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Path Space Filtering

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Monte Carlo and Quasi-Monte Carlo Methods

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 163))

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Abstract

We improve the efficiency of quasi-Monte Carlo integro-approximation by using weighted averages of samples instead of the samples themselves. The proposed deterministic algorithm is constructed such that it converges to the solution of the given integro-approximation problem. The improvements and wide applicability of the consistent method are demonstrated by visual evidence in the setting of light transport simulation for photorealistic image synthesis, where the weighted averages correspond to locally smoothed contributions of path space samples.

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Authors and Affiliations

  1. NVIDIA, Fasanenstr. 81, 10623, Berlin, Germany

    Alexander Keller, Ken Dahm & Nikolaus Binder

Authors
  1. Alexander Keller
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  2. Ken Dahm
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  3. Nikolaus Binder
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Corresponding author

Correspondence to Alexander Keller .

Editor information

Editors and Affiliations

  1. Department of Computer Science, KU Leuven, Heverlee, Belgium

    Ronald Cools

  2. Department of Computer Science, KU Leuven, Heverlee, Belgium

    Dirk Nuyens

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Keller, A., Dahm, K., Binder, N. (2016). Path Space Filtering. In: Cools, R., Nuyens, D. (eds) Monte Carlo and Quasi-Monte Carlo Methods. Springer Proceedings in Mathematics & Statistics, vol 163. Springer, Cham. https://doi.org/10.1007/978-3-319-33507-0_21

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