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dc.contributor.authorAgudo Martínez, Antonio
dc.contributor.authorMoreno-Noguer, Francesc
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.identifier.citationAgudo, A., Moreno-Noguer, F. Simultaneous pose and non-rigid shape with particle dynamics. A: IEEE Conference on Computer Vision and Pattern Recognition. "Proceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, Boston". Boston: 2015, p. 2179-2187.
dc.description.abstractIn this paper, we propose a sequential solution to simultaneously estimate camera pose and non-rigid 3D shape from a monocular video. In contrast to most existing approaches that rely on global representations of the shape, we model the object at a local level, as an ensemble of particles, each ruled by the linear equation of the Newton's second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency of the estimated shape and camera poses. The resulting approach is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, while it does not require any training data at all. Validation is done in a variety of real video sequences, including articulated and non-rigid motion, both for continuous and discontinuous shapes. Our system is shown to perform comparable to competing batch, computationally expensive, methods and shows remarkable improvement with respect to the sequential ones.
dc.format.extent9 p.
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.othercomputer vision
dc.titleSimultaneous pose and non-rigid shape with particle dynamics
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Optimisation
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/287617/EU/Aerial Robotics Cooperative Assembly System/ARCAS
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/606634/EU/Development of corneal biomechanical model. Dynamic topographical characterization based on 3D plenoptic imaging./POPCORN
local.citation.authorAgudo, A.; Moreno-Noguer, F.
local.citation.contributorIEEE Conference on Computer Vision and Pattern Recognition
local.citation.publicationNameProceedings of the 2015 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, Boston

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