Cloning the Repository

# SSH
git clone [email protected]:TarzanZhao/gaussian-splatting.git --recursive

Local Setup

Our default, provided install method is based on Conda package and environment management:

conda env create --file environment.yml
conda activate gaussian_splatting

If you want to use other name for this conda environment, you should change the name: field in the environment.yml

Then, we need to compile two dependent cuda repo diff-gaussian-rasterization and simple-knn. diff-gaussian-rasterization contains render cuda kernels, which will be continuously modified by us. Therefore, let us first install it in development mode.

pip install -e submodules/diff-gaussian-rasterization
pip install submodules/simple-knn

Training

# 1 GPU
python train.py \
    -s dataset path \
    --iterations 7000 \
    --log_interval 50 \
    --log_folder experiments/test_1gpu \
    --model_path experiments/test_1gpu \
    --benchmark_stats

# 4 GPU
torchrun --standalone --nnodes=1 --nproc-per-node=4 train.py \
    -s dataset path \
    --iterations 7000 \
    --log_interval 50 \
    --log_folder experiments/test_4gpu \
    --model_path experiments/test_4gpu \
    --render_distribution_mode "2" \
    --redistribute_gaussians_mode "1" \
    --loss_distribution_mode "general" \
    --benchmark_stats

In hz3496's account in greene, it is /scratch/hz3496/3dgs_data/tandt_db/tandt/train

Rendering

Evaluating metrics