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

Comparison of decarboxylation rates of acidic cannabinoids between secretory cavity contents and air-dried inflorescence extracts in Cannabis sativa cv. 'Cherry Wine'.

Scientific reports·2024

Same author

Degeneration of oil bodies by rough endoplasmic reticulum -associated protein during seed germination in <i>Cannabis sativa</i>.

AoB PLANTS·2023

Same author

Multichannel One-Dimensional Data Augmentation with Generative Adversarial Network.

Sensors (Basel, Switzerland)·2023

Same author

Role of polyvinylpyrrolidone in the electrochemical performance of Li<sub>2</sub>MnO<sub>3</sub> cathode for lithium-ion batteries.

RSC advances·2022

Same author

TiO<sub>2</sub>-coated LiCoO<sub>2</sub> electrodes fabricated by a sputtering deposition method for lithium-ion batteries with enhanced electrochemical performance.

RSC advances·2022

Same author

Field-of-view enhanced integral imaging with dual prism arrays based on perspective-dependent pixel mapping.

Optics express·2022

Same journal

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor.

Optics express·2026

Same journal

Robust 3D topography measurement of large-range high-aspect-ratio structures based on dual-domain statistical filtering in SD-OCT.

Optics express·2026

Same journal

Broadband transmissive terahertz metasurface for simultaneous quad-mode OAM multiplexing.

Optics express·2026

Same journal

Leveraging two-dimensional materials for high-sensitivity optical sensors: quasi-bound states in the continuum within hybrid metasurfaces.

Optics express·2026

Same journal

Resolution investigation for dual-spherical-wave optical scanning holographic microscopy: methods and performance.

Optics express·2026

Same journal

Robustness of parallel subnetwork-filtered diffractive deep neural networks.

Optics express·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 16, 2026

Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

Published on: November 21, 2016

Depth extraction of 3D objects using axially distributed image sensing.

Suk-Pyo Hong¹, Donghak Shin, Byung-Gook Lee

¹HoloDigilog Human Media Research Center(HoloDigilog), 3D Display Research Center(3DRC), Kwangwoon University, Wolgye-Dong, Nowon-Gu, Seoul 139-701, South Korea.

|November 29, 2012

Summary

This summary is machine-generated.

This study introduces a novel computational method for extracting 3D object depth using axially distributed image sensing (ADS). The technique reconstructs 3D images and employs block comparison for robust depth extraction.

More Related Videos

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Published on: April 26, 2016

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Related Experiment Videos

Last Updated: May 16, 2026

Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

Published on: November 21, 2016

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Published on: April 26, 2016

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Area of Science:

Optics and Photonics
Computer Vision
3D Imaging

Background:

Axially distributed image sensing (ADS) enables 3D object capture and high-resolution slice image reconstruction.
Existing methods for depth extraction from 3D data can be computationally intensive.

Purpose of the Study:

To propose a novel computational method for accurate depth extraction of 3D objects utilizing the ADS technique.
To develop a robust and computationally simple algorithm for depth determination.

Main Methods:

Elemental images of 3D objects were captured by moving a camera along the optical axis.
3D slice images were reconstructed using a computational algorithm based on ray back-projection.
A simple block comparison algorithm between the first elemental image and reconstructed 3D slice images was developed for depth extraction.

Main Results:

The proposed method successfully extracts depth information from 3D objects.
Preliminary experiments demonstrated the method's effectiveness across three different scenarios.
The block comparison algorithm proved to be computationally simple and robust.

Conclusions:

This research presents the first reported method for depth extraction using axially distributed image sensing (ADS).
The developed computational approach offers a straightforward and reliable way to obtain depth information from 3D objects.