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

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

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

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




<
Method Infoalllakes. 1llakes. 1ssand box 1lsand box 1sstora. room 1lstora. room 1sstora. room 2lstora. room 2sstora. room 2 1lstora. room 2 1sstora. room 2 2lstora. room 2 2sstora. room 3lstora. room 3stunnel 1ltunnel 1stunnel 2ltunnel 2stunnel 3ltunnel 3s
sort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
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
118
MDST_ROBtwo views8.37
137
0.32
2
9.03
128
4.18
161
2.42
114
26.86
169
6.14
134
19.36
158
13.52
119
27.09
164
22.75
141
9.47
87
4.74
79
15.06
147
6.34
96
0.02
65
0.02
70
0.00
1
0.00
1
0.02
14
0.13
75
SGM-Foresttwo views4.96
91
0.32
2
2.84
26
1.21
35
0.64
43
10.23
122
6.64
144
11.55
90
10.98
99
10.94
116
13.59
103
11.65
106
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
127
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
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
127
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
127
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
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
124
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
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
139
0.01
7
0.02
14
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
144
0.02
14
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
93
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
1
0.00
1
0.07
47
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
137
0.02
14
0.02
14
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
173
0.00
1
0.00
1
0.00
1
0.03
26
0.01
8
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
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
120
7.03
60
0.24
1
6.19
45
0.79
13
0.00
1
0.15
129
0.00
1
0.23
135
0.13
72
0.04
38
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
175
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
175
0.00
1
0.00
1
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
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
79
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
163
0.04
31
0.26
108
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
76
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
0.04
31
0.06
45
ac_64two views3.70
62
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
0.00
1
0.00
1
0.08
48
0.14
76
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
DSFCAtwo views4.21
80
0.50
24
5.45
83
1.34
39
1.68
88
7.04
97
4.51
122
10.73
79
7.00
53
10.78
114
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
FC-DCNNcopylefttwo views10.72
155
0.52
25
4.27
58
1.88
67
1.63
83
17.18
155
5.29
130
18.20
150
19.69
146
28.50
166
34.51
168
34.03
172
21.48
165
15.89
150
11.15
140
0.03
79
0.01
54
0.02
107
0.01
82
0.07
45
0.09
56
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
79
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
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
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
115
0.00
1
0.00
1
0.00
1
0.00
1
0.29
107
0.01
8
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
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
FCDSN-DCtwo views10.24
152
0.56
32
3.49
39
1.96
76
1.29
74
16.90
154
4.59
124
17.16
145
19.10
142
24.64
162
32.46
164
33.82
171
22.14
167
15.93
151
10.45
133
0.04
88
0.00
1
0.00
1
0.00
1
0.05
39
0.21
93
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
DISCOtwo views6.28
108
0.57
33
5.78
89
3.43
145
1.17
69
11.22
129
3.39
104
12.14
102
16.16
130
6.52
61
11.22
78
16.96
132
6.32
102
19.51
160
10.74
136
0.00
1
0.00
1
0.00
1
0.00
1
0.35
117
0.11
67
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
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.
SGM_RVCbinarytwo views10.08
149
0.60
36
3.42
38
2.30
100
0.32
29
19.41
158
6.33
140
18.95
154
14.64
122
25.14
163
24.32
146
33.34
170
18.79
157
19.86
161
12.55
145
0.25
129
0.26
148
0.22
150
0.24
137
0.34
116
0.40
121
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
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
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
86
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
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
86
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
86
RASNettwo views4.52
90
0.61
38
4.42
62
3.42
144
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
122
9.28
100
8.68
120
0.15
115
0.00
1
0.00
1
0.00
1
0.03
26
0.04
38
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
126
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
0.26
108
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
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
89
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
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
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
111
4.70
78
7.44
60
4.41
76
0.03
79
0.00
1
0.06
127
0.00
1
0.20
90
0.04
38
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
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
111
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
FAT-Stereotwo views6.78
120
0.68
49
6.80
111
2.30
100
1.77
94
5.63
80
4.20
115
18.79
152
18.62
139
10.53
111
17.15
124
18.52
141
13.74
147
8.86
91
7.38
103
0.03
79
0.15
129
0.01
95
0.07
115
0.12
64
0.26
108
delettwo views4.27
84
0.72
52
4.50
64
1.07
31
3.75
146
10.79
127
4.04
112
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
1
0.11
59
0.02
14
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
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
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
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
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
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
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
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
117
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
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
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
124
6.19
45
2.51
44
0.22
126
0.01
54
0.00
1
0.00
1
0.12
64
0.03
27
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
125
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
137
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
95
S-Stereotwo views6.63
115
0.84
64
9.67
132
3.15
137
3.48
136
6.49
90
6.22
136
12.99
112
22.84
153
9.48
100
15.51
116
12.00
108
8.43
118
8.04
72
10.70
135
0.12
112
0.17
134
0.00
1
0.38
149
0.13
72
1.92
164
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
WCMA_ROBtwo views9.21
143
0.87
67
7.37
117
2.54
115
2.13
101
13.59
142
5.80
132
11.64
91
14.01
121
24.43
161
32.99
165
27.09
161
18.02
153
12.51
130
9.85
132
0.81
158
0.07
107
0.01
95
0.01
82
0.16
81
0.23
99
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
stereogantwo views7.69
129
0.88
68
7.08
114
3.49
147
3.93
148
18.98
157
3.23
100
16.52
141
19.58
145
9.93
107
18.92
131
20.50
151
9.04
125
14.07
141
6.14
92
0.26
131
0.04
88
0.21
149
0.03
99
0.63
137
0.33
116
RYNettwo views6.34
110
0.89
70
5.88
91
1.41
44
4.48
158
15.97
149
4.18
114
13.41
115
16.49
131
10.81
115
7.00
41
14.33
120
8.72
121
9.43
104
13.71
147
0.00
1
0.01
54
0.00
1
0.00
1
0.02
14
0.07
47
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
0.10
53
0.09
56
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
112
12.97
98
13.91
118
3.71
59
8.72
87
5.30
85
0.00
1
0.00
1
0.00
1
0.00
1
0.03
26
0.10
64
CBMVpermissivetwo views5.35
96
0.91
71
3.67
45
1.62
57
0.44
35
10.09
120
7.19
150
12.49
106
12.33
114
12.22
122
14.69
112
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
84
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
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
120
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
114
ccnettwo views6.87
122
0.92
74
5.15
77
3.09
134
2.18
105
11.78
133
4.90
126
17.97
148
8.79
84
12.79
129
20.20
133
16.00
129
8.35
117
9.12
95
14.17
150
0.42
147
0.33
151
0.25
153
0.09
121
0.40
125
0.48
128
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
106
6.97
40
8.84
83
3.65
57
6.85
53
3.04
53
0.00
1
0.02
70
0.01
95
0.00
1
0.02
14
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
158
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
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
1
0.22
98
0.03
27
DRN-Testtwo views5.87
99
0.98
80
5.89
92
2.69
120
3.65
142
12.37
136
3.35
103
20.07
165
10.20
95
11.93
121
12.31
94
11.06
104
5.31
89
7.89
69
9.05
124
0.04
88
0.05
93
0.04
122
0.04
108
0.18
88
0.25
105
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
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
112
15.29
139
18.31
130
22.02
154
12.56
139
10.82
117
7.49
104
0.03
79
0.06
100
0.11
137
0.03
99
0.30
108
0.14
76
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
83
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
133
16.91
135
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
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
FBW_ROBtwo views8.50
140
1.03
86
7.98
123
1.93
73
1.28
73
13.10
140
6.23
137
22.50
169
18.98
140
18.82
146
14.91
114
19.06
144
10.04
129
18.41
157
9.83
130
0.62
154
0.22
140
1.82
174
0.82
162
0.99
149
1.36
157
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
110
7.94
77
3.50
53
6.43
50
5.70
89
0.00
1
0.00
1
0.04
122
0.00
1
0.20
90
0.08
52
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.
GANettwo views6.22
107
1.07
89
4.07
56
2.27
98
0.89
58
9.19
110
9.52
157
12.02
98
8.13
75
10.72
113
29.09
155
13.86
116
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
SGM-ForestMtwo views16.99
173
1.08
90
5.74
88
2.12
86
0.75
49
31.63
173
12.21
167
27.80
176
32.25
166
37.88
173
39.99
174
52.96
180
35.20
178
33.60
176
24.47
168
0.26
131
0.39
154
0.31
156
0.39
150
0.26
106
0.53
134
MSMD_ROBtwo views9.28
144
1.09
91
4.65
69
1.58
53
0.39
32
16.52
150
4.41
120
13.60
117
14.87
124
22.34
153
39.89
173
25.67
159
20.71
162
12.42
129
6.98
100
0.34
139
0.03
81
0.00
1
0.00
1
0.05
39
0.09
56
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
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
0.00
1
0.00
1
0.00
1
0.22
98
0.07
47
TDLMtwo views4.11
75
1.11
94
3.54
41
1.62
57
1.04
66
3.91
54
7.41
151
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
71
ARAFTtwo views3.13
45
1.11
94
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
FINETtwo views5.32
95
1.14
96
10.04
133
2.17
90
3.67
144
6.49
90
6.76
146
11.68
92
22.08
152
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
DeepPruner_ROBtwo views3.52
57
1.14
96
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
CVANet_RVCtwo views4.16
77
1.16
98
3.60
43
1.94
74
1.46
78
3.92
55
4.68
125
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
XPNet_ROBtwo views6.03
105
1.22
99
5.61
87
2.56
117
0.90
60
6.32
86
7.07
148
12.92
111
8.30
77
14.76
138
15.13
115
19.84
147
6.66
108
10.36
111
8.58
119
0.02
65
0.04
88
0.00
1
0.03
99
0.11
59
0.24
102
DANettwo views6.02
104
1.23
100
8.45
126
3.86
156
3.94
149
7.64
103
1.34
77
9.51
57
7.00
53
13.39
133
15.53
117
15.99
128
7.02
110
12.14
126
12.37
143
0.19
122
0.12
125
0.02
107
0.03
99
0.13
72
0.56
136
Z Ling, K Yang, J Li, Y Zhang, X Gao, L Luo, L Xie: Domain-adaptive modules for stereo matching network. Neurocomputing 2021
NaN_ROBtwo views6.00
103
1.24
101
6.29
98
1.34
39
1.68
88
9.60
116
10.31
162
15.09
131
15.79
128
12.62
125
8.95
62
11.67
107
5.83
97
11.78
125
6.41
97
0.05
96
0.13
126
0.08
132
0.20
131
0.22
98
0.79
145
Nwc_Nettwo views6.97
124
1.25
102
6.63
108
3.82
154
3.37
131
10.83
128
1.67
80
19.56
163
11.35
103
8.36
86
23.62
142
17.19
134
11.44
136
11.21
121
8.08
114
0.80
157
0.00
1
0.00
1
0.02
89
0.13
72
0.09
56
DMCA-RVCcopylefttwo views3.17
46
1.26
103
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
pmcnntwo views7.72
130
1.27
104
9.42
131
2.91
126
3.14
126
9.44
112
6.23
137
12.56
108
16.51
132
14.53
136
24.08
144
27.44
162
8.49
119
9.32
102
8.44
118
0.06
100
0.08
110
0.00
1
0.00
1
0.30
108
0.15
79
MANEtwo views19.47
178
1.27
104
5.07
76
4.69
164
5.55
166
30.49
172
9.94
160
34.01
179
37.27
175
44.13
178
51.57
182
52.51
179
40.41
180
33.58
175
24.81
169
0.89
160
0.86
165
1.11
170
9.72
180
0.38
120
1.06
152
BEATNet_4xtwo views3.24
48
1.27
104
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
1
0.23
103
0.23
99
LE_ROBtwo views16.73
172
1.28
107
11.61
139
3.72
151
1.65
84
16.67
152
9.17
155
14.39
126
55.91
182
63.81
181
40.86
176
35.94
174
37.73
179
14.24
142
26.87
173
0.05
96
0.10
116
0.13
141
0.22
134
0.12
64
0.15
79
RPtwo views6.84
121
1.29
108
5.53
85
3.92
157
5.18
164
6.32
86
3.53
105
11.73
94
15.31
126
9.54
102
22.38
139
18.25
139
14.47
148
10.11
109
7.49
104
0.91
161
0.01
54
0.12
139
0.15
127
0.33
113
0.19
89
ADCReftwo views7.27
125
1.38
109
16.37
154
2.52
114
3.30
130
11.63
132
3.16
99
10.80
81
9.35
86
13.03
132
25.27
149
8.17
78
8.92
123
8.06
74
21.81
164
0.15
115
0.08
110
0.16
144
0.34
147
0.38
120
0.58
137
DN-CSS_ROBtwo views2.69
37
1.40
110
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
iResNetv2_ROBtwo views4.28
85
1.43
111
7.17
115
2.91
126
1.26
72
4.36
61
1.62
79
13.64
119
10.25
96
9.83
105
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
AF-Nettwo views7.78
131
1.44
112
6.68
109
3.37
142
4.50
159
8.61
106
2.69
92
17.07
144
20.17
149
9.52
101
24.02
143
20.31
150
14.59
149
11.58
124
9.84
131
0.61
153
0.00
1
0.12
139
0.00
1
0.38
120
0.12
71
PA-Nettwo views4.98
92
1.47
113
7.42
118
2.40
107
2.14
102
8.73
107
3.64
109
12.42
105
13.11
116
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
153
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
Abc-Nettwo views6.77
118
1.49
114
6.48
104
2.92
128
4.40
152
7.43
99
3.61
107
19.52
161
13.29
117
8.39
87
16.91
121
15.96
126
12.13
137
12.85
131
7.70
106
1.47
167
0.11
120
0.01
95
0.42
152
0.14
79
0.24
102
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
118
1.49
114
6.48
104
2.92
128
4.40
152
7.43
99
3.61
107
19.52
161
13.29
117
8.39
87
16.91
121
15.96
126
12.13
137
12.85
131
7.70
106
1.47
167
0.11
120
0.01
95
0.42
152
0.14
79
0.24
102
MeshStereopermissivetwo views11.52
157
1.52
116
4.55
65
1.89
69
1.46
78
19.87
160
5.11
128
20.66
167
15.91
129
32.67
171
34.51
168
39.34
177
21.15
163
18.74
159
12.10
142
0.11
109
0.06
100
0.01
95
0.00
1
0.45
128
0.22
95
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
G-Nettwo views8.41
138
1.54
117
10.97
137
5.73
169
3.60
140
26.19
167
4.41
120
10.10
67
7.42
59
19.71
149
24.99
148
14.38
121
15.83
150
10.99
118
9.53
128
0.50
151
0.46
160
0.19
148
0.25
139
0.80
143
0.66
141
RAFT + AFFtwo views4.30
86
1.55
118
5.84
90
1.43
45
2.36
110
5.94
83
6.72
145
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
135
psmorigintwo views7.34
126
1.58
119
18.31
158
2.35
104
0.87
57
6.72
93
1.70
83
10.63
77
7.14
55
19.77
150
22.71
140
20.13
149
11.34
135
12.96
133
9.49
127
0.04
88
0.17
134
0.06
127
0.17
128
0.21
95
0.50
131
NCCL2two views5.88
100
1.59
120
5.44
82
1.87
66
0.92
61
9.55
114
11.55
166
12.11
99
9.94
93
9.67
104
8.85
59
22.28
155
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
99
CSANtwo views7.62
127
1.60
121
6.56
107
1.83
64
0.66
44
12.40
137
10.52
164
14.45
127
21.32
150
14.19
135
15.98
119
17.84
135
13.02
144
12.32
128
8.38
117
0.09
106
0.07
107
0.03
115
0.04
108
0.33
113
0.67
142
FADNet-RVC-Resampletwo views3.79
66
1.62
122
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
0.29
114
PSMNet_ROBtwo views5.02
94
1.63
123
6.03
95
1.90
70
1.83
96
9.57
115
6.35
141
15.58
138
7.23
57
6.15
51
10.48
73
12.22
109
4.16
66
8.02
71
8.71
121
0.02
65
0.01
54
0.01
95
0.10
123
0.20
90
0.12
71
FADNettwo views4.23
81
1.65
124
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
CroCo_RVCtwo views1.35
4
1.66
125
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
131
2.07
163
0.22
95
sAnonymous2two views1.35
4
1.66
125
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
131
2.07
163
0.22
95
FADNet_RVCtwo views3.91
71
1.67
127
12.95
147
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
156
0.91
146
0.95
150
APVNettwo views5.98
102
1.72
128
8.72
127
3.21
139
4.04
150
12.31
135
1.81
84
19.09
155
6.10
39
6.13
50
13.41
101
10.69
99
6.30
101
11.26
122
13.90
148
0.05
96
0.02
70
0.11
137
0.03
99
0.53
129
0.20
92
AANet_RVCtwo views5.01
93
1.74
129
6.38
102
1.96
76
1.29
74
2.26
19
1.69
81
10.07
65
18.53
138
7.88
80
18.15
129
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
71
StereoDRNettwo views5.59
97
1.75
130
6.80
111
3.12
136
4.45
156
10.61
125
4.35
119
18.80
153
9.73
89
12.22
122
6.87
38
11.44
105
4.65
77
8.09
76
8.26
116
0.02
65
0.11
120
0.00
1
0.03
99
0.20
90
0.28
113
ETE_ROBtwo views5.80
98
1.77
131
6.33
99
1.44
47
0.78
53
6.43
89
6.90
147
12.53
107
8.08
72
12.93
131
14.89
113
21.13
153
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
116
ADCP+two views8.09
134
1.79
132
14.50
152
1.54
50
4.28
151
16.57
151
5.20
129
12.80
110
11.20
102
12.83
130
17.07
123
11.02
103
10.80
133
17.59
156
23.18
167
0.03
79
0.05
93
0.01
95
0.18
129
0.39
124
0.81
146
LALA_ROBtwo views6.58
114
1.80
133
6.25
97
1.26
36
0.94
64
10.08
119
9.02
154
16.00
139
11.51
106
12.74
127
13.02
99
24.77
157
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
105
PS-NSSStwo views3.90
70
1.84
134
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
111
5.01
40
2.24
36
8.41
81
2.91
51
0.33
138
0.03
81
0.83
166
0.00
1
0.82
144
0.70
143
FADNet-RVCtwo views3.98
73
1.84
134
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
105
SANettwo views10.64
154
1.86
136
10.91
135
1.76
63
0.71
47
14.62
145
9.23
156
19.18
156
37.14
174
19.22
147
27.96
151
25.86
160
19.11
158
13.02
134
10.63
134
0.08
105
0.06
100
0.03
115
0.02
89
0.62
136
0.81
146
DPSNettwo views10.14
150
1.88
137
16.82
156
1.85
65
1.73
90
24.84
166
17.20
178
19.92
164
27.41
160
12.23
124
13.62
104
16.52
130
18.35
154
14.42
144
12.50
144
0.78
156
0.54
164
0.08
132
0.25
139
1.18
152
0.59
140
GwcNetcopylefttwo views6.42
111
1.97
138
10.92
136
2.59
118
5.58
167
11.55
131
2.21
87
14.10
123
16.52
134
10.04
108
17.19
126
10.86
101
5.61
95
9.23
98
8.84
123
0.01
48
0.06
100
0.03
115
0.08
117
0.56
130
0.40
121
PDISCO_ROBtwo views9.62
147
1.99
139
11.51
138
9.88
176
9.61
177
21.48
161
3.83
110
19.33
157
28.49
162
11.27
118
14.17
108
19.92
148
5.02
86
16.35
153
9.18
125
5.28
175
0.41
155
0.14
142
0.09
121
2.05
162
2.36
171
ADCLtwo views10.16
151
2.11
140
19.36
160
1.92
71
1.88
98
22.23
162
8.91
153
14.04
122
23.56
155
14.62
137
26.19
150
12.75
112
13.59
146
16.06
152
22.95
166
0.26
131
0.18
136
0.75
164
0.65
158
0.69
140
0.58
137
ELAScopylefttwo views16.72
171
2.14
141
9.23
129
4.92
165
4.53
160
32.66
175
15.11
175
27.40
175
28.68
163
40.27
176
44.90
178
38.33
176
30.50
175
26.44
172
21.94
165
0.88
159
1.23
169
0.67
163
0.89
164
1.49
158
2.18
169
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
RGCtwo views6.88
123
2.23
142
6.13
96
4.05
158
4.73
163
8.94
108
2.78
94
15.19
135
11.74
108
11.13
117
19.34
132
17.86
136
10.42
131
13.02
134
8.03
113
0.73
155
0.01
54
0.24
152
0.41
151
0.31
111
0.38
119
ELAS_RVCcopylefttwo views16.54
170
2.26
143
10.09
134
5.50
168
4.46
157
28.28
170
16.72
177
25.55
173
33.54
169
40.19
175
40.30
175
36.68
175
30.03
174
29.40
173
20.61
159
0.98
163
1.21
168
0.86
167
0.70
160
1.39
157
2.16
167
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ADCPNettwo views9.54
145
2.39
144
31.46
171
2.09
84
1.60
82
16.71
153
6.39
142
12.11
99
11.45
104
13.53
134
21.45
137
19.41
145
10.94
134
14.38
143
21.54
162
0.27
134
1.16
167
0.39
161
1.49
171
0.58
133
1.45
159
SHDtwo views9.61
146
2.60
145
12.46
143
3.69
150
3.54
138
9.47
113
1.25
73
20.16
166
37.84
176
18.19
145
21.24
136
16.96
132
12.83
143
14.47
145
16.05
153
0.32
137
0.13
126
0.01
95
0.08
117
0.38
120
0.48
128
DGSMNettwo views6.47
112
2.61
146
13.64
150
3.46
146
3.74
145
8.97
109
6.01
133
14.15
124
13.01
115
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
173
1.52
173
2.27
166
2.35
170
DeepPrunerFtwo views6.75
116
2.69
147
23.31
165
3.68
149
7.16
172
3.78
51
4.29
117
13.42
116
20.13
148
8.13
84
10.46
72
7.18
68
8.06
115
11.10
120
9.44
126
0.24
128
0.15
129
0.29
154
0.42
152
0.66
139
0.45
125
Syn2CoExtwo views6.32
109
2.72
148
9.33
130
2.45
111
1.67
86
8.59
105
3.32
101
18.08
149
12.27
113
8.53
89
21.08
135
10.30
95
9.80
128
9.30
101
7.86
109
0.01
48
0.00
1
0.00
1
0.00
1
0.63
137
0.43
124
SAMSARAtwo views14.63
163
2.74
149
12.38
142
12.65
178
6.74
171
36.50
176
72.93
184
19.36
158
23.77
156
16.20
142
13.04
100
29.21
164
12.78
141
16.98
154
15.21
152
0.11
109
0.26
148
0.03
115
0.14
126
0.76
142
0.77
144
GANetREF_RVCpermissivetwo views6.56
113
2.89
150
7.58
121
3.41
143
0.40
33
12.96
139
9.58
158
15.09
131
17.25
137
10.33
109
10.62
75
12.27
110
8.16
116
12.21
127
4.53
78
0.41
144
0.00
1
0.00
1
0.02
89
3.12
169
0.39
120
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
PVDtwo views15.44
165
2.93
151
14.67
153
4.21
162
3.39
134
17.43
156
4.16
113
27.84
177
48.84
181
31.02
169
43.54
177
29.76
166
30.81
176
25.97
171
21.40
161
0.23
127
0.41
155
0.04
122
0.33
146
0.41
126
1.33
156
RTSCtwo views9.15
142
3.00
152
13.57
149
3.72
151
1.76
93
11.82
134
0.46
37
16.95
143
36.83
173
15.80
140
15.53
117
12.91
113
7.46
113
20.01
163
21.76
163
0.31
136
0.13
126
0.01
95
0.08
117
0.57
132
0.41
123
ADCMidtwo views10.24
152
3.13
153
20.70
161
2.21
94
2.39
113
11.23
130
6.19
135
14.17
125
11.19
101
23.20
159
22.25
138
17.89
137
19.54
159
18.51
158
26.21
172
0.45
149
0.42
157
1.10
169
1.29
168
1.56
160
1.18
154
PWC_ROBbinarytwo views8.24
136
3.13
153
12.74
145
2.43
110
4.43
155
7.51
102
1.22
72
16.63
142
19.24
143
16.08
141
28.29
152
13.99
119
10.16
130
13.63
139
14.06
149
0.42
147
0.00
1
0.05
125
0.00
1
0.59
134
0.27
111
SuperBtwo views8.10
135
3.15
155
24.67
167
2.65
119
1.23
70
9.88
118
4.29
117
10.18
70
30.07
164
11.53
119
12.18
90
6.12
54
6.65
107
10.50
113
14.47
151
0.14
113
0.11
120
0.35
158
0.25
139
13.06
179
0.48
128
Anonymous Stereotwo views6.16
106
3.15
155
23.75
166
2.97
132
2.48
116
4.39
65
13.30
168
9.21
53
9.86
91
9.56
103
8.76
58
6.79
58
1.99
33
13.50
137
13.04
146
0.01
48
0.05
93
0.00
1
0.06
113
0.22
98
0.19
89
PWCDC_ROBbinarytwo views7.92
133
3.17
157
7.48
120
5.73
169
4.40
152
10.45
123
0.35
32
14.52
128
28.19
161
10.36
110
31.27
159
7.04
62
9.14
126
13.22
136
8.78
122
2.74
172
0.02
70
0.00
1
0.00
1
1.31
156
0.17
84
aanetorigintwo views8.72
141
3.29
158
27.55
169
1.95
75
3.84
147
4.93
74
5.39
131
5.49
24
10.05
94
27.85
165
31.47
160
15.80
125
12.78
141
9.04
94
11.98
141
0.25
129
0.22
140
0.22
150
0.25
139
1.28
155
0.84
148
XQCtwo views8.43
139
3.58
159
16.40
155
2.92
128
2.17
103
13.22
141
3.60
106
14.64
129
25.86
158
11.87
120
12.04
86
15.06
123
10.67
132
15.24
148
19.41
156
0.39
142
0.08
110
0.05
125
0.07
115
0.84
145
0.45
125