  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.
