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Multi-level k -nearest neighbors algorithm for direct point cloud-based engineering analysis.

Ashton M Corpuz1, Monu Jaiswal1, Ming-Chen Hsu1

  • 1Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA.

Computer Methods in Applied Mechanics and Engineering
|April 24, 2026
PubMed
Summary
This summary is machine-generated.

A new multi-level k-nearest neighbors (M-kNN) method improves point cloud processing for engineering analysis. This approach avoids manual tuning and mesh reconstruction, enabling direct use of raw point clouds.

Keywords:
CFDhemodynamicsimmersedk-nearest neighborspoint cloudreconstruction

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Area of Science:

  • Computational geometry
  • Computer-aided engineering
  • Geometric modeling

Background:

  • Point clouds are vital for geometric modeling in science and engineering due to advanced scanning technologies.
  • The unstructured nature of point clouds poses challenges for direct engineering analysis, often necessitating mesh reconstruction.
  • Existing mesh reconstruction methods, including k-nearest neighbors (kNN), require manual parameter tuning for complex or noisy data.

Purpose of the Study:

  • To develop an automated and robust method for processing point cloud data without manual intervention.
  • To enhance the accuracy of geometric processing and enable direct analysis of raw point clouds in engineering applications.
  • To overcome limitations of traditional kNN algorithms in handling varying point cloud densities and topological complexities.

Main Methods:

  • Introduction of a novel multi-level k-nearest neighbors (M-kNN) approach.
  • Iterative expansion of local neighborhoods to determine surface connectivity from point cloud data.
  • Application of M-kNN for point cloud resampling and geometry processing, particularly for complex structures.

Main Results:

  • M-kNN demonstrates improved point cloud resampling and more accurate geometry processing, especially for intricate geometries.
  • The method effectively handles noisy, low-density, and topologically ambiguous point clouds without manual tuning.
  • Validation on both synthetic and real-world datasets confirms the efficacy of the M-kNN approach.

Conclusions:

  • The M-kNN approach offers a significant advancement in processing point cloud data for engineering analysis.
  • It enables the direct utilization of raw point clouds, eliminating the need for mesh reconstruction and manual parameter adjustments.
  • This method enhances the flexibility and accuracy of geometric modeling and analysis in various scientific and engineering fields.