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The simplest case of a surface charge distribution is the uniformly charged disk. Calculating its electric field also helps us calculate the electric field of a large plane of charge.
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Related Experiment Video

Updated: Oct 12, 2025

Experimental Methods of Dust Charging and Mobilization on Surfaces with Exposure to Ultraviolet Radiation or Plasmas

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Published on: April 3, 2018

Point Cloud Resampling by Simulating Electric Charges on Metallic Surfaces.

Kyoungmin Han¹, Kyujin Jung¹, Jaeho Yoon²

¹Department of Electrical and Electronic Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Korea.

Sensors (Basel, Switzerland)

|November 27, 2021

Summary

This study introduces a novel 3D point cloud resampling algorithm using simulated electron repulsion forces for improved point distribution and stability. The method enhances uniformity and convergence for 3D data processing.

Keywords:

electric repulsion force local surface projection point cloud resampling

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Last Updated: Oct 12, 2025

Experimental Methods of Dust Charging and Mobilization on Surfaces with Exposure to Ultraviolet Radiation or Plasmas

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Published on: April 3, 2018

Finite Element Modelling of a Cellular Electric Microenvironment

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Published on: June 28, 2019

Area of Science:

Computational Geometry
Computer Graphics
Physics Simulation

Background:

3D point cloud resampling is a complex computational geometry problem.
Existing methods struggle with achieving uniform point distribution and stable convergence.

Purpose of the Study:

To develop a novel point cloud resampling algorithm inspired by electromagnetic repulsion forces.
To enhance the uniformity, convergence stability, and efficiency of 3D point cloud processing.

Main Methods:

Simulating electromagnetic repulsion forces between points treated as electrons on a virtual surface.
Incorporating acceleration and damping terms, akin to momentum methods in optimization.
Implementing a restriction method to keep points on the approximated local surface and projecting them afterward.
Approximating net repulsion forces using K-nearest neighbors for accelerated computation.

Main Results:

The algorithm achieves superior uniformization of point clouds compared to baseline methods.
Demonstrated enhanced convergence stability and improved run-time performance.
A new measurement criterion for evaluating point cloud uniformity was proposed and utilized.

Conclusions:

The proposed electron repulsion-based resampling algorithm effectively addresses challenges in 3D point cloud processing.
The method offers a promising approach for generating uniformly distributed and stable point clouds.
This technique shows significant advantages in uniformity, convergence, and computational efficiency.