Neural Acquisition & Representation of Subsurface Scattering
Loading...
Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
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 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.
Description
@inproceedings{10.2312:vmv.20251228,
booktitle = {Vision, Modeling, and Visualization},
editor = {Egger, Bernhard and Günther, Tobias},
title = {{Neural Acquisition & Representation of Subsurface Scattering}},
author = {Majumdar, Arjun and Braun, Raphael and Lensch, Hendrik},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {10.2312/vmv.20251228}
}