“Lessons Learned and Improvements when Building Screen-Space Samplers with Blue-Noise Error Distribution” by Belcour and Heitz

  • ©Laurent Belcour and Eric Heitz



Entry Number: 19


    Lessons Learned and Improvements when Building Screen-Space Samplers with Blue-Noise Error Distribution



    Recent work has shown that the error of Monte-Carlo rendering is visually more acceptable when distributed as blue-noise in screen- space. Despite recent efforts, building a screen-space sampler is still an open problem. In this talk, we present the lessons we learned while improving our previous screen-space sampler. Specifically: we advocate for a new criterion to assess the quality of such samplers; we introduce a new screen-space sampler based on rank-1 lattices; we provide a parallel optimization method that is compatible with a GPU implementation and that achieves better quality; we detail the pitfalls of using such samplers in renderers and how to cope with many dimensions; and we provide empirical proofs of the versatility of the optimization process.


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    Vassillen Chizhov, Iliyan Georgiev, Karol Myszkowski, and Gurprit Singh. 2020. Perceptual error optimization for Monte Carlo rendering. arXiv preprint arXiv:2012.02344 (2020).

    Iliyan Georgiev and Marcos Fajardo. 2016. Blue-noise dithered sampling. In ACM SIGGRAPH 2016 Talks. ACM, 35.

    Eric Heitz, Laurent Belcour, Victor Ostromoukhov, David Coeurjolly, and Jean-ClaudeIehl. 2019. A low-discrepancy sampler that distributes Monte Carlo errors as a blue noise in screen space. In ACM SIGGRAPH 2019 Talks. 1–2.

    Fred J Hickernell, Peter Kritzer, Frances Y Kuo, and Dirk Nuyens. 2012. Weighted compound integration rules with higher order convergence for all N. Numerical Algorithms 59, 2 (2012), 161–183.


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