3D Non-rigid Object Classification with Mesh Convolution Features

被引：0

作者：

Shi C.-W. ^{[1
]}

Zhao J.-Y. ^{[1
,2
]}

Chen Y. ^{[1
]}

机构：

[1] Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang

[2] Mobile Network Application Technology Key Laboratory of Zhejiang Province, Ningbo, 315211, Zhejiang

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 04期

关键词：

3D shape feature; Mesh convolution; Non-rigid 3D model; Support vector machine;

D O I：

10.3969/j.issn.0372-2112.2020.04.005

中图分类号：

学科分类号：

摘要：

3D object recognition with shape changes is a challenging task.The irregular data structure of the mesh model prevents the operation of the conventional convolution, which brings difficulties to feature extraction of the 3D non-rigid objects.In this paper, we propose a method of mesh convolution for 3D non-rigid objects to extract shape features and use them for classification.Firstly, we obtain the distribution of typical patch shapes by the mesh convolution.Then, we model the spatial co-occurrence relationship by Markov chains to complete the global feature description.Finally, we use the support vector machine to classify the 3D objects.Our method adopts the continuous polynomial function as the convolution kernel for the irregular data structure, and learn the kernel by an unsupervised learning method.Experimental results on the standard non-rigid 3D model datasets show our method can effectively extract the features and achieve classification accuracy of 92.88% on SHREC10 and 96.54% on SHREC15, respectively. © 2020, Chinese Institute of Electronics. All right reserved.

引用

页码：648 / 653

页数：5

共 15 条

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