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MSGCN: a multiscale spatio graph convolution network for 3D point clouds

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Abstract

We propose a multiscale spatio graph neural network (MSGCN) for 3D point cloud. The core of MSGCN is a multiscale spatio graph(MSG) that explicitly models the relations at various spatial scales. Different from many previous hierarchical structures, the MSG is built in a data adaptive fashion. MSG supports multiscale analysis of point clouds in the scale space and can obtain the dimensional features of point cloud data at different scales. Because traditional convolutional neural networks are not applicable to graph data with irregular vertex neighborhoods, this paper presents an sef-adaptive graph convolution kernel that uses the Chebyshev polynomial to fit an irregular convolution filter based on the theory of optimal approximation. In experiments conducted on four widely used public datasets, The results show that the proposed model outperforms most state-of-the-art methods.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This paper is supported by Opening Foundation of Key Laboratory of Computer Network and Information Integration(Southeast University), Ministry of Education (K93-9-2021-05).

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Authors and Affiliations

  1. School of Software, Nanchang Hangkong University, Nanchang, 330063, China

    Bo Wu

  2. Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, 211189, China

    Bo Wu

  3. Beihang University, Beijing, 100083, China

    Bo Wu & Bo Lang

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  1. Bo Wu
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Correspondence to Bo Wu.

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Wu, B., Lang, B. MSGCN: a multiscale spatio graph convolution network for 3D point clouds. Multimed Tools Appl 82, 35949–35968 (2023). https://doi.org/10.1007/s11042-023-14639-z

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