Gradient-Domain Photon Density Estimation

dc.contributor.authorHua, Binh-Sonen_US
dc.contributor.authorGruson, Adrienen_US
dc.contributor.authorNowrouzezahrai, Dereken_US
dc.contributor.authorHachisuka, Toshiyaen_US
dc.contributor.editorLoic Barthe and Bedrich Benesen_US
dc.date.accessioned2017-04-22T16:25:25Z
dc.date.available2017-04-22T16:25:25Z
dc.date.issued2017
dc.description.abstractThe most common solutions to the light transport problem rely on either Monte Carlo (MC) integration or density estimation methods, such as uni- & bi-directional path tracing or photon mapping. Recent gradient-domain extensions of MC approaches show great promise; here, gradients of the final image are estimated numerically (instead of the image intensities themselves) with coherent paths generated from a deterministic shift mapping. We extend gradient-domain approaches to light transport simulation based on density estimation. As with previous gradient-domain methods, we detail important considerations that arise when moving from a primal- to gradient-domain estimator. We provide an efficient and straightforward solution to these problems. Our solution supports stochastic progressive density estimation, so it is robust to complex transport effects. We show that gradient-domain photon density estimation converges faster than its primal-domain counterpart, as well as being generally more robust than gradient-domain uni- & bi-directional path tracing for scenes dominated by complex transport.en_US
dc.description.number2
dc.description.sectionheadersMonte Carlo
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.13104
dc.identifier.issn1467-8659
dc.identifier.pages031-038
dc.identifier.urihttps://doi.org/10.1111/cgf.13104
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13104
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.7 [Computer Graphics]
dc.subject3D Graphics & Realism
dc.subjectRaytracing
dc.titleGradient-Domain Photon Density Estimationen_US
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