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dc.contributor.authorFerraz Colomina, Luis
dc.contributor.authorBinefa, Xavier
dc.contributor.authorMoreno-Noguer, Francesc
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.identifier.citationFerraz, L., Binefa, X., Moreno-Noguer, F. Leveraging feature uncertainty in the PnP problem. A: British Machine Vision Conference. "Proceedings of the BMVC 2014 British Machine Vision Conference". Nottingham: 2014, p. 1-13.
dc.description.abstractWe propose a real-time and accurate solution to the Perspective-n-Point (PnP) problem –estimating the pose of a calibrated camera from n 3D-to-2D point correspondences– that exploits the fact that in practice the 2D position of not all 2D features is estimated with the same accuracy. Assuming a model of such feature uncertainties is known in advance, we reformulate the PnP problem as a maximum likelihood minimization approximated by an unconstrained Sampson error function, which naturally penalizes the most noisy correspondences. The advantages of this approach are thoroughly demonstrated in synthetic experiments where feature uncertainties are exactly known. Pre-estimating the features uncertainties in real experiments is, though, not easy. In this paper we model feature uncertainty as 2D Gaussian distributions representing the sensitivity of the 2D feature detectors to different camera viewpoints. When using these noise models with our PnP formulation we still obtain promising pose estimation results that outperform the most recent approaches.
dc.format.extent13 p.
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.othercomputer vision
dc.subject.otherfeature extraction
dc.subject.other3D pose estimation
dc.subject.otherperspective-n-point problem
dc.titleLeveraging feature uncertainty in the PnP problem
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Pattern recognition::Computer vision
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/287617/EU/Aerial Robotics Cooperative Assembly System/ARCAS
local.citation.authorFerraz, L.; Binefa, X.; Moreno-Noguer, F.
local.citation.contributorBritish Machine Vision Conference
local.citation.publicationNameProceedings of the BMVC 2014 British Machine Vision Conference

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