splatad

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用 3D 高斯泼溅实现自动驾驶场景下激光雷达与相机数据的实时渲染，为感知算法提供高保真仿真环境。

SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving

暂无版本433star34fork同步于 2026-11-07 07:30 UTC

3D高斯泼溅自动驾驶仿真激光雷达渲染实时渲染 3D重建

对比 GitHub 官网 3d-reconstruction autonomous-driving cuda gaussian-splatting

怎么上手

由 README 提炼，执行前请确认

原文

参照 gsplat 官方安装文档：https://github.com/nerfstudio-project/gsplat#installation

硬件要求按 README 说明

README 未说明具体显存与 GPU 型号要求，仅说明基于 gsplat 的 CUDA 加速光栅化，安装与开发环境需参照 gsplat 文档

需要 CUDA

下载：源码 zip·GitHub Releases（安装包）本站不镜像安装包

项目说明

由 README 自动翻译提炼

没人工点评？写下你的经验

基于 3D 高斯泼溅的自动驾驶激光雷达与相机实时渲染代码库

用 3D 高斯泼溅实现自动驾驶场景下激光雷达与相机数据的实时渲染，为感知算法提供高保真仿真环境。

核心能力

将激光雷达点投影到球面坐标后做深度与特征光栅化
支持非线性格点上的激光雷达光栅化
为相机与激光雷达提供卷帘快门补偿
提供激光雷达渲染与卷帘快门补偿的示例 notebook
基于 gsplat 扩展，无额外依赖

容易踩的坑

完整 SplatAD 模型（含数据加载、解码器等）尚未在本仓库发布，需等待 neurad-studio；本仓库只含渲染组件，安装依赖需按 gsplat 文档自行配置 CUDA 环境

内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README

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README 原文

未翻译

在 GitHub 打开

Project page

About

This is the official repository for the CVPR 2025 paper SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving. The code in this repository builds upon the open-source library gsplat, with modifications and extensions designed for autonomous driving data.

While the code contians all components needed to efficiently render camera and lidar data, the SplatAD-model itself, including dataloading, decoders, etc., will be released through neurad-studio.

We welcome all contributions!

Key Features

Efficient lidar rendering
- Projection to spherical coordinates
- Depth and feature rasterization for a non-linear grid of points
Rolling shutter compensation for camera and lidar

Installation

Our code introduce no additional dependencies. We thus refer to the original documentation from gsplat for both installation and development setup.

Usage

See rasterization and lidar_rasterization for entry points to camera and lidar rasterization. Additionally, we provide example notebooks under examples that demonstrate lidar rendering and rolling shutter compensation. For further examples, check out the test files.

Built On

gsplat - Collaboration friendly library for CUDA accelerated rasterization of Gaussians with python bindings
3dgs-deblur - Inspiration for the rolling shutter compensation

Citation

You can find our paper on arXiv.

If you use this code or find our paper useful, please consider citing:

@article{hess2024splatad,
  title={SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving},
  author={Hess, Georg and Lindstr{\"o}m, Carl and Fatemi, Maryam and Petersson, Christoffer and Svensson, Lennart},
  journal={arXiv preprint arXiv:2411.16816},
  year={2024}
}

Contributors

+ gsplat contributors