CliReg: Clique-Based Robust Point Cloud Registration

We propose a branch-and-bound algorithm for robust rigid registration of two point clouds in the presence of a large number of outlier correspondences. For this purpose, we consider a maximum consensus formulation of the registration problem and reformulate it as a (large) maximal clique search in a...

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Autores: Laserna Moratalla, Javier, San Segundo Carrillo, Pablo, Alvarez Sanchez, David
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/418492
Acceso en línea:http://hdl.handle.net/10261/418492
Access Level:acceso abierto
Palabra clave:Discrete optimization
maximum clique
mobile robotics
point cloud 3-D registration
scan matching
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spelling CliReg: Clique-Based Robust Point Cloud RegistrationLaserna Moratalla, JavierSan Segundo Carrillo, PabloAlvarez Sanchez, DavidDiscrete optimizationmaximum cliquemobile roboticspoint cloud 3-D registrationscan matchingWe propose a branch-and-bound algorithm for robust rigid registration of two point clouds in the presence of a large number of outlier correspondences. For this purpose, we consider a maximum consensus formulation of the registration problem and reformulate it as a (large) maximal clique search in a correspondence graph, where a clique represents a complete rigid transformation. Specifically, we use a maximum clique algorithm to enumerate large maximal cliques and a fitness procedure that evaluates each clique by solving a least-squares optimization problem. The main advantages of our approach are 1) it is possible to exploit the cutting-edge optimization techniques employed by current exact maximum clique algorithms, such as partial maximum satisfiability-based bounds, branching by partitioning or the use of bitstrings, etc.; 2) the correspondence graphs are expected to be sparse in real problems (confirmed empirically in our tests), and, consequently, the maximum clique problem is expected to be easy; 3) it is possible to have a good control of suboptimality with a k-nearest neighbor analysis that determines the size of the correspondence graph as a function of k. The new algorithm is called CliReg and has been implemented in C++. To evaluate CliReg, we have carried out extensive tests both on synthetic and real public datasets. The results show that CliReg clearly dominates the state of the art (e.g., RANSAC, FGR, and TEASER++) in terms of robustness, with a running time comparable to TEASER++ and RANSAC. In addition, we have implemented a fast variant called CliRegMutual that performs similarly to the fastest heuristic FGR.This work was supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement 2023–2026 with Universidad Politécnica de Madrid in the Line A, Emerging Ph.D. researchers. Funding for the open access charge was provided by Universidad Politécnica de Madrid/Consorcio Madroño.Peer reviewedInstitute of Electrical and Electronics EngineersComunidad de MadridLaserna Moratalla, Javier [0009-0004-2494-7458]San Segundo Carrillo, Pablo [0000-0001-7050-5563]Alvarez Sanchez, David [0000-0003-2056-640X]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/418492reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1109/TRO.2025.3542954Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4184922026-05-22T06:33:51Z
dc.title.none.fl_str_mv CliReg: Clique-Based Robust Point Cloud Registration
title CliReg: Clique-Based Robust Point Cloud Registration
spellingShingle CliReg: Clique-Based Robust Point Cloud Registration
Laserna Moratalla, Javier
Discrete optimization
maximum clique
mobile robotics
point cloud 3-D registration
scan matching
title_short CliReg: Clique-Based Robust Point Cloud Registration
title_full CliReg: Clique-Based Robust Point Cloud Registration
title_fullStr CliReg: Clique-Based Robust Point Cloud Registration
title_full_unstemmed CliReg: Clique-Based Robust Point Cloud Registration
title_sort CliReg: Clique-Based Robust Point Cloud Registration
dc.creator.none.fl_str_mv Laserna Moratalla, Javier
San Segundo Carrillo, Pablo
Alvarez Sanchez, David
author Laserna Moratalla, Javier
author_facet Laserna Moratalla, Javier
San Segundo Carrillo, Pablo
Alvarez Sanchez, David
author_role author
author2 San Segundo Carrillo, Pablo
Alvarez Sanchez, David
author2_role author
author
dc.contributor.none.fl_str_mv Comunidad de Madrid
Laserna Moratalla, Javier [0009-0004-2494-7458]
San Segundo Carrillo, Pablo [0000-0001-7050-5563]
Alvarez Sanchez, David [0000-0003-2056-640X]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Discrete optimization
maximum clique
mobile robotics
point cloud 3-D registration
scan matching
topic Discrete optimization
maximum clique
mobile robotics
point cloud 3-D registration
scan matching
description We propose a branch-and-bound algorithm for robust rigid registration of two point clouds in the presence of a large number of outlier correspondences. For this purpose, we consider a maximum consensus formulation of the registration problem and reformulate it as a (large) maximal clique search in a correspondence graph, where a clique represents a complete rigid transformation. Specifically, we use a maximum clique algorithm to enumerate large maximal cliques and a fitness procedure that evaluates each clique by solving a least-squares optimization problem. The main advantages of our approach are 1) it is possible to exploit the cutting-edge optimization techniques employed by current exact maximum clique algorithms, such as partial maximum satisfiability-based bounds, branching by partitioning or the use of bitstrings, etc.; 2) the correspondence graphs are expected to be sparse in real problems (confirmed empirically in our tests), and, consequently, the maximum clique problem is expected to be easy; 3) it is possible to have a good control of suboptimality with a k-nearest neighbor analysis that determines the size of the correspondence graph as a function of k. The new algorithm is called CliReg and has been implemented in C++. To evaluate CliReg, we have carried out extensive tests both on synthetic and real public datasets. The results show that CliReg clearly dominates the state of the art (e.g., RANSAC, FGR, and TEASER++) in terms of robustness, with a running time comparable to TEASER++ and RANSAC. In addition, we have implemented a fast variant called CliRegMutual that performs similarly to the fastest heuristic FGR.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/418492
url http://hdl.handle.net/10261/418492
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1109/TRO.2025.3542954
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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