Blue-noise remeshing with farthest point optimization

Handle URI:
http://hdl.handle.net/10754/575716
Title:
Blue-noise remeshing with farthest point optimization
Authors:
Yan, Dongming ( 0000-0003-2209-2404 ) ; Guo, Jianwei; Jia, Xiaohong; Zhang, Xiaopeng; Wonka, Peter ( 0000-0003-0627-9746 )
Abstract:
In this paper, we present a novel method for surface sampling and remeshing with good blue-noise properties. Our approach is based on the farthest point optimization (FPO), a relaxation technique that generates high quality blue-noise point sets in 2D. We propose two important generalizations of the original FPO framework: adaptive sampling and sampling on surfaces. A simple and efficient algorithm for accelerating the FPO framework is also proposed. Experimental results show that the generalized FPO generates point sets with excellent blue-noise properties for adaptive and surface sampling. Furthermore, we demonstrate that our remeshing quality is superior to the current state-of-the art approaches. © 2014 The Eurographics Association and John Wiley & Sons Ltd.
KAUST Department:
Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Wiley-Blackwell
Journal:
Computer Graphics Forum
Issue Date:
Aug-2014
DOI:
10.1111/cgf.12442
Type:
Article
ISSN:
01677055
Sponsors:
We are grateful to anonymous reviewers for their suggestive comments. We would like to thank Liyi Wei and Rui Wang for sharing the DDA tool, Zhonggui Chen, Ligang Liu and Esdras Medeiros for providing us with their results for comparison. This work was partially supported by the KAUST Visual Computing Center, the National Natural Science Foundation of China (nos. 61372168, 61331018, 61271431, 11201463), and the U.S. National Science Foundation.
Appears in Collections:
Articles; Computer Science Program; Computer Science Program; Visual Computing Center (VCC); Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorYan, Dongmingen
dc.contributor.authorGuo, Jianweien
dc.contributor.authorJia, Xiaohongen
dc.contributor.authorZhang, Xiaopengen
dc.contributor.authorWonka, Peteren
dc.date.accessioned2015-08-24T08:36:27Zen
dc.date.available2015-08-24T08:36:27Zen
dc.date.issued2014-08en
dc.identifier.issn01677055en
dc.identifier.doi10.1111/cgf.12442en
dc.identifier.urihttp://hdl.handle.net/10754/575716en
dc.description.abstractIn this paper, we present a novel method for surface sampling and remeshing with good blue-noise properties. Our approach is based on the farthest point optimization (FPO), a relaxation technique that generates high quality blue-noise point sets in 2D. We propose two important generalizations of the original FPO framework: adaptive sampling and sampling on surfaces. A simple and efficient algorithm for accelerating the FPO framework is also proposed. Experimental results show that the generalized FPO generates point sets with excellent blue-noise properties for adaptive and surface sampling. Furthermore, we demonstrate that our remeshing quality is superior to the current state-of-the art approaches. © 2014 The Eurographics Association and John Wiley & Sons Ltd.en
dc.description.sponsorshipWe are grateful to anonymous reviewers for their suggestive comments. We would like to thank Liyi Wei and Rui Wang for sharing the DDA tool, Zhonggui Chen, Ligang Liu and Esdras Medeiros for providing us with their results for comparison. This work was partially supported by the KAUST Visual Computing Center, the National Natural Science Foundation of China (nos. 61372168, 61331018, 61271431, 11201463), and the U.S. National Science Foundation.en
dc.publisherWiley-Blackwellen
dc.subjectCategories and Subject Descriptors (according to ACM CCS)en
dc.subjectI.3.6 [Computer Graphics]: Methodology and Techniques - Blue-noise sampling and remeshingen
dc.titleBlue-noise remeshing with farthest point optimizationen
dc.typeArticleen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalComputer Graphics Forumen
dc.contributor.institutionNLPR, Institute of Automation, CAS, United Kingdomen
dc.contributor.institutionKLMM, AMSS, CAS, United Kingdomen
dc.contributor.institutionArizona State Univ., United Statesen
kaust.authorYan, Dongmingen
kaust.authorWonka, Peteren
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.