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 bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysorted bysort bysort bysort by
CBMV_ROBtwo views4.14
5
0.52
4
3.14
3
1.30
3
0.77
10
6.92
9
1.97
10
10.11
5
9.58
8
8.92
8
14.20
18
7.12
6
5.90
17
8.65
10
3.50
6
0.01
5
0.05
17
0.00
1
0.00
1
0.04
4
0.09
4
XPNet_ROBtwo views6.03
17
1.22
17
5.61
14
2.56
24
0.90
12
6.32
6
7.07
26
12.92
15
8.30
6
14.76
24
15.13
22
19.84
23
6.66
20
10.36
17
8.58
20
0.02
8
0.04
15
0.00
1
0.03
22
0.11
12
0.24
19
PWCDC_ROBbinarytwo views7.92
22
3.17
35
7.48
24
5.73
34
4.40
30
10.45
19
0.35
3
14.52
19
28.19
34
10.36
10
31.27
32
7.04
5
9.14
23
13.22
24
8.78
22
2.74
35
0.02
12
0.00
1
0.00
1
1.31
32
0.17
14
DN-CSS_ROBtwo views2.69
1
1.40
21
5.34
12
2.31
21
0.75
9
3.14
2
0.06
1
6.11
1
3.87
1
5.34
2
12.18
10
2.34
1
1.22
1
7.84
1
1.48
1
0.03
15
0.00
1
0.00
1
0.00
1
0.35
25
0.03
3
pmcnntwo views7.72
21
1.27
19
9.42
29
2.91
26
3.14
28
9.44
12
6.23
20
12.56
14
16.51
26
14.53
23
24.08
26
27.44
32
8.49
22
9.32
15
8.44
19
0.06
21
0.08
25
0.00
1
0.00
1
0.30
22
0.15
12
NOSS_ROBtwo views3.30
2
0.46
3
2.62
1
2.08
17
1.01
16
5.60
4
0.74
6
10.37
6
11.48
14
5.15
1
8.43
3
5.67
3
1.73
2
7.97
3
2.34
2
0.02
8
0.06
20
0.00
1
0.00
1
0.07
7
0.14
11
MDST_ROBtwo views8.37
24
0.32
1
9.03
27
4.18
31
2.42
27
26.86
38
6.14
19
19.36
30
13.52
17
27.09
33
22.75
25
9.47
8
4.74
10
15.06
29
6.34
10
0.02
8
0.02
12
0.00
1
0.00
1
0.02
1
0.13
10
CBMVpermissivetwo views5.35
11
0.91
10
3.67
6
1.62
8
0.44
5
10.09
17
7.19
28
12.49
12
12.33
16
12.22
15
14.69
19
10.93
9
6.48
19
8.51
9
4.96
8
0.02
8
0.15
29
0.00
1
0.00
1
0.17
16
0.17
14
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
LALA_ROBtwo views6.58
19
1.80
28
6.25
18
1.26
2
0.94
15
10.08
16
9.02
29
16.00
23
11.51
15
12.74
20
13.02
14
24.77
28
5.25
12
10.56
18
8.02
15
0.04
16
0.05
17
0.00
1
0.02
19
0.10
9
0.25
20
iResNet_ROBtwo views4.23
6
1.02
14
4.90
11
2.18
18
0.93
14
2.92
1
0.37
4
15.10
21
16.91
27
7.89
7
10.51
7
7.03
4
3.07
4
8.16
8
3.46
5
0.01
5
0.00
1
0.00
1
0.00
1
0.10
9
0.02
1
ETE_ROBtwo views5.80
13
1.77
27
6.33
20
1.44
5
0.78
11
6.43
7
6.90
25
12.53
13
8.08
5
12.93
21
14.89
20
21.13
26
5.87
16
9.83
16
6.57
12
0.04
16
0.01
9
0.00
1
0.02
19
0.08
8
0.33
25
DISCOtwo views6.28
18
0.57
5
5.78
15
3.43
29
1.17
18
11.22
21
3.39
13
12.14
10
16.16
25
6.52
4
11.22
8
16.96
19
6.32
18
19.51
33
10.74
29
0.00
1
0.00
1
0.00
1
0.00
1
0.35
25
0.11
8
MSMD_ROBtwo views9.28
27
1.09
16
4.65
10
1.58
6
0.39
3
16.52
29
4.41
16
13.60
16
14.87
21
22.34
29
39.89
37
25.67
29
20.71
32
12.42
21
6.98
13
0.34
27
0.03
14
0.00
1
0.00
1
0.05
5
0.09
4
iResNettwo views3.68
3
0.91
10
7.94
25
2.97
27
0.34
2
4.44
3
0.48
5
7.70
2
9.74
10
7.72
6
12.74
12
4.03
2
2.87
3
8.05
5
3.37
4
0.02
8
0.01
9
0.00
1
0.00
1
0.10
9
0.09
4
StereoDRNettwo views5.59
12
1.75
26
6.80
22
3.12
28
4.45
32
10.61
20
4.35
15
18.80
26
9.73
9
12.22
15
6.87
1
11.44
11
4.65
9
8.09
7
8.26
17
0.02
8
0.11
27
0.00
1
0.03
22
0.20
18
0.28
24
SGM-Foresttwo views4.96
9
0.32
1
2.84
2
1.21
1
0.64
6
10.23
18
6.64
24
11.55
7
10.98
13
10.94
12
13.59
15
11.65
12
4.30
8
8.94
14
4.63
7
0.11
24
0.04
15
0.00
1
0.00
1
0.05
5
0.46
27
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
HSMtwo views4.00
4
0.79
8
3.16
4
1.59
7
2.17
26
6.77
8
1.11
7
12.28
11
6.35
2
6.75
5
8.11
2
13.90
15
5.37
14
8.85
13
2.71
3
0.00
1
0.00
1
0.00
1
0.00
1
0.02
1
0.02
1
DLCB_ROBtwo views4.51
8
0.91
10
3.78
7
2.19
19
1.07
17
6.28
5
3.09
11
9.78
3
7.72
4
10.65
11
12.97
13
13.91
16
3.71
5
8.72
11
5.30
9
0.00
1
0.00
1
0.00
1
0.00
1
0.03
3
0.10
7
WCMA_ROBtwo views9.21
26
0.87
9
7.37
23
2.54
23
2.13
25
13.59
26
5.80
18
11.64
8
14.01
18
24.43
31
32.99
33
27.09
31
18.02
27
12.51
22
9.85
27
0.81
31
0.07
23
0.01
19
0.01
18
0.16
15
0.23
17
PSMNet_ROBtwo views5.02
10
1.63
25
6.03
17
1.90
14
1.83
24
9.57
14
6.35
23
15.58
22
7.23
3
6.15
3
10.48
6
12.22
14
4.16
7
8.02
4
8.71
21
0.02
8
0.01
9
0.01
19
0.10
28
0.20
18
0.12
9
MeshStereopermissivetwo views11.52
32
1.52
22
4.55
9
1.89
13
1.46
20
19.87
33
5.11
17
20.66
34
15.91
24
32.67
37
34.51
35
39.34
40
21.15
33
18.74
32
12.10
31
0.11
24
0.06
20
0.01
19
0.00
1
0.45
27
0.22
16
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
StereoDRNet-Filteredtwo views4.46
7
0.62
7
3.80
8
1.92
15
0.40
4
9.35
11
0.15
2
10.02
4
8.83
7
12.69
19
11.62
9
9.34
7
3.87
6
8.06
6
8.02
15
0.00
1
0.00
1
0.01
19
0.05
26
0.20
18
0.26
22
CSANtwo views7.62
20
1.60
24
6.56
21
1.83
10
0.66
7
12.40
23
10.52
35
14.45
18
21.32
30
14.19
22
15.98
23
17.84
20
13.02
26
12.32
20
8.38
18
0.09
23
0.07
23
0.03
23
0.04
24
0.33
23
0.67
29
NCCL2two views5.88
15
1.59
23
5.44
13
1.87
12
0.92
13
9.55
13
11.55
36
12.11
9
9.94
11
9.67
9
8.85
4
22.28
27
7.41
21
8.78
12
7.17
14
0.01
5
0.00
1
0.03
23
0.00
1
0.13
14
0.23
17
SANettwo views10.64
31
1.86
29
10.91
31
1.76
9
0.71
8
14.62
28
9.23
31
19.18
28
37.14
39
19.22
27
27.96
28
25.86
30
19.11
31
13.02
23
10.63
28
0.08
22
0.06
20
0.03
23
0.02
19
0.62
29
0.81
31
DRN-Testtwo views5.87
14
0.98
13
5.89
16
2.69
25
3.65
29
12.37
22
3.35
12
20.07
33
10.20
12
11.93
14
12.31
11
11.06
10
5.31
13
7.89
2
9.05
23
0.04
16
0.05
17
0.04
26
0.04
24
0.18
17
0.25
20
PWC_ROBbinarytwo views8.24
23
3.13
34
12.74
34
2.43
22
4.43
31
7.51
10
1.22
8
16.63
25
19.24
29
16.08
25
28.29
29
13.99
17
10.16
25
13.63
26
14.06
34
0.42
28
0.00
1
0.05
27
0.00
1
0.59
28
0.27
23
NaN_ROBtwo views6.00
16
1.24
18
6.29
19
1.34
4
1.68
22
9.60
15
10.31
33
15.09
20
15.79
23
12.62
18
8.95
5
11.67
13
5.83
15
11.78
19
6.41
11
0.05
19
0.13
28
0.08
28
0.20
29
0.22
21
0.79
30
testNettwo views10.14
30
1.88
30
16.82
37
1.85
11
1.73
23
24.84
36
17.20
40
19.92
32
27.41
33
12.23
17
13.62
16
16.52
18
18.35
28
14.42
28
12.50
32
0.78
30
0.54
33
0.08
28
0.25
33
1.18
31
0.59
28
LE_ROBtwo views16.73
39
1.28
20
11.61
33
3.72
30
1.65
21
16.67
30
9.17
30
14.39
17
55.91
41
63.81
41
40.86
39
35.94
37
37.73
41
14.24
27
26.87
40
0.05
19
0.10
26
0.13
30
0.22
30
0.12
13
0.15
12
PDISCO_ROBtwo views9.62
28
1.99
31
11.51
32
9.88
38
9.61
38
21.48
34
3.83
14
19.33
29
28.49
35
11.27
13
14.17
17
19.92
24
5.02
11
16.35
30
9.18
24
5.28
37
0.41
32
0.14
31
0.09
27
2.05
35
2.36
35
SGM_ROBbinarytwo views10.08
29
0.60
6
3.42
5
2.30
20
0.32
1
19.41
31
6.33
22
18.95
27
14.64
19
25.14
32
24.32
27
33.34
35
18.79
30
19.86
34
12.55
33
0.25
26
0.26
31
0.22
32
0.24
32
0.34
24
0.40
26
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
MFMNet_retwo views13.29
33
8.60
39
18.29
38
9.75
37
7.25
36
19.65
32
14.84
37
20.71
35
30.72
37
23.03
30
28.77
30
18.85
21
26.09
38
13.55
25
9.82
25
2.44
34
1.35
36
0.34
33
0.23
31
4.78
37
6.69
37
ELAScopylefttwo views16.72
38
2.14
32
9.23
28
4.92
32
4.53
34
32.66
40
15.11
38
27.40
40
28.68
36
40.27
39
44.90
40
38.33
39
30.50
40
26.44
39
21.94
39
0.88
32
1.23
35
0.67
34
0.89
36
1.49
34
2.18
34
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAS_ROBcopylefttwo views16.54
37
2.26
33
10.09
30
5.50
33
4.46
33
28.28
39
16.72
39
25.55
39
33.54
38
40.19
38
40.30
38
36.68
38
30.03
39
29.40
40
20.61
37
0.98
33
1.21
34
0.86
35
0.70
34
1.39
33
2.16
33
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
FBW_ROBtwo views8.50
25
1.03
15
7.98
26
1.93
16
1.28
19
13.10
25
6.23
20
22.50
36
18.98
28
18.82
26
14.91
21
19.06
22
10.04
24
18.41
31
9.83
26
0.62
29
0.22
30
1.82
36
0.82
35
0.99
30
1.36
32
SPS-STEREOcopylefttwo views15.04
34
6.23
36
13.21
36
11.34
39
11.65
40
23.30
35
7.15
27
24.16
38
15.65
22
31.78
36
29.19
31
31.62
33
21.32
34
24.62
37
19.50
36
7.59
39
4.19
40
3.22
37
1.48
37
6.99
39
6.54
36
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
SGM+DAISYtwo views15.62
35
7.26
38
19.28
39
8.94
36
10.11
39
26.25
37
10.49
34
19.36
30
14.65
20
30.64
35
33.59
34
33.00
34
22.32
35
24.96
38
16.42
35
7.90
40
6.25
41
4.51
38
3.37
38
5.86
38
7.20
38
NVStereoNet_ROBtwo views16.04
36
6.75
37
12.90
35
6.37
35
7.42
37
12.89
24
9.74
32
22.78
37
25.12
31
30.32
34
46.19
41
34.37
36
25.38
36
21.48
35
21.38
38
5.94
38
3.10
38
6.07
39
10.09
41
4.01
36
8.54
40
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
PWCKtwo views30.53
41
44.32
41
47.25
42
29.76
41
7.23
35
40.78
41
27.10
41
44.73
41
44.32
40
47.31
40
36.37
36
47.16
41
26.05
37
41.26
41
31.87
41
21.83
41
4.03
39
29.50
40
4.67
39
27.17
41
7.80
39
DispFullNettwo views17.47
40
26.01
40
33.98
40
22.58
40
20.86
41
13.84
27
1.28
9
16.50
24
26.27
32
19.97
28
17.17
24
20.52
25
18.49
29
22.86
36
10.76
30
5.13
36
2.83
37
30.72
41
7.72
40
20.86
40
11.01
41
DPSimNet_ROBtwo views53.45
42
64.73
42
44.39
41
53.97
42
45.39
42
53.66
42
54.83
42
55.15
42
57.87
42
64.16
42
50.83
42
63.40
42
53.34
42
46.45
42
65.81
42
63.13
42
26.54
42
57.94
42
51.11
42
45.52
42
50.69
42
DPSMtwo views99.95
47
100.00
45
100.00
47
99.76
44
100.00
44
100.00
45
100.00
45
100.00
46
100.00
44
100.00
46
100.00
44
100.00
46
100.00
44
100.00
46
100.00
47
99.21
43
100.00
44
100.00
43
100.00
44
99.99
44
99.95
44
MEDIAN_ROBtwo views98.41
43
99.70
43
99.30
44
97.09
43
97.02
43
96.89
43
95.77
44
97.66
43
97.28
43
98.79
45
98.94
43
99.18
43
98.14
43
96.89
43
96.88
43
99.96
45
99.16
43
100.00
43
99.99
43
99.69
43
99.88
43
AVERAGE_ROBtwo views99.62
44
99.95
44
98.81
43
100.00
48
100.00
44
98.08
44
95.47
43
100.00
46
100.00
44
100.00
46
100.00
44
100.00
46
100.00
44
100.00
46
99.99
44
100.00
47
100.00
44
100.00
43
100.00
44
100.00
46
100.00
46
DPSM_ROBtwo views99.95
47
100.00
45
100.00
47
99.76
44
100.00
44
100.00
45
100.00
45
100.00
46
100.00
44
100.00
46
100.00
44
100.00
46
100.00
44
100.00
46
100.00
47
99.21
43
100.00
44
100.00
43
100.00
44
99.99
44
99.95
44
DPSMNet_ROBtwo views99.91
46
100.00
45
99.99
45
99.99
46
100.00
44
100.00
45
100.00
45
99.98
45
100.00
44
98.35
43
100.00
44
99.84
44
100.00
44
99.98
44
99.99
44
100.00
47
100.00
44
100.00
43
100.00
44
100.00
46
100.00
46
DGTPSM_ROBtwo views99.90
45
100.00
45
99.99
45
99.99
46
100.00
44
100.00
45
100.00
45
99.97
44
100.00
44
98.35
43
100.00
44
99.84
44
100.00
44
99.98
44
99.99
44
99.99
46
100.00
44
100.00
43
100.00
44
100.00
46
100.00
46