Intrinsic shape signature algorithm based on adaptive neighborhood

doi:10.11772/j.issn.1001-9081.2019091538

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (4): 1151-1156.DOI: 10.11772/j.issn.1001-9081.2019091538

• Virtual reality and multimedia computing • Previous Articles Next Articles

Intrinsic shape signature algorithm based on adaptive neighborhood

SHI Zhiliang, CAI Wangyue, WANG Guoqiang, XIONG Linjie

School of Mechanical and Electric Engineering, Wuhan University of Technology, Wuhan Hubei 430070, China

Received:2019-09-05 Revised:2019-11-06 Online:2020-04-10 Published:2019-11-18
Supported by:
This work is partially supported by the National Key Research and Development Program of China(2018YFB1105500).

基于自适应邻域的固有形状特征算法

石志良, 蔡旺月, 汪国强, 熊林杰

武汉理工大学机电工程学院, 武汉 430070

通讯作者: 蔡旺月
作者简介:石志良(1974-),男,湖北黄梅人,副教授,博士,主要研究方向:计算机辅助设计、增材制造、计算机视觉;蔡旺月(1995-),男,湖北襄阳人,硕士研究生,主要研究方向:数字化设计与制造;汪国强(1995-),男,湖北咸宁人,硕士研究生,主要研究方向:数字化设计与制造;熊林杰(1995-),男,云南昭通人,硕士研究生,主要研究方向:数字化设计与制造。
基金资助:
国家重点研发计划项目（2018YFB1105500）。

Abstract

Abstract: Concerning the problems that the determination of fixed scale in 3D point cloud feature point detection algorithm requires empirical knowledge,and the calculation of adaptive scale requires much time cost,an improved algorithm named Adaptive Neighborhood Intrinsic Shape Signature(ANISS)was proposed. Firstly,the local features were used to calculate the adaptive neighborhood k value of each point. Then,the k value was used as the neighborhood size of the ANISS algorithm,and by comparing the ratio of the continuous eigenvalues with the threshold,the approximate feature points were obtained. Finally,the k values of the approximate feature points were used as the neighborhood size of the Non-Maximum Suppression(NMS),and the NMS algorithm was executed to obtain the final feature points. The results of rotational translation invariance experiment and noise sensitivity experiment show that the repeatability of the feature points detected by ANISS algorithm is higher than that of Intrinsic Shape Signature(ISS)algorithm. ANISS algorithm not only reduces the inaccuracy caused by the neighborhood parameter input in ISS algorithm,but also has high computational efficiency.

Key words: adaptive neighborhood, Intrinsic Shape Signature (ISS), feature point, repeatability, rotational translation invariance, noise immunity

摘要： 针对三维点云特征点检测算法中固定尺度的确定需要经验知识的参与，自适应尺度的计算需消耗较多时间成本的问题，提出一种自适应邻域的固有形状特征（ANISS）改进算法。首先利用局部特征计算每一点的自适应邻域k值；然后将k值作为ANISS算法中的邻域大小，通过比较连续特征值的比率与阈值的大小来得到近似特征点；最后以近似特征点的k值作为非极大值抑制（NMS）的邻域大小，执行NMS算法，得到最终的特征点。旋转平移不变性实验和噪声敏感性实验的结果表明，ANISS算法检测出的特征点的可重复性均高于固有形状特征（ISS）算法，它不仅降低了ISS算法中邻域参数输入造成的不准确性，还具有较高的计算效率。

关键词: 自适应邻域, 固有形状特征, 特征点, 可重复性, 旋转平移不变性, 抗噪性

CLC Number:

TP391.41

SHI Zhiliang, CAI Wangyue, WANG Guoqiang, XIONG Linjie. Intrinsic shape signature algorithm based on adaptive neighborhood[J]. Journal of Computer Applications, 2020, 40(4): 1151-1156.

石志良, 蔡旺月, 汪国强, 熊林杰. 基于自适应邻域的固有形状特征算法[J]. 计算机应用, 2020, 40(4): 1151-1156.

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Intrinsic shape signature algorithm based on adaptive neighborhood

基于自适应邻域的固有形状特征算法

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