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	0.44 1	0.09 4	0.94 2	0.02 10	0.00 1	1.24 3	0.09 28	3.65 27	0.11 1	1.87 30	0.42 23	0.06 2	0.01 1	0.25 2	0.05 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.02 25

R-Stereo	0.44 1	0.09 4	0.94 2	0.02 10	0.00 1	1.24 3	0.09 28	3.65 27	0.11 1	1.87 30	0.42 23	0.06 2	0.01 1	0.25 2	0.05 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.02 25

DPM-Stereo	0.58 3	0.23 17	0.90 1	0.08 32	0.00 1	1.43 19	0.01 7	6.01 86	1.38 24	0.92 15	0.11 4	0.04 1	0.19 15	0.12 1	0.07 3	0.00 1	0.01 58	0.00 1	0.00 1	0.03 17	0.01 9

PMTNet	0.62 4	0.17 10	1.33 10	0.00 1	0.02 28	2.50 53	0.00 1	2.42 2	1.12 11	0.34 3	0.13 5	0.06 2	0.02 3	4.08 22	0.20 4	0.00 1	0.00 1	0.00 1	0.00 1	0.03 17	0.00 1

AdaStereo	0.65 5	0.09 4	1.12 5	0.02 10	0.00 1	1.45 21	0.23 49	4.38 54	1.44 33	1.17 22	0.37 21	1.56 20	0.18 13	0.56 4	0.37 9	0.00 1	0.00 1	0.00 1	0.00 1	0.01 6	0.00 1
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.
DN-CSS_ROB	0.77 6	0.90 79	2.01 26	0.85 90	0.00 1	1.47 23	0.01 7	2.92 6	0.93 7	0.12 1	0.53 27	0.44 6	0.16 11	4.33 27	0.35 7	0.00 1	0.00 1	0.00 1	0.00 1	0.30 88	0.01 9

CFNet_RVC	0.77 6	0.46 33	1.34 11	0.01 5	0.27 71	1.32 8	0.02 11	2.40 1	0.68 5	0.63 9	0.18 8	3.23 53	0.21 18	3.97 21	0.54 19	0.00 1	0.00 1	0.01 72	0.00 1	0.03 17	0.02 25

HITNet	0.80 8	0.58 50	2.14 29	0.69 86	0.00 1	1.35 10	0.02 11	4.50 58	1.15 12	1.76 27	0.16 6	0.60 7	0.30 22	1.13 5	1.64 39	0.00 1	0.00 1	0.00 1	0.00 1	0.06 36	0.00 1
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
StereoDRNet-Refined	0.83 9	0.12 8	1.18 7	0.02 10	0.01 21	1.31 7	0.00 1	3.33 15	1.07 8	1.92 33	1.24 44	2.48 40	0.18 13	1.86 7	1.67 40	0.00 1	0.00 1	0.00 1	0.00 1	0.14 55	0.05 44
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
STTStereo	0.83 9	0.34 22	2.44 34	0.24 54	0.15 58	1.41 17	0.10 33	3.02 7	1.55 41	1.34 24	0.17 7	1.77 24	0.45 31	3.42 12	0.30 5	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.01 9

DMCA	0.85 11	0.51 41	1.62 15	0.02 10	0.19 62	1.14 1	0.09 28	2.48 3	0.60 4	2.95 49	0.29 13	2.05 31	0.82 44	3.29 11	0.75 26	0.01 65	0.05 80	0.04 85	0.01 66	0.08 40	0.04 40

DeepPruner_ROB	0.86 12	0.50 40	1.80 20	0.01 5	0.00 1	1.57 30	0.17 40	3.48 23	0.42 3	3.31 56	0.02 1	2.09 32	0.17 12	3.01 10	0.61 22	0.00 1	0.00 1	0.00 1	0.00 1	0.12 53	0.01 9

ccs	0.89 13	0.16 9	1.12 5	0.02 10	0.02 28	1.53 27	0.24 51	6.55 91	2.18 58	0.94 16	0.22 10	1.52 19	0.36 25	2.04 8	0.51 17	0.03 80	0.02 68	0.12 95	0.05 80	0.11 52	0.06 51

BEATNet_4x	0.97 14	0.77 69	3.36 52	0.46 74	0.01 21	1.32 8	0.09 28	4.57 60	1.26 15	1.88 32	0.22 10	1.09 16	0.42 29	1.79 6	2.09 52	0.00 1	0.00 1	0.00 1	0.00 1	0.08 40	0.06 51

iResNet	1.00 15	0.45 32	3.08 47	0.86 93	0.01 21	1.97 43	0.01 7	3.67 29	1.49 39	1.77 28	1.09 36	0.35 5	0.13 10	3.83 19	1.27 34	0.00 1	0.00 1	0.00 1	0.00 1	0.07 38	0.01 9

MLCV	1.01 16	0.53 43	2.17 30	0.03 19	0.00 1	1.60 33	0.03 14	2.90 5	1.30 19	2.60 45	1.85 54	1.00 11	0.40 28	4.88 38	0.74 25	0.00 1	0.00 1	0.00 1	0.00 1	0.07 38	0.01 9

ccs_rob	1.03 17	0.66 60	2.06 27	0.20 51	0.02 28	1.30 5	0.03 14	4.09 42	2.05 54	1.08 19	1.20 41	1.07 15	0.20 16	5.59 50	0.72 24	0.00 1	0.00 1	0.00 1	0.00 1	0.16 58	0.05 44

NLCA_NET_v2_RVC	1.04 18	0.55 44	2.52 37	0.56 79	1.00 96	1.37 13	0.00 1	3.43 19	1.35 22	2.24 38	0.35 19	2.18 34	0.58 35	4.28 25	0.34 6	0.00 1	0.00 1	0.00 1	0.01 66	0.00 1	0.00 1
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	1.04 18	0.57 49	2.62 40	0.50 77	0.84 93	1.36 12	0.01 7	3.40 17	1.29 18	2.28 41	0.37 21	2.21 35	0.60 36	4.43 30	0.38 10	0.00 1	0.00 1	0.00 1	0.01 66	0.01 6	0.00 1

iResNet_ROB	1.05 20	0.76 67	2.06 27	0.07 28	0.00 1	1.35 10	0.03 14	6.80 96	2.57 69	0.79 12	1.20 41	0.94 10	0.12 8	3.81 17	0.41 11	0.00 1	0.00 1	0.00 1	0.00 1	0.08 40	0.01 9

iResNetv2_ROB	1.08 21	1.12 92	4.68 76	1.09 101	0.00 1	1.38 15	0.05 23	3.83 32	1.11 9	0.80 13	1.15 38	1.02 12	0.12 8	4.52 31	0.45 14	0.00 1	0.00 1	0.00 1	0.00 1	0.33 89	0.03 36

TDLM	1.08 21	0.49 38	1.43 13	0.27 59	0.00 1	1.56 29	2.12 80	3.10 10	1.75 48	1.36 25	0.93 34	1.06 14	0.49 32	6.21 58	0.70 23	0.00 1	0.00 1	0.00 1	0.00 1	0.16 58	0.02 25

FADNet-RVC-Resample	1.08 21	1.00 85	5.32 80	0.10 39	0.14 55	1.53 27	0.25 52	3.62 26	1.18 13	0.51 7	0.19 9	1.04 13	0.28 21	5.20 41	0.44 12	0.10 90	0.16 95	0.09 91	0.06 84	0.24 76	0.21 84

CFNet	1.08 21	0.62 55	2.26 31	0.26 58	0.03 33	1.40 16	0.04 19	4.45 57	2.09 55	0.48 6	0.69 30	1.31 17	0.24 19	6.63 68	0.83 27	0.00 1	0.00 1	0.00 1	0.00 1	0.17 63	0.04 40

PSMNet_ROB	1.09 25	0.69 63	2.92 43	0.03 19	0.07 41	1.68 36	0.16 38	3.44 20	1.30 19	0.96 17	0.32 14	2.81 44	0.63 38	4.23 23	2.48 55	0.00 1	0.00 1	0.00 1	0.00 1	0.08 40	0.04 40

NVstereo2D	1.09 25	0.17 10	4.40 73	0.00 1	0.00 1	1.59 32	0.18 43	4.29 51	1.40 28	0.85 14	0.36 20	2.30 37	0.62 37	5.26 44	0.35 7	0.00 1	0.00 1	0.00 1	0.00 1	0.01 6	0.01 9

GANetREF_RVC	1.10 27	0.95 81	2.98 44	0.06 25	0.00 1	2.23 45	0.18 43	3.95 38	2.53 67	0.56 8	0.32 14	1.89 27	0.66 42	5.03 39	0.51 17	0.00 1	0.00 1	0.00 1	0.00 1	0.05 32	0.04 40
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
FADNet-RVC	1.10 27	1.07 90	4.79 77	0.03 19	0.00 1	1.60 33	0.08 26	3.77 30	1.70 45	0.35 4	0.07 3	0.61 8	0.66 42	5.32 45	1.11 31	0.01 65	0.08 87	0.00 1	0.00 1	0.48 98	0.18 80

CVANet_RVC	1.10 27	0.39 26	1.69 17	0.28 60	0.00 1	1.45 21	0.48 59	3.26 12	1.64 44	2.93 48	1.22 43	1.51 18	0.37 27	6.09 57	0.50 16	0.00 1	0.00 1	0.00 1	0.00 1	0.18 67	0.01 9

FADNet_RVC	1.15 30	1.06 89	4.34 72	0.02 10	0.06 38	2.77 58	0.04 19	3.50 24	0.80 6	0.35 4	0.32 14	0.65 9	0.09 6	5.20 41	1.56 37	0.24 102	0.34 104	0.11 93	0.33 104	0.65 105	0.60 99

ETE_ROB	1.16 31	0.52 42	1.71 18	0.01 5	0.02 28	1.79 40	0.22 48	4.10 43	1.32 21	4.68 74	1.18 40	2.81 44	0.08 5	3.56 13	1.12 32	0.00 1	0.00 1	0.00 1	0.00 1	0.01 6	0.06 51

XPNet_ROB	1.17 32	0.41 29	1.68 16	0.04 23	0.01 21	1.65 35	0.17 40	3.46 22	1.39 25	3.89 64	1.01 35	1.60 21	0.53 33	4.35 29	3.07 62	0.00 1	0.00 1	0.00 1	0.00 1	0.03 17	0.02 25

DLCB_ROB	1.17 32	0.22 14	1.28 8	0.08 32	0.00 1	1.51 26	0.23 49	3.39 16	1.52 40	3.43 60	2.00 57	3.40 54	0.31 24	4.29 26	1.82 44	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1	0.00 1

FADNet	1.20 34	0.93 80	4.51 74	0.03 19	0.06 38	2.56 54	0.16 38	4.02 40	1.42 30	0.32 2	0.06 2	1.66 23	1.07 46	5.56 49	0.87 28	0.19 96	0.02 68	0.01 72	0.01 66	0.50 100	0.03 36

NOSS_ROB	1.29 35	0.22 14	1.35 12	0.64 84	0.16 60	2.56 54	0.00 1	4.14 45	2.37 62	2.29 42	1.76 53	2.84 46	0.02 3	6.80 73	0.45 14	0.00 1	0.00 1	0.00 1	0.00 1	0.04 27	0.11 65

NCCL2	1.30 36	0.63 56	2.31 32	0.02 10	0.03 33	1.57 30	3.41 89	3.91 37	1.39 25	3.14 54	0.34 17	3.90 58	0.30 22	3.75 15	1.19 33	0.00 1	0.00 1	0.02 82	0.00 1	0.03 17	0.05 44

PA-Net	1.31 37	0.59 52	3.65 60	0.06 25	0.15 58	1.44 20	0.54 62	4.08 41	2.71 71	0.78 11	1.16 39	2.32 39	0.64 39	5.96 54	1.92 47	0.01 65	0.00 1	0.00 1	0.00 1	0.04 27	0.06 51
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
LALA_ROB	1.33 38	0.59 52	1.74 19	0.02 10	0.06 38	3.25 63	0.50 61	3.45 21	1.39 25	3.70 62	0.59 28	4.59 66	0.25 20	3.58 14	2.79 59	0.00 1	0.00 1	0.00 1	0.00 1	0.04 27	0.02 25

HSM	1.36 39	0.40 27	1.07 4	0.01 5	0.30 74	1.73 39	0.03 14	5.89 84	1.22 14	1.83 29	1.36 47	4.78 69	1.30 51	6.61 67	0.57 20	0.00 1	0.00 1	0.00 1	0.00 1	0.02 15	0.00 1

HSM-Net_RVC	1.40 40	0.07 1	1.58 14	0.00 1	0.01 21	1.88 42	0.05 23	7.24 97	1.71 47	3.63 61	3.63 67	3.21 52	0.36 25	4.25 24	0.44 12	0.01 65	0.01 58	0.00 1	0.00 1	0.01 6	0.00 1
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
StereoDRNet	1.48 41	0.75 64	4.58 75	0.05 24	0.10 51	2.49 52	1.10 70	4.62 61	1.47 37	3.38 59	0.62 29	2.29 36	0.65 41	4.52 31	2.89 60	0.00 1	0.03 71	0.00 1	0.01 66	0.05 32	0.07 57

RYNet	1.52 42	0.35 23	3.38 53	0.25 55	0.04 36	2.31 49	0.03 14	4.62 61	1.47 37	1.14 21	0.34 17	4.52 64	1.38 52	6.36 62	4.23 72	0.00 1	0.00 1	0.00 1	0.00 1	0.01 6	0.02 25

RASNet	1.53 43	0.31 20	1.97 23	0.09 37	3.20 108	1.69 37	0.06 25	3.27 13	2.12 56	0.77 10	1.29 45	5.32 77	1.69 59	6.60 66	2.17 53	0.00 1	0.00 1	0.00 1	0.00 1	0.02 15	0.02 25

CBMV	1.56 44	0.24 19	1.92 22	0.31 63	0.03 33	2.90 60	4.33 95	3.79 31	1.81 49	4.98 75	1.73 51	2.70 42	1.68 58	3.81 17	0.92 29	0.00 1	0.00 1	0.00 1	0.00 1	0.05 32	0.08 59
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
PASM	1.58 45	1.05 88	7.51 91	0.56 79	0.09 45	1.37 13	0.28 53	2.85 4	1.44 33	3.91 65	0.44 26	3.02 49	0.64 39	6.38 63	1.59 38	0.00 1	0.01 58	0.00 1	0.03 76	0.26 79	0.15 74

DRN-Test	1.62 46	0.23 17	3.72 61	0.18 46	0.13 54	3.34 65	0.19 47	5.93 85	2.01 53	4.14 68	0.76 31	2.12 33	1.78 61	5.22 43	2.62 57	0.00 1	0.00 1	0.01 72	0.01 66	0.03 17	0.01 9

AANet_RVC	1.66 47	0.88 77	3.52 57	0.06 25	0.00 1	1.22 2	0.00 1	3.25 11	2.51 66	2.21 37	4.49 71	3.65 55	0.20 16	7.10 80	3.42 66	0.50 107	0.04 76	0.00 1	0.00 1	0.08 40	0.05 44

Abc-Net	1.67 48	0.56 47	3.10 48	0.08 32	0.22 63	2.24 46	0.13 35	5.05 74	1.42 30	2.27 39	2.72 58	4.29 59	3.53 82	5.76 52	2.01 49	0.04 81	0.00 1	0.00 1	0.00 1	0.03 17	0.02 25

NCC-stereo	1.67 48	0.56 47	3.10 48	0.08 32	0.22 63	2.24 46	0.13 35	5.05 74	1.42 30	2.27 39	2.72 58	4.29 59	3.53 82	5.76 52	2.01 49	0.04 81	0.00 1	0.00 1	0.00 1	0.03 17	0.02 25

CBMV_ROB	1.70 50	0.11 7	1.98 24	0.46 74	0.00 1	2.85 59	0.91 66	4.27 49	2.21 60	4.02 67	5.00 75	2.97 47	2.32 69	5.40 46	1.35 35	0.00 1	0.00 1	0.00 1	0.00 1	0.03 17	0.06 51

DANet	1.74 51	0.22 14	3.06 46	0.75 88	0.44 81	2.46 51	0.35 56	3.40 17	1.41 29	4.22 69	1.56 48	5.03 73	0.56 34	6.29 60	5.00 80	0.01 65	0.00 1	0.00 1	0.01 66	0.04 27	0.08 59

RP	1.76 52	0.55 44	2.59 39	0.22 53	0.60 91	1.70 38	0.98 68	4.31 53	1.99 52	3.12 53	5.04 76	4.41 62	2.96 74	4.72 34	1.90 45	0.01 65	0.00 1	0.00 1	0.01 66	0.14 55	0.01 9

Anonymous Stereo	1.78 53	1.37 97	7.79 92	0.42 71	0.07 41	1.49 25	3.20 87	3.31 14	1.84 50	2.75 46	1.14 37	1.84 25	0.09 6	6.89 76	3.32 65	0.00 1	0.00 1	0.00 1	0.00 1	0.09 48	0.01 9

SGM-Forest	1.84 54	0.08 3	1.31 9	0.15 43	0.22 63	3.52 66	2.46 83	3.85 34	2.16 57	5.29 76	3.90 68	3.71 56	1.56 55	6.28 59	1.90 45	0.01 65	0.02 68	0.00 1	0.00 1	0.03 17	0.30 89
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
RGC	1.86 55	0.67 61	3.45 56	0.07 28	0.26 68	3.29 64	0.11 34	4.78 68	1.46 36	3.26 55	4.51 72	5.14 74	2.68 72	5.46 47	1.94 48	0.00 1	0.00 1	0.00 1	0.00 1	0.08 40	0.09 62

GANet	1.90 56	0.43 30	1.83 21	0.39 69	0.00 1	2.24 46	3.37 88	3.90 36	1.44 33	2.17 36	9.66 87	2.57 41	1.09 47	6.88 75	1.81 43	0.00 1	0.00 1	0.02 82	0.00 1	0.10 49	0.03 36

DISCO	1.93 57	0.18 12	2.44 34	0.67 85	0.53 87	5.25 79	0.00 1	5.43 78	1.56 42	2.09 35	0.91 33	6.48 83	0.89 45	8.55 91	3.60 69	0.00 1	0.00 1	0.00 1	0.00 1	0.06 36	0.01 9

edge stereo	1.94 58	0.40 27	3.82 65	0.15 43	0.09 45	1.81 41	0.45 58	4.77 66	2.21 60	3.36 57	3.94 69	8.39 90	1.72 60	5.75 51	1.69 41	0.00 1	0.00 1	0.01 72	0.00 1	0.17 63	0.01 9

Nwc_Net	1.94 58	0.55 44	3.79 63	0.07 28	0.08 44	2.73 56	0.04 19	6.75 95	1.11 9	2.03 34	7.92 81	4.82 70	2.11 67	4.78 35	2.01 49	0.00 1	0.00 1	0.00 1	0.00 1	0.05 32	0.01 9

MFMNet_re	2.02 60	0.98 82	4.27 70	2.74 108	0.88 94	1.47 23	0.15 37	4.21 46	3.18 75	6.63 80	6.93 80	1.87 26	3.63 85	2.13 9	0.99 30	0.00 1	0.00 1	0.00 1	0.00 1	0.23 75	0.05 44

NaN_ROB	2.02 60	0.58 50	2.65 41	0.29 61	0.28 73	3.19 62	5.19 100	3.84 33	2.55 68	5.31 77	1.73 51	2.30 37	1.96 65	6.78 72	3.09 63	0.01 65	0.06 83	0.03 84	0.10 91	0.10 49	0.37 94

stereogan	2.13 62	0.31 20	3.75 62	0.01 5	0.09 45	7.44 92	0.60 63	4.27 49	3.29 76	3.91 65	4.62 73	6.41 82	1.84 63	5.17 40	0.58 21	0.00 1	0.01 58	0.01 72	0.00 1	0.24 76	0.05 44

AF-Net	2.17 63	0.65 58	3.53 58	0.00 1	0.09 45	2.02 44	0.48 59	6.44 88	1.70 45	3.36 57	10.22 88	4.95 72	2.81 73	4.34 28	2.66 58	0.00 1	0.00 1	0.00 1	0.00 1	0.13 54	0.01 9

PDISCO_ROB	2.21 64	0.78 70	3.43 55	0.96 96	2.04 107	7.04 89	0.09 28	7.36 99	5.72 91	1.21 23	1.67 49	3.16 51	1.28 50	7.52 85	1.37 36	0.00 1	0.00 1	0.00 1	0.00 1	0.34 90	0.13 73

ADCRef	2.29 65	0.65 58	5.67 85	0.10 39	0.09 45	4.81 75	0.76 64	4.75 65	1.37 23	2.88 47	1.67 49	1.62 22	2.30 68	3.77 16	14.93 99	0.02 76	0.00 1	0.01 72	0.10 91	0.20 72	0.11 65

FBW_ROB	2.36 66	0.36 24	3.16 50	0.37 68	0.14 55	4.42 74	0.18 43	6.64 92	3.05 74	2.98 51	2.85 60	4.86 71	1.11 48	11.00 102	4.53 73	0.16 95	0.05 80	0.37 105	0.09 88	0.19 71	0.78 104

DeepPrunerF	2.50 67	0.87 76	14.90 102	0.17 45	0.31 75	1.41 17	0.29 54	5.62 80	9.97 97	1.02 18	0.43 25	2.01 28	1.65 57	6.85 74	3.99 70	0.00 1	0.01 58	0.07 88	0.10 91	0.24 76	0.12 72

XQC	2.57 68	1.20 93	6.33 89	0.18 46	0.01 21	3.84 69	0.33 55	5.24 76	3.83 82	4.43 71	1.94 55	4.58 65	3.19 78	7.19 81	8.67 90	0.00 1	0.03 71	0.00 1	0.03 76	0.27 82	0.19 81

RTSC	2.66 69	1.24 94	6.04 86	0.18 46	0.02 28	4.40 73	0.08 26	4.29 51	4.98 89	6.00 78	3.15 62	2.97 47	1.56 55	6.71 70	10.89 95	0.00 1	0.07 85	0.00 1	0.05 80	0.40 95	0.24 88

CSAN	2.70 70	0.75 64	3.41 54	0.18 46	0.12 52	4.18 71	4.05 92	4.11 44	4.36 86	6.03 79	6.24 79	4.60 67	4.74 89	7.29 82	3.43 67	0.02 76	0.03 71	0.01 72	0.02 75	0.18 67	0.22 85

ADCP+	2.87 71	0.98 82	8.75 95	0.07 28	0.16 60	5.93 84	2.90 86	4.00 39	1.84 50	1.52 26	0.27 12	3.81 57	3.22 80	6.05 56	17.38 101	0.00 1	0.00 1	0.00 1	0.00 1	0.14 55	0.30 89

PWC_ROB	2.90 72	1.01 86	6.12 87	0.53 78	0.57 90	1.30 5	0.40 57	3.85 34	4.42 87	7.37 84	15.40 97	3.03 50	1.55 54	6.31 61	5.74 86	0.00 1	0.00 1	0.00 1	0.00 1	0.46 96	0.05 44

SHD	3.01 73	1.02 87	5.52 83	0.45 73	0.39 78	4.12 70	0.17 40	6.68 93	12.04 100	7.20 83	3.36 64	4.40 61	2.58 71	4.69 33	7.17 88	0.01 65	0.04 76	0.00 1	0.03 76	0.22 74	0.17 78

PWCDC_ROB	3.06 74	1.38 98	3.81 64	0.14 42	0.00 1	3.14 61	0.02 11	4.40 55	12.35 101	1.08 19	23.69 105	2.01 28	2.01 66	3.87 20	2.43 54	0.24 102	0.00 1	0.00 1	0.00 1	0.61 104	0.08 59

SPS-STEREO	3.08 75	0.49 38	2.49 36	0.21 52	0.24 66	3.67 68	1.24 71	5.80 83	2.43 65	10.68 95	8.00 82	9.48 94	3.59 84	8.11 88	5.08 81	0.01 65	0.01 58	0.00 1	0.00 1	0.04 27	0.11 65
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
NVStereoNet_ROB	3.25 76	0.46 33	2.71 42	0.34 66	0.56 89	2.36 50	0.87 65	4.56 59	4.47 88	4.55 72	14.09 96	11.93 96	3.23 81	9.79 98	3.14 64	0.43 105	0.03 71	0.53 107	0.31 103	0.36 93	0.38 95
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
ADCL	3.30 77	0.81 73	6.76 90	0.25 55	0.25 67	6.93 87	4.65 96	4.79 69	2.62 70	4.61 73	3.36 64	5.22 75	4.55 88	5.54 48	14.91 98	0.05 84	0.01 58	0.11 93	0.13 94	0.28 83	0.10 63

MFN_U_SF_DS_RVC	3.30 77	2.61 104	7.84 93	0.09 37	0.26 68	12.92 101	7.62 106	4.25 47	3.64 80	2.95 49	3.99 70	4.61 68	1.22 49	8.36 89	1.77 42	0.59 108	1.15 110	0.04 85	0.85 108	0.48 98	0.67 102

SAMSARA	3.32 79	1.07 90	6.25 88	0.86 93	0.31 75	7.55 93	3.87 91	5.26 77	4.20 85	7.43 85	3.35 63	10.04 95	3.83 87	6.98 79	4.83 77	0.00 1	0.14 92	0.00 1	0.05 80	0.17 63	0.22 85

DPSNet	3.38 80	0.60 54	9.55 96	0.61 82	0.46 82	5.10 78	1.44 75	11.17 106	3.92 83	2.57 44	0.88 32	5.90 78	9.68 103	7.63 86	7.25 89	0.08 86	0.16 95	0.01 72	0.07 85	0.34 90	0.17 78

STTStereo_v2	3.49 81	0.47 35	4.03 68	1.61 102	1.91 103	10.78 99	1.92 77	3.06 8	1.26 15	8.59 89	12.06 90	6.13 80	7.82 97	4.78 35	4.77 75	0.08 86	0.12 90	0.07 88	0.15 95	0.16 58	0.11 65

G-Net	3.49 81	0.47 35	4.03 68	1.61 102	1.91 103	10.78 99	1.92 77	3.06 8	1.26 15	8.59 89	12.06 90	6.13 80	7.82 97	4.78 35	4.77 75	0.08 86	0.12 90	0.07 88	0.15 95	0.16 58	0.11 65

MDST_ROB	3.50 83	0.07 1	3.94 66	2.66 107	1.46 101	12.94 102	0.97 67	7.32 98	2.42 64	14.82 103	8.08 83	2.74 43	1.51 53	8.44 90	2.48 55	0.00 1	0.00 1	0.00 1	0.00 1	0.01 6	0.11 65

pmcnn	3.61 84	0.68 62	5.38 82	0.61 82	1.46 101	2.75 57	1.26 72	5.04 73	1.57 43	8.77 91	11.67 89	18.69 105	2.38 70	6.03 55	5.61 85	0.00 1	0.07 85	0.00 1	0.00 1	0.21 73	0.07 57

ADCPNet	3.63 85	0.82 74	13.20 100	0.08 32	0.43 80	7.02 88	2.23 81	4.77 66	2.41 63	4.31 70	1.96 56	7.57 88	5.16 90	6.45 65	13.80 97	0.02 76	0.75 108	0.04 85	0.90 109	0.16 58	0.53 98

SuperB	3.70 86	1.77 100	15.73 103	0.31 63	0.05 37	4.37 72	1.67 76	3.58 25	11.57 98	2.36 43	1.32 46	2.03 30	1.91 64	6.95 78	9.01 91	0.05 84	0.03 71	0.10 92	0.08 86	11.00 110	0.10 63

AnyNet_C32	3.93 87	2.58 103	12.07 99	0.39 69	0.49 85	5.50 81	6.45 104	4.44 56	2.19 59	6.86 82	2.94 61	5.28 76	3.20 79	6.63 68	18.37 103	0.08 86	0.04 76	0.20 99	0.09 88	0.57 103	0.19 81

ADCMid	3.95 88	1.34 95	10.91 98	0.18 46	0.38 77	5.02 77	1.27 73	4.87 70	2.94 73	11.15 96	3.60 66	6.76 85	5.21 92	6.40 64	16.96 100	0.12 92	0.08 87	0.55 108	0.46 105	0.53 102	0.19 81

SGM_RVC	4.07 89	0.38 25	1.98 24	0.84 89	0.26 68	6.20 85	2.37 82	6.45 89	3.57 78	11.56 98	9.27 86	15.55 102	6.78 94	10.07 99	4.60 74	0.22 97	0.25 100	0.20 99	0.23 100	0.26 79	0.34 92
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
SGM+DAISY	4.39 90	1.34 95	5.18 79	1.01 98	0.88 94	5.61 82	2.86 85	4.88 71	3.53 77	13.29 100	13.95 95	12.66 98	7.55 95	7.98 87	5.39 84	0.23 101	0.18 98	0.15 97	0.24 101	0.29 84	0.64 100

WCMA_ROB	4.54 91	0.48 37	3.19 51	0.49 76	0.63 92	5.38 80	2.57 84	4.70 63	3.77 81	14.17 102	19.02 100	15.47 101	9.03 100	6.73 71	4.85 78	0.01 65	0.05 80	0.01 72	0.01 66	0.08 40	0.16 75

SANet	4.63 92	0.89 78	5.34 81	0.30 62	0.09 45	7.43 91	3.75 90	6.53 90	16.16 104	6.79 81	13.29 93	12.44 97	7.74 96	7.38 83	4.11 71	0.00 1	0.01 58	0.00 1	0.00 1	0.10 49	0.31 91

MSMD_ROB	4.94 93	0.43 30	2.58 38	0.12 41	0.01 21	8.43 96	1.09 69	4.25 47	4.11 84	13.11 99	26.36 107	13.95 100	13.88 106	7.45 84	2.92 61	0.10 90	0.01 58	0.00 1	0.00 1	0.01 6	0.02 25

ADCS	5.09 94	2.15 101	16.58 104	0.34 66	0.07 41	5.91 83	4.26 94	6.73 94	6.99 94	9.44 93	6.09 78	6.57 84	5.17 91	9.54 96	21.38 108	0.00 1	0.00 1	0.00 1	0.00 1	0.36 93	0.23 87

MSC_U_SF_DS_RVC	5.12 95	2.33 102	8.42 94	0.33 65	0.27 71	24.60 111	8.84 108	5.50 79	9.75 96	3.82 63	8.16 84	8.53 91	1.82 62	9.44 95	5.18 82	1.02 109	1.65 111	0.33 104	0.47 106	1.06 107	0.90 106

MeshStereo	5.78 96	0.75 64	3.04 45	0.25 55	0.14 55	7.88 95	2.09 79	9.64 102	5.18 90	20.48 105	19.06 101	22.80 106	9.54 102	9.64 97	4.88 79	0.00 1	0.00 1	0.00 1	0.00 1	0.29 84	0.03 36
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
PVD	5.81 97	0.99 84	5.56 84	0.72 87	0.51 86	6.72 86	0.18 43	9.90 104	23.69 108	11.45 97	19.26 102	8.18 89	9.46 101	9.24 93	9.49 92	0.02 76	0.16 95	0.00 1	0.08 86	0.18 67	0.40 96

FC-DCNN	6.09 98	0.18 12	2.35 33	0.42 71	0.53 87	7.80 94	1.42 74	8.14 100	7.07 95	19.05 104	22.10 104	22.85 107	13.84 105	9.42 94	6.44 87	0.00 1	0.01 58	0.00 1	0.00 1	0.01 6	0.06 51

AnyNet_C01	6.59 99	4.31 107	36.24 108	0.96 96	0.46 82	9.96 97	5.74 101	6.02 87	3.63 79	7.59 86	8.56 85	8.83 92	3.14 77	13.74 104	20.83 107	0.12 92	0.08 87	0.25 102	0.09 88	1.01 106	0.34 92

LSM	7.38 100	1.68 99	22.24 106	3.99 109	40.72 112	3.54 67	4.79 99	4.74 64	6.65 92	10.25 94	13.51 94	4.49 63	3.81 86	6.91 77	5.31 83	0.00 1	0.04 76	0.00 1	0.00 1	0.26 79	14.60 111

ELAS_RVC	7.69 101	0.80 71	4.90 78	1.06 99	1.02 97	10.00 98	9.43 109	9.75 103	14.69 103	21.83 106	20.27 103	17.88 103	16.41 108	13.01 103	10.44 94	0.22 97	0.55 106	0.28 103	0.19 97	0.35 92	0.65 101
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
DispFullNet	7.91 102	23.96 112	13.67 101	14.71 111	7.88 110	4.83 76	0.04 19	5.01 72	2.89 72	8.86 92	4.96 74	6.11 79	10.72 104	10.18 100	3.51 68	2.44 110	0.33 103	15.15 111	4.65 111	12.33 111	5.95 110

ELAS	8.05 103	0.76 67	4.02 67	0.95 95	1.02 97	14.85 105	6.99 105	12.80 108	11.73 99	22.43 107	27.75 108	17.88 103	15.19 107	10.96 101	11.31 96	0.22 97	0.56 107	0.23 101	0.22 99	0.50 100	0.71 103
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
PWCK	8.72 104	5.37 108	16.61 105	5.74 110	0.12 52	19.88 107	11.41 110	11.31 107	13.42 102	14.07 101	12.84 92	13.20 99	8.05 99	15.44 105	10.25 93	5.95 111	0.22 99	2.09 110	0.05 80	7.50 109	0.92 107

RTS	9.75 105	2.69 105	65.25 111	0.85 90	1.99 105	14.36 103	4.75 97	5.69 81	17.29 105	7.97 87	19.00 98	6.83 86	3.02 75	26.15 109	18.62 105	0.00 1	0.15 93	0.00 1	0.00 1	0.29 84	0.16 75

RTSA	9.75 105	2.69 105	65.25 111	0.85 90	1.99 105	14.36 103	4.75 97	5.69 81	17.29 105	7.97 87	19.00 98	6.83 86	3.02 75	26.15 109	18.62 105	0.00 1	0.15 93	0.00 1	0.00 1	0.29 84	0.16 75

MADNet+	10.71 107	14.57 110	70.07 113	0.58 81	0.47 84	19.89 108	4.20 93	11.05 105	6.87 93	3.03 52	5.99 77	9.01 93	6.35 93	31.95 112	28.31 111	0.22 97	0.26 102	0.01 72	0.04 79	0.46 96	0.87 105

SGM-ForestM	12.56 108	0.80 71	4.31 71	1.07 100	0.41 79	21.56 109	8.57 107	17.42 109	19.67 107	29.93 108	28.49 110	45.83 111	27.22 110	26.05 108	18.40 104	0.14 94	0.25 100	0.18 98	0.28 102	0.18 67	0.42 97

LE_ROB	12.79 109	0.64 57	10.26 97	2.13 105	1.29 99	7.25 90	5.81 102	9.09 101	49.27 112	57.55 112	24.58 106	23.68 108	31.13 112	9.00 92	23.57 110	0.04 81	0.06 83	0.13 96	0.20 98	0.08 40	0.11 65

MANE	14.02 110	0.85 75	3.53 58	2.13 105	3.75 109	18.44 106	6.14 103	22.67 111	27.90 110	33.01 110	41.08 112	41.26 110	30.90 111	23.39 106	17.99 102	0.47 106	0.76 109	0.91 109	4.04 110	0.17 63	1.00 108

BEATNet-Init1	17.91 111	7.45 109	36.82 109	1.88 104	1.38 100	37.29 112	14.71 111	21.90 110	24.20 109	38.07 111	36.59 111	50.41 112	32.00 113	29.42 111	21.83 109	0.33 104	0.39 105	0.47 106	0.54 107	1.50 108	1.07 109

DPSimNet_ROB	25.03 112	19.56 111	24.93 107	24.36 112	17.85 111	22.25 110	25.93 112	28.52 112	29.10 111	32.61 109	28.25 109	32.87 109	26.42 109	24.51 107	40.42 112	16.75 112	13.93 112	25.19 112	22.38 112	19.96 112	24.75 112

MADNet++	47.78 113	34.15 113	37.11 110	42.94 113	41.23 113	64.94 113	30.63 113	60.79 113	53.26 113	72.08 113	61.65 113	57.68 113	54.96 114	58.03 113	59.24 113	43.01 113	36.17 113	36.07 113	22.50 113	47.50 113	41.67 113

MEDIAN_ROB	96.83 114	99.41 114	98.66 115	94.75 114	94.23 114	93.08 114	90.54 114	95.61 114	94.75 114	97.65 116	97.73 114	98.30 114	96.57 115	94.51 114	93.74 114	99.78 119	98.24 114	99.99 116	99.89 114	99.48 116	99.67 116

DPSM	99.17 115	100.00 118	100.00 118	96.28 115	98.84 115	100.00 116	100.00 116	100.00 117	100.00 115	100.00 117	100.00 115	100.00 117	100.00 116	100.00 118	100.00 118	93.66 114	100.00 115	100.00 117	100.00 115	96.31 114	98.27 114

DPSM_ROB	99.17 115	100.00 118	100.00 118	96.28 115	98.84 115	100.00 116	100.00 116	100.00 117	100.00 115	100.00 117	100.00 115	100.00 117	100.00 116	100.00 118	100.00 118	93.66 114	100.00 115	100.00 117	100.00 115	96.31 114	98.27 114

AVERAGE_ROB	99.23 117	99.81 115	97.56 114	100.00 120	100.00 117	96.24 115	91.02 115	100.00 117	100.00 115	100.00 117	100.00 115	100.00 117	100.00 116	99.98 117	99.97 115	100.00 120	100.00 115	100.00 117	100.00 115	100.00 120	100.00 119

DGTPSM_ROB	99.45 118	99.94 116	99.98 116	96.58 117	100.00 117	100.00 116	100.00 116	99.81 115	100.00 115	95.86 114	100.00 115	99.32 115	100.00 116	99.79 115	99.99 116	98.34 116	100.00 115	99.86 114	100.00 115	99.52 117	99.99 118

DPSMNet_ROB	99.46 119	99.94 116	99.98 116	96.59 118	100.00 117	100.00 116	100.00 116	99.81 115	100.00 115	95.87 115	100.00 115	99.32 115	100.00 116	99.79 115	99.99 116	98.46 117	100.00 115	99.86 114	100.00 115	99.52 117	100.00 119

LSM0	99.94 120	100.00 118	100.00 118	99.84 119	100.00 117	100.00 116	100.00 116	100.00 117	100.00 115	100.00 117	100.00 115	100.00 117	100.00 116	100.00 118	100.00 118	99.22 118	100.00 115	100.00 117	100.00 115	99.99 119	99.83 117

MSMDNet													0.42 29

Low-res two-view benchmark