A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space
Résumé
We introduce a sampler that generates per-pixel samples achieving 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 magnitudes. 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.
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samplerBlueNoiseErrors2019_paper.pdf (3.64 Mo)
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samplerBlueNoiseErrors2019_video.mp4 (62 Mo)
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samplerCPP.zip (2.77 Mo)
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supplemental.zip (44.53 Mo)
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