Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Deer Antler Extract Promotes Osteoblast Differentiation and Improves Bone Microarchitecture in an Ovariectomy-Induced Osteoporosis Model.

Preventive nutrition and food science·2026

Same author

Lung Microbiome Dysbiosis in Pulmonary Fibrosis Induced by Multi-Walled Carbon Nanotubes and Bleomycin in Rats.

Medicina (Kaunas, Lithuania)·2026

Same author

Epidemiological surveillance and genomic characterisation of betacoronavirus in <i>Apodemus agrarius</i> from Gyeonggi and Gangwon Provinces<i>,</i> Republic of Korea, 2017-2019.

One health (Amsterdam, Netherlands)·2026

Same author

American Ginseng (<i>Panax quinquefolius</i>) Extracts (G1899) Ameliorate Immunosenescence via Regulation of T Cell Populations and Aging-Related Proteins in a Mouse Model Induced by D-Galactose and Tert-Butyl Hydroperoxide.

Current issues in molecular biology·2026

Same author

Fuzzy C-means clustering based vertical container stacking in container terminals.

Scientific reports·2026

Same author

Dammarenediol II enhances etoposide-induced apoptosis by targeting O-GlcNAc transferase and Akt/GSK3β/mTOR signaling in liver cancer.

Molecular oncology·2025

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 22, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

A Novel Method for Estimating Monocular Depth Using Cycle GAN and Segmentation.

Dong-Hoon Kwak¹, Seung-Ho Lee²

¹Department of Electronic Engineering, Hanbat National University, Daejeon 34158, Korea.

Sensors (Basel, Switzerland)

|May 6, 2020

Summary

This summary is machine-generated.

We developed a new method for monocular depth estimation using cycle generative adversarial networks (GANs) and segmentation. This approach improves depth estimation accuracy for 3D imaging applications.

Keywords:

adversarial loss cycle GAN cycle consistency loss monocular depth estimation multi-task learning segmentation

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Published on: March 7, 2017

Related Experiment Videos

Last Updated: Dec 22, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

Published on: March 7, 2017

Area of Science:

Computer Vision
Artificial Intelligence

Background:

Three-dimensional (3D) images with spatial information are crucial for virtual reality, augmented reality (AR), and autonomous driving.
Accurate depth estimation from single images (monocular depth estimation or MDE) is a challenging but vital task.

Purpose of the Study:

To propose a novel method for monocular depth estimation by integrating segmentation with cycle generative adversarial networks (GANs).
To enhance the accuracy and efficiency of depth estimation for 3D image processing applications.

Main Methods:

The proposed method combines segmentation and depth estimation processes.
It utilizes adversarial loss calculations to train the network.
Cycle consistency loss is employed to evaluate image similarity after estimation and restoration.

Main Results:

The proposed method was compared against existing monocular depth estimation techniques using the NYU Depth Dataset V2.
Our approach demonstrated superior performance, achieving better benchmark values than other methods.
The results indicate increased efficiency in depth estimation.

Conclusions:

The novel method effectively estimates monocular depth by combining segmentation and cycle GANs.
This technique offers a more efficient and reliable solution for depth estimation in 3D imaging.
The findings support the utility of this method in AR, VR, and autonomous driving.