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Adaptive Sampling and Bias Estimation in Path Tracing

  • Rasmus Tamstorf
  • Henrik Wann Jensen
Part of the Eurographics book series (EUROGRAPH)

Abstract

One of the major problems in Monte Carlo based methods for global illumination is noise. This paper investigates adaptive sampling as a method to alleviate the problem. We introduce a new refinement criterion, which takes human perception and limitations of display devices into account by incorporating the tone-operator. Our results indicate that this can lead to a significant reduction in the overall RMS-error, and even more important that noisy spots are eliminated. This leads to a very homogeneous image quality. As most adaptive sampling techniques our method is biased. In order to investigate the importance of this problem, a nonparametric bootstrap method is presented to estimate the actual bias. This provides a technique for bias correction and it shows that the bias is most significant in areas with indirect illumination.

Keywords

Display Device Adaptive Sampling Bootstrap Estimate Global Illumination Test Scene 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag/Wien 1997

Authors and Affiliations

  • Rasmus Tamstorf
    • 1
  • Henrik Wann Jensen
    • 2
  1. 1.Graphical CommunicationTechnical University of DenmarkLyngbyDenmark
  2. 2.mental images GmbH & Co. KGBerlinGermany

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