Combining local-physical and global-statistical models for sequential deformable shape from motion

The final publication is available at link.springer.com

Detalles Bibliográficos
Autores: Agudo Martínez, Antonio|||0000-0001-6845-4998, Moreno-Noguer, Francesc
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/108518
Acceso en línea:https://hdl.handle.net/2117/108518
https://dx.doi.org/10.1007/s11263-016-0972-8
Access Level:acceso abierto
Palabra clave:Sequential non-rigid structure from motion
Particle dynamics
Bundle adjustment
Low-rank models
Classificació INSPEC::Modelling
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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spelling Combining local-physical and global-statistical models for sequential deformable shape from motionAgudo Martínez, Antonio|||0000-0001-6845-4998Moreno-Noguer, FrancescSequential non-rigid structure from motionParticle dynamicsBundle adjustmentLow-rank modelsClassificació INSPEC::ModellingÀrees temàtiques de la UPC::Informàtica::Automàtica i controlThe final publication is available at link.springer.comIn this paper, we simultaneously estimate camera pose and non-rigid 3D shape from a monocular video, using a sequential solution that combines local and global representations. We model the object 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. The resulting approach allows to sequentially estimate shape and camera poses, while progressively learning a global low-rank model of the shape that is fed back into the optimization scheme, introducing thus, global constraints. The overall combination of local (physical) and global (statistical) constraints yields a solution that is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, without requiring any training data at all. Validation is done in a variety of real application domains, including articulated and non-rigid motion, both for continuous and discontinuous shapes. Our on-line methodology yields significantly more accurate reconstructions than competing sequential approaches, being even comparable to the more computationally demanding batch methods.Peer Reviewed20172017-04-0120172017-10-09journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/108518https://dx.doi.org/10.1007/s11263-016-0972-8reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1085182026-05-27T15:37:01Z
dc.title.none.fl_str_mv Combining local-physical and global-statistical models for sequential deformable shape from motion
title Combining local-physical and global-statistical models for sequential deformable shape from motion
spellingShingle Combining local-physical and global-statistical models for sequential deformable shape from motion
Agudo Martínez, Antonio|||0000-0001-6845-4998
Sequential non-rigid structure from motion
Particle dynamics
Bundle adjustment
Low-rank models
Classificació INSPEC::Modelling
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short Combining local-physical and global-statistical models for sequential deformable shape from motion
title_full Combining local-physical and global-statistical models for sequential deformable shape from motion
title_fullStr Combining local-physical and global-statistical models for sequential deformable shape from motion
title_full_unstemmed Combining local-physical and global-statistical models for sequential deformable shape from motion
title_sort Combining local-physical and global-statistical models for sequential deformable shape from motion
dc.creator.none.fl_str_mv Agudo Martínez, Antonio|||0000-0001-6845-4998
Moreno-Noguer, Francesc
author Agudo Martínez, Antonio|||0000-0001-6845-4998
author_facet Agudo Martínez, Antonio|||0000-0001-6845-4998
Moreno-Noguer, Francesc
author_role author
author2 Moreno-Noguer, Francesc
author2_role author
dc.subject.none.fl_str_mv Sequential non-rigid structure from motion
Particle dynamics
Bundle adjustment
Low-rank models
Classificació INSPEC::Modelling
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Sequential non-rigid structure from motion
Particle dynamics
Bundle adjustment
Low-rank models
Classificació INSPEC::Modelling
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description The final publication is available at link.springer.com
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-04-01
2017
2017-10-09
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/108518
https://dx.doi.org/10.1007/s11263-016-0972-8
url https://hdl.handle.net/2117/108518
https://dx.doi.org/10.1007/s11263-016-0972-8
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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