  We present a novel inverse rendering-based framework to estimate the 3D shape
(per-pixel surface normals and depth) of objects and scenes from single-view
polarization images, the problem popularly known as Shape from Polarization
(SfP). The existing physics-based and learning-based methods for SfP perform
under certain restrictions, i.e., (a) purely diffuse or purely specular
reflections, which are seldom in the real surfaces, (b) availability of the
ground truth surface normals for direct supervision that are hard to acquire
and are limited by the scanner's resolution, and (c) known refractive index. To
overcome these restrictions, we start by learning to separate the
partially-polarized diffuse and specular reflection components, which we call
reflectance cues, based on a modified polarization reflection model and then
estimate shape under mixed polarization through an inverse-rendering based
self-supervised deep learning framework called SS-SfP, guided by the
polarization data and estimated reflectance cues. Furthermore, we also obtain
the refractive index as a non-linear least squares solution. Through extensive
quantitative and qualitative evaluation, we establish the efficacy of the
proposed framework over simple single-object scenes from DeepSfP dataset and
complex in-the-wild scenes from SPW dataset in an entirely self-supervised
setting. To the best of our knowledge, this is the first learning-based
approach to address SfP under mixed polarization in a completely
self-supervised framework.
