OpenPoints is a library built for fairly benchmarking and easily reproducing point-based methods for point cloud understanding. It is born in the course of PointNeXt project and is used as an engine therein.

For any question related to OpenPoints, please open an issue in PointNeXt repo.

OpenPoints currently supports reproducing the following models:

• PointNet
• DGCNN
• DeepGCN
• PointNet++
• ASSANet
• PointMLP
• PointNeXt
• Pix4Point
• PointVector

1. Extensibility: supports many representative networks for point cloud understanding, such as PointNet, DGCNN, DeepGCN, PointNet++, ASSANet, PointMLP, and our PointNeXt. More networks can be built easily based on our framework since OpenPoints support a wide range of basic operations including graph convolutions, self-attention, farthest point sampling, ball query, e.t.c.

2. Ease of Use: Build model, optimizer, scheduler, loss function, and data loader easily from cfg. Train and validate different models on various tasks by simply changing the cfg\*\*.yaml file.

   model = build_model_from_cfg(cfg.model)
   criterion = build_criterion_from_cfg(cfg.criterion_args)
   

OpenPoints can be installed as the openpoints Python package:

pip install openpoints

The PyPI package installs the Python library (import openpoints) and the files needed by the datasets and configs. CUDA/C++ operators such as pointnet2_batch_cuda, pointops_cuda, chamfer, and emd_cuda are still built from a source checkout or source distribution for now because PyTorch/CUDA wheels must match the user's Python, PyTorch, CUDA, and platform versions.

For full training/evaluation with CUDA ops, install from source after installing PyTorch:

git clone --recursive https://github.com/guochengqian/openpoints.git
cd openpoints
pip install -e .[data,viz,wandb]
cd cpp/pointnet2_batch && python setup.py install && cd ../..
cd cpp/pointops && python setup.py install && cd ../..
cd cpp/chamfer_dist && python setup.py install && cd ../..
cd cpp/emd && python setup.py install && cd ../..

If the openpoints name is unavailable on a package index mirror, the same code can be published as openpoints-torch; the import name remains openpoints.

OpenPoints serves as the engine for PointNeXt. Please refer to PointNeXt for complete training, evaluation, and model-zoo examples.

If you use this library, please kindly acknowledge our work:

@Article{qian2022pointnext,
  author  = {Qian, Guocheng and Li, Yuchen and Peng, Houwen and Mai, Jinjie and Hammoud, Hasan and Elhoseiny, Mohamed and Ghanem, Bernard},
  title   = {PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies},
  journal = {arXiv:2206.04670},
  year    = {2022},
}