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Conference Paper: A fixed viewpoint approach for dense reconstruction of transparent objects

TitleA fixed viewpoint approach for dense reconstruction of transparent objects
Authors
Issue Date2015
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147
Citation
The 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA., 7-12 June 2015. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2015, p. 4001-4008 How to Cite?
AbstractThis paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we develop a fixed viewpoint approach for dense surface reconstruction of transparent objects based on refraction of light. We introduce a simple setup that allows us alter the incident light paths before light rays enter the object, and develop a method for recovering the object surface based on reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the shape of the object nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a complex shape and structure, with unknown and even inhomogeneous refractive index. Experimental results on both synthetic and real data are presented which demonstrate the feasibility and accuracy of our proposed approach.
Persistent Identifierhttp://hdl.handle.net/10722/219221
ISBN
ISSN
2020 SCImago Journal Rankings: 4.658
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHan, K-
dc.contributor.authorWong, KKY-
dc.contributor.authorLiu, M-
dc.date.accessioned2015-09-18T07:18:05Z-
dc.date.available2015-09-18T07:18:05Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA., 7-12 June 2015. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2015, p. 4001-4008-
dc.identifier.isbn9781467369640-
dc.identifier.issn1063-6919-
dc.identifier.urihttp://hdl.handle.net/10722/219221-
dc.description.abstractThis paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we develop a fixed viewpoint approach for dense surface reconstruction of transparent objects based on refraction of light. We introduce a simple setup that allows us alter the incident light paths before light rays enter the object, and develop a method for recovering the object surface based on reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the shape of the object nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a complex shape and structure, with unknown and even inhomogeneous refractive index. Experimental results on both synthetic and real data are presented which demonstrate the feasibility and accuracy of our proposed approach.-
dc.languageeng-
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147-
dc.relation.ispartofIEEE Conference on Computer Vision and Pattern Recognition Proceedings-
dc.titleA fixed viewpoint approach for dense reconstruction of transparent objects-
dc.typeConference_Paper-
dc.identifier.emailWong, KKY: kykwong@cs.hku.hk-
dc.identifier.authorityWong, KKY=rp01393-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/CVPR.2015.7299026-
dc.identifier.scopuseid_2-s2.0-84959225879-
dc.identifier.hkuros252040-
dc.identifier.spage4001-
dc.identifier.epage4008-
dc.identifier.isiWOS:000387959204005-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 151119-
dc.identifier.issnl1063-6919-

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