This repository contains the PyTorch implementation of the CVPR'2023 paper - GraVoS: Voxel Selection for 3D Point-Cloud Detection.
The code was tested in the following environment:
- Ubuntu 18.04/20.04
- Python 3.7
- CUDA 11.1
- Pytorch 1.9.0
- spconv 2.1.21
- spconv-cu111 2.1.21
conda create --name GraVoS python==3.7
conda activate GraVoS
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
git clone https://github.com/oshrout/GraVoS
cd GraVoS
pip install -r requirements.txt
- Install spconv with pip, see spconv for more details.
- Install pcdet by running
python setup.py develop
, see OpenPCDet for more details.
This project is realeased under the Apache License 2.0.
If you find this project useful, please consider cite:
@article{shrout2022gravos,
title={GraVoS: Gradient based Voxel Selection for 3D Detection},
author={Shrout, Oren and Ben-Shabat, Yizhak and Tal, Ayellet},
journal={arXiv preprint arXiv:2208.08780},
year={2022}
}
Our code is mostly based on OpenPCDet.