# L2M-Reg: Building-level Uncertainty-aware Registration of Outdoor LiDAR Point Clouds and Semantic 3D City Models

arXiv: https://arxiv.org/abs/2509.16832 · HTML: https://mlsanity.com/paper/2509.16832/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 83 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection](https://mlsanity.com/paper/2012.12195/) (2020)
- [Probabilistic Semantic Mapping for Urban Autonomous Driving Applications](https://mlsanity.com/paper/2006.04894/) (2020)
- [TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset](https://mlsanity.com/paper/2505.07396/) (2025)
- [Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models](https://mlsanity.com/paper/2304.11719/) (2023)
- [Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication](https://mlsanity.com/paper/2110.06401/) (2021)
- [Enriching thermal point clouds of buildings using semantic 3D building models](https://mlsanity.com/paper/2407.21436/) (2024)

## Built on by (most cited first, 1 of 1)

- [Radiometric fingerprinting of object surfaces using mobile laser scanning and semantic 3D road space models](https://mlsanity.com/paper/2603.11252/) (2026)

## Abstract (arXiv)

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.