CVPR 2023 - DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

In this episode we discuss DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering by Zongrui Li, Qian Zheng, Boxin Shi, Gang Pan, Xudong Jiang. The paper proposes a deep learning approach, called DANI-Net, to solve the challenging problem of uncalibrated photometric stereo (UPS) which is complicated by unknown lighting. UPS is particularly difficult for non-Lambertian objects with complex shapes and irregular shadows, and for general materials with complex reflectance such as anisotropic reflectance. Unlike previous methods that use non-differentiable shadow maps and assume isotropic material, DANI-Net benefits from cues of shadow and anisotropic reflectance through two differentiable paths, resulting in superior and robust performance on multiple real-world datasets.