{"ID":2868517,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16832","arxiv_id":"2509.16832","title":"L2M-Reg: Building-level Uncertainty-aware Registration of Outdoor LiDAR Point Clouds and Semantic 3D City Models","abstract":"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.","short_abstract":"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 registrati...","url_abs":"https://arxiv.org/abs/2509.16832","url_pdf":"https://arxiv.org/pdf/2509.16832v3","authors":"[\"Ziyang Xu\",\"Benedikt Schwab\",\"Yihui Yang\",\"Thomas H. Kolbe\",\"Christoph Holst\"]","published":"2025-09-20T23:13:27Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.RO\",\"eess.IV\"]","methods":"[]","has_code":false,"code_links":[{"ID":609598,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2868517,"paper_url":"https://arxiv.org/abs/2509.16832","paper_title":"L2M-Reg: Building-level Uncertainty-aware Registration of Outdoor LiDAR Point Clouds and Semantic 3D City Models","repo_url":"https://github.com/Ziyang-Geodesy/L2M-Reg","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}