Cross3DReg: Towards a Large-scale Real-world Cross-source Point Cloud Registration Benchmark

• URL: http://arxiv.org/abs/2509.06456v1
• Date: Mon, 08 Sep 2025 09:01:13 GMT
• Title: Cross3DReg: Towards a Large-scale Real-world Cross-source Point Cloud Registration Benchmark
• Authors: Zongyi Xu, Zhongpeng Lang, Yilong Chen, Shanshan Zhao, Xiaoshui Huang, Yifan Zuo, Yan Zhang, Qianni Zhang, Xinbo Gao,
• Abstract summary: Cross-source point cloud registration, which aims to align point cloud data from different sensors, is a fundamental task in 3D vision.<n>The lack of publicly available large-scale real-world datasets for training the deep registration models, and the inherent differences in point clouds captured by multiple sensors pose challenges.<n>We construct Cross3DReg, the currently largest and real-world multi-modal cross-source point cloud registration dataset.<n>A visual-geometric attention guided matching module is proposed to enhance the consistency of cross-source point cloud features.
• Score: 57.42211080221526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-source point cloud registration, which aims to align point cloud data from different sensors, is a fundamental task in 3D vision. However, compared to the same-source point cloud registration, cross-source registration faces two core challenges: the lack of publicly available large-scale real-world datasets for training the deep registration models, and the inherent differences in point clouds captured by multiple sensors. The diverse patterns induced by the sensors pose great challenges in robust and accurate point cloud feature extraction and matching, which negatively influence the registration accuracy. To advance research in this field, we construct Cross3DReg, the currently largest and real-world multi-modal cross-source point cloud registration dataset, which is collected by a rotating mechanical lidar and a hybrid semi-solid-state lidar, respectively. Moreover, we design an overlap-based cross-source registration framework, which utilizes unaligned images to predict the overlapping region between source and target point clouds, effectively filtering out redundant points in the irrelevant regions and significantly mitigating the interference caused by noise in non-overlapping areas. Then, a visual-geometric attention guided matching module is proposed to enhance the consistency of cross-source point cloud features by fusing image and geometric information to establish reliable correspondences and ultimately achieve accurate and robust registration. Extensive experiments show that our method achieves state-of-the-art registration performance. Our framework reduces the relative rotation error (RRE) and relative translation error (RTE) by $63.2\%$ and $40.2\%$, respectively, and improves the registration recall (RR) by $5.4\%$, which validates its effectiveness in achieving accurate cross-source registration.

Related papers

• Registration is a Powerful Rotation-Invariance Learner for 3D Anomaly Detection [64.0168648353038]
  3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability.<n>Current memory bank-based methods often suffer from inconsistent feature transformations and limited discriminative capacity.<n>We propose a registration-induced, rotation-invariant feature extraction framework that integrates the objectives of point-cloud registration and memory-based anomaly detection.
  arXiv  Detail & Related papers  (2025-10-19T14:56:38Z)
• EEPNet-V2: Patch-to-Pixel Solution for Efficient Cross-Modal Registration between LiDAR Point Cloud and Camera Image [15.975048012603914]
  We propose a framework that projects point clouds into several 2D representations for matching with camera images.<n>To tackle the challenges of cross modal differences and the limited overlap between LiDAR point clouds and images in the image matching task, we introduce a multi-scale feature extraction network.<n>We validate the performance of our model through experiments on the KITTI and nuScenes datasets.
  arXiv  Detail & Related papers  (2025-03-19T15:04:01Z)
• Fully-Geometric Cross-Attention for Point Cloud Registration [51.865371511201765]
  Point cloud registration approaches often fail when the overlap between point clouds is low due to noisy point correspondences.<n>This work introduces a novel cross-attention mechanism tailored for Transformer-based architectures that tackles this problem.<n>We integrate the Gromov-Wasserstein distance into the cross-attention formulation to jointly compute distances between points across different point clouds.<n>At the point level, we also devise a self-attention mechanism that aggregates the local geometric structure information into point features for fine matching.
  arXiv  Detail & Related papers  (2025-02-12T10:44:36Z)
• LPRnet: A self-supervised registration network for LiDAR and photogrammetric point clouds [38.42527849407057]
  LiDAR and photogrammetry are active and passive remote sensing techniques for point cloud acquisition, respectively.<n>Due to the fundamental differences in sensing mechanisms, spatial distributions and coordinate systems, their point clouds exhibit significant discrepancies in density, precision, noise, and overlap.<n>This paper proposes a self-supervised registration network based on a masked autoencoder, focusing on heterogeneous LiDAR and photogrammetric point clouds.
  arXiv  Detail & Related papers  (2025-01-10T02:36:37Z)
• VRHCF: Cross-Source Point Cloud Registration via Voxel Representation and Hierarchical Correspondence Filtering [0.7499722271664147]
  We present a novel framework for point cloud registration with broad applicability. In cross-source point cloud registration, our method attains the best RR on the 3DCSR dataset, demonstrating a 9.3 percentage points improvement.
  arXiv  Detail & Related papers  (2024-03-15T08:00:29Z)
• HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion [15.94976936555104]
  We propose a cross-source point cloud fusion algorithm called HybridFusion. It can register cross-source dense point clouds from different viewing angle in outdoor large scenes. The proposed approach is evaluated comprehensively through qualitative and quantitative experiments.
  arXiv  Detail & Related papers  (2023-04-10T10:54:54Z)
• RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving [15.26754768427011]
  We propose a novel network, named RDMNet, to find dense point correspondences coarse-to-fine. Our method outperforms existing state-of-the-art approaches in all tested datasets with a strong generalization ability.
  arXiv  Detail & Related papers  (2023-03-31T14:22:32Z)
• A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation [12.499361832561634]
  We present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation. Our method significantly outperforms existing methods on overlap prediction, especially in scenes with small overlaps.
  arXiv  Detail & Related papers  (2023-02-28T12:01:16Z)
• CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence [51.91791056908387]
  We propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P. Specifically, given a pair of a 2D image before a 3D point cloud, we first transform them into high-dimensional feature space feed the features into a symmetric overlapping region to determine the region where the image point cloud overlap.
  arXiv  Detail & Related papers  (2022-07-12T11:49:31Z)
• A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion [69.32451612060214]
  Real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications. Most existing point cloud completion methods use Chamfer Distance (CD) loss for training. We propose a novel Point Diffusion-Refinement (PDR) paradigm for point cloud completion.
  arXiv  Detail & Related papers  (2021-12-07T06:59:06Z)
• Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision [68.35777836993212]
  We propose a Pseudo-LiDAR point cloud network to generate temporally and spatially high-quality point cloud sequences. By exploiting the scene flow between point clouds, the proposed network is able to learn a more accurate representation of the 3D spatial motion relationship.
  arXiv  Detail & Related papers  (2020-06-20T03:11:04Z)

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.