A self-supervised method pretrains an encoder on eight PSP images per view to learn generalizable subsurface scattering representations that transfer to relighting and dense footprint reconstruction on unseen complex objects.
In: 2008 IEEE Conference on Computer Vision and Pattern Recognition
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U-Net learns dense pixel footprint responses for subsurface scattering from phase-shifted profilometry data, supporting relighting and generalization across materials.
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From Phase to Phenomenon: Self-Supervised Learning of Subsurface Scattering with Minimal Phase-shift Inputs
A self-supervised method pretrains an encoder on eight PSP images per view to learn generalizable subsurface scattering representations that transfer to relighting and dense footprint reconstruction on unseen complex objects.
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Neural Acquisition & Representation of Subsurface Scattering
U-Net learns dense pixel footprint responses for subsurface scattering from phase-shifted profilometry data, supporting relighting and generalization across materials.