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	deliv. area 1l	deliv. area 1s	deliv. area 2l	deliv. area 2s	deliv. area 3l	deliv. area 3s	elect. 1l	elect. 1s	elect. 2l	elect. 2s	elect. 3l	elect. 3s	facade 1s	forest 1s	forest 2s	playg. 1l	playg. 1s	playg. 2l	playg. 2s	playg. 3l	playg. 3s	terra. 1s	terra. 2s	terra. 1l	terra. 1s	terra. 2l	terra. 2s

TDLM	0.09 1	0.08 3	0.12 5	0.02 1	0.02 1	0.00 1	0.01 4	0.19 4	0.04 1	0.00 1	0.00 1	0.48 8	0.01 1	0.01 2	0.26 1	0.22 1	0.08 1	0.06 4	0.01 1	0.00 1	0.10 1	0.10 4	0.01 4	0.01 1	0.00 1	0.01 1	0.30 1	0.39 7

CVANet_RVC	0.21 2	0.15 4	0.61 18	0.42 17	0.06 3	0.06 16	0.01 4	1.53 21	0.05 3	0.08 4	0.04 6	0.62 13	0.04 2	0.03 5	0.46 2	0.32 2	0.14 4	0.09 7	0.05 2	0.01 2	0.19 3	0.07 3	0.00 1	0.03 4	0.00 1	0.01 1	0.55 3	0.08 1

AANet_RVC	0.25 3	0.05 2	0.29 8	0.06 3	0.19 6	0.00 1	0.23 25	0.03 2	0.17 8	0.35 11	0.01 5	0.07 2	0.04 2	0.01 2	1.37 5	0.48 4	0.10 2	0.06 4	0.43 5	0.17 8	0.10 1	0.25 5	0.03 10	0.05 5	0.02 3	0.06 4	1.15 5	0.89 14

GANet	0.29 4	0.56 18	0.39 15	0.27 9	0.11 5	0.88 32	0.14 20	0.27 7	0.06 4	0.02 2	0.28 11	1.85 27	0.04 2	0.03 5	0.62 3	0.39 3	0.37 12	0.14 10	0.24 3	0.01 2	0.20 4	0.05 2	0.06 15	0.02 2	0.06 5	0.01 1	0.48 2	0.16 3

RYNet	0.45 5	0.04 1	0.00 1	0.02 1	0.03 2	0.00 1	0.00 1	0.01 1	0.04 1	0.07 3	0.00 1	0.05 1	0.12 7	0.00 1	4.77 38	0.60 5	0.10 2	1.00 41	0.35 4	0.01 2	0.21 5	0.04 1	0.00 1	0.02 2	0.08 6	3.06 27	1.39 6	0.13 2

CFNet	0.57 6	0.46 11	0.64 19	0.43 18	0.34 15	0.04 15	0.00 1	0.58 9	0.09 5	0.66 15	0.05 7	0.64 14	0.07 5	0.05 10	2.25 7	0.65 6	0.35 8	0.06 4	0.73 8	0.34 14	0.85 11	0.73 6	0.00 1	0.08 7	0.45 18	0.16 5	3.22 15	1.51 20

HITNet	0.59 7	0.16 5	0.10 4	0.06 3	0.07 4	0.01 7	0.02 9	1.76 25	0.82 19	0.11 5	0.00 1	0.68 17	0.28 16	0.11 22	2.51 10	1.02 10	0.25 7	0.13 8	0.63 6	0.24 12	1.43 19	1.56 14	0.01 4	0.12 10	0.02 3	0.16 5	3.32 16	0.42 8
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
RASNet	0.63 8	0.18 6	0.57 17	0.41 16	0.28 9	0.10 18	0.05 15	0.94 13	0.14 7	1.07 23	0.09 9	0.48 8	0.34 19	0.02 4	1.28 4	1.47 23	0.35 8	0.21 12	0.99 9	0.02 5	1.15 14	1.53 12	0.10 17	0.34 23	0.22 12	0.30 9	3.55 20	0.73 11

DISCO	0.79 9	0.95 29	0.31 10	0.06 3	0.33 13	0.51 26	0.44 28	0.23 6	0.32 13	1.75 32	0.59 17	0.38 7	0.29 17	0.04 8	2.98 13	1.36 17	0.96 28	0.29 17	3.24 28	0.53 23	0.79 10	1.45 10	0.10 17	0.07 6	0.12 9	0.29 8	2.76 11	0.31 6

NVstereo2D	0.83 10	0.43 10	0.07 3	0.23 8	0.36 16	0.41 25	0.08 16	1.61 23	0.59 16	0.84 17	1.11 24	0.36 5	0.21 14	0.07 15	5.88 47	2.16 36	0.62 18	0.45 22	1.50 13	0.19 9	1.20 15	0.94 9	0.02 6	0.32 19	1.30 26	0.24 7	0.95 4	0.24 5

CFNet_RVC	0.86 11	0.59 19	0.74 23	0.33 11	0.32 12	0.11 19	0.02 9	0.11 3	0.17 8	0.87 20	0.74 20	0.57 12	0.16 10	0.08 18	3.11 15	1.19 16	0.50 14	0.34 19	1.28 11	0.15 6	1.68 22	1.48 11	0.02 6	0.25 12	0.86 24	1.89 22	3.64 22	2.13 24

DLCB_ROB	0.92 12	0.72 23	0.64 19	0.43 18	0.42 17	0.14 21	0.18 23	0.43 8	0.28 11	0.85 18	0.53 14	1.12 23	0.72 35	0.07 15	2.16 6	1.12 13	0.88 26	0.26 14	0.69 7	0.20 10	1.35 18	1.53 12	0.49 43	0.28 15	4.11 47	2.41 24	2.04 9	0.78 12

HSM	0.97 13	0.30 7	0.87 26	0.54 23	0.46 20	0.00 1	0.01 4	2.93 43	0.30 12	0.13 6	0.56 16	0.65 15	0.13 8	0.05 10	2.97 12	1.08 12	0.66 21	0.17 11	3.26 30	0.35 15	1.78 23	2.03 19	0.04 11	0.33 22	0.38 15	0.81 17	4.16 34	1.10 16

NLCA_NET_v2_RVC	0.99 14	0.82 26	0.34 13	0.18 7	0.43 18	0.03 13	1.10 38	1.20 17	2.50 46	2.06 38	0.11 10	0.69 18	0.07 5	0.54 39	3.65 25	0.91 7	0.19 6	0.22 13	1.65 17	0.15 6	0.73 9	0.92 8	0.08 16	0.18 11	0.11 8	3.29 29	2.58 10	2.09 23
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
PDISCO_ROB	1.03 15	0.55 17	0.06 2	0.67 24	0.28 9	0.00 1	0.21 24	0.71 11	0.49 15	1.28 25	0.99 23	0.25 4	0.32 18	0.19 27	3.02 14	2.03 33	0.69 22	1.42 50	2.26 18	0.58 25	2.32 27	3.22 34	0.14 20	0.27 14	1.36 27	0.37 10	3.84 28	0.23 4

iResNet_ROB	1.07 16	0.65 22	0.30 9	0.45 20	0.30 11	0.01 7	0.02 9	3.10 44	0.12 6	1.34 26	0.00 1	0.66 16	0.35 20	0.04 8	4.37 31	1.65 28	0.54 15	0.49 24	5.83 41	0.29 13	1.30 17	0.87 7	0.20 26	0.26 13	0.16 10	0.84 18	3.74 26	1.12 17

AF-Net	1.11 17	0.51 13	0.87 26	0.34 12	0.78 24	0.89 33	1.87 43	2.40 33	1.15 24	0.48 13	0.68 18	3.66 35	0.16 10	0.27 31	2.44 8	0.98 8	0.35 8	0.01 1	1.60 14	0.40 16	0.50 6	2.58 25	0.05 13	0.08 7	0.66 21	0.45 12	3.47 18	2.34 25

Nwc_Net	1.11 17	0.51 13	0.87 26	0.34 12	0.78 24	0.89 33	1.87 43	2.40 33	1.15 24	0.48 13	0.68 18	3.66 35	0.16 10	0.27 31	2.44 8	0.98 8	0.35 8	0.01 1	1.60 14	0.40 16	0.50 6	2.58 25	0.05 13	0.08 7	0.66 21	0.45 12	3.47 18	2.34 25

iResNetv2_ROB	1.26 19	0.75 24	0.32 11	1.29 40	0.33 13	0.02 12	0.02 9	3.19 45	0.20 10	2.00 37	0.29 12	2.24 30	0.41 24	0.06 13	3.64 24	1.45 22	0.75 23	0.13 8	5.77 39	0.43 20	2.86 33	1.71 16	0.16 23	0.37 25	0.38 15	0.47 14	4.08 32	0.72 10

RP	1.29 20	1.06 31	2.32 56	0.84 28	1.16 28	0.39 24	1.53 40	1.89 29	1.68 35	0.29 8	2.21 33	2.25 31	0.13 8	0.40 36	3.31 19	1.07 11	0.61 16	0.05 3	1.61 16	0.40 16	0.65 8	3.05 31	0.11 19	0.29 16	0.29 13	0.92 19	4.24 35	2.04 22

BEATNet_4x	1.40 21	0.54 16	0.27 7	0.13 6	0.27 8	0.01 7	0.10 17	1.80 26	1.74 37	0.41 12	0.53 14	0.82 20	0.59 30	0.54 39	4.30 28	2.92 44	1.87 42	1.84 58	1.39 12	0.71 28	4.68 47	6.21 62	0.02 6	0.81 36	0.21 11	0.40 11	3.69 23	0.96 15

DN-CSS_ROB	1.45 22	0.52 15	1.11 33	0.81 27	0.69 22	0.00 1	0.03 14	2.86 42	0.35 14	1.50 30	3.76 47	0.73 19	0.36 22	0.07 15	2.89 11	1.44 20	1.08 31	0.79 30	8.61 50	0.23 11	3.25 38	1.97 18	0.02 6	0.66 33	0.10 7	0.52 15	3.12 14	1.56 21

DeepPruner_ROB	1.57 23	0.37 9	0.71 21	0.50 22	0.55 21	0.37 23	1.60 41	1.74 24	0.84 20	1.37 28	2.40 35	0.49 10	0.51 28	0.10 21	5.38 43	2.16 36	1.10 32	1.08 43	2.28 19	0.40 16	4.83 51	3.42 35	0.66 50	0.34 23	0.57 20	5.04 43	3.07 12	0.53 9

PA-Net	1.61 24	0.81 25	1.43 43	0.27 9	1.99 47	0.12 20	0.34 26	0.19 4	1.90 39	0.85 18	0.41 13	0.24 3	0.20 13	0.78 53	4.61 36	1.44 20	0.17 5	0.26 14	1.08 10	0.53 23	1.44 20	3.18 33	0.65 48	0.44 28	0.99 25	7.71 56	1.78 8	9.78 47
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
NCC-stereo	1.86 25	1.63 37	1.33 39	0.95 34	1.22 32	0.80 29	2.29 49	2.62 39	2.93 49	0.34 9	2.87 42	2.16 28	1.17 39	0.21 28	3.63 21	1.13 14	0.63 19	0.96 39	2.79 24	0.75 29	0.98 12	2.96 29	0.36 36	0.32 19	2.31 35	4.50 39	3.69 23	4.78 33

Abc-Net	1.86 25	1.63 37	1.33 39	0.95 34	1.22 32	0.80 29	2.29 49	2.62 39	2.93 49	0.34 9	2.87 42	2.16 28	1.17 39	0.21 28	3.63 21	1.13 14	0.63 19	0.96 39	2.79 24	0.75 29	0.98 12	2.96 29	0.36 36	0.32 19	2.31 35	4.50 39	3.69 23	4.78 33

stereogan	1.89 27	2.00 45	1.17 35	1.47 42	2.05 50	1.72 41	0.87 34	1.84 28	1.11 22	1.36 27	1.51 27	5.81 44	0.48 27	0.32 35	3.87 26	1.69 29	0.80 24	0.39 21	3.96 33	0.78 31	1.23 16	4.02 42	0.26 29	0.51 31	0.30 14	1.30 21	6.30 48	3.84 31

NCCL2	2.12 28	2.27 48	1.29 36	1.28 39	1.85 44	0.18 22	0.77 33	1.24 18	1.25 27	1.62 31	2.71 39	1.06 21	0.51 28	0.31 33	4.40 32	2.33 40	1.57 38	0.62 27	2.35 20	0.68 27	2.68 32	4.29 46	1.52 62	1.06 38	4.65 51	4.17 35	4.04 31	6.58 39

R-Stereo Train	2.16 29	1.08 32	1.57 47	0.84 28	0.19 6	0.01 7	0.01 4	2.05 32	1.01 21	1.37 28	2.45 36	1.50 25	0.46 25	0.21 28	3.23 17	4.53 59	1.62 39	1.05 42	7.07 44	1.09 39	4.86 52	5.93 60	0.04 11	0.31 17	2.48 38	4.63 41	3.10 13	5.74 37

GANetREF_RVC	2.43 30	1.69 40	0.81 24	2.04 48	1.16 28	1.02 36	0.10 17	4.72 60	1.44 30	3.62 57	0.06 8	10.03 54	0.69 33	0.09 19	3.63 21	1.36 17	1.06 30	0.94 37	6.44 43	0.46 21	2.32 27	1.74 17	0.17 24	1.35 45	3.41 45	3.75 33	6.55 49	4.82 35
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
RGC	2.44 31	1.98 44	4.87 68	1.54 43	1.65 38	1.74 42	1.05 36	2.02 31	1.43 29	0.88 21	4.42 50	2.52 33	2.42 46	0.58 44	4.33 29	1.63 27	0.91 27	0.48 23	4.28 34	1.08 38	1.94 24	4.01 41	0.15 21	0.72 35	2.19 33	5.22 45	4.90 41	6.96 40

MLCV	2.45 32	0.61 20	0.81 24	2.48 51	1.21 31	0.95 35	0.50 29	3.57 49	0.61 17	3.75 60	4.26 48	5.13 41	3.34 49	0.06 13	4.63 37	1.78 31	1.32 35	0.86 34	10.05 52	1.09 39	2.30 26	3.92 39	0.29 30	2.16 51	1.87 30	3.06 27	4.01 30	1.40 18

R-Stereo	2.54 33	0.34 8	1.32 37	0.35 14	0.45 19	0.01 7	0.17 21	3.20 46	1.38 28	1.80 34	0.85 21	1.70 26	0.47 26	0.17 25	3.19 16	5.06 65	1.40 37	0.59 26	10.54 53	1.73 48	6.89 64	5.24 52	0.20 26	0.41 26	2.41 37	4.21 36	5.91 45	8.54 46

RTSC	2.56 34	0.89 28	1.50 44	0.47 21	0.76 23	0.87 31	2.29 49	1.56 22	1.91 40	2.12 39	2.86 41	1.23 24	0.97 38	1.03 62	7.27 60	3.36 52	4.68 63	1.74 55	7.73 46	1.11 41	3.95 42	5.39 55	0.21 28	1.11 41	0.40 17	0.95 20	4.56 39	8.26 45

DeepPrunerF	2.66 35	1.67 39	0.33 12	3.58 58	9.30 76	1.76 43	3.48 58	0.68 10	3.29 52	0.71 16	1.59 29	0.37 6	0.27 15	0.13 23	5.94 48	3.42 55	1.03 29	0.54 25	4.47 35	0.65 26	2.02 25	2.06 20	0.56 46	0.31 17	15.21 74	3.30 30	3.74 26	1.47 19

NOSS_ROB	2.78 36	3.78 61	0.39 15	2.02 47	1.58 37	2.36 48	0.00 1	2.66 41	1.54 32	3.63 58	1.79 32	2.55 34	3.51 50	0.83 56	5.82 46	5.57 67	2.69 48	1.83 57	2.63 23	0.91 35	4.62 45	4.13 43	0.42 39	2.58 55	2.29 34	4.46 38	7.67 56	2.88 27

SGM-Forest	2.81 37	2.03 46	0.36 14	6.32 69	1.79 43	2.66 49	0.02 9	1.42 19	0.77 18	2.52 40	0.96 22	6.57 46	5.74 53	0.31 33	4.36 30	2.94 46	0.61 16	0.33 18	3.08 26	0.47 22	2.46 30	2.73 27	0.84 55	0.45 29	3.66 46	3.74 32	11.30 62	7.44 42
Johannes L. Schönberger, Sudipta Sinha, Marc Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. ECCV 2018
PWCDC_ROB	2.82 38	0.49 12	0.17 6	0.38 15	0.79 26	0.03 13	0.10 17	1.16 16	8.93 67	0.14 7	2.49 37	0.55 11	31.02 80	0.57 41	5.58 45	3.32 51	1.73 40	0.91 36	2.53 22	0.88 33	2.92 34	7.18 66	0.15 21	0.53 32	0.55 19	0.69 16	1.43 7	0.88 13

HSM-Net_RVC	2.86 39	1.24 34	1.50 44	2.55 53	1.72 39	0.57 27	0.17 21	3.55 48	1.14 23	3.50 56	6.32 61	7.94 47	2.93 47	0.05 10	3.38 20	2.12 34	1.28 34	0.26 14	3.24 28	1.24 43	3.66 40	3.76 38	0.61 47	2.64 56	4.43 49	6.27 51	5.76 44	5.47 36
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
ADCRef	2.96 40	2.78 55	1.02 31	0.70 25	2.21 51	1.96 45	2.42 52	0.96 14	6.10 60	1.94 36	1.15 25	1.08 22	0.74 36	1.32 67	6.01 49	3.38 53	1.34 36	1.15 45	8.41 49	2.09 53	2.59 31	3.60 37	1.88 64	0.71 34	2.81 41	8.00 58	3.42 17	10.24 48

NaN_ROB	3.01 41	1.16 33	1.13 34	0.86 31	3.03 56	0.59 28	0.75 31	3.25 47	2.58 47	0.98 22	1.32 26	21.10 65	0.59 30	1.15 65	4.15 27	1.58 26	0.80 24	0.70 28	2.38 21	1.30 44	1.52 21	2.15 21	0.46 42	1.07 40	2.50 39	6.25 50	4.54 38	13.27 53

XPNet_ROB	3.02 42	2.54 51	1.33 39	0.73 26	1.45 36	1.04 37	0.61 30	0.83 12	1.45 31	1.16 24	3.44 44	26.85 70	0.35 20	0.40 36	3.24 18	2.22 39	3.28 53	0.79 30	3.20 27	1.19 42	2.94 36	5.51 56	0.76 52	0.46 30	0.72 23	2.37 23	9.22 58	3.37 30

ccs	3.29 43	0.63 21	1.94 51	0.99 36	2.02 48	0.06 16	0.01 4	3.87 52	1.79 38	4.48 72	9.13 72	4.94 39	4.74 52	0.87 57	6.01 49	5.69 68	1.83 41	1.39 48	5.37 37	1.03 37	5.86 59	7.35 68	0.32 33	1.56 46	3.32 44	6.28 52	4.08 32	3.36 29

ADCP+	3.47 44	2.05 47	2.64 59	1.31 41	1.93 45	1.19 38	3.99 59	1.07 15	9.83 69	1.78 33	5.75 57	8.79 51	0.37 23	0.90 58	5.04 42	2.20 38	0.44 13	0.90 35	5.82 40	0.88 33	2.38 29	3.43 36	0.33 34	0.41 26	3.03 43	12.72 68	3.59 21	10.90 49

FC-DCNN	3.82 45	4.38 65	5.67 75	9.61 73	4.14 60	8.01 70	3.26 56	4.21 53	1.69 36	2.95 48	1.54 28	5.87 45	2.09 45	0.13 23	4.40 32	2.15 35	2.54 47	1.11 44	3.38 31	0.82 32	3.11 37	2.20 22	0.65 48	3.29 58	6.33 61	4.91 42	6.98 52	7.80 44

ADCL	3.91 46	3.61 59	2.28 54	1.76 45	1.76 42	3.09 52	4.16 60	1.98 30	13.71 76	2.81 46	5.14 53	2.47 32	0.65 32	1.10 64	5.38 43	2.55 42	2.02 43	2.20 61	6.41 42	2.08 52	4.87 53	4.20 44	1.40 61	1.06 38	6.69 62	6.60 53	4.33 37	11.16 51

LALA_ROB	4.04 47	4.73 67	1.42 42	1.16 38	1.93 45	2.17 47	1.04 35	1.80 26	2.48 45	2.74 43	5.87 58	33.34 72	0.69 33	0.78 53	6.02 51	2.33 40	3.21 52	1.82 56	3.66 32	1.88 49	3.40 39	4.99 49	0.34 35	1.03 37	1.91 31	3.61 31	8.50 57	6.20 38

iResNet	4.14 48	0.84 27	0.71 21	2.34 50	2.59 52	4.58 59	1.07 37	4.67 58	1.21 26	3.72 59	3.61 45	12.28 56	16.06 64	0.03 5	6.64 57	3.14 49	2.35 46	1.89 59	16.43 60	2.53 57	3.76 41	2.90 28	0.17 24	3.88 63	1.66 29	4.23 37	5.19 42	3.23 28

SHD	4.34 49	1.02 30	2.50 57	1.71 44	1.33 34	1.46 39	2.01 45	4.90 61	4.30 58	2.63 41	4.99 52	4.39 38	9.41 57	0.58 44	8.20 71	4.81 61	4.43 62	2.44 65	13.01 58	2.95 62	5.61 58	6.88 65	0.30 31	3.26 57	4.11 47	5.16 44	3.88 29	10.98 50

CSAN	4.46 50	2.36 50	1.57 47	3.12 55	3.32 57	1.64 40	0.76 32	4.59 57	4.37 59	3.80 62	2.84 40	17.58 62	10.47 59	0.96 60	4.80 40	1.75 30	2.06 44	1.16 46	5.69 38	2.34 55	2.93 35	2.40 24	0.43 40	2.56 54	4.64 50	11.45 66	7.53 55	13.44 54

ETE_ROB	4.62 51	4.04 63	1.52 46	1.14 37	2.02 48	2.70 50	1.28 39	1.50 20	4.25 57	1.89 35	5.88 59	37.95 77	0.84 37	1.42 70	4.94 41	2.66 43	3.81 58	0.95 38	4.84 36	1.63 47	4.01 43	5.32 54	0.67 51	1.14 42	2.03 32	3.84 34	15.25 69	7.23 41

XQC	4.76 52	3.14 57	1.63 49	3.31 56	3.69 59	3.46 55	2.94 55	6.02 67	3.05 51	2.64 42	5.51 55	5.67 43	7.46 54	1.41 69	8.02 65	4.65 60	3.59 55	1.49 52	10.75 54	2.91 58	4.96 55	6.67 64	0.41 38	1.91 47	7.13 66	10.47 65	11.17 61	4.51 32

MSMDNet	4.98 53	2.59 53	3.57 62	2.54 52	0.90 27	3.99 56	1.63 42	4.52 56	1.56 33	4.54 73	18.92 89	20.82 64	12.09 60	0.60 48	6.57 56	5.84 69	2.28 45	1.52 53	7.08 45	1.94 51	6.09 60	6.12 61	0.31 32	2.29 52	1.47 28	2.83 26	4.25 36	7.49 43

CBMV	5.52 54	3.65 60	1.32 37	3.46 57	2.86 55	2.04 46	0.41 27	3.64 50	2.63 48	4.09 66	2.36 34	14.13 59	3.70 51	0.57 41	7.16 59	6.19 72	4.26 60	2.36 64	8.76 51	1.44 45	6.43 61	4.53 48	0.54 45	5.62 72	4.85 52	6.08 48	19.41 73	26.62 75
Konstantinos Batsos, Changjiang Cai, Philippos Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. Computer Vision and Pattern Recognition (CVPR) 2018
RTS	5.53 55	1.46 35	0.94 29	0.90 32	1.73 40	4.08 57	4.63 64	2.53 36	3.41 53	2.79 44	1.70 30	8.18 48	1.27 41	0.72 49	7.83 62	4.87 62	7.37 75	2.34 62	18.33 61	2.92 59	4.78 48	5.88 58	1.04 56	1.31 43	6.74 63	22.88 86	6.66 50	22.00 65

RTSA	5.53 55	1.46 35	0.94 29	0.90 32	1.73 40	4.08 57	4.63 64	2.53 36	3.41 53	2.79 44	1.70 30	8.18 48	1.27 41	0.72 49	7.83 62	4.87 62	7.37 75	2.34 62	18.33 61	2.92 59	4.78 48	5.88 58	1.04 56	1.31 43	6.74 63	22.88 86	6.66 50	22.00 65

AnyNet_C32	5.68 57	1.96 42	2.91 60	1.78 46	2.71 54	3.17 53	5.76 69	4.31 54	12.56 71	3.07 50	7.17 63	4.22 37	1.70 43	2.19 72	8.22 72	5.98 70	5.69 68	4.43 75	12.45 57	2.93 61	9.72 74	8.57 76	1.84 63	3.46 60	6.24 60	9.82 63	6.26 47	14.21 55

CBMV_ROB	6.09 58	2.65 54	2.29 55	8.35 72	5.74 66	5.08 60	2.76 54	5.03 63	2.35 43	3.78 61	4.70 51	21.22 66	13.56 62	0.09 19	6.04 52	3.70 57	2.84 49	1.39 48	11.35 55	8.80 79	4.92 54	3.11 32	0.43 40	2.10 50	7.06 65	10.12 64	12.96 64	11.93 52

ADCPNet	6.67 59	1.97 43	3.86 64	2.75 54	6.19 70	3.07 51	7.60 75	5.21 64	8.03 65	3.82 63	7.92 66	5.17 42	13.78 63	2.98 76	8.10 67	6.12 71	4.35 61	2.04 60	18.50 63	4.74 69	7.04 65	7.43 69	2.98 71	1.91 47	8.96 69	14.04 73	7.02 54	14.47 56

AMNet	6.77 60	3.87 62	2.62 58	4.00 62	5.86 67	6.75 66	3.31 57	2.51 35	2.47 44	3.44 54	6.68 62	9.34 52	2.05 44	7.44 87	4.79 39	9.81 83	13.48 84	7.65 84	7.95 48	7.83 74	11.47 78	7.62 71	2.74 69	4.18 64	5.52 55	8.25 60	13.24 65	17.97 61

ADCMid	6.83 61	4.46 66	4.95 69	2.16 49	4.31 63	5.71 64	7.29 73	8.83 68	12.78 73	3.11 51	8.10 68	9.83 53	9.65 58	2.84 73	8.51 74	5.42 66	4.68 63	2.83 67	18.87 65	3.22 63	7.09 66	7.70 72	5.37 78	2.33 53	5.12 54	8.71 61	5.21 43	15.24 57

PWC_ROB	7.15 62	1.85 41	5.57 73	0.85 30	1.17 30	1.95 44	4.77 66	2.56 38	17.27 86	3.83 64	7.66 65	4.97 40	39.20 88	0.81 55	8.35 73	4.96 64	6.51 71	4.27 72	15.99 59	4.39 66	7.48 68	7.59 70	0.53 44	4.79 69	2.74 40	2.63 25	7.01 53	23.26 68

FBW_ROB	7.98 63	9.24 77	5.57 73	20.21 80	8.25 74	12.08 75	8.09 76	4.92 62	1.58 34	3.30 53	5.15 54	13.88 58	3.03 48	7.45 88	6.23 53	1.49 24	2.89 50	0.79 30	33.18 82	1.93 50	5.24 56	4.40 47	9.43 84	8.29 80	4.88 53	7.69 55	4.88 40	21.40 63

pmcnn	8.05 64	2.96 56	2.25 53	6.23 67	5.03 64	6.98 67	4.33 62	4.70 59	2.02 42	4.19 67	9.20 74	19.11 63	20.39 70	0.73 51	7.05 58	6.73 73	6.29 70	4.51 76	22.54 72	4.66 68	9.62 73	5.08 51	4.46 77	4.23 65	5.99 56	5.22 45	17.26 71	25.55 74

ADCS	9.93 65	5.64 70	4.82 67	3.95 61	6.05 69	6.17 65	5.70 68	9.47 70	15.68 77	4.19 67	7.98 67	12.91 57	17.41 65	3.94 83	10.52 82	11.68 87	10.14 77	7.14 83	24.46 76	8.67 78	13.95 84	13.00 87	2.94 70	6.15 75	9.75 71	17.65 78	10.70 60	17.41 59

SGM_RVC	10.08 66	10.50 79	3.60 63	16.68 76	8.80 75	14.87 76	7.11 72	9.19 69	6.25 61	4.06 65	7.33 64	24.89 67	18.29 67	0.57 41	4.57 35	1.98 32	3.30 54	1.17 47	20.75 68	2.20 54	4.80 50	2.23 23	2.29 66	3.65 61	17.68 79	13.31 70	33.30 83	28.67 77
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
MDST_ROB	10.13 67	16.70 85	3.04 61	29.06 84	9.34 77	20.66 82	5.02 67	5.90 66	1.98 41	3.16 52	2.70 38	11.22 55	28.15 76	0.18 26	6.30 54	1.37 19	1.21 33	0.34 19	7.74 47	1.01 36	4.67 46	3.95 40	1.92 65	1.94 49	26.55 83	19.78 80	37.02 86	22.50 67

SANet	10.32 68	4.15 64	6.27 76	5.51 65	5.49 65	8.58 72	2.27 48	15.41 81	13.03 75	2.83 47	9.09 71	60.35 91	25.50 73	2.93 75	8.06 66	3.02 48	7.05 72	3.86 71	11.43 56	3.25 64	5.27 57	5.02 50	2.60 68	7.27 78	11.08 72	12.57 67	13.36 66	23.31 69

ccs_rob	10.35 69	5.69 71	2.14 52	4.64 64	2.67 53	5.53 63	6.85 71	4.40 55	4.18 56	5.98 78	10.34 76	26.00 68	37.16 86	3.25 79	19.47 92	10.00 84	11.64 82	3.40 69	48.57 87	13.46 83	7.76 69	10.45 80	0.80 53	4.63 67	2.91 42	6.10 49	6.08 46	15.43 58

STTStereo_v2	10.87 70	4.96 68	9.71 79	3.77 59	4.24 61	7.37 68	2.01 45	10.08 71	15.80 79	4.95 75	15.59 82	48.52 86	9.05 55	0.58 44	8.11 69	8.67 78	5.49 65	4.42 73	23.80 73	15.66 86	8.01 70	8.23 74	3.06 73	5.14 70	6.15 58	13.92 71	22.56 75	23.75 70

G-Net	10.87 70	4.96 68	9.71 79	3.77 59	4.24 61	7.37 68	2.01 45	10.08 71	15.80 79	4.95 75	15.59 82	48.52 86	9.05 55	0.58 44	8.11 69	8.67 78	5.49 65	4.42 73	23.80 73	15.66 86	8.01 70	8.23 74	3.06 73	5.14 70	6.15 58	13.92 71	22.56 75	23.75 70

edge stereo	11.35 72	6.83 75	10.52 81	14.06 74	12.00 81	16.55 78	12.81 80	12.42 76	7.44 63	6.06 79	15.12 81	38.81 79	17.75 66	1.25 66	7.59 61	7.29 75	5.73 69	3.11 68	25.18 77	4.53 67	6.71 62	8.01 73	4.16 75	3.71 62	8.94 68	7.65 54	13.41 67	28.72 78

SGM-ForestM	11.60 73	15.35 83	4.54 65	19.02 78	8.08 73	18.74 81	10.83 79	12.03 73	7.24 62	3.01 49	6.09 60	36.06 76	23.95 71	0.75 52	4.47 34	1.51 25	3.60 56	0.75 29	20.06 67	1.60 46	4.23 44	1.65 15	1.36 60	3.34 59	25.09 82	20.48 82	34.27 85	25.03 73

AnyNet_C01	12.12 74	6.13 74	5.39 71	6.24 68	6.03 68	5.20 61	9.20 77	12.30 75	16.87 83	5.85 77	9.03 70	26.16 69	18.91 68	4.53 85	11.14 85	12.98 88	16.27 85	15.82 90	34.24 83	10.36 81	16.44 85	13.34 88	7.06 79	6.93 77	11.61 73	9.46 62	9.77 59	19.91 62

PVD	12.14 75	5.88 72	8.51 78	7.70 71	6.65 71	10.05 73	6.16 70	21.11 89	19.74 89	3.44 54	8.69 69	16.07 60	27.50 75	2.86 74	8.93 76	7.52 77	11.20 80	6.11 81	24.24 75	6.83 73	11.08 75	11.21 82	1.11 58	5.71 73	15.30 75	21.90 84	17.71 72	34.49 83

MeshStereo	13.21 76	10.28 78	5.53 72	24.29 83	14.71 82	17.26 79	7.30 74	12.03 73	7.47 64	4.66 74	9.13 72	38.56 78	29.80 77	0.45 38	9.43 78	7.37 76	3.76 57	2.44 65	18.85 64	2.41 56	12.93 82	7.18 66	1.21 59	5.79 74	22.04 81	16.38 76	33.15 82	32.34 81
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
DPSNet	14.25 77	3.23 58	1.83 50	7.63 70	7.22 72	10.44 74	4.19 61	5.68 65	8.64 66	4.20 69	3.75 46	72.37 93	26.68 74	4.43 84	10.42 80	10.25 86	10.56 78	8.02 85	58.50 91	8.44 77	11.36 77	8.93 77	16.95 89	10.34 85	18.62 80	15.80 75	14.33 68	21.91 64

MFMNet_re	16.26 78	13.95 81	7.70 77	24.27 82	9.34 77	33.99 89	17.43 82	12.42 76	19.62 88	10.23 86	13.58 80	39.02 80	13.47 61	1.40 68	10.73 83	10.12 85	29.43 91	12.22 87	31.31 79	10.07 80	17.19 86	9.15 78	8.44 82	9.02 82	8.97 70	5.68 47	24.08 77	36.04 85

STStereo	19.12 79	25.20 89	16.96 91	40.13 89	26.93 89	32.21 87	21.77 88	19.35 87	16.49 82	7.75 83	17.60 86	46.83 84	36.96 85	1.06 63	8.10 67	2.99 47	3.81 58	1.57 54	31.85 80	5.18 71	6.77 63	5.31 53	3.04 72	9.71 84	29.57 84	23.01 88	40.22 87	35.81 84

ELAS_RVC	19.26 80	19.85 87	14.31 86	33.92 86	22.40 85	33.39 88	19.24 86	15.07 79	10.89 70	8.20 84	17.30 85	54.84 90	32.79 82	3.23 78	8.72 75	3.46 56	11.25 81	6.19 82	19.97 66	7.86 75	12.63 81	12.02 85	9.45 85	10.70 86	30.22 86	22.17 85	41.23 89	38.75 88
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
SAMSARA	19.29 81	2.55 52	5.07 70	4.09 63	3.44 58	3.26 54	4.38 63	15.32 80	9.58 68	4.44 70	5.63 56	8.73 50	24.82 72	3.30 81	10.45 81	6.81 74	5.49 65	3.46 70	21.26 69	4.77 70	17.96 88	24.74 93	0.82 54	4.77 68	80.12 96	91.25 96	77.91 95	76.45 95

MADNet+	19.30 82	7.92 76	15.32 88	17.51 77	11.42 80	8.09 71	18.41 84	28.03 92	17.21 85	12.46 87	12.08 77	33.63 73	30.37 78	9.62 89	18.64 91	14.89 90	25.78 89	10.10 86	40.05 85	16.50 88	17.34 87	12.76 86	9.48 86	18.60 91	35.02 92	20.22 81	21.19 74	38.41 86

ELAS	19.48 83	22.90 88	14.50 87	35.26 88	24.25 88	31.48 86	19.80 87	16.72 83	15.91 81	7.71 82	15.62 84	48.88 88	30.47 79	3.20 77	9.19 77	3.39 54	10.97 79	5.79 79	21.49 70	6.53 72	11.69 79	11.90 84	9.30 83	9.63 83	32.86 87	24.17 89	41.19 88	41.31 89
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
MANE	19.83 84	17.14 86	15.43 89	34.46 87	23.74 87	25.27 84	17.85 83	19.50 88	17.04 84	6.13 80	12.40 78	41.03 81	35.23 84	3.36 82	7.87 64	8.98 80	7.09 73	29.77 93	21.81 71	3.71 65	9.51 72	5.54 57	2.50 67	8.83 81	34.12 89	27.95 91	51.25 90	47.85 91

SPS-STEREO	20.13 85	14.92 82	11.88 82	19.84 79	20.39 83	18.24 80	9.27 78	16.06 82	19.74 89	19.91 91	20.06 91	33.66 74	37.68 87	7.23 86	13.17 88	16.66 92	26.75 90	16.01 91	32.30 81	23.65 92	30.25 93	22.84 92	13.67 87	13.47 89	16.56 77	15.26 74	29.55 80	24.45 72
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
SGM+DAISY	20.91 86	16.10 84	12.43 83	22.81 81	21.80 84	24.45 83	18.63 85	17.89 84	12.77 72	18.42 90	19.55 90	30.39 71	40.48 89	11.67 90	12.56 87	15.10 91	25.73 88	12.69 88	34.93 84	21.56 91	26.12 92	22.15 91	21.16 90	12.73 88	17.44 78	13.30 69	33.35 84	28.26 76

LE_ROB	22.46 87	10.87 80	12.67 84	32.44 85	37.93 90	31.36 85	33.43 90	23.22 90	21.14 92	6.73 81	17.81 87	52.15 89	50.94 94	1.56 71	6.43 55	2.92 44	3.14 51	1.46 51	30.14 78	8.43 76	7.23 67	4.28 45	7.08 80	25.69 92	34.50 91	36.15 92	53.30 91	53.30 92

LSM0	22.48 88	6.09 73	4.65 66	14.89 75	10.39 79	16.43 77	13.29 81	23.38 91	15.69 78	14.75 89	18.69 88	35.79 75	33.70 83	0.93 59	73.52 95	85.94 95	13.36 83	5.62 78	55.67 90	16.83 89	21.95 90	18.82 90	4.28 76	8.01 79	16.44 76	16.43 77	28.65 78	32.66 82

BEATNet-Init1	23.62 89	27.86 90	13.22 85	44.78 90	23.54 86	38.58 90	23.96 89	18.57 85	18.15 87	9.17 85	13.26 79	47.63 85	48.00 93	3.29 80	12.31 86	9.22 81	7.35 74	5.48 77	48.70 88	19.34 90	12.33 80	11.24 83	7.11 81	11.28 87	33.76 88	21.63 83	60.52 93	47.50 90

DispFullNet	26.27 90	39.81 91	17.89 92	52.75 91	38.74 91	45.03 91	55.21 92	13.74 78	12.89 74	13.75 88	9.88 75	43.75 83	32.46 81	86.51 95	11.01 84	13.68 89	29.48 92	16.23 92	51.78 89	14.64 84	13.87 83	10.46 81	14.59 88	6.22 76	8.62 67	8.03 59	16.08 70	32.10 80

PSMNet_ROB	31.36 91	48.75 92	16.32 90	64.48 95	46.97 93	60.77 94	61.30 93	19.02 86	20.70 91	27.57 93	24.90 92	41.43 82	42.18 90	83.15 94	13.95 89	4.06 58	17.90 86	5.94 80	42.51 86	15.37 85	11.21 76	9.54 79	57.95 94	14.27 90	29.64 85	19.74 79	29.38 79	17.81 60

PWCK	38.25 92	63.47 95	23.98 93	73.60 96	41.23 92	60.98 95	51.31 91	54.00 94	35.86 93	26.51 92	39.23 93	62.00 92	46.09 91	26.43 91	14.46 90	9.53 82	30.80 93	12.71 89	83.96 94	28.46 93	23.42 91	13.60 89	47.98 92	30.51 93	34.48 90	27.91 90	31.53 81	38.61 87

DPSimNet_ROB	58.77 93	55.98 93	59.21 95	62.88 94	73.92 94	60.20 93	64.84 95	28.34 93	91.63 96	80.59 95	52.65 94	74.49 94	47.24 92	90.64 96	55.91 94	45.99 94	54.62 94	42.30 94	68.03 92	59.25 94	51.18 94	59.54 95	44.54 91	45.19 94	44.85 93	61.56 93	56.15 92	55.05 93

CC-Net-ROB	70.08 94	57.59 94	33.20 94	55.61 92	76.68 95	84.28 96	62.79 94	83.71 96	83.30 95	76.21 94	74.67 95	74.97 95	61.84 95	62.54 93	41.03 93	41.52 93	86.04 95	88.34 96	89.47 95	90.97 95	75.66 95	54.62 94	53.92 93	50.83 95	74.81 95	79.04 95	83.52 96	94.87 97

MADNet++	76.54 95	72.15 96	83.11 96	61.53 93	81.21 96	58.42 92	69.47 96	68.24 95	78.58 94	85.63 96	92.37 96	80.60 96	85.04 96	36.71 92	81.19 96	89.82 96	88.15 96	69.64 95	82.54 93	91.39 96	92.32 96	93.48 96	65.75 95	90.26 96	59.09 94	74.29 94	74.25 94	61.32 94

MEDIAN_ROB	97.56 96	99.08 97	98.39 97	100.00 97	99.99 97	100.00 99	100.00 99	99.49 99	98.31 97	100.00 99	99.95 99	97.97 99	96.05 97	99.97 97	99.41 97	98.43 97	98.53 97	98.66 97	99.88 98	97.95 97	93.38 97	96.45 97	100.00 96	97.23 97	86.96 97	92.33 97	89.80 97	95.90 98

AVERAGE_ROB	98.95 97	100.00 100	99.79 98	100.00 97	100.00 98	100.00 99	100.00 99	100.00 100	100.00 98	100.00 99	100.00 100	100.00 102	100.00 100	100.00 98	99.77 98	99.85 98	100.00 100	100.00 100	100.00 99	100.00 100	98.12 98	99.51 98	100.00 96	100.00 98	93.43 98	93.75 98	94.35 98	93.16 96

DGTPSM_ROB	99.52 98	99.77 98	100.00 99	100.00 97	100.00 98	100.00 99	100.00 99	99.17 97	100.00 98	99.31 97	99.64 98	91.36 97	99.13 98	100.00 98	99.94 99	100.00 99	99.82 98	99.99 98	99.53 96	99.99 98	99.80 100	99.66 99	100.00 96	100.00 98	100.00 99	100.00 99	100.00 99	100.00 99

DPSMNet_ROB	99.53 99	99.78 99	100.00 99	100.00 97	100.00 98	100.00 99	100.00 99	99.17 97	100.00 98	99.31 97	99.63 97	91.42 98	99.14 99	100.00 98	99.94 99	100.00 99	99.82 98	99.99 98	99.62 97	99.99 98	99.79 99	99.66 99	100.00 96	100.00 98	100.00 99	100.00 99	100.00 99	100.00 99

DPSM	99.92 100	100.00 100	100.00 99	100.00 97	100.00 98	98.97 97	98.99 97	100.00 100	100.00 98	100.00 99	100.00 100	99.90 100	100.00 100	100.00 98	100.00 101	100.00 99	100.00 100	100.00 100	100.00 99	100.00 100	100.00 101	100.00 101	100.00 96	100.00 98	100.00 99	100.00 99	100.00 99	100.00 99

DPSM_ROB	99.92 100	100.00 100	100.00 99	100.00 97	100.00 98	98.97 97	98.99 97	100.00 100	100.00 98	100.00 99	100.00 100	99.90 100	100.00 100	100.00 98	100.00 101	100.00 99	100.00 100	100.00 100	100.00 99	100.00 100	100.00 101	100.00 101	100.00 96	100.00 98	100.00 99	100.00 99	100.00 99	100.00 99

STTRV1_RVC		2.31 49	1.06 32	5.61 66	1.39 35	5.42 62	2.59 53	3.68 51	3.77 55	4.47 71	4.40 49	16.44 61	20.14 69	1.02 61	9.83 79	3.26 50	18.06 87	0.81 33		11.45 82	18.05 89	6.27 63		4.23 65	6.12 57	7.90 57	11.99 63	28.90 79

Low-res two-view benchmark