Abstract
3D vision based on irregular point sequences has gained increasing attention, with current methods depending on random or farthest point sampling. However, the existing sampling methods either measure the distance in the Euclidean space and ignore the high-level properties, or just sample from point clouds only with the largest distance. To tackle these limitations, we introduce the Expectation-Maxi mization Attention module, to find the critical subset points and cluster the other points around them. Moreover, we explore a point cloud sampling strategy to sample points based on the critical subset. Extensive experiments demonstrate the effectiveness of our method for several popular point cloud analysis tasks. Our module achieves the accuracy of 93.3% on ModelNet40 with only 1024 points for classification task.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61972157 and No. 61902129), the National Key R&D Program of China (No. 2019YFC1521104), the Shanghai Pujiang Talent Program (No. 19PJ1403100), and the Economy and Informatization Commission of Shanghai Municipality (No. XX-RGZN-01-19-6348).
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Wang, W., Shao, Z., Zhong, W., Ma, L. (2020). CPCS: Critical Points Guided Clustering and Sampling for Point Cloud Analysis. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_37
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DOI: https://doi.org/10.1007/978-3-030-63820-7_37
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