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Adaptive Sampling and Bias Estimation in Path Tracing

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Rendering Techniques ’97 (EGSR 1997)

Part of the book series: Eurographics ((EUROGRAPH))

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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.

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Author information

Authors and Affiliations

  1. Graphical Communication, Technical University of Denmark, DK-2800, Lyngby, Denmark

    Rasmus Tamstorf

  2. mental images GmbH & Co. KG, Fasanenstrasse 81, D-10623, Berlin, Germany

    Henrik Wann Jensen

Authors
  1. Rasmus Tamstorf
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  2. Henrik Wann Jensen
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Editor information

Editors and Affiliations

  1. Massachusetts Institute of Technology, Cambridge, MA, USA

    Julie Dorsey

  2. Computer Graphics Laboratory, University of Erlangen, Erlangen, Germany

    Philipp Slusallek

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© 1997 Springer-Verlag/Wien

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Tamstorf, R., Jensen, H.W. (1997). Adaptive Sampling and Bias Estimation in Path Tracing. In: Dorsey, J., Slusallek, P. (eds) Rendering Techniques ’97. EGSR 1997. Eurographics. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6858-5_26

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  • DOI: https://doi.org/10.1007/978-3-7091-6858-5_26

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  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83001-7

  • Online ISBN: 978-3-7091-6858-5

  • eBook Packages: Springer Book Archive

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