Chinese Journal of Lasers, Volume. 46, Issue 4, 0404009(2019)

Point Cloud Feature Regularization Based on Fusion of Improved Field Force and Judging Criterion

Qing Liu*, Guang Zhang, and Xijiang Chen
Author Affiliations
  • School of Resources and Environmental, Wuhan University of Technology, Wuhan, Hubei 430079, China
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    Figures & Tables(15)
    Schematic of spatial dynamic grid Q
    Schematic of projection of Pi and k neighbor points onto their micro-cut plane
    Local two-dimensional coordinate system established in micro-cut plane
    Unitized projection vector. (a) Neighborhood points tilted to one side (boundary feature point); (b) neighborhood points distributed evenly (internal point)
    Using vector deflection angle to select next connection point
    Problems during vector deflection angle sorting. (a) Question 1; (b) question 2
    Schematic of traditional cubic B-spline algorithm
    Schematic of improved cubic B-spline algorithm
    Fitting connection comparison by three methods. (a) Circle; (b) sinusoid
    Partial extraction effect diagrams under different parameters. (a) k=30, ε=0.48; (b) k=30, ε=0.52; (c) k=28, ε=0.50; (d) k=30, ε=0.50
    Feature extraction comparison and fitting of point cloud model. (a)(f)(k) Original point clouds; (b)(g)(l) algorithmin Ref. [11]; (c)(h)(m) algorithm in Ref. [12]; (d)(i)(n) proposed algorithm; (e)(j)(o) feature regularization
    Feature extraction and fitting of Yangtze River Erqi Bridge. (a) Side view; (e) main view; (i) top view; (b)(f)(j) algorithm in Ref. [12]; (c)(g)(k) proposed algorithm; (d)(h)(l) feature regularization
    • Table 1. Extraction status of feature points and running time under different parameters

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      Table 1. Extraction status of feature points and running time under different parameters

      εk=26k=28k=30
      Feature pointnumberRunningtime /msFeature pointnumberRunningtime /msFeature pointnumberRunningtime /ms
      0.48378304135731414003216
      0.50321298730431253403244
      0.52271297225431232853198
    • Table 2. Extraction status of feature points and running time by two algorithms

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      Table 2. Extraction status of feature points and running time by two algorithms

      Sculpture point cloud modelOriginal point numberFeature point numberExtraction rate /%Running time /s
      Algorithm of Ref. [12]72902769710.618.34
      Proposed algorithm729021209516.612.57
    • Table 3. Deviations between randomly selected 40 points and boundary lines

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      Table 3. Deviations between randomly selected 40 points and boundary lines

      Positionμ /mm
      Bridge body0.04720.00630.03890.04120.06820.05610.10680.02310.05610.0968
      0.02100.01780.04920.06390.10780.11340.08920.06340.05480.1249
      Hook0.04370.08940.09470.10690.11270.03570.03680.04790.10180.0294
      0.04170.03960.10340.01470.10580.03690.06470.12490.10780.1095
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    Qing Liu, Guang Zhang, Xijiang Chen. Point Cloud Feature Regularization Based on Fusion of Improved Field Force and Judging Criterion[J]. Chinese Journal of Lasers, 2019, 46(4): 0404009

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    Paper Information

    Category: measurement and metrology

    Received: Oct. 22, 2018

    Accepted: Jan. 14, 2019

    Published Online: May. 9, 2019

    The Author Email:

    DOI:10.3788/CJL201946.0404009

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