A low-discrepancy sampler that distributes monte carlo errors as a blue noise in screen space
Eric Heitz, Laurent Belcour, Victor Ostromoukhov, David Cœurjolly, Jean‐Claude Iehl · 2019
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 sampler 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.