• Laser & Optoelectronics Progress
  • Vol. 59, Issue 16, 1633001 (2022)
Qibo Chen1、2, Baozhen Ge1、2、*, Yunpeng Li1、2, and Jianing Quan1、2
Author Affiliations
  • 1School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
  • 2Key Laboratory of Opto-Electronics Information Technology, Ministry of Education, Tianjin University, Tianjin 300072, China
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    DOI: 10.3788/LOP202259.1633001 Cite this Article Set citation alerts
    Qibo Chen, Baozhen Ge, Yunpeng Li, Jianing Quan. Stereo Matching Algorithm Based on Multi-Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1633001 Copy Citation Text show less
    Architecture overview of PSMnet
    Fig. 1. Architecture overview of PSMnet
    Architecture overview of proposed algorithm
    Fig. 2. Architecture overview of proposed algorithm
    Location-based channel attention module
    Fig. 3. Location-based channel attention module
    Multi-cross-attention module
    Fig. 4. Multi-cross-attention module
    Attention hourglass module
    Fig. 5. Attention hourglass module
    Comparison of ReLU function and Mish function
    Fig. 6. Comparison of ReLU function and Mish function
    Parallax evaluation results on KITTI2015 test set. (a1) (a3) Left images; (a2) (a4) partial enlargement of left images; (b1) (b3) disparity maps predicted by PSMnet; (b2) (b4) partial enlargement of disparity maps; (c1) (c3) the corresponding error maps; (c2) (c4) local enlargement of the corresponding error maps; (d1) (d3) disparity maps predicted by MAnet; (d2) (d4) partial enlargement of disparity maps; (e1) (e3) the corresponding error maps; (e2) (e4) partial enlargement of the corresponding error maps
    Fig. 7. Parallax evaluation results on KITTI2015 test set. (a1) (a3) Left images; (a2) (a4) partial enlargement of left images; (b1) (b3) disparity maps predicted by PSMnet; (b2) (b4) partial enlargement of disparity maps; (c1) (c3) the corresponding error maps; (c2) (c4) local enlargement of the corresponding error maps; (d1) (d3) disparity maps predicted by MAnet; (d2) (d4) partial enlargement of disparity maps; (e1) (e3) the corresponding error maps; (e2) (e4) partial enlargement of the corresponding error maps
    AlgorithmD1-bg /%D1-fg /%D1-all /%
    NocAllNocAllNocAll
    GC-NET42.022.215.586.162.612.87
    SegStereo221.761.883.704.072.082.25
    PSMnet51.711.864.314.622.142.32
    Bi3D231.791.953.113.482.012.21
    CTFnet61.681.843.694.032.012.20
    DeepPruner-Best241.711.873.183.561.952.15
    GWCnet251.611.743.493.931.922.11
    SSPCVNet261.611.753.403.891.912.11
    MAnet1.511.653.884.131.902.06
    Table 1. Performance evaluation of different algorithms on the KITTI2015 test set
    Algorithm2-pixels /%3-pixels /%4-pixels /%EPE /pixel
    NocAllNocAllNocAllNocAll
    GC-NET42.713.461.772.301.361.770.60.7
    SegStereo222.663.191.682.031.251.520.50.6
    PSMnet52.443.011.491.891.121.420.50.6
    SSPCVNet262.473.091.471.901.081.410.50.6
    MAnet2.342.971.441.871.091.420.50.6
    Table 2. Performance evaluation of different algorithms on the KITTI2012 test set
    Qibo Chen, Baozhen Ge, Yunpeng Li, Jianing Quan. Stereo Matching Algorithm Based on Multi-Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1633001
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