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Surface Reconstruction Pattern Recognition Technology Based on Scattered Point Cloud Data.

Feng Zeng1

  • 1Computer School, JiaYing University, Meizhou, China.

Big Data
|July 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel surface reconstruction method for scattered point cloud data, enhancing pattern recognition for applications like reverse engineering and smart cities. The technique refines point clouds by reducing data and adapting to non-uniformity.

Keywords:
pattern recognition technologypoint cloud denoisingpoint cloud reductionscattered point cloud datasurface reconstruction

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Area of Science:

  • Computer Vision
  • Geometric Modeling
  • 3D Reconstruction

Background:

  • Surface reconstruction from point cloud data is crucial for reverse engineering, cultural heritage, and smart cities.
  • Existing methods face challenges with scattered and non-uniform point cloud data.

Purpose of the Study:

  • To develop an effective surface reconstruction pattern recognition technology for scattered point cloud data.
  • To improve the accuracy and efficiency of 3D surface reconstruction.

Main Methods:

  • Candidate feature points extracted based on surface variation.
  • Clustering plane fitting and feature point selection.
  • Area increase method for initial grid construction without explicit normal vector separation.
  • Projection parameterization for projecting points onto curved surfaces.
  • Local feature size for refining point cloud data.

Main Results:

  • The proposed method successfully reconstructs surfaces from scattered point clouds.
  • Refinement using local feature size reduces point cloud data and removes redundancy.
  • The technique allows for dynamic adjustment and adaptive reconstruction of non-uniform point clouds.

Conclusions:

  • The developed surface reconstruction pattern recognition technology offers a robust solution for scattered point cloud data.
  • This method enhances the quality and adaptability of 3D models for various applications.
  • Further research can explore optimizing parameters for diverse datasets.