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	3.58 1	1.05 3	3.35 1	2.17 2	0.59 1	5.61 1	0.40 1	10.50 1	13.34 5	6.93 1	10.44 1	5.29 1	3.47 1	5.82 1	2.62 1	0.01 2	0.01 2	0.02 9	0.01 11	0.04 1	0.01 1

PMTNet	5.36 2	0.97 1	3.42 2	1.97 1	1.30 2	8.96 7	0.69 2	12.33 2	12.16 4	8.50 2	11.35 2	6.09 2	4.50 2	9.34 5	2.70 2	22.71 130	0.01 2	0.04 13	0.02 15	0.06 2	0.01 1

Gwc-CoAtRS	5.54 3	1.22 4	8.40 23	4.28 3	1.36 6	6.93 3	1.31 3	15.48 4	15.03 8	12.62 3	13.32 3	12.74 7	5.71 6	8.61 4	2.98 3	0.01 2	0.22 43	0.00 1	0.29 50	0.28 11	0.07 3

DPM-Stereo	6.74 4	1.93 21	10.02 33	4.28 3	1.64 8	13.54 23	1.52 5	15.16 3	6.63 1	14.30 5	26.81 19	10.90 4	5.19 3	14.78 19	7.56 9	0.00 1	0.06 23	0.05 15	0.00 1	0.17 9	0.17 14

R-Stereo Train	7.04 5	1.34 8	7.89 18	6.23 23	2.30 14	7.51 4	5.54 26	23.96 16	6.94 2	16.26 15	33.57 52	12.04 5	5.61 4	7.67 2	3.74 4	0.01 2	0.05 17	0.01 5	0.00 1	0.10 3	0.12 6

RAFT-Stereo	7.04 5	1.34 8	7.89 18	6.23 23	2.30 14	7.51 4	5.54 26	23.96 16	6.94 2	16.26 15	33.57 52	12.04 5	5.61 4	7.67 2	3.74 4	0.01 2	0.05 17	0.01 5	0.00 1	0.10 3	0.12 6
Lahav Lipson, Zachary Teed, and Jia Deng: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. 3DV
HITNet	7.83 7	2.93 38	8.39 22	4.76 5	1.48 7	12.75 16	4.64 18	20.97 6	14.37 7	15.14 11	22.15 6	14.57 10	10.62 11	14.38 17	8.14 13	0.04 8	0.01 2	0.48 48	0.02 15	0.57 25	0.10 5
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
FENet	7.94 8	1.01 2	6.72 11	5.87 16	2.07 10	11.11 11	2.03 6	21.39 8	20.81 15	17.14 22	22.65 7	14.43 9	11.54 15	12.97 13	7.89 11	0.15 20	0.24 46	0.03 11	0.02 15	0.14 6	0.62 32

DN-CSS_ROB	8.17 9	2.59 33	14.14 50	6.11 21	3.22 25	6.31 2	3.53 8	22.92 15	20.59 14	16.66 19	30.05 31	9.31 3	7.95 9	13.49 14	4.78 6	0.72 58	0.00 1	0.45 46	0.00 1	0.54 23	0.09 4

DMCA	8.51 10	1.90 20	9.96 32	5.16 6	4.14 34	10.29 10	4.46 17	25.52 24	19.55 12	16.33 17	20.69 5	17.59 14	11.26 13	10.42 6	11.36 32	0.50 48	0.08 29	0.35 38	0.05 25	0.36 13	0.31 20

MLCV	9.32 11	2.22 30	12.55 44	5.22 8	1.32 3	13.79 24	1.35 4	21.14 7	23.65 22	21.91 39	31.37 39	17.14 12	7.57 8	16.00 29	10.18 22	0.22 25	0.01 2	0.03 11	0.02 15	0.40 14	0.29 19

BEATNet_4x	9.37 12	4.85 65	12.05 41	5.48 10	1.35 5	13.15 20	6.15 36	25.06 22	16.74 9	16.18 14	24.65 11	17.29 13	12.51 18	16.91 34	10.92 27	0.38 36	0.18 40	0.81 64	0.05 25	1.95 64	0.65 35

GANet-RSSM	9.48 13	1.32 7	6.45 9	6.72 31	5.51 57	12.78 17	6.03 33	26.55 30	18.96 10	14.92 6	27.95 25	25.41 41	14.69 37	13.52 15	7.60 10	0.10 15	0.10 34	0.16 28	0.01 11	0.51 21	0.39 23

PSMNet-RSSM	9.71 14	1.73 16	6.09 7	7.47 39	4.27 37	10.06 8	6.00 31	29.14 41	21.74 17	15.76 12	32.88 49	22.63 23	13.18 19	12.94 12	9.12 19	0.09 14	0.02 9	0.07 20	0.10 36	0.70 31	0.24 16

ccs	9.74 15	1.25 6	7.47 16	5.77 13	2.16 12	7.74 6	3.54 9	33.83 69	35.91 62	13.42 4	23.80 9	22.01 20	11.97 16	11.51 7	9.09 18	0.41 40	1.75 95	0.17 29	0.17 41	0.63 27	2.12 72

CFNet-ft	9.87 16	2.18 28	5.49 4	7.45 37	5.25 50	15.76 36	5.98 29	21.89 10	21.87 18	14.96 7	30.02 29	25.68 42	13.68 23	12.46 9	10.94 28	0.08 12	0.06 23	1.83 85	0.22 43	1.09 42	0.43 26

CFNet_RVC	9.87 16	2.18 28	5.49 4	7.45 37	5.25 50	15.76 36	5.98 29	21.89 10	21.87 18	14.96 7	30.02 29	25.68 42	13.68 23	12.46 9	10.94 28	0.08 12	0.06 23	1.83 85	0.22 43	1.09 42	0.43 26

cf-r	10.06 18	1.60 11	7.99 20	5.82 14	5.70 62	12.92 19	4.78 19	24.18 19	27.47 32	16.91 21	31.02 37	25.12 39	14.05 29	11.63 8	11.01 30	0.03 7	0.03 12	0.05 15	0.03 22	0.43 15	0.39 23

AdaStereo	10.22 19	3.63 49	9.14 29	9.15 56	3.24 26	15.79 38	5.39 24	31.94 56	25.42 25	18.39 25	26.47 15	19.44 17	9.50 10	16.60 33	8.59 15	0.44 45	0.05 17	0.40 44	0.00 1	0.57 25	0.16 13
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.
iResNet	10.26 20	3.10 44	15.72 59	7.35 35	2.13 11	13.86 25	7.07 45	22.80 14	28.01 35	20.10 31	30.40 33	16.79 11	11.06 12	14.46 18	10.29 25	0.39 39	0.05 17	0.00 1	0.07 34	0.73 34	0.73 38

ACVNet	10.36 21	1.68 13	5.49 4	6.01 18	2.75 19	12.78 17	3.56 10	21.89 10	34.81 58	14.96 7	30.78 35	24.72 36	13.88 26	15.81 25	17.20 50	0.13 17	0.01 2	0.06 18	0.06 31	0.51 21	0.15 11

ccs_rob	10.52 22	2.12 26	7.47 16	6.02 20	3.07 22	19.32 52	4.16 13	25.06 22	27.77 34	20.21 32	27.93 24	30.56 56	13.32 21	14.95 21	7.25 8	0.04 8	0.05 17	0.21 30	0.05 25	0.55 24	0.27 18

GwcNet-RSSM	10.65 23	1.94 22	8.57 25	6.89 33	5.26 52	12.26 15	5.21 22	25.80 27	31.50 45	16.67 20	33.52 51	23.70 31	13.88 26	12.84 11	13.67 37	0.05 10	0.10 34	0.14 26	0.02 15	0.65 30	0.39 23

CFNet	10.67 24	2.33 31	8.90 26	6.66 29	4.09 33	16.05 39	4.26 15	30.44 48	31.01 44	18.53 26	26.48 16	23.69 30	13.68 23	15.27 23	10.59 26	0.05 10	0.02 9	0.44 45	0.05 25	0.73 34	0.23 15

DeepPruner_ROB	10.77 25	4.56 60	13.13 46	6.37 26	4.28 38	10.14 9	6.86 41	36.42 85	19.59 13	17.87 23	27.37 22	23.19 26	12.26 17	19.09 44	10.23 24	1.05 72	0.48 69	0.23 32	0.15 39	0.89 39	1.17 51

HSM-Net_RVC	10.88 26	1.23 5	5.46 3	5.57 11	2.63 17	20.12 56	6.06 34	27.67 34	28.35 36	20.63 35	25.51 12	34.01 71	14.38 36	16.18 31	9.29 20	0.11 16	0.07 27	0.01 5	0.00 1	0.13 5	0.14 10
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
NOSS_ROB	10.99 27	3.81 53	6.88 13	6.69 30	2.30 14	16.06 40	10.94 62	30.06 46	32.41 50	16.10 13	18.72 4	22.78 24	14.21 33	17.52 36	8.62 16	2.35 90	1.84 97	3.32 105	1.57 89	2.06 68	1.52 59

ac_64	11.10 28	1.49 10	9.88 31	8.24 52	5.45 55	19.12 50	6.46 38	24.10 18	14.34 6	22.80 43	23.87 10	33.28 69	15.54 41	18.87 42	17.26 52	0.28 30	0.12 37	0.02 9	0.02 15	0.26 10	0.63 33

HSM	11.33 29	1.80 17	7.04 15	6.13 22	3.99 32	16.16 43	6.92 43	29.69 44	19.39 11	22.95 44	26.05 14	41.08 96	15.79 44	20.25 50	8.89 17	0.02 6	0.03 12	0.01 5	0.00 1	0.15 7	0.34 22

DMCA-RVC	11.41 30	3.32 45	11.68 39	10.12 71	4.81 44	11.92 14	4.11 12	24.67 20	23.66 23	20.06 30	28.51 26	26.71 46	24.96 75	15.95 28	14.82 40	0.41 40	0.34 55	0.56 53	0.16 40	0.72 33	0.69 36

RASNet	11.59 31	1.71 15	10.36 34	6.30 25	5.66 60	15.48 35	6.12 35	34.50 75	20.86 16	16.61 18	32.51 46	22.04 22	23.00 67	19.44 45	16.15 47	0.71 56	0.03 12	0.05 15	0.01 11	0.15 7	0.13 9

iResNet_ROB	11.71 32	1.96 23	10.77 35	6.01 18	3.68 28	15.13 32	2.34 7	31.72 55	37.58 69	26.16 61	32.77 47	25.88 44	15.34 40	16.35 32	7.94 12	0.19 23	0.01 2	0.00 1	0.00 1	0.31 12	0.12 6

FADNet-RVC-Resample	11.72 33	4.35 58	30.33 94	9.46 61	3.90 30	14.11 26	7.17 46	25.78 25	23.31 21	23.27 47	29.52 28	19.50 18	15.00 39	15.38 24	8.33 14	0.53 49	0.50 70	0.49 49	0.50 63	1.38 55	1.67 63

CBMV_ROB	11.77 34	2.89 36	6.92 14	5.20 7	2.88 21	14.12 27	4.79 20	26.69 31	29.84 38	26.17 62	31.92 43	23.61 29	20.74 63	20.20 49	10.18 22	1.78 84	1.80 96	2.17 91	1.26 85	1.61 57	0.55 31

DSFCA	11.85 35	2.01 24	12.93 45	5.40 9	6.87 73	15.36 33	10.45 59	30.68 50	31.84 47	25.99 60	23.52 8	22.03 21	14.05 29	20.28 52	12.46 33	0.57 51	0.41 60	0.28 35	0.38 57	1.02 40	0.47 29

FADNet_RVC	11.94 36	5.17 69	39.74 107	6.44 27	3.46 27	11.42 12	4.17 14	29.16 42	26.89 29	17.97 24	25.96 13	13.41 8	15.67 42	15.84 27	13.54 36	0.90 65	0.75 81	0.55 52	1.20 83	3.65 88	2.85 80

iResNetv2_ROB	12.00 37	2.91 37	13.46 47	5.82 14	3.73 29	13.15 20	6.45 37	34.15 70	36.02 64	25.25 57	33.81 55	29.79 54	14.24 34	13.61 16	6.10 7	0.21 24	0.02 9	0.12 23	0.00 1	0.82 37	0.25 17

hitnet-ft	12.04 38	2.11 25	6.14 8	7.27 34	3.90 30	25.98 80	3.56 10	22.56 13	23.74 24	20.55 34	33.81 55	31.59 60	18.67 55	19.47 46	17.20 50	0.42 44	1.36 91	0.35 38	0.07 34	1.11 45	0.86 40

NLCA_NET_v2_RVC	12.05 39	3.43 47	16.25 64	7.62 42	5.58 58	14.57 31	6.00 31	32.98 66	27.41 31	21.69 38	31.40 40	25.21 40	13.47 22	14.96 22	15.60 43	0.84 60	0.33 53	0.35 38	0.35 54	1.29 51	1.78 65
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	12.13 40	3.51 48	15.82 60	7.52 40	5.50 56	14.30 29	6.60 40	32.79 63	27.59 33	21.97 40	32.14 45	24.76 38	14.02 28	14.82 20	16.06 44	0.85 62	0.35 56	0.34 37	0.35 54	1.30 53	1.94 68

acv_ft	12.16 41	1.68 13	11.97 40	7.70 44	6.20 64	19.61 54	5.52 25	25.78 25	34.81 58	25.62 59	30.78 35	24.72 36	13.20 20	15.81 25	18.80 53	0.13 17	0.01 2	0.06 18	0.06 31	0.63 27	0.15 11

HGLStereo	12.72 42	2.66 34	12.34 42	9.93 67	7.55 82	16.16 43	7.45 47	31.48 54	27.01 30	20.01 29	35.31 60	26.98 48	18.90 56	17.87 39	18.90 54	0.33 33	0.16 39	0.07 20	0.05 25	0.48 16	0.70 37

SGM-Forest	12.92 43	1.87 18	6.61 10	5.68 12	2.05 9	23.19 70	11.30 65	34.24 71	30.77 43	26.54 64	31.99 44	29.98 55	15.74 43	21.17 57	13.36 35	1.05 72	0.33 53	0.84 66	0.01 11	0.71 32	0.97 44
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
STTStereo	12.97 44	4.88 66	26.57 88	7.54 41	5.41 54	13.43 22	6.49 39	30.53 49	26.35 28	25.14 56	31.43 41	32.00 61	14.12 31	16.01 30	14.83 41	0.29 31	0.31 51	0.67 58	1.08 79	1.21 48	1.11 48

TDLM	12.98 45	3.78 52	9.77 30	10.75 76	4.94 46	16.10 41	14.08 76	35.05 78	31.87 48	22.36 41	26.51 18	26.09 45	14.97 38	24.73 69	13.11 34	1.05 72	0.05 17	1.21 80	0.27 48	1.83 60	1.00 46

AANet_RVC	13.16 46	5.34 71	10.83 37	8.20 51	4.44 39	14.13 28	9.46 52	28.78 40	37.67 70	23.44 49	37.47 67	23.48 27	15.83 45	22.39 59	16.63 48	1.67 82	0.86 85	0.24 33	0.02 15	0.63 27	1.60 61

CVANet_RVC	13.24 47	3.39 46	8.43 24	8.42 53	5.04 48	18.52 48	11.72 67	32.03 58	33.85 53	23.17 46	32.77 47	29.78 53	16.51 46	23.20 61	11.28 31	0.94 66	0.04 15	1.19 79	0.47 61	3.13 81	1.01 47

FADNet-RVC	13.27 48	11.57 105	39.71 106	7.94 49	4.50 40	15.41 34	6.95 44	27.96 35	22.98 20	20.37 33	30.73 34	26.86 47	11.31 14	20.50 55	14.28 39	0.14 19	0.14 38	0.13 25	0.18 42	2.83 75	0.86 40

DLCB_ROB	13.35 49	3.00 41	9.12 27	9.43 59	5.68 61	21.80 64	10.12 56	29.19 43	29.92 39	27.71 68	31.45 42	32.37 62	19.50 57	19.02 43	16.73 49	0.22 25	0.04 15	0.38 43	0.06 31	0.49 19	0.85 39

StereoDRNet-Refined	13.36 50	2.80 35	10.82 36	7.94 49	3.10 23	18.95 49	4.45 16	28.47 39	29.65 37	29.11 72	41.47 86	24.44 34	17.64 51	23.34 62	21.66 60	0.16 21	0.07 27	0.53 51	0.32 52	0.77 36	1.64 62
Rohan Chabra, Julian Straub, Chris Sweeney, Richard Newcombe, Henry Fuchs: StereoDRNet. CVPR
CBMV	13.69 51	3.63 49	8.02 21	5.93 17	2.69 18	22.57 67	12.44 69	29.81 45	31.57 46	31.20 81	33.79 54	31.04 57	17.41 50	25.28 72	14.11 38	0.71 56	0.60 74	0.60 55	0.11 37	1.11 45	1.13 50
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
FADNet	14.18 52	10.92 102	37.56 102	7.79 47	6.64 70	17.48 45	6.91 42	34.73 76	34.03 54	18.85 27	27.02 20	28.26 49	14.24 34	17.77 38	10.09 21	0.41 40	0.67 77	0.37 41	0.37 56	8.13 111	1.34 53

pmcnn	14.59 53	3.68 51	19.78 77	6.83 32	4.51 41	21.27 61	14.67 79	27.37 32	30.54 40	27.09 67	40.19 81	38.71 87	18.53 54	17.70 37	19.27 55	0.84 60	0.09 33	0.00 1	0.00 1	0.48 16	0.31 20

NVstereo2D	15.15 54	2.97 39	16.28 65	9.23 57	7.56 83	30.03 90	9.53 53	42.80 111	42.72 84	15.11 10	27.03 21	23.16 25	16.93 48	24.28 66	15.45 42	3.07 99	0.43 65	1.44 81	0.61 66	6.74 103	7.60 113

StereoDRNet	15.30 55	4.47 59	14.35 54	10.71 75	8.69 92	25.02 77	11.03 63	39.66 100	35.62 61	29.17 73	28.81 27	34.05 72	17.80 52	18.80 41	23.77 69	0.41 40	0.30 50	0.15 27	0.14 38	1.64 58	1.42 56

DRN-Test	15.88 56	3.83 54	14.70 55	9.96 69	8.02 89	28.93 89	14.65 78	42.34 107	38.25 73	28.18 71	34.31 59	29.22 52	18.35 53	20.01 48	22.68 65	0.37 35	0.41 60	0.46 47	0.28 49	1.28 50	1.34 53

PA-Net	16.29 57	6.88 83	27.34 89	9.88 66	11.12 105	21.77 63	28.15 119	32.06 59	39.49 74	19.99 28	27.91 23	23.50 28	20.59 62	20.25 50	24.04 72	0.24 27	2.26 105	0.22 31	4.59 113	1.13 47	4.48 98
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
DISCO	16.36 58	1.88 19	16.15 62	7.78 46	4.19 35	26.92 82	10.21 58	30.31 47	44.26 91	21.35 37	33.91 57	35.80 80	22.40 66	35.65 106	33.87 104	0.18 22	0.08 29	0.04 13	0.04 24	1.71 59	0.54 30

GwcNet	16.48 59	5.23 70	20.03 78	10.24 73	8.48 90	28.79 87	10.09 54	38.60 98	44.51 92	23.29 48	36.95 65	31.29 58	19.79 60	20.42 54	25.70 86	0.54 50	0.72 78	0.57 54	0.39 58	1.37 54	2.64 78

NaN_ROB	16.51 60	6.51 79	16.22 63	10.53 74	4.64 43	31.76 96	17.78 94	37.00 89	43.37 87	29.68 77	31.29 38	32.67 66	20.32 61	28.00 82	16.06 44	0.38 36	0.41 60	0.32 36	0.41 59	0.87 38	1.94 68

FAT-Stereo	16.81 61	2.97 39	17.56 68	9.80 65	6.21 65	16.12 42	13.23 73	38.18 97	39.67 76	27.00 66	39.73 80	37.55 84	31.30 97	20.39 53	21.52 59	3.44 101	2.05 99	0.81 64	1.10 80	1.89 63	5.72 102

S-Stereo	16.85 62	3.95 55	20.67 80	12.08 85	8.91 94	21.68 62	12.61 70	32.20 61	44.61 93	22.37 42	38.42 74	29.15 51	28.02 86	18.58 40	22.36 64	2.88 98	3.11 113	0.87 69	1.82 93	3.58 87	9.08 119

DANet	16.85 62	8.00 86	26.04 87	14.56 97	7.25 79	20.59 57	5.37 23	26.22 29	26.02 27	32.17 86	36.00 61	37.20 83	29.77 91	30.75 86	24.62 77	1.14 76	1.34 89	2.21 92	0.51 64	3.18 82	4.07 93

PSMNet_ROB	16.90 64	5.45 72	14.16 51	13.77 91	7.25 79	27.65 84	32.74 123	43.21 114	37.88 71	24.00 50	32.97 50	34.55 76	16.69 47	17.41 35	23.96 71	0.38 36	0.22 43	0.93 70	2.05 97	1.83 60	0.96 43

GANet	16.92 65	4.32 57	12.46 43	10.84 78	4.24 36	23.04 69	15.36 84	39.91 101	34.47 57	32.42 87	45.10 101	40.04 94	27.54 82	23.61 64	20.20 56	1.02 71	0.08 29	0.67 58	0.22 43	1.87 62	0.97 44

NCCL2	17.23 66	6.37 78	14.30 53	23.45 120	7.18 78	24.16 73	17.05 92	34.39 73	25.88 26	31.55 82	38.01 70	42.09 101	26.81 78	20.94 56	22.69 66	0.45 46	0.23 45	2.63 97	1.80 92	2.02 67	2.57 76

ADCRef	17.30 67	6.73 81	42.01 110	10.11 70	8.78 93	26.61 81	10.45 59	31.29 52	32.75 52	31.19 80	41.62 88	18.47 16	19.78 59	24.29 67	34.06 106	0.76 59	0.42 64	2.16 90	1.79 91	1.23 49	1.56 60

XPNet_ROB	17.33 68	4.84 64	15.44 58	11.15 81	6.64 70	20.88 58	16.10 86	36.26 84	39.54 75	31.80 85	41.32 85	41.30 98	23.91 68	24.67 68	27.40 92	1.07 75	0.74 79	0.79 62	0.25 46	1.10 44	1.31 52

RP	17.58 69	4.92 67	15.27 57	15.44 102	11.95 111	21.05 59	11.75 68	27.64 33	45.09 96	22.99 45	42.05 90	39.39 90	27.74 84	25.54 73	22.33 63	5.45 114	0.59 73	4.33 108	1.62 90	3.53 86	2.86 81

SuperB	17.60 70	6.22 77	56.40 123	7.91 48	5.07 49	19.70 55	9.03 50	24.87 21	50.15 108	26.25 63	39.13 75	17.61 15	20.84 64	22.28 58	25.31 82	0.70 55	0.31 51	1.10 75	0.71 69	16.58 127	1.86 66

ETE_ROB	17.67 71	9.48 93	17.66 69	13.60 89	4.57 42	23.03 68	21.09 104	32.49 62	35.24 60	30.49 78	38.06 71	46.18 112	24.49 70	26.40 76	24.21 74	0.46 47	0.24 46	1.14 76	0.84 73	1.52 56	2.13 73

PWCDC_ROB	17.80 72	9.62 94	18.38 73	17.95 108	7.74 84	24.61 75	5.56 28	34.41 74	52.79 112	29.66 76	50.20 110	20.63 19	19.69 58	29.85 85	20.87 57	6.18 117	0.26 49	0.10 22	0.05 25	5.36 101	2.00 70

MDST_ROB	17.92 73	1.60 11	13.68 48	13.88 92	6.44 68	43.05 116	14.86 81	42.74 110	41.66 81	43.25 109	42.85 93	28.43 50	17.28 49	29.35 84	16.06 44	0.88 63	0.11 36	0.63 57	0.47 61	0.50 20	0.64 34

Nwc_Net	17.98 74	4.67 62	17.79 72	14.26 93	11.65 110	25.14 78	14.88 82	40.85 104	39.83 77	21.07 36	43.24 96	34.50 75	27.64 83	26.44 77	25.03 78	3.22 100	0.18 40	2.52 96	3.18 104	1.98 65	1.51 58

ADCP+	18.16 75	4.61 61	32.94 97	9.31 58	10.23 101	28.87 88	11.69 66	33.43 67	36.49 65	29.28 75	40.49 82	24.18 32	24.61 72	33.89 96	35.33 107	0.25 28	0.40 59	2.00 87	1.17 82	2.16 69	1.87 67

PWC_ROB	18.37 76	10.55 99	25.19 86	10.21 72	6.29 67	23.75 71	4.79 20	35.93 83	48.75 101	32.56 88	44.46 100	34.68 77	24.78 73	30.99 87	25.72 87	1.38 79	0.08 29	1.57 82	0.26 47	2.18 70	3.17 85

AF-Net	18.38 77	5.60 73	14.21 52	15.95 103	10.78 103	22.26 65	10.45 59	35.39 80	50.22 109	24.71 55	37.77 68	41.55 99	29.24 90	29.11 83	26.67 88	4.35 109	0.06 23	4.56 109	0.90 75	2.42 71	1.38 55

Anonymous Stereo	18.60 78	11.75 106	49.81 116	14.43 95	12.02 113	14.33 30	23.23 113	32.16 60	43.08 86	24.32 53	34.16 58	24.24 33	14.12 31	31.67 90	30.84 101	0.89 64	0.91 86	1.74 84	1.92 96	3.23 83	3.23 86

stereogan	18.62 79	3.07 42	16.31 66	13.04 88	9.99 99	35.74 106	9.09 51	38.17 96	45.27 97	24.39 54	41.24 84	39.93 92	25.46 76	31.29 89	24.33 76	1.80 85	0.91 86	2.71 99	0.75 72	5.15 100	3.70 87

edge stereo	18.75 80	5.12 68	17.66 69	10.77 77	7.84 86	22.41 66	11.03 63	35.23 79	42.07 83	34.62 92	42.74 92	41.28 97	35.50 105	25.14 71	24.17 73	2.42 94	2.42 107	6.35 113	1.45 87	2.89 78	3.80 88

RYNet	18.92 81	4.69 63	16.88 67	10.99 79	15.84 120	46.72 118	16.57 88	37.84 93	48.03 99	26.92 65	26.48 16	37.84 86	24.92 74	19.80 47	31.04 102	0.25 28	0.25 48	0.62 56	0.03 22	6.34 102	6.39 105

LALA_ROB	19.19 82	7.15 85	15.93 61	12.96 87	5.30 53	28.30 86	23.21 112	42.51 108	36.69 66	33.45 91	40.81 83	50.96 118	24.53 71	27.92 79	25.66 85	0.60 52	0.44 66	2.04 88	1.22 84	2.54 73	1.47 57

aanetorigin	19.21 83	12.59 107	53.50 119	9.67 63	7.83 85	11.85 13	13.87 74	17.05 5	34.15 56	49.18 118	48.89 108	34.05 72	30.22 93	22.86 60	28.00 99	1.01 70	0.75 81	0.67 58	0.89 74	3.07 79	4.14 94

SGM_RVC	19.52 84	2.12 26	6.79 12	6.59 28	1.33 4	38.20 111	15.74 85	38.13 95	34.07 55	45.71 114	41.91 89	51.41 119	38.10 110	38.77 115	27.80 96	0.61 53	0.36 57	0.51 50	0.33 53	1.03 41	0.90 42
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
RTSC	19.64 85	9.36 90	31.06 95	10.99 79	6.24 66	28.10 85	10.14 57	37.50 92	58.34 125	31.74 84	36.70 64	32.50 63	21.03 65	36.00 109	37.24 112	1.23 77	0.44 66	0.12 23	0.31 51	1.98 65	1.69 64

Abc-Net	19.75 86	6.08 74	23.96 83	15.05 98	16.17 121	18.42 46	21.45 105	43.31 115	41.00 79	24.30 51	38.12 72	34.98 78	26.92 79	35.46 103	25.65 83	4.29 107	2.07 100	3.24 103	8.90 122	2.86 76	2.86 81
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
NCC-stereo	19.75 86	6.08 74	23.96 83	15.05 98	16.17 121	18.42 46	21.45 105	43.31 115	41.00 79	24.30 51	38.12 72	34.98 78	26.92 79	35.46 103	25.65 83	4.29 107	2.07 100	3.24 103	8.90 122	2.86 76	2.86 81

RGC	19.90 88	13.26 108	20.24 79	18.19 109	14.06 118	21.21 60	14.33 77	34.97 77	43.60 89	27.73 69	41.49 87	39.76 91	27.98 85	34.52 99	21.37 58	3.66 104	0.54 72	8.67 121	4.16 111	4.19 92	4.02 91

DeepPrunerF	19.92 89	11.02 104	44.29 111	20.74 113	17.75 124	19.19 51	22.57 107	36.86 88	49.93 104	25.48 58	36.62 63	24.69 35	23.97 69	23.44 63	21.79 61	2.68 97	1.63 94	5.70 111	3.95 110	3.38 85	2.67 79

WCMA_ROB	20.02 90	4.30 56	19.72 76	9.47 62	7.00 76	32.71 101	13.99 75	31.97 57	32.48 51	41.51 105	52.00 113	44.09 105	36.14 107	32.43 92	24.29 75	6.19 118	2.79 111	1.09 74	1.10 80	3.95 89	3.13 84

psmorigin	20.95 91	10.24 98	35.84 99	9.75 64	7.05 77	24.59 74	10.11 55	32.85 65	32.17 49	43.09 108	54.57 118	42.79 102	36.62 108	35.99 108	27.41 93	1.58 81	2.72 110	0.79 62	1.05 78	3.26 84	6.50 106

SANet	20.96 92	6.60 80	29.81 91	8.83 54	3.19 24	31.27 95	20.56 103	41.86 106	56.09 121	39.30 101	43.62 97	44.95 107	31.93 99	27.83 78	23.67 68	0.94 66	0.52 71	0.99 71	0.42 60	4.72 94	2.03 71

FBW_ROB	21.00 93	10.23 97	22.72 81	13.71 90	6.77 72	30.49 94	16.46 87	44.58 117	43.57 88	44.25 112	39.59 78	43.27 104	26.12 77	33.96 97	21.80 62	2.58 96	1.84 97	7.14 120	2.78 103	4.00 90	4.06 92

STTStereo_v2	21.25 94	8.24 87	37.97 104	15.14 100	9.26 96	50.87 121	16.80 90	27.96 35	30.54 40	38.92 99	42.93 94	33.27 67	32.17 100	27.99 80	25.25 80	4.40 110	3.83 119	3.60 106	0.97 76	7.91 109	6.95 109

G-Net	21.25 94	8.24 87	37.97 104	15.14 100	9.26 96	50.87 121	16.80 90	27.96 35	30.54 40	38.92 99	42.93 94	33.27 67	32.17 100	27.99 80	25.25 80	4.40 110	3.83 119	3.60 106	0.97 76	7.91 109	6.95 109

SHD	21.32 96	10.03 96	30.16 93	14.30 94	8.68 91	24.15 72	8.93 48	40.82 103	61.17 127	35.79 95	44.15 99	38.73 88	30.20 92	33.88 95	31.09 103	2.00 86	0.81 83	1.17 77	1.55 88	4.08 91	4.78 99

CSAN	21.34 97	9.45 92	23.34 82	20.99 115	4.95 47	32.57 99	34.26 124	38.83 99	49.95 106	36.97 97	39.72 79	44.97 108	31.73 98	26.05 74	23.94 70	1.52 80	0.47 68	0.85 67	1.43 86	2.73 74	2.14 75

ADCL	21.64 98	6.20 76	47.32 114	9.93 67	6.91 75	38.69 113	19.97 101	31.26 51	54.04 113	27.89 70	47.97 106	31.35 59	30.66 95	33.18 94	36.62 109	0.99 68	0.92 88	2.63 97	1.82 93	2.45 72	2.13 73

ADCPNet	21.93 99	9.38 91	57.92 124	11.76 84	6.88 74	36.03 107	18.44 96	32.80 64	35.93 63	32.91 90	43.80 98	39.21 89	26.99 81	31.14 88	36.94 111	2.05 87	2.67 109	2.30 94	3.45 107	4.22 93	3.82 89

MeshStereo	22.27 100	6.87 82	11.15 38	7.69 43	4.87 45	37.70 110	13.06 71	41.64 105	36.95 67	50.92 120	53.41 115	58.12 123	41.93 118	37.73 112	27.62 95	2.52 95	2.37 106	2.99 101	2.07 98	3.09 80	2.60 77
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
ADCMid	22.76 101	10.75 100	41.73 109	11.23 83	7.97 88	27.26 83	19.18 100	35.44 81	38.02 72	40.23 102	46.38 104	36.56 82	42.70 119	38.20 113	40.75 119	1.77 83	1.43 92	3.05 102	3.81 109	4.96 96	3.82 89

MSMD_ROB	22.90 102	9.64 95	14.76 56	17.46 107	9.88 98	34.94 104	18.83 97	33.67 68	37.47 68	40.97 104	59.80 123	45.93 110	38.34 111	31.74 91	23.24 67	6.22 119	3.58 117	8.78 122	11.03 124	6.76 104	5.06 101

LE_ROB	22.93 103	3.07 42	14.02 49	7.70 44	2.86 20	31.99 98	17.35 93	28.10 38	67.19 134	70.51 134	55.61 121	49.07 115	47.62 123	24.76 70	36.49 108	0.35 34	0.20 42	0.27 34	0.55 65	0.48 16	0.44 28

AnyNet_C32	23.37 104	13.28 109	40.29 108	12.36 86	11.44 107	30.39 91	29.15 120	31.36 53	44.90 95	35.36 93	48.86 107	33.61 70	34.20 103	43.15 119	41.62 122	1.27 78	1.35 90	1.18 78	2.58 101	4.77 95	6.27 104

SGM-ForestM	23.72 105	2.44 32	9.13 28	7.43 36	2.25 13	44.80 117	19.11 99	44.90 118	49.97 107	50.74 119	51.18 111	62.06 130	45.83 122	44.96 120	33.88 105	1.00 69	0.84 84	0.78 61	0.62 67	1.29 51	1.11 48

MFN_U_SF_DS_RVC	23.92 106	13.60 110	30.09 92	25.46 122	10.02 100	38.51 112	20.40 102	35.77 82	43.76 90	42.15 106	47.10 105	39.99 93	28.20 88	35.96 107	27.06 90	3.64 103	3.85 121	6.66 114	16.30 128	4.97 97	4.98 100

XQC	24.10 107	16.76 114	50.68 117	21.37 118	11.01 104	35.24 105	18.84 98	37.28 91	55.11 117	31.64 83	30.06 32	37.71 85	30.31 94	37.17 110	39.66 116	4.20 106	0.41 60	2.76 100	1.89 95	11.46 122	8.42 117

FC-DCNN	24.37 108	10.99 103	19.02 75	18.44 110	9.16 95	36.98 108	23.07 110	40.44 102	43.05 85	46.25 116	53.58 117	50.44 116	37.86 109	35.45 102	27.57 94	6.87 120	3.37 114	6.01 112	4.88 114	7.86 108	6.05 103

DPSNet	24.40 109	6.97 84	33.14 98	11.16 82	6.54 69	53.33 124	43.32 130	51.28 125	59.37 126	30.89 79	39.36 76	40.35 95	34.30 104	32.57 93	25.09 79	4.65 113	1.62 93	0.37 41	0.66 68	8.51 113	4.44 97

PDISCO_ROB	24.45 110	9.24 89	29.28 90	28.68 126	19.96 125	37.14 109	15.33 83	45.04 119	54.69 115	29.24 74	42.67 91	46.07 111	28.17 87	35.35 101	30.30 100	9.92 124	2.13 102	6.90 115	3.20 105	8.74 114	6.87 108

EDNetEfficient	25.16 111	22.23 122	77.22 131	9.09 55	7.38 81	19.38 53	16.78 89	21.41 9	55.74 120	61.52 130	53.51 116	36.22 81	40.65 117	24.22 65	38.27 113	0.62 54	0.74 79	0.86 68	2.60 102	7.70 107	7.09 111

GANetREF_RVC	25.41 112	29.28 126	24.63 85	25.18 121	6.04 63	30.44 92	35.39 126	47.84 120	50.60 110	36.88 96	36.04 62	34.11 74	31.23 96	37.46 111	27.96 98	8.58 121	2.66 108	16.29 127	4.95 115	14.37 126	8.32 116
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
FCDSN-DC	25.59 113	14.91 113	18.83 74	28.55 125	15.68 119	39.29 114	18.16 95	42.83 113	41.69 82	44.43 113	51.94 112	50.82 117	39.22 114	34.59 100	27.29 91	6.05 116	3.63 118	7.01 118	6.41 116	10.35 118	10.17 123
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
ADCS	26.20 114	13.71 111	46.90 113	14.51 96	10.34 102	30.46 93	22.72 109	42.81 112	57.61 124	42.58 107	49.37 109	41.91 100	40.43 115	41.90 118	44.58 127	2.37 91	2.19 104	2.37 95	3.29 106	7.32 105	6.52 107

MFN_U_SF_RVC	28.16 115	14.14 112	53.23 118	21.11 116	7.91 87	39.97 115	13.13 72	51.65 127	46.34 98	47.41 117	53.38 114	55.32 122	40.53 116	38.47 114	39.74 117	5.67 115	4.46 122	6.98 117	8.73 121	7.47 106	7.44 112

PASM	28.19 116	17.35 115	48.18 115	22.49 119	22.20 126	25.92 79	25.84 116	34.35 72	49.45 102	35.37 94	39.48 77	46.72 113	33.36 102	34.28 98	36.77 110	11.73 127	14.94 129	17.43 129	22.18 130	13.45 123	12.32 126

EDNetEfficientorigin	28.98 117	35.45 130	99.82 139	9.43 59	5.65 59	24.97 76	14.79 80	26.06 28	54.82 116	60.87 126	57.59 122	45.42 109	50.27 124	26.11 75	43.62 124	0.30 32	0.37 58	1.61 83	2.35 99	10.17 117	9.99 122

AnyNet_C01	29.76 118	25.78 125	75.37 130	17.03 106	11.34 106	49.00 120	27.12 118	37.99 94	40.05 78	40.73 103	65.13 132	46.98 114	38.86 113	46.63 121	48.19 129	2.14 88	2.13 102	2.13 89	2.56 100	8.14 112	7.98 115

LSM	29.98 119	19.52 120	55.11 122	19.06 111	53.75 133	32.57 99	23.17 111	42.61 109	48.57 100	45.97 115	55.04 120	42.93 103	36.02 106	35.63 105	27.03 89	2.33 89	9.19 128	4.87 110	7.71 120	10.65 120	27.91 131

RTS	30.15 120	23.12 123	96.33 134	16.71 104	11.63 108	58.66 128	24.28 114	36.80 86	62.63 129	43.79 110	46.10 102	32.56 64	43.90 120	51.29 125	40.92 120	2.38 92	0.66 75	1.06 72	0.74 70	5.11 98	4.34 95

RTSA	30.15 120	23.12 123	96.33 134	16.71 104	11.63 108	58.66 128	24.28 114	36.80 86	62.63 129	43.79 110	46.10 102	32.56 64	43.90 120	51.29 125	40.92 120	2.38 92	0.66 75	1.06 72	0.74 70	5.11 98	4.34 95

DispFullNet	30.27 122	31.74 127	58.63 125	30.13 127	28.18 128	31.79 97	8.94 49	37.16 90	54.68 114	32.60 89	37.99 69	44.62 106	28.80 89	41.55 117	27.93 97	11.94 128	3.46 115	35.53 131	12.34 125	30.08 130	17.26 127

MANE	31.97 123	10.75 100	17.71 71	20.94 114	13.77 116	47.80 119	26.85 117	53.00 130	55.20 118	59.29 123	64.30 130	64.93 132	56.31 131	51.83 128	38.69 115	9.61 123	4.72 123	6.91 116	19.93 129	9.20 115	7.64 114

PVD	33.19 124	18.59 117	37.64 103	26.09 123	16.99 123	33.25 102	42.25 129	51.42 126	66.03 132	52.65 122	64.31 131	51.93 120	55.13 128	51.61 127	40.40 118	4.00 105	3.51 116	9.53 123	4.26 112	10.62 119	23.56 128

ELAS	33.68 125	18.95 118	36.71 101	20.64 112	13.61 115	56.43 125	35.14 125	50.03 124	49.90 103	60.58 125	63.83 129	58.64 126	53.36 125	50.73 123	44.44 126	11.03 125	6.58 126	10.02 124	7.43 119	13.58 125	11.94 125
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
ELAS_RVC	33.79 126	19.15 119	35.97 100	21.28 117	13.79 117	52.58 123	36.11 127	48.96 123	55.66 119	61.47 129	62.91 125	58.35 125	53.55 126	53.37 130	42.23 123	11.25 126	6.64 127	10.32 125	7.05 118	13.56 124	11.58 124
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
SAMSARA	34.43 127	18.53 116	31.91 96	55.34 133	35.34 130	75.99 134	94.71 137	47.97 121	50.84 111	37.47 98	36.99 66	53.12 121	38.55 112	39.56 116	38.65 114	3.59 102	5.40 125	2.22 93	3.72 108	9.43 116	9.29 121

BEATNet-Init1	35.08 128	20.64 121	54.70 121	27.43 124	12.00 112	58.29 127	31.01 121	52.58 129	57.49 123	60.36 124	62.98 126	69.98 134	55.54 129	53.00 129	44.40 125	4.61 112	2.84 112	7.05 119	6.89 117	10.97 121	8.94 118

MSC_U_SF_DS_RVC	37.16 129	34.69 129	53.68 120	37.47 129	12.55 114	56.80 126	22.63 108	52.02 128	63.29 131	66.20 132	63.82 128	58.12 123	53.73 127	51.21 124	46.48 128	8.79 122	5.31 124	14.69 126	15.22 127	17.27 128	9.21 120

NVStereoNet_ROB	41.93 130	32.40 128	44.55 112	36.60 128	27.78 127	34.52 103	32.38 122	48.84 122	57.18 122	69.34 133	75.62 136	66.07 133	64.86 135	47.34 122	60.78 132	15.72 129	23.99 130	23.40 130	32.06 131	21.34 129	23.86 129
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. Arxiv
MADNet+	53.31 131	68.61 133	98.75 136	67.03 134	37.58 131	79.83 136	81.74 135	60.38 133	61.61 128	51.43 121	54.92 119	61.11 129	59.29 133	69.66 134	76.71 135	23.88 131	24.03 131	17.08 128	12.35 126	32.47 131	27.69 130

SGM+DAISY	54.67 132	56.61 131	62.73 127	40.21 130	53.22 132	61.95 131	48.59 132	55.05 131	44.62 94	61.03 128	63.68 127	60.46 128	55.62 130	60.68 132	55.34 130	56.34 132	48.56 134	50.16 132	49.63 134	52.25 132	56.76 134

SPS-STEREO	55.62 133	59.14 132	64.16 128	45.05 131	53.84 134	59.88 130	44.00 131	59.53 132	49.94 105	62.33 131	61.09 124	59.80 127	56.82 132	60.06 131	57.85 131	58.41 133	48.34 133	51.07 133	49.21 133	55.41 133	56.48 133
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
PWCK	65.90 134	88.63 136	79.40 132	80.32 136	29.96 129	63.85 132	39.45 128	72.07 135	71.26 135	78.74 135	68.99 133	77.82 135	61.77 134	82.81 136	62.64 134	80.17 134	39.88 132	81.90 136	47.37 132	73.54 135	37.51 132

MFMNet_re	72.05 135	79.64 134	64.23 129	52.73 132	84.71 136	77.93 135	68.35 133	63.96 134	66.93 133	61.00 127	71.29 134	63.84 131	68.91 136	72.42 135	61.11 133	82.03 136	74.22 136	80.34 135	74.44 135	83.62 136	89.34 136

DPSimNet_ROB	74.29 136	83.27 135	59.30 126	77.19 135	65.13 135	75.14 133	74.95 134	73.99 136	76.94 136	81.99 136	75.48 135	80.18 136	71.76 137	67.39 133	84.00 136	81.87 135	51.14 135	79.99 134	76.70 136	70.17 134	79.21 135

MADNet++	94.16 137	94.02 138	90.25 133	96.28 137	96.84 137	97.17 137	90.16 136	94.71 137	91.69 137	97.07 137	93.71 137	94.62 137	92.63 138	96.15 137	95.18 137	95.42 137	95.89 137	92.80 137	92.23 137	92.10 137	94.30 137

MEDIAN_ROB	99.19 138	99.84 139	99.62 138	98.49 138	98.51 138	98.58 138	97.81 139	98.80 138	98.56 138	99.36 140	99.49 138	99.56 138	99.06 139	98.35 138	98.31 138	99.99 138	99.63 138	100.00 138	100.00 138	99.81 138	99.95 138

AVERAGE_ROB	99.80 139	99.99 140	99.40 137	100.00 139	100.00 139	98.99 139	97.72 138	100.00 139	100.00 139	100.00 141	100.00 139	100.00 141	100.00 140	100.00 141	100.00 141	100.00 139	100.00 139	100.00 138	100.00 138	100.00 139	100.00 139

DPSMNet_ROB	99.95 140	100.00 141	100.00 140	100.00 139	100.00 139	100.00 140	100.00 140	100.00 139	100.00 139	99.15 139	100.00 139	99.96 139	100.00 140	99.99 139	99.99 139	100.00 139	100.00 139	100.00 138	100.00 138	100.00 139	100.00 139

DGTPSM_ROB	99.95 140	100.00 141	100.00 140	100.00 139	100.00 139	100.00 140	100.00 140	100.00 139	100.00 139	99.14 138	100.00 139	99.96 139	100.00 140	99.99 139	99.99 139	100.00 139	100.00 139	100.00 138	100.00 138	100.00 139	100.00 139

DPSM	100.00 142	100.00 141	100.00 140	100.00 139	100.00 139	100.00 140	100.00 140	100.00 139	100.00 139	100.00 141	100.00 139	100.00 141	100.00 140	100.00 141	100.00 141	100.00 139	100.00 139	100.00 138	100.00 138	100.00 139	100.00 139

LSM0	100.00 142	100.00 141	100.00 140	100.00 139	100.00 139	100.00 140	100.00 140	100.00 139	100.00 139	100.00 141	100.00 139	100.00 141	100.00 140	100.00 141	100.00 141	100.00 139	100.00 139	100.00 138	100.00 138	100.00 139	100.00 139

DPSM_ROB	100.00 142	100.00 141	100.00 140	100.00 139	100.00 139	100.00 140	100.00 140	100.00 139	100.00 139	100.00 141	100.00 139	100.00 141	100.00 140	100.00 141	100.00 141	100.00 139	100.00 139	100.00 138	100.00 138	100.00 139	100.00 139

MSMDNet													7.19 7

ASD4		90.49 137

Low-res two-view benchmark