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

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

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

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




Method Infoalllakes. 1llakes. 1ssand box 1lsand box 1sstora. room 1lstora. room 1sstora. room 2lstora. room 2sstora. room 2 1lstora. room 2 1sstora. room 2 2lstora. room 2 2sstora. room 3lstora. room 3stunnel 1ltunnel 1stunnel 2ltunnel 2stunnel 3ltunnel 3s
sort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort by
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
38
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
38
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
HSM-Net_RVCpermissivetwo views4.20
29
0.32
1
2.76
6
0.63
3
0.69
20
6.95
42
1.69
37
11.96
40
8.36
30
8.83
36
12.17
39
15.18
63
4.21
29
6.91
12
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
NVstereo2Dtwo views4.51
34
0.82
19
6.86
58
3.28
75
3.38
78
8.16
47
3.13
45
10.51
28
15.15
64
4.90
8
6.89
8
7.87
25
4.78
37
9.88
46
3.91
30
0.01
18
0.00
1
0.00
1
0.06
64
0.02
4
0.58
77
STTStereotwo views3.60
15
0.93
27
6.34
49
2.71
62
2.23
58
3.68
10
0.63
19
9.42
13
6.73
12
9.87
46
6.97
9
8.84
30
3.65
20
6.85
11
3.04
15
0.00
1
0.02
35
0.01
43
0.00
1
0.02
4
0.02
3
HSMtwo views4.00
25
0.79
18
3.16
12
1.59
20
2.17
56
6.77
40
1.11
28
12.28
46
6.35
10
6.75
20
8.11
16
13.90
55
5.37
43
8.85
37
2.71
11
0.00
1
0.00
1
0.00
1
0.00
1
0.02
4
0.02
3
RYNettwo views6.34
52
0.89
23
5.88
40
1.41
13
4.48
93
15.97
81
4.18
57
13.41
55
16.49
70
10.81
52
7.00
10
14.33
58
8.72
61
9.43
43
13.71
83
0.00
1
0.01
25
0.00
1
0.00
1
0.02
4
0.07
18
MDST_ROBtwo views8.37
72
0.32
1
9.03
68
4.18
92
2.42
63
26.86
101
6.14
70
19.36
90
13.52
58
27.09
99
22.75
76
9.47
33
4.74
35
15.06
80
6.34
40
0.02
23
0.02
35
0.00
1
0.00
1
0.02
4
0.13
38
AdaStereotwo views3.09
8
0.58
12
3.04
9
2.84
64
0.48
15
4.08
15
1.29
32
12.16
45
7.77
23
6.03
12
9.62
24
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.
DLCB_ROBtwo views4.51
34
0.91
24
3.78
17
2.19
45
1.07
36
6.28
35
3.09
44
9.78
16
7.72
21
10.65
50
12.97
46
13.91
56
3.71
22
8.72
34
5.30
36
0.00
1
0.00
1
0.00
1
0.00
1
0.03
10
0.10
30
RASNettwo views4.52
36
0.61
14
4.42
24
3.42
78
4.68
96
4.58
23
0.99
26
9.54
15
8.01
24
5.28
10
11.42
36
10.34
39
8.88
62
9.28
41
8.68
60
0.15
57
0.00
1
0.00
1
0.00
1
0.03
10
0.04
15
PMTNettwo views1.99
2
0.32
1
2.21
3
0.39
2
0.23
6
5.08
27
0.49
11
5.84
1
8.22
27
3.07
1
3.29
1
0.73
2
0.75
2
8.18
28
0.94
3
0.00
1
0.00
1
0.00
1
0.00
1
0.03
10
0.00
1
NLCA_NET_v2_RVCtwo views3.84
21
1.06
34
5.23
31
2.72
63
3.27
74
4.36
18
0.61
16
10.71
31
7.56
20
8.75
34
7.89
14
9.86
36
3.90
24
7.15
13
3.44
23
0.14
55
0.02
35
0.02
55
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.
CBMV_ROBtwo views4.14
27
0.52
9
3.14
11
1.30
10
0.77
25
6.92
41
1.97
39
10.11
22
9.58
35
8.92
37
14.20
53
7.12
22
5.90
46
8.65
33
3.50
25
0.01
18
0.05
48
0.00
1
0.00
1
0.04
13
0.09
23
SGM-Foresttwo views4.96
37
0.32
1
2.84
7
1.21
8
0.64
17
10.23
62
6.64
78
11.55
36
10.98
44
10.94
53
13.59
49
11.65
46
4.30
30
8.94
38
4.63
33
0.11
51
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
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
57
14.87
63
22.34
89
39.89
105
25.67
93
20.71
98
12.42
64
6.98
43
0.34
75
0.03
40
0.00
1
0.00
1
0.05
15
0.09
23
CC-Net-ROBtwo views3.84
21
1.07
35
5.23
31
2.65
59
2.96
72
4.22
16
0.69
22
10.43
27
7.72
21
8.78
35
8.29
17
9.61
34
4.02
26
7.16
14
3.65
26
0.13
54
0.03
40
0.02
55
0.03
50
0.05
15
0.03
9
DPM-Stereotwo views1.97
1
0.64
16
2.95
8
0.17
1
0.10
1
4.83
25
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
HITNettwo views2.79
6
0.77
17
4.02
19
2.03
40
0.11
3
5.58
31
0.59
14
9.24
12
5.15
8
6.42
16
7.26
11
3.66
6
2.92
16
4.07
4
3.87
29
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
NOSS_ROBtwo views3.30
11
0.46
8
2.62
4
2.08
41
1.01
34
5.60
32
0.74
23
10.37
26
11.48
49
5.15
9
8.43
18
5.67
13
1.73
8
7.97
20
2.34
9
0.02
23
0.06
54
0.00
1
0.00
1
0.07
20
0.14
39
FC-DCNNcopylefttwo views10.72
89
0.52
9
4.27
23
1.88
32
1.63
46
17.18
86
5.29
68
18.20
84
19.69
82
28.50
100
34.51
101
34.03
104
21.48
101
15.89
83
11.15
77
0.03
33
0.01
25
0.02
55
0.01
38
0.07
20
0.09
23
ETE_ROBtwo views5.80
43
1.77
72
6.33
47
1.44
15
0.78
26
6.43
39
6.90
79
12.53
49
8.08
25
12.93
67
14.89
55
21.13
87
5.87
45
9.83
45
6.57
42
0.04
38
0.01
25
0.00
1
0.02
42
0.08
22
0.33
61
MLCVtwo views3.44
13
0.88
21
5.60
36
1.39
12
0.25
7
4.36
18
0.33
6
7.25
5
7.28
16
9.17
40
12.24
42
5.09
10
2.47
12
9.15
40
3.23
19
0.00
1
0.00
1
0.00
1
0.00
1
0.10
23
0.02
3
iResNet_ROBtwo views4.23
30
1.02
31
4.90
28
2.18
44
0.93
32
2.92
5
0.37
8
15.10
71
16.91
73
7.89
27
10.51
28
7.03
19
3.07
17
8.16
27
3.46
24
0.01
18
0.00
1
0.00
1
0.00
1
0.10
23
0.02
3
iResNettwo views3.68
17
0.91
24
7.94
65
2.97
70
0.34
9
4.44
22
0.48
10
7.70
6
9.74
37
7.72
24
12.74
45
4.03
7
2.87
15
8.05
22
3.37
21
0.02
23
0.01
25
0.00
1
0.00
1
0.10
23
0.09
23
LALA_ROBtwo views6.58
54
1.80
74
6.25
45
1.26
9
0.94
33
10.08
59
9.02
85
16.00
77
11.51
50
12.74
64
13.02
47
24.77
91
5.25
41
10.56
51
8.02
52
0.04
38
0.05
48
0.00
1
0.02
42
0.10
23
0.25
54
PA-Nettwo views4.98
38
1.47
57
7.42
62
2.40
52
2.14
55
8.73
49
3.64
54
12.42
47
13.11
55
7.03
22
7.57
12
7.88
26
6.52
49
10.16
48
7.82
50
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.
XPNet_ROBtwo views6.03
48
1.22
44
5.61
37
2.56
58
0.90
29
6.32
36
7.07
80
12.92
52
8.30
28
14.76
74
15.13
57
19.84
82
6.66
52
10.36
49
8.58
59
0.02
23
0.04
44
0.00
1
0.03
50
0.11
27
0.24
51
LE_ROBtwo views16.73
105
1.28
51
11.61
76
3.72
84
1.65
47
16.67
84
9.17
86
14.39
64
55.91
113
63.81
113
40.86
108
35.94
106
37.73
112
14.24
76
26.87
105
0.05
43
0.10
65
0.13
79
0.22
78
0.12
29
0.15
41
GANettwo views6.22
50
1.07
35
4.07
21
2.27
48
0.89
28
9.19
51
9.52
88
12.02
41
8.13
26
10.72
51
29.09
91
13.86
54
7.52
56
11.00
56
4.39
31
0.36
76
0.00
1
0.02
55
0.02
42
0.12
29
0.08
21
AANet_RVCtwo views5.01
39
1.74
70
6.38
50
1.96
39
1.29
41
2.26
3
1.69
37
10.07
18
18.53
76
7.88
26
18.15
67
8.49
29
2.70
14
10.59
52
7.04
44
0.96
98
0.15
76
0.02
55
0.00
1
0.13
31
0.12
33
DANettwo views6.02
47
1.23
45
8.45
67
3.86
87
3.94
85
7.64
46
1.34
34
9.51
14
7.00
13
13.39
69
15.53
58
15.99
66
7.02
53
12.14
61
12.37
79
0.19
62
0.12
72
0.02
55
0.03
50
0.13
31
0.56
76
Nwc_Nettwo views6.97
61
1.25
47
6.63
54
3.82
86
3.37
77
10.83
65
1.67
36
19.56
95
11.35
47
8.36
29
23.62
77
17.19
71
11.44
73
11.21
58
8.08
55
0.80
93
0.00
1
0.00
1
0.02
42
0.13
31
0.09
23
NCCL2two views5.88
45
1.59
64
5.44
34
1.87
31
0.92
31
9.55
55
11.55
97
12.11
42
9.94
40
9.67
44
8.85
21
22.28
89
7.41
54
8.78
35
7.17
45
0.01
18
0.00
1
0.03
61
0.00
1
0.13
31
0.23
48
Abc-Nettwo views6.77
57
1.49
58
6.48
51
2.92
67
4.40
87
7.43
43
3.61
52
19.52
93
13.29
56
8.39
30
16.91
61
15.96
64
12.13
74
12.85
66
7.70
48
1.47
102
0.11
67
0.01
43
0.42
91
0.14
35
0.24
51
NCC-stereotwo views6.77
57
1.49
58
6.48
51
2.92
67
4.40
87
7.43
43
3.61
52
19.52
93
13.29
56
8.39
30
16.91
61
15.96
64
12.13
74
12.85
66
7.70
48
1.47
102
0.11
67
0.01
43
0.42
91
0.14
35
0.24
51
WCMA_ROBtwo views9.21
78
0.87
20
7.37
61
2.54
57
2.13
54
13.59
76
5.80
69
11.64
37
14.01
60
24.43
97
32.99
98
27.09
95
18.02
89
12.51
65
9.85
71
0.81
94
0.07
58
0.01
43
0.01
38
0.16
37
0.23
48
CBMVpermissivetwo views5.35
41
0.91
24
3.67
16
1.62
22
0.44
13
10.09
60
7.19
82
12.49
48
12.33
54
12.22
59
14.69
54
10.93
42
6.48
48
8.51
31
4.96
34
0.02
23
0.15
76
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
DRN-Testtwo views5.87
44
0.98
29
5.89
41
2.69
61
3.65
83
12.37
70
3.35
48
20.07
97
10.20
41
11.93
58
12.31
43
11.06
44
5.31
42
7.89
19
9.05
63
0.04
38
0.05
48
0.04
66
0.04
59
0.18
39
0.25
54
DMCAtwo views3.29
10
1.05
33
4.18
22
1.60
21
2.87
71
2.11
2
0.60
15
7.95
7
4.65
7
8.62
33
8.59
19
9.24
31
4.68
34
6.25
8
3.03
14
0.04
38
0.09
64
0.06
70
0.03
50
0.18
39
0.07
18
DMCA-RVCcopylefttwo views3.68
17
1.58
63
6.33
47
1.79
28
2.33
60
4.04
14
0.68
20
9.07
10
4.57
6
8.48
32
9.34
23
10.91
41
3.45
19
6.67
10
3.78
27
0.02
23
0.11
67
0.01
43
0.04
59
0.18
39
0.12
33
TDLMtwo views4.11
26
1.11
40
3.54
14
1.62
22
1.04
35
3.91
12
7.41
83
10.60
30
10.67
43
6.38
15
12.59
44
5.95
15
4.77
36
8.79
36
3.04
15
0.58
88
0.00
1
0.01
43
0.00
1
0.19
42
0.12
33
StereoDRNet-Refinedtwo views4.46
33
0.62
15
3.80
18
1.92
35
0.40
11
9.35
52
0.15
3
10.02
17
8.83
32
12.69
63
11.62
37
9.34
32
3.87
23
8.06
23
8.02
52
0.00
1
0.00
1
0.01
43
0.05
62
0.20
43
0.26
57
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
StereoDRNettwo views5.59
42
1.75
71
6.80
57
3.12
72
4.45
91
10.61
64
4.35
60
18.80
86
9.73
36
12.22
59
6.87
7
11.44
45
4.65
33
8.09
25
8.26
56
0.02
23
0.11
67
0.00
1
0.03
50
0.20
43
0.28
59
ccs_robtwo views3.63
16
1.12
41
4.42
24
2.52
55
0.91
30
5.50
30
0.21
4
10.11
22
9.11
33
6.55
19
11.28
34
8.32
28
2.55
13
7.66
15
2.01
8
0.00
1
0.00
1
0.00
1
0.00
1
0.20
43
0.08
21
PSMNet_ROBtwo views5.02
40
1.63
67
6.03
43
1.90
34
1.83
52
9.57
56
6.35
75
15.58
76
7.23
15
6.15
13
10.48
27
12.22
48
4.16
28
8.02
21
8.71
61
0.02
23
0.01
25
0.01
43
0.10
70
0.20
43
0.12
33
ccstwo views3.04
7
0.39
7
3.08
10
1.78
27
0.52
16
2.04
1
0.50
12
13.09
53
13.71
59
3.54
4
5.36
6
5.50
12
2.45
11
4.81
6
2.88
12
0.09
48
0.08
60
0.12
76
0.10
70
0.20
43
0.50
72
CVANet_RVCtwo views4.16
28
1.16
43
3.60
15
1.94
38
1.46
42
3.92
13
4.68
65
10.89
34
8.34
29
7.58
23
10.84
30
10.27
38
6.62
50
8.56
32
2.69
10
0.39
78
0.00
1
0.00
1
0.01
38
0.21
48
0.09
23
CFNettwo views3.72
19
1.10
39
5.03
29
2.49
54
1.59
44
4.90
26
0.22
5
11.38
35
9.88
39
4.80
7
11.25
33
6.44
17
3.68
21
8.33
29
3.00
13
0.00
1
0.00
1
0.00
1
0.00
1
0.22
49
0.07
18
Anonymous Stereotwo views6.16
49
3.15
92
23.75
101
2.97
70
2.48
65
4.39
21
13.30
99
9.21
11
9.86
38
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
64
0.22
49
0.19
45
NaN_ROBtwo views6.00
46
1.24
46
6.29
46
1.34
11
1.68
49
9.60
57
10.31
93
15.09
69
15.79
67
12.62
62
8.95
22
11.67
47
5.83
44
11.78
60
6.41
41
0.05
43
0.13
73
0.08
72
0.20
77
0.22
49
0.79
85
CFNet_RVCtwo views3.31
12
0.94
28
2.69
5
1.50
16
2.38
61
2.81
4
0.68
20
8.35
8
7.43
19
4.45
6
9.94
25
10.20
37
4.60
32
6.49
9
3.41
22
0.00
1
0.00
1
0.03
61
0.00
1
0.22
49
0.03
9
BEATNet_4xtwo views3.24
9
1.27
48
5.89
41
1.56
18
0.10
1
5.26
28
1.07
27
10.08
19
5.50
9
6.89
21
7.73
13
4.53
9
4.13
27
5.05
7
5.27
35
0.04
38
0.05
48
0.00
1
0.00
1
0.23
53
0.23
48
SGM-ForestMtwo views16.99
106
1.08
37
5.74
38
2.12
43
0.75
22
31.63
105
12.21
98
27.80
108
32.25
99
37.88
107
39.99
106
52.96
112
35.20
111
33.60
108
24.47
102
0.26
68
0.39
89
0.31
92
0.39
89
0.26
54
0.53
75
edge stereotwo views6.76
56
1.01
30
6.76
56
2.20
46
2.45
64
6.41
38
2.45
41
14.84
68
11.98
53
15.29
75
18.31
68
22.02
88
12.56
76
10.82
53
7.49
46
0.03
33
0.06
54
0.11
75
0.03
50
0.30
55
0.14
39
pmcnntwo views7.72
65
1.27
48
9.42
70
2.91
65
3.14
73
9.44
53
6.23
72
12.56
50
16.51
71
14.53
72
24.08
79
27.44
96
8.49
59
9.32
42
8.44
58
0.06
45
0.08
60
0.00
1
0.00
1
0.30
55
0.15
41
RGCtwo views6.88
60
2.23
81
6.13
44
4.05
89
4.73
97
8.94
50
2.78
43
15.19
73
11.74
52
11.13
54
19.34
70
17.86
73
10.42
69
13.02
68
8.03
54
0.73
91
0.01
25
0.24
89
0.41
90
0.31
57
0.38
63
DeepPruner_ROBtwo views3.52
14
1.14
42
4.06
20
1.12
7
1.65
47
3.65
7
0.83
25
13.96
61
4.47
5
7.80
25
10.84
30
7.05
21
2.16
10
8.14
26
3.08
17
0.07
46
0.03
40
0.00
1
0.01
38
0.32
58
0.06
17
CSANtwo views7.62
63
1.60
65
6.56
53
1.83
29
0.66
18
12.40
71
10.52
95
14.45
65
21.32
86
14.19
71
15.98
60
17.84
72
13.02
80
12.32
63
8.38
57
0.09
48
0.07
58
0.03
61
0.04
59
0.33
59
0.67
83
RPtwo views6.84
59
1.29
52
5.53
35
3.92
88
5.18
98
6.32
36
3.53
50
11.73
38
15.31
65
9.54
42
22.38
75
18.25
76
14.47
83
10.11
47
7.49
46
0.91
97
0.01
25
0.12
76
0.15
74
0.33
59
0.19
45
SGM_RVCbinarytwo views10.08
84
0.60
13
3.42
13
2.30
50
0.32
8
19.41
89
6.33
74
18.95
87
14.64
61
25.14
98
24.32
81
33.34
103
18.79
93
19.86
93
12.55
81
0.25
67
0.26
85
0.22
88
0.24
80
0.34
61
0.40
65
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DN-CSS_ROBtwo views2.69
5
1.40
54
5.34
33
2.31
51
0.75
22
3.14
6
0.06
1
6.11
2
3.87
3
5.34
11
12.18
40
2.34
5
1.22
5
7.84
18
1.48
4
0.03
33
0.00
1
0.00
1
0.00
1
0.35
62
0.03
9
DISCOtwo views6.28
51
0.57
11
5.78
39
3.43
79
1.17
37
11.22
66
3.39
49
12.14
44
16.16
69
6.52
18
11.22
32
16.96
69
6.32
47
19.51
92
10.74
73
0.00
1
0.00
1
0.00
1
0.00
1
0.35
62
0.11
32
iResNetv2_ROBtwo views4.28
32
1.43
55
7.17
60
2.91
65
1.26
39
4.36
18
1.62
35
13.64
59
10.25
42
9.83
45
11.41
35
7.68
24
4.00
25
7.75
17
1.85
6
0.00
1
0.00
1
0.00
1
0.00
1
0.37
64
0.09
23
AF-Nettwo views7.78
66
1.44
56
6.68
55
3.37
76
4.50
94
8.61
48
2.69
42
17.07
82
20.17
85
9.52
41
24.02
78
20.31
84
14.59
84
11.58
59
9.84
70
0.61
89
0.00
1
0.12
76
0.00
1
0.38
65
0.12
33
SHDtwo views9.61
81
2.60
84
12.46
80
3.69
83
3.54
80
9.47
54
1.25
30
20.16
98
37.84
107
18.19
81
21.24
72
16.96
69
12.83
79
14.47
79
16.05
87
0.32
74
0.13
73
0.01
43
0.08
67
0.38
65
0.48
70
MANEtwo views19.47
110
1.27
48
5.07
30
4.69
95
5.55
99
30.49
104
9.94
91
34.01
111
37.27
106
44.13
110
51.57
114
52.51
111
40.41
113
33.58
107
24.81
103
0.89
96
0.86
100
1.11
105
9.72
112
0.38
65
1.06
91
ADCReftwo views7.27
62
1.38
53
16.37
90
2.52
55
3.30
76
11.63
68
3.16
46
10.80
32
9.35
34
13.03
68
25.27
85
8.17
27
8.92
63
8.06
23
21.81
98
0.15
57
0.08
60
0.16
81
0.34
87
0.38
65
0.58
77
ADCP+two views8.09
69
1.79
73
14.50
88
1.54
17
4.28
86
16.57
83
5.20
67
12.80
51
11.20
46
12.83
66
17.07
63
11.02
43
10.80
71
17.59
88
23.18
101
0.03
33
0.05
48
0.01
43
0.18
75
0.39
69
0.81
86
FADNet-RVC-Resampletwo views3.79
20
1.62
66
12.06
78
1.43
14
0.66
18
5.94
34
2.41
40
10.18
24
8.58
31
6.28
14
4.22
3
5.33
11
4.80
38
7.71
16
3.19
18
0.17
60
0.21
81
0.17
82
0.12
72
0.41
70
0.29
60
PVDtwo views15.44
98
2.93
88
14.67
89
4.21
93
3.39
79
17.43
87
4.16
56
27.84
109
48.84
112
31.02
103
43.54
109
29.76
99
30.81
109
25.97
103
21.40
95
0.23
65
0.41
90
0.04
66
0.33
86
0.41
70
1.33
95
MeshStereopermissivetwo views11.52
91
1.52
60
4.55
26
1.89
33
1.46
42
19.87
91
5.11
66
20.66
99
15.91
68
32.67
105
34.51
101
39.34
109
21.15
99
18.74
91
12.10
78
0.11
51
0.06
54
0.01
43
0.00
1
0.45
72
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
RTSCtwo views9.15
77
3.00
89
13.57
86
3.72
84
1.76
51
11.82
69
0.46
9
16.95
81
36.83
104
15.80
76
15.53
58
12.91
51
7.46
55
20.01
95
21.76
97
0.31
73
0.13
73
0.01
43
0.08
67
0.57
73
0.41
66
ADCPNettwo views9.54
80
2.39
83
31.46
105
2.09
42
1.60
45
16.71
85
6.39
76
12.11
42
11.45
48
13.53
70
21.45
73
19.41
80
10.94
72
14.38
77
21.54
96
0.27
71
1.16
102
0.39
97
1.49
106
0.58
74
1.45
97
PWC_ROBbinarytwo views8.24
71
3.13
90
12.74
82
2.43
53
4.43
90
7.51
45
1.22
29
16.63
80
19.24
79
16.08
77
28.29
88
13.99
57
10.16
68
13.63
73
14.06
84
0.42
83
0.00
1
0.05
68
0.00
1
0.59
75
0.27
58
FADNet-RVCtwo views3.98
24
1.84
75
12.48
81
1.69
25
0.44
13
4.33
17
1.31
33
11.84
39
7.15
14
3.53
3
3.50
2
10.63
40
4.43
31
9.12
39
6.25
38
0.03
33
0.10
65
0.00
1
0.03
50
0.60
76
0.25
54
SANettwo views10.64
88
1.86
76
10.91
72
1.76
26
0.71
21
14.62
79
9.23
87
19.18
88
37.14
105
19.22
83
27.96
87
25.86
94
19.11
94
13.02
68
10.63
72
0.08
47
0.06
54
0.03
61
0.02
42
0.62
77
0.81
86
stereogantwo views7.69
64
0.88
21
7.08
59
3.49
80
3.93
84
18.98
88
3.23
47
16.52
79
19.58
81
9.93
47
18.92
69
20.50
85
9.04
64
14.07
75
6.14
37
0.26
68
0.04
44
0.21
87
0.03
50
0.63
78
0.33
61
DeepPrunerFtwo views6.75
55
2.69
85
23.31
100
3.68
82
7.16
104
3.78
11
4.29
58
13.42
56
20.13
84
8.13
28
10.46
26
7.18
23
8.06
57
11.10
57
9.44
65
0.24
66
0.15
76
0.29
90
0.42
91
0.66
79
0.45
67
ADCLtwo views10.16
86
2.11
79
19.36
95
1.92
35
1.88
53
22.23
93
8.91
84
14.04
62
23.56
88
14.62
73
26.19
86
12.75
50
13.59
82
16.06
84
22.95
100
0.26
68
0.18
79
0.75
100
0.65
96
0.69
80
0.58
77
MFN_U_SF_DS_RVCtwo views9.78
83
4.27
98
14.47
87
2.29
49
2.85
70
23.40
96
13.62
100
13.60
57
19.54
80
19.42
84
24.27
80
16.74
68
8.59
60
17.05
87
7.98
51
1.25
101
1.68
106
0.17
82
2.63
108
0.72
81
1.04
90
SAMSARAtwo views14.63
96
2.74
86
12.38
79
12.65
110
6.74
103
36.50
107
72.93
115
19.36
90
23.77
89
16.20
78
13.04
48
29.21
97
12.78
78
16.98
86
15.21
86
0.11
51
0.26
85
0.03
61
0.14
73
0.76
82
0.77
84
G-Nettwo views8.41
73
1.54
61
10.97
73
5.73
100
3.60
81
26.19
98
4.41
61
10.10
20
7.42
17
19.71
85
24.99
83
14.38
59
15.83
85
10.99
54
9.53
66
0.50
86
0.46
95
0.19
85
0.25
81
0.80
83
0.66
81
STTStereo_v2two views8.41
73
1.54
61
10.97
73
5.73
100
3.60
81
26.19
98
4.41
61
10.10
20
7.42
17
19.71
85
24.99
83
14.38
59
15.83
85
10.99
54
9.53
66
0.50
86
0.46
95
0.19
85
0.25
81
0.80
83
0.66
81
XQCtwo views8.43
75
3.58
95
16.40
91
2.92
67
2.17
56
13.22
75
3.60
51
14.64
67
25.86
91
11.87
57
12.04
38
15.06
62
10.67
70
15.24
81
19.41
90
0.39
78
0.08
60
0.05
68
0.07
66
0.84
85
0.45
67
FADNet_RVCtwo views3.91
23
1.67
69
12.95
84
0.96
6
0.75
22
5.71
33
0.54
13
10.83
33
6.60
11
3.46
2
8.09
15
4.10
8
3.40
18
9.43
43
6.33
39
0.36
76
0.44
93
0.17
82
0.46
95
0.91
86
0.95
89
RTSAtwo views18.87
108
9.32
108
86.48
113
4.95
97
6.10
101
42.08
110
14.70
103
15.49
74
41.06
109
22.65
91
32.32
95
13.77
52
19.54
95
37.98
109
28.96
106
0.41
80
0.23
83
0.00
1
0.02
42
0.91
86
0.50
72
RTStwo views18.87
108
9.32
108
86.48
113
4.95
97
6.10
101
42.08
110
14.70
103
15.49
74
41.06
109
22.65
91
32.32
95
13.77
52
19.54
95
37.98
109
28.96
106
0.41
80
0.23
83
0.00
1
0.02
42
0.91
86
0.50
72
FBW_ROBtwo views8.50
76
1.03
32
7.98
66
1.93
37
1.28
40
13.10
74
6.23
72
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
90
0.22
82
1.82
108
0.82
100
0.99
89
1.36
96
LSMtwo views14.01
95
5.95
101
33.49
106
6.78
105
43.61
113
10.22
61
9.98
92
15.16
72
22.93
87
23.07
94
32.34
97
18.52
77
12.67
77
15.45
82
11.10
76
0.16
59
0.51
98
0.09
74
0.32
85
1.08
90
16.85
113
FADNettwo views4.23
30
1.65
68
11.75
77
1.64
24
0.80
27
4.80
24
0.77
24
13.76
60
11.65
51
3.97
5
5.24
5
9.62
35
5.14
40
8.40
30
3.78
27
0.21
64
0.04
44
0.07
71
0.05
62
1.14
91
0.10
30
DPSNettwo views10.14
85
1.88
77
16.82
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1.85
30
1.73
50
24.84
97
17.20
109
19.92
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27.41
93
12.23
61
13.62
50
16.52
67
18.35
90
14.42
78
12.50
80
0.78
92
0.54
99
0.08
72
0.25
81
1.18
92
0.59
80
AnyNet_C32two views10.98
90
5.58
100
22.79
99
4.16
90
5.83
100
15.64
80
14.30
101
13.18
54
17.15
74
16.44
79
20.52
71
14.68
61
13.44
81
22.46
97
30.08
109
0.17
60
0.26
85
0.36
95
0.36
88
1.23
93
0.91
88
ADCStwo views13.02
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4.93
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28.38
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3.17
73
2.67
68
13.61
77
10.83
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18.70
85
33.46
100
22.59
90
24.78
82
19.59
81
18.51
92
23.40
100
32.16
111
0.10
50
0.19
80
0.37
96
0.18
75
1.26
94
1.46
98
PWCDC_ROBbinarytwo views7.92
68
3.17
94
7.48
63
5.73
100
4.40
87
10.45
63
0.35
7
14.52
66
28.19
94
10.36
49
31.27
94
7.04
20
9.14
65
13.22
70
8.78
62
2.74
107
0.02
35
0.00
1
0.00
1
1.31
95
0.17
43
ELAS_RVCcopylefttwo views16.54
103
2.26
82
10.09
71
5.50
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4.46
92
28.28
102
16.72
108
25.55
105
33.54
101
40.19
108
40.30
107
36.68
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30.03
107
29.40
105
20.61
93
0.98
99
1.21
103
0.86
102
0.70
98
1.39
96
2.16
102
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAScopylefttwo views16.72
104
2.14
80
9.23
69
4.92
96
4.53
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32.66
106
15.11
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27.40
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28.68
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40.27
109
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
99
0.88
95
1.23
104
0.67
99
0.89
101
1.49
97
2.18
104
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
PASMtwo views7.90
67
4.22
97
21.97
98
3.25
74
3.29
75
5.39
29
6.57
77
10.57
29
19.09
78
12.77
65
13.92
51
18.11
75
9.51
66
13.79
74
10.77
75
0.19
62
0.45
94
0.29
90
1.08
102
1.49
97
1.19
94
ADCMidtwo views10.24
87
3.13
90
20.70
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2.21
47
2.39
62
11.23
67
6.19
71
14.17
63
11.19
45
23.20
95
22.25
74
17.89
74
19.54
95
18.51
90
26.21
104
0.45
84
0.42
92
1.10
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1.29
103
1.56
99
1.18
93
MFN_U_SF_RVCtwo views12.94
92
3.66
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25.81
103
3.61
81
2.26
59
22.77
94
4.55
64
27.10
106
20.06
83
23.90
96
28.99
90
30.53
100
16.98
87
19.92
94
20.26
92
1.24
100
1.07
101
0.98
103
1.33
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1.80
100
2.04
101
PDISCO_ROBtwo views9.62
82
1.99
78
11.51
75
9.88
108
9.61
109
21.48
92
3.83
55
19.33
89
28.49
95
11.27
55
14.17
52
19.92
83
5.02
39
16.35
85
9.18
64
5.28
109
0.41
90
0.14
80
0.09
69
2.05
101
2.36
105
BEATNet-Init1two views23.31
111
9.03
107
41.67
108
4.17
91
2.53
67
45.68
112
19.47
110
33.43
110
38.45
108
47.59
112
49.10
112
59.31
113
41.80
114
38.35
111
29.21
108
0.47
85
0.50
97
0.81
101
0.66
97
2.10
102
1.86
99
AnyNet_C01two views16.12
101
10.81
110
59.36
111
4.42
94
2.49
66
30.06
103
15.15
107
17.51
83
16.51
71
17.88
80
37.69
104
24.04
90
17.54
88
29.60
106
33.29
112
0.28
72
0.38
88
0.43
98
0.42
91
2.57
103
1.98
100
GANetREF_RVCpermissivetwo views6.56
53
2.89
87
7.58
64
3.41
77
0.40
11
12.96
73
9.58
89
15.09
69
17.25
75
10.33
48
10.62
29
12.27
49
8.16
58
12.21
62
4.53
32
0.41
80
0.00
1
0.00
1
0.02
42
3.12
104
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
NVStereoNet_ROBtwo views16.04
100
6.75
103
12.90
83
6.37
104
7.42
107
12.89
72
9.74
90
22.78
102
25.12
90
30.32
101
46.19
111
34.37
105
25.38
104
21.48
96
21.38
94
5.94
110
3.10
110
6.07
111
10.09
113
4.01
105
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
MADNet+two views27.07
112
33.84
112
90.97
115
20.14
111
7.47
108
48.43
113
47.10
112
35.43
112
36.46
103
20.11
88
30.05
93
25.29
92
35.08
110
45.50
113
50.28
113
2.13
104
2.00
107
1.19
107
0.76
99
4.71
106
4.43
106
MFMNet_retwo views13.29
94
8.60
106
18.29
93
9.75
107
7.25
106
19.65
90
14.84
105
20.71
100
30.72
98
23.03
93
28.77
89
18.85
78
26.09
106
13.55
72
9.82
68
2.44
105
1.35
105
0.34
93
0.23
79
4.78
107
6.69
108
SGM+DAISYtwo views15.62
99
7.26
105
19.28
94
8.94
106
10.11
110
26.25
100
10.49
94
19.36
90
14.65
62
30.64
102
33.59
99
33.00
102
22.32
102
24.96
102
16.42
88
7.90
112
6.25
113
4.51
110
3.37
109
5.86
108
7.20
109
MSC_U_SF_DS_RVCtwo views16.41
102
6.93
104
21.83
97
5.94
103
2.81
69
38.71
108
14.59
102
24.55
104
34.87
102
33.66
106
34.35
100
29.24
98
24.20
103
22.59
98
17.95
89
2.52
106
2.81
108
1.17
106
1.51
107
5.89
109
2.16
102
SPS-STEREOcopylefttwo views15.04
97
6.23
102
13.21
85
11.34
109
11.65
111
23.30
95
7.15
81
24.16
103
15.65
66
31.78
104
29.19
92
31.62
101
21.32
100
24.62
101
19.50
91
7.59
111
4.19
112
3.22
109
1.48
105
6.99
110
6.54
107
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
SuperBtwo views8.10
70
3.15
92
24.67
102
2.65
59
1.23
38
9.88
58
4.29
58
10.18
24
30.07
97
11.53
56
12.18
40
6.12
16
6.65
51
10.50
50
14.47
85
0.14
55
0.11
67
0.35
94
0.25
81
13.06
111
0.48
70
DispFullNettwo views17.47
107
26.01
111
33.98
107
22.58
112
20.86
112
13.84
78
1.28
31
16.50
78
26.27
92
19.97
87
17.17
64
20.52
86
18.49
91
22.86
99
10.76
74
5.13
108
2.83
109
30.72
113
7.72
111
20.86
112
11.01
112
PWCKtwo views30.53
113
44.32
113
47.25
110
29.76
113
7.23
105
40.78
109
27.10
111
44.73
113
44.32
111
47.31
111
36.37
103
47.16
110
26.05
105
41.26
112
31.87
110
21.83
113
4.03
111
29.50
112
4.67
110
27.17
113
7.80
110
DPSimNet_ROBtwo views53.45
114
64.73
114
44.39
109
53.97
114
45.39
114
53.66
114
54.83
113
55.15
114
57.87
114
64.16
114
50.83
113
63.40
114
53.34
115
46.45
114
65.81
114
63.13
114
26.54
114
57.94
114
51.11
114
45.52
114
50.69
114
MADNet++two views82.84
115
82.38
115
73.57
112
87.72
115
82.97
115
93.14
115
69.15
114
86.42
115
82.50
115
93.46
115
86.70
115
86.28
115
80.92
116
88.34
115
88.84
115
86.83
115
84.17
115
72.64
115
68.92
115
80.47
115
81.42
115
MEDIAN_ROBtwo views98.41
116
99.70
116
99.30
117
97.09
116
97.02
116
96.89
116
95.77
117
97.66
116
97.28
116
98.79
118
98.94
116
99.18
116
98.14
117
96.89
116
96.88
116
99.96
118
99.16
116
100.00
116
99.99
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99.69
116
99.88
116
DPSMtwo views99.95
120
100.00
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100.00
120
99.76
117
100.00
117
100.00
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100.00
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100.00
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99.21
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100.00
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100.00
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99.95
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DPSM_ROBtwo views99.95
120
100.00
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100.00
120
99.76
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100.00
117
100.00
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100.00
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100.00
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99.21
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100.00
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DGTPSM_ROBtwo views99.90
118
100.00
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99.99
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99.99
119
100.00
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100.00
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100.00
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99.97
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100.00
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98.35
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100.00
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99.84
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100.00
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99.98
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99.99
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99.99
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100.00
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100.00
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100.00
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100.00
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AVERAGE_ROBtwo views99.62
117
99.95
117
98.81
116
100.00
121
100.00
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98.08
117
95.47
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100.00
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DPSMNet_ROBtwo views99.91
119
100.00
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99.99
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99.99
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100.00
117
100.00
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100.00
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99.98
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100.00
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98.35
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100.00
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99.84
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100.00
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99.98
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LSM0two views100.00
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100.00
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MSMDNettwo views1.26
6