Wasserstein Blue Noise Sampling

Wasserstein Blue Noise Sampling
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Wasserstein 蓝噪声采样

DOI:
10.1145/3119910
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发表时间:
2017-10
影响因子:
6.2
通讯作者:
Chen Baoquan
Chen Baoquan
中科院分区:
计算机科学1区
文献类型:
--
作者:
Qin Hongxing;Chen Yi;He Jinlong;Chen Baoquan

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本文提出了一种多类蓝噪声采样算法,该算法以多个密度分布的约束Wasserstein重心作为投放样本。利用熵正则化项,在最优运输问题中提供了一个受约束的运输计划,以打破以前的容量约束Voronoi Tessellation方法所需的分区。熵正则化项不仅可以控制蓝噪声采样的空间规则性,而且还可以减少多类采样中Vornoi细胞的期望质心之间的冲突。此外,自适应蓝噪声属性是保证每个单独的类,以及它们的组合类。我们的方法可以很容易地扩展到多类采样点集表面上。我们还演示了在对象分布和颜色点画的应用。
In this article, we present a multi-class blue noise sampling algorithm by throwing samples as the constrained Wasserstein barycenter of multiple density distributions. Using an entropic regularization term, a constrained transport plan in the optimal transport problem is provided to break the partition required by the previous Capacity-Constrained Voronoi Tessellation method. The entropic regularization term cannot only control spatial regularity of blue noise sampling, but it also reduces conflicts between the desired centroids of Vornoi cells for multi-class sampling. Moreover, the adaptive blue noise property is guaranteed for each individual class, as well as their combined class. Our method can be easily extended to multi-class sampling on a point set surface. We also demonstrate applications in object distribution and color stippling.
DOI: 10.1145/325334.325182
发表时间: 1985-07
期刊: Proceedings of the 12th annual conference on Computer graphics and interactive techniques
影响因子: --
作者:
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