Blue-noise remeshing with farthest point optimization

Dongming Yan, Jianwei Guo, Xiaohong Jia, Xiaopeng Zhang, Peter Wonka

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)167-176
Number of pages10
JournalComputer Graphics Forum
Issue number5
StatePublished - Aug 23 2014

ASJC Scopus subject areas

  • Computer Networks and Communications


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