LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS

Our complete codebase will be released within two weeks.

Setup

Local Setup

The codebase is based on gaussian-splatting

The used datasets, MipNeRF360 and Tank & Temple, are hosted by the paper authors here.

For installation:

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

note: we modified the "diff-gaussian-rasterization" in the submodule to get the Global Significant Score.

Compress to Compact Representation

Lightgaussian includes 3 ways to make the 3D Gaussians be compact

Option 1 Prune & Recovery

Users can directly prune a trained 3D-GS checkpoint using the following command (default setting):

bash scripts/run_prune_finetune.sh

One can also train from scratch and jointly prune redundant Gaussians in training using the following command (different setting from the paper):

bash scripts/run_train_densify_prune.sh

note: 3D-GS is trained for 20,000 iterations and then prune it. The resulting ply file is approximately 35% of the size of the original 3D-GS while ensuring a comparable quality level.

Option 2 SH distillation

Option 3 VecTree Quantization

Render

# Render with trajectory. By default ellipse, you can change it to spiral or others trajectory by changing to corresponding function.
python render_video.py --source_path PATH/TO/DATASET --model_path PATH/TO/MODEL --skip_train --skip_test --video

TODO List

Upload module 1: Prune & recovery
Upload module 2: SH distillation
Upload module 3: Vectree Quantization
Upload docker image

BibTeX

If you find our work useful for your project, please consider citing the following paper.

@misc{fan2023lightgaussian, 
title={LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS}, 
author={Zhiwen Fan and Kevin Wang and Kairun Wen and Zehao Zhu and Dejia Xu and Zhangyang Wang}, 
year={2023},
eprint={2311.17245},
archivePrefix={arXiv},
primaryClass={cs.CV} }

yaochengrong/LightGaussian