Neural SSS: Lightweight Object Appearance Representation

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dc.contributor.authorTg, Thomsonen_US
dc.contributor.authorTran, Duc Minhen_US
dc.contributor.authorJensen, Henrik W.en_US
dc.contributor.authorRamamoorthi, Ravien_US
dc.contributor.authorFrisvad, Jeppe Revallen_US
dc.contributor.editorGarces, Elenaen_US
dc.contributor.editorHaines, Ericen_US
dc.date.accessioned2024-06-25T10:20:18Z
dc.date.available2024-06-25T10:20:18Z
dc.date.issued2024
dc.description.abstractWe present a method for capturing the BSSRDF (bidirectional scattering-surface reflectance distribution function) of arbitrary geometry with a neural network. We demonstrate how a compact neural network can represent the full 8-dimensional light transport within an object including heterogeneous scattering. We develop an efficient rendering method using importance sampling that is able to render complex translucent objects under arbitrary lighting. Our method can also leverage the common planar half-space assumption, which allows it to represent one BSSRDF model that can be used across a variety of geometries. Our results demonstrate that we can render heterogeneous translucent objects under arbitrary lighting and obtain results that match the reference rendered using volumetric path tracing.en_US
dc.description.number4
dc.description.sectionheadersNeural Rendering
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15158
dc.identifier.issn1467-8659
dc.identifier.pages14 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15158
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15158
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.subjectCCS Concepts: Computing methodologies → Reflectance modeling; Neural networks
dc.subjectComputing methodologies → Reflectance modeling
dc.subjectNeural networks
dc.titleNeural SSS: Lightweight Object Appearance Representationen_US
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