• Laser & Optoelectronics Progress
  • Vol. 56, Issue 14, 142801 (2019)
Jintao Li1, Xiaojun Cheng1、2、*, Zexin Yang1, and Rongqi Yang3
Author Affiliations
  • 1 College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
  • 2 Key Laboratory of Advanced Engineering Surveying of NASMG (National Administration of Surveying, Mapping and Geoinformation), Shanghai 200092, China
  • 3 Shanghai Merchant Ship Design and Research Institute, Shanghai 201203, China
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    DOI: 10.3788/LOP56.142801 Cite this Article Set citation alerts
    Jintao Li, Xiaojun Cheng, Zexin Yang, Rongqi Yang. Curvature-Grading-Based Compression for Point Cloud Data[J]. Laser & Optoelectronics Progress, 2019, 56(14): 142801 Copy Citation Text show less
    References

    [1] Lee K H, Woo H, Suk T. Data reduction methods for reverse engineering[J]. The International Journal of Advanced Manufacturing Technology, 17, 735-743(2001).

    [2] Hu Z S, Yu J W, Shu T. A point cloud compression approach combined with rasterizing and feature estimate[J]. Journal of Liaoning Technical University (Natural Science), 34, 958-962(2015).

    [3] He Y B, Chen R L, Wu K et al. Point cloud simplification method based on k-means clustering[J]. Laser & Optoelectronics Progress, 56, 091002(2019).

    [4] Li R Z, Yang M, Ran Y et al. Point cloud denoising and simplification algorithm based on method library[J]. Laser & Optoelectronics Progress, 55, 011008(2018).

    [5] Zhou L, Lin H, Zhong Y X et al. The thinning method for measured points cloud in surface reconstruction[J]. Mie of China, 33, 102-104(2004).

    [6] Xing Z Q, Deng K Z, Xue J Q[J]. Point cloud data compression based on grid division and normal vector estimation Bulletin of Surveying and Mapping, 2012, 50-52.

    [7] Zhang H F, Cheng X J, Jia D F et al[J]. A study of improved registration and compression algorithm of multi-vision disorder point clouds Bulletin of Surveying and Mapping, 2012, 43-47.

    [8] Liang Z Y. Key techniques of complex object surface modeling based on point cloud[D]. Qingdao: Shangdong University of Science and Technology, 24-34(2018).

    [9] Liu Y M. Research on reconstructing triangular mesh from three-dimensional scattered point cloud[D]. Beijing: Beijing Institute of Technology, 12-14(2015).

    [10] Hoppe H. DeRose T, Duchamp T, et al. Surface reconstruction from unorganized points[J]. ACM SIGGRAPH Computer Graphics, 26, 71-78(1992).

    [11] Chen X J, Zhang G, Hua X H. Point cloud simplification based on the information entropy of normal vector angle[J]. Chinese Journal of Lasers, 42, 0814003(2015).

    [12] Cheng X J, Jia D F, Cheng X L[M]. Massive point cloud data processing theory and technology, 64-67(2014).

    [13] Liu C, Wu H B. Compress method for three dimension laser scanning data based on 3D triangulated irregular network[J]. Geomatics and Information Science of Wuhan University, 31, 908-911(2006).

    [14] Xi W F. Reduction of laser point cloud data compression[D]. Kunming: Kunming University of Science and Technology, 47-51(2011).

    [15] Chen P, Zhou D W. A point cloud data compression based on the point to plane distance[J]. Science of Surveying and Mapping, 40, 117-120(2015).

    Jintao Li, Xiaojun Cheng, Zexin Yang, Rongqi Yang. Curvature-Grading-Based Compression for Point Cloud Data[J]. Laser & Optoelectronics Progress, 2019, 56(14): 142801
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