解决城市场景中激光点云与语义3D模型的建筑级配准难题
L2M-Reg: Building-level Uncertainty-aware Registration of Outdoor LiDAR Point Clouds and Semantic 3D City Models
- 基于平面对应关系，构建考虑模型不确定性的约束优化模型
- 在五个真实数据集上精度优于主流ICP与平面方法，计算更高效
- 适合城市数字孪生、模型更新等需高精度配准的场景
激光雷达（LiDAR）点云与语义3D城市模型之间的精确配准是城市数字孪生的基础，也是数字建造、变化检测和模型优化的前提。然而，在细节等级2（LoD2）下，语义3D城市模型存在泛化不确定性，导致单体建筑级别的点云-模型配准仍具挑战。本文提出L2M-Reg，一种基于平面的精细配准方法，显式建模模型不确定性。该方法包含三个关键步骤：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、自适应估计垂直平移。在五个真实世界数据集上的大量实验表明，L2M-Reg在精度和计算效率方面均优于当前主流的ICP及平面方法。因此，L2M-Reg为存在模型不确定性的点云-模型建筑级配准提供了新解决方案。代码与数据集详见：https://github.com/Ziyang-Geodesy/L2M-Reg。
原文摘要 · Abstract (English)
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.
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。