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
DPM-Stereotwo views1.97
1
0.64
16
2.95
8
0.17
1
0.10
1
4.83
24
0.13
2
8.60
9
4.06
4
6.42
16
4.92
4
0.44
1
0.72
1
3.57
3
1.80
5
0.00
1
0.01
25
0.00
1
0.00
1
0.05
15
0.04
15
PMTNettwo views1.99
2
0.32
1
2.21
3
0.39
2
0.23
6
5.08
26
0.49
11
5.84
1
8.22
26
3.07
1
3.29
1
0.73
2
0.75
2
8.18
27
0.94
3
0.00
1
0.00
1
0.00
1
0.00
1
0.03
10
0.00
1
R-Stereotwo views2.44
3
0.32
1
1.93
1
0.94
4
0.16
4
3.67
8
0.61
16
6.37
3
3.08
1
9.14
37
17.44
65
1.80
3
0.77
3
1.76
1
0.70
1
0.00
1
0.01
25
0.00
1
0.00
1
0.01
1
0.03
9
R-Stereo Traintwo views2.44
3
0.32
1
1.93
1
0.94
4
0.16
4
3.67
8
0.61
16
6.37
3
3.08
1
9.14
37
17.44
65
1.80
3
0.77
3
1.76
1
0.70
1
0.00
1
0.01
25
0.00
1
0.00
1
0.01
1
0.03
9
DN-CSS_ROBtwo views2.69
5
1.40
55
5.34
33
2.31
50
0.75
22
3.14
6
0.06
1
6.11
2
3.87
3
5.34
11
12.18
39
2.34
5
1.22
5
7.84
17
1.48
4
0.03
32
0.00
1
0.00
1
0.00
1
0.35
61
0.03
9
HITNettwo views2.79
6
0.77
17
4.02
19
2.03
39
0.11
3
5.58
30
0.59
14
9.24
11
5.15
7
6.42
16
7.26
11
3.66
6
2.92
16
4.07
4
3.87
28
0.00
1
0.00
1
0.00
1
0.00
1
0.06
19
0.02
3
Vladimir Tankovich, Christian Häne, Yinda Zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz: HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching. CVPR 2021
ccstwo views3.04
7
0.39
7
3.08
10
1.78
27
0.52
16
2.04
1
0.50
12
13.09
52
13.71
59
3.54
4
5.36
6
5.50
12
2.45
11
4.81
6
2.88
12
0.09
47
0.08
60
0.12
75
0.10
69
0.20
42
0.50
72
AdaStereotwo views3.09
8
0.58
12
3.04
9
2.84
63
0.48
15
4.08
14
1.29
31
12.16
44
7.77
22
6.03
12
9.62
23
5.79
14
1.53
7
4.56
5
1.93
7
0.00
1
0.00
1
0.00
1
0.00
1
0.02
4
0.02
3
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi: AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
BEATNet_4xtwo views3.24
9
1.27
49
5.89
41
1.56
18
0.10
1
5.26
27
1.07
26
10.08
18
5.50
8
6.89
21
7.73
13
4.53
9
4.13
26
5.05
7
5.27
34
0.04
37
0.05
48
0.00
1
0.00
1
0.23
52
0.23
48
DMCAtwo views3.29
10
1.05
33
4.18
22
1.60
21
2.87
69
2.11
2
0.60
15
7.95
7
4.65
6
8.62
32
8.59
19
9.24
31
4.68
33
6.25
8
3.03
14
0.04
37
0.09
64
0.06
69
0.03
50
0.18
39
0.07
18
NOSS_ROBtwo views3.30
11
0.46
8
2.62
4
2.08
40
1.01
34
5.60
31
0.74
22
10.37
25
11.48
48
5.15
9
8.43
18
5.67
13
1.73
8
7.97
19
2.34
9
0.02
23
0.06
54
0.00
1
0.00
1
0.07
20
0.14
39
CFNet_RVCtwo views3.31
12
0.94
28
2.69
5
1.50
16
2.38
59
2.81
4
0.68
20
8.35
8
7.43
18
4.45
6
9.94
24
10.20
37
4.60
31
6.49
9
3.41
22
0.00
1
0.00
1
0.03
60
0.00
1
0.22
48
0.03
9
MLCVtwo views3.44
13
0.88
21
5.60
36
1.39
12
0.25
7
4.36
17
0.33
6
7.25
5
7.28
15
9.17
39
12.24
41
5.09
10
2.47
12
9.15
39
3.23
19
0.00
1
0.00
1
0.00
1
0.00
1
0.10
23
0.02
3
DeepPruner_ROBtwo views3.52
14
1.14
43
4.06
20
1.12
7
1.65
47
3.65
7
0.83
24
13.96
60
4.47
5
7.80
25
10.84
29
7.05
21
2.16
10
8.14
25
3.08
17
0.07
45
0.03
40
0.00
1
0.01
38
0.32
57
0.06
17
STTStereotwo views3.60
15
0.93
27
6.34
48
2.71
61
2.23
58
3.68
10
0.63
19
9.42
12
6.73
11
9.87
46
6.97
9
8.84
30
3.65
19
6.85
10
3.04
15
0.00
1
0.02
35
0.01
43
0.00
1
0.02
4
0.02
3
ccs_robtwo views3.63
16
1.12
42
4.42
24
2.52
54
0.91
30
5.50
29
0.21
4
10.11
21
9.11
32
6.55
19
11.28
33
8.32
28
2.55
13
7.66
14
2.01
8
0.00
1
0.00
1
0.00
1
0.00
1
0.20
42
0.08
21
iResNettwo views3.68
17
0.91
24
7.94
64
2.97
69
0.34
9
4.44
21
0.48
10
7.70
6
9.74
36
7.72
24
12.74
44
4.03
7
2.87
15
8.05
21
3.37
21
0.02
23
0.01
25
0.00
1
0.00
1
0.10
23
0.09
23
CFNettwo views3.72
18
1.10
39
5.03
29
2.49
53
1.59
44
4.90
25
0.22
5
11.38
34
9.88
38
4.80
7
11.25
32
6.44
17
3.68
20
8.33
28
3.00
13
0.00
1
0.00
1
0.00
1
0.00
1
0.22
48
0.07
18
FADNet-RVC-Resampletwo views3.79
19
1.62
66
12.06
77
1.43
14
0.66
18
5.94
33
2.41
39
10.18
23
8.58
30
6.28
14
4.22
3
5.33
11
4.80
37
7.71
15
3.19
18
0.17
59
0.21
80
0.17
81
0.12
71
0.41
69
0.29
60
NLCA_NET_v2_RVCtwo views3.84
20
1.06
34
5.23
31
2.72
62
3.27
72
4.36
17
0.61
16
10.71
30
7.56
19
8.75
33
7.89
14
9.86
36
3.90
23
7.15
12
3.44
23
0.14
54
0.02
35
0.02
54
0.03
50
0.04
13
0.03
9
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
CC-Net-ROBtwo views3.84
20
1.07
35
5.23
31
2.65
58
2.96
70
4.22
15
0.69
21
10.43
26
7.72
20
8.78
34
8.29
17
9.61
34
4.02
25
7.16
13
3.65
26
0.13
53
0.03
40
0.02
54
0.03
50
0.05
15
0.03
9
FADNet_RVCtwo views3.91
22
1.67
69
12.95
84
0.96
6
0.75
22
5.71
32
0.54
13
10.83
32
6.60
10
3.46
2
8.09
15
4.10
8
3.40
18
9.43
42
6.33
38
0.36
75
0.44
92
0.17
81
0.46
95
0.91
85
0.95
89
FADNet-RVCtwo views3.98
23
1.84
75
12.48
80
1.69
25
0.44
13
4.33
16
1.31
32
11.84
38
7.15
13
3.53
3
3.50
2
10.63
40
4.43
30
9.12
38
6.25
37
0.03
32
0.10
65
0.00
1
0.03
50
0.60
75
0.25
54
HSMtwo views4.00
24
0.79
18
3.16
12
1.59
20
2.17
56
6.77
39
1.11
27
12.28
45
6.35
9
6.75
20
8.11
16
13.90
55
5.37
42
8.85
36
2.71
11
0.00
1
0.00
1
0.00
1
0.00
1
0.02
4
0.02
3
TDLMtwo views4.11
25
1.11
41
3.54
14
1.62
22
1.04
35
3.91
12
7.41
82
10.60
29
10.67
42
6.38
15
12.59
43
5.95
15
4.77
35
8.79
35
3.04
15
0.58
87
0.00
1
0.01
43
0.00
1
0.19
41
0.12
34
CBMV_ROBtwo views4.14
26
0.52
9
3.14
11
1.30
10
0.77
25
6.92
40
1.97
38
10.11
21
9.58
34
8.92
36
14.20
52
7.12
22
5.90
45
8.65
32
3.50
25
0.01
18
0.05
48
0.00
1
0.00
1
0.04
13
0.09
23
CVANet_RVCtwo views4.16
27
1.16
44
3.60
15
1.94
37
1.46
42
3.92
13
4.68
64
10.89
33
8.34
28
7.58
23
10.84
29
10.27
38
6.62
49
8.56
31
2.69
10
0.39
77
0.00
1
0.00
1
0.01
38
0.21
47
0.09
23
HSM-Net_RVCpermissivetwo views4.20
28
0.32
1
2.76
6
0.63
3
0.69
20
6.95
41
1.69
36
11.96
39
8.36
29
8.83
35
12.17
38
15.18
63
4.21
28
6.91
11
3.30
20
0.02
23
0.02
35
0.00
1
0.00
1
0.01
1
0.01
2
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
iResNet_ROBtwo views4.23
29
1.02
31
4.90
28
2.18
43
0.93
32
2.92
5
0.37
8
15.10
71
16.91
73
7.89
27
10.51
27
7.03
19
3.07
17
8.16
26
3.46
24
0.01
18
0.00
1
0.00
1
0.00
1
0.10
23
0.02
3
FADNettwo views4.23
29
1.65
68
11.75
76
1.64
24
0.80
27
4.80
23
0.77
23
13.76
59
11.65
50
3.97
5
5.24
5
9.62
35
5.14
39
8.40
29
3.78
27
0.21
63
0.04
44
0.07
70
0.05
61
1.14
90
0.10
30
iResNetv2_ROBtwo views4.28
31
1.43
56
7.17
59
2.91
64
1.26
39
4.36
17
1.62
34
13.64
58
10.25
41
9.83
45
11.41
34
7.68
24
4.00
24
7.75
16
1.85
6
0.00
1
0.00
1
0.00
1
0.00
1
0.37
63
0.09
23
StereoDRNet-Refinedtwo views4.46
32
0.62
15
3.80
18
1.92
34
0.40
11
9.35
51
0.15
3
10.02
16
8.83
31
12.69
63
11.62
36
9.34
32
3.87
22
8.06
22
8.02
51
0.00
1
0.00
1
0.01
43
0.05
61
0.20
42
0.26
57
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
NVstereo2Dtwo views4.51
33
0.82
19
6.86
57
3.28
74
3.38
76
8.16
46
3.13
44
10.51
27
15.15
64
4.90
8
6.89
8
7.87
25
4.78
36
9.88
45
3.91
29
0.01
18
0.00
1
0.00
1
0.06
63
0.02
4
0.58
77
DLCB_ROBtwo views4.51
33
0.91
24
3.78
17
2.19
44
1.07
36
6.28
34
3.09
43
9.78
15
7.72
20
10.65
50
12.97
45
13.91
56
3.71
21
8.72
33
5.30
35
0.00
1
0.00
1
0.00
1
0.00
1
0.03
10
0.10
30
RASNettwo views4.52
35
0.61
14
4.42
24
3.42
78
4.68
94
4.58
22
0.99
25
9.54
14
8.01
23
5.28
10
11.42
35
10.34
39
8.88
62
9.28
40
8.68
60
0.15
56
0.00
1
0.00
1
0.00
1
0.03
10
0.04
15
SGM-Foresttwo views4.96
36
0.32
1
2.84
7
1.21
8
0.64
17
10.23
61
6.64
77
11.55
35
10.98
43
10.94
53
13.59
48
11.65
46
4.30
29
8.94
37
4.63
32
0.11
50
0.04
44
0.00
1
0.00
1
0.05
15
0.46
69
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
37
1.47
58
7.42
61
2.40
51
2.14
55
8.73
48
3.64
53
12.42
46
13.11
55
7.03
22
7.57
12
7.88
26
6.52
48
10.16
47
7.82
49
0.02
23
0.03
40
0.00
1
0.00
1
0.11
27
1.07
92
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
38
1.74
70
6.38
49
1.96
38
1.29
41
2.26
3
1.69
36
10.07
17
18.53
76
7.88
26
18.15
67
8.49
29
2.70
14
10.59
51
7.04
43
0.96
97
0.15
75
0.02
54
0.00
1
0.13
31
0.12
34
PSMNet_ROBtwo views5.02
39
1.63
67
6.03
43
1.90
33
1.83
52
9.57
55
6.35
74
15.58
76
7.23
14
6.15
13
10.48
26
12.22
48
4.16
27
8.02
20
8.71
61
0.02
23
0.01
25
0.01
43
0.10
69
0.20
42
0.12
34
CBMVpermissivetwo views5.35
40
0.91
24
3.67
16
1.62
22
0.44
13
10.09
59
7.19
81
12.49
47
12.33
54
12.22
59
14.69
54
10.93
41
6.48
47
8.51
30
4.96
33
0.02
23
0.15
75
0.00
1
0.00
1
0.17
38
0.17
43
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
41
1.75
71
6.80
56
3.12
71
4.45
89
10.61
63
4.35
60
18.80
86
9.73
35
12.22
59
6.87
7
11.44
44
4.65
32
8.09
24
8.26
56
0.02
23
0.11
67
0.00
1
0.03
50
0.20
42
0.28
59
ETE_ROBtwo views5.80
42
1.77
72
6.33
47
1.44
15
0.78
26
6.43
38
6.90
78
12.53
48
8.08
24
12.93
67
14.89
55
21.13
87
5.87
44
9.83
44
6.57
41
0.04
37
0.01
25
0.00
1
0.02
42
0.08
22
0.33
61
DRN-Testtwo views5.87
43
0.98
29
5.89
41
2.69
60
3.65
81
12.37
70
3.35
47
20.07
97
10.20
40
11.93
58
12.31
42
11.06
43
5.31
41
7.89
18
9.05
63
0.04
37
0.05
48
0.04
65
0.04
59
0.18
39
0.25
54
NCCL2two views5.88
44
1.59
64
5.44
34
1.87
30
0.92
31
9.55
54
11.55
97
12.11
41
9.94
39
9.67
44
8.85
21
22.28
89
7.41
53
8.78
34
7.17
44
0.01
18
0.00
1
0.03
60
0.00
1
0.13
31
0.23
48
NaN_ROBtwo views6.00
45
1.24
47
6.29
46
1.34
11
1.68
49
9.60
56
10.31
93
15.09
69
15.79
67
12.62
62
8.95
22
11.67
47
5.83
43
11.78
59
6.41
40
0.05
42
0.13
72
0.08
71
0.20
77
0.22
48
0.79
85
DANettwo views6.02
46
1.23
46
8.45
66
3.86
86
3.94
83
7.64
45
1.34
33
9.51
13
7.00
12
13.39
69
15.53
58
15.99
66
7.02
52
12.14
60
12.37
79
0.19
61
0.12
71
0.02
54
0.03
50
0.13
31
0.56
76
XPNet_ROBtwo views6.03
47
1.22
45
5.61
37
2.56
57
0.90
29
6.32
35
7.07
79
12.92
51
8.30
27
14.76
74
15.13
57
19.84
82
6.66
51
10.36
48
8.58
59
0.02
23
0.04
44
0.00
1
0.03
50
0.11
27
0.24
51
Anonymous Stereotwo views6.16
48
3.15
92
23.75
101
2.97
69
2.48
63
4.39
20
13.30
99
9.21
10
9.86
37
9.56
43
8.76
20
6.79
18
1.99
9
13.50
71
13.04
82
0.01
18
0.05
48
0.00
1
0.06
63
0.22
48
0.19
45
GANettwo views6.22
49
1.07
35
4.07
21
2.27
47
0.89
28
9.19
50
9.52
88
12.02
40
8.13
25
10.72
51
29.09
91
13.86
54
7.52
55
11.00
55
4.39
30
0.36
75
0.00
1
0.02
54
0.02
42
0.12
29
0.08
21
DISCOtwo views6.28
50
0.57
11
5.78
39
3.43
79
1.17
37
11.22
65
3.39
48
12.14
43
16.16
69
6.52
18
11.22
31
16.96
69
6.32
46
19.51
92
10.74
73
0.00
1
0.00
1
0.00
1
0.00
1
0.35
61
0.11
32
RYNettwo views6.34
51
0.89
23
5.88
40
1.41
13
4.48
91
15.97
81
4.18
57
13.41
54
16.49
70
10.81
52
7.00
10
14.33
58
8.72
60
9.43
42
13.71
83
0.00
1
0.01
25
0.00
1
0.00
1
0.02
4
0.07
18
GANetREF_RVCpermissivetwo views6.56
52
2.89
87
7.58
63
3.41
77
0.40
11
12.96
73
9.58
89
15.09
69
17.25
75
10.33
48
10.62
28
12.27
49
8.16
57
12.21
61
4.53
31
0.41
79
0.00
1
0.00
1
0.02
42
3.12
103
0.39
64
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
53
1.80
74
6.25
45
1.26
9
0.94
33
10.08
58
9.02
84
16.00
77
11.51
49
12.74
64
13.02
46
24.77
91
5.25
40
10.56
50
8.02
51
0.04
37
0.05
48
0.00
1
0.02
42
0.10
23
0.25
54
DeepPrunerFtwo views6.75
54
2.69
85
23.31
100
3.68
81
7.16
103
3.78
11
4.29
58
13.42
55
20.13
83
8.13
28
10.46
25
7.18
23
8.06
56
11.10
56
9.44
65
0.24
65
0.15
75
0.29
89
0.42
91
0.66
78
0.45
67
edge stereotwo views6.76
55
1.01
30
6.76
55
2.20
45
2.45
62
6.41
37
2.45
40
14.84
67
11.98
53
15.29
75
18.31
68
22.02
88
12.56
76
10.82
52
7.49
45
0.03
32
0.06
54
0.11
74
0.03
50
0.30
54
0.14
39
NCC-stereotwo views6.77
56
1.49
59
6.48
50
2.92
66
4.40
85
7.43
42
3.61
51
19.52
93
13.29
56
8.39
30
16.91
61
15.96
64
12.13
74
12.85
66
7.70
47
1.47
100
0.11
67
0.01
43
0.42
91
0.14
35
0.24
51
Abc-Nettwo views6.77
56
1.49
59
6.48
50
2.92
66
4.40
85
7.43
42
3.61
51
19.52
93
13.29
56
8.39
30
16.91
61
15.96
64
12.13
74
12.85
66
7.70
47
1.47
100
0.11
67
0.01
43
0.42
91
0.14
35
0.24
51
RPtwo views6.84
58
1.29
53
5.53
35
3.92
87
5.18
96
6.32
35
3.53
49
11.73
37
15.31
65
9.54
42
22.38
76
18.25
76
14.47
83
10.11
46
7.49
45
0.91
96
0.01
25
0.12
75
0.15
74
0.33
58
0.19
45
RGCtwo views6.88
59
2.23
81
6.13
44
4.05
88
4.73
95
8.94
49
2.78
42
15.19
73
11.74
51
11.13
54
19.34
71
17.86
73
10.42
69
13.02
68
8.03
53
0.73
90
0.01
25
0.24
88
0.41
90
0.31
56
0.38
63
Nwc_Nettwo views6.97
60
1.25
48
6.63
53
3.82
85
3.37
75
10.83
64
1.67
35
19.56
95
11.35
46
8.36
29
23.62
78
17.19
71
11.44
73
11.21
57
8.08
55
0.80
92
0.00
1
0.00
1
0.02
42
0.13
31
0.09
23
STTRV1_RVCtwo views7.02
61
1.10
39
12.88
82
3.32
75
6.92
102
11.90
69
4.00
55
15.07
68
11.94
52
9.51
40
14.57
53
11.63
45
8.73
61
12.65
65
8.06
54
3.32
106
2.75
106
0.41
97
0.12
71
1.38
95
0.11
32
ADCReftwo views7.27
62
1.38
54
16.37
90
2.52
54
3.30
74
11.63
67
3.16
45
10.80
31
9.35
33
13.03
68
25.27
86
8.17
27
8.92
63
8.06
22
21.81
97
0.15
56
0.08
60
0.16
80
0.34
87
0.38
64
0.58
77
CSANtwo views7.62
63
1.60
65
6.56
52
1.83
28
0.66
18
12.40
71
10.52
95
14.45
64
21.32
85
14.19
71
15.98
60
17.84
72
13.02
80
12.32
62
8.38
57
0.09
47
0.07
58
0.03
60
0.04
59
0.33
58
0.67
83
stereogantwo views7.69
64
0.88
21
7.08
58
3.49
80
3.93
82
18.98
88
3.23
46
16.52
79
19.58
81
9.93
47
18.92
69
20.50
85
9.04
64
14.07
75
6.14
36
0.26
67
0.04
44
0.21
86
0.03
50
0.63
77
0.33
61
pmcnntwo views7.72
65
1.27
49
9.42
69
2.91
64
3.14
71
9.44
52
6.23
71
12.56
49
16.51
71
14.53
72
24.08
80
27.44
97
8.49
58
9.32
41
8.44
58
0.06
44
0.08
60
0.00
1
0.00
1
0.30
54
0.15
41
AF-Nettwo views7.78
66
1.44
57
6.68
54
3.37
76
4.50
92
8.61
47
2.69
41
17.07
82
20.17
84
9.52
41
24.02
79
20.31
84
14.59
84
11.58
58
9.84
70
0.61
88
0.00
1
0.12
75
0.00
1
0.38
64
0.12
34
PASMtwo views7.90
67
4.22
96
21.97
98
3.25
73
3.29
73
5.39
28
6.57
76
10.57
28
19.09
78
12.77
65
13.92
50
18.11
75
9.51
66
13.79
74
10.77
75
0.19
61
0.45
93
0.29
89
1.08
102
1.49
97
1.19
94
PWCDC_ROBbinarytwo views7.92
68
3.17
94
7.48
62
5.73
99
4.40
85
10.45
62
0.35
7
14.52
65
28.19
93
10.36
49
31.27
94
7.04
20
9.14
65
13.22
70
8.78
62
2.74
105
0.02
35
0.00
1
0.00
1
1.31
94
0.17
43
ADCP+two views8.09
69
1.79
73
14.50
88
1.54
17
4.28
84
16.57
83
5.20
66
12.80
50
11.20
45
12.83
66
17.07
63
11.02
42
10.80
71
17.59
88
23.18
100
0.03
32
0.05
48
0.01
43
0.18
75
0.39
68
0.81
86
SuperBtwo views8.10
70
3.15
92
24.67
102
2.65
58
1.23
38
9.88
57
4.29
58
10.18
23
30.07
96
11.53
56
12.18
39
6.12
16
6.65
50
10.50
49
14.47
85
0.14
54
0.11
67
0.35
93
0.25
81
13.06
111
0.48
70
PWC_ROBbinarytwo views8.24
71
3.13
90
12.74
81
2.43
52
4.43
88
7.51
44
1.22
28
16.63
80
19.24
79
16.08
77
28.29
89
13.99
57
10.16
68
13.63
73
14.06
84
0.42
82
0.00
1
0.05
67
0.00
1
0.59
74
0.27
58
MDST_ROBtwo views8.37
72
0.32
1
9.03
67
4.18
91
2.42
61
26.86
101
6.14
69
19.36
90
13.52
58
27.09
99
22.75
77
9.47
33
4.74
34
15.06
80
6.34
39
0.02
23
0.02
35
0.00
1
0.00
1
0.02
4
0.13
38
G-Nettwo views8.41
73
1.54
62
10.97
72
5.73
99
3.60
79
26.19
98
4.41
61
10.10
19
7.42
16
19.71
85
24.99
84
14.38
59
15.83
85
10.99
53
9.53
66
0.50
85
0.46
94
0.19
84
0.25
81
0.80
82
0.66
81
STTStereo_v2two views8.41
73
1.54
62
10.97
72
5.73
99
3.60
79
26.19
98
4.41
61
10.10
19
7.42
16
19.71
85
24.99
84
14.38
59
15.83
85
10.99
53
9.53
66
0.50
85
0.46
94
0.19
84
0.25
81
0.80
82
0.66
81
XQCtwo views8.43
75
3.58
95
16.40
91
2.92
66
2.17
56
13.22
75
3.60
50
14.64
66
25.86
90
11.87
57
12.04
37
15.06
62
10.67
70
15.24
81
19.41
90
0.39
77
0.08
60
0.05
67
0.07
65
0.84
84
0.45
67
FBW_ROBtwo views8.50
76
1.03
32
7.98
65
1.93
36
1.28
40
13.10
74
6.23
71
22.50
101
18.98
77
18.82
82
14.91
56
19.06
79
10.04
67
18.41
89
9.83
69
0.62
89
0.22
81
1.82
107
0.82
100
0.99
88
1.36
96
RTSCtwo views9.15
77
3.00
89
13.57
86
3.72
83
1.76
51
11.82
68
0.46
9
16.95
81
36.83
103
15.80
76
15.53
58
12.91
51
7.46
54
20.01
94
21.76
96
0.31
72
0.13
72
0.01
43
0.08
66
0.57
72
0.41
66
WCMA_ROBtwo views9.21
78
0.87
20
7.37
60
2.54
56
2.13
54
13.59
76
5.80
68
11.64
36
14.01
60
24.43
97
32.99
98
27.09
96
18.02
88
12.51
64
9.85
71
0.81
93
0.07
58
0.01
43
0.01
38
0.16
37
0.23
48
MSMD_ROBtwo views9.28
79
1.09
38
4.65
27
1.58
19
0.39
10
16.52
82
4.41
61
13.60
56
14.87
63
22.34
90
39.89
105
25.67
93
20.71
97
12.42
63
6.98
42
0.34
74
0.03
40
0.00
1
0.00
1
0.05
15
0.09
23
ADCPNettwo views9.54
80
2.39
83
31.46
104
2.09
41
1.60
45
16.71
85
6.39
75
12.11
41
11.45
47
13.53
70
21.45
74
19.41
80
10.94
72
14.38
77
21.54
95
0.27
70
1.16
100
0.39
96
1.49
105
0.58
73
1.45
97
SHDtwo views9.61
81
2.60
84
12.46
79
3.69
82
3.54
78
9.47
53
1.25
29
20.16
98
37.84
106
18.19
81
21.24
73
16.96
69
12.83
79
14.47
79
16.05
87
0.32
73
0.13
72
0.01
43
0.08
66
0.38
64
0.48
70
PDISCO_ROBtwo views9.62
82
1.99
78
11.51
74
9.88
107
9.61
108
21.48
92
3.83
54
19.33
89
28.49
94
11.27
55
14.17
51
19.92
83
5.02
38
16.35
85
9.18
64
5.28
108
0.41
89
0.14
79
0.09
68
2.05
100
2.36
104
MFN_U_SF_DS_RVCtwo views9.78
83
4.27
97
14.47
87
2.29
48
2.85
68
23.40
96
13.62
100
13.60
56
19.54
80
19.42
84
24.27
81
16.74
68
8.59
59
17.05
87
7.98
50
1.25
99
1.68
104
0.17
81
2.63
107
0.72
80
1.04
90
SGM_RVCbinarytwo views10.08
84
0.60
13
3.42
13
2.30
49
0.32
8
19.41
89
6.33
73
18.95
87
14.64
61
25.14
98
24.32
82
33.34
103
18.79
92
19.86
93
12.55
81
0.25
66
0.26
84
0.22
87
0.24
80
0.34
60
0.40
65
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DPSNettwo views10.14
85
1.88
77
16.82
92
1.85
29
1.73
50
24.84
97
17.20
109
19.92
96
27.41
92
12.23
61
13.62
49
16.52
67
18.35
89
14.42
78
12.50
80
0.78
91
0.54
98
0.08
71
0.25
81
1.18
91
0.59
80
ADCLtwo views10.16
86
2.11
79
19.36
95
1.92
34
1.88
53
22.23
93
8.91
83
14.04
61
23.56
87
14.62
73
26.19
87
12.75
50
13.59
82
16.06
84
22.95
99
0.26
67
0.18
78
0.75
100
0.65
96
0.69
79
0.58
77
ADCMidtwo views10.24
87
3.13
90
20.70
96
2.21
46
2.39
60
11.23
66
6.19
70
14.17
62
11.19
44
23.20
96
22.25
75
17.89
74
19.54
94
18.51
90
26.21
103
0.45
83
0.42
91
1.10
103
1.29
103
1.56
99
1.18
93
SANettwo views10.64
88
1.86
76
10.91
71
1.76
26
0.71
21
14.62
79
9.23
87
19.18
88
37.14
104
19.22
83
27.96
88
25.86
94
19.11
93
13.02
68
10.63
72
0.08
46
0.06
54
0.03
60
0.02
42
0.62
76
0.81
86
FC-DCNNcopylefttwo views10.72
89
0.52
9
4.27
23
1.88
31
1.63
46
17.18
86
5.29
67
18.20
84
19.69
82
28.50
100
34.51
101
34.03
104
21.48
100
15.89
83
11.15
77
0.03
32
0.01
25
0.02
54
0.01
38
0.07
20
0.09
23
AnyNet_C32two views10.98
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5.58
99
22.79
99
4.16
89
5.83
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15.64
80
14.30
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13.18
53
17.15
74
16.44
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20.52
72
14.68
61
13.44
81
22.46
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30.08
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0.17
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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
91
1.52
61
4.55
26
1.89
32
1.46
42
19.87
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5.11
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20.66
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15.91
68
32.67
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34.51
101
39.34
109
21.15
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18.74
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12.10
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0.11
50
0.06
54
0.01
43
0.00
1
0.45
71
0.22
47
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
ADCStwo views13.02
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4.93
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28.38
103
3.17
72
2.67
66
13.61
77
10.83
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18.70
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33.46
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22.59
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24.78
83
19.59
81
18.51
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23.40
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32.16
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0.10
49
0.19
79
0.37
95
0.18
75
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
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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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26.09
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13.55
72
9.82
68
2.44
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1.35
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0.34
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0.23
79
4.78
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6.69
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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
113
10.22
60
9.98
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15.16
72
22.93
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23.07
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32.34
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18.52
77
12.67
77
15.45
82
11.10
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0.16
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0.51
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0.09
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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
47
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
50
0.26
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0.03
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0.14
73
0.76
81
0.77
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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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24.16
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15.65
66
31.78
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29.19
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31.62
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21.32
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24.62
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19.50
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7.59
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4.19
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3.22
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1.48
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6.99
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6.54
107
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
PVDtwo views15.44
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2.93
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14.67
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4.21
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3.39
77
17.43
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4.16
56
27.84
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48.84
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31.02
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43.54
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25.97
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21.40
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0.23
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1.33
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SGM+DAISYtwo views15.62
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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
90
14.65
62
30.64
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33.59
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33.00
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24.96
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16.42
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7.90
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6.25
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4.51
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3.37
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7.20
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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
72
9.74
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22.78
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25.12
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30.32
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46.19
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34.37
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25.38
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21.48
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21.38
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5.94
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3.10
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6.07
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10.09
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4.01
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8.54
111
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
64
30.06
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15.15
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17.51
83
16.51
71
17.88
80
37.69
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24.04
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17.54
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29.60
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33.29
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0.28
71
0.38
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0.43
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0.42
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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
67
38.71
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14.59
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33.66
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34.35
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22.59
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17.95
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2.52
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2.81
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1.17
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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
82
10.09
70
5.50
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4.46
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28.28
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16.72
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25.55
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33.54
100
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
98
1.21
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0.86
102
0.70
98
1.39
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2.16
101
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAScopylefttwo views16.72
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2.14
80
9.23
68
4.92
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4.53
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32.66
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15.11
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27.40
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28.68
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40.27
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44.90
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38.33
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30.50
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26.44
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21.94
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0.88
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1.23
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0.67
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0.89
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1.49
97
2.18
103
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
LE_ROBtwo views16.73
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1.28
52
11.61
75
3.72
83
1.65
47
16.67
84
9.17
85
14.39
63
55.91
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63.81
113
40.86
108
35.94
106
37.73
112
14.24
76
26.87
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0.05
42
0.10
65
0.13
78
0.22
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0.12
29
0.15
41
SGM-ForestMtwo views16.99
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1.08
37
5.74
38
2.12
42
0.75
22
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
106
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
67
0.39
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0.31
91
0.39
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0.26
53
0.53
75
DispFullNettwo views17.47
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26.01
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33.98
106
22.58
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20.86
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13.84
78
1.28
30
16.50
78
26.27
91
19.97
87
17.17
64
20.52
86
18.49
90
22.86
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10.76
74
5.13
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2.83
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30.72
112
7.72
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20.86
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11.01
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RTStwo views18.87
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9.32
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86.48
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4.95
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6.10
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42.08
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14.70
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15.49
74
41.06
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22.65
92
32.32
95
13.77
52
19.54
94
37.98
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28.96
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0.41
79
0.23
82
0.00
1
0.02
42
0.91
85
0.50
72
RTSAtwo views18.87
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9.32
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86.48
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4.95
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6.10
99
42.08
110
14.70
103
15.49
74
41.06
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22.65
92
32.32
95
13.77
52
19.54
94
37.98
108
28.96
105
0.41
79
0.23
82
0.00
1
0.02
42
0.91
85
0.50
72
MANEtwo views19.47
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1.27
49
5.07
30
4.69
94
5.55
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30.49
104
9.94
91
34.01
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37.27
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44.13
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51.57
114
52.51
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40.41
113
33.58
106
24.81
102
0.89
95
0.86
99
1.11
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9.72
111
0.38
64
1.06
91
BEATNet-Init1two views23.31
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9.03
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41.67
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4.17
90
2.53
65
45.68
112
19.47
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33.43
109
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
114
38.35
110
29.21
107
0.47
84
0.50
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0.81
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0.66
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2.10
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1.86
99
FADEtwo views25.68
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17.27
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50.60
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10.46
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9.90
109
22.50
94
9.17
85
35.80
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53.05
112
20.32
89
19.01
70
26.54
95
34.42
109
39.35
111
33.52
112
30.62
113
14.22
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38.39
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37.63
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5.22
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5.56
106
MADNet+two views27.07
112
33.84
112
90.97
115
20.14
111
7.47
107
48.43
113
47.10
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35.43
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36.46
102
20.11
88
30.05
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25.29
92
35.08
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45.50
113
50.28
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2.13
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2.00
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1.19
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0.76
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4.71
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4.43
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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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7.23
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40.78
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27.10
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44.73
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44.32
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47.31
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36.37
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47.16
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26.05
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41.26
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31.87
109
21.83
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4.03
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29.50
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4.67
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27.17
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7.80
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DPSimNet_ROBtwo views53.45
114
64.73
114
44.39
108
53.97
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45.39
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53.66
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54.83
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55.15
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57.87
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64.16
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50.83
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63.40
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53.34
115
46.45
114
65.81
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63.13
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26.54
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57.94
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51.11
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45.52
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50.69
114
MADNet++two views82.84
115
82.38
115
73.57
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87.72
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82.97
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93.14
115
69.15
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86.42
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82.50
115
93.46
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86.70
115
86.28
115
80.92
116
88.34
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88.84
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86.83
115
84.17
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72.64
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68.92
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80.47
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81.42
115
MEDIAN_ROBtwo views98.41
116
99.70
116
99.30
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97.09
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97.02
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96.89
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95.77
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97.66
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97.28
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98.79
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98.94
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99.18
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98.14
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96.89
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96.88
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99.96
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99.16
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100.00
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99.99
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99.69
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99.88
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AVERAGE_ROBtwo views99.62
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99.95
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98.81
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100.00
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100.00
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98.08
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95.47
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100.00
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DGTPSM_ROBtwo views99.90
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100.00
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99.99
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100.00
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100.00
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100.00
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DPSMNet_ROBtwo views99.91
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100.00
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99.99
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100.00
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100.00
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100.00
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DPSM_ROBtwo views99.95
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100.00
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99.76
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100.00
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100.00
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DPSMtwo views99.95
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100.00
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100.00
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99.76
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100.00
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100.00
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LSM0two views100.00
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MSMDNettwo views1.26
6