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

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

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

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




Method Infoalllakes. 1llakes. 1ssand box 1lsand box 1sstora. room 1lstora. room 1sstora. room 2lstora. room 2sstora. room 2 1lstora. room 2 1sstora. room 2 2lstora. room 2 2sstora. room 3lstora. room 3stunnel 1ltunnel 1stunnel 2ltunnel 2stunnel 3ltunnel 3s
sort bysort bysort bysort bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
DPM-Stereotwo views1.97
3
0.64
28
2.95
13
0.17
2
0.10
1
4.83
36
0.13
3
8.60
17
4.06
4
6.42
26
4.92
6
0.44
3
0.72
2
3.57
8
1.80
7
0.00
1
0.01
39
0.00
1
0.00
1
0.05
22
0.04
27
BEATNet_4xtwo views3.24
15
1.27
71
5.89
60
1.56
26
0.10
1
5.26
40
1.07
41
10.08
34
5.50
12
6.89
31
7.73
23
4.53
10
4.13
36
5.05
14
5.27
47
0.04
57
0.05
67
0.00
1
0.00
1
0.23
75
0.23
68
HITNettwo views2.79
10
0.77
33
4.02
31
2.03
52
0.11
3
5.58
44
0.59
23
9.24
24
5.15
9
6.42
26
7.26
19
3.66
7
2.92
20
4.07
10
3.87
36
0.00
1
0.00
1
0.00
1
0.00
1
0.06
27
0.02
7
Vladimir Tankovich, Christian Häne, Yinda Zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz: HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching. CVPR 2021
PMTNettwo views1.41
2
0.37
8
2.06
4
0.26
3
0.15
4
2.94
13
0.34
10
3.39
1
5.33
11
1.00
2
1.27
1
0.28
1
0.93
5
4.04
9
0.74
4
5.00
134
0.00
1
0.00
1
0.00
1
0.03
14
0.01
2
R-Stereo Traintwo views2.44
6
0.32
1
1.93
2
0.94
7
0.16
5
3.67
18
0.61
24
6.37
6
3.08
1
9.14
54
17.44
87
1.80
4
0.77
3
1.76
3
0.70
2
0.00
1
0.01
39
0.00
1
0.00
1
0.01
2
0.03
17
RAFT-Stereopermissivetwo views2.44
6
0.32
1
1.93
2
0.94
7
0.16
5
3.67
18
0.61
24
6.37
6
3.08
1
9.14
54
17.44
87
1.80
4
0.77
3
1.76
3
0.70
2
0.00
1
0.01
39
0.00
1
0.00
1
0.01
2
0.03
17
Lahav Lipson, Zachary Teed, and Jia Deng: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. 3DV
CREStereotwo views0.98
1
0.42
10
1.86
1
0.07
1
0.22
7
1.65
1
0.02
1
4.07
2
6.33
21
0.72
1
1.54
2
0.43
2
0.64
1
1.22
1
0.44
1
0.00
1
0.00
1
0.00
1
0.00
1
0.01
2
0.00
1
MLCVtwo views3.44
22
0.88
42
5.60
55
1.39
18
0.25
8
4.36
29
0.33
9
7.25
9
7.28
32
9.17
56
12.24
59
5.09
12
2.47
15
9.15
61
3.23
25
0.00
1
0.00
1
0.00
1
0.00
1
0.10
36
0.02
7
FENettwo views2.19
5
0.34
7
3.04
15
2.05
53
0.26
9
2.82
10
0.13
3
7.02
8
5.55
13
4.97
12
1.94
3
4.91
11
3.39
23
4.71
12
2.50
13
0.02
38
0.03
58
0.00
1
0.00
1
0.00
1
0.21
65
Gwc-CoAtRStwo views2.02
4
0.33
6
2.61
5
1.04
10
0.30
10
3.56
16
0.40
13
5.89
4
5.94
17
5.08
13
5.01
7
5.55
15
1.72
9
1.60
2
0.91
5
0.01
30
0.09
86
0.00
1
0.25
103
0.01
2
0.02
7
SGM_RVCbinarytwo views10.08
108
0.60
23
3.42
22
2.30
67
0.32
11
19.41
116
6.33
100
18.95
114
14.64
82
25.14
122
24.32
104
33.34
129
18.79
117
19.86
120
12.55
106
0.25
92
0.26
112
0.22
114
0.24
102
0.34
84
0.40
86
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
iResNettwo views3.68
27
0.91
45
7.94
86
2.97
95
0.34
12
4.44
33
0.48
15
7.70
10
9.74
57
7.72
38
12.74
63
4.03
8
2.87
19
8.05
42
3.37
27
0.02
38
0.01
39
0.00
1
0.00
1
0.10
36
0.09
40
MSMD_ROBtwo views9.28
103
1.09
60
4.65
42
1.58
27
0.39
13
16.52
108
4.41
82
13.60
81
14.87
84
22.34
112
39.89
132
25.67
118
20.71
122
12.42
88
6.98
61
0.34
101
0.03
58
0.00
1
0.00
1
0.05
22
0.09
40
StereoDRNet-Refinedtwo views4.46
52
0.62
27
3.80
29
1.92
44
0.40
14
9.35
73
0.15
5
10.02
32
8.83
52
12.69
85
11.62
54
9.34
47
3.87
32
8.06
43
8.02
71
0.00
1
0.00
1
0.01
59
0.05
81
0.20
61
0.26
77
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
GANetREF_RVCpermissivetwo views6.56
73
2.89
111
7.58
85
3.41
104
0.40
14
12.96
99
9.58
116
15.09
94
17.25
97
10.33
68
10.62
44
12.27
69
8.16
77
12.21
86
4.53
41
0.41
106
0.00
1
0.00
1
0.02
61
3.12
130
0.39
85
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
FADNet-RVCtwo views3.98
39
1.84
98
12.48
104
1.69
35
0.44
16
4.33
28
1.31
47
11.84
61
7.15
30
3.53
4
3.50
4
10.63
58
4.43
41
9.12
60
6.25
55
0.03
49
0.10
87
0.00
1
0.03
70
0.60
100
0.25
74
CBMVpermissivetwo views5.35
60
0.91
45
3.67
27
1.62
30
0.44
16
10.09
82
7.19
108
12.49
70
12.33
75
12.22
81
14.69
73
10.93
61
6.48
65
8.51
50
4.96
45
0.02
38
0.15
98
0.00
1
0.00
1
0.17
56
0.17
61
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
AdaStereotwo views3.09
13
0.58
22
3.04
15
2.84
87
0.48
18
4.08
25
1.29
46
12.16
67
7.77
41
6.03
20
9.62
36
5.79
19
1.53
8
4.56
11
1.93
9
0.00
1
0.00
1
0.00
1
0.00
1
0.02
7
0.02
7
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi: AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
ccstwo views3.04
12
0.39
9
3.08
17
1.78
37
0.52
19
2.04
2
0.50
17
13.09
76
13.71
80
3.54
5
5.36
10
5.50
14
2.45
14
4.81
13
2.88
17
0.09
70
0.08
82
0.12
101
0.10
91
0.20
61
0.50
94
ACVNettwo views2.58
8
0.53
18
2.69
7
0.84
5
0.59
20
2.61
6
0.55
19
8.35
12
5.70
14
4.45
7
9.26
34
5.77
17
3.50
25
2.24
5
4.41
39
0.00
1
0.00
1
0.00
1
0.00
1
0.17
56
0.01
2
SGM-Foresttwo views4.96
56
0.32
1
2.84
12
1.21
13
0.64
21
10.23
84
6.64
104
11.55
58
10.98
65
10.94
75
13.59
67
11.65
65
4.30
40
8.94
58
4.63
42
0.11
73
0.04
63
0.00
1
0.00
1
0.05
22
0.46
91
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
FADNet-RVC-Resampletwo views3.79
32
1.62
89
12.06
101
1.43
21
0.66
22
5.94
49
2.41
57
10.18
40
8.58
51
6.28
24
4.22
5
5.33
13
4.80
50
7.71
34
3.19
23
0.17
84
0.21
106
0.17
108
0.12
93
0.41
93
0.29
81
CSANtwo views7.62
86
1.60
88
6.56
73
1.83
38
0.66
22
12.40
97
10.52
122
14.45
90
21.32
110
14.19
93
15.98
80
17.84
94
13.02
103
12.32
87
8.38
77
0.09
70
0.07
80
0.03
79
0.04
79
0.33
82
0.67
106
HSM-Net_RVCpermissivetwo views4.20
45
0.32
1
2.76
10
0.63
4
0.69
24
6.95
60
1.69
52
11.96
62
8.36
49
8.83
52
12.17
56
15.18
84
4.21
39
6.91
24
3.30
26
0.02
38
0.02
51
0.00
1
0.00
1
0.01
2
0.01
2
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
SANettwo views10.64
113
1.86
99
10.91
94
1.76
36
0.71
25
14.62
105
9.23
114
19.18
115
37.14
132
19.22
105
27.96
110
25.86
119
19.11
118
13.02
93
10.63
95
0.08
69
0.06
73
0.03
79
0.02
61
0.62
101
0.81
109
hitnet-ftcopylefttwo views3.66
26
0.65
30
2.79
11
0.89
6
0.72
26
4.25
27
0.55
19
9.11
21
7.52
37
7.14
33
10.19
39
12.49
70
4.70
46
7.44
29
4.41
39
0.03
49
0.00
1
0.06
91
0.00
1
0.20
61
0.04
27
DN-CSS_ROBtwo views2.69
9
1.40
77
5.34
50
2.31
69
0.75
27
3.14
15
0.06
2
6.11
5
3.87
3
5.34
16
12.18
57
2.34
6
1.22
6
7.84
37
1.48
6
0.03
49
0.00
1
0.00
1
0.00
1
0.35
85
0.03
17
FADNet_RVCtwo views3.91
37
1.67
92
12.95
107
0.96
9
0.75
27
5.71
47
0.54
18
10.83
52
6.60
24
3.46
3
8.09
25
4.10
9
3.40
24
9.43
65
6.33
56
0.36
102
0.44
120
0.17
108
0.46
120
0.91
110
0.95
113
SGM-ForestMtwo views16.99
132
1.08
59
5.74
57
2.12
57
0.75
27
31.63
132
12.21
125
27.80
135
32.25
124
37.88
132
39.99
133
52.96
139
35.20
138
33.60
135
24.47
127
0.26
94
0.39
116
0.31
119
0.39
114
0.26
76
0.53
98
CBMV_ROBtwo views4.14
42
0.52
15
3.14
18
1.30
15
0.77
30
6.92
59
1.97
55
10.11
37
9.58
55
8.92
53
14.20
71
7.12
30
5.90
62
8.65
52
3.50
32
0.01
30
0.05
67
0.00
1
0.00
1
0.04
18
0.09
40
ETE_ROBtwo views5.80
62
1.77
95
6.33
66
1.44
22
0.78
31
6.43
54
6.90
105
12.53
71
8.08
45
12.93
89
14.89
74
21.13
112
5.87
61
9.83
67
6.57
60
0.04
57
0.01
39
0.00
1
0.02
61
0.08
31
0.33
82
FADNettwo views4.23
47
1.65
91
11.75
100
1.64
32
0.80
32
4.80
35
0.77
37
13.76
84
11.65
72
3.97
6
5.24
9
9.62
50
5.14
53
8.40
49
3.78
34
0.21
88
0.04
63
0.07
94
0.05
81
1.14
115
0.10
47
psmorigintwo views7.34
85
1.58
86
18.31
117
2.35
71
0.87
33
6.72
57
1.70
54
10.63
48
7.14
29
19.77
109
22.71
98
20.13
108
11.34
94
12.96
92
9.49
87
0.04
57
0.17
102
0.06
91
0.17
96
0.21
68
0.50
94
DMCAtwo views2.82
11
0.64
28
4.45
38
1.61
29
0.89
34
3.86
22
0.57
22
9.18
22
5.10
8
6.24
22
6.49
13
7.17
31
2.13
12
3.31
7
4.73
43
0.00
1
0.01
39
0.00
1
0.00
1
0.04
18
0.02
7
GANettwo views6.22
69
1.07
57
4.07
33
2.27
65
0.89
34
9.19
72
9.52
115
12.02
63
8.13
46
10.72
72
29.09
114
13.86
75
7.52
75
11.00
79
4.39
38
0.36
102
0.00
1
0.02
71
0.02
61
0.12
43
0.08
36
XPNet_ROBtwo views6.03
67
1.22
66
5.61
56
2.56
79
0.90
36
6.32
51
7.07
106
12.92
74
8.30
47
14.76
96
15.13
76
19.84
106
6.66
70
10.36
72
8.58
79
0.02
38
0.04
63
0.00
1
0.03
70
0.11
40
0.24
71
ccs_robtwo views3.63
25
1.12
63
4.42
36
2.52
76
0.91
37
5.50
43
0.21
6
10.11
37
9.11
53
6.55
29
11.28
49
8.32
40
2.55
16
7.66
33
2.01
10
0.00
1
0.00
1
0.00
1
0.00
1
0.20
61
0.08
36
NCCL2two views5.88
64
1.59
87
5.44
51
1.87
40
0.92
38
9.55
76
11.55
124
12.11
64
9.94
60
9.67
63
8.85
31
22.28
114
7.41
73
8.78
54
7.17
63
0.01
30
0.00
1
0.03
79
0.00
1
0.13
47
0.23
68
EDNetEfficientorigintwo views17.87
134
14.00
137
99.53
144
1.64
32
0.92
38
10.55
86
6.26
99
13.21
78
34.84
128
38.20
133
36.91
130
27.47
122
26.32
133
14.71
105
25.69
130
0.04
57
0.06
73
0.14
105
0.52
121
3.16
131
3.27
132
iResNet_ROBtwo views4.23
47
1.02
53
4.90
44
2.18
59
0.93
40
2.92
12
0.37
12
15.10
96
16.91
95
7.89
41
10.51
43
7.03
26
3.07
21
8.16
47
3.46
31
0.01
30
0.00
1
0.00
1
0.00
1
0.10
36
0.02
7
LALA_ROBtwo views6.58
74
1.80
97
6.25
64
1.26
14
0.94
41
10.08
81
9.02
112
16.00
102
11.51
71
12.74
86
13.02
65
24.77
116
5.25
54
10.56
74
8.02
71
0.04
57
0.05
67
0.00
1
0.02
61
0.10
36
0.25
74
NOSS_ROBtwo views3.30
17
0.46
11
2.62
6
2.08
54
1.01
42
5.60
45
0.74
36
10.37
42
11.48
70
5.15
14
8.43
29
5.67
16
1.73
10
7.97
39
2.34
12
0.02
38
0.06
73
0.00
1
0.00
1
0.07
29
0.14
56
TDLMtwo views4.11
41
1.11
62
3.54
25
1.62
30
1.04
43
3.91
23
7.41
109
10.60
47
10.67
64
6.38
25
12.59
62
5.95
20
4.77
48
8.79
55
3.04
19
0.58
114
0.00
1
0.01
59
0.00
1
0.19
60
0.12
51
DLCB_ROBtwo views4.51
53
0.91
45
3.78
28
2.19
60
1.07
44
6.28
50
3.09
62
9.78
29
7.72
39
10.65
71
12.97
64
13.91
77
3.71
30
8.72
53
5.30
48
0.00
1
0.00
1
0.00
1
0.00
1
0.03
14
0.10
47
DISCOtwo views6.28
70
0.57
21
5.78
58
3.43
106
1.17
45
11.22
91
3.39
68
12.14
66
16.16
90
6.52
28
11.22
47
16.96
91
6.32
64
19.51
119
10.74
97
0.00
1
0.00
1
0.00
1
0.00
1
0.35
85
0.11
50
SuperBtwo views8.10
93
3.15
116
24.67
126
2.65
81
1.23
46
9.88
80
4.29
79
10.18
40
30.07
122
11.53
78
12.18
57
6.12
21
6.65
69
10.50
73
14.47
110
0.14
78
0.11
89
0.35
121
0.25
103
13.06
138
0.48
92
iResNetv2_ROBtwo views4.28
51
1.43
78
7.17
81
2.91
89
1.26
47
4.36
29
1.62
50
13.64
83
10.25
63
9.83
64
11.41
50
7.68
34
4.00
34
7.75
36
1.85
8
0.00
1
0.00
1
0.00
1
0.00
1
0.37
87
0.09
40
FBW_ROBtwo views8.50
99
1.03
54
7.98
87
1.93
46
1.28
48
13.10
100
6.23
97
22.50
128
18.98
100
18.82
104
14.91
75
19.06
103
10.04
88
18.41
116
9.83
91
0.62
116
0.22
107
1.82
135
0.82
126
0.99
113
1.36
120
FCDSN-DCtwo views10.24
111
0.56
20
3.49
23
1.96
49
1.29
49
16.90
112
4.59
87
17.16
108
19.10
102
24.64
121
32.46
123
33.82
130
22.14
127
15.93
110
10.45
94
0.04
57
0.00
1
0.00
1
0.00
1
0.05
22
0.21
65
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
AANet_RVCtwo views5.01
58
1.74
93
6.38
68
1.96
49
1.29
49
2.26
3
1.69
52
10.07
33
18.53
98
7.88
40
18.15
89
8.49
44
2.70
17
10.59
75
7.04
62
0.96
124
0.15
98
0.02
71
0.00
1
0.13
47
0.12
51
CVANet_RVCtwo views4.16
43
1.16
65
3.60
26
1.94
47
1.46
51
3.92
24
4.68
88
10.89
54
8.34
48
7.58
37
10.84
45
10.27
55
6.62
68
8.56
51
2.69
15
0.39
104
0.00
1
0.00
1
0.01
57
0.21
68
0.09
40
MeshStereopermissivetwo views11.52
116
1.52
83
4.55
40
1.89
42
1.46
51
19.87
118
5.11
89
20.66
126
15.91
89
32.67
130
34.51
127
39.34
136
21.15
123
18.74
118
12.10
103
0.11
73
0.06
73
0.01
59
0.00
1
0.45
95
0.22
67
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
DMCA-RVCcopylefttwo views3.17
14
1.26
70
6.39
69
2.10
56
1.51
53
3.02
14
0.56
21
7.84
11
5.06
7
9.23
57
5.19
8
8.46
41
2.72
18
6.31
19
3.22
24
0.06
66
0.11
89
0.09
98
0.02
61
0.16
54
0.08
36
CFNettwo views3.72
30
1.10
61
5.03
45
2.49
75
1.59
54
4.90
38
0.22
7
11.38
57
9.88
59
4.80
10
11.25
48
6.44
22
3.68
29
8.33
48
3.00
18
0.00
1
0.00
1
0.00
1
0.00
1
0.22
70
0.07
33
ADCPNettwo views9.54
104
2.39
107
31.46
130
2.09
55
1.60
55
16.71
111
6.39
102
12.11
64
11.45
69
13.53
92
21.45
95
19.41
104
10.94
93
14.38
102
21.54
121
0.27
97
1.16
129
0.39
124
1.49
133
0.58
98
1.45
121
FC-DCNNcopylefttwo views10.72
114
0.52
15
4.27
34
1.88
41
1.63
56
17.18
113
5.29
91
18.20
110
19.69
106
28.50
125
34.51
127
34.03
131
21.48
125
15.89
109
11.15
101
0.03
49
0.01
39
0.02
71
0.01
57
0.07
29
0.09
40
DeepPruner_ROBtwo views3.52
23
1.14
64
4.06
32
1.12
12
1.65
57
3.65
17
0.83
38
13.96
85
4.47
6
7.80
39
10.84
45
7.05
29
2.16
13
8.14
46
3.08
21
0.07
68
0.03
58
0.00
1
0.01
57
0.32
81
0.06
31
LE_ROBtwo views16.73
131
1.28
74
11.61
99
3.72
111
1.65
57
16.67
110
9.17
113
14.39
89
55.91
140
63.81
140
40.86
135
35.94
133
37.73
139
14.24
101
26.87
132
0.05
64
0.10
87
0.13
104
0.22
100
0.12
43
0.15
59
MMNettwo views4.16
43
0.77
33
5.47
53
1.40
19
1.67
59
10.78
88
0.63
27
8.64
19
8.47
50
8.80
51
8.33
28
7.51
33
6.61
67
7.53
31
6.54
59
0.00
1
0.00
1
0.00
1
0.00
1
0.03
14
0.05
30
DSFCAtwo views4.21
46
0.50
14
5.45
52
1.34
16
1.68
60
7.04
61
4.51
85
10.73
50
7.00
27
10.78
73
6.80
14
8.48
43
4.63
44
9.91
69
5.12
46
0.02
38
0.06
73
0.02
71
0.03
70
0.08
31
0.03
17
NaN_ROBtwo views6.00
65
1.24
68
6.29
65
1.34
16
1.68
60
9.60
78
10.31
120
15.09
94
15.79
88
12.62
84
8.95
33
11.67
66
5.83
60
11.78
84
6.41
58
0.05
64
0.13
95
0.08
95
0.20
99
0.22
70
0.79
108
DPSNettwo views10.14
109
1.88
100
16.82
115
1.85
39
1.73
62
24.84
124
17.20
136
19.92
123
27.41
118
12.23
83
13.62
68
16.52
89
18.35
114
14.42
103
12.50
105
0.78
118
0.54
126
0.08
95
0.25
103
1.18
116
0.59
103
RTSCtwo views9.15
101
3.00
113
13.57
109
3.72
111
1.76
63
11.82
95
0.46
14
16.95
106
36.83
131
15.80
98
15.53
78
12.91
72
7.46
74
20.01
122
21.76
122
0.31
99
0.13
95
0.01
59
0.08
87
0.57
97
0.41
88
FAT-Stereotwo views6.78
80
0.68
31
6.80
77
2.30
67
1.77
64
5.63
46
4.20
78
18.79
112
18.62
99
10.53
70
17.15
84
18.52
100
13.74
106
8.86
57
7.38
64
0.03
49
0.15
98
0.01
59
0.07
85
0.12
43
0.26
77
PSMNet_ROBtwo views5.02
59
1.63
90
6.03
62
1.90
43
1.83
65
9.57
77
6.35
101
15.58
101
7.23
31
6.15
21
10.48
42
12.22
68
4.16
38
8.02
40
8.71
81
0.02
38
0.01
39
0.01
59
0.10
91
0.20
61
0.12
51
GwcNet-RSSMtwo views3.77
31
1.05
55
5.06
46
2.21
62
1.87
66
2.36
5
1.52
49
8.98
20
7.96
43
5.91
19
14.59
72
7.94
38
3.50
25
6.43
20
5.70
51
0.00
1
0.00
1
0.04
86
0.00
1
0.20
61
0.08
36
ADCLtwo views10.16
110
2.11
103
19.36
119
1.92
44
1.88
67
22.23
120
8.91
111
14.04
86
23.56
113
14.62
95
26.19
109
12.75
71
13.59
105
16.06
111
22.95
125
0.26
94
0.18
104
0.75
127
0.65
122
0.69
104
0.58
100
PSMNet-RSSMtwo views3.33
20
0.77
33
3.22
20
2.12
57
1.90
68
2.33
4
0.71
33
11.17
56
5.83
16
5.90
18
11.43
52
7.04
27
4.83
51
5.96
16
3.17
22
0.00
1
0.00
1
0.02
71
0.00
1
0.08
31
0.02
7
GANet-RSSMtwo views3.27
16
0.60
23
3.38
21
2.94
94
2.00
69
2.61
6
0.48
15
10.93
55
6.11
19
5.47
17
10.20
40
6.57
23
5.44
57
6.25
18
2.23
11
0.00
1
0.00
1
0.08
95
0.00
1
0.17
56
0.03
17
WCMA_ROBtwo views9.21
102
0.87
41
7.37
82
2.54
78
2.13
70
13.59
102
5.80
93
11.64
59
14.01
81
24.43
120
32.99
124
27.09
120
18.02
113
12.51
89
9.85
93
0.81
120
0.07
80
0.01
59
0.01
57
0.16
54
0.23
68
PA-Nettwo views4.98
57
1.47
80
7.42
83
2.40
72
2.14
71
8.73
70
3.64
73
12.42
69
13.11
76
7.03
32
7.57
22
7.88
36
6.52
66
10.16
71
7.82
69
0.02
38
0.03
58
0.00
1
0.00
1
0.11
40
1.07
116
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
HSMtwo views4.00
40
0.79
36
3.16
19
1.59
28
2.17
72
6.77
58
1.11
42
12.28
68
6.35
23
6.75
30
8.11
26
13.90
76
5.37
56
8.85
56
2.71
16
0.00
1
0.00
1
0.00
1
0.00
1
0.02
7
0.02
7
XQCtwo views8.43
98
3.58
120
16.40
114
2.92
91
2.17
72
13.22
101
3.60
70
14.64
92
25.86
116
11.87
79
12.04
55
15.06
83
10.67
91
15.24
107
19.41
115
0.39
104
0.08
82
0.05
89
0.07
85
0.84
109
0.45
89
STTStereotwo views3.60
24
0.93
48
6.34
67
2.71
84
2.23
74
3.68
20
0.63
27
9.42
25
6.73
25
9.87
65
6.97
17
8.84
45
3.65
28
6.85
23
3.04
19
0.00
1
0.02
51
0.01
59
0.00
1
0.02
7
0.02
7
MFN_U_SF_RVCtwo views12.94
117
3.66
121
25.81
127
3.61
108
2.26
75
22.77
121
4.55
86
27.10
133
20.06
107
23.90
119
28.99
113
30.53
126
16.98
111
19.92
121
20.26
117
1.24
126
1.07
128
0.98
130
1.33
131
1.80
125
2.04
127
CFNet_RVCtwo views3.31
18
0.94
49
2.69
7
1.50
23
2.38
76
2.81
8
0.68
30
8.35
12
7.43
35
4.45
7
9.94
37
10.20
53
4.60
42
6.49
21
3.41
28
0.00
1
0.00
1
0.03
79
0.00
1
0.22
70
0.03
17
CFNet-ftpermissivetwo views3.31
18
0.94
49
2.69
7
1.50
23
2.38
76
2.81
8
0.68
30
8.35
12
7.43
35
4.45
7
9.94
37
10.20
53
4.60
42
6.50
22
3.41
28
0.00
1
0.00
1
0.03
79
0.00
1
0.22
70
0.03
17
ADCMidtwo views10.24
111
3.13
114
20.70
120
2.21
62
2.39
78
11.23
92
6.19
95
14.17
88
11.19
66
23.20
118
22.25
96
17.89
96
19.54
119
18.51
117
26.21
131
0.45
110
0.42
119
1.10
131
1.29
130
1.56
124
1.18
117
MDST_ROBtwo views8.37
95
0.32
1
9.03
89
4.18
119
2.42
79
26.86
128
6.14
94
19.36
117
13.52
79
27.09
123
22.75
99
9.47
48
4.74
47
15.06
106
6.34
57
0.02
38
0.02
51
0.00
1
0.00
1
0.02
7
0.13
55
edge stereotwo views6.76
77
1.01
52
6.76
76
2.20
61
2.45
80
6.41
53
2.45
58
14.84
93
11.98
74
15.29
97
18.31
90
22.02
113
12.56
98
10.82
76
7.49
65
0.03
49
0.06
73
0.11
100
0.03
70
0.30
78
0.14
56
Anonymous Stereotwo views6.16
68
3.15
116
23.75
125
2.97
95
2.48
81
4.39
32
13.30
126
9.21
23
9.86
58
9.56
62
8.76
30
6.79
24
1.99
11
13.50
96
13.04
107
0.01
30
0.05
67
0.00
1
0.06
83
0.22
70
0.19
63
AnyNet_C01two views16.12
127
10.81
136
59.36
137
4.42
121
2.49
82
30.06
130
15.15
134
17.51
109
16.51
92
17.88
102
37.69
131
24.04
115
17.54
112
29.60
133
33.29
139
0.28
98
0.38
115
0.43
125
0.42
116
2.57
129
1.98
126
BEATNet-Init1two views23.31
138
9.03
133
41.67
133
4.17
118
2.53
83
45.68
139
19.47
137
33.43
137
38.45
135
47.59
139
49.10
139
59.31
140
41.80
141
38.35
138
29.21
135
0.47
111
0.50
124
0.81
128
0.66
123
2.10
127
1.86
124
psm_uptwo views3.85
36
0.82
38
3.01
14
2.00
51
2.58
84
5.28
41
3.34
66
10.85
53
6.33
21
7.27
35
7.32
20
10.15
52
9.02
84
6.19
17
2.51
14
0.22
89
0.01
39
0.00
1
0.00
1
0.12
43
0.03
17
ADCStwo views13.02
118
4.93
124
28.38
129
3.17
100
2.67
85
13.61
103
10.83
123
18.70
111
33.46
126
22.59
113
24.78
105
19.59
105
18.51
116
23.40
127
32.16
138
0.10
72
0.19
105
0.37
123
0.18
97
1.26
118
1.46
122
cf-rtwo views3.42
21
0.81
37
3.97
30
2.41
73
2.78
86
2.85
11
0.64
29
8.44
15
6.32
20
6.25
23
11.57
53
8.97
46
3.54
27
5.93
15
3.83
35
0.00
1
0.00
1
0.00
1
0.00
1
0.09
35
0.07
33
MSC_U_SF_DS_RVCtwo views16.41
128
6.93
130
21.83
121
5.94
130
2.81
87
38.71
135
14.59
129
24.55
131
34.87
129
33.66
131
34.35
126
29.24
124
24.20
129
22.59
125
17.95
114
2.52
132
2.81
135
1.17
133
1.51
134
5.89
136
2.16
128
MFN_U_SF_DS_RVCtwo views9.78
107
4.27
123
14.47
110
2.29
66
2.85
88
23.40
123
13.62
127
13.60
81
19.54
104
19.42
106
24.27
103
16.74
90
8.59
80
17.05
114
7.98
70
1.25
127
1.68
133
0.17
108
2.63
135
0.72
105
1.04
114
CC-Net-ROBtwo views3.84
34
1.07
57
5.23
48
2.65
81
2.96
89
4.22
26
0.69
32
10.43
44
7.72
39
8.78
50
8.29
27
9.61
49
4.02
35
7.16
28
3.65
33
0.13
77
0.03
58
0.02
71
0.03
70
0.05
22
0.03
17
pmcnntwo views7.72
88
1.27
71
9.42
91
2.91
89
3.14
90
9.44
74
6.23
97
12.56
72
16.51
92
14.53
94
24.08
102
27.44
121
8.49
79
9.32
64
8.44
78
0.06
66
0.08
82
0.00
1
0.00
1
0.30
78
0.15
59
UNettwo views3.97
38
0.61
25
6.50
72
1.68
34
3.22
91
9.82
79
0.31
8
9.50
26
5.32
10
7.20
34
6.35
12
10.59
57
5.91
63
6.93
25
5.49
49
0.00
1
0.00
1
0.00
1
0.00
1
0.02
7
0.01
2
NLCA_NET_v2_RVCtwo views3.84
34
1.06
56
5.23
48
2.72
85
3.27
92
4.36
29
0.61
24
10.71
49
7.56
38
8.75
49
7.89
24
9.86
51
3.90
33
7.15
27
3.44
30
0.14
78
0.02
51
0.02
71
0.03
70
0.04
18
0.03
17
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
PASMtwo views7.90
90
4.22
122
21.97
122
3.25
101
3.29
93
5.39
42
6.57
103
10.57
46
19.09
101
12.77
87
13.92
69
18.11
97
9.51
87
13.79
99
10.77
99
0.19
86
0.45
121
0.29
117
1.08
129
1.49
122
1.19
118
ADCReftwo views7.27
84
1.38
76
16.37
113
2.52
76
3.30
94
11.63
94
3.16
64
10.80
51
9.35
54
13.03
90
25.27
108
8.17
39
8.92
83
8.06
43
21.81
123
0.15
80
0.08
82
0.16
107
0.34
111
0.38
88
0.58
100
Nwc_Nettwo views6.97
83
1.25
69
6.63
74
3.82
113
3.37
95
10.83
90
1.67
51
19.56
122
11.35
68
8.36
46
23.62
100
17.19
93
11.44
95
11.21
81
8.08
74
0.80
119
0.00
1
0.00
1
0.02
61
0.13
47
0.09
40
NVstereo2Dtwo views4.51
53
0.82
38
6.86
79
3.28
102
3.38
96
8.16
68
3.13
63
10.51
45
15.15
85
4.90
11
6.89
16
7.87
35
4.78
49
9.88
68
3.91
37
0.01
30
0.00
1
0.00
1
0.06
83
0.02
7
0.58
100
UPFNettwo views3.82
33
0.47
12
4.34
35
2.21
62
3.38
96
7.26
62
2.69
59
9.89
30
5.96
18
7.98
42
7.53
21
7.89
37
3.15
22
7.72
35
5.88
53
0.01
30
0.02
51
0.00
1
0.00
1
0.04
18
0.06
31
PVDtwo views15.44
124
2.93
112
14.67
112
4.21
120
3.39
98
17.43
114
4.16
76
27.84
136
48.84
139
31.02
128
43.54
136
29.76
125
30.81
136
25.97
130
21.40
120
0.23
90
0.41
117
0.04
86
0.33
110
0.41
93
1.33
119
S-Stereotwo views6.63
75
0.84
40
9.67
92
3.15
99
3.48
99
6.49
55
6.22
96
12.99
75
22.84
111
9.48
59
15.51
77
12.00
67
8.43
78
8.04
41
10.70
96
0.12
76
0.17
102
0.00
1
0.38
113
0.13
47
1.92
125
ac_64two views3.70
29
0.47
12
3.50
24
2.87
88
3.50
100
5.91
48
0.72
34
10.17
39
4.34
5
8.16
45
5.80
11
10.82
59
5.64
59
7.08
26
4.73
43
0.03
49
0.00
1
0.00
1
0.00
1
0.08
31
0.14
56
SHDtwo views9.61
105
2.60
108
12.46
103
3.69
110
3.54
101
9.47
75
1.25
44
20.16
125
37.84
134
18.19
103
21.24
94
16.96
91
12.83
102
14.47
104
16.05
112
0.32
100
0.13
95
0.01
59
0.08
87
0.38
88
0.48
92
G-Nettwo views8.41
96
1.54
84
10.97
96
5.73
127
3.60
102
26.19
125
4.41
82
10.10
35
7.42
33
19.71
107
24.99
106
14.38
80
15.83
109
10.99
77
9.53
88
0.50
112
0.46
122
0.19
111
0.25
103
0.80
107
0.66
104
STTStereo_v2two views8.41
96
1.54
84
10.97
96
5.73
127
3.60
102
26.19
125
4.41
82
10.10
35
7.42
33
19.71
107
24.99
106
14.38
80
15.83
109
10.99
77
9.53
88
0.50
112
0.46
122
0.19
111
0.25
103
0.80
107
0.66
104
EDNetEfficienttwo views13.82
120
6.19
127
54.79
136
2.83
86
3.60
102
4.86
37
8.57
110
8.61
18
32.90
125
40.77
136
30.59
117
18.33
99
21.87
126
11.38
82
25.36
129
0.15
80
0.23
109
0.06
91
1.03
128
2.54
128
1.79
123
DRN-Testtwo views5.87
63
0.98
51
5.89
60
2.69
83
3.65
105
12.37
96
3.35
67
20.07
124
10.20
62
11.93
80
12.31
60
11.06
63
5.31
55
7.89
38
9.05
84
0.04
57
0.05
67
0.04
86
0.04
79
0.18
59
0.25
74
delettwo views4.27
50
0.72
32
4.50
39
1.07
11
3.75
106
10.79
89
4.04
75
9.95
31
7.89
42
8.09
43
8.89
32
8.46
41
3.81
31
7.56
32
5.80
52
0.00
1
0.00
1
0.00
1
0.00
1
0.11
40
0.02
7
aanetorigintwo views8.72
100
3.29
119
27.55
128
1.95
48
3.84
107
4.93
39
5.39
92
5.49
3
10.05
61
27.85
124
31.47
119
15.80
85
12.78
100
9.04
59
11.98
102
0.25
92
0.22
107
0.22
114
0.25
103
1.28
119
0.84
111
stereogantwo views7.69
87
0.88
42
7.08
80
3.49
107
3.93
108
18.98
115
3.23
65
16.52
104
19.58
105
9.93
66
18.92
91
20.50
110
9.04
85
14.07
100
6.14
54
0.26
94
0.04
63
0.21
113
0.03
70
0.63
102
0.33
82
DANettwo views6.02
66
1.23
67
8.45
88
3.86
114
3.94
109
7.64
67
1.34
48
9.51
27
7.00
27
13.39
91
15.53
78
15.99
88
7.02
71
12.14
85
12.37
104
0.19
86
0.12
94
0.02
71
0.03
70
0.13
47
0.56
99
ADCP+two views8.09
92
1.79
96
14.50
111
1.54
25
4.28
110
16.57
109
5.20
90
12.80
73
11.20
67
12.83
88
17.07
83
11.02
62
10.80
92
17.59
115
23.18
126
0.03
49
0.05
67
0.01
59
0.18
97
0.39
92
0.81
109
PWCDC_ROBbinarytwo views7.92
91
3.17
118
7.48
84
5.73
127
4.40
111
10.45
85
0.35
11
14.52
91
28.19
119
10.36
69
31.27
118
7.04
27
9.14
86
13.22
95
8.78
82
2.74
133
0.02
51
0.00
1
0.00
1
1.31
120
0.17
61
Abc-Nettwo views6.77
78
1.49
81
6.48
70
2.92
91
4.40
111
7.43
63
3.61
71
19.52
120
13.29
77
8.39
47
16.91
81
15.96
86
12.13
96
12.85
90
7.70
67
1.47
128
0.11
89
0.01
59
0.42
116
0.14
52
0.24
71
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
NCC-stereotwo views6.77
78
1.49
81
6.48
70
2.92
91
4.40
111
7.43
63
3.61
71
19.52
120
13.29
77
8.39
47
16.91
81
15.96
86
12.13
96
12.85
90
7.70
67
1.47
128
0.11
89
0.01
59
0.42
116
0.14
52
0.24
71
PWC_ROBbinarytwo views8.24
94
3.13
114
12.74
105
2.43
74
4.43
114
7.51
66
1.22
43
16.63
105
19.24
103
16.08
99
28.29
111
13.99
78
10.16
89
13.63
98
14.06
109
0.42
109
0.00
1
0.05
89
0.00
1
0.59
99
0.27
79
StereoDRNettwo views5.59
61
1.75
94
6.80
77
3.12
98
4.45
115
10.61
87
4.35
81
18.80
113
9.73
56
12.22
81
6.87
15
11.44
64
4.65
45
8.09
45
8.26
76
0.02
38
0.11
89
0.00
1
0.03
70
0.20
61
0.28
80
ELAS_RVCcopylefttwo views16.54
129
2.26
106
10.09
93
5.50
126
4.46
116
28.28
129
16.72
135
25.55
132
33.54
127
40.19
134
40.30
134
36.68
134
30.03
134
29.40
132
20.61
118
0.98
125
1.21
130
0.86
129
0.70
124
1.39
121
2.16
128
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
RYNettwo views6.34
71
0.89
44
5.88
59
1.41
20
4.48
117
15.97
107
4.18
77
13.41
79
16.49
91
10.81
74
7.00
18
14.33
79
8.72
81
9.43
65
13.71
108
0.00
1
0.01
39
0.00
1
0.00
1
0.02
7
0.07
33
AF-Nettwo views7.78
89
1.44
79
6.68
75
3.37
103
4.50
118
8.61
69
2.69
59
17.07
107
20.17
109
9.52
60
24.02
101
20.31
109
14.59
108
11.58
83
9.84
92
0.61
115
0.00
1
0.12
101
0.00
1
0.38
88
0.12
51
ELAScopylefttwo views16.72
130
2.14
104
9.23
90
4.92
123
4.53
119
32.66
133
15.11
133
27.40
134
28.68
121
40.27
135
44.90
137
38.33
135
30.50
135
26.44
131
21.94
124
0.88
121
1.23
131
0.67
126
0.89
127
1.49
122
2.18
130
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
RASNettwo views4.52
55
0.61
25
4.42
36
3.42
105
4.68
120
4.58
34
0.99
40
9.54
28
8.01
44
5.28
15
11.42
51
10.34
56
8.88
82
9.28
63
8.68
80
0.15
80
0.00
1
0.00
1
0.00
1
0.03
14
0.04
27
acv_fttwo views3.69
28
0.53
18
4.58
41
2.31
69
4.71
121
7.44
65
0.86
39
8.54
16
5.70
14
9.29
58
9.26
34
5.77
17
4.15
37
2.24
5
8.13
75
0.00
1
0.00
1
0.00
1
0.00
1
0.29
77
0.01
2
RGCtwo views6.88
82
2.23
105
6.13
63
4.05
116
4.73
122
8.94
71
2.78
61
15.19
98
11.74
73
11.13
76
19.34
92
17.86
95
10.42
90
13.02
93
8.03
73
0.73
117
0.01
39
0.24
116
0.41
115
0.31
80
0.38
84
HGLStereotwo views4.25
49
0.52
15
4.65
42
3.09
97
5.18
123
6.51
56
0.73
35
10.37
42
6.74
26
7.32
36
12.44
61
7.00
25
7.21
72
7.46
30
5.51
50
0.00
1
0.02
51
0.00
1
0.00
1
0.06
27
0.10
47
RPtwo views6.84
81
1.29
75
5.53
54
3.92
115
5.18
123
6.32
51
3.53
69
11.73
60
15.31
86
9.54
61
22.38
97
18.25
98
14.47
107
10.11
70
7.49
65
0.91
123
0.01
39
0.12
101
0.15
95
0.33
82
0.19
63
MANEtwo views19.47
137
1.27
71
5.07
47
4.69
122
5.55
125
30.49
131
9.94
118
34.01
138
37.27
133
44.13
137
51.57
141
52.51
138
40.41
140
33.58
134
24.81
128
0.89
122
0.86
127
1.11
132
9.72
139
0.38
88
1.06
115
GwcNetcopylefttwo views6.42
72
1.97
101
10.92
95
2.59
80
5.58
126
11.55
93
2.21
56
14.10
87
16.52
94
10.04
67
17.19
86
10.86
60
5.61
58
9.23
62
8.84
83
0.01
30
0.06
73
0.03
79
0.08
87
0.56
96
0.40
86
AnyNet_C32two views10.98
115
5.58
125
22.79
123
4.16
117
5.83
127
15.64
106
14.30
128
13.18
77
17.15
96
16.44
101
20.52
93
14.68
82
13.44
104
22.46
124
30.08
136
0.17
84
0.26
112
0.36
122
0.36
112
1.23
117
0.91
112
RTStwo views18.87
135
9.32
134
86.48
139
4.95
124
6.10
128
42.08
137
14.70
130
15.49
99
41.06
136
22.65
114
32.32
120
13.77
73
19.54
119
37.98
136
28.96
133
0.41
106
0.23
109
0.00
1
0.02
61
0.91
110
0.50
94
RTSAtwo views18.87
135
9.32
134
86.48
139
4.95
124
6.10
128
42.08
137
14.70
130
15.49
99
41.06
136
22.65
114
32.32
120
13.77
73
19.54
119
37.98
136
28.96
133
0.41
106
0.23
109
0.00
1
0.02
61
0.91
110
0.50
94
SAMSARAtwo views14.63
122
2.74
110
12.38
102
12.65
137
6.74
130
36.50
134
72.93
142
19.36
117
23.77
114
16.20
100
13.04
66
29.21
123
12.78
100
16.98
113
15.21
111
0.11
73
0.26
112
0.03
79
0.14
94
0.76
106
0.77
107
DeepPrunerFtwo views6.75
76
2.69
109
23.31
124
3.68
109
7.16
131
3.78
21
4.29
79
13.42
80
20.13
108
8.13
44
10.46
41
7.18
32
8.06
76
11.10
80
9.44
86
0.24
91
0.15
98
0.29
117
0.42
116
0.66
103
0.45
89
PWCKtwo views30.53
140
44.32
140
47.25
135
29.76
140
7.23
132
40.78
136
27.10
138
44.73
140
44.32
138
47.31
138
36.37
129
47.16
137
26.05
131
41.26
139
31.87
137
21.83
140
4.03
138
29.50
139
4.67
137
27.17
140
7.80
137
MFMNet_retwo views13.29
119
8.60
132
18.29
116
9.75
134
7.25
133
19.65
117
14.84
132
20.71
127
30.72
123
23.03
116
28.77
112
18.85
102
26.09
132
13.55
97
9.82
90
2.44
131
1.35
132
0.34
120
0.23
101
4.78
134
6.69
135
NVStereoNet_ROBtwo views16.04
126
6.75
129
12.90
106
6.37
131
7.42
134
12.89
98
9.74
117
22.78
129
25.12
115
30.32
126
46.19
138
34.37
132
25.38
130
21.48
123
21.38
119
5.94
137
3.10
137
6.07
138
10.09
140
4.01
132
8.54
138
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
MADNet+two views27.07
139
33.84
139
90.97
141
20.14
138
7.47
135
48.43
140
47.10
139
35.43
139
36.46
130
20.11
111
30.05
116
25.29
117
35.08
137
45.50
140
50.28
140
2.13
130
2.00
134
1.19
134
0.76
125
4.71
133
4.43
133
PDISCO_ROBtwo views9.62
106
1.99
102
11.51
98
9.88
135
9.61
136
21.48
119
3.83
74
19.33
116
28.49
120
11.27
77
14.17
70
19.92
107
5.02
52
16.35
112
9.18
85
5.28
136
0.41
117
0.14
105
0.09
90
2.05
126
2.36
131
SGM+DAISYtwo views15.62
125
7.26
131
19.28
118
8.94
133
10.11
137
26.25
127
10.49
121
19.36
117
14.65
83
30.64
127
33.59
125
33.00
128
22.32
128
24.96
129
16.42
113
7.90
139
6.25
140
4.51
137
3.37
136
5.86
135
7.20
136
SPS-STEREOcopylefttwo views15.04
123
6.23
128
13.21
108
11.34
136
11.65
138
23.30
122
7.15
107
24.16
130
15.65
87
31.78
129
29.19
115
31.62
127
21.32
124
24.62
128
19.50
116
7.59
138
4.19
139
3.22
136
1.48
132
6.99
137
6.54
134
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
DispFullNettwo views17.47
133
26.01
138
33.98
132
22.58
139
20.86
139
13.84
104
1.28
45
16.50
103
26.27
117
19.97
110
17.17
85
20.52
111
18.49
115
22.86
126
10.76
98
5.13
135
2.83
136
30.72
140
7.72
138
20.86
139
11.01
139
LSMtwo views14.01
121
5.95
126
33.49
131
6.78
132
43.61
140
10.22
83
9.98
119
15.16
97
22.93
112
23.07
117
32.34
122
18.52
100
12.67
99
15.45
108
11.10
100
0.16
83
0.51
125
0.09
98
0.32
109
1.08
114
16.85
140
DPSimNet_ROBtwo views53.45
141
64.73
141
44.39
134
53.97
141
45.39
141
53.66
141
54.83
140
55.15
141
57.87
141
64.16
141
50.83
140
63.40
141
53.34
142
46.45
141
65.81
141
63.13
141
26.54
141
57.94
141
51.11
141
45.52
141
50.69
141
MADNet++two views82.84
142
82.38
143
73.57
138
87.72
142
82.97
142
93.14
142
69.15
141
86.42
142
82.50
142
93.46
142
86.70
142
86.28
142
80.92
143
88.34
142
88.84
142
86.83
142
84.17
142
72.64
142
68.92
142
80.47
142
81.42
142
MEDIAN_ROBtwo views98.41
143
99.70
144
99.30
143
97.09
143
97.02
143
96.89
143
95.77
144
97.66
143
97.28
143
98.79
145
98.94
143
99.18
143
98.14
144
96.89
143
96.88
143
99.96
145
99.16
143
100.00
143
99.99
143
99.69
143
99.88
143
DGTPSM_ROBtwo views99.90
145
100.00
146
99.99
145
99.99
146
100.00
144
100.00
145
100.00
145
99.97
144
100.00
144
98.35
143
100.00
144
99.84
144
100.00
145
99.98
144
99.99
144
99.99
146
100.00
144
100.00
143
100.00
144
100.00
146
100.00
147
DPSMNet_ROBtwo views99.91
146
100.00
146
99.99
145
99.99
146
100.00
144
100.00
145
100.00
145
99.98
145
100.00
144
98.35
143
100.00
144
99.84
144
100.00
145
99.98
144
99.99
144
100.00
147
100.00
144
100.00
143
100.00
144
100.00
146
100.00
147
LSM0two views100.00
149
100.00
146
100.00
147
100.00
148
100.00
144
100.00
145
100.00
145
100.00
146
100.00
144
100.00
146
100.00
144
100.00
146
100.00
145
100.00
146
100.00
147
100.00
147
100.00
144
100.00
143
100.00
144
100.00
146
99.99
146
DPSM_ROBtwo views99.95
147
100.00
146
100.00
147
99.76
144
100.00
144
100.00
145
100.00
145
100.00
146
100.00
144
100.00
146
100.00
144
100.00
146
100.00
145
100.00
146
100.00
147
99.21
143
100.00
144
100.00
143
100.00
144
99.99
144
99.95
144
DPSMtwo views99.95
147
100.00
146
100.00
147
99.76
144
100.00
144
100.00
145
100.00
145
100.00
146
100.00
144
100.00
146
100.00
144
100.00
146
100.00
145
100.00
146
100.00
147
99.21
143
100.00
144
100.00
143
100.00
144
99.99
144
99.95
144
AVERAGE_ROBtwo views99.62
144
99.95
145
98.81
142
100.00
148
100.00
144
98.08
144
95.47
143
100.00
146
100.00
144
100.00
146
100.00
144
100.00
146
100.00
145
100.00
146
99.99
144
100.00
147
100.00
144
100.00
143
100.00
144
100.00
146
100.00
147
MSMDNettwo views1.26
7
ASD4two views81.57
142