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

Shinya Kitaoka, Yoshifumi Kitamura, Fu Mio Kishino

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)432-440
Number of pages9
JournalJournal of the Institute of Image Electronics Engineers of Japan
Volume38
Issue number4
DOIs
Publication statusPublished - 2009 Jan
Externally publishedYes

Keywords

  • Metropolis light transport
  • computer graphics
  • global illumination
  • multiple importance sampling
  • rendering

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering

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