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基于经验模态分解的点云数据平滑与增强

郭立新 王小超 郝爱民

郭立新, 王小超, 郝爱民等 . 基于经验模态分解的点云数据平滑与增强[J]. 北京航空航天大学学报, 2017, 43(5): 1045-1052. doi: 10.13700/j.bh.1001-5965.2016.0370
引用本文: 郭立新, 王小超, 郝爱民等 . 基于经验模态分解的点云数据平滑与增强[J]. 北京航空航天大学学报, 2017, 43(5): 1045-1052. doi: 10.13700/j.bh.1001-5965.2016.0370
GUO Lixin, WANG Xiaochao, HAO Aiminet al. Point clouds smoothing and enhancing based on empirical mode decomposition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(5): 1045-1052. doi: 10.13700/j.bh.1001-5965.2016.0370(in Chinese)
Citation: GUO Lixin, WANG Xiaochao, HAO Aiminet al. Point clouds smoothing and enhancing based on empirical mode decomposition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(5): 1045-1052. doi: 10.13700/j.bh.1001-5965.2016.0370(in Chinese)

基于经验模态分解的点云数据平滑与增强

doi: 10.13700/j.bh.1001-5965.2016.0370
基金项目: 

国家自然科学基金 61532002

国家自然科学基金 61672149

国家自然科学基金 61602341

国家自然科学基金 11626169

天津市自然科学基金 17JCQNJC00600

虚拟现实技术与系统国家重点实验室(北京航空天大学)开放基金 BUAA-VR-17KF-04

详细信息
    作者简介:

    郭立新,女,硕士研究生。主要研究方向:点云数据处理

    王小超,男,博士,讲师。主要研究方向:计算几何、三维数字几何处理

    郝爱民,男,博士,教授,博士生导师。主要研究方向:虚拟现实中的建模理论和绘制方法

    通讯作者:

    郝爱民, E-mail:ham@buaa.edu.cn

  • 中图分类号: TP391.41

Point clouds smoothing and enhancing based on empirical mode decomposition

Funds: 

National Natural Science Foundation of China 61532002

National Natural Science Foundation of China 61672149

National Natural Science Foundation of China 61602341

National Natural Science Foundation of China 11626169

Natural Science Foundation of Tianjin 17JCQNJC00600

Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang Univeristy BUAA-VR-17KF-04

More Information
  • 摘要:

    在计算机辅助设计与逆向工程应用中,针对缺乏拓扑连接关系的点云数据,提出了基于经验模态分解(EMD)的点云数据平滑与增强算法。首先,以点云模型的拉普拉斯矩阵坐标与法向的内积作为EMD输入信号,提取点云模型输入信号的极值点作为插值节点计算信号的上下包络;然后,为实现特征保持的EMD信号分解,通过检测点云数据上特征点,并在计算信号上下包络的过程中作为约束,克服传统EMD算法无法保持特征的局限;最后,迭代地从输入信号中减去上下包络的均值得到内蕴模态函数(IMF)和余量,并通过设计滤波器实现了点云数据平滑和增强。实验结果表明, 本文算法有效地将EMD推广到三维散乱点云数据中, 扩大EMD在三维几何中的应用范围,并在点云数据平滑和增强方面取得了很好的效果。

     

  • 图 1  八面体点云模型的EMD

    Figure 1.  EMD of octahedral point cloud model

    图 2  点云特征点提取结果

    Figure 2.  Feature point extraction results of point clouds

    图 3  Dragon点云模型的多尺度分解结果

    Figure 3.  Multi-scale decomposition results of Dragon point clouds model

    图 4  Dodecahandle和Venubody模型的平滑结果

    Figure 4.  Smoothing results of Dodecahandle and Venubody models

    图 5  Hand和Tweety模型的平滑结果

    Figure 5.  Smoothing results of Hand and Tweety models

    图 6  立方体模型的对比结果

    Figure 6.  Comparison results of cube model

    图 7  Fandisk模型的对比结果

    Figure 7.  Comparison results of Fandisk model

    图 8  Max Planck和Dog模型的平滑和增强结果

    Figure 8.  Smoothing and enhancing results of Max Planck and Dog models

    表  1  各模型参数设置及运行时间统计

    Table  1.   Parameter setting and running time statistics for each model

    模型 P NB nIMF 运行时间/s
    tL tEMD tTOTAL
    八面体 4 098 15 3 3.835 5.425 9.670
    Dragon 50 000 25 3 46.439 74.245 124.770
    Dodecahandle 38 390 15 3 34.358 52.278 89.298
    Venubody 11 362 15 3 10.502 15.229 26.529
    Hand 20 002 15 3 18.076 27.015 46.500
    Tweety 93 047 15 3 85.297 136.972 229.845
    立方体 24 578 15 3 22.526 34.611 59.771
    Fandisk 27 827 15 3 25.787 38.875 67.315
    Max Planck 49 132 25 3 43.872 70.614 119.321
    Dog 101 108 25 3 91.739 152.658 253.532
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-05-05
  • 录用日期:  2016-05-27
  • 网络出版日期:  2017-05-20

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