Blue noise sampling using an SPH-based method
Blue noise sampling using an SPH-based method
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DOI：
10.1145/2816795.2818102
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
Min Jiang;Yahan Zhou;Rui Wang;Richard Southern;Jian-Jun Zhang
中科院分区：
文献类型：
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作者：
Min Jiang;Yahan Zhou;Rui Wang;Richard Southern;Jian-Jun Zhang
我们提出了一种新的算法，蓝噪声采样的启发平滑粒子流体动力学（SPH）方法。SPH是流体模拟中的一种众所周知的方法-它计算粒子分布以最小化内部压力方差。我们发现，这导致样本点（即，粒子）具有高质量的蓝噪声谱。受此启发，我们定制了用于蓝噪声采样的SPH方法。我们的方法实现了快速采样的表面和体积的一般尺寸。通过改变一个参数，我们的方法可以产生各种各样的蓝噪声样本具有不同的分布特性，从劳埃德的松弛容量约束Voronoi Tessellations（CCVT）。我们的方法是快速的，并支持自适应采样和多类采样。我们还进行了实验研究的SPH内核及其对样本的分布特性的影响。我们用例子证明，我们的方法可以产生各种可控的蓝噪声样本模式，适用于应用程序，如图像点画和重新网格。
We propose a novel algorithm for blue noise sampling inspired by the Smoothed Particle Hydrodynamics (SPH) method. SPH is a well-known method in fluid simulation -- it computes particle distributions to minimize the internal pressure variance. We found that this results in sample points (i.e., particles) with a high quality blue-noise spectrum. Inspired by this, we tailor the SPH method for blue noise sampling. Our method achieves fast sampling in general dimensions for both surfaces and volumes. By varying a single parameter our method can generate a variety of blue noise samples with different distribution properties, ranging from Lloyd's relaxation to Capacity Constrained Voronoi Tessellations (CCVT). Our method is fast and supports adaptive sampling and multi-class sampling. We have also performed experimental studies of the SPH kernel and its influence on the distribution properties of samples. We demonstrate with examples that our method can generate a variety of controllable blue noise sample patterns, suitable for applications such as image stippling and re-meshing.