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 views3.58
1
1.05
5
3.35
2
2.17
2
0.59
8
5.61
15
0.40
5
10.50
6
13.34
17
6.93
5
10.44
5
5.29
10
3.47
7
5.82
5
2.62
6
0.01
8
0.01
4
0.02
29
0.01
26
0.04
4
0.01
1
Jiankun Li, Peisen Wang, Pengfei Xiong, Tao Cai, Ziwei Yan, Lei Yang, Jiangyu Liu, Haoqiang Fan, Shuaicheng Liu: Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation. CVPR 2022
XX-TBDtwo views4.45
2
10.67
141
3.01
1
10.28
110
0.57
6
5.84
18
0.07
1
7.57
1
12.24
13
11.43
17
14.74
13
4.07
4
1.66
1
4.51
1
2.05
1
0.01
8
0.01
4
0.00
1
0.00
1
0.26
23
0.01
1
s12784htwo views4.61
3
0.99
2
3.70
4
4.08
11
2.01
27
3.94
4
0.10
2
9.10
3
16.94
26
8.65
7
16.77
21
4.46
5
2.18
2
5.72
3
3.12
15
0.00
1
0.00
1
0.00
1
10.32
168
0.02
1
0.01
1
CREStereo++_RVCtwo views4.61
3
0.99
2
3.70
4
4.08
11
2.01
27
3.94
4
0.10
2
9.10
3
16.94
26
8.65
7
16.77
21
4.46
5
2.18
2
5.72
3
3.12
15
0.00
1
0.00
1
0.00
1
10.32
168
0.02
1
0.01
1
iRaftStereo_RVCtwo views4.68
5
1.51
20
6.89
22
4.91
19
0.72
13
5.26
13
0.46
6
17.60
30
10.47
9
9.53
10
15.13
15
5.75
11
3.24
5
8.93
17
2.22
4
0.00
1
0.09
54
0.00
1
0.00
1
0.46
39
0.41
43
TANstereotwo views4.70
6
1.08
6
4.01
6
3.53
6
0.47
5
14.26
59
0.15
4
8.67
2
12.54
14
12.50
22
18.43
25
5.20
9
4.54
12
5.68
2
2.07
2
0.00
1
0.13
64
0.00
1
0.01
26
0.02
1
0.63
57
sCroCo_RVCtwo views5.06
7
3.29
78
16.45
100
10.72
113
6.64
105
5.83
17
2.74
28
9.67
5
8.06
6
3.66
1
9.20
4
4.81
7
4.95
13
8.12
11
4.64
25
0.64
92
0.50
106
0.06
42
0.03
42
0.70
62
0.42
44
EAI-Stereotwo views5.21
8
1.74
37
8.46
45
5.69
30
1.37
21
5.67
16
3.52
29
18.77
31
5.65
2
9.75
11
16.08
16
6.36
13
3.57
8
11.73
34
3.64
18
0.01
8
0.33
87
0.03
31
1.06
119
0.21
19
0.65
63
MSMDNettwo views5.23
9
1.32
11
8.33
41
4.10
14
1.51
23
6.39
23
1.42
14
15.36
16
8.28
7
12.19
20
14.83
14
12.21
32
5.84
22
8.97
18
3.05
13
0.01
8
0.24
76
0.00
1
0.27
80
0.27
27
0.07
8
PMTNettwo views5.36
10
0.97
1
3.42
3
1.97
1
1.30
16
8.96
31
0.69
8
12.33
7
12.16
12
8.50
6
11.35
8
6.09
12
4.50
11
9.34
21
2.70
7
22.71
176
0.01
4
0.04
34
0.02
34
0.06
5
0.01
1
RAFT-Stereo + iAFFtwo views5.48
11
1.66
31
7.56
35
3.49
4
0.40
4
2.75
1
0.67
7
16.07
24
14.76
22
12.27
21
18.67
26
11.95
29
8.08
29
7.84
10
2.89
8
0.01
8
0.06
37
0.00
1
0.00
1
0.37
34
0.04
7
Gwc-CoAtRStwo views5.54
12
1.22
8
8.40
43
4.28
15
1.36
20
6.93
26
1.31
12
15.48
17
15.03
23
12.62
23
13.32
10
12.74
34
5.71
21
8.61
12
2.98
12
0.01
8
0.22
73
0.00
1
0.29
86
0.28
28
0.07
8
CFNet-RSSMtwo views5.55
13
1.08
6
8.60
47
4.30
17
1.21
15
6.42
24
1.25
11
16.12
25
17.84
28
13.12
26
11.14
7
11.84
28
5.52
15
9.14
20
2.35
5
0.01
8
0.20
71
0.00
1
0.36
93
0.34
31
0.07
8
raftrobusttwo views5.56
14
1.42
15
6.00
13
7.04
59
5.11
78
4.90
12
1.88
22
17.13
28
13.72
18
11.87
18
11.58
9
7.82
18
9.21
33
10.28
22
2.92
11
0.02
20
0.05
29
0.00
1
0.00
1
0.22
20
0.11
14
GMStereotwo views5.94
15
3.84
90
10.86
65
5.13
20
0.71
12
7.27
27
2.66
27
12.38
8
25.14
55
12.01
19
10.66
6
5.12
8
3.46
6
11.39
30
6.16
37
0.45
81
0.02
14
0.05
36
0.31
88
0.86
71
0.25
29
RALCasStereoNettwo views5.95
16
2.02
50
7.09
26
7.06
60
5.10
77
6.36
22
5.44
50
13.97
11
13.25
16
9.93
12
17.45
23
10.37
25
3.89
9
11.64
33
4.24
23
0.02
20
0.46
103
0.10
53
0.00
1
0.20
18
0.31
34
TestStereotwo views6.02
17
2.55
63
7.33
31
5.57
27
1.68
25
6.05
19
1.78
21
13.73
9
16.40
24
10.87
16
23.79
37
9.00
22
5.63
18
11.26
29
3.96
22
0.09
38
0.01
4
0.00
1
0.00
1
0.69
60
0.01
1
rafts_anoytwo views6.02
17
1.67
32
6.27
16
6.53
48
4.98
73
5.30
14
2.53
25
13.77
10
21.01
41
12.70
24
16.23
18
8.56
20
5.65
19
9.09
19
5.22
28
0.04
27
0.31
84
0.09
52
0.03
42
0.25
22
0.17
25
XX-Stereotwo views6.14
19
1.47
18
9.11
50
11.87
125
6.76
109
3.27
2
0.93
10
15.13
13
5.43
1
8.79
9
28.46
58
13.71
37
2.22
4
11.51
31
2.07
2
0.20
54
0.24
76
0.08
48
0.29
86
1.04
76
0.13
18
AFF-stereotwo views6.17
20
1.61
28
7.02
24
4.08
11
0.64
9
3.53
3
0.76
9
16.00
23
13.14
15
12.86
25
25.42
41
15.67
42
7.86
25
11.12
28
3.21
17
0.02
20
0.06
37
0.00
1
0.00
1
0.40
36
0.08
11
GwcNet-DCAtwo views6.46
21
1.59
25
7.28
27
3.51
5
0.68
10
4.77
11
1.75
20
15.33
15
23.62
50
15.55
41
20.73
32
7.31
17
11.56
46
8.83
16
5.26
29
0.00
1
0.05
29
0.00
1
0.00
1
0.26
23
1.11
83
RALAANettwo views6.49
22
2.49
61
9.69
55
7.09
61
4.98
73
6.23
20
1.68
16
19.90
32
14.26
19
15.04
34
14.56
11
12.69
33
4.38
10
12.91
41
3.07
14
0.02
20
0.07
43
0.00
1
0.10
61
0.51
46
0.15
21
222two views6.59
23
1.55
22
9.64
54
3.42
3
0.68
10
4.73
8
1.70
17
15.75
20
24.33
54
15.11
35
20.25
28
6.90
14
12.80
51
8.63
13
5.33
30
0.01
8
0.04
21
0.00
1
0.00
1
0.26
23
0.63
57
sAnonymous2two views6.59
23
6.21
115
19.05
114
8.91
85
5.27
83
4.58
6
6.69
69
15.56
18
11.12
10
4.86
3
8.71
2
3.81
2
7.93
26
7.38
6
2.89
8
0.09
38
0.07
43
0.05
36
0.28
83
11.25
164
7.10
154
CroCo_RVCtwo views6.59
23
6.21
115
19.05
114
8.91
85
5.27
83
4.58
6
6.69
69
15.56
18
11.12
10
4.86
3
8.71
2
3.81
2
7.93
26
7.38
6
2.89
8
0.09
38
0.07
43
0.05
36
0.28
83
11.25
164
7.10
154
raft+_RVCtwo views6.69
26
1.61
28
5.57
12
6.37
43
4.62
64
6.51
25
6.18
63
20.39
33
23.38
49
10.62
15
17.82
24
9.89
24
5.65
19
10.70
24
3.67
19
0.00
1
0.14
65
0.00
1
0.00
1
0.34
31
0.27
32
Anonymoustwo views6.73
27
2.83
67
18.61
111
11.88
126
7.83
126
14.52
61
9.37
83
14.49
12
10.15
8
4.28
2
6.89
1
3.13
1
7.67
24
11.82
35
5.66
33
0.83
98
0.35
91
0.08
48
0.20
71
2.86
115
1.09
82
DIP-Stereotwo views6.74
28
1.93
45
10.02
59
4.28
15
1.64
24
13.54
52
1.52
15
15.16
14
6.63
3
14.30
27
26.81
50
10.90
26
5.19
14
14.78
52
7.56
40
0.00
1
0.06
37
0.05
36
0.00
1
0.17
15
0.17
25
Zihua Zheng, Ni Nie, Zhi Ling, Pengfei Xiong, Jiangyu Liu, Hao Wang, Jiankun Li: DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow. cvpr2022
csctwo views6.83
29
1.55
22
7.28
27
3.56
7
0.32
1
4.73
8
1.70
17
15.75
20
31.87
84
15.11
35
20.25
28
6.90
14
12.80
51
8.63
13
5.33
30
0.01
8
0.04
21
0.00
1
0.00
1
0.09
6
0.63
57
cscssctwo views6.83
29
1.55
22
7.28
27
3.56
7
0.32
1
4.73
8
1.70
17
15.75
20
31.87
84
15.11
35
20.25
28
6.90
14
12.80
51
8.63
13
5.33
30
0.01
8
0.04
21
0.00
1
0.00
1
0.09
6
0.63
57
RAFT-Stereopermissivetwo views7.04
31
1.34
13
7.89
37
6.23
40
2.30
33
7.51
29
5.54
52
23.96
47
6.94
4
16.26
46
33.57
93
12.04
30
5.61
16
7.67
8
3.74
20
0.01
8
0.05
29
0.01
25
0.00
1
0.10
9
0.12
15
Lahav Lipson, Zachary Teed, and Jia Deng: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. 3DV
R-Stereo Traintwo views7.04
31
1.34
13
7.89
37
6.23
40
2.30
33
7.51
29
5.54
52
23.96
47
6.94
4
16.26
46
33.57
93
12.04
30
5.61
16
7.67
8
3.74
20
0.01
8
0.05
29
0.01
25
0.00
1
0.10
9
0.12
15
111two views7.26
33
1.65
30
7.28
27
3.56
7
0.32
1
10.76
35
3.53
30
20.55
34
27.51
67
10.07
13
16.62
20
14.30
38
9.53
36
12.02
36
5.87
35
0.14
47
0.01
4
0.00
1
0.00
1
0.31
29
1.14
87
HITNettwo views7.83
34
2.93
70
8.39
42
4.76
18
1.48
22
12.75
44
4.64
41
20.97
35
14.37
21
15.14
39
22.15
33
14.57
40
10.62
38
14.38
50
8.14
45
0.04
27
0.01
4
0.48
85
0.02
34
0.57
53
0.10
13
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
FENettwo views7.94
35
1.01
4
6.72
19
5.87
33
2.07
30
11.11
37
2.03
23
21.39
37
20.81
39
17.14
54
22.65
34
14.43
39
11.54
45
12.97
43
7.89
42
0.15
49
0.24
76
0.03
31
0.02
34
0.14
12
0.62
56
DN-CSS_ROBtwo views8.17
36
2.59
64
14.14
83
6.11
38
3.22
43
6.31
21
3.53
30
22.92
45
20.59
38
16.66
51
30.05
67
9.31
23
7.95
28
13.49
45
4.78
26
0.72
96
0.00
1
0.45
83
0.00
1
0.54
49
0.09
12
test_xeample3two views8.30
37
1.88
41
7.48
33
3.74
10
0.57
6
7.49
28
9.01
80
16.85
26
31.87
84
14.47
28
28.31
57
12.88
35
11.56
46
10.87
27
4.39
24
0.04
27
0.10
57
0.00
1
0.02
34
0.09
6
4.31
135
ARAFTtwo views8.50
38
3.19
77
16.58
101
5.52
26
0.91
14
12.13
41
11.20
101
17.19
29
18.28
30
18.40
59
16.33
19
17.10
44
8.58
31
18.56
73
5.00
27
0.11
43
0.04
21
0.00
1
0.00
1
0.55
51
0.25
29
DMCAtwo views8.51
39
1.90
43
9.96
58
5.16
21
4.14
55
10.29
34
4.46
38
25.52
55
19.55
35
16.33
48
20.69
31
17.59
47
11.26
41
10.42
23
11.36
66
0.50
84
0.08
50
0.35
75
0.05
50
0.36
33
0.31
34
GEStwo views8.51
39
1.98
48
11.82
69
6.39
45
1.77
26
12.36
43
6.22
64
21.55
39
21.34
42
19.02
62
26.02
44
8.79
21
9.34
34
13.94
48
7.24
39
0.38
71
0.10
57
0.30
73
0.14
66
0.69
60
0.73
68
HCRNettwo views8.80
41
1.43
16
7.74
36
14.41
140
5.25
79
10.85
36
2.56
26
26.07
61
18.04
29
15.48
40
25.03
40
18.63
50
9.93
37
13.01
44
6.76
38
0.04
27
0.04
21
0.10
53
0.01
26
0.18
17
0.38
38
CFNet-pseudotwo views9.29
42
1.31
10
7.48
33
6.39
45
3.68
47
13.69
53
4.61
39
32.46
99
28.01
68
17.36
55
29.86
64
11.43
27
8.43
30
10.79
25
9.29
53
0.04
27
0.09
54
0.08
48
0.04
47
0.17
15
0.60
55
MLCVtwo views9.32
43
2.22
58
12.55
74
5.22
23
1.32
17
13.79
54
1.35
13
21.14
36
23.65
51
21.91
80
31.37
77
17.14
45
7.57
23
16.00
60
10.18
56
0.22
56
0.01
4
0.03
31
0.02
34
0.40
36
0.29
33
BEATNet_4xtwo views9.37
44
4.85
101
12.05
71
5.48
25
1.35
19
13.15
49
6.15
62
25.06
54
16.74
25
16.18
45
24.65
39
17.29
46
12.51
49
16.91
66
10.92
61
0.38
71
0.18
69
0.81
102
0.05
50
1.95
101
0.65
63
pcwnet_v2two views9.48
45
1.46
17
7.39
32
5.95
35
3.44
45
15.21
65
4.63
40
28.87
75
18.88
32
16.52
49
38.32
117
15.41
41
11.27
42
10.79
25
8.99
50
0.26
61
0.30
82
0.07
45
0.11
63
0.48
40
1.19
89
UCFNet_RVCtwo views9.48
45
2.12
54
4.89
7
7.03
58
3.78
50
16.05
72
6.37
65
28.25
72
20.00
37
15.93
43
23.07
35
25.21
79
12.79
50
12.57
39
8.01
44
0.07
35
0.04
21
1.17
116
0.23
76
1.31
88
0.69
65
GANet-RSSMtwo views9.48
45
1.32
11
6.45
17
6.72
53
5.51
88
12.78
45
6.03
59
26.55
63
18.96
33
14.92
30
27.95
56
25.41
81
14.69
71
13.52
46
7.60
41
0.10
42
0.10
57
0.16
63
0.01
26
0.51
46
0.39
39
Anonymous3two views9.60
48
3.45
82
29.83
137
9.58
95
4.75
66
24.51
113
10.20
91
24.04
49
18.77
31
10.42
14
14.64
12
7.97
19
8.75
32
14.05
49
5.80
34
2.44
137
1.39
134
0.08
48
0.06
55
0.73
66
0.48
49
PSMNet-RSSMtwo views9.71
49
1.73
36
6.09
14
7.47
68
4.27
58
10.06
32
6.00
57
29.14
77
21.74
44
15.76
42
32.88
90
22.63
63
13.18
54
12.94
42
9.12
52
0.09
38
0.02
14
0.07
45
0.10
61
0.70
62
0.24
28
CFNet-ftpermissivetwo views9.87
50
2.18
56
5.49
9
7.45
66
5.25
79
15.76
69
5.98
55
21.89
40
21.87
45
14.96
31
30.02
65
25.68
82
13.68
58
12.46
37
10.94
62
0.08
36
0.06
37
1.83
126
0.22
73
1.09
77
0.43
45
CFNet_RVCtwo views9.87
50
2.18
56
5.49
9
7.45
66
5.25
79
15.76
69
5.98
55
21.89
40
21.87
45
14.96
31
30.02
65
25.68
82
13.68
58
12.46
37
10.94
62
0.08
36
0.06
37
1.83
126
0.22
73
1.09
77
0.43
45
cf-rtwo views10.06
52
1.60
26
7.99
39
5.82
31
5.70
94
12.92
47
4.78
44
24.18
51
27.47
66
16.91
53
31.02
74
25.12
78
14.05
63
11.63
32
11.01
64
0.03
25
0.03
18
0.05
36
0.03
42
0.43
38
0.39
39
AdaStereotwo views10.22
53
3.63
83
9.14
53
9.15
88
3.24
44
15.79
71
5.39
49
31.94
93
25.42
56
18.39
58
26.47
46
19.44
53
9.50
35
16.60
65
8.59
47
0.44
80
0.05
29
0.40
81
0.00
1
0.57
53
0.16
24
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.
iResNettwo views10.26
54
3.10
76
15.72
93
7.35
63
2.13
31
13.86
55
7.07
75
22.80
44
28.01
68
20.10
71
30.40
70
16.79
43
11.06
40
14.46
51
10.29
59
0.39
75
0.05
29
0.00
1
0.07
59
0.73
66
0.73
68
RAFT + AFFtwo views10.27
55
3.77
86
17.13
103
10.24
108
4.97
71
13.08
48
11.52
106
25.69
56
25.80
58
14.72
29
16.12
17
19.16
52
11.35
44
16.24
63
9.04
51
0.30
65
1.46
136
0.66
95
0.20
71
2.03
105
1.94
107
ACVNettwo views10.36
56
1.68
33
5.49
9
6.01
36
2.75
38
12.78
45
3.56
32
21.89
40
34.81
99
14.96
31
30.78
72
24.72
76
13.88
61
15.81
56
17.20
89
0.13
45
0.01
4
0.06
42
0.06
55
0.51
46
0.15
21
GwcNet-RSSMtwo views10.65
57
1.94
46
8.57
46
6.89
56
5.26
82
12.26
42
5.21
47
25.80
59
31.50
81
16.67
52
33.52
92
23.70
71
13.88
61
12.84
40
13.67
71
0.05
32
0.10
57
0.14
61
0.02
34
0.65
58
0.39
39
CFNettwo views10.67
58
2.33
59
8.90
49
6.66
50
4.09
54
16.05
72
4.26
36
30.44
84
31.01
80
18.53
60
26.48
47
23.69
70
13.68
58
15.27
54
10.59
60
0.05
32
0.02
14
0.44
82
0.05
50
0.73
66
0.23
27
DeepPruner_ROBtwo views10.77
59
4.56
96
13.13
77
6.37
43
4.28
59
10.14
33
6.86
71
36.42
124
19.59
36
17.87
56
27.37
54
23.19
66
12.26
48
19.09
80
10.23
58
1.05
112
0.48
105
0.23
67
0.15
68
0.89
73
1.17
88
HSM-Net_RVCpermissivetwo views10.88
60
1.23
9
5.46
8
5.57
27
2.63
36
20.12
92
6.06
60
27.67
67
28.35
71
20.63
75
25.51
42
34.01
114
14.38
70
16.18
62
9.29
53
0.11
43
0.07
43
0.01
25
0.00
1
0.13
11
0.14
20
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
NOSS_ROBtwo views10.99
61
3.81
88
6.88
21
6.69
51
2.30
33
16.06
74
10.94
98
30.06
82
32.41
90
16.10
44
18.72
27
22.78
64
14.21
67
17.52
69
8.62
48
2.35
132
1.84
140
3.32
147
1.57
130
2.06
106
1.52
98
ac_64two views11.10
62
1.49
19
9.88
57
8.24
82
5.45
87
19.12
86
6.46
67
24.10
50
14.34
20
22.80
83
23.87
38
33.28
112
15.54
75
18.87
77
17.26
91
0.28
63
0.12
63
0.02
29
0.02
34
0.26
23
0.63
57
HSMtwo views11.33
63
1.80
38
7.04
25
6.13
39
3.99
53
16.16
77
6.92
73
29.69
80
19.39
34
22.95
84
26.05
45
41.08
142
15.79
78
20.25
92
8.89
49
0.02
20
0.03
18
0.01
25
0.00
1
0.15
13
0.34
37
DMCA-RVCcopylefttwo views11.41
64
3.32
79
11.68
68
10.12
106
4.81
67
11.92
40
4.11
34
24.67
52
23.66
52
20.06
70
28.51
59
26.71
86
24.96
119
15.95
59
14.82
75
0.41
76
0.34
90
0.56
90
0.16
69
0.72
65
0.69
65
RASNettwo views11.59
65
1.71
35
10.36
61
6.30
42
5.66
92
15.48
68
6.12
61
34.50
113
20.86
40
16.61
50
32.51
87
22.04
62
23.00
108
19.44
83
16.15
84
0.71
94
0.03
18
0.05
36
0.01
26
0.15
13
0.13
18
iResNet_ROBtwo views11.71
66
1.96
47
10.77
62
6.01
36
3.68
47
15.13
63
2.34
24
31.72
92
37.58
110
26.16
104
32.77
88
25.88
84
15.34
74
16.35
64
7.94
43
0.19
52
0.01
4
0.00
1
0.00
1
0.31
29
0.12
15
UPFNettwo views11.72
67
1.52
21
10.33
60
7.38
64
7.14
116
20.05
91
9.62
86
28.90
76
25.94
60
20.02
68
29.73
63
20.91
57
16.34
81
18.61
75
16.17
85
0.26
61
0.17
68
0.25
69
0.03
42
0.55
51
0.56
53
FADNet-RVC-Resampletwo views11.72
67
4.35
94
30.33
140
9.46
93
3.90
51
14.11
56
7.17
76
25.78
57
23.31
48
23.27
87
29.52
62
19.50
55
15.00
73
15.38
55
8.33
46
0.53
85
0.50
106
0.49
86
0.50
102
1.38
90
1.67
102
CBMV_ROBtwo views11.77
69
2.89
68
6.92
23
5.20
22
2.88
40
14.12
57
4.79
45
26.69
64
29.84
73
26.17
105
31.92
81
23.61
69
20.74
104
20.20
91
10.18
56
1.78
125
1.80
139
2.17
132
1.26
126
1.61
92
0.55
51
DSFCAtwo views11.85
70
2.01
49
12.93
76
5.40
24
6.87
111
15.36
66
10.45
95
30.68
86
31.84
83
25.99
102
23.52
36
22.03
61
14.05
63
20.28
94
12.46
67
0.57
87
0.41
95
0.28
72
0.38
96
1.02
74
0.47
48
FADNet_RVCtwo views11.94
71
5.17
105
39.74
153
6.44
47
3.46
46
11.42
38
4.17
35
29.16
78
26.89
63
17.97
57
25.96
43
13.41
36
15.67
76
15.84
58
13.54
70
0.90
104
0.75
123
0.55
89
1.20
124
3.65
128
2.85
119
iResNetv2_ROBtwo views12.00
72
2.91
69
13.46
79
5.82
31
3.73
49
13.15
49
6.45
66
34.15
108
36.02
104
25.25
98
33.81
96
29.79
96
14.24
68
13.61
47
6.10
36
0.21
55
0.02
14
0.12
58
0.00
1
0.82
70
0.25
29
hitnet-ftcopylefttwo views12.04
73
2.11
53
6.14
15
7.27
62
3.90
51
25.98
121
3.56
32
22.56
43
23.74
53
20.55
74
33.81
96
31.59
103
18.67
96
19.47
84
17.20
89
0.42
79
1.36
133
0.35
75
0.07
59
1.11
80
0.86
72
NLCA_NET_v2_RVCtwo views12.05
74
3.43
81
16.25
97
7.62
70
5.58
89
14.57
62
6.00
57
32.98
103
27.41
65
21.69
79
31.40
78
25.21
79
13.47
57
14.96
53
15.60
80
0.84
100
0.33
87
0.35
75
0.35
92
1.29
86
1.78
104
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
acv_fttwo views12.16
75
1.68
33
11.97
70
7.70
72
6.20
97
19.61
89
5.52
51
25.78
57
34.81
99
25.62
101
30.78
72
24.72
76
13.20
56
15.81
56
18.80
96
0.13
45
0.01
4
0.06
42
0.06
55
0.63
56
0.15
21
UNettwo views12.64
76
2.02
50
13.36
78
6.85
55
7.76
125
25.31
119
4.74
43
34.10
107
28.19
70
20.03
69
31.07
75
27.02
89
17.36
88
20.18
90
13.87
72
0.06
34
0.05
29
0.27
70
0.01
26
0.23
21
0.39
39
delettwo views12.65
77
1.85
39
11.36
67
7.71
74
6.22
99
22.52
104
10.22
93
28.14
71
37.67
111
19.66
63
29.24
61
21.00
58
15.99
80
21.97
100
17.77
94
0.03
25
0.07
43
0.19
65
0.04
47
0.60
55
0.75
70
HGLStereotwo views12.72
78
2.66
65
12.34
72
9.93
102
7.55
121
16.16
77
7.45
77
31.48
91
27.01
64
20.01
67
35.31
102
26.98
88
18.90
97
17.87
72
18.90
97
0.33
67
0.16
67
0.07
45
0.05
50
0.48
40
0.70
67
GEStereo_RVCtwo views12.81
79
2.49
61
13.64
80
9.68
98
4.97
71
18.16
80
11.30
103
31.26
87
47.47
143
19.84
64
26.97
51
19.03
51
13.19
55
20.14
89
14.86
77
0.83
98
0.51
108
0.10
53
0.13
65
0.54
49
0.99
79
SGM-Foresttwo views12.92
80
1.87
40
6.61
18
5.68
29
2.05
29
23.19
109
11.30
103
34.24
109
30.77
78
26.54
107
31.99
82
29.98
97
15.74
77
21.17
99
13.36
69
1.05
112
0.33
87
0.84
104
0.01
26
0.71
64
0.97
77
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
STTStereotwo views12.97
81
4.88
102
26.57
132
7.54
69
5.41
86
13.43
51
6.49
68
30.53
85
26.35
62
25.14
97
31.43
79
32.00
104
14.12
65
16.01
61
14.83
76
0.29
64
0.31
84
0.67
96
1.08
120
1.21
83
1.11
83
MMNettwo views12.97
81
2.08
52
12.74
75
6.98
57
6.27
101
26.39
123
4.73
42
23.10
46
41.79
125
25.48
99
32.29
86
21.09
59
17.79
91
19.52
85
17.90
95
0.19
52
0.07
43
0.10
53
0.00
1
0.37
34
0.59
54
TDLMtwo views12.98
83
3.78
87
9.77
56
10.75
114
4.94
69
16.10
75
14.08
118
35.05
116
31.87
84
22.36
81
26.51
49
26.09
85
14.97
72
24.73
113
13.11
68
1.05
112
0.05
29
1.21
120
0.27
80
1.83
97
1.00
80
AANet_RVCtwo views13.16
84
5.34
107
10.83
64
8.20
81
4.44
60
14.13
58
9.46
84
28.78
74
37.67
111
23.44
89
37.47
110
23.48
67
15.83
79
22.39
102
16.63
87
1.67
123
0.86
127
0.24
68
0.02
34
0.63
56
1.60
100
psm_uptwo views13.18
85
1.91
44
8.81
48
9.59
96
5.84
95
15.17
64
11.37
105
38.74
139
30.81
79
20.66
76
30.32
69
30.00
98
23.12
109
19.19
81
16.02
81
0.58
88
0.10
57
0.17
64
0.01
26
0.65
58
0.55
51
CVANet_RVCtwo views13.24
86
3.39
80
8.43
44
8.42
83
5.04
75
18.52
84
11.72
108
32.03
95
33.85
94
23.17
86
32.77
88
29.78
95
16.51
83
23.20
104
11.28
65
0.94
105
0.04
21
1.19
119
0.47
100
3.13
121
1.01
81
FADNet-RVCtwo views13.27
87
11.57
147
39.71
152
7.94
78
4.50
61
15.41
67
6.95
74
27.96
68
22.98
47
20.37
73
30.73
71
26.86
87
11.31
43
20.50
97
14.28
74
0.14
47
0.14
65
0.13
60
0.18
70
2.83
114
0.86
72
DLCB_ROBtwo views13.35
88
3.00
73
9.12
51
9.43
91
5.68
93
21.80
100
10.12
89
29.19
79
29.92
74
27.71
111
31.45
80
32.37
105
19.50
98
19.02
78
16.73
88
0.22
56
0.04
21
0.38
80
0.06
55
0.49
44
0.85
71
StereoDRNet-Refinedtwo views13.36
89
2.80
66
10.82
63
7.94
78
3.10
41
18.95
85
4.45
37
28.47
73
29.65
72
29.11
116
41.47
132
24.44
74
17.64
90
23.34
105
21.66
103
0.16
50
0.07
43
0.53
88
0.32
90
0.77
69
1.64
101
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
CBMVpermissivetwo views13.69
90
3.63
83
8.02
40
5.93
34
2.69
37
22.57
105
12.44
110
29.81
81
31.57
82
31.20
126
33.79
95
31.04
99
17.41
89
25.28
116
14.11
73
0.71
94
0.60
115
0.60
92
0.11
63
1.11
80
1.13
86
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
PS-NSSStwo views13.70
91
7.41
126
14.68
88
6.70
52
5.63
90
18.43
83
12.59
111
38.89
141
25.56
57
20.27
72
35.71
103
21.11
60
16.35
82
17.16
67
17.56
92
2.28
130
0.53
110
5.37
152
0.36
93
4.28
134
3.09
123
FADNettwo views14.18
92
10.92
144
37.56
149
7.79
76
6.64
105
17.48
79
6.91
72
34.73
114
34.03
95
18.85
61
27.02
52
28.26
90
14.24
68
17.77
71
10.09
55
0.41
76
0.67
118
0.37
78
0.37
95
8.13
151
1.34
91
pmcnntwo views14.59
93
3.68
85
19.78
117
6.83
54
4.51
62
21.27
97
14.67
121
27.37
65
30.54
76
27.09
110
40.19
126
38.71
132
18.53
95
17.70
70
19.27
98
0.84
100
0.09
54
0.00
1
0.00
1
0.48
40
0.31
34
NVstereo2Dtwo views15.15
94
2.97
71
16.28
98
9.23
89
7.56
122
30.03
134
9.53
85
42.80
155
42.72
128
15.11
35
27.03
53
23.16
65
16.93
86
24.28
110
15.45
79
3.07
142
0.43
100
1.44
122
0.61
106
6.74
144
7.60
158
StereoDRNettwo views15.30
95
4.47
95
14.35
87
10.71
112
8.69
136
25.02
117
11.03
99
39.66
142
35.62
102
29.17
117
28.81
60
34.05
115
17.80
92
18.80
76
23.77
113
0.41
76
0.30
82
0.15
62
0.14
66
1.64
93
1.42
94
FINETtwo views15.49
96
6.08
111
24.99
128
8.12
80
7.69
123
21.80
100
22.35
152
33.76
106
48.27
145
25.99
102
32.27
85
19.48
54
16.83
85
19.07
79
16.38
86
1.37
119
0.70
119
0.95
109
0.27
80
2.61
112
0.90
74
DRN-Testtwo views15.88
97
3.83
89
14.70
89
9.96
104
8.02
131
28.93
132
14.65
120
42.34
151
38.25
115
28.18
114
34.31
101
29.22
93
18.35
94
20.01
87
22.68
108
0.37
70
0.41
95
0.46
84
0.28
83
1.28
85
1.34
91
PA-Nettwo views16.29
98
6.88
123
27.34
133
9.88
101
11.12
149
21.77
99
28.15
166
32.06
96
39.49
116
19.99
66
27.91
55
23.50
68
20.59
103
20.25
92
24.04
116
0.24
58
2.26
148
0.22
66
4.59
155
1.13
82
4.48
139
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
DISCOtwo views16.36
99
1.88
41
16.15
95
7.78
75
4.19
56
26.92
125
10.21
92
30.31
83
44.26
135
21.35
78
33.91
98
35.80
125
22.40
107
35.65
151
33.87
149
0.18
51
0.08
50
0.04
34
0.04
47
1.71
95
0.54
50
GwcNetcopylefttwo views16.48
100
5.23
106
20.03
118
10.24
108
8.48
134
28.79
130
10.09
87
38.60
138
44.51
136
23.29
88
36.95
108
31.29
101
19.79
101
20.42
96
25.70
129
0.54
86
0.72
120
0.57
91
0.39
97
1.37
89
2.64
117
NaN_ROBtwo views16.51
101
6.51
119
16.22
96
10.53
111
4.64
65
31.76
141
17.78
139
37.00
128
43.37
131
29.68
121
31.29
76
32.67
110
20.32
102
28.00
126
16.06
82
0.38
71
0.41
95
0.32
74
0.41
98
0.87
72
1.94
107
FAT-Stereotwo views16.81
102
2.97
71
17.56
104
9.80
100
6.21
98
16.12
76
13.23
115
38.18
137
39.67
118
27.00
109
39.73
124
37.55
129
31.30
143
20.39
95
21.52
102
3.44
144
2.05
142
0.81
102
1.10
121
1.89
100
5.72
144
DANettwo views16.85
103
8.00
127
26.04
131
14.56
143
7.25
118
20.59
93
5.37
48
26.22
62
26.02
61
32.17
131
36.00
104
37.20
128
29.77
137
30.75
131
24.62
121
1.14
116
1.34
131
2.21
133
0.51
103
3.18
122
4.07
133
Z Ling, K Yang, J Li, Y Zhang, X Gao, L Luo, L Xie: Domain-adaptive modules for stereo matching network. Neurocomputing 2021
S-Stereotwo views16.85
103
3.95
91
20.67
121
12.08
127
8.91
139
21.68
98
12.61
112
32.20
98
44.61
137
22.37
82
38.42
118
29.15
92
28.02
131
18.58
74
22.36
107
2.88
141
3.11
158
0.87
107
1.82
134
3.58
127
9.08
164
MaskLacGwcNet_RVCtwo views16.87
105
16.27
158
17.93
109
11.24
122
6.69
108
28.95
133
10.44
94
41.23
148
21.49
43
29.68
121
34.27
100
32.48
106
11.00
39
20.05
88
15.09
78
8.22
164
2.59
151
12.23
170
0.75
112
10.40
159
6.34
147
PSMNet_ROBtwo views16.90
106
5.45
108
14.16
84
13.77
134
7.25
118
27.65
127
32.74
170
43.21
158
37.88
113
24.00
90
32.97
91
34.55
119
16.69
84
17.41
68
23.96
115
0.38
71
0.22
73
0.93
108
2.05
138
1.83
97
0.96
76
GANettwo views16.92
107
4.32
93
12.46
73
10.84
116
4.24
57
23.04
108
15.36
128
39.91
143
34.47
98
32.42
132
45.10
146
40.04
140
27.54
127
23.61
108
20.20
99
1.02
111
0.08
50
0.67
96
0.22
73
1.87
99
0.97
77
Syn2CoExtwo views17.03
108
10.40
139
25.65
130
20.35
157
8.02
131
26.28
122
11.27
102
37.16
129
32.69
92
28.89
115
40.17
125
29.27
94
26.63
122
19.19
81
17.63
93
0.98
107
0.54
111
1.23
121
0.23
76
1.82
96
2.24
114
NCCL2two views17.23
109
6.37
118
14.30
86
23.45
166
7.18
117
24.16
112
17.05
137
34.39
111
25.88
59
31.55
127
38.01
113
42.09
147
26.81
123
20.94
98
22.69
109
0.45
81
0.23
75
2.63
138
1.80
133
2.02
104
2.57
115
ADCReftwo views17.30
110
6.73
121
42.01
156
10.11
105
8.78
138
26.61
124
10.45
95
31.29
89
32.75
93
31.19
125
41.62
134
18.47
49
19.78
100
24.29
111
34.06
151
0.76
97
0.42
99
2.16
131
1.79
132
1.23
84
1.56
99
XPNet_ROBtwo views17.33
111
4.84
100
15.44
92
11.15
119
6.64
105
20.88
94
16.10
131
36.26
123
39.54
117
31.80
130
41.32
131
41.30
144
23.91
111
24.67
112
27.40
135
1.07
115
0.74
121
0.79
100
0.25
78
1.10
79
1.31
90
RPtwo views17.58
112
4.92
103
15.27
91
15.44
147
11.95
155
21.05
95
11.75
109
27.64
66
45.09
140
22.99
85
42.05
136
39.39
136
27.74
129
25.54
117
22.33
106
5.45
157
0.59
114
4.33
149
1.62
131
3.53
126
2.86
120
SuperBtwo views17.60
113
6.22
117
56.40
169
7.91
77
5.07
76
19.70
90
9.03
81
24.87
53
50.15
155
26.25
106
39.13
119
17.61
48
20.84
105
22.28
101
25.31
125
0.70
93
0.31
84
1.10
114
0.71
109
16.58
173
1.86
105
ETE_ROBtwo views17.67
114
9.48
133
17.66
105
13.60
132
4.57
63
23.03
107
21.09
149
32.49
100
35.24
101
30.49
123
38.06
114
46.18
158
24.49
114
26.40
120
24.21
118
0.46
83
0.24
76
1.14
115
0.84
114
1.52
91
2.13
111
APVNettwo views17.75
115
5.46
109
20.35
120
12.31
128
8.40
133
30.16
135
24.10
160
41.14
147
30.12
75
25.08
96
41.09
129
31.24
100
23.12
109
27.07
122
27.86
140
0.34
68
0.56
113
3.02
143
0.52
104
1.68
94
1.45
95
PWCDC_ROBbinarytwo views17.80
116
9.62
134
18.38
110
17.95
153
7.74
124
24.61
115
5.56
54
34.41
112
52.79
159
29.66
120
50.20
156
20.63
56
19.69
99
29.85
130
20.87
100
6.18
160
0.26
81
0.10
53
0.05
50
5.36
142
2.00
109
MDST_ROBtwo views17.92
117
1.60
26
13.68
81
13.88
136
6.44
103
43.05
164
14.86
123
42.74
154
41.66
123
43.25
155
42.85
139
28.43
91
17.28
87
29.35
128
16.06
82
0.88
102
0.11
62
0.63
94
0.47
100
0.50
45
0.64
62
Nwc_Nettwo views17.98
118
4.67
98
17.79
108
14.26
138
11.65
154
25.14
118
14.88
124
40.85
146
39.83
119
21.07
77
43.24
141
34.50
118
27.64
128
26.44
121
25.03
122
3.22
143
0.18
69
2.52
137
3.18
145
1.98
102
1.51
97
ADCP+two views18.16
119
4.61
97
32.94
143
9.31
90
10.23
145
28.87
131
11.69
107
33.43
104
36.49
106
29.28
119
40.49
127
24.18
72
24.61
116
33.89
141
35.33
152
0.25
59
0.40
94
2.00
128
1.17
123
2.16
107
1.87
106
PWC_ROBbinarytwo views18.37
120
10.55
140
25.19
129
10.21
107
6.29
102
23.75
110
4.79
45
35.93
122
48.75
148
32.56
133
44.46
145
34.68
120
24.78
117
30.99
132
25.72
130
1.38
120
0.08
50
1.57
123
0.26
79
2.18
108
3.17
125
AF-Nettwo views18.38
121
5.60
110
14.21
85
15.95
148
10.78
147
22.26
102
10.45
95
35.39
118
50.22
156
24.71
95
37.77
111
41.55
145
29.24
135
29.11
127
26.67
131
4.35
153
0.06
37
4.56
150
0.90
116
2.42
109
1.38
93
Anonymous Stereotwo views18.60
122
11.75
148
49.81
162
14.43
141
12.02
157
14.33
60
23.23
159
32.16
97
43.08
130
24.32
93
34.16
99
24.24
73
14.12
65
31.67
135
30.84
146
0.89
103
0.91
128
1.74
125
1.92
137
3.23
123
3.23
126
stereogantwo views18.62
123
3.07
74
16.31
99
13.04
131
9.99
143
35.74
152
9.09
82
38.17
136
45.27
141
24.39
94
41.24
130
39.93
138
25.46
120
31.29
134
24.33
120
1.80
126
0.91
128
2.71
140
0.75
112
5.15
141
3.70
127
edge stereotwo views18.75
124
5.12
104
17.66
105
10.77
115
7.84
128
22.41
103
11.03
99
35.23
117
42.07
127
34.62
138
42.74
138
41.28
143
35.50
150
25.14
115
24.17
117
2.42
136
2.42
150
6.35
156
1.45
128
2.89
118
3.80
128
RYNettwo views18.92
125
4.69
99
16.88
102
10.99
117
15.84
164
46.72
166
16.57
134
37.84
133
48.03
144
26.92
108
26.48
47
37.84
131
24.92
118
19.80
86
31.04
147
0.25
59
0.25
80
0.62
93
0.03
42
6.34
143
6.39
148
LALA_ROBtwo views19.19
126
7.15
125
15.93
94
12.96
130
5.30
85
28.30
129
23.21
158
42.51
152
36.69
107
33.45
136
40.81
128
50.96
164
24.53
115
27.92
124
25.66
128
0.60
89
0.44
101
2.04
129
1.22
125
2.54
111
1.47
96
aanetorigintwo views19.21
127
12.59
150
53.50
165
9.67
97
7.83
126
11.85
39
13.87
116
17.05
27
34.15
97
49.18
164
48.89
154
34.05
115
30.22
139
22.86
103
28.00
144
1.01
110
0.75
123
0.67
96
0.89
115
3.07
119
4.14
134
SGM_RVCbinarytwo views19.52
128
2.12
54
6.79
20
6.59
49
1.33
18
38.20
158
15.74
129
38.13
135
34.07
96
45.71
160
41.91
135
51.41
165
38.10
155
38.77
161
27.80
139
0.61
90
0.36
92
0.51
87
0.33
91
1.03
75
0.90
74
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
RTSCtwo views19.64
129
9.36
130
31.06
141
10.99
117
6.24
100
28.10
128
10.14
90
37.50
132
58.34
172
31.74
129
36.70
107
32.50
107
21.03
106
36.00
154
37.24
157
1.23
117
0.44
101
0.12
58
0.31
88
1.98
102
1.69
103
NCC-stereotwo views19.75
130
6.08
111
23.96
125
15.05
144
16.17
165
18.42
81
21.45
150
43.31
159
41.00
121
24.30
91
38.12
115
34.98
121
26.92
124
35.46
148
25.65
126
4.29
151
2.07
143
3.24
145
8.90
166
2.86
115
2.86
120
Abc-Nettwo views19.75
130
6.08
111
23.96
125
15.05
144
16.17
165
18.42
81
21.45
150
43.31
159
41.00
121
24.30
91
38.12
115
34.98
121
26.92
124
35.46
148
25.65
126
4.29
151
2.07
143
3.24
145
8.90
166
2.86
115
2.86
120
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
DGSMNettwo views19.79
132
12.06
149
33.43
145
13.79
135
16.23
167
22.97
106
15.88
130
35.41
119
41.96
126
19.90
65
32.16
84
35.60
124
18.14
93
29.48
129
23.10
110
3.85
148
3.89
166
6.16
155
4.75
156
13.56
169
13.44
172
RGCtwo views19.90
133
13.26
151
20.24
119
18.19
154
14.06
162
21.21
96
14.33
119
34.97
115
43.60
133
27.73
112
41.49
133
39.76
137
27.98
130
34.52
144
21.37
101
3.66
147
0.54
111
8.67
164
4.16
153
4.19
132
4.02
131
DeepPrunerFtwo views19.92
134
11.02
146
44.29
157
20.74
159
17.75
169
19.19
87
22.57
153
36.86
127
49.93
151
25.48
99
36.62
106
24.69
75
23.97
112
23.44
106
21.79
104
2.68
140
1.63
138
5.70
153
3.95
152
3.38
125
2.67
118
WCMA_ROBtwo views20.02
135
4.30
92
19.72
116
9.47
94
7.00
114
32.71
146
13.99
117
31.97
94
32.48
91
41.51
151
52.00
159
44.09
151
36.14
152
32.43
137
24.29
119
6.19
161
2.79
155
1.09
113
1.10
121
3.95
129
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124
psmorigintwo views20.95
136
10.24
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35.84
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9.75
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7.05
115
24.59
114
10.11
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32.85
102
32.17
89
43.09
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54.57
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42.79
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36.62
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35.99
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27.41
136
1.58
122
2.72
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0.79
100
1.05
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3.26
124
6.50
149
SANettwo views20.96
137
6.60
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29.81
136
8.83
84
3.19
42
31.27
140
20.56
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41.86
150
56.09
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39.30
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44.95
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27.83
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23.67
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0.94
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0.52
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0.99
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0.42
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4.72
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2.03
110
FBW_ROBtwo views21.00
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10.23
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22.72
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13.71
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6.77
110
30.49
139
16.46
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44.58
161
43.57
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44.25
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43.27
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26.12
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33.96
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21.80
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2.58
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1.84
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7.14
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2.78
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4.00
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132
G-Nettwo views21.25
139
8.24
128
37.97
151
15.14
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9.26
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50.87
169
16.80
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27.96
68
30.54
76
38.92
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42.93
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33.27
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32.17
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27.99
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25.25
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4.40
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3.83
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3.60
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0.97
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6.95
152
SHDtwo views21.32
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10.03
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30.16
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14.30
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8.68
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24.15
111
8.93
78
40.82
145
61.17
174
35.79
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44.15
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38.73
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30.20
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33.88
140
31.09
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2.00
127
0.81
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1.17
116
1.55
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4.08
131
4.78
141
CSANtwo views21.34
141
9.45
132
23.34
124
20.99
161
4.95
70
32.57
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34.26
171
38.83
140
49.95
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36.97
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39.72
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44.97
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31.73
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26.05
118
23.94
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1.52
121
0.47
104
0.85
105
1.43
127
2.73
113
2.14
113
ADCLtwo views21.64
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6.20
114
47.32
160
9.93
102
6.91
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38.69
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19.97
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31.26
87
54.04
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27.89
113
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152
31.35
102
30.66
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33.18
139
36.62
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0.99
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0.92
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2.63
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1.82
134
2.45
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2.13
111
ADCPNettwo views21.93
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9.38
131
57.92
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11.76
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6.88
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36.03
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18.44
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32.80
101
35.93
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32.91
135
43.80
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39.21
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26.99
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31.14
133
36.94
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2.05
128
2.67
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2.30
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3.45
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4.22
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3.82
129
CC-Net-ROBtwo views22.04
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22.91
168
28.86
134
14.05
137
8.73
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33.79
148
15.26
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46.84
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36.06
105
33.72
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32.10
83
35.47
123
24.02
113
23.57
107
27.88
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8.95
167
2.95
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24.94
176
3.68
149
12.41
167
4.62
140
MeshStereopermissivetwo views22.27
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6.87
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11.15
66
7.69
71
4.87
68
37.70
156
13.06
113
41.64
149
36.95
108
50.92
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53.41
161
58.12
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41.93
163
37.73
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27.62
138
2.52
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2.37
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2.99
142
2.07
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3.09
120
2.60
116
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
ADCMidtwo views22.76
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10.75
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41.73
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11.23
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7.97
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27.26
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19.18
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35.44
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38.02
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40.23
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36.56
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42.70
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38.20
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40.75
164
1.77
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1.43
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3.05
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3.81
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4.96
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3.82
129
MSMD_ROBtwo views22.90
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9.64
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14.76
90
17.46
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9.88
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34.94
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18.83
142
33.67
105
37.47
109
40.97
150
59.80
169
45.93
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38.34
156
31.74
136
23.24
111
6.22
162
3.58
162
8.78
165
11.03
170
6.76
145
5.06
143
LE_ROBtwo views22.93
148
3.07
74
14.02
82
7.70
72
2.86
39
31.99
143
17.35
138
28.10
70
67.19
181
70.51
180
55.61
167
49.07
161
47.62
168
24.76
114
36.49
153
0.35
69
0.20
71
0.27
70
0.55
105
0.48
40
0.44
47
AnyNet_C32two views23.37
149
13.28
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40.29
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12.36
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11.44
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30.39
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29.15
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31.36
90
44.90
139
35.36
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48.86
153
33.61
113
34.20
148
43.15
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41.62
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1.27
118
1.35
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1.18
118
2.58
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4.77
136
6.27
146
SGM-ForestMtwo views23.72
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2.44
60
9.13
52
7.43
65
2.25
32
44.80
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19.11
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44.90
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49.97
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50.74
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51.18
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62.06
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45.83
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44.96
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33.88
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1.00
109
0.84
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0.78
99
0.62
107
1.29
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1.11
83
MFN_U_SF_DS_RVCtwo views23.92
151
13.60
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30.09
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25.46
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10.02
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38.51
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20.40
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35.77
121
43.76
134
42.15
152
47.10
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39.99
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28.20
133
35.96
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27.06
133
3.64
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3.85
165
6.66
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16.30
174
4.97
138
4.98
142
ccnettwo views24.02
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16.16
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21.70
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11.83
124
17.75
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37.87
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16.18
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45.46
164
31.93
88
36.99
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46.46
150
38.76
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29.52
136
37.55
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40.93
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9.20
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6.29
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9.63
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7.71
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11.19
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7.21
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XQCtwo views24.10
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16.76
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50.68
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21.37
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11.01
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35.24
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18.84
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37.28
131
55.11
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31.64
128
30.06
68
37.71
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30.31
140
37.17
155
39.66
161
4.20
150
0.41
95
2.76
141
1.89
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11.46
166
8.42
162
FC-DCNNcopylefttwo views24.37
154
10.99
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19.02
113
18.44
155
9.16
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36.98
154
23.07
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40.44
144
43.05
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46.25
162
53.58
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50.44
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37.86
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35.45
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27.57
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6.87
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3.37
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6.01
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4.88
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7.86
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6.05
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DPSNettwo views24.40
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6.97
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33.14
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11.16
120
6.54
104
53.33
171
43.32
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51.28
171
59.37
173
30.89
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39.36
120
40.35
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34.30
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32.57
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25.09
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4.65
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1.62
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0.37
78
0.66
108
8.51
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4.44
138
PDISCO_ROBtwo views24.45
156
9.24
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29.28
135
28.68
172
19.96
171
37.14
155
15.33
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45.04
163
54.69
162
29.24
118
42.67
137
46.07
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28.17
132
35.35
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30.30
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9.92
170
2.13
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6.90
158
3.20
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8.74
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6.87
151
EDNetEfficienttwo views25.16
157
22.23
167
77.22
178
9.09
87
7.38
120
19.38
88
16.78
135
21.41
38
55.74
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61.52
176
53.51
162
36.22
126
40.65
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24.22
109
38.27
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0.62
91
0.74
121
0.86
106
2.60
143
7.70
148
7.09
153
GANetREF_RVCpermissivetwo views25.41
158
29.28
172
24.63
127
25.18
167
6.04
96
30.44
137
35.39
173
47.84
166
50.60
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36.88
142
36.04
105
34.11
117
31.23
142
37.46
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27.96
143
8.58
165
2.66
152
16.29
172
4.95
158
14.37
172
8.32
161
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
FCDSN-DCtwo views25.59
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14.91
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18.83
112
28.55
171
15.68
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39.29
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18.16
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42.83
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41.69
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44.43
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51.94
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50.82
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39.22
159
34.59
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27.29
134
6.05
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3.63
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7.01
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6.41
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10.35
158
10.17
168
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
ADCStwo views26.20
160
13.71
154