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

R-Stereo	1.20 1	0.58 4	1.81 2	0.99 5	0.67 7	3.78 46	0.90 18	4.09 59	1.51 1	2.16 25	1.71 17	1.13 3	0.94 2	1.40 2	0.91 1	0.30 2	0.24 13	0.26 4	0.21 4	0.26 1	0.25 2

R-Stereo Train	1.20 1	0.58 4	1.81 2	0.99 5	0.67 7	3.78 46	0.90 18	4.09 59	1.51 1	2.16 25	1.71 17	1.13 3	0.94 2	1.40 2	0.91 1	0.30 2	0.24 13	0.26 4	0.21 4	0.26 1	0.25 2

DPM-Stereo	1.24 3	0.69 8	1.77 1	0.65 1	0.61 6	4.29 81	0.58 2	4.44 90	2.53 49	1.98 16	1.33 3	0.91 1	0.86 1	1.25 1	1.25 4	0.36 19	0.23 10	0.32 11	0.23 9	0.32 7	0.29 7

AdaStereo	1.45 4	0.83 18	2.34 7	1.28 37	0.81 14	3.21 7	1.14 33	4.20 71	2.46 44	2.05 20	1.77 22	2.48 24	1.18 7	1.71 4	1.44 5	0.43 35	0.25 17	0.43 33	0.28 15	0.44 17	0.34 13
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.
ccs	1.61 5	0.55 2	2.29 6	1.27 36	0.80 11	3.62 36	0.75 8	4.22 74	2.77 56	1.94 15	1.60 9	2.54 28	1.46 19	3.60 14	1.72 19	0.40 30	0.60 81	0.38 18	0.39 35	0.45 21	0.77 74

StereoDRNet-Refined	1.77 6	0.81 15	2.22 5	1.21 27	0.76 10	3.26 11	0.71 5	3.82 23	2.05 8	2.29 30	2.05 40	2.98 47	1.44 15	7.15 37	2.27 37	0.30 2	0.23 10	0.42 31	0.33 19	0.45 21	0.64 55
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
MLCV	1.87 7	0.91 21	8.59 19	1.10 14	0.57 4	3.59 34	0.53 1	3.70 19	2.20 16	2.80 55	2.25 55	2.00 11	1.42 14	4.13 18	1.86 26	0.30 2	0.20 3	0.32 11	0.21 4	0.33 9	0.36 15

iResNet_ROB	1.89 8	1.02 31	8.60 20	1.39 58	0.98 32	3.72 44	0.66 4	3.84 24	2.65 52	1.88 12	2.05 40	1.98 10	1.27 9	4.52 20	1.67 15	0.30 2	0.20 3	0.28 7	0.18 2	0.31 5	0.40 18

CFNet_RVC	1.90 9	0.95 25	3.56 13	1.07 9	1.54 66	4.09 71	0.90 18	2.85 2	1.82 7	1.78 8	1.70 13	2.75 35	1.48 20	9.33 52	1.72 19	0.33 12	0.30 22	0.61 73	0.36 26	0.52 30	0.39 17

DN-CSS_ROB	1.91 10	1.55 72	11.13 32	1.82 87	0.88 20	3.99 66	0.61 3	4.01 48	1.67 3	1.64 6	1.89 30	1.39 6	1.06 5	3.23 9	1.50 7	0.31 7	0.19 2	0.44 35	0.29 16	0.37 11	0.31 10

BEATNet_4x	1.92 11	1.36 60	8.75 21	1.20 26	0.56 2	3.46 22	1.03 26	4.00 47	2.43 39	2.25 29	1.70 13	2.10 16	1.59 26	2.57 8	3.05 57	0.37 20	0.22 8	0.47 41	0.29 16	0.60 48	0.40 18

LALA_ROB	1.95 12	1.48 69	3.11 11	1.05 8	0.98 32	3.99 66	1.82 64	4.44 90	2.28 27	3.31 70	1.85 28	2.76 39	1.67 33	4.04 17	3.23 59	0.46 42	0.41 48	0.55 63	0.52 64	0.60 48	0.57 44

DeepPruner_ROB	2.05 13	1.09 40	10.50 29	1.01 7	1.20 45	4.39 83	0.91 22	3.14 4	1.72 5	2.60 43	1.54 7	2.78 40	1.27 9	4.52 20	1.72 19	0.51 53	0.40 45	0.37 16	0.34 20	0.48 25	0.52 34

DLCB_ROB	2.05 13	0.95 25	2.37 8	1.24 30	1.02 36	2.92 5	1.30 39	3.68 17	2.30 29	2.91 58	2.17 49	3.32 52	1.52 24	10.37 67	2.50 41	0.40 30	0.33 25	0.40 26	0.36 26	0.41 14	0.47 25

ETE_ROB	2.07 15	1.40 65	3.01 10	1.09 10	0.92 25	3.94 59	1.53 52	4.25 75	2.23 19	3.31 70	2.06 42	2.35 20	1.52 24	8.74 44	2.07 32	0.44 38	0.36 35	0.51 52	0.48 57	0.54 35	0.62 51

HITNet	2.07 15	0.82 17	14.77 50	1.34 48	0.56 2	3.47 24	0.80 9	4.07 56	2.35 32	2.16 25	1.61 10	1.64 8	1.44 15	2.09 5	2.56 43	0.26 1	0.18 1	0.41 30	0.26 14	0.45 21	0.25 2
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
PMTNet	2.07 15	0.52 1	19.44 88	0.73 2	0.45 1	4.46 86	0.89 17	2.78 1	2.25 21	1.63 5	1.52 5	0.94 2	0.94 2	2.47 7	0.97 3	0.32 9	0.20 3	0.23 1	0.16 1	0.31 5	0.22 1

DMCA	2.09 18	1.05 34	6.92 16	1.16 20	1.61 76	2.41 1	0.91 22	3.18 5	1.70 4	3.46 74	1.52 5	2.43 23	1.69 34	9.58 54	1.87 27	0.39 28	0.34 32	0.45 39	0.41 44	0.46 24	0.36 15

DISCO	2.25 19	0.73 12	10.06 26	1.76 85	1.12 40	3.47 24	1.25 38	3.24 6	2.26 24	2.65 45	1.96 32	4.53 82	1.92 45	4.17 19	3.61 61	0.32 9	0.25 17	0.31 10	0.29 16	0.66 60	0.41 21

NOSS_ROB	2.27 20	0.73 12	3.00 9	1.71 82	1.00 34	3.90 55	0.97 24	4.29 79	2.81 63	2.94 60	2.19 51	3.88 65	1.19 8	11.77 88	1.62 10	0.59 64	0.54 71	0.56 64	0.52 64	0.56 41	0.54 38

XPNet_ROB	2.27 20	1.11 43	4.10 14	1.36 54	0.97 30	3.94 59	1.63 57	4.07 56	2.14 12	2.82 56	2.00 35	2.32 19	1.73 36	10.81 72	3.39 60	0.51 53	0.47 63	0.48 45	0.40 42	0.51 28	0.56 41

TDLM	2.29 22	1.08 38	4.61 15	1.29 40	1.01 35	4.26 80	2.17 73	3.75 22	2.79 59	2.31 32	1.98 34	2.06 15	1.59 26	12.45 95	1.72 19	0.51 53	0.28 21	0.52 58	0.35 23	0.56 41	0.50 30

CFNet	2.34 23	1.10 41	8.96 22	1.34 48	1.24 47	4.09 71	0.73 7	4.04 52	2.77 56	1.62 4	1.87 29	2.27 18	1.45 18	11.41 77	1.82 25	0.31 7	0.24 13	0.43 33	0.36 26	0.43 16	0.33 11

STTStereo	2.34 23	0.95 25	14.93 55	1.43 60	1.54 66	3.86 54	0.86 12	3.65 15	2.44 41	2.15 24	1.55 8	2.49 26	1.59 26	5.14 24	1.44 5	0.38 24	0.39 44	0.47 41	0.51 62	0.52 30	0.51 33

HSM	2.35 25	0.81 15	2.21 4	1.14 18	1.55 69	3.44 19	1.03 26	3.99 46	2.25 21	2.29 30	2.11 46	8.04 100	3.05 72	11.60 81	1.71 17	0.33 12	0.22 8	0.28 7	0.23 9	0.32 7	0.35 14

FADNet	2.49 26	1.76 80	13.17 39	1.17 21	0.95 27	3.27 13	0.88 16	3.94 43	2.27 26	1.40 2	1.29 2	2.51 27	2.06 47	9.93 61	1.94 28	0.43 35	0.45 59	0.40 26	0.43 49	1.06 90	0.53 35

FADNet-RVC	2.51 27	2.27 90	14.03 43	1.09 10	0.81 14	3.54 32	1.10 30	4.13 63	2.48 45	1.39 1	1.22 1	1.85 9	1.77 38	9.78 57	2.06 31	0.38 24	0.37 40	0.40 26	0.40 42	0.67 61	0.48 26

iResNet	2.52 28	0.94 24	21.17 101	1.84 89	0.72 9	3.75 45	0.81 10	4.14 66	2.55 51	2.33 33	2.03 36	1.35 5	1.44 15	3.27 10	2.16 35	0.35 18	0.23 10	0.28 7	0.22 8	0.44 17	0.45 23

NCCL2	2.53 29	1.35 59	10.90 30	1.15 19	0.97 30	3.53 30	2.56 78	3.65 15	2.23 19	2.76 51	1.80 25	2.41 22	1.66 32	10.35 66	2.07 32	0.44 38	0.40 45	0.57 65	0.53 67	0.59 45	0.65 58

ccs_rob	2.54 30	1.14 45	14.21 46	1.35 51	0.96 28	3.98 65	0.71 5	3.86 27	2.88 66	2.04 19	2.07 43	2.05 14	1.29 12	10.60 69	1.71 17	0.32 9	0.25 17	0.38 18	0.35 23	0.39 12	0.33 11

NLCA_NET_v2_RVC	2.54 30	1.04 33	16.73 70	1.53 70	2.00 96	3.96 62	0.87 15	3.87 29	2.31 30	2.54 39	1.67 12	2.85 43	1.63 30	5.55 28	1.62 10	0.46 42	0.33 25	0.38 18	0.38 33	0.53 34	0.62 51
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
NVstereo2D	2.54 30	0.90 19	11.10 31	1.32 42	1.47 64	3.58 33	1.38 43	4.13 63	2.45 42	1.90 14	1.71 17	2.86 44	1.76 37	10.86 73	1.66 14	0.60 65	0.43 54	0.53 59	0.41 44	0.78 72	0.94 86

CVANet_RVC	2.55 33	1.10 41	9.13 23	1.46 62	1.15 42	3.96 62	1.53 52	3.91 39	2.50 47	3.04 62	2.04 38	2.60 32	1.72 35	12.46 96	1.50 7	0.49 48	0.30 22	0.51 52	0.41 44	0.63 51	0.50 30

CC-Net-ROB	2.55 33	1.05 34	16.77 71	1.50 66	1.94 95	3.97 64	0.90 18	3.85 25	2.25 21	2.55 40	1.70 13	2.82 42	1.65 31	5.67 31	1.65 12	0.47 45	0.33 25	0.38 18	0.39 35	0.54 35	0.64 55

RASNet	2.56 35	0.70 9	10.06 26	1.60 75	2.25 102	3.67 40	1.00 25	3.38 9	3.14 75	1.81 9	2.11 46	4.60 84	2.30 53	10.25 65	2.61 44	0.37 20	0.24 13	0.25 3	0.24 13	0.28 3	0.26 5

DANet	2.63 36	1.07 37	8.10 17	1.83 88	1.70 82	3.25 10	1.16 34	4.31 84	2.38 36	3.40 73	2.18 50	4.08 71	1.80 40	9.77 56	3.89 64	0.52 58	0.55 73	0.59 68	0.44 50	0.63 51	0.86 80

iResNetv2_ROB	2.65 37	5.00 101	19.28 87	2.06 99	1.09 39	3.83 52	1.13 32	4.14 66	2.09 10	1.85 10	2.03 36	2.01 12	1.51 22	3.58 12	1.58 9	0.34 14	0.21 6	0.32 11	0.21 4	0.44 17	0.29 7

FADNet_RVC	2.65 37	2.28 91	16.51 66	0.98 4	0.92 25	4.09 71	0.83 11	4.26 76	1.73 6	1.46 3	1.78 24	1.60 7	1.50 21	8.88 46	2.39 39	0.47 45	0.38 42	0.44 35	0.58 75	0.89 80	0.97 87

PASM	2.67 39	2.05 88	9.71 25	1.73 84	1.37 57	2.53 2	1.52 51	3.10 3	2.17 15	2.79 54	1.84 26	2.90 45	1.85 43	11.89 91	2.19 36	0.81 89	0.85 98	0.87 99	1.01 101	1.14 94	1.05 91

PWC_ROB	2.69 40	2.01 85	8.36 18	1.33 44	1.90 93	3.23 8	1.18 35	4.33 86	3.25 82	3.11 65	3.41 84	2.71 33	2.18 51	9.15 50	4.48 67	0.56 62	0.31 24	0.61 73	0.36 26	0.69 64	0.74 68

RP	2.79 41	1.20 48	10.36 28	1.54 71	1.93 94	3.44 19	1.97 67	4.05 54	2.34 31	2.66 46	2.79 67	3.98 68	2.58 62	10.21 64	2.61 44	0.95 95	0.47 63	0.71 86	0.57 74	0.71 68	0.68 63

SHD	2.82 42	2.04 87	13.80 41	1.69 81	1.79 90	2.73 4	1.07 29	3.85 25	3.18 77	4.63 90	2.74 65	3.38 53	2.77 67	5.48 27	3.66 62	0.62 69	0.47 63	0.51 52	0.55 69	0.65 56	0.74 68

FADNet-RVC-Resample	2.83 43	2.02 86	21.07 98	1.12 15	0.91 24	3.53 30	1.37 42	4.08 58	2.09 10	1.68 7	1.33 3	2.03 13	1.51 22	9.54 53	1.69 16	0.38 24	0.37 40	0.42 31	0.44 50	0.56 41	0.56 41

StereoDRNet	2.84 44	1.49 70	14.48 49	1.33 44	1.57 74	3.44 19	2.06 69	3.69 18	2.36 34	3.26 69	1.84 26	2.79 41	1.80 40	10.88 74	3.16 58	0.41 33	0.33 25	0.39 23	0.36 26	0.59 45	0.58 47

PDISCO_ROB	2.86 45	1.53 71	14.27 48	1.98 96	2.19 99	4.52 87	1.35 41	4.65 97	3.19 78	2.09 22	2.16 48	3.53 58	2.45 60	5.32 26	2.12 34	1.30 106	0.78 95	0.72 89	0.59 77	1.24 100	1.28 96

AF-Net	2.87 46	1.30 57	16.24 61	1.33 44	1.75 86	3.26 11	1.59 56	4.09 59	2.43 39	2.69 47	2.94 71	4.51 81	3.20 76	5.84 33	2.75 49	0.76 84	0.36 35	0.72 89	0.49 58	0.70 65	0.56 41

HSM-Net_RVC	2.87 46	0.57 3	17.46 74	0.92 3	0.90 23	3.70 41	1.20 37	5.64 107	2.51 48	3.22 68	2.43 57	2.54 28	1.59 26	11.41 77	1.65 12	0.34 14	0.25 17	0.26 4	0.23 9	0.30 4	0.29 7
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
Nwc_Net	2.88 48	1.16 46	18.30 80	1.35 51	1.68 80	3.52 29	1.18 35	3.90 37	2.06 9	2.52 38	2.82 69	3.82 63	2.34 56	6.78 36	2.55 42	0.86 92	0.40 45	0.59 68	0.67 81	0.57 44	0.55 39

MFMNet_re	2.91 49	1.91 81	18.25 79	3.06 106	1.89 92	3.13 6	1.67 60	3.52 11	2.97 69	3.14 66	2.87 70	2.26 17	2.41 57	2.42 6	2.00 30	1.10 99	1.05 100	0.94 101	0.90 97	1.28 101	1.46 101

GANet	2.93 50	1.05 34	11.29 33	1.64 78	0.96 28	3.91 56	2.52 77	3.72 20	2.41 37	2.95 61	3.00 73	3.21 51	2.03 46	12.42 94	4.72 70	0.51 53	0.41 48	0.46 40	0.39 35	0.60 48	0.50 30

PSMNet_ROB	2.95 51	1.38 62	18.46 83	1.19 24	1.20 45	3.80 50	1.65 59	3.54 12	2.35 32	1.98 16	1.71 17	2.75 35	1.79 39	11.43 79	2.78 50	0.42 34	0.36 35	0.50 50	0.60 78	0.59 45	0.49 28

RTSC	2.95 51	2.51 93	15.49 60	1.54 71	1.30 53	3.51 28	0.86 12	3.89 32	2.48 45	3.64 76	3.12 79	3.04 49	2.30 53	5.70 32	6.67 78	0.55 61	0.35 34	0.37 16	0.39 35	0.70 65	0.60 49

PWCDC_ROB	2.95 51	3.26 95	14.06 44	1.59 74	1.56 72	4.19 77	0.86 12	4.30 83	4.08 94	2.06 21	7.37 91	2.75 35	2.42 58	3.59 13	2.67 47	1.34 108	0.36 35	0.39 23	0.34 20	1.13 93	0.62 51

SGM-Forest	2.96 54	0.62 7	3.49 12	1.09 10	0.80 11	4.06 68	3.30 89	4.37 87	3.09 73	4.18 85	3.27 81	4.67 85	3.86 87	11.69 86	7.91 87	0.51 53	0.46 61	0.49 48	0.39 35	0.48 25	0.49 28
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
XQC	2.96 54	2.42 92	14.85 51	1.36 54	1.32 55	3.29 14	1.53 52	4.28 78	2.78 58	3.21 67	2.46 58	3.53 58	2.92 70	6.58 34	4.67 69	0.70 81	0.41 48	0.59 68	0.56 71	0.96 87	0.83 79

RYNet	2.97 56	0.93 22	15.09 56	1.12 15	1.38 59	3.66 39	1.42 48	4.03 50	2.29 28	2.10 23	1.70 13	3.79 62	2.16 50	13.64 101	3.02 56	0.37 20	0.41 48	0.48 45	0.34 20	0.81 75	0.75 70

DRN-Test	3.03 57	0.99 29	18.18 78	1.35 51	1.60 75	3.59 34	1.48 49	4.13 63	2.45 42	3.47 75	1.92 31	2.58 31	2.53 61	10.88 74	2.80 51	0.39 28	0.36 35	0.44 35	0.41 44	0.55 38	0.57 44

AANet_RVC	3.03 57	1.72 77	12.78 37	1.17 21	1.12 40	3.79 49	1.10 30	3.86 27	3.13 74	2.62 44	4.26 89	3.96 67	1.41 13	11.43 79	5.23 76	0.94 94	0.47 63	0.34 15	0.23 9	0.40 13	0.55 39

Anonymous Stereo	3.09 59	3.14 94	16.69 69	1.52 69	1.25 49	3.33 15	2.95 82	4.02 49	2.42 38	2.35 34	2.04 38	2.57 30	1.27 9	11.88 90	2.92 53	0.49 48	0.49 69	0.57 65	0.56 71	0.70 65	0.65 58

PA-Net	3.09 59	1.24 54	18.84 84	1.38 57	1.19 44	3.42 18	1.75 62	3.89 32	3.27 85	1.88 12	2.10 44	3.03 48	1.88 44	11.60 81	2.88 52	0.40 30	0.54 71	0.44 35	0.62 79	0.52 30	1.02 89
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
stereogan	3.09 59	0.93 22	16.43 64	1.37 56	1.66 79	3.70 41	1.67 60	3.87 29	3.15 76	3.04 62	2.70 64	5.04 91	2.79 68	9.96 62	1.78 24	0.63 70	0.49 69	0.69 82	0.47 54	0.83 78	0.68 63

RGC	3.10 62	1.45 68	14.90 53	1.46 62	1.79 90	3.33 15	1.39 44	4.04 52	2.26 24	2.77 52	3.26 80	4.00 69	2.71 66	11.63 84	2.62 46	0.82 90	0.46 61	0.79 94	0.79 93	0.71 68	0.78 75

PVD	3.12 63	1.97 84	12.80 38	1.79 86	1.63 77	3.83 52	1.34 40	3.48 10	3.88 93	5.77 94	3.56 85	3.45 54	3.29 78	5.58 30	5.30 77	0.71 82	0.69 92	0.74 92	0.77 90	0.79 74	1.13 93

NCC-stereo	3.16 64	1.28 55	18.31 81	1.25 31	1.75 86	4.06 68	1.40 46	3.89 32	2.22 17	2.72 49	2.55 59	3.49 55	2.65 64	9.91 59	2.99 54	1.29 104	0.65 87	0.64 78	0.86 95	0.65 56	0.71 65

Abc-Net	3.16 64	1.28 55	18.31 81	1.25 31	1.75 86	4.06 68	1.40 46	3.89 32	2.22 17	2.72 49	2.55 59	3.49 55	2.65 64	9.91 59	2.99 54	1.29 104	0.65 87	0.64 78	0.86 95	0.65 56	0.71 65

GANetREF_RVC	3.20 66	1.93 82	18.84 84	1.51 68	0.80 11	4.12 75	1.82 64	4.16 68	2.91 68	1.87 11	1.74 21	2.48 24	1.84 42	13.18 98	1.74 23	0.80 87	0.57 75	0.79 94	0.74 87	1.20 99	0.86 80
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 67	1.02 31	9.21 24	1.49 65	1.07 37	3.78 46	1.48 49	4.31 84	3.08 72	2.46 35	2.32 56	4.20 75	2.08 48	15.19 106	7.16 86	0.84 91	0.62 84	1.33 107	0.92 98	0.99 88	1.31 97

ADCRef	3.30 68	1.23 53	14.90 53	1.17 21	1.41 62	3.95 61	1.64 58	3.88 31	2.36 34	3.05 64	2.21 53	2.40 21	2.81 69	5.04 22	16.63 100	0.46 42	0.34 32	0.63 75	0.62 79	0.54 35	0.67 61

CBMV	3.32 69	0.96 28	21.16 100	1.32 42	0.83 17	3.81 51	3.91 99	4.47 92	2.53 49	3.34 72	2.24 54	4.45 79	2.58 62	10.07 63	1.96 29	0.45 41	0.43 54	0.48 45	0.38 33	0.52 30	0.53 35
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
edge stereo	3.37 70	1.01 30	21.99 106	1.28 37	1.37 57	2.67 3	1.39 44	3.98 45	2.87 65	2.93 59	3.27 81	4.35 78	2.43 59	11.62 83	2.39 39	0.60 65	0.63 85	0.79 94	0.55 69	0.68 62	0.67 61

DPSNet	3.43 71	1.37 61	12.19 34	1.39 58	1.24 47	4.15 76	2.06 69	4.51 93	3.34 86	2.69 47	1.96 32	3.52 57	5.56 95	10.68 70	9.68 91	0.93 93	0.73 93	0.40 26	0.37 32	1.06 90	0.86 80

SAMSARA	3.55 72	2.16 89	14.18 45	1.86 91	1.49 65	4.62 88	2.65 79	3.58 14	2.80 61	7.05 100	3.05 76	5.16 92	4.07 88	9.18 51	4.80 71	0.64 72	0.67 90	0.59 68	0.68 83	0.89 80	0.91 85

CBMV_ROB	3.62 73	0.70 9	21.42 104	1.21 27	0.89 22	4.43 85	1.76 63	4.39 88	2.79 59	5.88 95	3.38 83	4.18 74	5.12 93	10.75 71	2.32 38	0.56 62	0.55 73	0.53 59	0.51 62	0.55 38	0.46 24

DeepPrunerF	3.70 74	1.72 77	14.87 52	1.33 44	1.74 85	3.70 41	1.55 55	4.20 71	14.00 109	2.02 18	1.77 22	2.74 34	2.30 53	10.88 74	6.88 83	0.68 79	0.60 81	0.78 93	0.78 92	0.81 75	0.71 65

ADCP+	3.74 75	1.96 83	16.90 72	1.09 10	1.55 69	4.35 82	3.65 96	3.93 40	2.98 70	2.22 28	1.66 11	3.95 66	4.83 92	3.41 11	18.87 106	0.38 24	0.41 48	0.63 75	0.53 67	0.64 55	0.81 76

ADCPNet	3.89 76	1.63 74	20.25 92	1.28 37	1.27 52	6.43 101	2.65 79	3.93 40	2.70 55	2.78 53	2.20 52	4.28 76	6.16 98	5.56 29	10.27 94	0.65 74	1.18 103	0.71 86	1.70 108	0.84 79	1.37 99

RTS	3.90 77	7.04 105	17.94 76	1.92 93	2.19 99	5.00 93	2.96 85	4.29 79	3.19 78	4.05 82	3.08 77	4.94 88	3.16 74	7.64 39	6.72 79	0.66 75	0.44 56	0.51 52	0.47 54	0.95 83	0.76 71

RTSA	3.90 77	7.04 105	17.94 76	1.92 93	2.19 99	5.00 93	2.96 85	4.29 79	3.19 78	4.05 82	3.08 77	4.94 88	3.16 74	7.64 39	6.72 79	0.66 75	0.44 56	0.51 52	0.47 54	0.95 83	0.76 71

MFN_U_SF_DS_RVC	3.94 79	10.88 113	12.30 35	1.26 33	1.55 69	5.19 95	3.33 90	4.42 89	2.82 64	2.58 41	2.62 62	5.00 90	2.11 49	14.03 104	2.73 48	1.28 103	2.34 110	0.67 81	1.83 109	0.81 75	1.03 90

ADCMid	3.96 80	3.27 96	15.48 59	1.26 33	1.40 60	4.66 89	2.28 74	4.06 55	3.54 88	3.94 81	3.02 74	3.85 64	3.67 84	4.03 16	18.65 104	0.68 79	0.61 83	1.09 105	1.18 104	1.38 103	1.08 92

SPS-STEREO	3.97 81	1.44 67	20.85 96	1.72 83	1.71 83	3.24 9	2.30 75	3.55 13	2.68 54	4.79 91	3.66 86	5.44 93	2.97 71	9.11 49	9.35 90	1.15 100	1.10 101	1.06 104	1.02 102	1.16 96	1.18 94
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
MADNet+	3.97 81	9.54 110	13.83 42	1.87 92	1.69 81	4.19 77	2.87 81	4.80 101	4.10 95	2.51 37	2.67 63	4.55 83	3.41 80	8.72 43	6.92 84	1.25 102	1.27 106	1.04 102	0.93 99	1.39 104	1.85 107

NaN_ROB	4.06 83	1.13 44	16.64 67	1.12 15	1.25 49	3.50 27	3.05 87	4.29 79	3.26 83	3.64 76	2.56 61	2.97 46	4.79 91	13.33 99	16.91 101	0.37 20	0.41 48	0.38 18	0.39 35	0.48 25	0.81 76

STTStereo_v2	4.11 84	1.21 49	19.47 89	2.31 102	2.94 106	4.74 90	2.95 82	3.33 7	2.14 12	4.44 87	9.43 100	6.93 96	3.84 85	9.01 47	4.92 73	0.79 85	0.80 96	0.70 84	0.50 59	0.95 83	0.87 83

G-Net	4.11 84	1.21 49	19.47 89	2.31 102	2.94 106	4.74 90	2.95 82	3.33 7	2.14 12	4.44 87	9.43 100	6.93 96	3.84 85	9.01 47	4.92 73	0.79 85	0.80 96	0.70 84	0.50 59	0.95 83	0.87 83

AnyNet_C32	4.17 86	5.75 103	12.62 36	1.60 75	1.54 66	5.51 96	4.09 100	3.90 37	3.23 81	4.22 86	3.03 75	3.10 50	3.41 80	8.01 41	18.85 105	0.53 60	0.58 77	0.53 59	0.74 87	1.19 98	0.97 87

ADCS	4.22 87	5.33 102	16.32 62	1.64 78	1.31 54	3.91 56	3.64 95	4.21 73	2.89 67	5.11 92	2.81 68	4.46 80	3.06 73	5.21 25	19.50 107	0.63 70	0.64 86	0.69 82	0.69 85	1.12 92	1.26 95

ADCL	4.35 88	1.63 74	14.21 46	1.29 40	1.26 51	5.56 97	11.16 106	3.97 44	3.37 87	6.07 96	4.67 90	4.30 77	5.72 96	5.08 23	14.51 97	0.50 51	0.48 67	0.89 100	0.77 90	0.78 72	0.76 71

AnyNet_C01	4.37 89	7.80 107	16.40 63	1.98 96	1.40 60	6.37 100	6.47 102	4.09 59	5.03 100	4.06 84	3.78 87	4.16 73	3.31 79	6.62 35	10.00 92	0.61 67	0.67 90	0.63 75	0.67 81	2.01 106	1.31 97

MSC_U_SF_DS_RVC	4.48 90	10.62 112	15.18 57	1.55 73	1.45 63	6.16 99	3.54 94	5.41 106	3.68 91	2.59 42	2.74 65	4.81 86	2.20 52	13.79 102	4.40 66	2.04 109	3.36 111	1.05 103	1.20 105	2.09 107	1.72 106

SGM_RVC	4.57 91	0.72 11	17.46 74	1.84 89	0.58 5	4.23 79	3.45 91	4.66 98	3.66 90	7.99 101	8.82 99	7.56 99	8.74 104	12.30 93	6.84 82	0.43 35	0.38 42	0.39 23	0.35 23	0.51 28	0.48 26
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DispFullNet	4.64 92	3.48 97	23.95 109	3.99 107	3.03 108	3.35 17	1.06 28	4.18 69	2.80 61	6.33 98	2.94 71	4.04 70	7.81 101	3.92 15	5.16 75	2.75 110	1.61 109	3.10 111	2.68 110	3.32 108	3.31 110

CSAN	4.71 93	1.39 64	20.23 91	1.19 24	0.87 19	3.65 38	3.50 93	4.57 95	3.87 92	3.89 80	7.82 94	3.54 61	6.16 98	23.47 114	6.82 81	0.52 58	0.45 59	0.49 48	0.52 64	0.63 51	0.66 60

SuperB	4.79 94	4.41 100	21.08 99	1.45 61	1.08 38	3.91 56	3.08 88	4.18 69	3.01 71	2.50 36	2.10 44	2.75 35	3.49 83	8.80 45	19.60 108	0.44 38	0.33 25	0.53 59	0.39 35	12.01 114	0.64 55

NVStereoNet_ROB	4.91 95	1.40 65	16.46 65	1.47 64	1.72 84	3.46 22	1.96 66	3.89 32	3.61 89	2.90 57	24.61 115	4.81 86	4.49 90	13.57 100	4.87 72	1.33 107	1.29 107	1.70 109	1.68 107	1.41 105	1.66 103
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 96	0.90 19	21.33 102	1.50 66	1.33 56	4.09 71	3.49 92	4.03 50	4.59 97	6.57 99	10.95 102	12.92 105	8.41 103	9.86 58	8.78 89	0.75 83	0.58 77	0.51 52	0.50 59	0.68 62	0.57 44

MeshStereo	5.35 97	1.32 58	21.34 103	1.34 48	1.15 42	4.40 84	3.90 98	5.18 102	4.55 96	10.14 107	11.82 104	11.47 103	5.84 97	13.84 103	7.03 85	0.61 67	0.58 77	0.60 72	0.58 75	0.72 70	0.59 48
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 98	1.22 52	17.40 73	1.60 75	2.41 103	3.48 26	2.12 72	4.52 94	3.26 83	3.67 78	13.15 105	22.10 110	3.46 82	11.92 92	17.16 102	0.34 14	0.21 6	0.23 1	0.19 3	0.36 10	0.28 6

MDST_ROB	5.53 99	0.59 6	23.11 107	2.55 104	2.69 104	21.20 111	1.98 68	4.77 100	2.65 52	9.36 104	4.25 88	4.14 72	4.45 89	22.57 110	3.68 63	0.48 47	0.33 25	0.47 41	0.44 50	0.44 17	0.44 22

MSMD_ROB	5.61 100	1.08 38	16.68 68	1.21 27	0.83 17	10.14 106	2.07 71	3.93 40	6.22 103	10.37 108	8.03 96	22.22 111	9.45 105	11.66 85	4.52 68	0.66 75	0.58 77	0.66 80	0.68 83	0.65 56	0.63 54

SANet	5.65 101	1.72 77	20.94 97	1.26 33	0.88 20	7.20 103	3.81 97	4.60 96	11.23 106	4.56 89	8.78 98	7.49 98	6.95 100	11.85 89	18.11 103	0.49 48	0.44 56	0.50 50	0.42 48	0.90 82	0.82 78

ELAS_RVC	5.72 102	1.64 76	20.40 94	2.09 100	2.01 97	4.95 92	11.47 107	4.66 98	6.31 104	8.09 102	8.01 95	8.06 102	11.65 107	8.36 42	10.22 93	0.99 98	1.17 102	0.81 97	0.76 89	1.14 94	1.58 102
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
PWCK	5.85 103	9.95 111	15.22 58	2.90 105	1.56 72	5.66 98	7.69 103	5.27 104	4.74 98	3.88 79	7.51 92	8.04 100	5.28 94	15.18 105	8.40 88	3.08 111	1.51 108	2.24 110	1.37 106	5.55 110	1.96 108

ELAS	5.99 104	1.62 73	20.29 93	1.96 95	2.01 97	15.09 110	8.38 105	5.23 103	5.54 101	9.02 103	8.61 97	6.74 95	9.75 106	7.48 38	11.40 95	0.96 96	1.18 103	0.72 89	0.93 99	1.30 102	1.67 104
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
FC-DCNN	6.64 105	0.77 14	19.26 86	1.68 80	1.64 78	8.27 105	2.35 76	6.35 108	9.23 105	10.85 109	11.27 103	19.89 108	13.33 108	12.51 97	11.79 96	0.66 75	0.57 75	0.58 67	0.56 71	0.63 51	0.61 50

MADNet++	7.57 106	5.80 104	13.28 40	6.38 110	5.26 110	7.18 102	7.77 104	5.32 105	5.62 102	6.11 97	7.78 93	6.02 94	8.01 102	11.74 87	16.36 99	9.36 114	4.95 112	8.57 114	5.66 112	4.83 109	5.34 111

SGM+DAISY	8.36 107	3.82 99	24.83 110	2.02 98	1.75 86	14.80 109	17.22 109	4.27 77	11.79 108	13.88 111	14.38 108	11.74 104	13.83 109	10.56 68	15.12 98	1.21 101	1.19 105	1.14 106	1.13 103	1.17 97	1.37 99

MANE	11.39 108	1.38 62	23.16 108	4.82 109	2.79 105	22.02 112	14.51 108	25.81 113	26.28 114	10.93 110	13.17 106	19.90 109	18.03 110	15.69 107	20.41 109	0.96 96	0.74 94	1.42 108	3.24 111	0.74 71	1.71 105

SGM-ForestM	11.55 109	1.16 46	21.62 105	2.14 101	0.81 14	22.25 113	19.08 110	16.50 112	11.34 107	15.94 114	14.44 109	27.12 114	22.61 115	24.76 115	28.39 114	0.50 51	0.48 67	0.47 41	0.44 50	0.55 38	0.53 35

LSM	12.71 110	3.52 98	20.79 95	55.63 118	84.76 118	3.63 37	6.30 101	3.73 21	4.93 99	5.55 93	13.92 107	3.53 58	3.25 77	9.72 55	4.12 65	0.64 72	0.85 98	0.71 86	0.81 94	1.03 89	26.84 116

DGTPSM_ROB	15.31 111	8.85 108	28.14 113	9.90 112	18.89 111	12.44 107	33.21 113	13.48 110	17.86 112	9.50 105	22.89 113	13.78 106	19.67 111	21.26 108	23.68 110	5.08 112	8.84 113	5.62 112	11.50 113	6.72 111	14.87 112

DPSMNet_ROB	15.52 112	8.89 109	31.44 114	10.07 113	18.91 112	12.44 107	33.22 114	13.51 111	17.94 113	9.58 106	22.91 114	13.79 107	19.67 111	21.43 109	23.73 111	5.17 113	8.84 113	5.63 113	11.52 116	6.79 112	14.90 113

LE_ROB	18.58 113	1.21 49	48.38 116	8.60 111	23.00 115	7.32 104	19.74 111	9.94 109	38.69 116	43.53 116	21.91 112	28.40 115	40.92 117	23.18 111	54.71 117	0.34 14	0.33 25	0.32 11	0.36 26	0.42 15	0.40 18

DPSM	20.78 114	20.03 114	26.57 111	23.62 114	22.46 113	31.10 114	39.97 116	33.52 115	17.41 110	15.25 112	15.80 110	24.06 112	20.71 113	23.30 112	27.62 112	10.78 115	9.41 115	11.69 115	11.50 113	14.49 115	16.36 114

DPSM_ROB	20.78 114	20.03 114	26.57 111	23.62 114	22.46 113	31.10 114	39.97 116	33.52 115	17.41 110	15.25 112	15.80 110	24.06 112	20.71 113	23.30 112	27.62 112	10.78 115	9.41 115	11.69 115	11.50 113	14.49 115	16.36 114

AVERAGE_ROB	44.46 116	45.91 117	46.59 115	40.59 117	38.66 116	32.99 116	30.52 112	46.19 117	43.26 117	49.59 117	48.97 117	44.62 116	39.89 116	45.04 116	43.94 115	48.74 118	50.69 118	50.80 118	49.79 118	45.54 118	46.97 117

MEDIAN_ROB	47.37 117	49.08 118	49.92 117	40.32 116	39.97 117	34.95 117	33.43 115	49.17 118	46.39 118	53.06 118	52.65 118	47.65 117	42.23 119	48.35 118	47.29 116	52.10 119	54.04 119	54.28 119	53.48 119	48.76 119	50.33 118

LSM0	49.58 118	40.04 116	53.62 118	97.15 119	128.68 120	62.75 118	79.89 119	67.63 119	35.11 115	33.02 115	32.61 116	48.43 118	41.60 118	46.60 117	55.43 118	21.63 117	18.99 117	23.62 117	23.13 117	29.47 117	52.26 119

BEATNet-Init1	49.72 119	64.32 119	97.77 119	4.68 108	4.28 109	84.75 119	60.42 118	73.38 120	68.59 119	88.29 119	88.51 120	91.94 119	97.99 120	85.26 120	70.96 119	0.80 87	0.65 87	0.85 98	0.70 86	7.96 113	2.33 109

DPSimNet_ROB	102.30 120	119.72 120	143.59 120	109.42 120	94.10 119	127.22 120	127.15 120	26.62 114	102.36 120	149.58 120	83.99 119	139.34 120	137.54 121	64.62 119	82.36 120	105.18 120	66.15 120	120.15 120	69.61 120	99.58 120	77.72 120

MSMDNet													1.11 6

Low-res two-view benchmark