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This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":6.822164,"end_time":"2026-04-16T03:02:00.397661+00:00","exception":false,"start_time":"2026-04-16T03:01:53.575497+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:52:52.609580Z","iopub.execute_input":"2026-04-16T16:52:52.610190Z","iopub.status.idle":"2026-04-16T16:52:57.858521Z","shell.execute_reply.started":"2026-04-16T16:52:52.610156Z","shell.execute_reply":"2026-04-16T16:52:57.857871Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# CELL 1 — Pre-flight (run this first, before anything else)\n# ============================================================\nimport os, sys, shutil, gc, pickle, warnings\nfrom pathlib import Path\nimport glob as _glob\n\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nwarnings.filterwarnings(\"ignore\", message=\".*trimesh.*\")\nwarnings.filterwarnings(\"ignore\", message=\".*RoPE2D.*\")\n\n# ── Install roma offline ──────────────────────────────────────────────────────\n_roma_wheels = _glob.glob(\"/kaggle/input/datasets/solomontneo/model7/roma*.whl\")\nif _roma_wheels:\n os.system(f\"pip install {_roma_wheels[0]} --no-index --no-deps --force-reinstall -q\")\n print(\"✅ roma installed\")\nelse:\n os.system(\"pip install roma -q\")\n print(\"✅ roma installed (from PyPI)\")\n\n# ── Copy weights to PyTorch hub cache (writable) ─────────────────────────────\nHUB_CACHE = Path(\"/root/.cache/torch/hub/checkpoints\")\nHUB_CACHE.mkdir(parents=True, exist_ok=True)\n\n# SuperPoint weights\n_SP_SRC = Path(\"/kaggle/input/datasets/solomontneo/model8/superpoint_v1.pth\")\n_SP_DST = HUB_CACHE / \"superpoint_v1.pth\"\nif not _SP_DST.exists():\n if _SP_SRC.exists():\n shutil.copy2(_SP_SRC, _SP_DST)\n print(f\"✅ SuperPoint weights copied to hub cache\")\n else:\n _candidates = list(Path(\"/kaggle/input\").rglob(\"superpoint_v1.pth\"))\n if _candidates:\n shutil.copy2(_candidates[0], _SP_DST)\n print(f\"✅ SuperPoint weights cached from {_candidates[0]}\")\n else:\n raise FileNotFoundError(\"superpoint_v1.pth not found in any dataset\")\nelse:\n print(f\"✅ SuperPoint weights already cached\")\n\n# LightGlue weights — CRITICAL: Exact filename LightGlue's source code expects\n_LG_SRC = Path(\"/kaggle/input/datasets/solomontneo/model9/superpoint_lightglue.pth\")\n_LG_DST = HUB_CACHE / \"superpoint_lightglue_v0-1_arxiv.pth\" # ← EXACT filename from LightGlue source\n\n# Remove any wrong versions first\nfor f in HUB_CACHE.glob(\"superpoint_lightglue*.pth\"):\n if f.name != \"superpoint_lightglue_v0-1_arxiv.pth\":\n f.unlink()\n print(f\"🗑️ Removed conflicting file: {f.name}\")\n\nif not _LG_DST.exists():\n if _LG_SRC.exists():\n shutil.copy2(_LG_SRC, _LG_DST)\n print(f\"✅ LightGlue weights copied → { _LG_DST.name}\")\n else:\n _candidates = list(Path(\"/kaggle/input\").rglob(\"superpoint_lightglue*.pth\"))\n if _candidates:\n shutil.copy2(_candidates[0], _LG_DST)\n print(f\"✅ LightGlue weights cached from {_candidates[0]}\")\n else:\n raise FileNotFoundError(\"superpoint_lightglue.pth not found in any dataset\")\nelse:\n print(f\"✅ LightGlue weights ready: {_LG_DST.name}\")\n\n# Verify both files exist with correct names\nprint(f\"\\nHub cache contents:\")\nfor f in sorted(HUB_CACHE.glob(\"*.pth\")):\n print(f\" 📁 {f.name} ({f.stat().st_size/1e6:.1f}MB)\")\n\nprint(\"\\n✅ Pre-flight complete — safe to import LightGlue now\")","metadata":{"papermill":{"duration":4.804831,"end_time":"2026-04-16T03:02:05.211391+00:00","exception":false,"start_time":"2026-04-16T03:02:00.406560+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:52:57.859851Z","iopub.execute_input":"2026-04-16T16:52:57.860212Z","iopub.status.idle":"2026-04-16T16:53:02.090229Z","shell.execute_reply.started":"2026-04-16T16:52:57.860188Z","shell.execute_reply":"2026-04-16T16:53:02.089496Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# Image Matching Challenge 2025 – COMPLETE SOLUTION\n# Strategy: DINOv3 + MASt3R + DUSt3R Global Alignment\n# NO PyColMap required.\n# ============================================================\n\n# ============================================================\n# SECTION 0: IMPORTS & CONFIG\n# ============================================================\n# ============================================================\n# CELL 2 — Imports\n# ============================================================\nimport cv2\nimport torch\nimport numpy as np\nimport pandas as pd\n\nfrom PIL import Image\nfrom tqdm.auto import tqdm\nfrom scipy.spatial.distance import cdist\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.cluster import DBSCAN\n\nimport networkx as nx\nfrom community import community_louvain\nfrom transformers import AutoImageProcessor, AutoModel\n\nLIGHTGLUE_ROOT = Path(\"/kaggle/input/datasets/solomontneo/models/LightGlue-offline/LightGlue-main\")\nMAST3R_ROOT = Path(\"/kaggle/input/datasets/solomontneo/model5/mast3r-offline\")\n\nfor _p in [MAST3R_ROOT, MAST3R_ROOT / \"dust3r\", LIGHTGLUE_ROOT]:\n if str(_p) not in sys.path:\n sys.path.insert(0, str(_p))\n\nimport mast3r.utils.path_to_dust3r\nimport dust3r.utils.path_to_croco\nfrom mast3r.model import AsymmetricMASt3R\nfrom dust3r.utils.image import load_images\nfrom dust3r.inference import inference\nfrom dust3r.cloud_opt import global_aligner, GlobalAlignerMode\n\n# LightGlue — weights already in hub cache, no download will occur\nfrom lightglue import LightGlue, SuperPoint\nfrom lightglue.utils import load_image, rbd\n\nprint(\"✅ All imports OK\")\n","metadata":{"papermill":{"duration":41.305053,"end_time":"2026-04-16T03:02:46.526026+00:00","exception":false,"start_time":"2026-04-16T03:02:05.220973+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:02.091305Z","iopub.execute_input":"2026-04-16T16:53:02.091688Z","iopub.status.idle":"2026-04-16T16:53:37.421768Z","shell.execute_reply.started":"2026-04-16T16:53:02.091640Z","shell.execute_reply":"2026-04-16T16:53:37.420996Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# SECTION 1: PATHS –– adjust to your Kaggle dataset paths\n# ============================================================\n# ============================================================\n# CELL 3 — Paths & Config (Kaggle-optimised)\n# ============================================================\nimport torch\nimport torch.backends.cudnn as cudnn\n\nif Path(\"/kaggle/input/competitions/image-matching-challenge-2025\").exists():\n DATA_ROOT = Path(\"/kaggle/input/competitions/image-matching-challenge-2025\")\nelse:\n DATA_ROOT = Path(\"/kaggle/input/image-matching-challenge-2025\")\n\nTEST_DIR = DATA_ROOT / \"train\" \nWORK_DIR = Path(\"/kaggle/working\")\nWORK_DIR.mkdir(parents=True, exist_ok=True)\n\nDINO_MODEL_DIR = Path(\"/kaggle/input/datasets/solomontneo/models/dinov3-offline/kaggle/working/dinov3-model\")\n\nCACHE_DIR = WORK_DIR / \"cache\"\nSTEP1_DINO_DIR = CACHE_DIR / \"step1_dino\"\nSTEP1_MAST3R_DIR = CACHE_DIR / \"step1_mast3r\"\nSTEP2_DIR = CACHE_DIR / \"step2\"\nSTEP3_DIR = CACHE_DIR / \"step3\"\nSTEP4_DIR = CACHE_DIR / \"step4_poses\"\nSUBMISSION_PATH = WORK_DIR / \"submission.csv\"\n\nfor _d in [CACHE_DIR, STEP1_DINO_DIR, STEP1_MAST3R_DIR, STEP2_DIR, STEP3_DIR, STEP4_DIR]:\n _d.mkdir(parents=True, exist_ok=True)\n\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nN_GPU = torch.cuda.device_count() if torch.cuda.is_available() else 0\n\n# GPU speed settings\nif torch.cuda.is_available():\n cudnn.benchmark = True\n cudnn.deterministic = False\n torch.backends.cuda.matmul.allow_tf32 = True\n torch.backends.cudnn.allow_tf32 = True\n\n# Conservative defaults first\nDINO_DTYPE = torch.float16 if DEVICE == \"cuda\" else torch.float32\nBATCH_SIZE_DINO = 8\nBATCH_SIZE_MAST3R = 4\n\n# Accelerator-aware tuning\nif DEVICE == \"cuda\":\n gpu_names = [torch.cuda.get_device_name(i) for i in range(N_GPU)]\n print(f\"GPUs detected ({N_GPU}): {gpu_names}\")\n\n if N_GPU >= 2:\n # T4 x2 or similar: bigger throughput, but still keep MASt3R moderate\n BATCH_SIZE_DINO = 16\n BATCH_SIZE_MAST3R = 8\n DINO_WORKERS = 2\n USE_DP_FOR_DINO = True\n USE_DP_FOR_MAST3R = False # safer: DUSt3R / MASt3R internals are not always DP-friendly\n else:\n gpu0 = gpu_names[0]\n if \"P100\" in gpu0:\n BATCH_SIZE_DINO = 12\n BATCH_SIZE_MAST3R = 6\n elif \"T4\" in gpu0:\n BATCH_SIZE_DINO = 12\n BATCH_SIZE_MAST3R = 6\n else:\n BATCH_SIZE_DINO = 10\n BATCH_SIZE_MAST3R = 4\n DINO_WORKERS = 1\n USE_DP_FOR_DINO = False\n USE_DP_FOR_MAST3R = False\nelse:\n DINO_WORKERS = 0\n USE_DP_FOR_DINO = False\n USE_DP_FOR_MAST3R = False\n\nK_EASY = 20\nK_MEDIUM = 30\nK_HARD = 50\n\nDBSCAN_EPS = 0.30\nDBSCAN_MIN_SAMPLES = 2\nMIN_INLIERS = 12\n\n# Keep this moderate — large scenes explode pair count\nMAX_IMAGES_PER_SCENE = 40\nNITER_GLOBAL = 200\nLR_GLOBAL = 0.07\n\n# For large clusters, avoid full graph\nFULLY_CONNECTED_LIMIT = 12\nPAIR_SKIP_LIST = [1, 2]\n\nNAN_R = \"0.997491479;-0.001187749;-0.070777766;-0.000589166;0.999685347;-0.025079396;0.070785277;0.025058180;0.997176886\"\nNAN_T = \"0.119209088;0.007134018;0.021655859\"\n\nprint(f\"✅ Config done — device={DEVICE}, n_gpu={N_GPU}\")\nprint(f\"✅ BATCH_SIZE_DINO={BATCH_SIZE_DINO}, BATCH_SIZE_MAST3R={BATCH_SIZE_MAST3R}\")\nprint(f\"✅ MAX_IMAGES_PER_SCENE={MAX_IMAGES_PER_SCENE}, NITER_GLOBAL={NITER_GLOBAL}\")","metadata":{"papermill":{"duration":0.068071,"end_time":"2026-04-16T03:02:46.602563+00:00","exception":false,"start_time":"2026-04-16T03:02:46.534492+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:37.422728Z","iopub.execute_input":"2026-04-16T16:53:37.423356Z","iopub.status.idle":"2026-04-16T16:53:37.483501Z","shell.execute_reply.started":"2026-04-16T16:53:37.423324Z","shell.execute_reply":"2026-04-16T16:53:37.482806Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# CELL 4 — Utilities\n# ============================================================\ndef safe_empty_cache():\n gc.collect()\n if torch.cuda.is_available():\n torch.cuda.empty_cache()\n\ndef format_R(R):\n return \";\".join(f\"{x:.9f}\" for x in np.asarray(R, dtype=float).reshape(-1))\n\ndef format_t(t):\n return \";\".join(f\"{x:.9f}\" for x in np.asarray(t, dtype=float).reshape(3))\n\ndef list_test_datasets(test_dir):\n out = {}\n for d in sorted(Path(test_dir).iterdir()):\n if d.is_dir():\n out[d.name] = sorted(d.glob(\"*.png\"))\n return out\n\ndef normalize_labels(labels):\n uniq = sorted(x for x in set(labels.tolist()) if x != -1)\n remap = {old: new for new, old in enumerate(uniq)}\n return np.array([remap[x] if x != -1 else -1 for x in labels], dtype=int)\n\ndef load_feature_pickles(base_dir: Path):\n feats = {}\n for pkl in sorted(Path(base_dir).rglob(\"*.pkl\")):\n try:\n data = torch.load(pkl, weights_only=False, map_location=\"cpu\")\n feats[pkl.stem] = data\n print(f\" ✅ {pkl.stem}: {len(data['image_names'])} images\")\n except Exception as e:\n print(f\" ❌ {pkl}: {e}\")\n return feats\n","metadata":{"papermill":{"duration":0.019819,"end_time":"2026-04-16T03:02:46.630605+00:00","exception":false,"start_time":"2026-04-16T03:02:46.610786+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:37.485299Z","iopub.execute_input":"2026-04-16T16:53:37.485594Z","iopub.status.idle":"2026-04-16T16:53:37.493464Z","shell.execute_reply.started":"2026-04-16T16:53:37.485536Z","shell.execute_reply":"2026-04-16T16:53:37.492756Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# CELL 5 — DINOv3 Feature Extraction\n# ============================================================\nclass DINOv3FeatureExtractor:\n def __init__(self, model_dir=DINO_MODEL_DIR, device=None):\n self.device = device or DEVICE\n self.processor = AutoImageProcessor.from_pretrained(model_dir, local_files_only=True)\n self.model = AutoModel.from_pretrained(\n model_dir, torch_dtype=DINO_DTYPE, local_files_only=True\n ).to(self.device).eval()\n self.embed_dim = self.model.config.hidden_size\n print(f\"DINOv3 loaded on {self.device}. dim={self.embed_dim}\")\n\n def extract_batch(self, image_paths, batch_size=BATCH_SIZE_DINO):\n image_names, all_features = [], []\n for i in tqdm(range(0, len(image_paths), batch_size), desc=\"DINOv3\"):\n batch = image_paths[i:i+batch_size]\n imgs, valid = [], []\n for p in batch:\n try:\n imgs.append(Image.open(p).convert(\"RGB\")); valid.append(p)\n except Exception as e:\n print(f\"Skip {p}: {e}\")\n if not imgs:\n continue\n inputs = {k: v.to(self.device) for k, v in\n self.processor(images=imgs, return_tensors=\"pt\").items()}\n with torch.inference_mode():\n out = self.model(**inputs)\n cls = out.pooler_output if out.pooler_output is not None else out.last_hidden_state[:, 0]\n cls = torch.nn.functional.normalize(cls, dim=-1)\n all_features.append(cls.detach().cpu().numpy())\n image_names.extend([Path(p).name for p in valid])\n del imgs, inputs, out, cls; safe_empty_cache()\n if not all_features:\n return [], np.array([], dtype=np.float32)\n return image_names, np.concatenate(all_features, axis=0)\n\n def extract_dataset(self, dataset_dir):\n paths = sorted(Path(dataset_dir).glob(\"*.png\"))\n return self.extract_batch(paths) if paths else ([], np.array([]))\n\n def release(self):\n del self.model, self.processor; safe_empty_cache()\n\ndef extract_all_dino_datasets(data_dir, save_dir):\n save_path = Path(save_dir); extractor = None\n for d in sorted(Path(data_dir).iterdir()):\n if not d.is_dir(): continue\n pkl = save_path / f\"{d.name}.pkl\"\n if pkl.exists(): continue\n if extractor is None: extractor = DINOv3FeatureExtractor()\n names, feats = extractor.extract_dataset(d)\n torch.save({\"image_names\": names, \"features\": feats}, pkl)\n safe_empty_cache()\n if extractor: extractor.release()\n","metadata":{"papermill":{"duration":0.021029,"end_time":"2026-04-16T03:02:46.659582+00:00","exception":false,"start_time":"2026-04-16T03:02:46.638553+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:37.494662Z","iopub.execute_input":"2026-04-16T16:53:37.495348Z","iopub.status.idle":"2026-04-16T16:53:37.732035Z","shell.execute_reply.started":"2026-04-16T16:53:37.495316Z","shell.execute_reply":"2026-04-16T16:53:37.731278Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# CELL 6 — MASt3R SPoC Extraction\n# ============================================================\nclass MASt3RSPoCExtractor:\n def __init__(self, image_size=512, device=None):\n self.device = device or DEVICE\n self.image_size = image_size\n weights_dir = MAST3R_ROOT / \"mast3r\" / \"weights\"\n self.model = AsymmetricMASt3R.from_pretrained(weights_dir).to(self.device).eval()\n print(\"MASt3R SPoC extractor loaded.\")\n\n def _spoc(self, img_dict):\n t = img_dict[\"img\"].to(self.device)\n if t.dim() == 3: t = t.unsqueeze(0)\n with torch.inference_mode():\n enc, _, _ = self.model._encode_image(t, img_dict[\"true_shape\"])\n spoc = torch.nn.functional.normalize(enc.mean(dim=1), dim=-1)\n return spoc.squeeze(0).detach().cpu().numpy()\n\n def extract_batch(self, image_paths, batch_size=BATCH_SIZE_MAST3R):\n names, feats = [], []\n for i in tqdm(range(0, len(image_paths), batch_size), desc=\"MASt3R SPoC\"):\n batch = image_paths[i:i+batch_size]\n try:\n imgs = load_images([str(p) for p in batch], size=self.image_size, verbose=False)\n except:\n continue\n for img_dict, path in zip(imgs, batch):\n try:\n feats.append(self._spoc(img_dict)); names.append(Path(path).name)\n except: pass\n safe_empty_cache()\n if not feats: return [], np.array([])\n return names, np.stack(feats, axis=0)\n\n def extract_dataset(self, dataset_dir):\n paths = sorted(Path(dataset_dir).glob(\"*.png\"))\n return self.extract_batch(paths) if paths else ([], np.array([]))\n\n def release(self):\n del self.model; safe_empty_cache()\n\ndef extract_all_mast3r_datasets(data_dir, save_dir):\n save_path = Path(save_dir); extractor = None\n for d in sorted(Path(data_dir).iterdir()):\n if not d.is_dir(): continue\n pkl = save_path / f\"{d.name}.pkl\"\n if pkl.exists(): continue\n if extractor is None: extractor = MASt3RSPoCExtractor()\n names, feats = extractor.extract_dataset(d)\n torch.save({\"image_names\": names, \"features\": feats}, pkl)\n safe_empty_cache()\n if extractor: extractor.release()\n","metadata":{"papermill":{"duration":0.02067,"end_time":"2026-04-16T03:02:46.688358+00:00","exception":false,"start_time":"2026-04-16T03:02:46.667688+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:37.733032Z","iopub.execute_input":"2026-04-16T16:53:37.733351Z","iopub.status.idle":"2026-04-16T16:53:37.750836Z","shell.execute_reply.started":"2026-04-16T16:53:37.733322Z","shell.execute_reply":"2026-04-16T16:53:37.750101Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# CELL 7 — Retrieval\n# ============================================================\ndef choose_strategy(sim_matrix):\n upper = sim_matrix[np.triu_indices_from(sim_matrix, k=1)]\n std = np.std(upper) if len(upper) > 0 else 0.0\n if std < 0.06: s = \"dinov3_only\"\n elif std > 0.18: s = \"full_geometric\"\n else: s = \"dinov3+geometric\"\n print(f\" std={std:.3f} → strategy={s}\")\n return s\n\ndef get_top_k_pairs(image_names, features, k=20):\n n = len(image_names)\n sim = cosine_similarity(features)\n np.fill_diagonal(sim, -1)\n pairs_set = set()\n for i in range(n):\n for j in np.argsort(sim[i])[-k:]:\n if sim[i, j] > 0:\n pairs_set.add((min(i, j), max(i, j)))\n pairs = [(i, j, sim[i, j]) for i, j in pairs_set]\n pairs.sort(key=lambda x: x[2], reverse=True)\n np.fill_diagonal(sim, 1.0)\n return pairs, sim\n\ndef merge_pairs(pair_lists):\n pair_map = {}\n for pairs in pair_lists:\n for i, j, s in pairs:\n key = (min(i, j), max(i, j))\n if key not in pair_map or s > pair_map[key]:\n pair_map[key] = s\n merged = [(i, j, s) for (i, j), s in pair_map.items()]\n merged.sort(key=lambda x: x[2], reverse=True)\n return merged\n\ndef retrieve_all_datasets(dino_features, mast3r_features=None):\n results = {}\n for dname, data in dino_features.items():\n names = data[\"image_names\"]\n feats = data[\"features\"]\n n = len(names)\n print(f\"\\nRetrieval: {dname} ({n} images)\")\n sim_mat = cosine_similarity(feats)\n strategy = choose_strategy(sim_mat)\n k = K_EASY if strategy == \"dinov3_only\" else (K_MEDIUM if strategy == \"dinov3+geometric\" else K_HARD)\n k = min(k, max(1, n - 1))\n dino_pairs, sim_mat = get_top_k_pairs(names, feats, k=k)\n\n if mast3r_features and dname in mast3r_features:\n mdata = mast3r_features[dname]\n mfeats = mdata[\"features\"]\n mnames = mdata[\"image_names\"]\n idx_map = {nm: i for i, nm in enumerate(mnames)}\n aligned = np.zeros((n, mfeats.shape[1]), dtype=np.float32)\n valid = []\n for i, nm in enumerate(names):\n if nm in idx_map:\n aligned[i] = mfeats[idx_map[nm]]; valid.append(i)\n if len(valid) >= n * 0.8:\n mast3r_pairs, _ = get_top_k_pairs(names, aligned, k=min(10, max(1, n - 1)))\n pairs = merge_pairs([dino_pairs, mast3r_pairs])\n else:\n pairs = dino_pairs\n else:\n pairs = dino_pairs\n\n results[dname] = {\n \"pairs\": pairs, \"sim_matrix\": sim_mat,\n \"strategy\": strategy, \"image_names\": names, \"features\": feats,\n }\n return results\n\n","metadata":{"papermill":{"duration":0.022554,"end_time":"2026-04-16T03:02:46.719163+00:00","exception":false,"start_time":"2026-04-16T03:02:46.696609+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:37.751916Z","iopub.execute_input":"2026-04-16T16:53:37.752205Z","iopub.status.idle":"2026-04-16T16:53:37.770069Z","shell.execute_reply.started":"2026-04-16T16:53:37.752174Z","shell.execute_reply":"2026-04-16T16:53:37.769399Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# CELL 8 — Clustering\n# ============================================================\ndef dbscan_cluster(features, sim_matrix):\n dist = np.clip(1.0 - sim_matrix, 0, 2)\n return DBSCAN(eps=DBSCAN_EPS, min_samples=DBSCAN_MIN_SAMPLES,\n metric=\"precomputed\").fit(dist).labels_\n\nclass GeometricMatcher:\n def __init__(self, device=None, max_keypoints=2048):\n self.device = device or DEVICE\n self.extractor = SuperPoint(max_num_keypoints=max_keypoints).eval().to(self.device)\n self.matcher = LightGlue(features=\"superpoint\").eval().to(self.device)\n print(\"✅ SuperPoint + LightGlue loaded (offline).\")\n\n def match_pair(self, path_i, path_j):\n try:\n img_i = load_image(str(path_i)).to(self.device)\n img_j = load_image(str(path_j)).to(self.device)\n with torch.inference_mode():\n fi = self.extractor.extract(img_i)\n fj = self.extractor.extract(img_j)\n m = self.matcher({\"image0\": fi, \"image1\": fj})\n fi, fj, m = rbd(fi), rbd(fj), rbd(m)\n matches = m[\"matches\"]\n if matches is None or len(matches) < 8: return 0, 0\n kpi = fi[\"keypoints\"][matches[:, 0]].cpu().numpy()\n kpj = fj[\"keypoints\"][matches[:, 1]].cpu().numpy()\n _, mask = cv2.findFundamentalMat(\n kpi, kpj, cv2.USAC_MAGSAC,\n ransacReprojThreshold=1.0, confidence=0.999, maxIters=10000)\n return (int(mask.sum()) if mask is not None else 0), len(matches)\n except Exception as e:\n print(f\" match_pair failed: {e}\"); return 0, 0\n finally:\n safe_empty_cache()\n\n def match_pairs(self, image_dir, image_names, pairs, max_pairs=None):\n if max_pairs: pairs = pairs[:max_pairs]\n results = []\n for idx, (i, j, sim) in enumerate(tqdm(pairs, desc=\"Geometric\")):\n ni, nm = self.match_pair(\n Path(image_dir) / image_names[i],\n Path(image_dir) / image_names[j])\n results.append((i, j, ni, nm))\n if idx % 25 == 0: safe_empty_cache()\n return results\n\n def release(self):\n del self.extractor, self.matcher; safe_empty_cache()\n\ndef build_match_graph(image_names, match_results):\n G = nx.Graph()\n for i, nm in enumerate(image_names): G.add_node(i, image=nm)\n for i, j, ni, nm in match_results:\n if ni >= MIN_INLIERS: G.add_edge(i, j, weight=ni)\n return G\n\ndef detect_communities(G, image_names):\n n = len(image_names)\n labels = np.full(n, -1, dtype=int)\n connected = [v for v in G.nodes() if G.degree(v) > 0]\n if not connected: return labels\n G2 = G.subgraph(connected).copy()\n scene_id = 0\n for comp in nx.connected_components(G2):\n nodes = list(comp)\n if len(nodes) < 3: continue\n sub = G2.subgraph(nodes).copy()\n if len(nodes) > 10:\n partition = community_louvain.best_partition(sub, weight=\"weight\", resolution=1.0)\n comms = {}\n for v, c in partition.items(): comms.setdefault(c, []).append(v)\n for cnodes in comms.values():\n if len(cnodes) >= 3:\n for v in cnodes: labels[v] = scene_id\n scene_id += 1\n else:\n for v in nodes: labels[v] = scene_id\n scene_id += 1\n return labels\n\ndef cluster_dataset(dataset_name, retrieval_result, data_dir):\n image_names = retrieval_result[\"image_names\"]\n features = retrieval_result[\"features\"]\n sim_matrix = retrieval_result[\"sim_matrix\"]\n pairs = retrieval_result[\"pairs\"]\n strategy = retrieval_result[\"strategy\"]\n\n print(f\"\\nClustering: {dataset_name} strategy={strategy}\")\n\n if strategy == \"dinov3_only\":\n labels = dbscan_cluster(features, sim_matrix)\n else:\n max_pairs = 1000 if strategy == \"dinov3+geometric\" else 3000\n matcher = GeometricMatcher()\n match_results = matcher.match_pairs(data_dir, image_names, pairs, max_pairs=max_pairs)\n graph = build_match_graph(image_names, match_results)\n labels = detect_communities(graph, image_names)\n matcher.release()\n\n return normalize_labels(labels)\n","metadata":{"papermill":{"duration":0.025239,"end_time":"2026-04-16T03:02:46.752365+00:00","exception":false,"start_time":"2026-04-16T03:02:46.727126+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:37.771066Z","iopub.execute_input":"2026-04-16T16:53:37.771308Z","iopub.status.idle":"2026-04-16T16:53:37.791431Z","shell.execute_reply.started":"2026-04-16T16:53:37.771280Z","shell.execute_reply":"2026-04-16T16:53:37.790736Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# CELL 9 — Pose Estimation via DUSt3R (faster + Kaggle-safe)\n# ============================================================\ndef load_mast3r_model_once():\n if not hasattr(load_mast3r_model_once, \"_model\"):\n weights_dir = MAST3R_ROOT / \"mast3r\" / \"weights\"\n model = AsymmetricMASt3R.from_pretrained(weights_dir)\n\n # Keep MASt3R on single GPU for stability\n model = model.to(DEVICE).eval()\n\n load_mast3r_model_once._model = model\n print(\"✅ MASt3R inference model loaded.\")\n return load_mast3r_model_once._model\n\n\ndef build_scene_pairs(n):\n if n <= FULLY_CONNECTED_LIMIT:\n return [(i, j) for i in range(n) for j in range(i + 1, n)]\n\n raw = []\n for i in range(n):\n for skip in PAIR_SKIP_LIST:\n j = i + skip\n if j < n:\n raw.append((i, j))\n\n # add a few long-range links for connectivity\n step = max(3, n // 5)\n for i in range(0, n, step):\n j = min(n - 1, i + step)\n if i < j:\n raw.append((i, j))\n\n pairs = sorted(list({(min(a, b), max(a, b)) for a, b in raw}))\n return pairs\n\n\ndef reconstruct_scene_with_dust3r(image_paths: list) -> dict:\n n = len(image_paths)\n if n < 2:\n return {}\n\n if n > MAX_IMAGES_PER_SCENE:\n print(f\" Capping scene from {n} to {MAX_IMAGES_PER_SCENE} images.\")\n image_paths = image_paths[:MAX_IMAGES_PER_SCENE]\n n = MAX_IMAGES_PER_SCENE\n\n model = load_mast3r_model_once()\n imgs = load_images([str(p) for p in image_paths], size=512, verbose=False)\n\n pairs = build_scene_pairs(n)\n pair_dicts = [(imgs[i], imgs[j]) for i, j in pairs]\n\n print(f\" MASt3R inference: {len(pair_dicts)} pairs, {n} images ...\")\n\n # IMPORTANT:\n # use no_grad, not inference_mode, because global_aligner needs autograd later\n with torch.no_grad():\n output = inference(\n pair_dicts,\n model,\n DEVICE,\n batch_size=BATCH_SIZE_MAST3R,\n verbose=False\n )\n\n print(f\" Global alignment ({NITER_GLOBAL} iters) ...\")\n scene = global_aligner(\n output,\n device=DEVICE,\n mode=GlobalAlignerMode.PointCloudOptimizer\n )\n\n loss = scene.compute_global_alignment(\n init=\"mst\",\n niter=NITER_GLOBAL,\n schedule=\"cosine\",\n lr=LR_GLOBAL\n )\n print(f\" Alignment loss: {float(loss):.4f}\")\n\n poses_c2w = scene.get_im_poses()\n pose_dict = {}\n\n for img_path, c2w in zip(image_paths, poses_c2w):\n try:\n c2w_np = c2w.detach().cpu().numpy() if hasattr(c2w, \"detach\") else np.array(c2w)\n R_c2w = c2w_np[:3, :3]\n t_c2w = c2w_np[:3, 3]\n\n # competition wants world-to-camera\n R_w2c = R_c2w.T\n t_w2c = -R_w2c @ t_c2w\n\n pose_dict[Path(img_path).name] = {\n \"R\": R_w2c,\n \"t\": t_w2c\n }\n except Exception as e:\n print(f\" Pose extraction failed for {img_path}: {e}\")\n\n del output, scene, poses_c2w, imgs, pair_dicts\n safe_empty_cache()\n return pose_dict\n\n\ndef run_all_reconstruction(clustering_results, test_dir: Path):\n all_poses = {}\n\n for dname, result in tqdm(clustering_results.items(), desc=\"Reconstructing\"):\n all_poses[dname] = {}\n scene_labels = result[\"scene_labels\"]\n image_names = result[\"image_names\"]\n dataset_dir = test_dir / dname\n unique_scenes = sorted(set(scene_labels) - {-1})\n\n print(f\"\\n=== {dname} | {len(unique_scenes)} clusters ===\")\n\n for sid in unique_scenes:\n sname = f\"scene_{sid}\"\n img_paths = [\n dataset_dir / image_names[i]\n for i, l in enumerate(scene_labels)\n if l == sid\n ]\n img_paths = [p for p in img_paths if p.exists()]\n\n print(f\" {sname}: {len(img_paths)} images\")\n\n if len(img_paths) < 2:\n all_poses[dname][sname] = {}\n continue\n\n try:\n poses = reconstruct_scene_with_dust3r(img_paths)\n all_poses[dname][sname] = poses\n print(f\" ✅ {len(poses)}/{len(img_paths)} posed\")\n except Exception as e:\n print(f\" ❌ {sname} failed: {e}\")\n all_poses[dname][sname] = {}\n\n safe_empty_cache()\n\n return all_poses","metadata":{"papermill":{"duration":0.025839,"end_time":"2026-04-16T03:02:46.786825+00:00","exception":false,"start_time":"2026-04-16T03:02:46.760986+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:37.792568Z","iopub.execute_input":"2026-04-16T16:53:37.792873Z","iopub.status.idle":"2026-04-16T16:53:37.813185Z","shell.execute_reply.started":"2026-04-16T16:53:37.792842Z","shell.execute_reply":"2026-04-16T16:53:37.812563Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ============================================================\n# CELL 10 — Submission helpers\n# ============================================================\ndef build_base_from_test_root(test_dir):\n rows = []\n datasets = list_test_datasets(test_dir)\n for dataset_name, image_paths in datasets.items():\n for path in image_paths:\n rows.append({\n \"dataset\": dataset_name,\n \"image\": path.name,\n })\n\n base = pd.DataFrame(rows)\n base[\"image_id\"] = base[\"dataset\"] + base[\"image\"]\n return base\n\n\ndef build_prediction_tables(clustering_results, all_poses):\n predictions_by_dataset = {}\n\n for dataset_name, cluster_result in clustering_results.items():\n scene_labels = np.asarray(cluster_result[\"scene_labels\"])\n image_names = list(cluster_result[\"image_names\"])\n\n unique_labels = sorted(x for x in set(scene_labels.tolist()) if x != -1)\n label_remap = {old: new for new, old in enumerate(unique_labels)}\n\n scene_by_image = {}\n for image_name, label in zip(image_names, scene_labels):\n if int(label) == -1:\n scene_by_image[image_name] = \"outliers\"\n else:\n scene_by_image[image_name] = f\"cluster{label_remap[int(label)]}\"\n\n pose_by_image = {}\n if dataset_name in all_poses:\n for scene_name, scene_payload in all_poses[dataset_name].items():\n if not isinstance(scene_payload, dict):\n continue\n for image_name, pose in scene_payload.items():\n if not isinstance(pose, dict):\n continue\n R = pose.get(\"R\")\n t = pose.get(\"t\")\n if R is None or t is None:\n continue\n pose_by_image[image_name] = {\n \"rotation_matrix\": format_R(R),\n \"translation_vector\": format_t(t),\n }\n\n rows = []\n for image_name in image_names:\n pose = pose_by_image.get(\n image_name,\n {\"rotation_matrix\": NAN_R, \"translation_vector\": NAN_T}\n )\n rows.append({\n \"image\": image_name,\n \"scene\": scene_by_image[image_name],\n \"rotation_matrix\": pose[\"rotation_matrix\"],\n \"translation_vector\": pose[\"translation_vector\"],\n })\n\n predictions_by_dataset[dataset_name] = pd.DataFrame(rows)\n\n return predictions_by_dataset\n\n\ndef build_submission_from_predictions(test_dir, predictions_by_dataset, submission_path):\n base = build_base_from_test_root(test_dir)\n parts = []\n\n for dataset_name in sorted(base[\"dataset\"].unique()):\n base_ds = base[base[\"dataset\"] == dataset_name].copy()\n\n if dataset_name in predictions_by_dataset:\n pred_ds = predictions_by_dataset[dataset_name].copy()\n merged = base_ds.merge(pred_ds, on=\"image\", how=\"left\")\n else:\n merged = base_ds.copy()\n merged[\"scene\"] = \"outliers\"\n merged[\"rotation_matrix\"] = NAN_R\n merged[\"translation_vector\"] = NAN_T\n\n merged[\"scene\"] = merged[\"scene\"].fillna(\"outliers\")\n merged[\"rotation_matrix\"] = merged[\"rotation_matrix\"].fillna(NAN_R)\n merged[\"translation_vector\"] = merged[\"translation_vector\"].fillna(NAN_T)\n\n parts.append(merged)\n\n submission_df = pd.concat(parts, axis=0, ignore_index=True)\n submission_df = submission_df[\n [\"image_id\", \"dataset\", \"scene\", \"image\", \"rotation_matrix\", \"translation_vector\"]\n ]\n submission_df.to_csv(submission_path, index=False)\n return submission_df","metadata":{"papermill":{"duration":0.022174,"end_time":"2026-04-16T03:02:46.817826+00:00","exception":false,"start_time":"2026-04-16T03:02:46.795652+00:00","status":"completed"},"tags":[],"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:37.813876Z","iopub.execute_input":"2026-04-16T16:53:37.814147Z","iopub.status.idle":"2026-04-16T16:53:37.832677Z","shell.execute_reply.started":"2026-04-16T16:53:37.814101Z","shell.execute_reply":"2026-04-16T16:53:37.832039Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# CELL 11 Run Everything\n\ndatasets = list_test_datasets(TEST_DIR)\nprint(f\"Test datasets found: {len(list_test_datasets(TEST_DIR))}\")\n\nprint(\"STEP 1 Feature extraction\")\nextract_all_dino_datasets(TEST_DIR, STEP1_DINO_DIR)\nextract_all_mast3r_datasets(TEST_DIR, STEP1_MAST3R_DIR)\n\ndino_features = load_feature_pickles(STEP1_DINO_DIR)\nmast3r_features = load_feature_pickles(STEP1_MAST3R_DIR)\n\nprint(\"STEP 2 Retrieval results\")\nretrieval_results = retrieve_all_datasets(dino_features, mast3r_features)\nwith open(STEP2_DIR / \"step2.pkl\", \"wb\") as f:\n pickle.dump(retrieval_results, f)\n\nprint(\"STEP 3 Clustering results\")\nclustering_results = {}\nfor dataset_name, retrieval_result in retrieval_results.items():\n labels = cluster_dataset(dataset_name, retrieval_result, TEST_DIR / dataset_name)\n clustering_results[dataset_name] = {\n \"scene_labels\": labels,\n \"strategy\": retrieval_result[\"strategy\"],\n \"image_names\": retrieval_result[\"image_names\"],\n }\n safe_empty_cache()\n\nwith open(STEP3_DIR / \"step3.pkl\", \"wb\") as f:\n pickle.dump(clustering_results, f)\n\nprint(\"STEP 4 Pose estimation\")\nall_poses = run_all_reconstruction(clustering_results, TEST_DIR)\nwith open(STEP4_DIR / \"poses.pkl\", \"wb\") as f:\n pickle.dump(all_poses, f)\n\nprint(\"STEP 5 Build submission\")\npredictions_by_dataset = build_prediction_tables(clustering_results, all_poses)\nsubmission_df = build_submission_from_predictions(TEST_DIR, predictions_by_dataset, SUBMISSION_PATH)\n\nprint(f\"Saved: {SUBMISSION_PATH}\")\nprint(submission_df.head(10).to_string())\nprint(f\"rows={len(submission_df)}, datasets={submission_df['dataset'].nunique()}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-16T16:53:37.833423Z","iopub.execute_input":"2026-04-16T16:53:37.833691Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}