{"id":36129,"url":"https://github.com/dashidhy/awesome-point-cloud-deep-learning","name":"awesome-point-cloud-deep-learning","description":"Paper list of deep learning on point clouds.","projects_count":42,"last_synced_at":"2026-09-06T23:00:27.454Z","repository":{"id":62005751,"uuid":"194451890","full_name":"dashidhy/awesome-point-cloud-deep-learning","owner":"dashidhy","description":"Paper list of deep learning on point clouds.","archived":false,"fork":false,"pushed_at":"2020-02-19T21:29:13.000Z","size":11,"stargazers_count":39,"open_issues_count":0,"forks_count":6,"subscribers_count":4,"default_branch":"master","last_synced_at":"2026-07-29T18:05:13.923Z","etag":null,"topics":["3d-deep-learning","3d-detection","3d-segmentation","3d-vision","paper-list","point-cloud"],"latest_commit_sha":null,"homepage":null,"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/dashidhy.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":"2019-06-29T22:01:41.000Z","updated_at":"2024-06-13T11:56:06.000Z","dependencies_parsed_at":"2022-10-25T02:45:16.341Z","dependency_job_id":null,"html_url":"https://github.com/dashidhy/awesome-point-cloud-deep-learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dashidhy/awesome-point-cloud-deep-learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dashidhy%2Fawesome-point-cloud-deep-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dashidhy%2Fawesome-point-cloud-deep-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dashidhy%2Fawesome-point-cloud-deep-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dashidhy%2Fawesome-point-cloud-deep-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dashidhy","download_url":"https://codeload.github.com/dashidhy/awesome-point-cloud-deep-learning/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dashidhy%2Fawesome-point-cloud-deep-learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36756479,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-06T04:43:03.162Z","status":"online","status_checked_at":"2026-08-18T02:00:06.197Z","response_time":52,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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"}},"created_at":"2024-01-13T12:59:29.569Z","updated_at":"2026-09-06T23:00:27.455Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["2. Detection","1. Feature extractor","4. Dataset","3. Segmentation"],"sub_categories":["2D proposal based","Only geometry as input","Autonomous driving (Lidar point cloud)","Multi-view/multi-sensor/multi-task","Other useful links","Shape understanding","Indoor scenes"],"readme":"# Awesome papers of deep learning on point clouds\n\nThis repo collects papers on point cloud deep learning. Note that the stars I give to each paper contain personal bias for my own project, but actually I do appreciate all the works that have been done in this area. For my own purpose, I can't include all the papers that have been published. A more complete paper list since 2017 is here: [https://github.com/Yochengliu/awesome-point-cloud-analysis](https://github.com/Yochengliu/awesome-point-cloud-analysis).\n\n## 1. Feature extractor\n\n- **Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models** (ICCV 2017), R. Klokov et al. [[pdf]](https://arxiv.org/pdf/1704.01222.pdf) :star: :star: :star: :star:\n- **PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation** (CVPR 2017), C. R. Qi et al. [[pdf]](https://arxiv.org/pdf/1612.00593.pdf) [[Github]](https://github.com/charlesq34/pointnet) :star: :star: :star: :star: :star:\n- **PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space** (NeurIPS 2017), C. R. Qi et al. [[pdf]](https://arxiv.org/pdf/1706.02413.pdf) [[Github]](https://github.com/charlesq34/pointnet2) :star: :star: :star: :star: :star:\n- **PointCNN: Convolution On X-Transformed Points** (NeurIPS 2018) Y. Li et al, [[pdf]](https://arxiv.org/pdf/1801.07791.pdf) [[Github]](https://github.com/yangyanli/PointCNN) :star: :star: :star:\n- **A-CNN: Annularly Convolutional Neural Networks on Point Clouds** (CVPR 2019), A. Komarichev et al. [[pdf]](https://arxiv.org/pdf/1904.08017.pdf) \u003cbr/\u003e :star: :star: :star:\n- **Relation-Shape Convolutional Neural Network for Point Cloud Analysis** (CVPR 2019), Y. Liu et al. [[pdf]](https://arxiv.org/pdf/1904.07601.pdf) \u003cbr/\u003e :star: :star: :star: :star:\n\n### Other useful links\n\n- [ModelNet Benchmark](http://modelnet.cs.princeton.edu/)\n\n## 2. Detection\n\n### Only geometry as input\n\n**Grid-based methods**\n\n- **Voting for Voting in Online Point Cloud Object Detection** (RSS 2015), D. Z. Wang et al. [[pdf]](http://www.robots.ox.ac.uk/~mobile/Papers/2015RSS_wang.pdf) :star: :star: :star:\n- **Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks** (ICRA 2017), M. Engelcke et al. [[pdf]](https://arxiv.org/pdf/1609.06666.pdf) :star: :star: :star:\n- **3D fully convolutional network for vehicle detection in point cloud** (IROS 2017) B. Li. [[pdf]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8205955) [[Github]](https://github.com/yukitsuji/3D_CNN_tensorflow) :star: :star: :star:\n- **VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection** (CVPR 2018), Y. Zhou et al. [[pdf]](https://arxiv.org/pdf/1711.06396.pdf) \u003cbr/\u003e :star: :star: :star: :star: :star:\n- **PIXOR: Real-time 3D Object Detection From Point Clouds** (CVPR 2018), B. Yang et al. [[pdf]](https://arxiv.org/pdf/1902.06326.pdf) :star: :star: :star: :star:\n- **SECOND: Sparsely Embedded Convolutional Detection** (Sensors 2018) Y. Yan et al. [[pdf]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210968/pdf/sensors-18-03337.pdf) [[Github]](https://github.com/traveller59/second.pytorch) :star: :star: :star:\n- **PointPillars: Fast Encoders for Object Detection from Point Clouds** (CVPR 2019), A. Lang et al. [[pdf]](https://arxiv.org/pdf/1812.05784.pdf) [[GIthub]](https://github.com/nutonomy/second.pytorch)  \u003cbr/\u003e :star: :star: :star: :star: :star:\n- **Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud** (ArXiv 2019) S. Shi et al. [[pdf]](https://arxiv.org/pdf/1907.03670.pdf) [[Github]](https://github.com/sshaoshuai/PartA2-Net) :star: :star: :star: :star:\n\n**Point-based methods**\n\n- **PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud** (CVPR 2019), S. Shi et al. [[pdf]](https://arxiv.org/pdf/1812.04244.pdf) [[Github]](https://github.com/sshaoshuai/PointRCNN) :star: :star: :star: :star: :star:\n- **Deep Hough Voting for 3D Object Detection in Point Clouds** (ICCV 2019) C. R. Qi et al. [[pdf]](https://arxiv.org/pdf/1904.09664.pdf) [[Github]](https://github.com/facebookresearch/votenet) \u003cbr/\u003e :star: :star: :star: :star: :star:\n\n**Combining point-based and grid-based methods**\n\n- **STD: Sparse-to-Dense 3D Object Detector for Point Cloud** (ICCV 2019), Z. Yang et al. [[pdf]](https://arxiv.org/pdf/1907.10471.pdf) :star: :star: :star: :star:\n- **Fast Point R-CNN** (ICCV 2019), Y. Chen et al. [[pdf]](https://arxiv.org/pdf/1908.02990.pdf) :star: :star: :star:\n- **PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection** (Arxiv 2019) S. Shi et al. [[pdf]](https://arxiv.org/pdf/1912.13192.pdf) \u003cbr/\u003e :star: :star: :star: :star: :star:\n\n### 2D proposal based\n\n- **IPOD: Intensive Point-based Object Detector for Point Cloud** (ArXiv 2018) Z. Yang et al. [[pdf]](https://arxiv.org/pdf/1812.05276.pdf) :star: :star: :star: :star:\n- **RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement** ((ArXiv 2018), K. Shin et al. [[pdf]](https://arxiv.org/pdf/1811.03818.pdf)\n- **Frustum PointNets for 3D Object Detection from RGB-D Data** (CVPR 2018), C. R. Qi et al. [[pdf]](https://arxiv.org/pdf/1711.08488.pdf) [[GIthub]](https://github.com/charlesq34/frustum-pointnets) \u003cbr/\u003e :star: :star: :star: :star: :star:\n- **Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection** (CVPR 2019), Z. Wang et al. [[pdf]](https://arxiv.org/pdf/1903.01864.pdf) :star: :star: :star:\n\n### Multi-view/multi-sensor/multi-task\n\n- **Multi-View 3D Object Detection Network for Autonomous Driving** (CVPR 2017), X. Chen et al. [[pdf]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Multi-View_3D_Object_CVPR_2017_paper.pdf) [[Github]](https://github.com/bostondiditeam/MV3D) \u003cbr/\u003e :star: :star: :star: :star:\n- **PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation** (CVPR 2018), D. Xu et al. [[pdf]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_PointFusion_Deep_Sensor_CVPR_2018_paper.pdf) :star: :star: :star: :star:\n- **Deep Continuous Fusion for Multi-Sensor 3D Object Detection** (ECCV 2018), M. Liang et al. [[pdf]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf) :star: :star: :star: :star:\n- **Multi-Task Multi-Sensor Fusion for 3D Object Detection** (CVPR 2019), M. Liang et al. [[pdf]](http://www.cs.toronto.edu/~byang/papers/mmf.pdf) :star: :star: :star: :star: :star:\n- **MVX-Net: Multimodal VoxelNet for 3D Object Detection** (ICRA 2019), V. A. Sindagi et al. [[pdf]](https://arxiv.org/pdf/1904.01649.pdf) :star: :star: :star: :star:\n\n### Other useful links\n- [KITTI Leaderboard](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d)\n- [nuScenes Leaderboard](https://www.nuscenes.org/object-detection?externalData=all\u0026mapData=all\u0026modalities=Any)\n\n## 3. Segmentation\n\n- **Recurrent Slice Networks for 3D Segmentation of Point Clouds** (CVPR 2018), Q. Huang et al. [[pdf]](https://arxiv.org/pdf/1802.04402.pdf) [[Github]](https://github.com/qianguih/RSNet) \u003cbr/\u003e :star: :star: :star: :star:\n- **SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation** (CVPR 2018), W. Wang et al. [[pdf]](https://arxiv.org/pdf/1711.08588.pdf) [[Github]](https://github.com/laughtervv/SGPN) :star: :star: :star: :star:\n- **Associatively Segmenting Instances and Semantics in Point Clouds** (CVPR 2019), X. Long et al. [[pdf]](https://arxiv.org/pdf/1902.09852.pdf) \u003cbr/\u003e :star: :star: :star: :star: :star:\n\n...(To be completed)\n\n## 4. Dataset\n\nNote that some of these datasets don't provide point cloud data, which means you need some toolboxes to convert data from mesh or RGB-D images.\n\n### Shape understanding\n\n- **ModelNet** [[pdf]](https://people.csail.mit.edu/khosla/papers/cvpr2015_wu.pdf) [[Project]](http://modelnet.cs.princeton.edu/)\n- **ShapeNet** [[pdf]](http://shapenet.cs.stanford.edu/shapenet/obj-zip/ShapeNetCore.v2-old/shapenet/tex/TechnicalReport/main.pdf) [[Project]](https://www.shapenet.org/)\n\n### Indoor scenes\n\n- **2D-3D-S** [[pdf]](http://buildingparser.stanford.edu/images/2D-3D-S_2017.pdf) [[Project]](http://buildingparser.stanford.edu/dataset.html)\n- **ScanNet** [[pdf]](https://arxiv.org/pdf/1702.04405.pdf) [[Project]](http://www.scan-net.org/)\n- **SUN RGB-D** [[pdf]](http://rgbd.cs.princeton.edu/paper.pdf) [[Project]](http://rgbd.cs.princeton.edu/)\n\n### Autonomous driving (Lidar point cloud)\n\n- **KITTI** [[pdf]](http://www.cvlibs.net/publications/Geiger2013IJRR.pdf) [[Project]](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d)\n- **nuScenes** [[pdf]](https://arxiv.org/pdf/1903.11027.pdf) [[Project]](https://www.nuscenes.org/)\n- **Waymo Open dataset** [[Project]](https://waymo.com/open/)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/dashidhy%2Fawesome-point-cloud-deep-learning/projects"}