gsplat

http://www.gsplat.studio/

gsplat is an open-source library for CUDA accelerated rasterization of gaussians with Python bindings. It is inspired by the SIGGRAPH paper 3D Gaussian Splatting for Real-Time Rendering of Radiance Fields, but we’ve made gsplat even faster, more memory efficient, and with a growing list of new features!

gsplat-quick-intro.mp4

Installation

Build from Source on Windows

Evaluation

This repo comes with a standalone script that reproduces the official Gaussian Splatting with exactly the same performance on PSNR, SSIM, LPIPS, and converged number of Gaussians. Powered by gsplat’s efficient CUDA implementation, the training takes up to 4x less GPU memory with up to 15% less time to finish than the official implementation.

Note

Full report can be found here.

pip install -r examples/requirements.txt
# download mipnerf_360 benchmark data
python examples/datasets/download_dataset.py
# run batch evaluation
bash examples/benchmarks/basic.sh

Examples

We provide a set of examples to get you started! Below you can find the details about the examples (requires to install some extra dependencies via pip install -r examples/requirements.txt)

• Train a 3D Gaussian splatting model on a COLMAP capture.
• Fit a 2D image with 3D Gaussians.
• Render a large scene in real-time.

Development and Contribution

This repository was born from the curiosity of people on the Nerfstudio team trying to understand a new rendering technique. We welcome contributions of any kind and are open to feedback, bug-reports, and improvements to help expand the capabilities of this software.

This project is developed by the following wonderful contributors (unordered):

Angjoo Kanazawa	UC Berkeley	Mentor of the project
Matthew Tancik	Luma AI	Mentor of the project
Vickie Ye	UC Berkeley	Project lead. v0.1 lead
Matias Turkulainen	Aalto University	Core developer
Ruilong Li	UC Berkeley	Core developer. v1.0 lead
Justin Kerr	UC Berkeley	Core developer
Brent Yi	UC Berkeley	Core developer
Zhuoyang Pan	ShanghaiTech University	Core developer
Jianbo Ye	Amazon	Core developer

We also have a white paper with about the project with benchmarking and mathematical supplement with conventions and derivations, available here. If you find this library useful in your projects or papers, please consider citing:

@article{ye2024gsplatopensourcelibrarygaussian,
    title={gsplat: An Open-Source Library for {Gaussian} Splatting}, 
    author={Vickie Ye and Ruilong Li and Justin Kerr and Matias Turkulainen and Brent Yi and Zhuoyang Pan and Otto Seiskari and Jianbo Ye and Jeffrey Hu and Matthew Tancik and Angjoo Kanazawa},
    year={2024},
    eprint={2409.06765},
    journal={arXiv preprint arXiv:2409.06765},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2409.06765}, 
}

We welcome contributions of any kind and are open to feedback, bug-reports, and improvements to help expand the capabilities of this software. Please check docs/DEV.md for more info about development.