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 bysorted bysort bysort bysort bysort bysort bysort bysort by
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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