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Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Related Experiment Video

Updated: Aug 31, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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A Meta-Device for Intelligent Depth Perception.

Mu Ku Chen1,2,3, Xiaoyuan Liu1, Yongfeng Wu1

  • 1Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, 999077, P. R. China.

Advanced Materials (Deerfield Beach, Fla.)
|August 20, 2022
PubMed
Summary
This summary is machine-generated.

This study presents a compact meta-device for accurate depth sensing, overcoming limitations of traditional imaging. It utilizes a meta-lens array and deep learning for reliable 3D information in various lighting conditions.

Keywords:
deep learningdepth perceptionlight field imagingmeta-lensneural networksstructured light

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Area of Science:

  • Optics
  • Computer Vision
  • Materials Science

Background:

  • Optical illusions and limited light-field data from single-lens imaging impede accurate depth sensing.
  • Incomplete depth information leads to cognitive errors and visual deception.
  • Existing depth-sensing technologies often lack miniaturization and versatility across different lighting conditions.

Purpose of the Study:

  • To develop an intelligent, compact, and integrated meta-device for robust depth sensing.
  • To enable depth measurement across diverse scenes and all light levels.
  • To provide a versatile solution for advanced imaging applications.

Main Methods:

  • Demonstration of a compact, multifunction stereo vision system utilizing an array of 3600 achromatic meta-lenses (1.2 × 1.2 mm²).
  • The meta-lens array functions as multiple imaging lenses for light-field information collection or as an active optical device for structured light projection.
  • Deep learning, specifically a convolutional neural network, is employed for analyzing and extracting depth information.

Main Results:

  • The meta-device successfully measures depth over a 30 cm range.
  • It operates effectively as both a light-field imaging system in bright conditions and a structured-light projection system in the dark.
  • Depth information is accurately extracted using the convolutional neural network.

Conclusions:

  • The demonstrated meta-device offers a novel approach to intelligent and compact depth sensing.
  • Its multifunctionality and performance in varied lighting conditions address key limitations of current technologies.
  • This advancement paves the way for enhanced applications in autonomous driving, augmented reality, and machine vision.