GA-NET: Global Attention Network for Point Cloud Semantic Segmentation | |
Deng, Shuang1,2,3; Dong, Qiulei1,2,3 | |
刊名 | IEEE SIGNAL PROCESSING LETTERS
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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 |
DOI | 10.1109/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 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000670537600003 |
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 |
内容类型 | 期刊论文 |
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. |
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