Screen-space blue-noise diffusion of monte carlo sampling error via hierarchical ordering of pixels
KAUST DepartmentComputer Science Program
Visual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/666257
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AbstractWe present a novel technique for diffusing Monte Carlo sampling error as a blue noise in screen space. We show that automatic diffusion of sampling error can be achieved by ordering the pixels in a way that preserves locality, such as Morton's Z-ordering, and assigning the samples to the pixels from successive sub-sequences of a single low-discrepancy sequence, thus securing well-distributed samples for each pixel, local neighborhoods, and the whole image. We further show that a blue-noise distribution of the error is attainable by scrambling the Z-ordering to induce isotropy. We present an efficient technique to implement this hierarchical scrambling by defining a context-free grammar that describes infinite self-similar lookup trees. Our concept is scalable to arbitrary image resolutions, sample dimensions, and sample count, and supports progressive and adaptive sampling.
CitationAhmed, A. G. M., & Wonka, P. (2020). Screen-space blue-noise diffusion of monte carlo sampling error via hierarchical ordering of pixels. ACM Transactions on Graphics, 39(6), 1–15. doi:10.1145/3414685.3417881
SponsorsThanks to the anonymous reviewers for the valuable comments. We credit reviewer #1 for pointing out the advantage of arithmetic hashing for GPU implementation. Thanks to the scientific editing team at KAUST for proofreading the paper and to Mohanad Ahmed for his insightful discussions.
JournalACM Transactions on Graphics