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

Weights

Pre-trained weights for certain scenes are released: Google Cloud

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/"

Tools and Preprocessing

Acknowledgements

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

License

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