Robust Manhattan Frame Estimation From a Single RGB-D Image

Handle URI:
http://hdl.handle.net/10754/556139
Title:
Robust Manhattan Frame Estimation From a Single RGB-D Image
Authors:
Bernard Ghanem; Heilbron, Fabian Caba; Niebles, Juan Carlos; Thabet, Ali Kassem ( 0000-0001-7513-0748 )
Abstract:
This paper proposes a new framework for estimating the Manhattan Frame (MF) of an indoor scene from a single RGB-D image. Our technique formulates this problem as the estimation of a rotation matrix that best aligns the normals of the captured scene to a canonical world axes. By introducing sparsity constraints, our method can simultaneously estimate the scene MF, the surfaces in the scene that are best aligned to one of three coordinate axes, and the outlier surfaces that do not align with any of the axes. To test our approach, we contribute a new set of annotations to determine ground truth MFs in each image of the popular NYUv2 dataset. We use this new benchmark to experimentally demonstrate that our method is more accurate, faster, more reliable and more robust than the methods used in the literature. We further motivate our technique by showing how it can be used to address the RGB-D SLAM problem in indoor scenes by incorporating it into and improving the performance of a popular RGB-D SLAM method.
KAUST Department:
Image and Video Understanding Lab
Publisher:
IEEE
Journal:
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Conference/Event name:
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Issue Date:
2-Jun-2015
Type:
Conference Paper
Sponsors:
IEEE Computer Society Computer Vision Foundation - CVF
Additional Links:
http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Ghanem_Robust_Manhattan_Frame_2015_CVPR_paper.html; https://dl.dropboxusercontent.com/u/18955644/website_files/0054-supp.zip
Appears in Collections:
Conference Papers

Full metadata record

DC FieldValue Language
dc.contributor.authorBernard Ghanemen
dc.contributor.authorHeilbron, Fabian Cabaen
dc.contributor.authorNiebles, Juan Carlosen
dc.contributor.authorThabet, Ali Kassemen
dc.date.accessioned2015-06-02T13:43:38Zen
dc.date.available2015-06-02T13:43:38Zen
dc.date.issued2015-06-02en
dc.identifier.urihttp://hdl.handle.net/10754/556139en
dc.description.abstractThis paper proposes a new framework for estimating the Manhattan Frame (MF) of an indoor scene from a single RGB-D image. Our technique formulates this problem as the estimation of a rotation matrix that best aligns the normals of the captured scene to a canonical world axes. By introducing sparsity constraints, our method can simultaneously estimate the scene MF, the surfaces in the scene that are best aligned to one of three coordinate axes, and the outlier surfaces that do not align with any of the axes. To test our approach, we contribute a new set of annotations to determine ground truth MFs in each image of the popular NYUv2 dataset. We use this new benchmark to experimentally demonstrate that our method is more accurate, faster, more reliable and more robust than the methods used in the literature. We further motivate our technique by showing how it can be used to address the RGB-D SLAM problem in indoor scenes by incorporating it into and improving the performance of a popular RGB-D SLAM method.en
dc.description.sponsorshipIEEE Computer Society Computer Vision Foundation - CVFen
dc.publisherIEEEen
dc.relation.urlhttp://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Ghanem_Robust_Manhattan_Frame_2015_CVPR_paper.htmlen
dc.relation.urlhttps://dl.dropboxusercontent.com/u/18955644/website_files/0054-supp.zipen
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectSparse Optimizationen
dc.subjectRobust Estimationen
dc.titleRobust Manhattan Frame Estimation From a Single RGB-D Imageen
dc.typeConference Paperen
dc.contributor.departmentImage and Video Understanding Laben
dc.identifier.journalProceedings of the IEEE Conference on Computer Vision and Pattern Recognitionen
dc.conference.date07 Jun - 12 Jun 2015en
dc.conference.name2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)en
dc.conference.locationHynes Convention Center 900 Boylston St Boston, MA, USAen
dc.eprint.versionPost-printen
dc.contributor.institutionUniversidad del Norte, Colombiaen
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