Hi All,\n
\n\nExplanation\n Unlocking Access to Self-Driving Research: The Lyft Level 5 Dataset and Competition\n Lyft Level 5 Self-Driving Dataset Competition Now Open
\n\nData Explanation\n Github\n Notebook
\n\nPapers with Code\n 3d Object Detection On Kitti Cars Moderate
\n\nPapers\nFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks\nVoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection\nFrustum PointNets for 3D Object Detection from RGB-D Data\n Deep Continuous Fusion for Multi-Sensor 3D Object Detection
\n\nThanks
", "messages": [ { "id": 625382, "postDate": "2019-09-13T02:54:08.457Z", "content": "Hi All,\n
\n\nExplanation\n Unlocking Access to Self-Driving Research: The Lyft Level 5 Dataset and Competition\n Lyft Level 5 Self-Driving Dataset Competition Now Open
\n\nData Explanation\n Github\n Notebook
\n\nPapers with Code\n 3d Object Detection On Kitti Cars Moderate
\n\nPapers\nFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks\nVoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection\nFrustum PointNets for 3D Object Detection from RGB-D Data\n Deep Continuous Fusion for Multi-Sensor 3D Object Detection
\n\nThanks
", "rawMarkdown": "Hi All,\n\n \n **Explanation**\n [Unlocking Access to Self-Driving Research: The Lyft Level 5 Dataset and Competition](https://medium.com/lyftlevel5/unlocking-access-to-self-driving-research-the-lyft-level-5-dataset-and-competition-d487c27b1b6c)\n [Lyft Level 5 Self-Driving Dataset Competition Now Open](https://medium.com/lyftlevel5/lyft-level-5-self-driving-dataset-competition-now-open-97493e9f154a)\n\n **Data Explanation**\n [Github](https://github.com/lyft/nuscenes-devkit)\n [Notebook](https://github.com/lyft/nuscenes-devkit/blob/master/notebooks/tutorial_lyft.ipynb)\n\n**Papers with Code**\n [3d Object Detection On Kitti Cars Moderate](https://paperswithcode.com/sota/3d-object-detection-on-kitti-cars-moderate)\n\n **Papers**\n[Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/pdf/1506.01497.pdf)\n[VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/3333.pdf)\n[Frustum PointNets for 3D Object Detection from RGB-D Data](http://zpascal.net/cvpr2018/Qi_Frustum_PointNets_for_CVPR_2018_paper.pdf)\n[ Deep Continuous Fusion for Multi-Sensor 3D Object Detection](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf)\n\n Thanks", "votes": 19 }, { "id": 656633, "postDate": "2019-10-24T13:37:22.400Z", "content": "I believe I was able to get the 2D bounding boxes. However, there are objects that are not visible. In fact I would like to get only objects that are visible, for example, in CAM_FRONT. can anybody help me?
", "rawMarkdown": "I believe I was able to get the 2D bounding boxes. However, there are objects that are not visible. In fact I would like to get only objects that are visible, for example, in CAM_FRONT. can anybody help me?" }, { "id": 626051, "postDate": "2019-09-13T18:20:13.940Z", "content": "Since this is a 3D object detection challenge, it might be a good idea to look some hello world examples to 3D object detection like kernels in 3-D MNIST Challenge.
\n\nhttps://www.kaggle.com/daavoo/3d-mnist/kernels
", "rawMarkdown": "Since this is a 3D object detection challenge, it might be a good idea to look some hello world examples to 3D object detection like kernels in **3-D MNIST Challenge**.\n\nhttps://www.kaggle.com/daavoo/3d-mnist/kernels" }, { "id": 625427, "postDate": "2019-09-13T04:34:02.920Z", "rawMarkdown": "", "isDeleted": true } ], "comments": [ { "id": 656633, "author_name": "Gledson Melotti", "author_url": "", "post_date": "2019-10-24T13:37:22.400000", "content": "I believe I was able to get the 2D bounding boxes. However, there are objects that are not visible. In fact I would like to get only objects that are visible, for example, in CAM_FRONT. can anybody help me?
", "votes": 0, "replies": [] }, { "id": 626051, "author_name": "Kurian Benoy", "author_url": "", "post_date": "2019-09-13T18:20:13.940000", "content": "Since this is a 3D object detection challenge, it might be a good idea to look some hello world examples to 3D object detection like kernels in 3-D MNIST Challenge.
\n\n", "votes": 0, "replies": [] }, { "id": 625427, "author_name": "", "author_url": "", "post_date": "2019-09-13T04:34:02.920000", "content": "", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "625382": "Hi All,\n\n \n **Explanation**\n [Unlocking Access to Self-Driving Research: The Lyft Level 5 Dataset and Competition](https://medium.com/lyftlevel5/unlocking-access-to-self-driving-research-the-lyft-level-5-dataset-and-competition-d487c27b1b6c)\n [Lyft Level 5 Self-Driving Dataset Competition Now Open](https://medium.com/lyftlevel5/lyft-level-5-self-driving-dataset-competition-now-open-97493e9f154a)\n\n **Data Explanation**\n [Github](https://github.com/lyft/nuscenes-devkit)\n [Notebook](https://github.com/lyft/nuscenes-devkit/blob/master/notebooks/tutorial_lyft.ipynb)\n\n**Papers with Code**\n [3d Object Detection On Kitti Cars Moderate](https://paperswithcode.com/sota/3d-object-detection-on-kitti-cars-moderate)\n\n **Papers**\n[Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/pdf/1506.01497.pdf)\n[VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/3333.pdf)\n[Frustum PointNets for 3D Object Detection from RGB-D Data](http://zpascal.net/cvpr2018/Qi_Frustum_PointNets_for_CVPR_2018_paper.pdf)\n[ Deep Continuous Fusion for Multi-Sensor 3D Object Detection](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf)\n\n Thanks", "656633": "I believe I was able to get the 2D bounding boxes. However, there are objects that are not visible. In fact I would like to get only objects that are visible, for example, in CAM_FRONT. can anybody help me?", "626051": "Since this is a 3D object detection challenge, it might be a good idea to look some hello world examples to 3D object detection like kernels in **3-D MNIST Challenge**.\n\nhttps://www.kaggle.com/daavoo/3d-mnist/kernels", "625427": "" } }