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
PMTNettwo views5.81
1
1.06
1
4.24
1
2.87
1
0.77
1
9.07
5
7.38
33
14.08
1
24.63
15
9.23
1
11.01
1
7.87
1
5.43
2
12.69
5
5.77
4
0.01
2
0.02
5
0.00
1
0.00
1
0.16
6
0.01
1
DPM-Stereotwo views6.74
2
1.93
11
10.02
21
4.28
2
1.64
6
13.54
13
1.52
2
15.16
2
6.63
1
14.30
3
26.81
13
10.90
3
5.19
1
14.78
11
7.56
7
0.00
1
0.06
18
0.05
11
0.00
1
0.17
7
0.17
10
R-Stereo Traintwo views7.04
3
1.34
4
7.89
11
6.23
16
2.30
11
7.51
2
5.54
15
23.96
9
6.94
2
16.26
9
33.57
39
12.04
4
5.61
3
7.67
1
3.74
1
0.01
2
0.05
12
0.01
5
0.00
1
0.10
1
0.12
4
R-Stereotwo views7.04
3
1.34
4
7.89
11
6.23
16
2.30
11
7.51
2
5.54
15
23.96
9
6.94
2
16.26
9
33.57
39
12.04
4
5.61
3
7.67
1
3.74
1
0.01
2
0.05
12
0.01
5
0.00
1
0.10
1
0.12
4
HITNettwo views7.83
5
2.93
24
8.39
14
4.76
3
1.48
5
12.75
9
4.64
10
20.97
3
14.37
4
15.14
6
22.15
3
14.57
7
10.62
9
14.38
9
8.14
9
0.04
6
0.01
2
0.48
35
0.02
14
0.57
16
0.10
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
DN-CSS_ROBtwo views8.17
6
2.59
19
14.14
35
6.11
14
3.22
21
6.31
1
3.53
4
22.92
7
20.59
9
16.66
12
30.05
22
9.31
2
7.95
7
13.49
7
4.78
3
0.72
41
0.00
1
0.45
33
0.00
1
0.54
14
0.09
2
MLCVtwo views9.32
7
2.22
16
12.55
30
5.22
5
1.32
2
13.79
14
1.35
1
21.14
4
23.65
14
21.91
27
31.37
27
17.14
9
7.57
6
16.00
18
10.18
17
0.22
16
0.01
2
0.03
9
0.02
14
0.40
9
0.29
14
BEATNet_4xtwo views9.37
8
4.85
48
12.05
28
5.48
6
1.35
4
13.15
10
6.15
22
25.06
12
16.74
5
16.18
8
24.65
5
17.29
10
12.51
14
16.91
23
10.92
22
0.38
25
0.18
29
0.81
47
0.05
19
1.95
47
0.65
23
ccstwo views9.74
9
1.25
3
7.47
9
5.77
9
2.16
9
7.74
4
3.54
5
33.83
49
35.91
45
13.42
2
23.80
4
22.01
16
11.97
12
11.51
3
9.09
14
0.41
29
1.75
76
0.17
18
0.17
28
0.63
18
2.12
57
CFNet_RVCtwo views9.87
10
2.18
15
5.49
3
7.45
27
5.25
42
15.76
25
5.98
18
21.89
5
21.87
11
14.96
4
30.02
21
25.68
32
13.68
17
12.46
4
10.94
23
0.08
9
0.06
18
1.83
64
0.22
30
1.09
29
0.43
18
AdaStereotwo views10.22
11
3.63
33
9.14
19
9.15
41
3.24
22
15.79
26
5.39
14
31.94
38
25.42
16
18.39
15
26.47
9
19.44
13
9.50
8
16.60
22
8.59
11
0.44
32
0.05
12
0.40
31
0.00
1
0.57
16
0.16
9
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.
iResNettwo views10.26
12
3.10
29
15.72
44
7.35
25
2.13
8
13.86
15
7.07
31
22.80
6
28.01
24
20.10
20
30.40
24
16.79
8
11.06
10
14.46
10
10.29
20
0.39
28
0.05
12
0.00
1
0.07
24
0.73
21
0.73
24
ccs_robtwo views10.52
13
2.12
13
7.47
9
6.02
13
3.07
18
19.32
37
4.16
6
25.06
12
27.77
23
20.21
21
27.93
18
30.56
42
13.32
15
14.95
13
7.25
6
0.04
6
0.05
12
0.21
19
0.05
19
0.55
15
0.27
13
CFNettwo views10.67
14
2.33
17
8.90
16
6.66
22
4.09
28
16.05
27
4.26
8
30.44
32
31.01
33
18.53
16
26.48
10
23.69
24
13.68
17
15.27
15
10.59
21
0.05
8
0.02
5
0.44
32
0.05
19
0.73
21
0.23
11
DeepPruner_ROBtwo views10.77
15
4.56
43
13.13
31
6.37
19
4.28
31
10.14
6
6.86
27
36.42
65
19.59
8
17.87
13
27.37
16
23.19
20
12.26
13
19.09
30
10.23
19
1.05
54
0.48
54
0.23
21
0.15
27
0.89
27
1.17
36
HSM-Net_RVCpermissivetwo views10.88
16
1.23
2
5.46
2
5.57
7
2.63
14
20.12
39
6.06
20
27.67
19
28.35
25
20.63
23
25.51
6
34.01
54
14.38
25
16.18
20
9.29
15
0.11
10
0.07
21
0.01
5
0.00
1
0.13
3
0.14
8
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
NOSS_ROBtwo views10.99
17
3.81
37
6.88
6
6.69
23
2.30
11
16.06
28
10.94
45
30.06
30
32.41
36
16.10
7
18.72
2
22.78
18
14.21
22
17.52
25
8.62
12
2.35
71
1.84
78
3.32
83
1.57
70
2.06
51
1.52
44
DMCAtwo views11.27
18
2.78
20
10.03
22
10.86
57
5.57
46
11.86
8
6.42
23
23.55
8
19.24
6
19.96
18
32.17
34
25.00
30
27.88
68
13.03
6
14.75
30
0.30
22
0.30
37
0.29
24
0.23
32
0.76
23
0.33
16
HSMtwo views11.33
19
1.80
8
7.04
8
6.13
15
3.99
27
16.16
30
6.92
29
29.69
28
19.39
7
22.95
30
26.05
8
41.08
76
15.79
31
20.25
35
8.89
13
0.02
5
0.03
8
0.01
5
0.00
1
0.15
4
0.34
17
RASNettwo views11.59
20
1.71
7
10.36
23
6.30
18
5.66
48
15.48
24
6.12
21
34.50
55
20.86
10
16.61
11
32.51
35
22.04
17
23.00
51
19.44
31
16.15
37
0.71
39
0.03
8
0.05
11
0.01
12
0.15
4
0.13
7
iResNet_ROBtwo views11.71
21
1.96
12
10.77
24
6.01
12
3.68
24
15.13
22
2.34
3
31.72
37
37.58
52
26.16
44
32.77
36
25.88
33
15.34
28
16.35
21
7.94
8
0.19
14
0.01
2
0.00
1
0.00
1
0.31
8
0.12
4
FADNet-RVC-Resampletwo views11.72
22
4.35
41
30.33
75
9.46
45
3.90
26
14.11
16
7.17
32
25.78
14
23.31
13
23.27
33
29.52
20
19.50
14
15.00
27
15.38
16
8.33
10
0.53
35
0.50
55
0.49
36
0.50
47
1.38
39
1.67
48
CBMV_ROBtwo views11.77
23
2.89
22
6.92
7
5.20
4
2.88
17
14.12
17
4.79
11
26.69
16
29.84
27
26.17
45
31.92
31
23.61
23
20.74
47
20.20
34
10.18
17
1.78
65
1.80
77
2.17
69
1.26
66
1.61
41
0.55
21
FADNet_RVCtwo views11.94
24
5.17
52
39.74
87
6.44
20
3.46
23
11.42
7
4.17
7
29.16
26
26.89
20
17.97
14
25.96
7
13.41
6
15.67
29
15.84
17
13.54
27
0.90
48
0.75
64
0.55
39
1.20
64
3.65
68
2.85
64
iResNetv2_ROBtwo views12.00
25
2.91
23
13.46
32
5.82
10
3.73
25
13.15
10
6.45
24
34.15
50
36.02
47
25.25
42
33.81
42
29.79
40
14.24
23
13.61
8
6.10
5
0.21
15
0.02
5
0.12
14
0.00
1
0.82
25
0.25
12
NLCA_NET_v2_RVCtwo views12.05
26
3.43
31
16.25
49
7.62
30
5.58
47
14.57
21
6.00
19
32.98
46
27.41
21
21.69
26
31.40
28
25.21
31
13.47
16
14.96
14
15.60
33
0.84
43
0.33
41
0.35
27
0.35
40
1.29
36
1.78
50
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
CC-Net-ROBtwo views12.13
27
3.51
32
15.82
45
7.52
28
5.50
45
14.30
19
6.60
26
32.79
44
27.59
22
21.97
28
32.14
33
24.76
29
14.02
19
14.82
12
16.06
34
0.85
45
0.35
43
0.34
26
0.35
40
1.30
38
1.94
53
SGM-Foresttwo views12.92
28
1.87
9
6.61
4
5.68
8
2.05
7
23.19
52
11.30
48
34.24
51
30.77
32
26.54
47
31.99
32
29.98
41
15.74
30
21.17
39
13.36
26
1.05
54
0.33
41
0.84
48
0.01
12
0.71
20
0.97
29
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
STTStereotwo views12.97
29
4.88
49
26.57
69
7.54
29
5.41
44
13.43
12
6.49
25
30.53
33
26.35
19
25.14
41
31.43
29
32.00
45
14.12
20
16.01
19
14.83
31
0.29
21
0.31
39
0.67
43
1.08
61
1.21
33
1.11
33
TDLMtwo views12.98
30
3.78
36
9.77
20
10.75
54
4.94
38
16.10
29
14.08
55
35.05
58
31.87
35
22.36
29
26.51
12
26.09
34
14.97
26
24.73
49
13.11
25
1.05
54
0.05
12
1.21
60
0.27
35
1.83
44
1.00
31
AANet_RVCtwo views13.16
31
5.34
53
10.83
26
8.20
38
4.44
32
14.13
18
9.46
38
28.78
25
37.67
53
23.44
34
37.47
51
23.48
21
15.83
32
22.39
41
16.63
38
1.67
63
0.86
67
0.24
22
0.02
14
0.63
18
1.60
46
CVANet_RVCtwo views13.24
32
3.39
30
8.43
15
8.42
39
5.04
40
18.52
34
11.72
50
32.03
40
33.85
39
23.17
32
32.77
36
29.78
39
16.51
33
23.20
42
11.28
24
0.94
49
0.04
10
1.19
59
0.47
45
3.13
63
1.01
32
FADNet-RVCtwo views13.27
33
11.57
86
39.71
86
7.94
36
4.50
33
15.41
23
6.95
30
27.96
20
22.98
12
20.37
22
30.73
25
26.86
35
11.31
11
20.50
37
14.28
29
0.14
11
0.14
28
0.13
16
0.18
29
2.83
58
0.86
26
DLCB_ROBtwo views13.35
34
3.00
26
9.12
17
9.43
44
5.68
49
21.80
46
10.12
40
29.19
27
29.92
28
27.71
50
31.45
30
32.37
46
19.50
42
19.02
29
16.73
39
0.22
16
0.04
10
0.38
30
0.06
23
0.49
12
0.85
25
StereoDRNet-Refinedtwo views13.36
35
2.80
21
10.82
25
7.94
36
3.10
19
18.95
35
4.45
9
28.47
24
29.65
26
29.11
54
41.47
68
24.44
27
17.64
38
23.34
43
21.66
44
0.16
12
0.07
21
0.53
38
0.32
38
0.77
24
1.64
47
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
CBMVpermissivetwo views13.69
36
3.63
33
8.02
13
5.93
11
2.69
15
22.57
49
12.44
52
29.81
29
31.57
34
31.20
63
33.79
41
31.04
43
17.41
37
25.28
52
14.11
28
0.71
39
0.60
59
0.60
40
0.11
25
1.11
31
1.13
35
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
FADNettwo views14.18
37
10.92
83
37.56
82
7.79
34
6.64
55
17.48
31
6.91
28
34.73
56
34.03
40
18.85
17
27.02
14
28.26
36
14.24
23
17.77
27
10.09
16
0.41
29
0.67
62
0.37
28
0.37
42
8.13
89
1.34
38
pmcnntwo views14.59
38
3.68
35
19.78
60
6.83
24
4.51
34
21.27
44
14.67
58
27.37
17
30.54
29
27.09
49
40.19
63
38.71
67
18.53
41
17.70
26
19.27
40
0.84
43
0.09
26
0.00
1
0.00
1
0.48
10
0.31
15
NVstereo2Dtwo views15.15
39
2.97
25
16.28
50
9.23
42
7.56
64
30.03
68
9.53
39
42.80
89
42.72
65
15.11
5
27.03
15
23.16
19
16.93
35
24.28
46
15.45
32
3.07
79
0.43
50
1.44
61
0.61
50
6.74
83
7.60
92
StereoDRNettwo views15.30
40
4.47
42
14.35
39
10.71
53
8.69
70
25.02
57
11.03
46
39.66
78
35.62
44
29.17
55
28.81
19
34.05
55
17.80
39
18.80
28
23.77
52
0.41
29
0.30
37
0.15
17
0.14
26
1.64
42
1.42
41
DRN-Testtwo views15.88
41
3.83
38
14.70
40
9.96
49
8.02
68
28.93
67
14.65
57
42.34
85
38.25
56
28.18
53
34.31
45
29.22
38
18.35
40
20.01
33
22.68
48
0.37
24
0.41
46
0.46
34
0.28
36
1.28
35
1.34
38
PA-Nettwo views16.29
42
6.88
65
27.34
70
9.88
47
11.12
82
21.77
45
28.15
95
32.06
41
39.49
57
19.99
19
27.91
17
23.50
22
20.59
46
20.25
35
24.04
55
0.24
18
2.26
85
0.22
20
4.59
91
1.13
32
4.48
81
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
DISCOtwo views16.36
43
1.88
10
16.15
47
7.78
33
4.19
29
26.92
61
10.21
42
30.31
31
44.26
72
21.35
25
33.91
43
35.80
62
22.40
50
35.65
84
33.87
83
0.18
13
0.08
23
0.04
10
0.04
18
1.71
43
0.54
20
NaN_ROBtwo views16.51
44
6.51
61
16.22
48
10.53
52
4.64
36
31.76
74
17.78
71
37.00
69
43.37
68
29.68
59
31.29
26
32.67
50
20.32
45
28.00
61
16.06
34
0.38
25
0.41
46
0.32
25
0.41
43
0.87
26
1.94
53
DANettwo views16.85
45
8.00
68
26.04
68
14.56
75
7.25
62
20.59
40
5.37
13
26.22
15
26.02
18
32.17
68
36.00
46
37.20
64
29.77
74
30.75
65
24.62
60
1.14
58
1.34
71
2.21
70
0.51
48
3.18
64
4.07
77
PSMNet_ROBtwo views16.90
46
5.45
54
14.16
36
13.77
69
7.25
62
27.65
63
32.74
99
43.21
91
37.88
54
24.00
35
32.97
38
34.55
58
16.69
34
17.41
24
23.96
54
0.38
25
0.22
32
0.93
50
2.05
77
1.83
44
0.96
28
GANettwo views16.92
47
4.32
40
12.46
29
10.84
56
4.24
30
23.04
51
15.36
62
39.91
79
34.47
42
32.42
69
45.10
83
40.04
74
27.54
65
23.61
45
20.20
41
1.02
53
0.08
23
0.67
43
0.22
30
1.87
46
0.97
29
NCCL2two views17.23
48
6.37
60
14.30
38
23.45
97
7.18
61
24.16
55
17.05
69
34.39
53
25.88
17
31.55
64
38.01
54
42.09
81
26.81
61
20.94
38
22.69
49
0.45
33
0.23
33
2.63
75
1.80
73
2.02
50
2.57
61
ADCReftwo views17.30
49
6.73
63
42.01
90
10.11
50
8.78
71
26.61
60
10.45
43
31.29
35
32.75
38
31.19
62
41.62
70
18.47
12
19.78
44
24.29
47
34.06
85
0.76
42
0.42
49
2.16
68
1.79
72
1.23
34
1.56
45
XPNet_ROBtwo views17.33
50
4.84
47
15.44
43
11.15
60
6.64
55
20.88
41
16.10
64
36.26
64
39.54
58
31.80
67
41.32
67
41.30
78
23.91
52
24.67
48
27.40
73
1.07
57
0.74
63
0.79
46
0.25
33
1.10
30
1.31
37
RPtwo views17.58
51
4.92
50
15.27
42
15.44
80
11.95
88
21.05
42
11.75
51
27.64
18
45.09
75
22.99
31
42.05
72
39.39
70
27.74
67
25.54
53
22.33
47
5.45
93
0.59
58
4.33
86
1.62
71
3.53
67
2.86
65
SuperBtwo views17.60
52
6.22
59
56.40
101
7.91
35
5.07
41
19.70
38
9.03
36
24.87
11
50.15
86
26.25
46
39.13
58
17.61
11
20.84
48
22.28
40
25.31
65
0.70
38
0.31
39
1.10
55
0.71
53
16.58
103
1.86
51
ETE_ROBtwo views17.67
53
9.48
75
17.66
53
13.60
67
4.57
35
23.03
50
21.09
80
32.49
43
35.24
43
30.49
60
38.06
55
46.18
90
24.49
54
26.40
55
24.21
57
0.46
34
0.24
34
1.14
56
0.84
57
1.52
40
2.13
58
PWCDC_ROBbinarytwo views17.80
54
9.62
76
18.38
57
17.95
86
7.74
65
24.61
56
5.56
17
34.41
54
52.79
90
29.66
58
50.20
91
20.63
15
19.69
43
29.85
64
20.87
42
6.18
94
0.26
36
0.10
13
0.05
19
5.36
81
2.00
55
MDST_ROBtwo views17.92
55
1.60
6
13.68
33
13.88
70
6.44
53
43.05
92
14.86
59
42.74
88
41.66
63
43.25
90
42.85
75
28.43
37
17.28
36
29.35
63
16.06
34
0.88
46
0.11
27
0.63
42
0.47
45
0.50
13
0.64
22
Nwc_Nettwo views17.98
56
4.67
45
17.79
56
14.26
71
11.65
87
25.14
58
14.88
60
40.85
82
39.83
59
21.07
24
43.24
78
34.50
57
27.64
66
26.44
56
25.03
61
3.22
80
0.18
29
2.52
74
3.18
82
1.98
48
1.51
43
ADCP+two views18.16
57
4.61
44
32.94
78
9.31
43
10.23
78
28.87
66
11.69
49
33.43
47
36.49
48
29.28
57
40.49
64
24.18
25
24.61
56
33.89
75
35.33
86
0.25
19
0.40
45
2.00
65
1.17
63
2.16
52
1.87
52
PWC_ROBbinarytwo views18.37
58
10.55
80
25.19
67
10.21
51
6.29
52
23.75
53
4.79
11
35.93
63
48.75
79
32.56
70
44.46
82
34.68
59
24.78
57
30.99
66
25.72
69
1.38
61
0.08
23
1.57
62
0.26
34
2.18
53
3.17
69
AF-Nettwo views18.38
59
5.60
55
14.21
37
15.95
81
10.78
80
22.26
47
10.45
43
35.39
60
50.22
87
24.71
40
37.77
52
41.55
79
29.24
73
29.11
62
26.67
70
4.35
88
0.06
18
4.56
87
0.90
58
2.42
54
1.38
40
Anonymous Stereotwo views18.60
60
11.75
87
49.81
96
14.43
73
12.02
90
14.33
20
23.23
89
32.16
42
43.08
67
24.32
38
34.16
44
24.24
26
14.12
20
31.67
69
30.84
80
0.89
47
0.91
68
1.74
63
1.92
76
3.23
65
3.23
70
stereogantwo views18.62
61
3.07
27
16.31
51
13.04
66
9.99
76
35.74
84
9.09
37
38.17
76
45.27
76
24.39
39
41.24
66
39.93
72
25.46
59
31.29
68
24.33
59
1.80
66
0.91
68
2.71
77
0.75
56
5.15
80
3.70
71
edge stereotwo views18.75
62
5.12
51
17.66
53
10.77
55
7.84
66
22.41
48
11.03
46
35.23
59
42.07
64
34.62
74
42.74
74
41.28
77
35.50
86
25.14
51
24.17
56
2.42
75
2.42
87
6.35
91
1.45
68
2.89
61
3.80
72
RYNettwo views18.92
63
4.69
46
16.88
52
10.99
58
15.84
96
46.72
94
16.57
66
37.84
73
48.03
77
26.92
48
26.48
10
37.84
66
24.92
58
19.80
32
31.04
81
0.25
19
0.25
35
0.62
41
0.03
17
6.34
82
6.39
87
LALA_ROBtwo views19.19
64
7.15
67
15.93
46
12.96
65
5.30
43
28.30
65
23.21
88
42.51
86
36.69
49
33.45
73
40.81
65
50.96
95
24.53
55
27.92
58
25.66
68
0.60
36
0.44
51
2.04
66
1.22
65
2.54
56
1.47
42
SGM_RVCbinarytwo views19.52
65
2.12
13
6.79
5
6.59
21
1.33
3
38.20
89
15.74
63
38.13
75
34.07
41
45.71
94
41.91
71
51.41
96
38.10
90
38.77
91
27.80
76
0.61
37
0.36
44
0.51
37
0.33
39
1.03
28
0.90
27
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
RTSCtwo views19.64
66
9.36
72
31.06
76
10.99
58
6.24
51
28.10
64
10.14
41
37.50
72
58.34
101
31.74
66
36.70
49
32.50
47
21.03
49
36.00
86
37.24
91
1.23
59
0.44
51
0.12
14
0.31
37
1.98
48
1.69
49
NCC-stereotwo views19.75
67
6.08
56
23.96
64
15.05
76
16.17
97
18.42
32
21.45
81
43.31
92
41.00
61
24.30
36
38.12
56
34.98
60
26.92
62
35.46
81
25.65
66
4.29
86
2.07
80
3.24
81
8.90
98
2.86
59
2.86
65
Abc-Nettwo views19.75
67
6.08
56
23.96
64
15.05
76
16.17
97
18.42
32
21.45
81
43.31
92
41.00
61
24.30
36
38.12
56
34.98
60
26.92
62
35.46
81
25.65
66
4.29
86
2.07
80
3.24
81
8.90
98
2.86
59
2.86
65
RGCtwo views19.90
69
13.26
88
20.24
61
18.19
87
14.06
95
21.21
43
14.33
56
34.97
57
43.60
70
27.73
51
41.49
69
39.76
71
27.98
69
34.52
78
21.37
43
3.66
83
0.54
57
8.67
97
4.16
89
4.19
72
4.02
75
DeepPrunerFtwo views19.92
70
11.02
85
44.29
91
20.74
91
17.75
100
19.19
36
22.57
83
36.86
68
49.93
82
25.48
43
36.62
48
24.69
28
23.97
53
23.44
44
21.79
45
2.68
78
1.63
75
5.70
89
3.95
88
3.38
66
2.67
63
WCMA_ROBtwo views20.02
71
4.30
39
19.72
59
9.47
46
7.00
60
32.71
79
13.99
54
31.97
39
32.48
37
41.51
87
52.00
93
44.09
84
36.14
88
32.43
71
24.29
58
6.19
95
2.79
90
1.09
54
1.10
62
3.95
69
3.13
68
SANettwo views20.96
72
6.60
62
29.81
72
8.83
40
3.19
20
31.27
73
20.56
79
41.86
84
56.09
97
39.30
83
43.62
79
44.95
86
31.93
80
27.83
57
23.67
51
0.94
49
0.52
56
0.99
51
0.42
44
4.72
74
2.03
56
FBW_ROBtwo views21.00
73
10.23
79
22.72
62
13.71
68
6.77
57
30.49
72
16.46
65
44.58
94
43.57
69
44.25
93
39.59
61
43.27
83
26.12
60
33.96
76
21.80
46
2.58
77
1.84
78
7.14
96
2.78
81
4.00
70
4.06
76
G-Nettwo views21.25
74
8.24
69
37.97
84
15.14
78
9.26
73
50.87
97
16.80
67
27.96
20
30.54
29
38.92
81
42.93
76
33.27
51
32.17
81
27.99
59
25.25
63
4.40
89
3.83
96
3.60
84
0.97
59
7.91
87
6.95
90
STTStereo_v2two views21.25
74
8.24
69
37.97
84
15.14
78
9.26
73
50.87
97
16.80
67
27.96
20
30.54
29
38.92
81
42.93
76
33.27
51
32.17
81
27.99
59
25.25
63
4.40
89
3.83
96
3.60
84
0.97
59
7.91
87
6.95
90
SHDtwo views21.32
76
10.03
78
30.16
74
14.30
72
8.68
69
24.15
54
8.93
34
40.82
81
61.17
103
35.79
77
44.15
81
38.73
68
30.20
75
33.88
74
31.09
82
2.00
67
0.81
65
1.17
57
1.55
69
4.08
71
4.78
82
CSANtwo views21.34
77
9.45
74
23.34
63
20.99
93
4.95
39
32.57
77
34.26
100
38.83
77
49.95
84
36.97
79
39.72
62
44.97
87
31.73
79
26.05
54
23.94
53
1.52
62
0.47
53
0.85
49
1.43
67
2.73
57
2.14
60
ADCLtwo views21.64
78
6.20
58
47.32
94
9.93
48
6.91
59
38.69
91
19.97
77
31.26
34
54.04
91
27.89
52
47.97
88
31.35
44
30.66
77
33.18
73
36.62
88
0.99
51
0.92
70
2.63
75
1.82
74
2.45
55
2.13
58
ADCPNettwo views21.93
79
9.38
73
57.92
102
11.76
63
6.88
58
36.03
85
18.44
72
32.80
45
35.93
46
32.91
72
43.80
80
39.21
69
26.99
64
31.14
67
36.94
90
2.05
68
2.67
89
2.30
72
3.45
85
4.22
73
3.82
73
MeshStereopermissivetwo views22.27
80
6.87
64
11.15
27
7.69
31
4.87
37
37.70
88
13.06
53
41.64
83
36.95
50
50.92
98
53.41
94
58.12
99
41.93
95
37.73
89
27.62
75
2.52
76
2.37
86
2.99
79
2.07
78
3.09
62
2.60
62
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
ADCMidtwo views22.76
81
10.75
81
41.73
89
11.23
62
7.97
67
27.26
62
19.18
76
35.44
61
38.02
55
40.23
84
46.38
86
36.56
63
42.70
96
38.20
90
40.75
96
1.77
64
1.43
73
3.05
80
3.81
87
4.96
76
3.82
73
MSMD_ROBtwo views22.90
82
9.64
77
14.76
41
17.46
85
9.88
75
34.94
82
18.83
73
33.67
48
37.47
51
40.97
86
59.80
99
45.93
88
38.34
91
31.74
70
23.24
50
6.22
96
3.58
95
8.78
98
11.03
100
6.76
84
5.06
84
LE_ROBtwo views22.93
83
3.07
27
14.02
34
7.70
32
2.86
16
31.99
76
17.35
70
28.10
23
67.19
110
70.51
110
55.61
98
49.07
93
47.62
100
24.76
50
36.49
87
0.35
23
0.20
31
0.27
23
0.55
49
0.48
10
0.44
19
AnyNet_C32two views23.37
84
13.28
89
40.29
88
12.36
64
11.44
84
30.39
69
29.15
96
31.36
36
44.90
74
35.36
75
48.86
89
33.61
53
34.20
84
43.15
95
41.62
99
1.27
60
1.35
72
1.18
58
2.58
80
4.77
75
6.27
86
SGM-ForestMtwo views23.72
85
2.44
18
9.13
18
7.43
26
2.25
10
44.80
93
19.11
75
44.90
95
49.97
85
50.74
97
51.18
92
62.06
106
45.83
99
44.96
96
33.88
84
1.00
52
0.84
66
0.78
45
0.62
51
1.29
36
1.11
33
MFN_U_SF_DS_RVCtwo views23.92
86
13.60
90
30.09
73
25.46
99
10.02
77
38.51
90
20.40
78
35.77
62
43.76
71
42.15
88
47.10
87
39.99
73
28.20
71
35.96
85
27.06
72
3.64
82
3.85
98
6.66
92
16.30
104
4.97
77
4.98
83
XQCtwo views24.10
87
16.76
92
50.68
97
21.37
95
11.01
81
35.24
83
18.84
74
37.28
71
55.11
94
31.64
65
30.06
23
37.71
65
30.31
76
37.17
87
39.66
94
4.20
85
0.41
46
2.76
78
1.89
75
11.46
98
8.42
96
FC-DCNNcopylefttwo views24.37
88
10.99
84
19.02
58
18.44
88
9.16
72
36.98
86
23.07
86
40.44
80
43.05
66
46.25
96
53.58
95
50.44
94
37.86
89
35.45
80
27.57
74
6.87
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3.37
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6.01
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4.88
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7.86
86
6.05
85
DPSNettwo views24.40
89
6.97
66
33.14
79
11.16
61
6.54
54
53.33
100
43.32
106
51.28
102
59.37
102
30.89
61
39.36
59
40.35
75
34.30
85
32.57
72
25.09
62
4.65
92
1.62
74
0.37
28
0.66
52
8.51
91
4.44
80
PDISCO_ROBtwo views24.45
90
9.24
71
29.28
71
28.68
102
19.96
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37.14
87
15.33
61
45.04
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54.69
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29.24
56
42.67
73
46.07
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28.17
70
35.35
79
30.30
79
9.92
101
2.13
82
6.90
93
3.20
83
8.74
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6.87
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GANetREF_RVCpermissivetwo views25.41
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29.28
103
24.63
66
25.18
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6.04
50
30.44
70
35.39
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47.84
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50.60
88
36.88
78
36.04
47
34.11
56
31.23
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37.46
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27.96
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8.58
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2.66
88
16.29
103
4.95
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14.37
102
8.32
95
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
ADCStwo views26.20
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13.71
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46.90
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14.51
74
10.34
79
30.46
71
22.72
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42.81
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57.61
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42.58
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49.37
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41.91
80
40.43
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41.90
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44.58
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2.37
72
2.19
84
2.37
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3.29
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7.32
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6.52
88
PASMtwo views28.19
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48.18
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22.49
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22.20
102
25.92
59
25.84
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34.35
52
49.45
80
35.37
76
39.48
60
46.72
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33.36
83
34.28
77
36.77
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11.73
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14.94
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17.43
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22.18
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13.45
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12.32
102
AnyNet_C01two views29.76
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25.78
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75.37
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17.03
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11.34
83
49.00
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27.12
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37.99
74
40.05
60
40.73
85
65.13
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46.98
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38.86
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46.63
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48.19
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2.14
69
2.13
82
2.13
67
2.56
79
8.14
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7.98
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LSMtwo views29.98
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55.11
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19.06
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53.75
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32.57
77
23.17
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42.61
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48.57
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45.97
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55.04
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42.93
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36.02
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35.63
83
27.03
71
2.33
70
9.19
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4.87
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7.71
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10.65
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27.91
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RTSAtwo views30.15
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96.33
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16.71
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11.63
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58.66
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24.28
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36.80
66
62.63
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43.79
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46.10
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32.56
48
43.90
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51.29
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40.92
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2.38
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0.66
60
1.06
52
0.74
54
5.11
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4.34
78
RTStwo views30.15
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23.12
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96.33
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16.71
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11.63
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58.66
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24.28
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36.80
66
62.63
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43.79
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46.10
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32.56
48
43.90
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51.29
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40.92
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2.38
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0.66
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1.06
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0.74
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5.11
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4.34
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DispFullNettwo views30.27
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31.79
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8.94
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32.60
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37.99
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44.62
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41.55
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MANEtwo views31.97
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17.71
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20.94
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47.80
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26.85
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53.00
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64.30
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51.83
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38.69
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9.61
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19.93
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PVDtwo views33.19
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37.64
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26.09
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33.25
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42.25
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51.42
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66.03
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52.65
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64.31
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51.93
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55.13
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51.61
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4.00
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ELAScopylefttwo views33.68
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18.95
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36.71
81
20.64
90
13.61
92
56.43
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35.14
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50.03
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49.90
81
60.58
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63.83
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58.64
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53.36
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50.73
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44.44
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11.03
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6.58
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10.02
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7.43
96
13.58
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11.94
101
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAS_RVCcopylefttwo views33.79
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19.15
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35.97
80
21.28
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13.79
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52.58
99
36.11
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48.96
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55.66
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61.47
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62.91
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58.35
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53.55
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53.37
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42.23
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11.25
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6.64
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10.32
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7.05
95
13.56
100
11.58
100
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
SAMSARAtwo views34.43
103
18.53
94
31.91
77
55.34
109
35.34
106
75.99
110
94.71
113
47.97
98
50.84
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37.47
80
36.99
50
53.12
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38.55
92
39.56
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38.65
92
3.59
81
5.40
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2.22
71
3.72
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9.43
94
9.29
99
BEATNet-Init1two views35.08
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20.64
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54.70
99
27.43
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12.00
89
58.29
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31.01
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52.58
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57.49
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60.36
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62.98
102
69.98
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55.54
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53.00
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44.40
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4.61
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2.84
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7.05
95
6.89
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10.97
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8.94
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MSC_U_SF_DS_RVCtwo views37.16
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34.69
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53.68
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37.47
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12.55
91
56.80
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22.63
84
52.02
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63.29
107
66.20
108
63.82
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58.12
99
53.73
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51.21
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46.48
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8.79
99
5.31
100
14.69
102
15.22
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17.27
104
9.21
98
NVStereoNet_ROBtwo views41.93
106
32.40
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44.55
92
36.60
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27.78
103
34.52
81
32.38
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48.84
99
57.18
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69.34
109
75.62
112
66.07
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64.86
111
47.34
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60.78
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15.72
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23.99
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23.40
106
32.06
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21.34
105
23.86
105
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
MADNet+two views53.31
107
68.61
109
98.75
113
67.03
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37.58
107
79.83
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81.74
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60.38
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61.61
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51.43
99
54.92
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61.11
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59.29
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69.66
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76.71
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23.88
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17.08
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12.35
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32.47
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27.69
106
SGM+DAISYtwo views54.67
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56.61
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62.73
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40.21
106
53.22
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61.95
107
48.59
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55.05
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44.62
73
61.03
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63.68
103
60.46
104
55.62
106
60.68
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55.34
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56.34
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48.56
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50.16
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49.63
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52.25
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56.76
110
SPS-STEREOcopylefttwo views55.62
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59.14
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64.16
106
45.05
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53.84
110
59.88
106
44.00
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59.53
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49.94
83
62.33
107
61.09
100
59.80
103
56.82
108
60.06
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57.85
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58.41
109
48.34
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51.07
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49.21
109
55.41
109
56.48
109
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
PWCKtwo views65.90
110
88.63
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79.40
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80.32
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29.96
105
63.85
108
39.45
104
72.07
111
71.26
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78.74
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68.99
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77.82
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61.77
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82.81
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62.64
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80.17
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39.88
108
81.90
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47.37
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73.54
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37.51
108
MFMNet_retwo views72.05
111
79.64
110
64.23
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52.73
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84.71
112
77.93
111
68.35
109
63.96
110
66.93
109
61.00
104
71.29
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63.84
107
68.91
112
72.42
111
61.11
109
82.03
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74.22
112
80.34
111
74.44
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83.62
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89.34
112
DPSimNet_ROBtwo views74.29
112
83.27
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59.30
104
77.19
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65.13
111
75.14
109
74.95
110
73.99
112
76.94
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81.99
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75.48
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80.18
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71.76
113
67.39
109
84.00
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81.87
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51.14
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79.99
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76.70
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70.17
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79.21
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MADNet++two views94.16
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94.02
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90.25
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96.28
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96.84
113
97.17
113
90.16
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94.71
113
91.69
113
97.07
113
93.71
113
94.62
113
92.63
114
96.15
113
95.18
113
95.42
113
95.89
113
92.80
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92.23
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92.10
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94.30
113
MEDIAN_ROBtwo views99.19
114
99.84
114
99.62
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98.49
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98.51
114
98.58
114
97.81
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98.80
114
98.56
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99.36
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99.49
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99.56
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99.06
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98.35
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98.31
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99.99
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99.63
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100.00
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100.00
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99.81
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99.95
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AVERAGE_ROBtwo views99.80
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99.99
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99.40
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100.00
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100.00
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98.99
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97.72
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100.00
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DPSMNet_ROBtwo views99.95
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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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100.00
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100.00
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99.15
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99.96
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100.00
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DGTPSM_ROBtwo views99.95
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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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100.00
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100.00
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99.14
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100.00
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99.96
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100.00
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99.99
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99.99
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100.00
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DPSM_ROBtwo views100.00
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LSM0two views100.00
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DPSMtwo views100.00
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MSMDNettwo views7.19
5