Towards Real-Time Monocular Depth Estimation for Robotics: A Survey

TL;DR

This survey comprehensively reviews 197 articles on monocular depth estimation, covering methods, datasets, evaluation metrics, open-source implementations, and robotic applications, highlighting future research directions.

Contribution

It provides the first extensive overview of MDE, including performance comparisons, datasets, open-source tools, and applications in robotics, filling a significant gap in the literature.

Findings

Summarizes 197 MDE methods and datasets.

Analyzes performance evaluation metrics.

Highlights open-source implementations and robotic applications.

Abstract

As an essential component for many autonomous driving and robotic activities such as ego-motion estimation, obstacle avoidance and scene understanding, monocular depth estimation (MDE) has attracted great attention from the computer vision and robotics communities. Over the past decades, a large number of methods have been developed. To the best of our knowledge, however, there is not a comprehensive survey of MDE. This paper aims to bridge this gap by reviewing 197 relevant articles published between 1970 and 2021. In particular, we provide a comprehensive survey of MDE covering various methods, introduce the popular performance evaluation metrics and summarize publically available datasets. We also summarize available open-source implementations of some representative methods and compare their performances. Furthermore, we review the application of MDE in some important robotic tasks. Finally, we conclude this paper by presenting some promising directions for future research. This survey is expected to assist readers to navigate this research field.