Low-res two-view results

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):

bad 0.5, 1.0, 2.0, 4.0 [%]: Fraction of pixels with errors larger than the given number of disparities.
Average error [px]: The per-pixel average disparity error.
Root mean square error [px]: The per-pixel root mean square disparity error.
50%, 90%, 95%, 99% error quantile [px]: The highest disparity error within the given percentage of best pixels (for 50%, this is the median error).
Time [s]: The runtime of the method.

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	all	lakes. 1l	lakes. 1s	sand box 1l	sand box 1s	stora. room 1l	stora. room 1s	stora. room 2l	stora. room 2s	stora. room 2 1l	stora. room 2 1s	stora. room 2 2l	stora. room 2 2s	stora. room 3l	stora. room 3s	tunnel 1l	tunnel 1s	tunnel 2l	tunnel 2s	tunnel 3l	tunnel 3s

CREStereo	0.92 1	0.53 3	1.45 1	0.61 1	0.34 1	4.42 109	0.40 1	2.21 2	1.54 3	0.94 1	1.12 3	0.87 2	0.87 2	1.12 1	0.72 1	0.28 3	0.19 2	0.22 1	0.15 1	0.22 1	0.19 1

PMTNet	0.99 2	0.48 1	1.73 2	0.62 2	0.56 2	4.20 99	0.43 2	1.88 1	1.58 5	1.00 2	1.05 1	0.78 1	0.98 5	1.50 8	0.85 2	1.21 124	0.20 4	0.23 2	0.15 1	0.22 1	0.19 1

RAFT-Stereo	1.20 3	0.58 7	1.81 4	0.99 8	0.67 9	3.78 58	0.90 24	4.09 80	1.51 1	2.16 37	1.71 23	1.13 4	0.94 3	1.40 6	0.91 4	0.30 6	0.24 16	0.26 6	0.21 5	0.26 3	0.25 4
Lahav Lipson, Zachary Teed, and Jia Deng: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. 3DV
R-Stereo Train	1.20 3	0.58 7	1.81 4	0.99 8	0.67 9	3.78 58	0.90 24	4.09 80	1.51 1	2.16 37	1.71 23	1.13 4	0.94 3	1.40 6	0.91 4	0.30 6	0.24 16	0.26 6	0.21 5	0.26 3	0.25 4

DPM-Stereo	1.24 5	0.69 15	1.77 3	0.65 3	0.61 8	4.29 104	0.58 4	4.44 114	2.53 65	1.98 25	1.33 6	0.91 3	0.86 1	1.25 3	1.25 6	0.36 28	0.23 13	0.32 16	0.23 11	0.32 10	0.29 11

AdaStereo	1.45 6	0.83 32	2.34 9	1.28 51	0.81 17	3.21 12	1.14 47	4.20 92	2.46 59	2.05 31	1.77 30	2.48 28	1.18 8	1.71 11	1.44 8	0.43 49	0.25 20	0.43 46	0.28 21	0.44 29	0.34 17
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi: AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
ACVNet	1.46 7	0.64 13	3.56 19	0.93 5	0.85 22	4.22 100	0.80 13	2.85 5	2.08 21	1.78 10	1.88 41	3.10 59	1.46 24	1.33 4	1.89 35	0.29 4	0.26 24	0.30 12	0.26 16	0.39 18	0.27 8

acv_ft	1.55 8	0.64 13	3.29 16	1.14 24	2.01 118	2.93 10	0.97 36	3.81 40	2.08 21	2.22 40	1.88 41	3.10 59	1.66 42	1.33 4	2.22 51	0.29 4	0.26 24	0.30 12	0.26 16	0.39 18	0.27 8

Gwc-CoAtRS	1.58 9	0.57 5	11.69 45	1.01 10	0.60 7	2.53 2	0.64 7	2.36 3	1.56 4	1.80 13	1.47 9	2.74 39	1.18 8	1.12 1	0.90 3	0.27 2	0.21 7	0.25 4	0.22 9	0.27 5	0.23 3

ccs	1.61 10	0.55 4	2.29 8	1.27 50	0.80 14	3.62 45	0.75 12	4.22 96	2.77 75	1.94 22	1.60 13	2.54 32	1.46 24	3.60 20	1.72 22	0.40 41	0.60 101	0.38 30	0.39 52	0.45 33	0.77 90

DMCA	1.72 11	0.74 23	6.63 24	1.14 24	0.96 33	3.98 77	0.90 24	3.75 37	2.76 74	2.83 72	1.58 12	2.35 23	1.18 8	1.58 9	1.98 40	0.40 41	0.27 27	0.42 43	0.26 16	0.36 15	0.38 23

StereoDRNet-Refined	1.77 12	0.81 28	2.22 7	1.21 36	0.76 13	3.26 16	0.71 9	3.82 41	2.05 19	2.29 44	2.05 54	2.98 56	1.44 19	7.15 47	2.27 53	0.30 6	0.23 13	0.42 43	0.33 30	0.45 33	0.64 70
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
MLCV	1.87 13	0.91 36	8.59 29	1.10 19	0.57 5	3.59 43	0.53 3	3.70 34	2.20 30	2.80 70	2.25 71	2.00 12	1.42 17	4.13 24	1.86 33	0.30 6	0.20 4	0.32 16	0.21 5	0.33 12	0.36 20

DSFCA	1.89 14	0.69 15	3.14 14	1.10 19	1.18 54	3.99 79	1.79 81	3.50 21	2.16 28	3.10 83	1.64 15	2.91 54	2.50 77	5.38 33	2.26 52	0.43 49	0.38 55	0.37 27	0.36 39	0.50 40	0.42 33

iResNet_ROB	1.89 14	1.02 48	8.60 30	1.39 77	0.98 40	3.72 54	0.66 8	3.84 42	2.65 69	1.88 18	2.05 54	1.98 11	1.27 12	4.52 26	1.67 18	0.30 6	0.20 4	0.28 9	0.18 3	0.31 9	0.40 28

CFNet_RVC	1.90 16	0.95 41	3.56 19	1.07 13	1.54 84	4.09 86	0.90 24	2.85 5	1.82 10	1.78 10	1.70 18	2.75 41	1.48 27	9.33 65	1.72 22	0.33 17	0.30 32	0.61 95	0.36 39	0.52 43	0.39 24

CFNet-ft	1.90 16	0.95 41	3.56 19	1.07 13	1.54 84	4.09 86	0.90 24	2.85 5	1.82 10	1.78 10	1.70 18	2.75 41	1.48 27	9.33 65	1.72 22	0.33 17	0.30 32	0.61 95	0.36 39	0.52 43	0.39 24

DN-CSS_ROB	1.91 18	1.55 91	11.13 43	1.82 110	0.88 24	3.99 79	0.61 6	4.01 68	1.67 6	1.64 7	1.89 43	1.39 7	1.06 6	3.23 15	1.50 10	0.31 11	0.19 2	0.44 49	0.29 23	0.37 17	0.31 14

BEATNet_4x	1.92 19	1.36 79	8.75 31	1.20 35	0.56 2	3.46 30	1.03 39	4.00 67	2.43 54	2.25 43	1.70 18	2.10 18	1.59 36	2.57 14	3.05 76	0.37 29	0.22 10	0.47 57	0.29 23	0.60 63	0.40 28

LALA_ROB	1.95 20	1.48 88	3.11 13	1.05 12	0.98 40	3.99 79	1.82 83	4.44 114	2.28 40	3.31 90	1.85 39	2.76 46	1.67 44	4.04 23	3.23 78	0.46 59	0.41 63	0.55 83	0.52 82	0.60 63	0.57 59

PSMNet-RSSM	1.96 21	0.77 25	3.26 15	1.29 54	1.31 68	4.19 95	0.96 35	3.68 30	1.89 13	2.05 31	1.80 34	2.60 36	1.70 46	9.84 72	1.81 30	0.35 26	0.29 30	0.35 25	0.37 47	0.42 26	0.35 18

GANet-RSSM	1.98 22	0.62 10	3.49 17	1.69 103	1.23 60	4.22 100	0.91 30	3.31 15	1.86 12	1.81 14	1.74 28	2.34 22	1.82 55	10.92 96	1.73 27	0.34 21	0.29 30	0.31 14	0.28 21	0.39 18	0.40 28

HGLStereo	2.03 23	0.74 23	7.50 25	1.23 40	1.56 91	3.62 45	0.95 34	3.78 39	1.89 13	2.03 29	2.20 67	3.12 62	2.14 65	5.68 40	2.04 43	0.37 29	0.31 36	0.32 16	0.29 23	0.40 22	0.45 36

DeepPruner_ROB	2.05 24	1.09 57	10.50 40	1.01 10	1.20 57	4.39 107	0.91 30	3.14 11	1.72 8	2.60 58	1.54 10	2.78 47	1.27 12	4.52 26	1.72 22	0.51 71	0.40 60	0.37 27	0.34 31	0.48 37	0.52 49

DLCB_ROB	2.05 24	0.95 41	2.37 10	1.24 42	1.02 44	2.92 9	1.30 54	3.68 30	2.30 42	2.91 74	2.17 64	3.32 65	1.52 33	10.37 84	2.50 59	0.40 41	0.33 39	0.40 38	0.36 39	0.41 25	0.47 39

HITNet	2.07 26	0.82 31	14.77 62	1.34 65	0.56 2	3.47 32	0.80 13	4.07 77	2.35 46	2.16 37	1.61 14	1.64 9	1.44 19	2.09 12	2.56 62	0.26 1	0.18 1	0.41 42	0.26 16	0.45 33	0.25 4
Vladimir Tankovich, Christian Häne, Yinda Zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz: HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching. CVPR 2021
ETE_ROB	2.07 26	1.40 84	3.01 12	1.09 15	0.92 30	3.94 71	1.53 67	4.25 98	2.23 33	3.31 90	2.06 56	2.35 23	1.52 33	8.74 57	2.07 46	0.44 53	0.36 48	0.51 72	0.48 74	0.54 50	0.62 66

DISCO	2.25 28	0.73 20	10.06 37	1.76 108	1.12 49	3.47 32	1.25 53	3.24 13	2.26 37	2.65 60	1.96 45	4.53 104	1.92 60	4.17 25	3.61 80	0.32 13	0.25 20	0.31 14	0.29 23	0.66 77	0.41 32

FENet	2.25 28	0.50 2	20.29 113	1.35 68	0.67 9	4.01 82	0.60 5	3.95 63	1.99 15	1.96 23	1.11 2	2.43 27	1.43 18	1.60 10	1.39 7	0.32 13	0.22 10	0.33 21	0.26 16	0.29 7	0.39 24

NOSS_ROB	2.27 30	0.73 20	3.00 11	1.71 105	1.00 42	3.90 67	0.97 36	4.29 102	2.81 82	2.94 77	2.19 66	3.88 83	1.19 11	11.77 110	1.62 13	0.59 83	0.54 90	0.56 84	0.52 82	0.56 56	0.54 53

XPNet_ROB	2.27 30	1.11 60	4.10 22	1.36 72	0.97 37	3.94 71	1.63 73	4.07 77	2.14 25	2.82 71	2.00 48	2.32 21	1.73 48	10.81 91	3.39 79	0.51 71	0.47 81	0.48 62	0.40 59	0.51 41	0.56 56

TDLM	2.29 32	1.08 55	4.61 23	1.29 54	1.01 43	4.26 103	2.17 93	3.75 37	2.79 78	2.31 46	1.98 47	2.06 17	1.59 36	12.45 119	1.72 22	0.51 71	0.28 29	0.52 78	0.35 34	0.56 56	0.50 45

CFNet	2.34 33	1.10 58	8.96 32	1.34 65	1.24 61	4.09 86	0.73 11	4.04 72	2.77 75	1.62 6	1.87 40	2.27 20	1.45 22	11.41 97	1.82 31	0.31 11	0.24 16	0.43 46	0.36 39	0.43 28	0.33 15

STTStereo	2.34 33	0.95 41	14.93 67	1.43 79	1.54 84	3.86 66	0.86 19	3.65 27	2.44 56	2.15 36	1.55 11	2.49 30	1.59 36	5.14 30	1.44 8	0.38 35	0.39 59	0.47 57	0.51 79	0.52 43	0.51 48

ac_64	2.35 35	0.62 10	9.77 36	1.32 57	1.71 103	3.27 18	0.93 32	3.48 19	2.50 62	1.74 9	1.73 27	3.91 84	1.77 50	10.21 80	1.86 33	0.40 41	0.31 36	0.33 21	0.30 27	0.34 13	0.39 24

HSM	2.35 35	0.81 28	2.21 6	1.14 24	1.55 88	3.44 26	1.03 39	3.99 66	2.25 35	2.29 44	2.11 60	8.04 123	3.05 90	11.60 101	1.71 20	0.33 17	0.22 10	0.28 9	0.23 11	0.32 10	0.35 18

hitnet-ft	2.37 37	0.73 20	8.34 27	0.96 6	0.89 26	3.77 57	0.80 13	3.55 24	2.58 68	2.24 42	2.04 51	3.65 78	1.49 29	11.71 108	1.89 35	0.44 53	0.53 89	0.44 49	0.35 34	0.52 43	0.48 40

DMCA-RVC	2.45 38	1.22 70	18.29 97	1.23 40	1.14 51	2.49 1	0.85 18	3.29 14	1.68 7	3.02 79	1.41 8	2.00 12	1.53 35	6.39 43	2.00 41	0.38 35	0.30 32	0.44 49	0.35 34	0.45 33	0.44 34

FADNet	2.49 39	1.76 101	13.17 51	1.17 28	0.95 32	3.27 18	0.88 23	3.94 62	2.27 39	1.40 4	1.29 5	2.51 31	2.06 62	9.93 76	1.94 37	0.43 49	0.45 76	0.40 38	0.43 65	1.06 110	0.53 50

FADNet-RVC	2.51 40	2.27 112	14.03 55	1.09 15	0.81 17	3.54 40	1.10 43	4.13 84	2.48 60	1.39 3	1.22 4	1.85 10	1.77 50	9.78 71	2.06 45	0.38 35	0.37 53	0.40 38	0.40 59	0.67 78	0.48 40

iResNet	2.52 41	0.94 40	21.17 122	1.84 112	0.72 12	3.75 56	0.81 16	4.14 87	2.55 67	2.33 47	2.03 49	1.35 6	1.44 19	3.27 16	2.16 49	0.35 26	0.23 13	0.28 9	0.22 9	0.44 29	0.45 36

NCCL2	2.53 42	1.35 78	10.90 41	1.15 27	0.97 37	3.53 38	2.56 99	3.65 27	2.23 33	2.76 66	1.80 34	2.41 26	1.66 42	10.35 83	2.07 46	0.44 53	0.40 60	0.57 85	0.53 86	0.59 60	0.65 73

NVstereo2D	2.54 43	0.90 34	11.10 42	1.32 57	1.47 81	3.58 42	1.38 58	4.13 84	2.45 57	1.90 20	1.71 23	2.86 52	1.76 49	10.86 92	1.66 17	0.60 84	0.43 69	0.53 79	0.41 61	0.78 92	0.94 106

NLCA_NET_v2_RVC	2.54 43	1.04 50	16.73 85	1.53 91	2.00 117	3.96 74	0.87 22	3.87 48	2.31 43	2.54 54	1.67 17	2.85 51	1.63 40	5.55 36	1.62 13	0.46 59	0.33 39	0.38 30	0.38 49	0.53 49	0.62 66
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
ccs_rob	2.54 43	1.14 62	14.21 58	1.35 68	0.96 33	3.98 77	0.71 9	3.86 46	2.88 86	2.04 30	2.07 57	2.05 16	1.29 15	10.60 87	1.71 20	0.32 13	0.25 20	0.38 30	0.35 34	0.39 18	0.33 15

CC-Net-ROB	2.55 46	1.05 51	16.77 87	1.50 86	1.94 116	3.97 76	0.90 24	3.85 44	2.25 35	2.55 55	1.70 18	2.82 49	1.65 41	5.67 39	1.65 15	0.47 62	0.33 39	0.38 30	0.39 52	0.54 50	0.64 70

CVANet_RVC	2.55 46	1.10 58	9.13 33	1.46 81	1.15 52	3.96 74	1.53 67	3.91 58	2.50 62	3.04 80	2.04 51	2.60 36	1.72 47	12.46 120	1.50 10	0.49 65	0.30 32	0.51 72	0.41 61	0.63 68	0.50 45

RASNet	2.56 48	0.70 17	10.06 37	1.60 96	2.25 125	3.67 50	1.00 38	3.38 18	3.14 95	1.81 14	2.11 60	4.60 106	2.30 69	10.25 82	2.61 63	0.37 29	0.24 16	0.25 4	0.24 15	0.28 6	0.26 7

cf-r	2.57 49	0.79 27	16.09 75	1.48 84	1.35 72	4.19 95	0.93 32	3.68 30	2.36 48	1.97 24	1.81 36	3.42 67	1.45 22	8.24 52	1.83 32	0.32 13	0.26 24	0.33 21	0.30 27	0.34 13	0.37 22

DANet	2.63 50	1.07 54	8.10 26	1.83 111	1.70 102	3.25 15	1.16 48	4.31 107	2.38 51	3.40 93	2.18 65	4.08 91	1.80 53	9.77 70	3.89 83	0.52 76	0.55 92	0.59 88	0.44 66	0.63 68	0.86 98

iResNetv2_ROB	2.65 51	5.00 124	19.28 106	2.06 123	1.09 47	3.83 64	1.13 46	4.14 87	2.09 23	1.85 16	2.03 49	2.01 14	1.51 31	3.58 18	1.58 12	0.34 21	0.21 7	0.32 16	0.21 5	0.44 29	0.29 11

FADNet_RVC	2.65 51	2.28 113	16.51 81	0.98 7	0.92 30	4.09 86	0.83 17	4.26 99	1.73 9	1.46 5	1.78 32	1.60 8	1.50 30	8.88 59	2.39 55	0.47 62	0.38 55	0.44 49	0.58 94	0.89 100	0.97 107

GwcNet-RSSM	2.66 53	1.05 51	15.91 74	1.22 39	1.19 55	4.12 91	1.12 45	3.84 42	2.02 16	1.93 21	1.79 33	3.12 62	1.46 24	10.37 84	2.05 44	0.33 17	0.27 27	0.35 25	0.30 27	0.40 22	0.36 20

PASM	2.67 54	2.05 109	9.71 35	1.73 107	1.37 73	2.53 2	1.52 66	3.10 10	2.17 29	2.79 69	1.84 37	2.90 53	1.85 58	11.89 113	2.19 50	0.81 111	0.85 121	0.87 122	1.01 123	1.14 114	1.05 111

PWC_ROB	2.69 55	2.01 106	8.36 28	1.33 61	1.90 114	3.23 13	1.18 49	4.33 109	3.25 102	3.11 84	3.41 102	2.71 38	2.18 67	9.15 63	4.48 86	0.56 81	0.31 36	0.61 95	0.36 39	0.69 82	0.74 83

RP	2.79 56	1.20 66	10.36 39	1.54 92	1.93 115	3.44 26	1.97 86	4.05 74	2.34 45	2.66 61	2.79 85	3.98 88	2.58 80	10.21 80	2.61 63	0.95 117	0.47 81	0.71 109	0.57 93	0.71 88	0.68 78

SHD	2.82 57	2.04 108	13.80 53	1.69 103	1.79 111	2.73 6	1.07 42	3.85 44	3.18 97	4.63 110	2.74 83	3.38 66	2.77 85	5.48 35	3.66 81	0.62 88	0.47 81	0.51 72	0.55 88	0.65 73	0.74 83

FADNet-RVC-Resample	2.83 58	2.02 107	21.07 119	1.12 21	0.91 29	3.53 38	1.37 57	4.08 79	2.09 23	1.68 8	1.33 6	2.03 15	1.51 31	9.54 68	1.69 19	0.38 35	0.37 53	0.42 43	0.44 66	0.56 56	0.56 56

StereoDRNet	2.84 59	1.49 89	14.48 61	1.33 61	1.57 94	3.44 26	2.06 89	3.69 33	2.36 48	3.26 89	1.84 37	2.79 48	1.80 53	10.88 93	3.16 77	0.41 46	0.33 39	0.39 35	0.36 39	0.59 60	0.58 62

PDISCO_ROB	2.86 60	1.53 90	14.27 60	1.98 120	2.19 122	4.52 111	1.35 56	4.65 121	3.19 98	2.09 34	2.16 63	3.53 73	2.45 76	5.32 32	2.12 48	1.30 130	0.78 118	0.72 112	0.59 96	1.24 121	1.28 117

AF-Net	2.87 61	1.30 76	16.24 76	1.33 61	1.75 107	3.26 16	1.59 72	4.09 80	2.43 54	2.69 62	2.94 89	4.51 103	3.20 94	5.84 42	2.75 68	0.76 106	0.36 48	0.72 112	0.49 75	0.70 83	0.56 56

HSM-Net_RVC	2.87 61	0.57 5	17.46 90	0.92 4	0.90 28	3.70 51	1.20 51	5.64 131	2.51 64	3.22 88	2.43 74	2.54 32	1.59 36	11.41 97	1.65 15	0.34 21	0.25 20	0.26 6	0.23 11	0.30 8	0.29 11
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
Nwc_Net	2.88 63	1.16 64	18.30 98	1.35 68	1.68 100	3.52 37	1.18 49	3.90 56	2.06 20	2.52 53	2.82 87	3.82 81	2.34 72	6.78 46	2.55 61	0.86 114	0.40 60	0.59 88	0.67 101	0.57 59	0.55 54

MFMNet_re	2.91 64	1.91 102	18.25 96	3.06 130	1.89 113	3.13 11	1.67 77	3.52 22	2.97 89	3.14 85	2.87 88	2.26 19	2.41 73	2.42 13	2.00 41	1.10 121	1.05 124	0.94 124	0.90 119	1.28 122	1.46 123

GANet	2.93 65	1.05 51	11.29 44	1.64 99	0.96 33	3.91 68	2.52 97	3.72 35	2.41 52	2.95 78	3.00 91	3.21 64	2.03 61	12.42 118	4.72 90	0.51 71	0.41 63	0.46 55	0.39 52	0.60 63	0.50 45

PWCDC_ROB	2.95 66	3.26 118	14.06 56	1.59 95	1.56 91	4.19 95	0.86 19	4.30 106	4.08 115	2.06 33	7.37 114	2.75 41	2.42 74	3.59 19	2.67 66	1.34 132	0.36 48	0.39 35	0.34 31	1.13 113	0.62 66

RTSC	2.95 66	2.51 115	15.49 73	1.54 92	1.30 67	3.51 36	0.86 19	3.89 51	2.48 60	3.64 95	3.12 97	3.04 58	2.30 69	5.70 41	6.67 99	0.55 79	0.35 47	0.37 27	0.39 52	0.70 83	0.60 64

PSMNet_ROB	2.95 66	1.38 81	18.46 101	1.19 32	1.20 57	3.80 62	1.65 76	3.54 23	2.35 46	1.98 25	1.71 23	2.75 41	1.79 52	11.43 99	2.78 69	0.42 48	0.36 48	0.50 70	0.60 97	0.59 60	0.49 43

XQC	2.96 69	2.42 114	14.85 63	1.36 72	1.32 70	3.29 21	1.53 67	4.28 101	2.78 77	3.21 87	2.46 75	3.53 73	2.92 88	6.58 44	4.67 88	0.70 103	0.41 63	0.59 88	0.56 90	0.96 107	0.83 97

SGM-Forest	2.96 69	0.62 10	3.49 17	1.09 15	0.80 14	4.06 83	3.30 111	4.37 110	3.09 93	4.18 105	3.27 99	4.67 107	3.86 107	11.69 107	7.91 108	0.51 71	0.46 79	0.49 68	0.39 52	0.48 37	0.49 43
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
RYNet	2.97 71	0.93 38	15.09 69	1.12 21	1.38 75	3.66 49	1.42 63	4.03 70	2.29 41	2.10 35	1.70 18	3.79 80	2.16 66	13.64 125	3.02 75	0.37 29	0.41 63	0.48 62	0.34 31	0.81 95	0.75 85

GwcNet	3.00 72	1.63 93	19.83 109	1.32 57	2.04 121	3.27 18	1.55 70	3.65 27	2.33 44	2.00 27	2.12 62	3.56 77	1.68 45	9.47 67	2.45 57	0.44 53	0.45 76	0.46 55	0.38 49	0.60 63	0.76 86

AANet_RVC	3.03 73	1.72 98	12.78 49	1.17 28	1.12 49	3.79 61	1.10 43	3.86 46	3.13 94	2.62 59	4.26 109	3.96 87	1.41 16	11.43 99	5.23 97	0.94 116	0.47 81	0.34 24	0.23 11	0.40 22	0.55 54

DRN-Test	3.03 73	0.99 46	18.18 95	1.35 68	1.60 96	3.59 43	1.48 64	4.13 84	2.45 57	3.47 94	1.92 44	2.58 35	2.53 78	10.88 93	2.80 70	0.39 40	0.36 48	0.44 49	0.41 61	0.55 53	0.57 59

Anonymous Stereo	3.09 75	3.14 117	16.69 84	1.52 89	1.25 63	3.33 22	2.95 104	4.02 69	2.42 53	2.35 48	2.04 51	2.57 34	1.27 12	11.88 112	2.92 72	0.49 65	0.49 87	0.57 85	0.56 90	0.70 83	0.65 73

stereogan	3.09 75	0.93 38	16.43 79	1.37 75	1.66 99	3.70 51	1.67 77	3.87 48	3.15 96	3.04 80	2.70 82	5.04 113	2.79 86	9.96 77	1.78 29	0.63 89	0.49 87	0.69 105	0.47 70	0.83 98	0.68 78

PA-Net	3.09 75	1.24 73	18.84 102	1.38 76	1.19 55	3.42 25	1.75 79	3.89 51	3.27 106	1.88 18	2.10 58	3.03 57	1.88 59	11.60 101	2.88 71	0.40 41	0.54 90	0.44 49	0.62 99	0.52 43	1.02 109
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
RGC	3.10 78	1.45 87	14.90 65	1.46 81	1.79 111	3.33 22	1.39 59	4.04 72	2.26 37	2.77 67	3.26 98	4.00 89	2.71 84	11.63 104	2.62 65	0.82 112	0.46 79	0.79 117	0.79 114	0.71 88	0.78 91

PVD	3.12 79	1.97 105	12.80 50	1.79 109	1.63 97	3.83 64	1.34 55	3.48 19	3.88 114	5.77 114	3.56 103	3.45 68	3.29 96	5.58 38	5.30 98	0.71 104	0.69 114	0.74 115	0.77 111	0.79 94	1.13 113

Abc-Net	3.16 80	1.28 74	18.31 99	1.25 44	1.75 107	4.06 83	1.40 61	3.89 51	2.22 31	2.72 64	2.55 77	3.49 69	2.65 82	9.91 74	2.99 73	1.29 128	0.65 107	0.64 101	0.86 117	0.65 73	0.71 80
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
NCC-stereo	3.16 80	1.28 74	18.31 99	1.25 44	1.75 107	4.06 83	1.40 61	3.89 51	2.22 31	2.72 64	2.55 77	3.49 69	2.65 82	9.91 74	2.99 73	1.29 128	0.65 107	0.64 101	0.86 117	0.65 73	0.71 80

GANetREF_RVC	3.20 82	1.93 103	18.84 102	1.51 88	0.80 14	4.12 91	1.82 83	4.16 89	2.91 88	1.87 17	1.74 28	2.48 28	1.84 56	13.18 122	1.74 28	0.80 109	0.57 94	0.79 117	0.74 107	1.20 120	0.86 98
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
FBW_ROB	3.24 83	1.02 48	9.21 34	1.49 85	1.07 45	3.78 58	1.48 64	4.31 107	3.08 92	2.46 50	2.32 72	4.20 96	2.08 63	15.19 130	7.16 107	0.84 113	0.62 104	1.33 131	0.92 120	0.99 108	1.31 118

aanetorigin	3.30 84	1.66 97	17.52 92	1.18 31	1.58 95	2.57 4	3.80 119	2.65 4	2.03 17	6.12 118	4.30 110	2.82 49	5.70 117	5.47 34	4.67 88	0.50 68	0.44 72	0.43 46	0.47 70	1.17 117	0.92 105

ADCRef	3.30 84	1.23 72	14.90 65	1.17 28	1.41 79	3.95 73	1.64 75	3.88 50	2.36 48	3.05 82	2.21 69	2.40 25	2.81 87	5.04 28	16.63 123	0.46 59	0.34 46	0.63 98	0.62 99	0.54 50	0.67 76

CBMV	3.32 86	0.96 45	21.16 121	1.32 57	0.83 20	3.81 63	3.91 122	4.47 116	2.53 65	3.34 92	2.24 70	4.45 101	2.58 80	10.07 79	1.96 39	0.45 58	0.43 69	0.48 62	0.38 49	0.52 43	0.53 50
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
FAT-Stereo	3.34 87	0.81 28	20.05 110	1.25 44	1.20 57	3.44 26	1.99 88	4.23 97	2.67 71	3.14 85	3.82 107	3.51 71	3.32 98	11.68 106	1.94 37	0.64 92	0.67 110	0.48 62	0.52 82	0.61 67	0.81 93

edge stereo	3.37 88	1.01 47	21.99 127	1.28 51	1.37 73	2.67 5	1.39 59	3.98 65	2.87 85	2.93 76	3.27 99	4.35 99	2.43 75	11.62 103	2.39 55	0.60 84	0.63 105	0.79 117	0.55 88	0.68 80	0.67 76

DPSNet	3.43 89	1.37 80	12.19 46	1.39 77	1.24 61	4.15 94	2.06 89	4.51 117	3.34 107	2.69 62	1.96 45	3.52 72	5.56 116	10.68 89	9.68 113	0.93 115	0.73 116	0.40 38	0.37 47	1.06 110	0.86 98

S-Stereo	3.48 90	0.91 36	22.06 128	1.36 72	1.50 83	3.55 41	2.53 98	4.21 94	2.81 82	2.37 49	3.81 106	3.75 79	1.84 56	11.90 114	2.51 60	0.63 89	0.71 115	0.48 62	0.76 109	0.67 78	1.19 115

SAMSARA	3.55 91	2.16 111	14.18 57	1.86 115	1.49 82	4.62 112	2.65 101	3.58 26	2.80 80	7.05 121	3.05 94	5.16 115	4.07 108	9.18 64	4.80 91	0.64 92	0.67 110	0.59 88	0.68 103	0.89 100	0.91 104

CBMV_ROB	3.62 92	0.70 17	21.42 125	1.21 36	0.89 26	4.43 110	1.76 80	4.39 112	2.79 78	5.88 115	3.38 101	4.18 94	5.12 114	10.75 90	2.32 54	0.56 81	0.55 92	0.53 79	0.51 79	0.55 53	0.46 38

DeepPrunerF	3.70 93	1.72 98	14.87 64	1.33 61	1.74 106	3.70 51	1.55 70	4.20 92	14.00 133	2.02 28	1.77 30	2.74 39	2.30 69	10.88 93	6.88 104	0.68 100	0.60 101	0.78 116	0.78 113	0.81 95	0.71 80

ADCP+	3.74 94	1.96 104	16.90 88	1.09 15	1.55 88	4.35 105	3.65 118	3.93 59	2.98 90	2.22 40	1.66 16	3.95 85	4.83 113	3.41 17	18.87 129	0.38 35	0.41 63	0.63 98	0.53 86	0.64 72	0.81 93

MFN_U_SF_RVC	3.81 95	10.78 136	15.01 68	1.84 112	1.39 76	3.73 55	1.63 73	4.05 74	3.26 103	2.91 74	2.38 73	4.36 100	3.65 103	8.43 54	4.91 93	1.11 122	1.04 123	0.99 125	1.31 129	1.58 128	1.75 129

ADCPNet	3.89 96	1.63 93	20.25 112	1.28 51	1.27 66	6.43 125	2.65 101	3.93 59	2.70 73	2.78 68	2.20 67	4.28 97	6.16 120	5.56 37	10.27 116	0.65 95	1.18 127	0.71 109	1.70 132	0.84 99	1.37 120

RTS	3.90 97	7.04 128	17.94 93	1.92 117	2.19 122	5.00 117	2.96 107	4.29 102	3.19 98	4.05 102	3.08 95	4.94 110	3.16 92	7.64 49	6.72 100	0.66 96	0.44 72	0.51 72	0.47 70	0.95 103	0.76 86

RTSA	3.90 97	7.04 128	17.94 93	1.92 117	2.19 122	5.00 117	2.96 107	4.29 102	3.19 98	4.05 102	3.08 95	4.94 110	3.16 92	7.64 49	6.72 100	0.66 96	0.44 72	0.51 72	0.47 70	0.95 103	0.76 86

MFN_U_SF_DS_RVC	3.94 99	10.88 137	12.30 47	1.26 47	1.55 88	5.19 119	3.33 112	4.42 113	2.82 84	2.58 56	2.62 80	5.00 112	2.11 64	14.03 128	2.73 67	1.28 127	2.34 134	0.67 104	1.83 133	0.81 95	1.03 110

ADCMid	3.96 100	3.27 119	15.48 72	1.26 47	1.40 77	4.66 113	2.28 94	4.06 76	3.54 109	3.94 101	3.02 92	3.85 82	3.67 104	4.03 22	18.65 127	0.68 100	0.61 103	1.09 129	1.18 127	1.38 125	1.08 112

MADNet+	3.97 101	9.54 133	13.83 54	1.87 116	1.69 101	4.19 95	2.87 103	4.80 125	4.10 116	2.51 52	2.67 81	4.55 105	3.41 99	8.72 56	6.92 105	1.25 126	1.27 130	1.04 126	0.93 121	1.39 126	1.85 130

SPS-STEREO	3.97 101	1.44 86	20.85 117	1.72 106	1.71 103	3.24 14	2.30 95	3.55 24	2.68 72	4.79 111	3.66 104	5.44 116	2.97 89	9.11 62	9.35 112	1.15 123	1.10 125	1.06 128	1.02 125	1.16 116	1.18 114
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
NaN_ROB	4.06 103	1.13 61	16.64 82	1.12 21	1.25 63	3.50 35	3.05 109	4.29 102	3.26 103	3.64 95	2.56 79	2.97 55	4.79 112	13.33 123	16.91 124	0.37 29	0.41 63	0.38 30	0.39 52	0.48 37	0.81 93

STTStereo_v2	4.11 104	1.21 67	19.47 107	2.31 126	2.94 129	4.74 114	2.95 104	3.33 16	2.14 25	4.44 107	9.43 123	6.93 119	3.84 105	9.01 60	4.92 94	0.79 107	0.80 119	0.70 107	0.50 76	0.95 103	0.87 101

G-Net	4.11 104	1.21 67	19.47 107	2.31 126	2.94 129	4.74 114	2.95 104	3.33 16	2.14 25	4.44 107	9.43 123	6.93 119	3.84 105	9.01 60	4.92 94	0.79 107	0.80 119	0.70 107	0.50 76	0.95 103	0.87 101

AnyNet_C32	4.17 106	5.75 126	12.62 48	1.60 96	1.54 84	5.51 120	4.09 123	3.90 56	3.23 101	4.22 106	3.03 93	3.10 59	3.41 99	8.01 51	18.85 128	0.53 78	0.58 97	0.53 79	0.74 107	1.19 119	0.97 107

ADCS	4.22 107	5.33 125	16.32 77	1.64 99	1.31 68	3.91 68	3.64 117	4.21 94	2.89 87	5.11 112	2.81 86	4.46 102	3.06 91	5.21 31	19.50 130	0.63 89	0.64 106	0.69 105	0.69 105	1.12 112	1.26 116

ADCL	4.35 108	1.63 93	14.21 58	1.29 54	1.26 65	5.56 121	11.16 130	3.97 64	3.37 108	6.07 116	4.67 111	4.30 98	5.72 118	5.08 29	14.51 120	0.50 68	0.48 85	0.89 123	0.77 111	0.78 92	0.76 86

AnyNet_C01	4.37 109	7.80 130	16.40 78	1.98 120	1.40 77	6.37 124	6.47 125	4.09 80	5.03 122	4.06 104	3.78 105	4.16 93	3.31 97	6.62 45	10.00 114	0.61 86	0.67 110	0.63 98	0.67 101	2.01 130	1.31 118

MSC_U_SF_DS_RVC	4.48 110	10.62 135	15.18 70	1.55 94	1.45 80	6.16 123	3.54 116	5.41 130	3.68 112	2.59 57	2.74 83	4.81 108	2.20 68	13.79 126	4.40 85	2.04 133	3.36 135	1.05 127	1.20 128	2.09 131	1.72 128

SGM_RVC	4.57 111	0.72 19	17.46 90	1.84 112	0.58 6	4.23 102	3.45 113	4.66 122	3.66 111	7.99 122	8.82 122	7.56 122	8.74 126	12.30 116	6.84 103	0.43 49	0.38 55	0.39 35	0.35 34	0.51 41	0.48 40
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DispFullNet	4.64 112	3.48 120	23.95 131	3.99 131	3.03 131	3.35 24	1.06 41	4.18 90	2.80 80	6.33 119	2.94 89	4.04 90	7.81 123	3.92 21	5.16 96	2.75 134	1.61 133	3.10 135	2.68 134	3.32 132	3.31 134

CSAN	4.71 113	1.39 83	20.23 111	1.19 32	0.87 23	3.65 48	3.50 115	4.57 119	3.87 113	3.89 100	7.82 117	3.54 76	6.16 120	23.47 138	6.82 102	0.52 76	0.45 76	0.49 68	0.52 82	0.63 68	0.66 75

SuperB	4.79 114	4.41 123	21.08 120	1.45 80	1.08 46	3.91 68	3.08 110	4.18 90	3.01 91	2.50 51	2.10 58	2.75 41	3.49 102	8.80 58	19.60 131	0.44 53	0.33 39	0.53 79	0.39 52	12.01 138	0.64 70

NVStereoNet_ROB	4.91 115	1.40 84	16.46 80	1.47 83	1.72 105	3.46 30	1.96 85	3.89 51	3.61 110	2.90 73	24.61 139	4.81 108	4.49 110	13.57 124	4.87 92	1.33 131	1.29 131	1.70 133	1.68 131	1.41 127	1.66 125
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
WCMA_ROB	5.12 116	0.90 34	21.33 123	1.50 86	1.33 71	4.09 86	3.49 114	4.03 70	4.59 118	6.57 120	10.95 125	12.92 128	8.41 125	9.86 73	8.78 111	0.75 105	0.58 97	0.51 72	0.50 76	0.68 80	0.57 59

MeshStereo	5.35 117	1.32 77	21.34 124	1.34 65	1.15 52	4.40 108	3.90 121	5.18 126	4.55 117	10.14 130	11.82 128	11.47 126	5.84 119	13.84 127	7.03 106	0.61 86	0.58 97	0.60 92	0.58 94	0.72 90	0.59 63
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
pmcnn	5.45 118	1.22 70	17.40 89	1.60 96	2.41 126	3.48 34	2.12 92	4.52 118	3.26 103	3.67 97	13.15 129	22.10 134	3.46 101	11.92 115	17.16 125	0.34 21	0.21 7	0.23 2	0.19 4	0.36 15	0.28 10

MDST_ROB	5.53 119	0.59 9	23.11 129	2.55 128	2.69 127	21.20 135	1.98 87	4.77 124	2.65 69	9.36 126	4.25 108	4.14 92	4.45 109	22.57 134	3.68 82	0.48 64	0.33 39	0.47 57	0.44 66	0.44 29	0.44 34

MSMD_ROB	5.61 120	1.08 55	16.68 83	1.21 36	0.83 20	10.14 130	2.07 91	3.93 59	6.22 127	10.37 131	8.03 119	22.22 135	9.45 127	11.66 105	4.52 87	0.66 96	0.58 97	0.66 103	0.68 103	0.65 73	0.63 69

EDNetEfficient	5.61 120	2.14 110	16.75 86	1.67 101	3.64 132	4.12 91	7.51 126	3.05 9	5.63 126	11.57 134	4.83 113	3.95 85	12.72 130	8.44 55	20.78 133	0.41 46	0.43 69	0.47 57	1.01 123	1.66 129	1.39 122

SANet	5.65 122	1.72 98	20.94 118	1.26 47	0.88 24	7.20 127	3.81 120	4.60 120	11.23 130	4.56 109	8.78 121	7.49 121	6.95 122	11.85 111	18.11 126	0.49 65	0.44 72	0.50 70	0.42 64	0.90 102	0.82 96

ELAS_RVC	5.72 123	1.64 96	20.40 115	2.09 124	2.01 118	4.95 116	11.47 131	4.66 122	6.31 128	8.09 123	8.01 118	8.06 125	11.65 129	8.36 53	10.22 115	0.99 120	1.17 126	0.81 120	0.76 109	1.14 114	1.58 124
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
PWCK	5.85 124	9.95 134	15.22 71	2.90 129	1.56 91	5.66 122	7.69 127	5.27 128	4.74 120	3.88 99	7.51 115	8.04 123	5.28 115	15.18 129	8.40 109	3.08 135	1.51 132	2.24 134	1.37 130	5.55 134	1.96 131

ELAS	5.99 125	1.62 92	20.29 113	1.96 119	2.01 118	15.09 134	8.38 129	5.23 127	5.54 124	9.02 125	8.61 120	6.74 118	9.75 128	7.48 48	11.40 118	0.96 118	1.18 127	0.72 112	0.93 121	1.30 123	1.67 126
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
FCDSN-DC	6.04 126	0.84 33	19.25 104	1.24 42	1.09 47	4.38 106	2.62 100	4.38 111	4.71 119	9.59 129	11.22 126	19.19 131	17.40 133	12.30 116	8.55 110	0.69 102	0.57 94	0.60 92	0.60 97	0.70 83	0.78 91
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
psmorigin	6.59 127	1.15 63	88.11 141	1.52 89	0.97 37	2.88 7	1.20 51	3.21 12	2.04 18	3.73 98	2.46 75	5.15 114	2.55 79	10.60 87	2.47 58	0.55 79	0.68 113	0.48 62	0.51 79	0.70 83	0.88 103

FC-DCNN	6.64 128	0.77 25	19.26 105	1.68 102	1.64 98	8.27 129	2.35 96	6.35 132	9.23 129	10.85 132	11.27 127	19.89 132	13.33 131	12.51 121	11.79 119	0.66 96	0.57 94	0.58 87	0.56 90	0.63 68	0.61 65

MADNet++	7.57 129	5.80 127	13.28 52	6.38 134	5.26 134	7.18 126	7.77 128	5.32 129	5.62 125	6.11 117	7.78 116	6.02 117	8.01 124	11.74 109	16.36 122	9.36 138	4.95 136	8.57 138	5.66 136	4.83 133	5.34 135

SGM+DAISY	8.36 130	3.82 122	24.83 132	2.02 122	1.75 107	14.80 133	17.22 133	4.27 100	11.79 132	13.88 135	14.38 132	11.74 127	13.83 132	10.56 86	15.12 121	1.21 124	1.19 129	1.14 130	1.13 126	1.17 117	1.37 120

MANE	11.39 131	1.38 81	23.16 130	4.82 133	2.79 128	22.02 136	14.51 132	25.81 137	26.28 138	10.93 133	13.17 130	19.90 133	18.03 134	15.69 131	20.41 132	0.96 118	0.74 117	1.42 132	3.24 135	0.74 91	1.71 127

SGM-ForestM	11.55 132	1.16 64	21.62 126	2.14 125	0.81 17	22.25 137	19.08 134	16.50 136	11.34 131	15.94 138	14.44 133	27.12 138	22.61 139	24.76 139	28.39 138	0.50 68	0.48 85	0.47 57	0.44 66	0.55 53	0.53 50

EDNetEfficientorigin	12.35 133	2.51 115	179.47 144	1.19 32	0.96 33	2.91 8	1.81 82	3.00 8	5.35 123	8.91 124	4.79 112	4.18 94	4.64 111	9.98 78	11.30 117	0.37 29	0.38 55	0.60 92	0.85 116	1.32 124	2.43 133

LSM	12.71 134	3.52 121	20.79 116	55.63 142	84.76 142	3.63 47	6.30 124	3.73 36	4.93 121	5.55 113	13.92 131	3.53 73	3.25 95	9.72 69	4.12 84	0.64 92	0.85 121	0.71 109	0.81 115	1.03 109	26.84 140

DGTPSM_ROB	15.31 135	8.85 131	28.14 135	9.90 136	18.89 135	12.44 131	33.21 137	13.48 134	17.86 136	9.50 127	22.89 137	13.78 129	19.67 135	21.26 132	23.68 134	5.08 136	8.84 137	5.62 136	11.50 137	6.72 135	14.87 136

DPSMNet_ROB	15.52 136	8.89 132	31.44 136	10.07 137	18.91 136	12.44 131	33.22 138	13.51 135	17.94 137	9.58 128	22.91 138	13.79 130	19.67 135	21.43 133	23.73 135	5.17 137	8.84 137	5.63 137	11.52 140	6.79 136	14.90 137

LE_ROB	18.58 137	1.21 67	48.38 138	8.60 135	23.00 139	7.32 128	19.74 135	9.94 133	38.69 140	43.53 140	21.91 136	28.40 139	40.92 141	23.18 135	54.71 141	0.34 21	0.33 39	0.32 16	0.36 39	0.42 26	0.40 28

DPSM	20.78 138	20.03 138	26.57 133	23.62 138	22.46 137	31.10 138	39.97 140	33.52 139	17.41 134	15.25 136	15.80 134	24.06 136	20.71 137	23.30 136	27.62 136	10.78 139	9.41 139	11.69 139	11.50 137	14.49 139	16.36 138

DPSM_ROB	20.78 138	20.03 138	26.57 133	23.62 138	22.46 137	31.10 138	39.97 140	33.52 139	17.41 134	15.25 136	15.80 134	24.06 136	20.71 137	23.30 136	27.62 136	10.78 139	9.41 139	11.69 139	11.50 137	14.49 139	16.36 138

AVERAGE_ROB	44.46 140	45.91 141	46.59 137	40.59 141	38.66 140	32.99 140	30.52 136	46.19 141	43.26 141	49.59 141	48.97 141	44.62 140	39.89 140	45.04 140	43.94 139	48.74 142	50.69 142	50.80 142	49.79 142	45.54 142	46.97 141

MEDIAN_ROB	47.37 141	49.08 142	49.92 139	40.32 140	39.97 141	34.95 141	33.43 139	49.17 142	46.39 142	53.06 142	52.65 142	47.65 141	42.23 143	48.35 142	47.29 140	52.10 143	54.04 143	54.28 143	53.48 143	48.76 143	50.33 142

LSM0	49.58 142	40.04 140	53.62 140	97.15 143	128.68 144	62.75 142	79.89 143	67.63 143	35.11 139	33.02 139	32.61 140	48.43 142	41.60 142	46.60 141	55.43 142	21.63 141	18.99 141	23.62 141	23.13 141	29.47 141	52.26 143

BEATNet-Init1	49.72 143	64.32 143	97.77 142	4.68 132	4.28 133	84.75 143	60.42 142	73.38 144	68.59 143	88.29 143	88.51 144	91.94 143	97.99 144	85.26 144	70.96 143	0.80 109	0.65 107	0.85 121	0.70 106	7.96 137	2.33 132

DPSimNet_ROB	102.30 144	119.72 145	143.59 143	109.42 144	94.10 143	127.22 144	127.15 144	26.62 138	102.36 144	149.58 144	83.99 143	139.34 144	137.54 145	64.62 143	82.36 144	105.18 144	66.15 144	120.15 144	69.61 144	99.58 144	77.72 144

MSMDNet													1.11 7

ASD4		113.98 144

Low-res two-view benchmark