Multi-view urban scene reconstruction in non-uniform volume

Mao, R; Wu, Q; Qiao, Y; Bai, L; Yang, J

Multi-view urban scene reconstruction in non-uniform volume

Mao, R Wu, Q

Qiao, Y Bai, L Yang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings of SPIE - The International Society for Optical Engineering, 2013, 9067
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Mao, R	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Qiao, Y	en_US
dc.contributor.author	Bai, L	en_US
dc.contributor.author	Yang, J	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings of SPIE - The International Society for Optical Engineering, 2013, 9067	en_US
dc.identifier.isbn	9780819499967	en_US
dc.identifier.issn	0277-786X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120709
dc.description.abstract	This paper presents a new fully automatic approach for multi-view urban scene reconstruction. Our algorithm is based on the Manhattan-World assumption, which can provide compact models while preserving fidelity of synthetic architectures. Starting from a dense point cloud, we extract its main axes by global optimization, and construct a nonuniform volume based on them. A graph model is created from volume facets rather than voxels. Appropriate edge weights are defined to ensure the validity and quality of the surface reconstruction. Compared with the common pointcloud- to-model methods, the proposed methodology exploits image information to unveil the real structures of holes in the point cloud. Experiments demonstrate the encouraging performance of the algorithm. © 2013 SPIE.	en_US
dc.relation.ispartof	Proceedings of SPIE - The International Society for Optical Engineering	en_US
dc.relation.isbasedon	10.1117/12.2049800	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Multi-view urban scene reconstruction in non-uniform volume	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9067	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	9067	en_US

Abstract:

This paper presents a new fully automatic approach for multi-view urban scene reconstruction. Our algorithm is based on the Manhattan-World assumption, which can provide compact models while preserving fidelity of synthetic architectures. Starting from a dense point cloud, we extract its main axes by global optimization, and construct a nonuniform volume based on them. A graph model is created from volume facets rather than voxels. Appropriate edge weights are defined to ensure the validity and quality of the surface reconstruction. Compared with the common pointcloud- to-model methods, the proposed methodology exploits image information to unveil the real structures of holes in the point cloud. Experiments demonstrate the encouraging performance of the algorithm. © 2013 SPIE.

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http://hdl.handle.net/10453/120709