[1] Lee K H, Woo H, Suk T. Data reduction methods for reverse engineering[J]. The International Journal of Advanced Manufacturing Technology, 2001, 17(10): 735-743.

[2] 胡志胜, 于敬武, 束涛. 一种结合了栅格化和特征判断的点云压缩方法[J]. 辽宁工程技术大学学报(自然科学版), 2015, 34(6): 958-962.

    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), 2015, 34(6): 958-962.

[3] 贺一波, 陈冉丽, 吴侃, 等. 基于k-means聚类的点云精简方法[J]. 激光与光电子学进展, 2019, 56(9): 091002.

    He Y B, Chen R L, Wu K, et al. Point cloud simplification method based on k-means clustering[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091002.

[4] 李仁忠, 杨曼, 冉媛, 等. 基于方法库的点云去噪与精简算法[J]. 激光与光电子学进展, 2018, 55(1): 011008.

    Li R Z, Yang M, Ran Y, et al. Point cloud denoising and simplification algorithm based on method library[J]. Laser & Optoelectronics Progress, 2018, 55(1): 011008.

[5] 周绿, 林亨, 钟约先, 等. 曲面重构中测量点云精简方法的研究[J]. 中国制造业信息化, 2004, 33(5): 102-104.

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

[6] 邢正全, 邓喀中, 薛继群. 基于栅格划分和法向量估计的点云数据压缩[J]. 测绘通报, 2012( 7): 50- 52.

    Xing ZQ, Deng KZ, Xue JQ. Point cloud data compression based on grid division and normal vector estimation[J]. Bulletin of Surveying and Mapping, 2012( 7): 50- 52.

[7] 张鸿飞, 程效军, 贾东峰, 等. 多视点散乱点云配准及压缩改进算法研究[J]. 测绘通报, 2012( 2): 43- 47.

    Zhang HF, Cheng XJ, Jia DF, et al. A study of improved registration and compression algorithm of multi-vision disorder point clouds[J]. Bulletin of Surveying and Mapping, 2012( 2): 43- 47.

[8] 梁周雁. 基于点云的复杂物体曲面重建关键技术研究[D]. 青岛: 山东科技大学, 2018: 24- 34.

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

[9] 刘咏梅. 基于三维散乱点云的三角网格重构关键技术研究[D]. 北京: 北京理工大学, 2015: 12- 14.

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

[10] Hoppe H. DeRose T, Duchamp T, et al. Surface reconstruction from unorganized points[J]. ACM SIGGRAPH Computer Graphics, 1992, 26(2): 71-78.

[11] 陈西江, 章光, 花向红. 于法向量夹角信息熵的点云简化算法[J]. 中国激光, 2015, 42(8): 0814003.

    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, 2015, 42(8): 0814003.

[12] 程效军, 贾东峰, 程小龙. 海量点云数据处理理论与技术[M]. 上海: 同济大学出版社, 2014: 64- 67.

    Cheng XJ, Jia DF, Cheng XL. Massive point cloud data processing theory and technology [M]. Shanghai: Tongji University Press, 2014: 64- 67.

[13] 刘春, 吴杭彬. 基于真三维TIN的三维激光扫描数据压缩方法[J]. 武汉大学学报(信息科学版), 2006, 31(10): 908-911.

    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, 2006, 31(10): 908-911.

[14] 喜文飞. 激光点云数据压缩的精简研究[D]. 昆明: 昆明理工大学, 2011: 47- 51.

    Xi WF. Reduction of laser point cloud data compression[D]. Kunming: Kunming University of Science and Technology, 2011: 47- 51.

[15] 陈朋, 周大伟. 点到平面距离的点云数据压缩方法[J]. 测绘科学, 2015, 40(8): 117-120.

    Chen P, Zhou D W. A point cloud data compression based on the point to plane distance[J]. Science of Surveying and Mapping, 2015, 40(8): 117-120.