3DGTN: 3-D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation-Reference-Cited by-同舟云学术

3DGTN: 3-D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation

Published:2024 Issue: Volume:62 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Lu Dening¹^ORCID,Gao Kyle¹^ORCID,Xie Qian²^ORCID,Xu Linlin¹^ORCID,Li Jonathan³^ORCID

Affiliation:

1. Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada

2. Department of Computer Science, University of Oxford, Oxford, U.K.

3. Department of Systems Design Engineering and the Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

China Scholarship Council under the Ph.D. Scholarship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10508408.pdf?arnumber=10508408

Reference63 articles.

1. Transformers in 3D point clouds: A survey;Lu;arXiv:2205.07417,2022

2. Hierarchical Point Cloud Transformer: A Unified Vegetation Semantic Segmentation Model for Multisource Point Clouds Based on Deep Learning

3. Dense Dual-Branch Cross Attention Network for Semantic Segmentation of Large-Scale Point Clouds

4. LSLPCT: An Enhanced Local Semantic Learning Transformer for 3-D Point Cloud Analysis

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