Feature detection on point clouds via Gabriel Triangles creation and l1 normal reconstruction  

作　　者：ZHANG Shaoguang WANG Xiaochao CAO Junjie WANG Jun 

机构地区：[1]School of Mathematical Sciences, Dalian University of Technology [2]ANSTEEL-DSIC Dalian Steel Service Co., Ltd

出　　处：《Computer Aided Drafting,Design and Manufacturing》2012年第4期29-35,共7页计算机辅助绘图设计与制造（英文版）

基　　金：Supported by National Natural Science Foundation of China(No.u0935004,61173102);the Fundamental Research Funds for the Central Unibersities(DUT11SX08)

摘　　要：In this paper, we present a robust subneighborhoods selection technique for feature detection on point clouds scattered over a piecewise smooth surface. The proposed method first identifies all potential features using covariance analysis of the local- neighborhoods. To further extract the accurate features from potential features, Gabriel triangles are created in local neighborhoods of each potential feature vertex. These triangles tightly attach to underlying surface and effectively reflect the local geometry struc- ture. Applying a shared nearest neighbor clustering algorithm on ~ 1 reconstructed normals of created triangle set, we classify the lo- cal neighborhoods of the potential feature vertex into multiple subneighborhoods. Each subneighborhood indicates a piecewise smooth surface. The final feature vertex is identified by checking whether it is locating on the intersection of the multiple surfaces. An advantage of this framework is that it is not only robust to noise, but also insensitive to the size of selected neighborhoods. Ex- perimental results on a variety of models are used to illustrate the effectiveness and robustness of our method.In this paper, we present a robust subneighborhoods selection technique for feature detection on point clouds scattered over a piecewise smooth surface. The proposed method first identifies all potential features using covariance analysis of the local- neighborhoods. To further extract the accurate features from potential features, Gabriel triangles are created in local neighborhoods of each potential feature vertex. These triangles tightly attach to underlying surface and effectively reflect the local geometry struc- ture. Applying a shared nearest neighbor clustering algorithm on ~ 1 reconstructed normals of created triangle set, we classify the lo- cal neighborhoods of the potential feature vertex into multiple subneighborhoods. Each subneighborhood indicates a piecewise smooth surface. The final feature vertex is identified by checking whether it is locating on the intersection of the multiple surfaces. An advantage of this framework is that it is not only robust to noise, but also insensitive to the size of selected neighborhoods. Ex- perimental results on a variety of models are used to illustrate the effectiveness and robustness of our method.

关 键 词：feature detection point clouds subneighborhoods selection Gabriel triangles creation l1 normal reconstruction 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]