The Visual Computer

, Volume 33, Issue 6–8, pp 833–843 | Cite as

Forced Random Sampling: fast generation of importance-guided blue-noise samples

  • Daniel Cornel
  • Robert F. Tobler
  • Hiroyuki Sakai
  • Christian Luksch
  • Michael Wimmer
Original Article

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.

Keywords

Blue noise Importance sampling Ordered dithering 

Notes

Acknowledgements

The competence center VRVis is funded by BMVIT, BMWFW and City of Vienna (ZIT) within the scope of COMET Competence Centers for Excellent Technologies. The program COMET is managed by FFG. Hiroyuki Sakai is partly supported by the Austrian Science Fund (FWF), project no. P 27974. We thank Christoph Weinzierl-Heigl for providing us access to his implementation of reflective shadow mapping.

Supplementary material

371_2017_1392_MOESM1_ESM.rar (16.8 mb)
Supplementary material 1 (rar 17210 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.VRVis Research CenterViennaAustria
  2. 2.TU WienViennaAustria

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