Awesome

DrivingGaussian👋

This is the official implementation of DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes.

Paper | Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, Ming-Hsuan Yang

Update

• 2024/05/10 - Code: please sign the application to obtain the code
• 2024/03/13 - Pre-trained weights are released
• 2024/02/27 - Paper: Accepted by CVPR 2024 👏
• 2023/12/07 - Webpage

Demo

Qualitative Comparison

Overview

Corner Case Simulation

Requirements

Setups

Please follow the 3DGS to install the relative packages.

git clone https://github.com/VDIGPKU/DrivingGaussian
cd DrivingGaussian
git submodule update --init --recursive
conda create -n DrivingGaussian python=3.8 
conda activate DrivingGaussian

pip install -r requirements.txt
pip install -e submodules/depth-diff-gaussian-rasterization
pip install -e submodules/simple-knn

Training

For training the driving scenes dataset,

python train.py -s "data/nuscenes/sceneID/" -m "saved/checkpoints/"

Rendering

Render the images with the following script

python render_combine.py -m "saved/checkpoints/"

Acknowledgements

The overall code and renderer are based on 3DGS, Deformable, and 4DGS. We sincerely thank the authors for their great work.

BibTex

@inproceedings{zhou2024drivinggaussian,
  title={Drivinggaussian: Composite gaussian splatting for surrounding dynamic autonomous driving scenes},
  author={Zhou, Xiaoyu and Lin, Zhiwei and Shan, Xiaojun and Wang, Yongtao and Sun, Deqing and Yang, Ming-Hsuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21634--21643},
  year={2024}
}

License

The project is only free for academic research purposes, but needs authorization forcommerce. For commerce permission, please contact wyt@pku.edu.cn.