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	0.36 1	0.21 1	1.70 7	0.29 3	0.18 4	0.55 1	0.26 8	0.78 5	0.36 1	0.57 11	0.67 29	0.33 1	0.24 1	0.32 1	0.23 1	0.10 3	0.08 4	0.09 2	0.07 3	0.09 1	0.12 5

R-Stereo Train	0.36 1	0.21 1	1.70 7	0.29 3	0.18 4	0.55 1	0.26 8	0.78 5	0.36 1	0.57 11	0.67 29	0.33 1	0.24 1	0.32 1	0.23 1	0.10 3	0.08 4	0.09 2	0.07 3	0.09 1	0.12 5

AdaStereo	0.44 3	0.38 5	1.79 14	0.34 16	0.20 7	0.57 5	0.28 13	0.92 36	0.69 13	0.52 7	0.55 5	0.61 10	0.33 4	0.48 3	0.39 5	0.17 29	0.08 4	0.17 31	0.11 13	0.18 8	0.12 5
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi: AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching. ArXiv
HITNet	0.46 4	0.92 40	2.09 26	0.33 10	0.14 1	0.55 1	0.22 2	0.83 17	0.56 5	0.51 6	0.49 1	0.48 5	0.37 8	0.48 3	0.54 23	0.09 1	0.06 1	0.10 7	0.08 7	0.23 22	0.09 1

DN-CSS_ROB	0.48 5	1.23 76	1.99 23	0.39 35	0.22 13	0.56 4	0.19 1	0.75 3	0.61 7	0.45 1	0.62 20	0.41 3	0.31 3	0.70 5	0.32 3	0.11 6	0.08 4	0.14 14	0.13 15	0.36 53	0.12 5

MLCV	0.48 5	0.89 39	1.85 16	0.29 3	0.14 1	0.59 9	0.23 6	0.72 2	0.59 6	0.66 29	0.66 28	0.55 6	0.36 5	1.03 16	0.49 19	0.09 1	0.06 1	0.09 2	0.07 3	0.19 12	0.11 2

StereoDRNet-Refined	0.50 7	0.41 7	1.50 2	0.33 10	0.21 11	0.68 29	0.26 8	0.81 10	0.68 12	0.67 31	0.68 34	0.73 26	0.42 18	1.10 20	0.59 27	0.11 6	0.08 4	0.14 14	0.14 20	0.25 29	0.21 26
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
iResNet_ROB	0.50 7	1.15 72	1.73 10	0.31 8	0.21 11	0.58 6	0.22 2	0.95 47	0.82 41	0.58 15	0.62 20	0.57 7	0.39 13	0.84 10	0.40 6	0.11 6	0.08 4	0.09 2	0.06 1	0.21 15	0.13 9

DeepPruner_ROB	0.50 7	0.70 20	1.94 20	0.29 3	0.26 23	0.63 17	0.31 22	0.80 8	0.48 3	0.61 19	0.55 5	0.66 18	0.38 9	0.98 12	0.40 6	0.19 39	0.14 32	0.14 14	0.14 20	0.23 22	0.17 20

iResNet	0.51 10	0.76 23	2.59 56	0.41 44	0.17 3	0.64 21	0.27 12	0.82 13	0.70 16	0.57 11	0.64 27	0.43 4	0.36 5	0.70 5	0.52 21	0.10 3	0.07 3	0.09 2	0.08 7	0.21 15	0.13 9

CFNet_RVC	0.51 10	0.76 23	1.79 14	0.28 2	0.30 32	0.63 17	0.28 13	0.66 1	0.55 4	0.45 1	0.57 9	0.74 27	0.41 15	1.46 30	0.42 9	0.14 20	0.10 16	0.28 65	0.13 15	0.18 8	0.14 13

DLCB_ROB	0.54 12	0.55 13	1.49 1	0.36 22	0.25 17	0.65 24	0.36 28	0.80 8	0.70 16	0.72 36	0.69 37	0.83 37	0.46 22	1.53 34	0.60 28	0.13 15	0.12 27	0.14 14	0.14 20	0.14 4	0.13 9

NLCA_NET_v2_RVC	0.55 13	0.92 40	2.36 42	0.38 31	0.37 50	0.60 10	0.28 13	0.82 13	0.62 9	0.62 22	0.55 5	0.72 23	0.45 21	1.00 14	0.43 12	0.15 22	0.11 19	0.14 14	0.14 20	0.15 6	0.16 16
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
CC-Net-ROB	0.55 13	0.93 45	2.43 46	0.37 25	0.36 45	0.60 10	0.29 18	0.81 10	0.61 7	0.63 25	0.56 8	0.72 23	0.46 22	1.03 16	0.44 13	0.15 22	0.11 19	0.14 14	0.14 20	0.16 7	0.16 16

DISCO	0.57 15	0.34 3	1.87 17	0.45 52	0.34 39	0.84 49	0.35 27	0.84 19	0.78 34	0.59 17	0.63 23	1.06 61	0.53 31	1.10 20	0.82 39	0.12 11	0.09 12	0.11 12	0.10 12	0.22 18	0.18 23

ccs_rob	0.57 15	0.99 55	1.97 22	0.32 9	0.20 7	0.63 17	0.22 2	0.82 13	0.72 21	0.53 9	0.61 16	0.61 10	0.38 9	1.98 50	0.44 13	0.12 11	0.09 12	0.14 14	0.14 20	0.28 39	0.16 16

NVstereo2D	0.58 17	0.40 6	2.03 25	0.35 19	0.35 41	0.69 30	0.39 29	0.91 31	0.80 37	0.47 4	0.53 2	0.67 19	0.46 22	1.99 51	0.48 17	0.19 39	0.11 19	0.17 31	0.13 15	0.25 29	0.25 39

CFNet	0.58 17	0.96 50	1.78 13	0.33 10	0.25 17	0.63 17	0.22 2	0.88 27	0.75 27	0.46 3	0.59 12	0.58 8	0.40 14	2.29 58	0.48 17	0.12 11	0.09 12	0.15 22	0.14 20	0.30 42	0.15 14

iResNetv2_ROB	0.59 19	1.43 84	2.96 66	0.40 41	0.22 13	0.58 6	0.28 13	0.91 31	0.73 23	0.57 11	0.63 23	0.60 9	0.41 15	0.74 7	0.35 4	0.11 6	0.08 4	0.10 7	0.07 3	0.43 66	0.17 20

ETE_ROB	0.60 20	0.71 21	1.55 3	0.37 25	0.25 17	0.69 30	0.51 41	1.47 80	0.70 16	0.80 43	0.69 37	0.85 40	0.49 28	1.36 29	0.58 26	0.16 26	0.13 30	0.19 39	0.16 34	0.21 15	0.23 35

LALA_ROB	0.60 20	0.76 23	1.69 6	0.36 22	0.29 29	0.79 44	0.55 45	0.94 40	0.77 32	0.81 46	0.67 29	0.94 51	0.49 28	0.99 13	0.73 35	0.21 46	0.15 38	0.20 45	0.18 43	0.23 22	0.21 26

ccs	0.60 20	1.05 61	2.11 29	0.35 19	0.20 7	0.61 14	0.24 7	0.78 5	0.70 16	0.50 5	0.61 16	0.61 10	0.36 5	2.53 68	0.44 13	0.12 11	0.10 16	0.13 13	0.13 15	0.24 27	0.13 9

XPNet_ROB	0.61 23	0.72 22	1.66 5	0.37 25	0.27 26	0.67 26	0.47 37	0.89 28	0.71 20	0.78 41	0.70 40	0.81 36	0.53 31	1.70 40	0.76 36	0.21 46	0.16 39	0.16 25	0.15 31	0.20 13	0.21 26

HSM	0.62 24	0.76 23	1.72 9	0.30 7	0.29 29	0.64 21	0.28 13	0.94 40	0.62 9	0.58 15	0.57 9	1.40 72	0.64 47	2.59 70	0.42 9	0.11 6	0.08 4	0.10 7	0.08 7	0.14 4	0.11 2

StereoDRNet	0.64 25	0.88 38	2.35 41	0.41 44	0.45 64	0.73 37	0.47 37	0.94 40	0.74 25	0.75 39	0.54 4	0.74 27	0.47 25	1.70 40	0.72 33	0.15 22	0.14 32	0.15 22	0.14 20	0.20 13	0.19 24

DRN-Test	0.64 25	0.43 11	2.44 48	0.39 35	0.44 60	0.80 45	0.41 30	1.02 61	0.78 34	0.80 43	0.63 23	0.69 21	0.56 35	1.79 46	0.69 31	0.15 22	0.12 27	0.17 31	0.16 34	0.18 8	0.17 20

NOSS_ROB	0.64 25	0.65 17	1.62 4	0.46 55	0.25 17	0.67 26	0.30 20	0.93 38	0.77 32	0.59 17	0.57 9	0.77 30	0.38 9	2.78 77	0.44 13	0.23 59	0.24 65	0.28 65	0.26 68	0.27 37	0.39 67

NCCL2	0.65 28	0.94 46	1.96 21	0.43 49	0.30 32	0.71 33	0.68 58	0.84 19	0.65 11	0.70 33	0.60 14	0.86 42	0.54 33	1.56 35	0.57 25	0.20 44	0.16 39	0.67 87	0.25 64	0.22 18	0.24 37

CVANet_RVC	0.65 28	0.80 33	2.13 31	0.36 22	0.26 23	0.62 15	0.42 32	0.84 19	0.72 21	0.69 32	0.68 34	0.68 20	0.52 30	2.64 71	0.42 9	0.19 39	0.12 27	0.22 52	0.17 39	0.37 58	0.15 14

PSMNet_ROB	0.66 30	1.01 60	2.33 39	0.38 31	0.31 35	0.72 35	0.55 45	0.86 22	0.69 13	0.54 10	0.60 14	0.75 29	0.47 25	2.00 52	0.82 39	0.17 29	0.13 30	0.19 39	0.19 46	0.24 27	0.27 43

TDLM	0.67 31	0.92 40	1.76 11	0.37 25	0.24 16	0.66 25	0.56 47	0.82 13	0.74 25	1.16 65	0.62 20	0.61 10	0.48 27	2.70 74	0.50 20	0.20 44	0.11 19	0.35 72	0.14 20	0.32 48	0.16 16

HSM-Net_RVC	0.67 31	0.34 3	2.23 36	0.26 1	0.19 6	0.67 26	0.29 18	1.26 74	0.69 13	0.72 36	0.70 40	0.78 33	0.42 18	3.77 85	0.41 8	0.13 15	0.09 12	0.10 7	0.09 10	0.12 3	0.11 2
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
AANet_RVC	0.68 33	1.00 57	2.09 26	0.33 10	0.25 17	0.58 6	0.32 25	0.81 10	0.80 37	0.61 19	0.94 53	0.80 35	0.41 15	2.46 66	1.02 47	0.36 79	0.14 32	0.10 7	0.09 10	0.23 22	0.22 31

stereogan	0.69 34	0.51 12	2.44 48	0.39 35	0.39 52	1.01 60	0.41 30	0.94 40	0.92 52	0.73 38	0.85 50	1.15 68	0.69 48	1.57 36	0.53 22	0.19 39	0.18 47	0.20 45	0.16 34	0.36 53	0.24 37

Anonymous Stereo	0.70 35	1.08 65	2.47 51	0.42 47	0.36 45	0.60 10	0.78 64	0.83 17	0.75 27	0.65 28	0.63 23	0.62 15	0.38 9	2.43 64	0.78 38	0.19 39	0.18 47	0.19 39	0.20 51	0.30 42	0.23 35

RYNet	0.70 35	0.56 14	2.25 37	0.35 19	0.41 58	0.86 51	0.43 33	0.94 40	0.82 41	0.62 22	0.53 2	0.91 49	0.60 40	2.70 74	1.00 46	0.14 20	0.11 19	0.16 25	0.14 20	0.25 29	0.27 43

PWC_ROB	0.70 35	0.94 46	1.88 18	0.39 35	0.39 52	0.62 15	0.30 20	0.98 53	0.94 53	0.91 52	1.29 65	0.77 30	0.62 45	1.62 38	1.03 49	0.18 34	0.11 19	0.18 35	0.13 15	0.51 79	0.22 31

CBMV	0.71 38	0.76 23	2.64 57	0.33 10	0.20 7	0.78 41	0.79 65	0.98 53	0.78 34	0.85 49	0.71 42	0.86 42	1.10 65	1.46 30	0.62 29	0.18 34	0.20 54	0.21 50	0.19 46	0.25 29	0.33 58
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
RTSC	0.72 39	1.00 57	2.22 34	0.40 41	0.29 29	0.82 47	0.31 22	0.96 48	1.10 67	0.92 54	0.81 46	0.78 33	0.58 37	1.23 25	1.55 59	0.18 34	0.17 46	0.14 14	0.17 39	0.43 66	0.30 48

PWCDC_ROB	0.72 39	1.06 63	2.09 26	0.47 57	0.36 45	0.78 41	0.26 8	0.98 53	1.34 74	0.61 19	2.07 76	0.64 16	0.61 41	0.83 8	0.71 32	0.31 74	0.14 32	0.15 22	0.12 14	0.60 83	0.21 26

PASM	0.72 39	0.68 19	1.91 19	0.47 57	0.44 60	0.60 10	0.51 41	0.76 4	0.80 37	0.77 40	0.67 29	0.87 44	0.61 41	2.40 62	0.67 30	0.32 75	0.35 77	0.36 74	0.41 80	0.45 70	0.39 67

SHD	0.72 39	0.77 28	2.36 42	0.45 52	0.40 56	0.71 33	0.32 25	1.02 61	1.25 73	1.07 60	0.84 49	0.90 48	0.73 51	1.09 19	0.98 44	0.21 46	0.18 47	0.20 45	0.21 53	0.32 48	0.31 51

XQC	0.73 43	0.98 54	2.39 45	0.46 55	0.35 41	0.81 46	0.46 35	1.00 58	1.00 58	0.80 43	0.69 37	0.88 46	0.74 52	1.32 27	1.20 52	0.24 61	0.16 39	0.23 57	0.20 51	0.41 65	0.33 58

GANet	0.75 44	0.84 35	2.01 24	0.40 41	0.25 17	0.74 38	0.66 56	0.87 24	0.73 23	0.71 35	1.07 61	0.84 38	0.59 38	2.71 76	1.15 50	0.17 29	0.16 39	0.44 81	0.16 34	0.31 46	0.20 25

PDISCO_ROB	0.75 44	1.06 63	2.11 29	0.64 76	0.59 79	1.13 66	0.43 33	1.14 70	1.05 62	0.64 27	0.71 42	0.96 53	0.61 41	1.21 24	0.76 36	0.37 80	0.18 47	0.27 63	0.24 58	0.56 81	0.32 53

GANetREF_RVC	0.75 44	1.15 72	2.46 50	0.47 57	0.26 23	0.83 48	0.61 52	0.97 52	0.89 48	0.62 22	0.61 16	0.72 23	0.59 38	2.42 63	0.55 24	0.32 75	0.25 69	0.36 74	0.24 58	0.39 62	0.31 51
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
CBMV_ROB	0.76 47	0.42 8	2.68 59	0.33 10	0.22 13	0.74 38	0.47 37	0.96 48	0.76 30	1.04 58	0.96 55	0.88 46	0.93 58	2.06 54	1.19 51	0.22 57	0.24 65	0.27 63	0.25 64	0.25 29	0.28 45

ADCRef	0.80 48	0.96 50	2.29 38	0.37 25	0.35 41	0.96 55	0.51 41	0.87 24	0.75 27	0.88 50	0.83 48	0.61 10	1.36 73	1.02 15	3.27 80	0.13 15	0.11 19	0.17 31	0.19 46	0.25 29	0.21 26

DANet	0.80 48	0.65 17	2.21 33	1.19 87	0.48 66	0.74 38	1.25 76	0.89 28	1.53 79	0.89 51	0.71 42	0.96 53	0.56 35	1.83 47	0.96 43	0.18 34	0.16 39	0.19 39	0.16 34	0.26 35	0.29 46

FBW_ROB	0.83 50	0.77 28	1.77 12	0.44 50	0.30 32	0.87 53	0.46 35	1.10 68	0.91 50	0.81 46	0.75 45	1.01 57	0.61 41	3.66 84	1.23 53	0.26 65	0.23 62	0.36 74	0.25 64	0.27 37	0.62 80

PA-Net	0.84 51	1.00 57	3.49 79	0.63 75	0.40 56	0.70 32	0.52 44	0.86 22	0.84 44	0.52 7	0.59 12	0.70 22	0.54 33	2.35 59	2.23 71	0.17 29	0.27 72	0.19 39	0.28 71	0.22 18	0.36 63
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
DPSNet	0.86 52	0.60 16	2.34 40	0.58 72	0.36 45	1.07 64	0.75 62	1.30 75	1.05 62	0.70 33	0.68 34	0.94 51	1.29 70	2.08 55	1.82 63	0.26 65	0.23 62	0.16 25	0.17 39	0.48 72	0.40 70

DeepPrunerF	0.86 52	0.86 37	2.91 65	0.45 52	0.50 67	0.64 21	0.48 40	1.14 70	2.64 88	0.63 25	0.61 16	0.65 17	0.62 45	2.21 56	1.31 55	0.23 59	0.19 51	0.30 69	0.28 71	0.36 53	0.25 39

PVD	0.90 54	0.85 36	2.15 32	0.53 65	0.50 67	0.99 57	0.56 47	1.15 72	1.64 81	1.41 77	1.41 70	1.11 66	1.13 67	1.35 28	1.31 55	0.26 65	0.28 73	0.28 65	0.26 68	0.37 58	0.49 77

SAMSARA	0.91 55	0.97 53	2.22 34	0.80 80	0.58 78	1.32 70	1.31 77	1.01 60	0.94 53	1.36 73	0.87 51	1.38 71	0.90 56	1.72 43	1.02 47	0.24 61	0.30 74	0.22 52	0.25 64	0.36 53	0.35 61

MFMNet_re	0.92 56	1.28 77	2.53 53	0.82 82	0.76 83	0.90 54	0.75 62	0.98 53	1.04 61	1.03 56	1.05 60	0.84 38	0.94 60	0.83 8	0.72 33	0.68 87	0.60 85	0.63 85	0.60 84	0.77 87	0.74 83

ADCP+	0.94 57	1.12 68	3.14 74	0.34 16	0.39 52	1.06 62	0.79 65	0.89 28	0.80 37	0.66 29	0.67 29	0.85 40	1.55 78	0.93 11	4.55 86	0.13 15	0.11 19	0.16 25	0.15 31	0.26 35	0.25 39

ADCMid	0.96 58	1.32 80	2.74 60	0.38 31	0.35 41	0.99 57	0.58 50	0.94 40	0.86 46	1.23 70	1.01 57	1.01 57	1.00 62	1.05 18	3.97 83	0.21 46	0.19 51	0.42 79	0.32 75	0.40 64	0.29 46

ADCPNet	0.99 59	0.99 55	3.93 84	0.37 25	0.68 81	1.36 71	0.62 53	0.87 24	0.76 30	0.78 41	0.81 46	1.08 62	1.18 68	1.14 22	2.49 75	0.21 46	0.69 88	0.21 50	0.95 90	0.29 41	0.37 65

ADCL	0.99 59	0.79 31	2.43 46	0.39 35	0.32 36	1.50 73	1.90 82	0.92 36	0.97 57	1.26 72	1.27 64	1.00 56	1.42 75	1.14 22	3.31 81	0.17 29	0.14 32	0.22 52	0.21 53	0.30 42	0.22 31

AnyNet_C32	1.00 61	1.33 81	2.53 53	0.49 61	0.54 74	1.15 68	1.14 74	0.91 31	0.89 48	1.11 62	0.95 54	0.87 44	0.93 58	1.50 32	4.07 84	0.21 46	0.20 54	0.25 62	0.24 58	0.38 60	0.33 58

SGM-Forest	1.04 62	0.42 8	3.59 81	0.41 44	0.32 36	1.05 61	1.32 78	0.94 40	0.83 43	1.16 65	2.23 79	1.03 59	1.39 74	2.49 67	2.35 72	0.21 46	0.21 56	0.20 45	0.19 46	0.23 22	0.32 53
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
ADCS	1.05 63	1.36 83	2.98 70	0.42 47	0.36 45	0.96 55	0.81 69	1.06 67	1.12 68	1.25 71	0.97 56	1.08 62	0.90 56	1.25 26	4.86 89	0.21 46	0.21 56	0.22 52	0.23 56	0.36 53	0.38 66

SPS-STEREO	1.07 64	1.13 70	2.88 63	0.65 77	0.69 82	0.99 57	0.68 58	1.05 66	0.87 47	1.36 73	1.23 63	1.46 73	0.94 60	1.87 48	1.72 61	0.66 86	0.61 86	0.58 84	0.57 82	0.65 84	0.77 86
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
WCMA_ROB	1.12 65	0.83 34	2.66 58	0.58 72	0.34 39	1.17 69	0.71 61	0.93 38	0.94 53	1.74 79	2.63 83	3.09 86	1.71 79	1.70 40	1.84 64	0.26 65	0.21 56	0.20 45	0.21 53	0.28 39	0.43 73

AnyNet_C01	1.14 66	1.49 86	4.23 85	0.54 68	0.53 73	1.51 74	1.37 79	1.04 65	1.07 64	1.17 68	1.33 69	1.15 68	1.08 64	1.61 37	2.74 77	0.25 64	0.22 59	0.28 65	0.24 58	0.48 72	0.40 70

NaN_ROB	1.14 66	0.94 46	2.96 66	0.53 65	0.56 77	1.13 66	0.99 71	0.98 53	1.00 58	1.09 61	0.87 51	0.77 30	1.06 63	5.90 90	2.72 76	0.16 26	0.23 62	0.16 25	0.24 58	0.22 18	0.36 63

NVStereoNet_ROB	1.16 68	0.95 49	2.54 55	0.58 72	0.55 76	0.78 41	0.63 55	1.00 58	1.08 65	1.03 56	4.07 90	1.48 74	1.22 69	2.88 80	1.27 54	0.50 82	0.46 81	0.54 83	0.62 85	0.50 77	0.63 81
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
MDST_ROB	1.17 69	0.42 8	3.87 83	0.54 68	0.44 60	4.11 89	0.58 50	1.31 76	0.84 44	2.12 82	1.12 62	0.98 55	0.71 50	4.24 88	0.99 45	0.16 26	0.14 32	0.19 39	0.17 39	0.18 8	0.35 61

SANet	1.20 70	1.21 75	3.33 77	0.38 31	0.27 26	1.73 80	0.85 70	1.10 68	2.49 87	1.16 65	1.83 74	1.72 75	1.50 77	2.37 60	2.84 78	0.18 34	0.16 39	0.16 25	0.15 31	0.32 48	0.32 53

RTSA	1.21 71	1.31 78	6.39 90	0.52 63	0.51 71	1.61 76	0.79 65	1.03 63	1.37 75	1.13 63	1.30 67	1.10 64	0.85 54	2.65 72	2.08 67	0.21 46	0.24 65	0.18 35	0.18 43	0.43 66	0.32 53

MeshStereo	1.21 71	1.15 72	3.01 71	0.34 16	0.28 28	1.10 65	0.69 60	1.35 77	1.01 60	2.71 85	2.41 81	2.96 85	1.45 76	2.80 78	1.40 58	0.22 57	0.22 59	0.22 52	0.19 46	0.50 77	0.26 42
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
RTS	1.21 71	1.31 78	6.39 90	0.52 63	0.51 71	1.61 76	0.79 65	1.03 63	1.37 75	1.13 63	1.30 67	1.10 64	0.85 54	2.65 72	2.08 67	0.21 46	0.24 65	0.18 35	0.18 43	0.43 66	0.32 53

CSAN	1.25 74	1.12 68	3.04 73	0.51 62	0.37 50	1.06 62	1.15 75	1.22 73	1.08 65	1.17 68	1.91 75	1.04 60	1.31 72	5.96 91	2.43 74	0.26 65	0.22 59	0.24 58	0.24 58	0.31 46	0.30 48

pmcnn	1.32 75	1.05 61	2.77 61	0.74 79	1.43 88	0.72 35	0.56 47	0.96 48	0.91 50	0.99 55	2.92 86	6.01 91	0.70 49	2.38 61	3.32 82	0.13 15	0.10 16	0.08 1	0.06 1	0.34 52	0.22 31

Abc-Net	1.34 76	1.10 66	3.03 72	0.57 71	0.43 59	2.03 84	2.00 83	3.50 90	1.15 70	1.06 59	1.56 71	2.48 82	2.23 84	1.77 45	2.01 65	0.28 71	0.19 51	0.24 58	0.23 56	0.48 72	0.45 74

MSMD_ROB	1.34 76	0.96 50	2.38 44	0.39 35	0.32 36	1.96 81	0.62 53	0.91 31	1.43 77	2.94 88	2.60 82	4.77 88	1.95 81	2.43 64	1.33 57	0.29 72	0.26 70	0.32 71	0.32 75	0.30 42	0.30 48

MADNet+	1.36 78	1.93 89	5.95 89	0.81 81	0.59 79	1.57 75	1.09 72	1.39 79	1.22 71	0.84 48	1.01 57	1.25 70	1.12 66	3.19 82	2.36 73	0.45 81	0.47 82	0.38 78	0.34 77	0.59 82	0.57 78

ELAS_RVC	1.37 79	0.78 30	3.21 75	0.55 70	0.50 67	1.49 72	2.48 87	1.48 81	1.71 83	2.13 83	2.09 77	2.07 80	2.56 86	1.90 49	2.02 66	0.35 77	0.36 78	0.37 77	0.34 77	0.49 75	0.47 75
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAS	1.39 80	0.79 31	2.86 62	0.53 65	0.50 67	2.81 87	2.20 84	1.61 84	1.50 78	2.28 84	2.35 80	1.82 76	2.13 83	1.73 44	2.22 70	0.35 77	0.37 80	0.35 72	0.35 79	0.49 75	0.47 75
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
Nwc_Net	1.49 81	0.92 40	3.40 78	0.47 57	0.44 60	1.98 83	2.58 89	3.38 89	1.14 69	0.91 52	1.29 65	1.88 78	2.51 85	2.02 53	5.25 90	0.24 61	0.16 39	0.18 35	0.14 20	0.45 70	0.40 70

FC-DCNN	1.49 81	0.59 15	2.88 63	0.44 50	0.39 52	1.97 82	0.66 56	1.67 85	1.70 82	2.72 86	2.89 84	4.22 87	2.97 89	2.55 69	2.19 69	0.29 72	0.26 70	0.31 70	0.30 74	0.32 48	0.39 67

PWCK	1.49 81	1.90 88	3.22 76	1.09 86	0.54 74	1.71 79	1.73 81	1.49 82	1.53 79	1.44 78	1.80 73	1.82 76	1.30 71	3.34 83	1.76 62	1.07 91	0.56 84	0.97 89	0.58 83	1.38 90	0.65 82

DispFullNet	1.67 84	2.10 90	5.22 87	1.53 89	8.90 91	0.86 51	0.31 22	0.96 48	0.96 56	1.37 76	1.01 57	1.13 67	1.98 82	1.51 33	0.91 42	0.54 84	0.31 75	1.18 90	0.63 87	1.13 89	0.80 87

SGM+DAISY	1.83 85	1.43 84	3.81 82	0.87 83	1.01 85	2.38 85	2.53 88	1.36 78	1.75 84	2.92 87	3.22 88	2.65 83	2.93 88	2.22 57	3.10 79	0.72 88	0.69 88	0.63 85	0.62 85	0.77 87	0.98 88

SGM_RVC	1.89 86	0.92 40	2.50 52	0.96 84	0.93 84	1.63 78	1.37 79	1.52 83	12.43 92	1.85 80	1.69 72	2.40 81	1.74 80	2.81 79	1.71 60	0.53 83	0.47 82	0.42 79	0.47 81	0.74 85	0.74 83
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
edge stereo	2.51 87	2.33 92	5.12 86	1.45 88	1.38 87	4.78 90	4.90 92	3.91 91	2.37 85	1.91 81	2.90 85	1.89 79	3.41 90	2.92 81	4.61 87	0.94 90	0.86 91	0.85 88	0.85 89	1.40 91	1.41 90

MADNet++	2.79 88	2.11 91	2.97 69	2.49 90	2.28 89	2.95 88	2.31 85	2.67 86	2.47 86	3.03 89	3.13 87	2.70 84	2.92 87	4.10 86	4.48 85	2.68 92	2.24 92	3.04 92	2.44 92	2.37 92	2.34 91

MANE	2.90 89	1.14 71	2.96 66	1.03 85	1.01 85	5.01 91	2.45 86	5.36 92	6.88 90	3.58 90	4.76 92	5.99 90	4.99 91	4.23 87	4.61 87	0.56 85	0.67 87	0.44 81	0.78 88	0.51 79	1.03 89

SGM-ForestM	2.98 90	1.70 87	3.52 80	0.67 78	0.47 65	5.18 92	3.36 90	3.30 88	2.86 89	4.44 91	3.79 89	9.80 94	5.50 92	6.29 92	6.52 91	0.27 70	0.36 78	0.24 58	0.27 70	0.38 60	0.58 79

LSM	3.31 91	1.11 67	5.29 88	9.40 94	30.65 99	0.84 49	1.12 73	0.91 31	1.22 71	1.36 73	2.19 78	0.93 50	0.83 53	1.66 39	0.90 41	0.21 46	0.31 75	0.24 58	0.28 71	0.39 62	6.33 92

LE_ROB	4.43 92	1.35 82	9.38 92	2.79 91	3.93 90	2.43 86	3.46 91	2.68 87	11.19 91	11.66 96	4.73 91	5.26 89	10.46 93	4.50 89	9.49 92	0.82 89	0.69 88	1.34 91	0.98 91	0.75 86	0.76 85

DGTPSM_ROB	8.89 93	5.62 93	12.25 93	5.92 92	11.32 92	8.54 93	24.18 96	6.40 93	13.49 93	5.15 92	11.46 95	6.19 92	10.92 94	7.00 93	13.72 93	4.47 93	7.65 93	3.92 93	7.45 95	4.38 93	7.87 93

DPSMNet_ROB	9.48 94	5.64 94	13.01 96	8.82 93	11.82 93	8.54 93	24.20 97	6.41 94	13.65 94	5.32 93	11.47 96	6.33 93	10.92 94	7.69 94	13.89 94	7.25 96	8.19 94	5.06 94	7.78 96	5.31 94	8.26 94

DPSM_ROB	12.34 95	11.21 95	12.51 94	12.91 95	12.80 94	19.84 97	28.28 98	14.38 96	14.28 95	10.58 94	11.26 93	12.03 95	11.85 96	13.15 96	15.34 96	7.11 94	8.31 95	6.50 95	7.35 93	8.84 95	8.31 95

DPSM	12.34 95	11.21 95	12.51 94	12.91 95	12.80 94	19.84 97	28.28 98	14.38 96	14.28 95	10.58 94	11.26 93	12.03 95	11.85 96	13.15 96	15.34 96	7.11 94	8.31 95	6.50 95	7.35 93	8.84 95	8.31 95

DPSimNet_ROB	17.97 97	20.20 97	24.98 97	17.62 97	15.92 96	21.53 99	21.00 95	13.70 95	15.61 97	23.89 98	14.69 97	22.87 97	24.58 100	12.70 95	14.56 95	18.48 98	12.91 97	20.21 98	12.05 97	17.76 97	14.17 97

MEDIAN_ROB	23.96 98	27.05 99	26.39 99	22.16 98	22.13 97	16.12 95	13.97 93	20.24 98	20.05 98	25.50 99	25.31 99	22.96 98	22.63 98	24.39 98	23.24 98	28.20 99	27.23 99	28.62 99	27.75 99	27.50 99	27.76 99

LSM0	26.58 99	22.63 98	25.88 98	30.13 100	52.21 100	39.94 100	56.74 100	28.97 100	28.72 100	21.14 97	22.26 98	24.43 99	23.88 99	26.54 99	30.87 100	14.32 97	16.55 98	13.12 97	14.71 98	17.92 98	20.60 98

AVERAGE_ROB	26.93 100	30.34 100	29.27 100	25.82 99	25.34 98	19.48 96	15.17 94	23.55 99	23.00 99	28.45 100	27.87 100	26.25 100	25.86 101	26.96 100	25.55 99	31.30 100	30.07 100	31.76 100	30.81 100	30.79 100	30.93 100

MSMDNet													0.43 20

Low-res two-view benchmark