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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted by
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
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
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
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
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
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.
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
MANEtwo views19.47
108
1.27
48
5.07
30
4.69
93
5.55
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30.49
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9.94
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37.27
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44.13
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51.57
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52.51
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40.41
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33.58
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24.81
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0.89
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0.86
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1.11
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9.72
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0.38
64
1.06
90
PA-Nettwo views4.98
37
1.47
57
7.42
61
2.40
51
2.14
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8.73
48
3.64
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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
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Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
ADCMidtwo views10.24
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2.21
46
2.39
60
11.23
66
6.19
69
14.17
62
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22.25
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17.89
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18.51
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1.18
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PASMtwo views7.90
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3.25
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3.29
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5.39
28
6.57
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10.57
28
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12.77
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13.92
50
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13.79
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10.77
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0.19
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0.45
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0.29
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1.49
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1.19
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PVDtwo views15.44
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4.21
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3.39
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17.43
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4.16
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27.84
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31.02
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25.97
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21.40
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0.23
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1.33
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FBW_ROBtwo views8.50
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1.03
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7.98
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1.93
36
1.28
40
13.10
73
6.23
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22.50
100
18.98
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18.82
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14.91
55
19.06
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10.04
66
18.41
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9.83
68
0.62
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0.22
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ADCPNettwo views9.54
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16.71
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6.39
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12.11
41
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47
13.53
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21.45
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10.94
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14.38
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21.54
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0.27
70
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ADCStwo views13.02
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28.38
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3.17
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2.67
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13.61
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10.83
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18.70
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33.46
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32.16
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0.10
49
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0.18
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1.26
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BEATNet-Init1two views23.31
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4.17
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2.53
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45.68
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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
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38.35
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29.21
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0.47
84
0.50
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AnyNet_C01two views16.12
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4.42
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2.49
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30.06
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17.51
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16.51
70
17.88
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37.69
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24.04
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17.54
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29.60
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33.29
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0.28
71
0.38
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0.43
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0.42
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1.98
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ELAS_RVCcopylefttwo views16.54
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2.26
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10.09
70
5.50
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4.46
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28.28
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16.72
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40.19
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36.68
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20.61
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0.98
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0.86
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0.70
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1.39
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2.16
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A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
MSC_U_SF_DS_RVCtwo views16.41
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2.81
67
38.71
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14.59
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34.87
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34.35
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29.24
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17.95
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2.52
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2.16
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ELAScopylefttwo views16.72
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2.14
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4.92
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4.53
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32.66
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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
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0.88
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1.23
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0.67
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0.89
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1.49
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2.18
102
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
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
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0.41
89
0.14
79
0.09
68
2.05
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2.36
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MADNet+two views27.07
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33.84
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90.97
113
20.14
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7.47
106
48.43
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47.10
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35.43
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36.46
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20.11
87
30.05
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25.29
91
35.08
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45.50
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50.28
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2.13
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2.00
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1.19
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0.76
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4.71
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4.43
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SPS-STEREOcopylefttwo views15.04
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6.23
100
13.21
84
11.34
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11.65
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23.30
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7.15
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24.16
102
15.65
65
31.78
102
29.19
90
31.62
99
21.32
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24.62
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19.50
90
7.59
109
4.19
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3.22
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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
MFMNet_retwo views13.29
92
8.60
104
18.29
92
9.75
105
7.25
104
19.65
89
14.84
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20.71
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30.72
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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
SGM+DAISYtwo views15.62
97
7.26
103
19.28
93
8.94
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10.11
108
26.25
98
10.49
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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
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6.25
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4.51
108
3.37
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5.86
106
7.20
107
PWCKtwo views30.53
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44.32
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47.25
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29.76
111
7.23
103
40.78
107
27.10
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44.73
111
44.32
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47.31
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36.37
101
47.16
108
26.05
103
41.26
110
31.87
108
21.83
111
4.03
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29.50
110
4.67
108
27.17
111
7.80
108
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
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
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22.86
97
10.76
73
5.13
106
2.83
107
30.72
111
7.72
109
20.86
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11.01
110
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
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16.85
111
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
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98.94
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99.18
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98.14
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96.89
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96.88
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99.96
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99.16
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100.00
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99.99
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99.69
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99.88
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DPSM_ROBtwo views99.95
118
100.00
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100.00
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99.76
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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
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100.00
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100.00
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100.00
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99.95
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DPSMtwo views99.95
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100.00
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100.00
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99.76
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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
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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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99.95
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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
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100.00
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100.00
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100.00
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DPSMNet_ROBtwo views99.91
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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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100.00
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100.00
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99.98
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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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100.00
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100.00
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100.00
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DGTPSM_ROBtwo views99.90
116
100.00
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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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100.00
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99.97
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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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AVERAGE_ROBtwo views99.62
115
99.95
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98.81
114
100.00
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100.00
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98.08
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95.47
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100.00
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100.00
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MSMDNettwo views1.26
6