3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929553433165824 |
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| author | Kim, Younggun Cho, Beomsik Ryoo, Seonghoon Lee, Soomok |
| author_facet | Kim, Younggun Cho, Beomsik Ryoo, Seonghoon Lee, Soomok |
| contents | Adapting deep learning networks for point cloud data recognition in self-driving vehicles faces challenges due to the variability in datasets and sensor technologies, emphasizing the need for adaptive techniques to maintain accuracy across different conditions. In this paper, we introduce the 3D Adaptive Structural Convolution Network (3D-ASCN), a cutting-edge framework for 3D point cloud recognition. It combines 3D convolution kernels, a structural tree structure, and adaptive neighborhood sampling for effective geometric feature extraction. This method obtains domain-invariant features and demonstrates robust, adaptable performance on a variety of point cloud datasets, ensuring compatibility across diverse sensor configurations without the need for parameter adjustments. This highlights its potential to significantly enhance the reliability and efficiency of self-driving vehicle technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04833 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition Kim, Younggun Cho, Beomsik Ryoo, Seonghoon Lee, Soomok Computer Vision and Pattern Recognition Artificial Intelligence I.2.10; I.5.1 Adapting deep learning networks for point cloud data recognition in self-driving vehicles faces challenges due to the variability in datasets and sensor technologies, emphasizing the need for adaptive techniques to maintain accuracy across different conditions. In this paper, we introduce the 3D Adaptive Structural Convolution Network (3D-ASCN), a cutting-edge framework for 3D point cloud recognition. It combines 3D convolution kernels, a structural tree structure, and adaptive neighborhood sampling for effective geometric feature extraction. This method obtains domain-invariant features and demonstrates robust, adaptable performance on a variety of point cloud datasets, ensuring compatibility across diverse sensor configurations without the need for parameter adjustments. This highlights its potential to significantly enhance the reliability and efficiency of self-driving vehicle technology. |
| title | 3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence I.2.10; I.5.1 |
| url | https://arxiv.org/abs/2407.04833 |