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
17
1.86
1
0.07
1
0.22
23
1.65
8
0.02
7
4.07
9
6.33
43
0.72
5
1.54
4
0.43
7
0.64
7
1.22
7
0.44
3
0.00
1
0.00
1
0.00
1
0.00
1
0.01
7
0.00
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
TANstereotwo views1.20
2
0.49
23
2.04
6
0.34
13
0.10
10
1.80
16
0.01
1
3.40
4
4.10
16
4.37
23
4.82
16
0.41
6
0.70
8
0.94
3
0.46
4
0.00
1
0.00
1
0.00
1
0.00
1
0.00
1
0.00
1
sCroCo_RVCtwo views1.21
3
0.65
47
5.16
78
1.09
32
1.29
74
0.92
1
0.19
22
3.92
6
2.68
2
0.30
1
3.04
9
0.52
11
1.56
26
1.58
8
0.78
12
0.09
106
0.25
146
0.00
1
0.00
1
0.07
45
0.11
67
sAnonymous2two views1.35
4
1.66
126
8.14
124
0.55
17
0.46
37
1.23
2
0.24
26
4.00
7
3.47
7
0.40
2
1.11
1
0.23
2
1.42
23
0.96
4
0.57
6
0.01
48
0.00
1
0.00
1
0.20
132
2.07
164
0.22
96
CroCo_RVCtwo views1.35
4
1.66
126
8.14
124
0.55
17
0.46
37
1.23
2
0.24
26
4.00
7
3.47
7
0.40
2
1.11
1
0.23
2
1.42
23
0.96
4
0.57
6
0.01
48
0.00
1
0.00
1
0.20
132
2.07
164
0.22
96
Anonymoustwo views1.37
6
0.52
25
4.23
57
1.38
41
2.70
121
1.85
17
0.42
36
4.77
12
3.54
9
0.53
4
1.56
5
0.19
1
1.32
20
3.11
23
0.92
19
0.05
96
0.08
110
0.00
1
0.00
1
0.17
83
0.15
80
PMTNettwo views1.41
7
0.37
11
2.06
8
0.26
8
0.15
16
2.94
36
0.34
31
3.39
3
5.33
26
1.00
6
1.27
3
0.28
5
0.93
15
4.04
30
0.74
10
5.00
174
0.00
1
0.00
1
0.00
1
0.03
26
0.01
8
XX-TBDtwo views1.44
8
0.85
66
2.04
6
2.45
111
0.05
3
2.34
21
0.01
1
3.46
5
7.51
62
2.88
12
4.98
20
0.26
4
0.25
2
1.15
6
0.46
4
0.00
1
0.00
1
0.00
1
0.00
1
0.00
1
0.00
1
CREStereo++_RVCtwo views1.59
9
0.39
15
2.26
9
0.36
14
0.01
1
1.67
9
0.01
1
2.95
1
8.12
73
3.69
19
7.44
46
0.47
9
0.32
3
0.74
1
0.39
1
0.00
1
0.00
1
0.00
1
3.03
176
0.00
1
0.00
1
s12784htwo views1.59
9
0.39
15
2.26
9
0.36
14
0.01
1
1.67
9
0.01
1
2.95
1
8.12
73
3.69
19
7.44
46
0.47
9
0.32
3
0.74
1
0.39
1
0.00
1
0.00
1
0.00
1
3.03
176
0.00
1
0.00
1
iRaftStereo_RVCtwo views1.62
11
0.68
49
1.98
5
0.67
21
0.05
3
2.99
37
0.04
8
5.36
18
3.74
11
3.82
21
4.96
18
2.54
28
0.62
6
3.35
25
0.97
20
0.00
1
0.07
107
0.00
1
0.00
1
0.30
108
0.27
112
rafts_anoytwo views1.72
12
0.79
59
2.58
15
2.03
80
1.75
91
2.47
23
0.16
20
5.48
23
6.01
38
3.24
15
2.11
7
1.92
24
1.33
21
3.08
22
1.13
25
0.01
48
0.18
136
0.00
1
0.01
82
0.03
26
0.07
47
TestStereotwo views1.74
13
1.02
84
3.64
44
0.20
3
0.28
26
1.33
4
0.01
1
5.24
17
4.01
13
3.18
13
4.97
19
2.07
25
0.70
8
7.32
59
0.75
11
0.02
65
0.00
1
0.00
1
0.00
1
0.01
7
0.00
1
Anonymous3two views1.76
14
0.44
18
6.37
101
0.33
12
0.50
40
4.33
59
0.54
42
4.43
10
2.48
1
2.21
8
3.24
10
1.53
19
1.14
17
5.64
41
1.29
26
0.17
119
0.23
142
0.00
1
0.00
1
0.12
64
0.15
80
raft+_RVCtwo views1.79
15
0.84
64
2.32
12
2.40
107
0.85
56
1.38
5
0.09
11
6.36
30
4.75
21
2.85
11
5.46
30
1.17
13
1.36
22
4.54
33
0.91
16
0.00
1
0.10
116
0.00
1
0.00
1
0.11
59
0.22
96
GMStereotwo views1.83
16
0.99
81
4.55
65
0.57
19
0.05
3
2.53
24
0.01
1
4.95
14
11.14
100
3.52
17
2.24
8
0.53
12
0.49
5
3.76
29
0.80
14
0.20
124
0.00
1
0.00
1
0.12
124
0.12
64
0.00
1
raftrobusttwo views1.86
17
0.74
54
1.94
4
2.15
89
2.24
108
2.74
28
0.53
40
4.89
13
4.05
14
6.09
49
3.89
12
1.64
20
2.55
39
2.44
21
1.12
24
0.00
1
0.03
81
0.00
1
0.00
1
0.12
64
0.08
52
RALCasStereoNettwo views1.93
18
1.01
82
2.75
23
1.88
67
1.75
91
2.88
34
0.18
21
4.64
11
4.36
19
2.27
9
5.14
22
2.22
26
0.79
13
7.10
57
1.00
22
0.00
1
0.34
152
0.00
1
0.00
1
0.10
53
0.16
84
DIP-Stereotwo views1.97
19
0.64
44
2.95
28
0.17
2
0.10
10
4.83
71
0.13
15
8.60
47
4.06
15
6.42
58
4.92
17
0.44
8
0.72
10
3.57
26
1.80
32
0.00
1
0.01
54
0.00
1
0.00
1
0.05
39
0.04
38
Zihua Zheng, Ni Nie, Zhi Ling, Pengfei Xiong, Jiangyu Liu, Hao Wang, Jiankun Li: DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow. cvpr2022
Gwc-CoAtRStwo views2.02
20
0.33
7
2.61
16
1.04
29
0.30
27
3.56
45
0.40
34
5.89
26
5.94
35
5.08
31
5.01
21
5.55
47
1.72
28
1.60
9
0.91
16
0.01
48
0.09
115
0.00
1
0.25
140
0.01
7
0.02
14
MSMDNettwo views2.02
20
0.37
11
2.84
26
1.14
34
0.34
30
3.73
50
0.53
40
6.21
29
3.55
10
5.32
37
5.17
23
5.85
52
1.74
30
2.23
18
0.91
16
0.01
48
0.10
116
0.00
1
0.24
138
0.02
14
0.02
14
GwcNet-DCAtwo views2.08
22
0.64
44
3.81
49
0.31
10
0.17
20
1.70
15
0.25
28
5.23
16
8.47
80
6.07
48
5.30
29
1.41
15
4.23
68
1.85
12
2.02
38
0.00
1
0.00
1
0.00
1
0.00
1
0.04
31
0.19
90
CFNet-RSSMtwo views2.11
23
0.33
7
2.71
21
1.04
29
0.30
27
3.21
40
0.41
35
6.67
34
6.96
51
5.62
40
4.58
14
5.40
45
1.71
27
1.86
13
0.87
15
0.00
1
0.08
110
0.00
1
0.32
145
0.02
14
0.02
14
RAFT-Stereo + iAFFtwo views2.12
24
0.58
34
2.43
14
1.27
37
0.16
17
1.60
7
0.14
17
5.17
15
5.90
34
7.10
68
10.10
69
3.06
31
1.74
30
1.87
14
0.97
20
0.01
48
0.02
70
0.00
1
0.00
1
0.23
103
0.02
14
FENettwo views2.19
25
0.34
9
3.04
31
2.05
82
0.26
25
2.82
31
0.13
15
7.02
36
5.55
30
4.97
30
1.94
6
4.91
39
3.39
51
4.71
35
2.50
43
0.02
65
0.03
81
0.00
1
0.00
1
0.00
1
0.21
94
222two views2.20
26
0.61
38
5.31
80
0.31
10
0.19
22
1.69
12
0.11
12
5.37
19
8.54
82
5.80
42
5.17
23
1.44
16
5.41
91
2.00
15
1.92
34
0.00
1
0.00
1
0.00
1
0.00
1
0.04
31
0.18
87
csctwo views2.28
27
0.61
38
3.81
49
0.22
4
0.07
7
1.69
12
0.11
12
5.37
19
11.84
109
5.80
42
5.17
23
1.44
16
5.41
91
2.00
15
1.92
34
0.00
1
0.00
1
0.00
1
0.00
1
0.02
14
0.18
87
cscssctwo views2.28
27
0.61
38
3.81
49
0.22
4
0.07
7
1.69
12
0.11
12
5.37
19
11.84
109
5.80
42
5.17
23
1.44
16
5.41
91
2.00
15
1.92
34
0.00
1
0.00
1
0.00
1
0.00
1
0.02
14
0.18
87
EAI-Stereotwo views2.31
29
0.44
18
2.72
22
0.68
22
0.56
41
3.51
44
2.62
90
11.42
89
4.14
17
2.09
7
7.23
43
1.21
14
0.91
14
5.67
42
1.51
29
0.01
48
0.25
146
0.00
1
0.88
164
0.04
31
0.26
109
R-Stereo Traintwo views2.44
30
0.32
2
1.93
2
0.94
26
0.16
17
3.67
47
0.61
49
6.37
31
3.08
4
9.14
95
17.44
128
1.80
21
0.77
11
1.76
10
0.70
8
0.00
1
0.01
54
0.00
1
0.00
1
0.01
7
0.03
27
RAFT-Stereopermissivetwo views2.44
30
0.32
2
1.93
2
0.94
26
0.16
17
3.67
47
0.61
49
6.37
31
3.08
4
9.14
95
17.44
128
1.80
21
0.77
11
1.76
10
0.70
8
0.00
1
0.01
54
0.00
1
0.00
1
0.01
7
0.03
27
Lahav Lipson, Zachary Teed, and Jia Deng: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. 3DV
RALAANettwo views2.46
32
1.09
91
3.70
46
2.55
116
0.80
54
3.37
41
0.15
18
7.44
39
5.54
29
5.65
41
6.01
32
3.54
33
1.13
16
6.34
49
1.58
30
0.00
1
0.01
54
0.00
1
0.05
110
0.21
95
0.03
27
GEStwo views2.49
33
0.38
13
4.29
59
1.58
53
0.05
3
4.58
68
1.07
69
9.70
59
3.39
6
5.21
34
6.64
36
1.84
23
3.36
50
4.86
37
2.54
45
0.06
100
0.01
54
0.06
127
0.01
82
0.12
64
0.03
27
HCRNettwo views2.56
34
0.53
29
3.35
36
0.24
7
3.39
134
2.19
18
0.23
25
6.89
35
5.50
27
5.26
35
4.79
15
5.27
43
2.78
44
7.71
64
2.89
50
0.00
1
0.02
70
0.00
1
0.00
1
0.04
31
0.04
38
ACVNettwo views2.58
35
0.53
29
2.69
18
0.84
23
0.59
42
2.61
25
0.55
44
8.35
42
5.70
31
4.45
24
9.26
64
5.77
49
3.50
53
2.24
19
4.41
76
0.00
1
0.00
1
0.00
1
0.00
1
0.17
83
0.01
8
AFF-stereotwo views2.59
36
0.68
49
2.35
13
1.55
51
0.17
20
1.67
9
0.08
10
5.75
25
6.60
46
6.20
52
12.13
87
6.16
55
2.36
37
4.75
36
1.11
23
0.02
65
0.02
70
0.00
1
0.00
1
0.23
103
0.05
43
DN-CSS_ROBtwo views2.69
37
1.40
111
5.34
81
2.31
102
0.75
49
3.14
39
0.06
9
6.11
28
3.87
12
5.34
38
12.18
90
2.34
27
1.22
18
7.84
68
1.48
28
0.03
79
0.00
1
0.00
1
0.00
1
0.35
117
0.03
27
XX-Stereotwo views2.71
38
0.38
13
5.96
94
2.25
97
2.20
106
1.54
6
0.21
23
5.44
22
3.05
3
2.28
10
16.16
121
7.03
60
0.24
1
6.19
45
0.79
13
0.00
1
0.15
129
0.00
1
0.23
136
0.13
72
0.04
38
111two views2.78
39
0.92
74
3.81
49
0.22
4
0.07
7
2.83
32
0.70
57
6.03
27
14.00
121
3.22
14
8.93
61
5.48
46
3.30
49
3.66
27
2.03
39
0.00
1
0.00
1
0.00
1
0.00
1
0.10
53
0.29
115
HITNettwo views2.79
40
0.77
55
4.02
54
2.03
80
0.11
13
5.58
78
0.59
48
9.24
54
5.15
24
6.42
58
7.26
44
3.66
34
2.92
46
4.07
31
3.87
71
0.00
1
0.00
1
0.00
1
0.00
1
0.06
43
0.02
14
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
41
0.64
44
4.45
63
1.61
56
0.89
58
3.86
53
0.57
47
9.18
52
5.10
23
6.24
54
6.49
34
7.17
67
2.13
34
3.31
24
4.73
80
0.00
1
0.01
54
0.00
1
0.00
1
0.04
31
0.02
14
pcwnet_v2two views2.93
42
0.29
1
3.01
29
2.39
106
1.07
67
2.73
27
0.89
66
11.37
87
6.25
41
6.20
52
11.49
83
2.71
30
2.74
43
4.22
32
2.75
49
0.06
100
0.05
93
0.00
1
0.00
1
0.01
7
0.37
119
AdaStereotwo views3.09
43
0.58
34
3.04
31
2.84
124
0.48
39
4.08
56
1.29
75
12.16
103
7.77
67
6.03
47
9.62
66
5.79
51
1.53
25
4.56
34
1.93
37
0.00
1
0.00
1
0.00
1
0.00
1
0.02
14
0.02
14
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.
UCFNet_RVCtwo views3.09
43
0.77
55
2.27
11
2.35
104
1.79
95
3.50
43
1.06
68
12.12
101
6.91
50
4.71
27
7.67
50
7.58
70
4.40
72
4.95
38
1.67
31
0.00
1
0.00
1
0.00
1
0.00
1
0.11
59
0.01
8
ARAFTtwo views3.13
45
1.11
95
7.34
116
0.42
16
0.14
15
4.52
67
2.99
95
7.41
38
5.97
37
8.85
93
7.06
42
5.20
42
1.25
19
8.49
82
1.44
27
0.00
1
0.03
81
0.00
1
0.00
1
0.33
113
0.11
67
DMCA-RVCcopylefttwo views3.17
46
1.26
104
6.39
103
2.10
85
1.51
80
3.02
38
0.56
46
7.84
41
5.06
22
9.23
98
5.19
27
8.46
79
2.72
42
6.31
48
3.22
60
0.06
100
0.11
120
0.09
135
0.02
89
0.16
81
0.08
52
CFNet-pseudotwo views3.23
47
0.36
10
3.58
42
2.13
88
1.45
77
3.78
51
0.70
57
12.67
109
9.52
87
5.16
33
12.28
93
2.62
29
1.81
32
5.36
40
3.08
55
0.01
48
0.00
1
0.00
1
0.00
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0.00
1
0.07
47
BEATNet_4xtwo views3.24
48
1.27
105
5.89
92
1.56
52
0.10
10
5.26
75
1.07
69
10.08
66
5.50
27
6.89
66
7.73
51
4.53
38
4.13
64
5.05
39
5.27
84
0.04
88
0.05
93
0.00
1
0.00
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0.23
103
0.23
100
GANet-RSSMtwo views3.27
49
0.60
36
3.38
37
2.94
131
2.00
100
2.61
25
0.48
38
10.93
85
6.11
40
5.47
39
10.20
71
6.57
57
5.44
94
6.25
47
2.23
41
0.00
1
0.00
1
0.08
132
0.00
1
0.17
83
0.03
27
NOSS_ROBtwo views3.30
50
0.46
20
2.62
17
2.08
83
1.01
65
5.60
79
0.74
62
10.37
72
11.48
105
5.15
32
8.43
56
5.67
48
1.73
29
7.97
70
2.34
42
0.02
65
0.06
100
0.00
1
0.00
1
0.07
45
0.14
77
CFNet-ftpermissivetwo views3.31
51
0.94
78
2.69
18
1.50
48
2.38
111
2.81
29
0.68
55
8.35
42
7.43
60
4.45
24
9.94
67
10.20
92
4.60
74
6.50
52
3.41
64
0.00
1
0.00
1
0.03
115
0.00
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0.22
98
0.03
27
CFNet_RVCtwo views3.31
51
0.94
78
2.69
18
1.50
48
2.38
111
2.81
29
0.68
55
8.35
42
7.43
60
4.45
24
9.94
67
10.20
92
4.60
74
6.49
51
3.41
64
0.00
1
0.00
1
0.03
115
0.00
1
0.22
98
0.03
27
PSMNet-RSSMtwo views3.33
53
0.77
55
3.22
35
2.12
86
1.90
99
2.33
20
0.71
59
11.17
86
5.83
33
5.90
45
11.43
82
7.04
62
4.83
85
5.96
44
3.17
58
0.00
1
0.00
1
0.02
107
0.00
1
0.08
48
0.02
14
test_xeample3two views3.34
54
0.93
76
4.33
60
0.28
9
0.12
14
3.46
42
3.98
111
6.58
33
11.84
109
6.79
64
13.56
102
3.52
32
4.23
68
3.71
28
2.05
40
0.01
48
0.01
54
0.00
1
0.00
1
0.02
14
1.40
159
cf-rtwo views3.42
55
0.81
61
3.97
53
2.41
109
2.78
122
2.85
33
0.64
54
8.44
45
6.32
42
6.25
55
11.57
84
8.97
84
3.54
55
5.93
43
3.83
70
0.00
1
0.00
1
0.00
1
0.00
1
0.09
52
0.07
47
MLCVtwo views3.44
56
0.88
68
5.60
86
1.39
42
0.25
24
4.36
61
0.33
30
7.25
37
7.28
58
9.17
97
12.24
92
5.09
41
2.47
38
9.15
97
3.23
61
0.00
1
0.00
1
0.00
1
0.00
1
0.10
53
0.02
14
DeepPruner_ROBtwo views3.52
57
1.14
97
4.06
55
1.12
33
1.65
84
3.65
46
0.83
64
13.96
121
4.47
20
7.80
79
10.84
76
7.05
64
2.16
35
8.14
77
3.08
55
0.07
104
0.03
81
0.00
1
0.01
82
0.32
112
0.06
45
STTStereotwo views3.60
58
0.93
76
6.34
100
2.71
121
2.23
107
3.68
49
0.63
52
9.42
55
6.73
48
9.87
107
6.97
40
8.84
83
3.65
57
6.85
53
3.04
53
0.00
1
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70
0.01
95
0.00
1
0.02
14
0.02
14
hitnet-ftcopylefttwo views3.66
59
0.65
47
2.79
25
0.89
25
0.72
48
4.25
58
0.55
44
9.11
51
7.52
63
7.14
69
10.19
70
12.49
112
4.70
78
7.44
60
4.41
76
0.03
79
0.00
1
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127
0.00
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0.20
90
0.04
38
iResNettwo views3.68
60
0.91
71
7.94
122
2.97
132
0.34
30
4.44
66
0.48
38
7.70
40
9.74
90
7.72
78
12.74
97
4.03
35
2.87
45
8.05
73
3.37
63
0.02
65
0.01
54
0.00
1
0.00
1
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53
0.09
56
acv_fttwo views3.69
61
0.53
29
4.58
68
2.31
102
4.71
162
7.44
101
0.86
65
8.54
46
5.70
31
9.29
99
9.26
64
5.77
49
4.15
65
2.24
19
8.13
116
0.00
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107
0.01
8
ac_64two views3.70
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0.47
21
3.50
40
2.87
125
3.50
137
5.91
82
0.72
60
10.17
69
4.34
18
8.16
85
5.80
31
10.82
100
5.64
96
7.08
56
4.73
80
0.03
79
0.00
1
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1
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48
0.14
77
CFNettwo views3.72
63
1.10
93
5.03
74
2.49
113
1.59
81
4.90
73
0.22
24
11.38
88
9.88
92
4.80
28
11.25
79
6.44
56
3.68
58
8.33
79
3.00
52
0.00
1
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98
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47
GEStereo_RVCtwo views3.74
64
0.73
53
4.95
72
2.01
79
1.24
71
4.36
61
3.07
96
11.77
95
7.80
68
6.87
65
6.52
35
7.70
73
4.31
71
8.98
93
4.19
74
0.03
79
0.01
54
0.02
107
0.02
89
0.13
72
0.11
67
GwcNet-RSSMtwo views3.77
65
1.05
87
5.06
75
2.21
94
1.87
97
2.36
22
1.52
78
8.98
50
7.96
70
5.91
46
14.59
111
7.94
77
3.50
53
6.43
50
5.70
89
0.00
1
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122
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90
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52
FADNet-RVC-Resampletwo views3.79
66
1.62
123
12.06
141
1.43
45
0.66
44
5.94
83
2.41
88
10.18
70
8.58
83
6.28
56
4.22
13
5.33
44
4.80
84
7.71
64
3.19
59
0.17
119
0.21
139
0.17
145
0.12
124
0.41
126
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115
UPFNettwo views3.82
67
0.47
21
4.34
61
2.21
94
3.38
132
7.26
98
2.69
92
9.89
61
5.96
36
7.98
82
7.53
48
7.89
76
3.15
48
7.72
66
5.88
91
0.01
48
0.02
70
0.00
1
0.00
1
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31
0.06
45
NLCA_NET_v2_RVCtwo views3.84
68
1.06
88
5.23
79
2.72
122
3.27
128
4.36
61
0.61
49
10.71
78
7.56
64
8.75
90
7.89
52
9.86
89
3.90
62
7.15
58
3.44
66
0.14
113
0.02
70
0.02
107
0.03
99
0.04
31
0.03
27
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
psm_uptwo views3.85
69
0.82
62
3.01
29
2.00
78
2.58
119
5.28
76
3.34
102
10.85
83
6.33
43
7.27
72
7.32
45
10.15
91
9.02
125
6.19
45
2.51
44
0.22
126
0.01
54
0.00
1
0.00
1
0.12
64
0.03
27
PS-NSSStwo views3.90
70
1.84
135
4.55
65
0.87
24
2.95
125
4.10
57
2.00
86
10.77
80
8.23
76
6.75
62
14.63
112
5.01
40
2.24
36
8.41
81
2.91
51
0.33
138
0.03
81
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167
0.00
1
0.82
144
0.70
144
FADNet_RVCtwo views3.91
71
1.67
128
12.95
148
0.96
28
0.75
49
5.71
81
0.54
42
10.83
82
6.60
46
3.46
16
8.09
53
4.10
36
3.40
52
9.43
104
6.33
95
0.36
140
0.44
158
0.17
145
0.46
157
0.91
146
0.95
151
UNettwo views3.97
72
0.61
38
6.50
106
1.68
61
3.22
127
9.82
117
0.31
29
9.50
56
5.32
25
7.20
70
6.35
33
10.59
97
5.91
100
6.93
55
5.49
87
0.00
1
0.00
1
0.00
1
0.00
1
0.02
14
0.01
8
FADNet-RVCtwo views3.98
73
1.84
135
12.48
144
1.69
62
0.44
35
4.33
59
1.31
76
11.84
96
7.15
56
3.53
18
3.50
11
10.63
98
4.43
73
9.12
95
6.25
94
0.03
79
0.10
116
0.00
1
0.03
99
0.60
135
0.25
106
HSMtwo views4.00
74
0.79
59
3.16
34
1.59
55
2.17
103
6.77
94
1.11
71
12.28
104
6.35
45
6.75
62
8.11
54
13.90
118
5.37
90
8.85
90
2.71
47
0.00
1
0.00
1
0.00
1
0.00
1
0.02
14
0.02
14
TDLMtwo views4.11
75
1.11
95
3.54
41
1.62
57
1.04
66
3.91
54
7.41
152
10.60
76
10.67
98
6.38
57
12.59
96
5.95
53
4.77
80
8.79
89
3.04
53
0.58
152
0.00
1
0.01
95
0.00
1
0.19
89
0.12
72
CBMV_ROBtwo views4.14
76
0.52
25
3.14
33
1.30
38
0.77
52
6.92
95
1.97
85
10.11
68
9.58
88
8.92
94
14.20
109
7.12
66
5.90
99
8.65
86
3.50
68
0.01
48
0.05
93
0.00
1
0.00
1
0.04
31
0.09
56
MMNettwo views4.16
77
0.77
55
5.47
84
1.40
43
1.67
86
10.78
126
0.63
52
8.64
49
8.47
80
8.80
91
8.33
55
7.51
69
6.61
105
7.53
62
6.54
98
0.00
1
0.00
1
0.00
1
0.00
1
0.03
26
0.05
43
CVANet_RVCtwo views4.16
77
1.16
99
3.60
43
1.94
74
1.46
78
3.92
55
4.68
126
10.89
84
8.34
78
7.58
77
10.84
76
10.27
94
6.62
106
8.56
84
2.69
46
0.39
142
0.00
1
0.00
1
0.01
82
0.21
95
0.09
56
HSM-Net_RVCpermissivetwo views4.20
79
0.32
2
2.76
24
0.63
20
0.69
46
6.95
96
1.69
81
11.96
97
8.36
79
8.83
92
12.17
89
15.18
125
4.21
67
6.91
54
3.30
62
0.02
65
0.02
70
0.00
1
0.00
1
0.01
7
0.01
8
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
DSFCAtwo views4.21
80
0.50
24
5.45
83
1.34
39
1.68
88
7.04
97
4.51
123
10.73
79
7.00
53
10.78
115
6.80
37
8.48
81
4.63
76
9.91
108
5.12
83
0.02
65
0.06
100
0.02
107
0.03
99
0.08
48
0.03
27
iResNet_ROBtwo views4.23
81
1.02
84
4.90
71
2.18
91
0.93
63
2.92
35
0.37
33
15.10
134
16.91
136
7.89
81
10.51
74
7.03
60
3.07
47
8.16
78
3.46
67
0.01
48
0.00
1
0.00
1
0.00
1
0.10
53
0.02
14
FADNettwo views4.23
81
1.65
125
11.75
140
1.64
59
0.80
54
4.80
70
0.77
63
13.76
120
11.65
107
3.97
22
5.24
28
9.62
88
5.14
87
8.40
80
3.78
69
0.21
125
0.04
88
0.07
131
0.05
110
1.14
151
0.10
64
HGLStereotwo views4.25
83
0.52
25
4.65
69
3.09
134
5.18
164
6.51
92
0.73
61
10.37
72
6.74
49
7.32
73
12.44
95
7.00
59
7.21
111
7.46
61
5.51
88
0.00
1
0.02
70
0.00
1
0.00
1
0.06
43
0.10
64
delettwo views4.27
84
0.72
52
4.50
64
1.07
31
3.75
146
10.79
127
4.04
113
9.95
62
7.89
69
8.09
83
8.89
60
8.46
79
3.81
60
7.56
63
5.80
90
0.00
1
0.00
1
0.00
1
0.00
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0.11
59
0.02
14
iResNetv2_ROBtwo views4.28
85
1.43
112
7.17
115
2.91
126
1.26
72
4.36
61
1.62
79
13.64
119
10.25
96
9.83
106
11.41
80
7.68
72
4.00
63
7.75
67
1.85
33
0.00
1
0.00
1
0.00
1
0.00
1
0.37
119
0.09
56
RAFT + AFFtwo views4.30
86
1.55
119
5.84
90
1.43
45
2.36
110
5.94
83
6.72
146
10.01
63
7.56
64
7.55
76
8.96
63
7.09
65
6.73
109
8.57
85
4.02
73
0.02
65
0.47
161
0.00
1
0.01
82
0.56
130
0.54
136
StereoDRNet-Refinedtwo views4.46
87
0.62
43
3.80
48
1.92
71
0.40
33
9.35
111
0.15
18
10.02
64
8.83
85
12.69
127
11.62
85
9.34
86
3.87
61
8.06
74
8.02
111
0.00
1
0.00
1
0.01
95
0.05
110
0.20
90
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109
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
NVstereo2Dtwo views4.51
88
0.82
62
6.86
113
3.28
141
3.38
132
8.16
104
3.13
98
10.51
74
15.15
126
4.90
29
6.89
39
7.87
74
4.78
82
9.88
107
3.91
72
0.01
48
0.00
1
0.00
1
0.06
113
0.02
14
0.58
138
DLCB_ROBtwo views4.51
88
0.91
71
3.78
47
2.19
92
1.07
67
6.28
85
3.09
97
9.78
60
7.72
66
10.65
113
12.97
98
13.91
119
3.71
59
8.72
87
5.30
85
0.00
1
0.00
1
0.00
1
0.00
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0.03
26
0.10
64
RASNettwo views4.52
90
0.61
38
4.42
62
3.42
145
4.68
161
4.58
68
0.99
67
9.54
58
8.01
71
5.28
36
11.42
81
10.34
96
8.88
123
9.28
100
8.68
121
0.15
115
0.00
1
0.00
1
0.00
1
0.03
26
0.04
38
SGM-Foresttwo views4.96
91
0.32
2
2.84
26
1.21
35
0.64
43
10.23
122
6.64
145
11.55
90
10.98
99
10.94
117
13.59
103
11.65
107
4.30
70
8.94
92
4.63
79
0.11
109
0.04
88
0.00
1
0.00
1
0.05
39
0.46
128
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
92
1.47
114
7.42
118
2.40
107
2.14
102
8.73
107
3.64
109
12.42
105
13.11
117
7.03
67
7.57
49
7.88
75
6.52
104
10.16
110
7.82
108
0.02
65
0.03
81
0.00
1
0.00
1
0.11
59
1.07
154
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
93
1.74
130
6.38
102
1.96
76
1.29
74
2.26
19
1.69
81
10.07
65
18.53
139
7.88
80
18.15
130
8.49
82
2.70
41
10.59
115
7.04
101
0.96
162
0.15
129
0.02
107
0.00
1
0.13
72
0.12
72
PSMNet_ROBtwo views5.02
94
1.63
124
6.03
95
1.90
70
1.83
96
9.57
115
6.35
142
15.58
139
7.23
57
6.15
51
10.48
73
12.22
110
4.16
66
8.02
71
8.71
122
0.02
65
0.01
54
0.01
95
0.10
123
0.20
90
0.12
72
FINETtwo views5.32
95
1.14
97
10.04
133
2.17
90
3.67
144
6.49
90
6.76
147
11.68
92
22.08
153
7.42
74
12.15
88
4.24
37
3.61
56
9.25
99
5.35
86
0.04
88
0.04
88
0.00
1
0.00
1
0.17
83
0.09
56
CBMVpermissivetwo views5.35
96
0.91
71
3.67
45
1.62
57
0.44
35
10.09
120
7.19
151
12.49
106
12.33
115
12.22
123
14.69
113
10.93
102
6.48
103
8.51
83
4.96
82
0.02
65
0.15
129
0.00
1
0.00
1
0.17
83
0.17
85
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
97
1.75
131
6.80
111
3.12
136
4.45
156
10.61
125
4.35
120
18.80
154
9.73
89
12.22
123
6.87
38
11.44
105
4.65
77
8.09
76
8.26
117
0.02
65
0.11
120
0.00
1
0.03
99
0.20
90
0.28
114
ETE_ROBtwo views5.80
98
1.77
132
6.33
99
1.44
47
0.78
53
6.43
89
6.90
148
12.53
107
8.08
72
12.93
132
14.89
114
21.13
154
5.87
98
9.83
106
6.57
99
0.04
88
0.01
54
0.00
1
0.02
89
0.08
48
0.33
117
DRN-Testtwo views5.87
99
0.98
80
5.89
92
2.69
120
3.65
142
12.37
137
3.35
103
20.07
166
10.20
95
11.93
122
12.31
94
11.06
104
5.31
89
7.89
69
9.05
125
0.04
88
0.05
93
0.04
122
0.04
108
0.18
88
0.25
106
NCCL2two views5.88
100
1.59
121
5.44
82
1.87
66
0.92
61
9.55
114
11.55
167
12.11
99
9.94
93
9.67
105
8.85
59
22.28
156
7.41
112
8.78
88
7.17
102
0.01
48
0.00
1
0.03
115
0.00
1
0.13
72
0.23
100
MaskLacGwcNet_RVCtwo views5.97
101
5.36
165
5.02
73
3.75
154
3.57
139
15.81
149
4.27
117
11.72
93
6.96
51
7.23
71
8.47
57
9.09
85
2.66
40
9.36
103
2.71
47
7.49
179
2.32
174
2.01
176
0.08
117
6.77
179
4.67
175
APVNettwo views5.98
102
1.72
129
8.72
127
3.21
139
4.04
150
12.31
136
1.81
84
19.09
156
6.10
39
6.13
50
13.41
101
10.69
99
6.30
101
11.26
122
13.90
149
0.05
96
0.02
70
0.11
137
0.03
99
0.53
129
0.20
93
NaN_ROBtwo views6.00
103
1.24
102
6.29
98
1.34
39
1.68
88
9.60
116
10.31
163
15.09
132
15.79
129
12.62
126
8.95
62
11.67
108
5.83
97
11.78
125
6.41
97
0.05
96
0.13
126
0.08
132
0.20
132
0.22
98
0.79
146
DANettwo views6.02
104
1.23
101
8.45
126
3.86
157
3.94
149
7.64
103
1.34
77
9.51
57
7.00
53
13.39
134
15.53
118
15.99
129
7.02
110
12.14
126
12.37
144
0.19
122
0.12
125
0.02
107
0.03
99
0.13
72
0.56
137
Z Ling, K Yang, J Li, Y Zhang, X Gao, L Luo, L Xie: Domain-adaptive modules for stereo matching network. Neurocomputing 2021
XPNet_ROBtwo views6.03
105
1.22
100
5.61
87
2.56
117
0.90
60
6.32
86
7.07
149
12.92
111
8.30
77
14.76
139
15.13
116
19.84
148
6.66
108
10.36
111
8.58
120
0.02
65
0.04
88
0.00
1
0.03
99
0.11
59
0.24
103
Anonymous Stereotwo views6.16
106
3.15
156
23.75
167
2.97
132
2.48
116
4.39
65
13.30
169
9.21
53
9.86
91
9.56
104
8.76
58
6.79
58
1.99
33
13.50
138
13.04
147
0.01
48
0.05
93
0.00
1
0.06
113
0.22
98
0.19
90
GANettwo views6.22
107
1.07
89
4.07
56
2.27
98
0.89
58
9.19
110
9.52
158
12.02
98
8.13
75
10.72
114
29.09
156
13.86
117
7.52
114
11.00
119
4.39
75
0.36
140
0.00
1
0.02
107
0.02
89
0.12
64
0.08
52
DISCOtwo views6.28
108
0.57
33
5.78
89
3.43
146
1.17
69
11.22
129
3.39
104
12.14
102
16.16
131
6.52
61
11.22
78
16.96
133
6.32
102
19.51
161
10.74
137
0.00
1
0.00
1
0.00
1
0.00
1
0.35
117
0.11
67
Syn2CoExtwo views6.32
109
2.72
149
9.33
130
2.45
111
1.67
86
8.59
105
3.32
101
18.08
150
12.27
114
8.53
89
21.08
136
10.30
95
9.80
129
9.30
101
7.86
109
0.01
48
0.00
1
0.00
1
0.00
1
0.63
137
0.43
125
RYNettwo views6.34
110
0.89
70
5.88
91
1.41
44
4.48
158
15.97
150
4.18
115
13.41
115
16.49
132
10.81
116
7.00
41
14.33
121
8.72
121
9.43
104
13.71
148
0.00
1
0.01
54
0.00
1
0.00
1
0.02
14
0.07
47
GwcNetcopylefttwo views6.42
111
1.97
139
10.92
136
2.59
118
5.58
167
11.55
131
2.21
87
14.10
123
16.52
135
10.04
109
17.19
127
10.86
101
5.61
95
9.23
98
8.84
124
0.01
48
0.06
100
0.03
115
0.08
117
0.56
130
0.40
122
DGSMNettwo views6.47
112
2.61
147
13.64
151
3.46
147
3.74
145
8.97
109
6.01
134
14.15
124
13.01
116
7.46
75
14.10
107
9.94
90
4.79
83
10.44
112
6.22
93
1.30
166
1.70
172
1.80
174
1.52
174
2.27
167
2.35
171
GANetREF_RVCpermissivetwo views6.56
113
2.89
151
7.58
121
3.41
144
0.40
33
12.96
140
9.58
159
15.09
132
17.25
138
10.33
110
10.62
75
12.27
111
8.16
116
12.21
127
4.53
78
0.41
144
0.00
1
0.00
1
0.02
89
3.12
170
0.39
121
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
114
1.80
134
6.25
97
1.26
36
0.94
64
10.08
119
9.02
155
16.00
140
11.51
106
12.74
128
13.02
99
24.77
158
5.25
88
10.56
114
8.02
111
0.04
88
0.05
93
0.00
1
0.02
89
0.10
53
0.25
106
S-Stereotwo views6.63
115
0.84
64
9.67
132
3.15
137
3.48
136
6.49
90
6.22
137
12.99
112
22.84
154
9.48
100
15.51
117
12.00
109
8.43
118
8.04
72
10.70
136
0.12
112
0.17
134
0.00
1
0.38
150
0.13
72
1.92
165
DeepPrunerFtwo views6.75
116
2.69
148
23.31
166
3.68
150
7.16
173
3.78
51
4.29
118
13.42
116
20.13
149
8.13
84
10.46
72
7.18
68
8.06
115
11.10
120
9.44
127
0.24
128
0.15
129
0.29
154
0.42
153
0.66
139
0.45
126
edge stereotwo views6.76
117
1.01
82
6.76
110
2.20
93
2.45
115
6.41
88
2.45
89
14.84
130
11.98
113
15.29
140
18.31
131
22.02
155
12.56
140
10.82
117
7.49
104
0.03
79
0.06
100
0.11
137
0.03
99
0.30
108
0.14
77
NCC-stereotwo views6.77
118
1.49
115
6.48
104
2.92
128
4.40
152
7.43
99
3.61
107
19.52
162
13.29
118
8.39
87
16.91
122
15.96
127
12.13
138
12.85
132
7.70
106
1.47
167
0.11
120
0.01
95
0.42
153
0.14
79
0.24
103
Abc-Nettwo views6.77
118
1.49
115
6.48
104
2.92
128
4.40
152
7.43
99
3.61
107
19.52
162
13.29
118
8.39
87
16.91
122
15.96
127
12.13
138
12.85
132
7.70
106
1.47
167
0.11
120
0.01
95
0.42
153
0.14
79
0.24
103
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
FAT-Stereotwo views6.78
120
0.68
49
6.80
111
2.30
100
1.77
94
5.63
80
4.20
116
18.79
153
18.62
140
10.53
112
17.15
125
18.52
142
13.74
148
8.86
91
7.38
103
0.03
79
0.15
129
0.01
95
0.07
115
0.12
64
0.26
109
RPtwo views6.84
121
1.29
109
5.53
85
3.92
158
5.18
164
6.32
86
3.53
105
11.73
94
15.31
127
9.54
103
22.38
140
18.25
140
14.47
149
10.11
109
7.49
104
0.91
161
0.01
54
0.12
139
0.15
128
0.33
113
0.19
90
ccnettwo views6.87
122
0.92
74
5.15
77
3.09
134
2.18
105
11.78
133
4.90
127
17.97
149
8.79
84
12.79
130
20.20
134
16.00
130
8.35
117
9.12
95
14.17
151
0.42
147
0.33
151
0.25
153
0.09
121
0.40
125
0.48
129
RGCtwo views6.88
123
2.23
143
6.13
96
4.05
159
4.73
163
8.94
108
2.78
94
15.19
136
11.74
108
11.13
118
19.34
133
17.86
137
10.42
132
13.02
135
8.03
113
0.73
155
0.01
54
0.24
152
0.41
152
0.31
111
0.38
120
Nwc_Nettwo views6.97
124
1.25
103
6.63
108
3.82
155
3.37
131
10.83
128
1.67
80
19.56
164
11.35
103
8.36
86
23.62
143
17.19
135
11.44
137
11.21
121
8.08
115
0.80
157
0.00
1
0.00
1
0.02
89
0.13
72
0.09
56
STTRV1_RVCtwo views7.02
125
1.10
93
12.88
146
3.32
142
6.92
172
11.90
135
4.00
112
15.07
131
11.94
112
9.51
101
14.57
110
11.63
106
8.73
122
12.65
131
8.06
114
3.32
173
2.75
176
0.41
162
0.12
124
1.38
157
0.11
67
ADCReftwo views7.27
126
1.38
110
16.37
155
2.52
114
3.30
130
11.63
132
3.16
99
10.80
81
9.35
86
13.03
133
25.27
150
8.17
78
8.92
124
8.06
74
21.81
165
0.15
115
0.08
110
0.16
144
0.34
148
0.38
120
0.58
138
psmorigintwo views7.34
127
1.58
120
18.31
159
2.35
104
0.87
57
6.72
93
1.70
83
10.63
77
7.14
55
19.77
151
22.71
141
20.13
150
11.34
136
12.96
134
9.49
128
0.04
88
0.17
134
0.06
127
0.17
129
0.21
95
0.50
132
CSANtwo views7.62
128
1.60
122
6.56
107
1.83
64
0.66
44
12.40
138
10.52
165
14.45
127
21.32
151
14.19
136
15.98
120
17.84
136
13.02
145
12.32
128
8.38
118
0.09
106
0.07
107
0.03
115
0.04
108
0.33
113
0.67
143
CC-Net-ROBtwo views7.68
129
9.21
175
7.46
119
3.82
155
3.66
143
15.68
148
5.02
128
17.95
148
10.45
97
6.44
60
14.00
106
7.63
71
4.77
80
10.75
116
3.10
57
5.99
178
2.46
175
16.28
180
1.08
167
6.30
178
1.56
162
stereogantwo views7.69
130
0.88
68
7.08
114
3.49
148
3.93
148
18.98
158
3.23
100
16.52
142
19.58
146
9.93
108
18.92
132
20.50
152
9.04
126
14.07
142
6.14
92
0.26
131
0.04
88
0.21
149
0.03
99
0.63
137
0.33
117
pmcnntwo views7.72
131
1.27
105
9.42
131
2.91
126
3.14
126
9.44
112
6.23
138
12.56
108
16.51
133
14.53
137
24.08
145
27.44
164
8.49
119
9.32
102
8.44
119
0.06
100
0.08
110
0.00
1
0.00
1
0.30
108
0.15
80
AF-Nettwo views7.78
132
1.44
113
6.68
109
3.37
143
4.50
159
8.61
106
2.69
92
17.07
145
20.17
150
9.52
102
24.02
144
20.31
151
14.59
150
11.58
124
9.84
132
0.61
153
0.00
1
0.12
139
0.00
1
0.38
120
0.12
72
PASMtwo views7.90
133
4.22
162
21.97
164
3.25
140
3.29
129
5.39
77
6.57
144
10.57
75
19.09
142
12.77
129
13.92
105
18.11
139
9.51
128
13.79
141
10.77
139
0.19
122
0.45
159
0.29
154
1.08
167
1.49
159
1.19
156
PWCDC_ROBbinarytwo views7.92
134
3.17
158
7.48
120
5.73
170
4.40
152
10.45
123
0.35
32
14.52
128
28.19
162
10.36
111
31.27
160
7.04
62
9.14
127
13.22
137
8.78
123
2.74
172
0.02
70
0.00
1
0.00
1
1.31
156
0.17
85
ADCP+two views8.09
135
1.79
133
14.50
153
1.54
50
4.28
151
16.57
152
5.20
130
12.80
110
11.20
102
12.83
131
17.07
124
11.02
103
10.80
134
17.59
157
23.18
168
0.03
79
0.05
93
0.01
95
0.18
130
0.39
124
0.81
147
SuperBtwo views8.10
136
3.15
156
24.67
168
2.65
119
1.23
70
9.88
118
4.29
118
10.18
70
30.07
165
11.53
120
12.18
90
6.12
54
6.65
107
10.50
113
14.47
152
0.14
113
0.11
120
0.35
158
0.25
140
13.06
181
0.48
129
PWC_ROBbinarytwo views8.24
137
3.13
154
12.74
145
2.43
110
4.43
155
7.51
102
1.22
72
16.63
143
19.24
144
16.08
142
28.29
153
13.99
120
10.16
131
13.63
140
14.06
150
0.42
147
0.00
1
0.05
125
0.00
1
0.59
134
0.27
112
MDST_ROBtwo views8.37
138
0.32
2
9.03
128
4.18
162
2.42
114
26.86
170
6.14
135
19.36
159
13.52
120
27.09
165
22.75
142
9.47
87
4.74
79
15.06
148
6.34
96
0.02
65
0.02
70
0.00
1
0.00
1
0.02
14
0.13
76
G-Nettwo views8.41
139
1.54
118
10.97
137
5.73
170
3.60
140
26.19
168
4.41
121
10.10
67
7.42
59
19.71
150
24.99
149
14.38
122
15.83
151
10.99
118
9.53
129
0.50
151
0.46
160
0.19
148
0.25
140
0.80
143
0.66
142
XQCtwo views8.43
140
3.58
160
16.40
156
2.92
128
2.17
103
13.22
142
3.60
106
14.64
129
25.86
159
11.87
121
12.04
86
15.06
124
10.67
133
15.24
149
19.41
157
0.39
142
0.08
110
0.05
125
0.07
115
0.84
145
0.45
126
FBW_ROBtwo views8.50
141
1.03
86
7.98
123
1.93
73
1.28
73
13.10
141
6.23
138
22.50
170
18.98
141
18.82
147
14.91
115
19.06
145
10.04
130
18.41
158
9.83
131
0.62
154
0.22
140
1.82
175
0.82
163
0.99
149
1.36
158
aanetorigintwo views8.72
142
3.29
159
27.55
170
1.95
75
3.84
147
4.93
74
5.39
132
5.49
24
10.05
94
27.85
166
31.47
161
15.80
126
12.78
142
9.04
94
11.98
142
0.25
129
0.22
140
0.22
150
0.25
140
1.28
155
0.84
149
RTSCtwo views9.15
143
3.00
153
13.57
150
3.72
152
1.76
93
11.82
134
0.46
37
16.95
144
36.83
174
15.80
141
15.53
118
12.91
114
7.46
113
20.01
164
21.76
164
0.31
136
0.13
126
0.01
95
0.08
117
0.57
132
0.41
124
WCMA_ROBtwo views9.21
144
0.87
67
7.37
117
2.54
115
2.13
101
13.59
143
5.80
133
11.64
91
14.01
122
24.43
162
32.99
166
27.09
163
18.02
154
12.51
130
9.85
133
0.81
158
0.07
107
0.01
95
0.01
82
0.16
81
0.23
100
MSMD_ROBtwo views9.28
145
1.09
91
4.65
69
1.58
53
0.39
32
16.52
151
4.41
121
13.60
117
14.87
125
22.34
154
39.89
174
25.67
160
20.71
163
12.42
129
6.98
100
0.34
139
0.03
81
0.00
1
0.00
1
0.05
39
0.09
56
ADCPNettwo views9.54
146
2.39
145
31.46
172
2.09
84
1.60
82
16.71
154
6.39
143
12.11
99
11.45
104
13.53
135
21.45
138
19.41
146
10.94
135
14.38
144
21.54
163
0.27
134
1.16
167
0.39
161
1.49
172
0.58
133
1.45
160
SHDtwo views9.61
147
2.60
146
12.46
143
3.69
151
3.54
138
9.47
113
1.25
73
20.16
167
37.84
177
18.19
146
21.24
137
16.96
133
12.83
144
14.47
146
16.05
154
0.32
137
0.13
126
0.01
95
0.08
117
0.38
120
0.48
129
PDISCO_ROBtwo views9.62
148
1.99
140
11.51
138
9.88
177
9.61
178
21.48
162
3.83
110
19.33
158
28.49
163
11.27
119
14.17
108
19.92
149
5.02
86
16.35
154
9.18
126
5.28
176
0.41
155
0.14
142
0.09
121
2.05
163
2.36
172
MFN_U_SF_DS_RVCtwo views9.78
149
4.27
163
14.47
152
2.29
99
2.85
124
23.40
166
13.62
170
13.60
117
19.54
145
19.42
149
24.27
146
16.74
132
8.59
120
17.05
156
7.98
110
1.25
165
1.68
171
0.17
145
2.63
175
0.72
141
1.04
152
SGM_RVCbinarytwo views10.08
150
0.60
36
3.42
38
2.30
100
0.32
29
19.41
159
6.33
141
18.95
155
14.64
123
25.14
164
24.32
147
33.34
172
18.79
158
19.86
162
12.55
146
0.25
129
0.26
148
0.22
150
0.24
138
0.34
116
0.40
122
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DPSNettwo views10.14
151
1.88
138
16.82
157
1.85
65
1.73
90
24.84
167
17.20
179
19.92
165
27.41
161
12.23
125
13.62
104
16.52
131
18.35
155
14.42
145
12.50
145
0.78
156
0.54
164
0.08
132
0.25
140
1.18
152
0.59
141
ADCLtwo views10.16
152
2.11
141
19.36
161
1.92
71
1.88
98
22.23
163
8.91
154
14.04
122
23.56
156
14.62
138
26.19
151
12.75
113
13.59
147
16.06
153
22.95
167
0.26
131
0.18
136
0.75
165
0.65
159
0.69
140
0.58
138
ADCMidtwo views10.24
153
3.13
154
20.70
162
2.21
94
2.39
113
11.23
130
6.19
136
14.17
125
11.19
101
23.20
160
22.25
139
17.89
138
19.54
160
18.51
159
26.21
173
0.45
149
0.42
157
1.10
170
1.29
169
1.56
161
1.18
155
FCDSN-DCtwo views10.24
153
0.56
32
3.49
39
1.96
76
1.29
74
16.90
155
4.59
125
17.16
146
19.10
143
24.64
163
32.46
165
33.82
173
22.14
168
15.93
152
10.45
134
0.04
88
0.00
1
0.00
1
0.00
1
0.05
39
0.21
94
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
SANettwo views10.64
155
1.86
137
10.91
135
1.76
63
0.71
47
14.62
146
9.23
157
19.18
157
37.14
175
19.22
148
27.96
152
25.86
161
19.11
159
13.02
135
10.63
135
0.08
105
0.06
100
0.03
115
0.02
89
0.62
136
0.81
147
FC-DCNNcopylefttwo views10.72
156
0.52
25
4.27
58
1.88
67
1.63
83
17.18
156
5.29
131
18.20
151
19.69
147
28.50
167
34.51
169
34.03
174
21.48
166
15.89
151
11.15
141
0.03
79
0.01
54
0.02
107
0.01
82
0.07
45
0.09
56
AnyNet_C32two views10.98
157
5.58
166
22.79
165
4.16
160
5.83
168
15.64
147
14.30
171
13.18
113
17.15
137
16.44
144
20.52
135
14.68
123
13.44
146
22.46
166
30.08
178
0.17
119
0.26
148
0.36
159
0.36
149
1.23
153
0.91
150
MeshStereopermissivetwo views11.52
158
1.52
117
4.55
65
1.89
69
1.46
78
19.87
161
5.11
129
20.66
168
15.91
130
32.67
172
34.51
169
39.34
179
21.15
164
18.74
160
12.10
143
0.11
109
0.06
100
0.01
95
0.00
1
0.45
128
0.22
96
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
159
3.66
161
25.81
169
3.61
149
2.26
109
22.77
164
4.55
124
27.10
175
20.06
148
23.90
161
28.99
155
30.53
169
16.98
152
19.92
163
20.26
159
1.24
164
1.07
166
0.98
169
1.33
170
1.80
162
2.04
167
ADCStwo views13.02
160