Deep Kernel Density Estimation for Photon Mapping

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dc.contributor.authorZhu, Shilinen_US
dc.contributor.authorXu, Zexiangen_US
dc.contributor.authorJensen, Henrik Wannen_US
dc.contributor.authorSu, Haoen_US
dc.contributor.authorRamamoorthi, Ravien_US
dc.contributor.editorDachsbacher, Carsten and Pharr, Matten_US
dc.date.accessioned2020-06-28T15:23:45Z
dc.date.available2020-06-28T15:23:45Z
dc.date.issued2020
dc.description.abstractRecently, deep learning-based denoising approaches have led to dramatic improvements in low sample-count Monte Carlo rendering. These approaches are aimed at path tracing, which is not ideal for simulating challenging light transport effects like caustics, where photon mapping is the method of choice. However, photon mapping requires very large numbers of traced photons to achieve high-quality reconstructions. In this paper, we develop the first deep learning-based method for particlebased rendering, and specifically focus on photon density estimation, the core of all particle-based methods. We train a novel deep neural network to predict a kernel function to aggregate photon contributions at shading points. Our network encodes individual photons into per-photon features, aggregates them in the neighborhood of a shading point to construct a photon local context vector, and infers a kernel function from the per-photon and photon local context features. This network is easy to incorporate in many previous photon mapping methods (by simply swapping the kernel density estimator) and can produce high-quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared to previous photon mapping methods. Our approach largely reduces the required number of photons, significantly advancing the computational efficiency in photon mapping.en_US
dc.description.number4
dc.description.sectionheadersGlobal Illumination
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.14052
dc.identifier.issn1467-8659
dc.identifier.pages35-45
dc.identifier.urihttps://doi.org/10.1111/cgf.14052
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14052
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectComputing methodologies
dc.subjectRendering
dc.titleDeep Kernel Density Estimation for Photon Mappingen_US
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