/ Machine Learning

Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

disprcnn

Code release for Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation (CVPR 2020).

This project contains the implementation of our CVPR 2020 paper arxiv.

Authors: Jiaming Sun, Linghao Chen, Yiming Xie, Siyu Zhang, Qinhong Jiang, Xiaowei Zhou, Hujun Bao.

Requirements

  • Ubuntu 16.04+
  • Python 3.7+
  • 8 Nvidia GPU with mem >= 12G (recommended, see Notes for details.)
  • GCC >= 4.9
  • PyTorch 1.2.0

Install

# Install webp support
sudo apt install libwebp-dev
# Clone repo
git clone https://github.com/zju3dv/disprcnn.git
cd disprcnn
# Install conda environment
conda env create -f environment.yaml
conda activate disprcnn
# Install Disp R-CNN
sh build_and_install.sh

Training and evaluation

See TRAIN_VAL.md

Sample results

qualitative

Citation

If you find this code useful for your research, please use the following BibTeX entry.

@inproceedings{sun2020disprcnn,
  title={Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation},
  author={Sun, Jiaming and Chen, Linghao and Xie, Yiming and Zhang, Siyu and Jiang, Qinhong and Zhou, Xiaowei and Bao, Hujun},
  booktitle={CVPR},
  year={2020}
}

Acknowledgment

This repo is built based on the Mask R-CNN implementation from maskrcnn-benchmark, and we also use the pretrained Stereo R-CNN weight from here for initialization.

License

Copyright (c) 2020 3D Vision Group of State Key Lab at CAD&CG, Zhejiang University

GitHub

Comments