This table lists the benchmark results for the low-res two-view scenario. This benchmark evaluates the Middlebury stereo metrics (for all metrics, smaller is better):

The mask determines whether the metric is evaluated for all pixels with ground truth, or only for pixels which are visible in both images (non-occluded).
The coverage selector allows to limit the table to results for all pixels (dense), or a given minimum fraction of pixels.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click one or more dataset result cells or column headers to show visualizations. Most visualizations are only available for training datasets. The visualizations may not work with mobile browsers.




Method Infoalldeliv. area 1ldeliv. area 1sdeliv. area 2ldeliv. area 2sdeliv. area 3ldeliv. area 3select. 1lelect. 1select. 2lelect. 2select. 3lelect. 3sfacade 1sforest 1sforest 2splayg. 1lplayg. 1splayg. 2lplayg. 2splayg. 3lplayg. 3sterra. 1sterra. 2sterra. 1lterra. 1sterra. 2lterra. 2s
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
TDLMtwo views0.09
1
0.08
3
0.12
5
0.02
1
0.02
1
0.00
1
0.01
4
0.19
6
0.04
1
0.00
1
0.00
1
0.48
9
0.01
1
0.01
2
0.26
1
0.22
1
0.08
1
0.06
4
0.01
1
0.00
1
0.10
1
0.10
4
0.01
4
0.01
1
0.00
1
0.01
1
0.30
1
0.39
7
CVANet_RVCtwo views0.21
2
0.15
4
0.61
22
0.42
21
0.06
3
0.06
19
0.01
4
1.53
27
0.05
3
0.08
4
0.04
6
0.62
15
0.04
2
0.03
5
0.46
2
0.32
2
0.14
4
0.09
7
0.05
2
0.01
2
0.19
3
0.07
3
0.00
1
0.03
4
0.00
1
0.01
1
0.55
3
0.08
1
AANet_RVCtwo views0.25
3
0.05
2
0.29
8
0.06
3
0.19
6
0.00
1
0.23
26
0.03
2
0.17
8
0.35
13
0.01
5
0.07
2
0.04
2
0.01
2
1.37
5
0.48
4
0.10
2
0.06
4
0.43
6
0.17
9
0.10
1
0.25
5
0.03
10
0.05
5
0.02
3
0.06
4
1.15
6
0.89
16
GANettwo views0.29
4
0.56
20
0.39
16
0.27
12
0.11
5
0.88
39
0.14
22
0.27
9
0.06
4
0.02
2
0.28
12
1.85
30
0.04
2
0.03
5
0.62
3
0.39
3
0.37
14
0.14
10
0.24
3
0.01
2
0.20
4
0.05
2
0.06
16
0.02
2
0.06
5
0.01
1
0.48
2
0.16
3
RYNettwo views0.45
5
0.04
1
0.00
1
0.02
1
0.03
2
0.00
1
0.00
1
0.01
1
0.04
1
0.07
3
0.00
1
0.05
1
0.12
9
0.00
1
4.77
43
0.60
5
0.10
2
1.00
45
0.35
5
0.01
2
0.21
5
0.04
1
0.00
1
0.02
2
0.08
7
3.06
32
1.39
7
0.13
2
CFNettwo views0.57
6
0.46
13
0.64
23
0.43
22
0.34
18
0.04
18
0.00
1
0.58
13
0.09
5
0.66
18
0.05
7
0.64
16
0.07
5
0.05
10
2.25
7
0.65
6
0.35
10
0.06
4
0.73
10
0.34
16
0.85
13
0.73
6
0.00
1
0.08
7
0.45
21
0.16
6
3.22
20
1.51
23
HITNettwo views0.59
7
0.16
5
0.10
4
0.06
3
0.07
4
0.01
8
0.02
10
1.76
32
0.82
22
0.11
5
0.00
1
0.68
19
0.28
19
0.11
24
2.51
11
1.02
12
0.25
7
0.13
8
0.63
8
0.24
14
1.43
21
1.56
17
0.01
4
0.12
11
0.02
3
0.16
6
3.32
22
0.42
8
Vladimir Tankovich, Christian Häne, Yinda Zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz: HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching. CVPR 2021
DMCAtwo views0.59
7
0.58
21
0.41
18
0.19
8
0.56
25
0.12
24
0.44
30
0.06
3
1.19
31
0.40
14
2.33
39
0.19
3
0.16
12
0.07
15
2.36
8
0.83
7
0.39
15
0.31
20
0.34
4
0.45
23
0.62
9
0.86
9
0.31
38
0.15
12
0.16
12
0.16
6
1.67
9
0.64
11
DMCA-RVCcopylefttwo views0.61
9
0.31
8
0.32
11
0.20
9
0.40
20
0.12
24
0.33
27
0.14
5
1.38
34
0.26
8
0.34
15
1.52
29
0.08
8
0.14
27
2.73
12
0.88
8
0.31
9
0.28
18
0.54
7
0.55
28
0.48
6
0.73
6
0.12
23
0.20
15
0.12
10
0.10
5
3.23
21
0.54
10
RASNettwo views0.63
10
0.18
6
0.57
19
0.41
20
0.28
11
0.10
22
0.05
16
0.94
18
0.14
7
1.07
29
0.09
9
0.48
9
0.34
23
0.02
4
1.28
4
1.47
26
0.35
10
0.21
12
0.99
11
0.02
5
1.15
16
1.53
15
0.10
20
0.34
28
0.22
15
0.30
11
3.55
28
0.73
13
DISCOtwo views0.79
11
0.95
34
0.31
10
0.06
3
0.33
16
0.51
33
0.44
30
0.23
8
0.32
14
1.75
41
0.59
21
0.38
8
0.29
20
0.04
8
2.98
15
1.36
20
0.96
34
0.29
19
3.24
32
0.53
26
0.79
12
1.45
13
0.10
20
0.07
6
0.12
10
0.29
10
2.76
14
0.31
6
NVstereo2Dtwo views0.83
12
0.43
11
0.07
3
0.23
10
0.36
19
0.41
32
0.08
18
1.61
30
0.59
18
0.84
21
1.11
29
0.36
6
0.21
17
0.07
15
5.88
54
2.16
42
0.62
22
0.45
26
1.50
16
0.19
10
1.20
17
0.94
12
0.02
6
0.32
24
1.30
31
0.24
9
0.95
4
0.24
5
CFNet_RVCtwo views0.86
13
0.59
22
0.74
28
0.33
14
0.32
15
0.11
23
0.02
10
0.11
4
0.17
8
0.87
24
0.74
25
0.57
14
0.16
12
0.08
19
3.11
17
1.19
19
0.50
17
0.34
22
1.28
14
0.15
7
1.68
26
1.48
14
0.02
6
0.25
16
0.86
29
1.89
27
3.64
30
2.13
27
DLCB_ROBtwo views0.92
14
0.72
27
0.64
23
0.43
22
0.42
21
0.14
27
0.18
24
0.43
10
0.28
12
0.85
22
0.53
18
1.12
25
0.72
42
0.07
15
2.16
6
1.12
16
0.88
32
0.26
15
0.69
9
0.20
11
1.35
20
1.53
15
0.49
50
0.28
19
4.11
55
2.41
29
2.04
11
0.78
14
HSMtwo views0.97
15
0.30
7
0.87
31
0.54
28
0.46
23
0.00
1
0.01
4
2.93
52
0.30
13
0.13
6
0.56
20
0.65
17
0.13
10
0.05
10
2.97
14
1.08
15
0.66
26
0.17
11
3.26
34
0.35
17
1.78
27
2.03
23
0.04
11
0.33
27
0.38
18
0.81
19
4.16
42
1.10
18
NLCA_NET_v2_RVCtwo views0.99
16
0.82
30
0.34
14
0.18
7
0.43
22
0.03
16
1.10
43
1.20
22
2.50
54
2.06
46
0.11
10
0.69
20
0.07
5
0.54
44
3.65
28
0.91
9
0.19
6
0.22
13
1.65
20
0.15
7
0.73
11
0.92
11
0.08
17
0.18
14
0.11
9
3.29
36
2.58
13
2.09
26
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
PDISCO_ROBtwo views1.03
17
0.55
19
0.06
2
0.67
30
0.28
11
0.00
1
0.21
25
0.71
15
0.49
17
1.28
31
0.99
28
0.25
5
0.32
22
0.19
29
3.02
16
2.03
39
0.69
27
1.42
57
2.26
21
0.58
29
2.32
32
3.22
41
0.14
25
0.27
18
1.36
32
0.37
12
3.84
36
0.23
4
STTStereotwo views1.06
18
0.85
32
0.57
19
0.36
18
0.29
13
0.08
21
0.64
34
0.43
10
1.56
40
1.40
37
0.26
11
1.95
31
0.07
5
0.29
36
4.26
31
1.06
13
0.25
7
0.23
14
1.16
13
0.14
6
1.46
23
0.84
8
0.12
23
0.10
10
0.07
6
1.81
26
3.51
27
4.84
41
PMTNettwo views1.06
18
1.07
37
1.62
57
0.25
11
0.21
9
0.01
8
0.05
16
1.53
27
0.64
20
0.31
10
0.44
17
0.54
12
0.51
34
0.08
19
3.20
18
2.70
50
0.61
19
0.41
25
2.84
29
0.22
12
4.32
51
2.47
30
0.08
17
0.15
12
0.47
22
1.51
24
1.09
5
1.15
20
iResNet_ROBtwo views1.07
20
0.65
25
0.30
9
0.45
24
0.30
14
0.01
8
0.02
10
3.10
53
0.12
6
1.34
32
0.00
1
0.66
18
0.35
24
0.04
8
4.37
35
1.65
31
0.54
18
0.49
28
5.83
46
0.29
15
1.30
19
0.87
10
0.20
32
0.26
17
0.16
12
0.84
20
3.74
34
1.12
19
Nwc_Nettwo views1.11
21
0.51
15
0.87
31
0.34
16
0.78
29
0.89
40
1.87
48
2.40
42
1.15
29
0.48
16
0.68
23
3.66
40
0.16
12
0.27
34
2.44
9
0.98
10
0.35
10
0.01
1
1.60
17
0.40
18
0.50
7
2.58
32
0.05
14
0.08
7
0.66
26
0.45
14
3.47
25
2.34
28
AF-Nettwo views1.11
21
0.51
15
0.87
31
0.34
16
0.78
29
0.89
40
1.87
48
2.40
42
1.15
29
0.48
16
0.68
23
3.66
40
0.16
12
0.27
34
2.44
9
0.98
10
0.35
10
0.01
1
1.60
17
0.40
18
0.50
7
2.58
32
0.05
14
0.08
7
0.66
26
0.45
14
3.47
25
2.34
28
iResNetv2_ROBtwo views1.26
23
0.75
28
0.32
11
1.29
47
0.33
16
0.02
15
0.02
10
3.19
54
0.20
10
2.00
45
0.29
13
2.24
34
0.41
28
0.06
13
3.64
27
1.45
25
0.75
29
0.13
8
5.77
44
0.43
22
2.86
39
1.71
19
0.16
28
0.37
31
0.38
18
0.47
16
4.08
40
0.72
12
RPtwo views1.29
24
1.06
36
2.32
65
0.84
34
1.16
34
0.39
31
1.53
45
1.89
36
1.68
43
0.29
9
2.21
38
2.25
35
0.13
10
0.40
41
3.31
22
1.07
14
0.61
19
0.05
3
1.61
19
0.40
18
0.65
10
3.05
38
0.11
22
0.29
20
0.29
16
0.92
21
4.24
43
2.04
25
BEATNet_4xtwo views1.40
25
0.54
18
0.27
7
0.13
6
0.27
10
0.01
8
0.10
19
1.80
33
1.74
45
0.41
15
0.53
18
0.82
22
0.59
37
0.54
44
4.30
32
2.92
51
1.87
50
1.84
65
1.39
15
0.71
32
4.68
54
6.21
70
0.02
6
0.81
42
0.21
14
0.40
13
3.69
31
0.96
17
DN-CSS_ROBtwo views1.45
26
0.52
17
1.11
40
0.81
33
0.69
27
0.00
1
0.03
15
2.86
51
0.35
15
1.50
38
3.76
54
0.73
21
0.36
26
0.07
15
2.89
13
1.44
23
1.08
38
0.79
34
8.61
57
0.23
13
3.25
44
1.97
21
0.02
6
0.66
39
0.10
8
0.52
17
3.12
19
1.56
24
DeepPruner_ROBtwo views1.57
27
0.37
10
0.71
26
0.50
26
0.55
24
0.37
30
1.60
46
1.74
31
0.84
24
1.37
34
2.40
41
0.49
11
0.51
34
0.10
23
5.38
49
2.16
42
1.10
39
1.08
48
2.28
22
0.40
18
4.83
58
3.42
42
0.66
58
0.34
28
0.57
25
5.04
51
3.07
15
0.53
9
FADNet-RVC-Resampletwo views1.60
28
0.67
26
0.97
36
0.50
26
0.64
26
0.18
28
1.05
40
1.27
24
0.83
23
0.96
26
0.97
27
5.23
48
0.42
29
1.02
67
4.81
46
1.68
32
1.05
36
1.96
67
3.78
37
1.06
42
1.52
24
2.28
28
0.21
33
1.35
51
0.55
23
3.16
35
2.13
12
2.92
31
PA-Nettwo views1.61
29
0.81
29
1.43
50
0.27
12
1.99
54
0.12
24
0.34
28
0.19
6
1.90
47
0.85
22
0.41
16
0.24
4
0.20
16
0.78
59
4.61
41
1.44
23
0.17
5
0.26
15
1.08
12
0.53
26
1.44
22
3.18
40
0.65
56
0.44
33
0.99
30
7.71
64
1.78
10
9.78
55
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
FADNet-RVCtwo views1.64
30
0.31
8
0.64
23
0.33
14
1.19
37
0.01
8
2.53
59
0.57
12
0.22
11
1.03
28
0.32
14
6.04
52
0.29
20
0.32
39
4.47
38
1.90
37
0.62
22
0.78
33
6.45
49
1.80
55
1.85
28
2.02
22
0.19
31
0.52
37
1.78
36
1.74
25
3.10
16
3.15
32
Abc-Nettwo views1.86
31
1.63
44
1.33
46
0.95
41
1.22
39
0.80
36
2.29
55
2.62
48
2.93
57
0.34
11
2.87
49
2.16
32
1.17
46
0.21
30
3.63
24
1.13
17
0.63
24
0.96
43
2.79
27
0.75
33
0.98
14
2.96
36
0.36
43
0.32
24
2.31
42
4.50
46
3.69
31
4.78
38
NCC-stereotwo views1.86
31
1.63
44
1.33
46
0.95
41
1.22
39
0.80
36
2.29
55
2.62
48
2.93
57
0.34
11
2.87
49
2.16
32
1.17
46
0.21
30
3.63
24
1.13
17
0.63
24
0.96
43
2.79
27
0.75
33
0.98
14
2.96
36
0.36
43
0.32
24
2.31
42
4.50
46
3.69
31
4.78
38
stereogantwo views1.89
33
2.00
52
1.17
42
1.47
49
2.05
57
1.72
49
0.87
38
1.84
35
1.11
27
1.36
33
1.51
32
5.81
50
0.48
33
0.32
39
3.87
29
1.69
33
0.80
30
0.39
24
3.96
38
0.78
35
1.23
18
4.02
49
0.26
35
0.51
36
0.30
17
1.30
23
6.30
56
3.84
36
FADNettwo views1.92
34
0.44
12
0.60
21
0.54
28
1.05
33
0.00
1
2.95
63
0.78
16
0.38
16
0.81
20
0.61
22
2.40
36
0.47
32
0.65
54
5.57
51
1.73
34
0.69
27
1.08
48
8.93
59
1.61
53
2.36
34
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31
0.09
19
0.36
30
2.54
47
3.77
41
3.43
24
5.46
42
NCCL2two views2.12
35
2.27
56
1.29
43
1.28
46
1.85
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0.18
28
0.77
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1.24
23
1.25
33
1.62
39
2.71
46
1.06
23
0.51
34
0.31
37
4.40
36
2.33
46
1.57
44
0.62
30
2.35
23
0.68
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2.68
38
4.29
53
1.52
71
1.06
44
4.65
59
4.17
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4.04
39
6.58
48
R-Stereotwo views2.16
36
1.08
38
1.57
54
0.84
34
0.19
6
0.01
8
0.01
4
2.05
39
1.01
25
1.37
34
2.45
42
1.50
27
0.46
30
0.21
30
3.23
19
4.53
68
1.62
45
1.05
46
7.07
50
1.09
44
4.86
59
5.93
67
0.04
11
0.31
21
2.48
44
4.63
48
3.10
16
5.74
44
R-Stereo Traintwo views2.16
36
1.08
38
1.57
54
0.84
34
0.19
6
0.01
8
0.01
4
2.05
39
1.01
25
1.37
34
2.45
42
1.50
27
0.46
30
0.21
30
3.23
19
4.53
68
1.62
45
1.05
46
7.07
50
1.09
44
4.86
59
5.93
67
0.04
11
0.31
21
2.48
44
4.63
48
3.10
16
5.74
44
GANetREF_RVCpermissivetwo views2.43
38
1.69
47
0.81
29
2.04
55
1.16
34
1.02
44
0.10
19
4.72
68
1.44
37
3.62
65
0.06
8
10.03
62
0.69
40
0.09
21
3.63
24
1.36
20
1.06
37
0.94
41
6.44
48
0.46
24
2.32
32
1.74
20
0.17
29
1.35
51
3.41
53
3.75
40
6.55
58
4.82
40
Zhang, Feihu and Prisacariu, Victor and Yang, Ruigang and Torr, Philip HS: GA-Net: Guided Aggregation Net for End- to-end Stereo Matching. CVPR 2019
RGCtwo views2.44
39
1.98
51
4.87
78
1.54
50
1.65
45
1.74
50
1.05
40
2.02
38
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36
0.88
25
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58
2.52
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53
0.58
49
4.33
33
1.63
30
0.91
33
0.48
27
4.28
39
1.08
43
1.94
29
4.01
48
0.15
26
0.72
41
2.19
40
5.22
53
4.90
50
6.96
49
MLCVtwo views2.45
40
0.61
23
0.81
29
2.48
58
1.21
38
0.95
42
0.50
32
3.57
57
0.61
19
3.75
68
4.26
56
5.13
46
3.34
56
0.06
13
4.63
42
1.78
36
1.32
42
0.86
38
10.05
60
1.09
44
2.30
31
3.92
46
0.29
36
2.16
60
1.87
37
3.06
32
4.01
38
1.40
21
RTSCtwo views2.56
41
0.89
33
1.50
51
0.47
25
0.76
28
0.87
38
2.29
55
1.56
29
1.91
48
2.12
47
2.86
48
1.23
26
0.97
45
1.03
69
7.27
67
3.36
61
4.68
74
1.74
62
7.73
53
1.11
47
3.95
48
5.39
61
0.21
33
1.11
47
0.40
20
0.95
22
4.56
47
8.26
54
DeepPrunerFtwo views2.66
42
1.67
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0.33
13
3.58
66
9.30
87
1.76
51
3.48
66
0.68
14
3.29
60
0.71
19
1.59
34
0.37
7
0.27
18
0.13
25
5.94
55
3.42
64
1.03
35
0.54
29
4.47
40
0.65
30
2.02
30
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24
0.56
53
0.31
21
15.21
85
3.30
37
3.74
34
1.47
22
NOSS_ROBtwo views2.78
43
3.78
70
0.39
16
2.02
54
1.58
44
2.36
56
0.00
1
2.66
50
1.54
39
3.63
66
1.79
37
2.55
39
3.51
57
0.83
62
5.82
53
5.57
77
2.69
56
1.83
64
2.63
26
0.91
39
4.62
52
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46
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64
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41
4.46
45
7.67
65
2.88
30
SGM-Foresttwo views2.81
44
2.03
53
0.36
15
6.32
77
1.79
50
2.66
57
0.02
10
1.42
25
0.77
21
2.52
48
0.96
26
6.57
53
5.74
60
0.31
37
4.36
34
2.94
53
0.61
19
0.33
21
3.08
30
0.47
25
2.46
36
2.73
34
0.84
63
0.45
34
3.66
54
3.74
39
11.30
73
7.44
51
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
PWCDC_ROBbinarytwo views2.82
45
0.49
14
0.17
6
0.38
19
0.79
31
0.03
16
0.10
19
1.16
21
8.93
77
0.14
7
2.49
44
0.55
13
31.02
91
0.57
46
5.58
52
3.32
60
1.73
47
0.91
40
2.53
25
0.88
37
2.92
40
7.18
74
0.15
26
0.53
38
0.55
23
0.69
18
1.43
8
0.88
15
HSM-Net_RVCpermissivetwo views2.86
46
1.24
41
1.50
51
2.55
60
1.72
46
0.57
34
0.17
23
3.55
56
1.14
28
3.50
64
6.32
71
7.94
54
2.93
54
0.05
10
3.38
23
2.12
40
1.28
41
0.26
15
3.24
32
1.24
49
3.66
46
3.76
45
0.61
54
2.64
65
4.43
57
6.27
59
5.76
53
5.47
43
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
ADCReftwo views2.96
47
2.78
63
1.02
37
0.70
31
2.21
58
1.96
53
2.42
58
0.96
19
6.10
68
1.94
44
1.15
30
1.08
24
0.74
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1.32
74
6.01
56
3.38
62
1.34
43
1.15
52
8.41
56
2.09
60
2.59
37
3.60
44
1.88
73
0.71
40
2.81
49
8.00
66
3.42
23
10.24
56
NaN_ROBtwo views3.01
48
1.16
40
1.13
41
0.86
38
3.03
63
0.59
35
0.75
35
3.25
55
2.58
55
0.98
27
1.32
31
21.10
75
0.59
37
1.15
72
4.15
30
1.58
29
0.80
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0.70
31
2.38
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50
1.52
24
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25
0.46
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1.07
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46
6.25
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4.54
46
13.27
61
XPNet_ROBtwo views3.02
49
2.54
59
1.33
46
0.73
32
1.45
43
1.04
45
0.61
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0.83
17
1.45
38
1.16
30
3.44
51
26.85
81
0.35
24
0.40
41
3.24
21
2.22
45
3.28
63
0.79
34
3.20
31
1.19
48
2.94
42
5.51
62
0.76
60
0.46
35
0.72
28
2.37
28
9.22
67
3.37
35
ccstwo views3.29
50
0.63
24
1.94
60
0.99
43
2.02
55
0.06
19
0.01
4
3.87
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46
4.48
80
9.13
82
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44
4.74
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0.87
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6.01
56
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1.83
49
1.39
55
5.37
42
1.03
41
5.86
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7.35
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0.32
40
1.56
54
3.32
52
6.28
60
4.08
40
3.36
34
ADCP+two views3.47
51
2.05
54
2.64
68
1.31
48
1.93
52
1.19
46
3.99
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1.07
20
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1.78
42
5.75
67
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58
0.37
27
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48
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37
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35
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43
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32
3.03
51
12.72
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29
10.90
57
FC-DCNNcopylefttwo views3.82
52
4.38
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5.67
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9.61
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4.14
69
8.01
80
3.26
64
4.21
61
1.69
44
2.95
56
1.54
33
5.87
51
2.09
52
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25
4.40
36
2.15
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2.54
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1.11
50
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0.82
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0.65
56
3.29
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6.33
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4.91
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6.98
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7.80
53
ADCLtwo views3.91
53
3.61
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2.28
63
1.76
52
1.76
49
3.09
60
4.16
68
1.98
37
13.71
86
2.81
54
5.14
63
2.47
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0.65
39
1.10
71
5.38
49
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2.02
51
2.20
69
6.41
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2.08
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4.87
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1.40
69
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44
6.69
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6.60
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4.33
45
11.16
59
FADNet_RVCtwo views3.95
54
2.06
55
1.30
44
3.33
64
3.36
65
0.99
43
2.06
53
2.30
41
1.39
35
1.72
40
4.72
60
9.29
59
5.82
62
1.76
79
8.23
81
3.12
56
2.98
59
4.27
81
16.83
68
3.30
73
4.99
64
5.51
62
0.63
55
1.39
53
1.45
33
3.14
34
4.86
48
5.92
46
LALA_ROBtwo views4.04
55
4.73
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1.42
49
1.16
45
1.93
52
2.17
55
1.04
39
1.80
33
2.48
53
2.74
51
5.87
68
33.34
83
0.69
40
0.78
59
6.02
58
2.33
46
3.21
62
1.82
63
3.66
36
1.88
56
3.40
45
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0.34
42
1.03
43
1.91
38
3.61
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8.50
66
6.20
47
iResNettwo views4.14
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0.84
31
0.71
26
2.34
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2.59
59
4.58
67
1.07
42
4.67
66
1.21
32
3.72
67
3.61
52
12.28
64
16.06
75
0.03
5
6.64
64
3.14
57
2.35
54
1.89
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16.43
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2.53
65
3.76
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0.17
29
3.88
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1.66
35
4.23
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5.19
51
3.23
33
SHDtwo views4.34
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1.02
35
2.50
66
1.71
51
1.33
41
1.46
47
2.01
50
4.90
69
4.30
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2.63
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4.99
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4.39
43
9.41
67
0.58
49
8.20
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4.81
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4.43
73
2.44
73
13.01
65
2.95
70
5.61
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6.88
73
0.30
37
3.26
66
4.11
55
5.16
52
3.88
37
10.98
58
CSANtwo views4.46
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2.36
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1.57
54
3.12
62
3.32
64
1.64
48
0.76
36
4.59
65
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67
3.80
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47
17.58
72
10.47
69
0.96
66
4.80
45
1.75
35
2.06
52
1.16
53
5.69
43
2.34
63
2.93
41
2.40
29
0.43
47
2.56
63
4.64
58
11.45
76
7.53
64
13.44
62
ETE_ROBtwo views4.62
59
4.04
72
1.52
53
1.14
44
2.02
55
2.70
58
1.28
44
1.50
26
4.25
65
1.89
43
5.88
69
37.95
88
0.84
44
1.42
77
4.94
47
2.66
49
3.81
68
0.95
42
4.84
41
1.63
54
4.01
49
5.32
60
0.67
59
1.14
48
2.03
39
3.84
42
15.25
80
7.23
50
XQCtwo views4.76
60
3.14
65
1.63
58
3.31
63
3.69
67
3.46
63
2.94
62
6.02
75
3.05
59
2.64
50
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65
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49
7.46
63
1.41
76
8.02
72
4.65
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3.59
65
1.49
59
10.75
61
2.91
66
4.96
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72
0.41
45
1.91
56
7.13
74
10.47
75
11.17
72
4.51
37
MSMDNettwo views4.98
61
2.59
61
3.57
71
2.54
59
0.90
32
3.99
64
1.63
47
4.52
64
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40
4.54
81
18.92
100
20.82
74
12.09
70
0.60
53
6.57
63
5.84
79
2.28
53
1.52
60
7.08
52
1.94
58
6.09
69
6.12
69
0.31
38
2.29
61
1.47
34
2.83
31
4.25
44
7.49
52
CBMVpermissivetwo views5.52
62
3.65
69
1.32
45
3.46
65
2.86
62
2.04
54
0.41
29
3.64
58
2.63
56
4.09
74
2.36
40
14.13
67
3.70
58
0.57
46
7.16
66
6.19
83
4.26
71
2.36
72
8.76
58
1.44
51
6.43
70
4.53
55
0.54
52
5.62
81
4.85
60
6.08
56
19.41
84
26.62
86
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
RTStwo views5.53
63
1.46
42
0.94
34
0.90
39
1.73
47
4.08
65
4.63
72
2.53
45
3.41
61
2.79
52
1.70
35
8.18
55
1.27
48
0.72
55
7.83
69
4.87
72
7.37
86
2.34
70
18.33
69
2.92
67
4.78
55
5.88
65
1.04
64
1.31
49
6.74
71
22.88
97
6.66
59
22.00
76
RTSAtwo views5.53
63
1.46
42
0.94
34
0.90
39
1.73
47
4.08
65
4.63
72
2.53
45
3.41
61
2.79
52
1.70
35
8.18
55
1.27
48
0.72
55
7.83
69
4.87
72
7.37
86
2.34
70
18.33
69
2.92
67
4.78
55
5.88
65
1.04
64
1.31
49
6.74
71
22.88
97
6.66
59
22.00
76
AnyNet_C32two views5.68
65
1.96
49
2.91
69
1.78
53
2.71
61
3.17
61
5.76
77
4.31
62
12.56
81
3.07
58
7.17
73
4.22
42
1.70
50
2.19
80
8.22
80
5.98
81
5.69
79
4.43
85
12.45
64
2.93
69
9.72
85
8.57
85
1.84
72
3.46
69
6.24
68
9.82
73
6.26
55
14.21
63
CBMV_ROBtwo views6.09
66
2.65
62
2.29
64
8.35
80
5.74
76
5.08
68
2.76
61
5.03
71
2.35
51
3.78
69
4.70
59
21.22
76
13.56
73
0.09
21
6.04
59
3.70
66
2.84
57
1.39
55
11.35
62
8.80
90
4.92
62
3.11
39
0.43
47
2.10
59
7.06
73
10.12
74
12.96
75
11.93
60
ADCPNettwo views6.67
67
1.97
50
3.86
74
2.75
61
6.19
80
3.07
59
7.60
84
5.21
72
8.03
74
3.82
71
7.92
76
5.17
47
13.78
74
2.98
84
8.10
75
6.12
82
4.35
72
2.04
68
18.50
71
4.74
78
7.04
74
7.43
77
2.98
80
1.91
56
8.96
77
14.04
83
7.02
63
14.47
65
AMNettwo views6.77
68
3.87
71
2.62
67
4.00
70
5.86
77
6.75
74
3.31
65
2.51
44
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52
3.44
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6.68
72
9.34
60
2.05
51
7.44
96
4.79
44
9.81
94
13.48
95
7.65
95
7.95
55
7.83
85
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89
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80
2.74
78
4.18
73
5.52
63
8.25
68
13.24
76
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ADCMidtwo views6.83
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PWC_ROBbinarytwo views7.15
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MFN_U_SF_DS_RVCtwo views7.30
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FBW_ROBtwo views7.98
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pmcnntwo views8.05
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ADCStwo views9.93
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SGM_RVCbinarytwo views10.08
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Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
MDST_ROBtwo views10.13
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SANettwo views10.32
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MFN_U_SF_RVCtwo views10.34
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ccs_robtwo views10.35
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G-Nettwo views10.87
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STTStereo_v2two views10.87
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MSC_U_SF_DS_RVCtwo views10.98
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edge stereotwo views11.35
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SGM-ForestMtwo views11.60
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AnyNet_C01two views12.12
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PVDtwo views12.14
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MeshStereopermissivetwo views13.21
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C. Zhang, Z. Li, Y. Cheng, R. Cai, H. Chao, Y. Rui: MeshStereo: A Global Stereo Model with Mesh Alignment Regularization for View Interpolation. ICCV 2015
DPSNettwo views14.25
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MFMNet_retwo views16.26
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STStereotwo views19.12
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ELAS_RVCcopylefttwo views19.26
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A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
SAMSARAtwo views19.29
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MADNet+two views19.30
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ELAScopylefttwo views19.48
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32.86
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A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
MANEtwo views19.83
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SPS-STEREOcopylefttwo views20.13
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K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
SGM+DAISYtwo views20.91
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LE_ROBtwo views22.46
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LSM0two views22.48
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BEATNet-Init1two views23.62
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DispFullNettwo views26.27
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PSMNet_ROBtwo views31.36
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PWCKtwo views38.25
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14.46
101
9.53
93
30.80
104
12.71
100
83.96
105
28.46
104
23.42
102
13.60
100
47.98
103
30.51
104
34.48
101
27.91
101
31.53
92
38.61
98
DPSimNet_ROBtwo views58.77
104
55.98
104
59.21
106
62.88
105
73.92
105
60.20
104
64.84
106
28.34
104
91.63
107
80.59
106
52.65
105
74.49
105
47.24
103
90.64
107
55.91
105
45.99
105
54.62
105
42.30
105
68.03
103
59.25
105
51.18
105
59.54
106
44.54
102
45.19
105
44.85
104
61.56
104
56.15
103
55.05
104
CC-Net-ROBtwo views70.08
105
57.59
105
33.20
105
55.61
103
76.68
106
84.28
107
62.79
105
83.71
107
83.30
106
76.21
105
74.67
106
74.97
106
61.84
106
62.54
104
41.03
104
41.52
104
86.04
106
88.34
107
89.47
106
90.97
106
75.66
106
54.62
105
53.92
104
50.83
106
74.81
106
79.04
106
83.52
107
94.87
108
MADNet++two views76.54
106
72.15
107
83.11
107
61.53
104
81.21
107
58.42
103
69.47
107
68.24
106
78.58
105
85.63
107
92.37
107
80.60
107
85.04
107
36.71
103
81.19
107
89.82
107
88.15
107
69.64
106
82.54
104
91.39
107
92.32
107
93.48
107
65.75
106
90.26
107
59.09
105
74.29
105
74.25
105
61.32
105
MEDIAN_ROBtwo views97.56
107
99.08
108
98.39
108
100.00
108
99.99
108
100.00
110
100.00
110
99.49
110
98.31
108
100.00
110
99.95
110
97.97
110
96.05
108
99.97
108
99.41
108
98.43
108
98.53
108
98.66
108
99.88
109
97.95
108
93.38
108
96.45
108
100.00
107
97.23
108
86.96
108
92.33
108
89.80
108
95.90
109
AVERAGE_ROBtwo views98.95
108
100.00
111
99.79
109
100.00
108
100.00
109
100.00
110
100.00
110
100.00
111
100.00
109
100.00
110
100.00
111
100.00
113
100.00
111
100.00
109
99.77
109
99.85
109
100.00
111
100.00
111
100.00
110
100.00
111
98.12
109
99.51
109
100.00
107
100.00
109
93.43
109
93.75
109
94.35
109
93.16
107
DGTPSM_ROBtwo views99.52
109
99.77
109
100.00
110
100.00
108
100.00
109
100.00
110
100.00
110
99.17
108
100.00
109
99.31
108
99.64
109
91.36
108
99.13
109
100.00
109
99.94
110
100.00
110
99.82
109
99.99
109
99.53
107
99.99
109
99.80
111
99.66
110
100.00
107
100.00
109
100.00
110
100.00
110
100.00
110
100.00
110
DPSMNet_ROBtwo views99.53
110
99.78
110
100.00
110
100.00
108
100.00
109
100.00
110
100.00
110
99.17
108
100.00
109
99.31
108
99.63
108
91.42
109
99.14
110
100.00
109
99.94
110
100.00
110
99.82
109
99.99
109
99.62
108
99.99
109
99.79
110
99.66
110
100.00
107
100.00
109
100.00
110
100.00
110
100.00
110
100.00
110
DPSM_ROBtwo views99.92
111
100.00
111
100.00
110
100.00
108
100.00
109
98.97
108
98.99
108
100.00
111
100.00
109
100.00
110
100.00
111
99.90
111
100.00
111
100.00
109
100.00
112
100.00
110
100.00
111
100.00
111
100.00
110
100.00
111
100.00
112
100.00
112
100.00
107
100.00
109
100.00
110
100.00
110
100.00
110
100.00
110
DPSMtwo views99.92
111
100.00
111
100.00
110
100.00
108
100.00
109
98.97
108
98.99
108
100.00
111
100.00
109
100.00
110
100.00
111
99.90
111
100.00
111
100.00
109
100.00
112
100.00
110
100.00
111
100.00
111
100.00
110
100.00
111
100.00
112
100.00
112
100.00
107
100.00
109
100.00
110
100.00
110
100.00
110
100.00
110
STTRV1_RVCtwo views2.31
57
1.06
38
5.61
74
1.39
42
5.42
70
2.59
60
3.68
59
3.77
63
4.47
79
4.40
57
16.44
70
20.14
80
1.02
67
9.83
89
3.26
58
18.06
98
0.81
37
11.45
93
18.05
100
6.27
71
4.23
74
6.12
65
7.90
65
11.99
74
28.90
90