A Review of Point Cloud 3D Object Detection Methods Based on Deep Learning

doi:10.1007/978-981-99-8764-1_3

	A Review of Point Cloud 3D Object Detection Methods Based on Deep Learning
	Wang, Xiyuan; Lin, Jie; Yang, Longrui; Wang, Sicong
	2024
Conference Name	38th CCF National Conference of Computer Applications, CCF NCCA 2023
Source Publication	Communications in Computer and Information Science
Volume	1959 CCIS
Pages	30-39
Conference Date	July 16, 2023 - July 20, 2023
Conference Place	Suzhou, China
Publisher	Springer Science and Business Media Deutschland GmbH
Abstract	Based on introducing the coupling relationship between deep learning and three-dimensional point clouds, this paper reviews the three characteristics and research problems of point clouds, randomness, sparsity, and unstructuredness, and discusses three-dimensional point cloud target detection based on deep neural networks, including point cloud detection techniques following graph convolution, detection techniques following the original point cloud, and detection algorithms based on fusion processing of graph convolution and the original point cloud. Focusing on future research direction and development, the field of point cloud analysis is currently undergoing further development through the application of deep learning techniques. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword	Convolutional neural networks - Deep neural networks - Learning systems - Object detection - Object recognition - Semantic Segmentation - Semantics 3D object - 3D point cloud - Coupling relationships - Deep leaning - Object detection method - Point-clouds - Research problems - Semantic segmentation - Targets detection - Three-dimensional point clouds
DOI	10.1007/978-981-99-8764-1_3
Indexed By	EI
Language	英语
EI Accession Number	20235215288801
EI Keywords	Convolution
EI Classification Number	461.4 Ergonomics and Human Factors Engineering - 716.1 Information Theory and Signal Processing - 723.2 Data Processing and Image Processing - 723.4 Artificial Intelligence
ISSN	1865-0929
Original literature type	Conference article (CA)
EISSN	1865-0937
Citation statistics	none
Document Type	会议论文
Identifier	https://ir.lut.edu.cn/handle/2XXMBERH/169323
Collection	电气工程与信息工程学院
Corresponding Author	Lin, Jie
Affiliation	College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
First Author Affilication	Lanzhou University of Technology
Corresponding Author Affilication	Lanzhou University of Technology
Recommended Citation GB/T 7714	Wang, Xiyuan,Lin, Jie,Yang, Longrui,et al. A Review of Point Cloud 3D Object Detection Methods Based on Deep Learning[C]:Springer Science and Business Media Deutschland GmbH,2024:30-39.

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