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 Train	1.20 1	0.58 2	1.81 1	0.99 2	0.67 4	3.78 37	0.90 13	4.09 49	1.51 1	2.16 17	1.71 8	1.13 1	0.94 1	1.40 1	0.91 1	0.30 2	0.24 10	0.26 2	0.21 3	0.26 1	0.25 1

R-Stereo	1.20 1	0.58 2	1.81 1	0.99 2	0.67 4	3.78 37	0.90 13	4.09 49	1.51 1	2.16 17	1.71 8	1.13 1	0.94 1	1.40 1	0.91 1	0.30 2	0.24 10	0.26 2	0.21 3	0.26 1	0.25 1

AdaStereo	1.45 3	0.83 14	2.34 5	1.28 27	0.81 10	3.21 5	1.14 24	4.20 58	2.46 33	2.05 13	1.77 13	2.48 18	1.18 5	1.71 3	1.44 3	0.43 27	0.25 13	0.43 27	0.28 12	0.44 15	0.34 10
Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi: AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching. ArXiv
StereoDRNet-Refined	1.77 4	0.81 11	2.22 4	1.21 19	0.76 7	3.26 9	0.71 4	3.82 18	2.05 6	2.29 21	2.05 30	2.98 37	1.44 14	7.15 32	2.27 30	0.30 2	0.23 8	0.42 26	0.33 15	0.45 19	0.64 45
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
MLCV	1.87 5	0.91 17	8.59 16	1.10 10	0.57 2	3.59 26	0.53 1	3.70 13	2.20 10	2.80 42	2.25 44	2.00 7	1.42 13	4.13 14	1.86 22	0.30 2	0.20 3	0.32 9	0.21 3	0.33 6	0.36 13

iResNet_ROB	1.89 6	1.02 25	8.60 17	1.39 45	0.98 25	3.72 35	0.66 3	3.84 20	2.65 39	1.88 7	2.05 30	1.98 6	1.27 8	4.52 16	1.67 13	0.30 2	0.20 3	0.28 5	0.18 1	0.31 4	0.40 15

CFNet_RVC	1.90 7	0.95 21	3.56 11	1.07 6	1.54 57	4.09 60	0.90 13	2.85 1	1.82 5	1.78 3	1.70 5	2.75 26	1.48 18	9.33 44	1.72 16	0.33 11	0.30 19	0.61 62	0.36 23	0.52 26	0.39 14

DN-CSS_ROB	1.91 8	1.55 61	11.13 27	1.82 73	0.88 15	3.99 56	0.61 2	4.01 39	1.67 3	1.64 2	1.89 20	1.39 4	1.06 3	3.23 6	1.50 4	0.31 7	0.19 2	0.44 29	0.29 13	0.37 8	0.31 7

LALA_ROB	1.95 9	1.48 58	3.11 9	1.05 5	0.98 25	3.99 56	1.82 51	4.44 74	2.28 18	3.31 56	1.85 18	2.76 29	1.67 27	4.04 13	3.23 44	0.46 32	0.41 37	0.55 53	0.52 52	0.60 41	0.57 34

DeepPruner_ROB	2.05 10	1.09 33	10.50 24	1.01 4	1.20 37	4.39 70	0.91 17	3.14 3	1.72 4	2.60 30	1.54 1	2.78 30	1.27 8	4.52 16	1.72 16	0.51 42	0.40 35	0.37 13	0.34 16	0.48 21	0.52 28

DLCB_ROB	2.05 10	0.95 21	2.37 6	1.24 22	1.02 29	2.92 3	1.30 29	3.68 11	2.30 20	2.91 45	2.17 38	3.32 42	1.52 20	10.37 53	2.50 32	0.40 23	0.33 22	0.40 22	0.36 23	0.41 12	0.47 21

ETE_ROB	2.07 12	1.40 54	3.01 8	1.09 7	0.92 20	3.94 49	1.53 40	4.25 61	2.23 12	3.31 56	2.06 32	2.35 14	1.52 20	8.74 40	2.07 25	0.44 29	0.36 30	0.51 43	0.48 49	0.54 30	0.62 41

HITNet	2.07 12	0.82 13	14.77 42	1.34 36	0.56 1	3.47 19	0.80 7	4.07 47	2.35 24	2.16 17	1.61 2	1.64 5	1.44 14	2.09 4	2.56 33	0.26 1	0.18 1	0.41 25	0.26 11	0.45 19	0.25 1

DISCO	2.25 14	0.73 8	10.06 22	1.76 71	1.12 32	3.47 19	1.25 28	3.24 4	2.26 16	2.65 32	1.96 22	4.53 69	1.92 37	4.17 15	3.61 47	0.32 9	0.25 13	0.31 8	0.29 13	0.66 51	0.41 17

NOSS_ROB	2.27 15	0.73 8	3.00 7	1.71 68	1.00 27	3.90 46	0.97 18	4.29 64	2.81 49	2.94 46	2.19 40	3.88 53	1.19 6	11.77 73	1.62 8	0.59 53	0.54 60	0.56 54	0.52 52	0.56 36	0.54 31

XPNet_ROB	2.27 15	1.11 36	4.10 12	1.36 41	0.97 23	3.94 49	1.63 44	4.07 47	2.14 8	2.82 43	2.00 25	2.32 13	1.73 29	10.81 58	3.39 46	0.51 42	0.47 52	0.48 35	0.40 38	0.51 24	0.56 33

TDLM	2.29 17	1.08 31	4.61 13	1.29 29	1.01 28	4.26 68	2.17 60	3.75 16	2.79 45	2.31 23	1.98 24	2.06 10	1.59 22	12.45 80	1.72 16	0.51 42	0.28 18	0.52 49	0.35 19	0.56 36	0.50 25

CFNet	2.34 18	1.10 34	8.96 18	1.34 36	1.24 39	4.09 60	0.73 6	4.04 43	2.77 43	1.62 1	1.87 19	2.27 12	1.45 17	11.41 63	1.82 21	0.31 7	0.24 10	0.43 27	0.36 23	0.43 14	0.33 8

HSM	2.35 19	0.81 11	2.21 3	1.14 13	1.55 59	3.44 15	1.03 19	3.99 38	2.25 14	2.29 21	2.11 35	8.04 83	3.05 58	11.60 67	1.71 14	0.33 11	0.22 7	0.28 5	0.23 8	0.32 5	0.35 12

iResNet	2.52 20	0.94 20	21.17 87	1.84 75	0.72 6	3.75 36	0.81 9	4.14 54	2.55 38	2.33 24	2.03 26	1.35 3	1.44 14	3.27 7	2.16 28	0.35 18	0.23 8	0.28 5	0.22 7	0.44 15	0.45 19

NCCL2	2.53 21	1.35 48	10.90 25	1.15 14	0.97 23	3.53 24	2.56 65	3.65 10	2.23 12	2.76 38	1.80 15	2.41 16	1.66 26	10.35 52	2.07 25	0.44 29	0.40 35	0.57 55	0.53 55	0.59 38	0.65 47

NVstereo2D	2.54 22	0.90 15	11.10 26	1.32 31	1.47 54	3.58 25	1.38 32	4.13 52	2.45 31	1.90 9	1.71 8	2.86 34	1.76 30	10.86 59	1.66 12	0.60 54	0.43 43	0.53 50	0.41 39	0.78 61	0.94 72

ccs_rob	2.54 22	1.14 38	14.21 38	1.35 39	0.96 21	3.98 55	0.71 4	3.86 23	2.88 50	2.04 12	2.07 33	2.05 9	1.29 11	10.60 55	1.71 14	0.32 9	0.25 13	0.38 15	0.35 19	0.39 9	0.33 8

NLCA_NET_v2_RVC	2.54 22	1.04 27	16.73 58	1.53 56	2.00 83	3.96 52	0.87 12	3.87 25	2.31 21	2.54 28	1.67 4	2.85 33	1.63 24	5.55 23	1.62 8	0.46 32	0.33 22	0.38 15	0.38 30	0.53 29	0.62 41
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
CVANet_RVC	2.55 25	1.10 34	9.13 19	1.46 47	1.15 34	3.96 52	1.53 40	3.91 33	2.50 35	3.04 48	2.04 28	2.60 23	1.72 28	12.46 81	1.50 4	0.49 37	0.30 19	0.51 43	0.41 39	0.63 43	0.50 25

CC-Net-ROB	2.55 25	1.05 28	16.77 59	1.50 52	1.94 82	3.97 54	0.90 13	3.85 21	2.25 14	2.55 29	1.70 5	2.82 32	1.65 25	5.67 26	1.65 10	0.47 35	0.33 22	0.38 15	0.39 33	0.54 30	0.64 45

DANet	2.63 27	1.07 30	8.10 14	1.83 74	1.70 71	3.25 8	1.16 25	4.31 69	2.38 28	3.40 59	2.18 39	4.08 59	1.80 32	9.77 46	3.89 50	0.52 47	0.55 63	0.59 58	0.44 43	0.63 43	0.86 68

iResNetv2_ROB	2.65 28	5.00 87	19.28 76	2.06 85	1.09 31	3.83 43	1.13 23	4.14 54	2.09 7	1.85 5	2.03 26	2.01 8	1.51 19	3.58 9	1.58 6	0.34 14	0.21 5	0.32 9	0.21 3	0.44 15	0.29 5

PASM	2.67 29	2.05 76	9.71 21	1.73 70	1.37 49	2.53 1	1.52 39	3.10 2	2.17 9	2.79 41	1.84 16	2.90 35	1.85 35	11.89 76	2.19 29	0.81 74	0.85 83	0.87 83	1.01 86	1.14 80	1.05 75

PWC_ROB	2.69 30	2.01 74	8.36 15	1.33 33	1.90 80	3.23 6	1.18 26	4.33 71	3.25 65	3.11 51	3.41 68	2.71 24	2.18 41	9.15 42	4.48 52	0.56 51	0.31 21	0.61 62	0.36 23	0.69 53	0.74 56

RP	2.79 31	1.20 41	10.36 23	1.54 58	1.93 81	3.44 15	1.97 54	4.05 45	2.34 23	2.66 33	2.79 54	3.98 56	2.58 49	10.21 51	2.61 34	0.95 80	0.47 52	0.71 73	0.57 61	0.71 57	0.68 51

SHD	2.82 32	2.04 75	13.80 34	1.69 67	1.79 77	2.73 2	1.07 21	3.85 21	3.18 60	4.63 74	2.74 53	3.38 43	2.77 53	5.48 22	3.66 48	0.62 57	0.47 52	0.51 43	0.55 57	0.65 48	0.74 56

StereoDRNet	2.84 33	1.49 59	14.48 41	1.33 33	1.57 63	3.44 15	2.06 56	3.69 12	2.36 26	3.26 55	1.84 16	2.79 31	1.80 32	10.88 60	3.16 43	0.41 25	0.33 22	0.39 19	0.36 23	0.59 38	0.58 37

PDISCO_ROB	2.86 34	1.53 60	14.27 40	1.98 82	2.19 86	4.52 73	1.35 31	4.65 80	3.19 61	2.09 15	2.16 37	3.53 47	2.45 46	5.32 21	2.12 27	1.30 90	0.78 82	0.72 76	0.59 63	1.24 86	1.28 81

HSM-Net_RVC	2.87 35	0.57 1	17.46 62	0.92 1	0.90 18	3.70 32	1.20 27	5.64 89	2.51 36	3.22 54	2.43 46	2.54 20	1.59 22	11.41 63	1.65 10	0.34 14	0.25 13	0.26 2	0.23 8	0.30 3	0.29 5
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
ccs	2.91 36	1.17 40	18.05 66	1.53 56	0.91 19	3.88 45	0.80 7	3.79 17	3.00 55	1.84 4	2.13 36	2.42 17	1.25 7	13.71 87	1.58 6	0.33 11	0.27 17	0.40 22	0.35 19	0.39 9	0.34 10

MFMNet_re	2.91 36	1.91 69	18.25 68	3.06 90	1.89 79	3.13 4	1.67 47	3.52 6	2.97 53	3.14 52	2.87 56	2.26 11	2.41 44	2.42 5	2.00 24	1.10 84	1.05 85	0.94 85	0.90 82	1.28 87	1.46 86

GANet	2.93 38	1.05 28	11.29 28	1.64 64	0.96 21	3.91 47	2.52 64	3.72 14	2.41 29	2.95 47	3.00 58	3.21 41	2.03 38	12.42 79	4.72 55	0.51 42	0.41 37	0.46 32	0.39 33	0.60 41	0.50 25

PWCDC_ROB	2.95 39	3.26 82	14.06 36	1.59 60	1.56 61	4.19 65	0.86 10	4.30 68	4.08 77	2.06 14	7.37 77	2.75 26	2.42 45	3.59 10	2.67 36	1.34 92	0.36 30	0.39 19	0.34 16	1.13 79	0.62 41

RTSC	2.95 39	2.51 79	15.49 50	1.54 58	1.30 45	3.51 23	0.86 10	3.89 28	2.48 34	3.64 61	3.12 64	3.04 39	2.30 42	5.70 28	6.67 61	0.55 50	0.35 29	0.37 13	0.39 33	0.70 55	0.60 39

PSMNet_ROB	2.95 39	1.38 51	18.46 71	1.19 17	1.20 37	3.80 41	1.65 46	3.54 7	2.35 24	1.98 10	1.71 8	2.75 26	1.79 31	11.43 65	2.78 37	0.42 26	0.36 30	0.50 40	0.60 64	0.59 38	0.49 23

XQC	2.96 42	2.42 78	14.85 43	1.36 41	1.32 47	3.29 10	1.53 40	4.28 63	2.78 44	3.21 53	2.46 47	3.53 47	2.92 56	6.58 30	4.67 54	0.70 69	0.41 37	0.59 58	0.56 58	0.96 72	0.83 66

SGM-Forest	2.96 42	0.62 5	3.49 10	1.09 7	0.80 8	4.06 58	3.30 73	4.37 72	3.09 57	4.18 71	3.27 66	4.67 71	3.86 69	11.69 71	7.91 70	0.51 42	0.46 49	0.49 38	0.39 33	0.48 21	0.49 23
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
RYNet	2.97 44	0.93 18	15.09 47	1.12 11	1.38 50	3.66 30	1.42 35	4.03 41	2.29 19	2.10 16	1.70 5	3.79 51	2.16 40	13.64 86	3.02 42	0.37 19	0.41 37	0.48 35	0.34 16	0.81 64	0.75 58

G-Net	3.01 45	1.37 49	18.87 74	1.26 24	1.66 68	3.69 31	1.49 38	3.82 18	2.31 21	2.68 34	2.50 48	4.38 66	2.47 47	5.67 26	3.27 45	1.27 88	0.64 74	0.64 68	0.77 75	0.69 53	0.72 55

AANet_RVC	3.03 46	1.72 66	12.78 31	1.17 15	1.12 32	3.79 40	1.10 22	3.86 23	3.13 58	2.62 31	4.26 74	3.96 55	1.41 12	11.43 65	5.23 59	0.94 79	0.47 52	0.34 12	0.23 8	0.40 11	0.55 32

DRN-Test	3.03 46	0.99 24	18.18 67	1.35 39	1.60 64	3.59 26	1.48 36	4.13 52	2.45 31	3.47 60	1.92 21	2.58 22	2.53 48	10.88 60	2.80 38	0.39 22	0.36 30	0.44 29	0.41 39	0.55 33	0.57 34

stereogan	3.09 48	0.93 18	16.43 53	1.37 43	1.66 68	3.70 32	1.67 47	3.87 25	3.15 59	3.04 48	2.70 52	5.04 75	2.79 54	9.96 49	1.78 20	0.63 58	0.49 58	0.69 71	0.47 46	0.83 66	0.68 51

Anonymous Stereo	3.09 48	3.14 81	16.69 57	1.52 55	1.25 41	3.33 11	2.95 69	4.02 40	2.42 30	2.35 25	2.04 28	2.57 21	1.27 8	11.88 75	2.92 40	0.49 37	0.49 58	0.57 55	0.56 58	0.70 55	0.65 47

PA-Net	3.09 48	1.24 45	18.84 72	1.38 44	1.19 36	3.42 14	1.75 49	3.89 28	3.27 68	1.88 7	2.10 34	3.03 38	1.88 36	11.60 67	2.88 39	0.40 23	0.54 60	0.44 29	0.62 66	0.52 26	1.02 74
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
RGC	3.10 51	1.45 57	14.90 45	1.46 47	1.79 77	3.33 11	1.39 33	4.04 43	2.26 16	2.77 39	3.26 65	4.00 57	2.71 52	11.63 69	2.62 35	0.82 75	0.46 49	0.79 80	0.79 79	0.71 57	0.78 62

PVD	3.12 52	1.97 73	12.80 32	1.79 72	1.63 66	3.83 43	1.34 30	3.48 5	3.88 76	5.77 78	3.56 69	3.45 44	3.29 63	5.58 25	5.30 60	0.71 70	0.69 79	0.74 78	0.77 75	0.79 63	1.13 78

NCC-stereo	3.16 53	1.28 46	18.31 69	1.25 23	1.75 75	4.06 58	1.40 34	3.89 28	2.22 11	2.72 36	2.55 49	3.49 45	2.65 51	9.91 48	2.99 41	1.29 89	0.65 76	0.64 68	0.86 81	0.65 48	0.71 53

GANetREF_RVC	3.20 54	1.93 70	18.84 72	1.51 54	0.80 8	4.12 63	1.82 51	4.16 56	2.91 52	1.87 6	1.74 12	2.48 18	1.84 34	13.18 83	1.74 19	0.80 73	0.57 65	0.79 80	0.74 72	1.20 85	0.86 68
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 55	1.02 25	9.21 20	1.49 51	1.07 30	3.78 37	1.48 36	4.31 69	3.08 56	2.46 26	2.32 45	4.20 63	2.08 39	15.19 91	7.16 69	0.84 76	0.62 73	1.33 90	0.92 83	0.99 74	1.31 82

ADCRef	3.30 56	1.23 44	14.90 45	1.17 15	1.41 53	3.95 51	1.64 45	3.88 27	2.36 26	3.05 50	2.21 42	2.40 15	2.81 55	5.04 18	16.63 84	0.46 32	0.34 28	0.63 64	0.62 66	0.54 30	0.67 50

CBMV	3.32 57	0.96 23	21.16 86	1.32 31	0.83 12	3.81 42	3.91 81	4.47 75	2.53 37	3.34 58	2.24 43	4.45 67	2.58 49	10.07 50	1.96 23	0.45 31	0.43 43	0.48 35	0.38 30	0.52 26	0.53 29
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
DPSNet	3.43 58	1.37 49	12.19 29	1.39 45	1.24 39	4.15 64	2.06 56	4.51 76	3.34 69	2.69 35	1.96 22	3.52 46	5.56 77	10.68 56	9.68 74	0.93 78	0.73 80	0.40 22	0.37 29	1.06 76	0.86 68

SAMSARA	3.55 59	2.16 77	14.18 37	1.86 77	1.49 56	4.62 74	2.65 66	3.58 9	2.80 47	7.05 84	3.05 61	5.16 76	4.07 70	9.18 43	4.80 56	0.64 60	0.67 77	0.59 58	0.68 69	0.89 68	0.91 71

CBMV_ROB	3.62 60	0.70 6	21.42 90	1.21 19	0.89 17	4.43 72	1.76 50	4.39 73	2.79 45	5.88 79	3.38 67	4.18 62	5.12 75	10.75 57	2.32 31	0.56 51	0.55 63	0.53 50	0.51 51	0.55 33	0.46 20

DeepPrunerF	3.70 61	1.72 66	14.87 44	1.33 33	1.74 74	3.70 32	1.55 43	4.20 58	14.00 94	2.02 11	1.77 13	2.74 25	2.30 42	10.88 60	6.88 66	0.68 67	0.60 71	0.78 79	0.78 78	0.81 64	0.71 53

ADCP+	3.74 62	1.96 72	16.90 60	1.09 7	1.55 59	4.35 69	3.65 78	3.93 34	2.98 54	2.22 20	1.66 3	3.95 54	4.83 74	3.41 8	18.87 90	0.38 21	0.41 37	0.63 64	0.53 55	0.64 47	0.81 63

ADCPNet	3.89 63	1.63 63	20.25 79	1.28 27	1.27 44	6.43 83	2.65 66	3.93 34	2.70 42	2.78 40	2.20 41	4.28 64	6.16 80	5.56 24	10.27 77	0.65 62	1.18 88	0.71 73	1.70 92	0.84 67	1.37 84

RTS	3.90 64	7.04 91	17.94 64	1.92 79	2.19 86	5.00 77	2.96 70	4.29 64	3.19 61	4.05 68	3.08 62	4.94 73	3.16 60	7.64 34	6.72 62	0.66 63	0.44 45	0.51 43	0.47 46	0.95 70	0.76 59

RTSA	3.90 64	7.04 91	17.94 64	1.92 79	2.19 86	5.00 77	2.96 70	4.29 64	3.19 61	4.05 68	3.08 62	4.94 73	3.16 60	7.64 34	6.72 62	0.66 63	0.44 45	0.51 43	0.47 46	0.95 70	0.76 59

ADCMid	3.96 66	3.27 83	15.48 49	1.26 24	1.40 51	4.66 75	2.28 61	4.06 46	3.54 71	3.94 66	3.02 59	3.85 52	3.67 68	4.03 12	18.65 88	0.68 67	0.61 72	1.09 88	1.18 89	1.38 89	1.08 76

MADNet+	3.97 67	9.54 96	13.83 35	1.87 78	1.69 70	4.19 65	2.87 68	4.80 84	4.10 78	2.51 27	2.67 51	4.55 70	3.41 65	8.72 39	6.92 67	1.25 87	1.27 91	1.04 86	0.93 84	1.39 90	1.85 91

SPS-STEREO	3.97 67	1.44 56	20.85 84	1.72 69	1.71 72	3.24 7	2.30 62	3.55 8	2.68 41	4.79 75	3.66 70	5.44 77	2.97 57	9.11 41	9.35 73	1.15 85	1.10 86	1.06 87	1.02 87	1.16 82	1.18 79
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
NaN_ROB	4.06 69	1.13 37	16.64 55	1.12 11	1.25 41	3.50 22	3.05 72	4.29 64	3.26 66	3.64 61	2.56 50	2.97 36	4.79 73	13.33 84	16.91 85	0.37 19	0.41 37	0.38 15	0.39 33	0.48 21	0.81 63

AnyNet_C32	4.17 70	5.75 89	12.62 30	1.60 61	1.54 57	5.51 79	4.09 82	3.90 32	3.23 64	4.22 72	3.03 60	3.10 40	3.41 65	8.01 37	18.85 89	0.53 49	0.58 67	0.53 50	0.74 72	1.19 84	0.97 73

ADCS	4.22 71	5.33 88	16.32 51	1.64 64	1.31 46	3.91 47	3.64 77	4.21 60	2.89 51	5.11 76	2.81 55	4.46 68	3.06 59	5.21 20	19.50 91	0.63 58	0.64 74	0.69 71	0.69 71	1.12 78	1.26 80

ADCL	4.35 72	1.63 63	14.21 38	1.29 29	1.26 43	5.56 80	11.16 88	3.97 37	3.37 70	6.07 80	4.67 75	4.30 65	5.72 78	5.08 19	14.51 81	0.50 40	0.48 56	0.89 84	0.77 75	0.78 61	0.76 59

AnyNet_C01	4.37 73	7.80 93	16.40 52	1.98 82	1.40 51	6.37 82	6.47 84	4.09 49	5.03 83	4.06 70	3.78 72	4.16 61	3.31 64	6.62 31	10.00 75	0.61 55	0.67 77	0.63 64	0.67 68	2.01 92	1.31 82

SGM_RVC	4.57 74	0.72 7	17.46 62	1.84 75	0.58 3	4.23 67	3.45 74	4.66 81	3.66 73	7.99 85	8.82 85	7.56 82	8.74 86	12.30 78	6.84 65	0.43 27	0.38 34	0.39 19	0.35 19	0.51 24	0.48 22
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DispFullNet	4.64 75	3.48 84	23.95 94	3.99 92	3.03 92	3.35 13	1.06 20	4.18 57	2.80 47	6.33 82	2.94 57	4.04 58	7.81 83	3.92 11	5.16 58	2.75 93	1.61 94	3.10 95	2.68 94	3.32 93	3.31 93

CSAN	4.71 76	1.39 53	20.23 78	1.19 17	0.87 14	3.65 29	3.50 76	4.57 78	3.87 75	3.89 65	7.82 80	3.54 50	6.16 80	23.47 99	6.82 64	0.52 47	0.45 48	0.49 38	0.52 52	0.63 43	0.66 49

NVStereoNet_ROB	4.91 77	1.40 54	16.46 54	1.47 50	1.72 73	3.46 18	1.96 53	3.89 28	3.61 72	2.90 44	24.61 100	4.81 72	4.49 72	13.57 85	4.87 57	1.33 91	1.29 92	1.70 92	1.68 91	1.41 91	1.66 88
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 78	0.90 15	21.33 88	1.50 52	1.33 48	4.09 60	3.49 75	4.03 41	4.59 80	6.57 83	10.95 87	12.92 89	8.41 85	9.86 47	8.78 72	0.75 71	0.58 67	0.51 43	0.50 50	0.68 52	0.57 34

MeshStereo	5.35 79	1.32 47	21.34 89	1.34 36	1.15 34	4.40 71	3.90 80	5.18 85	4.55 79	10.14 92	11.82 89	11.47 87	5.84 79	13.84 88	7.03 68	0.61 55	0.58 67	0.60 61	0.58 62	0.72 59	0.59 38
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 80	1.22 43	17.40 61	1.60 61	2.41 89	3.48 21	2.12 59	4.52 77	3.26 66	3.67 63	13.15 90	22.10 95	3.46 67	11.92 77	17.16 86	0.34 14	0.21 5	0.23 1	0.19 2	0.36 7	0.28 4

MDST_ROB	5.53 81	0.59 4	23.11 92	2.55 88	2.69 90	21.20 96	1.98 55	4.77 83	2.65 39	9.36 89	4.25 73	4.14 60	4.45 71	22.57 95	3.68 49	0.48 36	0.33 22	0.47 33	0.44 43	0.44 15	0.44 18

MSMD_ROB	5.61 82	1.08 31	16.68 56	1.21 19	0.83 12	10.14 89	2.07 58	3.93 34	6.22 87	10.37 93	8.03 82	22.22 96	9.45 87	11.66 70	4.52 53	0.66 63	0.58 67	0.66 70	0.68 69	0.65 48	0.63 44

SANet	5.65 83	1.72 66	20.94 85	1.26 24	0.88 15	7.20 85	3.81 79	4.60 79	11.23 91	4.56 73	8.78 84	7.49 81	6.95 82	11.85 74	18.11 87	0.49 37	0.44 45	0.50 40	0.42 42	0.90 69	0.82 65

ELAS_RVC	5.72 84	1.64 65	20.40 82	2.09 86	2.01 84	4.95 76	11.47 89	4.66 81	6.31 88	8.09 86	8.01 81	8.06 85	11.65 89	8.36 38	10.22 76	0.99 83	1.17 87	0.81 82	0.76 74	1.14 80	1.58 87
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
PWCK	5.85 85	9.95 97	15.22 48	2.90 89	1.56 61	5.66 81	7.69 85	5.27 87	4.74 81	3.88 64	7.51 78	8.04 83	5.28 76	15.18 90	8.40 71	3.08 95	1.51 93	2.24 93	1.37 90	5.55 96	1.96 92

ELAS	5.99 86	1.62 62	20.29 80	1.96 81	2.01 84	15.09 95	8.38 87	5.23 86	5.54 85	9.02 88	8.61 83	6.74 80	9.75 88	7.48 33	11.40 78	0.96 81	1.18 88	0.72 76	0.93 84	1.30 88	1.67 89
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
Abc-Net	6.58 87	2.94 80	18.36 70	1.62 63	1.62 65	9.96 88	11.53 90	20.69 97	5.05 84	3.97 67	5.61 76	14.40 92	12.47 90	6.52 29	12.11 80	0.75 71	0.54 60	0.63 64	0.61 65	1.07 77	1.09 77

FC-DCNN	6.64 88	0.77 10	19.26 75	1.68 66	1.64 67	8.27 87	2.35 63	6.35 90	9.23 89	10.85 94	11.27 88	19.89 93	13.33 91	12.51 82	11.79 79	0.66 63	0.57 65	0.58 57	0.56 58	0.63 43	0.61 40

Nwc_Net	6.84 89	1.95 71	19.38 77	1.46 47	1.47 54	11.20 90	14.98 92	20.44 96	3.76 74	2.72 36	3.72 71	8.25 86	14.42 93	7.90 36	21.04 94	0.89 77	0.46 49	0.50 40	0.38 30	0.98 73	0.83 66

MADNet++	7.57 90	5.80 90	13.28 33	6.38 94	5.26 94	7.18 84	7.77 86	5.32 88	5.62 86	6.11 81	7.78 79	6.02 78	8.01 84	11.74 72	16.36 83	9.36 98	4.95 96	8.57 98	5.66 96	4.83 95	5.34 95

SGM+DAISY	8.36 91	3.82 86	24.83 95	2.02 84	1.75 75	14.80 94	17.22 94	4.27 62	11.79 93	13.88 96	14.38 93	11.74 88	13.83 92	10.56 54	15.12 82	1.21 86	1.19 90	1.14 89	1.13 88	1.17 83	1.37 84

edge stereo	9.71 92	13.24 98	20.34 81	3.90 91	4.13 93	13.93 93	17.01 93	18.59 95	9.71 90	8.47 87	9.71 86	6.31 79	15.36 94	14.63 89	20.22 92	2.98 94	2.44 95	2.26 94	2.06 93	4.37 94	4.53 94

MANE	11.39 93	1.38 51	23.16 93	4.82 93	2.79 91	22.02 97	14.51 91	25.81 98	26.28 99	10.93 95	13.17 91	19.90 94	18.03 95	15.69 92	20.41 93	0.96 81	0.74 81	1.42 91	3.24 95	0.74 60	1.71 90

SGM-ForestM	11.55 94	1.16 39	21.62 91	2.14 87	0.81 10	22.25 98	19.08 95	16.50 94	11.34 92	15.94 99	14.44 94	27.12 99	22.61 100	24.76 100	28.39 99	0.50 40	0.48 56	0.47 33	0.44 43	0.55 33	0.53 29

LSM	12.71 95	3.52 85	20.79 83	55.63 102	84.76 102	3.63 28	6.30 83	3.73 15	4.93 82	5.55 77	13.92 92	3.53 47	3.25 62	9.72 45	4.12 51	0.64 60	0.85 83	0.71 73	0.81 80	1.03 75	26.84 100

DGTPSM_ROB	15.31 96	8.85 94	28.14 98	9.90 96	18.89 95	12.44 91	33.21 98	13.48 92	17.86 97	9.50 90	22.89 98	13.78 90	19.67 96	21.26 93	23.68 95	5.08 96	8.84 97	5.62 96	11.50 97	6.72 97	14.87 96

DPSMNet_ROB	15.52 97	8.89 95	31.44 99	10.07 97	18.91 96	12.44 91	33.22 99	13.51 93	17.94 98	9.58 91	22.91 99	13.79 91	19.67 96	21.43 94	23.73 96	5.17 97	8.84 97	5.63 97	11.52 100	6.79 98	14.90 97

LE_ROB	18.58 98	1.21 42	48.38 101	8.60 95	23.00 99	7.32 86	19.74 96	9.94 91	38.69 101	43.53 101	21.91 97	28.40 100	40.92 102	23.18 96	54.71 102	0.34 14	0.33 22	0.32 9	0.36 23	0.42 13	0.40 15

DPSM_ROB	20.78 99	20.03 99	26.57 96	23.62 98	22.46 97	31.10 99	39.97 101	33.52 100	17.41 95	15.25 97	15.80 95	24.06 97	20.71 98	23.30 97	27.62 97	10.78 99	9.41 99	11.69 99	11.50 97	14.49 99	16.36 98

DPSM	20.78 99	20.03 99	26.57 96	23.62 98	22.46 97	31.10 99	39.97 101	33.52 100	17.41 95	15.25 97	15.80 95	24.06 97	20.71 98	23.30 97	27.62 97	10.78 99	9.41 99	11.69 99	11.50 97	14.49 99	16.36 98

AVERAGE_ROB	44.46 101	45.91 102	46.59 100	40.59 101	38.66 100	32.99 101	30.52 97	46.19 102	43.26 102	49.59 102	48.97 102	44.62 101	39.89 101	45.04 101	43.94 100	48.74 102	50.69 102	50.80 102	49.79 102	45.54 102	46.97 101

MEDIAN_ROB	47.37 102	49.08 103	49.92 102	40.32 100	39.97 101	34.95 102	33.43 100	49.17 103	46.39 103	53.06 103	52.65 103	47.65 102	42.23 104	48.35 103	47.29 101	52.10 103	54.04 103	54.28 103	53.48 103	48.76 103	50.33 102

LSM0	49.58 103	40.04 101	53.62 103	97.15 103	128.68 104	62.75 103	79.89 103	67.63 104	35.11 100	33.02 100	32.61 101	48.43 103	41.60 103	46.60 102	55.43 103	21.63 101	18.99 101	23.62 101	23.13 101	29.47 101	52.26 103

DPSimNet_ROB	102.30 104	119.72 104	143.59 104	109.42 104	94.10 103	127.22 104	127.15 104	26.62 99	102.36 104	149.58 104	83.99 104	139.34 104	137.54 105	64.62 104	82.36 104	105.18 104	66.15 104	120.15 104	69.61 104	99.58 104	77.72 104

MSMDNet													1.11 4

Low-res two-view benchmark