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Laser & Optoelectronics Progress, Volume. 57, Issue 23, 231402(2020)

Point Cloud Simplification Optimization Strategy and Experimental Research Based on Multiple Algorithms

Raobo Li1, Xiping Yuan2,3, Shu Gan1,2、*, and Rui Bi1
Author Affiliations
  • 1Faculty of Land Resources and Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650093, China
  • 2Yunnan Provincial Plateau Mountain Survey Technique Application Engineering Research Center, Kunming University of Science and Technology, Kunming, Yunnan 650093, China
  • 3College of Engineering, West Yunnan University of Applied Sciences, Dali, Yunnan 671009, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
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    References (17)
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    Schematic of statistical filtering processing

    Fig. 1. Schematic of statistical filtering processing

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    Schematic of radius filtering processing

    Fig. 2. Schematic of radius filtering processing

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    Optimization strategy mainly deals with technical processes

    Fig. 3. Optimization strategy mainly deals with technical processes

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    Results of three buildings point clouds using the proposed filtering optimization strategy(statistical filtering and passthrough filtering).(a1)(a2)(a3) Original point clouds of the three buildings;(b1)(b2)(b3)results of statistical filtering ;(c1)(c2)(c3) results of passthrough filtering

    Fig. 4. Results of three buildings point clouds using the proposed filtering optimization strategy(statistical filtering and passthrough filtering).(a1)(a2)(a3) Original point clouds of the three buildings;(b1)(b2)(b3)results of statistical filtering ;(c1)(c2)(c3) results of passthrough filtering

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    Results of three buildings point clouds using the proposed filtering optimization strategy(CSF and radius filtering).(a1)(a2)(a3)Results of CSF ;(b1)(b2)(b3)local enlarged images of CSF;(c1)(c2)(c3)results of radius filtering ;(d1)(d2)(d3)local enlarged images of radius filtering

    Fig. 5. Results of three buildings point clouds using the proposed filtering optimization strategy(CSF and radius filtering).(a1)(a2)(a3)Results of CSF ;(b1)(b2)(b3)local enlarged images of CSF;(c1)(c2)(c3)results of radius filtering ;(d1)(d2)(d3)local enlarged images of radius filtering

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    Test results of 1 # building point cloud with three different compression methods.(a1)Point cloud after filtering;(a2)(a3)local enlarged images corresponding to box 1 and 2 in Fig. (a1) ;(b1)result of curvature graded compression;(b2)(b3)local enlarged images corresponding to box 1 and 2 in Fig. (b1);(c1)result are compressed by the Geomagic studio software;(c2)(c3)local enlarged images corresponding to box 1 and 2 in Fig. (c1);(d1)result of center point nearest point mesh compression;(d2)(d3)lo

    Fig. 6. Test results of 1 # building point cloud with three different compression methods.(a1)Point cloud after filtering;(a2)(a3)local enlarged images corresponding to box 1 and 2 in Fig. (a1) ;(b1)result of curvature graded compression;(b2)(b3)local enlarged images corresponding to box 1 and 2 in Fig. (b1);(c1)result are compressed by the Geomagic studio software;(c2)(c3)local enlarged images corresponding to box 1 and 2 in Fig. (c1);(d1)result of center point nearest point mesh compression;(d2)(d3)lo

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    Test results of 2 # building point cloud with three different compression methods.(a1)Point cloud after filtering;(a2)(a3)local enlarged images corresponding to box 1 and 2 in Fig. (a1);(b1)result of curvature graded compression;(b2)(b3)local enlarged images corresponding to box 1 and 2 in Fig. (b1);(c1)result are compressed by the Geomagic studio software;(c2)(c3)local enlarged images corresponding to box 1 and 2 in Fig. (c1);(d1)result of center point nearest point mesh compression;(d2)(d3)loc

    Fig. 7. Test results of 2 # building point cloud with three different compression methods.(a1)Point cloud after filtering;(a2)(a3)local enlarged images corresponding to box 1 and 2 in Fig. (a1);(b1)result of curvature graded compression;(b2)(b3)local enlarged images corresponding to box 1 and 2 in Fig. (b1);(c1)result are compressed by the Geomagic studio software;(c2)(c3)local enlarged images corresponding to box 1 and 2 in Fig. (c1);(d1)result of center point nearest point mesh compression;(d2)(d3)loc

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    Test results of 3 # building point cloud with three different compression methods.(a1)Point cloud after filtering;(a2)(a3)local enlarged images corresponding to box 1 and 2 in Fig. (a1);(b1)result of curvature graded compression;(b2)(b3)local enlarged images corresponding to box 1 and 2 in Fig. (b1);(c1)result are compressed by the Geomagic studio software;(c2)(c3)local enlarged images corresponding to box 1 and 2 in Fig. (c1);(d1)result of center point nearest point mesh compression;(d2)(d3)loc

    Fig. 8. Test results of 3 # building point cloud with three different compression methods.(a1)Point cloud after filtering;(a2)(a3)local enlarged images corresponding to box 1 and 2 in Fig. (a1);(b1)result of curvature graded compression;(b2)(b3)local enlarged images corresponding to box 1 and 2 in Fig. (b1);(c1)result are compressed by the Geomagic studio software;(c2)(c3)local enlarged images corresponding to box 1 and 2 in Fig. (c1);(d1)result of center point nearest point mesh compression;(d2)(d3)loc

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    • Table 1. Changes in the number of point clouds in each program

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      Table 1. Changes in the number of point clouds in each program

      SerialnumberPoint cloudobjectOriginalpoint cloudStatisticalfilteringCSFRadiusfiltering
      11# building1242341210639645596280
      22# building97255946889338693185
      33# building1018751003798326682053

    • Table 2. Comparison of entropy changes of different compression methods

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      Table 2. Comparison of entropy changes of different compression methods

      Point cloud objectCompression methodEntropy
      1# buildingPoint cloud after filtering2.29667×105
      Grade curvature1.15354×105
      Using Geomagic software1.14507×105
      Center point nearest point grid simplification method1.13119×105
      2# buildingPoint cloud after filtering2.29254×105
      Grade curvature9.33566×104
      Using Geomagic software9.25588×104
      Center point nearest point grid simplification method9.23096×104
      3# buildingPoint cloud after filtering1.99948×105
      Grade curvature1.10805×105
      Using Geomagic software1.10647×105
      Center point nearest point grid simplification method1.10517×105
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    Raobo Li, Xiping Yuan, Shu Gan, Rui Bi. Point Cloud Simplification Optimization Strategy and Experimental Research Based on Multiple Algorithms[J]. Laser & Optoelectronics Progress, 2020, 57(23): 231402

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    Paper Information

    Category: Lasers and Laser Optics

    Received: Mar. 2, 2020

    Accepted: Apr. 10, 2020

    Published Online: Dec. 9, 2020

    The Author Email: Gan Shu (bo5200909@163.com)

    DOI:10.3788/LOP57.231402

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology