2025/09/20 by Xu, Ziyang, Schwab, Benedikt, Yang, Yihui +2
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Robotics (cs.RO) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2509.16832
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 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. Experiments on three real-world datasets demonstrate that L2M-Reg is both more accurate and computationally efficient than existing ICP-based and plane-based methods. Overall, L2M-Reg provides a novel building-level solution regarding LiDAR-to-Model registration when model uncertainty is present.