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 bysort bysort bysort bysorted by
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
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
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
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
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
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.
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
MSMDNettwo views1.26
2