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dc.contributor.authorChoi, Hajinen_US
dc.contributor.authorMoon, Bochangen_US
dc.contributor.editorZhang, Fang-Lue and Eisemann, Elmar and Singh, Karanen_US
dc.description.abstractPhoton mapping is a light transport algorithm that simulates various rendering effects (e.g., caustics) robustly, and its progressive variants, progressive photon mapping (PPM) methods, can produce a biased but consistent rendering output. PPM estimates radiance using a kernel density estimation whose parameters (bandwidths) are adjusted progressively, and this refinement enables to reduce its estimation bias. Nonetheless, many iterations (and thus a large number of photons) are often required until PPM produces nearly converged estimates. This paper proposes a post-reconstruction that improves the performance of PPM by reducing residual errors in PPM estimates. Our key idea is to take multiple PPM estimates with multi-level correlation structures, and fuse the input images using a weight function trained by supervised learning with maintaining the consistency of PPM. We demonstrate that our technique boosts an existing PPM technique for various rendering scenes.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectRay tracing
dc.titleConsistent Post-Reconstruction for Progressive Photon Mappingen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersGlobal Illumination

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  • 40-Issue 7
    Pacific Graphics 2021 - Symposium Proceedings

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