采用密度空间聚类的散乱点云特征提取方法

doi:10.3969/j.issn.1001-2400.2017.02.020

摘要/Abstract

摘要：

针对现有点云特征提取算法中，采用全局特征度量阈值及仅使用点的局部信息进行特征提取而造成的特征尖锐程度敏感、对潜在曲面差异较大模型效果差等问题，提出一种基于密度空间聚类的散乱点云特征提取方法．首先，对点的反k近邻进行定义，并提出一种新的特征检测算子；然后，将反k近邻的尺度作为点密度，引入特征的全局约束性信息;最后，对基于密度空间聚类方法中的相关概念进行重定义并建立新的特征识别准则，提取特征点．实验结果表明，该算法简单、有效、鲁棒，同时考虑了特征的局部性信息及全局约束性信息，针对潜在曲面形状差异较大的模型表现出了较强的优越性．

关键词: 点云, 特征提取, 基于密度空间聚类, 全局约束性, 反k近邻

Abstract:

The existing feature extraction methods often depend on the global fixed thresholds and the local information of features, resulting in sensitivity to significance of features and failure in models with different surfaces. To overcome those problems, a novel method based on DBSCAN Clustering is proposed. First, a new reverse k nearest neighbors(kNN) of points are defined as a new feature detection operator. Second, the scales of the reverse k nearest neighbors of points are utilized as the density information of points and then the introduction of the global constraints information is proposed. Finally, based on the redefinition of the concepts of the DBSCAN clustering method and the creation of a new feature recognition criterion, an improved version of the DBSCAN clustering method is used to extract features. Experimental results show that the method is simple, effective and robust, which takes into account the local information and global constraint information and outperforms existing feature detection methods on point clouds with surfaces that have diverse geometries.

Key words: point clouds, feature extraction, DBSCAN clustering, global constraints, reverse kNN

张雨禾;耿国华;魏潇然;石晨晨;张顺利. 采用密度空间聚类的散乱点云特征提取方法[J]. 西安电子科技大学学报, 2017, 44(2): 114-120.

ZHANG Yuhe;GENG Guohua;WEI Xiaoran;SHI Chenchen;ZHANG Shunli. Feature extraction of point clouds using the DBSCAN clustering[J]. Journal of Xidian University, 2017, 44(2): 114-120.

参考文献

［1］魏潇然, 耿国华, 张雨禾. 几何信息预测的三角网格模型拓扑压缩［J］. 西安电子科技大学学报, 2015, 42(5): 194-199.
WEI Xiaoran, GENG Guohua, ZHANG Yuhe. Connectivity Compression of Triangle Meshes Based on Geometric Parameter Predict［J］. Journal of Xidian University, 2015, 42(5): 194-199.
［2］李姬俊男, 耿国华, 周明全, 等. 文物碎块虚拟拼接中的表面特征优化［J］. 计算机辅助设计与图形学学报, 2014, 26(12): 2149-2154.
LI Jijunnan, GENG Guohua, ZHOU Mingquan, et al. Surface Feature Optimization for Virtual Matching of Relic Fragments［J］. Journal of Computer-Aided Design ＆ Computer Graphics, 2014, 26(12): 2149-2154.
［3］冯筠, 陈雨, 仝鑫龙, 等. 三维颅骨特征点的自动标定［J］. 光学精密工程, 2014, 22(5): 1388-1394.
FENG Jun, CHEN Yu, TONG Xinlong, et al. Automatic Feature Point Extraction for Three-dimensional Skull［J］. Optics and Precision Engineering, 2014, 22(5): 1388-1394.
［4］张雨禾, 耿国华, 魏潇然. 散乱点云谷脊特征提取［J］. 光学精密工程, 2015, 23(1): 310-318.
ZHANG Yuhe, GENG Guohua, WEI Xiaoran. Valley-ridge Feature Extraction from Point Clouds［J］. Optics and Precision Engineering, 2015, 23(1): 310-318.
［5］ KARMAKAR D, MURTHY C A. Face Recognition Using Face-autocropping and Facial Feature Points Extraction ［C］//ACM International Conference Proceeding Series: 26-27-February-2015. New York: ACM, 2015: 116-122.
［6］ GUMHOLD S, WANG X L, MACLEOD R S. Feature Extraction from Point Clouds［C］//Proceedings of the 10th International Meshing Roundtable. Berlin: Springer, 2001: 293-305.
［7］ PAULY M, KEISER R, GROSS M. Multi-scale Feature Extraction on Point-sampled Surfaces［J］. Computer Graphics Forum, 2003, 22(3): 281-289.
［8］ HO H T, GIBBINS D. Curvature-based Approach for Multi-scale Feature Extraction from 3D Meshes and Unstructured Point Clouds ［J］. IET Computer Vision, 2009, 3(4): 201-212.
［9］ PARK M K, LEE S J, LEE K H. Multi-scale Tensor Voting for Feature Extraction from Unstructured Point Clouds［J］. Graphical Models, 2012, 74(4): 197-208.
［10］吾守尔·斯拉木, 曹巨明. 一种新的散乱点云尖锐特征提取方法［J］. 西安交通大学学报, 2012, 46(12): 1-5.
WUSHOUR SLAM, CAO Juming. An Extraction Algorithm for Sharp Feature Points from Point Clouds ［J］. Journal of Xi'an Jiaotong University, 2012, 46(12): 1-5.
［11］ DANIELS J, HA L K, OCHOTTA T, et al. Robust Smooth Feature Extraction from Point Clouds ［C］//Proceedings of the IEEE International Conference on Shape Modeling and Applications. Piscataway: IEEE Computer Society, 2007: 123-136.
［12］ DEMARSIN K, VANDERSTRAETEN D, VOLODINE T, et al. Detection of Closed Sharp Edges in Point Clouds Using Normal Estimation and Graph Theory ［J］. CAD Computer Aided Design, 2007, 39(4): 276-283.
［13］ JENKE P, WAND M, BOKELOH M, et al. Bayesian Point Cloud Reconstruction ［J］. Computer Graphics Forum, 2006, 25(3): 379-388
［14］ WEBER C, HAHMANN S, HAGEN H. Sharp Feature Detection in Point Clouds［C］//Proceedings of the International Conference on Shape Modeling and Applications. Piscataway: IEEE Computer Society, 2010: 175-186.
［15］王小超, 刘秀平, 李宝军, 等. 基于局部重建的点云特征点提取［J］. 计算机辅助设计与图形学学报, 2013, 25(5): 659-665.
WANG Xiaochao, LIU Xiuping, LI Baojun, et al. Feature Detection on Point Cloud via Local Reconstruction ［J］. Journal of Computer-Aided Design ＆ Computer Graphics, 2013, 25(5): 659-665.
［16］ LIN Y B, WANG C, CHENG J, et al. Line Segment Extraction for Large Scale Unorganized Point Clouds ［J］. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 102: 172-183.
［17］程效军, 方芳. 基于形态学的散乱点云轮廓特征线提取［J］. 同济大学学报(自然科学版), 2014, 42(11): 1738-1743.
CHENG Xiaojun,FANG Fang. Morphology-based Scattered Point Cloud Contour Extraction ［J］. Journal of Tongji University(Natural Science), 2014, 42(11): 1738-1743.
［18］ XIA C, HSU W, LEE M L, et al. BORDER: Efficient Computation of Boundary Points ［J］. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(3): 289-303.
［19］ ESTER M, KRIEGEL H P, SANDER J, et al. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise［C］//Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Menlo Park: AAAI, 1996: 226-231.

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