Progressive Expectation-Maximization for Hierarchical Volumetric Photon Mapping

State‐of‐the‐art density estimation methods for rendering participating media rely on a dense photon representation of the radiance distribution within a scene. A critical bottleneck of such kernel‐based approaches is the excessive number of photons that are required in practice to resolve fine illu...

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Bibliographic Details
Published in:Computer graphics forum Vol. 30; no. 4; pp. 1287 - 1297
Main Authors: Jakob, Wenzel, Regg, Christian, Jarosz, Wojciech
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01.06.2011
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ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary:State‐of‐the‐art density estimation methods for rendering participating media rely on a dense photon representation of the radiance distribution within a scene. A critical bottleneck of such kernel‐based approaches is the excessive number of photons that are required in practice to resolve fine illumination details, while controlling the amount of noise. In this paper, we propose a parametric density estimation technique that represents radiance using a hierarchical Gaussian mixture. We efficiently obtain the coefficients of this mixture using a progressive and accelerated form of the Expectation‐Maximization algorithm. After this step, we are able to create noise‐free renderings of high‐frequency illumination using only a few thousand Gaussian terms, where millions of photons are traditionally required. Temporal coherence is trivially supported within this framework, and the compact footprint is also useful in the context of real‐time visualization. We demonstrate a hierarchical ray tracing‐based implementation, as well as a fast splatting approach that can interactively render animated volume caustics.
Bibliography:istex:76CE68640BD530CF587F5DD1221ABBB2F771B44E
ArticleID:CGF1988
ark:/67375/WNG-HHK6P52S-W
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2011.01988.x