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Feature-preserving simplification framework for 3D point cloud.

Xueli Xu1,2,3, Kang Li4,5, Yifei Ma1,3

  • 1School of Information Science and Technology, Northwest University, Xi'an, 710127, Shaanxi, China.

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This study introduces a novel point cloud simplification framework that effectively preserves geometric details while achieving high simplification rates. The method uses deep neural networks to extract features from multi-angle images, enabling accurate point cloud simplification.

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Area of Science:

  • Computer Vision
  • Geometric Modeling
  • 3D Data Processing

Background:

  • Point cloud data is crucial in various fields but often requires simplification for efficient processing.
  • Existing simplification methods may struggle to balance simplification rate with geometric feature preservation.
  • Accurate extraction and preservation of geometric features are essential for meaningful point cloud analysis.

Purpose of the Study:

  • To develop a point cloud simplification framework that maximizes simplification rate while retaining critical geometric features.
  • To introduce a novel method for automatic feature point extraction in point clouds.
  • To evaluate the proposed method against existing algorithms for point cloud simplification.

Main Methods:

  • Generating multi-angle images of the original point cloud using a virtual camera.
  • Extracting 2D feature lines from images via a deep neural network.
  • Establishing a mapping relationship between 2D feature lines and the original point cloud for automatic feature point extraction.
  • Fusing extracted feature points with simplified non-feature points to create the final simplified point cloud.

Main Results:

  • The proposed framework successfully simplifies point clouds.
  • Experimental results on four datasets show superior performance compared to six other algorithms.
  • The method demonstrates effectiveness in retaining geometric features during simplification.
  • High simplification rates were achieved without compromising geometric integrity.

Conclusions:

  • The developed point cloud simplification framework offers a superior approach for balancing simplification rate and geometric feature preservation.
  • The integration of deep learning for feature extraction significantly enhances the accuracy of point cloud simplification.
  • This method provides a valuable tool for efficient processing of 3D point cloud data in various applications.