Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1815005(2022)

Binocular Depth Estimation Algorithm Based on Multi-Scale Attention Feature Fusion

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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    Figures & Tables(14)
    Overall structure of multi-scale attention fusion network
    Group-related attention fusion module
    Multi-scale convolution global attention module
    3D channel attention aggregation module
    Parallax maps obtained by different algorithms on SceneFlow dataset
    Visualization results of ablation experiment on KITTI2015 test set
    Qualitative evaluation results of different networks on KITTI2015 dataset
    Qualitative evaluation results of different networks on KITTI2012 dataset
    Qualitative evaluation results of different networks on Middlebury-v3 dataset
    • Table 1. Ablation study results on SceneFlow dataset

      View table

      Table 1. Ablation study results on SceneFlow 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 2. Comparison of EPE between MGNet and other methods

      View table

      Table 2. Comparison of EPE between MGNet and other methods

      ParameterMCCNNGCNetiResNeti2CRLPSMNetEdgeStereoSegStereoMGNet
      EPE /%3.791.841.401.321.091.111.450.662
    • Table 3. Benchmark results of designed module on KITTI2015 dataset

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      Table 3. Benchmark results of designed module on KITTI2015 dataset

      GAMAGwcCAA>3 pixel /%
      2.20
      2.18
      2.06
      2.01
    • Table 4. Comparison of different networks on KITTI2015 dataset

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      Table 4. Comparison of different networks 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 5. Comparison of different networks on KITTI2012 dataset

      View table

      Table 5. Comparison of different networks on KITTI2012 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
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    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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    Paper Information

    Category: Machine Vision

    Received: Jul. 15, 2021

    Accepted: Jul. 20, 2021

    Published Online: Aug. 29, 2022

    The Author Email: Lei Liang (leiliang@gdut.edu.cn)

    DOI:10.3788/LOP202259.1815005

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