ParkerEwen5441 / torch-splatting

A pure pytorch implementation of 3D gaussian splatting

torch-splatting

A pure pytorch implementation of 3D gaussian splatting.

Train

clone the repo:

git clone https://github.com/hbb1/torch-splatting.git --recursive

Run the following commands to setup the repo:

unzip B075X65R3X.zip
mkdir -p result/test

Lastly, set up the conda environment:

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

Run the code and train the model:

python train.py

Tile-based rendering is implemeted. Because running loop for python is slow, it uses 64x64-sized tile instead of 16x16 as 3DGSS did. The training time is about 2 hours for 512x512 resolution image for 30k iterartions, tested on a RTX 2080Ti. The number of 3D gaussians is fixed, of 16384 points. Under this setting, it matchs the original diff-gaussian-splatting implementation (~39 PSNR on my synthetic data).

Stay Tuned.

Reference

https://github.com/graphdeco-inria/gaussian-splatting/tree/main

https://github.com/graphdeco-inria/diff-gaussian-rasterization

https://github.com/openai/point-e/tree/main/point_e