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RFNet: 用于三维点云分类的卷积神经网络

单铉洋 孙战里 曾志刚

单铉洋, 孙战里, 曾志刚. RFNet: 用于三维点云分类的卷积神经网络. 自动化学报, 2023, 49(11): 2350−2359 doi: 10.16383/j.aas.c210532
引用本文: 单铉洋, 孙战里, 曾志刚. RFNet: 用于三维点云分类的卷积神经网络. 自动化学报, 2023, 49(11): 2350−2359 doi: 10.16383/j.aas.c210532
Shan Xuan-Yang, Sun Zhan-Li, Zeng Zhi-Gang. RFNet: Convolutional neural network for 3D point cloud classification. Acta Automatica Sinica, 2023, 49(11): 2350−2359 doi: 10.16383/j.aas.c210532
Citation: Shan Xuan-Yang, Sun Zhan-Li, Zeng Zhi-Gang. RFNet: Convolutional neural network for 3D point cloud classification. Acta Automatica Sinica, 2023, 49(11): 2350−2359 doi: 10.16383/j.aas.c210532

RFNet: 用于三维点云分类的卷积神经网络

doi: 10.16383/j.aas.c210532
基金项目: 国家自然科学基金(61972002)资助
详细信息
    作者简介:

    单铉洋:安徽大学电气工程与自动化学院硕士研究生. 主要研究方向为模式识别, 计算机视觉和深度学习. E-mail: shanxy128@163.com

    孙战里:安徽大学人工智能学院教授. 主要研究方向为模式识别, 机器学习和图像信号处理. 本文通信作者. E-mail: zhlsun2006@126.com

    曾志刚:华中科技大学人工智能与自动化学院教授. 主要研究方向为神经网络, 智能计算和模式识别. E-mail: zgzeng@hust.edu.cn

RFNet: Convolutional Neural Network for 3D Point Cloud Classification

Funds: Supported by National Natural Science Foundation of China (61972002)
More Information
    Author Bio:

    SHAN Xuan-Yang Master student at the School of Electrical Engineering and Automation, Anhui University. His research interest covers pattern recognition, computer vision, and deep learning

    SUN Zhan-Li Professor at the School of Artificial Intelligence, Anhui University. His research inter-est covers pattern recognition, machine learning, and image & signal processing. Corresponding author of this paper

    ZENG Zhi-Gang Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers neural networks, computational intelligence, and pattern recognition

  • 摘要: 由于点云的非结构性和无序性, 目前已有的点云分类网络在精度上仍然需要进一步提高. 通过考虑局部结构的构建、全局特征聚合和损失函数改进三个方面, 构造一个有效的点云分类网络. 首先, 针对点云的非结构性, 通过学习中心点特征与近邻点特征之间的关系, 为不规则的近邻点分配不同的权重, 以此构建局部结构; 然后, 使用注意力思想, 提出加权平均池化(Weighted average pooling, WAP), 通过自注意力方式, 学习每个高维特征的注意力分数, 在应对点云无序性的同时, 可以有效地聚合冗余的高维特征; 最后, 利用交叉熵损失与中心损失之间的互补关系, 提出联合损失函数(Joint loss function, JL), 在增大类间距离的同时, 减小类内距离, 进一步提高了网络的分类能力. 在合成数据集ModelNet40、ShapeNetCore和真实世界数据集ScanObjectNN上进行实验, 与目前性能最好的多个网络相比较, 验证了该整体网络结构的优越性.
  • 图  1  整体网络结构图

    Fig.  1  Diagram of overall network structure

    图  2  中心点和近邻点的局部结构图

    Fig.  2  Diagram of local structure between the central point and its neighbors

    图  3  局部特征提取结构图

    Fig.  3  Diagram of local feature extraction structure

    图  4  加权平均池化结构图

    Fig.  4  Diagram of weighted average pooling structure

    图  5  联合损失结构图

    Fig.  5  Diagram of joint loss structure

    图  6  混淆矩阵

    Fig.  6  Confusion matrix

    图  7  采样密度实验结果对比

    Fig.  7  Comparison of experiment results with different sampling densities

    图  8  添加高斯噪声前/后点云样本的对照图

    Fig.  8  Comparison of one sample with and without Gaussian noise

    表  1  实验配置

    Table  1  Experimental configuration

    项目详情
    操作系统CentOS Linux7
    GPUTesla V100 32 GB
    CUDA10.0
    CUDNN7.0
    Python3.6.0
    Pytorch1.3.0
    下载: 导出CSV

    表  2  在ModelNet40数据集上的实验结果

    Table  2  Experimental results on ModelNet40

    方法输入mAcc (%)OA (%)
    PointNet1 k86.089.2
    PointNet++1 k90.7
    Spec-GCN1 k91.5
    DGCNN1 k90.292.9
    RSCNN1 k92.9
    PCT1 k93.2
    ECC[22]1 k83.287.4
    RMFP-DNN[23]1 k88.992.6
    PointCNN[24]1 k88.192.2
    Point2sequence[25]1 k90.492.6
    Point Transformer [26]1 k92.8
    Octant-CNN[27]1 k88.791.9
    DRNet[28]1 k93.1
    AdaptConv[29]1 k90.793.4
    RFNet1 k91.293.6
    PointNet++5 k + nor91.9
    DGCNN2 k90.793.5
    RSCNN1 k + voting93.6
    SpiderCNN5 k + nor92.4
    RFNet2 k91.494.0
    下载: 导出CSV

    表  3  不同网络的易错模型分类对比

    Table  3  Classification comparison of error-prone models for different networks

    易错模型真实标签PointNetDGCNNRFNet
    植物植物植物植物
    花盆植物植物植物
    花瓶花盆花瓶花瓶
    花瓶瓶子花瓶花瓶
    杯子花盆花盆杯子
    下载: 导出CSV

    表  4  在ShapeNetCore数据集上的实验结果

    Table  4  Experimental results on ShapeNetCore

    方法输入OA (%)
    PointNet1 k83.7
    PointNet++1 k85.1
    DGCNN1 k84.7
    Point2sequence1 k85.2
    SpiderCNN1 k + nor85.3
    KPConv[30]1 k86.2
    RFNet1 k88.3
    下载: 导出CSV

    表  5  在ScanObjectNN数据集上的实验结果 (%)

    Table  5  Experimental results on ScanObjectNN (%)

    方法mAccOABagBinBoxSofaDeskShelfTableDoorBedCabinetChairDisplayPillowSinkToilet
    PointNet 63.4 68.2 36.1 69.8 10.5 76.7 50.0 72.6 67.8 93.8 61.8 62.6 89.0 73.0 67.6 64.2 55.3
    PointNet++ 75.4 77.9 49.4 84.4 31.6 90.5 74.0 72.6 72.6 85.2 75.5 77.4 91.3 79.4 81.0 80.8 85.9
    DGCNN 73.6 78.1 49.4 82.4 33.1 91.4 63.3 79.3 77.4 89.0 64.5 83.9 91.8 77.0 77.1 75.0 69.4
    PointCNN 75.1 78.5 57.8 82.9 33.1 91.9 65.3 84.2 67.4 84.8 80.0 83.6 92.6 78.4 80.0 72.5 71.8
    SpiderCNN 69.8 73.7 43.4 75.9 12.8 90.5 65.3 78.0 65.9 91.4 69.1 74.2 89.0 74.5 80.0 65.8 70.6
    RFNet 76.3 79.6 54.0 81.9 34.1 92.2 74.1 79.9 72.1 91.5 81.3 83.5 91.3 80.2 82.6 78.9 67.0
    下载: 导出CSV

    表  6  不同模块的消融实验 (%)

    Table  6  Ablation experiment of different modules (%)

    方法RFConvWAPJLmAccOA
    网络0×××90.292.9
    网络1××91.093.3
    网络2×91.193.5
    网络3×91.193.4
    网络491.293.6
    下载: 导出CSV

    表  7  高斯噪声鲁棒性实验 (%)

    Table  7  Robustness experiment of Gaussian noise (%)

    方法无高斯噪声 有高斯噪声
    mAccOAmAccOA
    PointNet86.089.2 83.487.6
    PointNet++90.789.6
    DGCNN90.292.989.792.6
    RSCNN92.992.3
    RFNet91.293.691.093.5
    下载: 导出CSV

    表  8  不同特征关系的消融实验 (%)

    Table  8  Ablation studies about different relationships between features (%)

    方法${e_{ij}}$$di{s_{ij}}$${l_{ij}}$${s_{ij}}$OA
    网络1×××93.3
    网络2××93.5
    网络3×93.6
    网络4××93.1
    网络5×93.5
    网络693.2
    下载: 导出CSV

    表  9  网络的复杂度对比

    Table  9  Comparison of network complexity

    方法参数量(M)浮点运算数(G)OA (%)
    PointNet3.470.4589.2
    PointNet++1.744.0991.9
    DGCNN1.812.4392.9
    PCT2.882.3293.2
    KPConv14.3092.9
    RFNet2.362.9593.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-15
  • 录用日期:  2022-02-10
  • 网络出版日期:  2022-05-05
  • 刊出日期:  2023-11-22

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