On the Efficiency of Multiple Importance Sampling

  • Approximating illumination by point light sources, as done in many professional applications, suffers from the problem of the weak singularity: Numerical exceptions caused by the division by the squared distance between the point light source and the point to be illuminated must be avoided. Multiple importance sampling overcomes these problems by combining multiple sampling techniques by weights. Such a set of weights is called a heuristic. So far the estimators resulting from a heuristic only have been analyzed for variance. Since the cost of sampling is not at all constant for different sampling techniques, it is possible to find more efficient heuristics, even though they may hove higher variance. Based on our new stratification heuristic, we present a robust and unbiased global illumination algorithm. By numerical examples, we show that it is more efficient than previous heuristics. The algorithm is as simple as a path tracer, but elegantly avoids the problem of the weak singularity.

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Verfasserangaben:T. Kollig, A. Keller
URN (Permalink):urn:nbn:de:hbz:386-kluedo-49794
Schriftenreihe (Bandnummer):Interner Bericht des Fachbereich Informatik (328)
Sprache der Veröffentlichung:Englisch
Veröffentlichungsdatum (online):27.10.2017
Jahr der Veröffentlichung:2004
Veröffentlichende Institution:Technische Universität Kaiserslautern
Datum der Publikation (Server):27.10.2017
Fachbereiche / Organisatorische Einheiten:Fachbereich Informatik
DDC-Sachgruppen:0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Lizenz (Deutsch):Creative Commons 4.0 - Namensnennung, nicht kommerziell, keine Bearbeitung (CC BY-NC-ND 4.0)