https://github.com/guochengqian/openpoints

OpenPoints: a library for easily reproducing point-based methods for point cloud understanding. The engine for [PointNeXt](https://arxiv.org/abs/2206.04670)
https://github.com/guochengqian/openpoints

pointcloud pointnext

Last synced: 16 days ago
JSON representation

OpenPoints: a library for easily reproducing point-based methods for point cloud understanding. The engine for [PointNeXt](https://arxiv.org/abs/2206.04670)

• Host: GitHub
• URL: https://github.com/guochengqian/openpoints
• Owner: guochengqian
• Created: 2022-05-26T12:15:04.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-12-01T08:58:25.000Z (12 months ago)
• Last Synced: 2024-08-01T03:43:32.478Z (3 months ago)
• Topics: pointcloud, pointnext
• Language: Python
• Homepage: https://guochengqian.github.io/PointNeXt/
• Size: 440 KB
• Stars: 278
• Watchers: 10
• Forks: 40
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# OpenPoints

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](https://github.com/guochengqian/PointNeXt) project and is used as an engine therein.

**For any question related to OpenPoints, please open an issue in [PointNeXt](https://github.com/guochengqian/PointNeXt) repo.**

OpenPoints currently supports reproducing the following models:

- PointNet

- DGCNN

- DeepGCN

- PointNet++

- ASSANet

- PointMLP

- PointNeXt

- Pix4Point

- PointVector

## Features

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)

   ```

## Usage

OpenPoints only serves as an engine. Please refer to [PointNeXt](https://github.com/guochengqian/PointNeXt) for a specific example of how to use and install

## Citation

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

```tex

@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},

}

```