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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
CFNet_RVCtwo views3.31
5
0.94
20
2.69
2
1.50
10
2.38
44
2.81
2
0.68
12
8.35
4
7.43
8
4.45
1
9.94
14
10.20
24
4.60
23
6.49
3
3.41
15
0.00
1
0.00
1
0.03
45
0.00
1
0.22
38
0.03
7
CFNettwo views3.72
11
1.10
29
5.03
22
2.49
36
1.59
35
4.90
16
0.22
4
11.38
21
9.88
25
4.80
2
11.25
22
6.44
8
3.68
14
8.33
19
3.00
9
0.00
1
0.00
1
0.00
1
0.00
1
0.22
38
0.07
13
NVstereo2Dtwo views4.51
22
0.82
13
6.86
42
3.28
51
3.38
51
8.16
29
3.13
26
10.51
16
15.15
40
4.90
3
6.89
2
7.87
16
4.78
27
9.88
32
3.91
21
0.01
14
0.00
1
0.00
1
0.06
48
0.02
2
0.58
50
NOSS_ROBtwo views3.30
4
0.46
4
2.62
1
2.08
27
1.01
25
5.60
20
0.74
14
10.37
14
11.48
31
5.15
4
8.43
9
5.67
5
1.73
4
7.97
12
2.34
6
0.02
19
0.06
41
0.00
1
0.00
1
0.07
15
0.14
29
DN-CSS_ROBtwo views2.69
1
1.40
41
5.34
27
2.31
33
0.75
14
3.14
4
0.06
1
6.11
1
3.87
1
5.34
5
12.18
27
2.34
1
1.22
1
7.84
10
1.48
1
0.03
28
0.00
1
0.00
1
0.00
1
0.35
47
0.03
7
ccstwo views3.37
6
1.16
33
3.89
14
2.94
46
0.78
17
4.78
15
0.33
5
9.00
5
7.77
12
5.90
6
10.84
18
7.74
15
2.31
7
7.76
9
1.98
4
0.00
1
0.00
1
0.00
1
0.00
1
0.16
28
0.06
11
AdaStereotwo views3.09
3
0.58
7
3.04
5
2.84
43
0.48
9
4.08
8
1.29
19
12.16
28
7.77
12
6.03
7
9.62
13
5.79
6
1.53
3
4.56
2
1.93
3
0.00
1
0.00
1
0.00
1
0.00
1
0.02
2
0.02
2
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi: AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching. ArXiv
PSMNet_ROBtwo views5.02
27
1.63
47
6.03
35
1.90
21
1.83
40
9.57
35
6.35
42
15.58
46
7.23
6
6.15
8
10.48
15
12.22
31
4.16
20
8.02
13
8.71
40
0.02
19
0.01
22
0.01
35
0.10
51
0.20
33
0.12
25
TDLMtwo views4.11
15
1.11
30
3.54
9
1.62
13
1.04
26
3.91
6
7.41
49
10.60
18
10.67
29
6.38
9
12.59
30
5.95
7
4.77
26
8.79
25
3.04
10
0.58
53
0.00
1
0.01
35
0.00
1
0.19
32
0.12
25
HITNettwo views2.79
2
0.77
11
4.02
15
2.03
26
0.11
1
5.58
19
0.59
10
9.24
7
5.15
3
6.42
10
7.26
4
3.66
2
2.92
12
4.07
1
3.87
20
0.00
1
0.00
1
0.00
1
0.00
1
0.06
14
0.02
2
Vladimir Tankovich, Christian Häne, Sean Fanello, Yinda Zhang, Shahram Izadi, Sofien Bouaziz: HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching.
DISCOtwo views6.28
38
0.57
6
5.78
32
3.43
53
1.17
28
11.22
43
3.39
28
12.14
27
16.16
44
6.52
11
11.22
21
16.96
41
6.32
35
19.51
58
10.74
49
0.00
1
0.00
1
0.00
1
0.00
1
0.35
47
0.11
23
ccs_robtwo views3.63
9
1.12
31
4.42
18
2.52
37
0.91
21
5.50
18
0.21
3
10.11
12
9.11
20
6.55
12
11.28
23
8.32
18
2.55
9
7.66
7
2.01
5
0.00
1
0.00
1
0.00
1
0.00
1
0.20
33
0.08
15
HSMtwo views4.00
14
0.79
12
3.16
7
1.59
12
2.17
43
6.77
24
1.11
16
12.28
29
6.35
4
6.75
13
8.11
7
13.90
34
5.37
31
8.85
26
2.71
8
0.00
1
0.00
1
0.00
1
0.00
1
0.02
2
0.02
2
PA-Nettwo views4.98
25
1.47
43
7.42
45
2.40
34
2.14
42
8.73
30
3.64
29
12.42
30
13.11
34
7.03
14
7.57
5
7.88
17
6.52
37
10.16
33
7.82
33
0.02
19
0.03
31
0.00
1
0.00
1
0.11
21
1.07
56
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
CVANet_RVCtwo views4.16
17
1.16
33
3.60
10
1.94
24
1.46
32
3.92
7
4.68
34
10.89
20
8.34
17
7.58
15
10.84
18
10.27
25
6.62
38
8.56
21
2.69
7
0.39
50
0.00
1
0.00
1
0.01
31
0.21
37
0.09
17
iResNettwo views3.68
10
0.91
17
7.94
48
2.97
47
0.34
4
4.44
14
0.48
9
7.70
3
9.74
23
7.72
16
12.74
31
4.03
3
2.87
11
8.05
14
3.37
14
0.02
19
0.01
22
0.00
1
0.00
1
0.10
17
0.09
17
DeepPruner_ROBtwo views3.52
8
1.14
32
4.06
16
1.12
2
1.65
36
3.65
5
0.83
15
13.96
38
4.47
2
7.80
17
10.84
18
7.05
12
2.16
6
8.14
17
3.08
11
0.07
36
0.03
31
0.00
1
0.01
31
0.32
44
0.06
11
AANet_RVCtwo views5.01
26
1.74
48
6.38
39
1.96
25
1.29
31
2.26
1
1.69
22
10.07
11
18.53
50
7.88
18
18.15
46
8.49
19
2.70
10
10.59
36
7.04
31
0.96
59
0.15
51
0.02
40
0.00
1
0.13
25
0.12
25
iResNet_ROBtwo views4.23
19
1.02
22
4.90
21
2.18
29
0.93
23
2.92
3
0.37
8
15.10
44
16.91
47
7.89
19
10.51
16
7.03
10
3.07
13
8.16
18
3.46
17
0.01
14
0.00
1
0.00
1
0.00
1
0.10
17
0.02
2
NLCA_NET_v2_RVCtwo views3.84
12
1.06
24
5.23
25
2.72
42
3.27
49
4.36
10
0.61
11
10.71
19
7.56
9
8.75
20
7.89
6
9.86
23
3.90
17
7.15
5
3.44
16
0.14
42
0.02
27
0.02
40
0.03
39
0.04
8
0.03
7
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
CC-Net-ROBtwo views3.84
12
1.07
25
5.23
25
2.65
40
2.96
47
4.22
9
0.69
13
10.43
15
7.72
10
8.78
21
8.29
8
9.61
22
4.02
19
7.16
6
3.65
19
0.13
41
0.03
31
0.02
40
0.03
39
0.05
10
0.03
7
HSM-Net_RVCpermissivetwo views4.20
18
0.32
1
2.76
3
0.63
1
0.69
12
6.95
26
1.69
22
11.96
24
8.36
18
8.83
22
12.17
26
15.18
38
4.21
21
6.91
4
3.30
13
0.02
19
0.02
27
0.00
1
0.00
1
0.01
1
0.01
1
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
CBMV_ROBtwo views4.14
16
0.52
5
3.14
6
1.30
5
0.77
16
6.92
25
1.97
24
10.11
12
9.58
21
8.92
23
14.20
38
7.12
13
5.90
34
8.65
22
3.50
18
0.01
14
0.05
37
0.00
1
0.00
1
0.04
8
0.09
17
MLCVtwo views3.44
7
0.88
15
5.60
29
1.39
7
0.25
2
4.36
10
0.33
5
7.25
2
7.28
7
9.17
24
12.24
28
5.09
4
2.47
8
9.15
28
3.23
12
0.00
1
0.00
1
0.00
1
0.00
1
0.10
17
0.02
2
Anonymous Stereotwo views6.16
36
3.15
59
23.75
65
2.97
47
2.48
46
4.39
13
13.30
63
9.21
6
9.86
24
9.56
25
8.76
10
6.79
9
1.99
5
13.50
46
13.04
57
0.01
14
0.05
37
0.00
1
0.06
48
0.22
38
0.19
34
NCCL2two views5.88
32
1.59
45
5.44
28
1.87
19
0.92
22
9.55
34
11.55
61
12.11
26
9.94
26
9.67
26
8.85
11
22.28
51
7.41
41
8.78
24
7.17
32
0.01
14
0.00
1
0.03
45
0.00
1
0.13
25
0.23
36
iResNetv2_ROBtwo views4.28
20
1.43
42
7.17
43
2.91
44
1.26
29
4.36
10
1.62
21
13.64
37
10.25
28
9.83
27
11.41
24
7.68
14
4.00
18
7.75
8
1.85
2
0.00
1
0.00
1
0.00
1
0.00
1
0.37
49
0.09
17
GANetREF_RVCpermissivetwo views6.56
40
2.89
57
7.58
47
3.41
52
0.40
6
12.96
47
9.58
54
15.09
42
17.25
48
10.33
28
10.62
17
12.27
32
8.16
43
12.21
40
4.53
23
0.41
51
0.00
1
0.00
1
0.02
34
3.12
62
0.39
45
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
PWCDC_ROBbinarytwo views7.92
45
3.17
60
7.48
46
5.73
60
4.40
54
10.45
41
0.35
7
14.52
41
28.19
59
10.36
29
31.27
55
7.04
11
9.14
46
13.22
45
8.78
41
2.74
62
0.02
27
0.00
1
0.00
1
1.31
57
0.17
32
DLCB_ROBtwo views4.51
22
0.91
17
3.78
12
2.19
30
1.07
27
6.28
21
3.09
25
9.78
9
7.72
10
10.65
30
12.97
32
13.91
35
3.71
15
8.72
23
5.30
26
0.00
1
0.00
1
0.00
1
0.00
1
0.03
7
0.10
22
GANettwo views6.22
37
1.07
25
4.07
17
2.27
31
0.89
19
9.19
31
9.52
53
12.02
25
8.13
15
10.72
31
29.09
53
13.86
33
7.52
42
11.00
37
4.39
22
0.36
49
0.00
1
0.02
40
0.02
34
0.12
23
0.08
15
RYNettwo views6.34
39
0.89
16
5.88
33
1.41
8
4.48
58
15.97
52
4.18
31
13.41
35
16.49
45
10.81
32
7.00
3
14.33
37
8.72
45
9.43
30
13.71
58
0.00
1
0.01
22
0.00
1
0.00
1
0.02
2
0.07
13
SGM-Foresttwo views4.96
24
0.32
1
2.84
4
1.21
3
0.64
10
10.23
40
6.64
44
11.55
22
10.98
30
10.94
33
13.59
34
11.65
29
4.30
22
8.94
27
4.63
24
0.11
39
0.04
35
0.00
1
0.00
1
0.05
10
0.46
47
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
PDISCO_ROBtwo views9.62
51
1.99
54
11.51
56
9.88
65
9.61
64
21.48
59
3.83
30
19.33
54
28.49
60
11.27
34
14.17
37
19.92
48
5.02
28
16.35
55
9.18
43
5.28
64
0.41
56
0.14
55
0.09
50
2.05
61
2.36
61
DRN-Testtwo views5.87
31
0.98
21
5.89
34
2.69
41
3.65
52
12.37
44
3.35
27
20.07
58
10.20
27
11.93
35
12.31
29
11.06
27
5.31
30
7.89
11
9.05
42
0.04
29
0.05
37
0.04
49
0.04
45
0.18
31
0.25
39
CBMVpermissivetwo views5.35
28
0.91
17
3.67
11
1.62
13
0.44
8
10.09
38
7.19
48
12.49
31
12.33
33
12.22
36
14.69
39
10.93
26
6.48
36
8.51
20
4.96
25
0.02
19
0.15
51
0.00
1
0.00
1
0.17
30
0.17
32
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
29
1.75
49
6.80
41
3.12
49
4.45
56
10.61
42
4.35
32
18.80
51
9.73
22
12.22
36
6.87
1
11.44
28
4.65
24
8.09
16
8.26
36
0.02
19
0.11
48
0.00
1
0.03
39
0.20
33
0.28
43
DPSNettwo views10.14
53
1.88
53
16.82
61
1.85
18
1.73
39
24.84
61
17.20
67
19.92
57
27.41
58
12.23
38
13.62
35
16.52
40
18.35
53
14.42
51
12.50
55
0.78
55
0.54
59
0.08
51
0.25
56
1.18
56
0.59
51
NaN_ROBtwo views6.00
33
1.24
37
6.29
37
1.34
6
1.68
38
9.60
36
10.31
58
15.09
42
15.79
42
12.62
39
8.95
12
11.67
30
5.83
32
11.78
38
6.41
28
0.05
32
0.13
50
0.08
51
0.20
52
0.22
38
0.79
53
StereoDRNet-Refinedtwo views4.46
21
0.62
9
3.80
13
1.92
22
0.40
6
9.35
32
0.15
2
10.02
10
8.83
19
12.69
40
11.62
25
9.34
20
3.87
16
8.06
15
8.02
34
0.00
1
0.00
1
0.01
35
0.05
47
0.20
33
0.26
41
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
LALA_ROBtwo views6.58
41
1.80
51
6.25
36
1.26
4
0.94
24
10.08
37
9.02
50
16.00
47
11.51
32
12.74
41
13.02
33
24.77
52
5.25
29
10.56
35
8.02
34
0.04
29
0.05
37
0.00
1
0.02
34
0.10
17
0.25
39
PASMtwo views7.90
44
4.22
61
21.97
64
3.25
50
3.29
50
5.39
17
6.57
43
10.57
17
19.09
52
12.77
42
13.92
36
18.11
43
9.51
47
13.79
49
10.77
51
0.19
44
0.45
57
0.29
57
1.08
62
1.49
59
1.19
57
ETE_ROBtwo views5.80
30
1.77
50
6.33
38
1.44
9
0.78
17
6.43
23
6.90
45
12.53
32
8.08
14
12.93
43
14.89
40
21.13
50
5.87
33
9.83
31
6.57
29
0.04
29
0.01
22
0.00
1
0.02
34
0.08
16
0.33
44
DANettwo views6.02
34
1.23
36
8.45
50
3.86
55
3.94
53
7.64
28
1.34
20
9.51
8
7.00
5
13.39
44
15.53
43
15.99
39
7.02
40
12.14
39
12.37
54
0.19
44
0.12
49
0.02
40
0.03
39
0.13
25
0.56
49
CSANtwo views7.62
42
1.60
46
6.56
40
1.83
17
0.66
11
12.40
45
10.52
60
14.45
40
21.32
54
14.19
45
15.98
44
17.84
42
13.02
51
12.32
41
8.38
37
0.09
38
0.07
44
0.03
45
0.04
45
0.33
45
0.67
52
pmcnntwo views7.72
43
1.27
38
9.42
53
2.91
44
3.14
48
9.44
33
6.23
39
12.56
33
16.51
46
14.53
46
24.08
48
27.44
56
8.49
44
9.32
29
8.44
38
0.06
34
0.08
46
0.00
1
0.00
1
0.30
43
0.15
30
XPNet_ROBtwo views6.03
35
1.22
35
5.61
30
2.56
39
0.90
20
6.32
22
7.07
46
12.92
34
8.30
16
14.76
47
15.13
42
19.84
47
6.66
39
10.36
34
8.58
39
0.02
19
0.04
35
0.00
1
0.03
39
0.11
21
0.24
38
PWC_ROBbinarytwo views8.24
46
3.13
58
12.74
58
2.43
35
4.43
55
7.51
27
1.22
17
16.63
49
19.24
53
16.08
48
28.29
51
13.99
36
10.16
49
13.63
48
14.06
59
0.42
52
0.00
1
0.05
50
0.00
1
0.59
52
0.27
42
FBW_ROBtwo views8.50
48
1.03
23
7.98
49
1.93
23
1.28
30
13.10
48
6.23
39
22.50
61
18.98
51
18.82
49
14.91
41
19.06
46
10.04
48
18.41
56
9.83
46
0.62
54
0.22
53
1.82
63
0.82
60
0.99
54
1.36
58
SANettwo views10.64
55
1.86
52
10.91
55
1.76
16
0.71
13
14.62
51
9.23
52
19.18
53
37.14
65
19.22
50
27.96
50
25.86
54
19.11
56
13.02
44
10.63
48
0.08
37
0.06
41
0.03
45
0.02
34
0.62
53
0.81
54
DispFullNettwo views17.47
66
26.01
67
33.98
67
22.58
67
20.86
67
13.84
50
1.28
18
16.50
48
26.27
57
19.97
51
17.17
45
20.52
49
18.49
54
22.86
61
10.76
50
5.13
63
2.83
64
30.72
68
7.72
66
20.86
67
11.01
67
MSMD_ROBtwo views9.28
50
1.09
28
4.65
20
1.58
11
0.39
5
16.52
53
4.41
33
13.60
36
14.87
39
22.34
52
39.89
62
25.67
53
20.71
58
12.42
42
6.98
30
0.34
48
0.03
31
0.00
1
0.00
1
0.05
10
0.09
17
MFMNet_retwo views13.29
57
8.60
66
18.29
62
9.75
64
7.25
62
19.65
57
14.84
64
20.71
60
30.72
62
23.03
53
28.77
52
18.85
45
26.09
64
13.55
47
9.82
45
2.44
61
1.35
63
0.34
59
0.23
54
4.78
64
6.69
63
LSMtwo views14.01
58
5.95
62
33.49
66
6.78
62
43.61
68
10.22
39
9.98
57
15.16
45
22.93
55
23.07
54
32.34
56
18.52
44
12.67
50
15.45
53
11.10
52
0.16
43
0.51
58
0.09
53
0.32
57
1.08
55
16.85
68
WCMA_ROBtwo views9.21
49
0.87
14
7.37
44
2.54
38
2.13
41
13.59
49
5.80
37
11.64
23
14.01
36
24.43
55
32.99
57
27.09
55
18.02
52
12.51
43
9.85
47
0.81
56
0.07
44
0.01
35
0.01
31
0.16
28
0.23
36
SGM_RVCbinarytwo views10.08
52
0.60
8
3.42
8
2.30
32
0.32
3
19.41
56
6.33
41
18.95
52
14.64
37
25.14
56
24.32
49
33.34
59
18.79
55
19.86
59
12.55
56
0.25
46
0.26
54
0.22
56
0.24
55
0.34
46
0.40
46
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
MDST_ROBtwo views8.37
47
0.32
1
9.03
51
4.18
56
2.42
45
26.86
63
6.14
38
19.36
55
13.52
35
27.09
57
22.75
47
9.47
21
4.74
25
15.06
52
6.34
27
0.02
19
0.02
27
0.00
1
0.00
1
0.02
2
0.13
28
FC-DCNNcopylefttwo views10.41
54
0.74
10
5.05
23
1.74
15
1.53
34
17.47
55
5.18
36
17.42
50
17.59
49
28.46
58
33.95
59
33.48
60
20.55
57
15.46
54
9.31
44
0.06
34
0.01
22
0.00
1
0.03
39
0.05
10
0.11
23
NVStereoNet_ROBtwo views16.04
61
6.75
64
12.90
59
6.37
61
7.42
63
12.89
46
9.74
55
22.78
62
25.12
56
30.32
59
46.19
67
34.37
61
25.38
62
21.48
60
21.38
63
5.94
65
3.10
65
6.07
66
10.09
68
4.01
63
8.54
66
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
SGM+DAISYtwo views15.62
60
7.26
65
19.28
63
8.94
63
10.11
65
26.25
62
10.49
59
19.36
55
14.65
38
30.64
60
33.59
58
33.00
58
22.32
61
24.96
63
16.42
60
7.90
67
6.25
68
4.51
65
3.37
64
5.86
65
7.20
64
SPS-STEREOcopylefttwo views15.04
59
6.23
63
13.21
60
11.34
66
11.65
66
23.30
60
7.15
47
24.16
63
15.65
41
31.78
61
29.19
54
31.62
57
21.32
60
24.62
62
19.50
61
7.59
66
4.19
67
3.22
64
1.48
63
6.99
66
6.54
62
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
MeshStereopermissivetwo views11.52
56
1.52
44
4.55
19
1.89
20
1.46
32
19.87
58
5.11
35
20.66
59
15.91
43
32.67
62
34.51
60
39.34
65
21.15
59
18.74
57
12.10
53
0.11
39
0.06
41
0.01
35
0.00
1
0.45
51
0.22
35
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
SGM-ForestMtwo views16.99
65
1.08
27
5.74
31
2.12
28
0.75
14
31.63
66
12.21
62
27.80
66
32.25
63
37.88
63
39.99
63
52.96
68
35.20
67
33.60
67
24.47
65
0.26
47
0.39
55
0.31
58
0.39
58
0.26
42
0.53
48
ELAS_RVCcopylefttwo views16.54
62
2.26
56
10.09
54
5.50
59
4.46
57
28.28
64
16.72
66
25.55
64
33.54
64
40.19
64
40.30
64
36.68
63
30.03
65
29.40
65
20.61
62
0.98
60
1.21
61
0.86
61
0.70
59
1.39
58
2.16
59
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAScopylefttwo views16.72
63
2.14
55
9.23
52
4.92
58
4.53
59
32.66
67
15.11
65
27.40
65
28.68
61
40.27
65
44.90
66
38.33
64
30.50
66
26.44
64
21.94
64
0.88
57
1.23
62
0.67
60
0.89
61
1.49
59
2.18
60
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
MANEtwo views19.47
67
1.27
38
5.07
24
4.69
57
5.55
60
30.49
65
9.94
56
34.01
67
37.27
66
44.13
66
51.57
69
52.51
67
40.41
69
33.58
66
24.81
66
0.89
58
0.86
60
1.11
62
9.72
67
0.38
50
1.06
55
PWCKtwo views30.53
68
44.32
68
47.25
69
29.76
68
7.23
61
40.78
68
27.10
68
44.73
68
44.32
67
47.31
67
36.37
61
47.16
66
26.05
63
41.26
68
31.87
68
21.83
68
4.03
66
29.50
67
4.67
65
27.17
68
7.80
65
LE_ROBtwo views16.73
64
1.28
40
11.61
57
3.72
54
1.65
36
16.67
54
9.17
51
14.39
39
55.91
68
63.81
68
40.86
65
35.94
62
37.73
68
14.24
50
26.87
67
0.05
32
0.10
47
0.13
54
0.22
53
0.12
23
0.15
30
DPSimNet_ROBtwo views53.45
69
64.73
69
44.39
68
53.97
69
45.39
69
53.66
69
54.83
69
55.15
69
57.87
69
64.16
69
50.83
68
63.40
69
53.34
70
46.45
69
65.81
69
63.13
69
26.54
69
57.94
69
51.11
69
45.52
69
50.69
69
DGTPSM_ROBtwo views99.90
72
100.00
72
99.99
72
99.99
73
100.00
71
100.00
72
100.00
72
99.97
71
100.00
71
98.35
70
100.00
71
99.84
71
100.00
72
99.98
71
99.99
71
99.99
73
100.00
71
100.00
70
100.00
71
100.00
73
100.00
74
DPSMNet_ROBtwo views99.91
73
100.00
72
99.99
72
99.99
73
100.00
71
100.00
72
100.00
72
99.98
72
100.00
71
98.35
70
100.00
71
99.84
71
100.00
72
99.98
71
99.99
71
100.00
74
100.00
71
100.00
70
100.00
71
100.00
73
100.00
74
MEDIAN_ROBtwo views98.41
70
99.70
70
99.30
71
97.09
70
97.02
70
96.89
70
95.77
71
97.66
70
97.28
70
98.79
72
98.94
70
99.18
70
98.14
71
96.89
70
96.88
70
99.96
72
99.16
70
100.00
70
99.99
70
99.69
70
99.88
70
AVERAGE_ROBtwo views99.62
71
99.95
71
98.81
70
100.00
75
100.00
71
98.08
71
95.47
70
100.00
73
100.00
71
100.00
73
100.00
71
100.00
73
100.00
72
100.00
73
99.99
71
100.00
74
100.00
71
100.00
70
100.00
71
100.00
73
100.00
74
DPSMtwo views99.95
74
100.00
72
100.00
74
99.76
71
100.00
71
100.00
72
100.00
72
100.00
73
100.00
71
100.00
73
100.00
71
100.00
73
100.00
72
100.00
73
100.00
74
99.21
70
100.00
71
100.00
70
100.00
71
99.99
71
99.95
71
DPSM_ROBtwo views99.95
74
100.00
72
100.00
74
99.76
71
100.00
71
100.00
72
100.00
72
100.00
73
100.00
71
100.00
73
100.00
71
100.00
73
100.00
72
100.00
73
100.00
74
99.21
70
100.00
71
100.00
70
100.00
71
99.99
71
99.95
71
LSM0two views100.00
76
100.00
72
100.00
74
100.00
75
100.00
71
100.00
72
100.00
72
100.00
73
100.00
71
100.00
73
100.00
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100.00
73
100.00
72
100.00
73
100.00
74
100.00
74
100.00
71
100.00
70
100.00
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100.00
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99.99
73
MSMDNettwo views1.26
2