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

被引：0
|
作者：
Hu H. [1 ,2 ]
Li Z. [3 ]
Qin S. [1 ]
Ma L. [4 ]
机构：
[1] Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou
[2] School of Science, Zhejiang A&F University, Hangzhou
[3] School of Science, Zhejiang Sci-Tech University, Hangzhou
[4] College of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai
来源：
Li, Zhong (lizhong@zstu.edu.cn) | 1600年 / Institute of Computing Technology卷 / 33期
关键词：
Point cloud models; Self-similarity; Shape analysis; Three-order tensor;
D O I：
10.3724/SP.J.1089.2021.18542
中图分类号：
学科分类号：
摘要：
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 diame-ter 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 effec-tively describe the shape and improves the recognition accuracy of similar sub-blocks of a point cloud model. © 2021, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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页码：590 / 600
页数：10
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