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 Infoalllakes. 1llakes. 1ssand box 1lsand box 1sstora. room 1lstora. room 1sstora. room 2lstora. room 2sstora. room 2 1lstora. room 2 1sstora. room 2 2lstora. room 2 2sstora. room 3lstora. room 3stunnel 1ltunnel 1stunnel 2ltunnel 2stunnel 3ltunnel 3s
sort bysort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
ccstwo views3.04
7
0.39
7
3.08
10
1.78
27
0.52
16
2.04
1
0.50
12
13.09
52
13.71
58
3.54
4
5.36
6
5.50
12
2.45
11
4.81
6
2.88
12
0.09
47
0.08
60
0.12
75
0.10
69
0.20
42
0.50
71
DMCAtwo views3.29
10
1.05
33
4.18
22
1.60
21
2.87
69
2.11
2
0.60
15
7.95
7
4.65
6
8.62
32
8.59
19
9.24
31
4.68
33
6.25
8
3.03
14
0.04
37
0.09
64
0.06
69
0.03
50
0.18
39
0.07
18
AANet_RVCtwo views5.01
38
1.74
69
6.38
49
1.96
38
1.29
41
2.26
3
1.69
36
10.07
17
18.53
75
7.88
26
18.15
66
8.49
29
2.70
14
10.59
51
7.04
43
0.96
97
0.15
75
0.02
54
0.00
1
0.13
31
0.12
33
CFNet_RVCtwo views3.31
12
0.94
28
2.69
5
1.50
16
2.38
59
2.81
4
0.68
20
8.35
8
7.43
18
4.45
6
9.94
24
10.20
37
4.60
31
6.49
9
3.41
22
0.00
1
0.00
1
0.03
60
0.00
1
0.22
48
0.03
9
iResNet_ROBtwo views4.23
29
1.02
31
4.90
28
2.18
43
0.93
32
2.92
5
0.37
8
15.10
70
16.91
72
7.89
27
10.51
27
7.03
19
3.07
17
8.16
26
3.46
24
0.01
18
0.00
1
0.00
1
0.00
1
0.10
23
0.02
3
DN-CSS_ROBtwo views2.69
5
1.40
54
5.34
33
2.31
50
0.75
22
3.14
6
0.06
1
6.11
2
3.87
3
5.34
11
12.18
39
2.34
5
1.22
5
7.84
17
1.48
4
0.03
32
0.00
1
0.00
1
0.00
1
0.35
61
0.03
9
DeepPruner_ROBtwo views3.52
14
1.14
42
4.06
20
1.12
7
1.65
47
3.65
7
0.83
24
13.96
60
4.47
5
7.80
25
10.84
29
7.05
21
2.16
10
8.14
25
3.08
17
0.07
45
0.03
40
0.00
1
0.01
38
0.32
57
0.06
17
R-Stereo Traintwo views2.44
3
0.32
1
1.93
1
0.94
4
0.16
4
3.67
8
0.61
16
6.37
3
3.08
1
9.14
37
17.44
64
1.80
3
0.77
3
1.76
1
0.70
1
0.00
1
0.01
25
0.00
1
0.00
1
0.01
1
0.03
9
R-Stereotwo views2.44
3
0.32
1
1.93
1
0.94
4
0.16
4
3.67
8
0.61
16
6.37
3
3.08
1
9.14
37
17.44
64
1.80
3
0.77
3
1.76
1
0.70
1
0.00
1
0.01
25
0.00
1
0.00
1
0.01
1
0.03
9
STTStereotwo views3.60
15
0.93
27
6.34
48
2.71
61
2.23
58
3.68
10
0.63
19
9.42
12
6.73
11
9.87
45
6.97
9
8.84
30
3.65
19
6.85
10
3.04
15
0.00
1
0.02
35
0.01
43
0.00
1
0.02
4
0.02
3
DeepPrunerFtwo views6.75
54
2.69
84
23.31
99
3.68
80
7.16
102
3.78
11
4.29
57
13.42
55
20.13
82
8.13
28
10.46
25
7.18
23
8.06
56
11.10
56
9.44
64
0.24
65
0.15
75
0.29
89
0.42
90
0.66
78
0.45
66
TDLMtwo views4.11
25
1.11
40
3.54
14
1.62
22
1.04
35
3.91
12
7.41
81
10.60
29
10.67
42
6.38
15
12.59
43
5.95
15
4.77
35
8.79
35
3.04
15
0.58
87
0.00
1
0.01
43
0.00
1
0.19
41
0.12
33
CVANet_RVCtwo views4.16
27
1.16
43
3.60
15
1.94
37
1.46
42
3.92
13
4.68
63
10.89
33
8.34
28
7.58
23
10.84
29
10.27
38
6.62
49
8.56
31
2.69
10
0.39
77
0.00
1
0.00
1
0.01
38
0.21
47
0.09
23
AdaStereotwo views3.09
8
0.58
12
3.04
9
2.84
63
0.48
15
4.08
14
1.29
31
12.16
44
7.77
22
6.03
12
9.62
23
5.79
14
1.53
7
4.56
5
1.93
7
0.00
1
0.00
1
0.00
1
0.00
1
0.02
4
0.02
3
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi: AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
CC-Net-ROBtwo views3.84
20
1.07
35
5.23
31
2.65
58
2.96
70
4.22
15
0.69
21
10.43
26
7.72
20
8.78
34
8.29
17
9.61
34
4.02
25
7.16
13
3.65
26
0.13
53
0.03
40
0.02
54
0.03
50
0.05
15
0.03
9
FADNet-RVCtwo views3.98
23
1.84
74
12.48
80
1.69
25
0.44
13
4.33
16
1.31
32
11.84
38
7.15
13
3.53
3
3.50
2
10.63
40
4.43
30
9.12
38
6.25
37
0.03
32
0.10
65
0.00
1
0.03
50
0.60
75
0.25
53
iResNetv2_ROBtwo views4.28
31
1.43
55
7.17
59
2.91
64
1.26
39
4.36
17
1.62
34
13.64
58
10.25
41
9.83
44
11.41
34
7.68
24
4.00
24
7.75
16
1.85
6
0.00
1
0.00
1
0.00
1
0.00
1
0.37
63
0.09
23
NLCA_NET_v2_RVCtwo views3.84
20
1.06
34
5.23
31
2.72
62
3.27
72
4.36
17
0.61
16
10.71
30
7.56
19
8.75
33
7.89
14
9.86
36
3.90
23
7.15
12
3.44
23
0.14
54
0.02
35
0.02
54
0.03
50
0.04
13
0.03
9
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
MLCVtwo views3.44
13
0.88
21
5.60
36
1.39
12
0.25
7
4.36
17
0.33
6
7.25
5
7.28
15
9.17
39
12.24
41
5.09
10
2.47
12
9.15
39
3.23
19
0.00
1
0.00
1
0.00
1
0.00
1
0.10
23
0.02
3
Anonymous Stereotwo views6.16
48
3.15
91
23.75
100
2.97
69
2.48
63
4.39
20
13.30
97
9.21
10
9.86
37
9.56
42
8.76
20
6.79
18
1.99
9
13.50
70
13.04
81
0.01
18
0.05
48
0.00
1
0.06
63
0.22
48
0.19
44
iResNettwo views3.68
17
0.91
24
7.94
64
2.97
69
0.34
9
4.44
21
0.48
10
7.70
6
9.74
36
7.72
24
12.74
44
4.03
7
2.87
15
8.05
21
3.37
21
0.02
23
0.01
25
0.00
1
0.00
1
0.10
23
0.09
23
RASNettwo views4.52
35
0.61
14
4.42
24
3.42
77
4.68
94
4.58
22
0.99
25
9.54
14
8.01
23
5.28
10
11.42
35
10.34
39
8.88
61
9.28
40
8.68
59
0.15
56
0.00
1
0.00
1
0.00
1
0.03
10
0.04
15
FADNettwo views4.23
29
1.65
67
11.75
76
1.64
24
0.80
27
4.80
23
0.77
23
13.76
59
11.65
50
3.97
5
5.24
5
9.62
35
5.14
39
8.40
29
3.78
27
0.21
63
0.04
44
0.07
70
0.05
61
1.14
90
0.10
30
DPM-Stereotwo views1.97
1
0.64
16
2.95
8
0.17
1
0.10
1
4.83
24
0.13
2
8.60
9
4.06
4
6.42
16
4.92
4
0.44
1
0.72
1
3.57
3
1.80
5
0.00
1
0.01
25
0.00
1
0.00
1
0.05
15
0.04
15
CFNettwo views3.72
18
1.10
39
5.03
29
2.49
53
1.59
44
4.90
25
0.22
5
11.38
34
9.88
38
4.80
7
11.25
32
6.44
17
3.68
20
8.33
28
3.00
13
0.00
1
0.00
1
0.00
1
0.00
1
0.22
48
0.07
18
PMTNettwo views1.99
2
0.32
1
2.21
3
0.39
2
0.23
6
5.08
26
0.49
11
5.84
1
8.22
26
3.07
1
3.29
1
0.73
2
0.75
2
8.18
27
0.94
3
0.00
1
0.00
1
0.00
1
0.00
1
0.03
10
0.00
1
BEATNet_4xtwo views3.24
9
1.27
48
5.89
41
1.56
18
0.10
1
5.26
27
1.07
26
10.08
18
5.50
8
6.89
21
7.73
13
4.53
9
4.13
26
5.05
7
5.27
34
0.04
37
0.05
48
0.00
1
0.00
1
0.23
52
0.23
47
PASMtwo views7.90
66
4.22
95
21.97
97
3.25
73
3.29
73
5.39
28
6.57
75
10.57
28
19.09
77
12.77
64
13.92
50
18.11
74
9.51
65
13.79
73
10.77
74
0.19
61
0.45
93
0.29
89
1.08
101
1.49
96
1.19
93
ccs_robtwo views3.63
16
1.12
41
4.42
24
2.52
54
0.91
30
5.50
29
0.21
4
10.11
21
9.11
32
6.55
19
11.28
33
8.32
28
2.55
13
7.66
14
2.01
8
0.00
1
0.00
1
0.00
1
0.00
1
0.20
42
0.08
21
HITNettwo views2.79
6
0.77
17
4.02
19
2.03
39
0.11
3
5.58
30
0.59
14
9.24
11
5.15
7
6.42
16
7.26
11
3.66
6
2.92
16
4.07
4
3.87
28
0.00
1
0.00
1
0.00
1
0.00
1
0.06
19
0.02
3
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
NOSS_ROBtwo views3.30
11
0.46
8
2.62
4
2.08
40
1.01
34
5.60
31
0.74
22
10.37
25
11.48
48
5.15
9
8.43
18
5.67
13
1.73
8
7.97
19
2.34
9
0.02
23
0.06
54
0.00
1
0.00
1
0.07
20
0.14
38
FADNet_RVCtwo views3.91
22
1.67
68
12.95
83
0.96
6
0.75
22
5.71
32
0.54
13
10.83
32
6.60
10
3.46
2
8.09
15
4.10
8
3.40
18
9.43
42
6.33
38
0.36
75
0.44
92
0.17
81
0.46
94
0.91
85
0.95
88
FADNet-RVC-Resampletwo views3.79
19
1.62
65
12.06
77
1.43
14
0.66
18
5.94
33
2.41
39
10.18
23
8.58
30
6.28
14
4.22
3
5.33
11
4.80
37
7.71
15
3.19
18
0.17
59
0.21
80
0.17
81
0.12
71
0.41
69
0.29
59
DLCB_ROBtwo views4.51
33
0.91
24
3.78
17
2.19
44
1.07
36
6.28
34
3.09
43
9.78
15
7.72
20
10.65
49
12.97
45
13.91
55
3.71
21
8.72
33
5.30
35
0.00
1
0.00
1
0.00
1
0.00
1
0.03
10
0.10
30
XPNet_ROBtwo views6.03
47
1.22
44
5.61
37
2.56
57
0.90
29
6.32
35
7.07
78
12.92
51
8.30
27
14.76
73
15.13
56
19.84
81
6.66
51
10.36
48
8.58
58
0.02
23
0.04
44
0.00
1
0.03
50
0.11
27
0.24
50
RPtwo views6.84
58
1.29
52
5.53
35
3.92
86
5.18
96
6.32
35
3.53
49
11.73
37
15.31
64
9.54
41
22.38
74
18.25
75
14.47
82
10.11
46
7.49
45
0.91
96
0.01
25
0.12
75
0.15
73
0.33
58
0.19
44
edge stereotwo views6.76
55
1.01
30
6.76
55
2.20
45
2.45
62
6.41
37
2.45
40
14.84
67
11.98
52
15.29
74
18.31
67
22.02
87
12.56
75
10.82
52
7.49
45
0.03
32
0.06
54
0.11
74
0.03
50
0.30
54
0.14
38
ETE_ROBtwo views5.80
42
1.77
71
6.33
47
1.44
15
0.78
26
6.43
38
6.90
77
12.53
48
8.08
24
12.93
66
14.89
54
21.13
86
5.87
44
9.83
44
6.57
41
0.04
37
0.01
25
0.00
1
0.02
42
0.08
22
0.33
60
HSMtwo views4.00
24
0.79
18
3.16
12
1.59
20
2.17
56
6.77
39
1.11
27
12.28
45
6.35
9
6.75
20
8.11
16
13.90
54
5.37
42
8.85
36
2.71
11
0.00
1
0.00
1
0.00
1
0.00
1
0.02
4
0.02
3
CBMV_ROBtwo views4.14
26
0.52
9
3.14
11
1.30
10
0.77
25
6.92
40
1.97
38
10.11
21
9.58
34
8.92
36
14.20
52
7.12
22
5.90
45
8.65
32
3.50
25
0.01
18
0.05
48
0.00
1
0.00
1
0.04
13
0.09
23
HSM-Net_RVCpermissivetwo views4.20
28
0.32
1
2.76
6
0.63
3
0.69
20
6.95
41
1.69
36
11.96
39
8.36
29
8.83
35
12.17
38
15.18
62
4.21
28
6.91
11
3.30
20
0.02
23
0.02
35
0.00
1
0.00
1
0.01
1
0.01
2
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
Abc-Nettwo views6.77
56
1.49
58
6.48
50
2.92
66
4.40
85
7.43
42
3.61
51
19.52
92
13.29
55
8.39
30
16.91
60
15.96
63
12.13
73
12.85
65
7.70
47
1.47
100
0.11
67
0.01
43
0.42
90
0.14
35
0.24
50
NCC-stereotwo views6.77
56
1.49
58
6.48
50
2.92
66
4.40
85
7.43
42
3.61
51
19.52
92
13.29
55
8.39
30
16.91
60
15.96
63
12.13
73
12.85
65
7.70
47
1.47
100
0.11
67
0.01
43
0.42
90
0.14
35
0.24
50
PWC_ROBbinarytwo views8.24
70
3.13
89
12.74
81
2.43
52
4.43
88
7.51
44
1.22
28
16.63
79
19.24
78
16.08
76
28.29
87
13.99
56
10.16
67
13.63
72
14.06
83
0.42
82
0.00
1
0.05
67
0.00
1
0.59
74
0.27
57
DANettwo views6.02
46
1.23
45
8.45
66
3.86
85
3.94
83
7.64
45
1.34
33
9.51
13
7.00
12
13.39
68
15.53
57
15.99
65
7.02
52
12.14
60
12.37
78
0.19
61
0.12
71
0.02
54
0.03
50
0.13
31
0.56
75
NVstereo2Dtwo views4.51
33
0.82
19
6.86
57
3.28
74
3.38
76
8.16
46
3.13
44
10.51
27
15.15
63
4.90
8
6.89
8
7.87
25
4.78
36
9.88
45
3.91
29
0.01
18
0.00
1
0.00
1
0.06
63
0.02
4
0.58
76
AF-Nettwo views7.78
65
1.44
56
6.68
54
3.37
75
4.50
92
8.61
47
2.69
41
17.07
81
20.17
83
9.52
40
24.02
77
20.31
83
14.59
83
11.58
58
9.84
69
0.61
88
0.00
1
0.12
75
0.00
1
0.38
64
0.12
33
PA-Nettwo views4.98
37
1.47
57
7.42
61
2.40
51
2.14
55
8.73
48
3.64
53
12.42
46
13.11
54
7.03
22
7.57
12
7.88
26
6.52
48
10.16
47
7.82
49
0.02
23
0.03
40
0.00
1
0.00
1
0.11
27
1.07
91
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
RGCtwo views6.88
59
2.23
80
6.13
44
4.05
87
4.73
95
8.94
49
2.78
42
15.19
72
11.74
51
11.13
53
19.34
69
17.86
72
10.42
68
13.02
67
8.03
53
0.73
90
0.01
25
0.24
88
0.41
89
0.31
56
0.38
62
GANettwo views6.22
49
1.07
35
4.07
21
2.27
47
0.89
28
9.19
50
9.52
86
12.02
40
8.13
25
10.72
50
29.09
89
13.86
53
7.52
55
11.00
55
4.39
30
0.36
75
0.00
1
0.02
54
0.02
42
0.12
29
0.08
21
StereoDRNet-Refinedtwo views4.46
32
0.62
15
3.80
18
1.92
34
0.40
11
9.35
51
0.15
3
10.02
16
8.83
31
12.69
62
11.62
36
9.34
32
3.87
22
8.06
22
8.02
51
0.00
1
0.00
1
0.01
43
0.05
61
0.20
42
0.26
56
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
pmcnntwo views7.72
64
1.27
48
9.42
69
2.91
64
3.14
71
9.44
52
6.23
70
12.56
49
16.51
70
14.53
71
24.08
78
27.44
95
8.49
58
9.32
41
8.44
57
0.06
44
0.08
60
0.00
1
0.00
1
0.30
54
0.15
40
SHDtwo views9.61
80
2.60
83
12.46
79
3.69
81
3.54
78
9.47
53
1.25
29
20.16
97
37.84
105
18.19
80
21.24
71
16.96
68
12.83
78
14.47
78
16.05
86
0.32
73
0.13
72
0.01
43
0.08
66
0.38
64
0.48
69
NCCL2two views5.88
44
1.59
63
5.44
34
1.87
30
0.92
31
9.55
54
11.55
95
12.11
41
9.94
39
9.67
43
8.85
21
22.28
88
7.41
53
8.78
34
7.17
44
0.01
18
0.00
1
0.03
60
0.00
1
0.13
31
0.23
47
PSMNet_ROBtwo views5.02
39
1.63
66
6.03
43
1.90
33
1.83
52
9.57
55
6.35
73
15.58
75
7.23
14
6.15
13
10.48
26
12.22
47
4.16
27
8.02
20
8.71
60
0.02
23
0.01
25
0.01
43
0.10
69
0.20
42
0.12
33
NaN_ROBtwo views6.00
45
1.24
46
6.29
46
1.34
11
1.68
49
9.60
56
10.31
91
15.09
68
15.79
66
12.62
61
8.95
22
11.67
46
5.83
43
11.78
59
6.41
40
0.05
42
0.13
72
0.08
71
0.20
76
0.22
48
0.79
84
SuperBtwo views8.10
69
3.15
91
24.67
101
2.65
58
1.23
38
9.88
57
4.29
57
10.18
23
30.07
95
11.53
55
12.18
39
6.12
16
6.65
50
10.50
49
14.47
84
0.14
54
0.11
67
0.35
93
0.25
80
13.06
109
0.48
69
LALA_ROBtwo views6.58
53
1.80
73
6.25
45
1.26
9
0.94
33
10.08
58
9.02
83
16.00
76
11.51
49
12.74
63
13.02
46
24.77
90
5.25
40
10.56
50
8.02
51
0.04
37
0.05
48
0.00
1
0.02
42
0.10
23
0.25
53
CBMVpermissivetwo views5.35
40
0.91
24
3.67
16
1.62
22
0.44
13
10.09
59
7.19
80
12.49
47
12.33
53
12.22
58
14.69
53
10.93
41
6.48
47
8.51
30
4.96
33
0.02
23
0.15
75
0.00
1
0.00
1
0.17
38
0.17
42
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
LSMtwo views14.01
93
5.95
99
33.49
104
6.78
103
43.61
111
10.22
60
9.98
90
15.16
71
22.93
85
23.07
93
32.34
95
18.52
76
12.67
76
15.45
81
11.10
75
0.16
58
0.51
97
0.09
73
0.32
84
1.08
89
16.85
111
SGM-Foresttwo views4.96
36
0.32
1
2.84
7
1.21
8
0.64
17
10.23
61
6.64
76
11.55
35
10.98
43
10.94
52
13.59
48
11.65
45
4.30
29
8.94
37
4.63
32
0.11
50
0.04
44
0.00
1
0.00
1
0.05
15
0.46
68
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
PWCDC_ROBbinarytwo views7.92
67
3.17
93
7.48
62
5.73
98
4.40
85
10.45
62
0.35
7
14.52
65
28.19
92
10.36
48
31.27
92
7.04
20
9.14
64
13.22
69
8.78
61
2.74
105
0.02
35
0.00
1
0.00
1
1.31
94
0.17
42
StereoDRNettwo views5.59
41
1.75
70
6.80
56
3.12
71
4.45
89
10.61
63
4.35
59
18.80
85
9.73
35
12.22
58
6.87
7
11.44
44
4.65
32
8.09
24
8.26
55
0.02
23
0.11
67
0.00
1
0.03
50
0.20
42
0.28
58
Nwc_Nettwo views6.97
60
1.25
47
6.63
53
3.82
84
3.37
75
10.83
64
1.67
35
19.56
94
11.35
46
8.36
29
23.62
76
17.19
70
11.44
72
11.21
57
8.08
54
0.80
92
0.00
1
0.00
1
0.02
42
0.13
31
0.09
23
DISCOtwo views6.28
50
0.57
11
5.78
39
3.43
78
1.17
37
11.22
65
3.39
48
12.14
43
16.16
68
6.52
18
11.22
31
16.96
68
6.32
46
19.51
91
10.74
72
0.00
1
0.00
1
0.00
1
0.00
1
0.35
61
0.11
32
ADCMidtwo views10.24
86
3.13
89
20.70
95
2.21
46
2.39
60
11.23
66
6.19
69
14.17
62
11.19
44
23.20
94
22.25
73
17.89
73
19.54
93
18.51
89
26.21
102
0.45
83
0.42
91
1.10
102
1.29
102
1.56
98
1.18
92
ADCReftwo views7.27
61
1.38
53
16.37
89
2.52
54
3.30
74
11.63
67
3.16
45
10.80
31
9.35
33
13.03
67
25.27
84
8.17
27
8.92
62
8.06
22
21.81
96
0.15
56
0.08
60
0.16
80
0.34
86
0.38
64
0.58
76
RTSCtwo views9.15
76
3.00
88
13.57
85
3.72
82
1.76
51
11.82
68
0.46
9
16.95
80
36.83
102
15.80
75
15.53
57
12.91
50
7.46
54
20.01
93
21.76
95
0.31
72
0.13
72
0.01
43
0.08
66
0.57
72
0.41
65
DRN-Testtwo views5.87
43
0.98
29
5.89
41
2.69
60
3.65
81
12.37
69
3.35
47
20.07
96
10.20
40
11.93
57
12.31
42
11.06
43
5.31
41
7.89
18
9.05
62
0.04
37
0.05
48
0.04
65
0.04
59
0.18
39
0.25
53
CSANtwo views7.62
62
1.60
64
6.56
52
1.83
28
0.66
18
12.40
70
10.52
93
14.45
64
21.32
84
14.19
70
15.98
59
17.84
71
13.02
79
12.32
62
8.38
56
0.09
47
0.07
58
0.03
60
0.04
59
0.33
58
0.67
82
NVStereoNet_ROBtwo views16.04
98
6.75
101
12.90
82
6.37
102
7.42
105
12.89
71
9.74
88
22.78
101
25.12
88
30.32
99
46.19
109
34.37
103
25.38
102
21.48
94
21.38
92
5.94
108
3.10
108
6.07
109
10.09
111
4.01
103
8.54
109
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
GANetREF_RVCpermissivetwo views6.56
52
2.89
86
7.58
63
3.41
76
0.40
11
12.96
72
9.58
87
15.09
68
17.25
74
10.33
47
10.62
28
12.27
48
8.16
57
12.21
61
4.53
31
0.41
79
0.00
1
0.00
1
0.02
42
3.12
102
0.39
63
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
FBW_ROBtwo views8.50
75
1.03
32
7.98
65
1.93
36
1.28
40
13.10
73
6.23
70
22.50
100
18.98
76
18.82
81
14.91
55
19.06
78
10.04
66
18.41
88
9.83
68
0.62
89
0.22
81
1.82
106
0.82
99
0.99
88
1.36
95
XQCtwo views8.43
74
3.58
94
16.40
90
2.92
66
2.17
56
13.22
74
3.60
50
14.64
66
25.86
89
11.87
56
12.04
37
15.06
61
10.67
69
15.24
80
19.41
89
0.39
77
0.08
60
0.05
67
0.07
65
0.84
84
0.45
66
WCMA_ROBtwo views9.21
77
0.87
20
7.37
60
2.54
56
2.13
54
13.59
75
5.80
67
11.64
36
14.01
59
24.43
95
32.99
96
27.09
94
18.02
87
12.51
64
9.85
70
0.81
93
0.07
58
0.01
43
0.01
38
0.16
37
0.23
47
ADCStwo views13.02
91
4.93
97
28.38
102
3.17
72
2.67
66
13.61
76
10.83
94
18.70
84
33.46
98
22.59
89
24.78
81
19.59
80
18.51
90
23.40
98
32.16
109
0.10
49
0.19
79
0.37
95
0.18
74
1.26
93
1.46
97
DispFullNettwo views17.47
105
26.01
109
33.98
105
22.58
110
20.86
110
13.84
77
1.28
30
16.50
77
26.27
90
19.97
86
17.17
63
20.52
85
18.49
89
22.86
97
10.76
73
5.13
106
2.83
107
30.72
111
7.72
109
20.86
110
11.01
110
SANettwo views10.64
87
1.86
75
10.91
71
1.76
26
0.71
21
14.62
78
9.23
85
19.18
87
37.14
103
19.22
82
27.96
86
25.86
93
19.11
92
13.02
67
10.63
71
0.08
46
0.06
54
0.03
60
0.02
42
0.62
76
0.81
85
AnyNet_C32two views10.98
89
5.58
98
22.79
98
4.16
88
5.83
98
15.64
79
14.30
99
13.18
53
17.15
73
16.44
78
20.52
70
14.68
60
13.44
80
22.46
95
30.08
107
0.17
59
0.26
84
0.36
94
0.36
87
1.23
92
0.91
87
RYNettwo views6.34
51
0.89
23
5.88
40
1.41
13
4.48
91
15.97
80
4.18
56
13.41
54
16.49
69
10.81
51
7.00
10
14.33
57
8.72
60
9.43
42
13.71
82
0.00
1
0.01
25
0.00
1
0.00
1
0.02
4
0.07
18
MSMD_ROBtwo views9.28
78
1.09
38
4.65
27
1.58
19
0.39
10
16.52
81
4.41
60
13.60
56
14.87
62
22.34
88
39.89
103
25.67
92
20.71
96
12.42
63
6.98
42
0.34
74
0.03
40
0.00
1
0.00
1
0.05
15
0.09
23
ADCP+two views8.09
68
1.79
72
14.50
87
1.54
17
4.28
84
16.57
82
5.20
65
12.80
50
11.20
45
12.83
65
17.07
62
11.02
42
10.80
70
17.59
87
23.18
99
0.03
32
0.05
48
0.01
43
0.18
74
0.39
68
0.81
85
LE_ROBtwo views16.73
103
1.28
51
11.61
75
3.72
82
1.65
47
16.67
83
9.17
84
14.39
63
55.91
111
63.81
111
40.86
106
35.94
104
37.73
110
14.24
75
26.87
103
0.05
42
0.10
65
0.13
78
0.22
77
0.12
29
0.15
40
ADCPNettwo views9.54
79
2.39
82
31.46
103
2.09
41
1.60
45
16.71
84
6.39
74
12.11
41
11.45
47
13.53
69
21.45
72
19.41
79
10.94
71
14.38
76
21.54
94
0.27
70
1.16
100
0.39
96
1.49
104
0.58
73
1.45
96
FC-DCNNcopylefttwo views10.72
88
0.52
9
4.27
23
1.88
31
1.63
46
17.18
85
5.29
66
18.20
83
19.69
81
28.50
98
34.51
99
34.03
102
21.48
99
15.89
82
11.15
76
0.03
32
0.01
25
0.02
54
0.01
38
0.07
20
0.09
23
PVDtwo views15.44
96
2.93
87
14.67
88
4.21
91
3.39
77
17.43
86
4.16
55
27.84
107
48.84
110
31.02
101
43.54
107
29.76
98
30.81
107
25.97
101
21.40
93
0.23
64
0.41
89
0.04
65
0.33
85
0.41
69
1.33
94
stereogantwo views7.69
63
0.88
21
7.08
58
3.49
79
3.93
82
18.98
87
3.23
46
16.52
78
19.58
80
9.93
46
18.92
68
20.50
84
9.04
63
14.07
74
6.14
36
0.26
67
0.04
44
0.21
86
0.03
50
0.63
77
0.33
60
SGM_RVCbinarytwo views10.08
83
0.60
13
3.42
13
2.30
49
0.32
8
19.41
88
6.33
72
18.95
86
14.64
60
25.14
96
24.32
80
33.34
101
18.79
91
19.86
92
12.55
80
0.25
66
0.26
84
0.22
87
0.24
79
0.34
60
0.40
64
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
MFMNet_retwo views13.29
92
8.60
104
18.29
92
9.75
105
7.25
104
19.65
89
14.84
103
20.71
99
30.72
96
23.03
92
28.77
88
18.85
77
26.09
104
13.55
71
9.82
67
2.44
103
1.35
103
0.34
92
0.23
78
4.78
105
6.69
106
MeshStereopermissivetwo views11.52
90
1.52
60
4.55
26
1.89
32
1.46
42
19.87
90
5.11
64
20.66
98
15.91
67
32.67
103
34.51
99
39.34
107
21.15
97
18.74
90
12.10
77
0.11
50
0.06
54
0.01
43
0.00
1
0.45
71
0.22
46
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
PDISCO_ROBtwo views9.62
81
1.99
77
11.51
74
9.88
106
9.61
107
21.48
91
3.83
54
19.33
88
28.49
93
11.27
54
14.17
51
19.92
82
5.02
38
16.35
84
9.18
63
5.28
107
0.41
89
0.14
79
0.09
68
2.05
99
2.36
103
ADCLtwo views10.16
85
2.11
78
19.36
94
1.92
34
1.88
53
22.23
92
8.91
82
14.04
61
23.56
86
14.62
72
26.19
85
12.75
49
13.59
81
16.06
83
22.95
98
0.26
67
0.18
78
0.75
99
0.65
95
0.69
79
0.58
76
SPS-STEREOcopylefttwo views15.04
95
6.23
100
13.21
84
11.34
107
11.65
109
23.30
93
7.15
79
24.16
102
15.65
65
31.78
102
29.19
90
31.62
99
21.32
98
24.62
99
19.50
90
7.59
109
4.19
110
3.22
107
1.48
103
6.99
108
6.54
105
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
MFN_U_SF_DS_RVCtwo views9.78
82
4.27
96
14.47
86
2.29
48
2.85
68
23.40
94
13.62
98
13.60
56
19.54
79
19.42
83
24.27
79
16.74
67
8.59
59
17.05
86
7.98
50
1.25
99
1.68
104
0.17
81
2.63
106
0.72
80
1.04
89
DPSNettwo views10.14
84
1.88
76
16.82
91
1.85
29
1.73
50
24.84
95
17.20
107
19.92
95
27.41
91
12.23
60
13.62
49
16.52
66
18.35
88
14.42
77
12.50
79
0.78
91
0.54
98
0.08
71
0.25
80
1.18
91
0.59
79
G-Nettwo views8.41
72
1.54
61
10.97
72
5.73
98
3.60
79
26.19
96
4.41
60
10.10
19
7.42
16
19.71
84
24.99
82
14.38
58
15.83
84
10.99
53
9.53
65
0.50
85
0.46
94
0.19
84
0.25
80
0.80
82
0.66
80
STTStereo_v2two views8.41
72
1.54
61
10.97
72
5.73
98
3.60
79
26.19
96
4.41
60
10.10
19
7.42
16
19.71
84
24.99
82
14.38
58
15.83
84
10.99
53
9.53
65
0.50
85
0.46
94
0.19
84
0.25
80
0.80
82
0.66
80
SGM+DAISYtwo views15.62
97
7.26
103
19.28
93
8.94
104
10.11
108
26.25
98
10.49
92
19.36
89
14.65
61
30.64
100
33.59
97
33.00
100
22.32
100
24.96
100
16.42
87
7.90
110
6.25
111
4.51
108
3.37
107
5.86
106
7.20
107
MDST_ROBtwo views8.37
71
0.32
1
9.03
67
4.18
90
2.42
61
26.86
99
6.14
68
19.36
89
13.52
57
27.09
97
22.75
75
9.47
33
4.74
34
15.06
79
6.34
39
0.02
23
0.02
35
0.00
1
0.00
1
0.02
4
0.13
37
ELAS_RVCcopylefttwo views16.54
101
2.26
81
10.09
70
5.50
97
4.46
90
28.28
100
16.72
106
25.55
104
33.54
99
40.19
106
40.30
105
36.68
105
30.03
105
29.40
103
20.61
91
0.98
98
1.21
101
0.86
101
0.70
97
1.39
95
2.16
100
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
AnyNet_C01two views16.12
99
10.81
108
59.36
109
4.42
92
2.49
64
30.06
101
15.15
105
17.51
82
16.51
70
17.88
79
37.69
102
24.04
89
17.54
86
29.60
104
33.29
110
0.28
71
0.38
87
0.43
97
0.42
90
2.57
101
1.98
99
MANEtwo views19.47
108
1.27
48
5.07
30
4.69
93
5.55
97
30.49
102
9.94
89
34.01
109
37.27
104
44.13
108
51.57
112
52.51
109
40.41
111
33.58
105
24.81
101
0.89
95
0.86
99
1.11
103
9.72
110
0.38
64
1.06
90
SGM-ForestMtwo views16.99
104
1.08
37
5.74
38
2.12
42
0.75
22
31.63
103
12.21
96
27.80
106
32.25
97
37.88
105
39.99
104
52.96
110
35.20
109
33.60
106
24.47
100
0.26
67
0.39
88
0.31
91
0.39
88
0.26
53
0.53
74
ELAScopylefttwo views16.72
102
2.14
79
9.23
68
4.92
94
4.53
93
32.66
104
15.11
104
27.40
105
28.68
94
40.27
107
44.90
108
38.33
106
30.50
106
26.44
102
21.94
97
0.88
94
1.23
102
0.67
98
0.89
100
1.49
96
2.18
102
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
SAMSARAtwo views14.63
94
2.74
85
12.38
78
12.65
108
6.74
101
36.50
105
72.93
113
19.36
89
23.77
87
16.20
77
13.04
47
29.21
96
12.78
77
16.98
85
15.21
85
0.11
50
0.26
84
0.03
60
0.14
72
0.76
81
0.77
83
MSC_U_SF_DS_RVCtwo views16.41
100
6.93
102
21.83
96
5.94
101
2.81
67
38.71
106
14.59
100
24.55
103
34.87
100
33.66
104
34.35
98
29.24
97
24.20
101
22.59
96
17.95
88
2.52
104
2.81
106
1.17
104
1.51
105
5.89
107
2.16
100
PWCKtwo views30.53
111
44.32
111
47.25
108
29.76
111
7.23
103
40.78
107
27.10
109
44.73
111
44.32
109
47.31
109
36.37
101
47.16
108
26.05
103
41.26
110
31.87
108
21.83
111
4.03
109
29.50
110
4.67
108
27.17
111
7.80
108
RTStwo views18.87
106
9.32
106
86.48
111
4.95
95
6.10
99
42.08
108
14.70
101
15.49
73
41.06
107
22.65
90
32.32
93
13.77
51
19.54
93
37.98
107
28.96
104
0.41
79
0.23
82
0.00
1
0.02
42
0.91
85
0.50
71
RTSAtwo views18.87
106
9.32
106
86.48
111
4.95
95
6.10
99
42.08
108
14.70
101
15.49
73
41.06
107
22.65
90
32.32
93
13.77
51
19.54
93
37.98
107
28.96
104
0.41
79
0.23
82
0.00
1
0.02
42
0.91
85
0.50
71
BEATNet-Init1two views23.31
109
9.03
105
41.67
106
4.17
89
2.53
65
45.68
110
19.47
108
33.43
108
38.45
106
47.59
110
49.10
110
59.31
111
41.80
112
38.35
109
29.21
106
0.47
84
0.50
96
0.81
100
0.66
96
2.10
100
1.86
98
MADNet+two views27.07
110
33.84
110
90.97
113
20.14
109
7.47
106
48.43
111
47.10
110
35.43
110
36.46
101
20.11
87
30.05
91
25.29
91
35.08
108
45.50
111
50.28
111
2.13
102
2.00
105
1.19
105
0.76
98
4.71
104
4.43
104
DPSimNet_ROBtwo views53.45
112
64.73
112
44.39
107
53.97
112
45.39
112
53.66
112
54.83
111
55.15
112
57.87
112
64.16
112
50.83
111
63.40
112
53.34
113
46.45
112
65.81
112
63.13
112
26.54
112
57.94
112
51.11
112
45.52
112
50.69
112
MADNet++two views82.84
113
82.38
113
73.57
110
87.72
113
82.97
113
93.14
113
69.15
112
86.42
113
82.50
113
93.46
113
86.70
113
86.28
113
80.92
114
88.34
113
88.84
113
86.83
113
84.17
113
72.64
113
68.92
113
80.47
113
81.42
113
MEDIAN_ROBtwo views98.41
114
99.70
114
99.30
115
97.09
114
97.02
114
96.89
114
95.77
115
97.66
114
97.28
114
98.79
116
98.94
114
99.18
114
98.14
115
96.89
114
96.88
114
99.96
116
99.16
114
100.00
114
99.99
114
99.69
114
99.88
114
AVERAGE_ROBtwo views99.62
115
99.95
115
98.81
114
100.00
119
100.00
115
98.08
115
95.47
114
100.00
117
100.00
115
100.00
117
100.00
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100.00
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100.00
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100.00
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99.99
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100.00
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100.00
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100.00
114
100.00
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100.00
117
100.00
118
DPSMtwo views99.95
118
100.00
116
100.00
118
99.76
115
100.00
115
100.00
116
100.00
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100.00
117
100.00
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100.00
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100.00
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100.00
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100.00
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100.00
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100.00
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99.21
114
100.00
115
100.00
114
100.00
115
99.99
115
99.95
115
DPSMNet_ROBtwo views99.91
117
100.00
116
99.99
116
99.99
117
100.00
115
100.00
116
100.00
116
99.98
116
100.00
115
98.35
114
100.00
115
99.84
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100.00
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99.98
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99.99
115
100.00
118
100.00
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100.00
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100.00
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100.00
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100.00
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DPSM_ROBtwo views99.95
118
100.00
116
100.00
118
99.76
115
100.00
115
100.00
116
100.00
116
100.00
117
100.00
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100.00
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100.00
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100.00
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100.00
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99.21
114
100.00
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100.00
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100.00
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99.99
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99.95
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DGTPSM_ROBtwo views99.90
116
100.00
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99.99
116
99.99
117
100.00
115
100.00
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100.00
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99.97
115
100.00
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98.35
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100.00
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99.84
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100.00
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99.98
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99.99
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99.99
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100.00
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100.00
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LSM0two views100.00
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100.00
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100.00
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100.00
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100.00
115
100.00
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100.00
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100.00
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MSMDNettwo views1.26
6