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hal.structure.identifierUniversity of North Carolina [Chapel Hill] [UNC]
dc.contributor.authorGUAN, Li
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierVisualization and manipulation of complex data on wireless mobile devices [IPARLA]
dc.contributor.authorFRANCO, Jean-Sébastien
hal.structure.identifierInterpretation and Modelling of Images and Videos [PERCEPTION]
dc.contributor.authorBOYER, Edmond
hal.structure.identifierUniversity of North Carolina [Chapel Hill] [UNC]
hal.structure.identifierEidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] [ETH Zürich]
dc.contributor.authorPOLLEFEYS, Marc
dc.date.accessioned2024-04-15T09:56:54Z
dc.date.available2024-04-15T09:56:54Z
dc.date.issued2010-06-07
dc.date.conference2010-06-13
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/198873
dc.description.abstractEnIn this paper we investigate shape and motion retrieval in the context of multi-camera systems. We propose a new low-level analysis based on latent silhouette cues, particularly suited for low-texture and outdoor datasets. Our analysis does not rely on explicit surface representations, instead using an EM framework to simultaneously update a set of volumetric voxel occupancy probabilities and retrieve a best estimate of the dense 3D motion field from the last consecutively observed multi-view frame set. As the framework uses only latent, probabilistic silhouette information, the method yields a promising 3D scene analysis method robust to many sources of noise and arbitrary scene objects. It can be used as input for higher level shape modeling and structural inference tasks. We validate the approach and demonstrate its practical use for shape and motion analysis experimentally.
dc.language.isoen
dc.publisherIEEE
dc.title.enProbabilistic 3D Occupancy Flow with Latent Silhouette Cues
dc.typeCommunication dans un congrès
dc.identifier.doi10.1109/CVPR.2010.5539807
dc.subject.halInformatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
bordeaux.page1379-1386
bordeaux.hal.laboratoriesLaboratoire Bordelais de Recherche en Informatique (LaBRI) - UMR 5800*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleCVPR 2010 - IEEE Computer Vision and Pattern Recognition
bordeaux.countryUS
bordeaux.conference.citySan Francisco
bordeaux.peerReviewedoui
hal.identifierinria-00463031
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2010-06-18
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//inria-00463031v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2010-06-07&rft.spage=1379-1386&rft.epage=1379-1386&rft.au=GUAN,%20Li&FRANCO,%20Jean-S%C3%A9bastien&BOYER,%20Edmond&POLLEFEYS,%20Marc&rft.genre=unknown


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