Related Experiment Video
Updated: Dec 23, 2025
03:31
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
Published on: December 15, 2023
931
Deep Learning-Based Monocular Depth Estimation Methods-A State-of-the-Art Review
Faisal Khan1, Saqib Salahuddin1, Hossein Javidnia2
1College of Engineering and Informatics, National University Ireland Galway, Galway H91 TK33, Ireland.
Sensors (Basel, Switzerland)
|April 23, 2020
Summary
This survey reviews deep learning methods for monocular depth estimation from RGB images. It covers traditional techniques, datasets, and 13 state-of-the-art approaches for computer vision applications.
Area of Science:
- Computer Vision
- Deep Learning
Background:
- Monocular depth estimation from RGB images is a challenging, ill-posed problem.
- Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), dominates recent research.
Purpose of the Study:
- To provide a comprehensive overview of monocular depth estimation.
- To review and evaluate state-of-the-art DL approaches.
Main Methods:
- Survey of traditional depth estimation methods.
- Review and analysis of 13 deep learning-based monocular depth estimation approaches.
- Discussion of relevant datasets.
Main Results:
- Recent DL approaches, primarily CNNs, are the focus.
- Key methods, datasets, and challenges are presented.
- Comparative evaluation of 13 state-of-the-art techniques.
Conclusions:
- Deep learning significantly advances monocular depth estimation.
- Future research should address remaining challenges in the field.
- Applications include scene reconstruction, robotics, and autonomous driving.
Related Concept Videos
01:15Depth Perception and Spatial Vision
1.6K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.6K
01:18Uniform Depth Channel Flow: Problem Solving
376
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
376