Neural Precomputed Radiance Transfer

dc.contributor.authorRainer, Gillesen_US
dc.contributor.authorBousseau, Adrienen_US
dc.contributor.authorRitschel, Tobiasen_US
dc.contributor.authorDrettakis, Georgeen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2022-04-22T06:29:04Z
dc.date.available2022-04-22T06:29:04Z
dc.date.issued2022
dc.description.abstractRecent advances in neural rendering indicate immense promise for architectures that learn light transport, allowing efficient rendering of global illumination effects once such methods are trained. The training phase of these methods can be seen as a form of pre-computation, which has a long standing history in Computer Graphics. In particular, Pre-computed Radiance Transfer (PRT) achieves real-time rendering by freezing some variables of the scene (geometry, materials) and encoding the distribution of others, allowing interactive rendering at runtime. We adopt the same configuration as PRT - global illumination of static scenes under dynamic environment lighting - and investigate different neural network architectures, inspired by the design principles and theoretical analysis of PRT. We introduce four different architectures, and show that those based on knowledge of light transport models and PRT-inspired principles improve the quality of global illumination predictions at equal training time and network size, without the need for high-end ray-tracing hardware.en_US
dc.description.number2
dc.description.sectionheadersRendering II
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume41
dc.identifier.doi10.1111/cgf.14480
dc.identifier.issn1467-8659
dc.identifier.pages365-378
dc.identifier.pages14 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14480
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14480
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleNeural Precomputed Radiance Transferen_US
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