Blue‐Noise Remeshing with Farthest Point Optimization
Blue‐Noise Remeshing with Farthest Point Optimization
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DOI：
10.1111/cgf.12442
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发表时间：
2014-08
影响因子：
2.5
通讯作者：
Dong‐Ming Yan;Jianwei Guo;Xiaohong Jia;Xiaopeng Zhang;Peter Wonka
中科院分区：
文献类型：
--
作者：
Dong‐Ming Yan;Jianwei Guo;Xiaohong Jia;Xiaopeng Zhang;Peter Wonka
在本文中，我们提出了一种新的方法，表面采样和重新网格具有良好的蓝噪声特性。我们的方法基于最远点优化（FPO），这是一种松弛技术，可以在2D中生成高质量的蓝噪声点集。我们提出了两个重要的推广原来的FPO框架：自适应采样和表面采样。提出了一种简单有效的FPO框架加速算法。实验结果表明，广义FPO生成的点集具有良好的自适应和表面采样的蓝噪声特性。此外，我们证明了我们的网格重划分质量上级当前最先进的方法。
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.