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 bysorted bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort bysort by
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
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
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
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
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
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
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
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
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
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.
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
71
100.00
73
100.00
72
100.00
73
100.00
74
100.00
74
100.00
71
100.00
70
100.00
71
100.00
73
99.99
73
MSMDNettwo views1.26
2