Skip to content

jb-ye/gsplat

 
 

Repository files navigation

gsplat

Core Tests. Docs

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

Dependence: Please install Pytorch first.

The easiest way is to install from PyPI. In this way it will build the CUDA code on the first run (JIT).

pip install gsplat

Or install from source. In this way it will build the CUDA code during installation.

pip install git+https://github.com/nerfstudio-project/gsplat.git

To install gsplat on Windows, please check this instruction.

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. Full report can be found here.

# under examples/
pip install -r requirements.txt
# download mipnerf_360 benchmark data
python datasets/download_dataset.py
# run batch evaluation
bash 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 exta dependencies via pip install -r examples/requirements.txt)

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):

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

@misc{ye2023mathematical,
    title={Mathematical Supplement for the $\texttt{gsplat}$ Library}, 
    author={Vickie Ye and Angjoo Kanazawa},
    year={2023},
    eprint={2312.02121},
    archivePrefix={arXiv},
    primaryClass={cs.MS}
}

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.

About

CUDA accelerated rasterization of gaussian splatting

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 48.3%
  • Cuda 47.5%
  • C++ 3.5%
  • Other 0.7%