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Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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.

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Monocular Depth Estimation Using Deep Learning: A Review.

Armin Masoumian^1,2, Hatem A Rashwan¹, Julián Cristiano¹

¹Department of Computer Engineering and Mathematics, University of Rovira i Virgili, 43007 Tarragona, Spain.

Sensors (Basel, Switzerland)

|July 27, 2022

Summary

This summary is machine-generated.

This review explores deep learning for monocular depth estimation (MDE), a crucial computer vision task for robotics and autonomous systems. It examines various methods, datasets, and limitations to guide future research in accurate depth prediction.

Keywords:

deep learning monocular depth estimation multi-task learning single image depth estimation supervised, semi-supervised, and unsupervised learning

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Deep Neural Networks for Image-Based Dietary Assessment

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Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Precise depth measurements are increasingly vital due to advancements in robotics and autonomous vehicles.
Depth estimation (DE) is a fundamental computer vision task with applications in augmented reality and target tracking.
Traditional monocular DE (MDE) relies on depth cues but faces challenges as an ill-posed problem.

Purpose of the Study:

To provide a state-of-the-art review of deep learning techniques for monocular depth estimation (MDE).
To highlight critical aspects of current MDE research, including data inputs, training methodologies, and evaluation metrics.

Main Methods:

Review of deep learning approaches applied to monocular depth estimation.
Analysis of different input data shapes and training paradigms (supervised, semi-supervised, unsupervised).
Examination of various datasets and evaluation indicators used in MDE research.

Main Results:

Deep learning techniques show significant potential in addressing the ill-posed nature of MDE.
The review categorizes and analyzes current MDE works based on input, training, and evaluation strategies.

Conclusions:

Deep learning-based MDE models offer promising results but face limitations in accuracy, computational cost, and generalization.
Future research directions include improving model accuracy, reducing computational demands for real-time inference, and enhancing domain adaptation and transferability.