Next Article in Journal
A Class-J Power Amplifier Implementation for Ultrasound Device Applications
Next Article in Special Issue
Measurement for the Thickness of Water Droplets/Film on a Curved Surface with Digital Image Projection (DIP) Technique
Previous Article in Journal
Wireless Motion Sensors—Useful in Assessing the Effectiveness of Physiotherapeutic Methods Used in Patients with Knee Osteoarthritis—Preliminary Report
Previous Article in Special Issue
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera
Review

Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

1
College of Engineering and Informatics, National University Ireland Galway, Galway H91 TK33, Ireland
2
ADAPT Centre, Trinity College Dublin, Dublin D02 PN40, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(8), 2272; https://doi.org/10.3390/s20082272
Received: 27 February 2020 / Revised: 9 April 2020 / Accepted: 12 April 2020 / Published: 16 April 2020
(This article belongs to the Collection Camera as a Smart-Sensor (CaaSS))
Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth estimation mostly rely on Convolutional Neural Networks (CNN). Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed, evaluated and discussed. We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges. View Full-Text
Keywords: monocular depth estimation; single image depth estimation; CNN monocular depth monocular depth estimation; single image depth estimation; CNN monocular depth
Show Figures

Figure 1

MDPI and ACS Style

Khan, F.; Salahuddin, S.; Javidnia, H. Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review. Sensors 2020, 20, 2272. https://doi.org/10.3390/s20082272

AMA Style

Khan F, Salahuddin S, Javidnia H. Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review. Sensors. 2020; 20(8):2272. https://doi.org/10.3390/s20082272

Chicago/Turabian Style

Khan, Faisal, Saqib Salahuddin, and Hossein Javidnia. 2020. "Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review" Sensors 20, no. 8: 2272. https://doi.org/10.3390/s20082272

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop