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dc.contributor.authorBriales Garcia, Jesus
dc.contributor.authorGonzález-Jiménez, Antonio Javier 
dc.date.accessioned2017-10-06T12:27:45Z
dc.date.available2017-10-06T12:27:45Z
dc.date.created2017
dc.date.issued2017
dc.identifier.citationIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017es_ES
dc.identifier.urihttp://hdl.handle.net/10630/14599
dc.description.abstractThe registration of 3D models by a Euclidean transformation is a fundamental task at the core of many application in computer vision. This problem is non-convex due to the presence of rotational constraints, making traditional local optimization methods prone to getting stuck in local minima. This paper addresses finding the globally optimal transformation in various 3D registration problems by a unified formulation that integrates common geometric registration modalities (namely point-to-point, point-to-line and point-to-plane). This formulation renders the optimization problem independent of both the number and nature of the correspondences. The main novelty of our proposal is the introduction of a strengthened Lagrangian dual relaxation for this problem, which surpasses previous similar approaches [32] in effectiveness. In fact, even though with no theoretical guarantees, exhaustive empirical evaluation in both synthetic and real experiments always resulted on a tight relaxation that allowed to recover a guaranteed globally optimal solution by exploiting duality theory. Thus, our approach allows for effectively solving the 3D registration with global optimality guarantees while running at a fraction of the time for the state-of-the-art alternative [34], based on a more computationally intensive Branch and Bound method.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectOptimización matemáticaes_ES
dc.subject.other3D registrationes_ES
dc.subject.otherConvex optimizationes_ES
dc.subject.otherGlobal optimizationes_ES
dc.subject.otherLagrangian dualityes_ES
dc.titleConvex Global 3D Registration with Lagrangian Dualityes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleIEEE Conference on Computer Vision and Pattern Recognitiones_ES
dc.relation.eventplaceHonolulu, Hawai, USAes_ES
dc.relation.eventdateJuly, 2017es_ES
dc.cclicenseby-nc-ndes_ES
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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