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Deep 3D point cloud classification and segmentation network based on GateNet

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Abstract

With the gradual growth of deep learning in machine vision, efficient extraction of 3D point clouds becomes significant. The raw data of the 3D point cloud are sparse, disordered, and immersed in noise, which makes it difficult to classify and segment. Whether 3D point clouds can be classified and segmented or not, the local feature is an essential ingredient. Therefore, this paper proposes a GateNet-based PointNet++ network (G-PointNet++). G-PointNet++ extracts local features more accurately than PointNet++ by suppressing irrelevant features and emphasizing important features. Meanwhile, it refines the feature adaptively. Besides, the SENet and attention mechanism are introduced into PointNet++. G-PointNet++ was evaluated on the public ModelNet dataset, ShapeNet dataset, and S3DIS dataset, and its effectiveness in classification and segmentation tasks was verified. In the classification task, G-PointNet++ achieves an overall classification accuracy (OA) of 95.5% on ModelNet10 and 93.3% on ModelNet40. In the segmentation task, the mIoU of G-PointNet++ reaches 85.5% on ShapeNet. These experiments show that G-PointNet++ achieves better performance and saves more time than PointNet++, and its overall accuracy is higher than that of PCT network on ModeNet40.

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Data availability statement

All data in the manuscript can be found at: https://modelnet.cs.princeton.edu/ and https://shapenet.org/. All other data are available from the authors upon reasonable request.

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Authors and Affiliations

  1. Department of Automation, School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China

    Hui Liu & Shuaihua Tian

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  1. Hui Liu
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This work was funded by the National Natural Science Foundation of China (Under Grant: 62176018).

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Liu, H., Tian, S. Deep 3D point cloud classification and segmentation network based on GateNet. Vis Comput 40, 971–981 (2024). https://doi.org/10.1007/s00371-023-02826-w

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