Handle URI:
http://hdl.handle.net/10754/598961
Title:
Near-Regular Structure Discovery Using Linear Programming
Authors:
Huang, Qixing; Guibas, Leonidas J.; Mitra, Niloy J.
Abstract:
Near-regular structures are common in manmade and natural objects. Algorithmic detection of such regularity greatly facilitates our understanding of shape structures, leads to compact encoding of input geometries, and enables efficient generation and manipulation of complex patterns on both acquired and synthesized objects. Such regularity manifests itself both in the repetition of certain geometric elements, as well as in the structured arrangement of the elements. We cast the regularity detection problem as an optimization and efficiently solve it using linear programming techniques. Our optimization has a discrete aspect, that is, the connectivity relationships among the elements, as well as a continuous aspect, namely the locations of the elements of interest. Both these aspects are captured by our near-regular structure extraction framework, which alternates between discrete and continuous optimizations. We demonstrate the effectiveness of our framework on a variety of problems including near-regular structure extraction, structure-preserving pattern manipulation, and markerless correspondence detection. Robustness results with respect to geometric and topological noise are presented on synthesized, real-world, and also benchmark datasets. © 2014 ACM.
Citation:
Huang Q, Guibas LJ, Mitra NJ (2014) Near-Regular Structure Discovery Using Linear Programming. ACM Transactions on Graphics 33: 1–17. Available: http://dx.doi.org/10.1145/2535596.
Publisher:
Association for Computing Machinery (ACM)
Journal:
ACM Transactions on Graphics
Issue Date:
2-Jun-2014
DOI:
10.1145/2535596
Type:
Article
ISSN:
0730-0301
Sponsors:
This work was supported by NSF grant CCF-1011228, Marie Curie Career Integration Grant 303541, ERC Starting Grant SmartGeometry 335373, a KAUST-Stanford AEA grant, a KAUST visiting scholarship, a Google research award, and a Stanford Graduate Fellowship.
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Full metadata record

DC FieldValue Language
dc.contributor.authorHuang, Qixingen
dc.contributor.authorGuibas, Leonidas J.en
dc.contributor.authorMitra, Niloy J.en
dc.date.accessioned2016-02-25T13:44:30Zen
dc.date.available2016-02-25T13:44:30Zen
dc.date.issued2014-06-02en
dc.identifier.citationHuang Q, Guibas LJ, Mitra NJ (2014) Near-Regular Structure Discovery Using Linear Programming. ACM Transactions on Graphics 33: 1–17. Available: http://dx.doi.org/10.1145/2535596.en
dc.identifier.issn0730-0301en
dc.identifier.doi10.1145/2535596en
dc.identifier.urihttp://hdl.handle.net/10754/598961en
dc.description.abstractNear-regular structures are common in manmade and natural objects. Algorithmic detection of such regularity greatly facilitates our understanding of shape structures, leads to compact encoding of input geometries, and enables efficient generation and manipulation of complex patterns on both acquired and synthesized objects. Such regularity manifests itself both in the repetition of certain geometric elements, as well as in the structured arrangement of the elements. We cast the regularity detection problem as an optimization and efficiently solve it using linear programming techniques. Our optimization has a discrete aspect, that is, the connectivity relationships among the elements, as well as a continuous aspect, namely the locations of the elements of interest. Both these aspects are captured by our near-regular structure extraction framework, which alternates between discrete and continuous optimizations. We demonstrate the effectiveness of our framework on a variety of problems including near-regular structure extraction, structure-preserving pattern manipulation, and markerless correspondence detection. Robustness results with respect to geometric and topological noise are presented on synthesized, real-world, and also benchmark datasets. © 2014 ACM.en
dc.description.sponsorshipThis work was supported by NSF grant CCF-1011228, Marie Curie Career Integration Grant 303541, ERC Starting Grant SmartGeometry 335373, a KAUST-Stanford AEA grant, a KAUST visiting scholarship, a Google research award, and a Stanford Graduate Fellowship.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.subjectInteger and linear programmingen
dc.subjectIntrinsic near-regular structureen
dc.subjectMarkerless correspondenceen
dc.subjectPattern manipulationen
dc.titleNear-Regular Structure Discovery Using Linear Programmingen
dc.typeArticleen
dc.identifier.journalACM Transactions on Graphicsen
dc.contributor.institutionStanford University, Palo Alto, United Statesen
dc.contributor.institutionUCL, London, United Kingdomen
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