3D-SMoE-Splatting
3D SMoE Splatting for Edge-aware Realtime Radiance Field Rendering
| Project page | Paper | Video |
This repository provides the official implementation of the paper “3D SMoE Splatting for Edge-aware Realtime Radiance Field Rendering.” The method represents a scene using a set of Gaussian density functions and incorporates compression and densification techniques to improve compactness and efficiency.
Installation
# download
git clone https://github.com/yihsinli/3d-SMoE-Splatting.git --recursive
# if you have an environment used for 3dgs, use it
# if not, create a new environment
conda env create --file environment.yml
conda activate 3dsmoesplatting
Training and Testing
To train a scene, simply use
python train.py -s <path to COLMAP or NeRF Synthetic dataset> -m <path to output folder> --init_path default
Commandline arguments for regularizations
--eval # evaluation mode or not
--densify_until_iter # iterations for add/remove Gaussians, default = 15000
--densify_grad_threshold # Grad threshold for densification, default = 0.0002
Tips for adjusting the parameters on your own dataset:
- For compact representation, we suggest using higher threshold, i.e.,
densify_grad_threshold=0.05, for low-rate regime. The testing results will be automatically generated in the same folder under ‘test’ and ‘all_results’ folders
Rendering
Navigation pose generator
To export a camera pose for rendering video, simply use
python generate_pose.py -s <path to COLMAP dataset> --m <path to pre-trained model> --iteration 30000
To export a rendered result (video), simply use
python render.py -m <path to pre-trained model> -s <path to COLMAP dataset> --iteration 30000
Commandline arguments you should adjust accordingly for meshing for bounded TSDF fusion, use
## Quick Examples
Assuming you have downloaded [MipNeRF360](https://jonbarron.info/mipnerf360/), simply use
```bash
python train.py -s <path to m360>/<counter> -m SMoEoutput/m360/counter
# use our pose generator
python generate_pose.py -s <path to m360>/<counter> -m SMoEoutput/m360/counter --iteration 30000
# or use the bounded mesh extraction if you focus on foreground
python render.py -s <path to m360>/<counter> -m output/m360/counter --iteration 30000
Custom Dataset: We use the same COLMAP loader as 3DGS, you can prepare your data following here.
Acknowledgements
This project is built upon 3DGS. We thank all the authors for their great repos.
Citation
If you find our code or paper helps, please consider citing:
@inproceedings{Li3DSMoE2025,
title={3D SMoE Splatting for Edge-aware Realtime Radiance Field Rendering},
author={Li, Yi-Hsin and Sikora, Thomas and Knorr, Sebastian and Sjöström, Mårten},
publisher = {Association for Computing Machinery},
booktitle = {SIGGRAPH Asia 2025 Conference Papers},
year = {2025}
}