Opto-Electronic Engineering, Volume. 50, Issue 10, 230166-1(2023)

Multi-resolution feature fusion for point cloud classification and segmentation network

Zhiyong Tao1, Heng Li1、*, Miaosen Dou1, and Sen Lin2
Author Affiliations
  • 1School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125100, China
  • 2School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, Liaoning 110159, China
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    To address the problem that existing networks find it difficult to learn local geometric information of point cloud effectively, a graph convolutional network that fuses multi-resolution features of point cloud is proposed. First, the local graph structure of the point cloud is constructed by the k-nearest neighbor algorithm to better represent the local geometric structure of the point cloud. Second, a parallel channel branch is proposed based on the farthest point sampling algorithm, which obtains point clouds with different resolutions by downsampling them and then groups them. To overcome the sparse characteristics of the point cloud, a geometric mapping module is proposed to perform normalization operations on the grouped point cloud. Finally, a feature fusion module is proposed to aggregate graph features and multi-resolution features to obtain global features more effectively. Experiments are evaluated using ModelNet40, ScanObjectNN, and ShapeNet Part datasets. The experimental results show that the proposed network has state-of-the-art classification and segmentation performance.

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    Zhiyong Tao, Heng Li, Miaosen Dou, Sen Lin. Multi-resolution feature fusion for point cloud classification and segmentation network[J]. Opto-Electronic Engineering, 2023, 50(10): 230166-1

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    Paper Information

    Category: Research Articles

    Received: Jul. 7, 2023

    Accepted: Sep. 20, 2023

    Published Online: Jan. 22, 2024

    The Author Email: Li Heng (李衡)

    DOI:10.12086/oee.2023.230166

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