Overlap-guided Gaussian Mixture Models for Point Cloud Registration

• URL: http://arxiv.org/abs/2210.09836v1
• Date: Mon, 17 Oct 2022 08:02:33 GMT
• Title: Overlap-guided Gaussian Mixture Models for Point Cloud Registration
• Authors: Guofeng Mei, Fabio Poiesi, Cristiano Saltori, Jian Zhang, Elisa Ricci, Nicu Sebe
• Abstract summary: 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.
• Score: 61.250516170418784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations. However, registering point cloud pairs in the case of partial overlap is still a challenge. This paper proposes a novel overlap-guided probabilistic registration approach that computes the optimal transformation from matched Gaussian Mixture Model (GMM) parameters. We reformulate the registration problem as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We introduce a Transformer-based detection module to detect overlapping regions, and represent the input point clouds using GMMs by guiding their alignment through overlap scores computed by this detection module. Experiments show that our method achieves superior registration accuracy and efficiency than state-of-the-art methods when handling point clouds with partial overlap and different densities on synthetic and real-world datasets. https://github.com/gfmei/ogmm

Related papers

• 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)
• Overlap Bias Matching is Necessary for Point Cloud Registration [21.584033532099134]
  Overlap between point clouds to be registered may be relatively small. We propose an unsupervised network Overlap Bias Matching Network (OBMNet) for partial point cloud registration.
  arXiv  Detail & Related papers  (2023-08-18T07:47:22Z)
• Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration [74.53755415380171]
  Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data. We propose UDPReg, an unsupervised deep probabilistic registration framework for point clouds with partial overlaps. Our UDPReg achieves competitive performance on the 3DMatch/3DLoMatch and ModelNet/ModelLoNet benchmarks.
  arXiv  Detail & Related papers  (2023-03-23T14:18:06Z)
• 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)
• LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration [1.8876415010297891]
  We propose a novel method called CPD with Local Surface Geometry (LSG-CPD) for rigid point cloud registration. Our method adaptively adds different levels of point-to-plane penalization on top of the point-to-point penalization based on the flatness of the local surface. It is significantly faster than modern implementations of CPD.
  arXiv  Detail & Related papers  (2021-03-28T03:46:41Z)
• Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment [85.32080531133799]
  This paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO. Two replacements for the distance transformation commonly used in edge registration are proposed: Approximate Nearest Neighbour Fields and Oriented Nearest Neighbour Fields. 3D2D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-12-15T11:42:17Z)
• DeepGMR: Learning Latent Gaussian Mixture Models for Registration [113.74060941036664]
  Point cloud registration is a fundamental problem in 3D computer vision, graphics and robotics. In this paper, we introduce Deep Gaussian Mixture Registration (DeepGMR), the first learning-based registration method. Our proposed method shows favorable performance when compared with state-of-the-art geometry-based and learning-based registration methods.
  arXiv  Detail & Related papers  (2020-08-20T17:25:16Z)

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.