Accurate registration between LiDAR (Light Detection and Ranging) point clouds and semantic 3D city models is a fundamental topic in urban digital twinning and a prerequisite for downstream tasks, such as digital construction, change detection, and model refinement. However, achieving accurate LiDAR-to-Model registration at the individual building level remains challenging, particularly due to the generalization uncertainty in semantic 3D city models at the Level of Detail 2 (LoD2). This paper addresses this gap by proposing L2M-Reg, a plane-based fine registration method that explicitly accounts for model uncertainty. L2M-Reg consists of three key steps: establishing reliable plane correspondence, building a pseudo-plane-constrained Gauss-Helmert model, and adaptively estimating vertical translation. Overall, extensive experiments on five real-world datasets demonstrate that L2M-Reg is both more accurate and computationally efficient than current leading ICP-based and plane-based methods. Therefore, L2M-Reg provides a novel building-level solution regarding LiDAR-to-Model registration when model uncertainty is present. The datasets and code for L2M-Reg can be found: https://github.com/Ziyang-Geodesy/L2M-Reg.

翻译：激光雷达点云与语义三维城市模型的精确配准是城市数字孪生的基础课题，也是数字建造、变化检测与模型精化等下游任务的前提。然而，在单体建筑层面实现精确的激光雷达到模型配准仍具挑战性，这尤其源于细节层次2语义三维城市模型中存在的泛化不确定性。本文针对这一空白，提出了L2M-Reg，一种显式考虑模型不确定性的基于平面的精细配准方法。L2M-Reg包含三个关键步骤：建立可靠的平面对应关系、构建伪平面约束的高斯-赫尔默特模型，以及自适应估计垂直平移。总体而言，在五个真实世界数据集上的大量实验表明，相较于当前主流的基于ICP和基于平面的方法，L2M-Reg在精度和计算效率上均更优。因此，当存在模型不确定性时，L2M-Reg为激光雷达到模型配准提供了一种新颖的单体建筑级解决方案。L2M-Reg的数据集与代码可见：https://github.com/Ziyang-Geodesy/L2M-Reg。