This table lists the benchmark results for the low-res two-view scenario. This benchmark evaluates the Middlebury stereo metrics (for all metrics, smaller is better):

The mask determines whether the metric is evaluated for all pixels with ground truth, or only for pixels which are visible in both images (non-occluded).
The coverage selector allows to limit the table to results for all pixels (dense), or a given minimum fraction of pixels.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click one or more dataset result cells or column headers to show visualizations. Most visualizations are only available for training datasets. The visualizations may not work with mobile browsers.




Method Infoalllakes. 1llakes. 1ssand box 1lsand box 1sstora. room 1lstora. room 1sstora. room 2lstora. room 2sstora. room 2 1lstora. room 2 1sstora. room 2 2lstora. room 2 2sstora. room 3lstora. room 3stunnel 1ltunnel 1stunnel 2ltunnel 2stunnel 3ltunnel 3s
sorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CREStereotwo views0.98
1
0.42
10
1.86
1
0.07
1
0.22
7
1.65
1
0.02
1
4.07
2
6.33
21
0.72
1
1.54
2
0.43
2
0.64
1
1.22
1
0.44
1
0.00
1
0.00
1
0.00
1
0.00
1
0.01
2
0.00
1
PMTNettwo views1.41
2
0.37
8
2.06
4
0.26
3
0.15
4
2.94
13
0.34
10
3.39
1
5.33
11
1.00
2
1.27
1
0.28
1
0.93
5
4.04
9
0.74
4
5.00
134
0.00
1
0.00
1
0.00
1
0.03
14
0.01
2
DPM-Stereotwo views1.97
3
0.64
28
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
39
0.00
1
0.00
1
0.05
22
0.04
27
Gwc-CoAtRStwo views2.02
4
0.33
6
2.61
5
1.04
10
0.30
10
3.56
16
0.40
13
5.89
4
5.94
17
5.08
13
5.01
7
5.55
15
1.72
9
1.60
2
0.91
5
0.01
30
0.09
86
0.00
1
0.25
103
0.01
2
0.02
7
FENettwo views2.19
5
0.34
7
3.04
15
2.05
53
0.26
9
2.82
10
0.13
3
7.02
8
5.55
13
4.97
12
1.94
3
4.91
11
3.39
23
4.71
12
2.50
13
0.02
38
0.03
58
0.00
1
0.00
1
0.00
1
0.21
65
RAFT-Stereopermissivetwo views2.44
6
0.32
1
1.93
2
0.94
7
0.16
5
3.67
18
0.61
24
6.37
6
3.08
1
9.14
54
17.44
87
1.80
4
0.77
3
1.76
3
0.70
2
0.00
1
0.01
39
0.00
1
0.00
1
0.01
2
0.03
17
Lahav Lipson, Zachary Teed, and Jia Deng: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. 3DV
R-Stereo Traintwo views2.44
6
0.32
1
1.93
2
0.94
7
0.16
5
3.67
18
0.61
24
6.37
6
3.08
1
9.14
54
17.44
87
1.80
4
0.77
3
1.76
3
0.70
2
0.00
1
0.01
39
0.00
1
0.00
1
0.01
2
0.03
17
ACVNettwo views2.58
8
0.53
18
2.69
7
0.84
5
0.59
20
2.61
6
0.55
19
8.35
12
5.70
14
4.45
7
9.26
34
5.77
17
3.50
25
2.24
5
4.41
39
0.00
1
0.00
1
0.00
1
0.00
1
0.17
56
0.01
2
DN-CSS_ROBtwo views2.69
9
1.40
77
5.34
50
2.31
69
0.75
27
3.14
15
0.06
2
6.11
5
3.87
3
5.34
16
12.18
57
2.34
6
1.22
6
7.84
37
1.48
6
0.03
49
0.00
1
0.00
1
0.00
1
0.35
85
0.03
17
HITNettwo views2.79
10
0.77
33
4.02
31
2.03
52
0.11
3
5.58
44
0.59
23
9.24
24
5.15
9
6.42
26
7.26
19
3.66
7
2.92
20
4.07
10
3.87
36
0.00
1
0.00
1
0.00
1
0.00
1
0.06
27
0.02
7
Vladimir Tankovich, Christian Häne, Yinda Zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz: HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching. CVPR 2021
DMCAtwo views2.82
11
0.64
28
4.45
38
1.61
29
0.89
34
3.86
22
0.57
22
9.18
22
5.10
8
6.24
22
6.49
13
7.17
31
2.13
12
3.31
7
4.73
43
0.00
1
0.01
39
0.00
1
0.00
1
0.04
18
0.02
7
ccstwo views3.04
12
0.39
9
3.08
17
1.78
37
0.52
19
2.04
2
0.50
17
13.09
76
13.71
80
3.54
5
5.36
10
5.50
14
2.45
14
4.81
13
2.88
17
0.09
70
0.08
82
0.12
101
0.10
91
0.20
61
0.50
94
AdaStereotwo views3.09
13
0.58
22
3.04
15
2.84
87
0.48
18
4.08
25
1.29
46
12.16
67
7.77
41
6.03
20
9.62
36
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
7
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi: AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
DMCA-RVCcopylefttwo views3.17
14
1.26
70
6.39
69
2.10
56
1.51
53
3.02
14
0.56
21
7.84
11
5.06
7
9.23
57
5.19
8
8.46
41
2.72
18
6.31
19
3.22
24
0.06
66
0.11
89
0.09
98
0.02
61
0.16
54
0.08
36
BEATNet_4xtwo views3.24
15
1.27
71
5.89
60
1.56
26
0.10
1
5.26
40
1.07
41
10.08
34
5.50
12
6.89
31
7.73
23
4.53
10
4.13
36
5.05
14
5.27
47
0.04
57
0.05
67
0.00
1
0.00
1
0.23
75
0.23
68
GANet-RSSMtwo views3.27
16
0.60
23
3.38
21
2.94
94
2.00
69
2.61
6
0.48
15
10.93
55
6.11
19
5.47
17
10.20
40
6.57
23
5.44
57
6.25
18
2.23
11
0.00
1
0.00
1
0.08
95
0.00
1
0.17
56
0.03
17
NOSS_ROBtwo views3.30
17
0.46
11
2.62
6
2.08
54
1.01
42
5.60
45
0.74
36
10.37
42
11.48
70
5.15
14
8.43
29
5.67
16
1.73
10
7.97
39
2.34
12
0.02
38
0.06
73
0.00
1
0.00
1
0.07
29
0.14
56
CFNet-ftpermissivetwo views3.31
18
0.94
49
2.69
7
1.50
23
2.38
76
2.81
8
0.68
30
8.35
12
7.43
35
4.45
7
9.94
37
10.20
53
4.60
42
6.50
22
3.41
28
0.00
1
0.00
1
0.03
79
0.00
1
0.22
70
0.03
17
CFNet_RVCtwo views3.31
18
0.94
49
2.69
7
1.50
23
2.38
76
2.81
8
0.68
30
8.35
12
7.43
35
4.45
7
9.94
37
10.20
53
4.60
42
6.49
21
3.41
28
0.00
1
0.00
1
0.03
79
0.00
1
0.22
70
0.03
17
PSMNet-RSSMtwo views3.33
20
0.77
33
3.22
20
2.12
57
1.90
68
2.33
4
0.71
33
11.17
56
5.83
16
5.90
18
11.43
52
7.04
27
4.83
51
5.96
16
3.17
22
0.00
1
0.00
1
0.02
71
0.00
1
0.08
31
0.02
7
cf-rtwo views3.42
21
0.81
37
3.97
30
2.41
73
2.78
86
2.85
11
0.64
29
8.44
15
6.32
20
6.25
23
11.57
53
8.97
46
3.54
27
5.93
15
3.83
35
0.00
1
0.00
1
0.00
1
0.00
1
0.09
35
0.07
33
MLCVtwo views3.44
22
0.88
42
5.60
55
1.39
18
0.25
8
4.36
29
0.33
9
7.25
9
7.28
32
9.17
56
12.24
59
5.09
12
2.47
15
9.15
61
3.23
25
0.00
1
0.00
1
0.00
1
0.00
1
0.10
36
0.02
7
DeepPruner_ROBtwo views3.52
23
1.14
64
4.06
32
1.12
12
1.65
57
3.65
17
0.83
38
13.96
85
4.47
6
7.80
39
10.84
45
7.05
29
2.16
13
8.14
46
3.08
21
0.07
68
0.03
58
0.00
1
0.01
57
0.32
81
0.06
31
STTStereotwo views3.60
24
0.93
48
6.34
67
2.71
84
2.23
74
3.68
20
0.63
27
9.42
25
6.73
25
9.87
65
6.97
17
8.84
45
3.65
28
6.85
23
3.04
19
0.00
1
0.02
51
0.01
59
0.00
1
0.02
7
0.02
7
ccs_robtwo views3.63
25
1.12
63
4.42
36
2.52
76
0.91
37
5.50
43
0.21
6
10.11
37
9.11
53
6.55
29
11.28
49
8.32
40
2.55
16
7.66
33
2.01
10
0.00
1
0.00
1
0.00
1
0.00
1
0.20
61
0.08
36
hitnet-ftcopylefttwo views3.66
26
0.65
30
2.79
11
0.89
6
0.72
26
4.25
27
0.55
19
9.11
21
7.52
37
7.14
33
10.19
39
12.49
70
4.70
46
7.44
29
4.41
39
0.03
49
0.00
1
0.06
91
0.00
1
0.20
61
0.04
27
iResNettwo views3.68
27
0.91
45
7.94
86
2.97
95
0.34
12
4.44
33
0.48
15
7.70
10
9.74
57
7.72
38
12.74
63
4.03
8
2.87
19
8.05
42
3.37
27
0.02
38
0.01
39
0.00
1
0.00
1
0.10
36
0.09
40
acv_fttwo views3.69
28
0.53
18
4.58
41
2.31
69
4.71
121
7.44
65
0.86
39
8.54
16
5.70
14
9.29
58
9.26
34
5.77
17
4.15
37
2.24
5
8.13
75
0.00
1
0.00
1
0.00
1
0.00
1
0.29
77
0.01
2
ac_64two views3.70
29
0.47
12
3.50
24
2.87
88
3.50
100
5.91
48
0.72
34
10.17
39
4.34
5
8.16
45
5.80
11
10.82
59
5.64
59
7.08
26
4.73
43
0.03
49
0.00
1
0.00
1
0.00
1
0.08
31
0.14
56
CFNettwo views3.72
30
1.10
61
5.03
45
2.49
75
1.59
54
4.90
38
0.22
7
11.38
57
9.88
59
4.80
10
11.25
48
6.44
22
3.68
29
8.33
48
3.00
18
0.00
1
0.00
1
0.00
1
0.00
1
0.22
70
0.07
33
GwcNet-RSSMtwo views3.77
31
1.05
55
5.06
46
2.21
62
1.87
66
2.36
5
1.52
49
8.98
20
7.96
43
5.91
19
14.59
72
7.94
38
3.50
25
6.43
20
5.70
51
0.00
1
0.00
1
0.04
86
0.00
1
0.20
61
0.08
36
FADNet-RVC-Resampletwo views3.79
32
1.62
89
12.06
101
1.43
21
0.66
22
5.94
49
2.41
57
10.18
40
8.58
51
6.28
24
4.22
5
5.33
13
4.80
50
7.71
34
3.19
23
0.17
84
0.21
106
0.17
108
0.12
93
0.41
93
0.29
81
UPFNettwo views3.82
33
0.47
12
4.34
35
2.21
62
3.38
96
7.26
62
2.69
59
9.89
30
5.96
18
7.98
42
7.53
21
7.89
37
3.15
22
7.72
35
5.88
53
0.01
30
0.02
51
0.00
1
0.00
1
0.04
18
0.06
31
NLCA_NET_v2_RVCtwo views3.84
34
1.06
56
5.23
48
2.72
85
3.27
92
4.36
29
0.61
24
10.71
49
7.56
38
8.75
49
7.89
24
9.86
51
3.90
33
7.15
27
3.44
30
0.14
78
0.02
51
0.02
71
0.03
70
0.04
18
0.03
17
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 views3.84
34
1.07
57
5.23
48
2.65
81
2.96
89
4.22
26
0.69
32
10.43
44
7.72
39
8.78
50
8.29
27
9.61
49
4.02
35
7.16
28
3.65
33
0.13
77
0.03
58
0.02
71
0.03
70
0.05
22
0.03
17
psm_uptwo views3.85
36
0.82
38
3.01
14
2.00
51
2.58
84
5.28
41
3.34
66
10.85
53
6.33
21
7.27
35
7.32
20
10.15
52
9.02
84
6.19
17
2.51
14
0.22
89
0.01
39
0.00
1
0.00
1
0.12
43
0.03
17
FADNet_RVCtwo views3.91
37
1.67
92
12.95
107
0.96
9
0.75
27
5.71
47
0.54
18
10.83
52
6.60
24
3.46
3
8.09
25
4.10
9
3.40
24
9.43
65
6.33
56
0.36
102
0.44
120
0.17
108
0.46
120
0.91
110
0.95
113
UNettwo views3.97
38
0.61
25
6.50
72
1.68
34
3.22
91
9.82
79
0.31
8
9.50
26
5.32
10
7.20
34
6.35
12
10.59
57
5.91
63
6.93
25
5.49
49
0.00
1
0.00
1
0.00
1
0.00
1
0.02
7
0.01
2
FADNet-RVCtwo views3.98
39
1.84
98
12.48
104
1.69
35
0.44
16
4.33
28
1.31
47
11.84
61
7.15
30
3.53
4
3.50
4
10.63
58
4.43
41
9.12
60
6.25
55
0.03
49
0.10
87
0.00
1
0.03
70
0.60
100
0.25
74
HSMtwo views4.00
40
0.79
36
3.16
19
1.59
28
2.17
72
6.77
58
1.11
42
12.28
68
6.35
23
6.75
30
8.11
26
13.90
76
5.37
56
8.85
56
2.71
16
0.00
1
0.00
1
0.00
1
0.00
1
0.02
7
0.02
7
TDLMtwo views4.11
41
1.11
62
3.54
25
1.62
30
1.04
43
3.91
23
7.41
109
10.60
47
10.67
64
6.38
25
12.59
62
5.95
20
4.77
48
8.79
55
3.04
19
0.58
114
0.00
1
0.01
59
0.00
1
0.19
60
0.12
51
CBMV_ROBtwo views4.14
42
0.52
15
3.14
18
1.30
15
0.77
30
6.92
59
1.97
55
10.11
37
9.58
55
8.92
53
14.20
71
7.12
30
5.90
62
8.65
52
3.50
32
0.01
30
0.05
67
0.00
1
0.00
1
0.04
18
0.09
40
CVANet_RVCtwo views4.16
43
1.16
65
3.60
26
1.94
47
1.46
51
3.92
24
4.68
88
10.89
54
8.34
48
7.58
37
10.84
45
10.27
55
6.62
68
8.56
51
2.69
15
0.39
104
0.00
1
0.00
1
0.01
57
0.21
68
0.09
40
MMNettwo views4.16
43
0.77
33
5.47
53
1.40
19
1.67
59
10.78
88
0.63
27
8.64
19
8.47
50
8.80
51
8.33
28
7.51
33
6.61
67
7.53
31
6.54
59
0.00
1
0.00
1
0.00
1
0.00
1
0.03
14
0.05
30
HSM-Net_RVCpermissivetwo views4.20
45
0.32
1
2.76
10
0.63
4
0.69
24
6.95
60
1.69
52
11.96
62
8.36
49
8.83
52
12.17
56
15.18
84
4.21
39
6.91
24
3.30
26
0.02
38
0.02
51
0.00
1
0.00
1
0.01
2
0.01
2
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
DSFCAtwo views4.21
46
0.50
14
5.45
52
1.34
16
1.68
60
7.04
61
4.51
85
10.73
50
7.00
27
10.78
73
6.80
14
8.48
43
4.63
44
9.91
69
5.12
46
0.02
38
0.06
73
0.02
71
0.03
70
0.08
31
0.03
17
FADNettwo views4.23
47
1.65
91
11.75
100
1.64
32
0.80
32
4.80
35
0.77
37
13.76
84
11.65
72
3.97
6
5.24
9
9.62
50
5.14
53
8.40
49
3.78
34
0.21
88
0.04
63
0.07
94
0.05
81
1.14
115
0.10
47
iResNet_ROBtwo views4.23
47
1.02
53
4.90
44
2.18
59
0.93
40
2.92
12
0.37
12
15.10
96
16.91
95
7.89
41
10.51
43
7.03
26
3.07
21
8.16
47
3.46
31
0.01
30
0.00
1
0.00
1
0.00
1
0.10
36
0.02
7
HGLStereotwo views4.25
49
0.52
15
4.65
42
3.09
97
5.18
123
6.51
56
0.73
35
10.37
42
6.74
26
7.32
36
12.44
61
7.00
25
7.21
72
7.46
30
5.51
50
0.00
1
0.02
51
0.00
1
0.00
1
0.06
27
0.10
47
delettwo views4.27
50
0.72
32
4.50
39
1.07
11
3.75
106
10.79
89
4.04
75
9.95
31
7.89
42
8.09
43
8.89
32
8.46
41
3.81
31
7.56
32
5.80
52
0.00
1
0.00
1
0.00
1
0.00
1
0.11
40
0.02
7
iResNetv2_ROBtwo views4.28
51
1.43
78
7.17
81
2.91
89
1.26
47
4.36
29
1.62
50
13.64
83
10.25
63
9.83
64
11.41
50
7.68
34
4.00
34
7.75
36
1.85
8
0.00
1
0.00
1
0.00
1
0.00
1
0.37
87
0.09
40
StereoDRNet-Refinedtwo views4.46
52
0.62
27
3.80
29
1.92
44
0.40
14
9.35
73
0.15
5
10.02
32
8.83
52
12.69
85
11.62
54
9.34
47
3.87
32
8.06
43
8.02
71
0.00
1
0.00
1
0.01
59
0.05
81
0.20
61
0.26
77
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
DLCB_ROBtwo views4.51
53
0.91
45
3.78
28
2.19
60
1.07
44
6.28
50
3.09
62
9.78
29
7.72
39
10.65
71
12.97
64
13.91
77
3.71
30
8.72
53
5.30
48
0.00
1
0.00
1
0.00
1
0.00
1
0.03
14
0.10
47
NVstereo2Dtwo views4.51
53
0.82
38
6.86
79
3.28
102
3.38
96
8.16
68
3.13
63
10.51
45
15.15
85
4.90
11
6.89
16
7.87
35
4.78
49
9.88
68
3.91
37
0.01
30
0.00
1
0.00
1
0.06
83
0.02
7
0.58
100
RASNettwo views4.52
55
0.61
25
4.42
36
3.42
105
4.68
120
4.58
34
0.99
40
9.54
28
8.01
44
5.28
15
11.42
51
10.34
56
8.88
82
9.28
63
8.68
80
0.15
80
0.00
1
0.00
1
0.00
1
0.03
14
0.04
27
SGM-Foresttwo views4.96
56
0.32
1
2.84
12
1.21
13
0.64
21
10.23
84
6.64
104
11.55
58
10.98
65
10.94
75
13.59
67
11.65
65
4.30
40
8.94
58
4.63
42
0.11
73
0.04
63
0.00
1
0.00
1
0.05
22
0.46
91
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
PA-Nettwo views4.98
57
1.47
80
7.42
83
2.40
72
2.14
71
8.73
70
3.64
73
12.42
69
13.11
76
7.03
32
7.57
22
7.88
36
6.52
66
10.16
71
7.82
69
0.02
38
0.03
58
0.00
1
0.00
1
0.11
40
1.07
116
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
AANet_RVCtwo views5.01
58
1.74
93
6.38
68
1.96
49
1.29
49
2.26
3
1.69
52
10.07
33
18.53
98
7.88
40
18.15
89
8.49
44
2.70
17
10.59
75
7.04
62
0.96
124
0.15
98
0.02
71
0.00
1
0.13
47
0.12
51
PSMNet_ROBtwo views5.02
59
1.63
90
6.03
62
1.90
43
1.83
65
9.57
77
6.35
101
15.58
101
7.23
31
6.15
21
10.48
42
12.22
68
4.16
38
8.02
40
8.71
81
0.02
38
0.01
39
0.01
59
0.10
91
0.20
61
0.12
51
CBMVpermissivetwo views5.35
60
0.91
45
3.67
27
1.62
30
0.44
16
10.09
82
7.19
108
12.49
70
12.33
75
12.22
81
14.69
73
10.93
61
6.48
65
8.51
50
4.96
45
0.02
38
0.15
98
0.00
1
0.00
1
0.17
56
0.17
61
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
StereoDRNettwo views5.59
61
1.75
94
6.80
77
3.12
98
4.45
115
10.61
87
4.35
81
18.80
113
9.73
56
12.22
81
6.87
15
11.44
64
4.65
45
8.09
45
8.26
76
0.02
38
0.11
89
0.00
1
0.03
70
0.20
61
0.28
80
ETE_ROBtwo views5.80
62
1.77
95
6.33
66
1.44
22
0.78
31
6.43
54
6.90
105
12.53
71
8.08
45
12.93
89
14.89
74
21.13
112
5.87
61
9.83
67
6.57
60
0.04
57
0.01
39
0.00
1
0.02
61
0.08
31
0.33
82
DRN-Testtwo views5.87
63
0.98
51
5.89
60
2.69
83
3.65
105
12.37
96
3.35
67
20.07
124
10.20
62
11.93
80
12.31
60
11.06
63
5.31
55
7.89
38
9.05
84
0.04
57
0.05
67
0.04
86
0.04
79
0.18
59
0.25
74
NCCL2two views5.88
64
1.59
87
5.44
51
1.87
40
0.92
38
9.55
76
11.55
124
12.11
64
9.94
60
9.67
63
8.85
31
22.28
114
7.41
73
8.78
54
7.17
63
0.01
30
0.00
1
0.03
79
0.00
1
0.13
47
0.23
68
NaN_ROBtwo views6.00
65
1.24
68
6.29
65
1.34
16
1.68
60
9.60
78
10.31
120
15.09
94
15.79
88
12.62
84
8.95
33
11.67
66
5.83
60
11.78
84
6.41
58
0.05
64
0.13
95
0.08
95
0.20
99
0.22
70
0.79
108
DANettwo views6.02
66
1.23
67
8.45
88
3.86
114
3.94
109
7.64
67
1.34
48
9.51
27
7.00
27
13.39
91
15.53
78
15.99
88
7.02
71
12.14
85
12.37
104
0.19
86
0.12
94
0.02
71
0.03
70
0.13
47
0.56
99
XPNet_ROBtwo views6.03
67
1.22
66
5.61
56
2.56
79
0.90
36
6.32
51
7.07
106
12.92
74
8.30
47
14.76
96
15.13
76
19.84
106
6.66
70
10.36
72
8.58
79
0.02
38
0.04
63
0.00
1
0.03
70
0.11
40
0.24
71
Anonymous Stereotwo views6.16
68
3.15
116
23.75
125
2.97
95
2.48
81
4.39
32
13.30
126
9.21
23
9.86
58
9.56
62
8.76
30
6.79
24
1.99
11
13.50
96
13.04
107
0.01
30
0.05
67
0.00
1
0.06
83
0.22
70
0.19
63
GANettwo views6.22
69
1.07
57
4.07
33
2.27
65
0.89
34
9.19
72
9.52
115
12.02
63
8.13
46
10.72
72
29.09
114
13.86
75
7.52
75
11.00
79
4.39
38
0.36
102
0.00
1
0.02
71
0.02
61
0.12
43
0.08
36
DISCOtwo views6.28
70
0.57
21
5.78
58
3.43
106
1.17
45
11.22
91
3.39
68
12.14
66
16.16
90
6.52
28
11.22
47
16.96
91
6.32
64
19.51
119
10.74
97
0.00
1
0.00
1
0.00
1
0.00
1
0.35
85
0.11
50
RYNettwo views6.34
71
0.89
44
5.88
59
1.41
20
4.48
117
15.97
107
4.18
77
13.41
79
16.49
91
10.81
74
7.00
18
14.33
79
8.72
81
9.43
65
13.71
108
0.00
1
0.01
39
0.00
1
0.00
1
0.02
7
0.07
33
GwcNetcopylefttwo views6.42
72
1.97
101
10.92
95
2.59
80
5.58
126
11.55
93
2.21
56
14.10
87
16.52
94
10.04
67
17.19
86
10.86
60
5.61
58
9.23
62
8.84
83
0.01
30
0.06
73
0.03
79
0.08
87
0.56
96
0.40
86
GANetREF_RVCpermissivetwo views6.56
73
2.89
111
7.58
85
3.41
104
0.40
14
12.96
99
9.58
116
15.09
94
17.25
97
10.33
68
10.62
44
12.27
69
8.16
77
12.21
86
4.53
41
0.41
106
0.00
1
0.00
1
0.02
61
3.12
130
0.39
85
Zhang, Feihu and Prisacariu, Victor and Yang, Ruigang and Torr, Philip HS: GA-Net: Guided Aggregation Net for End- to-end Stereo Matching. CVPR 2019
LALA_ROBtwo views6.58
74
1.80
97
6.25
64
1.26
14
0.94
41
10.08
81
9.02
112
16.00
102
11.51
71
12.74
86
13.02
65
24.77
116
5.25
54
10.56
74
8.02
71
0.04
57
0.05
67
0.00
1
0.02
61
0.10
36
0.25
74
S-Stereotwo views6.63
75
0.84
40
9.67
92
3.15
99
3.48
99
6.49
55
6.22
96
12.99
75
22.84
111
9.48
59
15.51
77
12.00
67
8.43
78
8.04
41
10.70
96
0.12
76
0.17
102
0.00
1
0.38
113
0.13
47
1.92
125
DeepPrunerFtwo views6.75
76
2.69
109
23.31
124
3.68
109
7.16
131
3.78
21
4.29
79
13.42
80
20.13
108
8.13
44
10.46
41
7.18
32
8.06
76
11.10
80
9.44
86
0.24
91
0.15
98
0.29
117
0.42
116
0.66
103
0.45
89
edge stereotwo views6.76
77
1.01
52
6.76
76
2.20
61
2.45
80
6.41
53
2.45
58
14.84
93
11.98
74
15.29
97
18.31
90
22.02
113
12.56
98
10.82
76
7.49
65
0.03
49
0.06
73
0.11
100
0.03
70
0.30
78
0.14
56
Abc-Nettwo views6.77
78
1.49
81
6.48
70
2.92
91
4.40
111
7.43
63
3.61
71
19.52
120
13.29
77
8.39
47
16.91
81
15.96
86
12.13
96
12.85
90
7.70
67
1.47
128
0.11
89
0.01
59
0.42
116
0.14
52
0.24
71
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
NCC-stereotwo views6.77
78
1.49
81
6.48
70
2.92
91
4.40
111
7.43
63
3.61
71
19.52
120
13.29
77
8.39
47
16.91
81
15.96
86
12.13
96
12.85
90
7.70
67
1.47
128
0.11
89
0.01
59
0.42
116
0.14
52
0.24
71
FAT-Stereotwo views6.78
80
0.68
31
6.80
77
2.30
67
1.77
64
5.63
46
4.20
78
18.79
112
18.62
99
10.53
70
17.15
84
18.52
100
13.74
106
8.86
57
7.38
64
0.03
49
0.15
98
0.01
59
0.07
85
0.12
43
0.26
77
RPtwo views6.84
81
1.29
75
5.53
54
3.92
115
5.18
123
6.32
51
3.53
69
11.73
60
15.31
86
9.54
61
22.38
97
18.25
98
14.47
107
10.11
70
7.49
65
0.91
123
0.01
39
0.12
101
0.15
95
0.33
82
0.19
63
RGCtwo views6.88
82
2.23
105
6.13
63
4.05
116
4.73
122
8.94
71
2.78
61
15.19
98
11.74
73
11.13
76
19.34
92
17.86
95
10.42
90
13.02
93
8.03
73
0.73
117
0.01
39
0.24
116
0.41
115
0.31
80
0.38
84
Nwc_Nettwo views6.97
83
1.25
69
6.63
74
3.82
113
3.37
95
10.83
90
1.67
51
19.56
122
11.35
68
8.36
46
23.62
100
17.19
93
11.44
95
11.21
81
8.08
74
0.80
119
0.00
1
0.00
1
0.02
61
0.13
47
0.09
40
ADCReftwo views7.27
84
1.38
76
16.37
113
2.52
76
3.30
94
11.63
94
3.16
64
10.80
51
9.35
54
13.03
90
25.27
108
8.17
39
8.92
83
8.06
43
21.81
123
0.15
80
0.08
82
0.16
107
0.34
111
0.38
88
0.58
100
psmorigintwo views7.34
85
1.58
86
18.31
117
2.35
71
0.87
33
6.72
57
1.70
54
10.63
48
7.14
29
19.77
109
22.71
98
20.13
108
11.34
94
12.96
92
9.49
87
0.04
57
0.17
102
0.06
91
0.17
96
0.21
68
0.50
94
CSANtwo views7.62
86
1.60
88
6.56
73
1.83
38
0.66
22
12.40
97
10.52
122
14.45
90
21.32
110
14.19
93
15.98
80
17.84
94
13.02
103
12.32
87
8.38
77
0.09
70
0.07
80
0.03
79
0.04
79
0.33
82
0.67
106
stereogantwo views7.69
87
0.88
42
7.08
80
3.49
107
3.93
108
18.98
115
3.23
65
16.52
104
19.58
105
9.93
66
18.92
91
20.50
110
9.04
85
14.07
100
6.14
54
0.26
94
0.04
63
0.21
113
0.03
70
0.63
102
0.33
82
pmcnntwo views7.72
88
1.27
71
9.42
91
2.91
89
3.14
90
9.44
74
6.23
97
12.56
72
16.51
92
14.53
94
24.08
102
27.44
121
8.49
79
9.32
64
8.44
78
0.06
66
0.08
82
0.00
1
0.00
1
0.30
78
0.15
59
AF-Nettwo views7.78
89
1.44
79
6.68
75
3.37
103
4.50
118
8.61
69
2.69
59
17.07
107
20.17
109
9.52
60
24.02
101
20.31
109
14.59
108
11.58
83
9.84
92
0.61
115
0.00
1
0.12
101
0.00
1
0.38
88
0.12
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PASMtwo views7.90
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4.22
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3.25
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3.29
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5.39
42
6.57
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10.57
46
19.09
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12.77
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13.92
69
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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0.45
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0.29
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1.08
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1.49
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PWCDC_ROBbinarytwo views7.92
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3.17
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7.48
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5.73
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4.40
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10.45
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0.35
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14.52
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28.19
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10.36
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31.27
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7.04
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9.14
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13.22
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8.78
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2.74
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0.02
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0.00
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0.00
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1.31
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0.17
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ADCP+two views8.09
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1.79
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14.50
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1.54
25
4.28
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16.57
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5.20
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12.80
73
11.20
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12.83
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17.07
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11.02
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10.80
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17.59
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0.03
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0.05
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0.81
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SuperBtwo views8.10
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2.65
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1.23
46
9.88
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4.29
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10.18
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11.53
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12.18
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6.12
21
6.65
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10.50
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14.47
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0.14
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0.35
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0.48
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PWC_ROBbinarytwo views8.24
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3.13
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12.74
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4.43
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7.51
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1.22
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16.63
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13.99
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0.42
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MDST_ROBtwo views8.37
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0.32
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4.18
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2.42
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26.86
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6.14
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19.36
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27.09
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22.75
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9.47
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4.74
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15.06
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6.34
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0.02
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0.00
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0.13
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STTStereo_v2two views8.41
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4.41
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10.10
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19.71
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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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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
35
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19.71
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10.99
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9.53
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XQCtwo views8.43
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3.58
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2.92
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13.22
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3.60
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14.64
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25.86
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11.87
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12.04
55
15.06
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10.67
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15.24
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19.41
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0.39
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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
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1.28
48
13.10
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6.23
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22.50
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18.98
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14.91
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19.06
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10.04
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18.41
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9.83
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0.62
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0.22
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1.82
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0.82
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0.99
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1.36
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aanetorigintwo views8.72
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3.29
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27.55
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1.95
48
3.84
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4.93
39
5.39
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5.49
3
10.05
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27.85
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31.47
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15.80
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12.78
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9.04
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11.98
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0.25
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0.22
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0.22
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0.25
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1.28
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0.84
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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
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0.46
14
16.95
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36.83
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15.80
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15.53
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12.91
72
7.46
74
20.01
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21.76
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0.31
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0.13
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0.01
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0.08
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0.57
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0.41
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WCMA_ROBtwo views9.21
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0.87
41
7.37
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2.54
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2.13
70
13.59
102
5.80
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11.64
59
14.01
81
24.43
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32.99
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27.09
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18.02
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12.51
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9.85
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0.81
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0.07
80
0.01
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0.01
57
0.16
54
0.23
68
MSMD_ROBtwo views9.28
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1.09
60
4.65
42
1.58
27
0.39
13
16.52
108
4.41
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13.60
81
14.87
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22.34
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39.89
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25.67
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20.71
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12.42
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6.98
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0.34
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0.03
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0.00
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0.00
1
0.05
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0.09
40
ADCPNettwo views9.54
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2.39
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31.46
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2.09
55
1.60
55
16.71
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6.39
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12.11
64
11.45
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13.53
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21.45
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19.41
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10.94
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14.38
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21.54
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0.27
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1.49
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1.45
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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
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1.25
44
20.16
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37.84
134
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
100
0.13
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0.01
59
0.08
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0.38
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0.48
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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
135
9.61
136
21.48
119
3.83
74
19.33
116
28.49
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11.27
77
14.17
70
19.92
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5.02
52
16.35
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9.18
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5.28
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0.41
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0.14
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0.09
90
2.05
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2.36
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MFN_U_SF_DS_RVCtwo views9.78
107
4.27
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14.47
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2.29
66
2.85
88
23.40
123
13.62
127
13.60
81
19.54
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19.42
106
24.27
103
16.74
90
8.59
80
17.05
114
7.98
70
1.25
127
1.68
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0.17
108
2.63
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0.72
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1.04
114
SGM_RVCbinarytwo views10.08
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0.60
23
3.42
22
2.30
67
0.32
11
19.41
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6.33
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18.95
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14.64
82
25.14
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24.32
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33.34
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18.79
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19.86
120
12.55
106
0.25
92
0.26
112
0.22
114
0.24
102
0.34
84
0.40
86
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DPSNettwo views10.14
109
1.88
100
16.82
115
1.85
39
1.73
62
24.84
124
17.20
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19.92
123
27.41
118
12.23
83
13.62
68
16.52
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18.35
114
14.42
103
12.50
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0.78
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0.54
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0.08
95
0.25
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1.18
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0.59
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ADCLtwo views10.16
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2.11
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19.36
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1.92
44
1.88
67
22.23
120
8.91
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14.04
86
23.56
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14.62
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26.19
109
12.75
71
13.59
105
16.06
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22.95
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0.26
94
0.18
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0.75
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0.69
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0.58
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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
62
2.39
78
11.23
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6.19
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14.17
88
11.19
66
23.20
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22.25
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17.89
96
19.54
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18.51
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26.21
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0.45
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0.42
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1.10
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1.29
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1.56
124
1.18
117
FCDSN-DCtwo views10.24
111
0.56
20
3.49
23
1.96
49
1.29
49
16.90
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4.59
87
17.16
108
19.10
102
24.64
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32.46
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33.82
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22.14
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15.93
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10.45
94
0.04
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0.00
1
0.00
1
0.00
1
0.05
22
0.21
65
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
SANettwo views10.64
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1.86
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10.91
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1.76
36
0.71
25
14.62
105
9.23
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19.18
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37.14
132
19.22
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27.96
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25.86
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19.11
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13.02
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10.63
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0.08
69
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0.03
79
0.02
61
0.62
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0.81
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FC-DCNNcopylefttwo views10.72
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0.52
15
4.27
34
1.88
41
1.63
56
17.18
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5.29
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18.20
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19.69
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28.50
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34.51
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34.03
131
21.48
125
15.89
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11.15
101
0.03
49
0.01
39
0.02
71
0.01
57
0.07
29
0.09
40
AnyNet_C32two views10.98
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5.58
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22.79
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4.16
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5.83
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15.64
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14.30
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13.18
77
17.15
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16.44
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20.52
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14.68
82
13.44
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22.46
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30.08
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0.17
84
0.26
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0.36
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0.36
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1.23
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0.91
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MeshStereopermissivetwo views11.52
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1.52
83
4.55
40
1.89
42
1.46
51
19.87
118
5.11
89
20.66
126
15.91
89
32.67
130
34.51
127
39.34
136
21.15
123
18.74
118
12.10
103
0.11
73
0.06
73
0.01
59
0.00
1
0.45
95
0.22
67
C. Zhang, Z. Li, Y. Cheng, R. Cai, H. Chao, Y. Rui: MeshStereo: A Global Stereo Model with Mesh Alignment Regularization for View Interpolation. ICCV 2015
MFN_U_SF_RVCtwo views12.94
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3.66
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25.81
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3.61
108
2.26
75
22.77
121
4.55
86
27.10
133
20.06
107
23.90
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28.99
113
30.53
126
16.98
111
19.92
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20.26
117
1.24
126
1.07
128
0.98
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1.33
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1.80
125
2.04
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ADCStwo views13.02
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4.93
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28.38
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3.17
100
2.67
85
13.61
103
10.83
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18.70
111
33.46
126
22.59
113
24.78
105
19.59
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18.51
116
23.40
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32.16
138
0.10
72
0.19
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0.37
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0.18
97
1.26
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1.46
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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
133
19.65
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14.84
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20.71
127
30.72
123
23.03
116
28.77
112
18.85
102
26.09
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13.55
97
9.82
90
2.44
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1.35
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0.34
120
0.23
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4.78
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6.69
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EDNetEfficienttwo views13.82
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6.19
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54.79
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2.83
86
3.60
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4.86
37
8.57
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8.61
18
32.90
125
40.77
136
30.59
117
18.33
99
21.87
126
11.38
82
25.36
129
0.15
80
0.23
109
0.06
91
1.03
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2.54
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1.79
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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
83
9.98
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15.16
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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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0.16
83
0.51
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0.09
98
0.32
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1.08
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16.85
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SAMSARAtwo views14.63
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2.74
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12.38
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12.65
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6.74
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36.50
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72.93
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19.36
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23.77
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16.20
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13.04
66
29.21
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12.78
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16.98
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15.21
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0.11
73
0.26
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0.03
79
0.14
94
0.76
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0.77
107
SPS-STEREOcopylefttwo views15.04
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6.23
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13.21
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11.34
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11.65
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23.30
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7.15
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15.65
87
31.78
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29.19
115
31.62
127
21.32
124
24.62
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19.50
116
7.59
138
4.19
139
3.22
136
1.48
132
6.99
137
6.54
134
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
PVDtwo views15.44
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3.39
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17.43
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4.16
76
27.84
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48.84
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31.02
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43.54
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29.76
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30.81
136
25.97
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21.40
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0.23
90
0.41
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0.04
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0.33
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0.41
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1.33
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SGM+DAISYtwo views15.62
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7.26
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19.28
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8.94
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10.11
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26.25
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10.49
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19.36
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14.65
83
30.64
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33.59
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33.00
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22.32
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24.96
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16.42
113
7.90
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6.25
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4.51
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5.86
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7.20
136
NVStereoNet_ROBtwo views16.04
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6.75
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12.90
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6.37
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7.42
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12.89
98
9.74
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22.78
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25.12
115
30.32
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46.19
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34.37
132
25.38
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21.48
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21.38
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5.94
137
3.10
137
6.07
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10.09
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4.01
132
8.54
138
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
AnyNet_C01two views16.12
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10.81
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59.36
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4.42
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2.49
82
30.06
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15.15
134
17.51
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16.51
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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
98
0.38
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0.43
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0.42
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2.57
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1.98
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MSC_U_SF_DS_RVCtwo views16.41
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6.93
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21.83
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5.94
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2.81
87
38.71
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14.59
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24.55
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34.87
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33.66
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34.35
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29.24
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22.59
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17.95
114
2.52
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2.81
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1.17
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1.51
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5.89
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2.16
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ELAS_RVCcopylefttwo views16.54
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2.26
106
10.09
93
5.50
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4.46
116
28.28
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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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29.40
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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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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
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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28.68
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40.27
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44.90
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38.33
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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
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A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
LE_ROBtwo views16.73
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1.28
74
11.61
99
3.72
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1.65
57
16.67
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9.17
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14.39
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55.91
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63.81
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40.86
135
35.94
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37.73
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14.24
101
26.87
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0.05
64
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0.13
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0.12
43
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59
SGM-ForestMtwo views16.99
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1.08
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5.74
57
2.12
57
0.75
27
31.63
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12.21
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27.80
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32.25
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37.88
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39.99
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52.96
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35.20
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33.60
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24.47
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0.26
94
0.39
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0.31
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0.39
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0.26
76
0.53
98
DispFullNettwo views17.47
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26.01
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33.98
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22.58
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20.86
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13.84
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1.28
45
16.50
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19.97
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17.17
85
20.52
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18.49
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22.86
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10.76
98
5.13
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30.72
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7.72
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20.86
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11.01
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EDNetEfficientorigintwo views17.87
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14.00
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99.53
144
1.64
32
0.92
38
10.55
86
6.26
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13.21
78
34.84
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38.20
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36.91
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27.47
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26.32
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14.71
105
25.69
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0.04
57
0.06
73
0.14
105
0.52
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3.16
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3.27
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RTStwo views18.87
135
9.32
134
86.48
139
4.95
124
6.10
128
42.08
137
14.70
130
15.49
99
41.06
136
22.65
114
32.32
120
13.77
73
19.54
119
37.98
136
28.96
133
0.41
106
0.23
109
0.00
1
0.02
61
0.91
110
0.50
94
RTSAtwo views18.87
135
9.32
134
86.48
139
4.95
124
6.10
128
42.08
137
14.70
130
15.49
99
41.06
136
22.65
114
32.32
120
13.77
73
19.54
119
37.98
136
28.96
133
0.41
106
0.23
109
0.00
1
0.02
61
0.91
110
0.50
94
MANEtwo views19.47
137
1.27
71
5.07
47
4.69
122
5.55
125
30.49
131
9.94
118
34.01
138
37.27
133
44.13
137
51.57
141
52.51
138
40.41
140
33.58
134
24.81
128
0.89
122
0.86
127
1.11
132
9.72
139
0.38
88
1.06
115
BEATNet-Init1two views23.31
138
9.03
133
41.67
133
4.17
118
2.53
83
45.68
139
19.47
137
33.43
137
38.45
135
47.59
139
49.10
139
59.31
140
41.80
141
38.35
138
29.21
135
0.47
111
0.50
124
0.81
128
0.66
123
2.10
127
1.86
124
MADNet+two views27.07
139
33.84
139
90.97
141
20.14
138
7.47
135
48.43
140
47.10
139
35.43
139
36.46
130
20.11
111
30.05
116
25.29
117
35.08
137
45.50
140
50.28
140
2.13
130
2.00
134
1.19
134
0.76
125
4.71
133
4.43
133
PWCKtwo views30.53
140
44.32
140
47.25
135
29.76
140
7.23
132
40.78
136
27.10
138
44.73
140
44.32
138
47.31
138
36.37
129
47.16
137
26.05
131
41.26
139
31.87
137
21.83
140
4.03
138
29.50
139
4.67
137
27.17
140
7.80
137
DPSimNet_ROBtwo views53.45
141
64.73
141
44.39
134
53.97
141
45.39
141
53.66
141
54.83
140
55.15
141
57.87
141
64.16
141
50.83
140
63.40
141
53.34
142
46.45
141
65.81
141
63.13
141
26.54
141
57.94
141
51.11
141
45.52
141
50.69
141
MADNet++two views82.84
142
82.38
143
73.57
138
87.72
142
82.97
142
93.14
142
69.15
141
86.42
142
82.50
142
93.46
142
86.70
142
86.28
142
80.92
143
88.34
142
88.84
142
86.83
142
84.17
142
72.64
142
68.92
142
80.47
142
81.42
142
MEDIAN_ROBtwo views98.41
143
99.70
144
99.30
143
97.09
143
97.02
143
96.89
143
95.77
144
97.66
143
97.28
143
98.79
145
98.94
143
99.18
143
98.14
144
96.89
143
96.88
143
99.96
145
99.16
143
100.00
143
99.99
143
99.69
143
99.88
143
AVERAGE_ROBtwo views99.62
144
99.95
145
98.81
142
100.00
148
100.00
144
98.08
144
95.47
143
100.00
146
100.00
144
100.00
146
100.00
144
100.00
146
100.00
145
100.00
146
99.99
144
100.00
147
100.00
144
100.00
143
100.00
144
100.00
146
100.00
147
DGTPSM_ROBtwo views99.90
145
100.00
146
99.99
145
99.99
146
100.00
144
100.00
145
100.00
145
99.97
144
100.00
144
98.35
143
100.00
144
99.84
144
100.00
145
99.98
144
99.99
144
99.99
146
100.00
144
100.00
143
100.00
144
100.00
146
100.00
147
DPSMNet_ROBtwo views99.91
146
100.00
146
99.99
145
99.99
146
100.00
144
100.00
145
100.00
145
99.98
145
100.00
144
98.35
143
100.00
144
99.84
144
100.00
145
99.98
144
99.99
144
100.00
147
100.00
144
100.00
143
100.00
144
100.00
146
100.00
147
DPSM_ROBtwo views99.95
147
100.00
146
100.00
147
99.76
144
100.00
144
100.00
145
100.00
145
100.00
146
100.00
144
100.00
146
100.00
144
100.00
146
100.00
145
100.00
146
100.00
147
99.21
143
100.00
144
100.00
143
100.00
144
99.99
144
99.95
144
DPSMtwo views99.95
147
100.00
146
100.00
147
99.76
144
100.00
144
100.00
145
100.00
145
100.00
146
100.00
144
100.00
146
100.00
144
100.00
146
100.00
145
100.00
146
100.00
147
99.21
143
100.00
144
100.00
143
100.00
144
99.99
144
99.95
144
LSM0two views100.00
149
100.00
146
100.00
147
100.00
148
100.00
144
100.00
145
100.00
145
100.00
146
100.00
144
100.00
146
100.00
144
100.00
146
100.00
145
100.00
146
100.00
147
100.00
147
100.00
144
100.00
143
100.00
144
100.00
146
99.99
146
MSMDNettwo views1.26
7
ASD4two views81.57
142