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一种面向散乱点云语义分割的深度残差特征金字塔网络框架

彭秀平 仝其胜 林洪彬 冯超 郑武

彭秀平, 仝其胜, 林洪彬, 冯超, 郑武. 一种面向散乱点云语义分割的深度残差−特征金字塔网络框架. 自动化学报, 2021, 47(12): 2831−2840 doi: 10.16383/j.aas.c190063
引用本文: 彭秀平, 仝其胜, 林洪彬, 冯超, 郑武. 一种面向散乱点云语义分割的深度残差−特征金字塔网络框架. 自动化学报, 2021, 47(12): 2831−2840 doi: 10.16383/j.aas.c190063
Peng Xiu-Ping, Tong Qi-Sheng, Lin Hong-Bin, Feng Chao, Zheng Wu. A deep residual — feature pyramid network framework for scattered point cloud semantic segmentation. Acta Automatica Sinica, 2021, 47(12): 2831−2840 doi: 10.16383/j.aas.c190063
Citation: Peng Xiu-Ping, Tong Qi-Sheng, Lin Hong-Bin, Feng Chao, Zheng Wu. A deep residual — feature pyramid network framework for scattered point cloud semantic segmentation. Acta Automatica Sinica, 2021, 47(12): 2831−2840 doi: 10.16383/j.aas.c190063

一种面向散乱点云语义分割的深度残差特征金字塔网络框架

doi: 10.16383/j.aas.c190063
基金项目: 国家重点研发计划(2017YFB0306402), 国家自然科学基金(51305390, 61601401), 河北省自然科学基金(F2016203312, E2020303188), 河北省高等学校青年拔尖人才计划项目(BJ2018018), 河北省教育厅高等学校科技计划重点项目(ZD2019039)资助
详细信息
    作者简介:

    彭秀平:燕山大学信息科学与工程学院副教授. 主要研究方向为扩频序列设计, 组合设计编码, 智能信息处理. E-mail: pengxp@ysu.edu.cn

    仝其胜:燕山大学信息科学与工程学院硕士研究生. 主要研究方向为计算机视觉, 点云感知. E-mail: tsisen@outlook.com

    林洪彬:燕山大学电气工程学院副教授. 主要研究方向为点云处理, 模式识别与计算机视觉. 本文通信作者. E-mail: honphin@ysu.edu.cn

    冯超:燕山大学信息科学与工程学院硕士研究生. 主要研究方向为计算机视觉, 同步定位与建图. E-mail: chaofenggo@163.com

    郑武:燕山大学信息科学与工程学院硕士研究生. 主要研究方向为计算机视觉, 点云感知. E-mail: zweducn@163.com

A Deep Residual Feature Pyramid Network Framework for Scattered Point Cloud Semantic Segmentation

Funds: Supported by National Key Research and Development Program of China (2017YFB0306402), National Natural Science Foundation of China (51305390, 61601401), Natural Science Foundation of Hebei Province (F2016203312, E2020303188), Young Talent Program of Colleges in Hebei Province (BJ2018018), and Key Foundation of Hebei Educational Committee (ZD2019039)
More Information
    Author Bio:

    PENG Xiu-Ping Associate professor at the School of Information Science and Engineering, Yanshan University. Her research interest covers spread spectrum sequence design, combination design coding, and intelligent information processing

    TONG Qi-Sheng Master student at the School of Information Science and Engineering, Yanshan University. His research interest covers computer vision and point cloud perception

    LIN Hong-Bin Associate professor at the School of Electrical Engineering, Yanshan University. His research interest covers point cloud processing, pattern recognition, and computer vision. Corresponding author of this paper

    FENG Chao Master student at the School of InformationScience and Engineering, Yanshan University. His research interest covers computer vision and simultaneous localization and mapping (SLAM)

    ZHENG Wu Master student at the School of Information Science and Engineering, Yanshan University. His research interest covers computer vision and point cloud perception

  • 摘要: 针对当前基于深度学习的散乱点云语义特征提取方法通用性差以及特征提取不足导致的分割精度和可靠性差的难题, 提出了一种散乱点云语义分割深度残差−特征金字塔网络框架. 首先, 针对当前残差网络在卷积方式上的局限性, 定义一种立方体卷积运算, 不仅可以通过二维卷积运算实现三维表示点的高层特征的抽取, 还可以解决现有的参数化卷积设计通用性差的问题;其次, 将定义的立方体卷积计算与残差网络相结合, 构建面向散乱点云语义分割的深度残差特征学习网络框架; 进一步, 将深度残差网络与特征金字塔网络相结合, 实现三维表示点高层特征多尺度学习与散乱点云场景语义分割. 实验结果表明, 本文提出的立方体卷积运算具有良好的适用性, 且本文提出的深度残差−特征金字塔网络框架在分割精度方面优于现存同类方法.
    1)  收稿日期 2019-01-26 录用日期 2019-07-30 Manuscript received January 26, 2019; accepted July 30, 2019 国家重点研发计划(2017YFB0306402), 国家自然科学基金 (51305390, 61601401), 河北省自然科学基金(F2016203312, E2020303188), 河北省高等学校青年拔尖人才计划项目(BJ2018018), 河北省教育厅高等学校科技计划重点项目(ZD2019039) 资助 Supported by National Key Research and Development Program of China (2017YFB0306402), National Natural Science Foundation of China (51305390, 61601401), Natural Science Foundation of Hebei Province (F2016203312, E2020303188), Young Talent Program of Colleges inHebei Province (BJ2018018), and KeyFoundation of Hebei Educational Committee (ZD2019039)
    2)  本文责任编委 黄庆明 Recommended by Associate Editor HUANG Qing-Ming 1. 燕山大学信息科学与工程学院 秦皇岛 066004 2. 燕山大学电气工程学院 秦皇岛 066004 1. School of Information Science and Engineering, YanshanUniversity, Qinhuangdao 066004 2. School of Electrical Engin-eering, Yanshan University, Qinhuangdao 066004
  • 图  1  深度残差−特征金字塔网络框架

    Fig.  1  Depth residual − feature pyramid network framework

    图  2  二维卷积

    Fig.  2  The 2D convolution

    图  3  立方体卷积

    Fig.  3  The cube convolution

    图  4  三维点云特征残差学习结构

    Fig.  4  The residual learning structure for 3D point cloud feature

    图  5  三维点云特征金字塔网络

    Fig.  5  The feature pyramid network for 3D point cloud

    表  1  参数设计

    Table  1  The parameter design

    卷积核大小 立方体边长 (m) 输出点个数 输出特征通道数
    立方体卷积 $1\times27,64$ 0.1 8 192 64
    立方体最大池化 $1\times27,64$ 0.1 2 048 64
    残差块1 $\left[\begin{aligned} 1\times1,64\;\\ 1\times27,64\\ 1\times1,256 \end{aligned}\right]\times3$ 0.2 2 048 256
    残差块2 $\left[\begin{aligned} 1\times1,12\;8\\ 1\times27,128\\ 1\times1,512\; \end{aligned}\right]\times4$ 0.4 512 512
    残差块3 $\left[ \begin{aligned}1\times1,256\;\\ 1\times27,256\\ 1\times1,1024\end{aligned}\right]\times6$ 0.8 128 1 024
    卷积层1 $1\times1,256$ 128 256
    卷积层2 $1\times1,256$ 512 256
    卷积层3 $1\times1,256$ 2 048 256
    卷积层4 $1\times1,256$ 8 192 256
    全连接层 8 192 20
    下载: 导出CSV

    表  2  S3DIS数据集分割结果比较 (%)

    Table  2  Segmentation result comparisons on the S3DIS dataset (%)

    评价指标 PointNet[7] RSNet[9] PointCNN[10] ResNet-FPN_C (本文) U-Net_C (本文)
    oAcc 78.5 88.14 89.6 88.53
    mAcc 66.2 66.45 75.61 76.83 76.98
    mIoU 47.6 56.47 65.39 67.05 67.37
    下载: 导出CSV

    表  3  S3DIS数据集各类别IoU分割结果比较 (%)

    Table  3  Comparison of IoU for all categories on the S3DIS dataset (%)

    类别 PointNet[7] RSNet[9] PointCNN[10] ResNet-FPN_C (本文) U-Net_C (本文)
    ceiling 88.0 92.48 94.78 91.99 91.46
    floor 88.7 92.83 97.30 94.99 94.12
    wall 69.3 78.56 75.82 77.04 79.00
    beam 42.4 32.75 63.25 50.29 51.92
    column 23.1 34.37 51.71 39.40 40.35
    window 47.5 51.62 58.38 65.57 65.63
    door 51.6 68.11 57.18 72.38 72.60
    table 54.1 60.13 71.63 72.20 71.57
    chair 42.0 59.72 69.12 77.10 77.56
    sofa 9.6 50.22 39.08 54.87 55.89
    bookcase 38.2 16.42 61.15 59.24 59.10
    board 29.4 44.85 52.19 53.44 54.54
    clutter 35.2 52.03 58.59 63.11 62.11
    下载: 导出CSV

    表  4  ScanNet数据集分割结果比较 (%)

    Table  4  Segmentation result comparisons on the ScanNet dataset (%)

    评价指标 ScanNet[22] PointNet[7] PointNet++[8] RSNet[9] PointCNN[10] ResNet-FPN_C (本文) U-Net_C (本文)
    $oAcc$ 73.0 73.90 84.50 85.1 85.5 85.3
    $mAcc$ 19.90 43.77 48.37 57.9 63.1 62.8
    $mIoU$ 14.69 34.26 39.35 43.7 45.0 46.5
    下载: 导出CSV

    表  5  ScanNet数据集各类别IoU分割结果比较 (%)

    Table  5  Comparison of IoU for all categories on the ScanNet dataset (%)

    类别 PointNet[7] PointNet++[8] RSNet[9] PointCNN[10] ResNet-FPN_C (本文) U-Net_C (本文)
    wall 69.44 77.48 79.23 74.5 77.1 77.8
    floor 88.59 92.50 94.10 90.7 90.6 90.8
    chair 35.93 64.55 64.99 68.8 76.4 76.6
    table 32.78 46.60 51.04 55.3 52.5 50.8
    desk 2.63 12.69 34.53 28.8 29.1 25.8
    bed 17.96 51.32 55.95 56.1 57.0 57.4
    bookshelf 3.18 52.93 53.02 38.9 42.7 42.3
    sofa 32.79 52.27 55.41 60.1 61.5 60.9
    sink 0.00 30.23 34.84 41.9 41.8 41.7
    bathtub 0.17 42.72 49.38 73.5 61.0 68.6
    toilet 0.00 31.37 54.16 73.4 69.8 73.2
    curtain 0.00 32.97 6.78 36.1 35.4 41.9
    counter 5.09 20.04 22.72 22.5 20.0 19.7
    door 0.00 2.02 3.00 7.5 26.0 28.4
    window 0.00 3.56 8.75 11.0 15.9 18.8
    shower curtain 0.00 27.43 29.92 40.6 38.1 42.6
    refrigerator 0.00 18.51 37.90 43.4 47.3 52.3
    picture 0.00 0.00 0.95 1.3 5.8 8.6
    cabinet 4.99 23.81 31.29 26.4 27.5 27.5
    other furniture 0.13 2.20 18.98 23.6 25.8 24.5
    下载: 导出CSV

    表  6  耗时比较

    Table  6  Comparison of running time

    PointNet[7] PointCNN[10] ResNet-FPN_C (本文) U-Net_C (本文)
    输入点个数 8 192 2 048 8 192 8 192
    单batch训练时间 (s) 0.035 0.047 0.060 0.140
    单batch前向传播时间 (s) 0.023 0.016 0.042 0.068
    下载: 导出CSV
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  • 收稿日期:  2019-01-26
  • 录用日期:  2019-07-30
  • 刊出日期:  2021-12-23

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