{"id":20062277,"url":"https://github.com/cuge1995/cvpr-2020-point-cloud-analysis","last_synced_at":"2026-02-26T08:40:46.820Z","repository":{"id":117637639,"uuid":"271494453","full_name":"cuge1995/CVPR-2020-point-cloud-analysis","owner":"cuge1995","description":"CVPR 2020 papers focusing on point cloud analysis","archived":false,"fork":false,"pushed_at":"2021-01-13T12:35:16.000Z","size":129,"stargazers_count":47,"open_issues_count":0,"forks_count":3,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-01-12T22:33:01.536Z","etag":null,"topics":["computer-vision","cvpr","cvpr2020","deep-learning","point-cloud"],"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/cuge1995.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-11T08:35:51.000Z","updated_at":"2024-10-26T09:39:45.000Z","dependencies_parsed_at":"2024-07-07T06:00:16.438Z","dependency_job_id":null,"html_url":"https://github.com/cuge1995/CVPR-2020-point-cloud-analysis","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/cuge1995%2FCVPR-2020-point-cloud-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FCVPR-2020-point-cloud-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FCVPR-2020-point-cloud-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FCVPR-2020-point-cloud-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cuge1995","download_url":"https://codeload.github.com/cuge1995/CVPR-2020-point-cloud-analysis/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241489613,"owners_count":19971086,"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":["computer-vision","cvpr","cvpr2020","deep-learning","point-cloud"],"created_at":"2024-11-13T13:28:16.326Z","updated_at":"2026-02-26T08:40:41.776Z","avatar_url":"https://github.com/cuge1995.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# CVPR-2020-point-cloud-analysis\nCVPR 2020 papers focusing on point cloud analysis\n\n\n- [D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features](https://arxiv.org/abs/2003.03164)\n  - [[Code](https://github.com/XuyangBai/D3Feat)]\n\n- [RPM-Net: Robust Point Matching using Learned Features](https://arxiv.org/abs/2003.13479)\n  - [[Code](https://github.com/yewzijian/RPMNet)]\n\n- [D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://arxiv.org/abs/2003.01060)\n\n- [Cascaded Refinement Network for Point Cloud Completion](https://arxiv.org/abs/2004.03327)\n  - [[Code](https://github.com/xiaogangw/cascaded-point-completion)]\n  \n- [PointAugment: an Auto-Augmentation Framework for Point Cloud Classification](https://arxiv.org/abs/2002.10876)\n  - [[Code](https://github.com/liruihui/PointAugment/)]\n\n- [Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer Labels.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Weakly_Supervised_Semantic_Point_Cloud_Segmentation_Towards_10x_Fewer_Labels_CVPR_2020_paper.pdf)  ` segmention ` \n  - [[Code](https://github.com/alex-xun-xu/WeakSupPointCloudSeg)]\n\n- [Neural Implicit Embedding for Point Cloud Analysis.](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fujiwara_Neural_Implicit_Embedding_for_Point_Cloud_Analysis_CVPR_2020_paper.pdf)  ` segmention ` ` classification `\n  - [[Code](https://github.com/kentfuji/NeuralEmbedding)]\n\n- [OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression.](https://arxiv.org/abs/2005.07178)  ` compression ` ` oral `\n\n- [PF-Net: Point Fractal Network for 3D Point Cloud Completion](https://arxiv.org/abs/2003.00410)   ` completion `\n  - [[Code](https://github.com/zztianzz/PF-Net-Point-Fractal-Network)]\n\n- [End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection](https://arxiv.org/abs/2004.03080)\n  - [[Code](https://github.com/mileyan/pseudo-LiDAR_e2e/tree/master/PIXOR)]\n\n- [Going Deeper with Point Networks](http://geometry.cs.ucl.ac.uk/projects/2020/deepleanpn/paper_docs/GoingDeeperWithPointNetworksLeKokkinosMitra.pdf) ` segmentation `\n  - [[Code](https://github.com/erictuanle/GoingDeeperwPointNetworks)]\n\n- [Learning multiview 3D point cloud registration](https://arxiv.org/abs/2001.05119)\n\n- [Geometry and Learning Co-supported Normal Estimation for UnstructuredPoint Cloud.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Geometry_and_Learning_Co-Supported_Normal_Estimation_for_Unstructured_Point_Cloud_CVPR_2020_paper.pdf)  ` normal estimation ` \n\n- [LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of\nPoint Cloud-based Deep Networks.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_LG-GAN_Label_Guided_Adversarial_Network_for_Flexible_Targeted_Attack_of_CVPR_2020_paper.pdf)  ` attack ` \n  - [[Code](https://github.com/RyanHangZhou/LG-GAN)]\n\n- [On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.pdf)  ` attack ` \n  - [[Code](https://github.com/skywalker6174/3d-isometry-robust)]\n\n- [Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lin_Convolution_in_the_Cloud_Learning_Deformable_Kernels_in_3D_Graph_CVPR_2020_paper.pdf)  ` classification ` ` segmentation ` \n  - [[Code](https://github.com/j1a0m0e4sNTU/3dgcn)]\n\n- [An Efficient PointLSTM for Point Clouds Based Gesture Recognition.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Min_An_Efficient_PointLSTM_for_Point_Clouds_Based_Gesture_Recognition_CVPR_2020_paper.pdf)  ` Gesture Recognition ` \n  - [[Code](https://github.com/Blueprintf/pointlstm-gesture-recognition-pytorch)]\n\n- [Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image](https://arxiv.org/abs/2002.12212)\n  \n- [In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction from 2D Landmarks](https://arxiv.org/pdf/1911.11924.pdf)\n\n- [Unsupervised Learning of Intrinsic Structural Representation Points](https://arxiv.org/pdf/2003.01661.pdf)\n  - [[Code](https://github.com/NolenChen/3DStructurePoints)]\n\n- [LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention](https://arxiv.org/pdf/2004.01389.pdf)\n  - [[Code](https://github.com/yinjunbo/3DVID)]\n\n- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://arxiv.org/abs/1911.11236)  ` segmentation `\n  - [[Code](https://github.com/QingyongHu/RandLA-Net)]\n  \n- [C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds](https://arxiv.org/abs/1912.07009)\n\n- [High-dimensional Convolutional Networks for Geometric Pattern Recognition.](https://arxiv.org/abs/2005.08144)  ` registrition ` ` oral `\n  - [[Code](https://github.com/chrischoy/HighDimConvNets)]\n\n- [Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs](https://arxiv.org/abs/2003.00287)\n\n- [PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation](https://arxiv.org/pdf/2004.01658.pdf)\n  - [[Code](https://github.com/Jia-Research-Lab/PointGroup)]\n  \n- [Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion](https://arxiv.org/abs/2003.01456)\n  - [[Code](https://github.com/RaminHasibi/SA_Net)]\n\n- [Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences](http://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_Feature-Metric_Registration_A_Fast_Semi-Supervised_Approach_for_Robust_Point_Cloud_CVPR_2020_paper.pdf)\n  - [[Code](https://github.com/XiaoshuiHuang/fmr)]\n  \n- [From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks](http://openaccess.thecvf.com/content_CVPR_2020/papers/Navaneet_From_Image_Collections_to_Point_Clouds_With_Self-Supervised_Shape_and_CVPR_2020_paper.pdf)\n  - [[Code](https://github.com/klnavaneet/ssl_3d_recon)]\n\n- [Associate-3Ddet: Perceptual-to-Conceptual association for 3D Point Cloud Object Detection](http://openaccess.thecvf.com/content_CVPR_2020/papers/Du_Associate-3Ddet_Perceptual-to-Conceptual_Association_for_3D_Point_Cloud_Object_Detection_CVPR_2020_paper.pdf) ` detection `\n  - [[Code](https://github.com/dleam/Associate-3Ddet)]\n \n- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://arxiv.org/pdf/2003.01251.pdf)  ` detection `\n  - [[Code](https://github.com/WeijingShi/Point-GNN)]\n\n- [PointGMM: a Neural GMM Network for Point Clouds](https://arxiv.org/pdf/2003.13326.pdf)\n  - [[Code](https://github.com/amirhertz/pointgmm)]\n\n- [3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation](https://arxiv.org/pdf/2003.13867.pdf)\n  - [[Code](https://github.com/francisengelmann/3D-MPA)]\n\n- [PointPainting: Sequential Fusion for 3D Object Detection](https://arxiv.org/abs/1911.10150)  ` detection `\n  - [[Code](https://github.com/rshilliday/painting)]\n\n- [Point Cloud Completion by Skip-attention Network with Hierarchical Folding](http://openaccess.thecvf.com/content_CVPR_2020/papers/Wen_Point_Cloud_Completion_by_Skip-Attention_Network_With_Hierarchical_Folding_CVPR_2020_paper.pdf)  ` completion `\n\n- [PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling.](https://arxiv.org/pdf/2003.00492.pdf)  ` classification ` ` segmentation `\n  - [[Code](https://github.com/yanx27/PointASNL)]\n\n- [PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation.](https://arxiv.org/pdf/2003.14032.pdf)  ` segmentation `\n  - [[Code](https://github.com/edwardzhou130/PolarSeg)]\n\n- [Grid-GCN for Fast and Scalable Point Cloud Learning.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Grid-GCN_for_Fast_and_Scalable_Point_Cloud_Learning_CVPR_2020_paper.pdf)  ` learning `\n  - [[Code](https://github.com/Xharlie/Grid-GCN)]\n\n- [End-to-End Learning Local Multi-View Descriptors for 3D Point Clouds.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_End-to-End_Learning_Local_Multi-View_Descriptors_for_3D_Point_Clouds_CVPR_2020_paper.pdf)  ` Registration `\n  - [[Code](https://github.com/craigleili/3DLocalMultiViewDesc)]\n\n- [On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhao_On_Isometry_Robustness_of_Deep_3D_Point_Cloud_Models_Under_CVPR_2020_paper.pdf)  ` attack `\n  - [[Code](https://github.com/skywalker6174/3d-isometry-robust)]\n\n- [SampleNet: Differentiable Point Cloud Sampling.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lang_SampleNet_Differentiable_Point_Cloud_Sampling_CVPR_2020_paper.pdf)  ` sampling `\n  - [[Code](https://github.com/itailang/SampleNet)]\n\n- [SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel.](http://openaccess.thecvf.com/content_CVPR_2020/papers/Lei_SegGCN_Efficient_3D_Point_Cloud_Segmentation_With_Fuzzy_Spherical_Kernel_CVPR_2020_paper.pdf)  ` segmentation `\n  - [[Code](https://github.com/hlei-ziyan/SegGCN)]\n\n- [ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes.](https://arxiv.org/abs/2001.10692)  `Detection ` \n  - [[Code](https://github.com/facebookresearch/imvotenet)]\n\n- [P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds.](https://arxiv.org/abs/2005.13888)  ` Tracking `   `Oral`\n  - [[Code](https://github.com/HaozheQi/P2B)]\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuge1995%2Fcvpr-2020-point-cloud-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcuge1995%2Fcvpr-2020-point-cloud-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuge1995%2Fcvpr-2020-point-cloud-analysis/lists"}