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Potential Due to a Polarized Object01:29

Potential Due to a Polarized Object

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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
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Reconstruction of Signal using Interpolation01:10

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Related Experiment Video

Updated: Aug 2, 2025

Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons
07:39

Determination of the Excitation and Coupling Rates Between Light Emitters and Surface Plasmon Polaritons

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Polarized Object Surface Reconstruction Algorithm Based on RU-GAN Network.

Xu Yang1, Cai Cheng1, Jin Duan1

  • 1College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for 3D object reconstruction using only polarized images, resolving surface normal vector ambiguity with a jump-compensated generative adversarial network (RU-Gan). This method enhances accuracy and simplifies 3D reconstruction.

Keywords:
polarization imagesreflective region localizationspecular reflection modelsurface normal vectorthree-dimensional reconstruction

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Area of Science:

  • Computer Vision
  • 3D Reconstruction
  • Photogrammetry

Background:

  • Ambiguity in surface normal vectors from polarization data complicates 3D reconstruction.
  • Existing methods often require additional data (e.g., shading), increasing computational burden.
  • Need for a more general and efficient 3D reconstruction approach.

Purpose of the Study:

  • To propose a comprehensive framework for 3D object surface reconstruction using solely polarized images.
  • To address and resolve the inherent ambiguity in surface normal vector estimation.
  • To improve the accuracy and applicability of 3D reconstruction techniques.

Main Methods:

  • Development of a jump-compensated U-shaped generative adversarial network (RU-Gan) to fuse six possible surface normal vectors.
  • Implementation of jump compensation in encoder and decoder modules.
  • Reconstruction of a content loss function and optimization of datasets using a specular reflection model to mitigate reflective regions.

Main Results:

  • The proposed RU-Gan framework significantly improves normal vector estimation accuracy.
  • Achieved approximately 20° improvement over conventional methods and 1.5° over recent neural network models.
  • Demonstrated high accuracy in reconstructed texture and simple implementation conditions.

Conclusions:

  • The novel RU-Gan network is highly suitable for normal vector estimation in 3D reconstruction.
  • The proposed framework offers a simplified and accurate method for 3D object surface reconstruction from polarized images.
  • This approach advances the field by enabling robust 3D reconstruction with reduced complexity.