“A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space”

  • ©Eric Heitz, Laurent Belcour, Victor Ostromoukhov, David Coeurjolly, and Jean-Claude Iehl

  • ©Eric Heitz, Laurent Belcour, Victor Ostromoukhov, David Coeurjolly, and Jean-Claude Iehl

Conference:


Entry Number: 68

Title:

    A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space

Presenter(s):



Abstract:


    We introduce a sampler that generates per-pixel samples achiev ing high visual quality thanks to two key properties related to the Monte Carlo errors that it produces. First, the sequence of each pixel is an Owen-scrambled Sobol sequence that has state-of-the-art convergence properties. The Monte Carlo errors have thus low mag nitudes. Second, these errors are distributed as a blue noise in screen space. This makes them visually even more acceptable. Our sam pler is lightweight and fast. We implement it with a small texture and two xor operations. Our supplemental material provides comparisons against previous work for different scenes and sample counts.

References:


    Iliyan Georgiev and Marcos Fajardo. 2016. Blue-noise dithered sampling. In ACM SIGGRAPH 2016 Talks. ACM, 35.
    Thomas Kollig and Alexander Keller. 2002. Efficient multidimensional sampling. In Computer Graphics Forum, Vol. 21. Wiley Online Library, 557–563.
    Art B Owen. 1998. Scrambling Sobol’ and Niederreiter-Xing Points. Journal of complexity 14, 4 (1998), 466–489.


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