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 bysorted bysort bysort bysort bysort by
FCDSN-DCtwo views10.24
106
0.56
19
3.49
22
1.96
46
1.29
49
16.90
107
4.59
82
17.16
103
19.10
97
24.64
116
32.46
118
33.82
125
22.14
122
15.93
105
10.45
89
0.04
53
0.00
1
0.00
1
0.00
1
0.05
19
0.21
60
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
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
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
80
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
72
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
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
129
0.00
1
0.00
1
0.00
1
0.03
13
0.01
2
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
71
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
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
iResNetv2_ROBtwo views4.28
46
1.43
73
7.17
76
2.91
84
1.26
47
4.36
29
1.62
48
13.64
78
10.25
58
9.83
59
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
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
80
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
95
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.
ccs_robtwo views3.63
25
1.12
58
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
NCCL2two views5.88
59
1.59
82
5.44
48
1.87
37
0.92
38
9.55
74
11.55
119
12.11
59
9.94
55
9.67
58
8.85
27
22.28
109
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
63
AF-Nettwo views7.78
84
1.44
74
6.68
70
3.37
98
4.50
113
8.61
67
2.69
57
17.07
102
20.17
104
9.52
55
24.02
96
20.31
104
14.59
103
11.58
78
9.84
87
0.61
110
0.00
1
0.12
96
0.00
1
0.38
83
0.12
46
CVANet_RVCtwo views4.16
40
1.16
60
3.60
25
1.94
44
1.46
51
3.92
24
4.68
83
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
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
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
Nwc_Nettwo views6.97
78
1.25
64
6.63
69
3.82
108
3.37
92
10.83
85
1.67
49
19.56
117
11.35
63
8.36
42
23.62
95
17.19
88
11.44
90
11.21
76
8.08
69
0.80
114
0.00
1
0.00
1
0.02
56
0.13
42
0.09
35
TDLMtwo views4.11
38
1.11
57
3.54
24
1.62
28
1.04
43
3.91
23
7.41
104
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
46
GANettwo views6.22
64
1.07
52
4.07
32
2.27
60
0.89
34
9.19
70
9.52
110
12.02
58
8.13
42
10.72
67
29.09
109
13.86
70
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
GANetREF_RVCpermissivetwo views6.56
68
2.89
106
7.58
80
3.41
99
0.40
14
12.96
94
9.58
111
15.09
89
17.25
92
10.33
63
10.62
39
12.27
64
8.16
73
12.21
81
4.53
40
0.41
101
0.00
1
0.00
1
0.02
56
3.12
125
0.39
80
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
DISCOtwo views6.28
65
0.57
20
5.78
54
3.43
101
1.17
45
11.22
86
3.39
64
12.14
61
16.16
85
6.52
28
11.22
42
16.96
86
6.32
61
19.51
114
10.74
92
0.00
1
0.00
1
0.00
1
0.00
1
0.35
80
0.11
45
RASNettwo views4.52
50
0.61
24
4.42
34
3.42
100
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
78
9.28
58
8.68
75
0.15
76
0.00
1
0.00
1
0.00
1
0.03
13
0.04
24
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
51
PWC_ROBbinarytwo views8.24
89
3.13
109
12.74
100
2.43
69
4.43
109
7.51
64
1.22
41
16.63
100
19.24
98
16.08
94
28.29
106
13.99
73
10.16
84
13.63
93
14.06
104
0.42
104
0.00
1
0.05
84
0.00
1
0.59
94
0.27
74
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
66
12.97
59
13.91
72
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
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
72
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
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
70
0.00
1
0.00
1
0.00
1
0.00
1
0.29
72
0.01
2
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
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
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
65
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
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
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
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
67
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
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
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
91
16.91
90
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
RGCtwo views6.88
77
2.23
100
6.13
59
4.05
111
4.73
117
8.94
69
2.78
58
15.19
93
11.74
68
11.13
71
19.34
87
17.86
90
10.42
85
13.02
88
8.03
68
0.73
112
0.01
36
0.24
111
0.41
110
0.31
75
0.38
79
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
82
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
PSMNet_ROBtwo views5.02
54
1.63
85
6.03
58
1.90
40
1.83
64
9.57
75
6.35
96
15.58
96
7.23
28
6.15
21
10.48
37
12.22
63
4.16
36
8.02
35
8.71
76
0.02
34
0.01
36
0.01
54
0.10
86
0.20
56
0.12
46
RYNettwo views6.34
66
0.89
39
5.88
55
1.41
18
4.48
112
15.97
102
4.18
72
13.41
74
16.49
86
10.81
69
7.00
17
14.33
74
8.72
77
9.43
60
13.71
103
0.00
1
0.01
36
0.00
1
0.00
1
0.02
7
0.07
28
ETE_ROBtwo views5.80
57
1.77
90
6.33
62
1.44
20
0.78
31
6.43
53
6.90
100
12.53
66
8.08
41
12.93
84
14.89
69
21.13
107
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
77
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
RPtwo views6.84
76
1.29
70
5.53
50
3.92
110
5.18
118
6.32
50
3.53
65
11.73
55
15.31
81
9.54
56
22.38
92
18.25
93
14.47
102
10.11
65
7.49
60
0.91
118
0.01
36
0.12
96
0.15
90
0.33
77
0.19
58
FC-DCNNcopylefttwo views10.72
109
0.52
14
4.27
33
1.88
38
1.63
56
17.18
108
5.29
86
18.20
105
19.69
101
28.50
120
34.51
122
34.03
126
21.48
120
15.89
104
11.15
96
0.03
45
0.01
36
0.02
66
0.01
52
0.07
26
0.09
35
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
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
82
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
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
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.
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
60
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
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
PWCDC_ROBbinarytwo views7.92
86
3.17
113
7.48
79
5.73
122
4.40
106
10.45
82
0.35
10
14.52
86
28.19
114
10.36
64
31.27
113
7.04
27
9.14
81
13.22
90
8.78
77
2.74
128
0.02
47
0.00
1
0.00
1
1.31
115
0.17
56
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
79
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
MDST_ROBtwo views8.37
90
0.32
1
9.03
84
4.18
114
2.42
78
26.86
123
6.14
89
19.36
112
13.52
74
27.09
118
22.75
94
9.47
45
4.74
45
15.06
101
6.34
53
0.02
34
0.02
47
0.00
1
0.00
1
0.02
7
0.13
50
MSMD_ROBtwo views9.28
98
1.09
55
4.65
39
1.58
25
0.39
13
16.52
103
4.41
77
13.60
76
14.87
79
22.34
107
39.89
127
25.67
113
20.71
117
12.42
83
6.98
56
0.34
96
0.03
53
0.00
1
0.00
1
0.05
19
0.09
35
DeepPruner_ROBtwo views3.52
23
1.14
59
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
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
PA-Nettwo views4.98
52
1.47
75
7.42
78
2.40
67
2.14
70
8.73
68
3.64
69
12.42
64
13.11
71
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
111
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
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
60
stereogantwo views7.69
82
0.88
37
7.08
75
3.49
102
3.93
103
18.98
110
3.23
62
16.52
99
19.58
100
9.93
61
18.92
86
20.50
105
9.04
80
14.07
95
6.14
50
0.26
89
0.04
58
0.21
108
0.03
65
0.63
97
0.33
77
FADNettwo views4.23
43
1.65
86
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
SGM-Foresttwo views4.96
51
0.32
1
2.84
12
1.21
12
0.64
21
10.23
81
6.64
99
11.55
53
10.98
60
10.94
70
13.59
62
11.65
60
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
86
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
XPNet_ROBtwo views6.03
62
1.22
61
5.61
52
2.56
74
0.90
36
6.32
50
7.07
101
12.92
69
8.30
43
14.76
91
15.13
71
19.84
101
6.66
66
10.36
67
8.58
74
0.02
34
0.04
58
0.00
1
0.03
65
0.11
37
0.24
66
LALA_ROBtwo views6.58
69
1.80
92
6.25
60
1.26
13
0.94
41
10.08
78
9.02
107
16.00
97
11.51
66
12.74
81
13.02
60
24.77
111
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
69
BEATNet_4xtwo views3.24
15
1.27
66
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
63
Anonymous Stereotwo views6.16
63
3.15
111
23.75
120
2.97
90
2.48
80
4.39
32
13.30
121
9.21
22
9.86
53
9.56
57
8.76
26
6.79
24
1.99
11
13.50
91
13.04
102
0.01
27
0.05
62
0.00
1
0.06
78
0.22
65
0.19
58
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
DRN-Testtwo views5.87
58
0.98
46
5.89
56
2.69
78
3.65
101
12.37
91
3.35
63
20.07
119
10.20
57
11.93
75
12.31
55
11.06
58
5.31
53
7.89
33
9.05
79
0.04
53
0.05
62
0.04
81
0.04
74
0.18
54
0.25
69
ADCP+two views8.09
87
1.79
91
14.50
106
1.54
23
4.28
105
16.57
104
5.20
85
12.80
68
11.20
62
12.83
83
17.07
78
11.02
57
10.80
87
17.59
110
23.18
121
0.03
45
0.05
62
0.01
54
0.18
92
0.39
87
0.81
104
EDNetEfficientorigintwo views17.87
129
14.00
132
99.53
139
1.64
30
0.92
38
10.55
83
6.26
94
13.21
73
34.84
123
38.20
128
36.91
125
27.47
117
26.32
128
14.71
100
25.69
125
0.04
53
0.06
68
0.14
100
0.52
116
3.16
126
3.27
127
GwcNetcopylefttwo views6.42
67
1.97
96
10.92
90
2.59
75
5.58
121
11.55
88
2.21
54
14.10
82
16.52
89
10.04
62
17.19
81
10.86
55
5.61
56
9.23
57
8.84
78
0.01
27
0.06
68
0.03
74
0.08
82
0.56
91
0.40
81
DSFCAtwo views4.21
42
0.50
13
5.45
49
1.34
15
1.68
59
7.04
60
4.51
80
10.73
46
7.00
24
10.78
68
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
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
51
SANettwo views10.64
108
1.86
94
10.91
89
1.76
33
0.71
25
14.62
100
9.23
109
19.18
110
37.14
127
19.22
100
27.96
105
25.86
114
19.11
113
13.02
88
10.63
90
0.08
65
0.06
68
0.03
74
0.02
56
0.62
96
0.81
104
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
69
15.29
92
18.31
85
22.02
108
12.56
93
10.82
71
7.49
60
0.03
45
0.06
68
0.11
95
0.03
65
0.30
73
0.14
51
MeshStereopermissivetwo views11.52
111
1.52
78
4.55
37
1.89
39
1.46
51
19.87
113
5.11
84
20.66
121
15.91
84
32.67
125
34.51
122
39.34
131
21.15
118
18.74
113
12.10
98
0.11
69
0.06
68
0.01
54
0.00
1
0.45
90
0.22
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
WCMA_ROBtwo views9.21
97
0.87
36
7.37
77
2.54
73
2.13
69
13.59
97
5.80
88
11.64
54
14.01
76
24.43
115
32.99
119
27.09
115
18.02
108
12.51
84
9.85
88
0.81
115
0.07
75
0.01
54
0.01
52
0.16
49
0.23
63
CSANtwo views7.62
81
1.60
83
6.56
68
1.83
35
0.66
22
12.40
92
10.52
117
14.45
85
21.32
105
14.19
88
15.98
75
17.84
89
13.02
98
12.32
82
8.38
72
0.09
66
0.07
75
0.03
74
0.04
74
0.33
77
0.67
101
pmcnntwo views7.72
83
1.27
66
9.42
86
2.91
84
3.14
88
9.44
72
6.23
92
12.56
67
16.51
87
14.53
89
24.08
97
27.44
116
8.49
75
9.32
59
8.44
73
0.06
62
0.08
77
0.00
1
0.00
1
0.30
73
0.15
54
XQCtwo views8.43
93
3.58
115
16.40
109
2.92
86
2.17
71
13.22
96
3.60
66
14.64
87
25.86
111
11.87
74
12.04
50
15.06
78
10.67
86
15.24
102
19.41
110
0.39
99
0.08
77
0.05
84
0.07
80
0.84
104
0.45
84
ADCReftwo views7.27
79
1.38
71
16.37
108
2.52
71
3.30
91
11.63
89
3.16
61
10.80
47
9.35
49
13.03
85
25.27
103
8.17
37
8.92
79
8.06
38
21.81
118
0.15
76
0.08
77
0.16
102
0.34
106
0.38
83
0.58
95
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
75
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
89
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
98
0.01
2
0.02
6
FADNet-RVCtwo views3.98
36
1.84
93
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
69
LE_ROBtwo views16.73
126
1.28
69
11.61
94
3.72
106
1.65
57
16.67
105
9.17
108
14.39
84
55.91
135
63.81
135
40.86
130
35.94
128
37.73
134
14.24
96
26.87
127
0.05
60
0.10
82
0.13
99
0.22
95
0.12
39
0.15
54
SuperBtwo views8.10
88
3.15
111
24.67
121
2.65
76
1.23
46
9.88
77
4.29
74
10.18
36
30.07
117
11.53
73
12.18
52
6.12
21
6.65
65
10.50
68
14.47
105
0.14
74
0.11
84
0.35
116
0.25
98
13.06
133
0.48
87
NCC-stereotwo views6.77
73
1.49
76
6.48
66
2.92
86
4.40
106
7.43
61
3.61
67
19.52
115
13.29
72
8.39
43
16.91
76
15.96
81
12.13
91
12.85
85
7.70
62
1.47
123
0.11
84
0.01
54
0.42
111
0.14
47
0.24
66
Abc-Nettwo views6.77
73
1.49
76
6.48
66
2.92
86
4.40
106
7.43
61
3.61
67
19.52
115
13.29
72
8.39
43
16.91
76
15.96
81
12.13
91
12.85
85
7.70
62
1.47
123
0.11
84
0.01
54
0.42
111
0.14
47
0.24
66
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
StereoDRNettwo views5.59
56
1.75
89
6.80
72
3.12
93
4.45
110
10.61
84
4.35
76
18.80
108
9.73
51
12.22
76
6.87
14
11.44
59
4.65
43
8.09
40
8.26
71
0.02
34
0.11
84
0.00
1
0.03
65
0.20
56
0.28
75
DMCA-RVCcopylefttwo views3.17
14
1.26
65
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
DANettwo views6.02
61
1.23
62
8.45
83
3.86
109
3.94
104
7.64
65
1.34
46
9.51
25
7.00
24
13.39
86
15.53
73
15.99
83
7.02
67
12.14
80
12.37
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0.19
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0.12
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0.02
66
0.03
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0.13
42
0.56
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SHDtwo views9.61
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2.60
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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
120
37.84
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18.19
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21.24
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16.96
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12.83
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14.47
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16.05
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0.32
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0.13
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0.01
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0.08
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0.38
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RTSCtwo views9.15
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3.00
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13.57
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3.72
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1.76
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11.82
90
0.46
13
16.95
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36.83
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15.80
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15.53
73
12.91
67
7.46
70
20.01
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21.76
117
0.31
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0.13
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0.01
54
0.08
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0.57
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0.41
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NaN_ROBtwo views6.00
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1.24
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6.29
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1.34
15
1.68
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9.60
76
10.31
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15.09
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15.79
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12.62
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8.95
28
11.67
61
5.83
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11.78
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6.41
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0.05
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0.22
65
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103
AANet_RVCtwo views5.01
53
1.74
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6.38
64
1.96
46
1.29
49
2.26
3
1.69
50
10.07
29
18.53
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7.88
38
18.15
84
8.49
41
2.70
17
10.59
70
7.04
57
0.96
119
0.15
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0.02
66
0.00
1
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42
0.12
46
FAT-Stereotwo views6.78
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0.68
29
6.80
72
2.30
62
1.77
63
5.63
45
4.20
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18.79
107
18.62
94
10.53
65
17.15
79
18.52
95
13.74
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8.86
52
7.38
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0.03
45
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0.01
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0.07
80
0.12
39
0.26
72
CBMVpermissivetwo views5.35
55
0.91
40
3.67
26
1.62
28
0.44
16
10.09
79
7.19
103
12.49
65
12.33
70
12.22
76
14.69
68
10.93
56
6.48
62
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45
4.96
44
0.02
34
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0.00
1
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1
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51
0.17
56
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
DeepPrunerFtwo views6.75
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3.68
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7.16
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3.78
21
4.29
74
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75
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36
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11.10
75
9.44
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0.24
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S-Stereotwo views6.63
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35
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6.49
54
6.22
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12.99
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22.84
106
9.48
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15.51
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12.00
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8.43
74
8.04
36
10.70
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0.12
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0.00
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psmorigintwo views7.34
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2.35
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0.87
33
6.72
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1.70
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44
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12.96
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ADCLtwo views10.16
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19.36
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1.92
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66
22.23
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8.91
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22.95
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0.26
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ADCStwo views13.02
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0.10
68
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FADNet-RVC-Resampletwo views3.79
32
1.62
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1.43
19
0.66
22
5.94
48
2.41
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10.18
36
8.58
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6.28
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4.22
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5.33
13
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48
7.71
30
3.19
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0.17
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aanetorigintwo views8.72
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1.95
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4.93
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15.80
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9.04
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0.25
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FBW_ROBtwo views8.50
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49
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43
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13.10
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18.98
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9.83
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RTStwo views18.87
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86.48
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4.95
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42.08
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13.77
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RTSAtwo views18.87
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86.48
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4.95
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42.08
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15.49
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41.06
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22.65
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13.77
68
19.54
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37.98
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28.96
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0.41
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EDNetEfficienttwo views13.82
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2.83
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4.86
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18
32.90
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40.77
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18.33
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11.38
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0.15
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AnyNet_C32two views10.98
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0.17
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SGM_RVCbinarytwo views10.08
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0.60
22
3.42
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2.30
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0.32
11
19.41
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6.33
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14.64
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25.14
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81
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
SAMSARAtwo views14.63
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15.21
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0.11
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AnyNet_C01two views16.12
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59.36
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4.42
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2.49
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30.06
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SGM-ForestMtwo views16.99
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31.63
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39.99
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33.60
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0.26
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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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48.84
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29.76
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30.81
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25.97
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21.40
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0.23
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PDISCO_ROBtwo views9.62
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1.99
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11.51
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9.88
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9.61
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21.48
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3.83
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19.33
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28.49
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5.02
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0.41
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ADCMidtwo views10.24
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3.13
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20.70
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2.21
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2.39
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11.23
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6.19
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14.17
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23.20
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22.25
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17.89
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19.54
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18.51
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26.21
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0.45
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1.10
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1.18
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FADNet_RVCtwo views3.91
35
1.67
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12.95
102
0.96
9
0.75
27
5.71
46
0.54
17
10.83
48
6.60
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3.46
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4.10
9
3.40
23
9.43
60
6.33
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0.36
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PASMtwo views7.90
85
4.22
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21.97
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3.25
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3.29
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5.39
41
6.57
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10.57
42
19.09
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12.77
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13.92
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18.11
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9.51
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13.79
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10.77
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0.19
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0.29
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1.08
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STTStereo_v2two views8.41
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1.54
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10.97
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5.73
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3.60
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26.19
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4.41
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10.10
31
7.42
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24.99
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14.38
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15.83
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10.99
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9.53
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0.50
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G-Nettwo views8.41
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10.97
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5.73
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3.60
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26.19
120
4.41
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10.10
31
7.42
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19.71
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24.99
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14.38
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10.99
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9.53
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0.50
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BEATNet-Init1two views23.31
133
9.03
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41.67
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4.17
113
2.53
82
45.68
134
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
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0.66
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2.10
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1.86
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LSMtwo views14.01
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5.95
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33.49
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6.78
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43.61
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10.22
80
9.98
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15.16
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22.93
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23.07
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32.34
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18.52
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12.67
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15.45
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11.10
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1.08
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16.85
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DPSNettwo views10.14
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16.82
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1.85
36
1.73
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24.84
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17.20
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19.92
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27.41
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12.23
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13.62
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16.52
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18.35
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14.42
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0.78
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0.54
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0.59
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MANEtwo views19.47
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1.27
66
5.07
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4.69
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5.55
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30.49
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9.94
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34.01
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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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0.38
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MFN_U_SF_RVCtwo views12.94
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3.66
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25.81
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3.61
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2.26
74
22.77
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4.55
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27.10
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28.99
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30.53
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16.98
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20.26
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1.24
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ADCPNettwo views9.54
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31.46
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2.09
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1.60
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16.71
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6.39
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12.11
59
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13.53
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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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1.45
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ELAS_RVCcopylefttwo views16.54
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2.26
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10.09
88
5.50
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4.46
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28.28
124
16.72
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25.55
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33.54
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40.19
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40.30
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36.68
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30.03
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20.61
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0.98
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1.21
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0.86
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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
ELAScopylefttwo views16.72
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2.14
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9.23
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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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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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0.89
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1.49
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A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
MFMNet_retwo views13.29
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8.60
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18.29
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9.75
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7.25
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19.65
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14.84
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20.71
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30.72
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23.03
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28.77
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18.85
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13.55
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9.82
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2.44
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1.35
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0.34
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MFN_U_SF_DS_RVCtwo views9.78
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4.27
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14.47
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2.29
61
2.85
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23.40
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13.62
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13.60
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19.54
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8.59
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17.05
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7.98
65
1.25
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MADNet+two views27.07
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33.84
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90.97
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20.14
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7.47
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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
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30.05
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35.08
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50.28
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2.13
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MSC_U_SF_DS_RVCtwo views16.41
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5.94
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2.81
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38.71
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14.59
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33.66
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17.95
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DispFullNettwo views17.47
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26.01
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33.98
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13.84
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1.28
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10.76
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NVStereoNet_ROBtwo views16.04
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6.75
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12.90
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12.89
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9.74
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5.94
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Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
PWCKtwo views30.53
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44.32
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47.25
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29.76
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40.78
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27.10
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31.87
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21.83
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4.03
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SPS-STEREOcopylefttwo views15.04
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11.34
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11.65
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15.65
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29.19
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K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
SGM+DAISYtwo views15.62
120
7.26
126
19.28
113
8.94
128
10.11
132
26.25
122
10.49
116
19.36
112
14.65
78
30.64
122
33.59
120
33.00
123
22.32
123
24.96
124
16.42
108
7.90
134
6.25
135
4.51
132
3.37
131
5.86
130
7.20
131
DPSimNet_ROBtwo views53.45
136
64.73
136
44.39
129
53.97
136
45.39
136
53.66
136
54.83
135
55.15
136
57.87
136
64.16
136
50.83
135
63.40
136
53.34
137
46.45
136
65.81
136
63.13
136
26.54
136
57.94
136
51.11
136
45.52
136
50.69
136
MADNet++two views82.84
137
82.38
138
73.57
133
87.72
137
82.97
137
93.14
137
69.15
136
86.42
137
82.50
137
93.46
137
86.70
137
86.28
137
80.92
138
88.34
137
88.84
137
86.83
137
84.17
137
72.64
137
68.92
137
80.47
137
81.42
137
MEDIAN_ROBtwo views98.41
138
99.70
139
99.30
138
97.09
138
97.02
138
96.89
138
95.77
139
97.66
138
97.28
138
98.79
140
98.94
138
99.18
138
98.14
139
96.89
138
96.88
138
99.96
140
99.16
138
100.00
138
99.99
138
99.69
138
99.88
138
LSM0two views100.00
144
100.00
141
100.00
142
100.00
143
100.00
139
100.00
140
100.00
140
100.00
141
100.00
139
100.00
141
100.00
139
100.00
141
100.00
140
100.00
141
100.00
142
100.00
142
100.00
139
100.00
138
100.00
139
100.00
141
99.99
141
AVERAGE_ROBtwo views99.62
139
99.95
140
98.81
137
100.00
143
100.00
139
98.08
139
95.47
138
100.00
141
100.00
139
100.00
141
100.00
139
100.00
141
100.00
140
100.00
141
99.99
139
100.00
142
100.00
139
100.00
138
100.00
139
100.00
141
100.00
142
DPSMNet_ROBtwo views99.91
141
100.00
141
99.99
140
99.99
141
100.00
139
100.00
140
100.00
140
99.98
140
100.00
139
98.35
138
100.00
139
99.84
139
100.00
140
99.98
139
99.99
139
100.00
142
100.00
139
100.00
138
100.00
139
100.00
141
100.00
142
DPSM_ROBtwo views99.95
142
100.00
141
100.00
142
99.76
139
100.00
139
100.00
140
100.00
140
100.00
141
100.00
139
100.00
141
100.00
139
100.00
141
100.00
140
100.00
141
100.00
142
99.21
138
100.00
139
100.00
138
100.00
139
99.99
139
99.95
139
DPSMtwo views99.95
142
100.00
141
100.00
142
99.76
139
100.00
139
100.00
140
100.00
140
100.00
141
100.00
139
100.00
141
100.00
139
100.00
141
100.00
140
100.00
141
100.00
142
99.21
138
100.00
139
100.00
138
100.00
139
99.99
139
99.95
139
DGTPSM_ROBtwo views99.90
140
100.00
141
99.99
140
99.99
141
100.00
139
100.00
140
100.00
140
99.97
139
100.00
139
98.35
138
100.00
139
99.84
139
100.00
140
99.98
139
99.99
139
99.99
141
100.00
139
100.00
138
100.00
139
100.00
141
100.00
142
MSMDNettwo views1.26
7
ASD4two views81.57
137