期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 10, 页码 16940-16961出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3160741
关键词
Estimation; Feature extraction; Robots; Cameras; Structure from motion; Three-dimensional displays; Task analysis; Monocular depth estimation; single image depth estimation; depth prediction; robotics; survey
资金
- University of New South Wales Tuition Fee Scholarship
This paper presents a comprehensive survey of monocular depth estimation (MDE), covering methods, performance evaluation metrics, datasets, and applications. It also summarizes open-source implementations of representative methods and discusses future research directions. The survey aims to assist readers in navigating this research field.
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.
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