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 achiev- ing 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 mag- nitudes. 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.

Publication
ACM SIGGRAPH Talk

@inproceedings{heitz19,
      author = {Eric Heitz and Laurent Belcour and Victor Ostromoukhov and David Coeurjolly and Jean-Claude Iehl},
      booktitle = {ACM SIGGRAPH Talk},
      doi = {10.1145/3306307.3328191},
      month = {July},
      title = {A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space},
      year = {2019}
}