Research on Graph-Based Point Cloud: A Survey

Liang Xun; Li Zhiying; Jiang Hongxun

doi:10.7544/issn1000-1239.202330077

Journal of Computer Research and Development > 2024 > 61(11): 2870-2896. > DOI: 10.7544/issn1000-1239.202330077 CSTR: 32373.14.issn1000-1239.202330077

Liang Xun, Li Zhiying, Jiang Hongxun. Research on Graph-Based Point Cloud: A Survey[J]. Journal of Computer Research and Development, 2024, 61(11): 2870-2896. DOI: 10.7544/issn1000-1239.202330077

Citation:

Liang Xun, Li Zhiying, Jiang Hongxun. Research on Graph-Based Point Cloud: A Survey[J]. Journal of Computer Research and Development, 2024, 61(11): 2870-2896. DOI: 10.7544/issn1000-1239.202330077

Citation:

Liang Xun, Li Zhiying, Jiang Hongxun. Research on Graph-Based Point Cloud: A Survey[J]. Journal of Computer Research and Development, 2024, 61(11): 2870-2896. DOI: 10.7544/issn1000-1239.202330077

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Research on Graph-Based Point Cloud: A Survey

School of Information, Renmin University of China, Beijing 100872

Funds: This work was supported by the National Natural Science Foundation of China (72071203).

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Author Bio:
Liang Xun: born in 1965. PhD, professor, PhD supervisor. His main research interests include neural networks, social computing, and natural language processing

Li Zhiying: born in 1996. PhD candidate. Her main research interests include point cloud analysis, graph neural networks, and deep learning

Jiang Hongxun: born in 1974. PhD, associate professor. His main research interests include information system engineering, network finance, and social computing
Received Date: February 15, 2023
Revised Date: December 25, 2023
Accepted Date: March 05, 2024
Available Online: March 06, 2024

Graphical Abstract

Abstract

Abstract

The processing, transmission, and semantic segmentation of point clouds are important analytical tasks in the field of 3D computer vision. Nowadays, the effectiveness of graph neural networks and graph structures in point cloud research has been confirmed, and graph-based point cloud (GPC) research continues to emerge. Therefore, a unified research perspective, framework and methodology need to be formed. This paper systematically sorts out various application scenarios of GPC research, including registration, denoising, compression, representation learning, classification, segmentation, detection and other tasks, summarizes the general framework of GPC research, and proposes a technical route covering the current GPC research. Specifically, this paper gives the hierarchical concept category of GPC research, including low-level data processing, intermediate representation learning, and high-level recognition tasks. GPC models or algorithms in various fields are reviewed, including static and dynamic point cloud processing algorithms, supervised and unsupervised representation learning models, and traditional or machine learning GPC recognition algorithms. The representative achievements and their core ideas are summarized, such as dynamically updating the nearest neighbor graph at each layer of feature space, hierarchical and parameter sharing dynamic point aggregation module, and segmentation accuracy improvement employing graph partitioning as well as graph convolution. The model performances are compared, including OA (overall accuracy), mAcc (mean accuracy) and mIoU (mean intersection over union). Based on the analysis and comparison of existing models and methods, the main challenges faced by GPC are summarized, the corresponding research issues are put forward, and the future research directions are explored. The GPC research framework established in this paper is general and comprehensive, which provides field positioning, technical summary and macro perspective for subsequent researchers to engage in this new cross-field research of GPC. The emergence of point cloud research is the result of the rapid progress of detector hardware technology. The current state of point cloud research indicates some challenges between theory and practical applications, and there are still some key issues to be addressed. However, the development of point cloud research is expected to propel artificial intelligence into a new era.
- point cloud,
- graph structure,
- graph-based point cloud (GPC),
- graph signal processing,
- spatial-temporal graph,
- graph neural network

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References (179)

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