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Laser & Optoelectronics Progress, Volume. 57, Issue 4, 040002(2020)

Review of Semantic Segmentation of Point Cloud Based on Deep Learning

Jiaying Zhang, Xiaoli Zhao*, and Zheng Chen
Author Affiliations
  • School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
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    Figures&Tables (26)
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    References (79)
    Cited By (7)
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    Figures & Tables(26)
    Fine-grained annotations of 24 objects in the PartNet dataset[26]

    Fig. 1. Fine-grained annotations of 24 objects in the PartNet dataset[26]

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    Models of chairs and laptops in the ShapeNet Part dataset[28]

    Fig. 2. Models of chairs and laptops in the ShapeNet Part dataset[28]

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    Point cloud scene and semantic segmentation diagram in S3DIS dataset[29]

    Fig. 3. Point cloud scene and semantic segmentation diagram in S3DIS dataset[29]

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    Annotated indoor scene maps in ScanNet dataset[30]

    Fig. 4. Annotated indoor scene maps in ScanNet dataset[30]

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    Examples of Semantic3D dataset[31]. (a) Point cloud scene; (b) diagram of intensity; (c) diagram of semantic segmentation

    Fig. 5. Examples of Semantic3D dataset[31]. (a) Point cloud scene; (b) diagram of intensity; (c) diagram of semantic segmentation

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    Semantic segmentation results of outdoor scene in vKITTI dataset[32]

    Fig. 6. Semantic segmentation results of outdoor scene in vKITTI dataset[32]

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    Visual representation of point cloud semantic segmentation methods

    Fig. 7. Visual representation of point cloud semantic segmentation methods

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    Framework of multi-view convolutional neural network (MVCNN)[37]

    Fig. 8. Framework of multi-view convolutional neural network (MVCNN)[37]

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    Overall flow of semantic marks of SnapNet-R network [39]

    Fig. 9. Overall flow of semantic marks of SnapNet-R network [39]

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    Overall architecture of semantic segmentation network of SEGCloud[49]

    Fig. 10. Overall architecture of semantic segmentation network of SEGCloud[49]

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    Framework of PointNet for point cloud classification and segmentation[16]

    Fig. 11. Framework of PointNet for point cloud classification and segmentation[16]

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    Overall framework of RSNet for point cloud semantic segmentation[53]

    Fig. 12. Overall framework of RSNet for point cloud semantic segmentation[53]

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    Point cloud semantic segmentation network of SO-Net formed by SOM[54]

    Fig. 13. Point cloud semantic segmentation network of SO-Net formed by SOM[54]

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    Applications of hierarchical convolution in regular gird and point clouds, and PointCNN framework used for semantic segmentation[18]. (a) Application of hierarchical convolution; (b) PointCNN framework

    Fig. 14. Applications of hierarchical convolution in regular gird and point clouds, and PointCNN framework used for semantic segmentation[18]. (a) Application of hierarchical convolution; (b) PointCNN framework

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    Architecture of PointNet++ for point cloud classification and segmentation[17]

    Fig. 15. Architecture of PointNet++ for point cloud classification and segmentation[17]

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    Overall architectures of PointSIFT module and point segementation of PointSIFT[58]. (a) Structure; (b) whole architecture

    Fig. 16. Overall architectures of PointSIFT module and point segementation of PointSIFT[58]. (a) Structure; (b) whole architecture

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    Architecture of A-CNN for point cloud classification and segmentation[61]

    Fig. 17. Architecture of A-CNN for point cloud classification and segmentation[61]

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    Point cloud semantic segmentation network of 3DMAX-Net[60] (MS-FLB: multi-scale feature learning block; LGAB: local and global feature aggregation block)

    Fig. 18. Point cloud semantic segmentation network of 3DMAX-Net[60] (MS-FLB: multi-scale feature learning block; LGAB: local and global feature aggregation block)

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    Framework of 3P-RNN for point cloud semantic segmentation[62]

    Fig. 19. Framework of 3P-RNN for point cloud semantic segmentation[62]

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    Network structural diagram of LDGCNN for point cloud classification and segmentation[65]

    Fig. 20. Network structural diagram of LDGCNN for point cloud classification and segmentation[65]

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    Network structural diagram of RGCNN for point cloud classification and segmentation[67]

    Fig. 21. Network structural diagram of RGCNN for point cloud classification and segmentation[67]

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    Network framework of GAPNet for point cloud semantic segmentation[68]

    Fig. 22. Network framework of GAPNet for point cloud semantic segmentation[68]

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    Forward time of different network models

    Fig. 23. Forward time of different network models

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    • Table 1. Common datasets of point cloud segmentation

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      Table 1. Common datasets of point cloud segmentation

      DatasetNumber ofcategoriesNumber oftraining setsNumber ofverification setsNumber oftesting sets
      PartNet[26]24---
      UWA Dataset[27]55---
      ShapeNet Part[28]161213718702874
      S3DIS[29]13224-48
      ScanNet[30]211201-312
      Semantic3D[31]815-15
      KITTI(Zhang)[34]10140-112
      KITTI(Ros)[35]11170-46
      vKITTI[32]13---

    • Table 2. Network parameter quantity of different semantic segmentation models

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      Table 2. Network parameter quantity of different semantic segmentation models

      ModelNetwork parameter quantity /MB
      Subvolume16.6
      MVCNN60
      PointNet3.48
      PointNet++1.48
      DGCNN1.84
      LDGCNN1.08
      PointCNN0.6

    • Table 3. Segmentation results of different models on typical point cloud datasets

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      Table 3. Segmentation results of different models on typical point cloud datasets

      ModelS3DISScanNetShapeNet PartSemantic 3DvKITTI
      mIoUOA /%mIoUOA /%mIoUmIoUOA /%mIoUOA /%
      Yi[69]----81.4----
      KD-Net[47]----82.3----
      SEGCloud[49]48.92--73.079.461.388.1--
      PointNet[16]47.7178.6214.6973.983.7--34.479.7
      PN++(SSG)[17]---83.3-----
      PN++(MSG+DP)[17]--34.2684.585.1----
      PN++(MRG+DP)[17]---83.4-----
      O-CNN+CRF[70]----85.9----
      SSCNN[71]----84.7----
      MS+CU[72]47.879.2-------
      G+RCU[72]49.781.1-----36.280.6
      DGCNN[64]56.184.1--85.1----
      RGCNN[67]----84.3----
      RSNet[53]53.8361.8139.35-84.9----
      SO-Net[54]----84.6----
      TMLC-MSR[73]-----54.286.2--
      DeePr3SS[74]-----58.588.9--
      SnapNet[38]-----59.188.6--
      SGPN[75]50.3780.78--85.8----
      SpiderCNN[59]----85.3----
      3DMAX-Net[60]47.579.5-------
      SPGraph[76]62.185.5---73.294.0--
      3P-RNN[62]56.386.9-----41.687.8
      PointCNN[18]62.7488.1-85.1-----
      PointSIFT[58]70.2388.72-86.2-----
      ASIS[77]59.386.2-------
      A-CNN[61]-87.3-------
      LDGCNN[65]----85.1----
      GAPNet[68]----84.7----
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    Jiaying Zhang, Xiaoli Zhao, Zheng Chen. Review of Semantic Segmentation of Point Cloud Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(4): 040002

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    Paper Information

    Category: Reviews

    Received: Jul. 1, 2019

    Accepted: Jul. 15, 2019

    Published Online: Feb. 20, 2020

    The Author Email: Xiaoli Zhao (evawhy@163.com)

    DOI:10.3788/LOP57.040002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology