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GA-NET: Global Attention Network for Point Cloud Semantic Segmentation | |
Deng, Shuang1,2,3; Dong, Qiulei1,2,3 | |
发表期刊 | IEEE SIGNAL PROCESSING LETTERS |
ISSN | 1070-9908 |
2021 | |
卷号 | 28页码:1300-1304 |
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. |
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 |
DOI | 10.1109/LSP.2021.3082851 |
收录类别 | SCI |
语种 | 英语 |
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 |
项目资助者 | 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 |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Electrical & Electronic |
WOS记录号 | WOS:000670537600003 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 三维视觉 |
国重实验室规划方向分类 | 其他 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45241 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 |
通讯作者 | Dong, Qiulei |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, China 3.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
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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