Abstract
This paper proposes an uncalibrated photometric stereo
method for non-Lambertian scenes based on deep learning.
Unlike previous approaches that heavily rely on assumptions of specific reflectances and light source distributions,
our method is able to determine both shape and light directions of a scene with unknown arbitrary reflectances observed under unknown varying light directions. To achieve
this goal, we propose a two-stage deep learning architecture, called SDPS-Net, which can effectively take advantage
of intermediate supervision, resulting in reduced learning
difficulty compared to a single-stage model. Experiments
on both synthetic and real datasets show that our proposed
approach significantly outperforms previous uncalibrated
photometric stereo methods.