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.

翻译：非标定光度立体视觉（UPS）因未知光照带来的固有歧义性而极具挑战。尽管非朗伯体物体可缓解该歧义，但对于具有复杂形状（引入不规则阴影）和复杂材料（如各向异性反射）的更通用物体，该问题仍难以求解。为利用阴影和反射线索解决UPS问题并提升对通用材料的性能，我们提出DANI-Net，一种集成可微阴影处理和各向异性反射建模的逆渲染框架。不同于多数先前方法采用不可微阴影映射并假设各向同性材料，我们的网络通过两条可微路径受益于阴影和各向异性反射的线索。在多个真实数据集上的实验证明了我们方法的优越性和鲁棒性。