LowDiscrepancy Blue Noise Sampling
ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2016)
Abdalla G. M. Ahmed^{1} Hélène Perrier^{2} David Coeurjolly^{2}^{ }^{ }Victor Ostromoukhov^{2}
Jianwei Guo^{3} Dongming Yan^{3} Hui Huang^{4,5} Oliver Deussen^{1,5}
^{1}University of Konstanz
^{2}Université de Lyon, CNRS/LIRIS
^{3}Institute of Automation ^{4}Shenzhen University^{ 5}SIAT
Starting from a template lowdiscrepancy (LD) point set (a), we use a segmented table of permutations to rearrange the LD set to match a reference set with the desired target spectrum (b). The permutations are localized and carefully constructed in such a way that they have minimal impact on the discrepancy of the underlying template set. The resulting set (c) inherits the spectral profile of the target set, while still retaining the discrepancy profile of the template set (d).
Abstract
We present a novel technique that produces twodimensional lowdiscrepancy (LD) blue noise point sets for sampling. Using onedimensional binary van der Corput sequences, we construct twodimensional LD point sets, and rearrange them to match a target spectral profile without loosing their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. To the best of our knowledge, our construction is the first one that combines bluenoise and lowdiscrepancy properties at the same time. We evaluate our technique and compare it to the stateoftheart sampling approaches.
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