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Blue‐Noise Remeshing with Farthest Point Optimization
Author(s) -
Yan DongMing,
Guo Jianwei,
Jia Xiaohong,
Zhang Xiaopeng,
Wonka Peter
Publication year - 2014
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12442
Subject(s) - sampling (signal processing) , adaptive sampling , computer science , noise (video) , algorithm , point (geometry) , mathematical optimization , relaxation (psychology) , surface (topology) , simple (philosophy) , colors of noise , mathematics , noise reduction , artificial intelligence , computer vision , statistics , geometry , psychology , social psychology , philosophy , filter (signal processing) , epistemology , monte carlo method , image (mathematics)
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‐theߚart approaches.