{"id":20062273,"url":"https://github.com/cuge1995/eccv-2020-point-cloud-analysis","last_synced_at":"2026-02-04T03:06:51.486Z","repository":{"id":117637836,"uuid":"277017553","full_name":"cuge1995/ECCV-2020-point-cloud-analysis","owner":"cuge1995","description":"ECCV 2020 papers focusing on point cloud analysis","archived":false,"fork":false,"pushed_at":"2021-04-10T02:20:29.000Z","size":28,"stargazers_count":22,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-02T10:26:22.203Z","etag":null,"topics":["deep-learning","eccv-2020","point","point-cloud","pointcloud"],"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":"2020-07-04T01:34:19.000Z","updated_at":"2024-10-26T09:39:40.000Z","dependencies_parsed_at":null,"dependency_job_id":"15ab7ba5-d0d9-4ddf-ae89-2ad9a2186dab","html_url":"https://github.com/cuge1995/ECCV-2020-point-cloud-analysis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/cuge1995/ECCV-2020-point-cloud-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FECCV-2020-point-cloud-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FECCV-2020-point-cloud-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FECCV-2020-point-cloud-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FECCV-2020-point-cloud-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cuge1995","download_url":"https://codeload.github.com/cuge1995/ECCV-2020-point-cloud-analysis/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FECCV-2020-point-cloud-analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264089672,"owners_count":23555785,"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":["deep-learning","eccv-2020","point","point-cloud","pointcloud"],"created_at":"2024-11-13T13:28:14.675Z","updated_at":"2026-02-04T03:06:51.428Z","avatar_url":"https://github.com/cuge1995.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# ECCV-2020-point-cloud-analysis\nECCV 2020 papers focusing on point cloud analysis\n\n- [CAD-Deform: Deformable Fitting of CAD Models to 3D Scans.](https://arxiv.org/pdf/2007.11965.pdf)  ` CAD reconstruction ` \n  - [[Code](https://github.com/alexeybokhovkin/CAD-Deform)]\n\n- [Weakly Supervised 3D Object Detection from Lidar Point Cloud.](https://arxiv.org/pdf/2007.11901.pdf)  ` object detection ` \n  - [[Code](https://github.com/hlesmqh/WS3D)]\n\n- [AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds.](https://arxiv.org/abs/1912.00461)  ` attack ` \n  - [[Code](https://github.com/ajhamdi/AdvPC)]\n\n- [Learning Graph-Convolutional Representations for Point Cloud Denoising.](https://arxiv.org/abs/2007.02578)  ` denoising ` \n  - [[Code](https://github.com/diegovalsesia/GPDNet)]\n\n- [Detail Preserved Point Cloud Completion via Separated Feature Aggregation.](https://arxiv.org/pdf/2007.02374.pdf)  ` completion ` \n  - [[Code](https://github.com/XLechter/Detail-Preserved-Point-Cloud-Completion-via-SFA)]\n\n- [GRNet: Gridding Residual Network for Dense Point Cloud Completion.](https://arxiv.org/abs/2006.03761)  ` completion ` \n  - [[Code](https://github.com/hzxie/GRNet)]\n\n- [EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection.]()  ` detection ` \n  - [[Code](https://github.com/happinesslz/EPNet)]\n\n- [3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection.](https://arxiv.org/pdf/2004.12636.pdf)  ` detection ` \n  - [[Code](https://github.com/rasd3/3D-CVF)]\n\n- [PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding.](https://arxiv.org/pdf/2007.10985.pdf)  ` representation learning `  \n  - [[Code](https://github.com/facebookresearch/PointContrast)]\n\n- [Finding Your (3D) Center: 3D Object Detection Using a Learned Loss.](https://arxiv.org/abs/2004.02693)  ` detection ` \n  - [[Code](https://github.com/dgriffiths3/finding-your-center)]\n\n- [H3DNet: 3D Object Detection Using Hybrid Geometric Primitives.](https://arxiv.org/pdf/2006.05682.pdf)  ` detection ` \n  - [[Code](https://github.com/zaiweizhang/H3DNet)]\n\n- [Progressive Point Cloud Deconvolution Generation Network.](https://arxiv.org/pdf/2007.05361.pdf)  ` generation ` \n  - [[Code](https://github.com/fpthink/PDGN)]\n\n- [Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions.](https://arxiv.org/abs/2003.13834.pdf)  ` segmentation ` \n  - [[Code](https://github.com/matheusgadelha/PointCloudLearningACD)]\n\n- [SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds.](https://arxiv.org/abs/2004.02774)  ` detection ` \n  - [[Code](https://github.com/xinge008/SSN)]\n\n- [DPDist : Comparing Point Clouds Using Deep Point Cloud Distance.](https://arxiv.org/abs/2004.11784.pdf)  ` distance ` \n  - [[Code](https://github.com/dahliau/DPDist)]\n\n- [A Closer Look at Local Aggregation Operators in Point Cloud Analysis.](https://arxiv.org/abs/2007.01294)  ` classification `  ` segmentation ` \n  - [[Code](https://github.com/zeliu98/CloserLook3D)]\n\n- [Quaternion Equivariant Capsule Networks for 3D Point Clouds.](https://arxiv.org/pdf/1912.12098.pdf)  ` classification ` \n  - [[Code](https://github.com/tolgabirdal/qenetworks)]\n\n- [ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds.](https://arxiv.org/abs/2003.12181.pdf)  ` Fitting `  \n  - [[Code](https://github.com/Hippogriff/parsenet-codebase)]\n\n- [PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling.](https://arxiv.org/pdf/2002.10277.pdf)  ` upsampling `  \n  - [[Code](https://github.com/ninaqy/PUGeo)]\n\n- [PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation.](https://arxiv.org/abs/1911.12408)  ` flow `  \n  - [[Code](https://github.com/DylanWusee/PointPWC)]\n\n- [Points2Surf: Learning Implicit Surfaces from Point Cloud Patches.](https://arxiv.org/pdf/2007.10453.pdf)  ` surface reconstruction `  \n  - [[Code](https://github.com/ErlerPhilipp/points2surf)]\n\n- [Weakly-supervised 3D Shape Completion in the Wild.](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500273.pdf)   ` completion ` \n\n- [Discrete Point Flow Networks for Efficient Point Cloud Generation.](https://arxiv.org/abs/2007.10170)  ` generation `  \n  - [[Code](https://github.com/Regenerator/dpf-nets)]\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuge1995%2Feccv-2020-point-cloud-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcuge1995%2Feccv-2020-point-cloud-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuge1995%2Feccv-2020-point-cloud-analysis/lists"}