A Survey on RGB-D Datasets

TL;DR

This survey reviews 203 RGB-D datasets across various categories, analyzing their types, applications, and trends to support the development of generalizable depth estimation models in computer vision.

Contribution

It categorizes and summarizes existing RGB-D datasets, providing insights into their applications, sensor types, and future directions for research and model development.

Findings

Datasets are grouped into scene/objects, body, and medical categories.

Trends indicate increasing use of RGB-D data in diverse applications.

Future directions include improving dataset diversity and sensor technology.

Abstract

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.