Blue-Noise Remeshing with Farthest Point Optimization

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dc.contributor.authorYan, Dong-Mingen_US
dc.contributor.authorGuo, Jianweien_US
dc.contributor.authorJia, Xiaohongen_US
dc.contributor.authorZhang, Xiaopengen_US
dc.contributor.authorWonka, Peteren_US
dc.contributor.editorThomas Funkhouser and Shi-Min Huen_US
dc.date.accessioned2015-03-03T12:41:53Z
dc.date.available2015-03-03T12:41:53Z
dc.date.issued2014en_US
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.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12442en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12442en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleBlue-Noise Remeshing with Farthest Point Optimizationen_US
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33-Issue 5
SGP14: Eurographics Symposium on Geometry Processing (CGF 33-5)