期刊
SENSORS
卷 22, 期 14, 页码 -出版社
MDPI
DOI: 10.3390/s22145353
关键词
monocular depth estimation; single image depth estimation; deep learning; multi-task learning; supervised; semi-supervised; and unsupervised learning
资金
- Secretariad Universitatsi Recercadel Departamentd Empresai Coneixement de la Generalitat de Catalunya [2020 FISDU 00405]
This paper provides a state-of-the-art review of the current developments in monocular depth estimation (MDE) based on deep learning techniques. It highlights the key points from various aspects and discusses limitations and future research directions in the field.
In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem. The principal purpose of this paper is to represent a state-of-the-art review of the current developments in MDE based on deep learning techniques. For this goal, this paper tries to highlight the critical points of the state-of-the-art works on MDE from disparate aspects. These aspects include input data shapes and training manners such as supervised, semi-supervised, and unsupervised learning approaches in combination with applying different datasets and evaluation indicators. At last, limitations regarding the accuracy of the DL-based MDE models, computational time requirements, real-time inference, transferability, input images shape and domain adaptation, and generalization are discussed to open new directions for future research.
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