Low-Discrepancy Blue Noise Sampling

Abstract : We present a novel technique that produces two-dimensional low- discrepancy (LD) blue noise point sets for sampling. Using one- dimensional binary van der Corput sequences, we construct two- dimensional LD point sets, and rearrange them to match a target spectral profile while preserving their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. We evaluate our tech- nique and compare it to the state-of-the-art sampling approaches.
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Abdalla Ahmed, Hélène Perrier, David Coeurjolly, Victor Ostromoukhov, Jianwei Guo, et al.. Low-Discrepancy Blue Noise Sampling. ACM Transactions on Graphics, Association for Computing Machinery, 2016, 35 (6), pp.247:1--247:13. ⟨10.1145/2980179.2980218⟩. ⟨hal-01372542⟩

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