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：无标定光度立体视觉（UPS）因未知光照带来的固有歧义而极具挑战性。尽管非朗伯物体上的此类歧义有所缓解，但对于具有复杂形状（引入不规则阴影）及各向异性反射等复杂材质的一般性物体，该问题仍难以求解。为利用阴影与反射线索求解UPS并提升一般性材质的性能，我们提出DANI-Net——一种集成可微分阴影处理与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非可微分阴影图并假设各向同性材质，本网络通过两条可微分路径从阴影及各向异性反射线索中获益。在多个真实世界数据集上的实验表明，该方法具有优越且鲁棒的性能。