Understanding Point Clouds of Electrical Substations Using a Multi-View Multi-Scale Fusion With Transformer
We propose a deep learning-based network model, relying on multi-view multi-scale fusion with transformer, for understanding semantical information of electrical substation point clouds. Our method is inspired by human recognition of 3D objects using information from multiple perspectives and with m...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11028987/ |
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| author | Xinghua Wang Yanxi Meng Hantuo Dong Tao Jia |
| author_facet | Xinghua Wang Yanxi Meng Hantuo Dong Tao Jia |
| author_sort | Xinghua Wang |
| collection | DOAJ |
| description | We propose a deep learning-based network model, relying on multi-view multi-scale fusion with transformer, for understanding semantical information of electrical substation point clouds. Our method is inspired by human recognition of 3D objects using information from multiple perspectives and with multiple levels of details, and it is constructed by a dynamic integration of multi-view 3D morphological representation and multi-scale 3D geometric characterization. Additionally, we leverage the channel and spatial attention mechanism to capture the relationship between morphological representations at multiple views and utilize the self and cross attention mechanism to understand the relationship between geometric characterization at multiple scales. To verify our model, experiments were conducted based on our substation point clouds dataset and two benchmark datasets. Our model shows a better performance than the state-of-the-art methods, and it achieves an overall accuracy of 91.15% on our dataset, 93.30% on ModelNet40, and 92.80% on ModelNet40-C, indicating its effectiveness and robustness. |
| format | Article |
| id | doaj-art-1d2c97b8f80c42b48dbbaf7e09ee9a08 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1d2c97b8f80c42b48dbbaf7e09ee9a082025-08-20T02:22:55ZengIEEEIEEE Access2169-35362025-01-011310049210050310.1109/ACCESS.2025.357822611028987Understanding Point Clouds of Electrical Substations Using a Multi-View Multi-Scale Fusion With TransformerXinghua Wang0Yanxi Meng1Hantuo Dong2Tao Jia3https://orcid.org/0000-0003-4921-6833Department of Infrastructure, Guangdong Power Grid Company Ltd., Guangzhou, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaPlanning Research Center, Guangdong Power Grid Company Ltd., Guangzhou, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaWe propose a deep learning-based network model, relying on multi-view multi-scale fusion with transformer, for understanding semantical information of electrical substation point clouds. Our method is inspired by human recognition of 3D objects using information from multiple perspectives and with multiple levels of details, and it is constructed by a dynamic integration of multi-view 3D morphological representation and multi-scale 3D geometric characterization. Additionally, we leverage the channel and spatial attention mechanism to capture the relationship between morphological representations at multiple views and utilize the self and cross attention mechanism to understand the relationship between geometric characterization at multiple scales. To verify our model, experiments were conducted based on our substation point clouds dataset and two benchmark datasets. Our model shows a better performance than the state-of-the-art methods, and it achieves an overall accuracy of 91.15% on our dataset, 93.30% on ModelNet40, and 92.80% on ModelNet40-C, indicating its effectiveness and robustness.https://ieeexplore.ieee.org/document/11028987/Deep learningmulti-view multi-scale fusiontransformer3D object classificationelectrical substation point clouds |
| spellingShingle | Xinghua Wang Yanxi Meng Hantuo Dong Tao Jia Understanding Point Clouds of Electrical Substations Using a Multi-View Multi-Scale Fusion With Transformer IEEE Access Deep learning multi-view multi-scale fusion transformer 3D object classification electrical substation point clouds |
| title | Understanding Point Clouds of Electrical Substations Using a Multi-View Multi-Scale Fusion With Transformer |
| title_full | Understanding Point Clouds of Electrical Substations Using a Multi-View Multi-Scale Fusion With Transformer |
| title_fullStr | Understanding Point Clouds of Electrical Substations Using a Multi-View Multi-Scale Fusion With Transformer |
| title_full_unstemmed | Understanding Point Clouds of Electrical Substations Using a Multi-View Multi-Scale Fusion With Transformer |
| title_short | Understanding Point Clouds of Electrical Substations Using a Multi-View Multi-Scale Fusion With Transformer |
| title_sort | understanding point clouds of electrical substations using a multi view multi scale fusion with transformer |
| topic | Deep learning multi-view multi-scale fusion transformer 3D object classification electrical substation point clouds |
| url | https://ieeexplore.ieee.org/document/11028987/ |
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