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Uncalibrated photometric stereo (UPS) is challenging due to the inherentambiguity brought by the unknown light. Although the ambiguity is alleviated onnon-Lambertian objects, the problem is still difficult to solve for moregeneral objects with complex shapes introducing irregular shadows and generalmaterials with complex reflectance like anisotropic reflectance. To exploitcues from shadow and reflectance to solve UPS and improve performance ongeneral materials, we propose DANI-Net, an inverse rendering framework withdifferentiable shadow handling and anisotropic reflectance modeling. Unlikemost previous methods that use non-differentiable shadow maps and assumeisotropic material, our network benefits from cues of shadow and anisotropicreflectance through two differentiable paths. Experiments on multiplereal-world datasets demonstrate our superior and robust performance.

DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering