Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/120419
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dc.contributor.authorPeñate Sánchez, Adrián-
dc.contributor.authorAndrade-Cetto, Juan-
dc.contributor.authorMoreno-Noguer, Francesc-
dc.date.accessioned2023-02-07T11:01:48Z-
dc.date.available2023-02-07T11:01:48Z-
dc.date.issued2013-
dc.identifier.issn0162-8828-
dc.identifier.urihttp://hdl.handle.net/10553/120419-
dc.description.abstractWe propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate than iterative ones. Our approach is inspired on the EPnP algorithm, a recent O(n) solution for the calibrated case. Yet we show that considering the focal length as an additional unknown renders the linearization and relinearization techniques of the original approach no longer valid, especially with large amounts of noise. We present new methodologies to circumvent this limitation termed exhaustive linearization and exhaustive relinearization which perform a systematic exploration of the solution space in closed form. The method is evaluated on both real and synthetic data, and our results show that besides producing precise focal length estimation, the retrieved camera pose is almost as accurate as the one computed using the EPnP, which assumes a calibrated camera.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Pattern Analysis and Machine Intelligence-
dc.sourceIEEE Transactions on Pattern Analysis and Machine Intelligence [ISSN 0162-8828], v. 35 (10), p. 2387-2400, (Octubre 2013)-
dc.subject1203 Ciencia de los ordenadores-
dc.subject.otherCamera calibration-
dc.subject.otherPerspective-n-point problem-
dc.titleExhaustive Linearization for Robust Camera Pose and Focal Length Estimation-
dc.typeinfo:eu-repo/semantics/article-
dc.typeArticle-
dc.identifier.doi10.1109/TPAMI.2013.36-
dc.identifier.pmid23969384-
dc.identifier.scopus2-s2.0-84883198236-
dc.identifier.isiWOS:000323175200006-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.identifier.eissn1939-3539-
dc.description.lastpage2400-
dc.identifier.issue10-
dc.description.firstpage2387-
dc.relation.volume35-
dc.investigacionIngeniería y Arquitectura-
dc.type2Artículo-
dc.identifier.external67238721-
dc.utils.revision-
dc.identifier.ulpgc-
dc.contributor.buulpgcBU-INF-
dc.description.sjr4,715
dc.description.jcr5,694
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0003-2876-3301-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNamePeñate Sánchez, Adrián-
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