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GA-NET: Global Attention Network for Point Cloud Semantic Segmentation

文献类型：期刊论文

作者Deng, Shuang1,2,3; Dong, Qiulei1,2,3
刊名IEEE SIGNAL PROCESSING LETTERS
出版日期2021
卷号28页码:1300-1304
关键词Three-dimensional displays Feature extraction Semantics Computational complexity Vegetation mapping Image segmentation Feeds 3D point cloud semantic segmentation global attention convolutional neural networks deep learning
ISSN号1070-9908
DOI10.1109/LSP.2021.3082851
通讯作者Dong, Qiulei(qldong@nlpr.ia.ac.cn)
英文摘要How to learn long-range dependencies from 3D point clouds is a challenging problem in 3D point cloud analysis. Addressing this problem, we propose a global attention network for point cloud semantic segmentation, named as GA-Net, consisting of a point-independent global attention module and a point-dependent global attention module for obtaining contextual information of 3D point clouds in this paper. The point-independent global attention module simply shares a global attention map for all 3D points. In the point-dependent global attention module, for each point, a novel random cross attention block using only two randomly sampled subsets is exploited to learn the contextual information of all the points. Additionally, we design a novel point-adaptive aggregation block to replace linear skip connection for aggregating more discriminate features. Extensive experimental results on three 3D public datasets demonstrate that our method outperforms state-of-the-art methods in most cases.
资助项目National Natural Science Foundation of China[U1805264] ; National Natural Science Foundation of China[61991423] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32050100] ; Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences
WOS研究方向Engineering
语种英语
WOS记录号WOS:000670537600003
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/45240]  
专题自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者Dong, Qiulei
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Deng, Shuang,Dong, Qiulei. GA-NET: Global Attention Network for Point Cloud Semantic Segmentation[J]. IEEE SIGNAL PROCESSING LETTERS,2021,28:1300-1304.
APA Deng, Shuang,&Dong, Qiulei.(2021).GA-NET: Global Attention Network for Point Cloud Semantic Segmentation.IEEE SIGNAL PROCESSING LETTERS,28,1300-1304.
MLA Deng, Shuang,et al."GA-NET: Global Attention Network for Point Cloud Semantic Segmentation".IEEE SIGNAL PROCESSING LETTERS 28(2021):1300-1304.

入库方式： OAI收割

来源：自动化研究所

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