Neural Acquisition & Representation of Subsurface Scattering

dc.contributor.authorMajumdar, Arjunen_US
dc.contributor.authorBraun, Raphaelen_US
dc.contributor.authorLensch, Hendriken_US
dc.contributor.editorEgger, Bernharden_US
dc.contributor.editorGünther, Tobiasen_US
dc.date.accessioned2025-09-24T10:36:56Z
dc.date.available2025-09-24T10:36:56Z
dc.date.issued2025
dc.description.abstractWe present a method to acquire and estimate the sub-surface scattering properties of light transport at a highly detailed level by learning the pixel footprint response at each point on the object surface. The reconstruction leverages 3D scanning techniques as input to a U-Net CNN. A stereo projector-camera setup using phase-shifted profilometry (PSP) patterns efficiently captures the data for a variety of scattering objects. Reconstructing dense pixel footprints allows for relighting with arbitrary high-resolution projector patterns. The final output is a relit color image. Qualitative and quantitative comparison against illuminated realworld captured images demonstrate that the predicted footprints are almost identical to the actual responses. The same model is trained for multiple views across multiple objects such that the learned representations can be used to generalize to unseen sub-surface scattering materials as well.en_US
dc.description.sectionheadersNeural and Differentiable Rendering
dc.description.seriesinformationVision, Modeling, and Visualization
dc.identifier.doi10.2312/vmv.20251228
dc.identifier.isbn978-3-03868-294-3
dc.identifier.pages8 pages
dc.identifier.urihttps://doi.org/10.2312/vmv.20251228
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/vmv20251228
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleNeural Acquisition & Representation of Subsurface Scatteringen_US
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