Improved Stochastic Progressive Photon Mapping with Metropolis Sampling

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dc.contributor.authorChen, Jiatingen_US
dc.contributor.authorWang, Binen_US
dc.contributor.authorYong, Jun-Haien_US
dc.contributor.editorRavi Ramamoorthi and Erik Reinharden_US
dc.date.accessioned2015-02-27T14:45:05Z
dc.date.available2015-02-27T14:45:05Z
dc.date.issued2011en_US
dc.description.abstractThis paper presents an improvement to the stochastic progressive photon mapping (SPPM), a method for robustly simulating complex global illumination with distributed ray tracing effects. Normally, similar to photon mapping and other particle tracing algorithms, SPPM would become inefficient when the photons are poorly distributed. An inordinate amount of photons are required to reduce the error caused by noise and bias to acceptable levels. In order to optimize the distribution of photons, we propose an extension of SPPM with a Metropolis-Hastings algorithm, effectively exploiting local coherence among the light paths that contribute to the rendered image. A well-designed scalar contribution function is introduced as our Metropolis sampling strategy, targeting at specific parts of image areas with large error to improve the efficiency of the radiance estimator. Experimental results demonstrate that the new Metropolis sampling based approach maintains the robustness of the standard SPPM method, while significantly improving the rendering efficiency for a wide range of scenes with complex lighting.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01979.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleImproved Stochastic Progressive Photon Mapping with Metropolis Samplingen_US
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EGSR11: 22th Eurographics Symposium on Rendering