Avoiding Statistical Bias of Metropolis Light Transport with Multiple Importance Sampling Based on the Primary Sample Space

Shinya Kitaoka; Yoshifumi Kitamura; Fu Mio Kishino

doi:10.11371/iieej.38.432

Avoiding Statistical Bias of Metropolis Light Transport with Multiple Importance Sampling Based on the Primary Sample Space

Shinya Kitaoka, Yoshifumi Kitamura, Fu Mio Kishino

RIEC - Human and Bio Information Systems Division

Osaka University

Research output: Contribution to journal › Article › peer-review

Abstract

We propose a method for avoiding the statistical bias of Metropolis light transport with multiple importance sampling based on the primary sample space. The statistical bias produces incorrect results which are much brighter or darker than true result in physically based rendering. This problem has been ignored because it appears only in special scenes. We solve the statistical bias problem by using two image buffers for storing sampling results.

Original language	English
Pages (from-to)	432-440
Number of pages	9
Journal	Journal of the Institute of Image Electronics Engineers of Japan
Volume	38
Issue number	4
DOIs	https://doi.org/10.11371/iieej.38.432
Publication status	Published - 2009 Jan

Keywords

computer graphics
global illumination
Metropolis light transport
multiple importance sampling
rendering

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10.11371/iieej.38.432

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abstract = "We propose a method for avoiding the statistical bias of Metropolis light transport with multiple importance sampling based on the primary sample space. The statistical bias produces incorrect results which are much brighter or darker than true result in physically based rendering. This problem has been ignored because it appears only in special scenes. We solve the statistical bias problem by using two image buffers for storing sampling results.",

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Avoiding Statistical Bias of Metropolis Light Transport with Multiple Importance Sampling Based on the Primary Sample Space

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Keywords

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