Adaptive Volumetric Shadow Maps

We introduce adaptive volumetric shadow maps (AVSM), a real‐time shadow algorithm that supports high‐quality shadowing from dynamic volumetric media such as hair and smoke. The key contribution of AVSM is the introduction of a streaming simplification algorithm that generates an accurate volumetric...

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Vydané v:Computer graphics forum Ročník 29; číslo 4; s. 1289 - 1296
Hlavní autori: Salvi, Marco, Vidimče, Kiril, Lauritzen, Andrew, Lefohn, Aaron
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford, UK Blackwell Publishing Ltd 01.06.2010
Predmet:
Algorithms
and texture
Coding and Information Theory (H.1.1) [E.4]: Data compaction and compression-Bitmap and framebuffer operations
Compressing
Computer graphics
Computer Graphics [I.3.3]: Three-Dimensional Graphics and Realism-Color
Computer Graphics [I.3.3]: Three‐Dimensional Graphics and Realism—Color, shading, shadowing, and texture
Computer programming
Data compression
Mathematical models
Real time
Rendering
shading
shadowing
Shadows
Simplification
Smoke
Studies
Volumetric analysis
ISSN:0167-7055, 1467-8659
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Popis
Shrnutí:We introduce adaptive volumetric shadow maps (AVSM), a real‐time shadow algorithm that supports high‐quality shadowing from dynamic volumetric media such as hair and smoke. The key contribution of AVSM is the introduction of a streaming simplification algorithm that generates an accurate volumetric light attenuation function using a small fixed memory footprint. This compression strategy leads to high performance because the visibility data can remain in on‐chip memory during simplification and can be efficiently sampled during rendering. We demonstrate that AVSM compression closely approximates the ground‐truth correct solution and performs competitively to existing real‐time rendering techniques while providing higher quality volumetric shadows.
Bibliografia:ark:/67375/WNG-7FPGVNCT-P
ArticleID:CGF1724
istex:A019D8759639F7CD38E0A7FF8F1B2A1C44BD10FC
marco.salvi@intel.com
kiril.vidimce@intel.com
aaron.lefohn@intel.com
,
andrew.t.lauritzen@intel.com
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ObjectType-Article-1
ObjectType-Feature-2
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ObjectType-Article-2
ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2010.01724.x