# PV-RCNN

**Repository Path**: victordoom/PV-RCNN

## Basic Information

- **Project Name**: PV-RCNN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PV-RCNN
An unofficial Pytorch implementation of [PV-RCNN](https://arxiv.org/pdf/1912.13192): Point-Voxel Feature Set Abstraction for 3D Object Detection.

![PV-RCNN](images/pvrcnn.png)

## 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](pvrcnn/inference.py).

## Installation
See [install.md](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](license.md). Note that the code in `pvrcnn/ops` is largely from [detectron2](https://github.com/facebookresearch/detectron2) and hence is subject to the Apache [license](pvrcnn/ops/LICENSE). Thank you to the authors of PV-RCNN for their research.