用激光雷达提升3D高斯点云重建质量，更少点数实现更高精度。
Structured-Li-GS: Structured 3D Gaussians Splatting with LiDAR Incorporation and Spatial Constraints
- 基于激光雷达与视觉惯性里程计融合，用稀疏点云锚定高斯分布。
- 仅用1.5万高斯点即达到比现有方法更高的重建精度。
- 适合需要轻量化、高保真3D重建的自动驾驶与机器人应用。
本文提出一种融合激光雷达的结构化3D高斯点云渲染框架（Structured-Li-GS）。该方法基于激光雷达-惯性-视觉联合SLAM，利用高密度彩色点云进行训练，在仅使用1.5万个高斯点的前提下实现了高质量3D重建。通过子采样点云锚定高斯分布，并从局部表面几何初始化其椭球参数。训练策略整合了光度、平坦度、偏移、深度和法向损失，由密集点云引导，无需高斯点云增密即可实现精确重建。实验使用自研硬件同步激光雷达-相机手持扫描仪，在基准数据集与自建真实场景数据集上验证，结果表明，相较于当前最优方法，本方法在减少高斯数量的同时显著提升了重建质量。
原文摘要 · Abstract (English)
In this study, we develop a Structured framework for Gaussian Splatting (3DGS) with LiDAR integration (Structured-Li-GS). It is a lightweight Gaussian Splatting pipeline that leverages LiDAR-inertial-visual SLAM. Structured-Li-GS achieves high-quality 3D reconstructions with fewer Gaussians by training on accurate, dense, colorized point clouds. Gaussian primitives are anchored using sub-sampled point clouds, and their ellipsoidal parameters are initialized from local surface geometry. Our training strategy integrates a comprehensive set of loss terms, including photometric, flattening, offset, depth, and normal losses, guided by the dense point cloud, enabling accurate reconstruction without Gaussian densification. This approach produces up-to-scale, high-fidelity results with a moderate model size. For experimental validation, we develop a custom hardware-synchronized LiDAR-camera handheld scanner. Experiments on both benchmark datasets and our real-world in-house dataset demonstrate that Structured-Li-GS surpasses state-of-the-art methods while using fewer Gaussians.
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