Guide to set up the Dynamic 3D Gaussians from JonathonLuiten using Conda and PyTorch.

Forked repository

I have forked the repository and added a requirements.txt file. The following sections will assume you use my fork instead. If you want to work on the original repository instead, you may check out the diff here.

Clone repository

git clone https://github.com/TCQian/Dynamic3DGaussians.git
cd Dynamic3DGaussians

Use conda to create a closed virtual environment

Try to use python 3.9 or above, as it’s the latest python version that PyTorch supports.

conda create -n 3dg python=3.10 -y
conda activate 3dg

Install packages (make sure you have a Nvidia GPU and Cuda runtime when running below installations)

Added requirements.txt file to include the necessary pip modules. I normally don’t use the conda environment files as they are often outdated, or the cuda runtime used is too old. Most of the time, you should use the most recent torch version that your cuda runtime can support to take advantage of improved library.

Pytorch no longer officially support installation using conda, so you should just use pip instead.

pip install -r requirements.txt

Installing submodule

diff-gaussian-rasterization-w-depth

The following is identical to the installation steps provided in the original repo.

git clone git@github.com:JonathonLuiten/diff-gaussian-rasterization-w-depth.git
cd diff-gaussian-rasterization-w-depth
python setup.py install
pip install .

Running

The following is also identical to the code run steps provided in the original repo.

Set up data folder.

cd Dynamic3DGaussians
wget https://omnomnom.vision.rwth-aachen.de/data/Dynamic3DGaussians/data.zip  # Download training data
unzip data.zip

Run training

python train.py