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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 12
3828-3833

3D shape classification method based on joint graph convolution learning of local geometry and global structure

Zhang Xiaohui1
He Jinhai1
Lan Pengyan1
Xu Shengsi2
1. School of Computer Science & Information Technology, Liaoning Normal University, Dalian Liaoning 116081, China
2. Information Technology Center, Dalian Polytechnic University, Dalian Liaoning 116034, China
DOI: 10.19734/j.issn.1001-3695.2023.04.0170

Abstract

Aiming at the issue of complex 3D shape analysis and recognition, this paper presented a novel 3D graph convolution classification method. It established a joint graph convolution learning mechanism of local geometry and global structure to provide both geometrical features and global context features, which effectively improved the robustness and stability of 3D data learning. Firstly, it constructed the local graph in spatial domain by farthest point sampling and K-nearest neighbor method, and designed a dynamic spectral graph convolution operator to extract local geometric features effectively. Meanwhile, it constructed the global feature graph based on random sampling in the feature domain, and obtained the global structure context by spectral graph convolution. Furthermore, it established a weighted graph convolution network with an attention mechanism to achieve adaptive feature fusion. Finally, under the optimization of objective function, it improved the performance of feature learning effectively. Experimental results show that the proposed joint network learning mechanism, which combined local geometric features with global structure features, enhances the representation ability and discrimination of deep features, and obtains better recognition and classification performance compared with advanced methods. This method can be used for large-scale point clouds recognition, 3D shape reconstruction and data compression. It has important research significance and broad application prospects in robot, product digital analysis, intelligent navigation, virtual reality and other fields.

Foundation Support

辽宁省科技厅资助项目(2023JH2/101300190)
辽宁省教育厅一般项目(LJ2020015)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.04.0170
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: Technology of Graphic & Image
Pages: 3828-3833
Serial Number: 1001-3695(2023)12-049-3828-06

Publish History

[2023-07-12] Accepted Paper
[2023-12-05] Printed Article

Cite This Article

张晓辉, 何金海, 兰鹏燕, 等. 局部几何与全局结构联合感知的三维形状分类方法 [J]. 计算机应用研究, 2023, 40 (12): 3828-3833. (Zhang Xiaohui, He Jinhai, Lan Pengyan, et al. 3D shape classification method based on joint graph convolution learning of local geometry and global structure [J]. Application Research of Computers, 2023, 40 (12): 3828-3833. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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