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Conference papers
A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space
Abstract : 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.
Cited literature [3 references]
https://hal.archives-ouvertes.fr/hal-02150657
Contributor : David Coeurjolly <>
Submitted on : Wednesday, June 12, 2019 - 2:42:17 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:52 PM
Contributor : David Coeurjolly <>
Submitted on : Wednesday, June 12, 2019 - 2:42:17 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:52 PM
