LBNP: Learning features between neighboring points for point cloud classification

Inspired by classical works, when constructing local relationships in point clouds, there is always a geometric description of the central point and its neighboring points. However, the basic geometric representation of the central point and its neighborhood is insufficient. Drawing inspiration from...

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Vydané v:	PloS one Ročník 20; číslo 1; s. e0314086
Hlavní autori:	Wang, Lei, Huang, Ming, Yang, Zhenqing, Wu, Rui, Qiu, Dashi, Xiao, Xingxing, Li, Dong, Chen, Cai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	San Francisco Public Library of Science 06.01.2025 Public Library of Science (PLoS)
Predmet:	Algorithms Analysis Biology and Life Sciences Classification Cloud classification Computer and Information Sciences Computer vision Deep learning Image processing Learning Machine vision Methods Neighborhoods Neural networks Physical Sciences Research and Analysis Methods Social Sciences China
ISSN:	1932-6203, 1932-6203
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Abstract	Inspired by classical works, when constructing local relationships in point clouds, there is always a geometric description of the central point and its neighboring points. However, the basic geometric representation of the central point and its neighborhood is insufficient. Drawing inspiration from local binary pattern algorithms used in image processing, we propose a novel method for representing point cloud neighborhoods, which we call Point Cloud Local Auxiliary Block (PLAB). This module explores useful neighborhood features by learning the relationships between neighboring points, thereby enhancing the learning capability of the model. In addition, we propose a pure Transformer structure that takes into account both local and global features, called Dual Attention Layer (DAL), which enables the network to learn valuable global features as well as local features in the aggregated feature space. Experimental results show that our method performs well on both coarse- and fine-grained point cloud datasets. We will publish the code and all experimental training logs on GitHub.
AbstractList	Inspired by classical works, when constructing local relationships in point clouds, there is always a geometric description of the central point and its neighboring points. However, the basic geometric representation of the central point and its neighborhood is insufficient. Drawing inspiration from local binary pattern algorithms used in image processing, we propose a novel method for representing point cloud neighborhoods, which we call Point Cloud Local Auxiliary Block (PLAB). This module explores useful neighborhood features by learning the relationships between neighboring points, thereby enhancing the learning capability of the model. In addition, we propose a pure Transformer structure that takes into account both local and global features, called Dual Attention Layer (DAL), which enables the network to learn valuable global features as well as local features in the aggregated feature space. Experimental results show that our method performs well on both coarse- and fine-grained point cloud datasets. We will publish the code and all experimental training logs on GitHub. Inspired by classical works, when constructing local relationships in point clouds, there is always a geometric description of the central point and its neighboring points. However, the basic geometric representation of the central point and its neighborhood is insufficient. Drawing inspiration from local binary pattern algorithms used in image processing, we propose a novel method for representing point cloud neighborhoods, which we call Point Cloud Local Auxiliary Block (PLAB). This module explores useful neighborhood features by learning the relationships between neighboring points, thereby enhancing the learning capability of the model. In addition, we propose a pure Transformer structure that takes into account both local and global features, called Dual Attention Layer (DAL), which enables the network to learn valuable global features as well as local features in the aggregated feature space. Experimental results show that our method performs well on both coarse- and fine-grained point cloud datasets. We will publish the code and all experimental training logs on GitHub.Inspired by classical works, when constructing local relationships in point clouds, there is always a geometric description of the central point and its neighboring points. However, the basic geometric representation of the central point and its neighborhood is insufficient. Drawing inspiration from local binary pattern algorithms used in image processing, we propose a novel method for representing point cloud neighborhoods, which we call Point Cloud Local Auxiliary Block (PLAB). This module explores useful neighborhood features by learning the relationships between neighboring points, thereby enhancing the learning capability of the model. In addition, we propose a pure Transformer structure that takes into account both local and global features, called Dual Attention Layer (DAL), which enables the network to learn valuable global features as well as local features in the aggregated feature space. Experimental results show that our method performs well on both coarse- and fine-grained point cloud datasets. We will publish the code and all experimental training logs on GitHub.
Audience	Academic
Author	Chen, Cai Wu, Rui Li, Dong Huang, Ming Qiu, Dashi Yang, Zhenqing Xiao, Xingxing Wang, Lei
AuthorAffiliation	1 School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing, China 3 Beijing Construction Engineering Group, Beijing, China National University of Sciences and Technology NUST, PAKISTAN 2 School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing, China
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Cites_doi	10.1109/CVPR52729.2023.00516 10.1109/TIP.2020.3019925 10.1109/ICCV48922.2021.00095 10.1007/s41095-021-0229-5 10.1109/CVPR52688.2022.01837 10.1109/CVPR46437.2021.00319 10.1109/TGRS.2024.3393845 10.1109/CVPR52733.2024.00463 10.1109/CVPR42600.2020.01112 10.1109/CVPR.2018.00979 10.1007/s00371-023-02826-w 10.1109/TPAMI.2002.1017623 10.1109/CVPR52688.2022.00831 10.1109/ICCV48922.2021.00986 10.1109/LRA.2018.2850061 10.1109/CVPR.2017.16 10.1109/TMM.2021.3074240 10.1007/s00371-023-02816-y 10.1109/ICCV.2015.114 10.1109/LRA.2022.3147907 10.1109/TIP.2020.3048623 10.1109/JAS.2023.123432 10.1109/TPAMI.2021.3137794 10.1145/3326362 10.1109/ICRA40945.2020.9197499
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Snippet	Inspired by classical works, when constructing local relationships in point clouds, there is always a geometric description of the central point and its...
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SubjectTerms	Algorithms Analysis Biology and Life Sciences Classification Cloud classification Computer and Information Sciences Computer vision Deep learning Image processing Learning Machine vision Methods Neighborhoods Neural networks Physical Sciences Research and Analysis Methods Social Sciences
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Title	LBNP: Learning features between neighboring points for point cloud classification
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