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	0.98 1	0.42 10	1.86 1	0.07 1	0.22 7	1.65 1	0.02 1	4.07 2	6.33 19	0.72 1	1.54 2	0.43 2	0.64 1	1.22 1	0.44 1	0.00 1	0.00 1	0.00 1	0.00 1	0.01 2	0.00 1

PMTNet	1.41 2	0.37 8	2.06 4	0.26 3	0.15 4	2.94 13	0.34 9	3.39 1	5.33 10	1.00 2	1.27 1	0.28 1	0.93 5	4.04 9	0.74 4	5.00 130	0.00 1	0.00 1	0.00 1	0.03 13	0.01 2

DPM-Stereo	1.97 3	0.64 26	2.95 13	0.17 2	0.10 1	4.83 36	0.13 3	8.60 17	4.06 4	6.42 26	4.92 6	0.44 3	0.72 2	3.57 8	1.80 7	0.00 1	0.01 36	0.00 1	0.00 1	0.05 19	0.04 24

Gwc-CoAtRS	2.02 4	0.33 6	2.61 5	1.04 10	0.30 10	3.56 16	0.40 12	5.89 4	5.94 16	5.08 13	5.01 7	5.55 15	1.72 9	1.60 2	0.91 5	0.01 27	0.09 81	0.00 1	0.25 99	0.01 2	0.02 6

FENet	2.19 5	0.34 7	3.04 14	2.05 49	0.26 9	2.82 10	0.13 3	7.02 8	5.55 12	4.97 12	1.94 3	4.91 11	3.39 22	4.71 12	2.50 13	0.02 34	0.03 53	0.00 1	0.00 1	0.00 1	0.21 61

R-Stereo Train	2.44 6	0.32 1	1.93 2	0.94 7	0.16 5	3.67 18	0.61 23	6.37 6	3.08 1	9.14 49	17.44 83	1.80 4	0.77 3	1.76 3	0.70 2	0.00 1	0.01 36	0.00 1	0.00 1	0.01 2	0.03 15

RAFT-Stereo	2.44 6	0.32 1	1.93 2	0.94 7	0.16 5	3.67 18	0.61 23	6.37 6	3.08 1	9.14 49	17.44 83	1.80 4	0.77 3	1.76 3	0.70 2	0.00 1	0.01 36	0.00 1	0.00 1	0.01 2	0.03 15
Lahav Lipson, Zachary Teed, and Jia Deng: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. 3DV
ACVNet	2.58 8	0.53 17	2.69 7	0.84 5	0.59 20	2.61 6	0.55 18	8.35 12	5.70 13	4.45 7	9.26 29	5.77 17	3.50 24	2.24 5	4.41 38	0.00 1	0.00 1	0.00 1	0.00 1	0.17 51	0.01 2

DN-CSS_ROB	2.69 9	1.40 73	5.34 47	2.31 64	0.75 27	3.14 15	0.06 2	6.11 5	3.87 3	5.34 16	12.18 52	2.34 6	1.22 6	7.84 32	1.48 6	0.03 45	0.00 1	0.00 1	0.00 1	0.35 80	0.03 15

HITNet	2.79 10	0.77 30	4.02 30	2.03 48	0.11 3	5.58 43	0.59 22	9.24 23	5.15 9	6.42 26	7.26 18	3.66 7	2.92 20	4.07 10	3.87 35	0.00 1	0.00 1	0.00 1	0.00 1	0.06 24	0.02 6
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
DMCA	2.82 11	0.64 26	4.45 36	1.61 27	0.89 34	3.86 22	0.57 21	9.18 21	5.10 8	6.24 22	6.49 12	7.17 31	2.13 12	3.31 7	4.73 42	0.00 1	0.01 36	0.00 1	0.00 1	0.04 16	0.02 6

ccs	3.04 12	0.39 9	3.08 16	1.78 34	0.52 19	2.04 2	0.50 16	13.09 71	13.71 76	3.54 5	5.36 10	5.50 14	2.45 14	4.81 13	2.88 16	0.09 66	0.08 77	0.12 96	0.10 86	0.20 56	0.50 90

AdaStereo	3.09 13	0.58 21	3.04 14	2.84 82	0.48 18	4.08 25	1.29 44	12.16 62	7.77 38	6.03 20	9.62 31	5.79 19	1.53 8	4.56 11	1.93 9	0.00 1	0.00 1	0.00 1	0.00 1	0.02 7	0.02 6
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.
DMCA-RVC	3.17 14	1.26 66	6.39 65	2.10 52	1.51 53	3.02 14	0.56 20	7.84 11	5.06 7	9.23 52	5.19 8	8.46 39	2.72 18	6.31 18	3.22 23	0.06 62	0.11 84	0.09 93	0.02 56	0.16 49	0.08 31

BEATNet_4x	3.24 15	1.27 67	5.89 56	1.56 24	0.10 1	5.26 40	1.07 39	10.08 30	5.50 11	6.89 31	7.73 20	4.53 10	4.13 34	5.05 14	5.27 46	0.04 53	0.05 62	0.00 1	0.00 1	0.23 70	0.23 64

GANet-RSSM	3.27 16	0.60 22	3.38 20	2.94 89	2.00 68	2.61 6	0.48 14	10.93 50	6.11 17	5.47 17	10.20 35	6.57 23	5.44 55	6.25 17	2.23 11	0.00 1	0.00 1	0.08 90	0.00 1	0.17 51	0.03 15

NOSS_ROB	3.30 17	0.46 11	2.62 6	2.08 50	1.01 42	5.60 44	0.74 34	10.37 38	11.48 65	5.15 14	8.43 25	5.67 16	1.73 10	7.97 34	2.34 12	0.02 34	0.06 68	0.00 1	0.00 1	0.07 26	0.14 52

CFNet-ft	3.31 18	0.94 44	2.69 7	1.50 21	2.38 75	2.81 8	0.68 28	8.35 12	7.43 32	4.45 7	9.94 32	10.20 49	4.60 40	6.50 21	3.41 27	0.00 1	0.00 1	0.03 74	0.00 1	0.22 65	0.03 15

CFNet_RVC	3.31 18	0.94 44	2.69 7	1.50 21	2.38 75	2.81 8	0.68 28	8.35 12	7.43 32	4.45 7	9.94 32	10.20 49	4.60 40	6.49 20	3.41 27	0.00 1	0.00 1	0.03 74	0.00 1	0.22 65	0.03 15

PSMNet-RSSM	3.33 20	0.77 30	3.22 19	2.12 53	1.90 67	2.33 4	0.71 31	11.17 51	5.83 15	5.90 18	11.43 47	7.04 27	4.83 49	5.96 16	3.17 21	0.00 1	0.00 1	0.02 66	0.00 1	0.08 28	0.02 6

cf-r	3.42 21	0.81 33	3.97 29	2.41 68	2.78 84	2.85 11	0.64 27	8.44 15	6.32 18	6.25 23	11.57 48	8.97 43	3.54 26	5.93 15	3.83 34	0.00 1	0.00 1	0.00 1	0.00 1	0.09 32	0.07 28

MLCV	3.44 22	0.88 37	5.60 51	1.39 17	0.25 8	4.36 29	0.33 8	7.25 9	7.28 29	9.17 51	12.24 54	5.09 12	2.47 15	9.15 56	3.23 24	0.00 1	0.00 1	0.00 1	0.00 1	0.10 33	0.02 6

DeepPruner_ROB	3.52 23	1.14 60	4.06 31	1.12 11	1.65 57	3.65 17	0.83 36	13.96 80	4.47 6	7.80 37	10.84 40	7.05 29	2.16 13	8.14 41	3.08 20	0.07 64	0.03 53	0.00 1	0.01 52	0.32 76	0.06 27

STTStereo	3.60 24	0.93 43	6.34 63	2.71 79	2.23 73	3.68 20	0.63 26	9.42 24	6.73 22	9.87 61	6.97 16	8.84 42	3.65 27	6.85 22	3.04 18	0.00 1	0.02 47	0.01 54	0.00 1	0.02 7	0.02 6

ccs_rob	3.63 25	1.12 59	4.42 34	2.52 71	0.91 37	5.50 42	0.21 6	10.11 33	9.11 48	6.55 29	11.28 44	8.32 38	2.55 16	7.66 29	2.01 10	0.00 1	0.00 1	0.00 1	0.00 1	0.20 56	0.08 31

hitnet-ft	3.66 26	0.65 28	2.79 11	0.89 6	0.72 26	4.25 27	0.55 18	9.11 20	7.52 34	7.14 33	10.19 34	12.49 66	4.70 44	7.44 27	4.41 38	0.03 45	0.00 1	0.06 86	0.00 1	0.20 56	0.04 24

iResNet	3.68 27	0.91 40	7.94 81	2.97 90	0.34 12	4.44 33	0.48 14	7.70 10	9.74 52	7.72 36	12.74 58	4.03 8	2.87 19	8.05 37	3.37 26	0.02 34	0.01 36	0.00 1	0.00 1	0.10 33	0.09 35

acv_ft	3.69 28	0.53 17	4.58 38	2.31 64	4.71 116	7.44 63	0.86 37	8.54 16	5.70 13	9.29 53	9.26 29	5.77 17	4.15 35	2.24 5	8.13 71	0.00 1	0.00 1	0.00 1	0.00 1	0.29 72	0.01 2

ac_64	3.70 29	0.47 12	3.50 23	2.87 83	3.50 96	5.91 47	0.72 32	10.17 35	4.34 5	8.16 41	5.80 11	10.82 54	5.64 57	7.08 24	4.73 42	0.03 45	0.00 1	0.00 1	0.00 1	0.08 28	0.14 52

CFNet	3.72 30	1.10 56	5.03 42	2.49 70	1.59 54	4.90 38	0.22 7	11.38 52	9.88 54	4.80 10	11.25 43	6.44 22	3.68 28	8.33 43	3.00 17	0.00 1	0.00 1	0.00 1	0.00 1	0.22 65	0.07 28

GwcNet-RSSM	3.77 31	1.05 50	5.06 43	2.21 58	1.87 65	2.36 5	1.52 47	8.98 19	7.96 39	5.91 19	14.59 68	7.94 36	3.50 24	6.43 19	5.70 49	0.00 1	0.00 1	0.04 81	0.00 1	0.20 56	0.08 31

FADNet-RVC-Resample	3.79 32	1.62 85	12.06 96	1.43 19	0.66 22	5.94 48	2.41 55	10.18 36	8.58 46	6.28 24	4.22 5	5.33 13	4.80 48	7.71 30	3.19 22	0.17 80	0.21 101	0.17 103	0.12 88	0.41 88	0.29 77

CC-Net-ROB	3.84 33	1.07 52	5.23 45	2.65 76	2.96 87	4.22 26	0.69 30	10.43 40	7.72 36	8.78 46	8.29 24	9.61 46	4.02 33	7.16 26	3.65 32	0.13 73	0.03 53	0.02 66	0.03 65	0.05 19	0.03 15

NLCA_NET_v2_RVC	3.84 33	1.06 51	5.23 45	2.72 80	3.27 89	4.36 29	0.61 23	10.71 45	7.56 35	8.75 45	7.89 21	9.86 48	3.90 31	7.15 25	3.44 29	0.14 74	0.02 47	0.02 66	0.03 65	0.04 16	0.03 15
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
FADNet_RVC	3.91 35	1.67 88	12.95 103	0.96 9	0.75 27	5.71 46	0.54 17	10.83 48	6.60 21	3.46 3	8.09 22	4.10 9	3.40 23	9.43 60	6.33 52	0.36 97	0.44 115	0.17 103	0.46 116	0.91 105	0.95 109

FADNet-RVC	3.98 36	1.84 94	12.48 99	1.69 32	0.44 16	4.33 28	1.31 45	11.84 56	7.15 27	3.53 4	3.50 4	10.63 53	4.43 39	9.12 55	6.25 51	0.03 45	0.10 82	0.00 1	0.03 65	0.60 95	0.25 70

HSM	4.00 37	0.79 32	3.16 18	1.59 26	2.17 71	6.77 57	1.11 40	12.28 63	6.35 20	6.75 30	8.11 23	13.90 72	5.37 54	8.85 51	2.71 15	0.00 1	0.00 1	0.00 1	0.00 1	0.02 7	0.02 6

TDLM	4.11 38	1.11 58	3.54 24	1.62 28	1.04 43	3.91 23	7.41 105	10.60 43	10.67 59	6.38 25	12.59 57	5.95 20	4.77 46	8.79 50	3.04 18	0.58 109	0.00 1	0.01 54	0.00 1	0.19 55	0.12 47

CBMV_ROB	4.14 39	0.52 14	3.14 17	1.30 14	0.77 30	6.92 58	1.97 53	10.11 33	9.58 50	8.92 48	14.20 66	7.12 30	5.90 60	8.65 47	3.50 31	0.01 27	0.05 62	0.00 1	0.00 1	0.04 16	0.09 35

CVANet_RVC	4.16 40	1.16 61	3.60 25	1.94 44	1.46 51	3.92 24	4.68 84	10.89 49	8.34 44	7.58 35	10.84 40	10.27 51	6.62 64	8.56 46	2.69 14	0.39 99	0.00 1	0.00 1	0.01 52	0.21 63	0.09 35

HSM-Net_RVC	4.20 41	0.32 1	2.76 10	0.63 4	0.69 24	6.95 59	1.69 50	11.96 57	8.36 45	8.83 47	12.17 51	15.18 80	4.21 37	6.91 23	3.30 25	0.02 34	0.02 47	0.00 1	0.00 1	0.01 2	0.01 2
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
DSFCA	4.21 42	0.50 13	5.45 49	1.34 15	1.68 59	7.04 60	4.51 81	10.73 46	7.00 24	10.78 69	6.80 13	8.48 40	4.63 42	9.91 64	5.12 45	0.02 34	0.06 68	0.02 66	0.03 65	0.08 28	0.03 15

FADNet	4.23 43	1.65 87	11.75 95	1.64 30	0.80 32	4.80 35	0.77 35	13.76 79	11.65 67	3.97 6	5.24 9	9.62 47	5.14 51	8.40 44	3.78 33	0.21 84	0.04 58	0.07 89	0.05 76	1.14 110	0.10 42

iResNet_ROB	4.23 43	1.02 48	4.90 41	2.18 55	0.93 40	2.92 12	0.37 11	15.10 92	16.91 91	7.89 39	10.51 38	7.03 26	3.07 21	8.16 42	3.46 30	0.01 27	0.00 1	0.00 1	0.00 1	0.10 33	0.02 6

HGLStereo	4.25 45	0.52 14	4.65 39	3.09 92	5.18 118	6.51 55	0.73 33	10.37 38	6.74 23	7.32 34	12.44 56	7.00 25	7.21 68	7.46 28	5.51 48	0.00 1	0.02 47	0.00 1	0.00 1	0.06 24	0.10 42

iResNetv2_ROB	4.28 46	1.43 74	7.17 76	2.91 84	1.26 47	4.36 29	1.62 48	13.64 78	10.25 58	9.83 60	11.41 45	7.68 33	4.00 32	7.75 31	1.85 8	0.00 1	0.00 1	0.00 1	0.00 1	0.37 82	0.09 35

StereoDRNet-Refined	4.46 47	0.62 25	3.80 28	1.92 41	0.40 14	9.35 71	0.15 5	10.02 28	8.83 47	12.69 81	11.62 49	9.34 44	3.87 30	8.06 38	8.02 66	0.00 1	0.00 1	0.01 54	0.05 76	0.20 56	0.26 73
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
NVstereo2D	4.51 48	0.82 34	6.86 74	3.28 97	3.38 93	8.16 66	3.13 60	10.51 41	15.15 81	4.90 11	6.89 15	7.87 34	4.78 47	9.88 63	3.91 36	0.01 27	0.00 1	0.00 1	0.06 78	0.02 7	0.58 96

DLCB_ROB	4.51 48	0.91 40	3.78 27	2.19 56	1.07 44	6.28 49	3.09 59	9.78 27	7.72 36	10.65 67	12.97 59	13.91 73	3.71 29	8.72 48	5.30 47	0.00 1	0.00 1	0.00 1	0.00 1	0.03 13	0.10 42

RASNet	4.52 50	0.61 24	4.42 34	3.42 101	4.68 115	4.58 34	0.99 38	9.54 26	8.01 40	5.28 15	11.42 46	10.34 52	8.88 79	9.28 58	8.68 76	0.15 76	0.00 1	0.00 1	0.00 1	0.03 13	0.04 24

SGM-Forest	4.96 51	0.32 1	2.84 12	1.21 12	0.64 21	10.23 81	6.64 100	11.55 53	10.98 60	10.94 71	13.59 62	11.65 61	4.30 38	8.94 53	4.63 41	0.11 69	0.04 58	0.00 1	0.00 1	0.05 19	0.46 87
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
PA-Net	4.98 52	1.47 76	7.42 78	2.40 67	2.14 70	8.73 68	3.64 69	12.42 64	13.11 72	7.03 32	7.57 19	7.88 35	6.52 63	10.16 66	7.82 64	0.02 34	0.03 53	0.00 1	0.00 1	0.11 37	1.07 112
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
AANet_RVC	5.01 53	1.74 89	6.38 64	1.96 46	1.29 49	2.26 3	1.69 50	10.07 29	18.53 94	7.88 38	18.15 85	8.49 41	2.70 17	10.59 70	7.04 57	0.96 119	0.15 93	0.02 66	0.00 1	0.13 42	0.12 47

PSMNet_ROB	5.02 54	1.63 86	6.03 58	1.90 40	1.83 64	9.57 75	6.35 97	15.58 97	7.23 28	6.15 21	10.48 37	12.22 64	4.16 36	8.02 35	8.71 77	0.02 34	0.01 36	0.01 54	0.10 86	0.20 56	0.12 47

CBMV	5.35 55	0.91 40	3.67 26	1.62 28	0.44 16	10.09 79	7.19 104	12.49 65	12.33 71	12.22 77	14.69 69	10.93 56	6.48 62	8.51 45	4.96 44	0.02 34	0.15 93	0.00 1	0.00 1	0.17 51	0.17 57
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
StereoDRNet	5.59 56	1.75 90	6.80 72	3.12 93	4.45 110	10.61 84	4.35 77	18.80 109	9.73 51	12.22 77	6.87 14	11.44 59	4.65 43	8.09 40	8.26 72	0.02 34	0.11 84	0.00 1	0.03 65	0.20 56	0.28 76

ETE_ROB	5.80 57	1.77 91	6.33 62	1.44 20	0.78 31	6.43 53	6.90 101	12.53 66	8.08 41	12.93 85	14.89 70	21.13 108	5.87 59	9.83 62	6.57 55	0.04 53	0.01 36	0.00 1	0.02 56	0.08 28	0.33 78

DRN-Test	5.87 58	0.98 46	5.89 56	2.69 78	3.65 101	12.37 92	3.35 63	20.07 120	10.20 57	11.93 76	12.31 55	11.06 58	5.31 53	7.89 33	9.05 80	0.04 53	0.05 62	0.04 81	0.04 74	0.18 54	0.25 70

NCCL2	5.88 59	1.59 83	5.44 48	1.87 37	0.92 38	9.55 74	11.55 120	12.11 59	9.94 55	9.67 59	8.85 27	22.28 110	7.41 69	8.78 49	7.17 58	0.01 27	0.00 1	0.03 74	0.00 1	0.13 42	0.23 64

NaN_ROB	6.00 60	1.24 64	6.29 61	1.34 15	1.68 59	9.60 76	10.31 116	15.09 90	15.79 84	12.62 80	8.95 28	11.67 62	5.83 58	11.78 79	6.41 54	0.05 60	0.13 90	0.08 90	0.20 95	0.22 65	0.79 104

DANet	6.02 61	1.23 63	8.45 83	3.86 110	3.94 104	7.64 65	1.34 46	9.51 25	7.00 24	13.39 87	15.53 74	15.99 84	7.02 67	12.14 80	12.37 100	0.19 82	0.12 89	0.02 66	0.03 65	0.13 42	0.56 95

XPNet_ROB	6.03 62	1.22 62	5.61 52	2.56 74	0.90 36	6.32 50	7.07 102	12.92 69	8.30 43	14.76 92	15.13 72	19.84 102	6.66 66	10.36 67	8.58 75	0.02 34	0.04 58	0.00 1	0.03 65	0.11 37	0.24 67

Anonymous Stereo	6.16 63	3.15 112	23.75 121	2.97 90	2.48 80	4.39 32	13.30 122	9.21 22	9.86 53	9.56 58	8.76 26	6.79 24	1.99 11	13.50 92	13.04 103	0.01 27	0.05 62	0.00 1	0.06 78	0.22 65	0.19 59

GANet	6.22 64	1.07 52	4.07 32	2.27 60	0.89 34	9.19 70	9.52 111	12.02 58	8.13 42	10.72 68	29.09 110	13.86 71	7.52 71	11.00 74	4.39 37	0.36 97	0.00 1	0.02 66	0.02 56	0.12 39	0.08 31

DISCO	6.28 65	0.57 20	5.78 54	3.43 102	1.17 45	11.22 86	3.39 64	12.14 61	16.16 86	6.52 28	11.22 42	16.96 87	6.32 61	19.51 115	10.74 93	0.00 1	0.00 1	0.00 1	0.00 1	0.35 80	0.11 45

RYNet	6.34 66	0.89 39	5.88 55	1.41 18	4.48 112	15.97 103	4.18 73	13.41 74	16.49 87	10.81 70	7.00 17	14.33 75	8.72 77	9.43 60	13.71 104	0.00 1	0.01 36	0.00 1	0.00 1	0.02 7	0.07 28

GwcNet	6.42 67	1.97 97	10.92 90	2.59 75	5.58 121	11.55 88	2.21 54	14.10 82	16.52 90	10.04 63	17.19 82	10.86 55	5.61 56	9.23 57	8.84 79	0.01 27	0.06 68	0.03 74	0.08 82	0.56 91	0.40 82

GANetREF_RVC	6.56 68	2.89 107	7.58 80	3.41 100	0.40 14	12.96 95	9.58 112	15.09 90	17.25 93	10.33 64	10.62 39	12.27 65	8.16 73	12.21 81	4.53 40	0.41 101	0.00 1	0.00 1	0.02 56	3.12 126	0.39 81
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
LALA_ROB	6.58 69	1.80 93	6.25 60	1.26 13	0.94 41	10.08 78	9.02 108	16.00 98	11.51 66	12.74 82	13.02 60	24.77 112	5.25 52	10.56 69	8.02 66	0.04 53	0.05 62	0.00 1	0.02 56	0.10 33	0.25 70

S-Stereo	6.63 70	0.84 35	9.67 87	3.15 94	3.48 95	6.49 54	6.22 92	12.99 70	22.84 107	9.48 54	15.51 73	12.00 63	8.43 74	8.04 36	10.70 92	0.12 72	0.17 97	0.00 1	0.38 109	0.13 42	1.92 121

DeepPrunerF	6.75 71	2.69 105	23.31 120	3.68 105	7.16 127	3.78 21	4.29 75	13.42 75	20.13 104	8.13 40	10.46 36	7.18 32	8.06 72	11.10 75	9.44 82	0.24 86	0.15 93	0.29 112	0.42 112	0.66 98	0.45 85

edge stereo	6.76 72	1.01 47	6.76 71	2.20 57	2.45 79	6.41 52	2.45 56	14.84 88	11.98 70	15.29 93	18.31 86	22.02 109	12.56 94	10.82 71	7.49 60	0.03 45	0.06 68	0.11 95	0.03 65	0.30 73	0.14 52

NCC-stereo	6.77 73	1.49 77	6.48 66	2.92 86	4.40 106	7.43 61	3.61 67	19.52 116	13.29 73	8.39 43	16.91 77	15.96 82	12.13 92	12.85 86	7.70 62	1.47 123	0.11 84	0.01 54	0.42 112	0.14 47	0.24 67

Abc-Net	6.77 73	1.49 77	6.48 66	2.92 86	4.40 106	7.43 61	3.61 67	19.52 116	13.29 73	8.39 43	16.91 77	15.96 82	12.13 92	12.85 86	7.70 62	1.47 123	0.11 84	0.01 54	0.42 112	0.14 47	0.24 67
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
FAT-Stereo	6.78 75	0.68 29	6.80 72	2.30 62	1.77 63	5.63 45	4.20 74	18.79 108	18.62 95	10.53 66	17.15 80	18.52 96	13.74 102	8.86 52	7.38 59	0.03 45	0.15 93	0.01 54	0.07 80	0.12 39	0.26 73

RP	6.84 76	1.29 71	5.53 50	3.92 111	5.18 118	6.32 50	3.53 65	11.73 55	15.31 82	9.54 57	22.38 93	18.25 94	14.47 103	10.11 65	7.49 60	0.91 118	0.01 36	0.12 96	0.15 91	0.33 77	0.19 59

RGC	6.88 77	2.23 101	6.13 59	4.05 112	4.73 117	8.94 69	2.78 58	15.19 94	11.74 68	11.13 72	19.34 88	17.86 91	10.42 86	13.02 89	8.03 68	0.73 112	0.01 36	0.24 111	0.41 111	0.31 75	0.38 80

Nwc_Net	6.97 78	1.25 65	6.63 69	3.82 109	3.37 92	10.83 85	1.67 49	19.56 118	11.35 63	8.36 42	23.62 96	17.19 89	11.44 91	11.21 76	8.08 70	0.80 114	0.00 1	0.00 1	0.02 56	0.13 42	0.09 35

STTRV1_RVC	7.02 79	1.10 56	12.88 101	3.32 98	6.92 126	11.90 91	4.00 71	15.07 89	11.94 69	9.51 55	14.57 67	11.63 60	8.73 78	12.65 85	8.06 69	3.32 129	2.75 130	0.41 120	0.12 88	1.38 116	0.11 45

ADCRef	7.27 80	1.38 72	16.37 109	2.52 71	3.30 91	11.63 89	3.16 61	10.80 47	9.35 49	13.03 86	25.27 104	8.17 37	8.92 80	8.06 38	21.81 119	0.15 76	0.08 77	0.16 102	0.34 107	0.38 83	0.58 96

psmorigin	7.34 81	1.58 82	18.31 113	2.35 66	0.87 33	6.72 56	1.70 52	10.63 44	7.14 26	19.77 105	22.71 94	20.13 104	11.34 90	12.96 88	9.49 83	0.04 53	0.17 97	0.06 86	0.17 92	0.21 63	0.50 90

CSAN	7.62 82	1.60 84	6.56 68	1.83 35	0.66 22	12.40 93	10.52 118	14.45 85	21.32 106	14.19 89	15.98 76	17.84 90	13.02 99	12.32 82	8.38 73	0.09 66	0.07 75	0.03 74	0.04 74	0.33 77	0.67 102

stereogan	7.69 83	0.88 37	7.08 75	3.49 103	3.93 103	18.98 111	3.23 62	16.52 100	19.58 101	9.93 62	18.92 87	20.50 106	9.04 81	14.07 96	6.14 50	0.26 89	0.04 58	0.21 108	0.03 65	0.63 97	0.33 78

pmcnn	7.72 84	1.27 67	9.42 86	2.91 84	3.14 88	9.44 72	6.23 93	12.56 67	16.51 88	14.53 90	24.08 98	27.44 118	8.49 75	9.32 59	8.44 74	0.06 62	0.08 77	0.00 1	0.00 1	0.30 73	0.15 55

AF-Net	7.78 85	1.44 75	6.68 70	3.37 99	4.50 113	8.61 67	2.69 57	17.07 103	20.17 105	9.52 56	24.02 97	20.31 105	14.59 104	11.58 78	9.84 88	0.61 110	0.00 1	0.12 96	0.00 1	0.38 83	0.12 47

PASM	7.90 86	4.22 118	21.97 118	3.25 96	3.29 90	5.39 41	6.57 99	10.57 42	19.09 97	12.77 83	13.92 64	18.11 93	9.51 83	13.79 95	10.77 95	0.19 82	0.45 116	0.29 112	1.08 125	1.49 118	1.19 114

PWCDC_ROB	7.92 87	3.17 114	7.48 79	5.73 123	4.40 106	10.45 82	0.35 10	14.52 86	28.19 115	10.36 65	31.27 114	7.04 27	9.14 82	13.22 91	8.78 78	2.74 128	0.02 47	0.00 1	0.00 1	1.31 115	0.17 57

ADCP+	8.09 88	1.79 92	14.50 107	1.54 23	4.28 105	16.57 105	5.20 86	12.80 68	11.20 62	12.83 84	17.07 79	11.02 57	10.80 88	17.59 111	23.18 122	0.03 45	0.05 62	0.01 54	0.18 93	0.39 87	0.81 105

SuperB	8.10 89	3.15 112	24.67 122	2.65 76	1.23 46	9.88 77	4.29 75	10.18 36	30.07 118	11.53 74	12.18 52	6.12 21	6.65 65	10.50 68	14.47 106	0.14 74	0.11 84	0.35 116	0.25 99	13.06 135	0.48 88

PWC_ROB	8.24 90	3.13 110	12.74 100	2.43 69	4.43 109	7.51 64	1.22 41	16.63 101	19.24 99	16.08 95	28.29 107	13.99 74	10.16 85	13.63 94	14.06 105	0.42 104	0.00 1	0.05 84	0.00 1	0.59 94	0.27 75

MDST_ROB	8.37 91	0.32 1	9.03 84	4.18 115	2.42 78	26.86 124	6.14 90	19.36 113	13.52 75	27.09 119	22.75 95	9.47 45	4.74 45	15.06 102	6.34 53	0.02 34	0.02 47	0.00 1	0.00 1	0.02 7	0.13 51

G-Net	8.41 92	1.54 80	10.97 91	5.73 123	3.60 98	26.19 121	4.41 78	10.10 31	7.42 30	19.71 103	24.99 102	14.38 76	15.83 105	10.99 72	9.53 84	0.50 107	0.46 117	0.19 106	0.25 99	0.80 102	0.66 100

STTStereo_v2	8.41 92	1.54 80	10.97 91	5.73 123	3.60 98	26.19 121	4.41 78	10.10 31	7.42 30	19.71 103	24.99 102	14.38 76	15.83 105	10.99 72	9.53 84	0.50 107	0.46 117	0.19 106	0.25 99	0.80 102	0.66 100

XQC	8.43 94	3.58 116	16.40 110	2.92 86	2.17 71	13.22 97	3.60 66	14.64 87	25.86 112	11.87 75	12.04 50	15.06 79	10.67 87	15.24 103	19.41 111	0.39 99	0.08 77	0.05 84	0.07 80	0.84 104	0.45 85

FBW_ROB	8.50 95	1.03 49	7.98 82	1.93 43	1.28 48	13.10 96	6.23 93	22.50 124	18.98 96	18.82 100	14.91 71	19.06 99	10.04 84	18.41 112	9.83 87	0.62 111	0.22 102	1.82 131	0.82 122	0.99 108	1.36 116

aanetorigin	8.72 96	3.29 115	27.55 124	1.95 45	3.84 102	4.93 39	5.39 88	5.49 3	10.05 56	27.85 120	31.47 115	15.80 81	12.78 96	9.04 54	11.98 98	0.25 87	0.22 102	0.22 109	0.25 99	1.28 114	0.84 107

RTSC	9.15 97	3.00 109	13.57 105	3.72 107	1.76 62	11.82 90	0.46 13	16.95 102	36.83 127	15.80 94	15.53 74	12.91 68	7.46 70	20.01 118	21.76 118	0.31 94	0.13 90	0.01 54	0.08 82	0.57 92	0.41 84

WCMA_ROB	9.21 98	0.87 36	7.37 77	2.54 73	2.13 69	13.59 98	5.80 89	11.64 54	14.01 77	24.43 116	32.99 120	27.09 117	18.02 109	12.51 84	9.85 89	0.81 115	0.07 75	0.01 54	0.01 52	0.16 49	0.23 64

MSMD_ROB	9.28 99	1.09 55	4.65 39	1.58 25	0.39 13	16.52 104	4.41 78	13.60 76	14.87 80	22.34 108	39.89 128	25.67 114	20.71 118	12.42 83	6.98 56	0.34 96	0.03 53	0.00 1	0.00 1	0.05 19	0.09 35

ADCPNet	9.54 100	2.39 103	31.46 126	2.09 51	1.60 55	16.71 107	6.39 98	12.11 59	11.45 64	13.53 88	21.45 91	19.41 100	10.94 89	14.38 98	21.54 117	0.27 92	1.16 124	0.39 119	1.49 129	0.58 93	1.45 117

SHD	9.61 101	2.60 104	12.46 98	3.69 106	3.54 97	9.47 73	1.25 42	20.16 121	37.84 130	18.19 99	21.24 90	16.96 87	12.83 98	14.47 100	16.05 108	0.32 95	0.13 90	0.01 54	0.08 82	0.38 83	0.48 88

PDISCO_ROB	9.62 102	1.99 98	11.51 93	9.88 131	9.61 132	21.48 115	3.83 70	19.33 112	28.49 116	11.27 73	14.17 65	19.92 103	5.02 50	16.35 108	9.18 81	5.28 132	0.41 112	0.14 100	0.09 85	2.05 122	2.36 127

MFN_U_SF_DS_RVC	9.78 103	4.27 119	14.47 106	2.29 61	2.85 86	23.40 119	13.62 123	13.60 76	19.54 100	19.42 102	24.27 99	16.74 86	8.59 76	17.05 110	7.98 65	1.25 122	1.68 128	0.17 103	2.63 131	0.72 100	1.04 110

SGM_RVC	10.08 104	0.60 22	3.42 21	2.30 62	0.32 11	19.41 112	6.33 96	18.95 110	14.64 78	25.14 118	24.32 100	33.34 126	18.79 113	19.86 116	12.55 102	0.25 87	0.26 107	0.22 109	0.24 98	0.34 79	0.40 82
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DPSNet	10.14 105	1.88 96	16.82 111	1.85 36	1.73 61	24.84 120	17.20 132	19.92 119	27.41 114	12.23 79	13.62 63	16.52 85	18.35 110	14.42 99	12.50 101	0.78 113	0.54 121	0.08 90	0.25 99	1.18 111	0.59 99

ADCL	10.16 106	2.11 99	19.36 115	1.92 41	1.88 66	22.23 116	8.91 107	14.04 81	23.56 109	14.62 91	26.19 105	12.75 67	13.59 101	16.06 107	22.95 121	0.26 89	0.18 99	0.75 123	0.65 118	0.69 99	0.58 96

FCDSN-DC	10.24 107	0.56 19	3.49 22	1.96 46	1.29 49	16.90 108	4.59 83	17.16 104	19.10 98	24.64 117	32.46 119	33.82 127	22.14 123	15.93 106	10.45 90	0.04 53	0.00 1	0.00 1	0.00 1	0.05 19	0.21 61
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
ADCMid	10.24 107	3.13 110	20.70 116	2.21 58	2.39 77	11.23 87	6.19 91	14.17 83	11.19 61	23.20 114	22.25 92	17.89 92	19.54 115	18.51 113	26.21 127	0.45 105	0.42 114	1.10 127	1.29 126	1.56 120	1.18 113

SANet	10.64 109	1.86 95	10.91 89	1.76 33	0.71 25	14.62 101	9.23 110	19.18 111	37.14 128	19.22 101	27.96 106	25.86 115	19.11 114	13.02 89	10.63 91	0.08 65	0.06 68	0.03 74	0.02 56	0.62 96	0.81 105

FC-DCNN	10.72 110	0.52 14	4.27 33	1.88 38	1.63 56	17.18 109	5.29 87	18.20 106	19.69 102	28.50 121	34.51 123	34.03 128	21.48 121	15.89 105	11.15 97	0.03 45	0.01 36	0.02 66	0.01 52	0.07 26	0.09 35

AnyNet_C32	10.98 111	5.58 121	22.79 119	4.16 113	5.83 122	15.64 102	14.30 124	13.18 72	17.15 92	16.44 97	20.52 89	14.68 78	13.44 100	22.46 120	30.08 132	0.17 80	0.26 107	0.36 117	0.36 108	1.23 112	0.91 108

MeshStereo	11.52 112	1.52 79	4.55 37	1.89 39	1.46 51	19.87 114	5.11 85	20.66 122	15.91 85	32.67 126	34.51 123	39.34 133	21.15 119	18.74 114	12.10 99	0.11 69	0.06 68	0.01 54	0.00 1	0.45 90	0.22 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
MFN_U_SF_RVC	12.94 113	3.66 117	25.81 123	3.61 104	2.26 74	22.77 117	4.55 82	27.10 129	20.06 103	23.90 115	28.99 109	30.53 123	16.98 107	19.92 117	20.26 113	1.24 121	1.07 123	0.98 126	1.33 127	1.80 121	2.04 123

ADCS	13.02 114	4.93 120	28.38 125	3.17 95	2.67 83	13.61 99	10.83 119	18.70 107	33.46 122	22.59 109	24.78 101	19.59 101	18.51 112	23.40 123	32.16 134	0.10 68	0.19 100	0.37 118	0.18 93	1.26 113	1.46 118

MFMNet_re	13.29 115	8.60 128	18.29 112	9.75 130	7.25 129	19.65 113	14.84 128	20.71 123	30.72 119	23.03 112	28.77 108	18.85 98	26.09 128	13.55 93	9.82 86	2.44 126	1.35 127	0.34 115	0.23 97	4.78 130	6.69 131

EDNetEfficient	13.82 116	6.19 123	54.79 132	2.83 81	3.60 98	4.86 37	8.57 106	8.61 18	32.90 121	40.77 132	30.59 113	18.33 95	21.87 122	11.38 77	25.36 125	0.15 76	0.23 104	0.06 86	1.03 124	2.54 124	1.79 119

LSM	14.01 117	5.95 122	33.49 127	6.78 128	43.61 137	10.22 80	9.98 115	15.16 93	22.93 108	23.07 113	32.34 118	18.52 96	12.67 95	15.45 104	11.10 96	0.16 79	0.51 120	0.09 93	0.32 105	1.08 109	16.85 136

SAMSARA	14.63 118	2.74 106	12.38 97	12.65 134	6.74 125	36.50 130	72.93 138	19.36 113	23.77 110	16.20 96	13.04 61	29.21 120	12.78 96	16.98 109	15.21 107	0.11 69	0.26 107	0.03 74	0.14 90	0.76 101	0.77 103

SPS-STEREO	15.04 119	6.23 124	13.21 104	11.34 133	11.65 135	23.30 118	7.15 103	24.16 126	15.65 83	31.78 125	29.19 111	31.62 124	21.32 120	24.62 124	19.50 112	7.59 134	4.19 135	3.22 132	1.48 128	6.99 134	6.54 130
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
PVD	15.44 120	2.93 108	14.67 108	4.21 116	3.39 94	17.43 110	4.16 72	27.84 132	48.84 135	31.02 124	43.54 132	29.76 122	30.81 132	25.97 126	21.40 116	0.23 85	0.41 112	0.04 81	0.33 106	0.41 88	1.33 115

SGM+DAISY	15.62 121	7.26 127	19.28 114	8.94 129	10.11 134	26.25 123	10.49 117	19.36 113	14.65 79	30.64 123	33.59 121	33.00 125	22.32 124	24.96 125	16.42 109	7.90 135	6.25 136	4.51 133	3.37 132	5.86 132	7.20 132

NVStereoNet_ROB	16.04 122	6.75 125	12.90 102	6.37 127	7.42 130	12.89 94	9.74 113	22.78 125	25.12 111	30.32 122	46.19 134	34.37 129	25.38 126	21.48 119	21.38 115	5.94 133	3.10 133	6.07 134	10.09 136	4.01 128	8.54 134
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
AnyNet_C01	16.12 123	10.81 132	59.36 133	4.42 117	2.49 81	30.06 126	15.15 130	17.51 105	16.51 88	17.88 98	37.69 127	24.04 111	17.54 108	29.60 129	33.29 135	0.28 93	0.38 110	0.43 121	0.42 112	2.57 125	1.98 122

MSC_U_SF_DS_RVC	16.41 124	6.93 126	21.83 117	5.94 126	2.81 85	38.71 131	14.59 125	24.55 127	34.87 125	33.66 127	34.35 122	29.24 121	24.20 125	22.59 121	17.95 110	2.52 127	2.81 131	1.17 129	1.51 130	5.89 133	2.16 124

ELAS_RVC	16.54 125	2.26 102	10.09 88	5.50 122	4.46 111	28.28 125	16.72 131	25.55 128	33.54 123	40.19 130	40.30 130	36.68 131	30.03 130	29.40 128	20.61 114	0.98 120	1.21 125	0.86 125	0.70 120	1.39 117	2.16 124
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAS	16.72 126	2.14 100	9.23 85	4.92 119	4.53 114	32.66 129	15.11 129	27.40 130	28.68 117	40.27 131	44.90 133	38.33 132	30.50 131	26.44 127	21.94 120	0.88 116	1.23 126	0.67 122	0.89 123	1.49 118	2.18 126
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
LE_ROB	16.73 127	1.28 70	11.61 94	3.72 107	1.65 57	16.67 106	9.17 109	14.39 84	55.91 136	63.81 136	40.86 131	35.94 130	37.73 135	14.24 97	26.87 128	0.05 60	0.10 82	0.13 99	0.22 96	0.12 39	0.15 55

SGM-ForestM	16.99 128	1.08 54	5.74 53	2.12 53	0.75 27	31.63 128	12.21 121	27.80 131	32.25 120	37.88 128	39.99 129	52.96 136	35.20 134	33.60 131	24.47 123	0.26 89	0.39 111	0.31 114	0.39 110	0.26 71	0.53 94

DispFullNet	17.47 129	26.01 135	33.98 128	22.58 136	20.86 136	13.84 100	1.28 43	16.50 99	26.27 113	19.97 106	17.17 81	20.52 107	18.49 111	22.86 122	10.76 94	5.13 131	2.83 132	30.72 136	7.72 134	20.86 136	11.01 135

EDNetEfficientorigin	17.87 130	14.00 133	99.53 141	1.64 30	0.92 38	10.55 83	6.26 95	13.21 73	34.84 124	38.20 129	36.91 126	27.47 119	26.32 129	14.71 101	25.69 126	0.04 53	0.06 68	0.14 100	0.52 117	3.16 127	3.27 128

RTSA	18.87 131	9.32 130	86.48 136	4.95 120	6.10 123	42.08 133	14.70 126	15.49 95	41.06 132	22.65 110	32.32 116	13.77 69	19.54 115	37.98 132	28.96 129	0.41 101	0.23 104	0.00 1	0.02 56	0.91 105	0.50 90

RTS	18.87 131	9.32 130	86.48 136	4.95 120	6.10 123	42.08 133	14.70 126	15.49 95	41.06 132	22.65 110	32.32 116	13.77 69	19.54 115	37.98 132	28.96 129	0.41 101	0.23 104	0.00 1	0.02 56	0.91 105	0.50 90

MANE	19.47 133	1.27 67	5.07 44	4.69 118	5.55 120	30.49 127	9.94 114	34.01 134	37.27 129	44.13 133	51.57 137	52.51 135	40.41 136	33.58 130	24.81 124	0.89 117	0.86 122	1.11 128	9.72 135	0.38 83	1.06 111

BEATNet-Init1	23.31 134	9.03 129	41.67 129	4.17 114	2.53 82	45.68 135	19.47 133	33.43 133	38.45 131	47.59 135	49.10 135	59.31 137	41.80 137	38.35 134	29.21 131	0.47 106	0.50 119	0.81 124	0.66 119	2.10 123	1.86 120

MADNet+	27.07 135	33.84 136	90.97 138	20.14 135	7.47 131	48.43 136	47.10 135	35.43 135	36.46 126	20.11 107	30.05 112	25.29 113	35.08 133	45.50 136	50.28 136	2.13 125	2.00 129	1.19 130	0.76 121	4.71 129	4.43 129

PWCK	30.53 136	44.32 137	47.25 131	29.76 137	7.23 128	40.78 132	27.10 134	44.73 136	44.32 134	47.31 134	36.37 125	47.16 134	26.05 127	41.26 135	31.87 133	21.83 136	4.03 134	29.50 135	4.67 133	27.17 137	7.80 133

DPSimNet_ROB	53.45 137	64.73 138	44.39 130	53.97 138	45.39 138	53.66 137	54.83 136	55.15 137	57.87 137	64.16 137	50.83 136	63.40 138	53.34 138	46.45 137	65.81 137	63.13 139	26.54 138	57.94 139	51.11 138	45.52 138	50.69 137

ASD4	75.85 138	81.57 139	69.25 134	68.23 139	80.87 139	96.24 139	99.15 141	77.50 138	93.47 139	77.59 138	85.63 138	78.32 139	76.43 139	77.89 138	82.61 138	58.30 138	62.67 139	55.02 138	62.43 139	68.35 139	65.49 138

MADNet++	82.84 139	82.38 140	73.57 135	87.72 140	82.97 140	93.14 138	69.15 137	86.42 139	82.50 138	93.46 139	86.70 139	86.28 140	80.92 140	88.34 139	88.84 139	86.83 140	84.17 140	72.64 140	68.92 140	80.47 140	81.42 139

MEDIAN_ROB	98.41 140	99.70 141	99.30 140	97.09 141	97.02 141	96.89 140	95.77 140	97.66 140	97.28 140	98.79 142	98.94 140	99.18 141	98.14 141	96.89 140	96.88 140	99.96 143	99.16 141	100.00 141	99.99 141	99.69 141	99.88 140

AVERAGE_ROB	99.62 141	99.95 142	98.81 139	100.00 146	100.00 142	98.08 141	95.47 139	100.00 143	100.00 141	100.00 143	100.00 141	100.00 144	100.00 142	100.00 143	99.99 141	100.00 145	100.00 142	100.00 141	100.00 142	100.00 144	100.00 144

DGTPSM_ROB	99.90 142	100.00 143	99.99 142	99.99 144	100.00 142	100.00 142	100.00 142	99.97 141	100.00 141	98.35 140	100.00 141	99.84 142	100.00 142	99.98 141	99.99 141	99.99 144	100.00 142	100.00 141	100.00 142	100.00 144	100.00 144

DPSMNet_ROB	99.91 143	100.00 143	99.99 142	99.99 144	100.00 142	100.00 142	100.00 142	99.98 142	100.00 141	98.35 140	100.00 141	99.84 142	100.00 142	99.98 141	99.99 141	100.00 145	100.00 142	100.00 141	100.00 142	100.00 144	100.00 144

DPSM_ROB	99.95 144	100.00 143	100.00 144	99.76 142	100.00 142	100.00 142	100.00 142	100.00 143	100.00 141	100.00 143	100.00 141	100.00 144	100.00 142	100.00 143	100.00 144	99.21 141	100.00 142	100.00 141	100.00 142	99.99 142	99.95 141

DPSM	99.95 144	100.00 143	100.00 144	99.76 142	100.00 142	100.00 142	100.00 142	100.00 143	100.00 141	100.00 143	100.00 141	100.00 144	100.00 142	100.00 143	100.00 144	99.21 141	100.00 142	100.00 141	100.00 142	99.99 142	99.95 141

LSM0	100.00 146	100.00 143	100.00 144	100.00 146	100.00 142	100.00 142	100.00 142	100.00 143	100.00 141	100.00 143	100.00 141	100.00 144	100.00 142	100.00 143	100.00 144	100.00 145	100.00 142	100.00 141	100.00 142	100.00 144	99.99 143

MSMDNet													1.26 7

FADE		17.27 134		10.46 132	9.90 133							26.54 116				30.62 137	14.22 137	38.39 137	37.63 137	5.22 131

Low-res two-view benchmark