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Feature-based point cloud simplification method: an effective solution for balancing accuracy and efficiency

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Abstract

Traditional point cloud simplification methods are slow to process large point clouds and prone to losing small features, which leads to a large loss of point cloud accuracy. In this paper, a new point cloud simplification method using a three-step strategy is proposed, which realizes efficient reduction of large point clouds while preserving fine features through point cloud down-sampling, normal vector calibration, and feature extraction based on the proposed feature descriptors and neighborhood subdivision strategy. In this paper, we validate the method using measured point clouds of large co-bottomed component surfaces, visualize the errors, and compare it with other methods. The results demonstrate that this method is well-suited for efficiently reducing large point clouds, even those on the order of ten million points, while maintaining high accuracy in feature retention, refinement precision, efficiency, and robustness to noise.

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Acknowledgements

This research was supported by the National Key Research and Development Program of China(2022YFB3404700), the Chinese Fundamental Research Funds for the Central Universities under Grant DUT22LAB505, the Changjiang Scholar Program of Chinese Ministry of Education (No. Q2021053, TE2022037).

Author information

Authors and Affiliations

  1. State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China

    Jiangsheng Wu, Xingliang Chai, Haibo Liu & Yongqing Wang

  2. Beijing Satellite Manufacturing Factory, Beijing, 100086, China

    Xiaoming Lai, Kai Yang & Tianming Wang

Authors
  1. Jiangsheng Wu
  2. Xiaoming Lai
  3. Xingliang Chai
  4. Kai Yang
  5. Tianming Wang
  6. Haibo Liu
  7. Yongqing Wang

Contributions

Haibo Liu, Yongqing Wang, and Xiaoming Lai contributed the central idea, Jiangsheng Wu and Xingliang Chai established the theoretical model and wrote the initial draft of the paper. Te Li and Yongqing Wang designed the experiment and analyzed most of the data, Chenglong Wang and JianChi Yu performed the experiment operation.

Corresponding author

Correspondence toXingliang Chai.

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The authors declare no competing interests.

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Wu, J., Lai, X., Chai, X.et al. Feature-based point cloud simplification method: an effective solution for balancing accuracy and efficiency.J Supercomput80, 14120–14142 (2024). https://doi.org/10.1007/s11227-024-06019-7

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