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

DPM-Stereo	1.97 1	0.64 16	2.95 8	0.17 1	0.10 1	4.83 24	0.13 2	8.60 9	4.06 4	6.42 16	4.92 4	0.44 1	0.72 1	3.57 3	1.80 5	0.00 1	0.01 25	0.00 1	0.00 1	0.05 15	0.04 15

PMTNet	1.99 2	0.32 1	2.21 3	0.39 2	0.23 6	5.08 26	0.49 11	5.84 1	8.22 26	3.07 1	3.29 1	0.73 2	0.75 2	8.18 27	0.94 3	0.00 1	0.00 1	0.00 1	0.00 1	0.03 10	0.00 1

R-Stereo	2.44 3	0.32 1	1.93 1	0.94 4	0.16 4	3.67 8	0.61 16	6.37 3	3.08 1	9.14 37	17.44 65	1.80 3	0.77 3	1.76 1	0.70 1	0.00 1	0.01 25	0.00 1	0.00 1	0.01 1	0.03 9

R-Stereo Train	2.44 3	0.32 1	1.93 1	0.94 4	0.16 4	3.67 8	0.61 16	6.37 3	3.08 1	9.14 37	17.44 65	1.80 3	0.77 3	1.76 1	0.70 1	0.00 1	0.01 25	0.00 1	0.00 1	0.01 1	0.03 9

DN-CSS_ROB	2.69 5	1.40 55	5.34 33	2.31 50	0.75 22	3.14 6	0.06 1	6.11 2	3.87 3	5.34 11	12.18 39	2.34 5	1.22 5	7.84 17	1.48 4	0.03 32	0.00 1	0.00 1	0.00 1	0.35 61	0.03 9

HITNet	2.79 6	0.77 17	4.02 19	2.03 39	0.11 3	5.58 30	0.59 14	9.24 11	5.15 7	6.42 16	7.26 11	3.66 6	2.92 16	4.07 4	3.87 28	0.00 1	0.00 1	0.00 1	0.00 1	0.06 19	0.02 3
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
ccs	3.04 7	0.39 7	3.08 10	1.78 27	0.52 16	2.04 1	0.50 12	13.09 52	13.71 59	3.54 4	5.36 6	5.50 12	2.45 11	4.81 6	2.88 12	0.09 47	0.08 60	0.12 75	0.10 69	0.20 42	0.50 72

AdaStereo	3.09 8	0.58 12	3.04 9	2.84 63	0.48 15	4.08 14	1.29 31	12.16 44	7.77 22	6.03 12	9.62 23	5.79 14	1.53 7	4.56 5	1.93 7	0.00 1	0.00 1	0.00 1	0.00 1	0.02 4	0.02 3
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.
BEATNet_4x	3.24 9	1.27 49	5.89 41	1.56 18	0.10 1	5.26 27	1.07 26	10.08 18	5.50 8	6.89 21	7.73 13	4.53 9	4.13 26	5.05 7	5.27 34	0.04 37	0.05 48	0.00 1	0.00 1	0.23 52	0.23 48

DMCA	3.29 10	1.05 33	4.18 22	1.60 21	2.87 69	2.11 2	0.60 15	7.95 7	4.65 6	8.62 32	8.59 19	9.24 31	4.68 33	6.25 8	3.03 14	0.04 37	0.09 64	0.06 69	0.03 50	0.18 39	0.07 18

NOSS_ROB	3.30 11	0.46 8	2.62 4	2.08 40	1.01 34	5.60 31	0.74 22	10.37 25	11.48 48	5.15 9	8.43 18	5.67 13	1.73 8	7.97 19	2.34 9	0.02 23	0.06 54	0.00 1	0.00 1	0.07 20	0.14 39

CFNet_RVC	3.31 12	0.94 28	2.69 5	1.50 16	2.38 59	2.81 4	0.68 20	8.35 8	7.43 18	4.45 6	9.94 24	10.20 37	4.60 31	6.49 9	3.41 22	0.00 1	0.00 1	0.03 60	0.00 1	0.22 48	0.03 9

MLCV	3.44 13	0.88 21	5.60 36	1.39 12	0.25 7	4.36 17	0.33 6	7.25 5	7.28 15	9.17 39	12.24 41	5.09 10	2.47 12	9.15 39	3.23 19	0.00 1	0.00 1	0.00 1	0.00 1	0.10 23	0.02 3

DeepPruner_ROB	3.52 14	1.14 43	4.06 20	1.12 7	1.65 47	3.65 7	0.83 24	13.96 60	4.47 5	7.80 25	10.84 29	7.05 21	2.16 10	8.14 25	3.08 17	0.07 45	0.03 40	0.00 1	0.01 38	0.32 57	0.06 17

STTStereo	3.60 15	0.93 27	6.34 48	2.71 61	2.23 58	3.68 10	0.63 19	9.42 12	6.73 11	9.87 46	6.97 9	8.84 30	3.65 19	6.85 10	3.04 15	0.00 1	0.02 35	0.01 43	0.00 1	0.02 4	0.02 3

ccs_rob	3.63 16	1.12 42	4.42 24	2.52 54	0.91 30	5.50 29	0.21 4	10.11 21	9.11 32	6.55 19	11.28 33	8.32 28	2.55 13	7.66 14	2.01 8	0.00 1	0.00 1	0.00 1	0.00 1	0.20 42	0.08 21

iResNet	3.68 17	0.91 24	7.94 64	2.97 69	0.34 9	4.44 21	0.48 10	7.70 6	9.74 36	7.72 24	12.74 44	4.03 7	2.87 15	8.05 21	3.37 21	0.02 23	0.01 25	0.00 1	0.00 1	0.10 23	0.09 23

CFNet	3.72 18	1.10 39	5.03 29	2.49 53	1.59 44	4.90 25	0.22 5	11.38 34	9.88 38	4.80 7	11.25 32	6.44 17	3.68 20	8.33 28	3.00 13	0.00 1	0.00 1	0.00 1	0.00 1	0.22 48	0.07 18

FADNet-RVC-Resample	3.79 19	1.62 66	12.06 77	1.43 14	0.66 18	5.94 33	2.41 39	10.18 23	8.58 30	6.28 14	4.22 3	5.33 11	4.80 37	7.71 15	3.19 18	0.17 59	0.21 80	0.17 81	0.12 71	0.41 69	0.29 60

NLCA_NET_v2_RVC	3.84 20	1.06 34	5.23 31	2.72 62	3.27 72	4.36 17	0.61 16	10.71 30	7.56 19	8.75 33	7.89 14	9.86 36	3.90 23	7.15 12	3.44 23	0.14 54	0.02 35	0.02 54	0.03 50	0.04 13	0.03 9
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	3.84 20	1.07 35	5.23 31	2.65 58	2.96 70	4.22 15	0.69 21	10.43 26	7.72 20	8.78 34	8.29 17	9.61 34	4.02 25	7.16 13	3.65 26	0.13 53	0.03 40	0.02 54	0.03 50	0.05 15	0.03 9

FADNet_RVC	3.91 22	1.67 69	12.95 84	0.96 6	0.75 22	5.71 32	0.54 13	10.83 32	6.60 10	3.46 2	8.09 15	4.10 8	3.40 18	9.43 42	6.33 38	0.36 75	0.44 92	0.17 81	0.46 95	0.91 85	0.95 89

FADNet-RVC	3.98 23	1.84 75	12.48 80	1.69 25	0.44 13	4.33 16	1.31 32	11.84 38	7.15 13	3.53 3	3.50 2	10.63 40	4.43 30	9.12 38	6.25 37	0.03 32	0.10 65	0.00 1	0.03 50	0.60 75	0.25 54

HSM	4.00 24	0.79 18	3.16 12	1.59 20	2.17 56	6.77 39	1.11 27	12.28 45	6.35 9	6.75 20	8.11 16	13.90 55	5.37 42	8.85 36	2.71 11	0.00 1	0.00 1	0.00 1	0.00 1	0.02 4	0.02 3

TDLM	4.11 25	1.11 41	3.54 14	1.62 22	1.04 35	3.91 12	7.41 82	10.60 29	10.67 42	6.38 15	12.59 43	5.95 15	4.77 35	8.79 35	3.04 15	0.58 87	0.00 1	0.01 43	0.00 1	0.19 41	0.12 34

CBMV_ROB	4.14 26	0.52 9	3.14 11	1.30 10	0.77 25	6.92 40	1.97 38	10.11 21	9.58 34	8.92 36	14.20 52	7.12 22	5.90 45	8.65 32	3.50 25	0.01 18	0.05 48	0.00 1	0.00 1	0.04 13	0.09 23

CVANet_RVC	4.16 27	1.16 44	3.60 15	1.94 37	1.46 42	3.92 13	4.68 64	10.89 33	8.34 28	7.58 23	10.84 29	10.27 38	6.62 49	8.56 31	2.69 10	0.39 77	0.00 1	0.00 1	0.01 38	0.21 47	0.09 23

HSM-Net_RVC	4.20 28	0.32 1	2.76 6	0.63 3	0.69 20	6.95 41	1.69 36	11.96 39	8.36 29	8.83 35	12.17 38	15.18 63	4.21 28	6.91 11	3.30 20	0.02 23	0.02 35	0.00 1	0.00 1	0.01 1	0.01 2
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
iResNet_ROB	4.23 29	1.02 31	4.90 28	2.18 43	0.93 32	2.92 5	0.37 8	15.10 71	16.91 73	7.89 27	10.51 27	7.03 19	3.07 17	8.16 26	3.46 24	0.01 18	0.00 1	0.00 1	0.00 1	0.10 23	0.02 3

FADNet	4.23 29	1.65 68	11.75 76	1.64 24	0.80 27	4.80 23	0.77 23	13.76 59	11.65 50	3.97 5	5.24 5	9.62 35	5.14 39	8.40 29	3.78 27	0.21 63	0.04 44	0.07 70	0.05 61	1.14 90	0.10 30

iResNetv2_ROB	4.28 31	1.43 56	7.17 59	2.91 64	1.26 39	4.36 17	1.62 34	13.64 58	10.25 41	9.83 45	11.41 34	7.68 24	4.00 24	7.75 16	1.85 6	0.00 1	0.00 1	0.00 1	0.00 1	0.37 63	0.09 23

StereoDRNet-Refined	4.46 32	0.62 15	3.80 18	1.92 34	0.40 11	9.35 51	0.15 3	10.02 16	8.83 31	12.69 63	11.62 36	9.34 32	3.87 22	8.06 22	8.02 51	0.00 1	0.00 1	0.01 43	0.05 61	0.20 42	0.26 57
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
NVstereo2D	4.51 33	0.82 19	6.86 57	3.28 74	3.38 76	8.16 46	3.13 44	10.51 27	15.15 64	4.90 8	6.89 8	7.87 25	4.78 36	9.88 45	3.91 29	0.01 18	0.00 1	0.00 1	0.06 63	0.02 4	0.58 77

DLCB_ROB	4.51 33	0.91 24	3.78 17	2.19 44	1.07 36	6.28 34	3.09 43	9.78 15	7.72 20	10.65 50	12.97 45	13.91 56	3.71 21	8.72 33	5.30 35	0.00 1	0.00 1	0.00 1	0.00 1	0.03 10	0.10 30

RASNet	4.52 35	0.61 14	4.42 24	3.42 78	4.68 94	4.58 22	0.99 25	9.54 14	8.01 23	5.28 10	11.42 35	10.34 39	8.88 62	9.28 40	8.68 60	0.15 56	0.00 1	0.00 1	0.00 1	0.03 10	0.04 15

SGM-Forest	4.96 36	0.32 1	2.84 7	1.21 8	0.64 17	10.23 61	6.64 77	11.55 35	10.98 43	10.94 53	13.59 48	11.65 46	4.30 29	8.94 37	4.63 32	0.11 50	0.04 44	0.00 1	0.00 1	0.05 15	0.46 69
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 37	1.47 58	7.42 61	2.40 51	2.14 55	8.73 48	3.64 53	12.42 46	13.11 55	7.03 22	7.57 12	7.88 26	6.52 48	10.16 47	7.82 49	0.02 23	0.03 40	0.00 1	0.00 1	0.11 27	1.07 92
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 38	1.74 70	6.38 49	1.96 38	1.29 41	2.26 3	1.69 36	10.07 17	18.53 76	7.88 26	18.15 67	8.49 29	2.70 14	10.59 51	7.04 43	0.96 97	0.15 75	0.02 54	0.00 1	0.13 31	0.12 34

PSMNet_ROB	5.02 39	1.63 67	6.03 43	1.90 33	1.83 52	9.57 55	6.35 74	15.58 76	7.23 14	6.15 13	10.48 26	12.22 48	4.16 27	8.02 20	8.71 61	0.02 23	0.01 25	0.01 43	0.10 69	0.20 42	0.12 34

CBMV	5.35 40	0.91 24	3.67 16	1.62 22	0.44 13	10.09 59	7.19 81	12.49 47	12.33 54	12.22 59	14.69 54	10.93 41	6.48 47	8.51 30	4.96 33	0.02 23	0.15 75	0.00 1	0.00 1	0.17 38	0.17 43
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 41	1.75 71	6.80 56	3.12 71	4.45 89	10.61 63	4.35 60	18.80 86	9.73 35	12.22 59	6.87 7	11.44 44	4.65 32	8.09 24	8.26 56	0.02 23	0.11 67	0.00 1	0.03 50	0.20 42	0.28 59

ETE_ROB	5.80 42	1.77 72	6.33 47	1.44 15	0.78 26	6.43 38	6.90 78	12.53 48	8.08 24	12.93 67	14.89 55	21.13 87	5.87 44	9.83 44	6.57 41	0.04 37	0.01 25	0.00 1	0.02 42	0.08 22	0.33 61

DRN-Test	5.87 43	0.98 29	5.89 41	2.69 60	3.65 81	12.37 70	3.35 47	20.07 97	10.20 40	11.93 58	12.31 42	11.06 43	5.31 41	7.89 18	9.05 63	0.04 37	0.05 48	0.04 65	0.04 59	0.18 39	0.25 54

NCCL2	5.88 44	1.59 64	5.44 34	1.87 30	0.92 31	9.55 54	11.55 97	12.11 41	9.94 39	9.67 44	8.85 21	22.28 89	7.41 53	8.78 34	7.17 44	0.01 18	0.00 1	0.03 60	0.00 1	0.13 31	0.23 48

NaN_ROB	6.00 45	1.24 47	6.29 46	1.34 11	1.68 49	9.60 56	10.31 93	15.09 69	15.79 67	12.62 62	8.95 22	11.67 47	5.83 43	11.78 59	6.41 40	0.05 42	0.13 72	0.08 71	0.20 77	0.22 48	0.79 85

DANet	6.02 46	1.23 46	8.45 66	3.86 86	3.94 83	7.64 45	1.34 33	9.51 13	7.00 12	13.39 69	15.53 58	15.99 66	7.02 52	12.14 60	12.37 79	0.19 61	0.12 71	0.02 54	0.03 50	0.13 31	0.56 76

XPNet_ROB	6.03 47	1.22 45	5.61 37	2.56 57	0.90 29	6.32 35	7.07 79	12.92 51	8.30 27	14.76 74	15.13 57	19.84 82	6.66 51	10.36 48	8.58 59	0.02 23	0.04 44	0.00 1	0.03 50	0.11 27	0.24 51

Anonymous Stereo	6.16 48	3.15 92	23.75 101	2.97 69	2.48 63	4.39 20	13.30 99	9.21 10	9.86 37	9.56 43	8.76 20	6.79 18	1.99 9	13.50 71	13.04 82	0.01 18	0.05 48	0.00 1	0.06 63	0.22 48	0.19 45

GANet	6.22 49	1.07 35	4.07 21	2.27 47	0.89 28	9.19 50	9.52 88	12.02 40	8.13 25	10.72 51	29.09 91	13.86 54	7.52 55	11.00 55	4.39 30	0.36 75	0.00 1	0.02 54	0.02 42	0.12 29	0.08 21

DISCO	6.28 50	0.57 11	5.78 39	3.43 79	1.17 37	11.22 65	3.39 48	12.14 43	16.16 69	6.52 18	11.22 31	16.96 69	6.32 46	19.51 92	10.74 73	0.00 1	0.00 1	0.00 1	0.00 1	0.35 61	0.11 32

RYNet	6.34 51	0.89 23	5.88 40	1.41 13	4.48 91	15.97 81	4.18 57	13.41 54	16.49 70	10.81 52	7.00 10	14.33 58	8.72 60	9.43 42	13.71 83	0.00 1	0.01 25	0.00 1	0.00 1	0.02 4	0.07 18

GANetREF_RVC	6.56 52	2.89 87	7.58 63	3.41 77	0.40 11	12.96 73	9.58 89	15.09 69	17.25 75	10.33 48	10.62 28	12.27 49	8.16 57	12.21 61	4.53 31	0.41 79	0.00 1	0.00 1	0.02 42	3.12 103	0.39 64
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 53	1.80 74	6.25 45	1.26 9	0.94 33	10.08 58	9.02 84	16.00 77	11.51 49	12.74 64	13.02 46	24.77 91	5.25 40	10.56 50	8.02 51	0.04 37	0.05 48	0.00 1	0.02 42	0.10 23	0.25 54

DeepPrunerF	6.75 54	2.69 85	23.31 100	3.68 81	7.16 103	3.78 11	4.29 58	13.42 55	20.13 83	8.13 28	10.46 25	7.18 23	8.06 56	11.10 56	9.44 65	0.24 65	0.15 75	0.29 89	0.42 91	0.66 78	0.45 67

edge stereo	6.76 55	1.01 30	6.76 55	2.20 45	2.45 62	6.41 37	2.45 40	14.84 67	11.98 53	15.29 75	18.31 68	22.02 88	12.56 76	10.82 52	7.49 45	0.03 32	0.06 54	0.11 74	0.03 50	0.30 54	0.14 39

NCC-stereo	6.77 56	1.49 59	6.48 50	2.92 66	4.40 85	7.43 42	3.61 51	19.52 93	13.29 56	8.39 30	16.91 61	15.96 64	12.13 74	12.85 66	7.70 47	1.47 100	0.11 67	0.01 43	0.42 91	0.14 35	0.24 51

Abc-Net	6.77 56	1.49 59	6.48 50	2.92 66	4.40 85	7.43 42	3.61 51	19.52 93	13.29 56	8.39 30	16.91 61	15.96 64	12.13 74	12.85 66	7.70 47	1.47 100	0.11 67	0.01 43	0.42 91	0.14 35	0.24 51

RP	6.84 58	1.29 53	5.53 35	3.92 87	5.18 96	6.32 35	3.53 49	11.73 37	15.31 65	9.54 42	22.38 76	18.25 76	14.47 83	10.11 46	7.49 45	0.91 96	0.01 25	0.12 75	0.15 74	0.33 58	0.19 45

RGC	6.88 59	2.23 81	6.13 44	4.05 88	4.73 95	8.94 49	2.78 42	15.19 73	11.74 51	11.13 54	19.34 71	17.86 73	10.42 69	13.02 68	8.03 53	0.73 90	0.01 25	0.24 88	0.41 90	0.31 56	0.38 63

Nwc_Net	6.97 60	1.25 48	6.63 53	3.82 85	3.37 75	10.83 64	1.67 35	19.56 95	11.35 46	8.36 29	23.62 78	17.19 71	11.44 73	11.21 57	8.08 55	0.80 92	0.00 1	0.00 1	0.02 42	0.13 31	0.09 23

STTRV1_RVC	7.02 61	1.10 39	12.88 82	3.32 75	6.92 102	11.90 69	4.00 55	15.07 68	11.94 52	9.51 40	14.57 53	11.63 45	8.73 61	12.65 65	8.06 54	3.32 106	2.75 106	0.41 97	0.12 71	1.38 95	0.11 32

ADCRef	7.27 62	1.38 54	16.37 90	2.52 54	3.30 74	11.63 67	3.16 45	10.80 31	9.35 33	13.03 68	25.27 86	8.17 27	8.92 63	8.06 22	21.81 97	0.15 56	0.08 60	0.16 80	0.34 87	0.38 64	0.58 77

CSAN	7.62 63	1.60 65	6.56 52	1.83 28	0.66 18	12.40 71	10.52 95	14.45 64	21.32 85	14.19 71	15.98 60	17.84 72	13.02 80	12.32 62	8.38 57	0.09 47	0.07 58	0.03 60	0.04 59	0.33 58	0.67 83

stereogan	7.69 64	0.88 21	7.08 58	3.49 80	3.93 82	18.98 88	3.23 46	16.52 79	19.58 81	9.93 47	18.92 69	20.50 85	9.04 64	14.07 75	6.14 36	0.26 67	0.04 44	0.21 86	0.03 50	0.63 77	0.33 61

pmcnn	7.72 65	1.27 49	9.42 69	2.91 64	3.14 71	9.44 52	6.23 71	12.56 49	16.51 71	14.53 72	24.08 80	27.44 97	8.49 58	9.32 41	8.44 58	0.06 44	0.08 60	0.00 1	0.00 1	0.30 54	0.15 41

AF-Net	7.78 66	1.44 57	6.68 54	3.37 76	4.50 92	8.61 47	2.69 41	17.07 82	20.17 84	9.52 41	24.02 79	20.31 84	14.59 84	11.58 58	9.84 70	0.61 88	0.00 1	0.12 75	0.00 1	0.38 64	0.12 34

PASM	7.90 67	4.22 96	21.97 98	3.25 73	3.29 73	5.39 28	6.57 76	10.57 28	19.09 78	12.77 65	13.92 50	18.11 75	9.51 66	13.79 74	10.77 75	0.19 61	0.45 93	0.29 89	1.08 102	1.49 97	1.19 94

PWCDC_ROB	7.92 68	3.17 94	7.48 62	5.73 99	4.40 85	10.45 62	0.35 7	14.52 65	28.19 93	10.36 49	31.27 94	7.04 20	9.14 65	13.22 70	8.78 62	2.74 105	0.02 35	0.00 1	0.00 1	1.31 94	0.17 43

ADCP+	8.09 69	1.79 73	14.50 88	1.54 17	4.28 84	16.57 83	5.20 66	12.80 50	11.20 45	12.83 66	17.07 63	11.02 42	10.80 71	17.59 88	23.18 100	0.03 32	0.05 48	0.01 43	0.18 75	0.39 68	0.81 86

SuperB	8.10 70	3.15 92	24.67 102	2.65 58	1.23 38	9.88 57	4.29 58	10.18 23	30.07 96	11.53 56	12.18 39	6.12 16	6.65 50	10.50 49	14.47 85	0.14 54	0.11 67	0.35 93	0.25 81	13.06 111	0.48 70

PWC_ROB	8.24 71	3.13 90	12.74 81	2.43 52	4.43 88	7.51 44	1.22 28	16.63 80	19.24 79	16.08 77	28.29 89	13.99 57	10.16 68	13.63 73	14.06 84	0.42 82	0.00 1	0.05 67	0.00 1	0.59 74	0.27 58

MDST_ROB	8.37 72	0.32 1	9.03 67	4.18 91	2.42 61	26.86 101	6.14 69	19.36 90	13.52 58	27.09 99	22.75 77	9.47 33	4.74 34	15.06 80	6.34 39	0.02 23	0.02 35	0.00 1	0.00 1	0.02 4	0.13 38

G-Net	8.41 73	1.54 62	10.97 72	5.73 99	3.60 79	26.19 98	4.41 61	10.10 19	7.42 16	19.71 85	24.99 84	14.38 59	15.83 85	10.99 53	9.53 66	0.50 85	0.46 94	0.19 84	0.25 81	0.80 82	0.66 81

STTStereo_v2	8.41 73	1.54 62	10.97 72	5.73 99	3.60 79	26.19 98	4.41 61	10.10 19	7.42 16	19.71 85	24.99 84	14.38 59	15.83 85	10.99 53	9.53 66	0.50 85	0.46 94	0.19 84	0.25 81	0.80 82	0.66 81

XQC	8.43 75	3.58 95	16.40 91	2.92 66	2.17 56	13.22 75	3.60 50	14.64 66	25.86 90	11.87 57	12.04 37	15.06 62	10.67 70	15.24 81	19.41 90	0.39 77	0.08 60	0.05 67	0.07 65	0.84 84	0.45 67

FBW_ROB	8.50 76	1.03 32	7.98 65	1.93 36	1.28 40	13.10 74	6.23 71	22.50 101	18.98 77	18.82 82	14.91 56	19.06 79	10.04 67	18.41 89	9.83 69	0.62 89	0.22 81	1.82 107	0.82 100	0.99 88	1.36 96

RTSC	9.15 77	3.00 89	13.57 86	3.72 83	1.76 51	11.82 68	0.46 9	16.95 81	36.83 103	15.80 76	15.53 58	12.91 51	7.46 54	20.01 94	21.76 96	0.31 72	0.13 72	0.01 43	0.08 66	0.57 72	0.41 66

WCMA_ROB	9.21 78	0.87 20	7.37 60	2.54 56	2.13 54	13.59 76	5.80 68	11.64 36	14.01 60	24.43 97	32.99 98	27.09 96	18.02 88	12.51 64	9.85 71	0.81 93	0.07 58	0.01 43	0.01 38	0.16 37	0.23 48

MSMD_ROB	9.28 79	1.09 38	4.65 27	1.58 19	0.39 10	16.52 82	4.41 61	13.60 56	14.87 63	22.34 90	39.89 105	25.67 93	20.71 97	12.42 63	6.98 42	0.34 74	0.03 40	0.00 1	0.00 1	0.05 15	0.09 23

ADCPNet	9.54 80	2.39 83	31.46 104	2.09 41	1.60 45	16.71 85	6.39 75	12.11 41	11.45 47	13.53 70	21.45 74	19.41 80	10.94 72	14.38 77	21.54 95	0.27 70	1.16 100	0.39 96	1.49 105	0.58 73	1.45 97

SHD	9.61 81	2.60 84	12.46 79	3.69 82	3.54 78	9.47 53	1.25 29	20.16 98	37.84 106	18.19 81	21.24 73	16.96 69	12.83 79	14.47 79	16.05 87	0.32 73	0.13 72	0.01 43	0.08 66	0.38 64	0.48 70

PDISCO_ROB	9.62 82	1.99 78	11.51 74	9.88 107	9.61 108	21.48 92	3.83 54	19.33 89	28.49 94	11.27 55	14.17 51	19.92 83	5.02 38	16.35 85	9.18 64	5.28 108	0.41 89	0.14 79	0.09 68	2.05 100	2.36 104

MFN_U_SF_DS_RVC	9.78 83	4.27 97	14.47 87	2.29 48	2.85 68	23.40 96	13.62 100	13.60 56	19.54 80	19.42 84	24.27 81	16.74 68	8.59 59	17.05 87	7.98 50	1.25 99	1.68 104	0.17 81	2.63 107	0.72 80	1.04 90

SGM_RVC	10.08 84	0.60 13	3.42 13	2.30 49	0.32 8	19.41 89	6.33 73	18.95 87	14.64 61	25.14 98	24.32 82	33.34 103	18.79 92	19.86 93	12.55 81	0.25 66	0.26 84	0.22 87	0.24 80	0.34 60	0.40 65
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
DPSNet	10.14 85	1.88 77	16.82 92	1.85 29	1.73 50	24.84 97	17.20 109	19.92 96	27.41 92	12.23 61	13.62 49	16.52 67	18.35 89	14.42 78	12.50 80	0.78 91	0.54 98	0.08 71	0.25 81	1.18 91	0.59 80

ADCL	10.16 86	2.11 79	19.36 95	1.92 34	1.88 53	22.23 93	8.91 83	14.04 61	23.56 87	14.62 73	26.19 87	12.75 50	13.59 82	16.06 84	22.95 99	0.26 67	0.18 78	0.75 100	0.65 96	0.69 79	0.58 77

ADCMid	10.24 87	3.13 90	20.70 96	2.21 46	2.39 60	11.23 66	6.19 70	14.17 62	11.19 44	23.20 96	22.25 75	17.89 74	19.54 94	18.51 90	26.21 103	0.45 83	0.42 91	1.10 103	1.29 103	1.56 99	1.18 93

SANet	10.64 88	1.86 76	10.91 71	1.76 26	0.71 21	14.62 79	9.23 87	19.18 88	37.14 104	19.22 83	27.96 88	25.86 94	19.11 93	13.02 68	10.63 72	0.08 46	0.06 54	0.03 60	0.02 42	0.62 76	0.81 86

FC-DCNN	10.72 89	0.52 9	4.27 23	1.88 31	1.63 46	17.18 86	5.29 67	18.20 84	19.69 82	28.50 100	34.51 101	34.03 104	21.48 100	15.89 83	11.15 77	0.03 32	0.01 25	0.02 54	0.01 38	0.07 20	0.09 23

AnyNet_C32	10.98 90	5.58 99	22.79 99	4.16 89	5.83 98	15.64 80	14.30 101	13.18 53	17.15 74	16.44 79	20.52 72	14.68 61	13.44 81	22.46 96	30.08 108	0.17 59	0.26 84	0.36 94	0.36 88	1.23 92	0.91 88

MeshStereo	11.52 91	1.52 61	4.55 26	1.89 32	1.46 42	19.87 91	5.11 65	20.66 99	15.91 68	32.67 105	34.51 101	39.34 109	21.15 98	18.74 91	12.10 78	0.11 50	0.06 54	0.01 43	0.00 1	0.45 71	0.22 47
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
ADCS	13.02 92	4.93 98	28.38 103	3.17 72	2.67 66	13.61 77	10.83 96	18.70 85	33.46 99	22.59 91	24.78 83	19.59 81	18.51 91	23.40 99	32.16 110	0.10 49	0.19 79	0.37 95	0.18 75	1.26 93	1.46 98

MFMNet_re	13.29 93	8.60 105	18.29 93	9.75 106	7.25 105	19.65 90	14.84 105	20.71 100	30.72 97	23.03 94	28.77 90	18.85 78	26.09 105	13.55 72	9.82 68	2.44 103	1.35 103	0.34 92	0.23 79	4.78 106	6.69 108

LSM	14.01 94	5.95 100	33.49 105	6.78 104	43.61 113	10.22 60	9.98 92	15.16 72	22.93 86	23.07 95	32.34 97	18.52 77	12.67 77	15.45 82	11.10 76	0.16 58	0.51 97	0.09 73	0.32 85	1.08 89	16.85 113

SAMSARA	14.63 95	2.74 86	12.38 78	12.65 110	6.74 101	36.50 107	72.93 115	19.36 90	23.77 88	16.20 78	13.04 47	29.21 98	12.78 78	16.98 86	15.21 86	0.11 50	0.26 84	0.03 60	0.14 73	0.76 81	0.77 84

SPS-STEREO	15.04 96	6.23 101	13.21 85	11.34 109	11.65 111	23.30 95	7.15 80	24.16 103	15.65 66	31.78 104	29.19 92	31.62 101	21.32 99	24.62 100	19.50 91	7.59 110	4.19 111	3.22 108	1.48 104	6.99 110	6.54 107
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
PVD	15.44 97	2.93 88	14.67 89	4.21 92	3.39 77	17.43 87	4.16 56	27.84 108	48.84 111	31.02 103	43.54 109	29.76 100	30.81 108	25.97 102	21.40 94	0.23 64	0.41 89	0.04 65	0.33 86	0.41 69	1.33 95

SGM+DAISY	15.62 98	7.26 104	19.28 94	8.94 105	10.11 110	26.25 100	10.49 94	19.36 90	14.65 62	30.64 102	33.59 99	33.00 102	22.32 101	24.96 101	16.42 88	7.90 111	6.25 112	4.51 109	3.37 108	5.86 108	7.20 109

NVStereoNet_ROB	16.04 99	6.75 102	12.90 83	6.37 103	7.42 106	12.89 72	9.74 90	22.78 102	25.12 89	30.32 101	46.19 111	34.37 105	25.38 103	21.48 95	21.38 93	5.94 109	3.10 109	6.07 110	10.09 112	4.01 104	8.54 111
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	16.12 100	10.81 109	59.36 111	4.42 93	2.49 64	30.06 103	15.15 107	17.51 83	16.51 71	17.88 80	37.69 104	24.04 90	17.54 87	29.60 105	33.29 111	0.28 71	0.38 87	0.43 98	0.42 91	2.57 102	1.98 100

MSC_U_SF_DS_RVC	16.41 101	6.93 103	21.83 97	5.94 102	2.81 67	38.71 108	14.59 102	24.55 104	34.87 101	33.66 106	34.35 100	29.24 99	24.20 102	22.59 97	17.95 89	2.52 104	2.81 107	1.17 105	1.51 106	5.89 109	2.16 101

ELAS_RVC	16.54 102	2.26 82	10.09 70	5.50 98	4.46 90	28.28 102	16.72 108	25.55 105	33.54 100	40.19 108	40.30 107	36.68 107	30.03 106	29.40 104	20.61 92	0.98 98	1.21 101	0.86 102	0.70 98	1.39 96	2.16 101
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAS	16.72 103	2.14 80	9.23 68	4.92 95	4.53 93	32.66 106	15.11 106	27.40 106	28.68 95	40.27 109	44.90 110	38.33 108	30.50 107	26.44 103	21.94 98	0.88 94	1.23 102	0.67 99	0.89 101	1.49 97	2.18 103
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
LE_ROB	16.73 104	1.28 52	11.61 75	3.72 83	1.65 47	16.67 84	9.17 85	14.39 63	55.91 113	63.81 113	40.86 108	35.94 106	37.73 112	14.24 76	26.87 104	0.05 42	0.10 65	0.13 78	0.22 78	0.12 29	0.15 41

SGM-ForestM	16.99 105	1.08 37	5.74 38	2.12 42	0.75 22	31.63 105	12.21 98	27.80 107	32.25 98	37.88 107	39.99 106	52.96 112	35.20 111	33.60 107	24.47 101	0.26 67	0.39 88	0.31 91	0.39 89	0.26 53	0.53 75

DispFullNet	17.47 106	26.01 111	33.98 106	22.58 112	20.86 112	13.84 78	1.28 30	16.50 78	26.27 91	19.97 87	17.17 64	20.52 86	18.49 90	22.86 98	10.76 74	5.13 107	2.83 108	30.72 112	7.72 110	20.86 112	11.01 112

RTS	18.87 107	9.32 107	86.48 113	4.95 96	6.10 99	42.08 110	14.70 103	15.49 74	41.06 108	22.65 92	32.32 95	13.77 52	19.54 94	37.98 108	28.96 105	0.41 79	0.23 82	0.00 1	0.02 42	0.91 85	0.50 72

RTSA	18.87 107	9.32 107	86.48 113	4.95 96	6.10 99	42.08 110	14.70 103	15.49 74	41.06 108	22.65 92	32.32 95	13.77 52	19.54 94	37.98 108	28.96 105	0.41 79	0.23 82	0.00 1	0.02 42	0.91 85	0.50 72

MANE	19.47 109	1.27 49	5.07 30	4.69 94	5.55 97	30.49 104	9.94 91	34.01 110	37.27 105	44.13 110	51.57 114	52.51 111	40.41 113	33.58 106	24.81 102	0.89 95	0.86 99	1.11 104	9.72 111	0.38 64	1.06 91

BEATNet-Init1	23.31 110	9.03 106	41.67 107	4.17 90	2.53 65	45.68 112	19.47 110	33.43 109	38.45 107	47.59 112	49.10 112	59.31 113	41.80 114	38.35 110	29.21 107	0.47 84	0.50 96	0.81 101	0.66 97	2.10 101	1.86 99

FADE	25.68 111	17.27 110	50.60 110	10.46 108	9.90 109	22.50 94	9.17 85	35.80 112	53.05 112	20.32 89	19.01 70	26.54 95	34.42 109	39.35 111	33.52 112	30.62 113	14.22 113	38.39 113	37.63 113	5.22 107	5.56 106

MADNet+	27.07 112	33.84 112	90.97 115	20.14 111	7.47 107	48.43 113	47.10 112	35.43 111	36.46 102	20.11 88	30.05 93	25.29 92	35.08 110	45.50 113	50.28 113	2.13 102	2.00 105	1.19 106	0.76 99	4.71 105	4.43 105

PWCK	30.53 113	44.32 113	47.25 109	29.76 113	7.23 104	40.78 109	27.10 111	44.73 113	44.32 110	47.31 111	36.37 103	47.16 110	26.05 104	41.26 112	31.87 109	21.83 112	4.03 110	29.50 111	4.67 109	27.17 113	7.80 110

DPSimNet_ROB	53.45 114	64.73 114	44.39 108	53.97 114	45.39 114	53.66 114	54.83 113	55.15 114	57.87 114	64.16 114	50.83 113	63.40 114	53.34 115	46.45 114	65.81 114	63.13 114	26.54 114	57.94 114	51.11 114	45.52 114	50.69 114

MADNet++	82.84 115	82.38 115	73.57 112	87.72 115	82.97 115	93.14 115	69.15 114	86.42 115	82.50 115	93.46 115	86.70 115	86.28 115	80.92 116	88.34 115	88.84 115	86.83 115	84.17 115	72.64 115	68.92 115	80.47 115	81.42 115

MEDIAN_ROB	98.41 116	99.70 116	99.30 117	97.09 116	97.02 116	96.89 116	95.77 117	97.66 116	97.28 116	98.79 118	98.94 116	99.18 116	98.14 117	96.89 116	96.88 116	99.96 118	99.16 116	100.00 116	99.99 116	99.69 116	99.88 116

AVERAGE_ROB	99.62 117	99.95 117	98.81 116	100.00 121	100.00 117	98.08 117	95.47 116	100.00 119	100.00 117	100.00 119	100.00 117	100.00 119	100.00 118	100.00 119	99.99 117	100.00 120	100.00 117	100.00 116	100.00 117	100.00 119	100.00 120

DGTPSM_ROB	99.90 118	100.00 118	99.99 118	99.99 119	100.00 117	100.00 118	100.00 118	99.97 117	100.00 117	98.35 116	100.00 117	99.84 117	100.00 118	99.98 117	99.99 117	99.99 119	100.00 117	100.00 116	100.00 117	100.00 119	100.00 120

DPSMNet_ROB	99.91 119	100.00 118	99.99 118	99.99 119	100.00 117	100.00 118	100.00 118	99.98 118	100.00 117	98.35 116	100.00 117	99.84 117	100.00 118	99.98 117	99.99 117	100.00 120	100.00 117	100.00 116	100.00 117	100.00 119	100.00 120

DPSM_ROB	99.95 120	100.00 118	100.00 120	99.76 117	100.00 117	100.00 118	100.00 118	100.00 119	100.00 117	100.00 119	100.00 117	100.00 119	100.00 118	100.00 119	100.00 120	99.21 116	100.00 117	100.00 116	100.00 117	99.99 117	99.95 117

DPSM	99.95 120	100.00 118	100.00 120	99.76 117	100.00 117	100.00 118	100.00 118	100.00 119	100.00 117	100.00 119	100.00 117	100.00 119	100.00 118	100.00 119	100.00 120	99.21 116	100.00 117	100.00 116	100.00 117	99.99 117	99.95 117

LSM0	100.00 122	100.00 118	100.00 120	100.00 121	100.00 117	100.00 118	100.00 118	100.00 119	100.00 117	100.00 119	100.00 117	100.00 119	100.00 118	100.00 119	100.00 120	100.00 120	100.00 117	100.00 116	100.00 117	100.00 119	99.99 119

MSMDNet													1.26 6

Low-res two-view benchmark