{"id":13444206,"url":"https://github.com/Yochengliu/Relation-Shape-CNN","last_synced_at":"2025-03-20T18:31:37.840Z","repository":{"id":41165420,"uuid":"179467230","full_name":"Yochengliu/Relation-Shape-CNN","owner":"Yochengliu","description":"Relation-Shape Convolutional Neural Network for Point Cloud Analysis (CVPR 2019 Oral \u0026 Best paper finalist)","archived":false,"fork":false,"pushed_at":"2021-09-30T11:36:21.000Z","size":19603,"stargazers_count":419,"open_issues_count":20,"forks_count":72,"subscribers_count":19,"default_branch":"master","last_synced_at":"2025-03-15T00:11:11.314Z","etag":null,"topics":["3d-convolutional-network","3d-graphics","3d-point-clouds","3d-shape-recognition","3d-shape-segmentation","artificial-intelligence","convolutional-neural-network","deep-learning","geometric-deep-learning","normal-estimation"],"latest_commit_sha":null,"homepage":"https://yochengliu.github.io/Relation-Shape-CNN/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Yochengliu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-04-04T09:34:58.000Z","updated_at":"2025-02-26T09:57:11.000Z","dependencies_parsed_at":"2022-07-25T01:47:49.390Z","dependency_job_id":null,"html_url":"https://github.com/Yochengliu/Relation-Shape-CNN","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/Yochengliu%2FRelation-Shape-CNN","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yochengliu%2FRelation-Shape-CNN/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yochengliu%2FRelation-Shape-CNN/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yochengliu%2FRelation-Shape-CNN/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Yochengliu","download_url":"https://codeload.github.com/Yochengliu/Relation-Shape-CNN/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244670196,"owners_count":20490938,"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-convolutional-network","3d-graphics","3d-point-clouds","3d-shape-recognition","3d-shape-segmentation","artificial-intelligence","convolutional-neural-network","deep-learning","geometric-deep-learning","normal-estimation"],"created_at":"2024-07-31T03:02:21.865Z","updated_at":"2025-03-20T18:31:34.946Z","avatar_url":"https://github.com/Yochengliu.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"Relation-Shape Convolutional Neural Network for Point Cloud Analysis\n===\nThis repository contains the author's implementation in Pytorch for the paper:\n\n__Relation-Shape Convolutional Neural Network for Point Cloud Analysis__ [[arXiv](https://arxiv.org/abs/1904.07601)] [[CVF](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Relation-Shape_Convolutional_Neural_Network_for_Point_Cloud_Analysis_CVPR_2019_paper.pdf)]\n\u003cbr\u003e\n[Yongcheng Liu](https://yochengliu.github.io/), [Bin Fan](http://www.nlpr.ia.ac.cn/fanbin/), [Shiming Xiang](https://scholar.google.com/citations?user=0ggsACEAAAAJ\u0026hl=zh-CN) and [Chunhong Pan](http://people.ucas.ac.cn/~0005314)\n\u003cbr\u003e\n[__CVPR 2019 Oral \u0026 Best paper finalist__](http://cvpr2019.thecvf.com/) \u0026nbsp;\u0026nbsp;\u0026nbsp; __Project Page__: [https://yochengliu.github.io/Relation-Shape-CNN/](https://yochengliu.github.io/Relation-Shape-CNN/)\n\n## Citation\n\nIf our paper is helpful for your research, please consider citing:   \n```BibTex\n        @inproceedings{liu2019rscnn,   \n            author = {Yongcheng Liu and    \n                            Bin Fan and    \n                      Shiming Xiang and   \n                           Chunhong Pan},   \n            title = {Relation-Shape Convolutional Neural Network for Point Cloud Analysis},   \n            booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},    \n            pages = {8895--8904},  \n            year = {2019}   \n        }   \n```\n## Usage: Preparation\n\n### Requirement\n\n- Ubuntu 14.04\n- Python 3 (recommend Anaconda3)\n- Pytorch 0.3.\\*/0.4.\\*\n- CMake \u003e 2.8\n- CUDA 8.0 + cuDNN 5.1\n\n### Building Kernel\n\n    git clone https://github.com/Yochengliu/Relation-Shape-CNN.git \n    cd Relation-Shape-CNN\n\n- mkdir build \u0026\u0026 cd build\n- cmake .. \u0026\u0026 make\n\n### Dataset\n__Shape Classification__\n\nDownload and unzip [ModelNet40](https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip) (415M). Replace `$data_root$` in `cfgs/config_*_cls.yaml` with the dataset parent path.\n\n__ShapeNet Part Segmentation__\n\nDownload and unzip [ShapeNet Part](https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip) (674M). Replace `$data_root$` in `cfgs/config_*_partseg.yaml` with the dataset path.\n\n## Usage: Training\n### Shape Classification\n\n    sh train_cls.sh\n        \nYou can modify `relation_prior` in `cfgs/config_*_cls.yaml`. We have trained a Single-Scale-Neighborhood classification model in `cls` folder, whose accuracy is 92.38%.\n        \n### Shape Part Segmentation\n\n    sh train_partseg.sh\n        \nWe have trained a Multi-Scale-Neighborhood part segmentation model in `seg` folder, whose class mIoU and instance mIoU is 84.18% and 85.81% respectively.\n\n## Usage: Evaluation\n### Shape Classification\n\n    Voting script: voting_evaluate_cls.py\n        \nYou can use our model `cls/model_cls_ssn_iter_16218_acc_0.923825.pth` as the checkpoint in `config_ssn_cls.yaml`, and after this voting you will get an accuracy of 92.71% if all things go right.\n\n### Shape Part Segmentation\n\n    Voting script: voting_evaluate_partseg.py\n        \nYou can use our model `seg/model_seg_msn_iter_57585_ins_0.858054_cls_0.841787.pth` as the checkpoint in `config_msn_partseg.yaml`.\n\n## License\n\nThe code is released under MIT License (see LICENSE file for details).\n\n## Acknowledgement\n\nThe code is heavily borrowed from [Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch).\n        \n## Contact\n\nIf you have some ideas or questions about our research to share with us, please contact \u003cyongcheng.liu@nlpr.ia.ac.cn\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FYochengliu%2FRelation-Shape-CNN","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FYochengliu%2FRelation-Shape-CNN","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FYochengliu%2FRelation-Shape-CNN/lists"}