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.

本篇论文提出一种基于反向渲染框架的可微分降噪网络 DANI-Net，该网络通过不同的可微分路径利用阴影和各向异性反射特征，有效地解决了非Lambertian对象和复杂反射材料所带来的影响，并在多个真实世界数据集上取得了优异的性能表现。

DANI-Net:通过可微分阴影处理、各向异性反射建模和神经反演渲染实施非标定光度立体成像