{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":91498,"databundleVersionId":11655853,"sourceType":"competition"},{"sourceId":7884485,"sourceType":"datasetVersion","datasetId":4628051},{"sourceId":12148061,"sourceType":"datasetVersion","datasetId":7651041},{"sourceId":12148116,"sourceType":"datasetVersion","datasetId":7651044},{"sourceId":12162657,"sourceType":"datasetVersion","datasetId":7652929},{"sourceId":244142953,"sourceType":"kernelVersion"},{"sourceId":4534,"sourceType":"modelInstanceVersion","modelInstanceId":3326,"modelId":986}],"dockerImageVersionId":31041,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**Inspired by the [first-place solution](https://www.kaggle.com/competitions/image-matching-challenge-2025/discussion/583058) by [@ns6464](https://www.kaggle.com/ns6464), I've put together a demo showcasing how to run MASt3R within Kaggle.**","metadata":{}},{"cell_type":"code","source":"import sys","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:20:03.848026Z","iopub.execute_input":"2025-12-09T04:20:03.848205Z","iopub.status.idle":"2025-12-09T04:20:03.854282Z","shell.execute_reply.started":"2025-12-09T04:20:03.848189Z","shell.execute_reply":"2025-12-09T04:20:03.853473Z"}},"outputs":[],"execution_count":1},{"cell_type":"code","source":"class CONFIG:\n    # DEBUG Settings\n    DRY_RUN = False\n    DRY_RUN_MAX_IMAGES = 10\n\n    # Pipeline settings\n    NUM_CORES = 2\n    MAST3R_MIN_PAIR = 15\n    MATCH_CONF_TH = 1.001","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:20:03.855995Z","iopub.execute_input":"2025-12-09T04:20:03.856391Z","iopub.status.idle":"2025-12-09T04:20:03.868192Z","shell.execute_reply.started":"2025-12-09T04:20:03.856375Z","shell.execute_reply":"2025-12-09T04:20:03.867555Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"!pip install torch torchvision torchaudio --no-index --find-links=/kaggle/input/mast3r-fix/mast3r-wheels","metadata":{"trusted":true,"_kg_hide-output":true,"scrolled":true,"execution":{"iopub.status.busy":"2025-12-09T04:20:03.868904Z","iopub.execute_input":"2025-12-09T04:20:03.869206Z","iopub.status.idle":"2025-12-09T04:22:21.23983Z","shell.execute_reply.started":"2025-12-09T04:20:03.869184Z","shell.execute_reply":"2025-12-09T04:22:21.239132Z"}},"outputs":[{"name":"stdout","text":"Looking in links: /kaggle/input/mast3r-fix/mast3r-wheels\nRequirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\nRequirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (0.21.0+cu124)\nRequirement already satisfied: torchaudio in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\nRequirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch) (3.18.0)\nRequirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch) (4.13.2)\nRequirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch) (3.4.2)\nRequirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.6)\nRequirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch) (2025.3.2)\nRequirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\nRequirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\nRequirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (from torch)\nINFO: pip is looking at multiple versions of torch to determine which version is compatible with other requirements. This could take a while.\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/torch-2.7.1-cp311-cp311-manylinux_2_28_x86_64.whl\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/sympy-1.14.0-py3-none-any.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/triton-3.3.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (from torch)\nRequirement already satisfied: setuptools>=40.8.0 in /usr/local/lib/python3.11/dist-packages (from triton==3.3.1->torch) (75.2.0)\nRequirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from torchvision) (1.26.4)\nRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/dist-packages (from torchvision) (11.1.0)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/torchvision-0.22.1-cp311-cp311-manylinux_2_28_x86_64.whl\nINFO: pip is looking at multiple versions of torchaudio to determine which version is compatible with other requirements. This could take a while.\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/torchaudio-2.5.1+cu121-cp311-cp311-linux_x86_64.whl\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/torchvision-0.20.1+cu121-cp311-cp311-linux_x86_64.whl\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/torch-2.5.1+cu121-cp311-cp311-linux_x86_64.whl\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (from nvidia-cudnn-cu12==9.5.1.17->torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (from nvidia-cusolver-cu12==11.7.1.2->torch)\nRequirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch) (2.21.5)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (from torch)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from torch)\nRequirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch) (1.13.1)\nRequirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.11/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.9.41)\nRequirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch) (1.3.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\nRequirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (1.3.8)\nRequirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (1.2.4)\nRequirement already satisfied: mkl_umath in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (0.1.1)\nRequirement already satisfied: mkl in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (2025.1.0)\nRequirement already satisfied: tbb4py in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (2022.1.0)\nRequirement already satisfied: mkl-service in /usr/local/lib/python3.11/dist-packages (from numpy->torchvision) (2.4.1)\nRequirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy->torchvision) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy->torchvision) (2022.1.0)\nRequirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy->torchvision) (1.3.0)\nRequirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy->torchvision) (2024.2.0)\nRequirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy->torchvision) (2024.2.0)\nInstalling collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, torchaudio, torchvision\n  Attempting uninstall: triton\n    Found existing installation: triton 3.2.0\n    Uninstalling triton-3.2.0:\n      Successfully uninstalled triton-3.2.0\n  Attempting uninstall: nvidia-nvtx-cu12\n    Found existing installation: nvidia-nvtx-cu12 12.4.127\n    Uninstalling nvidia-nvtx-cu12-12.4.127:\n      Successfully uninstalled nvidia-nvtx-cu12-12.4.127\n  Attempting uninstall: nvidia-nvjitlink-cu12\n    Found existing installation: nvidia-nvjitlink-cu12 12.9.41\n    Uninstalling nvidia-nvjitlink-cu12-12.9.41:\n      Successfully uninstalled nvidia-nvjitlink-cu12-12.9.41\n  Attempting uninstall: nvidia-curand-cu12\n    Found existing installation: nvidia-curand-cu12 10.3.10.19\n    Uninstalling nvidia-curand-cu12-10.3.10.19:\n      Successfully uninstalled nvidia-curand-cu12-10.3.10.19\n  Attempting uninstall: nvidia-cufft-cu12\n    Found existing installation: nvidia-cufft-cu12 11.4.0.6\n    Uninstalling nvidia-cufft-cu12-11.4.0.6:\n      Successfully uninstalled nvidia-cufft-cu12-11.4.0.6\n  Attempting uninstall: nvidia-cuda-runtime-cu12\n    Found existing installation: nvidia-cuda-runtime-cu12 12.4.127\n    Uninstalling nvidia-cuda-runtime-cu12-12.4.127:\n      Successfully uninstalled nvidia-cuda-runtime-cu12-12.4.127\n  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n    Found existing installation: nvidia-cuda-nvrtc-cu12 12.4.127\n    Uninstalling nvidia-cuda-nvrtc-cu12-12.4.127:\n      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.4.127\n  Attempting uninstall: nvidia-cuda-cupti-cu12\n    Found existing installation: nvidia-cuda-cupti-cu12 12.4.127\n    Uninstalling nvidia-cuda-cupti-cu12-12.4.127:\n      Successfully uninstalled nvidia-cuda-cupti-cu12-12.4.127\n  Attempting uninstall: nvidia-cublas-cu12\n    Found existing installation: nvidia-cublas-cu12 12.9.0.13\n    Uninstalling nvidia-cublas-cu12-12.9.0.13:\n      Successfully uninstalled nvidia-cublas-cu12-12.9.0.13\n  Attempting uninstall: nvidia-cusparse-cu12\n    Found existing installation: nvidia-cusparse-cu12 12.5.9.5\n    Uninstalling nvidia-cusparse-cu12-12.5.9.5:\n      Successfully uninstalled nvidia-cusparse-cu12-12.5.9.5\n  Attempting uninstall: nvidia-cudnn-cu12\n    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n  Attempting uninstall: nvidia-cusolver-cu12\n    Found existing installation: nvidia-cusolver-cu12 11.7.4.40\n    Uninstalling nvidia-cusolver-cu12-11.7.4.40:\n      Successfully uninstalled nvidia-cusolver-cu12-11.7.4.40\n  Attempting uninstall: torch\n    Found existing installation: torch 2.6.0+cu124\n    Uninstalling torch-2.6.0+cu124:\n      Successfully uninstalled torch-2.6.0+cu124\n  Attempting uninstall: torchaudio\n    Found existing installation: torchaudio 2.6.0+cu124\n    Uninstalling torchaudio-2.6.0+cu124:\n      Successfully uninstalled torchaudio-2.6.0+cu124\n  Attempting uninstall: torchvision\n    Found existing installation: torchvision 0.21.0+cu124\n    Uninstalling torchvision-0.21.0+cu124:\n      Successfully uninstalled torchvision-0.21.0+cu124\nSuccessfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nvjitlink-cu12-12.6.85 nvidia-nvtx-cu12-12.1.105 torch-2.5.1+cu121 torchaudio-2.5.1+cu121 torchvision-0.20.1+cu121 triton-3.1.0\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"!pip install faiss-gpu-cu12 --no-index --find-links=/kaggle/input/mast3r-fix/mast3r-wheels","metadata":{"trusted":true,"scrolled":true,"_kg_hide-output":false,"execution":{"iopub.status.busy":"2025-12-09T04:22:21.240928Z","iopub.execute_input":"2025-12-09T04:22:21.241609Z","iopub.status.idle":"2025-12-09T04:22:25.429911Z","shell.execute_reply.started":"2025-12-09T04:22:21.24158Z","shell.execute_reply":"2025-12-09T04:22:25.429261Z"}},"outputs":[{"name":"stdout","text":"Looking in links: /kaggle/input/mast3r-fix/mast3r-wheels\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/faiss_gpu_cu12-1.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\nRequirement already satisfied: numpy<2 in /usr/local/lib/python3.11/dist-packages (from faiss-gpu-cu12) (1.26.4)\nRequirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from faiss-gpu-cu12) (25.0)\nRequirement already satisfied: nvidia-cuda-runtime-cu12>=12.1.105 in /usr/local/lib/python3.11/dist-packages (from faiss-gpu-cu12) (12.1.105)\nRequirement already satisfied: nvidia-cublas-cu12>=12.1.3.1 in /usr/local/lib/python3.11/dist-packages (from faiss-gpu-cu12) (12.1.3.1)\nRequirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (1.3.8)\nRequirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (1.2.4)\nRequirement already satisfied: mkl_umath in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (0.1.1)\nRequirement already satisfied: mkl in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (2025.1.0)\nRequirement already satisfied: tbb4py in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (2022.1.0)\nRequirement already satisfied: mkl-service in /usr/local/lib/python3.11/dist-packages (from numpy<2->faiss-gpu-cu12) (2.4.1)\nRequirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy<2->faiss-gpu-cu12) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy<2->faiss-gpu-cu12) (2022.1.0)\nRequirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy<2->faiss-gpu-cu12) (1.3.0)\nRequirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy<2->faiss-gpu-cu12) (2024.2.0)\nRequirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy<2->faiss-gpu-cu12) (2024.2.0)\nInstalling collected packages: faiss-gpu-cu12\nSuccessfully installed faiss-gpu-cu12-1.11.0\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"\n!pip install --no-index --find-links=/kaggle/input/mast3r-fix/mast3r-wheels \\\n    -r /kaggle/input/mast3r-fix/mast3r/requirements.txt \\\n    -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt \\\n    -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt","metadata":{"trusted":true,"scrolled":true,"execution":{"iopub.status.busy":"2025-12-09T04:22:25.43082Z","iopub.execute_input":"2025-12-09T04:22:25.431117Z","iopub.status.idle":"2025-12-09T04:22:42.495995Z","shell.execute_reply.started":"2025-12-09T04:22:25.431088Z","shell.execute_reply":"2025-12-09T04:22:42.495334Z"}},"outputs":[{"name":"stdout","text":"Looking in links: /kaggle/input/mast3r-fix/mast3r-wheels\nRequirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (1.2.2)\nRequirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (2.5.1+cu121)\nRequirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 2)) (0.20.1+cu121)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/roma-1.5.3-py3-none-any.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 3))\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/gradio-5.33.0-py3-none-any.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\nRequirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 5)) (3.7.2)\nRequirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 6)) (4.67.1)\nRequirement already satisfied: opencv-python in /usr/local/lib/python3.11/dist-packages (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 7)) (4.11.0.86)\nRequirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from -r 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/kaggle/input/mast3r-fix/mast3r-wheels/pycolmap-3.11.1-cp311-cp311-manylinux_2_28_x86_64.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 6))\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/poselib-2.0.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from -r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 7))\nRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (1.26.4)\nRequirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (1.5.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (3.6.0)\nRequirement already satisfied: filelock in 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/kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (11.0.2.54)\nRequirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (10.3.2.106)\nRequirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (11.4.5.107)\nRequirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (12.1.0.106)\nRequirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 1)) (2.21.5)\nRequirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.11/dist-packages (from torch->-r 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/kaggle/input/mast3r-fix/mast3r-wheels/groovy-0.1.2-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\nRequirement already satisfied: httpx>=0.24.1 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (0.28.1)\nRequirement already satisfied: markupsafe<4.0,>=2.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (3.0.2)\nRequirement already satisfied: orjson~=3.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (3.10.16)\nRequirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (25.0)\nRequirement already satisfied: pandas<3.0,>=1.0 in /usr/local/lib/python3.11/dist-packages (from gradio->-r 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4))\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/safehttpx-0.1.6-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/semantic_version-2.10.0-py2.py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/starlette-0.46.2-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/tomlkit-0.13.3-py3-none-any.whl (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4))\nRequirement already satisfied: typer<1.0,>=0.12 in /usr/local/lib/python3.11/dist-packages (from gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (0.15.2)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/uvicorn-0.34.3-py3-none-any.whl (from gradio->-r 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/kaggle/input/mast3r-fix/mast3r-wheels/PyOpenGL-3.1.0.zip (from pyrender->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 2))\n  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\nRequirement already satisfied: numba>=0.42 in /usr/local/lib/python3.11/dist-packages (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3)) (0.60.0)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/piexif-1.1.3-py2.py3-none-any.whl (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3))\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/wcmatch-10.0-py3-none-any.whl (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3))\nRequirement already satisfied: tabulate>=0.8.7 in /usr/local/lib/python3.11/dist-packages (from kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3)) (0.9.0)\nRequirement already satisfied: pytz>=2021.1 in 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requests->huggingface-hub>=0.22->huggingface-hub[torch]>=0.22->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 13)) (3.4.2)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.22->huggingface-hub[torch]>=0.22->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 13)) (2.4.0)\nRequirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.11/dist-packages (from typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (8.1.8)\nRequirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.11/dist-packages (from typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (1.5.4)\nRequirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.11/dist-packages (from typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (14.0.0)\nProcessing /kaggle/input/mast3r-fix/mast3r-wheels/bracex-2.5.post1-py3-none-any.whl (from wcmatch>=5.0->kapture->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements_optional.txt (line 3))\nRequirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (3.0.0)\nRequirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio->-r /kaggle/input/mast3r-fix/mast3r/dust3r/requirements.txt (line 4)) (2.19.1)\nRequirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.17.3->scikit-learn->-r /kaggle/input/mast3r-fix/mast3r/requirements.txt (line 1)) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.17.3->scikit-learn->-r 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packages: PyOpenGL\n  Building wheel for PyOpenGL (setup.py) ... \u001b[?25l\u001b[?25hdone\n  Created wheel for PyOpenGL: filename=PyOpenGL-3.1.0-py3-none-any.whl size=1745193 sha256=22445cf50af02496fbe34f4414f5296eb10310e2edafa955b80e28e5c55aa1b5\n  Stored in directory: /root/.cache/pip/wheels/1e/f6/cf/2c2644cdb0ce0cc3bbbbe4338fb0e2eff890c26d0a1a225363\nSuccessfully built PyOpenGL\nInstalling collected packages: PyOpenGL, pyglet, uvicorn, tomlkit, semantic-version, ruff, roma, python-multipart, pyproject_hooks, pillow-heif, piexif, groovy, freetype-py, ffmpy, bracex, wcmatch, starlette, build, safehttpx, gradio-client, fastapi, numpy-quaternion, trimesh, kapture, pyrender, pycolmap, poselib, kapture-localization, gradio\n  Attempting uninstall: PyOpenGL\n    Found existing installation: PyOpenGL 3.1.9\n    Uninstalling PyOpenGL-3.1.9:\n      Successfully uninstalled PyOpenGL-3.1.9\nSuccessfully installed PyOpenGL-3.1.0 bracex-2.5.post1 build-1.2.2.post1 fastapi-0.115.12 ffmpy-0.6.0 freetype-py-2.5.1 gradio-5.33.0 gradio-client-1.10.2 groovy-0.1.2 kapture-1.1.10 kapture-localization-1.1.10 numpy-quaternion-2024.0.8 piexif-1.1.3 pillow-heif-0.22.0 poselib-2.0.4 pycolmap-3.11.1 pyglet-1.5.31 pyproject_hooks-1.2.0 pyrender-0.1.45 python-multipart-0.0.20 roma-1.5.3 ruff-0.11.13 safehttpx-0.1.6 semantic-version-2.10.0 starlette-0.46.2 tomlkit-0.13.3 trimesh-4.6.11 uvicorn-0.34.3 wcmatch-10.0\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"!pip install --no-index /kaggle/input/imc2024-packages-lightglue-rerun-kornia/* --no-deps","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:22:42.497016Z","iopub.execute_input":"2025-12-09T04:22:42.497315Z","iopub.status.idle":"2025-12-09T04:22:43.992433Z","shell.execute_reply.started":"2025-12-09T04:22:42.49728Z","shell.execute_reply":"2025-12-09T04:22:43.991522Z"}},"outputs":[{"name":"stdout","text":"Processing /kaggle/input/imc2024-packages-lightglue-rerun-kornia/kornia-0.7.2-py2.py3-none-any.whl\nProcessing /kaggle/input/imc2024-packages-lightglue-rerun-kornia/kornia_moons-0.2.9-py3-none-any.whl\n\u001b[31mERROR: kornia_rs-0.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl is not a supported wheel on this platform.\u001b[0m\u001b[31m\n\u001b[0m","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"!pip install --no-index /kaggle/input/pycolmap3-11/pycolmap-3.11.1-cp311-cp311-manylinux_2_28_x86_64.whl --no-deps","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:22:43.994675Z","iopub.execute_input":"2025-12-09T04:22:43.994905Z","iopub.status.idle":"2025-12-09T04:22:45.550158Z","shell.execute_reply.started":"2025-12-09T04:22:43.994882Z","shell.execute_reply":"2025-12-09T04:22:45.549447Z"}},"outputs":[{"name":"stdout","text":"Processing /kaggle/input/pycolmap3-11/pycolmap-3.11.1-cp311-cp311-manylinux_2_28_x86_64.whl\npycolmap is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"# !pip install kornia_rs\n# !pip install pycolmap","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:22:45.550984Z","iopub.execute_input":"2025-12-09T04:22:45.551275Z","iopub.status.idle":"2025-12-09T04:22:45.554911Z","shell.execute_reply.started":"2025-12-09T04:22:45.551249Z","shell.execute_reply":"2025-12-09T04:22:45.554171Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"\nsys.path.insert(0, \"/kaggle/input/mast3r-fix/mast3r\")\nsys.path.insert(0, '/kaggle/input/mast3r-fix/mast3r/asmk')\nsys.path.insert(0, '/kaggle/input/mast3r-fix/mast3r/dust3r/croco/models/curope')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:23:57.207397Z","iopub.execute_input":"2025-12-09T04:23:57.207686Z","iopub.status.idle":"2025-12-09T04:23:57.212604Z","shell.execute_reply.started":"2025-12-09T04:23:57.207661Z","shell.execute_reply":"2025-12-09T04:23:57.211763Z"}},"outputs":[],"execution_count":13},{"cell_type":"markdown","source":"# Run gradio demo with internet on mode:\n\n> !PYTHONPATH=\"/kaggle/input/mast3r-fix/mast3r/asmk:$PYTHONPATH\" python3 /kaggle/input/mast3r-fix/mast3r/demo.py --weights /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth --share\n\n![WechatIMG10.png](attachment:4aba1fc2-1244-4b2b-95e4-ce07df4566a2.png)","metadata":{},"attachments":{"4aba1fc2-1244-4b2b-95e4-ce07df4566a2.png":{"image/png":"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"}}},{"cell_type":"code","source":"import random\nimport os\nimport numpy as np\nimport torch\nimport dataclasses\n\ndef seed_everything(seed: int = 42):\n    \"\"\"Set seed for reproducibility across random, numpy, torch (CPU + CUDA).\"\"\"\n    random.seed(seed)\n    np.random.seed(seed)\n    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)  # for multi-GPU\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n\nseed_everything()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:23:57.213746Z","iopub.execute_input":"2025-12-09T04:23:57.213945Z","iopub.status.idle":"2025-12-09T04:23:57.229451Z","shell.execute_reply.started":"2025-12-09T04:23:57.213932Z","shell.execute_reply":"2025-12-09T04:23:57.228928Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"# !pip uninstall -y pycolmap\n# !pip install --no-cache-dir pycolmap==3.11.1 --index-url https://pypi.org/simple\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:23:57.230083Z","iopub.execute_input":"2025-12-09T04:23:57.230411Z","iopub.status.idle":"2025-12-09T04:23:57.238485Z","shell.execute_reply.started":"2025-12-09T04:23:57.230394Z","shell.execute_reply":"2025-12-09T04:23:57.237814Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"import pycolmap\n!pip show pycolmap\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:23:57.23911Z","iopub.execute_input":"2025-12-09T04:23:57.239339Z","iopub.status.idle":"2025-12-09T04:23:59.10442Z","shell.execute_reply.started":"2025-12-09T04:23:57.239316Z","shell.execute_reply":"2025-12-09T04:23:59.103641Z"}},"outputs":[{"name":"stdout","text":"Name: pycolmap\nVersion: 3.11.1\nSummary: COLMAP bindings\nHome-page: \nAuthor: \nAuthor-email: =?utf-8?q?Johannes_Sch=C3=B6nberger?= <jsch@demuc.de>, Mihai Dusmanu <mihai.dusmanu@gmail.com>, Paul-Edouard Sarlin <psarlin@ethz.ch>, Shaohui Liu <b1ueber2y@gmail.com>, Philipp Lindenberger <plindenbe@ethz.ch>\nLicense: BSD-3-Clause\nLocation: /usr/local/lib/python3.11/dist-packages\nRequires: numpy\nRequired-by: \n","output_type":"stream"}],"execution_count":16},{"cell_type":"code","source":"import pycolmap\nimport os\nprint(os.listdir(os.path.dirname(pycolmap.__file__)))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:23:59.106775Z","iopub.execute_input":"2025-12-09T04:23:59.107029Z","iopub.status.idle":"2025-12-09T04:23:59.112321Z","shell.execute_reply.started":"2025-12-09T04:23:59.107006Z","shell.execute_reply":"2025-12-09T04:23:59.111514Z"}},"outputs":[{"name":"stdout","text":"['__init__.py', 'cost_functions', '__pycache__', 'py.typed', 'utils.py', '_core', '_core.cpython-311-x86_64-linux-gnu.so', 'manifold']\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"import sys\nimport os\nfrom tqdm import tqdm\nfrom time import time, sleep\nimport gc\nimport numpy as np\nimport h5py\nimport dataclasses\nimport pandas as pd\nfrom IPython.display import clear_output\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom PIL import Image\n\nimport cv2\nimport torch\nimport torch.nn.functional as F\n\nimport torch\nfrom transformers import AutoImageProcessor, AutoModel\n\n\n# IMPORTANT Utilities: importing data into colmap and competition metric\nimport pycolmap\n\n\nsys.path.append('/kaggle/input/pycolmap3-11-imc-utils')\n# from database import *\nfrom h5_to_db import *\nimport metric\nfrom pycolmap import verify_matches, TwoViewGeometryOptions\n\nfrom fastprogress import progress_bar","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:23:59.113024Z","iopub.execute_input":"2025-12-09T04:23:59.113284Z","iopub.status.idle":"2025-12-09T04:24:16.695263Z","shell.execute_reply.started":"2025-12-09T04:23:59.113262Z","shell.execute_reply":"2025-12-09T04:24:16.694603Z"}},"outputs":[{"name":"stderr","text":"2025-12-09 04:24:04.366889: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1765254244.549049      37 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1765254244.602567      37 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}],"execution_count":18},{"cell_type":"code","source":"_ = verify_matches\n_ = TwoViewGeometryOptions()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:16.696005Z","iopub.execute_input":"2025-12-09T04:24:16.696489Z","iopub.status.idle":"2025-12-09T04:24:16.700748Z","shell.execute_reply.started":"2025-12-09T04:24:16.696471Z","shell.execute_reply":"2025-12-09T04:24:16.700088Z"}},"outputs":[],"execution_count":19},{"cell_type":"code","source":"from mast3r.model import AsymmetricMASt3R\nfrom mast3r.fast_nn import fast_reciprocal_NNs, extract_correspondences_nonsym\n\nimport mast3r.utils.path_to_dust3r\nfrom dust3r.inference import inference\nfrom dust3r.utils.image import load_images","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:16.701462Z","iopub.execute_input":"2025-12-09T04:24:16.701868Z","iopub.status.idle":"2025-12-09T04:24:17.016389Z","shell.execute_reply.started":"2025-12-09T04:24:16.701844Z","shell.execute_reply":"2025-12-09T04:24:17.015689Z"}},"outputs":[],"execution_count":20},{"cell_type":"code","source":"!rm -rf /kaggle/working/result\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:17.017206Z","iopub.execute_input":"2025-12-09T04:24:17.0174Z","iopub.status.idle":"2025-12-09T04:24:17.157926Z","shell.execute_reply.started":"2025-12-09T04:24:17.017385Z","shell.execute_reply":"2025-12-09T04:24:17.15722Z"}},"outputs":[],"execution_count":21},{"cell_type":"code","source":"# Configuration\nimport torch\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu' # Automatically use GPU if available\nprint(f\"Using device: {device}\")\n\nschedule = 'cosine' # These seem to be unused in the provided snippet, but keep for context\nlr = 0.01\nniter = 300\nlocal_model_path = \"/kaggle/input/mast3r-fix/mast3r/checkpoints/\"\nlocal_model_directory = \"/kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\"\nretrival_model_dir = '/kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth'\n\n# Now, we manually call `load_model` as suggested by `mast3r/model.py`'s `from_pretrained` logic\nfrom mast3r.model import load_model # Assuming load_model is defined in mast3r/model.py or accessible\n\nprint(f\"Loading model from local path: {local_model_directory}\")\nmast3r_model = load_model(local_model_directory, device=device) # Pass device to load_model\nprint(\"Model loaded successfully.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:17.158903Z","iopub.execute_input":"2025-12-09T04:24:17.159141Z","iopub.status.idle":"2025-12-09T04:24:42.053628Z","shell.execute_reply.started":"2025-12-09T04:24:17.159119Z","shell.execute_reply":"2025-12-09T04:24:42.052934Z"}},"outputs":[{"name":"stdout","text":"Using device: cuda\nLoading model from local path: /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\n... loading model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\n","output_type":"stream"},{"name":"stderr","text":"/kaggle/input/mast3r-fix/mast3r/mast3r/model.py:24: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n  ckpt = torch.load(model_path, map_location='cpu')\n","output_type":"stream"},{"name":"stdout","text":"instantiating : AsymmetricMASt3R(enc_depth=24, dec_depth=12, enc_embed_dim=1024, dec_embed_dim=768, enc_num_heads=16, dec_num_heads=12, pos_embed='RoPE100',img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False)\n<All keys matched successfully>\nModel loaded successfully.\n","output_type":"stream"}],"execution_count":22},{"cell_type":"code","source":"def transform_keypoints_to_original(\n    kpts_crop: np.ndarray,\n    original_size: tuple[int, int],#H,W\n    size_param: int = 512, # The 'size' parameter (e.g., 224, 512) used in load_images\n    square_ok: bool = False\n) -> np.ndarray:\n    \"\"\"\n    Transforms keypoint coordinates from a DUST3R-processed (resized and cropped)\n    image back to the original image's coordinate system.\n\n    Args:\n        kpts_crop: A NumPy array of shape (N, 2) where N is the number of keypoints,\n                   and each row is (x, y) coordinate on the processed image.\n        original_size: A tuple (original_width, original_height) of the original image.\n        resized_crop_size: A tuple (processed_width, processed_height) of the\n                           image after resizing and cropping (i.e., the dimensions\n                           of the input image to DUST3R). This is W2, H2 from the\n                           load_images function.\n        size_param: The 'size' parameter (e.g., 224, 512) used in the\n                    original load_images function.\n        square_ok: The 'square_ok' parameter used in the original load_images function.\n\n    Returns:\n        A NumPy array of shape (N, 2) with the transformed keypoint coordinates\n        on the original image.\n    \"\"\"\n    # print(f\"original_size: {original_size}\")\n    original_height, original_width = original_size\n    original_height = float(original_height)\n    original_width = float(original_width)\n\n    # --- 1. Determine the dimensions after resizing but *before* cropping (W_res, H_res) ---\n    # This logic mirrors the _resize_pil_image call in load_images\n    if size_param == 224:\n        # Target long side is used for resizing.\n        target_long_side = round(size_param * max(original_width / original_height, original_height / original_width))\n        if original_width >= original_height:\n            W_res = target_long_side\n            H_res = round(original_height * (target_long_side / original_width))\n        else:\n            H_res = target_long_side\n            W_res = round(original_width * (target_long_side / original_height))\n    else:\n        # Long side is resized to size_param.\n        if original_width >= original_height:\n            W_res = size_param\n            H_res = round(original_height * (size_param / original_width))\n        else:\n            H_res = size_param\n            W_res = round(original_width * (size_param / original_height))\n\n    # print(f\"H_res, W_res: {H_res}_{W_res}\")\n\n    # --- 2. Calculate the cropping offsets used during processing ---\n    cx, cy = W_res // 2, H_res // 2\n\n    if size_param == 224:\n        half = min(cx, cy)\n        crop_left = cx - half\n        crop_top = cy - half\n    else:\n        halfw = ((2 * cx) // 16) * 8\n        halfh = ((2 * cy) // 16) * 8\n        if not square_ok and W_res == H_res:\n            halfh = round(3 * halfw / 4)\n        \n        crop_left = cx - halfw\n        crop_top = cy - halfh\n\n    \n\n    # --- 4. Reverse the Resizing ---\n    # Determine the actual scaling factor applied during the initial resize\n    if original_width >= original_height:\n        scale_factor = size_param / original_width\n    else:\n        scale_factor = size_param / original_height\n    # --- 3. Reverse the Cropping ---\n    # Add the crop offsets to the keypoints from the cropped image\n    # print(crop_left, crop_top)\n    kpts_resized = kpts_crop.astype(float) # Ensure float for accurate division\n    kpts_resized[:, 0] = kpts_resized[:, 0] + crop_left\n    kpts_resized[:, 1] = kpts_resized[:, 1] + crop_top\n\n\n    \n    # Divide by the scale factor to get original coordinates\n    kpts_original = kpts_resized/ scale_factor \n\n    return kpts_original","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:42.054358Z","iopub.execute_input":"2025-12-09T04:24:42.054633Z","iopub.status.idle":"2025-12-09T04:24:42.06319Z","shell.execute_reply.started":"2025-12-09T04:24:42.054614Z","shell.execute_reply":"2025-12-09T04:24:42.062491Z"}},"outputs":[],"execution_count":23},{"cell_type":"code","source":"import os\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\ndef draw_matches_on_original_images(img_path1, img_path2, matches_im0, matches_im1, save_path, n_viz=100):\n  \n    os.makedirs(os.path.dirname(save_path), exist_ok=True)\n\n    # 读取图像\n    img0 = cv2.imread(img_path1)\n    img1 = cv2.imread(img_path2)\n    key1 = os.path.basename(img_path1)\n    key2 = os.path.basename(img_path2)\n\n\n    if img0 is None or img1 is None:\n        print(f\"Error: Cannot load {img_path1} or {img_path2}\")\n        return\n\n    img0 = cv2.cvtColor(img0, cv2.COLOR_BGR2RGB)\n    img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)\n\n    H0, W0 = img0.shape[:2]\n    H1, W1 = img1.shape[:2]\n    canvas_h = max(H0, H1)\n    canvas = np.zeros((canvas_h, W0 + W1, 3), dtype=np.uint8)\n    canvas[:H0, :W0] = img0\n    canvas[:H1, W0:] = img1\n\n   \n    num_matches = min(len(matches_im0), n_viz)\n    idxs = np.round(np.linspace(0, len(matches_im0) - 1, num_matches)).astype(int)\n    cmap = plt.get_cmap('rainbow')\n\n    for i, idx in enumerate(idxs):\n        (x0, y0) = matches_im0[idx]\n        (x1, y1) = matches_im1[idx]\n        color = tuple((np.array(cmap(i / n_viz))[:3] * 255).astype(int).tolist())\n\n        pt1 = (int(round(x0)), int(round(y0)))\n        pt2 = (int(round(x1 + W0)), int(round(y1)))\n\n        cv2.line(canvas, pt1, pt2, color, thickness=1, lineType=cv2.LINE_AA)\n        cv2.circle(canvas, pt1, 2, color, -1, lineType=cv2.LINE_AA)\n        cv2.circle(canvas, pt2, 2, color, -1, lineType=cv2.LINE_AA)\n\n   \n    cv2.imwrite(f'{save_path}/{key1}_{key2}.jpg', cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR))\n    # print(f\"Saved match debug image to {save_path}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:42.063968Z","iopub.execute_input":"2025-12-09T04:24:42.064214Z","iopub.status.idle":"2025-12-09T04:24:42.077915Z","shell.execute_reply.started":"2025-12-09T04:24:42.064191Z","shell.execute_reply":"2025-12-09T04:24:42.077388Z"}},"outputs":[],"execution_count":24},{"cell_type":"code","source":"img_path1 = '/kaggle/input/image-matching-challenge-2025/train/ETs/et_et003.png'\nimg_path2 = '/kaggle/input/image-matching-challenge-2025/train/ETs/et_et006.png'\nimages = load_images([img_path1, img_path2], size=512)\noutput = inference([tuple(images)], mast3r_model, device, batch_size=1, verbose=True)\n\n# at this stage, you have the raw dust3r predictions\nview1, pred1 = output['view1'], output['pred1']\nview2, pred2 = output['view2'], output['pred2']\n\ndesc1, desc2 = pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach()\n\n# find 2D-2D matches between the two images\n# matches_im0, matches_im1 = fast_reciprocal_NNs(desc1, desc2, subsample_or_initxy1=8,\n#                                                device=device, dist='dot', block_size=2**13)\nconf1, conf2 = pred1['desc_conf'].squeeze(0).detach(), pred2['desc_conf'].squeeze(0).detach()\nprint(f\"desc1 shape: {desc1.shape}\")\nprint(f\"conf1 shape: {conf1.shape}\")\ncorres = extract_correspondences_nonsym(desc1, desc2, conf1, conf2,\n                                        device=device, subsample=8, pixel_tol=5)\nprint(f\"corres[0].shape: {corres[0].shape}\")\nprint(f\"corres[2].shape: {corres[2].shape}\")\nscore = corres[2]\n\nprint(\"conf min:\",score.min().item())\nprint(\"conf max:\", score.max().item())\nprint(\"conf mean:\", score.float().mean().item())\nprint(\"conf std:\", score.float().std().item())\nprint(\"conf median:\", score.float().median().item())\n\nmask = score >= CONFIG.MATCH_CONF_TH\nmatches_im0 = corres[0][mask].cpu().numpy()\nmatches_im1 = corres[1][mask].cpu().numpy()\nprint(f\"matches_im0.shape: {matches_im0.shape}\")\n\n# matches_im0 = corres[0].cpu().numpy()\n# matches_im1 = corres[1].cpu().numpy()\n\n# ignore small border around the edge\nH0, W0 = view1['true_shape'][0]\nvalid_matches_im0 = (matches_im0[:, 0] >= 3) & (matches_im0[:, 0] < int(W0) - 3) & (\n    matches_im0[:, 1] >= 3) & (matches_im0[:, 1] < int(H0) - 3)\n\nH1, W1 = view2['true_shape'][0]\nvalid_matches_im1 = (matches_im1[:, 0] >= 3) & (matches_im1[:, 0] < int(W1) - 3) & (\n    matches_im1[:, 1] >= 3) & (matches_im1[:, 1] < int(H1) - 3)\n\nvalid_matches = valid_matches_im0 & valid_matches_im1\nmatches_im0, matches_im1 = matches_im0[valid_matches], matches_im1[valid_matches]\n\n# visualize a few matches\nimport numpy as np\nimport torch\nimport torchvision.transforms.functional\nfrom matplotlib import pyplot as pl\n\nn_viz = 100\nnum_matches = matches_im0.shape[0]\nmatch_idx_to_viz = np.round(np.linspace(0, num_matches - 1, n_viz)).astype(int)\nviz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz]\n\nimage_mean = torch.as_tensor([0.5, 0.5, 0.5], device='cpu').reshape(1, 3, 1, 1)\nimage_std = torch.as_tensor([0.5, 0.5, 0.5], device='cpu').reshape(1, 3, 1, 1)\n\nviz_imgs = []\nfor i, view in enumerate([view1, view2]):\n    rgb_tensor = view['img'] * image_std + image_mean\n    viz_imgs.append(rgb_tensor.squeeze(0).permute(1, 2, 0).cpu().numpy())\n\nH0, W0, H1, W1 = *viz_imgs[0].shape[:2], *viz_imgs[1].shape[:2]\nprint(H0,W0,H1,W1)\nimg0 = np.pad(viz_imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)\nimg1 = np.pad(viz_imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)\nimg = np.concatenate((img0, img1), axis=1)\npl.figure()\npl.imshow(img)\ncmap = pl.get_cmap('jet')\nfor i in range(n_viz):\n    (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T\n    pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)\npl.show(block=True)\n\nimg0 = cv2.imread(img_path1)\nimg1 = cv2.imread(img_path2)\nH0, W0 = img0.shape[:2]\nH1, W1 = img1.shape[:2]\nviz_matches_im0_org = transform_keypoints_to_original(viz_matches_im0, (H0, W0))\nviz_matches_im1_org = transform_keypoints_to_original(viz_matches_im1, (H1, W1))\n\nout_org_dir= os.path.join(\"/kaggle/working\", \"temp\")\nos.makedirs(out_org_dir, exist_ok=True)\n\ndraw_matches_on_original_images(img_path1, img_path2, viz_matches_im0_org, viz_matches_im1_org, out_org_dir, n_viz=100)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:42.078758Z","iopub.execute_input":"2025-12-09T04:24:42.079339Z","iopub.status.idle":"2025-12-09T04:24:43.987409Z","shell.execute_reply.started":"2025-12-09T04:24:42.079319Z","shell.execute_reply":"2025-12-09T04:24:43.986843Z"}},"outputs":[{"name":"stdout","text":">> Loading a list of 2 images\n - adding /kaggle/input/image-matching-challenge-2025/train/ETs/et_et003.png with resolution 480x640 --> 384x512\n - adding /kaggle/input/image-matching-challenge-2025/train/ETs/et_et006.png with resolution 480x640 --> 384x512\n (Found 2 images)\n>> Inference with model on 1 image pairs\n","output_type":"stream"},{"name":"stderr","text":"  0%|          | 0/1 [00:00<?, ?it/s]/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/inference.py:44: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n  with torch.cuda.amp.autocast(enabled=bool(use_amp)):\n/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/model.py:205: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n  with torch.cuda.amp.autocast(enabled=False):\n/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/inference.py:48: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n  with torch.cuda.amp.autocast(enabled=False):\n100%|██████████| 1/1 [00:00<00:00,  1.03it/s]\n","output_type":"stream"},{"name":"stdout","text":"desc1 shape: torch.Size([512, 384, 24])\nconf1 shape: torch.Size([512, 384])\ncorres[0].shape: torch.Size([1866, 2])\ncorres[2].shape: torch.Size([1866])\nconf min: 0.02820398658514023\nconf max: 3.819314956665039\nconf mean: 1.5521736145019531\nconf std: 0.7250813841819763\nconf median: 1.4392729997634888\nmatches_im0.shape: (1388, 2)\n512 384 512 384\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":25},{"cell_type":"code","source":"def get_img_pairs_exhaustive(img_fnames):\n    index_pairs = []\n    for i in range(len(img_fnames)):\n        for j in range(i+1, len(img_fnames)):\n            index_pairs.append((i,j))\n    return index_pairs","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:43.990225Z","iopub.execute_input":"2025-12-09T04:24:43.990421Z","iopub.status.idle":"2025-12-09T04:24:43.994099Z","shell.execute_reply.started":"2025-12-09T04:24:43.990407Z","shell.execute_reply":"2025-12-09T04:24:43.99339Z"}},"outputs":[],"execution_count":26},{"cell_type":"code","source":"import os\nimport torch\nimport PIL\nimport numpy as np # Ensure numpy is imported for checking np.ndarray\nfrom PIL import Image\nfrom mast3r.retrieval.processor import Retriever\nfrom mast3r.image_pairs import make_pairs\nfrom mast3r.model import AsymmetricMASt3R\n\n\ndef get_image_list(images_path):\n    \"\"\"\n    Scans the specified path for all image files and returns their relative paths.\n    Skips unidentifiable or corrupt image files.\n    \"\"\"\n    file_list = [os.path.relpath(os.path.join(dirpath, filename), images_path)\n                 for dirpath, _, filenames in os.walk(images_path)\n                 for filename in filenames]\n    file_list = sorted(file_list)\n    image_list = []\n    for filename in file_list:\n        try:\n            with Image.open(os.path.join(images_path, filename)) as im:\n                im.verify()  # Verify image file integrity\n                image_list.append(filename)\n        except (OSError, PIL.UnidentifiedImageError):\n            print(f'Skipping invalid image file: {filename}')\n    return image_list\n\n\ndef make_pair_with_mast3r_return_pairs(\n    image_dir: str,\n    weights_path: str,  # Path to the AsymmetricMASt3R model weights\n    retrieval_model_path: str,  # Path to the retrieval model (e.g., \"trainingfree.pth\")\n    scene_graph: str = 'retrieval-20-40',\n    device: str = 'cuda'\n):\n    \"\"\"\n    Generates image pairs using MASt3R + ASMK retrieval, returning a list of pairs.\n\n    Args:\n        image_dir (str): Path to the directory containing images.\n        weights_path (str): Path to the AsymmetricMASt3R model weights.\n        retrieval_model_path (str): Path to the retrieval model (e.g., \"trainingfree.pth\").\n        scene_graph (str, optional): String defining the scene graph construction strategy. \n                                     Defaults to 'retrieval-20-1-10-1'.\n        device (str, optional): PyTorch device to use ('cuda' or 'cpu'). Defaults to 'cuda'.\n\n    Returns:\n        sorted_pairs: List[Tuple[str, str]], where each tuple contains \n                      the relative paths of the paired images (img1, img2).\n    \"\"\"\n    print(\"🖼️ Scanning images...\")\n    imgs = get_image_list(image_dir)\n    imgs_fp = [os.path.join(image_dir, f) for f in imgs]\n\n    if not imgs:\n        print(\"⚠️ No valid images found in the directory. Returning empty pairs.\")\n        return []\n\n    print(f\"⚙️ Loading backbone model from {weights_path}...\")\n    backbone = AsymmetricMASt3R.from_pretrained(weights_path).to(device).eval()\n\n    # print(\"🔍 Running ASMK retrieval...\")\n    retriever = Retriever(retrieval_model_path, backbone=backbone)\n    \n    with torch.no_grad():\n        sim_matrix_np = retriever(imgs_fp) \n        \n    # Cleanup GPU cache\n    del retriever\n    del backbone \n    torch.cuda.empty_cache()\n\n    \n    raw_pairs = make_pairs(imgs, scene_graph, prefilter=None, symmetrize=True, sim_mat=sim_matrix_np)\n    # print(raw_pairs)\n    \n    sorted_pairs = sorted(set(tuple(sorted([a, b])) for a, b in raw_pairs))\n\n    print(f\"✅ Generated {len(sorted_pairs)} unique image pairs.\")\n    return sorted_pairs","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:43.994847Z","iopub.execute_input":"2025-12-09T04:24:43.995173Z","iopub.status.idle":"2025-12-09T04:24:44.142607Z","shell.execute_reply.started":"2025-12-09T04:24:43.995157Z","shell.execute_reply":"2025-12-09T04:24:44.142073Z"}},"outputs":[],"execution_count":27},{"cell_type":"code","source":"import kornia as K\nimport kornia.feature as KF\n# --- Helper function for image loading (if not already defined) ---\ndef load_torch_image(fname, device=torch.device('cpu')):\n    img = K.io.load_image(fname, K.io.ImageLoadType.RGB32, device=device)[None, ...]\n    return img","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:44.143234Z","iopub.execute_input":"2025-12-09T04:24:44.143797Z","iopub.status.idle":"2025-12-09T04:24:44.989049Z","shell.execute_reply.started":"2025-12-09T04:24:44.143778Z","shell.execute_reply":"2025-12-09T04:24:44.988311Z"}},"outputs":[],"execution_count":28},{"cell_type":"code","source":"# Must Use efficientnet global descriptor to get matching shortlists.\ndef get_global_desc(fnames, device = torch.device('cpu')):\n    processor = AutoImageProcessor.from_pretrained('/kaggle/input/dinov2/pytorch/base/1')\n    model = AutoModel.from_pretrained('/kaggle/input/dinov2/pytorch/base/1')\n    model = model.eval()\n    model = model.to(device)\n    global_descs_dinov2 = []\n    for i, img_fname_full in tqdm(enumerate(fnames),total= len(fnames)):\n        key = os.path.splitext(os.path.basename(img_fname_full))[0]\n        timg = load_torch_image(img_fname_full)\n        with torch.inference_mode():\n            inputs = processor(images=timg, return_tensors=\"pt\", do_rescale=False).to(device)\n            outputs = model(**inputs)\n            dino_mac = F.normalize(outputs.last_hidden_state[:,1:].max(dim=1)[0], dim=1, p=2)\n        global_descs_dinov2.append(dino_mac.detach().cpu())\n    global_descs_dinov2 = torch.cat(global_descs_dinov2, dim=0)\n    return global_descs_dinov2\n\n\ndef get_img_pairs_exhaustive(img_fnames):\n    index_pairs = []\n    for i in range(len(img_fnames)):\n        for j in range(i+1, len(img_fnames)):\n            index_pairs.append((i,j))\n    return index_pairs","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:44.989875Z","iopub.execute_input":"2025-12-09T04:24:44.990155Z","iopub.status.idle":"2025-12-09T04:24:44.996979Z","shell.execute_reply.started":"2025-12-09T04:24:44.990132Z","shell.execute_reply":"2025-12-09T04:24:44.996059Z"}},"outputs":[],"execution_count":29},{"cell_type":"code","source":"def get_image_pairs_shortlist_org(fnames,\n                              sim_th = 0.6, # should be strict\n                              min_pairs = 60,\n                              exhaustive_if_less = 20,\n                              device=torch.device('cpu')):\n    num_imgs = len(fnames)\n    if num_imgs <= exhaustive_if_less:\n        return get_img_pairs_exhaustive(fnames)\n    descs = get_global_desc(fnames, device=device)\n    dm = torch.cdist(descs, descs, p=2).detach().cpu().numpy()\n\n    \n    mask = dm <= sim_th\n    total = 0\n    matching_list = []\n    ar = np.arange(num_imgs)\n    already_there_set = []\n    for st_idx in range(num_imgs-1):\n        mask_idx = mask[st_idx]\n        to_match = ar[mask_idx]\n        if len(to_match) < min_pairs:\n            to_match = np.argsort(dm[st_idx])[:min_pairs]  \n        for idx in to_match:\n            if st_idx == idx:\n                continue\n            if dm[st_idx, idx] < 10000:\n                matching_list.append(tuple(sorted((st_idx, idx.item()))))\n                total+=1\n    matching_list = sorted(list(set(matching_list)))\n    return matching_list","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:44.99794Z","iopub.execute_input":"2025-12-09T04:24:44.998215Z","iopub.status.idle":"2025-12-09T04:24:45.015633Z","shell.execute_reply.started":"2025-12-09T04:24:44.9982Z","shell.execute_reply":"2025-12-09T04:24:45.014945Z"}},"outputs":[],"execution_count":30},{"cell_type":"code","source":"import pycolmap\nprint(f\"pycolmap version: {pycolmap.__version__}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.016307Z","iopub.execute_input":"2025-12-09T04:24:45.016532Z","iopub.status.idle":"2025-12-09T04:24:45.030459Z","shell.execute_reply.started":"2025-12-09T04:24:45.016517Z","shell.execute_reply":"2025-12-09T04:24:45.029916Z"}},"outputs":[{"name":"stdout","text":"pycolmap version: 3.11.1\n","output_type":"stream"}],"execution_count":31},{"cell_type":"code","source":"# Collect vital info from the dataset\n\n@dataclasses.dataclass\nclass Prediction:\n    image_id: str | None  # A unique identifier for the row -- unused otherwise. Used only on the hidden test set.\n    dataset: str\n    filename: str\n    cluster_index: int | None = None\n    rotation: np.ndarray | None = None\n    translation: np.ndarray | None = None\n\n# Set is_train=True to run the notebook on the training data.\n# Set is_train=False if submitting an entry to the competition (test data is hidden, and different from what you see on the \"test\" folder).\nis_train = False\ndata_dir = '/kaggle/input/image-matching-challenge-2025'\nworkdir = '/kaggle/working/result/'\nos.makedirs(workdir, exist_ok=True)\n\nif is_train:\n    sample_submission_csv = os.path.join(data_dir, 'train_labels.csv')\nelse:\n    sample_submission_csv = os.path.join(data_dir, 'sample_submission.csv')\n\nsamples = {}\ncompetition_data = pd.read_csv(sample_submission_csv)\nfor _, row in competition_data.iterrows():\n    # Note: For the test data, the \"scene\" column has no meaning, and the rotation_matrix and translation_vector columns are random.\n    if row.dataset not in samples:\n        samples[row.dataset] = []\n    samples[row.dataset].append(\n        Prediction(\n            image_id=None if is_train else row.image_id,\n            dataset=row.dataset,\n            filename=row.image\n        )\n    )\n\nfor dataset in samples:\n    print(f'Dataset \"{dataset}\" -> num_images={len(samples[dataset])}')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.031333Z","iopub.execute_input":"2025-12-09T04:24:45.031567Z","iopub.status.idle":"2025-12-09T04:24:45.179745Z","shell.execute_reply.started":"2025-12-09T04:24:45.031543Z","shell.execute_reply":"2025-12-09T04:24:45.179191Z"}},"outputs":[{"name":"stdout","text":"Dataset \"ETs\" -> num_images=22\nDataset \"amy_gardens\" -> num_images=200\nDataset \"fbk_vineyard\" -> num_images=163\nDataset \"imc2023_haiper\" -> num_images=54\nDataset \"imc2023_heritage\" -> num_images=209\nDataset \"imc2023_theather_imc2024_church\" -> num_images=76\nDataset \"imc2024_dioscuri_baalshamin\" -> num_images=138\nDataset \"imc2024_lizard_pond\" -> num_images=214\nDataset \"pt_brandenburg_british_buckingham\" -> num_images=225\nDataset \"pt_piazzasanmarco_grandplace\" -> num_images=168\nDataset \"pt_sacrecoeur_trevi_tajmahal\" -> num_images=225\nDataset \"pt_stpeters_stpauls\" -> num_images=200\nDataset \"stairs\" -> num_images=51\n","output_type":"stream"}],"execution_count":32},{"cell_type":"code","source":"import multiprocessing","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.180474Z","iopub.execute_input":"2025-12-09T04:24:45.181194Z","iopub.status.idle":"2025-12-09T04:24:45.18406Z","shell.execute_reply.started":"2025-12-09T04:24:45.181176Z","shell.execute_reply":"2025-12-09T04:24:45.183441Z"}},"outputs":[],"execution_count":33},{"cell_type":"code","source":"import os\nimport numpy as np\nimport torch\nimport matplotlib.pyplot as plt\nimport cv2\n\n\ndef save_match_viz_image(\n    key1,\n    key2,\n    view1,\n    view2,\n    matches_im0,\n    matches_im1,\n    feature_dir,\n    n_viz: int = 100\n):\n    \"\"\"\n    Save a visual match image for a pair of images using descriptor matches.\n\n    Parameters:\n        key1, key2: str\n            Base filenames of the matched image pair (used for naming the output).\n        view1, view2: dict\n            MASt3R inference outputs containing 'img' and 'true_shape'.\n        matches_im0, matches_im1: np.ndarray of shape (N, 2)\n            Coordinates of matched keypoints in image 0 and image 1.\n        feature_dir: str\n            Path to save the visualized match image.\n        n_viz: int\n            Number of matches to visualize (default: 100).\n    \"\"\"\n    if matches_im0.shape[0] == 0:\n        return  # nothing to draw\n\n    n_viz = min(n_viz, matches_im0.shape[0])\n    idx = np.round(np.linspace(0, matches_im0.shape[0] - 1, n_viz)).astype(int)\n    viz_matches_im0 = matches_im0[idx]\n    viz_matches_im1 = matches_im1[idx]\n\n    image_mean = torch.tensor([0.5, 0.5, 0.5]).reshape(1, 3, 1, 1)\n    image_std = torch.tensor([0.5, 0.5, 0.5]).reshape(1, 3, 1, 1)\n\n    viz_imgs = []\n    for view in [view1, view2]:\n        rgb_tensor = view['img'].cpu() * image_std + image_mean\n        rgb_np = rgb_tensor.squeeze(0).permute(1, 2, 0).clamp(0, 1).numpy()\n        viz_imgs.append((rgb_np * 255).astype(np.uint8))\n\n    H0, W0 = viz_imgs[0].shape[:2]\n    H1, W1 = viz_imgs[1].shape[:2]\n    img0 = np.pad(viz_imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant')\n    img1 = np.pad(viz_imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant')\n    img = np.concatenate((img0, img1), axis=1)\n\n    cmap = plt.get_cmap('jet')\n    for i in range(n_viz):\n        (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T\n        color = tuple(int(c * 255) for c in cmap(i / (n_viz - 1))[:3])\n        cv2.line(img, (int(x0), int(y0)), (int(x1 + W0), int(y1)), color, thickness=1)\n        cv2.circle(img, (int(x0), int(y0)), radius=2, color=color, thickness=-1)\n        cv2.circle(img, (int(x1 + W0), int(y1)), radius=2, color=color, thickness=-1)\n\n    out_dir = os.path.join(feature_dir, \"debug_vis\")\n    os.makedirs(out_dir, exist_ok=True)\n    out_path = os.path.join(out_dir, f\"{key1}-{key2}.jpg\")\n    cv2.imwrite(out_path, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))\n    print(f\"[Match Debug] Saved to {out_path}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.184827Z","iopub.execute_input":"2025-12-09T04:24:45.185267Z","iopub.status.idle":"2025-12-09T04:24:45.201402Z","shell.execute_reply.started":"2025-12-09T04:24:45.18525Z","shell.execute_reply":"2025-12-09T04:24:45.200876Z"}},"outputs":[],"execution_count":34},{"cell_type":"code","source":"from typing import Dict, Tuple\nfrom collections import defaultdict\nimport numpy as np\n\ndef unify_keypoints_and_matches(\n    out_match: Dict[str, Dict[str, np.ndarray]],\n    round_digits: int = 1\n) -> Tuple[\n    Dict[str, np.ndarray],                    # global_keypoints[img] = (N, 2)\n    Dict[Tuple[str, str], np.ndarray]         # global_matches[(img1, img2)] = (M, 2) (global IDs)\n]:\n   \n    keypoints_per_image = defaultdict(list)\n\n    for img1, subdict in out_match.items():\n        for img2, match in subdict.items():\n            pts1 = np.round(match[:, :2], decimals=round_digits)\n            pts2 = np.round(match[:, 2:], decimals=round_digits)\n            keypoints_per_image[img1].append(pts1)\n            keypoints_per_image[img2].append(pts2)\n\n    \n    global_keypoints = {}\n    coord_to_id = {}\n\n    for img, kpt_list in keypoints_per_image.items():\n        all_pts = np.concatenate(kpt_list, axis=0)\n        all_pts = np.round(all_pts, decimals=round_digits)\n        unique_pts, inverse = np.unique(all_pts, axis=0, return_inverse=True)\n        global_keypoints[img] = unique_pts\n\n       \n        coord_to_id[img] = {tuple(pt): idx for idx, pt in enumerate(unique_pts)}\n\n    \n    global_matches = {}\n\n    for img1, subdict in out_match.items():\n        for img2, match in subdict.items():\n            pts1 = np.round(match[:, :2], decimals=round_digits)\n            pts2 = np.round(match[:, 2:], decimals=round_digits)\n\n            ids1 = np.array([coord_to_id[img1][tuple(pt)] for pt in pts1])\n            ids2 = np.array([coord_to_id[img2][tuple(pt)] for pt in pts2])\n\n            global_matches[(img1, img2)] = np.stack([ids1, ids2], axis=1)\n\n    return global_keypoints, global_matches\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.202139Z","iopub.execute_input":"2025-12-09T04:24:45.20284Z","iopub.status.idle":"2025-12-09T04:24:45.216505Z","shell.execute_reply.started":"2025-12-09T04:24:45.202823Z","shell.execute_reply":"2025-12-09T04:24:45.215816Z"}},"outputs":[],"execution_count":35},{"cell_type":"code","source":"import os\nimport h5py\nimport numpy as np\n\ndef save_unified_keypoints_and_matches(global_keypoints, global_matches, feature_dir, lock=None):\n    os.makedirs(feature_dir, exist_ok=True)\n    save_kpts_file = os.path.join(feature_dir, 'keypoints.h5')\n    save_matches_file = os.path.join(feature_dir, 'matches.h5')\n    save_matches_txt_file = os.path.join(feature_dir, 'pairs.txt')\n\n    \n    for f in [save_kpts_file, save_matches_file, save_matches_txt_file]:\n        if os.path.exists(f):\n            os.remove(f)\n\n    \n    with h5py.File(save_kpts_file, 'w') as f_kp:\n        for img_name, kpts in global_keypoints.items():\n            f_kp[img_name] = kpts\n        #     f_kp.create_dataset(img_name, data=np.asarray(kpts, dtype=np.float32))\n        # f_kp.flush()\n    print(f\"✅ Saved keypoints to {save_kpts_file}\")\n\n    \n    with h5py.File(save_matches_file, 'w') as f_match:\n        for (img1, img2), match in global_matches.items():\n            group = f_match.require_group(img1)\n            if len(match) >= CONFIG.MAST3R_MIN_PAIR:\n            # match = np.asarray(match, dtype=np.int32)\n                group.create_dataset(img2, data=match)\n        # f_match.flush()\n    print(f\"✅ Saved matches to {save_matches_file}\")\n    \n    with h5py.File(save_matches_file, 'r') as f, open(save_matches_txt_file, 'w') as fout:\n        for k1 in f.keys():\n            group = f[k1]\n            for k2 in group.keys():\n                fout.write(f\"{k1} {k2}\\n\")\n    print(f\"✅ Saved matches to {save_matches_txt_file}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.217199Z","iopub.execute_input":"2025-12-09T04:24:45.217457Z","iopub.status.idle":"2025-12-09T04:24:45.229494Z","shell.execute_reply.started":"2025-12-09T04:24:45.21744Z","shell.execute_reply":"2025-12-09T04:24:45.228945Z"}},"outputs":[],"execution_count":36},{"cell_type":"code","source":"def match_with_mast3r_and_save(index_pairs, image_list, feature_dir, model, device, lock):\n    os.makedirs(feature_dir, exist_ok=True)\n    cache = {}\n    unique_keypoints = defaultdict(list)\n    out_match = defaultdict(dict)\n    local_cache = {}\n    \n    out_dir = os.path.join(feature_dir, \"pair_res\")\n    out_org_dir= os.path.join(feature_dir, \"pair_res_onorg\")\n    os.makedirs(out_dir, exist_ok=True)\n    os.makedirs(out_org_dir, exist_ok=True)\n    \n    for idx1, idx2 in tqdm(index_pairs):\n        name1, name2 = image_list[idx1], image_list[idx2]\n        key1, key2 = os.path.basename(name1), os.path.basename(name2)\n\n        # Only re-run inference for key1 if not in cache\n        images = load_images([name1, name2], size=512, verbose=False)\n        output = inference([tuple(images)], mast3r_model, device, batch_size=1, verbose=False)\n        \n        # at this stage, you have the raw dust3r predictions\n        view1, pred1 = output['view1'], output['pred1']\n        view2, pred2 = output['view2'], output['pred2']\n        \n        desc1, desc2 = pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach()\n\n        # matches_im0, matches_im1 = fast_reciprocal_NNs(desc1, desc2, subsample_or_initxy1=8, device=device)\n        # print(f\"get pair for {key1}_{key2}, {len(matches_im0)}\")\n\n        conf1, conf2 = pred1['desc_conf'].squeeze(0).detach(), pred2['desc_conf'].squeeze(0).detach()\n        corres = extract_correspondences_nonsym(desc1, desc2, conf1, conf2,\n                                        device=device, subsample=8, pixel_tol=5)\n        score = corres[2]\n        mask = score >= CONFIG.MATCH_CONF_TH\n        matches_im0 = corres[0][mask].cpu().numpy()\n        matches_im1 = corres[1][mask].cpu().numpy()\n\n        # matches_im0 = corres[0].cpu().numpy()\n        # matches_im1 = corres[1].cpu().numpy()\n\n        if len(matches_im0) < CONFIG.MAST3R_MIN_PAIR:\n            continue\n\n        H0, W0 = view1['true_shape'][0].tolist()\n        H1, W1 = view2['true_shape'][0].tolist()\n        \n        valid0 = (matches_im0[:, 0] >= 3) & (matches_im0[:, 0] < W0 - 3) & (matches_im0[:, 1] >= 3) & (matches_im0[:, 1] < H0 - 3)\n        valid1 = (matches_im1[:, 0] >= 3) & (matches_im1[:, 0] < W1 - 3) & (matches_im1[:, 1] >= 3) & (matches_im1[:, 1] < H1 - 3)\n        valid = valid0 & valid1\n\n        matches_im0 = matches_im0[valid]\n        matches_im1 = matches_im1[valid]\n        if len(matches_im0) < CONFIG.MAST3R_MIN_PAIR:\n            continue\n        # print(\"transform_keypoints_to_original begin\")\n        # print(f\"{key1}_{key2}: {len(matches_im0)} matches\")\n        img0 = cv2.imread(name1)\n        img1 = cv2.imread(name2)\n        H0, W0 = img0.shape[:2]\n        H1, W1 = img1.shape[:2]\n        matches_im0_org = transform_keypoints_to_original(matches_im0, (H0, W0))\n        matches_im1_org = transform_keypoints_to_original(matches_im1, (H1, W1))\n\n        # matches_im0_org = transform_keypoints_to_original(matches_im0, view1['true_shape'][0].tolist())\n        # matches_im1_org = transform_keypoints_to_original(matches_im1, view2['true_shape'][0].tolist())\n        # print(\"transform_keypoints_to_original end\")\n\n        unique_keypoints[key1].append(matches_im0_org)\n        unique_keypoints[key2].append(matches_im1_org)\n        out_match[key1][key2] = np.concatenate([matches_im0_org, matches_im1_org], axis=1)\n        if False:\n            save_match_viz_image(key1, key2, view1, view2, matches_im0, matches_im1, out_dir)\n        if False:\n            draw_matches_on_original_images(name1, name2, matches_im0_org, matches_im1_org, out_org_dir)\n    \n    # print(\"out of loop\")\n\n    keypoints_unified = {}\n    keypoints_id_map = {}\n    out_match_unified = defaultdict(dict)\n\n    global_keypoints, global_matches = unify_keypoints_and_matches(out_match)\n    # print(\"points and matches unified\")\n\n    save_unified_keypoints_and_matches(global_keypoints, global_matches, feature_dir, lock)\n    # print(f\"Saved keypoints and matches to {feature_dir}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.230306Z","iopub.execute_input":"2025-12-09T04:24:45.230561Z","iopub.status.idle":"2025-12-09T04:24:45.244266Z","shell.execute_reply.started":"2025-12-09T04:24:45.230537Z","shell.execute_reply":"2025-12-09T04:24:45.243572Z"}},"outputs":[],"execution_count":37},{"cell_type":"code","source":"import cv2\nimport h5py\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\n\n\ndef draw_keypoints_and_matches(images_input, unified_kp_path, remapped_matches_path, feature_dir='visualization_output'):\n    output_dir = os.path.join(feature_dir, 'visualization_output')\n    os.makedirs(output_dir, exist_ok=True)\n\n    # Load images and determine image_keys for HDF5 lookup\n    if isinstance(images_input[0], str):\n        loaded_images = [cv2.imread(img_path) for img_path in images_input]\n        image_keys = [os.path.basename(img_path) for img_path in images_input]\n    else:\n        loaded_images = images_input\n        # If images_input are already arrays, you need to provide the corresponding keys\n        # This part is crucial: image_keys MUST align with the HDF5 keys\n        image_keys = image_keys_in_h5 # Use the predefined list for the dummy case\n\n    # Load unified keypoints\n    keypoints_data = {}\n    with h5py.File(unified_kp_path, 'r') as f_kp:\n        for img_name_raw in f_kp.keys():\n            img_name = img_name_raw.decode('utf-8') if isinstance(img_name_raw, bytes) else img_name_raw\n            keypoints_data[img_name] = f_kp[img_name_raw][()] # Access with raw key if bytes\n\n    # Load remapped matches - CORRECTED LOGIC\n    # Store (img1_key, img2_key) directly with matches for robust iteration\n    matches_data_pairs = [] # Will store (img1_key, img2_key, matches_array)\n    with h5py.File(remapped_matches_path, 'r') as f_matches:\n        print(\"\\n--- Loading remapped matches from HDF5 ---\")\n        for img1_group_key_candidate in tqdm(f_matches.keys(), desc=\"Loading matches\"):\n            img1_key = img1_group_key_candidate.decode('utf-8') if isinstance(img1_group_key_candidate, bytes) else img1_group_key_candidate\n\n            img1_group = f_matches[img1_group_key_candidate] # Access with raw key\n\n            if isinstance(img1_group, h5py.Group):\n                for img2_dataset_key_candidate in img1_group.keys():\n                    img2_key = img2_dataset_key_candidate.decode('utf-8') if isinstance(img2_dataset_key_candidate, bytes) else img2_dataset_key_candidate\n\n                    try:\n                        matches_array = img1_group[img2_dataset_key_candidate][()]\n                        matches_data_pairs.append((img1_key, img2_key, matches_array))\n                    except Exception as e:\n                        print(f\"Error loading matches for pair ({img1_key}, {img2_key}): {e}\")\n            else:\n                print(f\"Warning: Expected '{img1_key}' to be a group, but found {type(img1_group)}. Skipping its contents.\")\n\n\n    # --- Drawing Keypoints ---\n    print(\"\\n--- Drawing Keypoints ---\")\n    for i, img_key in enumerate(image_keys):\n        if img_key in keypoints_data:\n            img = loaded_images[i].copy()\n            kpts = keypoints_data[img_key]\n\n            for kp in kpts:\n                x, y = int(kp[0]), int(kp[1])\n                cv2.circle(img, (x, y), 3, (0, 255, 0), -1) # Green circle for keypoint\n\n            output_kp_path = os.path.join(output_dir, f\"keypoints_{img_key}\")\n            if len(img.shape) == 2:\n                img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)\n            cv2.imwrite(output_kp_path, img)\n            print(f\"Keypoints drawn on {img_key}, saved to {output_kp_path}\")\n        else:\n            print(f\"No keypoints found for {img_key} in unified keypoints file.\")\n\n    # --- Drawing Matches ---\n    print(\"\\n--- Drawing Matches ---\")\n    # Iterate through the (img1_key, img2_key, matches) tuples directly\n    for img_name1, img_name2, matches in matches_data_pairs:\n        # We no longer need to split img_pair_key, as we have img_name1 and img_name2 directly\n\n        # Find the actual image objects and their keypoints using image_keys list\n        try:\n            img1_idx = image_keys.index(img_name1)\n            img2_idx = image_keys.index(img_name2)\n        except ValueError:\n            print(f\"Skipping matches for {img_name1}-{img_name2}: One or both image names not found in the provided 'images' list/keys.\")\n            continue\n\n        img1 = loaded_images[img1_idx].copy()\n        img2 = loaded_images[img2_idx].copy()\n\n        kpts1 = keypoints_data.get(img_name1)\n        kpts2 = keypoints_data.get(img_name2)\n\n        if kpts1 is None or kpts2 is None:\n            print(f\"Skipping matches for {img_name1}-{img_name2}: keypoints not found for one or both images in unified keypoints.\")\n            continue\n        if len(matches) == 0:\n            print(f\"No matches to draw for {img_name1}-{img_name2}.\")\n            continue\n\n        # Ensure images are 3 channels for drawing lines\n        if len(img1.shape) == 2:\n            img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2BGR)\n        if len(img2.shape) == 2:\n            img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2BGR)\n\n        # Create a concatenated image for drawing matches\n        h1, w1 = img1.shape[:2]\n        h2, w2 = img2.shape[:2]\n        max_h = max(h1, h2)\n        matched_img = np.zeros((max_h, w1 + w2, 3), dtype=np.uint8)\n        matched_img[0:h1, 0:w1] = img1\n        matched_img[0:h2, w1:w1+w2] = img2\n\n        num_matches_to_draw = min(len(matches), 200) # Draw up to 200 matches to avoid clutter, adjust as needed\n\n        for i in np.linspace(0, len(matches) - 1, num_matches_to_draw, dtype=int):\n            match = matches[i]\n            kp1_idx, kp2_idx = int(match[0]), int(match[1])\n\n            # Bounds check for keypoint indices\n            if kp1_idx >= len(kpts1) or kp2_idx >= len(kpts2):\n                # print(f\"Warning: Match index out of bounds for {img_name1}-{img_name2}. Skipping match {kp1_idx}-{kp2_idx}.\")\n                continue\n\n            pt1 = tuple(map(int, kpts1[kp1_idx][:2]))\n            pt2 = tuple(map(int, kpts2[kp2_idx][:2]))\n\n            # Draw circles on the concatenated image\n            cv2.circle(matched_img, pt1, 5, (0, 0, 255), 2) # Red circle on img1 side\n            cv2.circle(matched_img, (pt2[0] + w1, pt2[1]), 5, (255, 0, 0), 2) # Blue circle on img2 side\n\n            # Draw a line connecting the matched keypoints\n            color = tuple(np.random.randint(0, 255, 3).tolist())\n            cv2.line(matched_img, pt1, (pt2[0] + w1, pt2[1]), color, 1)\n\n        output_match_path = os.path.join(output_dir, f\"matches_{img_name1}_{img_name2}.png\")\n        cv2.imwrite(output_match_path, matched_img)\n        print(f\"Matches drawn between {img_name1} and {img_name2}, saved to {output_match_path}\")\n\n\n# Example call (replace with your actual 'images' list)\n# If your 'images' are file paths:\n# images_file_paths = ['path/to/your/image1.jpg', 'path/to/your/image2.jpg', ...]\n# draw_keypoints_and_matches(images_file_paths, unified_kp_path, remapped_matches_path)\n\n# If your 'images' are loaded numpy arrays (as in the dummy example above):\n# draw_keypoints_and_matches(images, unified_kp_path, remapped_matches_path)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.244968Z","iopub.execute_input":"2025-12-09T04:24:45.245587Z","iopub.status.idle":"2025-12-09T04:24:45.262617Z","shell.execute_reply.started":"2025-12-09T04:24:45.245562Z","shell.execute_reply":"2025-12-09T04:24:45.26197Z"}},"outputs":[],"execution_count":38},{"cell_type":"code","source":"import os\nimport gc\nimport time\nimport numpy as np\nimport concurrent.futures\nimport multiprocessing\nfrom pathlib import Path\nfrom time import sleep, time\n# from pycolmap import verify_matches, TwoViewGeometryOptions\n\ndef run_verify_matches_safe(database_path, pairs_path, max_retries=5):\n    def _safe_verify():\n        verify_matches(\n            database_path=database_path,\n            pairs_path=pairs_path,\n            options=TwoViewGeometryOptions()\n        )\n\n    for attempt in range(max_retries):\n        print(f\"🔁 Attempt {attempt + 1} to run verify_matches\")\n        proc = multiprocessing.Process(target=_safe_verify)\n        proc.start()\n        proc.join()\n\n        if proc.exitcode in [0, 1]:\n            print(\"✅ verify_matches succeeded\")\n            return\n        else:\n            print(f\"⚠️ verify_matches crashed with code {proc.exitcode}\")\n    raise RuntimeError(\"❌ verify_matches failed after multiple retries.\")\n\ndef reconstruct_from_db(feature_dir, img_dir):\n    result = {}\n    local_timings = {'RANSAC': [], 'Reconstruction': []}\n\n    database_path = f'{feature_dir}/colmap.db'\n    pairs_txt = f'{feature_dir}/pairs.txt'\n    if os.path.isfile(database_path):\n        os.remove(database_path)\n    gc.collect()\n    sleep(1)\n\n    import_into_colmap(img_dir, feature_dir=feature_dir, database_path=database_path)\n    sleep(1)\n    print(f\"import {database_path} done!\")\n    output_path = f'{feature_dir}/colmap_rec'\n    os.makedirs(output_path, exist_ok=True)\n    print(\"colmap database\")\n\n    t = time()\n    run_verify_matches_safe(database_path, pairs_txt)\n    print(\"verify matching done!!!!\")\n    local_timings['RANSAC'].append(time() - t)\n    print(f'RANSAC in {local_timings[\"RANSAC\"][-1]:.4f} sec')\n\n    t = time()\n    mapper_options = pycolmap.IncrementalPipelineOptions()\n    mapper_options.min_model_size = 3\n    mapper_options.max_num_models = 5\n    maps = pycolmap.incremental_mapping(database_path=database_path, image_path=img_dir, \n                                        output_path=output_path, options=mapper_options)\n    print(maps)\n    for map_index, rec in maps.items():\n        result[map_index]={}\n        for img_id, image in rec.images.items():\n            result[map_index][image.name] = {\n                'R': image.cam_from_world.rotation.matrix().tolist(),\n                't': image.cam_from_world.translation.tolist()\n            }\n    local_timings['Reconstruction'].append(time() - t)\n    print(f'Reconstruction done in {local_timings[\"Reconstruction\"][-1]:.4f} sec')\n\n    return result, local_timings\n\ndef run_one_dataset(dataset, predictions, data_dir, workdir, is_train, model, device, lock=None):\n    timings = {\n        \"shortlisting\": [],\n        \"feature_matching\": [],\n        \"RANSAC\": [],\n        \"Reconstruction\": [],\n    }\n\n    try:\n        images_dir = os.path.join(data_dir, 'train' if is_train else 'test', dataset)\n        images = [os.path.join(images_dir, p.filename) for p in predictions]\n\n        print(f'Processing dataset \"{dataset}\": {len(images)} images')\n        filename_to_index = {p.filename: idx for idx, p in enumerate(predictions)}\n        feature_dir = os.path.join(workdir, 'featureout', dataset)\n        os.makedirs(feature_dir, exist_ok=True)\n\n        t = time()\n        # index_pairs = get_image_pairs_shortlist_org(\n        #     images, sim_th=0.2, min_pairs=10,\n        #     exhaustive_if_less=20, device=device)\n\n        index_img_pairs = make_pair_with_mast3r_return_pairs(image_dir=images_dir,\n                      weights_path = local_model_directory,  # Path to the AsymmetricMASt3R model weights (e.g., \"naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric\" or local path)\n                      retrieval_model_path = retrival_model_dir,  # Path to the retrieval model (e.g., \"trainingfree.pth\")\n                      device = device)\n\n        indexed_pairs = []\n        for filename1, filename2 in index_img_pairs:\n            try:\n                idx1 = filename_to_index[filename1]\n                idx2 = filename_to_index[filename2]\n                indexed_pairs.append((idx1, idx2))\n            except KeyError as e:\n                print(f\"Warning: Filename not found in mapping: {e}. Skipping pair ({filename1}, {filename2}).\")\n\n        timings['shortlisting'].append(time() - t)\n        print(f'Shortlisting done: {len(indexed_pairs)} pairs')\n        gc.collect()\n\n        t = time()\n        match_with_mast3r_and_save(indexed_pairs, images, feature_dir, model, device, lock)\n        timings['feature_matching'].append(time() - t)\n        print(f'MASt3R matching done in {time() - t:.2f} sec')\n        gc.collect()\n\n        maps, local_timings = reconstruct_from_db(feature_dir, images_dir)\n        # print(maps)\n        \n        # Sort map clusters by number of registered images (ascending)\n        sorted_map_items = sorted(maps.items(), key=lambda x: len(x[1]))\n        # print(sorted_map_items)\n        \n        registered = 0\n        for new_cluster_idx, (original_map_index, cur_map) in enumerate(sorted_map_items):\n            for image_name, pose in cur_map.items():\n                idx = filename_to_index[image_name]\n                pred = predictions[idx]\n                pred.cluster_index = new_cluster_idx  # use the sorted order\n                pred.rotation = np.array(pose['R'])\n                pred.translation = np.array(pose['t'])\n                registered += 1\n\n        mapping_result_str = f\"Dataset {dataset} -> Registered {registered} / {len(images)} images with {len(maps)} clusters\"\n        return mapping_result_str, timings\n\n    except Exception as e:\n        print(f\"Error in dataset {dataset}: {e}\")\n        return f\"Dataset \\\"{dataset}\\\" -> Failed!\", timings\n\ndef run_mast3r_pipeline(samples, data_dir, workdir, is_train, model, device):\n    max_images = None\n    datasets_to_process = ['ETs'] if is_train else list(samples.keys())\n\n    overall_timings = {\n        \"shortlisting\": [],\n        \"feature_matching\": [],\n        \"RANSAC\": [],\n        \"Reconstruction\": [],\n    }\n    mapping_result_strs = []\n    lock = multiprocessing.Lock()\n\n    with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:\n        futures = []\n        for dataset, predictions in samples.items():\n            if datasets_to_process and dataset not in datasets_to_process:\n                print(f\"Skipping {dataset}\")\n                continue\n            futures.append(executor.submit(run_one_dataset, dataset, predictions, data_dir, workdir, is_train, model, device, lock))\n\n        for future in concurrent.futures.as_completed(futures):\n            result_str, timings = future.result()\n            mapping_result_strs.append(result_str)\n            for k in timings:\n                overall_timings[k].extend(timings[k])\n\n    print('\\nResults')\n    for s in mapping_result_strs:\n        print(s)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.264137Z","iopub.execute_input":"2025-12-09T04:24:45.26434Z","iopub.status.idle":"2025-12-09T04:24:45.282954Z","shell.execute_reply.started":"2025-12-09T04:24:45.264325Z","shell.execute_reply":"2025-12-09T04:24:45.282207Z"}},"outputs":[],"execution_count":39},{"cell_type":"code","source":"run_mast3r_pipeline(samples, data_dir, workdir, is_train, mast3r_model, device)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:24:45.283708Z","iopub.execute_input":"2025-12-09T04:24:45.283951Z","iopub.status.idle":"2025-12-09T04:57:36.200417Z","shell.execute_reply.started":"2025-12-09T04:24:45.283936Z","shell.execute_reply":"2025-12-09T04:57:36.199278Z"}},"outputs":[{"name":"stdout","text":"Processing dataset \"ETs\": 22 images\nProcessing dataset \"amy_gardens\": 200 images\n🖼️ Scanning images...\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"0it [00:00, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/amy_gardens/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/amy_gardens/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/amy_gardens/pairs.txt\nMASt3R matching done in 0.01 sec\nSkipping invalid image file: LICENSE.txt\n⚙️ Loading backbone model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth...\n... loading model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\n","output_type":"stream"},{"name":"stderr","text":"/kaggle/input/mast3r-fix/mast3r/mast3r/model.py:24: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n  ckpt = torch.load(model_path, map_location='cpu')\nAdding keypoints and images: 0it [00:00, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/amy_gardens/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\ninstantiating : AsymmetricMASt3R(enc_depth=24, dec_depth=12, enc_embed_dim=1024, dec_embed_dim=768, enc_num_heads=16, dec_num_heads=12, pos_embed='RoPE100',img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False)\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:24:48.411354 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:48.412224 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:48.412249 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:48.412763 137119733372480 sift.cc:979] Check failed: options_.Check() \nI20251209 04:24:48.412386 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:48.412802 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:48.412842 137119741765184 sift.cc:979] Check failed: options_.Check() \nI20251209 04:24:48.412446 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:24:48.412506 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:48.412953 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:48.412969 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:48.412995 137119750157888 sift.cc:979] Check failed: options_.Check() \nE20251209 04:24:48.413017 137120499443264 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254288 (unix time) try \"date -d @1765254288\" if you are using GNU date ***\nterminate called recursively\nterminate called recursively\nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x87) received by PID 135 (TID 0x7cb5acffd640) from PID 135; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:24:48.815954 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:48.816827 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:48.816866 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:48.817355 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254288 (unix time) try \"date -d @1765254288\" if you are using GNU date ***\nI20251209 04:24:48.817584 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:48.817601 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:48.817645 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:48.817750 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:48.817757 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:48.817783 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:48.817866 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:48.817872 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:48.817888 137120499443264 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x91) received by PID 145 (TID 0x7cb5acffd640) from PID 145; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:24:49.191716 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:49.192838 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:49.192853 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:24:49.193341 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:24:49.193216 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:49.193466 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.193512 137119733372480 sift.cc:979] Check failed: options_.Check() \nE20251209 04:24:49.193519 137119741765184 sift.cc:979] Check failed: options_.Check() \nE20251209 04:24:49.193489 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.193576 137120499443264 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254289 (unix time) try \"date -d @1765254289\" if you are using GNU date ***\nI20251209 04:24:49.195105 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nterminate called recursively\nE20251209 04:24:49.195120 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.195180 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x9b) received by PID 155 (TID 0x7cb5daa92640) from PID 155; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:24:49.571637 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:49.572880 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:49.572899 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.574937 137120499443264 sift.cc:979] Check failed: options_.Check() \nI20251209 04:24:49.572933 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:24:49.572974 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:49.574978 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.575015 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'I20251209 04:24:49.573007 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nstd::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254289 (unix time) try \"date -d @1765254289\" if you are using GNU date ***\nE20251209 04:24:49.575127 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.575177 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:24:49.575232 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.575258 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0xa5) received by PID 165 (TID 0x7cb5daa92640) from PID 165; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:24:49.944549 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:49.945598 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:49.945612 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.946057 137119733372480 sift.cc:979] Check failed: options_.Check() \nI20251209 04:24:49.945950 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:49.946117 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.946165 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254289 (unix time) try \"date -d @1765254289\" if you are using GNU date ***\nI20251209 04:24:49.946282 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:49.946291 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.946320 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:49.945732 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:49.947924 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:49.947965 137120499443264 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0xaf) received by PID 175 (TID 0x7cb5adfff640) from PID 175; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset amy_gardens: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"fbk_vineyard\": 163 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"0it [00:00, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/fbk_vineyard/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/fbk_vineyard/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/fbk_vineyard/pairs.txt\nMASt3R matching done in 0.01 sec\n","output_type":"stream"},{"name":"stderr","text":"Adding keypoints and images: 0it [00:00, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/fbk_vineyard/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:24:53.414676 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:53.415766 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:24:53.415778 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:24:53.415795 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:53.415786 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:53.416211 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254293 (unix time) try \"date -d @1765254293\" if you are using GNU date ***\nE20251209 04:24:53.415802 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:53.417516 137120499443264 sift.cc:979] Check failed: options_.Check() \nE20251209 04:24:53.415811 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:53.417637 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called recursively\nI20251209 04:24:53.418019 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:53.418048 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:53.418086 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0xb9) received by PID 185 (TID 0x7cb5acffd640) from PID 185; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:24:53.802099 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:53.803101 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:53.803116 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:53.803543 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254293 (unix time) try \"date -d @1765254293\" if you are using GNU date ***\nI20251209 04:24:53.803903 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:53.803923 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:53.803961 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:53.804099 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:53.804106 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:53.804140 137120499443264 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:53.804112 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:53.804212 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:53.804244 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0xc3) received by PID 195 (TID 0x7cb5acffd640) from PID 195; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:24:54.188825 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:54.189888 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.189919 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:24:54.189972 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.190334 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.190390 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254294 (unix time) try \"date -d @1765254294\" if you are using GNU date ***\nE20251209 04:24:54.191131 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:54.189902 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:24:54.190180 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.192912 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.192959 137120499443264 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:24:54.193192 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.193430 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0xcd) received by PID 205 (TID 0x7cb5ad7fe640) from PID 205; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:24:54.586539 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:54.587578 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.587592 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.588010 137119733372480 sift.cc:979] Check failed: options_.Check() \nI20251209 04:24:54.587937 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.588069 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.588108 137120499443264 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254294 (unix time) try \"date -d @1765254294\" if you are using GNU date ***\nI20251209 04:24:54.588241 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.588250 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.588278 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nI20251209 04:24:54.588403 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.588409 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.588434 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0xd7) received by PID 215 (TID 0x7cb5daa92640) from PID 215; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aaf5a04 clone\nI20251209 04:24:54.980074 137119503517248 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:54.981142 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:24:54.981169 137120499443264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.981202 137120499443264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.981602 137120499443264 sift.cc:979] Check failed: options_.Check() \nI20251209 04:24:54.981243 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.981632 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.981667 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254294 (unix time) try \"date -d @1765254294\" if you are using GNU date ***\nterminate called after throwing an instance of 'std::invalid_argument'\nE20251209 04:24:54.981157 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.984178 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:54.981307 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:54.984407 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:54.984452 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0xe1) received by PID 225 (TID 0x7cb5ad7fe640) from PID 225; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset fbk_vineyard: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"imc2023_haiper\": 54 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"0it [00:00, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/imc2023_haiper/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2023_haiper/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2023_haiper/pairs.txt\nMASt3R matching done in 0.01 sec\n","output_type":"stream"},{"name":"stderr","text":"Adding keypoints and images: 0it [00:00, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/imc2023_haiper/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:24:58.441836 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:58.442935 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:58.442956 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:24:58.442974 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:24:58.442985 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:58.443372 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:58.443391 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:24:58.443494 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:58.443504 137119741765184 sift.cc:979] Check failed: options_.Check() \nE20251209 04:24:58.443505 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:58.443543 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254298 (unix time) try \"date -d @1765254298\" if you are using GNU date ***\nE20251209 04:24:58.443468 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:24:58.443328 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0xec) received by PID 236 (TID 0x7cb59f4c8640) from PID 236; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:24:58.843810 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:58.844862 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:58.844878 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:58.845356 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254298 (unix time) try \"date -d @1765254298\" if you are using GNU date ***\nI20251209 04:24:58.846172 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:58.846183 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:58.846222 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:58.844985 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:58.846797 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:58.846845 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:58.844997 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:58.847019 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:58.847076 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0xf6) received by PID 246 (TID 0x7cb59f4c8640) from PID 246; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:24:59.248643 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:59.249824 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:59.249843 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:59.250279 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254299 (unix time) try \"date -d @1765254299\" if you are using GNU date ***\nI20251209 04:24:59.252125 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:59.252144 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:59.252184 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:24:59.252367 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:59.252381 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:24:59.252446 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:59.252454 137119750157888 sift.cc:979] Check failed: options_.Check() \nE20251209 04:24:59.252459 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nterminate called recursively\nE20251209 04:24:59.252492 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x100) received by PID 256 (TID 0x7cb59f4c8640) from PID 256; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aaf5a04 clone\nI20251209 04:24:59.650926 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:24:59.651970 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:59.652002 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:59.652415 137119741765184 sift.cc:979] Check failed: options_.Check() \nI20251209 04:24:59.652064 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:59.652662 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nterminate called after throwing an instance of 'std::invalid_argument'\nE20251209 04:24:59.652727 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254299 (unix time) try \"date -d @1765254299\" if you are using GNU date ***\nI20251209 04:24:59.652129 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:24:59.652197 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:24:59.652869 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:59.652899 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:24:59.652827 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:24:59.652957 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x10a) received by PID 266 (TID 0x7cb59f4c8640) from PID 266; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79954d42a __gnu_cxx::__verbose_terminate_handler()\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aaf5a04 clone\nI20251209 04:25:00.069176 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:00.070165 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:00.070189 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:00.070727 137119750157888 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:00.070682 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:00.070813 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:00.070849 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254300 (unix time) try \"date -d @1765254300\" if you are using GNU date ***\nterminate called recursively\nI20251209 04:25:00.071301 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:00.071311 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:00.071350 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:00.071441 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:00.071447 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:00.071473 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x114) received by PID 276 (TID 0x7cb5adfff640) from PID 276; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset imc2023_haiper: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"imc2023_heritage\": 209 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"0it [00:00, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/imc2023_heritage/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2023_heritage/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2023_heritage/pairs.txt\nMASt3R matching done in 0.01 sec\n<All keys matched successfully>\n","output_type":"stream"},{"name":"stderr","text":"Adding keypoints and images: 0it [00:00, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"Loading retrieval model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth\n","output_type":"stream"},{"name":"stderr","text":"/kaggle/input/mast3r-fix/mast3r/mast3r/retrieval/processor.py:70: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n  ckpt = torch.load(modelname, 'cpu')  # TODO from pretrained to download it automatically\n","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/imc2023_heritage/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:03.691685 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:03.692834 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:03.692855 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:03.692860 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:03.693287 137119503517248 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:03.692889 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:03.693371 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'I20251209 04:25:03.692992 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254303 (unix time) try \"date -d @1765254303\" if you are using GNU date ***\nE20251209 04:25:03.693464 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:03.693504 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:03.693076 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:03.693569 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:03.693606 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nstd::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x11e) received by PID 286 (TID 0x7cb59f4c8640) from PID 286; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:04.048795 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:04.049823 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.049859 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.050341 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254304 (unix time) try \"date -d @1765254304\" if you are using GNU date ***\nI20251209 04:25:04.050125 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.050616 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.050667 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:04.050217 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.050755 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.050792 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:04.050281 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.050846 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.050894 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x128) received by PID 296 (TID 0x7cb59f4c8640) from PID 296; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:04.406298 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:04.407218 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.407240 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.407644 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254304 (unix time) try \"date -d @1765254304\" if you are using GNU date ***\nI20251209 04:25:04.408582 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.408606 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.408668 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:04.408846 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.408864 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.408894 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:04.408989 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.408995 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.409018 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x132) received by PID 306 (TID 0x7cb59f4c8640) from PID 306; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:04.767427 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:04.768514 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:04.768567 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:04.768560 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.768604 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.768976 137119750157888 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:04.768568 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.768625 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.769083 137119741765184 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:04.769030 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254304 (unix time) try \"date -d @1765254304\" if you are using GNU date ***\nI20251209 04:25:04.768575 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:04.769249 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:04.769292 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x13c) received by PID 316 (TID 0x7cb59f4c8640) from PID 316; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:05.148811 137118994130496 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:05.150056 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:05.150097 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:05.150608 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254305 (unix time) try \"date -d @1765254305\" if you are using GNU date ***\nI20251209 04:25:05.151188 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:05.151206 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:05.151251 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:05.150116 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:05.150211 137119750157888 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:05.152352 137119750157888 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:05.152403 137119750157888 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:25:05.152470 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:05.152504 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x146) received by PID 326 (TID 0x7cb59f4c8640) from PID 326; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n/usr/local/lib/python3.11/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Found 22 images\n","output_type":"stream"},{"name":"stderr","text":"  0%|          | 0/22 [00:00<?, ?it/s]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset imc2023_heritage: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"imc2023_theather_imc2024_church\": 76 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"\n0it [00:00, ?it/s]\u001b[A\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/imc2023_theather_imc2024_church/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2023_theather_imc2024_church/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2023_theather_imc2024_church/pairs.txt\nMASt3R matching done in 0.22 sec\n","output_type":"stream"},{"name":"stderr","text":" 14%|█▎        | 3/22 [00:02<00:12,  1.57it/s]\nAdding keypoints and images: 0it [00:00, ?it/s]\u001b[A\n 45%|████▌     | 10/22 [00:03<00:01,  6.09it/s]","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/imc2023_theather_imc2024_church/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":" 50%|█████     | 11/22 [00:03<00:01,  5.74it/s]I20251209 04:25:09.209910 137117711713856 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:09.210725 137117728499264 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:09.210736 137117720106560 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:09.210765 137117720106560 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:09.211342 137117720106560 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:09.210738 137117728499264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:09.211176 137117736891968 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:09.211231 137119003551296 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:09.211459 137119003551296 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nterminate called after throwing an instance of 'std::invalid_argument'\nE20251209 04:25:09.211441 137117736891968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254309 (unix time) try \"date -d @1765254309\" if you are using GNU date ***\nE20251209 04:25:09.211508 137117728499264 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:25:09.211532 137117736891968 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:09.211514 137119003551296 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x18d) received by PID 397 (TID 0x7cb534ffd640) from PID 397; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n 55%|█████▍    | 12/22 [00:03<00:01,  5.63it/s]    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n 59%|█████▉    | 13/22 [00:04<00:01,  5.14it/s]I20251209 04:25:09.631562 137117711713856 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:09.632315 137117720106560 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:09.632341 137117720106560 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:09.632815 137117728499264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:09.632825 137117720106560 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:09.632832 137117728499264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:09.632885 137119003551296 sift.cc:1432] Creating SIFT CPU feature matcher\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:09.632915 137119003551296 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n*** Aborted at 1765254309 (unix time) try \"date -d @1765254309\" if you are using GNU date ***\nE20251209 04:25:09.632981 137117728499264 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:09.633055 137119003551296 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called recursively\nI20251209 04:25:09.632676 137117736891968 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:09.633497 137117736891968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:09.633528 137117736891968 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x197) received by PID 407 (TID 0x7cb534ffd640) from PID 407; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n 64%|██████▎   | 14/22 [00:04<00:01,  5.21it/s]    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n 68%|██████▊   | 15/22 [00:04<00:01,  4.94it/s]I20251209 04:25:10.040465 137117711713856 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:10.041425 137117720106560 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.041462 137117720106560 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.042148 137117720106560 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:10.041618 137117728499264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.042288 137117728499264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nterminate called after throwing an instance of 'std::invalid_argument'\nE20251209 04:25:10.042327 137117728499264 sift.cc:979] Check failed: options_.Check() \n  what():  [sift.cc:979] Check failed: options_.Check() \nterminate called recursively\n*** Aborted at 1765254310 (unix time) try \"date -d @1765254310\" if you are using GNU date ***\nI20251209 04:25:10.041677 137117736891968 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.042409 137117736891968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.042429 137117736891968 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:10.041752 137119003551296 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.042539 137119003551296 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.042560 137119003551296 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1a1) received by PID 417 (TID 0x7cb534ffd640) from PID 417; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n 73%|███████▎  | 16/22 [00:04<00:01,  5.07it/s]    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aaf5a04 clone\n 77%|███████▋  | 17/22 [00:05<00:01,  4.85it/s]I20251209 04:25:10.455251 137117711713856 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:10.456106 137117720106560 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.456132 137117720106560 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.456770 137117720106560 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:10.456330 137117728499264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.456813 137117728499264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.456852 137117728499264 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254310 (unix time) try \"date -d @1765254310\" if you are using GNU date ***\nI20251209 04:25:10.456392 137117736891968 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:10.456480 137119003551296 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.457060 137119003551296 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.457092 137119003551296 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:25:10.457027 137117736891968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.457162 137117736891968 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1ab) received by PID 427 (TID 0x7cb5357fe640) from PID 427; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n 82%|████████▏ | 18/22 [00:05<00:00,  4.99it/s]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":" 86%|████████▋ | 19/22 [00:05<00:00,  5.00it/s]I20251209 04:25:10.843156 137117711713856 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:10.844114 137117720106560 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.844149 137117720106560 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.844597 137117720106560 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:10.844284 137117728499264 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.844646 137117728499264 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.844683 137117728499264 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254310 (unix time) try \"date -d @1765254310\" if you are using GNU date ***\nI20251209 04:25:10.844353 137117736891968 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.845176 137117736891968 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.845220 137117736891968 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:10.844420 137119003551296 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:10.845276 137119003551296 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:10.845300 137119003551296 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1b5) received by PID 437 (TID 0x7cb5357fe640) from PID 437; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n 91%|█████████ | 20/22 [00:05<00:00,  5.48it/s]    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n 95%|█████████▌| 21/22 [00:05<00:00,  5.40it/s]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset imc2023_theather_imc2024_church: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"imc2024_dioscuri_baalshamin\": 138 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 22/22 [00:06<00:00,  3.04it/s]\n0it [00:00, ?it/s]\u001b[A\n100%|██████████| 22/22 [00:07<00:00,  3.02it/s]\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/imc2024_dioscuri_baalshamin/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2024_dioscuri_baalshamin/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2024_dioscuri_baalshamin/pairs.txt\nMASt3R matching done in 0.01 sec\n","output_type":"stream"},{"name":"stderr","text":"Adding keypoints and images: 0it [00:00, ?it/s]\n","output_type":"stream"},{"name":"stdout","text":"✅ Generated 231 unique image pairs.\nShortlisting done: 231 pairs\n","output_type":"stream"},{"name":"stderr","text":"  0%|          | 0/231 [00:00<?, ?it/s]/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/inference.py:44: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n  with torch.cuda.amp.autocast(enabled=bool(use_amp)):\n/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/model.py:205: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n  with torch.cuda.amp.autocast(enabled=False):\n/kaggle/input/mast3r-fix/mast3r/dust3r/dust3r/inference.py:48: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n  with torch.cuda.amp.autocast(enabled=False):\n","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/imc2024_dioscuri_baalshamin/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:14.784173 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:14.785545 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:14.785572 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:14.785658 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:14.786068 137118994130496 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:14.786079 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:14.786121 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254314 (unix time) try \"date -d @1765254314\" if you are using GNU date ***\nI20251209 04:25:14.786322 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:14.786334 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:14.786360 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:14.785552 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:14.786421 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:14.786453 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1c0) received by PID 448 (TID 0x7cb5ad7fe640) from PID 448; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n  0%|          | 1/231 [00:00<03:21,  1.14it/s]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:15.249806 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:15.250788 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:15.250833 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:15.251378 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:15.251546 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:15.251571 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254315 (unix time) try \"date -d @1765254315\" if you are using GNU date ***\nI20251209 04:25:15.251443 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:15.252102 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:15.252159 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:15.251511 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:15.252258 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:15.252298 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:25:15.251529 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1ca) received by PID 458 (TID 0x7cb59f4c8640) from PID 458; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:25:15.651700 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:15.652983 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:15.653009 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:15.653602 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254315 (unix time) try \"date -d @1765254315\" if you are using GNU date ***\nI20251209 04:25:15.653161 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:15.654014 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:15.653225 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:15.654220 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:15.654248 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:25:15.654369 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:15.654486 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:15.654497 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:15.654534 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1d4) received by PID 468 (TID 0x7cb5acffd640) from PID 468; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n  1%|          | 2/231 [00:01<03:14,  1.18it/s]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:16.026536 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:16.028353 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:16.028384 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:16.028800 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254316 (unix time) try \"date -d @1765254316\" if you are using GNU date ***\nI20251209 04:25:16.029173 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:16.029194 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:16.029246 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:16.029356 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:16.029364 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:16.029392 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:16.029676 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:16.029730 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:16.029771 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1de) received by PID 478 (TID 0x7cb580efe640) from PID 478; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:16.432168 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:16.433463 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:16.433497 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:16.433903 137119741765184 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:16.433574 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:16.433946 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:16.433985 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254316 (unix time) try \"date -d @1765254316\" if you are using GNU date ***\nI20251209 04:25:16.433641 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:16.434894 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:16.434931 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:16.433707 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:16.434998 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:16.435024 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1e8) received by PID 488 (TID 0x7cb580efe640) from PID 488; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset imc2024_dioscuri_baalshamin: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"imc2024_lizard_pond\": 214 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"\n0it [00:00, ?it/s]\u001b[A\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/imc2024_lizard_pond/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2024_lizard_pond/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/imc2024_lizard_pond/pairs.txt\nMASt3R matching done in 0.01 sec\n","output_type":"stream"},{"name":"stderr","text":"  2%|▏         | 4/231 [00:04<04:06,  1.09s/it]\nAdding keypoints and images: 0it [00:00, ?it/s]\u001b[A\n  2%|▏         | 5/231 [00:05<03:33,  1.06it/s]","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/imc2024_lizard_pond/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:19.900268 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:19.901764 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:19.901796 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:19.902312 137119733372480 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:19.901892 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:19.902350 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:19.902394 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'terminate called after throwing an instance of 'I20251209 04:25:19.902020 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nstd::invalid_argumentI20251209 04:25:19.901966 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nstd::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:19.902566 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n*** Aborted at 1765254319 (unix time) try \"date -d @1765254319\" if you are using GNU date ***\nE20251209 04:25:19.902596 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\n'\n  what():  [sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:19.902532 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:19.903111 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1f2) received by PID 498 (TID 0x7cb580efe640) from PID 498; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n  3%|▎         | 6/231 [00:05<03:18,  1.13it/s]    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:20.306794 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:20.307774 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:20.307798 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:20.308510 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254320 (unix time) try \"date -d @1765254320\" if you are using GNU date ***\nI20251209 04:25:20.308344 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:20.309485 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:20.309522 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:20.308408 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:20.309585 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:20.309614 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:20.308476 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:20.309652 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:20.309678 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x1fc) received by PID 508 (TID 0x7cb580efe640) from PID 508; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aaf5a04 clone\nI20251209 04:25:20.739292 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:20.740688 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:20.740719 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:20.741329 137118994130496 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:20.741130 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:20.741459 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nterminate called after throwing an instance of 'std::invalid_argument'\nE20251209 04:25:20.741509 137119741765184 sift.cc:979] Check failed: options_.Check() \n  what():  [sift.cc:979] Check failed: options_.Check() \nterminate called recursively\n*** Aborted at 1765254320 (unix time) try \"date -d @1765254320\" if you are using GNU date ***\nI20251209 04:25:20.741214 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:20.741634 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:20.741765 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:20.741268 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:20.741836 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:20.741866 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x206) received by PID 518 (TID 0x7cb580efe640) from PID 518; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n  3%|▎         | 7/231 [00:06<03:17,  1.13it/s]    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:21.194821 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:21.196553 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:21.196579 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:21.196601 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:21.196793 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:21.197078 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:21.196867 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:21.197130 137119733372480 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:21.197136 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:21.197164 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\nE20251209 04:25:21.197026 137118994130496 sift.cc:979] Check failed: options_.Check() \n  what():  [sift.cc:979] Check failed: options_.Check() \nterminate called recursively\n*** Aborted at 1765254321 (unix time) try \"date -d @1765254321\" if you are using GNU date ***\nE20251209 04:25:21.197078 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:21.197362 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x210) received by PID 528 (TID 0x7cb59f4c8640) from PID 528; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:21.538025 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:21.540079 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:21.540102 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:21.540614 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254321 (unix time) try \"date -d @1765254321\" if you are using GNU date ***\nI20251209 04:25:21.540248 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:21.541638 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:21.541689 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:21.541812 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:21.541817 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:21.541841 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:21.542117 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:21.542134 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:21.542169 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x21a) received by PID 538 (TID 0x7cb580efe640) from PID 538; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n  3%|▎         | 8/231 [00:07<03:19,  1.12it/s]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset imc2024_lizard_pond: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"pt_brandenburg_british_buckingham\": 225 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"\n0it [00:00, ?it/s]\u001b[A\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/pt_brandenburg_british_buckingham/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/pt_brandenburg_british_buckingham/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/pt_brandenburg_british_buckingham/pairs.txt\nMASt3R matching done in 0.01 sec\n","output_type":"stream"},{"name":"stderr","text":"  4%|▍         | 9/231 [00:09<04:00,  1.08s/it]\nAdding keypoints and images: 0it [00:00, ?it/s]\u001b[A\n  5%|▍         | 11/231 [00:10<03:28,  1.05it/s]","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/pt_brandenburg_british_buckingham/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:25.057268 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:25.058482 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:25.058498 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.058511 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.058920 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254325 (unix time) try \"date -d @1765254325\" if you are using GNU date ***\nE20251209 04:25:25.058525 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.060285 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:25.058626 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.060526 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.060703 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:25.061114 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.061125 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.061159 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x224) received by PID 548 (TID 0x7cb580efe640) from PID 548; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:25.473887 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:25.474700 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.474739 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.475323 137118994130496 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:25.475352 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.475373 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.475405 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254325 (unix time) try \"date -d @1765254325\" if you are using GNU date ***\nI20251209 04:25:25.475546 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.475556 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.475592 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called recursively\nI20251209 04:25:25.475901 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.475917 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.475974 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x22e) received by PID 558 (TID 0x7cb580efe640) from PID 558; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:25:25.893081 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:25.894331 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.894366 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.895114 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254325 (unix time) try \"date -d @1765254325\" if you are using GNU date ***\nI20251209 04:25:25.894990 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.895574 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.895623 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:25.895029 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.895925 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.895987 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:25.894931 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:25.896067 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:25.896111 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x238) received by PID 568 (TID 0x7cb580efe640) from PID 568; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n  5%|▌         | 12/231 [00:11<03:34,  1.02it/s]    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:26.249662 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:26.250555 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:26.250584 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:26.251391 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:26.251578 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\n*** Aborted at 1765254326 (unix time) try \"date -d @1765254326\" if you are using GNU date ***\nE20251209 04:25:26.251592 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:26.251617 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:26.251726 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:26.251739 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:26.251765 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:26.251847 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:26.251853 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:26.251878 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x242) received by PID 578 (TID 0x7cb580efe640) from PID 578; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:25:26.721273 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:26.722671 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:26.722700 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:26.723270 137118994130496 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:26.723367 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:26.723379 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254326 (unix time) try \"date -d @1765254326\" if you are using GNU date ***\nE20251209 04:25:26.723415 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:26.723842 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:26.723859 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:26.723889 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:26.724145 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:26.724161 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:26.724206 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x24c) received by PID 588 (TID 0x7cb580efe640) from PID 588; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset pt_brandenburg_british_buckingham: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"pt_piazzasanmarco_grandplace\": 168 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"  6%|▌         | 13/231 [00:12<03:36,  1.01it/s]\n0it [00:00, ?it/s]\u001b[A\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/pt_piazzasanmarco_grandplace/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/pt_piazzasanmarco_grandplace/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/pt_piazzasanmarco_grandplace/pairs.txt\nMASt3R matching done in 0.01 sec\n","output_type":"stream"},{"name":"stderr","text":"  6%|▌         | 14/231 [00:14<04:32,  1.26s/it]\nAdding keypoints and images: 0it [00:00, ?it/s]\u001b[A\n  6%|▋         | 15/231 [00:15<04:03,  1.13s/it]","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/pt_piazzasanmarco_grandplace/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:30.168004 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:30.169375 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.169409 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.170161 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254330 (unix time) try \"date -d @1765254330\" if you are using GNU date ***\nI20251209 04:25:30.169610 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.171168 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.171223 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:30.169687 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.171309 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.171329 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:30.169762 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.171375 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.171406 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x256) received by PID 598 (TID 0x7cb5ad7fe640) from PID 598; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:30.529515 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:30.530773 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.530800 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.531432 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254330 (unix time) try \"date -d @1765254330\" if you are using GNU date ***\nI20251209 04:25:30.531651 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.531671 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.531711 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:30.531853 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.531863 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.531917 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:30.532034 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.532081 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.532130 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x260) received by PID 608 (TID 0x7cb580efe640) from PID 608; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n  7%|▋         | 16/231 [00:16<03:50,  1.07s/it]    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:30.930516 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:30.932287 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.932321 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.932945 137118994130496 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:30.932580 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.933086 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.933120 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254330 (unix time) try \"date -d @1765254330\" if you are using GNU date ***\nI20251209 04:25:30.934142 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.934159 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.934207 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:30.934321 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:30.934330 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:30.934357 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x26a) received by PID 618 (TID 0x7cb59f4c8640) from PID 618; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:25:31.376184 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:31.377483 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:31.377515 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:31.377938 137118994130496 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:31.377539 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254331 (unix time) try \"date -d @1765254331\" if you are using GNU date ***\nE20251209 04:25:31.378113 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:31.378173 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:31.379660 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:31.379683 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:31.379724 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:31.379841 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:31.379892 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:31.379959 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x274) received by PID 628 (TID 0x7cb580efe640) from PID 628; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n  7%|▋         | 17/231 [00:17<03:45,  1.06s/it]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:31.760104 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:31.761199 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:31.761216 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:31.761649 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254331 (unix time) try \"date -d @1765254331\" if you are using GNU date ***\nI20251209 04:25:31.762450 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:31.762463 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:31.762500 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:31.762597 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:31.762602 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:31.762626 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:31.763132 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:31.763150 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:31.763331 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x27e) received by PID 638 (TID 0x7cb580efe640) from PID 638; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset pt_piazzasanmarco_grandplace: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"pt_sacrecoeur_trevi_tajmahal\": 225 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"\n0it [00:00, ?it/s]\u001b[A\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/pt_sacrecoeur_trevi_tajmahal/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/pt_sacrecoeur_trevi_tajmahal/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/pt_sacrecoeur_trevi_tajmahal/pairs.txt\nMASt3R matching done in 0.01 sec\n","output_type":"stream"},{"name":"stderr","text":"  8%|▊         | 18/231 [00:19<04:30,  1.27s/it]\nAdding keypoints and images: 0it [00:00, ?it/s]\u001b[A\n  9%|▊         | 20/231 [00:20<03:33,  1.01s/it]","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/pt_sacrecoeur_trevi_tajmahal/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:35.227288 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:35.228424 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:35.228468 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:35.229220 137118994130496 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:35.229413 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:35.229590 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:35.229626 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254335 (unix time) try \"date -d @1765254335\" if you are using GNU date ***\nterminate called recursively\nI20251209 04:25:35.229475 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:35.229983 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:35.230012 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:35.229139 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:35.230071 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:35.230093 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x288) received by PID 648 (TID 0x7cb5acffd640) from PID 648; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:25:35.629325 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:35.630612 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:35.630642 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:35.631134 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254335 (unix time) try \"date -d @1765254335\" if you are using GNU date ***\nI20251209 04:25:35.630732 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:35.631859 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:35.632094 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:35.632186 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:35.632199 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:35.632238 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:35.632112 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:35.632319 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:35.632342 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x292) received by PID 658 (TID 0x7cb580efe640) from PID 658; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n  9%|▉         | 21/231 [00:21<03:28,  1.01it/s]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:36.039900 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:36.040923 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.040971 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.041797 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254336 (unix time) try \"date -d @1765254336\" if you are using GNU date ***\nI20251209 04:25:36.041542 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.042192 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.042233 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:36.041610 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.042305 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.042342 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:36.041730 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.042392 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.042427 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x29c) received by PID 668 (TID 0x7cb580efe640) from PID 668; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:36.393887 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:36.395665 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.395694 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.396934 137118994130496 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:36.396003 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.396991 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.397060 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'I20251209 04:25:36.396138 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.397140 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.397180 137119733372480 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:36.396247 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.397283 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.397311 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\n*** Aborted at 1765254336 (unix time) try \"date -d @1765254336\" if you are using GNU date ***\nstd::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x2a6) received by PID 678 (TID 0x7cb5ad7fe640) from PID 678; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79954d42a __gnu_cxx::__verbose_terminate_handler()\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n 10%|▉         | 22/231 [00:22<03:14,  1.07it/s]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:36.800835 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:36.802247 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.802271 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.802860 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254336 (unix time) try \"date -d @1765254336\" if you are using GNU date ***\nI20251209 04:25:36.804163 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.804181 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.804231 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:36.804354 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.804363 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.804395 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:36.805119 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:36.805133 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:36.805170 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x2b0) received by PID 688 (TID 0x7cb580efe640) from PID 688; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset pt_sacrecoeur_trevi_tajmahal: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"pt_stpeters_stpauls\": 200 images\n🖼️ Scanning images...\n⚠️ No valid images found in the directory. Returning empty pairs.\nShortlisting done: 0 pairs\n","output_type":"stream"},{"name":"stderr","text":"\n0it [00:00, ?it/s]\u001b[A\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/pt_stpeters_stpauls/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/pt_stpeters_stpauls/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/pt_stpeters_stpauls/pairs.txt\nMASt3R matching done in 0.01 sec\n","output_type":"stream"},{"name":"stderr","text":" 10%|█         | 24/231 [00:24<03:32,  1.03s/it]\nAdding keypoints and images: 0it [00:00, ?it/s]\u001b[A\n 11%|█         | 25/231 [00:25<03:12,  1.07it/s]","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/pt_stpeters_stpauls/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:40.299745 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:40.301496 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:40.301515 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:40.301520 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:40.302125 137119741765184 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:40.301525 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:40.302313 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'terminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nstd::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:40.301617 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\n*** Aborted at 1765254340 (unix time) try \"date -d @1765254340\" if you are using GNU date ***\nE20251209 04:25:40.302596 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:40.301658 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:40.302889 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:40.302937 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:25:40.303118 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x2ba) received by PID 698 (TID 0x7cb5acffd640) from PID 698; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 2 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:40.686331 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:40.687871 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:40.687896 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:25:40.687975 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:40.687900 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:40.688488 137119733372480 sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:40.687948 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:40.688542 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254340 (unix time) try \"date -d @1765254340\" if you are using GNU date ***\nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:40.687989 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nI20251209 04:25:40.688152 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:40.689145 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:40.689209 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nE20251209 04:25:40.689295 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x2c4) received by PID 708 (TID 0x7cb580efe640) from PID 708; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n 11%|█▏        | 26/231 [00:26<03:09,  1.08it/s]    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 3 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:41.101493 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:41.102735 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.102761 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.103548 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254341 (unix time) try \"date -d @1765254341\" if you are using GNU date ***\nI20251209 04:25:41.105107 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.105133 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.105187 137119733372480 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:41.105218 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nterminate called recursively\nE20251209 04:25:41.105226 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.105258 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:41.105158 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.105301 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.105318 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x2ce) received by PID 718 (TID 0x7cb580efe640) from PID 718; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 4 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:25:41.476678 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:41.477810 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.477847 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.478557 137118994130496 sift.cc:979] Check failed: options_.Check() \nI20251209 04:25:41.478384 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.478604 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.478642 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'terminate called after throwing an instance of 'I20251209 04:25:41.478312 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nstd::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nE20251209 04:25:41.478750 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\n*** Aborted at 1765254341 (unix time) try \"date -d @1765254341\" if you are using GNU date ***\nE20251209 04:25:41.478786 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:41.478227 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.478831 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.478847 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nstd::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x2d8) received by PID 728 (TID 0x7cb580efe640) from PID 728; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n 12%|█▏        | 27/231 [00:27<03:06,  1.09it/s]    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\n🔁 Attempt 5 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\nI20251209 04:25:41.906946 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:25:41.907801 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.907838 137118994130496 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.908565 137118994130496 sift.cc:979] Check failed: options_.Check() \nterminate called after throwing an instance of 'std::invalid_argument'\n  what():  [sift.cc:979] Check failed: options_.Check() \n*** Aborted at 1765254341 (unix time) try \"date -d @1765254341\" if you are using GNU date ***\nI20251209 04:25:41.909363 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.909382 137119503517248 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.909420 137119503517248 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:41.909534 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.909544 137119733372480 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.909581 137119733372480 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nI20251209 04:25:41.909708 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nE20251209 04:25:41.909719 137119741765184 logging.h:126] [sift.cc:89] Check failed: max_num_matches > 0 (0 vs. 0)\nE20251209 04:25:41.909985 137119741765184 sift.cc:979] Check failed: options_.Check() \nterminate called recursively\nPC: @     0x7cb79aa669fc pthread_kill\n*** SIGABRT (@0x2e2) received by PID 738 (TID 0x7cb580efe640) from PID 738; stack trace: ***\n    @     0x7cb79aa69ee8 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x99ee7)\n    @     0x7cb6a8e7c388 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x69d387)\n    @     0x7cb79aa12520 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x4251f)\n    @     0x7cb79aa669fc pthread_kill\n    @     0x7cb79aa12476 raise\n    @     0x7cb79a9f87f3 abort\n    @     0x7cb79953fb9e (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xa2b9d)\n    @     0x7cb79954b20c (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xae20b)\n    @     0x7cb79954b277 std::terminate()\n    @     0x7cb79954b4d8 __cxa_throw\n    @     0x7cb6a89ea373 (/usr/local/lib/python3.11/dist-packages/pycolmap/_core.cpython-311-x86_64-linux-gnu.so+0x20b372)\n    @     0x7cb6a9098881 colmap::CreateSiftFeatureMatcher(colmap::SiftMatchingOptions const&)\n    @     0x7cb6a900da4b colmap::FeatureMatcherWorker::Run()\n    @     0x7cb6a99ce872 colmap::Thread::RunFunc()\n    @     0x7cb799579253 (/usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.30+0xdc252)\n    @     0x7cb79aa64ac3 (/usr/lib/x86_64-linux-gnu/libc.so.6+0x94ac2)\n    @     0x7cb79aaf5a04 clone\n 12%|█▏        | 28/231 [00:28<02:59,  1.13it/s]","output_type":"stream"},{"name":"stdout","text":"⚠️ verify_matches crashed with code -6\nError in dataset pt_stpeters_stpauls: ❌ verify_matches failed after multiple retries.\nProcessing dataset \"stairs\": 51 images\n🖼️ Scanning images...\nSkipping invalid image file: LICENSE.txt\n","output_type":"stream"},{"name":"stderr","text":" 13%|█▎        | 29/231 [00:28<02:49,  1.19it/s]","output_type":"stream"},{"name":"stdout","text":"⚙️ Loading backbone model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth...\n... loading model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth\n","output_type":"stream"},{"name":"stderr","text":" 13%|█▎        | 31/231 [00:30<02:51,  1.17it/s]","output_type":"stream"},{"name":"stdout","text":"instantiating : AsymmetricMASt3R(enc_depth=24, dec_depth=12, enc_embed_dim=1024, dec_embed_dim=768, enc_num_heads=16, dec_num_heads=12, pos_embed='RoPE100',img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), patch_embed_cls='PatchEmbedDust3R', two_confs=True, desc_conf_mode=('exp', 0, inf), landscape_only=False)\n","output_type":"stream"},{"name":"stderr","text":" 17%|█▋        | 40/231 [00:38<02:38,  1.21it/s]","output_type":"stream"},{"name":"stdout","text":"<All keys matched successfully>\n","output_type":"stream"},{"name":"stderr","text":" 18%|█▊        | 41/231 [00:39<03:14,  1.02s/it]","output_type":"stream"},{"name":"stdout","text":"Loading retrieval model from /kaggle/input/mast3r-fix/mast3r/checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric_retrieval_trainingfree.pth\nFound 51 images\n","output_type":"stream"},{"name":"stderr","text":"\n  0%|          | 0/51 [00:00<?, ?it/s]\u001b[A\n  2%|▏         | 1/51 [00:01<01:18,  1.57s/it]\u001b[A\n  4%|▍         | 2/51 [00:01<00:39,  1.25it/s]\u001b[A\n  6%|▌         | 3/51 [00:02<00:26,  1.83it/s]\u001b[A\n  8%|▊         | 4/51 [00:02<00:20,  2.32it/s]\u001b[A\n 10%|▉         | 5/51 [00:02<00:16,  2.73it/s]\u001b[A\n 12%|█▏        | 6/51 [00:02<00:14,  3.06it/s]\u001b[A\n 14%|█▎        | 7/51 [00:03<00:13,  3.33it/s]\u001b[A\n 16%|█▌        | 8/51 [00:03<00:12,  3.52it/s]\u001b[A\n 18%|█▊        | 9/51 [00:03<00:11,  3.66it/s]\u001b[A\n 20%|█▉        | 10/51 [00:03<00:10,  3.76it/s]\u001b[A\n 22%|██▏       | 11/51 [00:04<00:10,  3.83it/s]\u001b[A\n 24%|██▎       | 12/51 [00:04<00:10,  3.88it/s]\u001b[A\n 25%|██▌       | 13/51 [00:04<00:09,  3.91it/s]\u001b[A\n 27%|██▋       | 14/51 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224/231 [06:11<00:13,  1.97s/it]]\u001b[A\n 98%|█████████▊| 226/231 [06:14<00:09,  1.81s/it]]\u001b[A\n 98%|█████████▊| 227/231 [06:16<00:07,  1.89s/it]]\u001b[A\n 99%|█████████▉| 229/231 [06:19<00:03,  1.77s/it]]\u001b[A\n100%|██████████| 231/231 [06:22<00:00,  1.66s/it]]\u001b[A\n\n 11%|█▏        | 133/1160 [05:23<43:42,  2.55s/it]\u001b[A","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/ETs/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/ETs/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/ETs/pairs.txt\nMASt3R matching done in 383.95 sec\n","output_type":"stream"},{"name":"stderr","text":"\nAdding keypoints and images: 100%|██████████| 19/19 [00:00<00:00, 75.00it/s]\n\n 12%|█▏        | 135/1160 [05:27<37:15,  2.18s/it]\u001b[A","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/ETs/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:31:41.232102 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:31:41.233929 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:31:41.233929 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:31:41.234028 137119495124544 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:31:41.234103 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:31:41.234124 137119045514816 pairing.cc:742] Importing image pairs...\nI20251209 04:31:41.234427 137119045514816 pairing.cc:775] Matching block [1/1]\n\n 12%|█▏        | 136/1160 [05:29<34:33,  2.02s/it]\u001b[AI20251209 04:31:42.892770 137119045514816 feature_matching.cc:46] in 1.658s\nI20251209 04:31:42.896505 137119045514816 timer.cc:91] Elapsed time: 0.028 [minutes]\n","output_type":"stream"},{"name":"stdout","text":"✅ verify_matches succeeded\nverify matching done!!!!\nRANSAC in 1.9882 sec\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:31:43.104184 137119503517248 incremental_pipeline.cc:237] Loading database\nI20251209 04:31:43.106653 137119503517248 database_cache.cc:66] Loading cameras...\nI20251209 04:31:43.106734 137119503517248 database_cache.cc:76]  19 in 0.000s\nI20251209 04:31:43.106757 137119503517248 database_cache.cc:84] Loading matches...\nI20251209 04:31:43.108856 137119503517248 database_cache.cc:89]  80 in 0.002s\nI20251209 04:31:43.108881 137119503517248 database_cache.cc:105] Loading images...\nI20251209 04:31:43.117104 137119503517248 database_cache.cc:153]  19 in 0.008s (connected 19)\nI20251209 04:31:43.117132 137119503517248 database_cache.cc:164] Loading pose priors...\nI20251209 04:31:43.117273 137119503517248 database_cache.cc:175]  0 in 0.000s\nI20251209 04:31:43.117293 137119503517248 database_cache.cc:184] Building correspondence graph...\nW20251209 04:31:43.118975 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5259 in image_id=1 and point2D_idx=5353 in image_id=2\nW20251209 04:31:43.119211 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8284 in image_id=1 and point2D_idx=9131 in image_id=2\nW20251209 04:31:43.119254 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5266 in image_id=1 and point2D_idx=5362 in image_id=2\nW20251209 04:31:43.119359 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3272 in image_id=1 and point2D_idx=3211 in image_id=2\nW20251209 04:31:43.119454 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5745 in image_id=1 and point2D_idx=5790 in image_id=2\nW20251209 04:31:43.119614 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1531 in image_id=1 and point2D_idx=1214 in image_id=2\nW20251209 04:31:43.119716 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10825 in image_id=1 and point2D_idx=11839 in image_id=2\nW20251209 04:31:43.119747 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9393 in image_id=1 and point2D_idx=10189 in image_id=2\nW20251209 04:31:43.119761 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10826 in image_id=1 and point2D_idx=11840 in image_id=2\nW20251209 04:31:43.119858 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7912 in image_id=1 and point2D_idx=8329 in image_id=2\nW20251209 04:31:43.119958 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9409 in image_id=1 and point2D_idx=10160 in image_id=2\nW20251209 04:31:43.119980 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11142 in image_id=1 and point2D_idx=12071 in image_id=2\nW20251209 04:31:43.120133 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5709 in image_id=1 and point2D_idx=3338 in image_id=3\nW20251209 04:31:43.120183 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7832 in image_id=1 and point2D_idx=5753 in image_id=3\nW20251209 04:31:43.120211 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2764 in image_id=1 and point2D_idx=522 in image_id=3\nW20251209 04:31:43.120289 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6981 in image_id=1 and point2D_idx=4727 in image_id=3\nW20251209 04:31:43.120353 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6565 in image_id=1 and point2D_idx=4287 in image_id=3\nW20251209 04:31:43.120372 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8312 in image_id=1 and point2D_idx=5820 in image_id=3\nW20251209 04:31:43.120383 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9019 in image_id=1 and point2D_idx=6434 in image_id=3\nW20251209 04:31:43.120418 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9725 in image_id=1 and point2D_idx=7014 in image_id=3\nW20251209 04:31:43.120453 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1909 in image_id=1 and point2D_idx=473 in image_id=3\nW20251209 04:31:43.120476 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9729 in image_id=1 and point2D_idx=7016 in image_id=3\nW20251209 04:31:43.120494 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2796 in image_id=1 and point2D_idx=1316 in image_id=3\nW20251209 04:31:43.120532 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2332 in image_id=1 and point2D_idx=555 in image_id=3\nW20251209 04:31:43.120577 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2808 in image_id=1 and point2D_idx=818 in image_id=3\nW20251209 04:31:43.120595 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3842 in image_id=1 and point2D_idx=1989 in image_id=3\nW20251209 04:31:43.120647 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6204 in image_id=1 and point2D_idx=3841 in image_id=3\nW20251209 04:31:43.120763 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9434 in image_id=1 and point2D_idx=6935 in image_id=4\nW20251209 04:31:43.120782 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7869 in image_id=1 and point2D_idx=6225 in image_id=4\nW20251209 04:31:43.120806 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9747 in image_id=1 and point2D_idx=8356 in image_id=4\nW20251209 04:31:43.120850 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10132 in image_id=1 and point2D_idx=8820 in image_id=4\nW20251209 04:31:43.120885 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3327 in image_id=1 and point2D_idx=2099 in image_id=4\nW20251209 04:31:43.121026 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6164 in image_id=1 and point2D_idx=5418 in image_id=5\nW20251209 04:31:43.121108 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6996 in image_id=1 and point2D_idx=6760 in image_id=5\nW20251209 04:31:43.121187 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7011 in image_id=1 and point2D_idx=6773 in image_id=5\nW20251209 04:31:43.121228 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1149 in image_id=1 and point2D_idx=357 in image_id=5\nW20251209 04:31:43.121350 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10814 in image_id=1 and point2D_idx=10615 in image_id=5\nW20251209 04:31:43.121416 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10822 in image_id=1 and point2D_idx=10670 in image_id=5\nW20251209 04:31:43.121554 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7442 in image_id=1 and point2D_idx=7565 in image_id=5\nW20251209 04:31:43.121656 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9406 in image_id=1 and point2D_idx=9213 in image_id=5\nW20251209 04:31:43.121692 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3876 in image_id=1 and point2D_idx=3296 in image_id=5\nW20251209 04:31:43.121759 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5353 in image_id=1 and point2D_idx=4575 in image_id=5\nW20251209 04:31:43.121896 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7483 in image_id=1 and point2D_idx=9277 in image_id=6\nW20251209 04:31:43.121944 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11072 in image_id=1 and point2D_idx=3305 in image_id=6\nW20251209 04:31:43.121968 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9363 in image_id=1 and point2D_idx=9287 in image_id=6\nW20251209 04:31:43.121984 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9365 in image_id=1 and point2D_idx=9374 in image_id=6\nW20251209 04:31:43.122011 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8760 in image_id=1 and point2D_idx=9298 in image_id=6\nW20251209 04:31:43.122078 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9380 in image_id=1 and point2D_idx=10880 in image_id=6\nW20251209 04:31:43.122100 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=864 in image_id=1 and point2D_idx=1570 in image_id=6\nW20251209 04:31:43.122123 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10129 in image_id=1 and point2D_idx=11931 in image_id=6\nW20251209 04:31:43.122133 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2345 in image_id=1 and point2D_idx=382 in image_id=6\nW20251209 04:31:43.122195 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7438 in image_id=1 and point2D_idx=8304 in image_id=6\nW20251209 04:31:43.122226 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1561 in image_id=1 and point2D_idx=2338 in image_id=6\nW20251209 04:31:43.122306 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4431 in image_id=1 and point2D_idx=9857 in image_id=6\nW20251209 04:31:43.122371 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4762 in image_id=1 and point2D_idx=5714 in image_id=7\nW20251209 04:31:43.122397 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6171 in image_id=1 and point2D_idx=4707 in image_id=7\nW20251209 04:31:43.122422 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4332 in image_id=1 and point2D_idx=3075 in image_id=7\nW20251209 04:31:43.122436 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1512 in image_id=1 and point2D_idx=1061 in image_id=7\nW20251209 04:31:43.122444 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2329 in image_id=1 and point2D_idx=1542 in image_id=7\nW20251209 04:31:43.122465 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1912 in image_id=1 and point2D_idx=1743 in image_id=7\nW20251209 04:31:43.122482 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1519 in image_id=1 and point2D_idx=2246 in image_id=7\nW20251209 04:31:43.122491 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7416 in image_id=1 and point2D_idx=6949 in image_id=7\nW20251209 04:31:43.122523 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2340 in image_id=1 and point2D_idx=4737 in image_id=7\nW20251209 04:31:43.122545 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2813 in image_id=1 and point2D_idx=6077 in image_id=7\nW20251209 04:31:43.122567 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2820 in image_id=1 and point2D_idx=526 in image_id=7\nW20251209 04:31:43.122581 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11923 in image_id=1 and point2D_idx=8636 in image_id=7\nW20251209 04:31:43.122593 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2824 in image_id=1 and point2D_idx=2501 in image_id=7\nW20251209 04:31:43.122601 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3857 in image_id=1 and point2D_idx=3919 in image_id=7\nW20251209 04:31:43.122617 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11924 in image_id=1 and point2D_idx=9135 in image_id=7\nW20251209 04:31:43.122656 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3338 in image_id=1 and point2D_idx=4323 in image_id=7\nW20251209 04:31:43.122668 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1956 in image_id=1 and point2D_idx=2852 in image_id=7\nW20251209 04:31:43.122709 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6613 in image_id=1 and point2D_idx=9400 in image_id=7\nW20251209 04:31:43.122725 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3876 in image_id=1 and point2D_idx=8011 in image_id=7\nW20251209 04:31:43.122772 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4308 in image_id=1 and point2D_idx=7931 in image_id=8\nW20251209 04:31:43.122779 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4748 in image_id=1 and point2D_idx=8321 in image_id=8\nW20251209 04:31:43.122793 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3269 in image_id=1 and point2D_idx=6193 in image_id=8\nW20251209 04:31:43.122805 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6167 in image_id=1 and point2D_idx=3768 in image_id=8\nW20251209 04:31:43.122824 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2326 in image_id=1 and point2D_idx=1457 in image_id=8\nW20251209 04:31:43.122840 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2796 in image_id=1 and point2D_idx=2043 in image_id=8\nW20251209 04:31:43.122848 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9880 in image_id=1 and point2D_idx=6507 in image_id=8\nW20251209 04:31:43.122865 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1151 in image_id=1 and point2D_idx=3208 in image_id=8\nW20251209 04:31:43.122876 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7951 in image_id=1 and point2D_idx=6132 in image_id=8\nW20251209 04:31:43.122883 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9745 in image_id=1 and point2D_idx=8356 in image_id=8\nW20251209 04:31:43.122891 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7875 in image_id=1 and point2D_idx=6136 in image_id=8\nW20251209 04:31:43.122927 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=435 in image_id=1 and point2D_idx=1065 in image_id=8\nW20251209 04:31:43.122940 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1956 in image_id=1 and point2D_idx=3394 in image_id=8\nW20251209 04:31:43.122954 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7970 in image_id=1 and point2D_idx=7227 in image_id=8\nW20251209 04:31:43.122969 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3358 in image_id=1 and point2D_idx=7281 in image_id=8\nW20251209 04:31:43.123002 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5742 in image_id=1 and point2D_idx=3015 in image_id=9\nW20251209 04:31:43.123021 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6174 in image_id=1 and point2D_idx=4222 in image_id=9\nW20251209 04:31:43.123031 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4780 in image_id=1 and point2D_idx=3553 in image_id=9\nW20251209 04:31:43.123130 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9386 in image_id=1 and point2D_idx=6198 in image_id=9\nW20251209 04:31:43.123193 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1547 in image_id=1 and point2D_idx=1140 in image_id=9\nW20251209 04:31:43.123261 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3258 in image_id=1 and point2D_idx=5313 in image_id=10\nW20251209 04:31:43.123280 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2491 in image_id=1 and point2D_idx=4866 in image_id=10\nW20251209 04:31:43.123301 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3285 in image_id=1 and point2D_idx=1783 in image_id=10\nW20251209 04:31:43.123336 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2817 in image_id=1 and point2D_idx=421 in image_id=10\nW20251209 04:31:43.123352 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2820 in image_id=1 and point2D_idx=754 in image_id=10\nW20251209 04:31:43.123368 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1941 in image_id=1 and point2D_idx=1141 in image_id=10\nW20251209 04:31:43.123381 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3316 in image_id=1 and point2D_idx=2978 in image_id=10\nW20251209 04:31:43.123460 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5755 in image_id=2 and point2D_idx=3328 in image_id=3\nW20251209 04:31:43.123513 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5351 in image_id=2 and point2D_idx=2894 in image_id=3\nW20251209 04:31:43.123532 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6576 in image_id=2 and point2D_idx=4001 in image_id=3\nW20251209 04:31:43.123662 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8681 in image_id=2 and point2D_idx=5812 in image_id=3\nW20251209 04:31:43.123733 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4479 in image_id=2 and point2D_idx=2675 in image_id=3\nW20251209 04:31:43.123768 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4943 in image_id=2 and point2D_idx=2922 in image_id=3\nW20251209 04:31:43.123838 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7450 in image_id=2 and point2D_idx=4733 in image_id=3\nW20251209 04:31:43.123977 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7860 in image_id=2 and point2D_idx=4928 in image_id=3\nW20251209 04:31:43.124015 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9097 in image_id=2 and point2D_idx=5840 in image_id=3\nW20251209 04:31:43.124050 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10844 in image_id=2 and point2D_idx=7237 in image_id=3\nW20251209 04:31:43.124071 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10122 in image_id=2 and point2D_idx=6638 in image_id=3\nW20251209 04:31:43.124110 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5424 in image_id=2 and point2D_idx=3189 in image_id=3\nW20251209 04:31:43.124141 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5429 in image_id=2 and point2D_idx=3108 in image_id=3\nW20251209 04:31:43.124208 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6166 in image_id=2 and point2D_idx=4450 in image_id=4\nW20251209 04:31:43.124240 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7412 in image_id=2 and point2D_idx=5478 in image_id=4\nW20251209 04:31:43.124300 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7418 in image_id=2 and point2D_idx=5481 in image_id=4\nW20251209 04:31:43.124408 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6178 in image_id=2 and point2D_idx=4465 in image_id=4\nW20251209 04:31:43.124467 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4927 in image_id=2 and point2D_idx=3499 in image_id=4\nW20251209 04:31:43.124518 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8252 in image_id=2 and point2D_idx=6146 in image_id=4\nW20251209 04:31:43.124567 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8260 in image_id=2 and point2D_idx=6154 in image_id=4\nW20251209 04:31:43.124651 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=873 in image_id=2 and point2D_idx=66 in image_id=4\nW20251209 04:31:43.124675 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7034 in image_id=2 and point2D_idx=5265 in image_id=4\nW20251209 04:31:43.124726 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4066 in image_id=2 and point2D_idx=2911 in image_id=4\nW20251209 04:31:43.124755 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1209 in image_id=2 and point2D_idx=74 in image_id=4\nW20251209 04:31:43.124798 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4071 in image_id=2 and point2D_idx=2916 in image_id=4\nW20251209 04:31:43.124844 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9473 in image_id=2 and point2D_idx=7233 in image_id=4\nW20251209 04:31:43.124903 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5826 in image_id=2 and point2D_idx=4303 in image_id=4\nW20251209 04:31:43.124992 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3266 in image_id=2 and point2D_idx=2101 in image_id=4\nW20251209 04:31:43.125081 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10442 in image_id=2 and point2D_idx=10652 in image_id=5\nW20251209 04:31:43.125175 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6597 in image_id=2 and point2D_idx=5705 in image_id=5\nW20251209 04:31:43.125270 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11821 in image_id=2 and point2D_idx=10668 in image_id=5\nW20251209 04:31:43.125301 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12049 in image_id=2 and point2D_idx=10853 in image_id=5\nW20251209 04:31:43.125312 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2815 in image_id=2 and point2D_idx=2884 in image_id=5\nW20251209 04:31:43.125332 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6637 in image_id=2 and point2D_idx=6797 in image_id=5\nW20251209 04:31:43.125461 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7087 in image_id=2 and point2D_idx=7216 in image_id=6\nW20251209 04:31:43.125518 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5464 in image_id=2 and point2D_idx=7234 in image_id=6\nW20251209 04:31:43.125535 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9064 in image_id=2 and point2D_idx=10239 in image_id=6\nW20251209 04:31:43.125547 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9776 in image_id=2 and point2D_idx=1163 in image_id=6\nW20251209 04:31:43.125570 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10173 in image_id=2 and point2D_idx=1906 in image_id=6\nW20251209 04:31:43.125592 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3224 in image_id=2 and point2D_idx=1913 in image_id=6\nW20251209 04:31:43.125638 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4950 in image_id=2 and point2D_idx=4896 in image_id=6\nW20251209 04:31:43.125658 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10902 in image_id=2 and point2D_idx=10592 in image_id=6\nW20251209 04:31:43.125720 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9101 in image_id=2 and point2D_idx=9315 in image_id=6\nW20251209 04:31:43.125743 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1225 in image_id=2 and point2D_idx=409 in image_id=6\nW20251209 04:31:43.125777 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10493 in image_id=2 and point2D_idx=11381 in image_id=6\nW20251209 04:31:43.125806 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5503 in image_id=2 and point2D_idx=6293 in image_id=6\nW20251209 04:31:43.125846 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6712 in image_id=2 and point2D_idx=8827 in image_id=6\nW20251209 04:31:43.125857 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=337 in image_id=2 and point2D_idx=2345 in image_id=6\nW20251209 04:31:43.125877 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7081 in image_id=2 and point2D_idx=9914 in image_id=6\nW20251209 04:31:43.125890 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9533 in image_id=2 and point2D_idx=11401 in image_id=6\nW20251209 04:31:43.125900 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3760 in image_id=2 and point2D_idx=7309 in image_id=6\nW20251209 04:31:43.125922 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4022 in image_id=2 and point2D_idx=5112 in image_id=7\nW20251209 04:31:43.125938 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6999 in image_id=2 and point2D_idx=9290 in image_id=7\nW20251209 04:31:43.125948 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7412 in image_id=2 and point2D_idx=9666 in image_id=7\nW20251209 04:31:43.125972 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7097 in image_id=2 and point2D_idx=9675 in image_id=7\nW20251209 04:31:43.126067 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9083 in image_id=2 and point2D_idx=7947 in image_id=7\nW20251209 04:31:43.126102 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11530 in image_id=2 and point2D_idx=9327 in image_id=7\nW20251209 04:31:43.126114 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=880 in image_id=2 and point2D_idx=1503 in image_id=7\nW20251209 04:31:43.126121 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2426 in image_id=2 and point2D_idx=3900 in image_id=7\nW20251209 04:31:43.126134 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2365 in image_id=2 and point2D_idx=4302 in image_id=7\nW20251209 04:31:43.126142 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12089 in image_id=2 and point2D_idx=810 in image_id=7\nW20251209 04:31:43.126158 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10479 in image_id=2 and point2D_idx=10201 in image_id=7\nW20251209 04:31:43.126168 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7051 in image_id=2 and point2D_idx=6505 in image_id=7\nW20251209 04:31:43.126176 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9525 in image_id=2 and point2D_idx=7962 in image_id=7\nW20251209 04:31:43.126204 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1225 in image_id=2 and point2D_idx=217 in image_id=7\nW20251209 04:31:43.126244 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=118 in image_id=2 and point2D_idx=407 in image_id=7\nW20251209 04:31:43.126291 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6233 in image_id=2 and point2D_idx=7481 in image_id=7\nW20251209 04:31:43.126355 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4909 in image_id=2 and point2D_idx=8313 in image_id=8\nW20251209 04:31:43.126381 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4124 in image_id=2 and point2D_idx=7193 in image_id=8\nW20251209 04:31:43.126390 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4045 in image_id=2 and point2D_idx=6925 in image_id=8\nW20251209 04:31:43.126399 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6184 in image_id=2 and point2D_idx=7941 in image_id=8\nW20251209 04:31:43.126424 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4945 in image_id=2 and point2D_idx=3775 in image_id=8\nW20251209 04:31:43.126452 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2562 in image_id=2 and point2D_idx=3720 in image_id=8\nW20251209 04:31:43.126469 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=882 in image_id=2 and point2D_idx=3105 in image_id=8\nW20251209 04:31:43.126490 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1559 in image_id=2 and point2D_idx=5408 in image_id=8\nW20251209 04:31:43.126502 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10542 in image_id=2 and point2D_idx=8761 in image_id=8\nW20251209 04:31:43.126512 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3245 in image_id=2 and point2D_idx=7472 in image_id=8\nW20251209 04:31:43.126588 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3294 in image_id=2 and point2D_idx=7240 in image_id=8\nW20251209 04:31:43.126641 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2786 in image_id=2 and point2D_idx=2092 in image_id=9\nW20251209 04:31:43.126651 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4947 in image_id=2 and point2D_idx=3550 in image_id=9\nW20251209 04:31:43.126662 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4949 in image_id=2 and point2D_idx=3654 in image_id=9\nW20251209 04:31:43.126682 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=883 in image_id=2 and point2D_idx=4748 in image_id=9\nW20251209 04:31:43.126738 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=329 in image_id=2 and point2D_idx=2075 in image_id=9\nW20251209 04:31:43.126753 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1981 in image_id=2 and point2D_idx=3929 in image_id=9\nW20251209 04:31:43.126804 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1962 in image_id=2 and point2D_idx=1018 in image_id=10\nW20251209 04:31:43.126844 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3697 in image_id=2 and point2D_idx=4900 in image_id=10\nW20251209 04:31:43.126950 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3127 in image_id=3 and point2D_idx=3965 in image_id=4\nW20251209 04:31:43.127011 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3135 in image_id=3 and point2D_idx=3975 in image_id=4\nW20251209 04:31:43.127021 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4659 in image_id=3 and point2D_idx=5407 in image_id=4\nW20251209 04:31:43.127040 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3136 in image_id=3 and point2D_idx=3976 in image_id=4\nW20251209 04:31:43.127095 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6235 in image_id=3 and point2D_idx=6913 in image_id=4\nW20251209 04:31:43.127125 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4955 in image_id=3 and point2D_idx=5658 in image_id=4\nW20251209 04:31:43.127141 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3145 in image_id=3 and point2D_idx=3990 in image_id=4\nW20251209 04:31:43.127153 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3148 in image_id=3 and point2D_idx=3993 in image_id=4\nW20251209 04:31:43.127200 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1042 in image_id=3 and point2D_idx=1447 in image_id=4\nW20251209 04:31:43.127228 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6057 in image_id=3 and point2D_idx=6945 in image_id=4\nW20251209 04:31:43.127250 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6629 in image_id=3 and point2D_idx=7648 in image_id=4\nW20251209 04:31:43.127417 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6799 in image_id=3 and point2D_idx=8118 in image_id=4\nW20251209 04:31:43.127426 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7691 in image_id=3 and point2D_idx=9008 in image_id=4\nW20251209 04:31:43.127511 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1014 in image_id=3 and point2D_idx=1786 in image_id=5\nW20251209 04:31:43.127526 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2155 in image_id=3 and point2D_idx=3207 in image_id=5\nW20251209 04:31:43.127535 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=55 in image_id=3 and point2D_idx=604 in image_id=5\nW20251209 04:31:43.127552 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=57 in image_id=3 and point2D_idx=605 in image_id=5\nW20251209 04:31:43.127610 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3627 in image_id=3 and point2D_idx=4924 in image_id=5\nW20251209 04:31:43.127632 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5108 in image_id=3 and point2D_idx=6743 in image_id=5\nW20251209 04:31:43.127643 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4023 in image_id=3 and point2D_idx=5707 in image_id=5\nW20251209 04:31:43.127672 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4913 in image_id=3 and point2D_idx=7118 in image_id=5\nW20251209 04:31:43.127695 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7208 in image_id=3 and point2D_idx=9768 in image_id=5\nW20251209 04:31:43.127713 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1500 in image_id=3 and point2D_idx=2793 in image_id=5\nW20251209 04:31:43.127723 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5826 in image_id=3 and point2D_idx=8138 in image_id=5\nW20251209 04:31:43.127770 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5607 in image_id=3 and point2D_idx=8155 in image_id=5\nW20251209 04:31:43.127777 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7679 in image_id=3 and point2D_idx=10084 in image_id=5\nW20251209 04:31:43.127784 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3608 in image_id=3 and point2D_idx=5743 in image_id=5\nW20251209 04:31:43.127809 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7227 in image_id=3 and point2D_idx=9461 in image_id=5\nW20251209 04:31:43.127830 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3838 in image_id=3 and point2D_idx=6095 in image_id=5\nW20251209 04:31:43.127903 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4389 in image_id=3 and point2D_idx=7711 in image_id=6\nW20251209 04:31:43.127949 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1533 in image_id=3 and point2D_idx=5265 in image_id=6\nW20251209 04:31:43.127967 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6034 in image_id=3 and point2D_idx=10228 in image_id=6\nW20251209 04:31:43.127974 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6605 in image_id=3 and point2D_idx=10572 in image_id=6\nW20251209 04:31:43.127996 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4280 in image_id=3 and point2D_idx=8756 in image_id=6\nW20251209 04:31:43.128005 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2958 in image_id=3 and point2D_idx=7232 in image_id=6\nW20251209 04:31:43.128018 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4658 in image_id=3 and point2D_idx=9266 in image_id=6\nW20251209 04:31:43.128054 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5291 in image_id=3 and point2D_idx=9779 in image_id=6\nW20251209 04:31:43.128062 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1719 in image_id=3 and point2D_idx=4799 in image_id=6\nW20251209 04:31:43.128070 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3351 in image_id=3 and point2D_idx=7740 in image_id=6\nW20251209 04:31:43.128087 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4239 in image_id=3 and point2D_idx=8247 in image_id=6\nW20251209 04:31:43.128100 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4427 in image_id=3 and point2D_idx=6244 in image_id=6\nW20251209 04:31:43.128113 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5582 in image_id=3 and point2D_idx=8259 in image_id=6\nW20251209 04:31:43.128120 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6963 in image_id=3 and point2D_idx=9287 in image_id=6\nW20251209 04:31:43.128164 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3822 in image_id=3 and point2D_idx=6259 in image_id=6\nW20251209 04:31:43.128193 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6632 in image_id=3 and point2D_idx=9805 in image_id=6\nW20251209 04:31:43.128205 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4205 in image_id=3 and point2D_idx=7272 in image_id=6\nW20251209 04:31:43.128237 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6192 in image_id=3 and point2D_idx=9815 in image_id=6\nW20251209 04:31:43.128251 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=819 in image_id=3 and point2D_idx=4838 in image_id=6\nW20251209 04:31:43.128278 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5646 in image_id=3 and point2D_idx=9321 in image_id=6\nW20251209 04:31:43.128286 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6920 in image_id=3 and point2D_idx=11375 in image_id=6\nW20251209 04:31:43.128308 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4868 in image_id=3 and point2D_idx=8435 in image_id=7\nW20251209 04:31:43.128325 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5153 in image_id=3 and point2D_idx=9283 in image_id=7\nW20251209 04:31:43.128338 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6214 in image_id=3 and point2D_idx=10047 in image_id=7\nW20251209 04:31:43.128353 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2371 in image_id=3 and point2D_idx=7404 in image_id=7\nW20251209 04:31:43.128392 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5066 in image_id=3 and point2D_idx=10062 in image_id=7\nW20251209 04:31:43.128416 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1719 in image_id=3 and point2D_idx=5565 in image_id=7\nW20251209 04:31:43.128427 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7852 in image_id=3 and point2D_idx=373 in image_id=7\nW20251209 04:31:43.128439 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3037 in image_id=3 and point2D_idx=6922 in image_id=7\nW20251209 04:31:43.128451 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7481 in image_id=3 and point2D_idx=379 in image_id=7\nW20251209 04:31:43.128479 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5591 in image_id=3 and point2D_idx=6937 in image_id=7\nW20251209 04:31:43.128501 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4257 in image_id=3 and point2D_idx=6051 in image_id=7\nW20251209 04:31:43.128514 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=275 in image_id=3 and point2D_idx=2427 in image_id=7\nW20251209 04:31:43.128525 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7484 in image_id=3 and point2D_idx=9328 in image_id=7\nW20251209 04:31:43.128547 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4266 in image_id=3 and point2D_idx=6433 in image_id=7\nW20251209 04:31:43.128554 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5371 in image_id=3 and point2D_idx=7458 in image_id=7\nW20251209 04:31:43.128565 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6451 in image_id=3 and point2D_idx=8118 in image_id=7\nW20251209 04:31:43.128578 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4705 in image_id=3 and point2D_idx=6963 in image_id=7\nW20251209 04:31:43.128586 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7463 in image_id=3 and point2D_idx=10764 in image_id=7\nW20251209 04:31:43.128613 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=825 in image_id=3 and point2D_idx=2849 in image_id=7\nW20251209 04:31:43.128624 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4567 in image_id=3 and point2D_idx=8295 in image_id=8\nW20251209 04:31:43.128635 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2891 in image_id=3 and point2D_idx=7914 in image_id=8\nW20251209 04:31:43.128646 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3198 in image_id=3 and point2D_idx=8306 in image_id=8\nW20251209 04:31:43.128655 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4879 in image_id=3 and point2D_idx=9199 in image_id=8\nW20251209 04:31:43.128665 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3544 in image_id=3 and point2D_idx=8719 in image_id=8\nW20251209 04:31:43.128673 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6221 in image_id=3 and point2D_idx=9631 in image_id=8\nW20251209 04:31:43.128688 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1022 in image_id=3 and point2D_idx=7182 in image_id=8\nW20251209 04:31:43.128698 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4009 in image_id=3 and point2D_idx=9209 in image_id=8\nW20251209 04:31:43.128723 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3350 in image_id=3 and point2D_idx=8732 in image_id=8\nW20251209 04:31:43.128735 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4667 in image_id=3 and point2D_idx=9225 in image_id=8\nW20251209 04:31:43.128754 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1269 in image_id=3 and point2D_idx=1212 in image_id=8\nW20251209 04:31:43.128761 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3629 in image_id=3 and point2D_idx=4094 in image_id=8\nW20251209 04:31:43.128770 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2922 in image_id=3 and point2D_idx=3347 in image_id=8\nW20251209 04:31:43.128802 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7867 in image_id=3 and point2D_idx=7046 in image_id=8\nW20251209 04:31:43.128830 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=824 in image_id=3 and point2D_idx=2073 in image_id=8\nW20251209 04:31:43.128843 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1916 in image_id=3 and point2D_idx=7342 in image_id=9\nW20251209 04:31:43.128857 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1711 in image_id=3 and point2D_idx=7357 in image_id=9\nW20251209 04:31:43.128863 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=364 in image_id=3 and point2D_idx=5867 in image_id=9\nW20251209 04:31:43.128874 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1029 in image_id=3 and point2D_idx=6363 in image_id=9\nW20251209 04:31:43.128881 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1715 in image_id=3 and point2D_idx=7074 in image_id=9\nW20251209 04:31:43.128891 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3203 in image_id=3 and point2D_idx=3201 in image_id=9\nW20251209 04:31:43.128900 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3207 in image_id=3 and point2D_idx=3546 in image_id=9\nW20251209 04:31:43.128926 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1751 in image_id=3 and point2D_idx=3233 in image_id=9\nW20251209 04:31:43.128933 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7486 in image_id=3 and point2D_idx=6194 in image_id=9\nW20251209 04:31:43.128955 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6121 in image_id=4 and point2D_idx=6724 in image_id=5\nW20251209 04:31:43.128980 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7597 in image_id=4 and point2D_idx=8379 in image_id=5\nW20251209 04:31:43.129004 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2616 in image_id=4 and point2D_idx=2758 in image_id=5\nW20251209 04:31:43.129049 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3686 in image_id=4 and point2D_idx=4068 in image_id=5\nW20251209 04:31:43.129079 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5916 in image_id=4 and point2D_idx=6740 in image_id=5\nW20251209 04:31:43.129102 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3500 in image_id=4 and point2D_idx=4076 in image_id=5\nW20251209 04:31:43.129211 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8592 in image_id=4 and point2D_idx=10079 in image_id=5\nW20251209 04:31:43.129218 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5735 in image_id=4 and point2D_idx=7123 in image_id=5\nW20251209 04:31:43.129342 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8371 in image_id=4 and point2D_idx=9478 in image_id=5\nW20251209 04:31:43.129389 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7930 in image_id=4 and point2D_idx=9198 in image_id=5\nW20251209 04:31:43.129408 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1832 in image_id=4 and point2D_idx=2827 in image_id=5\nW20251209 04:31:43.129426 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6448 in image_id=4 and point2D_idx=8180 in image_id=5\nW20251209 04:31:43.129474 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5241 in image_id=4 and point2D_idx=8750 in image_id=6\nW20251209 04:31:43.129515 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1415 in image_id=4 and point2D_idx=3803 in image_id=6\nW20251209 04:31:43.129548 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2358 in image_id=4 and point2D_idx=5275 in image_id=6\nW20251209 04:31:43.129612 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4533 in image_id=4 and point2D_idx=7745 in image_id=6\nW20251209 04:31:43.129639 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5495 in image_id=4 and point2D_idx=8249 in image_id=6\nW20251209 04:31:43.129688 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3704 in image_id=4 and point2D_idx=4332 in image_id=6\nW20251209 04:31:43.129722 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2381 in image_id=4 and point2D_idx=1981 in image_id=6\nW20251209 04:31:43.129778 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7863 in image_id=4 and point2D_idx=9797 in image_id=6\nW20251209 04:31:43.129812 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1499 in image_id=4 and point2D_idx=3856 in image_id=6\nW20251209 04:31:43.129851 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6422 in image_id=4 and point2D_idx=8796 in image_id=6\nW20251209 04:31:43.129862 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7655 in image_id=4 and point2D_idx=9621 in image_id=6\nW20251209 04:31:43.129880 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=903 in image_id=4 and point2D_idx=3863 in image_id=6\nW20251209 04:31:43.129917 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6361 in image_id=4 and point2D_idx=8800 in image_id=6\nW20251209 04:31:43.130022 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1166 in image_id=4 and point2D_idx=1583 in image_id=6\nW20251209 04:31:43.130069 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5737 in image_id=4 and point2D_idx=7796 in image_id=6\nW20251209 04:31:43.130124 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6632 in image_id=4 and point2D_idx=10047 in image_id=7\nW20251209 04:31:43.130140 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6451 in image_id=4 and point2D_idx=10050 in image_id=7\nW20251209 04:31:43.130193 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1093 in image_id=4 and point2D_idx=4255 in image_id=7\nW20251209 04:31:43.130272 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5188 in image_id=4 and point2D_idx=9315 in image_id=7\nW20251209 04:31:43.130290 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6136 in image_id=4 and point2D_idx=9685 in image_id=7\nW20251209 04:31:43.130306 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5744 in image_id=4 and point2D_idx=9316 in image_id=7\nW20251209 04:31:43.130335 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3700 in image_id=4 and point2D_idx=3130 in image_id=7\nW20251209 04:31:43.130373 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5257 in image_id=4 and point2D_idx=5145 in image_id=7\nW20251209 04:31:43.130457 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=617 in image_id=4 and point2D_idx=1280 in image_id=7\nW20251209 04:31:43.130484 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1454 in image_id=4 and point2D_idx=1742 in image_id=7\nW20251209 04:31:43.130519 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2909 in image_id=4 and point2D_idx=2872 in image_id=7\nW20251209 04:31:43.130535 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6732 in image_id=4 and point2D_idx=7955 in image_id=7\nW20251209 04:31:43.130608 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8357 in image_id=4 and point2D_idx=10095 in image_id=7\nW20251209 04:31:43.130636 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=117 in image_id=4 and point2D_idx=4309 in image_id=7\nW20251209 04:31:43.130665 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9010 in image_id=4 and point2D_idx=11615 in image_id=7\nW20251209 04:31:43.130731 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8128 in image_id=4 and point2D_idx=10777 in image_id=7\nW20251209 04:31:43.130750 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=329 in image_id=4 and point2D_idx=1312 in image_id=7\nW20251209 04:31:43.130802 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7678 in image_id=4 and point2D_idx=9345 in image_id=7\nW20251209 04:31:43.130819 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=126 in image_id=4 and point2D_idx=1773 in image_id=7\nW20251209 04:31:43.130832 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1484 in image_id=4 and point2D_idx=3927 in image_id=7\nW20251209 04:31:43.130877 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3752 in image_id=4 and point2D_idx=7914 in image_id=8\nW20251209 04:31:43.130896 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2602 in image_id=4 and point2D_idx=7536 in image_id=8\nW20251209 04:31:43.130927 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5005 in image_id=4 and point2D_idx=9205 in image_id=8\nW20251209 04:31:43.130947 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3969 in image_id=4 and point2D_idx=8723 in image_id=8\nW20251209 04:31:43.130976 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1421 in image_id=4 and point2D_idx=6491 in image_id=8\nW20251209 04:31:43.131025 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5197 in image_id=4 and point2D_idx=4484 in image_id=8\nW20251209 04:31:43.131084 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6678 in image_id=4 and point2D_idx=6116 in image_id=8\nW20251209 04:31:43.131114 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1808 in image_id=4 and point2D_idx=1726 in image_id=8\nW20251209 04:31:43.131129 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3502 in image_id=4 and point2D_idx=3349 in image_id=8\nW20251209 04:31:43.131155 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4766 in image_id=4 and point2D_idx=4895 in image_id=8\nW20251209 04:31:43.131182 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8342 in image_id=4 and point2D_idx=6507 in image_id=8\nW20251209 04:31:43.131376 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2907 in image_id=4 and point2D_idx=2682 in image_id=9\nW20251209 04:31:43.131406 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1819 in image_id=4 and point2D_idx=481 in image_id=9\nW20251209 04:31:43.131414 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1504 in image_id=4 and point2D_idx=575 in image_id=9\nW20251209 04:31:43.131432 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1167 in image_id=4 and point2D_idx=3293 in image_id=9\nW20251209 04:31:43.131510 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1306 in image_id=5 and point2D_idx=3918 in image_id=6\nW20251209 04:31:43.131579 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8112 in image_id=5 and point2D_idx=10582 in image_id=6\nW20251209 04:31:43.131775 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8400 in image_id=5 and point2D_idx=9294 in image_id=6\nW20251209 04:31:43.131808 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3367 in image_id=5 and point2D_idx=2794 in image_id=6\nW20251209 04:31:43.131826 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10603 in image_id=5 and point2D_idx=11086 in image_id=6\nW20251209 04:31:43.131884 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7488 in image_id=5 and point2D_idx=8289 in image_id=6\nW20251209 04:31:43.131913 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1385 in image_id=5 and point2D_idx=123 in image_id=6\nW20251209 04:31:43.131965 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3278 in image_id=5 and point2D_idx=4368 in image_id=6\nW20251209 04:31:43.131981 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6802 in image_id=5 and point2D_idx=6855 in image_id=6\nW20251209 04:31:43.131990 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11101 in image_id=5 and point2D_idx=12409 in image_id=6\nW20251209 04:31:43.132027 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10150 in image_id=5 and point2D_idx=11392 in image_id=6\nW20251209 04:31:43.132073 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4626 in image_id=5 and point2D_idx=7812 in image_id=6\nW20251209 04:31:43.132090 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10118 in image_id=5 and point2D_idx=11956 in image_id=6\nW20251209 04:31:43.132116 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9855 in image_id=5 and point2D_idx=11958 in image_id=6\nW20251209 04:31:43.132135 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11340 in image_id=5 and point2D_idx=12782 in image_id=6\nW20251209 04:31:43.132163 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10879 in image_id=5 and point2D_idx=12784 in image_id=6\nW20251209 04:31:43.132248 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7440 in image_id=5 and point2D_idx=10446 in image_id=7\nW20251209 04:31:43.132316 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6043 in image_id=5 and point2D_idx=9680 in image_id=7\nW20251209 04:31:43.132353 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6509 in image_id=5 and point2D_idx=9316 in image_id=7\nW20251209 04:31:43.132449 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=611 in image_id=5 and point2D_idx=1498 in image_id=7\nW20251209 04:31:43.132469 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3788 in image_id=5 and point2D_idx=2423 in image_id=7\nW20251209 04:31:43.132486 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3790 in image_id=5 and point2D_idx=2424 in image_id=7\nW20251209 04:31:43.132514 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6184 in image_id=5 and point2D_idx=6061 in image_id=7\nW20251209 04:31:43.132526 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9465 in image_id=5 and point2D_idx=10765 in image_id=7\nW20251209 04:31:43.132544 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1851 in image_id=5 and point2D_idx=6515 in image_id=7\nW20251209 04:31:43.132596 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=633 in image_id=5 and point2D_idx=888 in image_id=7\nW20251209 04:31:43.132625 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3282 in image_id=5 and point2D_idx=4323 in image_id=7\nW20251209 04:31:43.132639 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7204 in image_id=5 and point2D_idx=6983 in image_id=7\nW20251209 04:31:43.132646 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6492 in image_id=5 and point2D_idx=6527 in image_id=7\nW20251209 04:31:43.132680 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6814 in image_id=5 and point2D_idx=9399 in image_id=7\nW20251209 04:31:43.132690 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1942 in image_id=5 and point2D_idx=6544 in image_id=7\nW20251209 04:31:43.132698 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10118 in image_id=5 and point2D_idx=11361 in image_id=7\nW20251209 04:31:43.132705 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11362 in image_id=5 and point2D_idx=11843 in image_id=7\nW20251209 04:31:43.132735 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7568 in image_id=5 and point2D_idx=11372 in image_id=7\nW20251209 04:31:43.132778 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5710 in image_id=5 and point2D_idx=3698 in image_id=8\nW20251209 04:31:43.132802 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1835 in image_id=5 and point2D_idx=1213 in image_id=8\nW20251209 04:31:43.132817 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=948 in image_id=5 and point2D_idx=1297 in image_id=8\nW20251209 04:31:43.132828 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=949 in image_id=5 and point2D_idx=1893 in image_id=8\nW20251209 04:31:43.132848 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10716 in image_id=5 and point2D_idx=8750 in image_id=8\nW20251209 04:31:43.132855 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7813 in image_id=5 and point2D_idx=6129 in image_id=8\nW20251209 04:31:43.132883 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2329 in image_id=5 and point2D_idx=7588 in image_id=8\nW20251209 04:31:43.132892 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9292 in image_id=5 and point2D_idx=8763 in image_id=8\nW20251209 04:31:43.132935 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1413 in image_id=5 and point2D_idx=4549 in image_id=8\nW20251209 04:31:43.132987 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2264 in image_id=5 and point2D_idx=7120 in image_id=9\nW20251209 04:31:43.133010 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6047 in image_id=5 and point2D_idx=7082 in image_id=9\nW20251209 04:31:43.133028 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2786 in image_id=5 and point2D_idx=2305 in image_id=9\nW20251209 04:31:43.133051 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6072 in image_id=5 and point2D_idx=4222 in image_id=9\nW20251209 04:31:43.133065 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3786 in image_id=5 and point2D_idx=2616 in image_id=9\nW20251209 04:31:43.133086 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10144 in image_id=5 and point2D_idx=6482 in image_id=9\nW20251209 04:31:43.133094 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3256 in image_id=5 and point2D_idx=2052 in image_id=9\nW20251209 04:31:43.133110 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2814 in image_id=5 and point2D_idx=320 in image_id=9\nW20251209 04:31:43.133122 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=24 in image_id=5 and point2D_idx=698 in image_id=9\nW20251209 04:31:43.133139 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=29 in image_id=5 and point2D_idx=1818 in image_id=9\nW20251209 04:31:43.133148 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=640 in image_id=5 and point2D_idx=2648 in image_id=9\nW20251209 04:31:43.133184 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5069 in image_id=5 and point2D_idx=1978 in image_id=10\nW20251209 04:31:43.133205 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3259 in image_id=5 and point2D_idx=2599 in image_id=10\nW20251209 04:31:43.133213 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2813 in image_id=5 and point2D_idx=501 in image_id=10\nW20251209 04:31:43.133226 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1393 in image_id=5 and point2D_idx=1648 in image_id=10\nW20251209 04:31:43.133237 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=632 in image_id=5 and point2D_idx=2252 in image_id=10\nW20251209 04:31:43.133247 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1398 in image_id=5 and point2D_idx=2485 in image_id=10\nW20251209 04:31:43.133326 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3295 in image_id=6 and point2D_idx=4262 in image_id=7\nW20251209 04:31:43.133368 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12386 in image_id=6 and point2D_idx=7511 in image_id=7\nW20251209 04:31:43.133376 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12586 in image_id=6 and point2D_idx=7940 in image_id=7\nW20251209 04:31:43.133391 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12254 in image_id=6 and point2D_idx=7434 in image_id=7\nW20251209 04:31:43.133411 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12636 in image_id=6 and point2D_idx=8474 in image_id=7\nW20251209 04:31:43.133436 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12594 in image_id=6 and point2D_idx=9780 in image_id=7\nW20251209 04:31:43.133455 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5310 in image_id=6 and point2D_idx=4723 in image_id=7\nW20251209 04:31:43.133486 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3332 in image_id=6 and point2D_idx=3900 in image_id=7\nW20251209 04:31:43.133515 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1567 in image_id=6 and point2D_idx=2432 in image_id=7\nW20251209 04:31:43.133526 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12184 in image_id=6 and point2D_idx=11617 in image_id=7\nW20251209 04:31:43.133540 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11934 in image_id=6 and point2D_idx=11342 in image_id=7\nW20251209 04:31:43.133601 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9908 in image_id=6 and point2D_idx=8512 in image_id=7\nW20251209 04:31:43.133609 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11388 in image_id=6 and point2D_idx=10164 in image_id=7\nW20251209 04:31:43.133625 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7291 in image_id=6 and point2D_idx=6086 in image_id=7\nW20251209 04:31:43.133685 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2336 in image_id=6 and point2D_idx=2811 in image_id=7\nW20251209 04:31:43.133701 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11961 in image_id=6 and point2D_idx=11643 in image_id=7\nW20251209 04:31:43.133842 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6737 in image_id=6 and point2D_idx=4489 in image_id=8\nW20251209 04:31:43.133854 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3833 in image_id=6 and point2D_idx=2705 in image_id=8\nW20251209 04:31:43.133919 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11931 in image_id=6 and point2D_idx=9965 in image_id=8\nW20251209 04:31:43.133945 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1627 in image_id=6 and point2D_idx=4529 in image_id=8\nW20251209 04:31:43.133955 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1199 in image_id=6 and point2D_idx=176 in image_id=8\nW20251209 04:31:43.133983 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2813 in image_id=6 and point2D_idx=2400 in image_id=8\nW20251209 04:31:43.133993 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12409 in image_id=6 and point2D_idx=9723 in image_id=8\nW20251209 04:31:43.134023 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2903 in image_id=6 and point2D_idx=3386 in image_id=8\nW20251209 04:31:43.134220 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4375 in image_id=6 and point2D_idx=4544 in image_id=8\nW20251209 04:31:43.134273 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9415 in image_id=6 and point2D_idx=7602 in image_id=8\nW20251209 04:31:43.134433 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2414 in image_id=6 and point2D_idx=2273 in image_id=9\nW20251209 04:31:43.134524 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4404 in image_id=6 and point2D_idx=3194 in image_id=9\nW20251209 04:31:43.134578 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7250 in image_id=6 and point2D_idx=3871 in image_id=9\nW20251209 04:31:43.134649 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2786 in image_id=6 and point2D_idx=2362 in image_id=9\nW20251209 04:31:43.134698 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4339 in image_id=6 and point2D_idx=3277 in image_id=9\nW20251209 04:31:43.134754 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1917 in image_id=6 and point2D_idx=2307 in image_id=9\nW20251209 04:31:43.134815 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10924 in image_id=6 and point2D_idx=5147 in image_id=9\nW20251209 04:31:43.134866 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11650 in image_id=6 and point2D_idx=6191 in image_id=9\nW20251209 04:31:43.134932 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7778 in image_id=6 and point2D_idx=4494 in image_id=9\nW20251209 04:31:43.134976 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1199 in image_id=6 and point2D_idx=263 in image_id=9\nW20251209 04:31:43.134998 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2314 in image_id=6 and point2D_idx=5910 in image_id=9\nW20251209 04:31:43.135011 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2896 in image_id=6 and point2D_idx=2326 in image_id=9\nW20251209 04:31:43.135029 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2813 in image_id=6 and point2D_idx=2399 in image_id=9\nW20251209 04:31:43.135096 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3886 in image_id=6 and point2D_idx=4520 in image_id=9\nW20251209 04:31:43.135125 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=606 in image_id=6 and point2D_idx=2381 in image_id=9\nW20251209 04:31:43.135145 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3891 in image_id=6 and point2D_idx=4527 in image_id=9\nW20251209 04:31:43.135221 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2750 in image_id=6 and point2D_idx=4599 in image_id=10\nW20251209 04:31:43.135242 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4879 in image_id=6 and point2D_idx=5313 in image_id=10\nW20251209 04:31:43.135281 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3465 in image_id=6 and point2D_idx=1621 in image_id=10\nW20251209 04:31:43.135311 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10872 in image_id=6 and point2D_idx=2942 in image_id=10\nW20251209 04:31:43.135337 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2303 in image_id=6 and point2D_idx=4271 in image_id=10\nW20251209 04:31:43.135365 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=858 in image_id=6 and point2D_idx=656 in image_id=10\nW20251209 04:31:43.135396 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=132 in image_id=6 and point2D_idx=2255 in image_id=10\nW20251209 04:31:43.135509 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8908 in image_id=7 and point2D_idx=8732 in image_id=8\nW20251209 04:31:43.135531 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6468 in image_id=7 and point2D_idx=7193 in image_id=8\nW20251209 04:31:43.135583 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3955 in image_id=7 and point2D_idx=3708 in image_id=8\nW20251209 04:31:43.135635 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=816 in image_id=7 and point2D_idx=1223 in image_id=8\nW20251209 04:31:43.135651 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2776 in image_id=7 and point2D_idx=3424 in image_id=8\nW20251209 04:31:43.135666 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11062 in image_id=7 and point2D_idx=9330 in image_id=8\nW20251209 04:31:43.135682 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=586 in image_id=7 and point2D_idx=36 in image_id=8\nW20251209 04:31:43.135697 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=587 in image_id=7 and point2D_idx=67 in image_id=8\nW20251209 04:31:43.135741 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4309 in image_id=7 and point2D_idx=5754 in image_id=8\nW20251209 04:31:43.135805 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=427 in image_id=7 and point2D_idx=488 in image_id=8\nW20251209 04:31:43.135932 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11581 in image_id=7 and point2D_idx=7 in image_id=9\nW20251209 04:31:43.135939 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11867 in image_id=7 and point2D_idx=105 in image_id=9\nW20251209 04:31:43.135955 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1590 in image_id=7 and point2D_idx=1012 in image_id=9\nW20251209 04:31:43.135992 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1792 in image_id=7 and point2D_idx=1324 in image_id=9\nW20251209 04:31:43.136010 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2398 in image_id=7 and point2D_idx=2591 in image_id=9\nW20251209 04:31:43.136018 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7422 in image_id=7 and point2D_idx=7075 in image_id=9\nW20251209 04:31:43.136029 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6919 in image_id=7 and point2D_idx=6786 in image_id=9\nW20251209 04:31:43.136055 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2747 in image_id=7 and point2D_idx=2959 in image_id=9\nW20251209 04:31:43.136063 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3122 in image_id=7 and point2D_idx=3189 in image_id=9\nW20251209 04:31:43.136070 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6564 in image_id=7 and point2D_idx=6477 in image_id=9\nW20251209 04:31:43.136097 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3877 in image_id=7 and point2D_idx=3201 in image_id=9\nW20251209 04:31:43.136108 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10081 in image_id=7 and point2D_idx=657 in image_id=9\nW20251209 04:31:43.136118 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3881 in image_id=7 and point2D_idx=3274 in image_id=9\nW20251209 04:31:43.136155 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=579 in image_id=7 and point2D_idx=1208 in image_id=9\nW20251209 04:31:43.136168 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=581 in image_id=7 and point2D_idx=1389 in image_id=9\nW20251209 04:31:43.136177 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11390 in image_id=7 and point2D_idx=6794 in image_id=9\nW20251209 04:31:43.136188 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5274 in image_id=7 and point2D_idx=4494 in image_id=9\nW20251209 04:31:43.136205 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3156 in image_id=7 and point2D_idx=4742 in image_id=9\nW20251209 04:31:43.136236 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6965 in image_id=7 and point2D_idx=6720 in image_id=9\nW20251209 04:31:43.136248 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2787 in image_id=7 and point2D_idx=4956 in image_id=9\nW20251209 04:31:43.136303 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=847 in image_id=7 and point2D_idx=1371 in image_id=9\nW20251209 04:31:43.136318 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11997 in image_id=7 and point2D_idx=7588 in image_id=9\nW20251209 04:31:43.136332 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11856 in image_id=7 and point2D_idx=7589 in image_id=9\nW20251209 04:31:43.136362 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=860 in image_id=7 and point2D_idx=2654 in image_id=9\nW20251209 04:31:43.136419 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2041 in image_id=7 and point2D_idx=2282 in image_id=10\nW20251209 04:31:43.136426 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3117 in image_id=7 and point2D_idx=4105 in image_id=10\nW20251209 04:31:43.136444 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2748 in image_id=7 and point2D_idx=2204 in image_id=10\nW20251209 04:31:43.136465 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11442 in image_id=7 and point2D_idx=113 in image_id=10\nW20251209 04:31:43.136484 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3510 in image_id=7 and point2D_idx=1776 in image_id=10\nW20251209 04:31:43.136494 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10460 in image_id=7 and point2D_idx=60 in image_id=10\nW20251209 04:31:43.136508 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1731 in image_id=7 and point2D_idx=1834 in image_id=10\nW20251209 04:31:43.136516 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11661 in image_id=7 and point2D_idx=672 in image_id=10\nW20251209 04:31:43.136535 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11974 in image_id=7 and point2D_idx=933 in image_id=10\nW20251209 04:31:43.136562 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=582 in image_id=7 and point2D_idx=2468 in image_id=10\nW20251209 04:31:43.136573 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10473 in image_id=7 and point2D_idx=682 in image_id=10\nW20251209 04:31:43.136590 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10772 in image_id=7 and point2D_idx=3445 in image_id=10\nW20251209 04:31:43.136603 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1750 in image_id=7 and point2D_idx=4276 in image_id=10\nW20251209 04:31:43.136631 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=61 in image_id=7 and point2D_idx=493 in image_id=10\nW20251209 04:31:43.136663 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2455 in image_id=7 and point2D_idx=2879 in image_id=10\nW20251209 04:31:43.136673 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=222 in image_id=7 and point2D_idx=2254 in image_id=10\nW20251209 04:31:43.136680 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=606 in image_id=7 and point2D_idx=2517 in image_id=10\nW20251209 04:31:43.136703 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5245 in image_id=7 and point2D_idx=4742 in image_id=10\nW20251209 04:31:43.136729 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11654 in image_id=7 and point2D_idx=5501 in image_id=10\nW20251209 04:31:43.136770 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1437 in image_id=8 and point2D_idx=1314 in image_id=9\nW20251209 04:31:43.136780 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4471 in image_id=8 and point2D_idx=4979 in image_id=9\nW20251209 04:31:43.136794 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5697 in image_id=8 and point2D_idx=5862 in image_id=9\nW20251209 04:31:43.136801 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6581 in image_id=8 and point2D_idx=6467 in image_id=9\nW20251209 04:31:43.136859 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2028 in image_id=8 and point2D_idx=1402 in image_id=9\nW20251209 04:31:43.136926 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2043 in image_id=8 and point2D_idx=2312 in image_id=9\nW20251209 04:31:43.136947 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=466 in image_id=8 and point2D_idx=271 in image_id=9\nW20251209 04:31:43.137000 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=172 in image_id=8 and point2D_idx=145 in image_id=9\nW20251209 04:31:43.137146 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1240 in image_id=8 and point2D_idx=1187 in image_id=9\nW20251209 04:31:43.137259 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2419 in image_id=8 and point2D_idx=3257 in image_id=9\nW20251209 04:31:43.137308 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7170 in image_id=8 and point2D_idx=4245 in image_id=10\nW20251209 04:31:43.137378 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9684 in image_id=8 and point2D_idx=55 in image_id=10\nW20251209 04:31:43.137501 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6500 in image_id=8 and point2D_idx=14 in image_id=10\nW20251209 04:31:43.137519 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3701 in image_id=8 and point2D_idx=2210 in image_id=10\nW20251209 04:31:43.137626 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1736 in image_id=8 and point2D_idx=3266 in image_id=10\nW20251209 04:31:43.137698 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2401 in image_id=8 and point2D_idx=2475 in image_id=10\nW20251209 04:31:43.137715 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=48 in image_id=8 and point2D_idx=721 in image_id=10\nW20251209 04:31:43.137723 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3380 in image_id=8 and point2D_idx=3235 in image_id=10\nW20251209 04:31:43.137750 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1485 in image_id=8 and point2D_idx=2484 in image_id=10\nW20251209 04:31:43.137776 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9752 in image_id=8 and point2D_idx=5338 in image_id=10\nW20251209 04:31:43.137801 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6183 in image_id=8 and point2D_idx=5445 in image_id=10\nW20251209 04:31:43.137812 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3508 in image_id=9 and point2D_idx=2258 in image_id=10\nW20251209 04:31:43.137840 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1517 in image_id=9 and point2D_idx=1203 in image_id=10\nW20251209 04:31:43.137975 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3212 in image_id=9 and point2D_idx=2695 in image_id=10\nW20251209 04:31:43.137982 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4229 in image_id=9 and point2D_idx=2950 in image_id=10\nW20251209 04:31:43.137992 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4231 in image_id=9 and point2D_idx=2951 in image_id=10\nW20251209 04:31:43.138053 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2972 in image_id=9 and point2D_idx=3712 in image_id=10\nW20251209 04:31:43.138077 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=897 in image_id=9 and point2D_idx=911 in image_id=10\nW20251209 04:31:43.138099 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4508 in image_id=9 and point2D_idx=4582 in image_id=10\nW20251209 04:31:43.138116 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3233 in image_id=9 and point2D_idx=3018 in image_id=10\nW20251209 04:31:43.138155 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4520 in image_id=9 and point2D_idx=3946 in image_id=10\nW20251209 04:31:43.138190 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=807 in image_id=9 and point2D_idx=1492 in image_id=10\nW20251209 04:31:43.138219 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1611 in image_id=9 and point2D_idx=2998 in image_id=10\nW20251209 04:31:43.138330 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10655 in image_id=11 and point2D_idx=14697 in image_id=12\nW20251209 04:31:43.138338 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7063 in image_id=11 and point2D_idx=12658 in image_id=12\nW20251209 04:31:43.138519 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7093 in image_id=11 and point2D_idx=7680 in image_id=12\nW20251209 04:31:43.138610 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6790 in image_id=11 and point2D_idx=6986 in image_id=12\nW20251209 04:31:43.138688 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3518 in image_id=11 and point2D_idx=4797 in image_id=12\nW20251209 04:31:43.138736 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9865 in image_id=11 and point2D_idx=12067 in image_id=12\nW20251209 04:31:43.138751 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=298 in image_id=11 and point2D_idx=1215 in image_id=12\nW20251209 04:31:43.138763 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9440 in image_id=11 and point2D_idx=11743 in image_id=12\nW20251209 04:31:43.138845 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1015 in image_id=11 and point2D_idx=1449 in image_id=12\nW20251209 04:31:43.138974 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1518 in image_id=11 and point2D_idx=7796 in image_id=13\nW20251209 04:31:43.138987 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1609 in image_id=11 and point2D_idx=7376 in image_id=13\nW20251209 04:31:43.139059 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=693 in image_id=11 and point2D_idx=5064 in image_id=13\nW20251209 04:31:43.139155 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11088 in image_id=11 and point2D_idx=10742 in image_id=13\nW20251209 04:31:43.139188 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5858 in image_id=11 and point2D_idx=4702 in image_id=13\nW20251209 04:31:43.139297 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8184 in image_id=11 and point2D_idx=8682 in image_id=13\nW20251209 04:31:43.139357 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=63 in image_id=11 and point2D_idx=1788 in image_id=13\nW20251209 04:31:43.139405 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8270 in image_id=11 and point2D_idx=8268 in image_id=13\nW20251209 04:31:43.139480 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=112 in image_id=11 and point2D_idx=1229 in image_id=13\nW20251209 04:31:43.139682 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7916 in image_id=11 and point2D_idx=6634 in image_id=13\nW20251209 04:31:43.139714 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7175 in image_id=11 and point2D_idx=5503 in image_id=13\nW20251209 04:31:43.140151 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2637 in image_id=11 and point2D_idx=1645 in image_id=14\nW20251209 04:31:43.140447 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5854 in image_id=11 and point2D_idx=5694 in image_id=15\nW20251209 04:31:43.140482 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11462 in image_id=11 and point2D_idx=12136 in image_id=15\nW20251209 04:31:43.140497 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3693 in image_id=11 and point2D_idx=7931 in image_id=15\nW20251209 04:31:43.140523 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3282 in image_id=11 and point2D_idx=6582 in image_id=15\nW20251209 04:31:43.140619 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1565 in image_id=11 and point2D_idx=3119 in image_id=15\nW20251209 04:31:43.140656 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10695 in image_id=11 and point2D_idx=10665 in image_id=15\nW20251209 04:31:43.140703 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5603 in image_id=11 and point2D_idx=4346 in image_id=15\nW20251209 04:31:43.140736 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=294 in image_id=11 and point2D_idx=579 in image_id=15\nW20251209 04:31:43.140808 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3322 in image_id=11 and point2D_idx=2013 in image_id=15\nW20251209 04:31:43.140828 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7483 in image_id=11 and point2D_idx=5744 in image_id=15\nW20251209 04:31:43.140882 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3120 in image_id=11 and point2D_idx=1020 in image_id=15\nW20251209 04:31:43.140984 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9416 in image_id=11 and point2D_idx=6903 in image_id=16\nW20251209 04:31:43.140991 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10695 in image_id=11 and point2D_idx=9672 in image_id=16\nW20251209 04:31:43.141012 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11763 in image_id=11 and point2D_idx=8399 in image_id=16\nW20251209 04:31:43.141150 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7141 in image_id=11 and point2D_idx=1937 in image_id=16\nW20251209 04:31:43.141175 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3743 in image_id=11 and point2D_idx=1123 in image_id=16\nW20251209 04:31:43.141203 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11072 in image_id=11 and point2D_idx=7836 in image_id=17\nW20251209 04:31:43.141229 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11093 in image_id=11 and point2D_idx=6936 in image_id=17\nW20251209 04:31:43.141246 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8547 in image_id=11 and point2D_idx=3847 in image_id=17\nW20251209 04:31:43.141253 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10680 in image_id=11 and point2D_idx=6284 in image_id=17\nW20251209 04:31:43.141282 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2442 in image_id=11 and point2D_idx=11344 in image_id=17\nW20251209 04:31:43.141289 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7160 in image_id=11 and point2D_idx=5400 in image_id=17\nW20251209 04:31:43.141317 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6483 in image_id=11 and point2D_idx=4777 in image_id=17\nW20251209 04:31:43.141366 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9869 in image_id=11 and point2D_idx=7422 in image_id=17\nW20251209 04:31:43.141427 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8667 in image_id=11 and point2D_idx=1714 in image_id=17\nW20251209 04:31:43.141435 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9787 in image_id=11 and point2D_idx=6369 in image_id=18\nW20251209 04:31:43.141450 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11271 in image_id=11 and point2D_idx=6503 in image_id=18\nW20251209 04:31:43.141465 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11322 in image_id=11 and point2D_idx=6504 in image_id=18\nW20251209 04:31:43.141484 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1031 in image_id=11 and point2D_idx=10768 in image_id=18\nW20251209 04:31:43.141500 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2848 in image_id=11 and point2D_idx=11204 in image_id=18\nW20251209 04:31:43.141512 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6177 in image_id=11 and point2D_idx=4547 in image_id=18\nW20251209 04:31:43.141523 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8559 in image_id=11 and point2D_idx=5219 in image_id=18\nW20251209 04:31:43.141545 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8811 in image_id=11 and point2D_idx=5169 in image_id=18\nW20251209 04:31:43.141570 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10716 in image_id=11 and point2D_idx=7279 in image_id=18\nW20251209 04:31:43.141588 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7482 in image_id=11 and point2D_idx=3336 in image_id=18\nW20251209 04:31:43.141596 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1899 in image_id=11 and point2D_idx=1902 in image_id=18\nW20251209 04:31:43.141604 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1601 in image_id=11 and point2D_idx=1658 in image_id=18\nW20251209 04:31:43.141614 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9496 in image_id=11 and point2D_idx=2509 in image_id=18\nW20251209 04:31:43.141660 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=278 in image_id=11 and point2D_idx=7450 in image_id=19\nW20251209 04:31:43.141701 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11776 in image_id=11 and point2D_idx=4910 in image_id=19\nW20251209 04:31:43.141716 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12190 in image_id=11 and point2D_idx=4880 in image_id=19\nW20251209 04:31:43.141779 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1236 in image_id=12 and point2D_idx=3477 in image_id=13\nW20251209 04:31:43.141817 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7286 in image_id=12 and point2D_idx=11482 in image_id=13\nW20251209 04:31:43.142015 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=766 in image_id=12 and point2D_idx=1480 in image_id=13\nW20251209 04:31:43.142025 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2563 in image_id=12 and point2D_idx=3509 in image_id=13\nW20251209 04:31:43.142099 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12685 in image_id=12 and point2D_idx=13962 in image_id=13\nW20251209 04:31:43.142134 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5534 in image_id=12 and point2D_idx=4315 in image_id=13\nW20251209 04:31:43.142201 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10237 in image_id=12 and point2D_idx=8331 in image_id=13\nW20251209 04:31:43.142240 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10764 in image_id=12 and point2D_idx=8280 in image_id=13\nW20251209 04:31:43.142258 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8145 in image_id=12 and point2D_idx=6621 in image_id=13\nW20251209 04:31:43.142274 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=94 in image_id=12 and point2D_idx=161 in image_id=13\nW20251209 04:31:43.142297 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8507 in image_id=12 and point2D_idx=7016 in image_id=13\nW20251209 04:31:43.142306 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13788 in image_id=12 and point2D_idx=12866 in image_id=13\nW20251209 04:31:43.142400 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12722 in image_id=12 and point2D_idx=10723 in image_id=13\nW20251209 04:31:43.142471 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5926 in image_id=12 and point2D_idx=3189 in image_id=13\nW20251209 04:31:43.142525 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14165 in image_id=12 and point2D_idx=8326 in image_id=14\nW20251209 04:31:43.142583 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5151 in image_id=12 and point2D_idx=1902 in image_id=14\nW20251209 04:31:43.142591 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8840 in image_id=12 and point2D_idx=4024 in image_id=14\nW20251209 04:31:43.142710 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6602 in image_id=12 and point2D_idx=5880 in image_id=14\nW20251209 04:31:43.142819 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12046 in image_id=12 and point2D_idx=8804 in image_id=14\nW20251209 04:31:43.142832 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4203 in image_id=12 and point2D_idx=1686 in image_id=14\nW20251209 04:31:43.142844 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3556 in image_id=12 and point2D_idx=1181 in image_id=14\nW20251209 04:31:43.142980 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7713 in image_id=12 and point2D_idx=5656 in image_id=14\nW20251209 04:31:43.143042 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1707 in image_id=12 and point2D_idx=414 in image_id=14\nW20251209 04:31:43.143072 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2292 in image_id=12 and point2D_idx=1440 in image_id=14\nW20251209 04:31:43.143101 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4562 in image_id=12 and point2D_idx=3404 in image_id=14\nW20251209 04:31:43.143177 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13341 in image_id=12 and point2D_idx=10924 in image_id=14\nW20251209 04:31:43.143217 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12354 in image_id=12 and point2D_idx=12602 in image_id=15\nW20251209 04:31:43.143336 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11636 in image_id=12 and point2D_idx=11936 in image_id=15\nW20251209 04:31:43.143385 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6601 in image_id=12 and point2D_idx=6302 in image_id=15\nW20251209 04:31:43.143412 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7324 in image_id=12 and point2D_idx=6932 in image_id=15\nW20251209 04:31:43.143427 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11193 in image_id=12 and point2D_idx=11255 in image_id=15\nW20251209 04:31:43.143468 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14038 in image_id=12 and point2D_idx=12801 in image_id=15\nW20251209 04:31:43.143539 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2881 in image_id=12 and point2D_idx=2811 in image_id=15\nW20251209 04:31:43.143553 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7332 in image_id=12 and point2D_idx=6613 in image_id=15\nW20251209 04:31:43.143600 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4526 in image_id=12 and point2D_idx=4276 in image_id=15\nW20251209 04:31:43.143625 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5191 in image_id=12 and point2D_idx=4343 in image_id=15\nW20251209 04:31:43.143686 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8086 in image_id=12 and point2D_idx=6326 in image_id=15\nW20251209 04:31:43.143718 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10756 in image_id=12 and point2D_idx=8342 in image_id=15\nW20251209 04:31:43.143818 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14739 in image_id=12 and point2D_idx=11927 in image_id=15\nW20251209 04:31:43.143850 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9341 in image_id=12 and point2D_idx=6048 in image_id=15\nW20251209 04:31:43.143879 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2930 in image_id=12 and point2D_idx=1021 in image_id=15\nW20251209 04:31:43.143895 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6662 in image_id=12 and point2D_idx=3777 in image_id=15\nW20251209 04:31:43.143904 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4572 in image_id=12 and point2D_idx=2031 in image_id=15\nW20251209 04:31:43.143915 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9731 in image_id=12 and point2D_idx=7140 in image_id=16\nW20251209 04:31:43.143962 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9749 in image_id=12 and point2D_idx=5022 in image_id=16\nW20251209 04:31:43.144010 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10738 in image_id=12 and point2D_idx=8383 in image_id=16\nW20251209 04:31:43.144022 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10742 in image_id=12 and point2D_idx=7873 in image_id=16\nW20251209 04:31:43.144053 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10226 in image_id=12 and point2D_idx=6257 in image_id=16\nW20251209 04:31:43.144092 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12628 in image_id=12 and point2D_idx=10192 in image_id=16\nW20251209 04:31:43.144127 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13295 in image_id=12 and point2D_idx=7393 in image_id=16\nW20251209 04:31:43.144166 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11247 in image_id=12 and point2D_idx=3647 in image_id=16\nW20251209 04:31:43.144201 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4569 in image_id=12 and point2D_idx=1127 in image_id=16\nW20251209 04:31:43.144293 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9746 in image_id=12 and point2D_idx=4825 in image_id=17\nW20251209 04:31:43.144318 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8864 in image_id=12 and point2D_idx=6857 in image_id=17\nW20251209 04:31:43.144327 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2007 in image_id=12 and point2D_idx=10117 in image_id=17\nW20251209 04:31:43.144337 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3926 in image_id=12 and point2D_idx=11336 in image_id=17\nW20251209 04:31:43.144345 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7678 in image_id=12 and point2D_idx=5674 in image_id=17\nW20251209 04:31:43.144362 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7683 in image_id=12 and point2D_idx=5400 in image_id=17\nW20251209 04:31:43.144375 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6612 in image_id=12 and point2D_idx=5096 in image_id=17\nW20251209 04:31:43.144383 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9299 in image_id=12 and point2D_idx=6298 in image_id=17\nW20251209 04:31:43.144396 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10730 in image_id=12 and point2D_idx=8589 in image_id=17\nW20251209 04:31:43.144424 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5887 in image_id=12 and point2D_idx=3855 in image_id=17\nW20251209 04:31:43.144449 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6319 in image_id=12 and point2D_idx=2792 in image_id=17\nW20251209 04:31:43.144498 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12711 in image_id=12 and point2D_idx=8131 in image_id=17\nW20251209 04:31:43.144507 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10770 in image_id=12 and point2D_idx=6892 in image_id=17\nW20251209 04:31:43.144519 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12072 in image_id=12 and point2D_idx=6924 in image_id=17\nW20251209 04:31:43.144550 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5563 in image_id=12 and point2D_idx=2368 in image_id=17\nW20251209 04:31:43.144559 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10779 in image_id=12 and point2D_idx=3892 in image_id=17\nW20251209 04:31:43.144572 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8524 in image_id=12 and point2D_idx=2607 in image_id=17\nW20251209 04:31:43.144596 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4926 in image_id=12 and point2D_idx=774 in image_id=17\nW20251209 04:31:43.144611 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9355 in image_id=12 and point2D_idx=1340 in image_id=17\nW20251209 04:31:43.144625 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11066 in image_id=12 and point2D_idx=6977 in image_id=18\nW20251209 04:31:43.144634 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9063 in image_id=12 and point2D_idx=4819 in image_id=18\nW20251209 04:31:43.144647 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10181 in image_id=12 and point2D_idx=5438 in image_id=18\nW20251209 04:31:43.144689 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8863 in image_id=12 and point2D_idx=3611 in image_id=18\nW20251209 04:31:43.144742 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2262 in image_id=12 and point2D_idx=8190 in image_id=18\nW20251209 04:31:43.144752 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6976 in image_id=12 and point2D_idx=4233 in image_id=18\nW20251209 04:31:43.144758 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7688 in image_id=12 and point2D_idx=4236 in image_id=18\nW20251209 04:31:43.144798 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12432 in image_id=12 and point2D_idx=6538 in image_id=18\nW20251209 04:31:43.144826 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12403 in image_id=12 and point2D_idx=6336 in image_id=18\nW20251209 04:31:43.144840 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10254 in image_id=12 and point2D_idx=4271 in image_id=18\nW20251209 04:31:43.144851 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8916 in image_id=12 and point2D_idx=3040 in image_id=18\nW20251209 04:31:43.144864 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5224 in image_id=12 and point2D_idx=1670 in image_id=18\nW20251209 04:31:43.144872 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8919 in image_id=12 and point2D_idx=2303 in image_id=18\nW20251209 04:31:43.144881 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5568 in image_id=12 and point2D_idx=1459 in image_id=18\nW20251209 04:31:43.144898 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5271 in image_id=12 and point2D_idx=732 in image_id=18\nW20251209 04:31:43.144906 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7736 in image_id=12 and point2D_idx=1067 in image_id=18\nW20251209 04:31:43.144932 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8866 in image_id=12 and point2D_idx=4604 in image_id=19\nW20251209 04:31:43.144941 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9755 in image_id=12 and point2D_idx=6772 in image_id=19\nW20251209 04:31:43.144962 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10730 in image_id=12 and point2D_idx=5520 in image_id=19\nW20251209 04:31:43.144979 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10218 in image_id=12 and point2D_idx=4616 in image_id=19\nW20251209 04:31:43.145000 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10745 in image_id=12 and point2D_idx=4858 in image_id=19\nW20251209 04:31:43.145021 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=592 in image_id=12 and point2D_idx=6422 in image_id=19\nW20251209 04:31:43.145050 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11219 in image_id=12 and point2D_idx=6791 in image_id=19\nW20251209 04:31:43.145095 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14565 in image_id=12 and point2D_idx=3821 in image_id=19\nW20251209 04:31:43.145102 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9241 in image_id=12 and point2D_idx=3493 in image_id=19\nW20251209 04:31:43.145142 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9407 in image_id=12 and point2D_idx=465 in image_id=19\nW20251209 04:31:43.145165 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13899 in image_id=13 and point2D_idx=5296 in image_id=14\nW20251209 04:31:43.145211 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13930 in image_id=13 and point2D_idx=5023 in image_id=14\nW20251209 04:31:43.145243 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13936 in image_id=13 and point2D_idx=5028 in image_id=14\nW20251209 04:31:43.145315 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6582 in image_id=13 and point2D_idx=6842 in image_id=14\nW20251209 04:31:43.145336 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4910 in image_id=13 and point2D_idx=888 in image_id=14\nW20251209 04:31:43.145363 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3216 in image_id=13 and point2D_idx=75 in image_id=14\nW20251209 04:31:43.145392 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3157 in image_id=13 and point2D_idx=88 in image_id=14\nW20251209 04:31:43.145410 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3531 in image_id=13 and point2D_idx=4292 in image_id=14\nW20251209 04:31:43.145424 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3926 in image_id=13 and point2D_idx=4856 in image_id=14\nW20251209 04:31:43.145498 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5838 in image_id=13 and point2D_idx=5369 in image_id=14\nW20251209 04:31:43.145532 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4749 in image_id=13 and point2D_idx=4863 in image_id=14\nW20251209 04:31:43.145563 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13343 in image_id=13 and point2D_idx=11087 in image_id=14\nW20251209 04:31:43.145616 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2155 in image_id=13 and point2D_idx=3627 in image_id=14\nW20251209 04:31:43.145659 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2500 in image_id=13 and point2D_idx=4324 in image_id=14\nW20251209 04:31:43.145697 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9604 in image_id=13 and point2D_idx=11016 in image_id=15\nW20251209 04:31:43.145731 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6192 in image_id=13 and point2D_idx=7250 in image_id=15\nW20251209 04:31:43.145745 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11065 in image_id=13 and point2D_idx=11877 in image_id=15\nW20251209 04:31:43.145864 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5804 in image_id=13 and point2D_idx=6606 in image_id=15\nW20251209 04:31:43.145959 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5448 in image_id=13 and point2D_idx=4265 in image_id=15\nW20251209 04:31:43.146003 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9274 in image_id=13 and point2D_idx=6907 in image_id=15\nW20251209 04:31:43.146037 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14141 in image_id=13 and point2D_idx=12123 in image_id=15\nW20251209 04:31:43.146099 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2116 in image_id=13 and point2D_idx=1510 in image_id=15\nW20251209 04:31:43.146153 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2122 in image_id=13 and point2D_idx=1450 in image_id=15\nW20251209 04:31:43.146202 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3155 in image_id=13 and point2D_idx=2251 in image_id=15\nW20251209 04:31:43.146239 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=965 in image_id=13 and point2D_idx=425 in image_id=15\nW20251209 04:31:43.146286 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3162 in image_id=13 and point2D_idx=2547 in image_id=15\nW20251209 04:31:43.146430 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10198 in image_id=13 and point2D_idx=8347 in image_id=15\nW20251209 04:31:43.146582 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12578 in image_id=13 and point2D_idx=10630 in image_id=15\nW20251209 04:31:43.146630 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2160 in image_id=13 and point2D_idx=2587 in image_id=15\nW20251209 04:31:43.146664 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=244 in image_id=13 and point2D_idx=92 in image_id=15\nW20251209 04:31:43.146708 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11502 in image_id=13 and point2D_idx=8516 in image_id=16\nW20251209 04:31:43.146788 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1777 in image_id=13 and point2D_idx=10401 in image_id=16\nW20251209 04:31:43.146805 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10666 in image_id=13 and point2D_idx=7351 in image_id=16\nW20251209 04:31:43.146824 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9142 in image_id=13 and point2D_idx=5423 in image_id=16\nW20251209 04:31:43.146864 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7052 in image_id=13 and point2D_idx=5636 in image_id=16\nW20251209 04:31:43.146878 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8686 in image_id=13 and point2D_idx=6869 in image_id=16\nW20251209 04:31:43.147011 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7434 in image_id=13 and point2D_idx=3912 in image_id=16\nW20251209 04:31:43.147124 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8725 in image_id=13 and point2D_idx=6308 in image_id=16\nW20251209 04:31:43.147190 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10208 in image_id=13 and point2D_idx=5680 in image_id=16\nW20251209 04:31:43.147227 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5143 in image_id=13 and point2D_idx=2698 in image_id=16\nW20251209 04:31:43.147262 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11138 in image_id=13 and point2D_idx=4804 in image_id=16\nW20251209 04:31:43.147349 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1182 in image_id=13 and point2D_idx=9517 in image_id=17\nW20251209 04:31:43.147389 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5792 in image_id=13 and point2D_idx=4243 in image_id=17\nW20251209 04:31:43.147411 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10647 in image_id=13 and point2D_idx=7434 in image_id=17\nW20251209 04:31:43.147534 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5521 in image_id=13 and point2D_idx=4767 in image_id=17\nW20251209 04:31:43.147569 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=114 in image_id=13 and point2D_idx=4260 in image_id=17\nW20251209 04:31:43.147577 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6700 in image_id=13 and point2D_idx=5102 in image_id=17\nW20251209 04:31:43.147599 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6224 in image_id=13 and point2D_idx=4837 in image_id=17\nW20251209 04:31:43.147616 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7424 in image_id=13 and point2D_idx=5478 in image_id=17\nW20251209 04:31:43.147710 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2150 in image_id=13 and point2D_idx=2362 in image_id=17\nW20251209 04:31:43.147729 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5847 in image_id=13 and point2D_idx=3882 in image_id=17\nW20251209 04:31:43.147736 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7504 in image_id=13 and point2D_idx=5143 in image_id=17\nW20251209 04:31:43.147800 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10647 in image_id=13 and point2D_idx=6258 in image_id=18\nW20251209 04:31:43.147820 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2782 in image_id=13 and point2D_idx=10520 in image_id=18\nW20251209 04:31:43.147861 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=738 in image_id=13 and point2D_idx=8632 in image_id=18\nW20251209 04:31:43.147892 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6981 in image_id=13 and point2D_idx=4835 in image_id=18\nW20251209 04:31:43.147899 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8249 in image_id=13 and point2D_idx=5792 in image_id=18\nW20251209 04:31:43.147911 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9632 in image_id=13 and point2D_idx=7684 in image_id=18\nW20251209 04:31:43.147943 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4792 in image_id=13 and point2D_idx=3309 in image_id=18\nW20251209 04:31:43.147950 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10169 in image_id=13 and point2D_idx=7253 in image_id=18\nW20251209 04:31:43.147976 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10088 in image_id=13 and point2D_idx=7012 in image_id=18\nW20251209 04:31:43.147989 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7005 in image_id=13 and point2D_idx=3023 in image_id=18\nW20251209 04:31:43.148017 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9186 in image_id=13 and point2D_idx=3695 in image_id=18\nW20251209 04:31:43.148059 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11617 in image_id=13 and point2D_idx=6550 in image_id=18\nW20251209 04:31:43.148097 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5859 in image_id=13 and point2D_idx=1682 in image_id=18\nW20251209 04:31:43.148110 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8299 in image_id=13 and point2D_idx=2120 in image_id=18\nW20251209 04:31:43.148119 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6265 in image_id=13 and point2D_idx=1310 in image_id=18\nW20251209 04:31:43.148131 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=156 in image_id=13 and point2D_idx=8388 in image_id=19\nW20251209 04:31:43.148148 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7835 in image_id=13 and point2D_idx=4070 in image_id=19\nW20251209 04:31:43.148188 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9649 in image_id=13 and point2D_idx=4848 in image_id=19\nW20251209 04:31:43.148206 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10172 in image_id=13 and point2D_idx=5135 in image_id=19\nW20251209 04:31:43.148216 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10176 in image_id=13 and point2D_idx=5090 in image_id=19\nW20251209 04:31:43.148247 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11115 in image_id=13 and point2D_idx=6966 in image_id=19\nW20251209 04:31:43.148266 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11118 in image_id=13 and point2D_idx=5828 in image_id=19\nW20251209 04:31:43.148283 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13984 in image_id=13 and point2D_idx=5762 in image_id=19\nW20251209 04:31:43.148310 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13318 in image_id=13 and point2D_idx=3516 in image_id=19\nW20251209 04:31:43.148343 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13322 in image_id=13 and point2D_idx=1678 in image_id=19\nW20251209 04:31:43.148368 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13024 in image_id=13 and point2D_idx=845 in image_id=19\nW20251209 04:31:43.148391 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6164 in image_id=14 and point2D_idx=6286 in image_id=15\nW20251209 04:31:43.148439 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10637 in image_id=14 and point2D_idx=11883 in image_id=15\nW20251209 04:31:43.148562 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6520 in image_id=14 and point2D_idx=6626 in image_id=15\nW20251209 04:31:43.148592 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2322 in image_id=14 and point2D_idx=4012 in image_id=15\nW20251209 04:31:43.148638 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11538 in image_id=14 and point2D_idx=12445 in image_id=15\nW20251209 04:31:43.148657 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3612 in image_id=14 and point2D_idx=3453 in image_id=15\nW20251209 04:31:43.148673 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10906 in image_id=14 and point2D_idx=11000 in image_id=15\nW20251209 04:31:43.148686 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=103 in image_id=14 and point2D_idx=587 in image_id=15\nW20251209 04:31:43.148772 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5389 in image_id=14 and point2D_idx=3773 in image_id=15\nW20251209 04:31:43.148811 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8352 in image_id=14 and point2D_idx=4131 in image_id=16\nW20251209 04:31:43.148818 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10232 in image_id=14 and point2D_idx=5986 in image_id=16\nW20251209 04:31:43.148826 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8353 in image_id=14 and point2D_idx=4132 in image_id=16\nW20251209 04:31:43.148852 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8364 in image_id=14 and point2D_idx=7362 in image_id=16\nW20251209 04:31:43.148882 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9099 in image_id=14 and point2D_idx=9671 in image_id=16\nW20251209 04:31:43.148899 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10251 in image_id=14 and point2D_idx=8649 in image_id=16\nW20251209 04:31:43.148934 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8760 in image_id=14 and point2D_idx=7642 in image_id=16\nW20251209 04:31:43.148947 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7627 in image_id=14 and point2D_idx=6275 in image_id=16\nW20251209 04:31:43.148959 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8396 in image_id=14 and point2D_idx=6003 in image_id=16\nW20251209 04:31:43.149005 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2582 in image_id=14 and point2D_idx=1697 in image_id=16\nW20251209 04:31:43.149019 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1898 in image_id=14 and point2D_idx=1478 in image_id=16\nW20251209 04:31:43.149058 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9792 in image_id=14 and point2D_idx=7113 in image_id=17\nW20251209 04:31:43.149069 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10204 in image_id=14 and point2D_idx=7605 in image_id=17\nW20251209 04:31:43.149086 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6827 in image_id=14 and point2D_idx=3544 in image_id=17\nW20251209 04:31:43.149102 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10633 in image_id=14 and point2D_idx=7611 in image_id=17\nW20251209 04:31:43.149229 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6867 in image_id=14 and point2D_idx=3328 in image_id=17\nW20251209 04:31:43.149256 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7973 in image_id=14 and point2D_idx=4493 in image_id=17\nW20251209 04:31:43.149281 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8576 in image_id=14 and point2D_idx=4790 in image_id=17\nW20251209 04:31:43.149296 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9111 in image_id=14 and point2D_idx=5130 in image_id=17\nW20251209 04:31:43.149319 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10271 in image_id=14 and point2D_idx=10166 in image_id=17\nW20251209 04:31:43.149411 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9792 in image_id=14 and point2D_idx=5718 in image_id=18\nW20251209 04:31:43.149448 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=608 in image_id=14 and point2D_idx=11189 in image_id=18\nW20251209 04:31:43.149543 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8751 in image_id=14 and point2D_idx=3636 in image_id=18\nW20251209 04:31:43.149568 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10677 in image_id=14 and point2D_idx=7945 in image_id=18\nW20251209 04:31:43.149577 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7501 in image_id=14 and point2D_idx=6039 in image_id=18\nW20251209 04:31:43.149596 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6880 in image_id=14 and point2D_idx=5486 in image_id=18\nW20251209 04:31:43.149645 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10642 in image_id=14 and point2D_idx=4337 in image_id=19\nW20251209 04:31:43.149725 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9127 in image_id=14 and point2D_idx=2575 in image_id=19\nW20251209 04:31:43.149803 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3484 in image_id=15 and point2D_idx=10724 in image_id=16\nW20251209 04:31:43.149833 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2596 in image_id=15 and point2D_idx=9636 in image_id=16\nW20251209 04:31:43.149842 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10945 in image_id=15 and point2D_idx=7330 in image_id=16\nW20251209 04:31:43.149890 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3426 in image_id=15 and point2D_idx=10479 in image_id=16\nW20251209 04:31:43.149906 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10955 in image_id=15 and point2D_idx=6235 in image_id=16\nW20251209 04:31:43.149947 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8709 in image_id=15 and point2D_idx=6872 in image_id=16\nW20251209 04:31:43.149990 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8725 in image_id=15 and point2D_idx=6550 in image_id=16\nW20251209 04:31:43.150159 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8352 in image_id=15 and point2D_idx=6913 in image_id=16\nW20251209 04:31:43.150204 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1523 in image_id=15 and point2D_idx=2427 in image_id=16\nW20251209 04:31:43.150250 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7995 in image_id=15 and point2D_idx=6003 in image_id=16\nW20251209 04:31:43.150434 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4052 in image_id=15 and point2D_idx=11336 in image_id=17\nW20251209 04:31:43.150462 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3782 in image_id=15 and point2D_idx=11051 in image_id=17\nW20251209 04:31:43.150471 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7945 in image_id=15 and point2D_idx=5065 in image_id=17\nW20251209 04:31:43.150498 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3995 in image_id=15 and point2D_idx=11056 in image_id=17\nW20251209 04:31:43.150525 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10178 in image_id=15 and point2D_idx=6060 in image_id=17\nW20251209 04:31:43.150650 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7274 in image_id=15 and point2D_idx=5687 in image_id=17\nW20251209 04:31:43.150662 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8029 in image_id=15 and point2D_idx=5981 in image_id=17\nW20251209 04:31:43.150709 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10971 in image_id=15 and point2D_idx=10451 in image_id=17\nW20251209 04:31:43.150730 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8725 in image_id=15 and point2D_idx=6648 in image_id=17\nW20251209 04:31:43.150749 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8729 in image_id=15 and point2D_idx=6945 in image_id=17\nW20251209 04:31:43.150780 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4859 in image_id=15 and point2D_idx=2645 in image_id=17\nW20251209 04:31:43.150821 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2057 in image_id=15 and point2D_idx=2592 in image_id=17\nW20251209 04:31:43.150838 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11318 in image_id=15 and point2D_idx=9140 in image_id=17\nW20251209 04:31:43.150874 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5145 in image_id=15 and point2D_idx=3298 in image_id=17\nW20251209 04:31:43.150944 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1333 in image_id=15 and point2D_idx=9373 in image_id=18\nW20251209 04:31:43.151008 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10949 in image_id=15 and point2D_idx=5433 in image_id=18\nW20251209 04:31:43.151021 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10950 in image_id=15 and point2D_idx=5500 in image_id=18\nW20251209 04:31:43.151048 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3120 in image_id=15 and point2D_idx=9038 in image_id=18\nW20251209 04:31:43.151156 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10269 in image_id=15 and point2D_idx=8411 in image_id=18\nW20251209 04:31:43.151205 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6067 in image_id=15 and point2D_idx=3927 in image_id=18\nW20251209 04:31:43.151228 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11032 in image_id=15 and point2D_idx=7926 in image_id=18\nW20251209 04:31:43.151261 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11303 in image_id=15 and point2D_idx=10057 in image_id=18\nW20251209 04:31:43.151334 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9858 in image_id=15 and point2D_idx=7034 in image_id=18\nW20251209 04:31:43.151370 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3766 in image_id=15 and point2D_idx=2101 in image_id=18\nW20251209 04:31:43.151402 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4070 in image_id=15 and point2D_idx=1675 in image_id=18\nW20251209 04:31:43.151417 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4612 in image_id=15 and point2D_idx=1921 in image_id=18\nW20251209 04:31:43.151441 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6053 in image_id=15 and point2D_idx=1927 in image_id=18\nW20251209 04:31:43.151480 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11557 in image_id=15 and point2D_idx=4585 in image_id=19\nW20251209 04:31:43.151497 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11875 in image_id=15 and point2D_idx=4825 in image_id=19\nW20251209 04:31:43.151523 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=207 in image_id=15 and point2D_idx=6588 in image_id=19\nW20251209 04:31:43.151531 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3123 in image_id=15 and point2D_idx=8194 in image_id=19\nW20251209 04:31:43.151547 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3428 in image_id=15 and point2D_idx=8427 in image_id=19\nW20251209 04:31:43.151567 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7963 in image_id=15 and point2D_idx=4128 in image_id=19\nW20251209 04:31:43.151578 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4013 in image_id=15 and point2D_idx=7076 in image_id=19\nW20251209 04:31:43.151622 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11596 in image_id=15 and point2D_idx=6464 in image_id=19\nW20251209 04:31:43.151654 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10615 in image_id=15 and point2D_idx=4648 in image_id=19\nW20251209 04:31:43.151708 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11326 in image_id=15 and point2D_idx=1871 in image_id=19\nW20251209 04:31:43.151720 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11616 in image_id=15 and point2D_idx=1651 in image_id=19\nW20251209 04:31:43.151752 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11229 in image_id=16 and point2D_idx=9764 in image_id=17\nW20251209 04:31:43.151801 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8066 in image_id=16 and point2D_idx=7358 in image_id=17\nW20251209 04:31:43.151856 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7335 in image_id=16 and point2D_idx=6838 in image_id=17\nW20251209 04:31:43.151866 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10111 in image_id=16 and point2D_idx=9056 in image_id=17\nW20251209 04:31:43.151896 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4710 in image_id=16 and point2D_idx=4458 in image_id=17\nW20251209 04:31:43.151911 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4123 in image_id=16 and point2D_idx=3839 in image_id=17\nW20251209 04:31:43.151966 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9102 in image_id=16 and point2D_idx=8567 in image_id=17\nW20251209 04:31:43.152139 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4149 in image_id=16 and point2D_idx=4183 in image_id=17\nW20251209 04:31:43.152203 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10761 in image_id=16 and point2D_idx=11075 in image_id=17\nW20251209 04:31:43.152210 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11032 in image_id=16 and point2D_idx=11367 in image_id=17\nW20251209 04:31:43.152234 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=826 in image_id=16 and point2D_idx=772 in image_id=17\nW20251209 04:31:43.152348 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9142 in image_id=16 and point2D_idx=9831 in image_id=17\nW20251209 04:31:43.152441 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4404 in image_id=16 and point2D_idx=3276 in image_id=18\nW20251209 04:31:43.152536 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4129 in image_id=16 and point2D_idx=2990 in image_id=18\nW20251209 04:31:43.152629 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=704 in image_id=16 and point2D_idx=573 in image_id=18\nW20251209 04:31:43.152832 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2457 in image_id=16 and point2D_idx=1929 in image_id=18\nW20251209 04:31:43.152875 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5387 in image_id=16 and point2D_idx=7428 in image_id=19\nW20251209 04:31:43.152896 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8087 in image_id=16 and point2D_idx=8252 in image_id=19\nW20251209 04:31:43.152929 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7325 in image_id=16 and point2D_idx=7563 in image_id=19\nW20251209 04:31:43.152946 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8843 in image_id=16 and point2D_idx=8301 in image_id=19\nW20251209 04:31:43.152993 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=131 in image_id=16 and point2D_idx=1598 in image_id=19\nW20251209 04:31:43.153008 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=101 in image_id=16 and point2D_idx=1599 in image_id=19\nW20251209 04:31:43.153213 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8861 in image_id=16 and point2D_idx=6254 in image_id=19\nW20251209 04:31:43.153271 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6295 in image_id=16 and point2D_idx=3773 in image_id=19\nW20251209 04:31:43.153318 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6296 in image_id=16 and point2D_idx=3775 in image_id=19\nW20251209 04:31:43.153338 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10480 in image_id=16 and point2D_idx=8427 in image_id=19\nW20251209 04:31:43.153402 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4442 in image_id=16 and point2D_idx=2513 in image_id=19\nW20251209 04:31:43.153515 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9705 in image_id=16 and point2D_idx=6270 in image_id=19\nW20251209 04:31:43.153545 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2871 in image_id=16 and point2D_idx=810 in image_id=19\nW20251209 04:31:43.153577 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6305 in image_id=16 and point2D_idx=3151 in image_id=19\nW20251209 04:31:43.153681 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6576 in image_id=16 and point2D_idx=3171 in image_id=19\nW20251209 04:31:43.153694 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3645 in image_id=16 and point2D_idx=1014 in image_id=19\nW20251209 04:31:43.153727 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8925 in image_id=16 and point2D_idx=4876 in image_id=19\nW20251209 04:31:43.153792 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4454 in image_id=17 and point2D_idx=3589 in image_id=18\nW20251209 04:31:43.153811 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4456 in image_id=17 and point2D_idx=3592 in image_id=18\nW20251209 04:31:43.153889 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5084 in image_id=17 and point2D_idx=3910 in image_id=18\nW20251209 04:31:43.154019 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10748 in image_id=17 and point2D_idx=10327 in image_id=18\nW20251209 04:31:43.154162 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9449 in image_id=17 and point2D_idx=7927 in image_id=18\nW20251209 04:31:43.154199 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6322 in image_id=17 and point2D_idx=5481 in image_id=18\nW20251209 04:31:43.154270 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6895 in image_id=17 and point2D_idx=5775 in image_id=18\nW20251209 04:31:43.154312 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7670 in image_id=17 and point2D_idx=6315 in image_id=18\nW20251209 04:31:43.154360 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8570 in image_id=17 and point2D_idx=8311 in image_id=19\nW20251209 04:31:43.154374 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9414 in image_id=17 and point2D_idx=8518 in image_id=19\nW20251209 04:31:43.154451 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8105 in image_id=17 and point2D_idx=5089 in image_id=19\nW20251209 04:31:43.154469 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5119 in image_id=17 and point2D_idx=2520 in image_id=19\nW20251209 04:31:43.154485 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11070 in image_id=17 and point2D_idx=6940 in image_id=19\nW20251209 04:31:43.154495 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=638 in image_id=17 and point2D_idx=1610 in image_id=19\nW20251209 04:31:43.154540 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10453 in image_id=17 and point2D_idx=7082 in image_id=19\nW20251209 04:31:43.154583 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10157 in image_id=17 and point2D_idx=6307 in image_id=19\nW20251209 04:31:43.154600 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11084 in image_id=17 and point2D_idx=6458 in image_id=19\nW20251209 04:31:43.154642 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7426 in image_id=17 and point2D_idx=2839 in image_id=19\nW20251209 04:31:43.154678 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12 in image_id=18 and point2D_idx=1786 in image_id=19\nW20251209 04:31:43.154697 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4827 in image_id=18 and point2D_idx=2778 in image_id=19\nW20251209 04:31:43.154785 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6284 in image_id=18 and point2D_idx=4349 in image_id=19\nW20251209 04:31:43.154813 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7914 in image_id=18 and point2D_idx=6098 in image_id=19\nW20251209 04:31:43.154834 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10793 in image_id=18 and point2D_idx=8293 in image_id=19\nW20251209 04:31:43.154861 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6527 in image_id=18 and point2D_idx=4406 in image_id=19\nW20251209 04:31:43.154883 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7476 in image_id=18 and point2D_idx=5323 in image_id=19\nW20251209 04:31:43.154966 137119503517248 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8177 in image_id=18 and point2D_idx=5338 in image_id=19\nI20251209 04:31:43.167616 137119503517248 database_cache.cc:210]  in 0.050s (ignored 0)\nI20251209 04:31:43.167663 137119503517248 timer.cc:91] Elapsed time: 0.001 [minutes]\nI20251209 04:31:43.171314 137119503517248 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:31:43.672199 137119503517248 incremental_pipeline.cc:306] Initializing with image pair #12 and #16\nI20251209 04:31:43.678831 137119503517248 incremental_pipeline.cc:311] Global bundle adjustment\n\n 12%|█▏        | 137/1160 [05:30<31:36,  1.85s/it]\u001b[AI20251209 04:31:44.478247 137119503517248 incremental_pipeline.cc:390] Registering image #17 (3)\nI20251209 04:31:44.478277 137119503517248 incremental_pipeline.cc:393] => Image sees 1378 / 11365 points\n\n 12%|█▏        | 138/1160 [05:32<29:14,  1.72s/it]\u001b[AI20251209 04:31:45.681990 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:31:45.930988 137119503517248 incremental_pipeline.cc:390] Registering image #18 (4)\nI20251209 04:31:45.931018 137119503517248 incremental_pipeline.cc:393] => Image sees 4563 / 11224 points\n\n 12%|█▏        | 139/1160 [05:33<27:01,  1.59s/it]\u001b[AI20251209 04:31:46.992523 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:31:47.546394 137119503517248 incremental_pipeline.cc:390] Registering image #19 (5)\nI20251209 04:31:47.546428 137119503517248 incremental_pipeline.cc:393] => Image sees 4385 / 8745 points\n\n 12%|█▏        | 140/1160 [05:34<26:11,  1.54s/it]\u001b[AI20251209 04:31:49.462207 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 12%|█▏        | 141/1160 [05:36<25:08,  1.48s/it]\u001b[AI20251209 04:31:50.136478 137119503517248 incremental_pipeline.cc:390] Registering image #15 (6)\nI20251209 04:31:50.136512 137119503517248 incremental_pipeline.cc:393] => Image sees 4918 / 12807 points\n\n 12%|█▏        | 142/1160 [05:37<24:27,  1.44s/it]\u001b[A\n 12%|█▏        | 143/1160 [05:38<24:06,  1.42s/it]\u001b[A\n 12%|█▏        | 144/1160 [05:40<23:46,  1.40s/it]\u001b[AI20251209 04:31:54.606364 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 12%|█▎        | 145/1160 [05:41<23:36,  1.40s/it]\u001b[AI20251209 04:31:55.553368 137119503517248 incremental_pipeline.cc:390] Registering image #13 (7)\nI20251209 04:31:55.553416 137119503517248 incremental_pipeline.cc:393] => Image sees 7707 / 14171 points\n\n 13%|█▎        | 146/1160 [05:42<23:32,  1.39s/it]\u001b[A\n 13%|█▎        | 147/1160 [05:44<23:32,  1.39s/it]\u001b[A\n 13%|█▎        | 148/1160 [05:45<23:20,  1.38s/it]\u001b[AI20251209 04:31:59.139112 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 13%|█▎        | 149/1160 [05:47<23:01,  1.37s/it]\u001b[AI20251209 04:32:00.621390 137119503517248 incremental_pipeline.cc:390] Registering image #11 (8)\nI20251209 04:32:00.621459 137119503517248 incremental_pipeline.cc:393] => Image sees 7758 / 12944 points\n\n 13%|█▎        | 150/1160 [05:48<22:53,  1.36s/it]\u001b[A\n 13%|█▎        | 151/1160 [05:49<22:53,  1.36s/it]\u001b[A\n 13%|█▎        | 152/1160 [05:51<23:26,  1.40s/it]\u001b[AI20251209 04:32:04.790383 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 13%|█▎        | 153/1160 [05:52<23:49,  1.42s/it]\u001b[A\n 13%|█▎        | 154/1160 [05:54<23:30,  1.40s/it]\u001b[AI20251209 04:32:07.789806 137119503517248 incremental_pipeline.cc:390] Registering image #14 (9)\nI20251209 04:32:07.789856 137119503517248 incremental_pipeline.cc:393] => Image sees 9204 / 11803 points\n\n 13%|█▎        | 155/1160 [05:55<23:20,  1.39s/it]\u001b[A\n 13%|█▎        | 156/1160 [05:56<23:09,  1.38s/it]\u001b[A\n 14%|█▎        | 157/1160 [05:58<23:01,  1.38s/it]\u001b[A\n 14%|█▎        | 158/1160 [05:59<22:55,  1.37s/it]\u001b[AI20251209 04:32:13.735507 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 14%|█▎        | 159/1160 [06:00<22:32,  1.35s/it]\u001b[A\n 14%|█▍        | 160/1160 [06:02<22:37,  1.36s/it]\u001b[A\n 14%|█▍        | 161/1160 [06:03<22:37,  1.36s/it]\u001b[AI20251209 04:32:17.496062 137119503517248 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:32:17.950088 137119503517248 incremental_pipeline.cc:306] Initializing with image pair #2 and #9\nI20251209 04:32:17.954607 137119503517248 incremental_pipeline.cc:311] Global bundle adjustment\n\n 14%|█▍        | 162/1160 [06:04<22:34,  1.36s/it]\u001b[AI20251209 04:32:18.339397 137119503517248 incremental_pipeline.cc:390] Registering image #8 (3)\nI20251209 04:32:18.339429 137119503517248 incremental_pipeline.cc:393] => Image sees 893 / 10473 points\nI20251209 04:32:18.758682 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:32:18.861392 137119503517248 incremental_pipeline.cc:390] Registering image #7 (4)\nI20251209 04:32:18.861422 137119503517248 incremental_pipeline.cc:393] => Image sees 3047 / 11979 points\n\n 14%|█▍        | 163/1160 [06:06<22:13,  1.34s/it]\u001b[AI20251209 04:32:19.771539 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:32:20.086897 137119503517248 incremental_pipeline.cc:390] Registering image #6 (5)\nI20251209 04:32:20.086930 137119503517248 incremental_pipeline.cc:393] => Image sees 4464 / 12770 points\n\n 14%|█▍        | 164/1160 [06:07<22:07,  1.33s/it]\u001b[AI20251209 04:32:21.542917 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:32:22.076008 137119503517248 incremental_pipeline.cc:390] Registering image #5 (6)\nI20251209 04:32:22.076055 137119503517248 incremental_pipeline.cc:393] => Image sees 4704 / 11542 points\n\n 14%|█▍        | 165/1160 [06:08<22:19,  1.35s/it]\u001b[A\n 14%|█▍        | 166/1160 [06:10<22:23,  1.35s/it]\u001b[AI20251209 04:32:24.459073 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 14%|█▍        | 167/1160 [06:11<22:18,  1.35s/it]\u001b[AI20251209 04:32:25.888381 137119503517248 incremental_pipeline.cc:390] Registering image #4 (7)\nI20251209 04:32:25.888418 137119503517248 incremental_pipeline.cc:393] => Image sees 4763 / 8977 points\n\n 14%|█▍        | 168/1160 [06:12<22:08,  1.34s/it]\u001b[A\n 15%|█▍        | 169/1160 [06:14<22:20,  1.35s/it]\u001b[A\n 15%|█▍        | 170/1160 [06:15<22:18,  1.35s/it]\u001b[AI20251209 04:32:29.300941 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 15%|█▍        | 171/1160 [06:17<22:26,  1.36s/it]\u001b[AI20251209 04:32:30.506435 137119503517248 incremental_pipeline.cc:390] Registering image #3 (8)\nI20251209 04:32:30.506475 137119503517248 incremental_pipeline.cc:393] => Image sees 5400 / 7840 points\n\n 15%|█▍        | 172/1160 [06:18<22:12,  1.35s/it]\u001b[A\n 15%|█▍        | 173/1160 [06:19<22:19,  1.36s/it]\u001b[AI20251209 04:32:33.964206 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 15%|█▌        | 174/1160 [06:21<22:23,  1.36s/it]\u001b[A\n 15%|█▌        | 175/1160 [06:22<22:23,  1.36s/it]\u001b[AI20251209 04:32:36.415673 137119503517248 incremental_pipeline.cc:390] Registering image #1 (9)\nI20251209 04:32:36.415710 137119503517248 incremental_pipeline.cc:393] => Image sees 9926 / 12149 points\n\n 15%|█▌        | 176/1160 [06:23<22:23,  1.37s/it]\u001b[A\n 15%|█▌        | 177/1160 [06:25<23:15,  1.42s/it]\u001b[A\n 15%|█▌        | 178/1160 [06:26<23:16,  1.42s/it]\u001b[AI20251209 04:32:40.803671 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 15%|█▌        | 179/1160 [06:28<23:39,  1.45s/it]\u001b[A\n 16%|█▌        | 180/1160 [06:29<23:16,  1.43s/it]\u001b[AI20251209 04:32:43.990657 137119503517248 incremental_pipeline.cc:390] Registering image #10 (10)\nI20251209 04:32:43.990728 137119503517248 incremental_pipeline.cc:393] => Image sees 4012 / 5534 points\n\n 16%|█▌        | 181/1160 [06:31<22:58,  1.41s/it]\u001b[A\n 16%|█▌        | 182/1160 [06:32<22:53,  1.40s/it]\u001b[AI20251209 04:32:47.132727 137119503517248 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\n\n 16%|█▌        | 183/1160 [06:33<22:34,  1.39s/it]\u001b[A\n 16%|█▌        | 184/1160 [06:35<22:18,  1.37s/it]\u001b[A\n 16%|█▌        | 185/1160 [06:36<22:14,  1.37s/it]\u001b[AI20251209 04:32:50.310452 137119503517248 timer.cc:91] Elapsed time: 1.120 [minutes]\n","output_type":"stream"},{"name":"stdout","text":"{0: Reconstruction(num_cameras=9, num_images=9, num_reg_images=9, num_points3D=17537), 1: Reconstruction(num_cameras=10, num_images=10, num_reg_images=10, num_points3D=14668)}\nReconstruction done in 67.2429 sec\n","output_type":"stream"},{"name":"stderr","text":"\n 16%|█▌        | 186/1160 [06:37<22:07,  1.36s/it]\u001b[A\n 16%|█▌        | 187/1160 [06:39<21:45,  1.34s/it]\u001b[A\n 16%|█▌        | 188/1160 [06:40<21:28,  1.33s/it]\u001b[A\n 16%|█▋        | 189/1160 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1.45s/it]\u001b[A\n","output_type":"stream"},{"name":"stdout","text":"✅ Saved keypoints to /kaggle/working/result/featureout/stairs/keypoints.h5\n✅ Saved matches to /kaggle/working/result/featureout/stairs/matches.h5\n✅ Saved matches to /kaggle/working/result/featureout/stairs/pairs.txt\nMASt3R matching done in 1681.40 sec\n","output_type":"stream"},{"name":"stderr","text":"Adding keypoints and images: 100%|██████████| 51/51 [00:02<00:00, 21.85it/s]\n","output_type":"stream"},{"name":"stdout","text":"import /kaggle/working/result/featureout/stairs/colmap.db done!\ncolmap database\n🔁 Attempt 1 to run verify_matches\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:54:20.052993 137119045514816 misc.cc:44] \n==============================================================================\nFeature matching\n==============================================================================\nI20251209 04:54:20.055431 137118994130496 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:54:20.055612 137119503517248 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:54:20.055725 137119733372480 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:54:20.055817 137119741765184 sift.cc:1432] Creating SIFT CPU feature matcher\nI20251209 04:54:20.055849 137119045514816 pairing.cc:742] Importing image pairs...\nI20251209 04:54:20.056348 137119045514816 pairing.cc:775] Matching block [1/1]\nI20251209 04:54:24.718503 137119045514816 feature_matching.cc:46] in 4.662s\nI20251209 04:54:24.724399 137119045514816 timer.cc:91] Elapsed time: 0.078 [minutes]\n","output_type":"stream"},{"name":"stdout","text":"✅ verify_matches succeeded\nverify matching done!!!!\nRANSAC in 5.0059 sec\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:54:24.940371 137119495124544 incremental_pipeline.cc:237] Loading database\nI20251209 04:54:24.941831 137119495124544 database_cache.cc:66] Loading cameras...\nI20251209 04:54:24.941920 137119495124544 database_cache.cc:76]  51 in 0.000s\nI20251209 04:54:24.941945 137119495124544 database_cache.cc:84] Loading matches...\nI20251209 04:54:24.945451 137119495124544 database_cache.cc:89]  323 in 0.004s\nI20251209 04:54:24.945476 137119495124544 database_cache.cc:105] Loading images...\nI20251209 04:54:24.958127 137119495124544 database_cache.cc:153]  51 in 0.013s (connected 51)\nI20251209 04:54:24.958157 137119495124544 database_cache.cc:164] Loading pose priors...\nI20251209 04:54:24.958447 137119495124544 database_cache.cc:175]  0 in 0.000s\nI20251209 04:54:24.958462 137119495124544 database_cache.cc:184] Building correspondence graph...\nW20251209 04:54:24.960804 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4126 in image_id=1 and point2D_idx=8293 in image_id=2\nW20251209 04:54:24.960906 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=251 in image_id=1 and point2D_idx=2425 in image_id=2\nW20251209 04:54:24.960962 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=257 in image_id=1 and point2D_idx=2097 in image_id=2\nW20251209 04:54:24.960995 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4248 in image_id=1 and point2D_idx=8355 in image_id=2\nW20251209 04:54:24.961019 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4250 in image_id=1 and point2D_idx=8357 in image_id=2\nW20251209 04:54:24.961062 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=265 in image_id=1 and point2D_idx=1826 in image_id=2\nW20251209 04:54:24.961115 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1741 in image_id=1 and point2D_idx=4577 in image_id=2\nW20251209 04:54:24.961168 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4401 in image_id=1 and point2D_idx=8469 in image_id=2\nW20251209 04:54:24.961237 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=284 in image_id=1 and point2D_idx=833 in image_id=2\nW20251209 04:54:24.961256 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4153 in image_id=1 and point2D_idx=8326 in image_id=2\nW20251209 04:54:24.961294 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=288 in image_id=1 and point2D_idx=738 in image_id=2\nW20251209 04:54:24.961312 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=904 in image_id=1 and point2D_idx=1783 in image_id=2\nW20251209 04:54:24.961326 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2729 in image_id=1 and point2D_idx=6557 in image_id=2\nW20251209 04:54:24.961369 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=292 in image_id=1 and point2D_idx=419 in image_id=2\nW20251209 04:54:24.961448 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3012 in image_id=1 and point2D_idx=6763 in image_id=2\nW20251209 04:54:24.961470 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3731 in image_id=1 and point2D_idx=7784 in image_id=2\nW20251209 04:54:24.961508 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2517 in image_id=1 and point2D_idx=5423 in image_id=2\nW20251209 04:54:24.961539 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4558 in image_id=1 and point2D_idx=8597 in image_id=2\nW20251209 04:54:24.961600 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2973 in image_id=1 and point2D_idx=6580 in image_id=3\nW20251209 04:54:24.961620 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2980 in image_id=1 and point2D_idx=6584 in image_id=3\nW20251209 04:54:24.961662 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=673 in image_id=1 and point2D_idx=404 in image_id=3\nW20251209 04:54:24.961675 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2496 in image_id=1 and point2D_idx=6041 in image_id=3\nW20251209 04:54:24.961685 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2714 in image_id=1 and point2D_idx=6380 in image_id=3\nW20251209 04:54:24.961730 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4163 in image_id=1 and point2D_idx=8119 in image_id=9\nW20251209 04:54:24.961741 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3436 in image_id=1 and point2D_idx=7333 in image_id=9\nW20251209 04:54:24.961797 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2496 in image_id=1 and point2D_idx=6567 in image_id=24\nW20251209 04:54:24.961827 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2999 in image_id=1 and point2D_idx=7402 in image_id=24\nW20251209 04:54:24.961840 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=685 in image_id=1 and point2D_idx=751 in image_id=24\nW20251209 04:54:24.961866 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3392 in image_id=1 and point2D_idx=912 in image_id=25\nW20251209 04:54:24.961875 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3396 in image_id=1 and point2D_idx=895 in image_id=25\nW20251209 04:54:24.961882 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4605 in image_id=1 and point2D_idx=2287 in image_id=25\nW20251209 04:54:24.961898 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4401 in image_id=1 and point2D_idx=1929 in image_id=25\nW20251209 04:54:24.961945 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2704 in image_id=1 and point2D_idx=7978 in image_id=28\nW20251209 04:54:24.961957 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=879 in image_id=1 and point2D_idx=4315 in image_id=28\nW20251209 04:54:24.961968 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2708 in image_id=1 and point2D_idx=8037 in image_id=28\nW20251209 04:54:24.961991 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2957 in image_id=1 and point2D_idx=8462 in image_id=28\nW20251209 04:54:24.961998 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3380 in image_id=1 and point2D_idx=9042 in image_id=28\nW20251209 04:54:24.962013 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2964 in image_id=1 and point2D_idx=8466 in image_id=28\nW20251209 04:54:24.962020 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3384 in image_id=1 and point2D_idx=9044 in image_id=28\nW20251209 04:54:24.962027 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3705 in image_id=1 and point2D_idx=9371 in image_id=28\nW20251209 04:54:24.962062 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2980 in image_id=1 and point2D_idx=8510 in image_id=28\nW20251209 04:54:24.962091 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=671 in image_id=1 and point2D_idx=2439 in image_id=28\nW20251209 04:54:24.962131 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1997 in image_id=1 and point2D_idx=6243 in image_id=28\nW20251209 04:54:24.962179 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1615 in image_id=1 and point2D_idx=4548 in image_id=28\nW20251209 04:54:24.962252 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1860 in image_id=1 and point2D_idx=5552 in image_id=28\nW20251209 04:54:24.962270 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2004 in image_id=1 and point2D_idx=6110 in image_id=28\nW20251209 04:54:24.962285 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3410 in image_id=1 and point2D_idx=9360 in image_id=28\nW20251209 04:54:24.962335 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1299 in image_id=1 and point2D_idx=2709 in image_id=28\nW20251209 04:54:24.962354 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1474 in image_id=1 and point2D_idx=3402 in image_id=28\nW20251209 04:54:24.962366 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2141 in image_id=1 and point2D_idx=6682 in image_id=28\nW20251209 04:54:24.962380 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2291 in image_id=1 and point2D_idx=7198 in image_id=28\nW20251209 04:54:24.962393 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2508 in image_id=1 and point2D_idx=7869 in image_id=28\nW20251209 04:54:24.962436 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=687 in image_id=1 and point2D_idx=549 in image_id=28\nW20251209 04:54:24.962495 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3418 in image_id=1 and point2D_idx=5364 in image_id=36\nW20251209 04:54:24.962524 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2990 in image_id=1 and point2D_idx=3534 in image_id=40\nW20251209 04:54:24.962544 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2136 in image_id=1 and point2D_idx=1920 in image_id=40\nW20251209 04:54:24.962566 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3009 in image_id=1 and point2D_idx=4042 in image_id=40\nW20251209 04:54:24.962587 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=907 in image_id=1 and point2D_idx=1273 in image_id=45\nW20251209 04:54:24.962675 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1852 in image_id=1 and point2D_idx=5232 in image_id=51\nW20251209 04:54:24.962709 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2980 in image_id=1 and point2D_idx=7823 in image_id=51\nW20251209 04:54:24.962720 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3711 in image_id=1 and point2D_idx=8691 in image_id=51\nW20251209 04:54:24.962740 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3401 in image_id=1 and point2D_idx=8383 in image_id=51\nW20251209 04:54:24.962766 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1858 in image_id=1 and point2D_idx=4796 in image_id=51\nW20251209 04:54:24.962789 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3723 in image_id=1 and point2D_idx=8754 in image_id=51\nW20251209 04:54:24.962824 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3957 in image_id=1 and point2D_idx=9126 in image_id=51\nW20251209 04:54:24.962932 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=51 in image_id=1 and point2D_idx=547 in image_id=51\nW20251209 04:54:24.963006 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6684 in image_id=2 and point2D_idx=6043 in image_id=3\nW20251209 04:54:24.963024 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6289 in image_id=2 and point2D_idx=5733 in image_id=3\nW20251209 04:54:24.963058 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5906 in image_id=2 and point2D_idx=5472 in image_id=3\nW20251209 04:54:24.963076 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5162 in image_id=2 and point2D_idx=4748 in image_id=3\nW20251209 04:54:24.963090 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4757 in image_id=2 and point2D_idx=4248 in image_id=3\nW20251209 04:54:24.963115 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4565 in image_id=2 and point2D_idx=4088 in image_id=3\nW20251209 04:54:24.963133 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5361 in image_id=2 and point2D_idx=5078 in image_id=3\nW20251209 04:54:24.963141 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7085 in image_id=2 and point2D_idx=6329 in image_id=3\nW20251209 04:54:24.963168 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3393 in image_id=2 and point2D_idx=2786 in image_id=3\nW20251209 04:54:24.963189 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4385 in image_id=2 and point2D_idx=3966 in image_id=3\nW20251209 04:54:24.963198 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6697 in image_id=2 and point2D_idx=6098 in image_id=3\nW20251209 04:54:24.963240 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2352 in image_id=2 and point2D_idx=455 in image_id=3\nW20251209 04:54:24.963260 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2107 in image_id=2 and point2D_idx=84 in image_id=3\nW20251209 04:54:24.963286 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2800 in image_id=2 and point2D_idx=885 in image_id=3\nW20251209 04:54:24.963328 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6506 in image_id=2 and point2D_idx=6121 in image_id=3\nW20251209 04:54:24.963353 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6496 in image_id=2 and point2D_idx=6498 in image_id=9\nW20251209 04:54:24.963367 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6498 in image_id=2 and point2D_idx=6500 in image_id=9\nW20251209 04:54:24.963378 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4572 in image_id=2 and point2D_idx=5005 in image_id=9\nW20251209 04:54:24.963388 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1173 in image_id=2 and point2D_idx=2969 in image_id=9\nW20251209 04:54:24.963398 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1174 in image_id=2 and point2D_idx=2970 in image_id=9\nW20251209 04:54:24.963447 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5572 in image_id=2 and point2D_idx=6178 in image_id=9\nW20251209 04:54:24.963467 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2768 in image_id=2 and point2D_idx=4321 in image_id=9\nW20251209 04:54:24.963531 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=549 in image_id=2 and point2D_idx=1208 in image_id=13\nW20251209 04:54:24.963554 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1581 in image_id=2 and point2D_idx=1997 in image_id=13\nW20251209 04:54:24.963572 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1759 in image_id=2 and point2D_idx=1429 in image_id=14\nW20251209 04:54:24.963581 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1190 in image_id=2 and point2D_idx=710 in image_id=14\nW20251209 04:54:24.963604 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3593 in image_id=2 and point2D_idx=2823 in image_id=23\nW20251209 04:54:24.963620 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4086 in image_id=2 and point2D_idx=3110 in image_id=23\nW20251209 04:54:24.963626 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4574 in image_id=2 and point2D_idx=3385 in image_id=23\nW20251209 04:54:24.963673 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6756 in image_id=2 and point2D_idx=4524 in image_id=23\nW20251209 04:54:24.963681 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7478 in image_id=2 and point2D_idx=4788 in image_id=23\nW20251209 04:54:24.963717 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7149 in image_id=2 and point2D_idx=4683 in image_id=23\nW20251209 04:54:24.963731 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=578 in image_id=2 and point2D_idx=1123 in image_id=23\nW20251209 04:54:24.963752 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5582 in image_id=2 and point2D_idx=4201 in image_id=23\nW20251209 04:54:24.963760 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7283 in image_id=2 and point2D_idx=4796 in image_id=23\nW20251209 04:54:24.963769 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2133 in image_id=2 and point2D_idx=2633 in image_id=23\nW20251209 04:54:24.963807 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=677 in image_id=2 and point2D_idx=1325 in image_id=23\nW20251209 04:54:24.963827 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4038 in image_id=2 and point2D_idx=3651 in image_id=23\nW20251209 04:54:24.963846 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7150 in image_id=2 and point2D_idx=4800 in image_id=23\nW20251209 04:54:24.963854 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6476 in image_id=2 and point2D_idx=4610 in image_id=23\nW20251209 04:54:24.963862 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3878 in image_id=2 and point2D_idx=3653 in image_id=23\nW20251209 04:54:24.963870 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5046 in image_id=2 and point2D_idx=4092 in image_id=23\nW20251209 04:54:24.963887 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6311 in image_id=2 and point2D_idx=6402 in image_id=24\nW20251209 04:54:24.963901 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6508 in image_id=2 and point2D_idx=6688 in image_id=24\nW20251209 04:54:24.964003 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7832 in image_id=2 and point2D_idx=9201 in image_id=28\nW20251209 04:54:24.964068 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=446 in image_id=2 and point2D_idx=39 in image_id=28\nW20251209 04:54:24.964321 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2582 in image_id=2 and point2D_idx=2198 in image_id=28\nW20251209 04:54:24.964359 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7460 in image_id=2 and point2D_idx=8768 in image_id=28\nW20251209 04:54:24.964412 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=980 in image_id=2 and point2D_idx=244 in image_id=28\nW20251209 04:54:24.964575 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7475 in image_id=2 and point2D_idx=9003 in image_id=28\nW20251209 04:54:24.964621 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4395 in image_id=2 and point2D_idx=5128 in image_id=28\nW20251209 04:54:24.964630 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5376 in image_id=2 and point2D_idx=6513 in image_id=28\nW20251209 04:54:24.964693 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4586 in image_id=2 and point2D_idx=5554 in image_id=28\nW20251209 04:54:24.964733 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2816 in image_id=2 and point2D_idx=2462 in image_id=28\nW20251209 04:54:24.964784 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4023 in image_id=2 and point2D_idx=4613 in image_id=28\nW20251209 04:54:24.964801 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4782 in image_id=2 and point2D_idx=5934 in image_id=28\nW20251209 04:54:24.964818 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7637 in image_id=2 and point2D_idx=9466 in image_id=28\nW20251209 04:54:24.964868 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4592 in image_id=2 and point2D_idx=5744 in image_id=28\nW20251209 04:54:24.964913 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5745 in image_id=2 and point2D_idx=7546 in image_id=28\nW20251209 04:54:24.964942 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2824 in image_id=2 and point2D_idx=2473 in image_id=28\nW20251209 04:54:24.964995 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=985 in image_id=2 and point2D_idx=497 in image_id=29\nW20251209 04:54:24.965015 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=782 in image_id=2 and point2D_idx=499 in image_id=29\nW20251209 04:54:24.965029 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3401 in image_id=2 and point2D_idx=2827 in image_id=29\nW20251209 04:54:24.965077 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1394 in image_id=2 and point2D_idx=980 in image_id=29\nW20251209 04:54:24.965134 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1960 in image_id=2 and point2D_idx=2021 in image_id=29\nW20251209 04:54:24.965177 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6656 in image_id=2 and point2D_idx=6214 in image_id=29\nW20251209 04:54:24.965211 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1185 in image_id=2 and point2D_idx=1644 in image_id=29\nW20251209 04:54:24.965228 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7641 in image_id=2 and point2D_idx=6880 in image_id=29\nW20251209 04:54:24.965253 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=226 in image_id=2 and point2D_idx=512 in image_id=29\nW20251209 04:54:24.965263 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2606 in image_id=2 and point2D_idx=3439 in image_id=29\nW20251209 04:54:24.965280 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5603 in image_id=2 and point2D_idx=5646 in image_id=29\nW20251209 04:54:24.965319 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4068 in image_id=2 and point2D_idx=4721 in image_id=29\nW20251209 04:54:24.965336 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6152 in image_id=2 and point2D_idx=6396 in image_id=29\nW20251209 04:54:24.965343 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3430 in image_id=2 and point2D_idx=4723 in image_id=29\nW20251209 04:54:24.965387 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1912 in image_id=2 and point2D_idx=2968 in image_id=34\nW20251209 04:54:24.965447 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1034 in image_id=2 and point2D_idx=2977 in image_id=34\nW20251209 04:54:24.965475 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3858 in image_id=2 and point2D_idx=6853 in image_id=34\nW20251209 04:54:24.965485 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=677 in image_id=2 and point2D_idx=2982 in image_id=34\nW20251209 04:54:24.965506 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9 in image_id=2 and point2D_idx=1099 in image_id=34\nW20251209 04:54:24.965542 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1616 in image_id=2 and point2D_idx=5353 in image_id=34\nW20251209 04:54:24.965571 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1404 in image_id=2 and point2D_idx=1917 in image_id=39\nW20251209 04:54:24.965598 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=474 in image_id=2 and point2D_idx=2172 in image_id=39\nW20251209 04:54:24.965615 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=335 in image_id=2 and point2D_idx=6744 in image_id=42\nW20251209 04:54:24.965643 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3222 in image_id=2 and point2D_idx=11171 in image_id=42\nW20251209 04:54:24.965700 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=296 in image_id=2 and point2D_idx=805 in image_id=43\nW20251209 04:54:24.965739 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5582 in image_id=2 and point2D_idx=5625 in image_id=43\nW20251209 04:54:24.965749 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=209 in image_id=2 and point2D_idx=1127 in image_id=43\nW20251209 04:54:24.965759 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3450 in image_id=2 and point2D_idx=4017 in image_id=43\nW20251209 04:54:24.965787 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3629 in image_id=2 and point2D_idx=4419 in image_id=43\nW20251209 04:54:24.965798 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4039 in image_id=2 and point2D_idx=4846 in image_id=43\nW20251209 04:54:24.965814 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=819 in image_id=2 and point2D_idx=2597 in image_id=43\nW20251209 04:54:24.965824 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13 in image_id=2 and point2D_idx=1075 in image_id=43\nW20251209 04:54:24.965853 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1005 in image_id=2 and point2D_idx=5563 in image_id=48\nW20251209 04:54:24.965882 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=171 in image_id=2 and point2D_idx=4501 in image_id=48\nW20251209 04:54:24.965899 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1381 in image_id=2 and point2D_idx=1356 in image_id=51\nW20251209 04:54:24.965927 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1172 in image_id=2 and point2D_idx=1363 in image_id=51\nW20251209 04:54:24.965934 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1902 in image_id=2 and point2D_idx=1747 in image_id=51\nW20251209 04:54:24.965963 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3193 in image_id=2 and point2D_idx=3060 in image_id=51\nW20251209 04:54:24.965979 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5781 in image_id=2 and point2D_idx=6071 in image_id=51\nW20251209 04:54:24.966030 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=957 in image_id=2 and point2D_idx=2167 in image_id=51\nW20251209 04:54:24.966084 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=168 in image_id=2 and point2D_idx=1392 in image_id=51\nW20251209 04:54:24.966249 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=218 in image_id=3 and point2D_idx=1927 in image_id=4\nW20251209 04:54:24.966273 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1465 in image_id=3 and point2D_idx=2048 in image_id=4\nW20251209 04:54:24.966288 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1953 in image_id=3 and point2D_idx=2131 in image_id=4\nW20251209 04:54:24.966338 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1566 in image_id=3 and point2D_idx=490 in image_id=10\nW20251209 04:54:24.966361 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1031 in image_id=3 and point2D_idx=283 in image_id=10\nW20251209 04:54:24.966389 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2607 in image_id=3 and point2D_idx=1366 in image_id=10\nW20251209 04:54:24.966420 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=574 in image_id=3 and point2D_idx=185 in image_id=10\nW20251209 04:54:24.966432 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=756 in image_id=3 and point2D_idx=304 in image_id=10\nW20251209 04:54:24.966445 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3170 in image_id=3 and point2D_idx=1878 in image_id=10\nW20251209 04:54:24.966454 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=318 in image_id=3 and point2D_idx=35 in image_id=10\nW20251209 04:54:24.966468 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2623 in image_id=3 and point2D_idx=1635 in image_id=10\nW20251209 04:54:24.966476 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3337 in image_id=3 and point2D_idx=2016 in image_id=10\nW20251209 04:54:24.966486 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=736 in image_id=3 and point2D_idx=380 in image_id=10\nW20251209 04:54:24.966510 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1357 in image_id=3 and point2D_idx=808 in image_id=10\nW20251209 04:54:24.966528 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2286 in image_id=3 and point2D_idx=1645 in image_id=10\nW20251209 04:54:24.966538 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1360 in image_id=3 and point2D_idx=894 in image_id=10\nW20251209 04:54:24.966553 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1838 in image_id=3 and point2D_idx=1409 in image_id=10\nW20251209 04:54:24.966560 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2289 in image_id=3 and point2D_idx=1742 in image_id=10\nW20251209 04:54:24.966579 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2637 in image_id=3 and point2D_idx=1965 in image_id=10\nW20251209 04:54:24.966613 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2444 in image_id=3 and point2D_idx=2855 in image_id=14\nW20251209 04:54:24.966633 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1975 in image_id=3 and point2D_idx=1953 in image_id=24\nW20251209 04:54:24.966656 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2823 in image_id=3 and point2D_idx=2819 in image_id=24\nW20251209 04:54:24.966667 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6767 in image_id=3 and point2D_idx=7473 in image_id=24\nW20251209 04:54:24.966694 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=527 in image_id=3 and point2D_idx=1507 in image_id=24\nW20251209 04:54:24.966703 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3349 in image_id=3 and point2D_idx=3290 in image_id=24\nW20251209 04:54:24.966739 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2956 in image_id=3 and point2D_idx=2881 in image_id=24\nW20251209 04:54:24.966748 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4774 in image_id=3 and point2D_idx=4738 in image_id=24\nW20251209 04:54:24.966822 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4473 in image_id=3 and point2D_idx=4391 in image_id=24\nW20251209 04:54:24.966831 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5316 in image_id=3 and point2D_idx=5260 in image_id=24\nW20251209 04:54:24.966886 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4101 in image_id=3 and point2D_idx=3993 in image_id=24\nW20251209 04:54:24.966901 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5940 in image_id=3 and point2D_idx=6235 in image_id=24\nW20251209 04:54:24.966915 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6645 in image_id=3 and point2D_idx=7368 in image_id=24\nW20251209 04:54:24.966937 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4275 in image_id=3 and point2D_idx=4207 in image_id=24\nW20251209 04:54:24.966947 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3329 in image_id=3 and point2D_idx=3070 in image_id=24\nW20251209 04:54:24.966973 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3500 in image_id=3 and point2D_idx=3568 in image_id=24\nW20251209 04:54:24.966997 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5858 in image_id=3 and point2D_idx=6244 in image_id=24\nW20251209 04:54:24.967009 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2452 in image_id=3 and point2D_idx=2857 in image_id=24\nW20251209 04:54:24.967018 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5860 in image_id=3 and point2D_idx=6292 in image_id=24\nW20251209 04:54:24.967025 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6118 in image_id=3 and point2D_idx=6617 in image_id=24\nW20251209 04:54:24.967123 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5 in image_id=3 and point2D_idx=541 in image_id=27\nW20251209 04:54:24.967182 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4594 in image_id=3 and point2D_idx=4921 in image_id=27\nW20251209 04:54:24.967237 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3620 in image_id=3 and point2D_idx=4105 in image_id=27\nW20251209 04:54:24.967266 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4932 in image_id=3 and point2D_idx=5053 in image_id=27\nW20251209 04:54:24.967285 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=21 in image_id=3 and point2D_idx=344 in image_id=27\nW20251209 04:54:24.967368 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4790 in image_id=3 and point2D_idx=4787 in image_id=27\nW20251209 04:54:24.967400 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4615 in image_id=3 and point2D_idx=4626 in image_id=27\nW20251209 04:54:24.967423 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3782 in image_id=3 and point2D_idx=4045 in image_id=27\nW20251209 04:54:24.967433 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4098 in image_id=3 and point2D_idx=4259 in image_id=27\nW20251209 04:54:24.967440 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5089 in image_id=3 and point2D_idx=4987 in image_id=27\nW20251209 04:54:24.967475 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5216 in image_id=3 and point2D_idx=5044 in image_id=27\nW20251209 04:54:24.967489 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1337 in image_id=3 and point2D_idx=1859 in image_id=27\nW20251209 04:54:24.967507 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4800 in image_id=3 and point2D_idx=4635 in image_id=27\nW20251209 04:54:24.967526 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4623 in image_id=3 and point2D_idx=4493 in image_id=27\nW20251209 04:54:24.967545 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2799 in image_id=3 and point2D_idx=2753 in image_id=27\nW20251209 04:54:24.967592 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4582 in image_id=3 and point2D_idx=5686 in image_id=28\nW20251209 04:54:24.967619 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6525 in image_id=3 and point2D_idx=8323 in image_id=28\nW20251209 04:54:24.967632 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4410 in image_id=3 and point2D_idx=5501 in image_id=28\nW20251209 04:54:24.967644 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7005 in image_id=3 and point2D_idx=9199 in image_id=28\nW20251209 04:54:24.967667 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=146 in image_id=3 and point2D_idx=2166 in image_id=28\nW20251209 04:54:24.967734 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3139 in image_id=3 and point2D_idx=3841 in image_id=28\nW20251209 04:54:24.967748 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=292 in image_id=3 and point2D_idx=2175 in image_id=28\nW20251209 04:54:24.967780 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=425 in image_id=3 and point2D_idx=2179 in image_id=28\nW20251209 04:54:24.967792 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6711 in image_id=3 and point2D_idx=8696 in image_id=28\nW20251209 04:54:24.967803 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3627 in image_id=3 and point2D_idx=4329 in image_id=28\nW20251209 04:54:24.967823 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6395 in image_id=3 and point2D_idx=7995 in image_id=28\nW20251209 04:54:24.967894 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6982 in image_id=3 and point2D_idx=9215 in image_id=28\nW20251209 04:54:24.967932 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=899 in image_id=3 and point2D_idx=2198 in image_id=28\nW20251209 04:54:24.967947 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6027 in image_id=3 and point2D_idx=7349 in image_id=28\nW20251209 04:54:24.967971 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5222 in image_id=3 and point2D_idx=6098 in image_id=28\nW20251209 04:54:24.967990 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3333 in image_id=3 and point2D_idx=4101 in image_id=28\nW20251209 04:54:24.968008 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=539 in image_id=3 and point2D_idx=1972 in image_id=28\nW20251209 04:54:24.968015 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6991 in image_id=3 and point2D_idx=9225 in image_id=28\nW20251209 04:54:24.968031 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4285 in image_id=3 and point2D_idx=6101 in image_id=28\nW20251209 04:54:24.968055 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3662 in image_id=3 and point2D_idx=5544 in image_id=28\nW20251209 04:54:24.968073 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3618 in image_id=3 and point2D_idx=2754 in image_id=51\nW20251209 04:54:24.968084 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3632 in image_id=3 and point2D_idx=2839 in image_id=51\nW20251209 04:54:24.968105 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=892 in image_id=4 and point2D_idx=1942 in image_id=5\nW20251209 04:54:24.968112 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1374 in image_id=4 and point2D_idx=2350 in image_id=5\nW20251209 04:54:24.968144 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=648 in image_id=4 and point2D_idx=1593 in image_id=5\nW20251209 04:54:24.968203 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=325 in image_id=4 and point2D_idx=47 in image_id=5\nW20251209 04:54:24.968214 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=273 in image_id=4 and point2D_idx=30 in image_id=5\nW20251209 04:54:24.968229 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1040 in image_id=4 and point2D_idx=1393 in image_id=5\nW20251209 04:54:24.968242 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1280 in image_id=4 and point2D_idx=1429 in image_id=5\nW20251209 04:54:24.968252 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=208 in image_id=4 and point2D_idx=600 in image_id=5\nW20251209 04:54:24.968269 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=802 in image_id=4 and point2D_idx=775 in image_id=5\nW20251209 04:54:24.968290 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=903 in image_id=4 and point2D_idx=3077 in image_id=12\nW20251209 04:54:24.968304 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=517 in image_id=4 and point2D_idx=2750 in image_id=12\nW20251209 04:54:24.968311 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=782 in image_id=4 and point2D_idx=2915 in image_id=12\nW20251209 04:54:24.968337 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2352 in image_id=4 and point2D_idx=1411 in image_id=27\nW20251209 04:54:24.968356 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1247 in image_id=4 and point2D_idx=111 in image_id=27\nW20251209 04:54:24.968366 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2359 in image_id=4 and point2D_idx=1416 in image_id=27\nW20251209 04:54:24.968375 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1248 in image_id=4 and point2D_idx=105 in image_id=27\nW20251209 04:54:24.968396 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3647 in image_id=4 and point2D_idx=2982 in image_id=27\nW20251209 04:54:24.968433 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3821 in image_id=4 and point2D_idx=3247 in image_id=27\nW20251209 04:54:24.968452 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3547 in image_id=4 and point2D_idx=2855 in image_id=27\nW20251209 04:54:24.968463 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3877 in image_id=4 and point2D_idx=3387 in image_id=27\nW20251209 04:54:24.968471 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3255 in image_id=4 and point2D_idx=2509 in image_id=27\nW20251209 04:54:24.968500 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1273 in image_id=4 and point2D_idx=27 in image_id=27\nW20251209 04:54:24.968526 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4132 in image_id=4 and point2D_idx=3810 in image_id=27\nW20251209 04:54:24.968564 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2171 in image_id=4 and point2D_idx=1738 in image_id=27\nW20251209 04:54:24.968588 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3364 in image_id=4 and point2D_idx=2745 in image_id=27\nW20251209 04:54:24.968613 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4067 in image_id=4 and point2D_idx=3820 in image_id=27\nW20251209 04:54:24.968623 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2064 in image_id=4 and point2D_idx=2078 in image_id=27\nW20251209 04:54:24.968667 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3559 in image_id=4 and point2D_idx=2899 in image_id=28\nW20251209 04:54:24.968675 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3378 in image_id=4 and point2D_idx=2676 in image_id=28\nW20251209 04:54:24.968696 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2413 in image_id=5 and point2D_idx=662 in image_id=11\nW20251209 04:54:24.968711 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2499 in image_id=5 and point2D_idx=863 in image_id=11\nW20251209 04:54:24.968721 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2502 in image_id=5 and point2D_idx=888 in image_id=11\nW20251209 04:54:24.968746 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=89 in image_id=5 and point2D_idx=522 in image_id=12\nW20251209 04:54:24.968753 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=118 in image_id=5 and point2D_idx=627 in image_id=12\nW20251209 04:54:24.968767 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=267 in image_id=5 and point2D_idx=1216 in image_id=12\nW20251209 04:54:24.968775 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=804 in image_id=5 and point2D_idx=2109 in image_id=12\nW20251209 04:54:24.968822 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1743 in image_id=5 and point2D_idx=3026 in image_id=12\nW20251209 04:54:24.968853 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=537 in image_id=5 and point2D_idx=1636 in image_id=12\nW20251209 04:54:24.968874 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1375 in image_id=5 and point2D_idx=2592 in image_id=12\nW20251209 04:54:24.968887 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=274 in image_id=5 and point2D_idx=3076 in image_id=13\nW20251209 04:54:24.968896 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=478 in image_id=5 and point2D_idx=3637 in image_id=13\nW20251209 04:54:24.968917 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=540 in image_id=5 and point2D_idx=3061 in image_id=13\nW20251209 04:54:24.968951 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=485 in image_id=5 and point2D_idx=2632 in image_id=13\nW20251209 04:54:24.968961 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1455 in image_id=6 and point2D_idx=1880 in image_id=7\nW20251209 04:54:24.968975 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1459 in image_id=6 and point2D_idx=1885 in image_id=7\nW20251209 04:54:24.968982 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1569 in image_id=6 and point2D_idx=2129 in image_id=7\nW20251209 04:54:24.969014 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1927 in image_id=6 and point2D_idx=2467 in image_id=7\nW20251209 04:54:24.969061 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2127 in image_id=6 and point2D_idx=2569 in image_id=7\nW20251209 04:54:24.969080 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1023 in image_id=6 and point2D_idx=34 in image_id=7\nW20251209 04:54:24.969091 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1938 in image_id=6 and point2D_idx=2475 in image_id=7\nW20251209 04:54:24.969108 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=340 in image_id=6 and point2D_idx=561 in image_id=9\nW20251209 04:54:24.969136 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=506 in image_id=6 and point2D_idx=753 in image_id=9\nW20251209 04:54:24.969149 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1037 in image_id=6 and point2D_idx=2542 in image_id=9\nW20251209 04:54:24.969182 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=107 in image_id=6 and point2D_idx=86 in image_id=9\nW20251209 04:54:24.969197 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=844 in image_id=6 and point2D_idx=1601 in image_id=9\nW20251209 04:54:24.969219 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=520 in image_id=6 and point2D_idx=583 in image_id=9\nW20251209 04:54:24.969229 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1052 in image_id=6 and point2D_idx=2305 in image_id=9\nW20251209 04:54:24.969237 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1093 in image_id=6 and point2D_idx=2556 in image_id=9\nW20251209 04:54:24.969250 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1884 in image_id=6 and point2D_idx=623 in image_id=12\nW20251209 04:54:24.969264 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2099 in image_id=6 and point2D_idx=951 in image_id=12\nW20251209 04:54:24.969274 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2291 in image_id=6 and point2D_idx=1388 in image_id=12\nW20251209 04:54:24.969282 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1204 in image_id=6 and point2D_idx=60 in image_id=12\nW20251209 04:54:24.969315 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1478 in image_id=6 and point2D_idx=317 in image_id=12\nW20251209 04:54:24.969331 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1995 in image_id=6 and point2D_idx=954 in image_id=12\nW20251209 04:54:24.969349 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1669 in image_id=6 and point2D_idx=630 in image_id=12\nW20251209 04:54:24.969377 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1350 in image_id=6 and point2D_idx=319 in image_id=12\nW20251209 04:54:24.969418 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2190 in image_id=6 and point2D_idx=1396 in image_id=12\nW20251209 04:54:24.969435 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2407 in image_id=6 and point2D_idx=1707 in image_id=12\nW20251209 04:54:24.969468 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2380 in image_id=6 and point2D_idx=5200 in image_id=13\nW20251209 04:54:24.969529 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=715 in image_id=6 and point2D_idx=865 in image_id=13\nW20251209 04:54:24.969539 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2294 in image_id=6 and point2D_idx=4840 in image_id=13\nW20251209 04:54:24.969561 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1930 in image_id=6 and point2D_idx=4419 in image_id=23\nW20251209 04:54:24.969572 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2264 in image_id=6 and point2D_idx=1942 in image_id=25\nW20251209 04:54:24.969585 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1576 in image_id=6 and point2D_idx=3611 in image_id=26\nW20251209 04:54:24.969598 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=336 in image_id=6 and point2D_idx=3710 in image_id=32\nW20251209 04:54:24.969613 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=139 in image_id=6 and point2D_idx=3131 in image_id=32\nW20251209 04:54:24.969633 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=219 in image_id=6 and point2D_idx=2952 in image_id=32\nW20251209 04:54:24.969666 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=435 in image_id=6 and point2D_idx=2295 in image_id=33\nW20251209 04:54:24.969676 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=201 in image_id=6 and point2D_idx=1737 in image_id=33\nW20251209 04:54:24.969685 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=65 in image_id=6 and point2D_idx=1225 in image_id=33\nW20251209 04:54:24.969707 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=354 in image_id=6 and point2D_idx=5178 in image_id=42\nW20251209 04:54:24.969715 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=206 in image_id=6 and point2D_idx=4652 in image_id=42\nW20251209 04:54:24.969728 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=769 in image_id=6 and point2D_idx=4501 in image_id=48\nW20251209 04:54:24.969752 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=970 in image_id=6 and point2D_idx=4941 in image_id=48\nW20251209 04:54:24.969761 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=437 in image_id=6 and point2D_idx=2773 in image_id=48\nW20251209 04:54:24.969776 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1041 in image_id=6 and point2D_idx=4945 in image_id=48\nW20251209 04:54:24.969792 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=844 in image_id=6 and point2D_idx=4088 in image_id=48\nW20251209 04:54:24.969811 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=627 in image_id=7 and point2D_idx=3633 in image_id=8\nW20251209 04:54:24.969819 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1084 in image_id=7 and point2D_idx=3861 in image_id=8\nW20251209 04:54:24.969830 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=852 in image_id=7 and point2D_idx=3571 in image_id=8\nW20251209 04:54:24.969841 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1045 in image_id=7 and point2D_idx=3619 in image_id=8\nW20251209 04:54:24.969856 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=391 in image_id=7 and point2D_idx=3148 in image_id=8\nW20251209 04:54:24.969868 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=824 in image_id=7 and point2D_idx=2967 in image_id=8\nW20251209 04:54:24.969897 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1163 in image_id=7 and point2D_idx=3048 in image_id=26\nW20251209 04:54:24.969909 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=600 in image_id=7 and point2D_idx=2662 in image_id=26\nW20251209 04:54:24.969930 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=211 in image_id=7 and point2D_idx=2206 in image_id=26\nW20251209 04:54:24.969947 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1470 in image_id=7 and point2D_idx=3284 in image_id=26\nW20251209 04:54:24.969964 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1677 in image_id=7 and point2D_idx=3396 in image_id=26\nW20251209 04:54:24.969985 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=135 in image_id=7 and point2D_idx=276 in image_id=26\nW20251209 04:54:24.969992 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=611 in image_id=7 and point2D_idx=2527 in image_id=26\nW20251209 04:54:24.970028 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1481 in image_id=7 and point2D_idx=3069 in image_id=26\nW20251209 04:54:24.970144 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1791 in image_id=7 and point2D_idx=3301 in image_id=26\nW20251209 04:54:24.970183 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=619 in image_id=7 and point2D_idx=2333 in image_id=26\nW20251209 04:54:24.970238 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2137 in image_id=7 and point2D_idx=3571 in image_id=26\nW20251209 04:54:24.970282 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2299 in image_id=7 and point2D_idx=3751 in image_id=26\nW20251209 04:54:24.970335 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1907 in image_id=7 and point2D_idx=3204 in image_id=26\nW20251209 04:54:24.970357 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2261 in image_id=7 and point2D_idx=3702 in image_id=26\nW20251209 04:54:24.970394 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=969 in image_id=7 and point2D_idx=2102 in image_id=26\nW20251209 04:54:24.970434 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=476 in image_id=7 and point2D_idx=1630 in image_id=26\nW20251209 04:54:24.970472 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=973 in image_id=7 and point2D_idx=1823 in image_id=26\nW20251209 04:54:24.970516 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2649 in image_id=8 and point2D_idx=526 in image_id=16\nW20251209 04:54:24.970531 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2209 in image_id=8 and point2D_idx=197 in image_id=16\nW20251209 04:54:24.970569 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2037 in image_id=8 and point2D_idx=467 in image_id=16\nW20251209 04:54:24.970586 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2386 in image_id=8 and point2D_idx=792 in image_id=16\nW20251209 04:54:24.970680 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=421 in image_id=8 and point2D_idx=349 in image_id=16\nW20251209 04:54:24.970715 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=78 in image_id=8 and point2D_idx=2165 in image_id=22\nW20251209 04:54:24.970727 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=472 in image_id=8 and point2D_idx=2817 in image_id=22\nW20251209 04:54:24.970738 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=79 in image_id=8 and point2D_idx=1995 in image_id=22\nW20251209 04:54:24.970750 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=133 in image_id=8 and point2D_idx=2089 in image_id=22\nW20251209 04:54:24.970796 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1607 in image_id=8 and point2D_idx=3840 in image_id=25\nW20251209 04:54:24.970824 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1862 in image_id=8 and point2D_idx=3920 in image_id=25\nW20251209 04:54:24.970854 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=833 in image_id=8 and point2D_idx=3237 in image_id=25\nW20251209 04:54:24.970943 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=490 in image_id=8 and point2D_idx=2600 in image_id=25\nW20251209 04:54:24.970966 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1249 in image_id=8 and point2D_idx=3296 in image_id=25\nW20251209 04:54:24.971052 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1256 in image_id=8 and point2D_idx=3073 in image_id=25\nW20251209 04:54:24.971138 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=264 in image_id=8 and point2D_idx=905 in image_id=25\nW20251209 04:54:24.971169 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3419 in image_id=8 and point2D_idx=678 in image_id=26\nW20251209 04:54:24.971211 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3050 in image_id=8 and point2D_idx=412 in image_id=26\nW20251209 04:54:24.971234 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2375 in image_id=8 and point2D_idx=6 in image_id=26\nW20251209 04:54:24.971266 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3530 in image_id=8 and point2D_idx=949 in image_id=26\nW20251209 04:54:24.971282 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2301 in image_id=8 and point2D_idx=116 in image_id=26\nW20251209 04:54:24.971323 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2245 in image_id=8 and point2D_idx=211 in image_id=26\nW20251209 04:54:24.971360 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1045 in image_id=8 and point2D_idx=47 in image_id=26\nW20251209 04:54:24.971419 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3266 in image_id=8 and point2D_idx=1251 in image_id=26\nW20251209 04:54:24.971455 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3812 in image_id=8 and point2D_idx=1833 in image_id=26\nW20251209 04:54:24.971477 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=609 in image_id=8 and point2D_idx=98 in image_id=26\nW20251209 04:54:24.971496 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2533 in image_id=8 and point2D_idx=611 in image_id=26\nW20251209 04:54:24.971521 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1764 in image_id=8 and point2D_idx=387 in image_id=26\nW20251209 04:54:24.971552 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2879 in image_id=8 and point2D_idx=849 in image_id=26\nW20251209 04:54:24.971589 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=783 in image_id=8 and point2D_idx=267 in image_id=26\nW20251209 04:54:24.971601 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1153 in image_id=8 and point2D_idx=290 in image_id=26\nW20251209 04:54:24.971617 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3065 in image_id=8 and point2D_idx=1255 in image_id=26\nW20251209 04:54:24.971649 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1516 in image_id=8 and point2D_idx=472 in image_id=26\nW20251209 04:54:24.971670 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3081 in image_id=8 and point2D_idx=1510 in image_id=26\nW20251209 04:54:24.971698 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3809 in image_id=8 and point2D_idx=2227 in image_id=26\nW20251209 04:54:24.971709 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3260 in image_id=8 and point2D_idx=1934 in image_id=26\nW20251209 04:54:24.971726 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1796 in image_id=8 and point2D_idx=860 in image_id=26\nW20251209 04:54:24.971735 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2910 in image_id=8 and point2D_idx=1740 in image_id=26\nW20251209 04:54:24.971748 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2954 in image_id=8 and point2D_idx=1847 in image_id=26\nW20251209 04:54:24.971770 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4051 in image_id=8 and point2D_idx=2543 in image_id=26\nW20251209 04:54:24.971777 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3744 in image_id=8 and point2D_idx=2451 in image_id=26\nW20251209 04:54:24.971786 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1073 in image_id=8 and point2D_idx=864 in image_id=26\nW20251209 04:54:24.971793 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1282 in image_id=8 and point2D_idx=977 in image_id=26\nW20251209 04:54:24.971812 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=964 in image_id=8 and point2D_idx=979 in image_id=26\nW20251209 04:54:24.971820 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1657 in image_id=8 and point2D_idx=1405 in image_id=26\nW20251209 04:54:24.971839 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7101 in image_id=9 and point2D_idx=3414 in image_id=10\nW20251209 04:54:24.971852 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7104 in image_id=9 and point2D_idx=3396 in image_id=10\nW20251209 04:54:24.971866 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7412 in image_id=9 and point2D_idx=3565 in image_id=10\nW20251209 04:54:24.971893 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=266 in image_id=9 and point2D_idx=110 in image_id=13\nW20251209 04:54:24.971907 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2975 in image_id=9 and point2D_idx=3060 in image_id=18\nW20251209 04:54:24.971928 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4146 in image_id=9 and point2D_idx=4807 in image_id=18\nW20251209 04:54:24.971953 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6235 in image_id=9 and point2D_idx=7657 in image_id=18\nW20251209 04:54:24.971971 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2743 in image_id=9 and point2D_idx=2798 in image_id=18\nW20251209 04:54:24.972000 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4556 in image_id=9 and point2D_idx=5279 in image_id=18\nW20251209 04:54:24.972132 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1574 in image_id=9 and point2D_idx=1418 in image_id=18\nW20251209 04:54:24.972197 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2289 in image_id=9 and point2D_idx=2174 in image_id=18\nW20251209 04:54:24.972217 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2041 in image_id=9 and point2D_idx=1691 in image_id=18\nW20251209 04:54:24.972293 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3772 in image_id=9 and point2D_idx=1698 in image_id=24\nW20251209 04:54:24.972382 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3586 in image_id=9 and point2D_idx=529 in image_id=24\nW20251209 04:54:24.972390 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4853 in image_id=9 and point2D_idx=2428 in image_id=24\nW20251209 04:54:24.972419 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6361 in image_id=9 and point2D_idx=401 in image_id=25\nW20251209 04:54:24.972455 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8053 in image_id=9 and point2D_idx=1580 in image_id=25\nW20251209 04:54:24.972475 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7599 in image_id=9 and point2D_idx=1321 in image_id=25\nW20251209 04:54:24.972492 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5875 in image_id=9 and point2D_idx=746 in image_id=25\nW20251209 04:54:24.972519 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7600 in image_id=9 and point2D_idx=1457 in image_id=25\nW20251209 04:54:24.972533 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5835 in image_id=9 and point2D_idx=937 in image_id=25\nW20251209 04:54:24.972548 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4363 in image_id=9 and point2D_idx=419 in image_id=25\nW20251209 04:54:24.972559 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5562 in image_id=9 and point2D_idx=939 in image_id=25\nW20251209 04:54:24.972645 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5063 in image_id=9 and point2D_idx=4352 in image_id=29\nW20251209 04:54:24.972658 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5019 in image_id=9 and point2D_idx=4538 in image_id=29\nW20251209 04:54:24.972679 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2510 in image_id=9 and point2D_idx=1660 in image_id=29\nW20251209 04:54:24.972696 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2997 in image_id=9 and point2D_idx=2454 in image_id=29\nW20251209 04:54:24.972737 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1576 in image_id=9 and point2D_idx=1403 in image_id=29\nW20251209 04:54:24.972769 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2776 in image_id=9 and point2D_idx=3475 in image_id=29\nW20251209 04:54:24.972835 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7114 in image_id=9 and point2D_idx=9523 in image_id=34\nW20251209 04:54:24.972862 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6073 in image_id=9 and point2D_idx=7787 in image_id=34\nW20251209 04:54:24.972893 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1560 in image_id=9 and point2D_idx=879 in image_id=34\nW20251209 04:54:24.972923 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6074 in image_id=9 and point2D_idx=8099 in image_id=34\nW20251209 04:54:24.972952 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4515 in image_id=9 and point2D_idx=5708 in image_id=34\nW20251209 04:54:24.972981 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5743 in image_id=9 and point2D_idx=7804 in image_id=34\nW20251209 04:54:24.973003 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8189 in image_id=9 and point2D_idx=12213 in image_id=34\nW20251209 04:54:24.973030 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=886 in image_id=9 and point2D_idx=610 in image_id=34\nW20251209 04:54:24.973123 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6531 in image_id=9 and point2D_idx=9283 in image_id=34\nW20251209 04:54:24.973228 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8355 in image_id=9 and point2D_idx=12639 in image_id=34\nW20251209 04:54:24.973269 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=710 in image_id=9 and point2D_idx=842 in image_id=34\nW20251209 04:54:24.973294 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5551 in image_id=9 and point2D_idx=8122 in image_id=34\nW20251209 04:54:24.973309 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6701 in image_id=9 and point2D_idx=9848 in image_id=34\nW20251209 04:54:24.973386 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6241 in image_id=9 and point2D_idx=9294 in image_id=34\nW20251209 04:54:24.973403 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7806 in image_id=9 and point2D_idx=11563 in image_id=34\nW20251209 04:54:24.973433 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8201 in image_id=9 and point2D_idx=12440 in image_id=34\nW20251209 04:54:24.973476 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2815 in image_id=9 and point2D_idx=4462 in image_id=34\nW20251209 04:54:24.973550 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6874 in image_id=9 and point2D_idx=10393 in image_id=34\nW20251209 04:54:24.973630 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8146 in image_id=9 and point2D_idx=12453 in image_id=34\nW20251209 04:54:24.973700 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1602 in image_id=9 and point2D_idx=3817 in image_id=34\nW20251209 04:54:24.973728 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5401 in image_id=9 and point2D_idx=8733 in image_id=34\nW20251209 04:54:24.973763 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5056 in image_id=9 and point2D_idx=8447 in image_id=34\nW20251209 04:54:24.973879 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5390 in image_id=9 and point2D_idx=4761 in image_id=38\nW20251209 04:54:24.973914 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1410 in image_id=9 and point2D_idx=2400 in image_id=38\nW20251209 04:54:24.973942 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1662 in image_id=9 and point2D_idx=2640 in image_id=38\nW20251209 04:54:24.973967 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2496 in image_id=9 and point2D_idx=1176 in image_id=39\nW20251209 04:54:24.974056 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2311 in image_id=9 and point2D_idx=7307 in image_id=42\nW20251209 04:54:24.974109 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1353 in image_id=9 and point2D_idx=5675 in image_id=42\nW20251209 04:54:24.974135 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3765 in image_id=9 and point2D_idx=9132 in image_id=42\nW20251209 04:54:24.974174 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5090 in image_id=9 and point2D_idx=10746 in image_id=42\nW20251209 04:54:24.974200 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=855 in image_id=9 and point2D_idx=4650 in image_id=42\nW20251209 04:54:24.974224 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=134 in image_id=9 and point2D_idx=3137 in image_id=42\nW20251209 04:54:24.974269 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3457 in image_id=9 and point2D_idx=7335 in image_id=42\nW20251209 04:54:24.974287 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=334 in image_id=9 and point2D_idx=3641 in image_id=42\nW20251209 04:54:24.974355 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=212 in image_id=9 and point2D_idx=3161 in image_id=42\nW20251209 04:54:24.974407 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8405 in image_id=9 and point2D_idx=6081 in image_id=49\nW20251209 04:54:24.974447 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6364 in image_id=9 and point2D_idx=4193 in image_id=49\nW20251209 04:54:24.974484 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6830 in image_id=9 and point2D_idx=4628 in image_id=49\nW20251209 04:54:24.974514 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8014 in image_id=9 and point2D_idx=5727 in image_id=49\nW20251209 04:54:24.974584 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7795 in image_id=9 and point2D_idx=5642 in image_id=49\nW20251209 04:54:24.974614 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5032 in image_id=9 and point2D_idx=3486 in image_id=49\nW20251209 04:54:24.974631 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=422 in image_id=9 and point2D_idx=46 in image_id=49\nW20251209 04:54:24.974644 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5872 in image_id=9 and point2D_idx=4166 in image_id=49\nW20251209 04:54:24.974668 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3802 in image_id=9 and point2D_idx=2714 in image_id=49\nW20251209 04:54:24.974688 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1019 in image_id=9 and point2D_idx=493 in image_id=49\nW20251209 04:54:24.974708 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3410 in image_id=9 and point2D_idx=2553 in image_id=49\nW20251209 04:54:24.974846 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=721 in image_id=10 and point2D_idx=1005 in image_id=11\nW20251209 04:54:24.974872 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2615 in image_id=10 and point2D_idx=5075 in image_id=24\nW20251209 04:54:24.974898 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3144 in image_id=10 and point2D_idx=6381 in image_id=24\nW20251209 04:54:24.974937 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2715 in image_id=10 and point2D_idx=5421 in image_id=24\nW20251209 04:54:24.974979 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3167 in image_id=10 and point2D_idx=6532 in image_id=24\nW20251209 04:54:24.974986 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3306 in image_id=10 and point2D_idx=6799 in image_id=24\nW20251209 04:54:24.974994 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3479 in image_id=10 and point2D_idx=7101 in image_id=24\nW20251209 04:54:24.975004 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2070 in image_id=10 and point2D_idx=3779 in image_id=24\nW20251209 04:54:24.975031 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3128 in image_id=10 and point2D_idx=6535 in image_id=24\nW20251209 04:54:24.975077 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2409 in image_id=10 and point2D_idx=4588 in image_id=24\nW20251209 04:54:24.975085 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2762 in image_id=10 and point2D_idx=5604 in image_id=24\nW20251209 04:54:24.975114 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3319 in image_id=10 and point2D_idx=6806 in image_id=24\nW20251209 04:54:24.975141 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3493 in image_id=10 and point2D_idx=7108 in image_id=24\nW20251209 04:54:24.975149 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3003 in image_id=10 and point2D_idx=6078 in image_id=24\nW20251209 04:54:24.975189 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2998 in image_id=10 and point2D_idx=5923 in image_id=24\nW20251209 04:54:24.975205 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1476 in image_id=10 and point2D_idx=2634 in image_id=24\nW20251209 04:54:24.975223 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2316 in image_id=10 and point2D_idx=4212 in image_id=24\nW20251209 04:54:24.975231 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1564 in image_id=10 and point2D_idx=2858 in image_id=24\nW20251209 04:54:24.975239 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1689 in image_id=10 and point2D_idx=3080 in image_id=24\nW20251209 04:54:24.975259 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3299 in image_id=10 and point2D_idx=196 in image_id=25\nW20251209 04:54:24.975271 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3303 in image_id=10 and point2D_idx=198 in image_id=25\nW20251209 04:54:24.975288 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1704 in image_id=10 and point2D_idx=3622 in image_id=28\nW20251209 04:54:24.975330 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3114 in image_id=10 and point2D_idx=2130 in image_id=50\nW20251209 04:54:24.975365 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3132 in image_id=10 and point2D_idx=2856 in image_id=50\nW20251209 04:54:24.975375 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3373 in image_id=10 and point2D_idx=3117 in image_id=50\nW20251209 04:54:24.975410 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=392 in image_id=11 and point2D_idx=3094 in image_id=12\nW20251209 04:54:24.975419 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=180 in image_id=11 and point2D_idx=2925 in image_id=12\nW20251209 04:54:24.975464 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1687 in image_id=11 and point2D_idx=3567 in image_id=14\nW20251209 04:54:24.975478 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1212 in image_id=11 and point2D_idx=3291 in image_id=14\nW20251209 04:54:24.975498 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1897 in image_id=11 and point2D_idx=3048 in image_id=24\nW20251209 04:54:24.975513 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1557 in image_id=11 and point2D_idx=2386 in image_id=24\nW20251209 04:54:24.975522 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1388 in image_id=11 and point2D_idx=1967 in image_id=24\nW20251209 04:54:24.975540 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1270 in image_id=11 and point2D_idx=1819 in image_id=24\nW20251209 04:54:24.975555 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1628 in image_id=11 and point2D_idx=2842 in image_id=24\nW20251209 04:54:24.975565 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1911 in image_id=11 and point2D_idx=3544 in image_id=24\nW20251209 04:54:24.975598 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1769 in image_id=11 and point2D_idx=4140 in image_id=24\nW20251209 04:54:24.975610 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1049 in image_id=11 and point2D_idx=2853 in image_id=24\nW20251209 04:54:24.975741 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=346 in image_id=12 and point2D_idx=286 in image_id=13\nW20251209 04:54:24.975764 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=736 in image_id=12 and point2D_idx=4458 in image_id=17\nW20251209 04:54:24.975782 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1 in image_id=12 and point2D_idx=1114 in image_id=17\nW20251209 04:54:24.975842 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=872 in image_id=12 and point2D_idx=1579 in image_id=17\nW20251209 04:54:24.975864 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=456 in image_id=12 and point2D_idx=152 in image_id=17\nW20251209 04:54:24.975879 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2995 in image_id=13 and point2D_idx=4191 in image_id=14\nW20251209 04:54:24.975887 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3427 in image_id=13 and point2D_idx=4848 in image_id=14\nW20251209 04:54:24.975895 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3676 in image_id=13 and point2D_idx=5341 in image_id=14\nW20251209 04:54:24.975907 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2579 in image_id=13 and point2D_idx=3504 in image_id=14\nW20251209 04:54:24.975914 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2997 in image_id=13 and point2D_idx=4192 in image_id=14\nW20251209 04:54:24.975923 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3430 in image_id=13 and point2D_idx=4849 in image_id=14\nW20251209 04:54:24.975961 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4313 in image_id=13 and point2D_idx=6171 in image_id=14\nW20251209 04:54:24.975969 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3528 in image_id=13 and point2D_idx=5045 in image_id=14\nW20251209 04:54:24.975979 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3006 in image_id=13 and point2D_idx=4262 in image_id=17\nW20251209 04:54:24.975986 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3162 in image_id=13 and point2D_idx=4449 in image_id=17\nW20251209 04:54:24.976159 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=871 in image_id=13 and point2D_idx=1561 in image_id=17\nW20251209 04:54:24.976228 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=143 in image_id=13 and point2D_idx=578 in image_id=17\nW20251209 04:54:24.976294 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=32 in image_id=13 and point2D_idx=308 in image_id=17\nW20251209 04:54:24.976350 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1048 in image_id=13 and point2D_idx=1791 in image_id=17\nW20251209 04:54:24.976372 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1907 in image_id=13 and point2D_idx=2641 in image_id=17\nW20251209 04:54:24.976447 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5052 in image_id=13 and point2D_idx=5042 in image_id=23\nW20251209 04:54:24.976543 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=523 in image_id=13 and point2D_idx=6 in image_id=23\nW20251209 04:54:24.976579 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2585 in image_id=13 and point2D_idx=5626 in image_id=34\nW20251209 04:54:24.976617 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4693 in image_id=13 and point2D_idx=10873 in image_id=34\nW20251209 04:54:24.976636 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4360 in image_id=13 and point2D_idx=10084 in image_id=34\nW20251209 04:54:24.976681 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3481 in image_id=13 and point2D_idx=8122 in image_id=34\nW20251209 04:54:24.976737 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=150 in image_id=13 and point2D_idx=3707 in image_id=42\nW20251209 04:54:24.976784 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=220 in image_id=13 and point2D_idx=3632 in image_id=42\nW20251209 04:54:24.976859 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=339 in image_id=13 and point2D_idx=3161 in image_id=42\nW20251209 04:54:24.976927 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1461 in image_id=13 and point2D_idx=4162 in image_id=42\nW20251209 04:54:24.976979 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1415 in image_id=13 and point2D_idx=1975 in image_id=43\nW20251209 04:54:24.977090 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4209 in image_id=13 and point2D_idx=6213 in image_id=43\nW20251209 04:54:24.977110 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5070 in image_id=13 and point2D_idx=7429 in image_id=43\nW20251209 04:54:24.977119 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5341 in image_id=13 and point2D_idx=7853 in image_id=43\nW20251209 04:54:24.977136 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5103 in image_id=13 and point2D_idx=7430 in image_id=43\nW20251209 04:54:24.977218 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3713 in image_id=13 and point2D_idx=5682 in image_id=43\nW20251209 04:54:24.977235 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5086 in image_id=13 and point2D_idx=7590 in image_id=43\nW20251209 04:54:24.977309 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1731 in image_id=13 and point2D_idx=1616 in image_id=49\nW20251209 04:54:24.977324 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4937 in image_id=13 and point2D_idx=5135 in image_id=49\nW20251209 04:54:24.977370 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3448 in image_id=13 and point2D_idx=3606 in image_id=49\nW20251209 04:54:24.977379 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4856 in image_id=13 and point2D_idx=5160 in image_id=49\nW20251209 04:54:24.977392 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4700 in image_id=13 and point2D_idx=5027 in image_id=49\nW20251209 04:54:24.977403 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2873 in image_id=13 and point2D_idx=3080 in image_id=49\nW20251209 04:54:24.977443 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4086 in image_id=13 and point2D_idx=4437 in image_id=49\nW20251209 04:54:24.977458 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4955 in image_id=13 and point2D_idx=5429 in image_id=49\nW20251209 04:54:24.977465 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5079 in image_id=13 and point2D_idx=5541 in image_id=49\nW20251209 04:54:24.977564 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4437 in image_id=13 and point2D_idx=8770 in image_id=51\nW20251209 04:54:24.977575 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4547 in image_id=13 and point2D_idx=9142 in image_id=51\nW20251209 04:54:24.977596 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1632 in image_id=14 and point2D_idx=381 in image_id=15\nW20251209 04:54:24.977623 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4253 in image_id=14 and point2D_idx=2550 in image_id=15\nW20251209 04:54:24.977673 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5525 in image_id=14 and point2D_idx=3352 in image_id=15\nW20251209 04:54:24.977697 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3769 in image_id=14 and point2D_idx=2162 in image_id=15\nW20251209 04:54:24.977707 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5132 in image_id=14 and point2D_idx=3022 in image_id=15\nW20251209 04:54:24.977733 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3570 in image_id=14 and point2D_idx=2020 in image_id=15\nW20251209 04:54:24.977766 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1652 in image_id=14 and point2D_idx=262 in image_id=15\nW20251209 04:54:24.977773 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2171 in image_id=14 and point2D_idx=932 in image_id=15\nW20251209 04:54:24.977781 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2858 in image_id=14 and point2D_idx=1531 in image_id=15\nW20251209 04:54:24.977788 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3679 in image_id=14 and point2D_idx=2016 in image_id=15\nW20251209 04:54:24.977796 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4448 in image_id=14 and point2D_idx=2467 in image_id=15\nW20251209 04:54:24.977807 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5022 in image_id=14 and point2D_idx=2899 in image_id=15\nW20251209 04:54:24.977813 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5223 in image_id=14 and point2D_idx=3051 in image_id=15\nW20251209 04:54:24.977850 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3252 in image_id=14 and point2D_idx=1663 in image_id=15\nW20251209 04:54:24.977866 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5023 in image_id=14 and point2D_idx=2853 in image_id=15\nW20251209 04:54:24.977874 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5720 in image_id=14 and point2D_idx=3466 in image_id=15\nW20251209 04:54:24.977897 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3778 in image_id=14 and point2D_idx=1998 in image_id=15\nW20251209 04:54:24.977905 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4921 in image_id=14 and point2D_idx=2774 in image_id=15\nW20251209 04:54:24.977956 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3782 in image_id=14 and point2D_idx=2004 in image_id=15\nW20251209 04:54:24.977964 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4824 in image_id=14 and point2D_idx=2673 in image_id=15\nW20251209 04:54:24.977988 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2181 in image_id=14 and point2D_idx=557 in image_id=15\nW20251209 04:54:24.977995 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2752 in image_id=14 and point2D_idx=1084 in image_id=15\nW20251209 04:54:24.978025 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2016 in image_id=14 and point2D_idx=4324 in image_id=18\nW20251209 04:54:24.978074 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1719 in image_id=14 and point2D_idx=4331 in image_id=18\nW20251209 04:54:24.978088 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2457 in image_id=14 and point2D_idx=5043 in image_id=18\nW20251209 04:54:24.978116 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2500 in image_id=14 and point2D_idx=5047 in image_id=18\nW20251209 04:54:24.978153 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1708 in image_id=14 and point2D_idx=4578 in image_id=18\nW20251209 04:54:24.978271 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1093 in image_id=14 and point2D_idx=992 in image_id=20\nW20251209 04:54:24.978399 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1627 in image_id=14 and point2D_idx=2359 in image_id=23\nW20251209 04:54:24.978445 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6103 in image_id=14 and point2D_idx=4772 in image_id=23\nW20251209 04:54:24.978538 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3401 in image_id=14 and point2D_idx=3251 in image_id=23\nW20251209 04:54:24.978562 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3126 in image_id=14 and point2D_idx=3113 in image_id=23\nW20251209 04:54:24.978614 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2648 in image_id=14 and point2D_idx=2795 in image_id=23\nW20251209 04:54:24.978657 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3135 in image_id=14 and point2D_idx=3119 in image_id=23\nW20251209 04:54:24.978668 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3402 in image_id=14 and point2D_idx=3258 in image_id=23\nW20251209 04:54:24.978704 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=22 in image_id=14 and point2D_idx=1117 in image_id=23\nW20251209 04:54:24.978790 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3461 in image_id=14 and point2D_idx=3319 in image_id=24\nW20251209 04:54:24.978834 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4054 in image_id=14 and point2D_idx=4401 in image_id=24\nW20251209 04:54:24.978861 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4255 in image_id=14 and point2D_idx=4772 in image_id=24\nW20251209 04:54:24.978875 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1614 in image_id=14 and point2D_idx=1201 in image_id=24\nW20251209 04:54:24.978896 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4602 in image_id=14 and point2D_idx=5433 in image_id=24\nW20251209 04:54:24.978957 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1066 in image_id=14 and point2D_idx=963 in image_id=29\nW20251209 04:54:24.978968 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1698 in image_id=14 and point2D_idx=1624 in image_id=29\nW20251209 04:54:24.978977 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1267 in image_id=14 and point2D_idx=1342 in image_id=29\nW20251209 04:54:24.978989 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1658 in image_id=14 and point2D_idx=1627 in image_id=29\nW20251209 04:54:24.979003 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2066 in image_id=14 and point2D_idx=2216 in image_id=29\nW20251209 04:54:24.979031 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=952 in image_id=14 and point2D_idx=1838 in image_id=29\nW20251209 04:54:24.979063 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2045 in image_id=14 and point2D_idx=2629 in image_id=29\nW20251209 04:54:24.979077 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=397 in image_id=14 and point2D_idx=1371 in image_id=29\nW20251209 04:54:24.979099 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2725 in image_id=14 and point2D_idx=3625 in image_id=29\nW20251209 04:54:24.979115 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2687 in image_id=14 and point2D_idx=1149 in image_id=30\nW20251209 04:54:24.979128 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2334 in image_id=14 and point2D_idx=1153 in image_id=30\nW20251209 04:54:24.979154 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1079 in image_id=14 and point2D_idx=623 in image_id=30\nW20251209 04:54:24.979161 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1849 in image_id=14 and point2D_idx=1134 in image_id=30\nW20251209 04:54:24.979181 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=318 in image_id=14 and point2D_idx=80 in image_id=30\nW20251209 04:54:24.979203 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1527 in image_id=14 and point2D_idx=1511 in image_id=30\nW20251209 04:54:24.979252 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2760 in image_id=14 and point2D_idx=4414 in image_id=34\nW20251209 04:54:24.979262 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=210 in image_id=14 and point2D_idx=1657 in image_id=34\nW20251209 04:54:24.979273 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3010 in image_id=14 and point2D_idx=5103 in image_id=34\nW20251209 04:54:24.979292 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3588 in image_id=14 and point2D_idx=5926 in image_id=34\nW20251209 04:54:24.979318 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=812 in image_id=14 and point2D_idx=2704 in image_id=34\nW20251209 04:54:24.979345 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2581 in image_id=14 and point2D_idx=5034 in image_id=34\nW20251209 04:54:24.979365 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3813 in image_id=14 and point2D_idx=6545 in image_id=34\nW20251209 04:54:24.979387 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=93 in image_id=14 and point2D_idx=1098 in image_id=39\nW20251209 04:54:24.979400 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2881 in image_id=14 and point2D_idx=3581 in image_id=39\nW20251209 04:54:24.979425 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=311 in image_id=14 and point2D_idx=7298 in image_id=42\nW20251209 04:54:24.979445 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1854 in image_id=14 and point2D_idx=9114 in image_id=42\nW20251209 04:54:24.979453 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2896 in image_id=14 and point2D_idx=10725 in image_id=42\nW20251209 04:54:24.979477 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=232 in image_id=14 and point2D_idx=6754 in image_id=42\nW20251209 04:54:24.979489 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2504 in image_id=14 and point2D_idx=9589 in image_id=42\nW20251209 04:54:24.979500 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2589 in image_id=14 and point2D_idx=9716 in image_id=42\nW20251209 04:54:24.979508 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1112 in image_id=14 and point2D_idx=7909 in image_id=42\nW20251209 04:54:24.979527 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=218 in image_id=14 and point2D_idx=201 in image_id=45\nW20251209 04:54:24.979535 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1032 in image_id=14 and point2D_idx=4906 in image_id=48\nW20251209 04:54:24.979550 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=725 in image_id=15 and point2D_idx=2132 in image_id=16\nW20251209 04:54:24.979558 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=617 in image_id=15 and point2D_idx=2084 in image_id=16\nW20251209 04:54:24.979595 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1337 in image_id=15 and point2D_idx=2163 in image_id=16\nW20251209 04:54:24.979629 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=171 in image_id=15 and point2D_idx=1051 in image_id=16\nW20251209 04:54:24.979641 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1249 in image_id=15 and point2D_idx=1588 in image_id=16\nW20251209 04:54:24.979664 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=534 in image_id=15 and point2D_idx=989 in image_id=16\nW20251209 04:54:24.979687 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=749 in image_id=15 and point2D_idx=828 in image_id=16\nW20251209 04:54:24.979701 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1062 in image_id=15 and point2D_idx=813 in image_id=16\nW20251209 04:54:24.979724 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=48 in image_id=16 and point2D_idx=4965 in image_id=22\nW20251209 04:54:24.979735 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=49 in image_id=16 and point2D_idx=4943 in image_id=22\nW20251209 04:54:24.979759 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=179 in image_id=16 and point2D_idx=4801 in image_id=22\nW20251209 04:54:24.979794 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=129 in image_id=16 and point2D_idx=1977 in image_id=22\nW20251209 04:54:24.979829 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1772 in image_id=16 and point2D_idx=628 in image_id=27\nW20251209 04:54:24.979865 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2315 in image_id=16 and point2D_idx=1009 in image_id=27\nW20251209 04:54:24.979878 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1383 in image_id=16 and point2D_idx=306 in image_id=27\nW20251209 04:54:24.979888 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2266 in image_id=16 and point2D_idx=963 in image_id=27\nW20251209 04:54:24.979910 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2672 in image_id=16 and point2D_idx=1856 in image_id=27\nW20251209 04:54:24.979944 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1582 in image_id=16 and point2D_idx=417 in image_id=27\nW20251209 04:54:24.979951 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1794 in image_id=16 and point2D_idx=571 in image_id=27\nW20251209 04:54:24.979958 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1986 in image_id=16 and point2D_idx=711 in image_id=27\nW20251209 04:54:24.979964 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2169 in image_id=16 and point2D_idx=851 in image_id=27\nW20251209 04:54:24.979995 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1325 in image_id=17 and point2D_idx=272 in image_id=18\nW20251209 04:54:24.980002 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6841 in image_id=17 and point2D_idx=10551 in image_id=18\nW20251209 04:54:24.980013 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6591 in image_id=17 and point2D_idx=10214 in image_id=18\nW20251209 04:54:24.980019 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6846 in image_id=17 and point2D_idx=10527 in image_id=18\nW20251209 04:54:24.980040 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4465 in image_id=17 and point2D_idx=6243 in image_id=18\nW20251209 04:54:24.980098 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2841 in image_id=17 and point2D_idx=1909 in image_id=18\nW20251209 04:54:24.980116 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1969 in image_id=17 and point2D_idx=401 in image_id=19\nW20251209 04:54:24.980154 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6833 in image_id=17 and point2D_idx=4301 in image_id=19\nW20251209 04:54:24.980173 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6052 in image_id=17 and point2D_idx=3871 in image_id=19\nW20251209 04:54:24.980232 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8750 in image_id=17 and point2D_idx=2979 in image_id=22\nW20251209 04:54:24.980288 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8500 in image_id=17 and point2D_idx=3164 in image_id=22\nW20251209 04:54:24.980361 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8609 in image_id=17 and point2D_idx=3551 in image_id=22\nW20251209 04:54:24.980413 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4654 in image_id=17 and point2D_idx=1414 in image_id=22\nW20251209 04:54:24.980462 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4293 in image_id=17 and point2D_idx=1277 in image_id=22\nW20251209 04:54:24.980481 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5896 in image_id=17 and point2D_idx=2230 in image_id=22\nW20251209 04:54:24.980568 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6618 in image_id=17 and point2D_idx=2817 in image_id=22\nW20251209 04:54:24.980626 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8561 in image_id=17 and point2D_idx=4122 in image_id=22\nW20251209 04:54:24.980670 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5505 in image_id=17 and point2D_idx=2519 in image_id=22\nW20251209 04:54:24.980688 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8008 in image_id=17 and point2D_idx=3745 in image_id=22\nW20251209 04:54:24.980725 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7135 in image_id=17 and point2D_idx=3381 in image_id=22\nW20251209 04:54:24.980753 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5731 in image_id=17 and point2D_idx=2823 in image_id=22\nW20251209 04:54:24.980833 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5945 in image_id=17 and point2D_idx=3194 in image_id=22\nW20251209 04:54:24.980903 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9091 in image_id=17 and point2D_idx=4631 in image_id=22\nW20251209 04:54:24.980938 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7861 in image_id=17 and point2D_idx=4295 in image_id=22\nW20251209 04:54:24.980982 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8291 in image_id=17 and point2D_idx=4485 in image_id=22\nW20251209 04:54:24.981006 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8547 in image_id=17 and point2D_idx=4564 in image_id=22\nW20251209 04:54:24.981051 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9189 in image_id=17 and point2D_idx=4734 in image_id=22\nW20251209 04:54:24.981074 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3769 in image_id=17 and point2D_idx=2683 in image_id=22\nW20251209 04:54:24.981082 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4690 in image_id=17 and point2D_idx=3205 in image_id=22\nW20251209 04:54:24.981092 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7154 in image_id=17 and point2D_idx=4301 in image_id=22\nW20251209 04:54:24.981106 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7501 in image_id=17 and point2D_idx=2131 in image_id=25\nW20251209 04:54:24.981113 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9147 in image_id=17 and point2D_idx=3021 in image_id=25\nW20251209 04:54:24.981143 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9192 in image_id=17 and point2D_idx=3051 in image_id=25\nW20251209 04:54:24.981150 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7431 in image_id=17 and point2D_idx=2208 in image_id=25\nW20251209 04:54:24.981160 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9238 in image_id=17 and point2D_idx=3129 in image_id=25\nW20251209 04:54:24.981174 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8644 in image_id=17 and point2D_idx=2815 in image_id=25\nW20251209 04:54:24.981190 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9070 in image_id=17 and point2D_idx=3055 in image_id=25\nW20251209 04:54:24.981199 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7120 in image_id=17 and point2D_idx=2298 in image_id=25\nW20251209 04:54:24.981219 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8652 in image_id=17 and point2D_idx=2897 in image_id=25\nW20251209 04:54:24.981249 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4516 in image_id=17 and point2D_idx=1842 in image_id=25\nW20251209 04:54:24.981265 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8382 in image_id=17 and point2D_idx=2904 in image_id=25\nW20251209 04:54:24.981273 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8891 in image_id=17 and point2D_idx=3222 in image_id=25\nW20251209 04:54:24.981346 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5866 in image_id=17 and point2D_idx=8666 in image_id=33\nW20251209 04:54:24.981447 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8639 in image_id=17 and point2D_idx=10340 in image_id=33\nW20251209 04:54:24.981467 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8141 in image_id=17 and point2D_idx=10079 in image_id=33\nW20251209 04:54:24.981521 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7510 in image_id=17 and point2D_idx=9586 in image_id=33\nW20251209 04:54:24.981556 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6357 in image_id=17 and point2D_idx=8327 in image_id=33\nW20251209 04:54:24.981580 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7927 in image_id=17 and point2D_idx=9864 in image_id=33\nW20251209 04:54:24.981599 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7967 in image_id=17 and point2D_idx=9867 in image_id=33\nW20251209 04:54:24.981629 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8375 in image_id=17 and point2D_idx=10093 in image_id=33\nW20251209 04:54:24.981729 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8382 in image_id=17 and point2D_idx=10015 in image_id=33\nW20251209 04:54:24.981736 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8919 in image_id=17 and point2D_idx=10282 in image_id=33\nW20251209 04:54:24.981752 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3295 in image_id=17 and point2D_idx=4612 in image_id=33\nW20251209 04:54:24.981789 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8118 in image_id=17 and point2D_idx=9633 in image_id=33\nW20251209 04:54:24.981887 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=97 in image_id=17 and point2D_idx=236 in image_id=33\nW20251209 04:54:24.981923 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1185 in image_id=17 and point2D_idx=1257 in image_id=33\nW20251209 04:54:24.981946 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5472 in image_id=17 and point2D_idx=6407 in image_id=33\nW20251209 04:54:24.982023 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6840 in image_id=17 and point2D_idx=6511 in image_id=38\nW20251209 04:54:24.982066 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3960 in image_id=17 and point2D_idx=4489 in image_id=38\nW20251209 04:54:24.982088 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5286 in image_id=17 and point2D_idx=5457 in image_id=38\nW20251209 04:54:24.982102 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5434 in image_id=17 and point2D_idx=5566 in image_id=38\nW20251209 04:54:24.982122 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3961 in image_id=17 and point2D_idx=4491 in image_id=38\nW20251209 04:54:24.982188 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=573 in image_id=17 and point2D_idx=957 in image_id=38\nW20251209 04:54:24.982370 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2466 in image_id=17 and point2D_idx=2652 in image_id=38\nW20251209 04:54:24.982421 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2433 in image_id=17 and point2D_idx=2421 in image_id=38\nW20251209 04:54:24.982446 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1150 in image_id=17 and point2D_idx=1183 in image_id=38\nW20251209 04:54:24.982484 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1381 in image_id=17 and point2D_idx=1191 in image_id=38\nW20251209 04:54:24.982504 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2441 in image_id=17 and point2D_idx=2198 in image_id=38\nW20251209 04:54:24.982524 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=462 in image_id=17 and point2D_idx=308 in image_id=38\nW20251209 04:54:24.982544 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5861 in image_id=17 and point2D_idx=14629 in image_id=42\nW20251209 04:54:24.982559 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=564 in image_id=17 and point2D_idx=4333 in image_id=42\nW20251209 04:54:24.982585 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=926 in image_id=17 and point2D_idx=5157 in image_id=42\nW20251209 04:54:24.982613 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=571 in image_id=17 and point2D_idx=3769 in image_id=42\nW20251209 04:54:24.982661 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3278 in image_id=17 and point2D_idx=7338 in image_id=42\nW20251209 04:54:24.982734 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7132 in image_id=17 and point2D_idx=12369 in image_id=42\nW20251209 04:54:24.982740 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=957 in image_id=17 and point2D_idx=2680 in image_id=42\nW20251209 04:54:24.982747 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5943 in image_id=17 and point2D_idx=10776 in image_id=42\nW20251209 04:54:24.982755 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=86 in image_id=17 and point2D_idx=1154 in image_id=42\nW20251209 04:54:24.982802 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5946 in image_id=17 and point2D_idx=9181 in image_id=42\nW20251209 04:54:24.982834 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3313 in image_id=17 and point2D_idx=3188 in image_id=42\nW20251209 04:54:24.982841 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5091 in image_id=17 and point2D_idx=5739 in image_id=42\nW20251209 04:54:24.982858 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2470 in image_id=17 and point2D_idx=2293 in image_id=42\nW20251209 04:54:24.982868 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2909 in image_id=17 and point2D_idx=2702 in image_id=42\nW20251209 04:54:24.982903 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7946 in image_id=17 and point2D_idx=11147 in image_id=51\nW20251209 04:54:24.982919 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7312 in image_id=17 and point2D_idx=10372 in image_id=51\nW20251209 04:54:24.982936 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8871 in image_id=17 and point2D_idx=11753 in image_id=51\nW20251209 04:54:24.982962 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8688 in image_id=17 and point2D_idx=11680 in image_id=51\nW20251209 04:54:24.983004 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8239 in image_id=17 and point2D_idx=11576 in image_id=51\nW20251209 04:54:24.983027 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8242 in image_id=17 and point2D_idx=11634 in image_id=51\nW20251209 04:54:24.983181 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8421 in image_id=18 and point2D_idx=3132 in image_id=19\nW20251209 04:54:24.983288 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7446 in image_id=18 and point2D_idx=2425 in image_id=19\nW20251209 04:54:24.983428 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10189 in image_id=18 and point2D_idx=4237 in image_id=19\nW20251209 04:54:24.983469 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9835 in image_id=18 and point2D_idx=4132 in image_id=19\nW20251209 04:54:24.983497 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7459 in image_id=18 and point2D_idx=2307 in image_id=19\nW20251209 04:54:24.983516 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10358 in image_id=18 and point2D_idx=4297 in image_id=19\nW20251209 04:54:24.983611 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11123 in image_id=18 and point2D_idx=1395 in image_id=22\nW20251209 04:54:24.983664 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10768 in image_id=18 and point2D_idx=1557 in image_id=22\nW20251209 04:54:24.983681 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11111 in image_id=18 and point2D_idx=1756 in image_id=22\nW20251209 04:54:24.983722 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8969 in image_id=18 and point2D_idx=1134 in image_id=22\nW20251209 04:54:24.983788 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7847 in image_id=18 and point2D_idx=5133 in image_id=29\nW20251209 04:54:24.983823 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8645 in image_id=18 and point2D_idx=6026 in image_id=29\nW20251209 04:54:24.983863 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2152 in image_id=18 and point2D_idx=512 in image_id=29\nW20251209 04:54:24.984067 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7500 in image_id=18 and point2D_idx=6411 in image_id=29\nW20251209 04:54:24.984098 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7464 in image_id=18 and point2D_idx=6446 in image_id=29\nW20251209 04:54:24.984115 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=161 in image_id=18 and point2D_idx=14 in image_id=29\nW20251209 04:54:24.984124 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1418 in image_id=18 and point2D_idx=1034 in image_id=29\nW20251209 04:54:24.984189 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10245 in image_id=18 and point2D_idx=7843 in image_id=29\nW20251209 04:54:24.984227 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5118 in image_id=18 and point2D_idx=5120 in image_id=29\nW20251209 04:54:24.984247 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2365 in image_id=18 and point2D_idx=2666 in image_id=29\nW20251209 04:54:24.984317 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6640 in image_id=18 and point2D_idx=6738 in image_id=29\nW20251209 04:54:24.984334 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8286 in image_id=18 and point2D_idx=7465 in image_id=29\nW20251209 04:54:24.984353 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11252 in image_id=18 and point2D_idx=8175 in image_id=29\nW20251209 04:54:24.984375 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8468 in image_id=18 and point2D_idx=7652 in image_id=29\nW20251209 04:54:24.984383 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9164 in image_id=18 and point2D_idx=7809 in image_id=29\nW20251209 04:54:24.984395 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6222 in image_id=18 and point2D_idx=6877 in image_id=29\nW20251209 04:54:24.984410 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10672 in image_id=18 and point2D_idx=8106 in image_id=29\nW20251209 04:54:24.984425 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9031 in image_id=18 and point2D_idx=7861 in image_id=29\nW20251209 04:54:24.984448 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=872 in image_id=18 and point2D_idx=3888 in image_id=32\nW20251209 04:54:24.984467 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3379 in image_id=18 and point2D_idx=3975 in image_id=32\nW20251209 04:54:24.984493 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=577 in image_id=18 and point2D_idx=1723 in image_id=33\nW20251209 04:54:24.984514 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=256 in image_id=18 and point2D_idx=1457 in image_id=33\nW20251209 04:54:24.984556 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3099 in image_id=18 and point2D_idx=4263 in image_id=33\nW20251209 04:54:24.984563 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10260 in image_id=18 and point2D_idx=9418 in image_id=33\nW20251209 04:54:24.984576 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6458 in image_id=18 and point2D_idx=6577 in image_id=33\nW20251209 04:54:24.984583 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7055 in image_id=18 and point2D_idx=6971 in image_id=33\nW20251209 04:54:24.984601 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8975 in image_id=18 and point2D_idx=8328 in image_id=33\nW20251209 04:54:24.984614 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2400 in image_id=18 and point2D_idx=3599 in image_id=33\nW20251209 04:54:24.984626 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1687 in image_id=18 and point2D_idx=2909 in image_id=33\nW20251209 04:54:24.984633 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9154 in image_id=18 and point2D_idx=8531 in image_id=33\nW20251209 04:54:24.984644 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4890 in image_id=18 and point2D_idx=5525 in image_id=33\nW20251209 04:54:24.984666 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3107 in image_id=18 and point2D_idx=4270 in image_id=33\nW20251209 04:54:24.984681 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6216 in image_id=18 and point2D_idx=6384 in image_id=33\nW20251209 04:54:24.984690 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6859 in image_id=18 and point2D_idx=6787 in image_id=33\nW20251209 04:54:24.984719 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=913 in image_id=18 and point2D_idx=2311 in image_id=33\nW20251209 04:54:24.984817 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10502 in image_id=18 and point2D_idx=11753 in image_id=34\nW20251209 04:54:24.984843 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5987 in image_id=18 and point2D_idx=6548 in image_id=34\nW20251209 04:54:24.984855 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6030 in image_id=18 and point2D_idx=6549 in image_id=34\nW20251209 04:54:24.984866 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5063 in image_id=18 and point2D_idx=5650 in image_id=34\nW20251209 04:54:24.984905 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10807 in image_id=18 and point2D_idx=12431 in image_id=34\nW20251209 04:54:24.984924 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8086 in image_id=18 and point2D_idx=9848 in image_id=34\nW20251209 04:54:24.984937 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8934 in image_id=18 and point2D_idx=10899 in image_id=34\nW20251209 04:54:24.984974 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10969 in image_id=18 and point2D_idx=13043 in image_id=34\nW20251209 04:54:24.984983 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11256 in image_id=18 and point2D_idx=13341 in image_id=34\nW20251209 04:54:24.984999 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9693 in image_id=18 and point2D_idx=12016 in image_id=34\nW20251209 04:54:24.985065 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9993 in image_id=18 and point2D_idx=9300 in image_id=35\nW20251209 04:54:24.985165 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4583 in image_id=18 and point2D_idx=3737 in image_id=35\nW20251209 04:54:24.985237 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10508 in image_id=18 and point2D_idx=10207 in image_id=35\nW20251209 04:54:24.985271 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10016 in image_id=18 and point2D_idx=9862 in image_id=35\nW20251209 04:54:24.985342 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10653 in image_id=18 and point2D_idx=10534 in image_id=35\nW20251209 04:54:24.985402 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1883 in image_id=18 and point2D_idx=2093 in image_id=35\nW20251209 04:54:24.985411 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8452 in image_id=18 and point2D_idx=8944 in image_id=35\nW20251209 04:54:24.985440 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10813 in image_id=18 and point2D_idx=10878 in image_id=35\nW20251209 04:54:24.985467 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1275 in image_id=18 and point2D_idx=1215 in image_id=35\nW20251209 04:54:24.985477 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9319 in image_id=18 and point2D_idx=9705 in image_id=35\nW20251209 04:54:24.985499 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3370 in image_id=18 and point2D_idx=3707 in image_id=35\nW20251209 04:54:24.985509 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9354 in image_id=18 and point2D_idx=9706 in image_id=35\nW20251209 04:54:24.985526 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5587 in image_id=18 and point2D_idx=6134 in image_id=35\nW20251209 04:54:24.985547 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8981 in image_id=18 and point2D_idx=9523 in image_id=35\nW20251209 04:54:24.985607 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4857 in image_id=18 and point2D_idx=6139 in image_id=35\nW20251209 04:54:24.985618 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10168 in image_id=18 and point2D_idx=10707 in image_id=35\nW20251209 04:54:24.985630 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=587 in image_id=18 and point2D_idx=1227 in image_id=35\nW20251209 04:54:24.985642 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9859 in image_id=18 and point2D_idx=10575 in image_id=35\nW20251209 04:54:24.985702 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5325 in image_id=18 and point2D_idx=7136 in image_id=35\nW20251209 04:54:24.985709 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4625 in image_id=18 and point2D_idx=6403 in image_id=35\nW20251209 04:54:24.985750 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10003 in image_id=18 and point2D_idx=7227 in image_id=39\nW20251209 04:54:24.985780 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5763 in image_id=18 and point2D_idx=4262 in image_id=39\nW20251209 04:54:24.985789 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9005 in image_id=18 and point2D_idx=6738 in image_id=39\nW20251209 04:54:24.985804 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9006 in image_id=18 and point2D_idx=6739 in image_id=39\nW20251209 04:54:24.985826 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=528 in image_id=18 and point2D_idx=200 in image_id=39\nW20251209 04:54:24.985833 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=685 in image_id=18 and point2D_idx=254 in image_id=39\nW20251209 04:54:24.985849 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8307 in image_id=18 and point2D_idx=6248 in image_id=39\nW20251209 04:54:24.985864 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=856 in image_id=18 and point2D_idx=374 in image_id=39\nW20251209 04:54:24.985872 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3084 in image_id=18 and point2D_idx=2563 in image_id=39\nW20251209 04:54:24.985893 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2816 in image_id=18 and point2D_idx=2425 in image_id=39\nW20251209 04:54:24.985901 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5297 in image_id=18 and point2D_idx=4268 in image_id=39\nW20251209 04:54:24.985924 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9491 in image_id=18 and point2D_idx=7237 in image_id=39\nW20251209 04:54:24.985931 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9661 in image_id=18 and point2D_idx=7336 in image_id=39\nW20251209 04:54:24.985952 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9492 in image_id=18 and point2D_idx=7238 in image_id=39\nW20251209 04:54:24.985985 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5301 in image_id=18 and point2D_idx=4272 in image_id=39\nW20251209 04:54:24.986003 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=49 in image_id=18 and point2D_idx=6 in image_id=39\nW20251209 04:54:24.986028 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1419 in image_id=18 and point2D_idx=1088 in image_id=39\nW20251209 04:54:24.986150 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10204 in image_id=18 and point2D_idx=7575 in image_id=39\nW20251209 04:54:24.986188 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=397 in image_id=18 and point2D_idx=266 in image_id=39\nW20251209 04:54:24.986216 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9845 in image_id=18 and point2D_idx=7499 in image_id=39\nW20251209 04:54:24.986233 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11054 in image_id=18 and point2D_idx=7801 in image_id=39\nW20251209 04:54:24.986309 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=907 in image_id=18 and point2D_idx=634 in image_id=39\nW20251209 04:54:24.986396 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=279 in image_id=18 and point2D_idx=278 in image_id=39\nW20251209 04:54:24.986478 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11253 in image_id=18 and point2D_idx=7978 in image_id=39\nW20251209 04:54:24.986505 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11384 in image_id=18 and point2D_idx=8123 in image_id=39\nW20251209 04:54:24.986524 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=150 in image_id=18 and point2D_idx=286 in image_id=39\nW20251209 04:54:24.986541 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=562 in image_id=18 and point2D_idx=726 in image_id=39\nW20251209 04:54:24.986578 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=682 in image_id=18 and point2D_idx=4216 in image_id=42\nW20251209 04:54:24.986626 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5581 in image_id=18 and point2D_idx=9732 in image_id=42\nW20251209 04:54:24.986677 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4120 in image_id=18 and point2D_idx=7335 in image_id=42\nW20251209 04:54:24.986705 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2595 in image_id=18 and point2D_idx=5697 in image_id=42\nW20251209 04:54:24.986778 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7066 in image_id=18 and point2D_idx=10259 in image_id=42\nW20251209 04:54:24.986801 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9323 in image_id=18 and point2D_idx=12688 in image_id=42\nW20251209 04:54:24.986827 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9358 in image_id=18 and point2D_idx=12358 in image_id=42\nW20251209 04:54:24.986849 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8536 in image_id=18 and point2D_idx=11203 in image_id=42\nW20251209 04:54:24.986946 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=998 in image_id=18 and point2D_idx=2768 in image_id=48\nW20251209 04:54:24.986976 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=698 in image_id=18 and point2D_idx=2427 in image_id=48\nW20251209 04:54:24.986993 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=52 in image_id=18 and point2D_idx=1824 in image_id=48\nW20251209 04:54:24.987028 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3372 in image_id=18 and point2D_idx=4949 in image_id=48\nW20251209 04:54:24.987086 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3118 in image_id=18 and point2D_idx=4474 in image_id=48\nW20251209 04:54:24.987127 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10678 in image_id=18 and point2D_idx=6357 in image_id=49\nW20251209 04:54:24.987174 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3008 in image_id=19 and point2D_idx=4704 in image_id=20\nW20251209 04:54:24.987196 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3551 in image_id=19 and point2D_idx=6203 in image_id=20\nW20251209 04:54:24.987270 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1930 in image_id=19 and point2D_idx=3461 in image_id=20\nW20251209 04:54:24.987302 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3119 in image_id=19 and point2D_idx=5615 in image_id=20\nW20251209 04:54:24.987340 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3559 in image_id=19 and point2D_idx=6461 in image_id=20\nW20251209 04:54:24.987370 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3649 in image_id=19 and point2D_idx=6590 in image_id=20\nW20251209 04:54:24.987394 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2041 in image_id=19 and point2D_idx=3868 in image_id=20\nW20251209 04:54:24.987430 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3334 in image_id=19 and point2D_idx=6217 in image_id=20\nW20251209 04:54:24.987446 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3563 in image_id=19 and point2D_idx=6467 in image_id=20\nW20251209 04:54:24.987491 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1464 in image_id=19 and point2D_idx=2896 in image_id=20\nW20251209 04:54:24.987517 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3959 in image_id=19 and point2D_idx=7060 in image_id=20\nW20251209 04:54:24.987576 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2049 in image_id=19 and point2D_idx=4119 in image_id=20\nW20251209 04:54:24.987628 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3765 in image_id=19 and point2D_idx=7006 in image_id=20\nW20251209 04:54:24.987665 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3341 in image_id=19 and point2D_idx=6863 in image_id=20\nW20251209 04:54:24.987706 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1711 in image_id=19 and point2D_idx=997 in image_id=21\nW20251209 04:54:24.987756 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2596 in image_id=19 and point2D_idx=2895 in image_id=21\nW20251209 04:54:24.987789 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4305 in image_id=19 and point2D_idx=1015 in image_id=22\nW20251209 04:54:24.987803 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4040 in image_id=19 and point2D_idx=684 in image_id=22\nW20251209 04:54:24.987815 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3259 in image_id=19 and point2D_idx=112 in image_id=22\nW20251209 04:54:24.987842 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2895 in image_id=19 and point2D_idx=116 in image_id=22\nW20251209 04:54:24.987863 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3926 in image_id=19 and point2D_idx=811 in image_id=22\nW20251209 04:54:24.987894 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2610 in image_id=19 and point2D_idx=118 in image_id=22\nW20251209 04:54:24.987906 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2815 in image_id=19 and point2D_idx=231 in image_id=22\nW20251209 04:54:24.987918 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3852 in image_id=19 and point2D_idx=812 in image_id=22\nW20251209 04:54:24.987962 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=326 in image_id=19 and point2D_idx=5010 in image_id=32\nW20251209 04:54:24.987980 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=199 in image_id=19 and point2D_idx=4690 in image_id=32\nW20251209 04:54:24.987993 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=251 in image_id=19 and point2D_idx=4691 in image_id=32\nW20251209 04:54:24.988062 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=266 in image_id=19 and point2D_idx=3201 in image_id=33\nW20251209 04:54:24.988075 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=67 in image_id=19 and point2D_idx=2880 in image_id=33\nW20251209 04:54:24.988087 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=529 in image_id=19 and point2D_idx=3928 in image_id=33\nW20251209 04:54:24.988098 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1045 in image_id=19 and point2D_idx=5271 in image_id=33\nW20251209 04:54:24.988108 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=390 in image_id=19 and point2D_idx=3803 in image_id=37\nW20251209 04:54:24.988120 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=266 in image_id=19 and point2D_idx=3590 in image_id=37\nW20251209 04:54:24.988140 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=195 in image_id=19 and point2D_idx=2602 in image_id=38\nW20251209 04:54:24.988170 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=125 in image_id=19 and point2D_idx=3124 in image_id=48\nW20251209 04:54:24.988202 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1208 in image_id=19 and point2D_idx=6055 in image_id=48\nW20251209 04:54:24.988219 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=26 in image_id=19 and point2D_idx=2773 in image_id=48\nW20251209 04:54:24.988231 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3910 in image_id=19 and point2D_idx=8379 in image_id=51\nW20251209 04:54:24.988246 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3003 in image_id=19 and point2D_idx=6527 in image_id=51\nW20251209 04:54:24.988253 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3201 in image_id=19 and point2D_idx=6952 in image_id=51\nW20251209 04:54:24.988280 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3569 in image_id=19 and point2D_idx=7845 in image_id=51\nW20251209 04:54:24.988309 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3932 in image_id=19 and point2D_idx=8766 in image_id=51\nW20251209 04:54:24.988352 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1789 in image_id=19 and point2D_idx=5254 in image_id=51\nW20251209 04:54:24.988362 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2623 in image_id=19 and point2D_idx=6549 in image_id=51\nW20251209 04:54:24.988375 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3678 in image_id=19 and point2D_idx=8409 in image_id=51\nW20251209 04:54:24.988402 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2290 in image_id=19 and point2D_idx=6099 in image_id=51\nW20251209 04:54:24.988420 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2411 in image_id=19 and point2D_idx=6372 in image_id=51\nW20251209 04:54:24.988435 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4214 in image_id=19 and point2D_idx=9558 in image_id=51\nW20251209 04:54:24.988471 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5759 in image_id=20 and point2D_idx=2169 in image_id=21\nW20251209 04:54:24.988503 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6966 in image_id=20 and point2D_idx=3727 in image_id=21\nW20251209 04:54:24.988541 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6161 in image_id=20 and point2D_idx=2566 in image_id=21\nW20251209 04:54:24.988622 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6891 in image_id=20 and point2D_idx=3866 in image_id=21\nW20251209 04:54:24.988639 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5610 in image_id=20 and point2D_idx=2881 in image_id=21\nW20251209 04:54:24.988658 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3243 in image_id=20 and point2D_idx=1081 in image_id=21\nW20251209 04:54:24.988715 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3134 in image_id=20 and point2D_idx=999 in image_id=21\nW20251209 04:54:24.988734 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3465 in image_id=20 and point2D_idx=1475 in image_id=21\nW20251209 04:54:24.988741 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4525 in image_id=20 and point2D_idx=2401 in image_id=21\nW20251209 04:54:24.988784 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6066 in image_id=20 and point2D_idx=333 in image_id=22\nW20251209 04:54:24.988793 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6644 in image_id=20 and point2D_idx=465 in image_id=22\nW20251209 04:54:24.988815 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6557 in image_id=20 and point2D_idx=7540 in image_id=24\nW20251209 04:54:24.988822 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6780 in image_id=20 and point2D_idx=680 in image_id=25\nW20251209 04:54:24.988837 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5312 in image_id=20 and point2D_idx=7678 in image_id=28\nW20251209 04:54:24.988848 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4193 in image_id=20 and point2D_idx=6465 in image_id=28\nW20251209 04:54:24.988878 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3571 in image_id=20 and point2D_idx=5539 in image_id=28\nW20251209 04:54:24.988884 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3696 in image_id=20 and point2D_idx=5722 in image_id=28\nW20251209 04:54:24.988906 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=438 in image_id=20 and point2D_idx=971 in image_id=29\nW20251209 04:54:24.988914 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=415 in image_id=20 and point2D_idx=974 in image_id=29\nW20251209 04:54:24.988929 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=172 in image_id=20 and point2D_idx=1002 in image_id=29\nW20251209 04:54:24.988951 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=441 in image_id=20 and point2D_idx=153 in image_id=30\nW20251209 04:54:24.988973 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=119 in image_id=20 and point2D_idx=100 in image_id=30\nW20251209 04:54:24.988986 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=997 in image_id=20 and point2D_idx=1184 in image_id=30\nW20251209 04:54:24.989122 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2733 in image_id=20 and point2D_idx=1132 in image_id=36\nW20251209 04:54:24.989180 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4929 in image_id=20 and point2D_idx=3464 in image_id=36\nW20251209 04:54:24.989257 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5077 in image_id=20 and point2D_idx=3598 in image_id=36\nW20251209 04:54:24.989276 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5352 in image_id=20 and point2D_idx=3861 in image_id=36\nW20251209 04:54:24.989399 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1133 in image_id=20 and point2D_idx=156 in image_id=36\nW20251209 04:54:24.989420 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1851 in image_id=20 and point2D_idx=622 in image_id=36\nW20251209 04:54:24.989449 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2634 in image_id=20 and point2D_idx=1235 in image_id=36\nW20251209 04:54:24.989464 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2724 in image_id=20 and point2D_idx=1322 in image_id=36\nW20251209 04:54:24.989476 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2847 in image_id=20 and point2D_idx=1423 in image_id=36\nW20251209 04:54:24.989497 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2374 in image_id=20 and point2D_idx=1072 in image_id=36\nW20251209 04:54:24.989510 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2510 in image_id=20 and point2D_idx=1150 in image_id=36\nW20251209 04:54:24.989567 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5964 in image_id=20 and point2D_idx=2975 in image_id=40\nW20251209 04:54:24.989636 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5970 in image_id=20 and point2D_idx=2982 in image_id=40\nW20251209 04:54:24.989660 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4523 in image_id=20 and point2D_idx=1704 in image_id=40\nW20251209 04:54:24.989739 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4994 in image_id=20 and point2D_idx=2446 in image_id=40\nW20251209 04:54:24.989778 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3116 in image_id=20 and point2D_idx=2572 in image_id=45\nW20251209 04:54:24.989857 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=953 in image_id=20 and point2D_idx=335 in image_id=45\nW20251209 04:54:24.989904 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1884 in image_id=20 and point2D_idx=1226 in image_id=45\nW20251209 04:54:24.989938 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1126 in image_id=20 and point2D_idx=580 in image_id=45\nW20251209 04:54:24.989951 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=673 in image_id=20 and point2D_idx=207 in image_id=45\nW20251209 04:54:24.990050 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=439 in image_id=20 and point2D_idx=96 in image_id=45\nW20251209 04:54:24.990110 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1582 in image_id=20 and point2D_idx=1387 in image_id=45\nW20251209 04:54:24.990183 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5401 in image_id=20 and point2D_idx=6104 in image_id=45\nW20251209 04:54:24.990245 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4561 in image_id=20 and point2D_idx=5467 in image_id=45\nW20251209 04:54:24.990321 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2903 in image_id=20 and point2D_idx=3421 in image_id=45\nW20251209 04:54:24.990347 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1005 in image_id=20 and point2D_idx=1412 in image_id=45\nW20251209 04:54:24.990363 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=837 in image_id=20 and point2D_idx=4906 in image_id=48\nW20251209 04:54:24.990373 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=989 in image_id=20 and point2D_idx=4917 in image_id=48\nW20251209 04:54:24.990393 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1539 in image_id=20 and point2D_idx=2155 in image_id=51\nW20251209 04:54:24.990409 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2114 in image_id=21 and point2D_idx=215 in image_id=22\nW20251209 04:54:24.990416 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4202 in image_id=21 and point2D_idx=802 in image_id=22\nW20251209 04:54:24.990468 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3063 in image_id=21 and point2D_idx=4797 in image_id=36\nW20251209 04:54:24.990497 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=993 in image_id=21 and point2D_idx=2371 in image_id=36\nW20251209 04:54:24.990514 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3441 in image_id=21 and point2D_idx=4905 in image_id=36\nW20251209 04:54:24.990549 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1644 in image_id=21 and point2D_idx=4919 in image_id=45\nW20251209 04:54:24.990569 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2794 in image_id=21 and point2D_idx=6532 in image_id=45\nW20251209 04:54:24.990610 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=345 in image_id=21 and point2D_idx=214 in image_id=46\nW20251209 04:54:24.990635 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2997 in image_id=21 and point2D_idx=1717 in image_id=46\nW20251209 04:54:24.990687 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2157 in image_id=21 and point2D_idx=1369 in image_id=50\nW20251209 04:54:24.990699 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1172 in image_id=21 and point2D_idx=638 in image_id=50\nW20251209 04:54:24.990732 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3759 in image_id=21 and point2D_idx=4019 in image_id=50\nW20251209 04:54:24.990751 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=754 in image_id=21 and point2D_idx=273 in image_id=50\nW20251209 04:54:24.990777 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3053 in image_id=21 and point2D_idx=2251 in image_id=50\nW20251209 04:54:24.990801 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3416 in image_id=21 and point2D_idx=3900 in image_id=50\nW20251209 04:54:24.990811 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=861 in image_id=21 and point2D_idx=404 in image_id=50\nW20251209 04:54:24.990837 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=865 in image_id=21 and point2D_idx=421 in image_id=50\nW20251209 04:54:24.990853 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3883 in image_id=21 and point2D_idx=4096 in image_id=50\nW20251209 04:54:24.990902 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3903 in image_id=21 and point2D_idx=4106 in image_id=50\nW20251209 04:54:24.990938 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3641 in image_id=21 and point2D_idx=4115 in image_id=50\nW20251209 04:54:24.990957 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=546 in image_id=21 and point2D_idx=228 in image_id=50\nW20251209 04:54:24.990974 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3330 in image_id=21 and point2D_idx=3779 in image_id=50\nW20251209 04:54:24.990991 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3461 in image_id=21 and point2D_idx=7795 in image_id=51\nW20251209 04:54:24.991000 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4360 in image_id=21 and point2D_idx=9429 in image_id=51\nW20251209 04:54:24.991012 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3435 in image_id=21 and point2D_idx=7813 in image_id=51\nW20251209 04:54:24.991028 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4553 in image_id=22 and point2D_idx=3414 in image_id=25\nW20251209 04:54:24.991054 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4398 in image_id=22 and point2D_idx=3226 in image_id=25\nW20251209 04:54:24.991070 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4834 in image_id=22 and point2D_idx=3890 in image_id=25\nW20251209 04:54:24.991102 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4737 in image_id=22 and point2D_idx=3396 in image_id=41\nW20251209 04:54:24.991130 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4765 in image_id=22 and point2D_idx=3273 in image_id=41\nW20251209 04:54:24.991181 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=595 in image_id=22 and point2D_idx=6778 in image_id=42\nW20251209 04:54:24.991218 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=914 in image_id=22 and point2D_idx=6254 in image_id=42\nW20251209 04:54:24.991270 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=717 in image_id=22 and point2D_idx=4162 in image_id=42\nW20251209 04:54:24.991316 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=503 in image_id=22 and point2D_idx=2280 in image_id=42\nW20251209 04:54:24.991371 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2397 in image_id=22 and point2D_idx=3192 in image_id=42\nW20251209 04:54:24.991497 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4096 in image_id=22 and point2D_idx=12101 in image_id=51\nW20251209 04:54:24.991596 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3924 in image_id=22 and point2D_idx=11944 in image_id=51\nW20251209 04:54:24.991657 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3369 in image_id=22 and point2D_idx=11567 in image_id=51\nW20251209 04:54:24.991673 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3555 in image_id=22 and point2D_idx=11690 in image_id=51\nW20251209 04:54:24.991728 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2660 in image_id=22 and point2D_idx=10616 in image_id=51\nW20251209 04:54:24.991982 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2808 in image_id=23 and point2D_idx=5342 in image_id=34\nW20251209 04:54:24.992105 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1376 in image_id=23 and point2D_idx=2529 in image_id=35\nW20251209 04:54:24.992152 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=984 in image_id=23 and point2D_idx=2302 in image_id=35\nW20251209 04:54:24.992191 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=262 in image_id=23 and point2D_idx=632 in image_id=35\nW20251209 04:54:24.992224 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=346 in image_id=23 and point2D_idx=1004 in image_id=35\nW20251209 04:54:24.992263 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=268 in image_id=23 and point2D_idx=1008 in image_id=35\nW20251209 04:54:24.992314 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1100 in image_id=23 and point2D_idx=9121 in image_id=42\nW20251209 04:54:24.992347 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1578 in image_id=23 and point2D_idx=9716 in image_id=42\nW20251209 04:54:24.992417 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1562 in image_id=23 and point2D_idx=2413 in image_id=43\nW20251209 04:54:24.992485 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1829 in image_id=23 and point2D_idx=3026 in image_id=43\nW20251209 04:54:24.992549 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2130 in image_id=23 and point2D_idx=3485 in image_id=43\nW20251209 04:54:24.992636 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1132 in image_id=23 and point2D_idx=1225 in image_id=44\nW20251209 04:54:24.992698 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=419 in image_id=23 and point2D_idx=527 in image_id=44\nW20251209 04:54:24.992716 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1142 in image_id=23 and point2D_idx=1607 in image_id=44\nW20251209 04:54:24.992725 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4343 in image_id=23 and point2D_idx=4394 in image_id=44\nW20251209 04:54:24.992741 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=262 in image_id=23 and point2D_idx=414 in image_id=44\nW20251209 04:54:24.992749 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3415 in image_id=23 and point2D_idx=3409 in image_id=44\nW20251209 04:54:24.992763 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1574 in image_id=23 and point2D_idx=2064 in image_id=44\nW20251209 04:54:24.992774 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=346 in image_id=23 and point2D_idx=653 in image_id=44\nW20251209 04:54:24.992783 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2672 in image_id=23 and point2D_idx=2743 in image_id=44\nW20251209 04:54:24.992803 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1579 in image_id=23 and point2D_idx=2342 in image_id=44\nW20251209 04:54:24.992828 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1805 in image_id=23 and point2D_idx=685 in image_id=45\nW20251209 04:54:24.992839 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1316 in image_id=23 and point2D_idx=157 in image_id=45\nW20251209 04:54:24.992847 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1318 in image_id=23 and point2D_idx=328 in image_id=45\nW20251209 04:54:24.992863 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1327 in image_id=23 and point2D_idx=5812 in image_id=48\nW20251209 04:54:24.992880 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2999 in image_id=23 and point2D_idx=6836 in image_id=48\nW20251209 04:54:24.992890 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1294 in image_id=23 and point2D_idx=5820 in image_id=48\nW20251209 04:54:24.992904 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2838 in image_id=23 and point2D_idx=6711 in image_id=48\nW20251209 04:54:24.992930 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=442 in image_id=23 and point2D_idx=4504 in image_id=48\nW20251209 04:54:24.992939 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=816 in image_id=23 and point2D_idx=5269 in image_id=48\nW20251209 04:54:24.992963 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=761 in image_id=23 and point2D_idx=1079 in image_id=51\nW20251209 04:54:24.992997 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6790 in image_id=24 and point2D_idx=196 in image_id=25\nW20251209 04:54:24.993006 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7214 in image_id=24 and point2D_idx=389 in image_id=25\nW20251209 04:54:24.993129 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3036 in image_id=24 and point2D_idx=4060 in image_id=28\nW20251209 04:54:24.993202 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5575 in image_id=24 and point2D_idx=6633 in image_id=28\nW20251209 04:54:24.993318 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6784 in image_id=24 and point2D_idx=7834 in image_id=28\nW20251209 04:54:24.993405 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5087 in image_id=24 and point2D_idx=6084 in image_id=28\nW20251209 04:54:24.993493 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2175 in image_id=24 and point2D_idx=2912 in image_id=28\nW20251209 04:54:24.993556 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2851 in image_id=24 and point2D_idx=3671 in image_id=28\nW20251209 04:54:24.993564 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3324 in image_id=24 and point2D_idx=4097 in image_id=28\nW20251209 04:54:24.993798 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1218 in image_id=24 and point2D_idx=1460 in image_id=30\nW20251209 04:54:24.993809 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=949 in image_id=24 and point2D_idx=1197 in image_id=30\nW20251209 04:54:24.993818 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=679 in image_id=24 and point2D_idx=875 in image_id=30\nW20251209 04:54:24.993869 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=40 in image_id=24 and point2D_idx=1098 in image_id=39\nW20251209 04:54:24.993883 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=363 in image_id=24 and point2D_idx=1539 in image_id=43\nW20251209 04:54:24.993928 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=355 in image_id=24 and point2D_idx=68 in image_id=45\nW20251209 04:54:24.993939 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=283 in image_id=24 and point2D_idx=72 in image_id=45\nW20251209 04:54:24.993955 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=363 in image_id=24 and point2D_idx=412 in image_id=45\nW20251209 04:54:24.993972 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=451 in image_id=24 and point2D_idx=746 in image_id=45\nW20251209 04:54:24.994005 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=29 in image_id=24 and point2D_idx=3554 in image_id=48\nW20251209 04:54:24.994066 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3424 in image_id=27 and point2D_idx=3349 in image_id=28\nW20251209 04:54:24.994093 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4004 in image_id=27 and point2D_idx=4059 in image_id=28\nW20251209 04:54:24.994116 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3790 in image_id=27 and point2D_idx=3832 in image_id=28\nW20251209 04:54:24.994163 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2857 in image_id=27 and point2D_idx=2903 in image_id=28\nW20251209 04:54:24.994176 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3672 in image_id=27 and point2D_idx=3847 in image_id=28\nW20251209 04:54:24.994190 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4613 in image_id=27 and point2D_idx=5307 in image_id=28\nW20251209 04:54:24.994233 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3274 in image_id=27 and point2D_idx=3628 in image_id=28\nW20251209 04:54:24.994242 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3713 in image_id=27 and point2D_idx=4097 in image_id=28\nW20251209 04:54:24.994260 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1381 in image_id=28 and point2D_idx=1830 in image_id=29\nW20251209 04:54:24.994290 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1155 in image_id=28 and point2D_idx=619 in image_id=30\nW20251209 04:54:24.994308 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1161 in image_id=28 and point2D_idx=870 in image_id=30\nW20251209 04:54:24.994340 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1359 in image_id=28 and point2D_idx=1613 in image_id=30\nW20251209 04:54:24.994367 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1723 in image_id=28 and point2D_idx=2052 in image_id=30\nW20251209 04:54:24.994387 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=122 in image_id=28 and point2D_idx=1662 in image_id=34\nW20251209 04:54:24.994428 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5915 in image_id=28 and point2D_idx=2663 in image_id=36\nW20251209 04:54:24.994473 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4876 in image_id=28 and point2D_idx=2102 in image_id=36\nW20251209 04:54:24.994495 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1983 in image_id=28 and point2D_idx=677 in image_id=36\nW20251209 04:54:24.994527 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=719 in image_id=28 and point2D_idx=1338 in image_id=39\nW20251209 04:54:24.994549 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1987 in image_id=28 and point2D_idx=2718 in image_id=39\nW20251209 04:54:24.994561 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=340 in image_id=28 and point2D_idx=1638 in image_id=39\nW20251209 04:54:24.994569 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=737 in image_id=28 and point2D_idx=2161 in image_id=39\nW20251209 04:54:24.994592 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=78 in image_id=28 and point2D_idx=1361 in image_id=39\nW20251209 04:54:24.994650 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1168 in image_id=28 and point2D_idx=964 in image_id=45\nW20251209 04:54:24.994658 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1777 in image_id=28 and point2D_idx=1526 in image_id=45\nW20251209 04:54:24.994696 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7175 in image_id=29 and point2D_idx=6217 in image_id=30\nW20251209 04:54:24.994725 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6367 in image_id=29 and point2D_idx=4253 in image_id=30\nW20251209 04:54:24.994750 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1820 in image_id=29 and point2D_idx=949 in image_id=30\nW20251209 04:54:24.994758 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3225 in image_id=29 and point2D_idx=1938 in image_id=30\nW20251209 04:54:24.994767 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4695 in image_id=29 and point2D_idx=2837 in image_id=30\nW20251209 04:54:24.994786 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5627 in image_id=29 and point2D_idx=4570 in image_id=30\nW20251209 04:54:24.994811 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5261 in image_id=29 and point2D_idx=4161 in image_id=30\nW20251209 04:54:24.994830 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1347 in image_id=29 and point2D_idx=470 in image_id=30\nW20251209 04:54:24.994839 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4333 in image_id=29 and point2D_idx=3030 in image_id=30\nW20251209 04:54:24.994863 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3799 in image_id=29 and point2D_idx=2642 in image_id=30\nW20251209 04:54:24.994871 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5077 in image_id=29 and point2D_idx=4183 in image_id=30\nW20251209 04:54:24.994912 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5446 in image_id=29 and point2D_idx=4543 in image_id=30\nW20251209 04:54:24.994935 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3243 in image_id=29 and point2D_idx=2300 in image_id=30\nW20251209 04:54:24.994965 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2935 in image_id=29 and point2D_idx=2134 in image_id=30\nW20251209 04:54:24.994973 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1634 in image_id=29 and point2D_idx=847 in image_id=30\nW20251209 04:54:24.994981 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2431 in image_id=29 and point2D_idx=1703 in image_id=30\nW20251209 04:54:24.994988 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2526 in image_id=29 and point2D_idx=1799 in image_id=30\nW20251209 04:54:24.995004 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1636 in image_id=29 and point2D_idx=882 in image_id=30\nW20251209 04:54:24.995200 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2075 in image_id=29 and point2D_idx=1404 in image_id=30\nW20251209 04:54:24.995233 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2229 in image_id=29 and point2D_idx=1625 in image_id=30\nW20251209 04:54:24.995247 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2603 in image_id=29 and point2D_idx=1975 in image_id=30\nW20251209 04:54:24.995267 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4718 in image_id=29 and point2D_idx=4442 in image_id=30\nW20251209 04:54:24.995333 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3970 in image_id=29 and point2D_idx=167 in image_id=31\nW20251209 04:54:24.995379 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1653 in image_id=29 and point2D_idx=5482 in image_id=32\nW20251209 04:54:24.995437 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3259 in image_id=29 and point2D_idx=5265 in image_id=33\nW20251209 04:54:24.995463 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1851 in image_id=29 and point2D_idx=3928 in image_id=33\nW20251209 04:54:24.995473 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7687 in image_id=29 and point2D_idx=9555 in image_id=33\nW20251209 04:54:24.995536 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=19 in image_id=29 and point2D_idx=11 in image_id=34\nW20251209 04:54:24.995569 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1860 in image_id=29 and point2D_idx=3708 in image_id=37\nW20251209 04:54:24.995589 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3464 in image_id=29 and point2D_idx=3672 in image_id=37\nW20251209 04:54:24.995612 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3082 in image_id=29 and point2D_idx=3460 in image_id=37\nW20251209 04:54:24.995628 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1873 in image_id=29 and point2D_idx=3092 in image_id=37\nW20251209 04:54:24.995671 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4507 in image_id=29 and point2D_idx=983 in image_id=40\nW20251209 04:54:24.995680 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5819 in image_id=29 and point2D_idx=1889 in image_id=40\nW20251209 04:54:24.995705 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3601 in image_id=29 and point2D_idx=533 in image_id=40\nW20251209 04:54:24.995726 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7353 in image_id=29 and point2D_idx=3614 in image_id=40\nW20251209 04:54:24.995755 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5631 in image_id=29 and point2D_idx=2601 in image_id=40\nW20251209 04:54:24.995769 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5077 in image_id=29 and point2D_idx=2334 in image_id=40\nW20251209 04:54:24.995803 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3643 in image_id=29 and point2D_idx=8040 in image_id=42\nW20251209 04:54:24.995811 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4085 in image_id=29 and point2D_idx=8555 in image_id=42\nW20251209 04:54:24.995827 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1034 in image_id=29 and point2D_idx=4657 in image_id=42\nW20251209 04:54:24.995837 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2919 in image_id=29 and point2D_idx=5697 in image_id=42\nW20251209 04:54:24.995852 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3083 in image_id=29 and point2D_idx=5703 in image_id=42\nW20251209 04:54:24.995865 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7737 in image_id=29 and point2D_idx=7740 in image_id=43\nW20251209 04:54:24.995873 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7799 in image_id=29 and point2D_idx=7741 in image_id=43\nW20251209 04:54:24.995914 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3632 in image_id=29 and point2D_idx=6416 in image_id=48\nW20251209 04:54:24.995923 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1654 in image_id=29 and point2D_idx=4917 in image_id=48\nW20251209 04:54:24.995939 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4190 in image_id=29 and point2D_idx=6572 in image_id=48\nW20251209 04:54:24.995964 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1590 in image_id=29 and point2D_idx=4066 in image_id=48\nW20251209 04:54:24.995994 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=143 in image_id=29 and point2D_idx=2415 in image_id=48\nW20251209 04:54:24.996013 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5678 in image_id=29 and point2D_idx=6431 in image_id=48\nW20251209 04:54:24.996032 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5356 in image_id=30 and point2D_idx=1969 in image_id=31\nW20251209 04:54:24.996091 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6054 in image_id=30 and point2D_idx=2788 in image_id=31\nW20251209 04:54:24.996120 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3672 in image_id=30 and point2D_idx=1131 in image_id=31\nW20251209 04:54:24.996139 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3275 in image_id=30 and point2D_idx=992 in image_id=31\nW20251209 04:54:24.996149 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5369 in image_id=30 and point2D_idx=2804 in image_id=31\nW20251209 04:54:24.996167 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3406 in image_id=30 and point2D_idx=1193 in image_id=31\nW20251209 04:54:24.996176 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4660 in image_id=30 and point2D_idx=2315 in image_id=31\nW20251209 04:54:24.996201 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5079 in image_id=30 and point2D_idx=3029 in image_id=31\nW20251209 04:54:24.996226 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4934 in image_id=30 and point2D_idx=3127 in image_id=31\nW20251209 04:54:24.996243 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3287 in image_id=30 and point2D_idx=1556 in image_id=31\nW20251209 04:54:24.996274 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5380 in image_id=30 and point2D_idx=3477 in image_id=31\nW20251209 04:54:24.996292 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4945 in image_id=30 and point2D_idx=3306 in image_id=31\nW20251209 04:54:24.996304 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1463 in image_id=30 and point2D_idx=5322 in image_id=32\nW20251209 04:54:24.996312 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=339 in image_id=30 and point2D_idx=4522 in image_id=32\nW20251209 04:54:24.996334 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4535 in image_id=30 and point2D_idx=6830 in image_id=35\nW20251209 04:54:24.996366 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5663 in image_id=30 and point2D_idx=7323 in image_id=35\nW20251209 04:54:24.996446 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5124 in image_id=30 and point2D_idx=5562 in image_id=39\nW20251209 04:54:24.996455 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5871 in image_id=30 and point2D_idx=6236 in image_id=39\nW20251209 04:54:24.996465 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6103 in image_id=30 and point2D_idx=6730 in image_id=39\nW20251209 04:54:24.996528 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3277 in image_id=30 and point2D_idx=1463 in image_id=40\nW20251209 04:54:24.996548 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5374 in image_id=30 and point2D_idx=3510 in image_id=40\nW20251209 04:54:24.996559 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4529 in image_id=30 and point2D_idx=2624 in image_id=40\nW20251209 04:54:24.996573 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4665 in image_id=30 and point2D_idx=2728 in image_id=40\nW20251209 04:54:24.996583 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4666 in image_id=30 and point2D_idx=2697 in image_id=40\nW20251209 04:54:24.996598 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3825 in image_id=30 and point2D_idx=1920 in image_id=40\nW20251209 04:54:24.996606 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3826 in image_id=30 and point2D_idx=1921 in image_id=40\nW20251209 04:54:24.996618 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5910 in image_id=30 and point2D_idx=3981 in image_id=40\nW20251209 04:54:24.996666 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5972 in image_id=30 and point2D_idx=7520 in image_id=45\nW20251209 04:54:24.996692 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5990 in image_id=30 and point2D_idx=7521 in image_id=45\nW20251209 04:54:24.996711 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5846 in image_id=30 and point2D_idx=7313 in image_id=45\nW20251209 04:54:24.996758 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3428 in image_id=30 and point2D_idx=4584 in image_id=45\nW20251209 04:54:24.996796 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4956 in image_id=30 and point2D_idx=6268 in image_id=45\nW20251209 04:54:24.996803 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5102 in image_id=30 and point2D_idx=6414 in image_id=45\nW20251209 04:54:24.996809 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5270 in image_id=30 and point2D_idx=6555 in image_id=45\nW20251209 04:54:24.996838 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2549 in image_id=30 and point2D_idx=3061 in image_id=45\nW20251209 04:54:24.996845 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2644 in image_id=30 and point2D_idx=3237 in image_id=45\nW20251209 04:54:24.996881 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1410 in image_id=30 and point2D_idx=1397 in image_id=45\nW20251209 04:54:24.996897 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2046 in image_id=30 and point2D_idx=2198 in image_id=45\nW20251209 04:54:24.996920 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2048 in image_id=30 and point2D_idx=2200 in image_id=45\nW20251209 04:54:24.996935 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2136 in image_id=30 and point2D_idx=2338 in image_id=45\nW20251209 04:54:24.996962 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=985 in image_id=30 and point2D_idx=906 in image_id=45\nW20251209 04:54:24.996971 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2314 in image_id=30 and point2D_idx=2608 in image_id=45\nW20251209 04:54:24.996992 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2411 in image_id=30 and point2D_idx=2751 in image_id=45\nW20251209 04:54:24.997011 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3180 in image_id=30 and point2D_idx=4113 in image_id=45\nW20251209 04:54:24.997083 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2637 in image_id=30 and point2D_idx=3073 in image_id=45\nW20251209 04:54:24.997126 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1093 in image_id=30 and point2D_idx=1016 in image_id=45\nW20251209 04:54:24.997163 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3461 in image_id=30 and point2D_idx=4280 in image_id=45\nW20251209 04:54:24.997197 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6266 in image_id=30 and point2D_idx=4718 in image_id=49\nW20251209 04:54:24.997257 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2244 in image_id=30 and point2D_idx=1776 in image_id=50\nW20251209 04:54:24.997312 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2774 in image_id=30 and point2D_idx=1644 in image_id=50\nW20251209 04:54:24.997346 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2545 in image_id=30 and point2D_idx=1582 in image_id=50\nW20251209 04:54:24.997387 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3428 in image_id=31 and point2D_idx=1476 in image_id=32\nW20251209 04:54:24.997427 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2796 in image_id=31 and point2D_idx=2081 in image_id=32\nW20251209 04:54:24.997485 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1996 in image_id=31 and point2D_idx=3264 in image_id=32\nW20251209 04:54:24.997525 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2201 in image_id=31 and point2D_idx=4114 in image_id=32\nW20251209 04:54:24.997563 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2808 in image_id=31 and point2D_idx=4739 in image_id=32\nW20251209 04:54:24.997676 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2186 in image_id=31 and point2D_idx=3365 in image_id=40\nW20251209 04:54:24.997705 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2424 in image_id=31 and point2D_idx=3497 in image_id=40\nW20251209 04:54:24.997717 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2091 in image_id=31 and point2D_idx=3104 in image_id=40\nW20251209 04:54:24.997735 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=874 in image_id=31 and point2D_idx=1458 in image_id=40\nW20251209 04:54:24.997790 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=330 in image_id=31 and point2D_idx=125 in image_id=41\nW20251209 04:54:24.997797 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=857 in image_id=31 and point2D_idx=641 in image_id=41\nW20251209 04:54:24.997807 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2559 in image_id=31 and point2D_idx=3019 in image_id=41\nW20251209 04:54:24.997879 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=251 in image_id=31 and point2D_idx=126 in image_id=41\nW20251209 04:54:24.997890 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1899 in image_id=31 and point2D_idx=2805 in image_id=41\nW20251209 04:54:24.997925 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=460 in image_id=31 and point2D_idx=356 in image_id=41\nW20251209 04:54:24.997954 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3223 in image_id=31 and point2D_idx=3986 in image_id=41\nW20251209 04:54:24.997966 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1057 in image_id=31 and point2D_idx=1726 in image_id=41\nW20251209 04:54:24.997979 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3055 in image_id=31 and point2D_idx=3909 in image_id=41\nW20251209 04:54:24.997995 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1566 in image_id=31 and point2D_idx=2703 in image_id=41\nW20251209 04:54:24.998003 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1928 in image_id=31 and point2D_idx=3146 in image_id=41\nW20251209 04:54:24.998082 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5041 in image_id=32 and point2D_idx=9259 in image_id=33\nW20251209 04:54:24.998100 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5516 in image_id=32 and point2D_idx=9879 in image_id=33\nW20251209 04:54:24.998124 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5163 in image_id=32 and point2D_idx=2370 in image_id=38\nW20251209 04:54:24.998136 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5494 in image_id=32 and point2D_idx=4981 in image_id=39\nW20251209 04:54:24.998193 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=885 in image_id=32 and point2D_idx=2661 in image_id=41\nW20251209 04:54:24.998209 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1044 in image_id=32 and point2D_idx=2774 in image_id=41\nW20251209 04:54:24.998223 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=943 in image_id=32 and point2D_idx=2395 in image_id=41\nW20251209 04:54:24.998238 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=514 in image_id=32 and point2D_idx=1527 in image_id=41\nW20251209 04:54:24.998251 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=923 in image_id=32 and point2D_idx=2111 in image_id=41\nW20251209 04:54:24.998273 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=182 in image_id=32 and point2D_idx=987 in image_id=41\nW20251209 04:54:24.998289 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=587 in image_id=32 and point2D_idx=1251 in image_id=41\nW20251209 04:54:24.998312 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1338 in image_id=32 and point2D_idx=2118 in image_id=41\nW20251209 04:54:24.998326 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1308 in image_id=32 and point2D_idx=1974 in image_id=41\nW20251209 04:54:24.998355 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=590 in image_id=32 and point2D_idx=891 in image_id=41\nW20251209 04:54:24.998366 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1431 in image_id=32 and point2D_idx=1831 in image_id=41\nW20251209 04:54:24.998387 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2092 in image_id=32 and point2D_idx=2124 in image_id=41\nW20251209 04:54:24.998397 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=428 in image_id=32 and point2D_idx=707 in image_id=41\nW20251209 04:54:24.998404 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3117 in image_id=32 and point2D_idx=2911 in image_id=41\nW20251209 04:54:24.998412 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=94 in image_id=32 and point2D_idx=539 in image_id=41\nW20251209 04:54:24.998431 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1703 in image_id=32 and point2D_idx=1688 in image_id=41\nW20251209 04:54:24.998451 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2349 in image_id=32 and point2D_idx=1983 in image_id=41\nW20251209 04:54:24.998459 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=945 in image_id=32 and point2D_idx=896 in image_id=41\nW20251209 04:54:24.998489 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2939 in image_id=32 and point2D_idx=1323 in image_id=42\nW20251209 04:54:24.998496 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3705 in image_id=32 and point2D_idx=2495 in image_id=42\nW20251209 04:54:24.998528 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3056 in image_id=32 and point2D_idx=1726 in image_id=42\nW20251209 04:54:24.998551 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3588 in image_id=32 and point2D_idx=2654 in image_id=42\nW20251209 04:54:24.998573 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1760 in image_id=32 and point2D_idx=183 in image_id=42\nW20251209 04:54:24.998599 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3107 in image_id=32 and point2D_idx=2263 in image_id=42\nW20251209 04:54:24.998623 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3722 in image_id=32 and point2D_idx=4243 in image_id=42\nW20251209 04:54:24.998673 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2071 in image_id=32 and point2D_idx=586 in image_id=42\nW20251209 04:54:24.998736 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3221 in image_id=32 and point2D_idx=7377 in image_id=42\nW20251209 04:54:24.998749 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1665 in image_id=32 and point2D_idx=985 in image_id=42\nW20251209 04:54:24.998759 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4555 in image_id=32 and point2D_idx=11214 in image_id=42\nW20251209 04:54:24.998811 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2577 in image_id=32 and point2D_idx=6823 in image_id=42\nW20251209 04:54:24.998910 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5528 in image_id=32 and point2D_idx=14477 in image_id=42\nW20251209 04:54:24.998924 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4901 in image_id=32 and point2D_idx=14481 in image_id=42\nW20251209 04:54:24.998938 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3425 in image_id=32 and point2D_idx=14339 in image_id=42\nW20251209 04:54:24.998964 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2582 in image_id=33 and point2D_idx=216 in image_id=34\nW20251209 04:54:24.998974 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2282 in image_id=33 and point2D_idx=76 in image_id=34\nW20251209 04:54:24.998985 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2586 in image_id=33 and point2D_idx=324 in image_id=34\nW20251209 04:54:24.999003 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3073 in image_id=33 and point2D_idx=1142 in image_id=34\nW20251209 04:54:24.999029 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9235 in image_id=33 and point2D_idx=13905 in image_id=34\nW20251209 04:54:24.999096 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5569 in image_id=33 and point2D_idx=3291 in image_id=37\nW20251209 04:54:24.999114 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3670 in image_id=33 and point2D_idx=2639 in image_id=37\nW20251209 04:54:24.999130 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2012 in image_id=33 and point2D_idx=2338 in image_id=37\nW20251209 04:54:24.999176 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3673 in image_id=33 and point2D_idx=2305 in image_id=37\nW20251209 04:54:24.999207 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2669 in image_id=33 and point2D_idx=2068 in image_id=37\nW20251209 04:54:24.999232 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4815 in image_id=33 and point2D_idx=2419 in image_id=37\nW20251209 04:54:24.999253 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4816 in image_id=33 and point2D_idx=2421 in image_id=37\nW20251209 04:54:24.999292 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2926 in image_id=33 and point2D_idx=1969 in image_id=37\nW20251209 04:54:24.999314 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1488 in image_id=33 and point2D_idx=1547 in image_id=37\nW20251209 04:54:24.999322 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4283 in image_id=33 and point2D_idx=1972 in image_id=37\nW20251209 04:54:24.999336 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2626 in image_id=33 and point2D_idx=1651 in image_id=37\nW20251209 04:54:24.999349 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2627 in image_id=33 and point2D_idx=1653 in image_id=37\nW20251209 04:54:24.999394 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5796 in image_id=33 and point2D_idx=1979 in image_id=37\nW20251209 04:54:24.999455 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2568 in image_id=33 and point2D_idx=1794 in image_id=38\nW20251209 04:54:24.999479 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8862 in image_id=33 and point2D_idx=6118 in image_id=38\nW20251209 04:54:24.999491 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9198 in image_id=33 and point2D_idx=6350 in image_id=38\nW20251209 04:54:24.999516 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8120 in image_id=33 and point2D_idx=5672 in image_id=38\nW20251209 04:54:24.999531 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5028 in image_id=33 and point2D_idx=3559 in image_id=38\nW20251209 04:54:24.999738 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3584 in image_id=33 and point2D_idx=2620 in image_id=38\nW20251209 04:54:24.999819 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=645 in image_id=33 and point2D_idx=309 in image_id=38\nW20251209 04:54:24.999826 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=826 in image_id=33 and point2D_idx=386 in image_id=38\nW20251209 04:54:24.999869 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6586 in image_id=33 and point2D_idx=4895 in image_id=38\nW20251209 04:54:24.999915 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=841 in image_id=33 and point2D_idx=538 in image_id=38\nW20251209 04:54:24.999928 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=295 in image_id=33 and point2D_idx=55 in image_id=38\nW20251209 04:54:24.999959 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=668 in image_id=33 and point2D_idx=408 in image_id=38\nW20251209 04:54:24.999992 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10336 in image_id=33 and point2D_idx=4329 in image_id=41\nW20251209 04:54:25.000017 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1511 in image_id=33 and point2D_idx=4100 in image_id=42\nW20251209 04:54:25.000028 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9048 in image_id=33 and point2D_idx=14633 in image_id=42\nW20251209 04:54:25.000136 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9846 in image_id=33 and point2D_idx=14801 in image_id=42\nW20251209 04:54:25.000200 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9781 in image_id=33 and point2D_idx=14787 in image_id=42\nW20251209 04:54:25.000243 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=414 in image_id=33 and point2D_idx=2234 in image_id=42\nW20251209 04:54:25.000314 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9053 in image_id=33 and point2D_idx=14565 in image_id=42\nW20251209 04:54:25.000385 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=234 in image_id=33 and point2D_idx=1577 in image_id=42\nW20251209 04:54:25.000418 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2280 in image_id=33 and point2D_idx=4642 in image_id=42\nW20251209 04:54:25.000452 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7212 in image_id=33 and point2D_idx=11973 in image_id=42\nW20251209 04:54:25.000509 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1067 in image_id=33 and point2D_idx=2651 in image_id=42\nW20251209 04:54:25.000533 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9 in image_id=33 and point2D_idx=763 in image_id=42\nW20251209 04:54:25.000587 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7944 in image_id=33 and point2D_idx=12679 in image_id=42\nW20251209 04:54:25.000645 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8748 in image_id=33 and point2D_idx=13302 in image_id=42\nW20251209 04:54:25.000678 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9420 in image_id=33 and point2D_idx=14126 in image_id=42\nW20251209 04:54:25.000771 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7768 in image_id=33 and point2D_idx=11613 in image_id=42\nW20251209 04:54:25.000793 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5533 in image_id=33 and point2D_idx=7348 in image_id=42\nW20251209 04:54:25.000818 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6792 in image_id=33 and point2D_idx=9759 in image_id=42\nW20251209 04:54:25.000882 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4559 in image_id=33 and point2D_idx=5721 in image_id=42\nW20251209 04:54:25.000916 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=293 in image_id=33 and point2D_idx=1114 in image_id=42\nW20251209 04:54:25.001056 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1761 in image_id=33 and point2D_idx=2285 in image_id=42\nW20251209 04:54:25.001151 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6628 in image_id=33 and point2D_idx=7974 in image_id=42\nW20251209 04:54:25.001209 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8173 in image_id=33 and point2D_idx=10791 in image_id=42\nW20251209 04:54:25.001340 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5087 in image_id=33 and point2D_idx=4705 in image_id=42\nW20251209 04:54:25.001482 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8512 in image_id=33 and point2D_idx=7720 in image_id=48\nW20251209 04:54:25.001504 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8693 in image_id=33 and point2D_idx=7784 in image_id=48\nW20251209 04:54:25.001545 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8873 in image_id=33 and point2D_idx=7850 in image_id=48\nW20251209 04:54:25.001633 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7751 in image_id=33 and point2D_idx=7288 in image_id=48\nW20251209 04:54:25.001710 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5737 in image_id=33 and point2D_idx=5848 in image_id=48\nW20251209 04:54:25.001795 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=694 in image_id=33 and point2D_idx=730 in image_id=48\nW20251209 04:54:25.001886 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4817 in image_id=33 and point2D_idx=4109 in image_id=48\nW20251209 04:54:25.002019 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3235 in image_id=33 and point2D_idx=745 in image_id=51\nW20251209 04:54:25.002085 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8332 in image_id=33 and point2D_idx=10404 in image_id=51\nW20251209 04:54:25.002182 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12837 in image_id=34 and point2D_idx=11810 in image_id=35\nW20251209 04:54:25.002287 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10086 in image_id=34 and point2D_idx=8933 in image_id=35\nW20251209 04:54:25.002331 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7797 in image_id=34 and point2D_idx=6855 in image_id=35\nW20251209 04:54:25.002411 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10366 in image_id=34 and point2D_idx=9326 in image_id=35\nW20251209 04:54:25.002459 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9573 in image_id=34 and point2D_idx=8726 in image_id=35\nW20251209 04:54:25.002505 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1677 in image_id=34 and point2D_idx=634 in image_id=35\nW20251209 04:54:25.002586 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5943 in image_id=34 and point2D_idx=5154 in image_id=35\nW20251209 04:54:25.002662 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1688 in image_id=34 and point2D_idx=595 in image_id=35\nW20251209 04:54:25.002682 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6250 in image_id=34 and point2D_idx=5398 in image_id=35\nW20251209 04:54:25.002704 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7183 in image_id=34 and point2D_idx=6386 in image_id=35\nW20251209 04:54:25.002719 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8118 in image_id=34 and point2D_idx=7361 in image_id=35\nW20251209 04:54:25.002732 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10647 in image_id=34 and point2D_idx=9883 in image_id=35\nW20251209 04:54:25.002782 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5949 in image_id=34 and point2D_idx=5158 in image_id=35\nW20251209 04:54:25.002799 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8425 in image_id=34 and point2D_idx=7842 in image_id=35\nW20251209 04:54:25.002965 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12002 in image_id=34 and point2D_idx=11161 in image_id=35\nW20251209 04:54:25.003009 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3554 in image_id=34 and point2D_idx=2499 in image_id=35\nW20251209 04:54:25.003159 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3001 in image_id=34 and point2D_idx=1897 in image_id=35\nW20251209 04:54:25.003186 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3558 in image_id=34 and point2D_idx=2574 in image_id=35\nW20251209 04:54:25.003208 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4149 in image_id=34 and point2D_idx=3261 in image_id=35\nW20251209 04:54:25.003238 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12457 in image_id=34 and point2D_idx=11849 in image_id=35\nW20251209 04:54:25.003300 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7157 in image_id=34 and point2D_idx=3772 in image_id=36\nW20251209 04:54:25.003319 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7787 in image_id=34 and point2D_idx=4279 in image_id=36\nW20251209 04:54:25.003339 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2722 in image_id=34 and point2D_idx=3913 in image_id=37\nW20251209 04:54:25.003405 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2512 in image_id=34 and point2D_idx=3092 in image_id=37\nW20251209 04:54:25.003434 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=209 in image_id=34 and point2D_idx=2515 in image_id=37\nW20251209 04:54:25.003465 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6584 in image_id=34 and point2D_idx=3463 in image_id=37\nW20251209 04:54:25.003516 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5693 in image_id=34 and point2D_idx=2910 in image_id=37\nW20251209 04:54:25.003547 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6320 in image_id=34 and point2D_idx=2941 in image_id=37\nW20251209 04:54:25.003585 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2460 in image_id=34 and point2D_idx=2616 in image_id=38\nW20251209 04:54:25.003623 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2475 in image_id=34 and point2D_idx=2387 in image_id=38\nW20251209 04:54:25.003645 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3858 in image_id=34 and point2D_idx=3051 in image_id=38\nW20251209 04:54:25.003666 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4462 in image_id=34 and point2D_idx=3249 in image_id=38\nW20251209 04:54:25.003698 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5069 in image_id=34 and point2D_idx=3420 in image_id=38\nW20251209 04:54:25.003728 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8139 in image_id=34 and point2D_idx=4507 in image_id=38\nW20251209 04:54:25.003742 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=59 in image_id=34 and point2D_idx=689 in image_id=38\nW20251209 04:54:25.003796 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=314 in image_id=34 and point2D_idx=968 in image_id=38\nW20251209 04:54:25.003849 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9 in image_id=34 and point2D_idx=392 in image_id=38\nW20251209 04:54:25.003888 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=330 in image_id=34 and point2D_idx=807 in image_id=38\nW20251209 04:54:25.003952 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10346 in image_id=34 and point2D_idx=4130 in image_id=40\nW20251209 04:54:25.004007 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1097 in image_id=34 and point2D_idx=5674 in image_id=42\nW20251209 04:54:25.004096 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7190 in image_id=34 and point2D_idx=10243 in image_id=42\nW20251209 04:54:25.004207 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8491 in image_id=34 and point2D_idx=8578 in image_id=42\nW20251209 04:54:25.004248 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7908 in image_id=34 and point2D_idx=7350 in image_id=42\nW20251209 04:54:25.004309 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10417 in image_id=34 and point2D_idx=8588 in image_id=42\nW20251209 04:54:25.004361 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7155 in image_id=34 and point2D_idx=4608 in image_id=43\nW20251209 04:54:25.004401 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7158 in image_id=34 and point2D_idx=4611 in image_id=43\nW20251209 04:54:25.004434 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7875 in image_id=34 and point2D_idx=5048 in image_id=43\nW20251209 04:54:25.004492 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10364 in image_id=34 and point2D_idx=6538 in image_id=43\nW20251209 04:54:25.004512 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10092 in image_id=34 and point2D_idx=6355 in image_id=43\nW20251209 04:54:25.004532 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10636 in image_id=34 and point2D_idx=6738 in image_id=43\nW20251209 04:54:25.004579 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12219 in image_id=34 and point2D_idx=7778 in image_id=43\nW20251209 04:54:25.004699 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=295 in image_id=34 and point2D_idx=347 in image_id=43\nW20251209 04:54:25.004738 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=296 in image_id=34 and point2D_idx=256 in image_id=43\nW20251209 04:54:25.004988 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5016 in image_id=34 and point2D_idx=3452 in image_id=43\nW20251209 04:54:25.005027 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7853 in image_id=34 and point2D_idx=5093 in image_id=43\nW20251209 04:54:25.005073 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=678 in image_id=34 and point2D_idx=623 in image_id=43\nW20251209 04:54:25.005160 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7907 in image_id=34 and point2D_idx=5099 in image_id=43\nW20251209 04:54:25.005246 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=74 in image_id=34 and point2D_idx=165 in image_id=43\nW20251209 04:54:25.005376 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13378 in image_id=34 and point2D_idx=8270 in image_id=43\nW20251209 04:54:25.005399 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11189 in image_id=34 and point2D_idx=6973 in image_id=43\nW20251209 04:54:25.005476 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8403 in image_id=34 and point2D_idx=3849 in image_id=44\nW20251209 04:54:25.005534 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10359 in image_id=34 and point2D_idx=4874 in image_id=44\nW20251209 04:54:25.005617 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2981 in image_id=34 and point2D_idx=1228 in image_id=44\nW20251209 04:54:25.005639 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5939 in image_id=34 and point2D_idx=2920 in image_id=44\nW20251209 04:54:25.005739 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5359 in image_id=34 and point2D_idx=2712 in image_id=44\nW20251209 04:54:25.005770 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9063 in image_id=34 and point2D_idx=4528 in image_id=44\nW20251209 04:54:25.005819 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6260 in image_id=34 and point2D_idx=3167 in image_id=44\nW20251209 04:54:25.005849 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4452 in image_id=34 and point2D_idx=2205 in image_id=44\nW20251209 04:54:25.005868 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4743 in image_id=34 and point2D_idx=2347 in image_id=44\nW20251209 04:54:25.005955 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5965 in image_id=34 and point2D_idx=3200 in image_id=44\nW20251209 04:54:25.005994 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=632 in image_id=34 and point2D_idx=172 in image_id=44\nW20251209 04:54:25.006025 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9308 in image_id=34 and point2D_idx=4775 in image_id=44\nW20251209 04:54:25.006106 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=638 in image_id=34 and point2D_idx=178 in image_id=44\nW20251209 04:54:25.006145 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=852 in image_id=34 and point2D_idx=240 in image_id=44\nW20251209 04:54:25.006190 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1722 in image_id=34 and point2D_idx=677 in image_id=44\nW20251209 04:54:25.006228 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6894 in image_id=34 and point2D_idx=3760 in image_id=44\nW20251209 04:54:25.006278 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2951 in image_id=34 and point2D_idx=4929 in image_id=48\nW20251209 04:54:25.006319 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3292 in image_id=34 and point2D_idx=4512 in image_id=48\nW20251209 04:54:25.006373 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5972 in image_id=34 and point2D_idx=5593 in image_id=48\nW20251209 04:54:25.006420 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=471 in image_id=34 and point2D_idx=1832 in image_id=48\nW20251209 04:54:25.006447 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1169 in image_id=34 and point2D_idx=2144 in image_id=48\nW20251209 04:54:25.006463 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7747 in image_id=34 and point2D_idx=5851 in image_id=48\nW20251209 04:54:25.006491 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=326 in image_id=34 and point2D_idx=1572 in image_id=48\nW20251209 04:54:25.006592 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4728 in image_id=34 and point2D_idx=2388 in image_id=49\nW20251209 04:54:25.006674 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4732 in image_id=34 and point2D_idx=2432 in image_id=49\nW20251209 04:54:25.006717 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12854 in image_id=34 and point2D_idx=6231 in image_id=49\nW20251209 04:54:25.006783 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13034 in image_id=34 and point2D_idx=6290 in image_id=49\nW20251209 04:54:25.006907 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8432 in image_id=34 and point2D_idx=4311 in image_id=49\nW20251209 04:54:25.006924 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8718 in image_id=34 and point2D_idx=4485 in image_id=49\nW20251209 04:54:25.006966 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3289 in image_id=34 and point2D_idx=1652 in image_id=49\nW20251209 04:54:25.007020 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1710 in image_id=34 and point2D_idx=722 in image_id=49\nW20251209 04:54:25.007180 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13685 in image_id=34 and point2D_idx=10779 in image_id=51\nW20251209 04:54:25.007213 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13599 in image_id=34 and point2D_idx=10782 in image_id=51\nW20251209 04:54:25.007244 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13027 in image_id=34 and point2D_idx=10369 in image_id=51\nW20251209 04:54:25.007272 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13330 in image_id=34 and point2D_idx=10578 in image_id=51\nW20251209 04:54:25.007336 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13572 in image_id=34 and point2D_idx=10796 in image_id=51\nW20251209 04:54:25.007355 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=616 in image_id=34 and point2D_idx=403 in image_id=51\nW20251209 04:54:25.007410 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5369 in image_id=34 and point2D_idx=3407 in image_id=51\nW20251209 04:54:25.007463 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=890 in image_id=34 and point2D_idx=407 in image_id=51\nW20251209 04:54:25.007531 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11150 in image_id=34 and point2D_idx=8801 in image_id=51\nW20251209 04:54:25.007556 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5077 in image_id=34 and point2D_idx=3108 in image_id=51\nW20251209 04:54:25.007619 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4205 in image_id=34 and point2D_idx=2503 in image_id=51\nW20251209 04:54:25.007639 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10711 in image_id=34 and point2D_idx=7889 in image_id=51\nW20251209 04:54:25.007650 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11803 in image_id=34 and point2D_idx=9163 in image_id=51\nW20251209 04:54:25.007726 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2504 in image_id=35 and point2D_idx=620 in image_id=36\nW20251209 04:54:25.007849 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3203 in image_id=35 and point2D_idx=1048 in image_id=36\nW20251209 04:54:25.007922 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5116 in image_id=35 and point2D_idx=2855 in image_id=36\nW20251209 04:54:25.007966 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6604 in image_id=35 and point2D_idx=4137 in image_id=36\nW20251209 04:54:25.008011 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2284 in image_id=35 and point2D_idx=437 in image_id=36\nW20251209 04:54:25.008116 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5857 in image_id=35 and point2D_idx=3640 in image_id=36\nW20251209 04:54:25.008405 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=623 in image_id=35 and point2D_idx=616 in image_id=39\nW20251209 04:54:25.008434 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1398 in image_id=35 and point2D_idx=1431 in image_id=39\nW20251209 04:54:25.008443 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1674 in image_id=35 and point2D_idx=1658 in image_id=39\nW20251209 04:54:25.008694 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3013 in image_id=35 and point2D_idx=2747 in image_id=39\nW20251209 04:54:25.008738 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3053 in image_id=35 and point2D_idx=2748 in image_id=39\nW20251209 04:54:25.008745 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3276 in image_id=35 and point2D_idx=2917 in image_id=39\nW20251209 04:54:25.008768 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3054 in image_id=35 and point2D_idx=2749 in image_id=39\nW20251209 04:54:25.008787 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3277 in image_id=35 and point2D_idx=2918 in image_id=39\nW20251209 04:54:25.008800 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3949 in image_id=35 and point2D_idx=3439 in image_id=39\nW20251209 04:54:25.008842 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3745 in image_id=35 and point2D_idx=3272 in image_id=39\nW20251209 04:54:25.008858 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4422 in image_id=35 and point2D_idx=3778 in image_id=39\nW20251209 04:54:25.008877 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10549 in image_id=35 and point2D_idx=7679 in image_id=39\nW20251209 04:54:25.008959 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10551 in image_id=35 and point2D_idx=7681 in image_id=39\nW20251209 04:54:25.009003 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10711 in image_id=35 and point2D_idx=7709 in image_id=39\nW20251209 04:54:25.009092 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10735 in image_id=35 and point2D_idx=7711 in image_id=39\nW20251209 04:54:25.009170 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10264 in image_id=35 and point2D_idx=7592 in image_id=39\nW20251209 04:54:25.009187 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10400 in image_id=35 and point2D_idx=7650 in image_id=39\nW20251209 04:54:25.009392 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7789 in image_id=35 and point2D_idx=3381 in image_id=40\nW20251209 04:54:25.009424 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1386 in image_id=35 and point2D_idx=8519 in image_id=42\nW20251209 04:54:25.009455 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2524 in image_id=35 and point2D_idx=9116 in image_id=42\nW20251209 04:54:25.009504 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3227 in image_id=35 and point2D_idx=8540 in image_id=42\nW20251209 04:54:25.009533 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4697 in image_id=35 and point2D_idx=9726 in image_id=42\nW20251209 04:54:25.009572 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4413 in image_id=35 and point2D_idx=8550 in image_id=42\nW20251209 04:54:25.009620 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=480 in image_id=35 and point2D_idx=4650 in image_id=42\nW20251209 04:54:25.009675 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=213 in image_id=35 and point2D_idx=3641 in image_id=42\nW20251209 04:54:25.009707 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6151 in image_id=35 and point2D_idx=7347 in image_id=42\nW20251209 04:54:25.009894 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4148 in image_id=35 and point2D_idx=2449 in image_id=44\nW20251209 04:54:25.009956 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4390 in image_id=35 and point2D_idx=2595 in image_id=44\nW20251209 04:54:25.009999 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4629 in image_id=35 and point2D_idx=2728 in image_id=44\nW20251209 04:54:25.010173 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5137 in image_id=35 and point2D_idx=3013 in image_id=44\nW20251209 04:54:25.010196 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5377 in image_id=35 and point2D_idx=3153 in image_id=44\nW20251209 04:54:25.010221 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5622 in image_id=35 and point2D_idx=3295 in image_id=44\nW20251209 04:54:25.010349 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5873 in image_id=35 and point2D_idx=3481 in image_id=44\nW20251209 04:54:25.010367 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6373 in image_id=35 and point2D_idx=3734 in image_id=44\nW20251209 04:54:25.010401 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=319 in image_id=35 and point2D_idx=253 in image_id=44\nW20251209 04:54:25.010500 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=635 in image_id=35 and point2D_idx=415 in image_id=44\nW20251209 04:54:25.010560 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8071 in image_id=35 and point2D_idx=4647 in image_id=44\nW20251209 04:54:25.010610 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1871 in image_id=35 and point2D_idx=1242 in image_id=44\nW20251209 04:54:25.010643 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1250 in image_id=35 and point2D_idx=802 in image_id=44\nW20251209 04:54:25.010682 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8783 in image_id=35 and point2D_idx=5010 in image_id=44\nW20251209 04:54:25.010724 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8959 in image_id=35 and point2D_idx=5150 in image_id=44\nW20251209 04:54:25.010763 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9346 in image_id=35 and point2D_idx=5355 in image_id=44\nW20251209 04:54:25.010775 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9569 in image_id=35 and point2D_idx=5457 in image_id=44\nW20251209 04:54:25.010899 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4914 in image_id=35 and point2D_idx=3041 in image_id=44\nW20251209 04:54:25.010936 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4670 in image_id=35 and point2D_idx=2902 in image_id=44\nW20251209 04:54:25.011135 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1409 in image_id=35 and point2D_idx=4501 in image_id=48\nW20251209 04:54:25.011184 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=86 in image_id=35 and point2D_idx=2758 in image_id=48\nW20251209 04:54:25.011224 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10673 in image_id=35 and point2D_idx=9715 in image_id=51\nW20251209 04:54:25.011245 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10354 in image_id=35 and point2D_idx=9547 in image_id=51\nW20251209 04:54:25.011266 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10023 in image_id=35 and point2D_idx=9446 in image_id=51\nW20251209 04:54:25.011290 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10025 in image_id=35 and point2D_idx=9448 in image_id=51\nW20251209 04:54:25.011438 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9127 in image_id=35 and point2D_idx=8414 in image_id=51\nW20251209 04:54:25.011468 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5926 in image_id=35 and point2D_idx=4822 in image_id=51\nW20251209 04:54:25.011492 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2765 in image_id=35 and point2D_idx=2477 in image_id=51\nW20251209 04:54:25.011521 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1919 in image_id=35 and point2D_idx=1773 in image_id=51\nW20251209 04:54:25.011541 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1001 in image_id=35 and point2D_idx=1031 in image_id=51\nW20251209 04:54:25.011576 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9874 in image_id=35 and point2D_idx=8781 in image_id=51\nW20251209 04:54:25.011594 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4173 in image_id=35 and point2D_idx=3392 in image_id=51\nW20251209 04:54:25.011607 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4651 in image_id=35 and point2D_idx=3712 in image_id=51\nW20251209 04:54:25.011625 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1003 in image_id=35 and point2D_idx=1035 in image_id=51\nW20251209 04:54:25.011645 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6125 in image_id=35 and point2D_idx=4824 in image_id=51\nW20251209 04:54:25.011658 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7159 in image_id=35 and point2D_idx=5676 in image_id=51\nW20251209 04:54:25.011708 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=10384 in image_id=35 and point2D_idx=9469 in image_id=51\nW20251209 04:54:25.011759 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1366 in image_id=35 and point2D_idx=1403 in image_id=51\nW20251209 04:54:25.011814 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7408 in image_id=35 and point2D_idx=5682 in image_id=51\nW20251209 04:54:25.011834 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6881 in image_id=35 and point2D_idx=5266 in image_id=51\nW20251209 04:54:25.011847 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8786 in image_id=35 and point2D_idx=6988 in image_id=51\nW20251209 04:54:25.011860 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9189 in image_id=35 and point2D_idx=7419 in image_id=51\nW20251209 04:54:25.011926 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8964 in image_id=35 and point2D_idx=6989 in image_id=51\nW20251209 04:54:25.011962 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9892 in image_id=35 and point2D_idx=8427 in image_id=51\nW20251209 04:54:25.012001 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7854 in image_id=35 and point2D_idx=5689 in image_id=51\nW20251209 04:54:25.012025 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8317 in image_id=35 and point2D_idx=6115 in image_id=51\nW20251209 04:54:25.012115 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5449 in image_id=36 and point2D_idx=7847 in image_id=45\nW20251209 04:54:25.012175 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3602 in image_id=36 and point2D_idx=5269 in image_id=45\nW20251209 04:54:25.012211 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5792 in image_id=36 and point2D_idx=8196 in image_id=45\nW20251209 04:54:25.012397 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4917 in image_id=36 and point2D_idx=7197 in image_id=45\nW20251209 04:54:25.012413 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5013 in image_id=36 and point2D_idx=7308 in image_id=45\nW20251209 04:54:25.012447 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5570 in image_id=36 and point2D_idx=8011 in image_id=45\nW20251209 04:54:25.012478 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6035 in image_id=36 and point2D_idx=8470 in image_id=45\nW20251209 04:54:25.012512 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6036 in image_id=36 and point2D_idx=8446 in image_id=45\nW20251209 04:54:25.012951 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1636 in image_id=36 and point2D_idx=4035 in image_id=51\nW20251209 04:54:25.012970 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1989 in image_id=36 and point2D_idx=4393 in image_id=51\nW20251209 04:54:25.012979 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3853 in image_id=36 and point2D_idx=7377 in image_id=51\nW20251209 04:54:25.012987 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=323 in image_id=36 and point2D_idx=2155 in image_id=51\nW20251209 04:54:25.013001 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5600 in image_id=36 and point2D_idx=9114 in image_id=51\nW20251209 04:54:25.013026 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4845 in image_id=36 and point2D_idx=7826 in image_id=51\nW20251209 04:54:25.013070 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5104 in image_id=36 and point2D_idx=8383 in image_id=51\nW20251209 04:54:25.013119 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4158 in image_id=36 and point2D_idx=6528 in image_id=51\nW20251209 04:54:25.013151 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1046 in image_id=36 and point2D_idx=3062 in image_id=51\nW20251209 04:54:25.013213 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1084 in image_id=36 and point2D_idx=3065 in image_id=51\nW20251209 04:54:25.013316 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4191 in image_id=37 and point2D_idx=5356 in image_id=38\nW20251209 04:54:25.013370 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4521 in image_id=37 and point2D_idx=6844 in image_id=38\nW20251209 04:54:25.013396 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4485 in image_id=37 and point2D_idx=6786 in image_id=38\nW20251209 04:54:25.013428 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3612 in image_id=37 and point2D_idx=5712 in image_id=38\nW20251209 04:54:25.013467 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3986 in image_id=37 and point2D_idx=6617 in image_id=38\nW20251209 04:54:25.013512 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1635 in image_id=37 and point2D_idx=973 in image_id=46\nW20251209 04:54:25.013539 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1278 in image_id=37 and point2D_idx=740 in image_id=46\nW20251209 04:54:25.013557 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2412 in image_id=37 and point2D_idx=1279 in image_id=46\nW20251209 04:54:25.013607 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2401 in image_id=37 and point2D_idx=1282 in image_id=46\nW20251209 04:54:25.013670 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=920 in image_id=37 and point2D_idx=562 in image_id=47\nW20251209 04:54:25.013701 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=404 in image_id=37 and point2D_idx=216 in image_id=47\nW20251209 04:54:25.013724 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=883 in image_id=37 and point2D_idx=552 in image_id=47\nW20251209 04:54:25.013765 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=780 in image_id=37 and point2D_idx=450 in image_id=47\nW20251209 04:54:25.013792 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=763 in image_id=37 and point2D_idx=422 in image_id=47\nW20251209 04:54:25.013818 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3295 in image_id=37 and point2D_idx=1691 in image_id=47\nW20251209 04:54:25.013866 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4197 in image_id=37 and point2D_idx=1772 in image_id=47\nW20251209 04:54:25.013895 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3137 in image_id=37 and point2D_idx=1456 in image_id=47\nW20251209 04:54:25.013959 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2954 in image_id=37 and point2D_idx=1203 in image_id=47\nW20251209 04:54:25.013982 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2083 in image_id=37 and point2D_idx=858 in image_id=47\nW20251209 04:54:25.013994 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3617 in image_id=37 and point2D_idx=1557 in image_id=47\nW20251209 04:54:25.014044 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3947 in image_id=37 and point2D_idx=1471 in image_id=47\nW20251209 04:54:25.014097 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4073 in image_id=37 and point2D_idx=1569 in image_id=47\nW20251209 04:54:25.014111 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4000 in image_id=37 and point2D_idx=1479 in image_id=47\nW20251209 04:54:25.014150 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1076 in image_id=37 and point2D_idx=104 in image_id=48\nW20251209 04:54:25.014187 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=121 in image_id=37 and point2D_idx=3421 in image_id=50\nW20251209 04:54:25.014223 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=178 in image_id=37 and point2D_idx=3425 in image_id=50\nW20251209 04:54:25.014279 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=298 in image_id=37 and point2D_idx=3430 in image_id=50\nW20251209 04:54:25.014313 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1254 in image_id=37 and point2D_idx=3980 in image_id=50\nW20251209 04:54:25.014348 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=676 in image_id=37 and point2D_idx=3533 in image_id=50\nW20251209 04:54:25.014391 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=251 in image_id=37 and point2D_idx=2992 in image_id=50\nW20251209 04:54:25.014418 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=167 in image_id=37 and point2D_idx=2602 in image_id=50\nW20251209 04:54:25.014548 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=252 in image_id=37 and point2D_idx=2143 in image_id=50\nW20251209 04:54:25.014591 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1039 in image_id=37 and point2D_idx=2501 in image_id=50\nW20251209 04:54:25.014617 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1690 in image_id=37 and point2D_idx=2504 in image_id=50\nW20251209 04:54:25.014645 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1677 in image_id=37 and point2D_idx=2531 in image_id=50\nW20251209 04:54:25.014714 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2149 in image_id=38 and point2D_idx=4 in image_id=39\nW20251209 04:54:25.014774 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5569 in image_id=38 and point2D_idx=7519 in image_id=39\nW20251209 04:54:25.014794 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6020 in image_id=38 and point2D_idx=7775 in image_id=39\nW20251209 04:54:25.014880 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5776 in image_id=38 and point2D_idx=14297 in image_id=42\nW20251209 04:54:25.014899 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5940 in image_id=38 and point2D_idx=14456 in image_id=42\nW20251209 04:54:25.014910 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6859 in image_id=38 and point2D_idx=14787 in image_id=42\nW20251209 04:54:25.014929 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3031 in image_id=38 and point2D_idx=7901 in image_id=42\nW20251209 04:54:25.014958 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1597 in image_id=38 and point2D_idx=5151 in image_id=42\nW20251209 04:54:25.014980 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4623 in image_id=38 and point2D_idx=11572 in image_id=42\nW20251209 04:54:25.015065 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=829 in image_id=38 and point2D_idx=3615 in image_id=42\nW20251209 04:54:25.015111 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=595 in image_id=38 and point2D_idx=3128 in image_id=42\nW20251209 04:54:25.015305 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1565 in image_id=38 and point2D_idx=3645 in image_id=42\nW20251209 04:54:25.015355 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5158 in image_id=38 and point2D_idx=10265 in image_id=42\nW20251209 04:54:25.015381 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4391 in image_id=38 and point2D_idx=7958 in image_id=42\nW20251209 04:54:25.015426 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3071 in image_id=38 and point2D_idx=5210 in image_id=42\nW20251209 04:54:25.015497 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6042 in image_id=38 and point2D_idx=12004 in image_id=42\nW20251209 04:54:25.015543 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1208 in image_id=38 and point2D_idx=2281 in image_id=42\nW20251209 04:54:25.015578 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=136 in image_id=38 and point2D_idx=1119 in image_id=42\nW20251209 04:54:25.015627 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3303 in image_id=38 and point2D_idx=4693 in image_id=42\nW20251209 04:54:25.015650 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=205 in image_id=38 and point2D_idx=1122 in image_id=42\nW20251209 04:54:25.015713 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4064 in image_id=38 and point2D_idx=5735 in image_id=42\nW20251209 04:54:25.015778 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3558 in image_id=38 and point2D_idx=5817 in image_id=48\nW20251209 04:54:25.015796 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3708 in image_id=38 and point2D_idx=6044 in image_id=48\nW20251209 04:54:25.015896 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2158 in image_id=38 and point2D_idx=2773 in image_id=48\nW20251209 04:54:25.015951 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5002 in image_id=38 and point2D_idx=6956 in image_id=48\nW20251209 04:54:25.016147 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3638 in image_id=38 and point2D_idx=4110 in image_id=48\nW20251209 04:54:25.016184 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3278 in image_id=38 and point2D_idx=3180 in image_id=48\nW20251209 04:54:25.016199 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4234 in image_id=38 and point2D_idx=5305 in image_id=48\nW20251209 04:54:25.016219 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2186 in image_id=38 and point2D_idx=1855 in image_id=48\nW20251209 04:54:25.016310 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2883 in image_id=38 and point2D_idx=2171 in image_id=48\nW20251209 04:54:25.016335 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1990 in image_id=38 and point2D_idx=1397 in image_id=48\nW20251209 04:54:25.016372 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1895 in image_id=39 and point2D_idx=55 in image_id=40\nW20251209 04:54:25.016408 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5396 in image_id=39 and point2D_idx=1358 in image_id=40\nW20251209 04:54:25.016452 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1897 in image_id=39 and point2D_idx=57 in image_id=40\nW20251209 04:54:25.016503 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3048 in image_id=39 and point2D_idx=301 in image_id=40\nW20251209 04:54:25.016548 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5400 in image_id=39 and point2D_idx=2637 in image_id=40\nW20251209 04:54:25.016560 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6057 in image_id=39 and point2D_idx=3516 in image_id=40\nW20251209 04:54:25.016596 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2350 in image_id=39 and point2D_idx=150 in image_id=40\nW20251209 04:54:25.016616 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3053 in image_id=39 and point2D_idx=328 in image_id=40\nW20251209 04:54:25.016631 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5062 in image_id=39 and point2D_idx=2175 in image_id=40\nW20251209 04:54:25.016667 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6060 in image_id=39 and point2D_idx=3387 in image_id=40\nW20251209 04:54:25.016727 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2354 in image_id=39 and point2D_idx=154 in image_id=40\nW20251209 04:54:25.016745 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3233 in image_id=39 and point2D_idx=380 in image_id=40\nW20251209 04:54:25.016760 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4245 in image_id=39 and point2D_idx=1381 in image_id=40\nW20251209 04:54:25.016774 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4740 in image_id=39 and point2D_idx=2058 in image_id=40\nW20251209 04:54:25.016796 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5232 in image_id=39 and point2D_idx=2745 in image_id=40\nW20251209 04:54:25.016813 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6235 in image_id=39 and point2D_idx=3989 in image_id=40\nW20251209 04:54:25.016862 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3743 in image_id=39 and point2D_idx=794 in image_id=40\nW20251209 04:54:25.016914 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=933 in image_id=39 and point2D_idx=4135 in image_id=42\nW20251209 04:54:25.016932 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=503 in image_id=39 and point2D_idx=3641 in image_id=42\nW20251209 04:54:25.016949 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2579 in image_id=39 and point2D_idx=5194 in image_id=42\nW20251209 04:54:25.016964 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1955 in image_id=39 and point2D_idx=4668 in image_id=42\nW20251209 04:54:25.016981 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=635 in image_id=39 and point2D_idx=3645 in image_id=42\nW20251209 04:54:25.017012 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2334 in image_id=39 and point2D_idx=4153 in image_id=42\nW20251209 04:54:25.017087 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7917 in image_id=39 and point2D_idx=6433 in image_id=44\nW20251209 04:54:25.017153 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3077 in image_id=39 and point2D_idx=5581 in image_id=48\nW20251209 04:54:25.017179 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2783 in image_id=39 and point2D_idx=5269 in image_id=48\nW20251209 04:54:25.017199 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2143 in image_id=39 and point2D_idx=4507 in image_id=48\nW20251209 04:54:25.017227 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2382 in image_id=39 and point2D_idx=4512 in image_id=48\nW20251209 04:54:25.017247 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=889 in image_id=39 and point2D_idx=3148 in image_id=48\nW20251209 04:54:25.017331 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1891 in image_id=39 and point2D_idx=3160 in image_id=48\nW20251209 04:54:25.017371 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1964 in image_id=39 and point2D_idx=2789 in image_id=48\nW20251209 04:54:25.017408 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=726 in image_id=39 and point2D_idx=2147 in image_id=48\nW20251209 04:54:25.017427 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1409 in image_id=39 and point2D_idx=2445 in image_id=48\nW20251209 04:54:25.017474 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7168 in image_id=39 and point2D_idx=8758 in image_id=51\nW20251209 04:54:25.017507 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1174 in image_id=39 and point2D_idx=1382 in image_id=51\nW20251209 04:54:25.017549 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6897 in image_id=39 and point2D_idx=8402 in image_id=51\nW20251209 04:54:25.017576 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5243 in image_id=39 and point2D_idx=5663 in image_id=51\nW20251209 04:54:25.017600 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=743 in image_id=39 and point2D_idx=1017 in image_id=51\nW20251209 04:54:25.017662 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3251 in image_id=39 and point2D_idx=3081 in image_id=51\nW20251209 04:54:25.017696 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3596 in image_id=39 and point2D_idx=3387 in image_id=51\nW20251209 04:54:25.017715 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4759 in image_id=39 and point2D_idx=4817 in image_id=51\nW20251209 04:54:25.017732 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6941 in image_id=39 and point2D_idx=7859 in image_id=51\nW20251209 04:54:25.017745 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7096 in image_id=39 and point2D_idx=8409 in image_id=51\nW20251209 04:54:25.017804 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1673 in image_id=40 and point2D_idx=212 in image_id=41\nW20251209 04:54:25.017874 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1793 in image_id=40 and point2D_idx=470 in image_id=41\nW20251209 04:54:25.017917 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1178 in image_id=40 and point2D_idx=276 in image_id=41\nW20251209 04:54:25.017966 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=995 in image_id=40 and point2D_idx=277 in image_id=41\nW20251209 04:54:25.017985 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1286 in image_id=40 and point2D_idx=444 in image_id=41\nW20251209 04:54:25.018002 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1796 in image_id=40 and point2D_idx=931 in image_id=41\nW20251209 04:54:25.018061 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1083 in image_id=40 and point2D_idx=445 in image_id=41\nW20251209 04:54:25.018101 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2152 in image_id=40 and point2D_idx=1694 in image_id=41\nW20251209 04:54:25.018153 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=720 in image_id=40 and point2D_idx=272 in image_id=41\nW20251209 04:54:25.018174 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=842 in image_id=40 and point2D_idx=394 in image_id=41\nW20251209 04:54:25.018193 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2970 in image_id=40 and point2D_idx=2911 in image_id=41\nW20251209 04:54:25.018242 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1463 in image_id=40 and point2D_idx=1583 in image_id=41\nW20251209 04:54:25.018366 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3577 in image_id=41 and point2D_idx=500 in image_id=42\nW20251209 04:54:25.018401 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3115 in image_id=41 and point2D_idx=1408 in image_id=42\nW20251209 04:54:25.018428 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3232 in image_id=41 and point2D_idx=2306 in image_id=42\nW20251209 04:54:25.018564 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3701 in image_id=41 and point2D_idx=14819 in image_id=42\nW20251209 04:54:25.018595 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14681 in image_id=42 and point2D_idx=6926 in image_id=43\nW20251209 04:54:25.018621 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14732 in image_id=42 and point2D_idx=7578 in image_id=43\nW20251209 04:54:25.018657 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14465 in image_id=42 and point2D_idx=7586 in image_id=43\nW20251209 04:54:25.018690 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13577 in image_id=42 and point2D_idx=6830 in image_id=43\nW20251209 04:54:25.018739 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12345 in image_id=42 and point2D_idx=7481 in image_id=43\nW20251209 04:54:25.018769 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13001 in image_id=42 and point2D_idx=8021 in image_id=43\nW20251209 04:54:25.018796 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3652 in image_id=42 and point2D_idx=1820 in image_id=43\nW20251209 04:54:25.018812 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13869 in image_id=42 and point2D_idx=8490 in image_id=43\nW20251209 04:54:25.018831 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7348 in image_id=42 and point2D_idx=5296 in image_id=43\nW20251209 04:54:25.018858 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6794 in image_id=42 and point2D_idx=5110 in image_id=43\nW20251209 04:54:25.018888 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8580 in image_id=42 and point2D_idx=6404 in image_id=43\nW20251209 04:54:25.018902 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13591 in image_id=42 and point2D_idx=8370 in image_id=43\nW20251209 04:54:25.018937 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13874 in image_id=42 and point2D_idx=8734 in image_id=43\nW20251209 04:54:25.019015 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4101 in image_id=42 and point2D_idx=1546 in image_id=48\nW20251209 04:54:25.019034 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1572 in image_id=42 and point2D_idx=390 in image_id=48\nW20251209 04:54:25.019086 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1094 in image_id=42 and point2D_idx=263 in image_id=48\nW20251209 04:54:25.019106 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=11961 in image_id=42 and point2D_idx=6942 in image_id=48\nW20251209 04:54:25.019205 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7317 in image_id=42 and point2D_idx=4584 in image_id=48\nW20251209 04:54:25.019248 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4116 in image_id=42 and point2D_idx=1816 in image_id=48\nW20251209 04:54:25.019299 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12329 in image_id=42 and point2D_idx=7133 in image_id=48\nW20251209 04:54:25.019338 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6230 in image_id=42 and point2D_idx=3586 in image_id=48\nW20251209 04:54:25.019389 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=13918 in image_id=42 and point2D_idx=7724 in image_id=48\nW20251209 04:54:25.019447 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=770 in image_id=42 and point2D_idx=221 in image_id=48\nW20251209 04:54:25.019507 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12998 in image_id=42 and point2D_idx=7433 in image_id=48\nW20251209 04:54:25.019605 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=9753 in image_id=42 and point2D_idx=6442 in image_id=48\nW20251209 04:54:25.019692 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7892 in image_id=42 and point2D_idx=741 in image_id=49\nW20251209 04:54:25.019760 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12329 in image_id=42 and point2D_idx=3886 in image_id=49\nW20251209 04:54:25.019800 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=12998 in image_id=42 and point2D_idx=5071 in image_id=49\nW20251209 04:54:25.019848 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=14130 in image_id=42 and point2D_idx=6065 in image_id=49\nW20251209 04:54:25.019901 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2842 in image_id=43 and point2D_idx=4520 in image_id=48\nW20251209 04:54:25.019922 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1573 in image_id=43 and point2D_idx=2843 in image_id=48\nW20251209 04:54:25.019946 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1139 in image_id=43 and point2D_idx=2142 in image_id=48\nW20251209 04:54:25.020007 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3450 in image_id=43 and point2D_idx=4099 in image_id=48\nW20251209 04:54:25.020036 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=623 in image_id=43 and point2D_idx=1417 in image_id=48\nW20251209 04:54:25.020072 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=97 in image_id=43 and point2D_idx=877 in image_id=48\nW20251209 04:54:25.020134 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5844 in image_id=43 and point2D_idx=5862 in image_id=48\nW20251209 04:54:25.020186 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=23 in image_id=43 and point2D_idx=503 in image_id=48\nW20251209 04:54:25.020409 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1785 in image_id=43 and point2D_idx=1168 in image_id=49\nW20251209 04:54:25.020492 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=421 in image_id=43 and point2D_idx=242 in image_id=49\nW20251209 04:54:25.020584 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6419 in image_id=43 and point2D_idx=4898 in image_id=49\nW20251209 04:54:25.020637 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2437 in image_id=43 and point2D_idx=1810 in image_id=49\nW20251209 04:54:25.020692 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1142 in image_id=43 and point2D_idx=720 in image_id=49\nW20251209 04:54:25.020754 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4665 in image_id=43 and point2D_idx=3638 in image_id=49\nW20251209 04:54:25.020792 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=38 in image_id=43 and point2D_idx=5 in image_id=49\nW20251209 04:54:25.020899 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5429 in image_id=43 and point2D_idx=6967 in image_id=51\nW20251209 04:54:25.020967 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2874 in image_id=43 and point2D_idx=2791 in image_id=51\nW20251209 04:54:25.020992 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5245 in image_id=43 and point2D_idx=6555 in image_id=51\nW20251209 04:54:25.021014 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6156 in image_id=43 and point2D_idx=8414 in image_id=51\nW20251209 04:54:25.021124 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5636 in image_id=43 and point2D_idx=6989 in image_id=51\nW20251209 04:54:25.021157 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6742 in image_id=43 and point2D_idx=9150 in image_id=51\nW20251209 04:54:25.021178 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=644 in image_id=43 and point2D_idx=286 in image_id=51\nW20251209 04:54:25.021266 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5226 in image_id=43 and point2D_idx=6118 in image_id=51\nW20251209 04:54:25.021363 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6045 in image_id=43 and point2D_idx=7441 in image_id=51\nW20251209 04:54:25.021497 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6788 in image_id=43 and point2D_idx=8811 in image_id=51\nW20251209 04:54:25.021524 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5284 in image_id=43 and point2D_idx=5710 in image_id=51\nW20251209 04:54:25.021568 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=164 in image_id=43 and point2D_idx=33 in image_id=51\nW20251209 04:54:25.021611 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3260 in image_id=43 and point2D_idx=2522 in image_id=51\nW20251209 04:54:25.021641 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=859 in image_id=43 and point2D_idx=161 in image_id=51\nW20251209 04:54:25.021659 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5314 in image_id=43 and point2D_idx=5716 in image_id=51\nW20251209 04:54:25.021747 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2595 in image_id=44 and point2D_idx=4122 in image_id=45\nW20251209 04:54:25.021794 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1771 in image_id=44 and point2D_idx=2237 in image_id=45\nW20251209 04:54:25.021831 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2460 in image_id=44 and point2D_idx=3795 in image_id=45\nW20251209 04:54:25.021873 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5429 in image_id=44 and point2D_idx=9122 in image_id=51\nW20251209 04:54:25.021903 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=781 in image_id=44 and point2D_idx=1387 in image_id=51\nW20251209 04:54:25.021916 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4128 in image_id=44 and point2D_idx=6620 in image_id=51\nW20251209 04:54:25.021930 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5329 in image_id=44 and point2D_idx=9130 in image_id=51\nW20251209 04:54:25.021954 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7508 in image_id=45 and point2D_idx=2383 in image_id=46\nW20251209 04:54:25.021996 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7705 in image_id=45 and point2D_idx=2351 in image_id=46\nW20251209 04:54:25.022082 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1233 in image_id=45 and point2D_idx=33 in image_id=46\nW20251209 04:54:25.022127 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5613 in image_id=45 and point2D_idx=1449 in image_id=46\nW20251209 04:54:25.022167 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8009 in image_id=45 and point2D_idx=2661 in image_id=46\nW20251209 04:54:25.022200 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5282 in image_id=45 and point2D_idx=1552 in image_id=46\nW20251209 04:54:25.022270 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5288 in image_id=45 and point2D_idx=1563 in image_id=46\nW20251209 04:54:25.022294 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7525 in image_id=45 and point2D_idx=2512 in image_id=46\nW20251209 04:54:25.022352 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3406 in image_id=45 and point2D_idx=148 in image_id=47\nW20251209 04:54:25.022379 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7345 in image_id=45 and point2D_idx=4418 in image_id=49\nW20251209 04:54:25.022399 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3026 in image_id=45 and point2D_idx=151 in image_id=50\nW20251209 04:54:25.022433 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2869 in image_id=45 and point2D_idx=104 in image_id=50\nW20251209 04:54:25.022467 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=969 in image_id=45 and point2D_idx=2266 in image_id=51\nW20251209 04:54:25.022491 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7181 in image_id=45 and point2D_idx=7818 in image_id=51\nW20251209 04:54:25.022505 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=822 in image_id=45 and point2D_idx=2155 in image_id=51\nW20251209 04:54:25.022564 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1949 in image_id=46 and point2D_idx=2849 in image_id=50\nW20251209 04:54:25.022575 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2484 in image_id=46 and point2D_idx=4102 in image_id=50\nW20251209 04:54:25.022628 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=456 in image_id=46 and point2D_idx=490 in image_id=50\nW20251209 04:54:25.022683 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1488 in image_id=46 and point2D_idx=2286 in image_id=50\nW20251209 04:54:25.022745 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1387 in image_id=46 and point2D_idx=2153 in image_id=50\nW20251209 04:54:25.022795 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1708 in image_id=46 and point2D_idx=3012 in image_id=50\nW20251209 04:54:25.022821 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1544 in image_id=46 and point2D_idx=2636 in image_id=50\nW20251209 04:54:25.022871 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=512 in image_id=46 and point2D_idx=5228 in image_id=51\nW20251209 04:54:25.022895 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=285 in image_id=46 and point2D_idx=4388 in image_id=51\nW20251209 04:54:25.022937 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=925 in image_id=46 and point2D_idx=6949 in image_id=51\nW20251209 04:54:25.022979 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=578 in image_id=47 and point2D_idx=637 in image_id=48\nW20251209 04:54:25.023026 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=983 in image_id=47 and point2D_idx=6616 in image_id=48\nW20251209 04:54:25.023077 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1194 in image_id=47 and point2D_idx=7599 in image_id=48\nW20251209 04:54:25.023121 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=95 in image_id=47 and point2D_idx=2727 in image_id=50\nW20251209 04:54:25.023152 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=133 in image_id=47 and point2D_idx=2730 in image_id=50\nW20251209 04:54:25.023191 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=923 in image_id=47 and point2D_idx=7377 in image_id=51\nW20251209 04:54:25.023250 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6958 in image_id=48 and point2D_idx=5341 in image_id=49\nW20251209 04:54:25.023277 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7056 in image_id=48 and point2D_idx=5475 in image_id=49\nW20251209 04:54:25.023311 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7081 in image_id=48 and point2D_idx=5929 in image_id=49\nW20251209 04:54:25.023331 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7437 in image_id=48 and point2D_idx=6320 in image_id=49\nW20251209 04:54:25.023346 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=8025 in image_id=48 and point2D_idx=6551 in image_id=49\nW20251209 04:54:25.023380 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=7798 in image_id=48 and point2D_idx=6599 in image_id=49\nW20251209 04:54:25.023438 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1012 in image_id=49 and point2D_idx=773 in image_id=51\nW20251209 04:54:25.023495 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2434 in image_id=49 and point2D_idx=2818 in image_id=51\nW20251209 04:54:25.023514 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2946 in image_id=49 and point2D_idx=3730 in image_id=51\nW20251209 04:54:25.023539 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=580 in image_id=49 and point2D_idx=297 in image_id=51\nW20251209 04:54:25.023598 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3240 in image_id=49 and point2D_idx=4087 in image_id=51\nW20251209 04:54:25.023621 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6184 in image_id=49 and point2D_idx=10813 in image_id=51\nW20251209 04:54:25.023663 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5334 in image_id=49 and point2D_idx=8811 in image_id=51\nW20251209 04:54:25.023699 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2957 in image_id=49 and point2D_idx=3426 in image_id=51\nW20251209 04:54:25.023720 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5335 in image_id=49 and point2D_idx=8813 in image_id=51\nW20251209 04:54:25.023730 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5452 in image_id=49 and point2D_idx=9173 in image_id=51\nW20251209 04:54:25.023748 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=2436 in image_id=49 and point2D_idx=2515 in image_id=51\nW20251209 04:54:25.023771 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=3911 in image_id=49 and point2D_idx=5293 in image_id=51\nW20251209 04:54:25.023823 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4465 in image_id=49 and point2D_idx=6589 in image_id=51\nW20251209 04:54:25.023878 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=6362 in image_id=49 and point2D_idx=11138 in image_id=51\nW20251209 04:54:25.023918 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=5200 in image_id=49 and point2D_idx=8452 in image_id=51\nW20251209 04:54:25.023939 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=4469 in image_id=49 and point2D_idx=6593 in image_id=51\nW20251209 04:54:25.023981 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1001 in image_id=50 and point2D_idx=6929 in image_id=51\nW20251209 04:54:25.024055 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=1225 in image_id=50 and point2D_idx=7365 in image_id=51\nW20251209 04:54:25.024079 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=810 in image_id=50 and point2D_idx=6051 in image_id=51\nW20251209 04:54:25.024148 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=539 in image_id=50 and point2D_idx=5222 in image_id=51\nW20251209 04:54:25.024220 137119495124544 correspondence_graph.cc:156] Duplicate correspondence between point2D_idx=819 in image_id=50 and point2D_idx=5645 in image_id=51\nI20251209 04:54:25.043330 137119495124544 database_cache.cc:210]  in 0.085s (ignored 0)\nI20251209 04:54:25.043417 137119495124544 timer.cc:91] Elapsed time: 0.002 [minutes]\nI20251209 04:54:25.047529 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:54:25.301937 137119495124544 incremental_pipeline.cc:306] Initializing with image pair #34 and #44\nI20251209 04:54:25.306167 137119495124544 incremental_pipeline.cc:311] Global bundle adjustment\nI20251209 04:54:25.387969 137119495124544 incremental_pipeline.cc:390] Registering image #35 (3)\nI20251209 04:54:25.387990 137119495124544 incremental_pipeline.cc:393] => Image sees 38 / 12036 points\nI20251209 04:54:25.389268 137119495124544 incremental_pipeline.cc:404] => Could not register, trying another image.\nI20251209 04:54:25.389285 137119495124544 incremental_pipeline.cc:390] Registering image #43 (3)\nI20251209 04:54:25.389291 137119495124544 incremental_pipeline.cc:393] => Image sees 31 / 8861 points\nI20251209 04:54:26.351551 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:54:27.490114 137119495124544 incremental_pipeline.cc:390] Registering image #49 (4)\nI20251209 04:54:27.490143 137119495124544 incremental_pipeline.cc:393] => Image sees 1288 / 6492 points\nI20251209 04:54:28.437563 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:54:29.258496 137119495124544 incremental_pipeline.cc:390] Registering image #51 (5)\nI20251209 04:54:29.258526 137119495124544 incremental_pipeline.cc:393] => Image sees 2156 / 12054 points\nI20251209 04:54:30.692028 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:54:32.537409 137119495124544 incremental_pipeline.cc:390] Registering image #9 (6)\nI20251209 04:54:32.537444 137119495124544 incremental_pipeline.cc:393] => Image sees 1223 / 8355 points\nI20251209 04:54:34.482238 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:54:36.004523 137119495124544 incremental_pipeline.cc:390] Registering image #18 (7)\nI20251209 04:54:36.004555 137119495124544 incremental_pipeline.cc:393] => Image sees 1110 / 11339 points\nI20251209 04:54:37.641596 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:54:39.242014 137119495124544 incremental_pipeline.cc:390] Registering image #29 (8)\nI20251209 04:54:39.242064 137119495124544 incremental_pipeline.cc:393] => Image sees 1686 / 8074 points\nI20251209 04:54:41.620658 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:54:43.045233 137119495124544 incremental_pipeline.cc:390] Registering image #39 (9)\nI20251209 04:54:43.045267 137119495124544 incremental_pipeline.cc:393] => Image sees 1456 / 8149 points\nI20251209 04:54:45.090143 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:54:45.974268 137119495124544 incremental_pipeline.cc:390] Registering image #13 (10)\nI20251209 04:54:45.974314 137119495124544 incremental_pipeline.cc:393] => Image sees 1093 / 5639 points\nI20251209 04:54:47.447249 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:54:49.165633 137119495124544 incremental_pipeline.cc:390] Registering image #2 (11)\nI20251209 04:54:49.165670 137119495124544 incremental_pipeline.cc:393] => Image sees 1368 / 8557 points\nI20251209 04:54:50.864134 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:54:55.223773 137119495124544 incremental_pipeline.cc:390] Registering image #19 (12)\nI20251209 04:54:55.223848 137119495124544 incremental_pipeline.cc:393] => Image sees 790 / 4372 points\nI20251209 04:54:57.015011 137119495124544 incremental_pipeline.cc:390] Registering image #42 (13)\nI20251209 04:54:57.015060 137119495124544 incremental_pipeline.cc:393] => Image sees 3212 / 14737 points\nI20251209 04:54:58.254783 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:55:00.787127 137119495124544 incremental_pipeline.cc:390] Registering image #17 (14)\nI20251209 04:55:00.787159 137119495124544 incremental_pipeline.cc:393] => Image sees 1697 / 9338 points\nI20251209 04:55:02.967566 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:55:05.564942 137119495124544 incremental_pipeline.cc:390] Registering image #33 (15)\nI20251209 04:55:05.564980 137119495124544 incremental_pipeline.cc:393] => Image sees 4016 / 10204 points\nI20251209 04:55:07.096229 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:55:10.018095 137119495124544 incremental_pipeline.cc:390] Registering image #38 (16)\nI20251209 04:55:10.018130 137119495124544 incremental_pipeline.cc:393] => Image sees 3563 / 6860 points\nI20251209 04:55:12.701298 137119495124544 incremental_pipeline.cc:390] Registering image #48 (17)\nI20251209 04:55:12.701331 137119495124544 incremental_pipeline.cc:393] => Image sees 4831 / 7974 points\nI20251209 04:55:15.337224 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:55:19.346851 137119495124544 incremental_pipeline.cc:390] Registering image #22 (18)\nI20251209 04:55:19.346889 137119495124544 incremental_pipeline.cc:393] => Image sees 2305 / 4969 points\nI20251209 04:55:21.387453 137119495124544 incremental_pipeline.cc:390] Registering image #23 (19)\nI20251209 04:55:21.387486 137119495124544 incremental_pipeline.cc:393] => Image sees 1495 / 5088 points\nI20251209 04:55:22.376051 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:55:24.616649 137119495124544 incremental_pipeline.cc:390] Registering image #32 (20)\nI20251209 04:55:24.616690 137119495124544 incremental_pipeline.cc:393] => Image sees 1431 / 5493 points\nI20251209 04:55:26.267634 137119495124544 incremental_pipeline.cc:390] Registering image #25 (21)\nI20251209 04:55:26.267693 137119495124544 incremental_pipeline.cc:393] => Image sees 1128 / 3883 points\nI20251209 04:55:27.526415 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:55:29.687633 137119495124544 incremental_pipeline.cc:390] Registering image #28 (22)\nI20251209 04:55:29.687672 137119495124544 incremental_pipeline.cc:393] => Image sees 617 / 9435 points\nI20251209 04:55:31.475587 137119495124544 incremental_pipeline.cc:390] Registering image #24 (23)\nI20251209 04:55:31.475623 137119495124544 incremental_pipeline.cc:393] => Image sees 1572 / 7722 points\nI20251209 04:55:33.223334 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:55:37.763239 137119495124544 incremental_pipeline.cc:390] Registering image #3 (24)\nI20251209 04:55:37.763274 137119495124544 incremental_pipeline.cc:393] => Image sees 1696 / 6986 points\nI20251209 04:55:39.568318 137119495124544 incremental_pipeline.cc:390] Registering image #14 (25)\nI20251209 04:55:39.568351 137119495124544 incremental_pipeline.cc:393] => Image sees 1107 / 6082 points\nI20251209 04:55:41.438871 137119495124544 incremental_pipeline.cc:390] Registering image #10 (26)\nI20251209 04:55:41.438905 137119495124544 incremental_pipeline.cc:393] => Image sees 866 / 3587 points\nI20251209 04:55:43.521665 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:55:48.780796 137119495124544 incremental_pipeline.cc:390] Registering image #27 (27)\nI20251209 04:55:48.780843 137119495124544 incremental_pipeline.cc:393] => Image sees 707 / 5033 points\nI20251209 04:55:50.655520 137119495124544 incremental_pipeline.cc:390] Registering image #30 (28)\nI20251209 04:55:50.655557 137119495124544 incremental_pipeline.cc:393] => Image sees 846 / 6281 points\nI20251209 04:55:52.413798 137119495124544 incremental_pipeline.cc:390] Registering image #45 (29)\nI20251209 04:55:52.413832 137119495124544 incremental_pipeline.cc:393] => Image sees 1005 / 8485 points\nI20251209 04:55:54.253600 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:56:00.632832 137119495124544 incremental_pipeline.cc:390] Registering image #20 (30)\nI20251209 04:56:00.632906 137119495124544 incremental_pipeline.cc:393] => Image sees 1242 / 7017 points\nI20251209 04:56:01.976068 137119495124544 incremental_pipeline.cc:390] Registering image #36 (31)\nI20251209 04:56:01.976099 137119495124544 incremental_pipeline.cc:393] => Image sees 1056 / 6195 points\nI20251209 04:56:03.897956 137119495124544 incremental_pipeline.cc:390] Registering image #4 (32)\nI20251209 04:56:03.897994 137119495124544 incremental_pipeline.cc:393] => Image sees 651 / 4184 points\nI20251209 04:56:05.117808 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:56:17.806138 137119495124544 incremental_pipeline.cc:390] Registering image #1 (33)\nI20251209 04:56:17.806172 137119495124544 incremental_pipeline.cc:393] => Image sees 2610 / 4584 points\nI20251209 04:56:19.008570 137119495124544 incremental_pipeline.cc:390] Registering image #12 (34)\nI20251209 04:56:19.008605 137119495124544 incremental_pipeline.cc:393] => Image sees 690 / 3260 points\nI20251209 04:56:20.068377 137119495124544 incremental_pipeline.cc:390] Registering image #6 (35)\nI20251209 04:56:20.068412 137119495124544 incremental_pipeline.cc:393] => Image sees 885 / 2505 points\nI20251209 04:56:21.600185 137119495124544 incremental_pipeline.cc:390] Registering image #37 (36)\nI20251209 04:56:21.600220 137119495124544 incremental_pipeline.cc:393] => Image sees 1579 / 4555 points\nI20251209 04:56:22.621128 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:56:34.209783 137119495124544 incremental_pipeline.cc:390] Registering image #46 (37)\nI20251209 04:56:34.209844 137119495124544 incremental_pipeline.cc:393] => Image sees 579 / 2643 points\nI20251209 04:56:35.386348 137119495124544 incremental_pipeline.cc:390] Registering image #11 (38)\nI20251209 04:56:35.386381 137119495124544 incremental_pipeline.cc:393] => Image sees 389 / 1946 points\nI20251209 04:56:36.721368 137119495124544 incremental_pipeline.cc:390] Registering image #21 (39)\nI20251209 04:56:36.721404 137119495124544 incremental_pipeline.cc:393] => Image sees 485 / 4368 points\nI20251209 04:56:38.377836 137119495124544 incremental_pipeline.cc:390] Registering image #50 (40)\nI20251209 04:56:38.377867 137119495124544 incremental_pipeline.cc:393] => Image sees 509 / 4614 points\nI20251209 04:56:39.365500 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:57:05.109638 137119495124544 incremental_pipeline.cc:390] Registering image #40 (41)\nI20251209 04:57:05.109699 137119495124544 incremental_pipeline.cc:393] => Image sees 391 / 4090 points\nI20251209 04:57:06.334216 137119495124544 incremental_pipeline.cc:390] Registering image #31 (42)\nI20251209 04:57:06.334249 137119495124544 incremental_pipeline.cc:393] => Image sees 704 / 3589 points\nI20251209 04:57:06.989057 137119495124544 incremental_pipeline.cc:390] Registering image #41 (43)\nI20251209 04:57:06.989088 137119495124544 incremental_pipeline.cc:393] => Image sees 843 / 4342 points\nI20251209 04:57:08.254266 137119495124544 incremental_pipeline.cc:390] Registering image #15 (44)\nI20251209 04:57:08.254299 137119495124544 incremental_pipeline.cc:393] => Image sees 262 / 3619 points\nI20251209 04:57:09.067509 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:57:13.391471 137119495124544 incremental_pipeline.cc:390] Registering image #47 (45)\nI20251209 04:57:13.391508 137119495124544 incremental_pipeline.cc:393] => Image sees 322 / 1772 points\nI20251209 04:57:13.891519 137119495124544 incremental_pipeline.cc:390] Registering image #5 (46)\nI20251209 04:57:13.891554 137119495124544 incremental_pipeline.cc:393] => Image sees 259 / 2542 points\nI20251209 04:57:14.550805 137119495124544 incremental_pipeline.cc:390] Registering image #16 (47)\nI20251209 04:57:14.550836 137119495124544 incremental_pipeline.cc:393] => Image sees 208 / 2606 points\nI20251209 04:57:15.083178 137119495124544 incremental_pipeline.cc:390] Registering image #8 (48)\nI20251209 04:57:15.083207 137119495124544 incremental_pipeline.cc:393] => Image sees 59 / 4075 points\nI20251209 04:57:15.086978 137119495124544 incremental_pipeline.cc:404] => Could not register, trying another image.\nI20251209 04:57:15.087002 137119495124544 incremental_pipeline.cc:390] Registering image #35 (48)\nI20251209 04:57:15.087012 137119495124544 incremental_pipeline.cc:393] => Image sees 5834 / 12036 points\nI20251209 04:57:17.948467 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:57:26.326138 137119495124544 incremental_pipeline.cc:390] Registering image #8 (49)\nI20251209 04:57:26.326174 137119495124544 incremental_pipeline.cc:393] => Image sees 59 / 4075 points\nI20251209 04:57:26.329245 137119495124544 incremental_pipeline.cc:404] => Could not register, trying another image.\nI20251209 04:57:26.329265 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:57:30.326276 137119495124544 incremental_pipeline.cc:390] Registering image #8 (49)\nI20251209 04:57:30.326319 137119495124544 incremental_pipeline.cc:393] => Image sees 59 / 4075 points\nI20251209 04:57:30.944613 137119495124544 incremental_pipeline.cc:42] Retriangulation and Global bundle adjustment\nI20251209 04:57:35.700263 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.743231 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.745832 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.746145 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.748626 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.748932 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.750914 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.751224 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.752970 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.753329 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.755014 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.755339 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.757007 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.757336 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.758977 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.759293 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.760928 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.761243 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.762867 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.763174 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.764886 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.765191 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.766831 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.767140 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.768763 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.769124 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.770753 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.771062 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.772671 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.772966 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.774592 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.774891 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.776513 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.776817 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.778493 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.778813 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.780429 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.780731 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.782374 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.782761 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.784516 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.784850 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.786577 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.786897 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.788571 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.788883 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.790510 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.790816 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.792440 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.792745 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.794352 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.794661 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.796272 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.796589 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.798219 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.798528 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.800146 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.800462 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.802253 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.802792 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.804795 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.805167 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.806939 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.807254 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.808935 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.809248 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.810917 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.811236 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.812984 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.813313 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.814966 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.815287 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.816964 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.817282 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.818933 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.819249 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.821117 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.821440 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.823078 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.823423 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.825049 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.825354 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.826981 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.827306 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.828926 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.829245 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.830879 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.831196 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.832822 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.833138 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.834771 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.835088 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.836732 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.837059 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.838652 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.838943 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.840861 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.841182 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.842851 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.843170 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.844807 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.845126 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.846792 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.847109 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.848735 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.849065 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.850699 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.851006 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.852645 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.852964 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.854602 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.854909 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.856683 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.857008 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.858932 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.859302 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.861080 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.861388 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.863079 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.863385 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.865047 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.865325 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.867045 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.867330 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.868977 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.869300 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.870964 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.871300 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.872965 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.873287 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.874947 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.875271 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.877597 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.877968 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.879775 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.880108 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.881769 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.882096 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.883752 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.884073 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.885718 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.886025 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.887669 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.887977 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.889658 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.889957 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.891648 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.891943 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.893615 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.893927 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.895595 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.895906 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.897585 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.897885 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.899580 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.899831 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.901473 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.901811 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.903755 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.904080 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.905805 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.906126 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.907812 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.908118 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.909787 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.910102 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.911746 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.912055 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.913695 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.913996 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.915763 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.916078 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.917707 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.918003 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.919642 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.919945 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.921566 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.921870 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.923492 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.923798 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.925399 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.925705 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.927357 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.927661 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.929283 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.929576 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.931186 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.931483 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.933105 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.933396 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.934986 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.935295 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.936910 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.937211 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.938824 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.939132 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.940754 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.941062 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.942661 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.942958 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.944569 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.944876 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.946564 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.946955 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.948801 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.949144 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.950810 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.951128 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.952756 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.953074 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.954709 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.955014 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.956660 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.956964 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.958750 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.959091 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.960726 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.961059 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.962693 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.962986 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.964625 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.964932 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.966561 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.966859 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.968480 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.968785 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.970418 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.970722 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.972334 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.972629 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.974243 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.974538 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.976200 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.976532 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.978214 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.978506 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.980117 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.980408 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.982010 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.982315 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.984021 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.984323 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.985919 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.986230 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.987842 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.988141 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.989753 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.990050 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.991658 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.991933 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.993545 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.993855 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.995466 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.995758 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.997370 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.997662 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:35.999263 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:35.999542 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.001168 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.001447 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.003244 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.003521 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.005176 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.005455 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.007096 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.007371 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.008965 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.009284 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.010890 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.011181 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.012775 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.013060 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.014636 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.014948 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.016564 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.016848 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.018442 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.018725 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.020330 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.020607 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.022204 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.022479 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.024068 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.024343 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.025920 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.026215 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.027815 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.028106 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.029686 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.029939 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.031553 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.031836 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.033405 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.033688 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.035275 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.035547 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.037216 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.037496 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.039083 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.039360 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.041114 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.041379 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.043020 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.043307 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.044921 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.045221 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.046833 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.047136 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.048826 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.049115 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.050811 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.051122 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.052751 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.053043 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.054681 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.054980 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.056628 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.056942 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.058555 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.058839 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.060438 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.060724 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.062309 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.062584 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.064168 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.064407 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.065990 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.066259 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.067855 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.068126 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.069735 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.070010 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.071654 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.071948 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.073557 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.073855 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.075446 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.075745 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.077352 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.077654 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.079252 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.079526 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.081118 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.081411 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.083004 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.083309 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.084909 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.085204 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.086804 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.087098 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.088699 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.088975 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.090602 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.090888 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.092472 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.092753 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.094329 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.094609 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.096188 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.096496 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.098109 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.098394 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.099976 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.100268 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.101871 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\n","output_type":"stream"},{"name":"stdout","text":"{0: Reconstruction(num_cameras=49, num_images=49, num_reg_images=49, num_points3D=48157)}\nReconstruction done in 191.2472 sec\n\nResults\nDataset \"amy_gardens\" -> Failed!\nDataset \"fbk_vineyard\" -> Failed!\nDataset \"imc2023_haiper\" -> Failed!\nDataset \"imc2023_heritage\" -> Failed!\nDataset \"imc2023_theather_imc2024_church\" -> Failed!\nDataset \"imc2024_dioscuri_baalshamin\" -> Failed!\nDataset \"imc2024_lizard_pond\" -> Failed!\nDataset \"pt_brandenburg_british_buckingham\" -> Failed!\nDataset \"pt_piazzasanmarco_grandplace\" -> Failed!\nDataset \"pt_sacrecoeur_trevi_tajmahal\" -> Failed!\nDataset \"pt_stpeters_stpauls\" -> Failed!\nDataset ETs -> Registered 19 / 22 images with 2 clusters\nDataset stairs -> Registered 49 / 51 images with 1 clusters\n","output_type":"stream"},{"name":"stderr","text":"I20251209 04:57:36.102198 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.104101 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.104416 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.106268 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.106573 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.108248 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.108529 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.110188 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.110461 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.112070 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.112356 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.113956 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.114253 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.115834 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.116131 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.117736 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.118018 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.119639 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.119935 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.121543 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.121834 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.123432 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.123722 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.125320 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.125612 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.127231 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.127521 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.129113 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.129397 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.130981 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.131314 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.132944 137119495124544 incremental_pipeline.cc:282] Finding good initial image pair\nI20251209 04:57:36.133242 137119495124544 incremental_pipeline.cc:286] => No good initial image pair found.\nI20251209 04:57:36.133365 137119495124544 timer.cc:91] Elapsed time: 3.187 [minutes]\n","output_type":"stream"}],"execution_count":40},{"cell_type":"code","source":"array_to_str = lambda array: ';'.join([f\"{x:.09f}\" for x in array])\nnone_to_str = lambda n: ';'.join(['nan'] * n)\nsubmission_file = '/kaggle/working/submission.csv'\nwith open(submission_file, 'w') as f:\n    if is_train:\n        f.write('dataset,scene,image,rotation_matrix,translation_vector\\n')\n        for dataset, predictions in samples.items():\n            for prediction in predictions:\n                cluster_name = 'outliers' if prediction.cluster_index is None else f'cluster{prediction.cluster_index}'\n\n                # ✅ `rotation` is a list of lists, flatten it\n                if prediction.rotation is None:\n                    rotation_str = none_to_str(9)\n                else:\n                    rotation_flat =  prediction.rotation.flatten()  # flatten 3x3 list -> 9 elems\n                    rotation_str = array_to_str(rotation_flat)\n\n                # ✅ `translation` is a flat list\n                if prediction.translation is None:\n                    translation_str = none_to_str(3)\n                else:\n                    translation_str = array_to_str(prediction.translation)\n\n                f.write(f'{prediction.dataset},{cluster_name},{prediction.filename},{rotation_str},{translation_str}\\n')\n    else:\n        f.write('image_id,dataset,scene,image,rotation_matrix,translation_vector\\n')\n        for dataset, predictions in samples.items():\n            for prediction in predictions:\n                cluster_name = 'outliers' if prediction.cluster_index is None else f'cluster{prediction.cluster_index}'\n\n                if prediction.rotation is None:\n                    rotation_str = none_to_str(9)\n                else:\n                    rotation_flat =  prediction.rotation.flatten()\n                    rotation_str = array_to_str(rotation_flat)\n\n                if prediction.translation is None:\n                    translation_str = none_to_str(3)\n                else:\n                    translation_str = array_to_str(prediction.translation)\n\n                f.write(f'{prediction.image_id},{prediction.dataset},{cluster_name},{prediction.filename},{rotation_str},{translation_str}\\n')\n\n# Preview the output\n!head {submission_file}\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:57:36.201673Z","iopub.execute_input":"2025-12-09T04:57:36.202117Z","iopub.status.idle":"2025-12-09T04:57:36.605639Z","shell.execute_reply.started":"2025-12-09T04:57:36.202084Z","shell.execute_reply":"2025-12-09T04:57:36.604726Z"}},"outputs":[{"name":"stdout","text":"image_id,dataset,scene,image,rotation_matrix,translation_vector\nETs_another_et_another_et001.png_public,ETs,cluster1,another_et_another_et001.png,0.999901122;-0.006022093;0.012707487;0.005732015;0.999724943;0.022741628;-0.012840944;-0.022666539;0.999660611,-2.810862083;-1.938197515;2.734789485\nETs_another_et_another_et002.png_public,ETs,cluster1,another_et_another_et002.png,0.999990905;-0.003492048;-0.002448712;0.003485311;0.999990144;-0.002750249;0.002458292;0.002741690;0.999993220,-2.656899281;-1.163056178;1.202037024\nETs_another_et_another_et003.png_public,ETs,cluster1,another_et_another_et003.png,0.997897729;-0.043131049;0.048371835;0.046276132;0.996752351;-0.065903486;-0.045372254;0.068003401;0.996652846,-2.864245076;0.351968712;-0.599432525\nETs_another_et_another_et004.png_public,ETs,cluster1,another_et_another_et004.png,0.999324094;-0.012796008;0.034461810;0.008239038;0.991575723;0.129266022;-0.035825583;-0.128894718;0.991010938,-2.772501780;-1.431491010;-0.140841990\nETs_another_et_another_et005.png_public,ETs,cluster1,another_et_another_et005.png,0.995498998;0.001146848;0.094765134;-0.007473143;0.997763119;0.066429746;-0.094476971;-0.066838939;0.993280755,-3.298440810;-2.123986635;1.325921346\nETs_another_et_another_et006.png_public,ETs,cluster1,another_et_another_et006.png,0.919834772;0.189919983;-0.343270146;-0.215986817;0.975617624;-0.038986502;0.327496088;0.110002966;0.938427227,-0.573114075;-0.731431989;1.084820369\nETs_another_et_another_et007.png_public,ETs,cluster1,another_et_another_et007.png,0.779595606;0.259614760;-0.569939354;-0.307037104;0.951602015;0.013484158;0.545856124;0.164480338;0.821576114,1.132292675;-0.432583411;0.585784571\nETs_another_et_another_et008.png_public,ETs,cluster1,another_et_another_et008.png,0.565464703;0.305603269;-0.766065475;-0.389897638;0.917529529;0.078226566;0.726793988;0.254452758;0.637984555,2.830325819;-0.746015949;1.381209091\nETs_another_et_another_et009.png_public,ETs,cluster1,another_et_another_et009.png,0.312116576;0.340570588;-0.886901865;-0.486884755;0.858965166;0.158499462;0.815798062;0.382348687;0.433915894,4.484310177;-1.071450912;2.003105554\n","output_type":"stream"}],"execution_count":41},{"cell_type":"code","source":"# Definitely Compute results if running on the training set.\n# Do not do this when submitting a notebook for scoring. All you have to do is save your submission to /kaggle/working/submission.csv.\n\nif is_train:\n    t = time()\n    final_score, dataset_scores = metric.score(\n        gt_csv='/kaggle/input/image-matching-challenge-2025/train_labels.csv',\n        user_csv=submission_file,\n        thresholds_csv='/kaggle/input/image-matching-challenge-2025/train_thresholds.csv',\n        mask_csv=None if is_train else os.path.join(data_dir, 'mask.csv'),\n        inl_cf=0,\n        strict_cf=-1,\n        verbose=True,\n    )\n    print(f'Computed metric in: {time() - t:.02f} sec.')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-09T04:57:36.606813Z","iopub.execute_input":"2025-12-09T04:57:36.607087Z","iopub.status.idle":"2025-12-09T04:57:36.612152Z","shell.execute_reply.started":"2025-12-09T04:57:36.607054Z","shell.execute_reply":"2025-12-09T04:57:36.611437Z"}},"outputs":[],"execution_count":42}]}