  Photometric stereo recovers the surface normals of an object from multiple
images with varying shading cues, i.e., modeling the relationship between
surface orientation and intensity at each pixel. Photometric stereo prevails in
superior per-pixel resolution and fine reconstruction details. However, it is a
complicated problem because of the non-linear relationship caused by
non-Lambertian surface reflectance. Recently, various deep learning methods
have shown a powerful ability in the context of photometric stereo against
non-Lambertian surfaces. This paper provides a comprehensive review of existing
deep learning-based calibrated photometric stereo methods. We first analyze
these methods from different perspectives, including input processing,
supervision, and network architecture. We summarize the performance of deep
learning photometric stereo models on the most widely-used benchmark data set.
This demonstrates the advanced performance of deep learning-based photometric
stereo methods. Finally, we give suggestions and propose future research trends
based on the limitations of existing models.
