Global Parametrization of Range Image Sets

Pietroni, N; Tarini, M; Sorkine, O; Zorin, D

Global Parametrization of Range Image Sets

Pietroni, N

Tarini, M Sorkine, O Zorin, D

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Graphics, 2011, 30, (6), pp. 1-10
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Pietroni, N https://orcid.org/0000-0002-8271-2102
dc.contributor.author	Tarini, M
dc.contributor.author	Sorkine, O
dc.contributor.author	Zorin, D
dc.date.accessioned	2023-03-17T04:41:13Z
dc.date.available	2023-03-17T04:41:13Z
dc.date.issued	2011-12-01
dc.identifier.citation	ACM Transactions on Graphics, 2011, 30, (6), pp. 1-10
dc.identifier.issn	0730-0301
dc.identifier.issn	1557-7368
dc.identifier.uri	http://hdl.handle.net/10453/167470
dc.description.abstract	We present a method to globally parameterize a surface represented by height maps over a set of planes (range images). In contrast to other parametrization techniques, we do not start with a manifold mesh. The parametrization we compute defines a manifold structure, it is seamless and globally smooth, can be aligned to geometric features and shows good quality in terms of angle and area preservation, comparable to current parametrization techniques for meshes. Computing such global seamless parametrization makes it possible to perform quad remeshing, texture mapping and texture synthesis and many other types of geometry processing operations. Our approach is based on a formulation of the Poisson equation on a manifold structure defined for the surface by the range images. Construction of such global parametrization requires only a way to project surface data onto a set of planes, and can be applied directly to implicit surfaces, nonmanifold surfaces, very large meshes, and collections of range scans. We demonstrate application of our technique to all these geometry types. © 2011, ACM. All rights reserved.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Graphics
dc.relation.isbasedon	10.1145/2070781.2024183
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Software Engineering
dc.title	Global Parametrization of Range Image Sets
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-17T04:41:09Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	6

Abstract:

We present a method to globally parameterize a surface represented by height maps over a set of planes (range images). In contrast to other parametrization techniques, we do not start with a manifold mesh. The parametrization we compute defines a manifold structure, it is seamless and globally smooth, can be aligned to geometric features and shows good quality in terms of angle and area preservation, comparable to current parametrization techniques for meshes. Computing such global seamless parametrization makes it possible to perform quad remeshing, texture mapping and texture synthesis and many other types of geometry processing operations. Our approach is based on a formulation of the Poisson equation on a manifold structure defined for the surface by the range images. Construction of such global parametrization requires only a way to project surface data onto a set of planes, and can be applied directly to implicit surfaces, nonmanifold surfaces, very large meshes, and collections of range scans. We demonstrate application of our technique to all these geometry types. © 2011, ACM. All rights reserved.

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http://hdl.handle.net/10453/167470