We present a novel method for efficient acquisition of shape and spatially
varying reflectance of 3D objects using polarization cues. Unlike previous
works that have exploited polarization to estimate material or object
appearance under certain constraints (known shape or multiview acquisition), we
lift such restrictions by coupling polarization imaging with deep learning to
achieve high quality estimate of 3D object shape (surface normals and depth)
and SVBRDF using single-view polarization imaging under frontal flash
illumination. In addition to acquired polarization images, we provide our deep
network with strong novel cues related to shape and reflectance, in the form of
a normalized Stokes map and an estimate of diffuse color. We additionally
describe modifications to network architecture and training loss which provide
further qualitative improvements. We demonstrate our approach to achieve
superior results compared to recent works employing deep learning in
conjunction with flash illumination.

本文提出了一种使用偏振线索高效获取物体形状和空间变化反射的新方法，其中偏振成像与深度学习相结合，可以在正面闪光照射下使用单视角偏振成像实现高质量估计物体形状和反射。