A step-by-step guide to setting up the SC-GS 3D Gaussian Splatting framework from CVMI-Lab using Conda and PyTorch.

Forked repository

I have forked the repository and updated the 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, initialise submodule in the repository

git clone https://github.com/TCQian/SC-GS.git
cd SC-GS
git submodule init; git submodule update

Use conda to create a closed virtual environment

conda create -n scgs python=3.10
conda activate scgs

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

There was some changes made to requirements.txt file because:

1. pytorch-ssim module is removed because it is found to be no longer used in the repository
2. all module versions removed, since we can rely on pip to resolve dependencies

Here, we follow https://pytorch.org/ guidelines and always install the latest torch version that the Nvidia GPU can support for optimal performance.

pip install torch torchvision torchaudio
pip install -r requirements.txt

Installing submodule toolkit

diff-gaussian-rasterization

While installing, you might encounter some header files not found issue. It’s likely that because the system packages are not yet installed in your Linux system. If you have sudo privileges, you may google the header files and install them directly on your Linux system.

For me, I do not have sudo privileges, and I encounter a glm header file not found issue. So I used conda to install the corresponding system packages. I also need to export some variable such that the installation will not use the default system path.

conda install -c conda-forge glm ninja
export CPLUS_INCLUDE_PATH=$CONDA_PREFIX/include

Then, I can install diff-gaussian-rasterization successfully.

pip install -e submodules/diff-gaussian-rasterization

simple-knn

While installing, the error prompt some missing variables:

error: identifier "FLT_MAX" is undefined

To resolve this, you need to include the missing import statement #include <cfloat> at the top of these two files:

simple_knn.cu
spatial.cu

Afterwards, you should be able to install simple-knn successfully.

pip install -e submodules/simple-knn

Running

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

Set up data folder by downloading the D-NeRF dataset from dropbox

Your folder should look like this:

├── data
│   ├── jumpingjacks
│   ├── bouncingballs
│   ├── hook
│   ├── ...

Run training.

CUDA_VISIBLE_DEVICES=0 python train_gui.py --source_path YOUR/PATH/TO/DATASET/jumpingjacks --model_path outputs/jumpingjacks --deform_type node --node_num 512 --hyper_dim 8 --is_blender --eval --gt_alpha_mask_as_scene_mask --local_frame --resolution 2 --W 800 --H 800