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dc.contributor.authorReinbold, Christianen_US
dc.contributor.authorWestermann, Rüdigeren_US
dc.contributor.editorPelechano, Nuriaen_US
dc.contributor.editorVanderhaeghe, Daviden_US
dc.description.abstractWe propose a novel encoder/decoder-based neural network architecture that learns view-dependent shape and appearance of geometry represented by voxel representations. Since the network is trained on local geometry patches, it generalizes to arbitrary models. A geometry model is first encoded into a sparse voxel octree of features learned by a network, and this model representation can then be decoded by another network in-turn for the intended task. We utilize the network for adaptive supersampling in ray-tracing, to predict super-sampling patterns when seeing coarse-scale geometry. We discuss and evaluate the proposed network design, and demonstrate that the decoder network is compact and can be integrated seamlessly into on-chip ray-tracing kernels. We compare the results to previous screen-space super-sampling strategies as well as non-network-based world-space approaches.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.titleLearning Generic Local Shape Properties for Adaptive Super-Samplingen_US
dc.description.seriesinformationEurographics 2022 - Short Papers
dc.identifier.pages4 pages

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License