DOGS

DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus

[Project Page | arXiv] (NeurIPS 2024)

1. Introduction

Our method accelerates the training of 3DGS by 6+ times when evaluated on large-scale scenes while concurrently achieving state-of-the-art rendering quality.

2. TODO & Roadmap

• [ ] Release evaluation code
• [ ] Release pre-trained models on Mill19, UrbanScene3D, and MatrixCity
• [ ] Release web-viewer.
• [ ] Release training code
• [ ] Test on street-view scenes
• [ ] Support distributed training of Scaffold-GS and Octree-GS

3. Train & Test

Visualize scene splitting

Please check and compile my modification of COLMAP. After installation, launch COLMAP's GUI. I extended the original model files of COLMAP with an additional cluster.txt file, where each line of the file follows the format: [image_id, cluster_id]. Once COLMAP's GUI finds this file, it will render each image with its color corresponding to its cluster ID. Below are some examples of scene splitting:

Cite

If you find this project useful for your research, please consider citing our paper:

@inproceedings{yuchen2024dogaussian,
    title={DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus},
    author={Yu Chen, Gim Hee Lee},
    booktitle={arXiv},
    year={2024},
}