An approach to robust ICP initialization

• URL: http://arxiv.org/abs/2212.05332v4
• Date: Sun, 25 Jun 2023 22:57:54 GMT
• Title: An approach to robust ICP initialization
• Authors: Alexander Kolpakov, Michael Werman
• Abstract summary: We propose an approach to initialize the Iterative Closest Point (ICP) algorithm to match unlabelled point clouds related by rigid transformations. We derive bounds on the robustness of our approach to noise and numerical experiments confirm our theoretical findings.
• Score: 77.45039118761837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this note, we propose an approach to initialize the Iterative Closest Point (ICP) algorithm to match unlabelled point clouds related by rigid transformations. The method is based on matching the ellipsoids defined by the points' covariance matrices and then testing the various principal half-axes matchings that differ by elements of a finite reflection group. We derive bounds on the robustness of our approach to noise and numerical experiments confirm our theoretical findings.

Related papers

• Regularized Projection Matrix Approximation with Applications to Community Detection [1.3761665705201904]
  This paper introduces a regularized projection matrix approximation framework designed to recover cluster information from the affinity matrix. We investigate three distinct penalty functions, each specifically tailored to address bounded, positive, and sparse scenarios. Numerical experiments conducted on both synthetic and real-world datasets reveal that our regularized projection matrix approximation approach significantly outperforms state-of-the-art methods in clustering performance.
  arXiv  Detail & Related papers  (2024-05-26T15:18:22Z)
• A Unified Theory of Stochastic Proximal Point Methods without Smoothness [52.30944052987393]
  Proximal point methods have attracted considerable interest owing to their numerical stability and robustness against imperfect tuning. This paper presents a comprehensive analysis of a broad range of variations of the proximal point method (SPPM)
  arXiv  Detail & Related papers  (2024-05-24T21:09:19Z)
• Variable Substitution and Bilinear Programming for Aligning Partially Overlapping Point Sets [48.1015832267945]
  This research presents a method to meet requirements through the minimization objective function of the RPM algorithm. A branch-and-bound (BnB) algorithm is devised, which solely branches over the parameters, thereby boosting convergence rate. Empirical evaluations demonstrate better robustness of the proposed methodology against non-rigid deformation, positional noise, and outliers, when compared with prevailing state-of-the-art transformations.
  arXiv  Detail & Related papers  (2024-05-14T13:28:57Z)
• Bounded Projection Matrix Approximation with Applications to Community Detection [1.8876415010297891]
  We introduce a new differentiable convex penalty and derive an alternating direction method of multipliers (ADMM) algorithm. Numerical experiments demonstrate the superiority of our algorithm over its competitors.
  arXiv  Detail & Related papers  (2023-05-21T06:55:10Z)
• Gradient Based Clustering [72.15857783681658]
  We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality. The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and is applicable to a wide range of functions.
  arXiv  Detail & Related papers  (2022-02-01T19:31:15Z)
• Determinantal consensus clustering [77.34726150561087]
  We propose the use of determinantal point processes or DPP for the random restart of clustering algorithms. DPPs favor diversity of the center points within subsets. We show through simulations that, contrary to DPP, this technique fails both to ensure diversity, and to obtain a good coverage of all data facets.
  arXiv  Detail & Related papers  (2021-02-07T23:48:24Z)
• Rigid and Articulated Point Registration with Expectation Conditional Maximization [20.096170794358315]
  We introduce an innovative EM-like algorithm, namely the Conditional Expectation Maximization for Point Registration (ECMPR) algorithm. We analyse in detail the associated consequences in terms of estimation of the registration parameters. We extend rigid registration to articulated registration.
  arXiv  Detail & Related papers  (2020-12-09T17:36:11Z)
• FKAConv: Feature-Kernel Alignment for Point Cloud Convolution [75.85619090748939]
  We provide a formulation to relate and analyze a number of point convolution methods. We also propose our own convolution variant, that separates the estimation of geometry-less kernel weights. We show competitive results on classification and semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2020-04-09T10:12:45Z)

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.