• Laser & Optoelectronics Progress
  • Vol. 58, Issue 24, 2428007 (2021)
Lili Yu, Haiyang Yu*, Zixin He, and Liangxuan Chen
Author Affiliations
  • Key Laboratory of Spatio-temporal Information and Ecological Restoration of Mines, Ministry of Natural Resources, Henan University of Technology, Jiaozuo, Henan 454000, China
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    DOI: 10.3788/LOP202158.2428007 Cite this Article Set citation alerts
    Lili Yu, Haiyang Yu, Zixin He, Liangxuan Chen. Point Cloud Scene Segmentation Based on Dual Attention Mechanism and Multi-Scale Features[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2428007 Copy Citation Text show less
    Convolution network structure after embedding attention mechanism
    Fig. 1. Convolution network structure after embedding attention mechanism
    Spatial attention mechanism module
    Fig. 2. Spatial attention mechanism module
    Channel attention mechanism
    Fig. 3. Channel attention mechanism
    Segmentation network structure with embedded attention mechanism
    Fig. 4. Segmentation network structure with embedded attention mechanism
    Multi-scale data fusion
    Fig. 5. Multi-scale data fusion
    Experimental data set. (a) Training data set; (b) image corresponding to training data set; (c) test data set; (d) image corresponding to test data set
    Fig. 6. Experimental data set. (a) Training data set; (b) image corresponding to training data set; (c) test data set; (d) image corresponding to test data set
    Visualization of test set splitting effect
    Fig. 7. Visualization of test set splitting effect
    Comparison chart of misclassification results. (a) Attention mechanism not embedded; (b) embedded attention mechanism
    Fig. 8. Comparison chart of misclassification results. (a) Attention mechanism not embedded; (b) embedded attention mechanism
    MethodF1 scoreOA /%
    Power lineLow_vegationImpervious surfaceCarFence/hedgeRoofFacadeShrubTree
    BIJ_W13.878.590.556.436.392.253.243.378.481.5
    WhuY231.980.088.940.824.593.149.141.177.381.0
    LUH59.677.591.173.134.094.256.346.683.181.6
    RIT_137.577.991.573.418.094.049.345.982.581.6
    WhuY337.181.490.163.423.993.447.539.978.082.3
    Proposed algorithm55.881.594.973.845.594.430.043.883.886.3
    Table 1. Comparison of proposed algorithm and results provided by ISPR network
    MethodF1 scoreOA /%
    Power lineLow_vegationImpervious surfaceCarFence/hedgeRoofFacadeShrubTree
    PointNet++57.979.690.666.131.591.654.341.677.081.2
    PointNetSIFT55.780.790.977.830.592.55.944.479.682.2
    PointNetCNN61.582.791.875.835.992.757.849.178.183.3
    Proposed algorithm55.881.594.973.845.594.430.043.883.886.3
    Table 2. Comparison of proposed algorithm and deep learning algorithms
    MethodMean_IoUPower lineLow_vegationImpervious surfaceCarFence/hedgeRoofFacadeShrubTree
    PointNet32.00.832.147.623.22.384.75.715.476.2
    Proposed algorithm52.535.667.789.553.419.788.622.229.566.4
    Table 3. IoU of each category on Vaihingen unit: %
    Lili Yu, Haiyang Yu, Zixin He, Liangxuan Chen. Point Cloud Scene Segmentation Based on Dual Attention Mechanism and Multi-Scale Features[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2428007
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