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Environment

First, clone this repository.

git clone https://github.com/SainingZhang/CRUISE.git

Configure Python environment of CRUISE

# conda environment
conda create -n cruise python=3.8
conda activate cruise

# CUDA 11.8
pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu118

# Install requirements
pip install -r requirments.txt

# Install submodules
pip install ./submodules/diff-gaussian-rasterization
pip install ./submodules/simple-knn
pip install ./submodules/simple-waymo-open-dataset-reader

Configure environment for generating masks GroudingDINO, and download the SAM checkpoint

Configure Python environment of Relightable3DGaussian

Dataset

  • Download the original dataset: DAIR-V2X-SPD

    • Use data_process.ipynb for data pre-processing

    • Use generate_mask.ipynb to generate sky mask and ego mask

  • Or you can directly download and use the processed synthetic dataset: (comming soon)

  • Download the high-quality vehicle dataset for Relightable3DGaussian: (comming soon)

Training

If you want to modify the training command, change the content in train.sh and specify the corresponding config.

./script/train.sh

Rendering

Use following command to render.

python render.py --config configs/xxxx.yaml mode edit

Generate Synthetic dataset

Use the following command to perform preliminary organization and packaging of the render data.

python generate_dataset.py

Then use the command below to merge the synthetic dataset with the original dataset for downstream tasks.

python append_dataset.py

Downstream tasks

Please complete the corresponding downstream as shown in the corresponding document.