  This paper makes a first attempt to bring the Shape from Polarization (SfP)
problem to the realm of deep learning. The previous state-of-the-art methods
for SfP have been purely physics-based. We see value in these principled
models, and blend these physical models as priors into a neural network
architecture. This proposed approach achieves results that exceed the previous
state-of-the-art on a challenging dataset we introduce. This dataset consists
of polarization images taken over a range of object textures, paints, and
lighting conditions. We report that our proposed method achieves the lowest
test error on each tested condition in our dataset, showing the value of
blending data-driven and physics-driven approaches.
