SDRSAC: semidefinite-based randomized approach for robust point cloud registration without correspondences

Huu M. Le, Thanh-Toan Do, Tuan Hoang, Ngai-Man Cheung

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

14 Citations (Scopus)

Abstract

This paper presents a novel randomized algorithm for robust point cloud registration without correspondences. Most existing registration approaches require a set of putative correspondences obtained by extracting invariant descriptors. However, such descriptors could become unreliable in noisy and contaminated settings. In these settings, methods that directly handle input point sets are preferable. Without correspondences, however, conventional randomized techniques require a very large number of samples in order to reach satisfactory solutions. In this paper, we propose a novel approach to address this problem. In particular, our work enables the use of randomized methods for point cloud registration without the need of putative correspondences. By considering point cloud alignment as a special instance of graph matching and employing an efficient semi-definite relaxation, we propose a novel sampling mechanism, in which the size of the sampled subsets can be larger-than-minimal. Our tight relaxation scheme enables fast rejection of the outliers in the sampled sets, resulting in high quality hypotheses. We conduct extensive experiments to demonstrate that our approach outperforms other state-of-the-art methods. Importantly, our proposed method serves as a generic framework which can be extended to problems with known correspondences.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
EditorsAbhinav Gupta, Derek Hoiem, Gang Hua, Zhuowen Tu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages124-133
Number of pages10
ISBN (Electronic)9781728132938
ISBN (Print)9781728132945
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2019 - Long Beach, United States of America
Duration: 16 Jun 201920 Jun 2019
Conference number: 32nd
http://cvpr2019.thecvf.com/
https://ieeexplore.ieee.org/xpl/conhome/8938205/proceeding (Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2019-June
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2019
Abbreviated titleCVPR 2019
CountryUnited States of America
CityLong Beach
Period16/06/1920/06/19
Internet address

Keywords

  • 3D from Multiview and Sensors

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