Laser & Optoelectronics Progress, Volume. 56, Issue 14, 142801(2019)
Curvature-Grading-Based Compression for Point Cloud Data
Fig. 1. Point cloud 1 and point number-curvature distribution. (a) Point cloud 1; (b) point number-curvature distribution
Fig. 2. Point cloud 2 and point number-curvature distribution. (a) Point cloud 2; (b) point number-curvature distribution
Fig. 6. Cloud data before compression and the results of three compression methods. (a) Original data; (b) compression result using Geomagic software; (c) compression result using the method in Ref. [8]; (d) compression result using the method proposed in this paper
Fig. 7. A local area and compression results under different compression ratios. (a) Original data of a local area; (b) compression ratio 70%, S=53.0; (c) compression ratio 80%, S=10.5; (d) compression ratio 90%, S=1.1
Fig. 8. Surface model built before compression and after compression by three methods. (a) Model built from original data; (b) model built from result compressed by Geomagic software; (c) model built from result compressed by the method in Ref. [8]; (d) model built from result compressed by the method proposed in this paper
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Jintao Li, Xiaojun Cheng, Zexin Yang, Rongqi Yang. Curvature-Grading-Based Compression for Point Cloud Data[J]. Laser & Optoelectronics Progress, 2019, 56(14): 142801
Category: Remote Sensing and Sensors
Received: Dec. 19, 2018
Accepted: Feb. 20, 2019
Published Online: Jul. 12, 2019
The Author Email: Xiaojun Cheng (cxj@tongji.edu.cn)