Estimating the surface of translucent objects from photometric data poses significant challenges due to complex internal light scattering. We introduce a novel method that computes a depth map from single-viewpoint photographs of a material sample, captured under multiple illuminations. Our approach leverages inverse rendering to derive a volumetric representation, including density, albedo, and phase function, from which a surface mesh is reconstructed. Beyond validation with synthetic and 3D-printed physical models, we illustrate our technique's power by successfully applying it to the digitization of fabrics, a notoriously difficult material due to its intricate translucent structure. This work advances the state-of-the-art texture stack acquisition via enhanced surface reconstruction.
@article{Sagredo:2026:SurfaceEstimation,
title = {{ Surface estimation of translucent materials: an application to fabric digitization }},
author = {Sagredo, Diego and Fabre, Javier and Lopez-Moreno, Jorge},
year = 2026,
month = mar,
journal = {IEEE Transactions on Visualization \& Computer Graphics},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
number = {01},
pages = {1--12},
doi = {10.1109/TVCG.2026.3676259},
issn = {1941-0506},
url = {https://doi.ieeecomputersociety.org/10.1109/TVCG.2026.3676259}
}