Deep Polarization Cues for Single-shot Shape and Subsurface Scattering Estimation

Trung Thanh Ngo, Hajime Nagahara, Chenhao Li

Abstract:

In this work, we propose a novel learning-based method to
jointly estimate the shape and subsurface scattering (SSS) parameters of
translucent objects by utilizing polarization cues. Although polarization
cues have been used in various applications, such as shape from polarization (SfP), BRDF estimation, and reflection removal, their application
in SSS estimation has not yet been explored. Our observations indicate
that the SSS affects not only the light intensity but also the polarization signal. Hence, the polarization signal can provide additional cues for
SSS estimation. We also introduce the first large-scale synthetic dataset
of polarized translucent objects for training our model. Our method outperforms several baselines from the SfP and inverse rendering realms on
both synthetic and real data, as demonstrated by qualitative and quantitative results.