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
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CREStereotwo views0.98
1
0.42
10
1.86
1
0.07
1
0.22
7
1.65
1
0.02
1
4.07
2
6.33
19
0.72
1
1.54
2
0.43
2
0.64
1
1.22
1
0.44
1
0.00
1
0.00
1
0.00
1
0.00
1
0.01
2
0.00
1
PMTNettwo views1.41
2
0.37
8
2.06
4
0.26
3
0.15
4
2.94
13
0.34
9
3.39
1
5.33
10
1.00
2
1.27
1
0.28
1
0.93
5
4.04
9
0.74
4
5.00
130
0.00
1
0.00
1
0.00
1
0.03
13
0.01
2
DPM-Stereotwo views1.97
3
0.64
26
2.95
13
0.17
2
0.10
1
4.83
36
0.13
3
8.60
17
4.06
4
6.42
26
4.92
6
0.44
3
0.72
2
3.57
8
1.80
7
0.00
1
0.01
36
0.00
1
0.00
1
0.05
19
0.04
24
Gwc-CoAtRStwo views2.02
4
0.33
6
2.61
5
1.04
10
0.30
10
3.56
16
0.40
12
5.89
4
5.94
16
5.08
13
5.01
7
5.55
15
1.72
9
1.60
2
0.91
5
0.01
27
0.09
81
0.00
1
0.25
99
0.01
2
0.02
6
FENettwo views2.19
5
0.34
7
3.04
14
2.05
49
0.26
9
2.82
10
0.13
3
7.02
8
5.55
12
4.97
12
1.94
3
4.91
11
3.39
22
4.71
12
2.50
13
0.02
34
0.03
53
0.00
1
0.00
1
0.00
1
0.21
61
R-Stereo Traintwo views2.44
6
0.32
1
1.93
2
0.94
7
0.16
5
3.67
18
0.61
23
6.37
6
3.08
1
9.14
49
17.44
83
1.80
4
0.77
3
1.76
3
0.70
2
0.00
1
0.01
36
0.00
1
0.00
1
0.01
2
0.03
15
RAFT-Stereopermissivetwo views2.44
6
0.32
1
1.93
2
0.94
7
0.16
5
3.67
18
0.61
23
6.37
6
3.08
1
9.14
49
17.44
83
1.80
4
0.77
3
1.76
3
0.70
2
0.00
1
0.01
36
0.00
1
0.00
1
0.01
2
0.03
15
Lahav Lipson, Zachary Teed, and Jia Deng: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. 3DV
ACVNettwo views2.58
8
0.53
17
2.69
7
0.84
5
0.59
20
2.61
6
0.55
18
8.35
12
5.70
13
4.45
7
9.26
29
5.77
17
3.50
24
2.24
5
4.41
38
0.00
1
0.00
1
0.00
1
0.00
1
0.17
51
0.01
2
DN-CSS_ROBtwo views2.69
9
1.40
73
5.34
47
2.31
64
0.75
27
3.14
15
0.06
2
6.11
5
3.87
3
5.34
16
12.18
52
2.34
6
1.22
6
7.84
32
1.48
6
0.03
45
0.00
1
0.00
1
0.00
1
0.35
80
0.03
15
HITNettwo views2.79
10
0.77
30
4.02
30
2.03
48
0.11
3
5.58
43
0.59
22
9.24
23
5.15
9
6.42
26
7.26
18
3.66
7
2.92
20
4.07
10
3.87
35
0.00
1
0.00
1
0.00
1
0.00
1
0.06
24
0.02
6
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 views2.82
11
0.64
26
4.45
36
1.61
27
0.89
34
3.86
22
0.57
21
9.18
21
5.10
8
6.24
22
6.49
12
7.17
31
2.13
12
3.31
7
4.73
42
0.00
1
0.01
36
0.00
1
0.00
1
0.04
16
0.02
6
ccstwo views3.04
12
0.39
9
3.08
16
1.78
34
0.52
19
2.04
2
0.50
16
13.09
71
13.71
76
3.54
5
5.36
10
5.50
14
2.45
14
4.81
13
2.88
16
0.09
66
0.08
77
0.12
96
0.10
86
0.20
56
0.50
90
AdaStereotwo views3.09
13
0.58
21
3.04
14
2.84
82
0.48
18
4.08
25
1.29
44
12.16
62
7.77
38
6.03
20
9.62
31
5.79
19
1.53
8
4.56
11
1.93
9
0.00
1
0.00
1
0.00
1
0.00
1
0.02
7
0.02
6
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.
DMCA-RVCcopylefttwo views3.17
14
1.26
66
6.39
65
2.10
52
1.51
53
3.02
14
0.56
20
7.84
11
5.06
7
9.23
52
5.19
8
8.46
39
2.72
18
6.31
18
3.22
23
0.06
62
0.11
84
0.09
93
0.02
56
0.16
49
0.08
31
BEATNet_4xtwo views3.24
15
1.27
67
5.89
56
1.56
24
0.10
1
5.26
40
1.07
39
10.08
30
5.50
11
6.89
31
7.73
20
4.53
10
4.13
34
5.05
14
5.27
46
0.04
53
0.05
62
0.00
1
0.00
1
0.23
70
0.23
64
GANet-RSSMtwo views3.27
16
0.60
22
3.38
20
2.94
89
2.00
68
2.61
6
0.48
14
10.93
50
6.11
17
5.47
17
10.20
35
6.57
23
5.44
55
6.25
17
2.23
11
0.00
1
0.00
1
0.08
90
0.00
1
0.17
51
0.03
15
NOSS_ROBtwo views3.30
17
0.46
11
2.62
6
2.08
50
1.01
42
5.60
44
0.74
34
10.37
38
11.48
65
5.15
14
8.43
25
5.67
16
1.73
10
7.97
34
2.34
12
0.02
34
0.06
68
0.00
1
0.00
1
0.07
26
0.14
52
CFNet-ftpermissivetwo views3.31
18
0.94
44
2.69
7
1.50
21
2.38
75
2.81
8
0.68
28
8.35
12
7.43
32
4.45
7
9.94
32
10.20
49
4.60
40
6.50
21
3.41
27
0.00
1
0.00
1
0.03
74
0.00
1
0.22
65
0.03
15
CFNet_RVCtwo views3.31
18
0.94
44
2.69
7
1.50
21
2.38
75
2.81
8
0.68
28
8.35
12
7.43
32
4.45
7
9.94
32
10.20
49
4.60
40
6.49
20
3.41
27
0.00
1
0.00
1
0.03
74
0.00
1
0.22
65
0.03
15
PSMNet-RSSMtwo views3.33
20
0.77
30
3.22
19
2.12
53
1.90
67
2.33
4
0.71
31
11.17
51
5.83
15
5.90
18
11.43
47
7.04
27
4.83
49
5.96
16
3.17
21
0.00
1
0.00
1
0.02
66
0.00
1
0.08
28
0.02
6
cf-rtwo views3.42
21
0.81
33
3.97
29
2.41
68
2.78
84
2.85
11
0.64
27
8.44
15
6.32
18
6.25
23
11.57
48
8.97
43
3.54
26
5.93
15
3.83
34
0.00
1
0.00
1
0.00
1
0.00
1
0.09
32
0.07
28
MLCVtwo views3.44
22
0.88
37
5.60
51
1.39
17
0.25
8
4.36
29
0.33
8
7.25
9
7.28
29
9.17
51
12.24
54
5.09
12
2.47
15
9.15
56
3.23
24
0.00
1
0.00
1
0.00
1
0.00
1
0.10
33
0.02
6
DeepPruner_ROBtwo views3.52
23
1.14
60
4.06
31
1.12
11
1.65
57
3.65
17
0.83
36
13.96
80
4.47
6
7.80
37
10.84
40
7.05
29
2.16
13
8.14
41
3.08
20
0.07
64
0.03
53
0.00
1
0.01
52
0.32
76
0.06
27
STTStereotwo views3.60
24
0.93
43
6.34
63
2.71
79
2.23
73
3.68
20
0.63
26
9.42
24
6.73
22
9.87
61
6.97
16
8.84
42
3.65
27
6.85
22
3.04
18
0.00
1
0.02
47
0.01
54
0.00
1
0.02
7
0.02
6
ccs_robtwo views3.63
25
1.12
59
4.42
34
2.52
71
0.91
37
5.50
42
0.21
6
10.11
33
9.11
48
6.55
29
11.28
44
8.32
38
2.55
16
7.66
29
2.01
10
0.00
1
0.00
1
0.00
1
0.00
1
0.20
56
0.08
31
hitnet-ftcopylefttwo views3.66
26
0.65
28
2.79
11
0.89
6
0.72
26
4.25
27
0.55
18
9.11
20
7.52
34
7.14
33
10.19
34
12.49
66
4.70
44
7.44
27
4.41
38
0.03
45
0.00
1
0.06
86
0.00
1
0.20
56
0.04
24
iResNettwo views3.68
27
0.91
40
7.94
81
2.97
90
0.34
12
4.44
33
0.48
14
7.70
10
9.74
52
7.72
36
12.74
58
4.03
8
2.87
19
8.05
37
3.37
26
0.02
34
0.01
36
0.00
1
0.00
1
0.10
33
0.09
35
acv_fttwo views3.69
28
0.53
17
4.58
38
2.31
64
4.71
116
7.44
63
0.86
37
8.54
16
5.70
13
9.29
53
9.26
29
5.77
17
4.15
35
2.24
5
8.13
71
0.00
1
0.00
1
0.00
1
0.00
1
0.29
72
0.01
2
ac_64two views3.70
29
0.47
12
3.50
23
2.87
83
3.50
96
5.91
47
0.72
32
10.17
35
4.34
5
8.16
41
5.80
11
10.82
54
5.64
57
7.08
24
4.73
42
0.03
45
0.00
1
0.00
1
0.00
1
0.08
28
0.14
52
CFNettwo views3.72
30
1.10
56
5.03
42
2.49
70
1.59
54
4.90
38
0.22
7
11.38
52
9.88
54
4.80
10
11.25
43
6.44
22
3.68
28
8.33
43
3.00
17
0.00
1
0.00
1
0.00
1
0.00
1
0.22
65
0.07
28
GwcNet-RSSMtwo views3.77
31
1.05
50
5.06
43
2.21
58
1.87
65
2.36
5
1.52
47
8.98
19
7.96
39
5.91
19
14.59
68
7.94
36
3.50
24
6.43
19
5.70
49
0.00
1
0.00
1
0.04
81
0.00
1
0.20
56
0.08
31
FADNet-RVC-Resampletwo views3.79
32
1.62
85
12.06
96
1.43
19
0.66
22
5.94
48
2.41
55
10.18
36
8.58
46
6.28
24
4.22
5
5.33
13
4.80
48
7.71
30
3.19
22
0.17
80
0.21
101
0.17
103
0.12
88
0.41
88
0.29
77
CC-Net-ROBtwo views3.84
33
1.07
52
5.23
45
2.65
76
2.96
87
4.22
26
0.69
30
10.43
40
7.72
36
8.78
46
8.29
24
9.61
46
4.02
33
7.16
26
3.65
32
0.13
73
0.03
53
0.02
66
0.03
65
0.05
19
0.03
15
NLCA_NET_v2_RVCtwo views3.84
33
1.06
51
5.23
45
2.72
80
3.27
89
4.36
29
0.61
23
10.71
45
7.56
35
8.75
45
7.89
21
9.86
48
3.90
31
7.15
25
3.44
29
0.14
74
0.02
47
0.02
66
0.03
65
0.04
16
0.03
15
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
FADNet_RVCtwo views3.91
35
1.67
88
12.95
103
0.96
9
0.75
27
5.71
46
0.54
17
10.83
48
6.60
21
3.46
3
8.09
22
4.10
9
3.40
23
9.43
60
6.33
52
0.36
97
0.44
115
0.17
103
0.46
116
0.91
105
0.95
109
FADNet-RVCtwo views3.98
36
1.84
94
12.48
99
1.69
32
0.44
16
4.33
28
1.31
45
11.84
56
7.15
27
3.53
4
3.50
4
10.63
53
4.43
39
9.12
55
6.25
51
0.03
45
0.10
82
0.00
1
0.03
65
0.60
95
0.25
70
HSMtwo views4.00
37
0.79
32
3.16
18
1.59
26
2.17
71
6.77
57
1.11
40
12.28
63
6.35
20
6.75
30
8.11
23
13.90
72
5.37
54
8.85
51
2.71
15
0.00
1
0.00
1
0.00
1
0.00
1
0.02
7
0.02
6
TDLMtwo views4.11
38
1.11
58
3.54
24
1.62
28
1.04
43
3.91
23
7.41
105
10.60
43
10.67
59
6.38
25
12.59
57
5.95
20
4.77
46
8.79
50
3.04
18
0.58
109
0.00
1
0.01
54
0.00
1
0.19
55
0.12
47
CBMV_ROBtwo views4.14
39
0.52
14
3.14
17
1.30
14
0.77
30
6.92
58
1.97
53
10.11
33
9.58
50
8.92
48
14.20
66
7.12
30
5.90
60
8.65
47
3.50
31
0.01
27
0.05
62
0.00
1
0.00
1
0.04
16
0.09
35
CVANet_RVCtwo views4.16
40
1.16
61
3.60
25
1.94
44
1.46
51
3.92
24
4.68
84
10.89
49
8.34
44
7.58
35
10.84
40
10.27
51
6.62
64
8.56
46
2.69
14
0.39
99
0.00
1
0.00
1
0.01
52
0.21
63
0.09
35
HSM-Net_RVCpermissivetwo views4.20
41
0.32
1
2.76
10
0.63
4
0.69
24
6.95
59
1.69
50
11.96
57
8.36
45
8.83
47
12.17
51
15.18
80
4.21
37
6.91
23
3.30
25
0.02
34
0.02
47
0.00
1
0.00
1
0.01
2
0.01
2
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
DSFCAtwo views4.21
42
0.50
13
5.45
49
1.34
15
1.68
59
7.04
60
4.51
81
10.73
46
7.00
24
10.78
69
6.80
13
8.48
40
4.63
42
9.91
64
5.12
45
0.02
34
0.06
68
0.02
66
0.03
65
0.08
28
0.03
15
FADNettwo views4.23
43
1.65
87
11.75
95
1.64
30
0.80
32
4.80
35
0.77
35
13.76
79
11.65
67
3.97
6
5.24
9
9.62
47
5.14
51
8.40
44
3.78
33
0.21
84
0.04
58
0.07
89
0.05
76
1.14
110
0.10
42
iResNet_ROBtwo views4.23
43
1.02
48
4.90
41
2.18
55
0.93
40
2.92
12
0.37
11
15.10
92
16.91
91
7.89
39
10.51
38
7.03
26
3.07
21
8.16
42
3.46
30
0.01
27
0.00
1
0.00
1
0.00
1
0.10
33
0.02
6
HGLStereotwo views4.25
45
0.52
14
4.65
39
3.09
92
5.18
118
6.51
55
0.73
33
10.37
38
6.74
23
7.32
34
12.44
56
7.00
25
7.21
68
7.46
28
5.51
48
0.00
1
0.02
47
0.00
1
0.00
1
0.06
24
0.10
42
iResNetv2_ROBtwo views4.28
46
1.43
74
7.17
76
2.91
84
1.26
47
4.36
29
1.62
48
13.64
78
10.25
58
9.83
60
11.41
45
7.68
33
4.00
32
7.75
31
1.85
8
0.00
1
0.00
1
0.00
1
0.00
1
0.37
82
0.09
35
StereoDRNet-Refinedtwo views4.46
47
0.62
25
3.80
28
1.92
41
0.40
14
9.35
71
0.15
5
10.02
28
8.83
47
12.69
81
11.62
49
9.34
44
3.87
30
8.06
38
8.02
66
0.00
1
0.00
1
0.01
54
0.05
76
0.20
56
0.26
73
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
NVstereo2Dtwo views4.51
48
0.82
34
6.86
74
3.28
97
3.38
93
8.16
66
3.13
60
10.51
41
15.15
81
4.90
11
6.89
15
7.87
34
4.78
47
9.88
63
3.91
36
0.01
27
0.00
1
0.00
1
0.06
78
0.02
7
0.58
96
DLCB_ROBtwo views4.51
48
0.91
40
3.78
27
2.19
56
1.07
44
6.28
49
3.09
59
9.78
27
7.72
36
10.65
67
12.97
59
13.91
73
3.71
29
8.72
48
5.30
47
0.00
1
0.00
1
0.00
1
0.00
1
0.03
13
0.10
42
RASNettwo views4.52
50
0.61
24
4.42
34
3.42
101
4.68
115
4.58
34
0.99
38
9.54
26
8.01
40
5.28
15
11.42
46
10.34
52
8.88
79
9.28
58
8.68
76
0.15
76
0.00
1
0.00
1
0.00
1
0.03
13
0.04
24
SGM-Foresttwo views4.96
51
0.32
1
2.84
12
1.21
12
0.64
21
10.23
81
6.64
100
11.55
53
10.98
60
10.94
71
13.59
62
11.65
61
4.30
38
8.94
53
4.63
41
0.11
69
0.04
58
0.00
1
0.00
1
0.05
19
0.46
87
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
PA-Nettwo views4.98
52
1.47
76
7.42
78
2.40
67
2.14
70
8.73
68
3.64
69
12.42
64
13.11
72
7.03
32
7.57
19
7.88
35
6.52
63
10.16
66
7.82
64
0.02
34
0.03
53
0.00
1
0.00
1
0.11
37
1.07
112
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
AANet_RVCtwo views5.01
53
1.74
89
6.38
64
1.96
46
1.29
49
2.26
3
1.69
50
10.07
29
18.53
94
7.88
38
18.15
85
8.49
41
2.70
17
10.59
70
7.04
57
0.96
119
0.15
93
0.02
66
0.00
1
0.13
42
0.12
47
PSMNet_ROBtwo views5.02
54
1.63
86
6.03
58
1.90
40
1.83
64
9.57
75
6.35
97
15.58
97
7.23
28
6.15
21
10.48
37
12.22
64
4.16
36
8.02
35
8.71
77
0.02
34
0.01
36
0.01
54
0.10
86
0.20
56
0.12
47
CBMVpermissivetwo views5.35
55
0.91
40
3.67
26
1.62
28
0.44
16
10.09
79
7.19
104
12.49
65
12.33
71
12.22
77
14.69
69
10.93
56
6.48
62
8.51
45
4.96
44
0.02
34
0.15
93
0.00
1
0.00
1
0.17
51
0.17
57
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
StereoDRNettwo views5.59
56
1.75
90
6.80
72
3.12
93
4.45
110
10.61
84
4.35
77
18.80
109
9.73
51
12.22
77
6.87
14
11.44
59
4.65
43
8.09
40
8.26
72
0.02
34
0.11
84
0.00
1
0.03
65
0.20
56
0.28
76
ETE_ROBtwo views5.80
57
1.77
91
6.33
62
1.44
20
0.78
31
6.43
53
6.90
101
12.53
66
8.08
41
12.93
85
14.89
70
21.13
108
5.87
59
9.83
62
6.57
55
0.04
53
0.01
36
0.00
1
0.02
56
0.08
28
0.33
78
DRN-Testtwo views5.87
58
0.98
46
5.89
56
2.69
78
3.65
101
12.37
92
3.35
63
20.07
120
10.20
57
11.93
76
12.31
55
11.06
58
5.31
53
7.89
33
9.05
80
0.04
53
0.05
62
0.04
81
0.04
74
0.18
54
0.25
70
NCCL2two views5.88
59
1.59
83
5.44
48
1.87
37
0.92
38
9.55
74
11.55
120
12.11
59
9.94
55
9.67
59
8.85
27
22.28
110
7.41
69
8.78
49
7.17
58
0.01
27
0.00
1
0.03
74
0.00
1
0.13
42
0.23
64
NaN_ROBtwo views6.00
60
1.24
64
6.29
61
1.34
15
1.68
59
9.60
76
10.31
116
15.09
90
15.79
84
12.62
80
8.95
28
11.67
62
5.83
58
11.78
79
6.41
54
0.05
60
0.13
90
0.08
90
0.20
95
0.22
65
0.79
104
DANettwo views6.02
61
1.23
63
8.45
83
3.86
110
3.94
104
7.64
65
1.34
46
9.51
25
7.00
24
13.39
87
15.53
74
15.99
84
7.02
67
12.14
80
12.37
100
0.19
82
0.12
89
0.02
66
0.03
65
0.13
42
0.56
95
XPNet_ROBtwo views6.03
62
1.22
62
5.61
52
2.56
74
0.90
36
6.32
50
7.07
102
12.92
69
8.30
43
14.76
92
15.13
72
19.84
102
6.66
66
10.36
67
8.58
75
0.02
34
0.04
58
0.00
1
0.03
65
0.11
37
0.24
67
Anonymous Stereotwo views6.16
63
3.15
112
23.75
121
2.97
90
2.48
80
4.39
32
13.30
122
9.21
22
9.86
53
9.56
58
8.76
26
6.79
24
1.99
11
13.50
92
13.04
103
0.01
27
0.05
62
0.00
1
0.06
78
0.22
65
0.19
59
GANettwo views6.22
64
1.07
52
4.07
32
2.27
60
0.89
34
9.19
70
9.52
111
12.02
58
8.13
42
10.72
68
29.09
110
13.86
71
7.52
71
11.00
74
4.39
37
0.36
97
0.00
1
0.02
66
0.02
56
0.12
39
0.08
31
DISCOtwo views6.28
65
0.57
20
5.78
54
3.43
102
1.17
45
11.22
86
3.39
64
12.14
61
16.16
86
6.52
28
11.22
42
16.96
87
6.32
61
19.51
115
10.74
93
0.00
1
0.00
1
0.00
1
0.00
1
0.35
80
0.11
45
RYNettwo views6.34
66
0.89
39
5.88
55
1.41
18
4.48
112
15.97
103
4.18
73
13.41
74
16.49
87
10.81
70
7.00
17
14.33
75
8.72
77
9.43
60
13.71
104
0.00
1
0.01
36
0.00
1
0.00
1
0.02
7
0.07
28
GwcNetcopylefttwo views6.42
67
1.97
97
10.92
90
2.59
75
5.58
121
11.55
88
2.21
54
14.10
82
16.52
90
10.04
63
17.19
82
10.86
55
5.61
56
9.23
57
8.84
79
0.01
27
0.06
68
0.03
74
0.08
82
0.56
91
0.40
82
GANetREF_RVCpermissivetwo views6.56
68
2.89
107
7.58
80
3.41
100
0.40
14
12.96
95
9.58
112
15.09
90
17.25
93
10.33
64
10.62
39
12.27
65
8.16
73
12.21
81
4.53
40
0.41
101
0.00
1
0.00
1
0.02
56
3.12
126
0.39
81
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
LALA_ROBtwo views6.58
69
1.80
93
6.25
60
1.26
13
0.94
41
10.08
78
9.02
108
16.00
98
11.51
66
12.74
82
13.02
60
24.77
112
5.25
52
10.56
69
8.02
66
0.04
53
0.05
62
0.00
1
0.02
56
0.10
33
0.25
70
S-Stereotwo views6.63
70
0.84
35
9.67
87
3.15
94
3.48
95
6.49
54
6.22
92
12.99
70
22.84
107
9.48
54
15.51
73
12.00
63
8.43
74
8.04
36
10.70
92
0.12
72
0.17
97
0.00
1
0.38
109
0.13
42
1.92
121
DeepPrunerFtwo views6.75
71
2.69
105
23.31
120
3.68
105
7.16
127
3.78
21
4.29
75
13.42
75
20.13
104
8.13
40
10.46
36
7.18
32
8.06
72
11.10
75
9.44
82
0.24
86
0.15
93
0.29
112
0.42
112
0.66
98
0.45
85
edge stereotwo views6.76
72
1.01
47
6.76
71
2.20
57
2.45
79
6.41
52
2.45
56
14.84
88
11.98
70
15.29
93
18.31
86
22.02
109
12.56
94
10.82
71
7.49
60
0.03
45
0.06
68
0.11
95
0.03
65
0.30
73
0.14
52
NCC-stereotwo views6.77
73
1.49
77
6.48
66
2.92
86
4.40
106
7.43
61
3.61
67
19.52
116
13.29
73
8.39
43
16.91
77
15.96
82
12.13
92
12.85
86
7.70
62
1.47
123
0.11
84
0.01
54
0.42
112
0.14
47
0.24
67
Abc-Nettwo views6.77
73
1.49
77
6.48
66
2.92
86
4.40
106
7.43
61
3.61
67
19.52
116
13.29
73
8.39
43
16.91
77
15.96
82
12.13
92
12.85
86
7.70
62
1.47
123
0.11
84
0.01
54
0.42
112
0.14
47
0.24
67
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
FAT-Stereotwo views6.78
75
0.68
29
6.80
72
2.30
62
1.77
63
5.63
45
4.20
74
18.79
108
18.62
95
10.53
66
17.15
80
18.52
96
13.74
102
8.86
52
7.38
59
0.03
45
0.15
93
0.01
54
0.07
80
0.12
39
0.26
73
RPtwo views6.84
76
1.29
71
5.53
50
3.92
111
5.18
118
6.32
50
3.53
65
11.73
55
15.31
82
9.54
57
22.38
93
18.25
94
14.47
103
10.11
65
7.49
60
0.91
118
0.01
36
0.12
96
0.15
91
0.33
77
0.19
59
RGCtwo views6.88
77
2.23
101
6.13
59
4.05
112
4.73
117
8.94
69
2.78
58
15.19
94
11.74
68
11.13
72
19.34
88
17.86
91
10.42
86
13.02
89
8.03
68
0.73
112
0.01
36
0.24
111
0.41
111
0.31
75
0.38
80
Nwc_Nettwo views6.97
78
1.25
65
6.63
69
3.82
109
3.37
92
10.83
85
1.67
49
19.56
118
11.35
63
8.36
42
23.62
96
17.19
89
11.44
91
11.21
76
8.08
70
0.80
114
0.00
1
0.00
1
0.02
56
0.13
42
0.09
35
STTRV1_RVCtwo views7.02
79
1.10
56
12.88
101
3.32
98
6.92
126
11.90
91
4.00
71
15.07
89
11.94
69
9.51
55
14.57
67
11.63
60
8.73
78
12.65
85
8.06
69
3.32
129
2.75
130
0.41
120
0.12
88
1.38
116
0.11
45
ADCReftwo views7.27
80
1.38
72
16.37
109
2.52
71
3.30
91
11.63
89
3.16
61
10.80
47
9.35
49
13.03
86
25.27
104
8.17
37
8.92
80
8.06
38
21.81
119
0.15
76
0.08
77
0.16
102
0.34
107
0.38
83
0.58
96
psmorigintwo views7.34
81
1.58
82
18.31
113
2.35
66
0.87
33
6.72
56
1.70
52
10.63
44
7.14
26
19.77
105
22.71
94
20.13
104
11.34
90
12.96
88
9.49
83
0.04
53
0.17
97
0.06
86
0.17
92
0.21
63
0.50
90
CSANtwo views7.62
82
1.60
84
6.56
68
1.83
35
0.66
22
12.40
93
10.52
118
14.45
85
21.32
106
14.19
89
15.98
76
17.84
90
13.02
99
12.32
82
8.38
73
0.09
66
0.07
75
0.03
74
0.04
74
0.33
77
0.67
102
stereogantwo views7.69
83
0.88
37
7.08
75
3.49
103
3.93
103
18.98
111
3.23
62
16.52
100
19.58
101
9.93
62
18.92
87
20.50
106
9.04
81
14.07
96
6.14
50
0.26
89
0.04
58
0.21
108
0.03
65
0.63
97
0.33
78
pmcnntwo views7.72
84
1.27
67
9.42
86
2.91
84
3.14
88
9.44
72
6.23
93
12.56
67
16.51
88
14.53
90
24.08
98
27.44
118
8.49
75
9.32
59
8.44
74
0.06
62
0.08
77
0.00
1
0.00
1
0.30
73
0.15
55
AF-Nettwo views7.78
85
1.44
75
6.68
70
3.37
99
4.50
113
8.61
67
2.69
57
17.07
103
20.17
105
9.52
56
24.02
97
20.31
105
14.59
104
11.58
78
9.84
88
0.61
110
0.00
1
0.12
96
0.00
1
0.38
83
0.12
47
PASMtwo views7.90
86
4.22
118
21.97
118
3.25
96
3.29
90
5.39
41
6.57
99
10.57
42
19.09
97
12.77
83
13.92
64
18.11
93
9.51
83
13.79
95
10.77
95
0.19
82
0.45
116
0.29
112
1.08
125
1.49
118
1.19
114
PWCDC_ROBbinarytwo views7.92
87
3.17
114
7.48
79
5.73
123
4.40
106
10.45
82
0.35
10
14.52
86
28.19
115
10.36
65
31.27
114
7.04
27
9.14
82
13.22
91
8.78
78
2.74
128
0.02
47
0.00
1
0.00
1
1.31
115
0.17
57
ADCP+two views8.09
88
1.79
92
14.50
107
1.54
23
4.28
105
16.57
105
5.20
86
12.80
68
11.20
62
12.83
84
17.07
79
11.02
57
10.80
88
17.59
111
23.18
122
0.03
45
0.05
62
0.01
54
0.18
93
0.39
87
0.81
105
SuperBtwo views8.10
89
3.15
112
24.67
122
2.65
76
1.23
46
9.88
77
4.29
75
10.18
36
30.07
118
11.53
74
12.18
52
6.12
21
6.65
65
10.50
68
14.47
106
0.14
74
0.11
84
0.35
116
0.25
99
13.06
135
0.48
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PWC_ROBbinarytwo views8.24
90
3.13
110
12.74
100
2.43
69
4.43
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7.51
64
1.22
41
16.63
101
19.24
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16.08
95
28.29
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13.99
74
10.16
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13.63
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14.06
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0.42
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0.00
1
0.05
84
0.00
1
0.59
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0.27
75
MDST_ROBtwo views8.37
91
0.32
1
9.03
84
4.18
115
2.42
78
26.86
124
6.14
90
19.36
113
13.52
75
27.09
119
22.75
95
9.47
45
4.74
45
15.06
102
6.34
53
0.02
34
0.02
47
0.00
1
0.00
1
0.02
7
0.13
51
G-Nettwo views8.41
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1.54
80
10.97
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5.73
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3.60
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26.19
121
4.41
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10.10
31
7.42
30
19.71
103
24.99
102
14.38
76
15.83
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10.99
72
9.53
84
0.50
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0.46
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0.19
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0.25
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0.80
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0.66
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STTStereo_v2two views8.41
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1.54
80
10.97
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5.73
123
3.60
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26.19
121
4.41
78
10.10
31
7.42
30
19.71
103
24.99
102
14.38
76
15.83
105
10.99
72
9.53
84
0.50
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0.46
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0.19
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0.25
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0.80
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0.66
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XQCtwo views8.43
94
3.58
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16.40
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2.92
86
2.17
71
13.22
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3.60
66
14.64
87
25.86
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11.87
75
12.04
50
15.06
79
10.67
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15.24
103
19.41
111
0.39
99
0.08
77
0.05
84
0.07
80
0.84
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0.45
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FBW_ROBtwo views8.50
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1.03
49
7.98
82
1.93
43
1.28
48
13.10
96
6.23
93
22.50
124
18.98
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18.82
100
14.91
71
19.06
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10.04
84
18.41
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9.83
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0.62
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0.22
102
1.82
131
0.82
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0.99
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1.36
116
aanetorigintwo views8.72
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3.29
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27.55
124
1.95
45
3.84
102
4.93
39
5.39
88
5.49
3
10.05
56
27.85
120
31.47
115
15.80
81
12.78
96
9.04
54
11.98
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0.25
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0.22
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0.22
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0.25
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1.28
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0.84
107
RTSCtwo views9.15
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3.00
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13.57
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3.72
107
1.76
62
11.82
90
0.46
13
16.95
102
36.83
127
15.80
94
15.53
74
12.91
68
7.46
70
20.01
118
21.76
118
0.31
94
0.13
90
0.01
54
0.08
82
0.57
92
0.41
84
WCMA_ROBtwo views9.21
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0.87
36
7.37
77
2.54
73
2.13
69
13.59
98
5.80
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11.64
54
14.01
77
24.43
116
32.99
120
27.09
117
18.02
109
12.51
84
9.85
89
0.81
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0.07
75
0.01
54
0.01
52
0.16
49
0.23
64
MSMD_ROBtwo views9.28
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1.09
55
4.65
39
1.58
25
0.39
13
16.52
104
4.41
78
13.60
76
14.87
80
22.34
108
39.89
128
25.67
114
20.71
118
12.42
83
6.98
56
0.34
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0.03
53
0.00
1
0.00
1
0.05
19
0.09
35
ADCPNettwo views9.54
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2.39
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31.46
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2.09
51
1.60
55
16.71
107
6.39
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12.11
59
11.45
64
13.53
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21.45
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19.41
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10.94
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14.38
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21.54
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0.27
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1.16
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0.39
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1.49
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0.58
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1.45
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SHDtwo views9.61
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12.46
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3.69
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3.54
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9.47
73
1.25
42
20.16
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37.84
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18.19
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21.24
90
16.96
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12.83
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14.47
100
16.05
108
0.32
95
0.13
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0.01
54
0.08
82
0.38
83
0.48
88
PDISCO_ROBtwo views9.62
102
1.99
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11.51
93
9.88
131
9.61
132
21.48
115
3.83
70
19.33
112
28.49
116
11.27
73
14.17
65
19.92
103
5.02
50
16.35
108
9.18
81
5.28
132
0.41
112
0.14
100
0.09
85
2.05
122
2.36
127
MFN_U_SF_DS_RVCtwo views9.78
103
4.27
119
14.47
106
2.29
61
2.85
86
23.40
119
13.62
123
13.60
76
19.54
100
19.42
102
24.27
99
16.74
86
8.59
76
17.05
110
7.98
65
1.25
122
1.68
128
0.17
103
2.63
131
0.72
100
1.04
110
SGM_RVCbinarytwo views10.08
104
0.60
22
3.42
21
2.30
62
0.32
11
19.41
112
6.33
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18.95
110
14.64
78
25.14
118
24.32
100
33.34
126
18.79
113
19.86
116
12.55
102
0.25
87
0.26
107
0.22
109
0.24
98
0.34
79
0.40
82
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DPSNettwo views10.14
105
1.88
96
16.82
111
1.85
36
1.73
61
24.84
120
17.20
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19.92
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27.41
114
12.23
79
13.62
63
16.52
85
18.35
110
14.42
99
12.50
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0.78
113
0.54
121
0.08
90
0.25
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1.18
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0.59
99
ADCLtwo views10.16
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2.11
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19.36
115
1.92
41
1.88
66
22.23
116
8.91
107
14.04
81
23.56
109
14.62
91
26.19
105
12.75
67
13.59
101
16.06
107
22.95
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0.26
89
0.18
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0.75
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0.65
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0.69
99
0.58
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FCDSN-DCtwo views10.24
107
0.56
19
3.49
22
1.96
46
1.29
49
16.90
108
4.59
83
17.16
104
19.10
98
24.64
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32.46
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33.82
127
22.14
123
15.93
106
10.45
90
0.04
53
0.00
1
0.00
1
0.00
1
0.05
19
0.21
61
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
ADCMidtwo views10.24
107
3.13
110
20.70
116
2.21
58
2.39
77
11.23
87
6.19
91
14.17
83
11.19
61
23.20
114
22.25
92
17.89
92
19.54
115
18.51
113
26.21
127
0.45
105
0.42
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1.10
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1.29
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1.56
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1.18
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SANettwo views10.64
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1.86
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10.91
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1.76
33
0.71
25
14.62
101
9.23
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19.18
111
37.14
128
19.22
101
27.96
106
25.86
115
19.11
114
13.02
89
10.63
91
0.08
65
0.06
68
0.03
74
0.02
56
0.62
96
0.81
105
FC-DCNNcopylefttwo views10.72
110
0.52
14
4.27
33
1.88
38
1.63
56
17.18
109
5.29
87
18.20
106
19.69
102
28.50
121
34.51
123
34.03
128
21.48
121
15.89
105
11.15
97
0.03
45
0.01
36
0.02
66
0.01
52
0.07
26
0.09
35
AnyNet_C32two views10.98
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5.58
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22.79
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4.16
113
5.83
122
15.64
102
14.30
124
13.18
72
17.15
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16.44
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20.52
89
14.68
78
13.44
100
22.46
120
30.08
132
0.17
80
0.26
107
0.36
117
0.36
108
1.23
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0.91
108
MeshStereopermissivetwo views11.52
112
1.52
79
4.55
37
1.89
39
1.46
51
19.87
114
5.11
85
20.66
122
15.91
85
32.67
126
34.51
123
39.34
133
21.15
119
18.74
114
12.10
99
0.11
69
0.06
68
0.01
54
0.00
1
0.45
90
0.22
63
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
MFN_U_SF_RVCtwo views12.94
113
3.66
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25.81
123
3.61
104
2.26
74
22.77
117
4.55
82
27.10
129
20.06
103
23.90
115
28.99
109
30.53
123
16.98
107
19.92
117
20.26
113
1.24
121
1.07
123
0.98
126
1.33
127
1.80
121
2.04
123
ADCStwo views13.02
114
4.93
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28.38
125
3.17
95
2.67
83
13.61
99
10.83
119
18.70
107
33.46
122
22.59
109
24.78
101
19.59
101
18.51
112
23.40
123
32.16
134
0.10
68
0.19
100
0.37
118
0.18
93
1.26
113
1.46
118
MFMNet_retwo views13.29
115
8.60
128
18.29
112
9.75
130
7.25
129
19.65
113
14.84
128
20.71
123
30.72
119
23.03
112
28.77
108
18.85
98
26.09
128
13.55
93
9.82
86
2.44
126
1.35
127
0.34
115
0.23
97
4.78
130
6.69
131
EDNetEfficienttwo views13.82
116
6.19
123
54.79
132
2.83
81
3.60
98
4.86
37
8.57
106
8.61
18
32.90
121
40.77
132
30.59
113
18.33
95
21.87
122
11.38
77
25.36
125
0.15
76
0.23
104
0.06
86
1.03
124
2.54
124
1.79
119
LSMtwo views14.01
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5.95
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33.49
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6.78
128
43.61
137
10.22
80
9.98
115
15.16
93
22.93
108
23.07
113
32.34
118
18.52
96
12.67
95
15.45
104
11.10
96
0.16
79
0.51
120
0.09
93
0.32
105
1.08
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16.85
136
SAMSARAtwo views14.63
118
2.74
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12.38
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12.65
134
6.74
125
36.50
130
72.93
138
19.36
113
23.77
110
16.20
96
13.04
61
29.21
120
12.78
96
16.98
109
15.21
107
0.11
69
0.26
107
0.03
74
0.14
90
0.76
101
0.77
103
SPS-STEREOcopylefttwo views15.04
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6.23
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13.21
104
11.34
133
11.65
135
23.30
118
7.15
103
24.16
126
15.65
83
31.78
125
29.19
111
31.62
124
21.32
120
24.62
124
19.50
112
7.59
134
4.19
135
3.22
132
1.48
128
6.99
134
6.54
130
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
PVDtwo views15.44
120
2.93
108
14.67
108
4.21
116
3.39
94
17.43
110
4.16
72
27.84
132
48.84
135
31.02
124
43.54
132
29.76
122
30.81
132
25.97
126
21.40
116
0.23
85
0.41
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0.04
81
0.33
106
0.41
88
1.33
115
SGM+DAISYtwo views15.62
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7.26
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19.28
114
8.94
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10.11
134
26.25
123
10.49
117
19.36
113
14.65
79
30.64
123
33.59
121
33.00
125
22.32
124
24.96
125
16.42
109
7.90
135
6.25
136
4.51
133
3.37
132
5.86
132
7.20
132
NVStereoNet_ROBtwo views16.04
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6.75
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12.90
102
6.37
127
7.42
130
12.89
94
9.74
113
22.78
125
25.12
111
30.32
122
46.19
134
34.37
129
25.38
126
21.48
119
21.38
115
5.94
133
3.10
133
6.07
134
10.09
136
4.01
128
8.54
134
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
AnyNet_C01two views16.12
123
10.81
132
59.36
133
4.42
117
2.49
81
30.06
126
15.15
130
17.51
105
16.51
88
17.88
98
37.69
127
24.04
111
17.54
108
29.60
129
33.29
135
0.28
93
0.38
110
0.43
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0.42
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2.57
125
1.98
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MSC_U_SF_DS_RVCtwo views16.41
124
6.93
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21.83
117
5.94
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2.81
85
38.71
131
14.59
125
24.55
127
34.87
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33.66
127
34.35
122
29.24
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24.20
125
22.59
121
17.95
110
2.52
127
2.81
131
1.17
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1.51
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5.89
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2.16
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ELAS_RVCcopylefttwo views16.54
125
2.26
102
10.09
88
5.50
122
4.46
111
28.28
125
16.72
131
25.55
128
33.54
123
40.19
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40.30
130
36.68
131
30.03
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29.40
128
20.61
114
0.98
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1.21
125
0.86
125
0.70
120
1.39
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2.16
124
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAScopylefttwo views16.72
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2.14
100
9.23
85
4.92
119
4.53
114
32.66
129
15.11
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27.40
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28.68
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40.27
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44.90
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38.33
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30.50
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26.44
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21.94
120
0.88
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1.23
126
0.67
122
0.89
123
1.49
118
2.18
126
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
LE_ROBtwo views16.73
127
1.28
70
11.61
94
3.72
107
1.65
57
16.67
106
9.17
109
14.39
84
55.91
136
63.81
136
40.86
131
35.94
130
37.73
135
14.24
97
26.87
128
0.05
60
0.10
82
0.13
99
0.22
96
0.12
39
0.15
55
SGM-ForestMtwo views16.99
128
1.08
54
5.74
53
2.12
53
0.75
27
31.63
128
12.21
121
27.80
131
32.25
120
37.88
128
39.99
129
52.96
136
35.20
134
33.60
131
24.47
123
0.26
89
0.39
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0.31
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0.39
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0.26
71
0.53
94
DispFullNettwo views17.47
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26.01
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33.98
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22.58
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20.86
136
13.84
100
1.28
43
16.50
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26.27
113
19.97
106
17.17
81
20.52
107
18.49
111
22.86
122
10.76
94
5.13
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2.83
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30.72
136
7.72
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20.86
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11.01
135
EDNetEfficientorigintwo views17.87
130
14.00
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99.53
141
1.64
30
0.92
38
10.55
83
6.26
95
13.21
73
34.84
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38.20
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36.91
126
27.47
119
26.32
129
14.71
101
25.69
126
0.04
53
0.06
68
0.14
100
0.52
117
3.16
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3.27
128
RTSAtwo views18.87
131
9.32
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86.48
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4.95
120
6.10
123
42.08
133
14.70
126
15.49
95
41.06
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22.65
110
32.32
116
13.77
69
19.54
115
37.98
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28.96
129
0.41
101
0.23
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0.00
1
0.02
56
0.91
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0.50
90
RTStwo views18.87
131
9.32
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86.48
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4.95
120
6.10
123
42.08
133
14.70
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15.49
95
41.06
132
22.65
110
32.32
116
13.77
69
19.54
115
37.98
132
28.96
129
0.41
101
0.23
104
0.00
1
0.02
56
0.91
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0.50
90
MANEtwo views19.47
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1.27
67
5.07
44
4.69
118
5.55
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30.49
127
9.94
114
34.01
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37.27
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44.13
133
51.57
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52.51
135
40.41
136
33.58
130
24.81
124
0.89
117
0.86
122
1.11
128
9.72
135
0.38
83
1.06
111
BEATNet-Init1two views23.31
134
9.03
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41.67
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4.17
114
2.53
82
45.68
135
19.47
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33.43
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38.45
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47.59
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49.10
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59.31
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41.80
137
38.35
134
29.21
131
0.47
106
0.50
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0.81
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0.66
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2.10
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1.86
120
MADNet+two views27.07
135
33.84
136
90.97
138
20.14
135
7.47
131
48.43
136
47.10
135
35.43
135
36.46
126
20.11
107
30.05
112
25.29
113
35.08
133
45.50
136
50.28
136
2.13
125
2.00
129
1.19
130
0.76
121
4.71
129
4.43
129
PWCKtwo views30.53
136
44.32
137
47.25
131
29.76
137
7.23
128
40.78
132
27.10
134
44.73
136
44.32
134
47.31
134
36.37
125
47.16
134
26.05
127
41.26
135
31.87
133
21.83
136
4.03
134
29.50
135
4.67
133
27.17
137
7.80
133
DPSimNet_ROBtwo views53.45
137
64.73
138
44.39
130
53.97
138
45.39
138
53.66
137
54.83
136
55.15
137
57.87
137
64.16
137
50.83
136
63.40
138
53.34
138
46.45
137
65.81
137
63.13
139
26.54
138
57.94
139
51.11
138
45.52
138
50.69
137
ASD4two views75.85
138
81.57
139
69.25
134
68.23
139
80.87
139
96.24
139
99.15
141
77.50
138
93.47
139
77.59
138
85.63
138
78.32
139
76.43
139
77.89
138
82.61
138
58.30
138
62.67
139
55.02
138
62.43
139
68.35
139
65.49
138
MADNet++two views82.84
139
82.38
140
73.57
135
87.72
140
82.97
140
93.14
138
69.15
137
86.42
139
82.50
138
93.46
139
86.70
139
86.28
140
80.92
140
88.34
139
88.84
139
86.83
140
84.17
140
72.64
140
68.92
140
80.47
140
81.42
139
MEDIAN_ROBtwo views98.41
140
99.70
141
99.30
140
97.09
141
97.02
141
96.89
140
95.77
140
97.66
140
97.28
140
98.79
142
98.94
140
99.18
141
98.14
141
96.89
140
96.88
140
99.96
143
99.16
141
100.00
141
99.99
141
99.69
141
99.88
140
AVERAGE_ROBtwo views99.62
141
99.95
142
98.81
139
100.00
146
100.00
142
98.08
141
95.47
139
100.00
143
100.00
141
100.00
143
100.00
141
100.00
144
100.00
142
100.00
143
99.99
141
100.00
145
100.00
142
100.00
141
100.00
142
100.00
144
100.00
144
DGTPSM_ROBtwo views99.90
142
100.00
143
99.99
142
99.99
144
100.00
142
100.00
142
100.00
142
99.97
141
100.00
141
98.35
140
100.00
141
99.84
142
100.00
142
99.98
141
99.99
141
99.99
144
100.00
142
100.00
141
100.00
142
100.00
144
100.00
144
DPSMNet_ROBtwo views99.91
143
100.00
143
99.99
142
99.99
144
100.00
142
100.00
142
100.00
142
99.98
142
100.00
141
98.35
140
100.00
141
99.84
142
100.00
142
99.98
141
99.99
141
100.00
145
100.00
142
100.00
141
100.00
142
100.00
144
100.00
144
DPSM_ROBtwo views99.95
144
100.00
143
100.00
144
99.76
142
100.00
142
100.00
142
100.00
142
100.00
143
100.00
141
100.00
143
100.00
141
100.00
144
100.00
142
100.00
143
100.00
144
99.21
141
100.00
142
100.00
141
100.00
142
99.99
142
99.95
141
DPSMtwo views99.95
144
100.00
143
100.00
144
99.76
142
100.00
142
100.00
142
100.00
142
100.00
143
100.00
141
100.00
143
100.00
141
100.00
144
100.00
142
100.00
143
100.00
144
99.21
141
100.00
142
100.00
141
100.00
142
99.99
142
99.95
141
LSM0two views100.00
146
100.00
143
100.00
144
100.00
146
100.00
142
100.00
142
100.00
142
100.00
143
100.00
141
100.00
143
100.00
141
100.00
144
100.00
142
100.00
143
100.00
144
100.00
145
100.00
142
100.00
141
100.00
142
100.00
144
99.99
143
MSMDNettwo views1.26
7
FADEtwo views17.27
134
10.46
132
9.90
133
26.54
116
30.62
137
14.22
137
38.39
137
37.63
137
5.22
131