Progressive Expectation-Maximization for Hierarchical Volumetric Photon Mapping

dc.contributor.authorJakob, Wenzelen_US
dc.contributor.authorRegg, Christianen_US
dc.contributor.authorJarosz, Wojciechen_US
dc.contributor.editorRavi Ramamoorthi and Erik Reinharden_US
dc.date.accessioned2015-02-27T14:45:40Z
dc.date.available2015-02-27T14:45:40Z
dc.date.issued2011en_US
dc.description.abstractState-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.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01988.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleProgressive Expectation-Maximization for Hierarchical Volumetric Photon Mappingen_US
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