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基于马尔科夫随机场的散乱点云全局特征提取

张靖 周明全 张雨禾 耿国华

张靖, 周明全, 张雨禾, 耿国华. 基于马尔科夫随机场的散乱点云全局特征提取. 自动化学报, 2016, 42(7): 1090-1099. doi: 10.16383/j.aas.2016.c150627
引用本文: 张靖, 周明全, 张雨禾, 耿国华. 基于马尔科夫随机场的散乱点云全局特征提取. 自动化学报, 2016, 42(7): 1090-1099. doi: 10.16383/j.aas.2016.c150627
ZHANG Jing, ZHOU Ming-Quan, ZHANG Yu-He, GENG Guo-Hua. Global Feature Extraction from Scattered Point Clouds Based on Markov Random Field. ACTA AUTOMATICA SINICA, 2016, 42(7): 1090-1099. doi: 10.16383/j.aas.2016.c150627
Citation: ZHANG Jing, ZHOU Ming-Quan, ZHANG Yu-He, GENG Guo-Hua. Global Feature Extraction from Scattered Point Clouds Based on Markov Random Field. ACTA AUTOMATICA SINICA, 2016, 42(7): 1090-1099. doi: 10.16383/j.aas.2016.c150627

基于马尔科夫随机场的散乱点云全局特征提取

doi: 10.16383/j.aas.2016.c150627
基金项目: 

陕西省教育厅科研专项 2013JK1180

国家自然科学基金 61373117

高等学校博士学科点专项科研基金 20136101110019

国家自然科学基金 61305032

详细信息
    作者简介:

    张靖西北大学信息科学与技术学院硕士研究生.主要研究方向为图像处理, 可视化技术.E-mail:zj18710812436@163.com

    张雨禾西北大学信息科学与技术学院博士研究生.主要研究方向为计算机图形图像处理与可视化技术.E-mail:zhangyuhe0601@126.com

    耿国华西北大学信息科学与技术学院教授.主要研究方向为智能信息处理, 数据库与知识库, 图形图像处理.E-mail:ghgeng@nwu.edu.cn

    通讯作者:

    周明全北京师范大学信息科学与技术学院教授.主要研究方向为虚拟现实与可视化技术, 智能信息处理, 数据库与知识库, 图形图像处理.本文通信作者.E-mail:mqzhou@nwu.edu.cn

Global Feature Extraction from Scattered Point Clouds Based on Markov Random Field

More Information
    Author Bio:

    Master student at the College of Information Science and Technology, Northwestern University. Her research interest covers computer graphics and visualization

    Ph. D. candidate at the College of Information Science and Technology, Northwestern University. Her research interest covers computer graphics and visualization

    Ph. D. candidate at the College of Information Science and Technology, Northwestern University. Her research interest covers computer graphics and visualization

    Corresponding author: ZHOU Ming-Quan Professor at the College of Information Science and Technology, Beijing Normal University. His research interest covers virtual reality and visualization technology, intelligent information processing, database and knowledge base, graphics and image processing. Corresponding author of this paper
  • 摘要: 为了精确提取点云数据中的特征信息,针对激光扫描获取的三维散乱点云数据,提出一种基于马尔科夫随机场(Markov random field, MRF)的散乱点云特征提取方法.首先,根据散乱点的曲率估计及阈值初始化点标号并判定稳定点,将稳定点标记存储在数组中;然后,将优化不稳定点的标号问题转化为随机场标号的能量函数问题,引用贝叶斯估计求后验概率分布函数及MAP-MRF(Maximum a posteriori-Markov random field)框架归约得到目标函数;最后,根据图割法α-expansion算法,利用标号调整过程中标号集相对能量变化得到不稳定点的最优标号集,将其与存储稳定点的数组综合,根据点标号提取特征点.实验结果表明,该方法简单、高效、无需人工调参,能够依据全局能量的变化自适应提取特征,特征提取结果令人满意.
  • 图  1  本文方法流程图

    Fig.  1  The flowchart of our method

    图  2  兵马俑1号碎片特征提取

    Fig.  2  Feature extraction of Terracotta Army 1

    图  3  兵马俑2号碎片特征提取

    Fig.  3  Feature extraction of Terracotta Army 2

    图  4  兵马俑3号碎片特征提取

    Fig.  4  Feature extraction of Terracotta Army 3

    图  5  Bunny模型特征提取

    Fig.  5  Feature extraction of Bunny model

    图  6  Fandisk模型特征提取

    Fig.  6  Feature extraction of Fandisk model

    图  7  Dragon模型特征提取

    Fig.  7  Feature extraction of Dragon model

    图  8  带噪声模型提取特征点数对比

    Fig.  8  Fandisk feature extraction with different noise amplitude

    图  9  Bunny简化模型特征提取结果

    Fig.  9  Feature extraction of simplified Bunny model

    表  1  参数列表

    Table  1  The parameter list

    参数 取值 作用
    α 0.00001 保证两点间势能的正定性
    β 8.0 决定先验信息在MRF中所占比重
    m 20 m个近邻点, 用来判断稳定点
    L 2 标号数, 即分类个数
    下载: 导出CSV

    表  2  含噪声模型特征提取对比表

    Table  2  Comparison of feature points extracted with noise model

    点云名称 采样点数 噪声幅度(ldb) 特征点数
    15 115 0.000 2 478
    Fandisk 15 115 0.001 1 989
    15 115 0.003 1 664
    下载: 导出CSV

    表  3  算法时间效率对比表

    Table  3  Comparison table of algorithm time efficiency

    点云名称 采样点数 特征点数 时间耗费(s)
    本文算法 张量投票 阈值检测
    Bunny 34 863 3 428 6.36 7.34 8.89
    Fandisk 15 115 2 478 3.67 3.89 4.16
    Dragon 464 869 103 721 189.69 273.56 403.56
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-10-10
  • 录用日期:  2016-01-19
  • 刊出日期:  2016-07-01

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