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

Acceptability of long-acting injectables for TB preventative treatment among high-risk groups.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease·2026

Same author

Cavitary lung lesions and quality of life after TB.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease·2024

Same author

Low BMI increases all-cause mortality rates in patients with drug-resistant TB.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease·2022

Same author

Rates and risk factors for nephrotoxicity and ototoxicity among tuberculosis patients in Tbilisi, Georgia.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease·2019

Same author

IPT in people living with HIV in Myanmar: a five-fold decrease in incidence of TB disease and all-cause mortality.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease·2019

Same author

Convergence of non-communicable diseases and tuberculosis: a two-way street?

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease·2018

Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 29, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Experiments in intensity guided range sensing recognition of three-dimensional objects.

M J Magee¹, B A Boyter, C H Chien

¹Laboratory for Image and Signal Analysis, the University of Texas, Austin, TX 78712; Department of Computer Science, University of Wyoming, Laramie, WY 82071.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This summary is machine-generated.

This study introduces a novel method for 3-D object recognition by efficiently combining intensity and range imaging. This approach significantly reduces range sensing time while enabling accurate 3-D object descriptor construction and recognition.

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

Published on: October 18, 2024

Related Experiment Videos

Last Updated: May 29, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

Published on: October 18, 2024

Area of Science:

Computer Vision
Robotics
3-D Imaging

Background:

3-D object recognition is crucial in image processing.
Traditional range sensing is time-consuming.
Intensity images lack depth information for 3-D object descriptors.

Purpose of the Study:

To develop a method for efficient 3-D object recognition.
To reduce the time required for range sensing.
To combine intensity and range data for improved 3-D object descriptor construction.

Main Methods:

Extracting points of interest from intensity images.
Selectively sensing range data at feature points.
Constructing a graph structure from range data.
Comparing object graphs using partial matching algorithms.

Main Results:

Demonstrated a method to significantly reduce range sensing time.
Successfully constructed 3-D object descriptors by combining intensity and range data.
Validated the approach using synthetic and real-world intensity/range images.

Conclusions:

The proposed method offers an efficient way to perform 3-D object recognition.
Combining intensity and range sensing enhances descriptor accuracy and reduces acquisition time.
This technique is effective for both simulated and real-world 3-D imaging applications.