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Hierarchical Poisson disk sampling distributions
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Fast hierarchical importance sampling with blue noise properties
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ACM SIGGRAPH 2008 papers
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ACM Transactions on Graphics (TOG)
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IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
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ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
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This paper proposes a novel method for the computation of hierarchical Poisson disk samplings on polygonal surfaces. The algorithm generates a pointerless hierarchical structure such that each level is a uniform Poisson disk sampling and a subset of the next level. As the main result, given a dynamically-varying importance sampling function defined over a surface, the hierarchy is capable of generating adaptive samplings with blue noise characteristics, temporal-coherence and real-time computation. Classical algorithms produce hierarchies in tight ratios, which is a serious bottleneck specially for a large number of samples. Instead, our method uses sparse ratios and decreases the adaptation error of the hierarchy through a fast optimization process. Therefore, we save a considerable amount of time (up to 74% in our experiments) while preserving the good blue noise properties. We present applications on Non-Photo Realistic rendering (NPR), more specifically, on surface stippling effects. First, we apply our method by taking illumination to be the importance sampling to shade the surface, and second, we dynamically deform a surface with a predefined stippled texture.