Author Affiliations
1Faculty of Land Resources and Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650093, China2Yunnan Provincial Plateau Mountain Survey Technique Application Engineering Research Center, Kunming University of Science and Technology, Kunming, Yunnan 650093, China3College of Engineering, West Yunnan University of Applied Sciences, Dali, Yunnan 671009, Chinashow less
Fig. 1. Schematic of statistical filtering processing
Fig. 2. Schematic of radius filtering processing
Fig. 3. Optimization strategy mainly deals with technical processes
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
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
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
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
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
Serialnumber | Point cloudobject | Originalpoint cloud | Statisticalfiltering | CSF | Radiusfiltering |
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1 | 1# building | 124234 | 121063 | 96455 | 96280 | 2 | 2# building | 97255 | 94688 | 93386 | 93185 | 3 | 3# building | 101875 | 100379 | 83266 | 82053 |
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Table 1. Changes in the number of point clouds in each program
Point cloud object | Compression method | Entropy |
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1# building | Point cloud after filtering | 2.29667×105 | Grade curvature | 1.15354×105 | Using Geomagic software | 1.14507×105 | Center point nearest point grid simplification method | 1.13119×105 | 2# building | Point cloud after filtering | 2.29254×105 | Grade curvature | 9.33566×104 | Using Geomagic software | 9.25588×104 | Center point nearest point grid simplification method | 9.23096×104 | 3# building | Point cloud after filtering | 1.99948×105 | Grade curvature | 1.10805×105 | Using Geomagic software | 1.10647×105 | Center point nearest point grid simplification method | 1.10517×105 |
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Table 2. Comparison of entropy changes of different compression methods