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Feature Line Extraction from Point Clouds Based on Geometric Structure of Point Space

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3D Research

Abstract

In order to improve the accuracy and rapidity of feature line extraction from point clouds, the work proposed a feature line extraction method based on geometric structure of point space. Firstly, a spatial grid dynamic division method is designed to locate the feature region of the model. A new feature points detection operator based on the linear intercept ratio is proposed according to the geometric information of points. Then, the feature points are refined by the Laplacian operator. Finally, the refined feature points are connected into the characteristic curve by the improved method of polyline growth. Compared with the feature points detection method based on surface variation (MSSV) or the angle of normal vector (SM-PD), the proposed method has low rate of error recognition with the increased noise intensity. Meanwhile, the computation time is 224.42 ms for the standard Armadillo model, less than 530.23 ms of the MSSV and 350.75 ms of the SM-PD. The experimental results show that the proposed method can accurately extract the feature points, with good noise immunity, especially suitable for the massive point cloud model.

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Abbreviations

PCA:

Principal component analysis

RANSAC:

Random Sample Consensus:

MSSV:

The method based on the multi-scale surface variation [5]

SM-PD:

Statistical model based on the Poisson distribution algorithm [16]

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Acknowledgements

Thank the anonymous reviewers for their comments and constructive suggestions. The Armadillo and Fandisk data were from The Stanford 3D Scanning Repository.

Funding

The work was funded by the National Natural Science Foundation of China through Projects (Grant Nos. 51065021, 51365037,and 51705229), and was supported in part by the Science and Technology Project of Jiangxi Education Department of China (GJJ181032).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Nanchang University, Nanchang, China

    Siyong Fu & Lushen Wu

  2. ZTE School of Communication and Information Engineering, Xinyu University, Xinyu, China

    Siyong Fu

Authors
  1. Siyong Fu
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  2. Lushen Wu
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Contributions

FSY conceived the original idea for the project and improved the algorithm; the first version of the paper is written by him. WLS confirmed the performance of the algorithm. All authors contributed to the helpful discussions and analyzed the results. Both authors read and approved the final manuscript.

Corresponding authors

Correspondence to Siyong Fu or Lushen Wu.

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Fu, S., Wu, L. Feature Line Extraction from Point Clouds Based on Geometric Structure of Point Space. 3D Res 10, 16 (2019). https://doi.org/10.1007/s13319-019-0227-x

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