{"id":20062270,"url":"https://github.com/cuge1995/iccv-2021-point-cloud-analysis","last_synced_at":"2026-01-30T01:41:17.344Z","repository":{"id":117637925,"uuid":"393678072","full_name":"cuge1995/ICCV-2021-point-cloud-analysis","owner":"cuge1995","description":"ICCV 2021 papers and code focus on point cloud analysis","archived":false,"fork":false,"pushed_at":"2021-10-21T08:13:33.000Z","size":16,"stargazers_count":15,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-12T22:33:01.309Z","etag":null,"topics":["3d-object-detection","deep-learning","point-cloud-detection","point-cloud-registration","point-cloud-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/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":"2021-08-07T12:30:02.000Z","updated_at":"2024-11-26T12:25:41.000Z","dependencies_parsed_at":null,"dependency_job_id":"e6e4f4ea-8f6d-46e0-b1af-dcc3b626e4f2","html_url":"https://github.com/cuge1995/ICCV-2021-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%2FICCV-2021-point-cloud-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FICCV-2021-point-cloud-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FICCV-2021-point-cloud-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FICCV-2021-point-cloud-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cuge1995","download_url":"https://codeload.github.com/cuge1995/ICCV-2021-point-cloud-analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241489551,"owners_count":19971072,"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-object-detection","deep-learning","point-cloud-detection","point-cloud-registration","point-cloud-segmentation"],"created_at":"2024-11-13T13:28:14.227Z","updated_at":"2026-01-30T01:41:12.323Z","avatar_url":"https://github.com/cuge1995.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# ICCV-2021-point-cloud-analysis\nICCV 2021 papers and code focus on point cloud analysis\n\n\n- [Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis](https://arxiv.org/abs/2105.01288) `classification` \n  - [[Code](https://github.com/tiangexiang/CurveNet)]\n\n- [Score-Based Point Cloud Denoising](https://arxiv.org/abs/2107.10981) `Denoising`\n  - [[Code](https://github.com/luost26/score-denoise)]\n\n- [ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation](https://arxiv.org/abs/2107.11769) `Segmentation`\n\n- [HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration](https://arxiv.org/abs/2107.11992) `registration`\n  - [[Code](https://ispc-group.github.io/hregnet)]\n\n- [Learning with Noisy Labels for Robust Point Cloud Segmentation](https://arxiv.org/abs/2107.14230) `segmentation` `oral`\n  - [[Code](https://shuquanye.com/PNAL_website/)]\n\n- [Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation](https://arxiv.org/abs/2107.14724) `segmentation` `Domain Adaptation`\n  - [[Code](https://github.com/leolyj/DsCML)]\n \n- [Unsupervised Point Cloud Pre-Training via View-Point Occlusion, Completion](https://arxiv.org/abs/2010.01089) `Unsupervised learning`\n  - [[Code](https://github.com/hansen7/OcCo)]\n\n- [Group-Free 3D Object Detection via Transformers](https://arxiv.org/abs/2104.00678) `3D Object Detection`\n\n- [Hierarchical Aggregation for 3D Instance Segmentation](https://arxiv.org/abs/2108.02350) `segmentation`\n  - [[Code](https://github.com/hustvl/HAIS)]\n\n- [3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds.](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_3DVG-Transformer_Relation_Modeling_for_Visual_Grounding_on_Point_Clouds_ICCV_2021_paper.pdf) `3D Visual Grounding`\n\n- [Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud\nSemantic Segmentation.](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Perturbed_Self-Distillation_Weakly_Supervised_Large-Scale_Point_Cloud_Semantic_Segmentation_ICCV_2021_paper.pdf) `segmentation`\n\n- [TempNet: Online Semantic Segmentation on Large-scale Point Cloud Series.](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhou_TempNet_Online_Semantic_Segmentation_on_Large-Scale_Point_Cloud_Series_ICCV_2021_paper.pdf) `segmentation`\n\n- [Robustness Certification for Point Cloud Models.](https://openaccess.thecvf.com/content/ICCV2021/papers/Lorenz_Robustness_Certification_for_Point_Cloud_Models_ICCV_2021_paper.pdf) `robustness`\n  - [[Code](https://github.com/eth-sri/3dcertify)]\n\n- [Shape Self-Correction for Unsupervised Point Cloud Understanding.](https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Shape_Self-Correction_for_Unsupervised_Point_Cloud_Understanding_ICCV_2021_paper.pdf) `Unsupervised learning`\n\n- [Pyramid Point Cloud Transformer for Large-Scale Place Recognition.](https://openaccess.thecvf.com/content/ICCV2021/papers/Hui_Pyramid_Point_Cloud_Transformer_for_Large-Scale_Place_Recognition_ICCV_2021_paper.pdf) `Place_Recognition`\n  - [[Code](https://github.com/fpthink/PPT-Net)]\n\n- [Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds.](https://openaccess.thecvf.com/content/ICCV2021/papers/Zou_Geometry-Aware_Self-Training_for_Unsupervised_Domain_Adaptation_on_Object_Point_Clouds_ICCV_2021_paper.pdf) `Domain Adaptation`\n  - [[Code](https://github.com/zou-longkun/GAST)]\n\n- [Differentiable Convolution Search for Point Cloud Processing.](https://openaccess.thecvf.com/content/ICCV2021/papers/Nie_Differentiable_Convolution_Search_for_Point_Cloud_Processing_ICCV_2021_paper.pdf) `NAS`\n\n- [Learning Inner-group Relations on Point Clouds.](https://openaccess.thecvf.com/content/ICCV2021/papers/Ran_Learning_Inner-Group_Relations_on_Point_Clouds_ICCV_2021_paper.pdf) `classification`\n  - [[Code](https://github.com/hancyran/RPNet-Point-Clouds)]\n\n- [Point-Based Modeling of Human Clothing.](https://openaccess.thecvf.com/content/ICCV2021/papers/Zakharkin_Point-Based_Modeling_of_Human_Clothing_ICCV_2021_paper.pdf) \n  - [[Code](https://github.com/saic-vul/point_based_clothing)]\n\n- [Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks.](https://arxiv.org/pdf/2007.11679.pdf) `classification`\n  - [[Code](https://github.com/saic-vul/cloud_transformers)]\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuge1995%2Ficcv-2021-point-cloud-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcuge1995%2Ficcv-2021-point-cloud-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuge1995%2Ficcv-2021-point-cloud-analysis/lists"}