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

Triple Spectral Fusion for Sensor-based Human Activity Recognition.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Fully automated, deep learning, cardiac CT-based multimodal network for cardiovascular risk stratification in high-risk perioperative patients.

European heart journal. Digital health·2026

Same author

Trait Association for Flowering Time in Lentil from Global Multi-Environment Data Using GWAS and Machine Learning.

Plants (Basel, Switzerland)·2026

Same author

AquaticCLIP: A Vision-Language Foundation Model and Dataset for Underwater Scene Analysis.

IEEE transactions on neural networks and learning systems·2026

Same author

Unleashing the Power of Text-to-Image Diffusion Models for Category-Agnostic Pose Estimation.

IEEE transactions on pattern analysis and machine intelligence·2026

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: Dec 5, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation.

Hamid Laga, Laurent Valentin Jospin, Farid Boussaid

IEEE Transactions on Pattern Analysis and Machine Intelligence

|October 20, 2020

Summary

This summary is machine-generated.

Deep learning significantly improves stereo matching for depth estimation from RGB images, overcoming limitations of traditional methods. This survey covers recent advancements and future directions in this rapidly evolving field.

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

Related Experiment Videos

Last Updated: Dec 5, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

Area of Science:

Computer Vision
Machine Learning
3D Graphics

Background:

Estimating depth from RGB images is a challenging, ill-posed problem.
Stereo matching, inspired by human vision, is a common approach.
Traditional stereo methods struggle with texture, uniform regions, and occlusions.

Purpose of the Study:

To provide a comprehensive survey of deep learning for stereo-based depth estimation.
To summarize common deep learning pipelines, their benefits, and limitations.
To discuss future research directions in this domain.

Main Methods:

Reviewing over 150 papers published between 2014 and 2019 on deep learning for stereo depth estimation.
Analyzing and categorizing various deep learning architectures and techniques.
Comparing performance and identifying challenges of different methods.

Main Results:

Deep learning methods show significant performance improvements over traditional techniques.
These advancements enable new applications in autonomous driving and augmented reality.
The field has rapidly grown, with continuous innovation.

Conclusions:

Deep learning has revolutionized stereo-based depth estimation.
Further research is needed to address remaining challenges and explore future potential.
The survey provides a valuable overview for researchers and practitioners.