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Blue Noise through Optimal Transport

Abstract : We present a fast, scalable algorithm to generate high-quality blue noise point distributions of arbitrary density functions. At its core is a novel formulation of the recently-introduced concept of capacity-constrained Voronoi tessellation as an optimal transport problem. This insight leads to a continuous formulation able to enforce the capacity constraints exactly, unlike previous work. We exploit the variational nature of this formulation to design an efficient optimization technique of point distributions via constrained minimization in the space of power diagrams. Our mathematical, algorithmic, and practical contributions lead to high-quality blue noise point sets with improved spectral and spatial properties.
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Fernando de Goes, Katherine Breeden, Victor Ostromoukhov, Mathieu Desbrun. Blue Noise through Optimal Transport. ACM Transactions on Graphics, Association for Computing Machinery, 2012, 6, 31, pp.171:1-171:1. ⟨10.1145/2366145.2366190⟩. ⟨hal-01353135⟩



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