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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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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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Related Experiment Video

Updated: Jan 13, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

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

Published on: December 3, 2013

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Fringe-Based Structured-Light 3D Reconstruction: Principles, Projection Technologies, and Deep Learning Integration.

Zhongyuan Zhang1,2, Hao Wang1,2, Yiming Li2

  • 1Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary

This review compares Fringe Projection Profilometry (FPP) for diffuse surfaces and Phase Measuring Deflectometry (PMD) for specular surfaces. It highlights Micro-Electro-Mechanical Systems (MEMS) and deep learning for advanced 3D reconstruction.

Keywords:
3D measurementdeep learningfringe projection profilometryfringe structured lightphase measuring deflectometry

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Last Updated: Jan 13, 2026

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Area of Science:

  • Optics and Photonics
  • Computer Vision
  • Metrology

Background:

  • Structured-light 3D reconstruction is a key active measurement technique for capturing object geometry.
  • It is widely used in industrial inspection, cultural heritage, and virtual reality.
  • Existing reviews often lack systematic comparisons between major fringe-based methods like Fringe Projection Profilometry (FPP) and Phase Measuring Deflectometry (PMD).

Purpose of the Study:

  • To provide a comprehensive comparative analysis of mainstream fringe-based 3D reconstruction methods.
  • To clarify the impact of different projection schemes (e.g., Digital Light Processing (DLP), MEMS) on system performance.
  • To explore the integration of deep learning with FPP and PMD for enhanced accuracy.

Main Methods:

  • Systematic comparison of Fringe Projection Profilometry (FPP) and Phase Measuring Deflectometry (PMD).
  • Analysis of measurement principles, system implementation, calibration, and error control.
  • Investigation of projection technologies (DLP, MEMS) and deep learning integration.

Main Results:

  • FPP and PMD are compared across multiple technical dimensions.
  • The review clarifies the influence of projection schemes like DLP and MEMS.
  • Deep learning shows potential for improving phase retrieval and 3D reconstruction accuracy.

Conclusions:

  • Micro-Electro-Mechanical Systems (MEMS) offer potential for lightweight, high-dynamic-range measurements.
  • Deep learning is emerging as a crucial tool for enhancing 3D reconstruction.
  • Future research should focus on system modeling, intelligent reconstruction, and performance evaluation.