gaussian-splatting-mcmc 1.1.10

Refactored python training code for 3D Gaussian Splatting as Markov Chain Monte Carlo

3D Gaussian Splatting as Markov Chain Monte Carlo (Packaged Python Version)

This repository contains the refactored Python code for 3dgs-mcmc. It is forked from commit 7b4fc9f76a1c7b775f69603cb96e70f80c7e6d13. The original code has been refactored to follow the standard Python package structure, while maintaining the same algorithms as the original version.

Features

• Code organized as a standard Python package
• Markov Chain Monte Carlo trainer for 3D Gaussian Splatting
• Integration with reduced-3dgs

Prerequisites

• Pytorch (v2.4 or higher recommended)
• CUDA Toolkit (12.4 recommended, should match with PyTorch version)
• (Optional) cuML for faster vector quantization

(Optional) If you have trouble with gaussian-splatting and reduced-3dgs, try to install it from source:

pip install wheel setuptools
pip install --upgrade git+https://github.com/yindaheng98/gaussian-splatting.git@master --no-build-isolation
pip install --upgrade git+https://github.com/yindaheng98/reduced-3dgs.git@main --no-build-isolation

PyPI Install

pip install --upgrade gaussian-splatting-mcmc

or build latest from source:

pip install wheel setuptools
pip install --upgrade git+https://github.com/yindaheng98/3dgs-mcmc.git@main --no-build-isolation

Development Install

git clone --recursive https://github.com/yindaheng98/reduced-3dgs
cd 3dgs-mcmc
pip install --target . --upgrade --no-deps .

Quick Start

1. Download dataset (T&T+DB COLMAP dataset, size 650MB):

wget https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/input/tandt_db.zip -P ./data
unzip data/tandt_db.zip -d data/

2. Train 3DGS-MCMC:

python -m gaussian_splatting_mcmc.train -s data/truck -d output/truck -i 30000 --mode base

3. Render:

python -m gaussian_splatting.render -s data/truck -d output/truck -i 30000 --load_camera output/truck/cameras.json

** NeurIPS 2024 SPOTLIGHT **

3D Gaussian Splatting as Markov Chain Monte Carlo

Shakiba Kheradmand, Daniel Rebain, Gopal Sharma, Weiwei Sun, Yang-Che Tseng, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, Kwang Moo Yi

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BibTeX

@inproceedings{kheradmand20243d,
    title = {3D Gaussian Splatting as Markov Chain Monte Carlo},
    author = {Kheradmand, Shakiba and Rebain, Daniel and Sharma, Gopal and Sun, Weiwei and Tseng, Yang-Che and Isack, Hossam and Kar, Abhishek and Tagliasacchi, Andrea and Yi, Kwang Moo},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    year = {2024},
    note = {Spotlight Presentation},
   }