• Laser & Optoelectronics Progress
  • Vol. 62, Issue 8, 0815012 (2025)
Fanna Meng1,*, ZouYongjia1, Yang Cao1, Jin Lü2, and Hongfei Yu1
Author Affiliations
  • 1School of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113000, Liaoning , China
  • 2Neusoft Reach Automotive Technology (Shenyang) Co., Ltd., Shenyang 110179, Liaoning , China
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    DOI: 10.3788/LOP241894 Cite this Article Set citation alerts
    Fanna Meng, ZouYongjia, Yang Cao, Jin Lü, Hongfei Yu. Stereo Matching Algorithm Based on Adaptive Spatial Convolution[J]. Laser & Optoelectronics Progress, 2025, 62(8): 0815012 Copy Citation Text show less
    Overall framework of network
    Fig. 1. Overall framework of network
    Adaptive spatial convolution module
    Fig. 2. Adaptive spatial convolution module
    Comparison of disparity maps in KITTI reflection areas. (a) Left view; (b) benchmark algorithm; (c) ours
    Fig. 3. Comparison of disparity maps in KITTI reflection areas. (a) Left view; (b) benchmark algorithm; (c) ours
    Comparison of disparity maps in KITTI occlusion regions. (a) Left view; (b) benchmark algorithm; (c) ours
    Fig. 4. Comparison of disparity maps in KITTI occlusion regions. (a) Left view; (b) benchmark algorithm; (c) ours
    Visualization results comparison on Middlebury dataset. (a) Left view; (b) benchmark algorithm; (c) ours
    Fig. 5. Visualization results comparison on Middlebury dataset. (a) Left view; (b) benchmark algorithm; (c) ours
    Parameter nameParameter value
    Step2×105
    Learning rate2×10-4
    Iteration round22
    Batch size8
    Table 1. Pre-training model parameters
    Ablation moduleEPE /pixelD1 /%Param /M
    RAFT-Stereo0.536.1011.23
    RAFT-Stereo+ASCT0.495.6311.47
    RAFT-Stereo+multi-scale GRUs0.505.3811.64
    RAFT-Stereo+ASCT+multi-scale GRUs (ours)0.465.3011.81
    Table 2. Ablation experiment
    Kernel numberEPE /pixelD1 /%
    20.506.08
    30.495.96
    40.495.63
    Table 3. Only ablation analysis of changing ASCT convolutional kernel number κ
    Kernel sizeEPE /pixelD1 /%
    3×30.536.10
    3×3+5×50.525.84
    3×3+1×10.505.38
    Table 4. Only ablation analysis of changing GRU convolutional kernel size
    MethodKITTI-2015 (all)KITTI-2015 (noc)Runtime/ s
    D1-bg/%D1-fg/%D1-all/%D1-bg/%D1-fg/%D1-all/%
    RAFT-Stereo121.583.051.821.452.941.690.38
    CRE-Stereo251.452.861.691.332.601.540.41
    IGEV-Stereo131.382.671.591.272.621.490.18
    Los261.422.811.651.292.661.520.19
    CroCo-Stereo271.382.651.591.302.561.510.93
    MDA281.372.641.581.262.581.480.32
    Ours1.422.691.581.242.641.470.44
    Table 5. Quantitative results with existing mainstream stereo matching methods on KITTI 2015 test
    MethodKITTI-2012 (all)KITTI-2012 (reflective)
    2-noc2-all3-noc3-allEPE-nocEPE-all2-noc2-all3-noc3-all
    RAFT-Stereo121.922.421.301.660.40.58.419.875.406.48
    CRE-Stereo251.722.181.141.460.40.59.7111.266.277.27
    IGEV-Stereo131.712.171.121.440.40.47.298.484.114.76
    MS-ACV291.752.301.111.440.40.5
    DVANet301.782.391.091.520.40.59.6311.995.687.48
    HCR311.692.181.091.420.40.49.7811.936.017.68
    MDA281.762.261.091.430.40.59.7911.895.647.22
    Ours1.632.061.071.400.40.57.138.014.394.62
    Table 6. Quantitative results with existing mainstream stereo matching methods on KITTI 2012 test
    MethodScene FlowETH3DMiddlebury
    EPE /pixelD-1 /%Half error /%Quarter error /%
    GANet320.846.513.58.5
    RAFT-Stereo120.543.28.77.3
    IGEV-Stereo130.473.67.16.2
    DLNR330.489.5
    IGEV++340.503.57.8
    Ours0.463.86.66.0
    Table 7. Quantitative results with mainstream stereo matching methods on Scene Flow, ETH3D and Middlebury