  Uncalibrated photometric stereo (UPS) is challenging due to the inherent
ambiguity brought by the unknown light. Although the ambiguity is alleviated on
non-Lambertian objects, the problem is still difficult to solve for more
general objects with complex shapes introducing irregular shadows and general
materials with complex reflectance like anisotropic reflectance. To exploit
cues from shadow and reflectance to solve UPS and improve performance on
general materials, we propose DANI-Net, an inverse rendering framework with
differentiable shadow handling and anisotropic reflectance modeling. Unlike
most previous methods that use non-differentiable shadow maps and assume
isotropic material, our network benefits from cues of shadow and anisotropic
reflectance through two differentiable paths. Experiments on multiple
real-world datasets demonstrate our superior and robust performance.
