Multi-view SoftPool attention convolutional networks for 3D model classification
IntroductionExisting multi-view-based 3D model classification methods have the problems of insufficient view refinement feature extraction and poor generalization ability of the network model, which makes it difficult to further improve the classification accuracy. To this end, this paper proposes a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2022.1029968/full |
| _version_ | 1811225892743020544 |
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| author | Wenju Wang Xiaolin Wang Gang Chen Haoran Zhou |
| author_facet | Wenju Wang Xiaolin Wang Gang Chen Haoran Zhou |
| author_sort | Wenju Wang |
| collection | DOAJ |
| description | IntroductionExisting multi-view-based 3D model classification methods have the problems of insufficient view refinement feature extraction and poor generalization ability of the network model, which makes it difficult to further improve the classification accuracy. To this end, this paper proposes a multi-view SoftPool attention convolutional network for 3D model classification tasks.MethodsThis method extracts multi-view features through ResNest and adaptive pooling modules, and the extracted features can better represent 3D models. Then, the results of the multi-view feature extraction processed using SoftPool are used as the Query for the self-attentive calculation, which enables the subsequent refinement extraction. We then input the attention scores calculated by Query and Key in the self-attention calculation into the mobile inverted bottleneck convolution, which effectively improves the generalization of the network model. Based on our proposed method, a compact 3D global descriptor is finally generated, achieving a high-accuracy 3D model classification performance.ResultsExperimental results showed that our method achieves 96.96% OA and 95.68% AA on ModelNet40 and 98.57% OA and 98.42% AA on ModelNet10.DiscussionCompared with a multitude of popular methods, our algorithm model achieves the state-of-the-art classification accuracy. |
| first_indexed | 2024-04-12T09:15:01Z |
| format | Article |
| id | doaj.art-72ac2611a78a405191ffa25263f48ba9 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-04-12T09:15:01Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-72ac2611a78a405191ffa25263f48ba92022-12-22T03:38:52ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182022-11-011610.3389/fnbot.2022.10299681029968Multi-view SoftPool attention convolutional networks for 3D model classificationWenju WangXiaolin WangGang ChenHaoran ZhouIntroductionExisting multi-view-based 3D model classification methods have the problems of insufficient view refinement feature extraction and poor generalization ability of the network model, which makes it difficult to further improve the classification accuracy. To this end, this paper proposes a multi-view SoftPool attention convolutional network for 3D model classification tasks.MethodsThis method extracts multi-view features through ResNest and adaptive pooling modules, and the extracted features can better represent 3D models. Then, the results of the multi-view feature extraction processed using SoftPool are used as the Query for the self-attentive calculation, which enables the subsequent refinement extraction. We then input the attention scores calculated by Query and Key in the self-attention calculation into the mobile inverted bottleneck convolution, which effectively improves the generalization of the network model. Based on our proposed method, a compact 3D global descriptor is finally generated, achieving a high-accuracy 3D model classification performance.ResultsExperimental results showed that our method achieves 96.96% OA and 95.68% AA on ModelNet40 and 98.57% OA and 98.42% AA on ModelNet10.DiscussionCompared with a multitude of popular methods, our algorithm model achieves the state-of-the-art classification accuracy.https://www.frontiersin.org/articles/10.3389/fnbot.2022.1029968/full3D model classificationmulti-viewattentionSoftPoolconvolutional |
| spellingShingle | Wenju Wang Xiaolin Wang Gang Chen Haoran Zhou Multi-view SoftPool attention convolutional networks for 3D model classification Frontiers in Neurorobotics 3D model classification multi-view attention SoftPool convolutional |
| title | Multi-view SoftPool attention convolutional networks for 3D model classification |
| title_full | Multi-view SoftPool attention convolutional networks for 3D model classification |
| title_fullStr | Multi-view SoftPool attention convolutional networks for 3D model classification |
| title_full_unstemmed | Multi-view SoftPool attention convolutional networks for 3D model classification |
| title_short | Multi-view SoftPool attention convolutional networks for 3D model classification |
| title_sort | multi view softpool attention convolutional networks for 3d model classification |
| topic | 3D model classification multi-view attention SoftPool convolutional |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2022.1029968/full |
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