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 only serves as an engine. Please refer to PointNeXt for a specific example of how to use and install

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},
}