{"id":13434536,"url":"https://github.com/QingyongHu/SoTA-Point-Cloud","last_synced_at":"2025-03-17T18:31:03.381Z","repository":{"id":37672496,"uuid":"230131765","full_name":"QingyongHu/SoTA-Point-Cloud","owner":"QingyongHu","description":"🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey","archived":false,"fork":false,"pushed_at":"2021-06-08T07:29:25.000Z","size":2385,"stargazers_count":1594,"open_issues_count":4,"forks_count":190,"subscribers_count":74,"default_branch":"master","last_synced_at":"2025-02-01T09:15:43.854Z","etag":null,"topics":["3d-classification","3d-deep-learning","3d-detection","3d-segmentation","3d-tracking","instance-segmentation","pointclouds","semantic-segmentation"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/QingyongHu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-12-25T17:08:03.000Z","updated_at":"2025-01-26T19:08:13.000Z","dependencies_parsed_at":"2022-08-08T21:15:42.692Z","dependency_job_id":null,"html_url":"https://github.com/QingyongHu/SoTA-Point-Cloud","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QingyongHu%2FSoTA-Point-Cloud","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QingyongHu%2FSoTA-Point-Cloud/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QingyongHu%2FSoTA-Point-Cloud/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QingyongHu%2FSoTA-Point-Cloud/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/QingyongHu","download_url":"https://codeload.github.com/QingyongHu/SoTA-Point-Cloud/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244088118,"owners_count":20396077,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["3d-classification","3d-deep-learning","3d-detection","3d-segmentation","3d-tracking","instance-segmentation","pointclouds","semantic-segmentation"],"created_at":"2024-07-31T02:02:00.130Z","updated_at":"2025-03-17T18:31:02.901Z","avatar_url":"https://github.com/QingyongHu.png","language":null,"funding_links":[],"categories":["Others","Uncategorized"],"sub_categories":["Uncategorized"],"readme":"[![arXiv](https://img.shields.io/badge/arXiv-1912.12033-b31b1b.svg)](https://arxiv.org/abs/1912.12033)\n[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/QingyongHu/SoTA-Point-Cloud/graphs/commit-activity)\n[![GitHub issues](https://img.shields.io/github/issues/QingyongHu/SoTA-Point-Cloud)](https://GitHub.com/QingyongHu/SoTA-Point-Cloud/issues/)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)\n\n\n# Deep Learning for 3D Point Clouds: A Survey (IEEE TPAMI, 2020)\n\nThis is the official repository of **Deep Learning for 3D Point Clouds: A Survey** (IEEE TPAMI), a comprehensive survey \nof recent progress in deep learning methods for point clouds. For details, please refer to:\n \n**Deep Learning for 3D Point Clouds: A Survey** \u003cbr /\u003e\n\n[Yulan Guo\u003csup\u003e∗\u003c/sup\u003e](http://yulanguo.me/), \n[Hanyun Wang\u003csup\u003e∗\u003c/sup\u003e](https://scholar.google.com.hk/citations?user=QG3LdUcAAAAJ\u0026hl=zh-CN), \n[Qingyong Hu\u003csup\u003e∗\u003c/sup\u003e](https://qingyonghu.github.io/), Hao Liu\u003csup\u003e∗\u003c/sup\u003e,\n[Li Liu](http://www.ee.oulu.fi/~lili/LiLiuHomepage.html), \nand [Mohammed Bennamoun](http://staffhome.ecm.uwa.edu.au/~00051632/). \u003cbr /\u003e\n(* *indicates equal contribution*)\n\n**[[Paper](https://arxiv.org/abs/1912.12033)] [[Blog](https://mp.weixin.qq.com/s/5RJAv_cOlhee1R9uZzkmHQ)]** \u003cbr /\u003e\n\n\u003cp align=\"center\"\u003e \u003cimg src=\"./taxonomy.png\" width=\"95%\"\u003e \u003c/p\u003e\n\n\n## Introduction\nWe present a comprehensive review of recent deep learning methods for point clouds. It covers major tasks in 3D point cloud analysis, \nincluding 3D shape classification, 3D object detection, and 3D point cloud segmentation. It also presents comparative \nresults on several publicly available datasets, together with insightful observations and inspiring future research directions.\nPlease feel free to \u003ca href=\"mailto:qingyong.hu@cs.ox.ac.uk\"\u003econtact me\u003c/a\u003e \nor [create an issue](https://help.github.com/en/github/managing-your-work-on-github/creating-an-issue) on this page if you have new results to add or any suggestions! \n\nWe will update this page on a regular basis! So stay tuned~ :tada::tada::tada:\n\n### (1) Datasets\n\u003cp align=\"center\"\u003e \u003cimg src=\"./Dataset.png\" width=\"95%\"\u003e \u003c/p\u003e\n\n### (2) 3D Shape Classification\n#### Public Datasets\n- ModelNet (CVPR'15) [[paper]](http://3dvision.princeton.edu/projects/2014/3DShapeNets/paper.pdf) [[project page]](http://modelnet.cs.princeton.edu/)\n    - ModelNet10 [[data]](http://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip) [[results]](http://modelnet.cs.princeton.edu/) \n    - ModelNet40 [[data]](http://modelnet.cs.princeton.edu/ModelNet40.zip) [[results]](http://modelnet.cs.princeton.edu/) \n- PartNet (CVPR'19) [[paper]](https://arxiv.org/abs/1812.02713) [[data]](https://github.com/daerduoCarey/partnet_dataset) [[project page]](https://cs.stanford.edu/~kaichun/partnet/)\n- ScanObjectNN (ICCV'19) [[paper]](https://arxiv.org/pdf/1908.04616.pdf) [[data]](https://github.com/hkust-vgd/scanobjectnn) [[project page]](https://hkust-vgd.github.io/scanobjectnn/)\n\n#### Benchmark Results\n\u003cp align=\"center\"\u003e \u003cimg src=\"./classification.png\" width=\"95%\"\u003e \u003c/p\u003e\n\n### (3) 3D Object Detection\n#### Public Datasets\n- KITTI (CVPR'12) [[paper]](http://www.cvlibs.net/publications/Geiger2012CVPR.pdf) [[project page]](http://www.cvlibs.net/datasets/kitti/eval_3dobject.php)\n    - _3D objecct detection_ [[data]](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) [[results]](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d)\n    - _BEV_ [[data]](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=bev) [[results]](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=bev)\n- ApolloScape (TPAMI'19) [[paper]](http://ad-apolloscape.bj.bcebos.com/public%2FApolloScape%20Dataset.pdf) [[data]](http://apolloscape.auto/tracking.html#to_data_href) [[results]](http://apolloscape.auto/leader_board.html)\n- Argoverse (CVPR'19) [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf) [[data]](https://www.argoverse.org/data.html#download-link) [[project page]](https://www.argoverse.org/index.html)\n- A*3D (arXiv'19) [[paper]](https://arxiv.org/pdf/1909.07541) [[data]](https://github.com/I2RDL2/ASTAR-3D#Download) [[project page]](https://github.com/I2RDL2/ASTAR-3D)\n- Waymo (arXiv'19) [[paper]](https://arxiv.org/pdf/1912.04838) [[data]](https://waymo.com/open/licensing/) [[project page]](https://waymo.com/open/)\n\n#### Benchmark Results\n\u003cp align=\"center\"\u003e \u003cimg src=\"./detection.png\" width=\"95%\"\u003e \u003c/p\u003e\n\u003cp align=\"center\"\u003e \u003cimg src=\"./detection_bev.png\" width=\"95%\"\u003e \u003c/p\u003e\n\n\n### (4) 3D Point Cloud Segmentation\n#### Public Datasets\n- Semantic3D (ISPRS'17) [[paper]](https://www.ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/Papers/Hackel-etal-cmrt2017.pdf) [[project page]](http://www.semantic3d.net/)\n    - _semantic-8_ [[data]](http://www.semantic3d.net/view_dbase.php?chl=1#download) [[results]](http://www.semantic3d.net/view_results.php?chl=1)\n    - _reduced-8_ [[data]](http://www.semantic3d.net/view_dbase.php?chl=2#download) [[results]](http://www.semantic3d.net/view_results.php?chl=2)\n- S3DIS (CVPR'17) [[paper]](http://buildingparser.stanford.edu/images/3D_Semantic_Parsing.pdf) [[data]](https://docs.google.com/forms/d/e/1FAIpQLScDimvNMCGhy_rmBA2gHfDu3naktRm6A8BPwAWWDv-Uhm6Shw/viewform?c=0\u0026w=1) [[project page]](http://buildingparser.stanford.edu/dataset.html#Download)\n- ScanNet (CVPR'17) [[paper]](https://arxiv.org/pdf/1702.04405) [[data]](https://github.com/ScanNet/ScanNet) [[project page]](http://www.scan-net.org/) [[results]](http://kaldir.vc.in.tum.de/scannet_benchmark/)  \n- NPM3D (IJRR'18) [[paper]](https://arxiv.org/pdf/1712.00032) [[data]](https://cloud.mines-paristech.fr/index.php/s/JhIxgyt0ALgRZ1O) [[project page]](http://npm3d.fr/) [[results]](http://npm3d.fr/paris-lille-3d) \n- DublinCity (BMVC'19) [[paper]](https://arxiv.org/abs/1909.03613) [[data]](https://v-sense.scss.tcd.ie/dublincity/) [[project page]](https://v-sense.scss.tcd.ie/dublincity/) \n- SemanticKITTI (ICCV'19) [[paper]](https://arxiv.org/pdf/1904.01416) [[data]](http://semantic-kitti.org/dataset.html#download) [[project page]](http://semantic-kitti.org/index.html) [[results]](https://competitions.codalab.org/competitions/20331#results)\n- nuScenes (CVPR'20) [[paper]](https://arxiv.org/abs/1903.11027) [[data]](https://www.nuscenes.org/lidar-segmentation) [[project page]](https://www.nuscenes.org/lidar-segmentation) [[results]](https://www.nuscenes.org/lidar-segmentation)\n- Toronto-3D (CVPRW'20) [[paper]](https://arxiv.org/abs/2003.08284) [[data]](https://github.com/WeikaiTan/Toronto-3D) [[project page]](https://github.com/WeikaiTan/Toronto-3D) [[results]](https://github.com/WeikaiTan/Toronto-3D)\n- DALES (CVPRW'20) [[paper]](https://arxiv.org/abs/2004.11985) [[data]](https://docs.google.com/forms/d/e/1FAIpQLSe3IaTxCS7wKH01SHn_o7U86ToIw9K26vc0bkwiELn6wwh8gg/viewform) [[project page]](https://udayton.edu/engineering/research/centers/vision_lab/research/was_data_analysis_and_processing/dale.php) [[results]](https://arxiv.org/abs/2004.11985)\n- Campus3D (ACM MM'20) [[paper]](https://arxiv.org/abs/2008.04968) [[data]](https://3d.dataset.site/) [[project page]](https://github.com/shinke-li/Campus3D) [[results]](https://arxiv.org/abs/2008.04968)\n- SensatUrban (CVPR'21) [[paper]](https://arxiv.org/abs/2009.03137) [data][[project page]](http://point-cloud-analysis.cs.ox.ac.uk/) [[results]](https://arxiv.org/abs/2009.03137)\n\n\n#### Benchmark Results\n\u003cp align=\"center\"\u003e \u003cimg src=\"./Segmentation.png\" width=\"95%\"\u003e \u003c/p\u003e\n\n\n### Citation\nIf you find our work useful in your research, please consider citing:\n\n    @article{guo2020deep,\n      title={Deep learning for 3d point clouds: A survey},\n      author={Guo, Yulan and Wang, Hanyun and Hu, Qingyong and Liu, Hao and Liu, Li and Bennamoun, Mohammed},\n      journal={IEEE transactions on pattern analysis and machine intelligence},\n      year={2020},\n      publisher={IEEE}\n    }\n\n## Updates\n* 26/02/2020: Adding the dataset information\n* 27/12/2019: Initial release.\n\n\n## Related Repos\n1. [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://github.com/QingyongHu/RandLA-Net) ![GitHub stars](https://img.shields.io/github/stars/QingyongHu/RandLA-Net.svg?style=flat\u0026label=Star)\n2. [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://github.com/QingyongHu/SensatUrban) ![GitHub stars](https://img.shields.io/github/stars/QingyongHu/SensatUrban.svg?style=flat\u0026label=Star)\n3. [3D-BoNet: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds](https://github.com/Yang7879/3D-BoNet) ![GitHub stars](https://img.shields.io/github/stars/Yang7879/3D-BoNet.svg?style=flat\u0026label=Star)\n4. [SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration](https://github.com/QingyongHu/SpinNet) ![GitHub stars](https://img.shields.io/github/stars/QingyongHu/SpinNet.svg?style=flat\u0026label=Star)\n5. [SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000x Fewer Labels](https://github.com/QingyongHu/SQN) ![GitHub stars](https://img.shields.io/github/stars/QingyongHu/SQN.svg?style=flat\u0026label=Star)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FQingyongHu%2FSoTA-Point-Cloud","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FQingyongHu%2FSoTA-Point-Cloud","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FQingyongHu%2FSoTA-Point-Cloud/lists"}