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

In 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. Th...

ver descrição completa

Detalhes bibliográficos
Autores: Agudo Martínez, Antonio, Moreno-Noguer, Francesc
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/166698
Acesso em linha:http://hdl.handle.net/10261/166698
Access Level:acceso abierto
Palavra-chave:Low-rank models
Sequential non-rigid structure from motion
Particle dynamics
Bundle adjustment
id ES_8b3d605ea7a1a88a3351ffbeccda1b75
oai_identifier_str oai:digital.csic.es:10261/166698
network_acronym_str ES
network_name_str España
repository_id_str
spelling Combining local-physical and global-statistical models for sequential deformable shape from motionAgudo Martínez, AntonioMoreno-Noguer, FrancescLow-rank modelsSequential non-rigid structure from motionParticle dynamicsBundle adjustmentIn 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.This work has been partially supported by the Spanish Ministry of Science and Innovation under project RobInstruct TIN2014-58178-R; by a scholarship FPU12/04886 from the Spanish MECD; and by the ERA-net CHISTERA projects VISEN PCIN-2013-047 and I-DRESS PCIN-2015-147.Peer ReviewedSpringer NatureMinisterio de Educación, Cultura y Deporte (España)Ministerio de Economía y Competitividad (España)Ministerio de Ciencia e Innovación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2018201820172018info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/166698reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2014-58178-Rinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PCIN-2013-047info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PCIN-2015-147https://doi.org/10.1007/s11263-016-0972-8Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1666982026-05-22T06:33:51Z
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
Low-rank models
Sequential non-rigid structure from motion
Particle dynamics
Bundle adjustment
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
Moreno-Noguer, Francesc
author Agudo Martínez, Antonio
author_facet Agudo Martínez, Antonio
Moreno-Noguer, Francesc
author_role author
author2 Moreno-Noguer, Francesc
author2_role author
dc.contributor.none.fl_str_mv Ministerio de Educación, Cultura y Deporte (España)
Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia e Innovación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Low-rank models
Sequential non-rigid structure from motion
Particle dynamics
Bundle adjustment
topic Low-rank models
Sequential non-rigid structure from motion
Particle dynamics
Bundle adjustment
description In 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.
publishDate 2017
dc.date.none.fl_str_mv 2017
2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/166698
url http://hdl.handle.net/10261/166698
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2014-58178-R
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PCIN-2013-047
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PCIN-2015-147
https://doi.org/10.1007/s11263-016-0972-8
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869412794164576256
score 15.812455

Registos relacionados