三维点云模型特征张量描述符的构造及自相似性分析  

Construction of Feature Tensor Descriptor and Self-Similarity Analysis for 3D Point Cloud Models

作　　者：胡海龙 李重[3] 秦胜伟[1] 马利庄 Hu Hailong;Li Zhong;Qin Shengwei;Ma Lizhuang(Faculty of Mechanical Engineering&Automation,Zhejiang Sci-Tech University,Hangzhou 310018;School of Science,Zhejiang A&F University,Hangzhou 311300;School of Science,Zhejiang Sci-Tech University,Hangzhou 310018;College of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240)

机构地区：[1]浙江理工大学机械与自动控制学院,杭州310018 [2]浙江农林大学理学院,杭州311300 [3]浙江理工大学理学院,杭州310018 [4]上海交通大学电子信息与电气工程学院,上海200240

出　　处：《计算机辅助设计与图形学学报》2021年第4期590-600,共11页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(11671009);浙江省自然科学基金(LY19F010014,LZ19A010002)。

摘　　要：三维模型局部自相似性是物体形状分析中的一个基本问题,其中,局部形状描述符的构建对自相似性分析的最终结果至关重要.针对此问题,提出了一种基于张量融合特征描述符的自相似性分析方法.首先利用相关面和反向点对点云模型进行形状直径函数(shapediameterfunction,SDF)的近似计算;然后利用谱聚类对模型进行过分割成模型子块,由K近邻(K-nearest neighbor, KNN)邻域点的SDF、形状指数(shape index, SI)和高斯曲率(Gauss curvature,GS)矩阵构造三维特征张量;最后利用张量范数构造映射得到形状描述符,并定义相似性度量分析模型子块之间的自相似性.对几种最新的方法(包括部分匹配和显著性检测)进行了实验,无论是直观视觉效果,还是相似性测度和相对误差上的评价指标,结果均表明,该方法可有效地对形状进行描述,提高了点云模型相似子块的识别精度.Local self-similarity of 3D model is a fundamental problem in the shape analysis.The construction of a local shape descriptor is very important to the final result of self-similarity analysis.To solve this problem,a self-similarity analysis method based on the tensor fusion feature descriptor is proposed.Firstly,the shape diameter function(SDF)of a point cloud model is approximately calculated by using relevant facets and antipodal points.Then,spectral clustering is used to segment the model into sub-blocks,and the three-dimensional feature tensor is constructed from the SDF,shape index(SI)and Gauss curvature(GS)matrix of KNN neighborhood points.Finally,the shape descriptor is obtained by constructing the mapping with the tensor norm,and then the similarity measure is defined and the self-similarity between the sub-blocks of the model is analyzed.Several state-of-the-art methods(including partial matching and saliency detection)are tested.In terms of not only the visual effect,but also the similarity measure and the relative errors,the results show that this method can effectively describe the shape and improves the recognition accuracy of similar sub-blocks of a point cloud model.

关 键 词：自相似性 三阶张量 形状分析 点云模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]