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This paper presents an uncalibrated deep neural network framework for thephotometric stereo problem. For training models to solve the problem, existingneural network-based methods either require exact light directions orground-truth surface normals of the object or both. However, in practice, it ischallenging to procure both of this information precisely, which restricts thebroader adoption of photometric stereo algorithms for vision application. Tobypass this difficulty, we propose an uncalibrated neural inverse renderingapproach to this problem. Our method first estimates the light directions fromthe input images and then optimizes an image reconstruction loss to calculatethe surface normals, bidirectional reflectance distribution function value, anddepth. Additionally, our formulation explicitly models the concave and convexparts of a complex surface to consider the effects of interreflections in theimage formation process. Extensive evaluation of the proposed method on thechallenging subjects generally shows comparable or better results than thesupervised and classical approaches.

Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces