gaussian-splatting-mcmc 1.1.4

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)

Install

PyPI Install

pip install --upgrade gaussian-splatting-mcmc

Install (Development)

Install gaussian-splatting. You can download the wheel from PyPI:

pip install --upgrade gaussian-splatting

Alternatively, install the latest version from the source:

pip install --upgrade git+https://github.com/yindaheng98/gaussian-splatting.git@master

Install reduced-3dgs. You can download the wheel from PyPI:

pip install --upgrade reduced-3dgs

Alternatively, install the latest version from the source:

pip install --upgrade git+https://github.com/yindaheng98/reduced-3dgs.git@main

(Optional) If you prefer not to install gaussian-splatting and reduced-3dgs in your environment, you can install them in your lapis-gs directory:

pip install --target . --no-deps --upgrade git+https://github.com/yindaheng98/gaussian-splatting.git@master
pip install --target . --no-deps --upgrade git+https://github.com/yindaheng98/reduced-3dgs.git@main

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},
   }