用高斯点云实现户外实时逼真激光惯性视觉建图
GS-LIVM: Real-Time Photo-Realistic LiDAR-Inertial-Visual Mapping with Gaussian Splatting
- 结合高斯点云与贝叶斯回归，解决激光点稀疏不均问题
- 自研CUDA加速实现大规模室外场景实时稠密建图
- 适合自动驾驶、机器人导航等需高保真环境感知的场景
本文提出GS-LIVM，一种面向室外场景的实时逼真激光惯性视觉建图框架，基于高斯点云（Gaussian Splatting）。相比基于神经辐射场（NeRF）和3D高斯点云（3DGS）的方法，本方法在大规模无界室外环境中实现了实时逼真渲染。通过高斯过程回归（GPR）缓解激光雷达观测稀疏且分布不均带来的问题。基于体素的3D高斯地图表示结合自定义CUDA内核，支持大范围室外环境下的实时稠密建图。整体框架采用协方差中心设计，利用估计协方差初始化3D高斯的尺度与旋转，并更新GPR参数。在多个室外数据集上的评估表明，该方法在建图效率与渲染质量上达到当前最优水平。源代码已公开于GitHub。
原文摘要 · Abstract (English)
In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), our approach enables real-time photo-realistic mapping while ensuring high-quality image rendering in large-scale unbounded outdoor environments. In this work, Gaussian Process Regression (GPR) is employed to mitigate the issues resulting from sparse and unevenly distributed LiDAR observations. The voxel-based 3D Gaussians map representation facilitates real-time dense mapping in large outdoor environments with acceleration governed by custom CUDA kernels. Moreover, the overall framework is designed in a covariance-centered manner, where the estimated covariance is used to initialize the scale and rotation of 3D Gaussians, as well as update the parameters of the GPR. We evaluate our algorithm on several outdoor datasets, and the results demonstrate that our method achieves state-of-the-art performance in terms of mapping efficiency and rendering quality. The source code is available on GitHub.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。