This paper presents an uncalibrated deep neural network framework for the
photometric stereo problem. For training models to solve the problem, existing
neural network-based methods either require exact light directions or
ground-truth surface normals of the object or both. However, in practice, it is
challenging to procure both of this information precisely, which restricts the
broader adoption of photometric stereo algorithms for vision application. To
bypass this difficulty, we propose an uncalibrated neural inverse rendering
approach to this problem. Our method first estimates the light directions from
the input images and then optimizes an image reconstruction loss to calculate
the surface normals, bidirectional reflectance distribution function value, and
depth. Additionally, our formulation explicitly models the concave and convex
parts of a complex surface to consider the effects of interreflections in the
image formation process. Extensive evaluation of the proposed method on the
challenging subjects generally shows comparable or better results than the
supervised and classical approaches.

本文提出了一个针对光度立体问题的不需要校准的深度神经网络框架，通过估计输入图像的光线方向来计算表面法线、双向反射分布函数值和深度，同时显式模拟图像形成过程中的凹凸面部分的互反射效应，结果表明该方法在挑战性主题上表现优于监督和经典方法。