Laurent Belcour
ABSTRACT
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.
Supplemental Material
Available for Download
- Abdalla GM Ahmed and Peter Wonka. 2020. Screen-space blue-noise diffusion of Monte Carlo sampling error via hierarchical ordering of pixels. ACM Transactions on Graphics (TOG) 39, 6 (2020), 1–15.Google Scholar
Digital Library
- Vassillen Chizhov, Iliyan Georgiev, Karol Myszkowski, and Gurprit Singh. 2020. Perceptual error optimization for Monte Carlo rendering. arXiv preprint arXiv:2012.02344(2020).Google Scholar
- Iliyan Georgiev and Marcos Fajardo. 2016. Blue-noise dithered sampling. In ACM SIGGRAPH 2016 Talks. ACM, 35.Google ScholarDigital Library
- Eric Heitz, Laurent Belcour, Victor Ostromoukhov, David Coeurjolly, and Jean-Claude Iehl. 2019. A low-discrepancy sampler that distributes Monte Carlo errors as a blue noise in screen space. In ACM SIGGRAPH 2019 Talks. 1–2.Google ScholarDigital Library
- 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.Google ScholarDigital Library
Index Terms
Lessons Learned and Improvements when Building Screen-Space Samplers with Blue-Noise Error Distribution
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