• Laser & Optoelectronics Progress
  • Vol. 59, Issue 18, 1815005 (2022)
Huitong Yang, Liang Lei*, and Yongchun Lin
Author Affiliations
  • School of Physics & Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510006, Guangdong , China
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    DOI: 10.3788/LOP202259.1815005 Cite this Article Set citation alerts
    Huitong Yang, Liang Lei, Yongchun Lin. Binocular Depth Estimation Algorithm Based on Multi-Scale Attention Feature Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815005 Copy Citation Text show less
    Overall structure of multi-scale attention fusion network
    Fig. 1. Overall structure of multi-scale attention fusion network
    Group-related attention fusion module
    Fig. 2. Group-related attention fusion module
    Multi-scale convolution global attention module
    Fig. 3. Multi-scale convolution global attention module
    3D channel attention aggregation module
    Fig. 4. 3D channel attention aggregation module
    Parallax maps obtained by different algorithms on SceneFlow dataset
    Fig. 5. Parallax maps obtained by different algorithms on SceneFlow dataset
    Visualization results of ablation experiment on KITTI2015 test set
    Fig. 6. Visualization results of ablation experiment on KITTI2015 test set
    Qualitative evaluation results of different networks on KITTI2015 dataset
    Fig. 7. Qualitative evaluation results of different networks on KITTI2015 dataset
    Qualitative evaluation results of different networks on KITTI2012 dataset
    Fig. 8. Qualitative evaluation results of different networks on KITTI2012 dataset
    Qualitative evaluation results of different networks on Middlebury-v3 dataset
    Fig. 9. Qualitative evaluation results of different networks on Middlebury-v3 dataset
    Module>1 pixel>2 pixel>3 pixelD1-allEPE /%
    GAMACAA
    0.08090.04380.03190.02600.757
    0.077800.04290.03160.02580.746
    0.07020.03840.02810.02260.662
    Table 1. Ablation study results on SceneFlow dataset
    ParameterMCCNNGCNetiResNeti2CRLPSMNetEdgeStereoSegStereoMGNet
    EPE /%3.791.841.401.321.091.111.450.662
    Table 2. Comparison of EPE between MGNet and other methods
    GAMAGwcCAA>3 pixel /%
    2.20
    2.18
    2.06
    2.01
    Table 3. Benchmark results of designed module on KITTI2015 dataset
    NetworkALLNoc
    D1-bgD1-fgD1-allD1-bgD1-fgD1-all
    DispNetC4.324.414.344.113.724.05
    CRL2.483.592.672.323.122.45
    PDSNet2.294.052.582.093.682.36
    GCNet2.216.162.872.025.582.61
    PSMNet1.864.622.321.714.312.14
    AANet1.995.392.551.804.932.32
    EdgeStereo2.274.182.592.123.852.40
    Big3D1.953.482.211.793.112.01
    MGNet1.653.842.011.513.491.84
    Table 4. Comparison of different networks on KITTI2015 dataset
    Network>2 pixel>3 pixel>4 pixel>5 pixel
    NocALLNocALLNocALLNocALL
    DispNetC7.388.114.114.652.773.202.052.39
    PDSNet3.824.651.922.531.381.851.121.51
    GCNet2.713.461.772.301.361.771.121.46
    PSMNet2.443.011.491.891.121.420.901.15
    Edgestereo2.792.431.732.181.301.641.041.32
    SegStereo2.663.191.682.031.251.521.001.21
    SSPCVNET2.473.091.471.901.081.410.871.14
    EdgestereoV22.322.881.461.831.071.340.831.04
    AANet2.302.961.552.041.201.580.981.30
    MGNet2.122.711.341.761.011.340.821.08
    Table 5. Comparison of different networks on KITTI2012 dataset
    Huitong Yang, Liang Lei, Yongchun Lin. Binocular Depth Estimation Algorithm Based on Multi-Scale Attention Feature Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815005
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