A Hierarchical Architecture for Neural Materials

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dc.contributor.authorXue, Bowenen_US
dc.contributor.authorZhao, Shuangen_US
dc.contributor.authorJensen, Henrik Wannen_US
dc.contributor.authorMontazeri, Zahraen_US
dc.contributor.editorAlliez, Pierreen_US
dc.contributor.editorWimmer, Michaelen_US
dc.date.accessioned2024-12-19T11:15:40Z
dc.date.available2024-12-19T11:15:40Z
dc.date.issued2024
dc.description.abstractNeural reflectance models are capable of reproducing the spatially‐varying appearance of many real‐world materials at different scales. Unfortunately, existing techniques such as NeuMIP have difficulties handling materials with strong shadowing effects or detailed specular highlights. In this paper, we introduce a neural appearance model that offers a new level of accuracy. Central to our model is an inception‐based core network structure that captures material appearances at multiple scales using parallel‐operating kernels and ensures multi‐stage features through specialized convolution layers. Furthermore, we encode the inputs into frequency space, introduce a gradient‐based loss, and employ it adaptive to the progress of the learning phase. We demonstrate the effectiveness of our method using a variety of synthetic and real examples.en_US
dc.description.number6
dc.description.sectionheadersMajor Revision from Eurographics Conference
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15116
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15116
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15116
dc.publisher© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectappearance modelling
dc.subjectBTF
dc.subjectmultiresolution
dc.subjectneural networks neural rendering
dc.titleA Hierarchical Architecture for Neural Materialsen_US
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