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
135
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
104
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
66
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
88
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
88
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
78
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
81
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
95
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
71
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
72
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
69
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
57
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
65
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
66
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
64
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
71
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
76
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
57
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
73
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
90
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
82
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
85
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
93
12.95
108
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
121
0.91
110
0.95
114
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
99
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
75
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
77
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
63
3.54
25
1.62
30
1.04
43
3.91
23
7.41
110
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
52
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
66
3.60
26
1.94
47
1.46
51
3.92
24
4.68
89
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
85
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
86
10.73
50
7.00
27
10.78
74
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
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
97
16.91
96
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
FADNettwo views4.23
47
1.65
92
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
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
76
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
79
7.17
81
2.91
89
1.26
47
4.36
29
1.62
50
13.64
83
10.25
63
9.83
65
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
86
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
78
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
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
86
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
101
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
72
12.97
64
13.91
78
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
RASNettwo views4.52
55
0.61
25
4.42
36
3.42
106
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
83
9.28
63
8.68
81
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
105
11.55
58
10.98
65
10.94
76
13.59
67
11.65
66
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
92
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
81
7.42
83
2.40
72
2.14
71
8.73
70
3.64
73
12.42
69
13.11
77
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
117
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
94
6.38
68
1.96
49
1.29
49
2.26
3
1.69
52
10.07
33
18.53
99
7.88
40
18.15
90
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
52
PSMNet_ROBtwo views5.02
59
1.63
91
6.03
62
1.90
43
1.83
65
9.57
77
6.35
102
15.58
102
7.23
31
6.15
21
10.48
42
12.22
69
4.16
38
8.02
40
8.71
82
0.02
38
0.01
39
0.01
59
0.10
91
0.20
61
0.12
52
CBMVpermissivetwo views5.35
60
0.91
45
3.67
27
1.62
30
0.44
16
10.09
82
7.19
109
12.49
70
12.33
76
12.22
82
14.69
74
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
62
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
95
6.80
77
3.12
98
4.45
115
10.61
87
4.35
82
18.80
114
9.73
56
12.22
82
6.87
15
11.44
64
4.65
45
8.09
45
8.26
77
0.02
38
0.11
89
0.00
1
0.03
70
0.20
61
0.28
81
ETE_ROBtwo views5.80
62
1.77
96
6.33
66
1.44
22
0.78
31
6.43
54
6.90
106
12.53
71
8.08
45
12.93
90
14.89
75
21.13
113
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
83
DRN-Testtwo views5.87
63
0.98
51
5.89
60
2.69
83
3.65
105
12.37
97
3.35
67
20.07
125
10.20
62
11.93
81
12.31
60
11.06
63
5.31
55
7.89
38
9.05
85
0.04
57
0.05
67
0.04
86
0.04
79
0.18
59
0.25
75
NCCL2two views5.88
64
1.59
88
5.44
51
1.87
40
0.92
38
9.55
76
11.55
125
12.11
64
9.94
60
9.67
64
8.85
31
22.28
115
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
69
NaN_ROBtwo views6.00
65
1.24
69
6.29
65
1.34
16
1.68
60
9.60
78
10.31
121
15.09
95
15.79
89
12.62
85
8.95
33
11.67
67
5.83
60
11.78
84
6.41
58
0.05
64
0.13
95
0.08
95
0.20
100
0.22
70
0.79
109
DANettwo views6.02
66
1.23
68
8.45
88
3.86
115
3.94
109
7.64
67
1.34
48
9.51
27
7.00
27
13.39
92
15.53
79
15.99
89
7.02
71
12.14
85
12.37
105
0.19
86
0.12
94
0.02
71
0.03
70
0.13
47
0.56
100
XPNet_ROBtwo views6.03
67
1.22
67
5.61
56
2.56
79
0.90
36
6.32
51
7.07
107
12.92
74
8.30
47
14.76
97
15.13
77
19.84
107
6.66
70
10.36
72
8.58
80
0.02
38
0.04
63
0.00
1
0.03
70
0.11
40
0.24
72
Anonymous Stereotwo views6.16
68
3.15
117
23.75
126
2.97
95
2.48
81
4.39
32
13.30
127
9.21
23
9.86
58
9.56
63
8.76
30
6.79
24
1.99
11
13.50
97
13.04
108
0.01
30
0.05
67
0.00
1
0.06
83
0.22
70
0.19
64
GANettwo views6.22
69
1.07
57
4.07
33
2.27
65
0.89
34
9.19
72
9.52
116
12.02
63
8.13
46
10.72
73
29.09
115
13.86
76
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
107
1.17
45
11.22
91
3.39
68
12.14
66
16.16
91
6.52
28
11.22
47
16.96
92
6.32
64
19.51
120
10.74
98
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
108
4.18
78
13.41
79
16.49
92
10.81
75
7.00
18
14.33
80
8.72
81
9.43
65
13.71
109
0.00
1
0.01
39
0.00
1
0.00
1
0.02
7
0.07
33
GwcNetcopylefttwo views6.42
72
1.97
102
10.92
95
2.59
80
5.58
126
11.55
93
2.21
56
14.10
87
16.52
95
10.04
68
17.19
87
10.86
60
5.61
58
9.23
62
8.84
84
0.01
30
0.06
73
0.03
79
0.08
87
0.56
96
0.40
87
GANetREF_RVCpermissivetwo views6.56
73
2.89
112
7.58
85
3.41
105
0.40
14
12.96
100
9.58
117
15.09
95
17.25
98
10.33
69
10.62
44
12.27
70
8.16
77
12.21
86
4.53
41
0.41
106
0.00
1
0.00
1
0.02
61
3.12
131
0.39
86
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
98
6.25
64
1.26
14
0.94
41
10.08
81
9.02
113
16.00
103
11.51
71
12.74
87
13.02
65
24.77
117
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
75
S-Stereotwo views6.63
75
0.84
40
9.67
92
3.15
99
3.48
99
6.49
55
6.22
97
12.99
75
22.84
112
9.48
59
15.51
78
12.00
68
8.43
78
8.04
41
10.70
97
0.12
76
0.17
102
0.00
1
0.38
114
0.13
47
1.92
126
DeepPrunerFtwo views6.75
76
2.69
110
23.31
125
3.68
110
7.16
132
3.78
21
4.29
80
13.42
80
20.13
109
8.13
44
10.46
41
7.18
32
8.06
76
11.10
80
9.44
87
0.24
91
0.15
98
0.29
117
0.42
117
0.66
103
0.45
90
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
75
15.29
98
18.31
91
22.02
114
12.56
99
10.82
76
7.49
65
0.03
49
0.06
73
0.11
100
0.03
70
0.30
78
0.14
57
Abc-Nettwo views6.77
78
1.49
82
6.48
70
2.92
91
4.40
111
7.43
63
3.61
71
19.52
121
13.29
78
8.39
47
16.91
82
15.96
87
12.13
97
12.85
91
7.70
67
1.47
128
0.11
89
0.01
59
0.42
117
0.14
52
0.24
72
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
82
6.48
70
2.92
91
4.40
111
7.43
63
3.61
71
19.52
121
13.29
78
8.39
47
16.91
82
15.96
87
12.13
97
12.85
91
7.70
67
1.47
128
0.11
89
0.01
59
0.42
117
0.14
52
0.24
72
FAT-Stereotwo views6.78
80
0.68
31
6.80
77
2.30
67
1.77
64
5.63
46
4.20
79
18.79
113
18.62
100
10.53
71
17.15
85
18.52
101
13.74
107
8.86
57
7.38
64
0.03
49
0.15
98
0.01
59
0.07
85
0.12
43
0.26
78
RPtwo views6.84
81
1.29
76
5.53
54
3.92
116
5.18
123
6.32
51
3.53
69
11.73
60
15.31
87
9.54
62
22.38
98
18.25
99
14.47
108
10.11
70
7.49
65
0.91
123
0.01
39
0.12
101
0.15
96
0.33
82
0.19
64
RGCtwo views6.88
82
2.23
106
6.13
63
4.05
117
4.73
122
8.94
71
2.78
61
15.19
99
11.74
73
11.13
77
19.34
93
17.86
96
10.42
91
13.02
94
8.03
73
0.73
117
0.01
39
0.24
116
0.41
116
0.31
80
0.38
85
Nwc_Nettwo views6.97
83
1.25
70
6.63
74
3.82
114
3.37
95
10.83
90
1.67
51
19.56
123
11.35
68
8.36
46
23.62
101
17.19
94
11.44
96
11.21
81
8.08
75
0.80
119
0.00
1
0.00
1
0.02
61
0.13
47
0.09
40
STTRV1_RVCtwo views7.02
84
1.10
61
12.88
106
3.32
103
6.92
131
11.90
96
4.00
75
15.07
94
11.94
74
9.51
60
14.57
72
11.63
65
8.73
82
12.65
90
8.06
74
3.32
134
2.75
135
0.41
125
0.12
93
1.38
121
0.11
50
ADCReftwo views7.27
85
1.38
77
16.37
114
2.52
76
3.30
94
11.63
94
3.16
64
10.80
51
9.35
54
13.03
91
25.27
109
8.17
39
8.92
84
8.06
43
21.81
124
0.15
80
0.08
82
0.16
107
0.34
112
0.38
88
0.58
101
psmorigintwo views7.34
86
1.58
87
18.31
118
2.35
71
0.87
33
6.72
57
1.70
54
10.63
48
7.14
29
19.77
110
22.71
99
20.13
109
11.34
95
12.96
93
9.49
88
0.04
57
0.17
102
0.06
91
0.17
97
0.21
68
0.50
95
CSANtwo views7.62
87
1.60
89
6.56
73
1.83
38
0.66
22
12.40
98
10.52
123
14.45
90
21.32
111
14.19
94
15.98
81
17.84
95
13.02
104
12.32
87
8.38
78
0.09
70
0.07
80
0.03
79
0.04
79
0.33
82
0.67
107
stereogantwo views7.69
88
0.88
42
7.08
80
3.49
108
3.93
108
18.98
116
3.23
65
16.52
105
19.58
106
9.93
67
18.92
92
20.50
111
9.04
86
14.07
101
6.14
54
0.26
94
0.04
63
0.21
113
0.03
70
0.63
102
0.33
83
pmcnntwo views7.72
89
1.27
72
9.42
91
2.91
89
3.14
90
9.44
74
6.23
98
12.56
72
16.51
93
14.53
95
24.08
103
27.44
123
8.49
79
9.32
64
8.44
79
0.06
66
0.08
82
0.00
1
0.00
1
0.30
78
0.15
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AF-Nettwo views7.78
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1.44
80
6.68
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3.37
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8.61
69
2.69
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17.07
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61
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11.58
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9.84
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0.61
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1
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0.12
52
PASMtwo views7.90
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4.22
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3.25
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5.39
42
6.57
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10.57
46
19.09
102
12.77
88
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
86
0.45
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1.08
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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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70
31.27
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7.04
27
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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
1
0.00
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1.31
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0.17
62
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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10.80
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17.59
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0.03
49
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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
80
4.29
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40
30.07
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11.53
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12.18
57
6.12
21
6.65
69
10.50
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14.47
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0.14
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0.11
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0.35
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0.25
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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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2.43
74
4.43
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7.51
66
1.22
43
16.63
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13.99
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10.16
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0.42
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0.00
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0.27
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MDST_ROBtwo views8.37
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0.32
1
9.03
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4.18
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2.42
79
26.86
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6.14
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19.36
118
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80
27.09
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22.75
100
9.47
48
4.74
47
15.06
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6.34
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0.02
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0.02
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0.00
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0.13
56
G-Nettwo views8.41
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26.19
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4.41
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10.10
35
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33
19.71
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10.99
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9.53
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0.50
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0.19
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STTStereo_v2two views8.41
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1.54
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10.97
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5.73
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3.60
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26.19
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4.41
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10.10
35
7.42
33
19.71
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15.83
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10.99
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9.53
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0.50
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XQCtwo views8.43
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2.92
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2.17
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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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12.04
55
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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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0.08
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FBW_ROBtwo views8.50
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7.98
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1.93
46
1.28
48
13.10
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6.23
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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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aanetorigintwo views8.72
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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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15.80
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12.78
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9.04
59
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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13.57
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3.72
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1.76
63
11.82
95
0.46
14
16.95
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36.83
132
15.80
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15.53
79
12.91
73
7.46
74
20.01
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21.76
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0.31
99
0.13
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0.01
59
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
82
2.54
78
2.13
70
13.59
103
5.80
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11.64
59
14.01
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24.43
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32.99
125
27.09
122
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
59
0.01
57
0.16
54
0.23
69
MSMD_ROBtwo views9.28
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1.09
60
4.65
42
1.58
27
0.39
13
16.52
109
4.41
83
13.60
81
14.87
85
22.34
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39.89
133
25.67
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20.71
123
12.42
88
6.98
61
0.34
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0.03
58
0.00
1
0.00
1
0.05
22
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
112
6.39
103
12.11
64
11.45
69
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
97
1.16
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0.39
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1.49
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0.58
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1.45
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SHDtwo views9.61
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12.46
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3.69
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3.54
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9.47
75
1.25
44
20.16
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37.84
135
18.19
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21.24
95
16.96
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12.83
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14.47
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16.05
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0.32
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0.13
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0.01
59
0.08
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0.38
88
0.48
93
PDISCO_ROBtwo views9.62
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1.99
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11.51
98
9.88
136
9.61
137
21.48
120
3.83
74
19.33
117
28.49
121
11.27
78
14.17
70
19.92
108
5.02
52
16.35
113
9.18
86
5.28
137
0.41
117
0.14
105
0.09
90
2.05
127
2.36
132
MFN_U_SF_DS_RVCtwo views9.78
108
4.27
124
14.47
111
2.29
66
2.85
88
23.40
124
13.62
128
13.60
81
19.54
105
19.42
107
24.27
104
16.74
91
8.59
80
17.05
115
7.98
70
1.25
127
1.68
133
0.17
108
2.63
136
0.72
105
1.04
115
SGM_RVCbinarytwo views10.08
109
0.60
23
3.42
22
2.30
67
0.32
11
19.41
117
6.33
101
18.95
115
14.64
83
25.14
124
24.32
105
33.34
131
18.79
118
19.86
121
12.55
107
0.25
92
0.26
112
0.22
114
0.24
103
0.34
84
0.40
87
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DPSNettwo views10.14
110
1.88
101
16.82
116
1.85
39
1.73
62
24.84
125
17.20
137
19.92
124
27.41
119
12.23
84
13.62
68
16.52
90
18.35
115
14.42
104
12.50
106
0.78
118
0.54
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0.08
95
0.25
104
1.18
116
0.59
104
ADCLtwo views10.16
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2.11
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19.36
120
1.92
44
1.88
67
22.23
121
8.91
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14.04
86
23.56
114
14.62
96
26.19
110
12.75
72
13.59
106
16.06
112
22.95
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0.26
94
0.18
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0.75
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0.65
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0.69
104
0.58
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FCDSN-DCtwo views10.24
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0.56
20
3.49
23
1.96
49
1.29
49
16.90
113
4.59
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17.16
109
19.10
103
24.64
123
32.46
124
33.82
132
22.14
128
15.93
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10.45
95
0.04
57
0.00
1
0.00
1
0.00
1
0.05
22
0.21
66
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
ADCMidtwo views10.24
112
3.13
115
20.70
121
2.21
62
2.39
78
11.23
92
6.19
96
14.17
88
11.19
66
23.20
120
22.25
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17.89
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19.54
120
18.51
118
26.21
132
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
125
1.18
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SANettwo views10.64
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1.86
100
10.91
94
1.76
36
0.71
25
14.62
106
9.23
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19.18
116
37.14
133
19.22
106
27.96
111
25.86
120
19.11
119
13.02
94
10.63
96
0.08
69
0.06
73
0.03
79
0.02
61
0.62
101
0.81
110
FC-DCNNcopylefttwo views10.72
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0.52
15
4.27
34
1.88
41
1.63
56
17.18
114
5.29
92
18.20
111
19.69
107
28.50
127
34.51
128
34.03
133
21.48
126
15.89
110
11.15
102
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
124
4.16
118
5.83
127
15.64
107
14.30
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13.18
77
17.15
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16.44
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20.52
94
14.68
83
13.44
105
22.46
125
30.08
137
0.17
84
0.26
112
0.36
122
0.36
113
1.23
117
0.91
113
MeshStereopermissivetwo views11.52
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1.52
84
4.55
40
1.89
42
1.46
51
19.87
119
5.11
90
20.66
127
15.91
90
32.67
132
34.51
128
39.34
138
21.15
124
18.74
119
12.10
104
0.11
73
0.06
73
0.01
59
0.00
1
0.45
95
0.22
68
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
118
3.66
122
25.81
128
3.61
109
2.26
75
22.77
122
4.55
87
27.10
134
20.06
108
23.90
121
28.99
114
30.53
128
16.98
112
19.92
122
20.26
118
1.24
126
1.07
128
0.98
131
1.33
132
1.80
126
2.04
128
ADCStwo views13.02
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4.93
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28.38
130
3.17
100
2.67
85
13.61
104
10.83
124
18.70
112
33.46
127
22.59
115
24.78
106
19.59
106
18.51
117
23.40
128
32.16
139
0.10
72
0.19
105
0.37
123
0.18
98
1.26
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1.46
123
MFMNet_retwo views13.29
120
8.60
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18.29
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9.75
135
7.25
134
19.65
118
14.84
133
20.71
128
30.72
124
23.03
118
28.77
113
18.85
103
26.09
133
13.55
98
9.82
91
2.44
131
1.35
132
0.34
120
0.23
102
4.78
135
6.69
137
EDNetEfficienttwo views13.82
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6.19
128
54.79
137
2.83
86
3.60
102
4.86
37
8.57
111
8.61
18
32.90
126
40.77
138
30.59
118
18.33
100
21.87
127
11.38
82
25.36
130
0.15
80
0.23
109
0.06
91
1.03
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2.54
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1.79
124
LSMtwo views14.01
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5.95
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33.49
132
6.78
133
43.61
142
10.22
83
9.98
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15.16
98
22.93
113
23.07
119
32.34
123
18.52
101
12.67
100
15.45
109
11.10
101
0.16
83
0.51
125
0.09
98
0.32
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1.08
114
16.85
142
SAMSARAtwo views14.63
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2.74
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12.38
102
12.65
139
6.74
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36.50
135
72.93
143
19.36
118
23.77
115
16.20
101
13.04
66
29.21
125
12.78
101
16.98
114
15.21
112
0.11
73
0.26
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0.03
79
0.14
95
0.76
106
0.77
108
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
140
23.30
123
7.15
108
24.16
131
15.65
88
31.78
131
29.19
116
31.62
129
21.32
125
24.62
129
19.50
117
7.59
139
4.19
140
3.22
137
1.48
133
6.99
139
6.54
136
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
98
17.43
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4.16
77
27.84
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48.84
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31.02
130
43.54
137
29.76
127
30.81
137
25.97
131
21.40
121
0.23
90
0.41
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0.04
86
0.33
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0.41
93
1.33
120
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
139
26.25
128
10.49
122
19.36
118
14.65
84
30.64
129
33.59
126
33.00
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22.32
129
24.96
130
16.42
114
7.90
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6.25
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4.51
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3.37
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5.86
137
7.20
138
NVStereoNet_ROBtwo views16.04
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6.75
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12.90
107
6.37
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7.42
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12.89
99
9.74
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22.78
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25.12
116
30.32
128
46.19
139
34.37
134
25.38
131
21.48
124
21.38
120
5.94
138
3.10
138
6.07
139
10.09
141
4.01
133
8.54
140
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
128
10.81
137
59.36
138
4.42
122
2.49
82
30.06
131
15.15
135
17.51
110
16.51
93
17.88
103
37.69
132
24.04
116
17.54
113
29.60
134
33.29
140
0.28
98
0.38
115
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
136
14.59
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24.55
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34.87
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33.66
133
34.35
127
29.24
126
24.20
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22.59
126
17.95
115
2.52
132
2.81
136
1.17
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1.51
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5.89
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2.16
129
ELAS_RVCcopylefttwo views16.54
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2.26
107
10.09
93
5.50
127
4.46
116
28.28
130
16.72
136
25.55
133
33.54
128
40.19
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135
36.68
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30.03
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29.40
133
20.61
119
0.98
125
1.21
130
0.86
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0.70
125
1.39
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2.16
129
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
90
4.92
124
4.53
119
32.66
134
15.11
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27.40
135
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
136
26.44
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21.94
125
0.88
121
1.23
131
0.67
127
0.89
128
1.49
123
2.18
131
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
LE_ROBtwo views16.73
132
1.28
75
11.61
99
3.72
112
1.65
57
16.67
111
9.17
114
14.39
89
55.91
142
63.81
142
40.86
136
35.94
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37.73
140
14.24
102
26.87
133
0.05
64
0.10
87
0.13
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0.22
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0.12
43
0.15
60
SGM-ForestMtwo views16.99
133
1.08
59
5.74
57
2.12
57
0.75
27
31.63
133
12.21
126
27.80
136
32.25
125
37.88
134
39.99
134
52.96
141
35.20
139
33.60
136
24.47
128
0.26
94
0.39
116
0.31
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0.39
115
0.26
76
0.53
99
DispFullNettwo views17.47
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26.01
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33.98
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22.58
141
20.86
141
13.84
105
1.28
45
16.50
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26.27
118
19.97
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17.17
86
20.52
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18.49
116
22.86
127
10.76
99
5.13
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30.72
141
7.72
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20.86
141
11.01
141
EDNetEfficientorigintwo views17.87
135
14.00
138
99.53
146
1.64
32
0.92
38
10.55
86
6.26
100
13.21
78
34.84
129
38.20
135
36.91
131
27.47
124
26.32
134
14.71
106
25.69
131
0.04
57
0.06
73
0.14
105
0.52
122
3.16
132
3.27
133
RTStwo views18.87
136
9.32
135
86.48
141
4.95
125
6.10
128
42.08
138
14.70
131
15.49
100
41.06
137
22.65
116
32.32
121
13.77
74
19.54
120
37.98
137
28.96
134
0.41
106
0.23
109
0.00
1
0.02
61
0.91
110
0.50
95
RTSAtwo views18.87
136
9.32
135
86.48
141
4.95
125
6.10
128
42.08
138
14.70
131
15.49
100
41.06
137
22.65
116
32.32
121
13.77
74
19.54
120
37.98
137
28.96
134
0.41
106
0.23
109
0.00
1
0.02
61
0.91
110
0.50
95
MANEtwo views19.47
138
1.27
72
5.07
47
4.69
123
5.55
125
30.49
132
9.94
119
34.01
139
37.27
134
44.13
139
51.57
142
52.51
140
40.41
141
33.58
135
24.81
129
0.89
122
0.86
127
1.11
133
9.72
140
0.38
88
1.06
116
BEATNet-Init1two views23.31
139
9.03
134
41.67
134
4.17
119
2.53
83
45.68
140
19.47
138
33.43
138
38.45
136
47.59
141
49.10
140
59.31
142
41.80
142
38.35
139
29.21
136
0.47
111
0.50
124
0.81
129
0.66
124
2.10
128
1.86
125
MADNet+two views27.07
140
33.84
141
90.97
143
20.14
140
7.47
136
48.43
141
47.10
140
35.43
140
36.46
131
20.11
112
30.05
117
25.29
118
35.08
138
45.50
142
50.28
141
2.13
130
2.00
134
1.19
135
0.76
126
4.71
134
4.43
134
PWCKtwo views30.53
141
44.32
142
47.25
136
29.76
142
7.23
133
40.78
137
27.10
139
44.73
142
44.32
139
47.31
140
36.37
130
47.16
139
26.05
132
41.26
141
31.87
138
21.83
141
4.03
139
29.50
140
4.67
138
27.17
142
7.80
139
DPSimNet_ROBtwo views53.45
142
64.73
143
44.39
135
53.97
143
45.39
143
53.66
142
54.83
141
55.15
143
57.87
143
64.16
143
50.83
141
63.40
143
53.34
143
46.45
143
65.81
142
63.13
144
26.54
143
57.94
144
51.11
143
45.52
143
50.69
143
ASD4two views75.85
143
81.57
144
69.25
139
68.23
144
80.87
144
96.24
144
99.15
146
77.50
144
93.47
145
77.59
144
85.63
143
78.32
144
76.43
144
77.89
144
82.61
143
58.30
143
62.67
144
55.02
143
62.43
144
68.35
144
65.49
144
MADNet++two views82.84
144
82.38
145
73.57
140
87.72
145
82.97
145
93.14
143
69.15
142
86.42
145
82.50
144
93.46
145
86.70
144
86.28
145
80.92
145
88.34
145
88.84
144
86.83
145
84.17
145
72.64
145
68.92
145
80.47
145
81.42
145
MEDIAN_ROBtwo views98.41
145
99.70
146
99.30
145
97.09
146
97.02
146
96.89
145
95.77
145
97.66
146
97.28
146
98.79
148
98.94
145
99.18
146
98.14
146
96.89
146
96.88
145
99.96
148
99.16
146
100.00
146
99.99
146
99.69
146
99.88
146
AVERAGE_ROBtwo views99.62
146
99.95
147
98.81
144
100.00
151
100.00
147
98.08
146
95.47
144
100.00
149
100.00
147
100.00
149
100.00
146
100.00
149
100.00
147
100.00
149
99.99
146
100.00
150
100.00
147
100.00
146
100.00
147
100.00
149
100.00
150
DGTPSM_ROBtwo views99.90
147
100.00
148
99.99
147
99.99
149
100.00
147
100.00
147
100.00
147
99.97
147
100.00
147
98.35
146
100.00
146
99.84
147
100.00
147
99.98
147
99.99
146
99.99
149
100.00
147
100.00
146
100.00
147
100.00
149
100.00
150
DPSMNet_ROBtwo views99.91
148
100.00
148
99.99
147
99.99
149
100.00
147
100.00
147
100.00
147
99.98
148
100.00
147
98.35
146
100.00
146
99.84
147
100.00
147
99.98
147
99.99
146
100.00
150
100.00
147
100.00
146
100.00
147
100.00
149
100.00
150
DPSM_ROBtwo views99.95
149
100.00
148
100.00
149
99.76
147
100.00
147
100.00
147
100.00
147
100.00
149
100.00
147
100.00
149
100.00
146
100.00
149
100.00
147
100.00
149
100.00
149
99.21
146
100.00
147
100.00
146
100.00
147
99.99
147
99.95
147
DPSMtwo views99.95
149
100.00
148
100.00
149
99.76
147
100.00
147
100.00
147
100.00
147
100.00
149
100.00
147
100.00
149
100.00
146
100.00
149
100.00
147
100.00
149
100.00
149
99.21
146
100.00
147
100.00
146
100.00
147
99.99
147
99.95
147
LSM0two views100.00
151
100.00
148
100.00
149
100.00
151
100.00
147
100.00
147
100.00
147
100.00
149
100.00
147
100.00
149
100.00
146
100.00
149
100.00
147
100.00
149
100.00
149
100.00
150
100.00
147
100.00
146
100.00
147
100.00
149
99.99
149
FADEtwo views17.27
139
10.46
137
9.90
138
35.80
141
53.05
141
20.32
113
26.54
121
39.35
140
30.62
142
14.22
142
38.39
142
37.63
142
5.22
136
5.56
135
MSMDNettwo views1.26
7