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

News

Unreleased

Changes on main since the v1.5.3 tag (not yet on PyPI).

• [May 2026] Inference Rendering (HiGS) -- An experimental inference-only rendering path based on HiGS (Hierarchically Tiled Gaussian Splatting) is now available under the experimental package. The inference path uses macro-tile fused rasterization with fp16 scene packing for low-latency rendering of pre-trained Gaussian scenes. For more details, see the Inference Rendering section and the HiGS project page.
• [May 2026] Native CUDA MCMC perturb (inject_noise) speeds up the noise-injection step used in MCMC-style Gaussian optimization.
• [Apr 2026] AccuTile adds a conservative ellipse-based tile–Gaussian intersection test on the 3DGS path for tighter work scheduling before rasterization (PR #927).
• [Apr 2026] NCore v4 capture support, including richer camera models and point-cloud loading via PointCloudsSourceProtocol. See the NCore example.
• [Mar 2026] LiDAR rasterization for 3D Gaussian splatting: spinning-lidar camera models, eval3d rendering, depth / hit-distance modes, and related tooling (optional SciPy via pip install "gsplat[lidar]").
• [Mar 2026] TorchScript-oriented deployment: camera models and distortions are also available through PyTorch custom operators and custom classes, not only Python callables.
• [Mar 2026] 3DGUT extensions: external distortion (e.g. windshield-style rigs), optional per-ray inputs with gradients, optional ray-normal outputs, and refactored render modes / extra signals (see 3DGUT notes).
• [Jan 2026] PPISP is integrated as an alternative way of bilateral grid to compensate the training views.

v1.5.3

• [May 2025] Arbitrary batching (over multiple scenes and multiple viewpoints) is supported now!! Checkout here for more details! Kudos to Junchen Liu.
• [May 2025] Jonathan Stephens makes a great tutorial video for Windows users on how to install gsplat and get start with 3DGUT.
• [April 2025] NVIDIA 3DGUT is now integrated in gsplat! Checkout here for more details. [NVIDIA Tech Blog] [NVIDIA Sweepstakes]

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

Alternatively you can install gsplat from source. In this way it will build the CUDA code during installation.

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

We also provide pre-compiled wheels for both linux and windows on certain python-torch-CUDA combinations (please check first which versions are supported). Note this way you would have to manually install gsplat's dependencies. For example, to install gsplat for pytorch 2.0 and cuda 11.8 you can run

pip install ninja numpy jaxtyping rich
pip install gsplat --index-url https://docs.gsplat.studio/whl/pt20cu118

To build gsplat from source 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.

cd 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 installing some extrra dependencies via pip install -r examples/requirements.txt --no-build-isolation)

• Train a 3D Gaussian splatting model on a COLMAP capture.
• Fit a 2D image with 3D Gaussians.
• Render a large scene in real-time.
• Train on an NCore v4 capture.

Inference Rendering

gsplat includes an experimental inference-only rendering path based on HiGS (Hierarchically Tiled Gaussian Splatting) in the standalone experimental package, designed for low-latency rendering of pre-trained Gaussian scenes where training gradients are not needed. The inference path packs scene data into compact fp16 layouts and uses a macro-tile fused rasterization pipeline for fast single-camera rendering.

from experimental import render_scene, GaussianInferenceScene

The simple_viewer.py example supports the Inference path via the --use_gaussian_render_inference_scene flag. A standalone benchmark comparing Inference rendering against the default rasterization() path is available in examples/benchmarks/gaussian_render_inference_scene/; run gaussian_render_inference_scene_bench.py from the repo root. For more details, see the HiGS project page.

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 contributors coming from following institutes (unordered):

• UC Berkeley
• NVIDIA
• ShanghaiTech University
• Amazon
• Meta
• IIIT
• LumaAI
• SpectacularAI
• Aalto University
• CMU

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{ye2025gsplat,
  title={gsplat: An open-source library for Gaussian splatting},
  author={Ye, Vickie and Li, Ruilong and Kerr, Justin and Turkulainen, Matias and Yi, Brent and Pan, Zhuoyang and Seiskari, Otto and Ye, Jianbo and Hu, Jeffrey and Tancik, Matthew and Angjoo Kanazawa},
  journal={Journal of Machine Learning Research},
  volume={26},
  number={34},
  pages={1--17},
  year={2025}
}

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.