@inproceedings{83ade4b5d9db4739b7ff36da92807a55,
title = "CPCS: Critical Points Guided Clustering and Sampling for Point Cloud Analysis",
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.",
keywords = "Attention mechanism, Expectation maximization, Point cloud, Sampling",
author = "Wei Wang and Zhiwen Shao and Wencai Zhong and Lizhuang Ma",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 27th International Conference on Neural Information Processing, ICONIP 2020 ; Conference date: 18-11-2020 Through 22-11-2020",
year = "2020",
doi = "10.1007/978-3-030-63820-7\_37",
language = "英语",
isbn = "9783030638191",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "327--335",
editor = "Haiqin Yang and Kitsuchart Pasupa and Leung, \{Andrew Chi-Sing\} and Kwok, \{James T.\} and Chan, \{Jonathan H.\} and Irwin King",
booktitle = "Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings",
address = "德国",
}