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首页> 外文期刊>Visualization and Computer Graphics, IEEE Transactions on >Variational Blue Noise Sampling
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Variational Blue Noise Sampling

机译：可变蓝噪声采样

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摘要

Blue noise point sampling is one of the core algorithms in computer graphics. In this paper, we present a new and versatile variational framework for generating point distributions with high-quality blue noise characteristics while precisely adapting to given density functions. Different from previous approaches based on discrete settings of capacity-constrained Voronoi tessellation, we cast the blue noise sampling generation as a variational problem with continuous settings. Based on an accurate evaluation of the gradient of an energy function, an efficient optimization is developed which delivers significantly faster performance than the previous optimization-based methods. Our framework can easily be extended to generating blue noise point samples on manifold surfaces and for multi-class sampling. The optimization formulation also allows us to naturally deal with dynamic domains, such as deformable surfaces, and to yield blue noise samplings with temporal coherence. We present experimental results to validate the efficacy of our variational framework. Finally, we show a variety of applications of the proposed methods, including nonphotorealistic image stippling, color stippling, and blue noise sampling on deformable surfaces.
机译：蓝噪声点采样是计算机图形学的核心算法之一。在本文中，我们提出了一个新的通用变体框架，该框架可以生成具有高质量蓝噪声特征的点分布，同时精确地适应给定的密度函数。与先前基于容量受限Voronoi细分的离散设置的方法不同，我们将蓝噪声采样生成转换为具有连续设置的变分问题。基于对能量函数梯度的准确评估，开发了一种有效的优化方法，该方法比以前的基于优化方法的性能要快得多。我们的框架可以轻松扩展为在流形表面上生成蓝噪声点样本以及进行多类采样。优化公式还使我们能够自然地处理动态域（例如可变形表面），并产生具有时间相干性的蓝噪声采样。我们提出实验结果以验证我们的变体框架的功效。最后，我们展示了所提出方法的各种应用，包括非真实感图像点画，颜色点画和可变形表面上的蓝噪声采样。

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