Abstract
In this paper, we propose an attention-based dynamic graph CNN method for point cloud classification. We introduce an efficient channel attention module into each edge convolution block of dynamic graph CNN (DGCNN) to obtain more discriminative and stable features. Our experimental results show that, compared with DGCNN, our method improves not only the classification accuracy but also the stability.
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Acknowledgment
This work was supported by the State Key Laboratory of Precision Blasting, Jianghan University (No. PBSKL2022201), the Scientific Research Program of Jianghan University (No. 2021yb052), the Graduate Innovation Fund of the Jianghan University, the National College Students’ Innovation and Entrepreneurship Training Program of China (Grant No. S202211072042), and the Undergraduate Research Projects of the Jianghan University (Grant Nos. 2021Bczd006, 2021Bczd007, and 2022zd096).
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Wang, J. et al. (2022). Attention-Based Dynamic Graph CNN for Point Cloud Classification. In: Yang, S., Lu, H. (eds) Artificial Intelligence and Robotics. ISAIR 2022. Communications in Computer and Information Science, vol 1700. Springer, Singapore. https://doi.org/10.1007/978-981-19-7946-0_30
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DOI: https://doi.org/10.1007/978-981-19-7946-0_30
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