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

• URL: http://arxiv.org/abs/2509.16832v2
• Date: Tue, 23 Sep 2025 08:02:05 GMT
• Title: L2M-Reg: Building-level Uncertainty-aware Registration of Outdoor LiDAR Point Clouds and Semantic 3D City Models
• Abstract summary: This paper proposes L2M-Reg, a plane-based fine registration method that explicitly accounts for model uncertainty.<n>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.
• Score: 3.564261673240843
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• SemCityLoc: Aerial 6DoF Localization Using Semantic 3D City Models [51.121529170856064]
  We propose SemCityLoc, a semantic-geometric alignment system that reframes aerial pose estimation.<n>Instead of matching sparse contours or dense texture, our method aligns semantic surfaces and monocular depth with lightweight semantic 3D building models.<n>Experiments demonstrate substantial improvements over existing map-based approaches.
  arXiv  Detail & Related papers  (2026-06-25T18:13:39Z)
• RegFormer++: An Efficient Large-Scale 3D LiDAR Point Registration Network with Projection-Aware 2D Transformer [71.59462491691819]
  We propose a novel end-to-end differential transformer network, termed RegFormer++, for large-scale point cloud alignment.<n>Because we fill original 3D coordinates into 2D projected positions, our designed transformer can benefit from both high efficiency in 2D processing and accuracy from 3D geometric information.<n>Experiments on KITTI, NuScenes, and Argoverse datasets demonstrate that our model achieves state-of-the-art performance in terms of both accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-03-15T09:03:10Z)
• DIMM: Decoupled Multi-hierarchy Kalman Filter for 3D Object Tracking [50.038098341549095]
  State estimation is challenging for 3D object tracking with high maneuverability.<n>We propose a novel framework, DIMM, to effectively combine estimates from different motion models in each direction.<n>DIMM significantly improves the tracking accuracy of existing state estimation methods by 31.61%99.23%.
  arXiv  Detail & Related papers  (2025-05-18T10:12:41Z)
• Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction [18.671925059007478]
  We investigate a state-of-the-art MHE model trained purely on synthetic data.<n>We find that the model relies heavily on shadow cues, a factor that can lead to overestimation or underestimation of heights.<n>We propose a novel correction pipeline that integrates sparse, imperfect global LiDAR measurements.
  arXiv  Detail & Related papers  (2025-05-11T08:54:09Z)
• LiDAR-based Registration against Georeferenced Models for Globally Consistent Allocentric Maps [16.335109366948473]
  Modern unmanned aerial vehicles (UAVs) are irreplaceable in search and rescue (SAR) missions to obtain a situational overview or provide closeups without endangering personnel.<n>However, UAVs heavily rely on global navigation satellite system (GNSS) for localization which works well in open spaces, but the precision drastically degrades in the vicinity of buildings.<n>In contrast, CityGML models provide approximate building shapes with accurate georeferenced poses.
  arXiv  Detail & Related papers  (2024-12-03T16:25:08Z)
• Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene [22.297964850282177]
  We propose LiDAR-2D Self-paced Learning (LiSe) for unsupervised 3D detection. RGB images serve as a valuable complement to LiDAR data, offering precise 2D localization cues. Our framework devises a self-paced learning pipeline that incorporates adaptive sampling and weak model aggregation strategies.
  arXiv  Detail & Related papers  (2024-07-11T14:58:49Z)
• 4D Contrastive Superflows are Dense 3D Representation Learners [62.433137130087445]
  We introduce SuperFlow, a novel framework designed to harness consecutive LiDAR-camera pairs for establishing pretraining objectives. To further boost learning efficiency, we incorporate a plug-and-play view consistency module that enhances alignment of the knowledge distilled from camera views.
  arXiv  Detail & Related papers  (2024-07-08T17:59:54Z)
• FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models [59.13757801286343]
  Few-shot class-incremental learning aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data.<n>We introduce the FILP-3D framework with two novel components: the Redundant Feature Eliminator (RFE) for feature space misalignment and the Spatial Noise Compensator (SNC) for significant noise.
  arXiv  Detail & Related papers  (2023-12-28T14:52:07Z)
• Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection [0.5156484100374059]
  Background modeling is widely used for intelligent surveillance systems to detect moving targets by subtracting the static background components. Most roadside LiDAR object detection methods filter out foreground points by comparing new data points to pre-trained background references. In this paper, we transform the raw LiDAR data into a structured representation based on the elevation and azimuth value of each LiDAR point. The proposed method was compared against two state-of-the-art roadside LiDAR background models, computer vision benchmark, and deep learning baselines, evaluated at point, object, and path levels under heavy traffic and challenging weather.
  arXiv  Detail & Related papers  (2022-04-20T22:48:05Z)
• Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling [67.75095378830694]
  We develop a two-stage odometry estimation network, where we obtain the ego-motion by estimating a set of sub-region transformations. In this paper, we aim to alleviate the influence of unreliable structures in training, inference and mapping phases. Our two-frame odometry outperforms the previous state of the arts by 16%/12% in terms of translational/rotational errors.
  arXiv  Detail & Related papers  (2022-02-27T12:52:27Z)
• Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception [122.53774221136193]
  State-of-the-art methods for driving-scene LiDAR-based perception often project the point clouds to 2D space and then process them via 2D convolution. A natural remedy is to utilize the 3D voxelization and 3D convolution network. We propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern.
  arXiv  Detail & Related papers  (2021-09-12T06:25:11Z)
• Global Unifying Intrinsic Calibration for Spinning and Solid-State LiDARs [1.6252896527001484]
  We propose a new type of calibration model for spinning and solid-state LiDARs. We prove mathematically that the proposed model is well-constrained (has a unique answer) given four appropriately orientated targets. For spinning LiDARs, we show with experimental data that the proposed matrix Lie Group model performs equally well in terms of reducing the P2P distance, while being more robust to noise.
  arXiv  Detail & Related papers  (2020-12-06T16:55:58Z)
• SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks [81.64530401885476]
  We propose a self-supervised LiDAR odometry method, dubbed SelfVoxeLO, to tackle these two difficulties. Specifically, we propose a 3D convolution network to process the raw LiDAR data directly, which extracts features that better encode the 3D geometric patterns. We evaluate our method's performances on two large-scale datasets, i.e., KITTI and Apollo-SouthBay.
  arXiv  Detail & Related papers  (2020-10-19T09:23:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.