A Survey of Monocular Depth Estimation based on Deep Learning

Abstract

Depth information is very important for machines to perceive the environment and estimate their own state. Significant advances in robotics engineering and self-driving cars in recent decades have increased the demand for accurate depth measurements. Traditional depth estimation methods include motion structure and stereo vision matching, but these are based on the feature correspondence of multiple viewpoints, and at the same time, the predicted depth map is sparse. Depth estimation is a traditional task in computer vision that can be properly predicted by applying a variety of procedures, whereas inferring depth information from a single image is an ill-posed problem. The main objective of this paper is to provide a brief overview of the development of monocular depth estimation techniques based on deep learning. This article attempts to give an overview of supervised, unsupervised, and datasets and evaluation metrics. We conclude with a brief analysis of future developments.