• Acta Optica Sinica
  • Vol. 40, Issue 9, 0915001 (2020)
Lingyin Kong, Jiangping Zhu, and Sancong Ying*
Author Affiliations
  • College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China
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    DOI: 10.3788/AOS202040.0915001 Cite this Article Set citation alerts
    Lingyin Kong, Jiangping Zhu, Sancong Ying. Stereo Matching Based on Guidance Image and Adaptive Support Region[J]. Acta Optica Sinica, 2020, 40(9): 0915001 Copy Citation Text show less
    Flowchart of proposed algorithm
    Fig. 1. Flowchart of proposed algorithm
    Schematic of constructing adaptive support region based on cross method
    Fig. 2. Schematic of constructing adaptive support region based on cross method
    Disparity maps of three cost computation methods. (a) C1; (b) C2; (c) proposed gradient calculation method
    Fig. 3. Disparity maps of three cost computation methods. (a) C1; (b) C2; (c) proposed gradient calculation method
    Weighted averages for all regions and non-occluded regions. (a) Avgerr; (b) RMSE
    Fig. 4. Weighted averages for all regions and non-occluded regions. (a) Avgerr; (b) RMSE
    Weighted average after disparity refinement on each step. (a)(b) Avgerr; (c)(d) RMSE
    Fig. 5. Weighted average after disparity refinement on each step. (a)(b) Avgerr; (c)(d) RMSE
    Comparison of disparity results. (a) Adirondack; (b) Jadeplant; (c) Piano; (d) Motorcycle; (e) Recycle
    Fig. 6. Comparison of disparity results. (a) Adirondack; (b) Jadeplant; (c) Piano; (d) Motorcycle; (e) Recycle
    ParameterWeighted average /pixelReducedpercentage /%
    Before disparity refinementAfter disparity refinement
    Avgerr(all)21.111.3046.4
    Avgerr(nonocc)12.17.8135.5
    RMSE(all)47.227.7041.3
    RMSE(nonocc)31.620.9033.9
    Table 1. Weighted average of errors before and after disparity refinement and reduced percentage (disparity map of proposed gradient calculation method)
    ParameterWeighted average /pixelReduced percentage /%
    Before disparity refinementAfter disparity refinement
    Avgerr(all)22.812.4045.6
    Avgerr(nonocc)13.68.6336.5
    RMSE(all)49.629.8039.9
    RMSE(nonocc)34.522.9033.6
    Table 2. Weighted average of errors before and after disparity refinement and reduced percentage (disparity map of C1)
    ParameterWeighted average /pixelReduced percentage /%
    Before disparity refinementAfter disparity refinement
    Avgerr(all)24.514.939.2
    Avgerr(nonocc)15.110.729.1
    RMSE(all)51.534.632.8
    RMSE(nonocc)36.327.125.3
    Table 3. Weighted average of errors before and after disparity refinement and reduced percentage (disparity map of C2)
    Image nameLE-ELASIEBIMstSPSSM-AWPDSGCADoGGuidedProposed algorithm
    Adirondack9.3127.306.5110.507.6820.106.40
    ArtL5.9015.1015.2019.9021.7028.009.00
    Jadeplant64.5055.6040.0062.7045.0056.5026.10
    Motorcycle7.245.548.3511.0010.6013.808.11
    MotorcycleE7.658.218.4512.5010.4016.8011.40
    Piano6.256.4012.009.0811.5013.406.15
    PianoL9.6918.9025.0029.7024.5037.3034.00
    Pipes12.8011.8016.1021.1019.9023.8014.90
    Playroom10.1018.0025.2020.7024.6030.3010.50
    Playtable23.9017.9015.709.5034.5030.8016.70
    PlaytableP4.274.9512.409.7514.8013.0010.00
    Recycle7.395.298.817.187.569.134.20
    Shelves8.4817.1023.7011.4017.3019.009.97
    Teddy2.985.318.019.4412.2013.403.35
    Vintage14.0010.9053.7016.8043.8013.6010.90
    Australia15.209.178.6419.1016.6018.2012.00
    AustraliaP6.945.548.7718.2012.4012.608.31
    Bicycle26.687.5411.4016.0012.9017.6013.70
    Classroom224.6027.9020.2029.3032.6034.909.09
    Classroom2E69.6055.0027.0051.1039.3076.3067.10
    Computer12.4013.8022.2022.5020.6022.1013.20
    Crusade21.7074.3050.8091.8049.5073.4036.30
    CrusadeP21.0074.6050.2094.9050.5071.3035.60
    Djembe2.732.103.657.335.716.642.98
    DjembeL13.8029.1017.2031.8024.5039.0019.50
    Hoops22.8045.0038.7037.7036.3056.6023.00
    Livingroom10.309.4930.4016.8022.9025.907.18
    Newkuba16.2013.3020.3028.5023.2028.7011.30
    Plants43.3023.3026.2032.2027.7033.9025.80
    Staircase21.3030.9039.4036.4039.8057.5029.80
    Weighted average15.9520.9520.7026.5022.8029.2015.20
    Table 4. Comparison of Avgerr in all regionspixel
    Image nameLE-ELASIEBIMstSPSSM-AWPDSGCADoGGuidedProposed algorithm
    Adirondack8.4626.103.576.313.2515.204.84
    ArtL3.834.675.349.655.959.574.62
    Jadeplant41.1041.9022.8031.8018.9027.1016.10
    Motorcycle5.122.723.114.713.605.644.58
    MotorcycleE5.804.993.156.393.418.317.72
    Piano5.545.699.346.687.178.095.20
    PianoL8.9717.5022.9028.4021.1032.4034.40
    Pipes7.445.476.7810.607.239.677.53
    Playroom8.7612.9012.509.089.3614.005.05
    Playtable22.4014.809.705.0929.4024.5013.00
    PlaytableP3.473.267.645.187.945.325.67
    Recycle6.934.996.273.863.805.563.37
    Shelves8.2616.4022.309.7314.7016.209.49
    Teddy2.292.641.523.643.514.152.15
    Vintage13.1010.4052.6010.7039.7015.009.64
    Australia13.406.535.3213.5011.0012.308.48
    AustraliaP5.273.365.4812.706.756.625.70
    Bicycle24.885.047.7011.007.0111.2010.50
    Classroom219.3019.305.6017.5013.7016.305.35
    Classroom2E66.5045.7012.5041.8021.5062.6064.80
    Computer6.063.418.0511.005.906.833.92
    Crusade15.6051.3015.1070.106.7234.0019.20
    CrusadeP13.7046.4013.1072.305.8530.6015.30
    Djembe1.941.521.844.012.783.652.23
    DjembeL13.3029.2016.1028.3022.2037.0019.20
    Hoops18.2039.2022.8025.8017.2035.0016.90
    Livingroom9.628.7719.407.8811.9013.406.19
    Newkuba12.808.1912.6021.5011.1014.207.60
    Plants35.4016.9014.5019.7014.1019.1017.90
    Staircase19.1027.6027.5021.7023.8034.4024.40
    Weighted average12.4315.1011.0017.3610.2315.8510.36
    Table 5. Comparison of Avgerr in non-occluded regionspixel
    Image nameLE-ELASIEBIMstSPSSM-AWPDSGCADoGGuidedProposed algorithm
    Adirondack25.4058.9023.0030.3025.1048.9019.20
    ArtL16.8038.8039.9038.7048.6059.5022.80
    Jadeplant120.00128.0095.80119.00102.00118.0062.60
    Motorcycle23.3020.1029.1033.8032.5039.4023.20
    MotorcycleE24.0027.4029.8036.9032.4044.6028.90
    Piano13.9013.9032.1019.3029.2031.6012.40
    PianoL18.7039.0053.8056.3050.8065.8065.30
    Pipes30.7030.2041.0047.3047.4052.6035.10
    Playroom25.9038.0059.5047.8058.3066.7027.60
    Playtable52.3038.6043.3025.0068.3059.5040.10
    PlaytableP12.6014.2037.2025.5038.4034.4028.00
    Recycle17.6017.5026.5022.5023.3025.7010.60
    Shelves15.3030.4044.4023.8033.5033.9019.40
    Teddy8.3218.6027.8025.9034.9036.7013.60
    Vintage27.4028.80131.0045.80105.0066.1027.70
    Australia34.9027.9030.0046.6040.8045.2032.60
    AustraliaP26.6022.9030.9045.8035.9038.3027.50
    Bicycle221.0023.5032.1037.7032.6040.2032.30
    Classroom255.8068.7060.9064.1082.6085.4027.30
    Classroom2E112.00106.0076.4093.1088.30148.00127.00
    Computer28.2034.1053.7042.3046.7048.0031.70
    Crusade58.70156.00141.00152.00131.00151.0079.40
    CrusadeP59.00160.00140.00156.00134.00150.0080.10
    Djembe8.107.3015.0027.8019.7021.509.28
    DjembeL31.0058.6042.8061.3051.4066.4042.70
    Hoops51.7077.9078.2072.6073.7099.9050.10
    Livingroom23.0024.9067.9039.8052.8058.6017.60
    Newkuba53.3038.1065.2078.2068.0081.9033.70
    Plants72.6054.6059.3063.9062.9069.4055.70
    Staircase46.0048.2085.8073.6078.70102.0051.20
    Weighted average36.2548.1554.8555.2056.1564.5035.70
    Table 6. Comparison of RMSE in all regionspixel
    Image nameLE-ELASIEBIMstSPSSM-AWPDSGCADoGGuidedProposed algorithm
    Adirondack24.6058.0016.4021.3013.2042.2015.80
    ArtL13.8015.6017.2022.7017.9028.3014.10
    Jadeplant91.90121.0075.7081.5063.6075.7046.80
    Motorcycle18.3011.4015.5019.1014.9021.4014.80
    MotorcycleE20.0020.9015.5024.0014.5028.8022.30
    Piano13.1012.7027.5014.5020.2019.4010.50
    PianoL17.9037.4052.0056.2047.3060.9066.80
    Pipes23.3019.2024.5032.2024.6029.6024.50
    Playroom25.5031.5035.8027.2025.8037.5013.90
    Playtable51.2032.4033.1014.3063.9052.2033.70
    PlaytableP11.309.2029.1015.3025.6015.5015.30
    Recycle17.1016.9021.9012.9013.6018.108.36
    Shelves15.1029.4043.5020.2029.8029.7018.60
    Teddy6.8610.706.6312.1014.4014.209.72
    Vintage26.3029.00134.0026.20104.0050.7025.00
    Australia31.5021.3022.4037.8031.0035.4025.20
    AustraliaP22.8016.4023.5036.7024.9025.9022.40
    Bicycle217.1017.4026.0029.9022.0030.7027.30
    Classroom249.8057.5026.1048.5047.1052.6019.50
    Classroom2E112.0099.2053.3086.3059.10136.00126.00
    Computer16.609.4730.0022.5017.5020.5011.30
    Crusade49.80131.0076.70127.0032.9084.2051.00
    CrusadeP48.60123.0071.20131.0031.1079.2037.90
    Djembe5.735.549.1317.9011.8014.007.17
    DjembeL30.8059.2041.7055.0049.2065.1043.00
    Hoops47.5072.7059.7060.1046.3073.2043.40
    Livingroom22.7024.3051.8021.3032.6035.3016.00
    Newkuba52.0026.5054.0073.8042.5050.5027.90
    Plants62.4045.0042.4046.6042.9047.9044.20
    Staircase42.0041.8075.4056.2060.6066.2043.50
    Weighted average31.4539.1537.4541.2031.4541.5026.65
    Table 7. Comparison of RMSE in non-occluded regionspixel
    Lingyin Kong, Jiangping Zhu, Sancong Ying. Stereo Matching Based on Guidance Image and Adaptive Support Region[J]. Acta Optica Sinica, 2020, 40(9): 0915001
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