Combined correlated and importance sampling in direct light source computation and environment mapping
dc.contributor.author
dc.date.accessioned
2017-05-31T09:28:30Z
dc.date.available
2017-05-31T09:28:30Z
dc.date.issued
2004-08-27
dc.identifier.issn
0167-7055
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dc.description.abstract
This paper presents a general variance reduction method that is a quasi-optimal combination of correlated and importance sampling. The weights of the combination are selected automatically in order to keep the merits of both importance and correlated sampling. The proposed sampling method is used for efficient direct light source computation of large area sources and for the calculation of the reflected illumination of environment maps. Importance sampling would be good in these cases if the sources are hidden, while correlated sampling is efficient if the sources are fully visible. The proposed method automatically detects the particular case and provides results that inherit the advantages of both techniques
dc.description.sponsorship
This work has been supported by the National Scientific Research Fund (OTKA ref. No.: T042735), IKTA ref. No.: 00159/2002, the Bolyai Scholarship, by Intel, and by TIC 2001 2416-C03-01 from the Spanish Government
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Wiley
dc.relation
MICYT/PN 2001-2004/TIC2001-2416-C03-01
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Reproducció digital del document publicat a: http://dx.doi.org/10.1111/j.1467-8659.2004.00790.x
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© Computer Graphics Forum, 2004, vol. 23, núm. 3, p. 585-593
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Articles publicats (D-IMA)
dc.rights
Tots els drets reservats
dc.subject
dc.title
Combined correlated and importance sampling in direct light source computation and environment mapping
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/embargoedAccess
dc.embargo.terms
Cap
dc.date.embargoEndDate
info:eu-repo/date/embargoEnd/2026-01-01
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
000611
dc.contributor.funder
dc.identifier.eissn
1467-8659