VRHCF: Cross-Source Point Cloud Registration via Voxel Representation and Hierarchical Correspondence Filtering

• URL: http://arxiv.org/abs/2403.10085v1
• Date: Fri, 15 Mar 2024 08:00:29 GMT
• Title: VRHCF: Cross-Source Point Cloud Registration via Voxel Representation and Hierarchical Correspondence Filtering
• Authors: Guiyu Zhao, Zewen Du, Zhentao Guo, Hongbin Ma,
• Abstract summary: 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.
• Score: 0.7499722271664147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Addressing the challenges posed by the substantial gap in point cloud data collected from diverse sensors, achieving robust cross-source point cloud registration becomes a formidable task. In response, we present a novel framework for point cloud registration with broad applicability, suitable for both homologous and cross-source registration scenarios. To tackle the issues arising from different densities and distributions in cross-source point cloud data, we introduce a feature representation based on spherical voxels. Furthermore, addressing the challenge of numerous outliers and mismatches in cross-source registration, we propose a hierarchical correspondence filtering approach. This method progressively filters out mismatches, yielding a set of high-quality correspondences. Our method exhibits versatile applicability and excels in both traditional homologous registration and challenging cross-source registration scenarios. Specifically, in homologous registration using the 3DMatch dataset, we achieve the highest registration recall of 95.1% and an inlier ratio of 87.8%. In cross-source point cloud registration, our method attains the best RR on the 3DCSR dataset, demonstrating a 9.3 percentage points improvement. The code is available at https://github.com/GuiyuZhao/VRHCF.

Related papers

• Register Any Point: Scaling 3D Point Cloud Registration by Flow Matching [41.014082260379304]
  Point cloud registration is a core step for 3D reconstruction and robot localization.<n>We cast registration as conditional generation: a learned continuous, point-wise velocity field transports noisy points to a registered scene.<n>Our approach achieves state-of-the-art results on pairwise and multi-view registration benchmarks.
  arXiv  Detail & Related papers  (2025-12-01T16:36:51Z)
• Cross3DReg: Towards a Large-scale Real-world Cross-source Point Cloud Registration Benchmark [57.42211080221526]
  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.
  arXiv  Detail & Related papers  (2025-09-08T09:01:13Z)
• 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. This work introduces a novel cross-attention mechanism tailored for Transformer-based architectures that tackles this problem. We integrate the Gromov-Wasserstein distance into the cross-attention formulation to jointly compute distances between points across different point clouds. 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)
• Cross-PCR: A Robust Cross-Source Point Cloud Registration Framework [0.7499722271664147]
  We propose a density-robust feature extraction and matching scheme to achieve robust and accurate cross-source registration. On the challenging Kinect-LiDAR scene in the cross-source 3DCSR dataset, our method improves feature matching recall by 63.5 percentage points (pp) and registration recall by 57.6 pp.
  arXiv  Detail & Related papers  (2024-12-25T11:14:59Z)
• Multiway Point Cloud Mosaicking with Diffusion and Global Optimization [74.3802812773891]
  We introduce a novel framework for multiway point cloud mosaicking (named Wednesday) At the core of our approach is ODIN, a learned pairwise registration algorithm that identifies overlaps and refines attention scores. Tested on four diverse, large-scale datasets, our method state-of-the-art pairwise and rotation registration results by a large margin on all benchmarks.
  arXiv  Detail & Related papers  (2024-03-30T17:29:13Z)
• ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models [77.84408427496025]
  State-of-the-art 3D point cloud registration methods rely on labeled 3D datasets for training. We introduce ZeroReg, a zero-shot registration approach that utilizes 2D foundation models to predict 3D correspondences.
  arXiv  Detail & Related papers  (2023-12-05T11:33:16Z)
• 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)
• Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting [63.95845583460312]
  We present a new method for the multiview registration of point cloud. Our method achieves 11% higher registration recall on the 3DMatch dataset and 13% lower registration errors on the ScanNet dataset.
  arXiv  Detail & Related papers  (2023-04-02T06:43:40Z)
• Overlap-guided Gaussian Mixture Models for Point Cloud Registration [61.250516170418784]
  Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. This paper proposes a novel overlap-guided probabilistic registration approach that computes the optimal transformation from matched Gaussian Mixture Model (GMM) parameters.
  arXiv  Detail & Related papers  (2022-10-17T08:02:33Z)
• Learning to Register Unbalanced Point Pairs [10.369750912567714]
  Recent 3D registration methods can effectively handle large-scale or partially overlapping point pairs. We present a novel 3D registration method, called UPPNet, for the unbalanced point pairs.
  arXiv  Detail & Related papers  (2022-07-09T08:03:59Z)
• COTReg:Coupled Optimal Transport based Point Cloud Registration [28.730827908402286]
  This paper proposes a learning framework COTReg to predict correspondences of 3D point cloud registration. We transform the two matchings into a Wasserstein distance-based and a Gromov-Wasserstein distance-based optimizations. Our correspondence prediction pipeline can be easily integrated into either learning-based features like FCGF or traditional descriptors like FPFH.
  arXiv  Detail & Related papers  (2021-12-29T03:20:18Z)
• Fast and Robust Registration of Partially Overlapping Point Clouds [5.073765501263891]
  Real-time registration of partially overlapping point clouds has emerging applications in cooperative perception for autonomous vehicles. Relative translation between point clouds in these applications is higher than in traditional SLAM and odometry applications. We propose a novel registration method for partially overlapping point clouds where correspondences are learned using an efficient point-wise feature encoder.
  arXiv  Detail & Related papers  (2021-12-18T12:39:05Z)
• GenReg: Deep Generative Method for Fast Point Cloud Registration [18.66568286698704]
  We propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration. The experiments on both ModelNet40 and 7Scene datasets demonstrate that the proposed algorithm achieves state-of-the-art accuracy and efficiency.
  arXiv  Detail & Related papers  (2021-11-23T10:52:09Z)
• Deep Hough Voting for Robust Global Registration [52.40611370293272]
  We present an efficient framework for pairwise registration of real-world 3D scans, leveraging Hough voting in the 6D transformation parameter space. Our method outperforms state-of-the-art methods on 3DMatch and 3DLoMatch benchmarks while achieving comparable performance on KITTI odometry dataset.
  arXiv  Detail & Related papers  (2021-09-09T14:38:06Z)
• UPDesc: Unsupervised Point Descriptor Learning for Robust Registration [54.95201961399334]
  UPDesc is an unsupervised method to learn point descriptors for robust point cloud registration. We show that our learned descriptors yield superior performance over existing unsupervised methods.
  arXiv  Detail & Related papers  (2021-08-05T17:11:08Z)
• Learning and Matching Multi-View Descriptors for Registration of Point Clouds [48.25586496457587]
  We first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets.
  arXiv  Detail & Related papers  (2018-07-16T01:58:27Z)

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.