Title | Curvature-Variation-Inspired Sampling for Point Cloud Classification and Segmentation |
Author | |
Publication Years | 2022
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DOI | |
Source Title | |
ISSN | 1070-9908
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EISSN | 1558-2361
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Volume | 29Pages:1868-1872 |
Abstract | Point cloud is a discrete and unordered expression of 3D data. A lot of methods have been proposed to solve the problem in 3D object classification and scene recognition. To handle the huge amount of unordered point cloud, down-sampling before processing is needed. The shortage of existing sampling methods is the lack of geometry information consideration, which is essential for point cloud classification and segmentation tasks. Our method is mainly motivated by the observation that points with a high curvature variation can depict the outlines of objects. Thus, we propose a curvature variation based sampling method for point cloud classification and segmentation tasks. We aim to sample points with high curvature variations, which are considered to be more suitable for classification and segmentation tasks than the traditional sampling method. We combine the proposed sampling algorithm with the existing sampling method for multiple information fusion, and a higher accuracy and mean IoU can be achieved. The experimental results verify the advantage of considering curvature variation in classification and segmentation tasks. |
Keywords | |
URL | [Source Record] |
Indexed By | |
Language | English
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SUSTech Authorship | Others
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Funding Project | Leading Talents of Guangdong Province Program["2016LJ06G498","2019QN01X761"]
; Program for Guangdong Yangfan Innovative and Entrepreneurial Teams[2017YT05G026]
; Guangdong Provincial Special Fund for Modern Agriculture Common Key Technology R&D Innovation Team[2019KJ129]
; China Postdoctoral Science Foundation[2021M701576]
; National Natural Science Foundation of China["62103179","62173096"]
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WOS Research Area | Engineering
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WOS Subject | Engineering, Electrical & Electronic
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WOS Accession No | WOS:000852825500004
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Publisher | |
ESI Research Field | ENGINEERING
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Scopus EID | 2-s2.0-85137551614
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Data Source | Scopus
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PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9864034 |
Citation statistics |
Cited Times [WOS]:0
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Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/401652 |
Department | Department of Electrical and Electronic Engineering |
Affiliation | 1.Biomimetic and Intelligent Robotics Lab (BIRL), Guangdong University of Technology, Guangzhou, China 2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China |
Recommended Citation GB/T 7714 |
Zhu,Lei,Chen,Weinan,Lin,Xubin,et al. Curvature-Variation-Inspired Sampling for Point Cloud Classification and Segmentation[J]. IEEE SIGNAL PROCESSING LETTERS,2022,29:1868-1872.
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APA |
Zhu,Lei,Chen,Weinan,Lin,Xubin,He,Li,&Guan,Yisheng.(2022).Curvature-Variation-Inspired Sampling for Point Cloud Classification and Segmentation.IEEE SIGNAL PROCESSING LETTERS,29,1868-1872.
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MLA |
Zhu,Lei,et al."Curvature-Variation-Inspired Sampling for Point Cloud Classification and Segmentation".IEEE SIGNAL PROCESSING LETTERS 29(2022):1868-1872.
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