Monocular depth estimation based on a single image: a literature review

Yuan Tian; Xiaodong Hu

doi:10.1117/12.2589510

27 January 2021 Monocular depth estimation based on a single image: a literature review

Yuan Tian, Xiaodong Hu

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117201Z (2021) https://doi.org/10.1117/12.2589510
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Monocular depth estimation is a very valuable but also very challenging problem. In order to solve this ill-posed problem, traditional approaches apply depth cues such as defocusing, atmospheric scattering and shading to estimate depth information and approaches based on machine learning apply frameworks such as MRF and data-driven learning. With the development of deep learning, the monocular depth estimation approaches based on CNN and other networks have achieved good results and gradually become the mainstream. In this paper, we summarize some typical and representative literature on monocular depth estimation based on a single image in the past two decades and depict our analysis involved in these approaches. In addition, this paper also analyzes and compares the results obtained by some typical approaches, which may provide some guidance for those who are interested in this field.

Citation Download Citation

Yuan Tian and Xiaodong Hu "Monocular depth estimation based on a single image: a literature review", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117201Z (27 January 2021); https://doi.org/10.1117/12.2589510

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