3D Matched Manifold Detection for Optimizing Point Cloud Registration

Amit Efraim, Joseph M. Francos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Point cloud registration is usually performed by matching key points to obtain an approximate global alignment, followed by a local optimization algorithm such as the iterative closest point (I CP) and its variants, to refine the initial estimate. These refinement algorithms, however, converge in many cases to a false local extremum. We propose a new matched manifold detection approach over the group of rigid 3-D transformations, by employing a novel correlation operator between functions defined on sparsely and non-uniformly sampled point clouds. Correlation between point clouds is evaluated using a method-ology inspired by the definition of the Kernel Point Convolution (KPConv), but instead of performing convolution with a kernel, the inner-product of feature vectors evaluated on the points in the two point clouds are aggregated. The proposed approach is shown to outperform state of the art local registration methods in terms of accuracy on challenging data sets.

Original languageEnglish
Title of host publicationInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781665470957
DOIs
StatePublished - 1 Jan 2022
Event2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022 - Male, Maldives
Duration: 16 Nov 202218 Nov 2022

Publication series

NameInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

Conference

Conference2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022
Country/TerritoryMaldives
CityMale
Period16/11/2218/11/22

Keywords

  • Manifold optimization
  • Matched manifold detection
  • Point clouds
  • Registration
  • Rigid transformation

ASJC Scopus subject areas

  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Renewable Energy, Sustainability and the Environment

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