{"id":13443485,"url":"https://github.com/ma-xu/pointMLP-pytorch","last_synced_at":"2025-03-20T16:31:33.356Z","repository":{"id":40324063,"uuid":"413309145","full_name":"ma-xu/pointMLP-pytorch","owner":"ma-xu","description":"[ICLR 2022 poster] Official PyTorch implementation of \"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework\"","archived":false,"fork":false,"pushed_at":"2024-04-22T15:23:21.000Z","size":3954,"stargazers_count":472,"open_issues_count":1,"forks_count":59,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-08-01T03:43:46.278Z","etag":null,"topics":["modelnet40","pointcloud","pytorch","scanobjectnn"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ma-xu.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,"governance":null,"roadmap":null,"authors":null}},"created_at":"2021-10-04T07:01:37.000Z","updated_at":"2024-07-25T11:58:32.000Z","dependencies_parsed_at":"2024-01-18T14:44:02.885Z","dependency_job_id":"a5881557-4a74-419c-a900-954cd29d6f8b","html_url":"https://github.com/ma-xu/pointMLP-pytorch","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/ma-xu%2FpointMLP-pytorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ma-xu%2FpointMLP-pytorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ma-xu%2FpointMLP-pytorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ma-xu%2FpointMLP-pytorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ma-xu","download_url":"https://codeload.github.com/ma-xu/pointMLP-pytorch/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221780001,"owners_count":16879040,"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":["modelnet40","pointcloud","pytorch","scanobjectnn"],"created_at":"2024-07-31T03:02:01.846Z","updated_at":"2024-10-28T04:31:09.726Z","avatar_url":"https://github.com/ma-xu.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework （ICLR 2022）\n\n\n\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rethinking-network-design-and-local-geometry-1/3d-point-cloud-classification-on-modelnet40)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40?p=rethinking-network-design-and-local-geometry-1)\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rethinking-network-design-and-local-geometry-1/3d-point-cloud-classification-on-scanobjectnn)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-scanobjectnn?p=rethinking-network-design-and-local-geometry-1)\n\n\n[![github](https://img.shields.io/github/stars/ma-xu/pointMLP-pytorch?style=social)](https://github.com/ma-xu/pointMLP-pytorch)\n\n\n\u003cdiv align=\"left\"\u003e\n    \u003ca\u003e\u003cimg src=\"images/smile.png\"  height=\"70px\" \u003e\u003c/a\u003e\n    \u003ca\u003e\u003cimg src=\"images/neu.png\"  height=\"70px\" \u003e\u003c/a\u003e\n    \u003ca\u003e\u003cimg src=\"images/columbia.png\"  height=\"70px\" \u003e\u003c/a\u003e\n\u003c/div\u003e\n\n [open review](https://openreview.net/forum?id=3Pbra-_u76D) | [arXiv](https://arxiv.org/abs/2202.07123) | Primary contact: [Xu Ma](mailto:ma.xu1@northeastern.edu)\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"images/overview.png\" width=\"650px\" height=\"300px\"\u003e\n\u003c/div\u003e\n\nOverview of one stage in PointMLP. Given an input point cloud, PointMLP progressively extracts local features using residual point MLP blocks. In each stage, we first transform the local point using a geometric affine module, and then local points are extracted before and after aggregation, respectively. By repeating multiple stages, PointMLP progressively enlarges the receptive field and models entire point cloud geometric information.\n\n\n## BibTeX\n\n    @article{ma2022rethinking,\n        title={Rethinking network design and local geometry in point cloud: A simple residual MLP framework},\n        author={Ma, Xu and Qin, Can and You, Haoxuan and Ran, Haoxi and Fu, Yun},\n        journal={arXiv preprint arXiv:2202.07123},\n        year={2022}\n    }\n\n## Model Zoo\n\n  **Questions on ModelNet40 classification results (a common issue for ModelNet40 dataset in the community)**\n  \n  The performance on ModelNet40 of almost all methods are not stable, see (https://github.com/CVMI-Lab/PAConv/issues/9#issuecomment-873371422).\u003cbr\u003e\n  If you run the same codes for several times, you will get different results (even with fixed seed).\u003cbr\u003e\n  The best way to reproduce the results is to test with a pretrained model for ModelNet40. \u003cbr\u003e\n  Also, the randomness of ModelNet40 is our motivation to experiment on ScanObjectNN, and to report the mean/std results of several runs.\n\n\n\n------\n\nThe codes/models/logs for submission version (without bug fixed) can be found here [commit:d2b8dbaa](http://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026).\n\nOn ModelNet40, fixed pointMLP achieves a result of **91.5% mAcc** and **94.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/modelnet40/pointMLP-20220209053148-404/).\n\nOn ScanObjectNN, fixed pointMLP achieves a result of **84.4% mAcc** and **86.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/scanobjectnn/pointMLP-20220204021453/). Fixed pointMLP-elite achieves a result of **81.7% mAcc** and **84.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/scanobjectnn/model313Elite-20220220015842-2956/).\n\nStay tuned. More elite versions and voting results will be uploaded.\n\n\n\n## News \u0026 Updates:\n\n- [x] **Apr/24/2024**: University server is down. Update the ScanobjectNN dataset link.\n- [x] fix the uncomplete utils in partseg by Mar/10, caused by error uplaoded folder.\n- [x] upload test code for ModelNet40\n- [x] update std bug (unstable testing in previous version)\n- [x] paper/codes release\n\n:point_right::point_right::point_right:**NOTE:** The codes/models/logs for submission version (without bug fixed) can be found here [commit:d2b8dbaa](http://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026).\n\n\n\n\n## Install\n\n```bash\n# step 1. clone this repo\ngit clone https://github.com/ma-xu/pointMLP-pytorch.git\ncd pointMLP-pytorch\n\n# step 2. create a conda virtual environment and activate it\nconda env create\nconda activate pointmlp\n```\n\n```bash\n# Optional solution for step 2: install libs step by step\nconda create -n pointmlp python=3.7 -y\nconda activate pointmlp\nconda install pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=10.2 -c pytorch -y\n# if you are using Ampere GPUs (e.g., A100 and 30X0), please install compatible Pytorch and CUDA versions, like:\n# pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html\npip install cycler einops h5py pyyaml==5.4.1 scikit-learn==0.24.2 scipy tqdm matplotlib==3.4.2\npip install pointnet2_ops_lib/.\n```\n\n\n## Useage\n\n### Classification ModelNet40\n**Train**: The dataset will be automatically downloaded, run following command to train.\n\nBy default, it will create a folder named \"checkpoints/{modelName}-{msg}-{randomseed}\", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.\n```bash\ncd classification_ModelNet40\n# train pointMLP\npython main.py --model pointMLP\n# train pointMLP-elite\npython main.py --model pointMLPElite\n# please add other paramemters as you wish.\n```\n\n\nTo conduct voting testing, run\n```bash\n# please modify the msg accrodingly\npython voting.py --model pointMLP --msg demo\n```\n\n\n### Classification ScanObjectNN\n\nThe dataset will be automatically downloaded\n\n- Train pointMLP/pointMLPElite \n```bash\ncd classification_ScanObjectNN\n# train pointMLP\npython main.py --model pointMLP\n# train pointMLP-elite\npython main.py --model pointMLPElite\n# please add other paramemters as you wish.\n```\nBy default, it will create a fold named \"checkpoints/{modelName}-{msg}-{randomseed}\", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.\n\n\n### Part segmentation\n\n- Make data folder and download the dataset\n```bash\ncd part_segmentation\nmkdir data\ncd data\nwget https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip --no-check-certificate\nunzip shapenetcore_partanno_segmentation_benchmark_v0_normal.zip\n```\n\n- Train pointMLP\n```bash\n# train pointMLP\npython main.py --model pointMLP\n# please add other paramemters as you wish.\n```\n\n\n## Acknowledgment\n\nOur implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.\n\n[CurveNet](https://github.com/tiangexiang/CurveNet),\n[PAConv](https://github.com/CVMI-Lab/PAConv),\n[GDANet](https://github.com/mutianxu/GDANet),\n[Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch)\n\n## LICENSE\nPointMLP is under the Apache-2.0 license. \n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fma-xu%2FpointMLP-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fma-xu%2FpointMLP-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fma-xu%2FpointMLP-pytorch/lists"}