中文版 | English
Title

Curvature-Variation-Inspired Sampling for Point Cloud Classification and Segmentation

Author
Publication Years
2022
DOI
Source Title
ISSN
1070-9908
EISSN
1558-2361
Volume29Pages: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
SUSTech Authorship
Others
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"]
WOS Research Area
Engineering
WOS Subject
Engineering, Electrical & Electronic
WOS Accession No
WOS:000852825500004
Publisher
ESI Research Field
ENGINEERING
Scopus EID
2-s2.0-85137551614
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9864034
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/401652
DepartmentDepartment 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.
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.
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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