@article{PB-VRVis-2017-012,
author = {Cornel, Daniel and Tobler, Robert and Sakai, Hiroyuki and Luksch, Christian and Wimmer, Michael},
title = {Forced Random Sampling: fast generation of importance-guided
  blue-noise samples},
year = {2017},
journaltitle = {The Visual Computer},
doi = {http://dx.doi.org/10.1007/s00371-017-1392-7},
url = {https://www.vrvis.at/publications/PB-VRVis-2017-012},
issn = {1432-2315},
pages = {833--843},
volume = {33},
abstract = {In computer graphics, stochastic sampling is frequently used to  efficiently approximate complex functions and integrals. The  error of approximation can be reduced by distributing samples  according to an importance function, but cannot be eliminated  completely. To avoid visible artifacts, sample distributions are  sought to be random, but spatially uniform, which is called  blue-noise sampling. The generation of unbiased,  importance-guided blue-noise samples is expensive and not  feasible for real-time applications. Sampling algorithms for  these applications focus on runtime performance at the cost of  having weak blue-noise properties. Blue-noise distributions have  also been proposed for digital halftoning in the form of  precomputed dither matrices. Ordered dithering with such matrices  allows to distribute dots with blue-noise properties according to  a grayscale image. By the nature of ordered dithering, this  process can be parallelized easily. We introduce a novel sampling  method called forced random sampling that is based on forced  random dithering, a variant of ordered dithering with blue noise.  By shifting the main computational effort into the generation of  a precomputed dither matrix, our sampling method runs efficiently  on GPUs and allows real-time importance sampling with blue noise  for a finite number of samples. We demonstrate the quality of our  method in two different rendering applications.},
number = {6},
}