pv-rcnn's Introduction

PV-RCNN

An unofficial Pytorch implementation of PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection.

News (03/02/2020)

Added implementation of SECOND.

Project goals

Emphasis on simple codebase (no 1,000 LOC functions).
General 3D detection library (easy to extend to new models).
Hope to reproduce results of paper.

Status and plans

This repo is still under active development.
I will post a pretrained model when codebase stabilizes and results are good.
I will add more detailed training and inference instructions.
I will add description of codebase and design choices.

Usage

See inference.py.

Installation

See install.md and please ask if you have any questions. I will supply a Docker build soon.

Citing

If you find this work helpful in your research, please consider starring this repo and citing:

@article{pvrcnnpytorch,
  author={Jacob Hultman},
  title={PV-RCNN PyTorch},
  journal={https://github.com/jhultman/PV-RCNN},
  year={2020}
}

and the original PV-RCNN paper (note I am not an author of this paper):

@article{shi2019pv,
  author={Shi, Shaoshuai and Guo, Chaoxu and Jiang, Li and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng},
  title={PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection},
  journal={arXiv preprint arXiv:1912.13192},
  year={2019}
}

Contributions

Contributions are welcome. Please post an issue if you find any bugs.

Acknowledgements and licensing

Please see license.md. Note that the code in pvrcnn/ops is largely from detectron2 and hence is subject to the Apache license. Thank you to the authors of PV-RCNN for their research.

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