Department of Computer Engineering and Mathematics, University of Rovira i Virgili, 43007 Tarragona, Spain.
Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA.
Sensors (Basel). 2022 Jul 18;22(14):5353. doi: 10.3390/s22145353.
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.
在最近几十年，机器人工程和自动驾驶汽车领域的重大进展提高了对精确深度测量的要求。深度估计（Depth Estimation，DE）是计算机视觉中的一项传统任务，可以通过应用多种程序来进行适当的预测。这项任务在增强现实和目标跟踪等不同应用中至关重要。传统的单目深度估计（Monocular DE，MDE）程序基于深度线索进行深度预测。各种深度学习技术已证明它们在管理和支持传统不适定问题方面的潜在应用。本文的主要目的是代表基于深度学习技术的 MDE 最新进展的最新综述。为此，本文试图从不同方面突出 MDE 最新工作的关键点。这些方面包括输入数据的形状和训练方式，例如监督、半监督和无监督学习方法，以及结合不同的数据集和评估指标的应用。最后，讨论了基于深度学习的 MDE 模型的准确性、计算时间要求、实时推断、可转移性、输入图像形状和域自适应以及泛化方面的局限性，为未来的研究开辟了新的方向。
