Neural Acquisition & Representation of Subsurface Scattering
We 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 real-world captured images demonstrate that the predict...
| Authors | Arjun Majumdar, Raphael Braun, Hendrik Lensch |
|---|---|
| Primary category | cs.CV |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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