3D Model Classification Based on GCN and SVM
3D model classification is an important task. Now, 3D model is usually expressed as point cloud. Disorder of point cloud brings great difficulty into 3D model classification. In order to classify 3D model correctly, a new classification method combining Graph Convolution Network (GCN) and Support Ve...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9955507/ |
| _version_ | 1797980093092462592 |
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| author | Xue-Yao Gao Qing-Xian Yuan Chun-Xiang Zhang |
| author_facet | Xue-Yao Gao Qing-Xian Yuan Chun-Xiang Zhang |
| author_sort | Xue-Yao Gao |
| collection | DOAJ |
| description | 3D model classification is an important task. Now, 3D model is usually expressed as point cloud. Disorder of point cloud brings great difficulty into 3D model classification. In order to classify 3D model correctly, a new classification method combining Graph Convolution Network (GCN) and Support Vector Machine (SVM) is proposed in this paper. Point cloud is sampled. K-Nearest Neighbor (KNN) algorithm is used to find K nearest points of sampling point, and adjacency matrix is established for graph convolution operation. Shape features D1, D2, D3 and A3 of sampling point are computed based on its K nearest points. Coordinates and shape features of sampling point are combined as discriminative feature. 2-layer graph convolution is used to aggregate disambiguation information of 1-degree and 2-degree adjacent points of sampling point for describing point cloud comprehensively. At the same time, maximum pooling and average pooling are adopted to retain representative information. Finally, SVM is used to classify point clouds. Experimental results show that compared with GCN based on coordinates, the proposed network improves accuracy of 3D model classification by 1.67%. Global and local information can be extracted adequately when 1024 points are sampled from point cloud. When we select 20 nearest points to compute shape features D1, D2, D3, A3, local information of point can be described better. Shape features D1, D2, D3, A3 are combined with coordinates to describe shape and structure of point cloud better. 2-layer graph convolutions are adopted to aggregate information of 1-degree and 2-degree nodes for extracting effective disambiguation features. |
| first_indexed | 2024-04-11T05:48:52Z |
| format | Article |
| id | doaj.art-6e53862c95244c3e994dafa2117d2257 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T05:48:52Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6e53862c95244c3e994dafa2117d22572022-12-22T04:42:08ZengIEEEIEEE Access2169-35362022-01-011012149412150710.1109/ACCESS.2022.322338499555073D Model Classification Based on GCN and SVMXue-Yao Gao0Qing-Xian Yuan1https://orcid.org/0000-0001-6576-3429Chun-Xiang Zhang2https://orcid.org/0000-0002-7676-6630School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China3D model classification is an important task. Now, 3D model is usually expressed as point cloud. Disorder of point cloud brings great difficulty into 3D model classification. In order to classify 3D model correctly, a new classification method combining Graph Convolution Network (GCN) and Support Vector Machine (SVM) is proposed in this paper. Point cloud is sampled. K-Nearest Neighbor (KNN) algorithm is used to find K nearest points of sampling point, and adjacency matrix is established for graph convolution operation. Shape features D1, D2, D3 and A3 of sampling point are computed based on its K nearest points. Coordinates and shape features of sampling point are combined as discriminative feature. 2-layer graph convolution is used to aggregate disambiguation information of 1-degree and 2-degree adjacent points of sampling point for describing point cloud comprehensively. At the same time, maximum pooling and average pooling are adopted to retain representative information. Finally, SVM is used to classify point clouds. Experimental results show that compared with GCN based on coordinates, the proposed network improves accuracy of 3D model classification by 1.67%. Global and local information can be extracted adequately when 1024 points are sampled from point cloud. When we select 20 nearest points to compute shape features D1, D2, D3, A3, local information of point can be described better. Shape features D1, D2, D3, A3 are combined with coordinates to describe shape and structure of point cloud better. 2-layer graph convolutions are adopted to aggregate information of 1-degree and 2-degree nodes for extracting effective disambiguation features.https://ieeexplore.ieee.org/document/9955507/3D modelpoint cloudgraph convolution networksupport vector machinek-nearest neighborshape features |
| spellingShingle | Xue-Yao Gao Qing-Xian Yuan Chun-Xiang Zhang 3D Model Classification Based on GCN and SVM IEEE Access 3D model point cloud graph convolution network support vector machine k-nearest neighbor shape features |
| title | 3D Model Classification Based on GCN and SVM |
| title_full | 3D Model Classification Based on GCN and SVM |
| title_fullStr | 3D Model Classification Based on GCN and SVM |
| title_full_unstemmed | 3D Model Classification Based on GCN and SVM |
| title_short | 3D Model Classification Based on GCN and SVM |
| title_sort | 3d model classification based on gcn and svm |
| topic | 3D model point cloud graph convolution network support vector machine k-nearest neighbor shape features |
| url | https://ieeexplore.ieee.org/document/9955507/ |
| work_keys_str_mv | AT xueyaogao 3dmodelclassificationbasedongcnandsvm AT qingxianyuan 3dmodelclassificationbasedongcnandsvm AT chunxiangzhang 3dmodelclassificationbasedongcnandsvm |