Gsplat

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gsplat is an open-source library for CUDA accelerated rasterization of gaussians with Python bindings. It offers faster and more memory-efficient real-time rendering of radiance fields compared to official implementations.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is gsplat?

gsplat is an open-source library designed for CUDA accelerated rasterization of gaussians, complete with Python bindings. Inspired by the SIGGRAPH paper '3D Gaussian Splatting for Real-Time Rendering of Radiance Fields,' gsplat significantly enhances performance. It boasts up to 4x less GPU memory usage and up to 15% faster training times compared to the official implementation, making it a highly efficient solution for real-time rendering. The library supports arbitrary batching over multiple scenes and viewpoints and integrates with NVIDIA 3DGUT. It provides examples for training 3D Gaussian splatting models on COLMAP captures, fitting 2D images with 3D Gaussians, and rendering large scenes in real-time, catering to both research and practical application needs.

Best used for

Ideal for researchers and developers who need to implement and experiment with 3D Gaussian splatting for real-time rendering. Especially valuable for those requiring high performance, memory efficiency, and the ability to train models on various datasets like COLMAP captures.

Common actions

render radiance fields

train 3D models

accelerate rendering

process gaussian splatting

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Capabilities

Key features

CUDA accelerated rasterization
Python bindings
Memory-efficient training
Real-time rendering
Arbitrary batching
NVIDIA 3DGUT integration

Target Audience

3d graphics developersmachine learning researcherscomputer vision engineersgame developers

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the primary benefits of using gsplat over other Gaussian splatting implementations?

gsplat offers significant advantages including up to 4x less GPU memory usage and up to 15% faster training times compared to the official implementation. It also provides CUDA accelerated rasterization and Python bindings for ease of use and integration.

Can gsplat be used for real-time rendering of large scenes?

Yes, gsplat is specifically designed for efficient real-time rendering. It includes examples and features that demonstrate its capability to render large scenes effectively, making it suitable for demanding applications.

What are the installation requirements for gsplat?

To install gsplat, you need Pytorch. The easiest way is via pip, which builds the CUDA code on the first run. Alternatively, you can install from source or use pre-compiled wheels for specific Python-Torch-CUDA combinations, requiring manual dependency installation.

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