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HSPC-Net: A hierarchical shape-preserving completion network for machine part point cloud completion.

Yuchao Jiang1, Honghui Fan1, Hongjin Zhu1

  • 1Jiangsu University of Technology, Changzhou, Jiangsu, China.

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|August 11, 2025
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Summary
This summary is machine-generated.

This study introduces a novel Hierarchical Shape-Preserving Completion Network (HSPC-Net) for reconstructing 3D mechanical components from sparse point cloud data. The method effectively restores missing geometric details using 2D image assistance, improving accuracy in industrial applications.

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Area of Science:

  • Computer Vision
  • Geometric Deep Learning
  • 3D Reconstruction

Background:

  • 3D scanning generates point cloud data crucial for industrial design, manufacturing, and repair.
  • Point cloud data often suffers from sparsity and missing information due to scanning limitations, especially for complex mechanical shapes.
  • Accurate data completion is vital for preserving global morphology and local details.

Purpose of the Study:

  • To propose a novel method for the accurate completion of sparse 3D point cloud data of mechanical components.
  • To enhance the recovery of missing geometric details while maintaining structural consistency.
  • To improve the robustness and accuracy of 3D point cloud completion.

Main Methods:

  • Introduced the Hierarchical Shape-Preserving Completion Network (HSPC-Net).
  • Integrated a multi-receptive field Transformer with cross-modal geometric information fusion.
  • Leveraged 2D image information to aid in 3D point cloud completion.

Main Results:

  • HSPC-Net demonstrated superior performance in completing sparse 3D point clouds compared to existing methods.
  • The network achieved high accuracy in restoring both global shape and intricate local details.
  • Experimental results confirmed enhanced completion accuracy, structural consistency, and detail recovery on benchmark datasets.

Conclusions:

  • HSPC-Net offers an effective solution for the challenging problem of 3D point cloud completion for mechanical components.
  • The proposed method successfully addresses limitations in scanning precision and acquisition conditions.
  • This approach significantly advances the state-of-the-art in 3D data recovery for industrial applications.