• Laser & Optoelectronics Progress
  • Vol. 57, Issue 23, 231402 (2020)
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
  • show less
    DOI: 10.3788/LOP57.231402 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Schematic of statistical filtering processing
    Fig. 1. Schematic of statistical filtering processing
    Schematic of radius filtering processing
    Fig. 2. Schematic of radius filtering processing
    Optimization strategy mainly deals with technical processes
    Fig. 3. Optimization strategy mainly deals with technical processes
    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
    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
    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
    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
    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
    SerialnumberPoint cloudobjectOriginalpoint cloudStatisticalfilteringCSFRadiusfiltering
    11# building1242341210639645596280
    22# building97255946889338693185
    33# building1018751003798326682053
    Table 1. Changes in the number of point clouds in each program
    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
    Table 2. Comparison of entropy changes of different compression methods
    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
    Download Citation