@inproceedings{324b56fd130441a7a1f497e412c422a7,
title = "Point-BLS: 3D Point Cloud Classification Combining Deep Learning and Broad Learning System",
abstract = "3D object recognition and detection based on point clouds is an important research topic in computer vision and autonomous navigation. Nowadays, deep learning algorithms have significantly improved the accuracy and robustness of 3D point cloud classification. However, deep learning networks usually suffer from complex network structures and time-consuming training process. In this paper, we proposed a 3D point cloud classification network Point-BLS, which combines deep learning and broad learning system together. Specifically, we first extract point cloud features through a deep learning-based feature extraction network, and then classifies them with the broad learning system. Experiments on the ModelNet40 dataset showed that our proposed network can achieve high 3D point cloud recognition accuracy of over 87%, which is better than that of a pure deep learning network with an identical backbone. In addition, the shortest training time of Point-BLS is 10.31 seconds in our experiments.",
keywords = "3D Classification, Broad Learning System, Deep Learning, Point Cloud",
author = "Yixuan Chen and Mengyin Fu and Kai Shen",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 34th Chinese Control and Decision Conference, CCDC 2022 ; Conference date: 15-08-2022 Through 17-08-2022",
year = "2022",
doi = "10.1109/CCDC55256.2022.10033601",
language = "English",
series = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2810--2815",
booktitle = "Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022",
address = "United States",
}