  RGB-D data is essential for solving many problems in computer vision.
Hundreds of public RGB-D datasets containing various scenes, such as indoor,
outdoor, aerial, driving, and medical, have been proposed. These datasets are
useful for different applications and are fundamental for addressing classic
computer vision tasks, such as monocular depth estimation. This paper reviewed
and categorized image datasets that include depth information. We gathered 203
datasets that contain accessible data and grouped them into three categories:
scene/objects, body, and medical. We also provided an overview of the different
types of sensors, depth applications, and we examined trends and future
directions of the usage and creation of datasets containing depth data, and how
they can be applied to investigate the development of generalizable machine
learning models in the monocular depth estimation field.
