M-GCN: Multi-scale Graph Convolutional Network for 3D Point Cloud Classification-Reference-Cited by-同舟云学术

M-GCN: Multi-scale Graph Convolutional Network for 3D Point Cloud Classification

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Multimedia and Expo (ICME)
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Author:

Hu Jing¹,Wang Xincheng²,Liao Ziheng¹,Xiao Tingsong³

Affiliation:

1. University of Electronic Science and Technology of China,Department of Information and Communication Engineering,Chengdu,China

2. University of Melbourne,Department of Engineering and Information Technology,Melbourne,Australia

3. University of Florida,Department of Computer & Information Science & Engineering,Gainesville,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10219544/10219545/10219655.pdf?arnumber=10219655

Reference28 articles.

1. Cross-Modality Feature Fusion Network for Few-Shot 3D Point Cloud Classification

2. A deep-shallow and global–local multi-feature fusion network for photometric stereo

3. Dual-graph learning convolutional networks for interpretable alzheimer’s disease diagnosis;xiao;Medical Image Computing and Computer Assisted Intervention– MICCAI 2022 25th International Conference Singapore September 18–22 2022 Proceedings Part VIII,2022

4. Graph convolutional network with sample and feature weights for Alzheimer’s disease diagnosis

5. Learning conditional photometric stereo with high-resolution features

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