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Weighted Importance Sampling Techniques for Monte Carlo Radiosity

  • Philippe Bekaert
  • Mateu Sbert
  • Yves D. Willems
Part of the Eurographics book series (EUROGRAPH)

Abstract

This paper presents weighted importance sampling techniques for Monte Carlo form factor computation and for stochastic Jacobi radiosity system solution. Weighted importance sampling is a generalisation of importance sampling. The basic idea is to compute a-posteriori a correction factor to the importance sampling estimates, based on sample weights accumulated during sampling. With proper weights, the correction factor will compensate for statistical fluctuations and lead to a lower mean square error. Although weighted importance sampling is a simple extension to importance sampling, our experiments indicate that it can lead to a substantial reduction of the error at a very low additional computation and storage cost.

Keywords

Form Factor Mean Square Error Importance Sampling Local Line Spherical Triangle 
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 2000

Authors and Affiliations

  • Philippe Bekaert
    • 1
  • Mateu Sbert
    • 2
  • Yves D. Willems
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenLeuvenBelgium
  2. 2.Institut d’Informàtica i AplicacionsUniversitat de GironaGironaSpain

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