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dc.contributor.authorPicos, Kenia-
dc.contributor.authorOrozco Rosas, Ulises-
dc.contributor.authorCuesta-Infante, Alfredo-
dc.contributor.authorSanz, Antonio-
dc.contributor.authorPantrigo, Juan José-
dc.coverage.spatialSilicon, Valley,USAes_ES
dc.date.accessioned2022-09-13T00:38:37Z-
dc.date.available2022-09-13T00:38:37Z-
dc.date.issued2020-03-
dc.identifier.otherP21924-
dc.identifier.urihttps://repositorio.cetys.mx/handle/60000/1468-
dc.description.abstractPose estimation is an important task for novel engineering applications, such as virtual and augmented reality (VR/AR), pose-based video games, object reconstruction, target tracking, driving assistance and recent sports analytics. Commonly, an efficient pose estimation system depends on the pose visualization given by a 3D configuration of location, orientation, and scaling parameters of the target. In this work we implement Capsule Networks to solve 3D pose estimation in computer graphics of rigid objects using a multi-GPU architecturees_ES
dc.language.isoen_USes_ES
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 México*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/mx/*
dc.subjectCapsule networkses_ES
dc.subject3D pose estimationes_ES
dc.titleCapsule networks for 3D pose estimation in computer graphicses_ES
dc.typePresentationes_ES
dc.subject.sedeCampus Tijuanaes_ES
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