# Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework (ICLR 2022) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rethinking-network-design-and-local-geometry/3d-point-cloud-classification-on-modelnet40)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40?p=rethinking-network-design-and-local-geometry) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rethinking-network-design-and-local-geometry/3d-point-cloud-classification-on-scanobjectnn)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-scanobjectnn?p=rethinking-network-design-and-local-geometry)
[Project Sites]() | [arXiv]() | Primary contact: [Xu Ma](mailto:ma.xu1@northeastern.edu)
Overview of one stage in PointMLP. Given an input point cloud, PointMLP progressively extract local features using residual point MLP blocks. In each stage, we first transform local point using a geometric affine module, then local points are are extracted before and after aggregation respectively. By repeating multiple stages, PointMLP progressively enlarge the receptive field and model entire point cloud geometric information. ## BibTeX @inproceedings{ ma2022rethinking, title={Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual {MLP} Framework}, author={Xu Ma and Can Qin and Haoxuan You and Haoxi Ran and Yun Fu}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=3Pbra-_u76D} } ## News & Updates: - [ ] updated pretrained models - [ ] project page - [ ] update std bug (unstable testing in previous version) - [ ] paper/codes release :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). ## Install ```bash # 1. clone this repo git clone https://github.com/ma-xu/pointMLP-pytorch.git cd pointMLP-pytorch # 2. create a conda virtual environment and activate it conda create -n pointmlp python=3.7 -y conda activate pointmlp # 3. install required libs, pytorch 1.8.1, torchvision 0.9.1, etc. pip install -r requirements.txt # 4. install CUDA kernels pip install pointnet2_ops_lib/. ``` ## Useage ### Classification ModelNet40 **Train**: The dataset will be automatically downloaded, run following command to train. By 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. ```bash cd pointMLP-pytorch/classification_ModelNet40 # train pointMLP python main.py --model pointMLP # train pointMLP-elite python main.py --model pointMLPElite # please add other paramemters as you wish. ``` To conduct voting testing, run ```bash # please modify the msg accrodingly python voting.py --model pointMLP --msg demo ``` ### Classification ScanObjectNN - Make data folder and download the dataset ```bash cd pointMLP-pytorch/classification_ScanObjectNN mkdir data cd data wget http://103.24.77.34/scanobjectnn/h5_files.zip unzip h5_files.zip ``` - Train pointMLP/pointMLPElite ```bash # train pointMLP python main.py --model pointMLP # train pointMLP-elite python main.py --model pointMLPElite # please add other paramemters as you wish. ``` By 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. ### Part segmentation - Make data folder and download the dataset ```bash cd pointMLP-pytorch/part_segmentation mkdir data cd data wget https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip --no-check-certificate unzip shapenetcore_partanno_segmentation_benchmark_v0_normal.zip ``` - Train pointMLP ```bash # train pointMLP python main.py --model pointMLP # please add other paramemters as you wish. ``` ## Acknowledgment Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works. [CurveNet](https://github.com/tiangexiang/CurveNet), [PAConv](https://github.com/CVMI-Lab/PAConv), [GDANet](https://github.com/mutianxu/GDANet), [Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch) ## LICENSE PointMLP is under the Apache-2.0 license. Please contact the authors for commercial use.