@inproceedings{3664e795102b4c8c9bd6e22e35eae183,
title = "Simple and Effective Spatial-Attended Graph CNN with Learnable Aggregation for Classification in Point Clouds",
abstract = "The main task of point cloud classification is to extract unique features from each point cloud and to accurately distinguish individual point clouds. How to extract meaningful features in point clouds to improve recognition accuracy has always been a challenge. In this work, the double-KNN dynamic graphs with different neighbor sets are proposed to process the input point cloud separately and to extract respective features. These two separate features are individually gone through the convolutional block attention module (CBAM) [1], followed by learnable aggression mechanisms to construct the final salient features for classification. The simulation shows that the proposed architecture has better performance in overall accuracy and average accuracy when testing in the ModelNet40 dataset.",
keywords = "Convolution Network, Deep learning, Point cloud classification, self-Attention module",
author = "Chang, {Min Kuan} and Wu, {Yu Lin} and Chien, {Feng Tsun} and Yang, {Guu Chang}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE Global Conference on Consumer Electronics, GCCE 2023 ; Conference date: 10-10-2023 Through 13-10-2023",
year = "2023",
doi = "10.1109/GCCE59613.2023.10315484",
language = "English",
series = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "105--106",
booktitle = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
address = "美國",
}