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 4	0.12 6	0.02 1	0.02 1	0.00 1	0.01 4	0.19 9	0.04 2	0.00 1	0.00 1	0.48 15	0.01 1	0.01 2	0.26 1	0.22 1	0.08 1	0.06 5	0.01 1	0.00 1	0.10 1	0.10 4	0.01 7	0.01 1	0.00 1	0.01 1	0.30 1	0.39 11

delet	0.19 2	0.07 3	0.25 9	0.06 4	0.04 3	0.02 20	0.02 11	0.01 1	0.04 2	0.34 10	0.00 1	0.10 3	0.03 3	0.02 5	1.36 5	0.46 4	0.16 6	0.05 3	0.13 3	0.05 6	0.13 3	0.18 6	0.00 1	0.04 6	0.00 1	0.01 1	1.41 10	0.02 1

CVANet_RVC	0.21 3	0.15 5	0.61 28	0.42 29	0.06 5	0.06 28	0.01 4	1.53 35	0.05 6	0.08 4	0.04 12	0.62 22	0.04 5	0.03 7	0.46 2	0.32 2	0.14 4	0.09 8	0.05 2	0.01 2	0.19 4	0.07 3	0.00 1	0.03 4	0.00 1	0.01 1	0.55 3	0.08 4

AANet_RVC	0.25 4	0.05 2	0.29 11	0.06 4	0.19 10	0.00 1	0.23 34	0.03 5	0.17 14	0.35 13	0.01 11	0.07 2	0.04 5	0.01 2	1.37 6	0.48 5	0.10 2	0.06 5	0.43 8	0.17 13	0.10 1	0.25 8	0.03 14	0.05 7	0.02 4	0.06 7	1.15 5	0.89 23

DMCA	0.27 5	0.37 15	0.20 8	0.17 9	0.09 7	0.02 20	0.01 4	0.02 4	0.04 2	0.59 19	0.00 1	0.34 10	0.05 8	0.01 2	1.81 7	0.88 9	0.31 12	0.12 9	0.22 4	0.10 8	0.38 9	0.23 7	0.00 1	0.03 4	0.03 7	0.06 7	1.19 6	0.15 6

GANet	0.29 6	0.56 28	0.39 19	0.27 16	0.11 8	0.88 47	0.14 29	0.27 13	0.06 7	0.02 2	0.28 18	1.85 39	0.04 5	0.03 7	0.62 3	0.39 3	0.37 18	0.14 14	0.24 5	0.01 2	0.20 5	0.05 2	0.06 21	0.02 2	0.06 10	0.01 1	0.48 2	0.16 7

MMNet	0.30 7	0.21 8	0.07 3	0.05 3	0.05 4	0.00 1	0.06 22	0.01 1	0.03 1	0.22 8	0.00 1	0.19 5	0.01 1	0.05 13	2.24 10	0.62 7	0.24 9	0.12 9	0.35 6	0.07 7	0.30 7	0.11 5	0.04 15	0.08 10	0.03 7	0.04 6	2.84 20	0.02 1

UPFNet	0.44 8	0.29 9	0.59 26	0.21 11	0.26 15	0.01 9	0.02 11	0.08 7	0.08 8	0.39 14	0.00 1	0.27 8	0.03 3	0.24 41	3.08 24	0.92 11	0.14 4	0.12 9	0.57 9	0.31 20	0.36 8	0.39 9	0.01 7	0.12 15	0.02 4	0.01 1	3.44 32	0.05 3

RYNet	0.45 9	0.04 1	0.00 1	0.02 1	0.03 2	0.00 1	0.00 1	0.01 1	0.04 2	0.07 3	0.00 1	0.05 1	0.12 14	0.00 1	4.77 51	0.60 6	0.10 2	1.00 53	0.35 6	0.01 2	0.21 6	0.04 1	0.00 1	0.02 2	0.08 12	3.06 40	1.39 9	0.13 5

UNet	0.56 10	0.30 10	0.39 19	0.26 15	0.19 10	0.03 23	0.08 24	0.52 18	0.13 12	1.15 35	0.00 1	0.44 14	0.16 19	0.16 34	2.13 8	1.28 24	0.34 13	0.20 16	1.42 22	0.19 14	0.62 12	0.50 10	0.13 31	0.24 21	0.03 7	0.26 14	3.32 28	0.62 17

CFNet	0.57 11	0.46 21	0.64 29	0.43 30	0.34 25	0.04 26	0.00 1	0.58 20	0.09 9	0.66 21	0.05 13	0.64 23	0.07 9	0.05 13	2.25 11	0.65 8	0.35 14	0.06 5	0.73 12	0.34 22	0.85 17	0.73 11	0.00 1	0.08 10	0.45 26	0.16 11	3.22 27	1.51 31

HGLStereo	0.58 12	0.32 14	0.38 18	0.23 12	0.26 15	0.01 9	0.06 22	0.06 6	0.12 10	1.22 38	0.70 34	0.14 4	0.15 18	0.09 25	2.33 12	1.33 26	0.37 18	0.24 20	1.13 17	0.15 10	0.97 18	1.51 21	0.06 21	0.07 8	0.08 12	0.08 10	3.08 22	0.47 13

HITNet	0.59 13	0.16 6	0.10 5	0.06 4	0.07 6	0.01 9	0.02 11	1.76 39	0.82 31	0.11 5	0.00 1	0.68 26	0.28 26	0.11 30	2.51 16	1.02 14	0.25 10	0.13 12	0.63 10	0.24 18	1.43 28	1.56 24	0.01 7	0.12 15	0.02 4	0.16 11	3.32 28	0.42 12
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 14	0.18 7	0.57 24	0.41 28	0.28 18	0.10 31	0.05 21	0.94 26	0.14 13	1.07 34	0.09 15	0.48 15	0.34 30	0.02 5	1.28 4	1.47 33	0.35 14	0.21 17	0.99 14	0.02 5	1.15 23	1.53 22	0.10 27	0.34 35	0.22 20	0.30 16	3.55 36	0.73 19

DMCA-RVC	0.65 15	0.42 17	0.43 22	0.31 20	0.58 31	0.15 35	0.44 37	0.29 14	1.57 50	1.31 40	0.35 22	0.55 19	0.13 15	0.11 30	2.54 17	1.10 18	0.51 22	0.46 33	0.84 13	0.36 25	0.76 15	0.83 12	0.19 38	0.14 19	0.11 15	0.06 7	2.59 17	0.52 15

DSFCA	0.70 16	0.43 18	0.51 23	0.29 19	0.58 31	0.04 26	0.59 40	0.22 11	0.54 25	0.49 18	0.56 27	0.33 9	0.08 12	0.04 10	2.86 18	1.28 24	0.67 33	0.73 39	1.00 15	0.44 31	1.01 21	1.14 18	0.21 41	0.24 21	0.54 27	0.82 26	2.81 19	0.47 13

DISCO	0.79 17	0.95 44	0.31 13	0.06 4	0.33 23	0.51 41	0.44 37	0.23 12	0.32 20	1.75 50	0.59 30	0.38 13	0.29 27	0.04 10	2.98 22	1.36 27	0.96 42	0.29 24	3.24 39	0.53 35	0.79 16	1.45 19	0.10 27	0.07 8	0.12 17	0.29 15	2.76 18	0.31 10

NVstereo2D	0.83 18	0.43 18	0.07 3	0.23 12	0.36 26	0.41 40	0.08 24	1.61 37	0.59 28	0.84 26	1.11 39	0.36 11	0.21 24	0.07 20	5.88 62	2.16 50	0.62 27	0.45 32	1.50 23	0.19 14	1.20 24	0.94 17	0.02 10	0.32 31	1.30 38	0.24 13	0.95 4	0.24 9

CFNet_RVC	0.86 19	0.59 30	0.74 34	0.33 21	0.32 22	0.11 32	0.02 11	0.11 8	0.17 14	0.87 29	0.74 35	0.57 21	0.16 19	0.08 24	3.11 25	1.19 23	0.50 21	0.34 26	1.28 20	0.15 10	1.68 33	1.48 20	0.02 10	0.25 23	0.86 34	1.89 35	3.64 38	2.13 35

DLCB_ROB	0.92 20	0.72 36	0.64 29	0.43 30	0.42 27	0.14 34	0.18 32	0.43 15	0.28 18	0.85 27	0.53 25	1.12 34	0.72 51	0.07 20	2.16 9	1.12 19	0.88 40	0.26 21	0.69 11	0.20 16	1.35 27	1.53 22	0.49 58	0.28 26	4.11 66	2.41 37	2.04 13	0.78 20

HSM	0.97 21	0.30 10	0.87 38	0.54 36	0.46 29	0.00 1	0.01 4	2.93 61	0.30 19	0.13 6	0.56 27	0.65 24	0.13 15	0.05 13	2.97 20	1.08 17	0.66 32	0.17 15	3.26 41	0.35 23	1.78 34	2.03 30	0.04 15	0.33 34	0.38 23	0.81 25	4.16 51	1.10 25

NLCA_NET_v2_RVC	0.99 22	0.82 39	0.34 16	0.18 10	0.43 28	0.03 23	1.10 52	1.20 30	2.50 63	2.06 55	0.11 16	0.69 27	0.07 9	0.54 53	3.65 36	0.91 10	0.19 8	0.22 18	1.65 27	0.15 10	0.73 14	0.92 16	0.08 24	0.18 20	0.11 15	3.29 45	2.58 16	2.09 34
Zhibo Rao, Mingyi He, Yuchao Dai, Zhidong Zhu, Bo Li, and Renjie He.: NLCA-Net: A non-local context attention network for stereo matching.
psm_up	1.01 23	0.60 31	0.96 43	0.38 26	0.75 35	0.01 9	0.03 18	2.33 49	1.18 40	0.78 24	0.00 1	0.94 31	0.09 13	0.45 51	2.41 13	1.18 22	0.62 27	0.44 31	3.67 44	0.32 21	1.14 22	0.85 14	0.00 1	0.58 46	1.06 36	1.42 32	3.75 44	1.44 29

PDISCO_ROB	1.03 24	0.55 27	0.06 2	0.67 38	0.28 18	0.00 1	0.21 33	0.71 22	0.49 24	1.28 39	0.99 38	0.25 7	0.32 29	0.19 36	3.02 23	2.03 47	0.69 34	1.42 65	2.26 29	0.58 38	2.32 39	3.22 50	0.14 32	0.27 25	1.36 39	0.37 17	3.84 45	0.23 8

CREStereo	1.03 24	0.31 12	0.74 34	0.24 14	0.16 9	0.01 9	0.04 20	2.66 58	0.55 26	0.63 20	0.56 27	0.71 28	0.59 45	0.05 13	2.97 20	3.31 67	0.60 24	0.43 30	2.12 28	0.57 37	3.00 51	2.30 36	0.06 21	0.12 15	1.18 37	0.53 23	2.42 15	0.88 21

STTStereo	1.06 26	0.85 42	0.57 24	0.36 25	0.29 20	0.08 30	0.64 42	0.43 15	1.56 48	1.40 46	0.26 17	1.95 40	0.07 9	0.29 44	4.26 39	1.06 15	0.25 10	0.23 19	1.16 18	0.14 9	1.46 30	0.84 13	0.12 30	0.10 14	0.07 11	1.81 34	3.51 35	4.84 50

iResNet_ROB	1.07 27	0.65 34	0.30 12	0.45 32	0.30 21	0.01 9	0.02 11	3.10 62	0.12 10	1.34 41	0.00 1	0.66 25	0.35 31	0.04 10	4.37 43	1.65 38	0.54 23	0.49 35	5.83 54	0.29 19	1.30 26	0.87 15	0.20 40	0.26 24	0.16 18	0.84 27	3.74 42	1.12 26

AF-Net	1.11 28	0.51 23	0.87 38	0.34 23	0.78 37	0.89 48	1.87 57	2.40 50	1.15 38	0.48 16	0.68 32	3.66 49	0.16 19	0.27 42	2.44 14	0.98 12	0.35 14	0.01 1	1.60 24	0.40 26	0.50 10	2.58 40	0.05 19	0.08 10	0.66 31	0.45 19	3.47 33	2.34 36

Nwc_Net	1.11 28	0.51 23	0.87 38	0.34 23	0.78 37	0.89 48	1.87 57	2.40 50	1.15 38	0.48 16	0.68 32	3.66 49	0.16 19	0.27 42	2.44 14	0.98 12	0.35 14	0.01 1	1.60 24	0.40 26	0.50 10	2.58 40	0.05 19	0.08 10	0.66 31	0.45 19	3.47 33	2.34 36

iResNetv2_ROB	1.26 30	0.75 37	0.32 14	1.29 55	0.33 23	0.02 20	0.02 11	3.19 63	0.20 16	2.00 54	0.29 19	2.24 43	0.41 35	0.06 18	3.64 35	1.45 32	0.75 37	0.13 12	5.77 52	0.43 30	2.86 47	1.71 26	0.16 35	0.37 38	0.38 23	0.47 21	4.08 49	0.72 18

PMTNet	1.28 31	0.58 29	0.96 43	0.28 18	0.22 14	0.01 9	0.16 30	0.50 17	0.57 27	0.72 23	0.35 22	0.54 18	0.48 40	0.09 25	3.27 29	3.32 68	0.72 36	0.39 28	6.17 55	0.35 23	3.67 56	2.41 38	0.09 25	0.12 15	3.12 62	1.00 30	1.23 7	3.32 42

RP	1.29 32	1.06 46	2.32 74	0.84 42	1.16 42	0.39 39	1.53 54	1.89 43	1.68 52	0.29 9	2.21 48	2.25 44	0.13 15	0.40 49	3.31 30	1.07 16	0.61 25	0.05 3	1.61 26	0.40 26	0.65 13	3.05 47	0.11 29	0.29 27	0.29 21	0.92 28	4.24 52	2.04 33

BEATNet_4x	1.40 33	0.54 26	0.27 10	0.13 8	0.27 17	0.01 9	0.10 26	1.80 40	1.74 54	0.41 15	0.53 25	0.82 30	0.59 45	0.54 53	4.30 40	2.92 58	1.87 58	1.84 73	1.39 21	0.71 41	4.68 63	6.21 79	0.02 10	0.81 50	0.21 19	0.40 18	3.69 39	0.96 24

DN-CSS_ROB	1.45 34	0.52 25	1.11 49	0.81 41	0.69 34	0.00 1	0.03 18	2.86 60	0.35 21	1.50 47	3.76 63	0.73 29	0.36 33	0.07 20	2.89 19	1.44 30	1.08 46	0.79 42	8.61 66	0.23 17	3.25 53	1.97 28	0.02 10	0.66 47	0.10 14	0.52 22	3.12 26	1.56 32

DeepPruner_ROB	1.57 35	0.37 15	0.71 32	0.50 34	0.55 30	0.37 38	1.60 55	1.74 38	0.84 33	1.37 43	2.40 50	0.49 17	0.51 43	0.10 29	5.38 57	2.16 50	1.10 47	1.08 56	2.28 30	0.40 26	4.83 67	3.42 51	0.66 67	0.34 35	0.57 30	5.04 60	3.07 21	0.53 16

FCDSN-DC	1.58 36	0.84 40	2.05 69	1.66 59	1.43 51	1.08 54	0.99 47	0.72 23	0.44 23	1.18 37	0.31 20	1.45 36	0.49 42	0.07 20	6.05 68	1.96 45	2.13 61	1.97 76	1.16 18	0.44 31	2.39 43	2.62 42	0.49 58	3.18 74	1.72 43	3.24 44	1.34 8	1.27 27
Dominik Hirner, Friedrich Fraundorfer: FCDSN-DC: An accurate but lightweight end-to-end trainable neural network for stereo estimation with depth completion.
FADNet-RVC-Resample	1.60 37	0.67 35	0.97 45	0.50 34	0.64 33	0.18 36	1.05 49	1.27 32	0.83 32	0.96 31	0.97 37	5.23 57	0.42 36	1.02 77	4.81 54	1.68 39	1.05 44	1.96 75	3.78 45	1.06 51	1.52 31	2.28 35	0.21 41	1.35 59	0.55 28	3.16 43	2.13 14	2.92 39

PA-Net	1.61 38	0.81 38	1.43 59	0.27 16	1.99 63	0.12 33	0.34 35	0.19 9	1.90 56	0.85 27	0.41 24	0.24 6	0.20 23	0.78 69	4.61 49	1.44 30	0.17 7	0.26 21	1.08 16	0.53 35	1.44 29	3.18 49	0.65 65	0.44 40	0.99 35	7.71 75	1.78 12	9.78 64
Zhibo Rao, Mingyi He, Yuchao Dai, Zhelun Shen: Patch Attention Network with Generative Adversarial Model for Semi-Supervised Binocular Disparity Prediction.
FADNet-RVC	1.64 39	0.31 12	0.64 29	0.33 21	1.19 45	0.01 9	2.53 68	0.57 19	0.22 17	1.03 33	0.32 21	6.04 61	0.29 27	0.32 47	4.47 46	1.90 44	0.62 27	0.78 41	6.45 58	1.80 64	1.85 35	2.02 29	0.19 38	0.52 44	1.78 44	1.74 33	3.10 23	3.15 40

NCC-stereo	1.86 40	1.63 53	1.33 55	0.95 49	1.22 47	0.80 44	2.29 64	2.62 56	2.93 66	0.34 10	2.87 58	2.16 41	1.17 55	0.21 37	3.63 32	1.13 20	0.63 30	0.96 51	2.79 35	0.75 42	0.98 19	2.96 45	0.36 51	0.32 31	2.31 50	4.50 55	3.69 39	4.78 47

Abc-Net	1.86 40	1.63 53	1.33 55	0.95 49	1.22 47	0.80 44	2.29 64	2.62 56	2.93 66	0.34 10	2.87 58	2.16 41	1.17 55	0.21 37	3.63 32	1.13 20	0.63 30	0.96 51	2.79 35	0.75 42	0.98 19	2.96 45	0.36 51	0.32 31	2.31 50	4.50 55	3.69 39	4.78 47
Xing Li, Yangyu Fan, Guoyun Lv, and Haoyue Ma: Area-based Correlation and Non-local Attention Network for Stereo Matching. The Visual Computer
stereogan	1.89 42	2.00 62	1.17 51	1.47 57	2.05 66	1.72 58	0.87 46	1.84 42	1.11 36	1.36 42	1.51 42	5.81 59	0.48 40	0.32 47	3.87 37	1.69 40	0.80 38	0.39 28	3.96 46	0.78 44	1.23 25	4.02 58	0.26 44	0.51 43	0.30 22	1.30 31	6.30 65	3.84 45

FADNet	1.92 43	0.44 20	0.60 27	0.54 36	1.05 41	0.00 1	2.95 72	0.78 24	0.38 22	0.81 25	0.61 31	2.40 45	0.47 39	0.65 63	5.57 59	1.73 41	0.69 34	1.08 56	8.93 68	1.61 62	2.36 41	2.52 39	0.09 25	0.36 37	2.54 55	3.77 50	3.43 31	5.46 51

NCCL2	2.12 44	2.27 67	1.29 52	1.28 54	1.85 60	0.18 36	0.77 45	1.24 31	1.25 42	1.62 48	2.71 55	1.06 32	0.51 43	0.31 45	4.40 44	2.33 54	1.57 52	0.62 37	2.35 31	0.68 40	2.68 46	4.29 62	1.52 81	1.06 52	4.65 70	4.17 52	4.04 48	6.58 57

R-Stereo Train	2.16 45	1.08 47	1.57 63	0.84 42	0.19 10	0.01 9	0.01 4	2.05 46	1.01 34	1.37 43	2.45 51	1.50 37	0.46 37	0.21 37	3.23 26	4.53 77	1.62 53	1.05 54	7.07 59	1.09 53	4.86 68	5.93 76	0.04 15	0.31 28	2.48 52	4.63 57	3.10 23	5.74 53

RAFT-Stereo	2.16 45	1.08 47	1.57 63	0.84 42	0.19 10	0.01 9	0.01 4	2.05 46	1.01 34	1.37 43	2.45 51	1.50 37	0.46 37	0.21 37	3.23 26	4.53 77	1.62 53	1.05 54	7.07 59	1.09 53	4.86 68	5.93 76	0.04 15	0.31 28	2.48 52	4.63 57	3.10 23	5.74 53
Lahav Lipson, Zachary Teed, and Jia Deng: RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching. 3DV
GANetREF_RVC	2.43 47	1.69 56	0.81 36	2.04 64	1.16 42	1.02 52	0.10 26	4.72 77	1.44 45	3.62 74	0.06 14	10.03 71	0.69 49	0.09 25	3.63 32	1.36 27	1.06 45	0.94 49	6.44 57	0.46 33	2.32 39	1.74 27	0.17 36	1.35 59	3.41 64	3.75 49	6.55 67	4.82 49
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 48	1.98 61	4.87 88	1.54 58	1.65 54	1.74 59	1.05 49	2.02 45	1.43 44	0.88 30	4.42 67	2.52 47	2.42 62	0.58 58	4.33 41	1.63 37	0.91 41	0.48 34	4.28 47	1.08 52	1.94 36	4.01 57	0.15 33	0.72 49	2.19 48	5.22 62	4.90 59	6.96 58

MLCV	2.45 49	0.61 32	0.81 36	2.48 67	1.21 46	0.95 50	0.50 39	3.57 66	0.61 29	3.75 77	4.26 65	5.13 55	3.34 65	0.06 18	4.63 50	1.78 43	1.32 50	0.86 46	10.05 69	1.09 53	2.30 38	3.92 55	0.29 45	2.16 68	1.87 45	3.06 40	4.01 47	1.40 28

RTSC	2.56 50	0.89 43	1.50 60	0.47 33	0.76 36	0.87 46	2.29 64	1.56 36	1.91 57	2.12 56	2.86 57	1.23 35	0.97 54	1.03 79	7.27 76	3.36 70	4.68 85	1.74 70	7.73 62	1.11 56	3.95 58	5.39 70	0.21 41	1.11 55	0.40 25	0.95 29	4.56 56	8.26 63

DeepPrunerF	2.66 51	1.67 55	0.33 15	3.58 75	9.30 97	1.76 60	3.48 76	0.68 21	3.29 69	0.71 22	1.59 44	0.37 12	0.27 25	0.13 32	5.94 63	3.42 73	1.03 43	0.54 36	4.47 48	0.65 39	2.02 37	2.06 31	0.56 62	0.31 28	15.21 96	3.30 46	3.74 42	1.47 30

NOSS_ROB	2.78 52	3.78 81	0.39 19	2.02 63	1.58 53	2.36 65	0.00 1	2.66 58	1.54 47	3.63 75	1.79 47	2.55 48	3.51 66	0.83 72	5.82 61	5.57 86	2.69 65	1.83 72	2.63 34	0.91 48	4.62 61	4.13 59	0.42 54	2.58 72	2.29 49	4.46 54	7.67 76	2.88 38

SGM-Forest	2.81 53	2.03 63	0.36 17	6.32 86	1.79 59	2.66 66	0.02 11	1.42 33	0.77 30	2.52 57	0.96 36	6.57 62	5.74 69	0.31 45	4.36 42	2.94 60	0.61 25	0.33 25	3.08 37	0.47 34	2.46 44	2.73 43	0.84 73	0.45 41	3.66 65	3.74 48	11.30 84	7.44 60
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 54	0.49 22	0.17 7	0.38 26	0.79 39	0.03 23	0.10 26	1.16 29	8.93 88	0.14 7	2.49 53	0.55 19	31.02 102	0.57 55	5.58 60	3.32 68	1.73 55	0.91 48	2.53 33	0.88 46	2.92 48	7.18 84	0.15 33	0.53 45	0.55 28	0.69 24	1.43 11	0.88 21

HSM-Net_RVC	2.86 55	1.24 50	1.50 60	2.55 69	1.72 55	0.57 42	0.17 31	3.55 65	1.14 37	3.50 73	6.32 80	7.94 63	2.93 63	0.05 13	3.38 31	2.12 48	1.28 49	0.26 21	3.24 39	1.24 58	3.66 55	3.76 54	0.61 63	2.64 73	4.43 68	6.27 68	5.76 62	5.47 52
Gengshan Yang, Joshua Manela, Michael Happold, and Deva Ramanan: Hierarchical Deep Stereo Matching on High-resolution Images. CVPR 2019
ADCRef	2.96 56	2.78 74	1.02 46	0.70 39	2.21 67	1.96 62	2.42 67	0.96 27	6.10 78	1.94 53	1.15 40	1.08 33	0.74 52	1.32 84	6.01 64	3.38 71	1.34 51	1.15 60	8.41 65	2.09 69	2.59 45	3.60 53	1.88 83	0.71 48	2.81 58	8.00 77	3.42 30	10.24 65

NaN_ROB	3.01 57	1.16 49	1.13 50	0.86 46	3.03 72	0.59 43	0.75 43	3.25 64	2.58 64	0.98 32	1.32 41	21.10 85	0.59 45	1.15 82	4.15 38	1.58 36	0.80 38	0.70 38	2.38 32	1.30 59	1.52 31	2.15 32	0.46 57	1.07 54	2.50 54	6.25 67	4.54 55	13.27 70

XPNet_ROB	3.02 58	2.54 70	1.33 55	0.73 40	1.45 52	1.04 53	0.61 41	0.83 25	1.45 46	1.16 36	3.44 60	26.85 91	0.35 31	0.40 49	3.24 28	2.22 53	3.28 72	0.79 42	3.20 38	1.19 57	2.94 50	5.51 71	0.76 70	0.46 42	0.72 33	2.37 36	9.22 78	3.37 44

ccs	3.29 59	0.63 33	1.94 68	0.99 51	2.02 64	0.06 28	0.01 4	3.87 69	1.79 55	4.48 89	9.13 91	4.94 53	4.74 68	0.87 73	6.01 64	5.69 87	1.83 57	1.39 63	5.37 50	1.03 50	5.86 77	7.35 86	0.32 48	1.56 62	3.32 63	6.28 69	4.08 49	3.36 43

ADCP+	3.47 60	2.05 64	2.64 77	1.31 56	1.93 61	1.19 55	3.99 77	1.07 28	9.83 90	1.78 51	5.75 76	8.79 67	0.37 34	0.90 74	5.04 56	2.20 52	0.44 20	0.90 47	5.82 53	0.88 46	2.38 42	3.43 52	0.33 49	0.41 39	3.03 61	12.72 89	3.59 37	10.90 66

FC-DCNN	3.82 61	4.38 85	5.67 95	9.61 94	4.14 78	8.01 90	3.26 74	4.21 70	1.69 53	2.95 65	1.54 43	5.87 60	2.09 61	0.13 32	4.40 44	2.15 49	2.54 64	1.11 58	3.38 42	0.82 45	3.11 52	2.20 33	0.65 65	3.29 76	6.33 80	4.91 59	6.98 71	7.80 62

ADCL	3.91 62	3.61 79	2.28 72	1.76 61	1.76 58	3.09 69	4.16 78	1.98 44	13.71 97	2.81 63	5.14 72	2.47 46	0.65 48	1.10 81	5.38 57	2.55 56	2.02 59	2.20 78	6.41 56	2.08 68	4.87 70	4.20 60	1.40 79	1.06 52	6.69 81	6.60 70	4.33 54	11.16 68

FADNet_RVC	3.95 63	2.06 65	1.30 53	3.33 73	3.36 74	0.99 51	2.06 62	2.30 48	1.39 43	1.72 49	4.72 69	9.29 68	5.82 71	1.76 90	8.23 90	3.12 63	2.98 68	4.27 91	16.83 78	3.30 82	4.99 73	5.51 71	0.63 64	1.39 61	1.45 40	3.14 42	4.86 57	5.92 55

LALA_ROB	4.04 64	4.73 87	1.42 58	1.16 53	1.93 61	2.17 64	1.04 48	1.80 40	2.48 62	2.74 60	5.87 77	33.34 94	0.69 49	0.78 69	6.02 66	2.33 54	3.21 71	1.82 71	3.66 43	1.88 65	3.40 54	4.99 65	0.34 50	1.03 51	1.91 46	3.61 47	8.50 77	6.20 56

iResNet	4.14 65	0.84 40	0.71 32	2.34 66	2.59 68	4.58 76	1.07 51	4.67 75	1.21 41	3.72 76	3.61 61	12.28 73	16.06 86	0.03 7	6.64 73	3.14 64	2.35 63	1.89 74	16.43 77	2.53 74	3.76 57	2.90 44	0.17 36	3.88 81	1.66 42	4.23 53	5.19 60	3.23 41

SHD	4.34 66	1.02 45	2.50 75	1.71 60	1.33 49	1.46 56	2.01 59	4.90 78	4.30 76	2.63 58	4.99 71	4.39 52	9.41 78	0.58 58	8.20 88	4.81 80	4.43 84	2.44 82	13.01 74	2.95 79	5.61 76	6.88 82	0.30 46	3.26 75	4.11 66	5.16 61	3.88 46	10.98 67

CSAN	4.46 67	2.36 69	1.57 63	3.12 71	3.32 73	1.64 57	0.76 44	4.59 74	4.37 77	3.80 79	2.84 56	17.58 81	10.47 80	0.96 76	4.80 53	1.75 42	2.06 60	1.16 61	5.69 51	2.34 72	2.93 49	2.40 37	0.43 55	2.56 71	4.64 69	11.45 87	7.53 75	13.44 71

ETE_ROB	4.62 68	4.04 83	1.52 62	1.14 52	2.02 64	2.70 67	1.28 53	1.50 34	4.25 75	1.89 52	5.88 78	37.95 99	0.84 53	1.42 88	4.94 55	2.66 57	3.81 79	0.95 50	4.84 49	1.63 63	4.01 59	5.32 69	0.67 68	1.14 56	2.03 47	3.84 51	15.25 91	7.23 59

XQC	4.76 69	3.14 76	1.63 66	3.31 72	3.69 76	3.46 72	2.94 71	6.02 85	3.05 68	2.64 59	5.51 74	5.67 58	7.46 73	1.41 86	8.02 81	4.65 79	3.59 74	1.49 67	10.75 70	2.91 75	4.96 72	6.67 81	0.41 53	1.91 64	7.13 85	10.47 86	11.17 83	4.51 46

MSMDNet	4.98 70	2.59 72	3.57 81	2.54 68	0.90 40	3.99 73	1.63 56	4.52 73	1.56 48	4.54 90	18.92 110	20.82 84	12.09 81	0.60 62	6.57 72	5.84 88	2.28 62	1.52 68	7.08 61	1.94 67	6.09 78	6.12 78	0.31 47	2.29 69	1.47 41	2.83 39	4.25 53	7.49 61

CBMV	5.52 71	3.65 80	1.32 54	3.46 74	2.86 71	2.04 63	0.41 36	3.64 67	2.63 65	4.09 83	2.36 49	14.13 76	3.70 67	0.57 55	7.16 75	6.19 92	4.26 82	2.36 81	8.76 67	1.44 60	6.43 80	4.53 64	0.54 61	5.62 91	4.85 71	6.08 65	19.41 95	26.62 97
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 72	1.46 51	0.94 41	0.90 47	1.73 56	4.08 74	4.63 82	2.53 53	3.41 70	2.79 61	1.70 45	8.18 64	1.27 57	0.72 65	7.83 78	4.87 81	7.37 97	2.34 79	18.33 80	2.92 76	4.78 64	5.88 74	1.04 74	1.31 57	6.74 82	22.88 108	6.66 68	22.00 87

RTSA	5.53 72	1.46 51	0.94 41	0.90 47	1.73 56	4.08 74	4.63 82	2.53 53	3.41 70	2.79 61	1.70 45	8.18 64	1.27 57	0.72 65	7.83 78	4.87 81	7.37 97	2.34 79	18.33 80	2.92 76	4.78 64	5.88 74	1.04 74	1.31 57	6.74 82	22.88 108	6.66 68	22.00 87

AnyNet_C32	5.68 74	1.96 59	2.91 78	1.78 62	2.71 70	3.17 70	5.76 87	4.31 71	12.56 92	3.07 67	7.17 82	4.22 51	1.70 59	2.19 91	8.22 89	5.98 90	5.69 90	4.43 95	12.45 73	2.93 78	9.72 96	8.57 96	1.84 82	3.46 78	6.24 79	9.82 84	6.26 64	14.21 72

CBMV_ROB	6.09 75	2.65 73	2.29 73	8.35 91	5.74 85	5.08 77	2.76 70	5.03 80	2.35 60	3.78 78	4.70 68	21.22 86	13.56 84	0.09 25	6.04 67	3.70 75	2.84 66	1.39 63	11.35 71	8.80 101	4.92 71	3.11 48	0.43 55	2.10 67	7.06 84	10.12 85	12.96 86	11.93 69

ADCPNet	6.67 76	1.97 60	3.86 84	2.75 70	6.19 89	3.07 68	7.60 94	5.21 82	8.03 85	3.82 80	7.92 85	5.17 56	13.78 85	2.98 95	8.10 84	6.12 91	4.35 83	2.04 77	18.50 82	4.74 87	7.04 85	7.43 87	2.98 90	1.91 64	8.96 88	14.04 94	7.02 73	14.47 74

AMNet	6.77 77	3.87 82	2.62 76	4.00 79	5.86 86	6.75 84	3.31 75	2.51 52	2.47 61	3.44 71	6.68 81	9.34 69	2.05 60	7.44 107	4.79 52	9.81 105	13.48 106	7.65 106	7.95 64	7.83 96	11.47 100	7.62 91	2.74 88	4.18 82	5.52 74	8.25 79	13.24 87	17.97 81

ADCMid	6.83 78	4.46 86	4.95 89	2.16 65	4.31 81	5.71 81	7.29 91	8.83 87	12.78 94	3.11 68	8.10 87	9.83 70	9.65 79	2.84 92	8.51 92	5.42 85	4.68 85	2.83 84	18.87 85	3.22 80	7.09 86	7.70 92	5.37 98	2.33 70	5.12 73	8.71 81	5.21 61	15.24 75

PWC_ROB	7.15 79	1.85 57	5.57 93	0.85 45	1.17 44	1.95 61	4.77 84	2.56 55	17.27 107	3.83 81	7.66 84	4.97 54	39.20 110	0.81 71	8.35 91	4.96 84	6.51 93	4.27 91	15.99 76	4.39 84	7.48 88	7.59 90	0.53 60	4.79 87	2.74 56	2.63 38	7.01 72	23.26 90

FAT-Stereo	7.20 80	2.13 66	8.68 100	7.00 87	11.87 102	5.95 82	3.19 73	7.26 86	3.52 72	5.84 95	13.06 98	18.26 82	8.49 74	1.41 86	8.81 94	6.34 93	3.65 76	4.07 90	15.49 75	6.21 91	6.75 83	6.94 83	0.74 69	4.80 88	2.92 60	7.26 72	7.50 74	16.30 77

MFN_U_SF_DS_RVC	7.30 81	3.59 78	1.09 48	9.20 92	3.71 77	7.69 88	7.51 93	10.81 92	8.52 86	4.93 92	4.20 64	16.73 80	9.30 77	5.45 105	8.09 83	3.29 66	1.80 56	1.13 59	18.77 83	2.10 70	6.48 81	7.49 88	3.17 94	1.74 63	12.06 94	14.29 95	9.71 80	14.36 73

FBW_ROB	7.98 82	9.24 98	5.57 93	20.21 102	8.25 95	12.08 95	8.09 95	4.92 79	1.58 51	3.30 70	5.15 73	13.88 75	3.03 64	7.45 108	6.23 69	1.49 34	2.89 67	0.79 42	33.18 102	1.93 66	5.24 74	4.40 63	9.43 105	8.29 102	4.88 72	7.69 74	4.88 58	21.40 85

pmcnn	8.05 83	2.96 75	2.25 71	6.23 84	5.03 82	6.98 85	4.33 80	4.70 76	2.02 59	4.19 84	9.20 93	19.11 83	20.39 92	0.73 67	7.05 74	6.73 94	6.29 92	4.51 96	22.54 92	4.66 86	9.62 95	5.08 67	4.46 97	4.23 83	5.99 75	5.22 62	17.26 93	25.55 96

S-Stereo	9.11 84	1.88 58	3.41 80	7.93 90	7.92 93	12.47 96	19.24 107	5.20 81	6.26 80	6.18 100	20.72 113	29.27 92	7.13 72	0.65 63	9.55 98	7.50 98	3.80 78	5.87 101	17.77 79	6.30 92	6.21 79	7.52 89	12.23 108	6.88 97	2.77 57	6.68 71	6.74 70	18.03 82

ADCS	9.93 85	5.64 90	4.82 87	3.95 78	6.05 88	6.17 83	5.70 86	9.47 89	15.68 98	4.19 84	7.98 86	12.91 74	17.41 87	3.94 102	10.52 103	11.68 109	10.14 99	7.14 105	24.46 96	8.67 100	13.95 106	13.00 109	2.94 89	6.15 94	9.75 91	17.65 100	10.70 82	17.41 78

SGM_RVC	10.08 86	10.50 100	3.60 82	16.68 97	8.80 96	14.87 98	7.11 90	9.19 88	6.25 79	4.06 82	7.33 83	24.89 88	18.29 89	0.57 55	4.57 48	1.98 46	3.30 73	1.17 62	20.75 88	2.20 71	4.80 66	2.23 34	2.29 85	3.65 79	17.68 101	13.31 91	33.30 105	28.67 99
Heiko Hirschmueller: Stereo processing by semiglobal matching and mutual information. TPAMI 2008, Volume 30(2), pp. 328-341
MDST_ROB	10.13 87	16.70 107	3.04 79	29.06 106	9.34 98	20.66 104	5.02 85	5.90 84	1.98 58	3.16 69	2.70 54	11.22 72	28.15 98	0.18 35	6.30 70	1.37 29	1.21 48	0.34 26	7.74 63	1.01 49	4.67 62	3.95 56	1.92 84	1.94 66	26.55 105	19.78 102	37.02 108	22.50 89

SANet	10.32 88	4.15 84	6.27 96	5.51 82	5.49 84	8.58 92	2.27 63	15.41 102	13.03 96	2.83 64	9.09 90	60.35 113	25.50 95	2.93 94	8.06 82	3.02 62	7.05 94	3.86 89	11.43 72	3.25 81	5.27 75	5.02 66	2.60 87	7.27 100	11.08 92	12.57 88	13.36 88	23.31 91

MFN_U_SF_RVC	10.34 89	6.17 95	3.69 83	9.55 93	5.13 83	12.52 97	10.34 98	12.25 95	7.05 81	12.23 108	14.31 101	22.82 87	13.03 82	8.82 109	11.37 107	4.87 81	3.83 81	3.31 86	39.34 106	5.97 90	8.56 93	8.81 97	1.45 80	6.40 96	9.49 90	8.57 80	9.56 79	19.71 83

ccs_rob	10.35 90	5.69 91	2.14 70	4.64 81	2.67 69	5.53 80	6.85 89	4.40 72	4.18 74	5.98 97	10.34 95	26.00 89	37.16 108	3.25 98	19.47 114	10.00 106	11.64 104	3.40 87	48.57 109	13.46 105	7.76 89	10.45 102	0.80 71	4.63 85	2.91 59	6.10 66	6.08 63	15.43 76

G-Net	10.87 91	4.96 88	9.71 101	3.77 76	4.24 79	7.37 86	2.01 59	10.08 90	15.80 100	4.95 93	15.59 103	48.52 108	9.05 75	0.58 58	8.11 86	8.67 100	5.49 87	4.42 93	23.80 93	15.66 108	8.01 91	8.23 94	3.06 92	5.14 89	6.15 77	13.92 92	22.56 97	23.75 92

STTStereo_v2	10.87 91	4.96 88	9.71 101	3.77 76	4.24 79	7.37 86	2.01 59	10.08 90	15.80 100	4.95 93	15.59 103	48.52 108	9.05 75	0.58 58	8.11 86	8.67 100	5.49 87	4.42 93	23.80 93	15.66 108	8.01 91	8.23 94	3.06 92	5.14 89	6.15 77	13.92 92	22.56 97	23.75 92

MSC_U_SF_DS_RVC	10.98 93	10.59 101	6.33 97	17.49 98	6.29 90	7.85 89	10.69 99	18.03 106	23.43 114	11.13 107	4.86 70	14.66 77	5.81 70	9.65 111	9.78 99	5.91 89	3.18 70	5.17 97	39.06 105	7.03 95	7.92 90	9.81 101	7.04 99	7.22 99	14.77 95	8.85 82	6.40 66	17.43 79

edge stereo	11.35 94	6.83 96	10.52 103	14.06 95	12.00 103	16.55 100	12.81 101	12.42 97	7.44 83	6.06 98	15.12 102	38.81 101	17.75 88	1.25 83	7.59 77	7.29 96	5.73 91	3.11 85	25.18 97	4.53 85	6.71 82	8.01 93	4.16 95	3.71 80	8.94 87	7.65 73	13.41 89	28.72 100

SGM-ForestM	11.60 95	15.35 105	4.54 85	19.02 100	8.08 94	18.74 103	10.83 100	12.03 93	7.24 82	3.01 66	6.09 79	36.06 98	23.95 93	0.75 68	4.47 46	1.51 35	3.60 75	0.75 40	20.06 87	1.60 61	4.23 60	1.65 25	1.36 78	3.34 77	25.09 104	20.48 104	34.27 107	25.03 95

AnyNet_C01	12.12 96	6.13 94	5.39 91	6.24 85	6.03 87	5.20 78	9.20 96	12.30 96	16.87 104	5.85 96	9.03 89	26.16 90	18.91 90	4.53 104	11.14 106	12.98 110	16.27 107	15.82 112	34.24 103	10.36 103	16.44 107	13.34 110	7.06 100	6.93 98	11.61 93	9.46 83	9.77 81	19.91 84

PVD	12.14 97	5.88 92	8.51 99	7.70 89	6.65 91	10.05 93	6.16 88	21.11 111	19.74 110	3.44 71	8.69 88	16.07 78	27.50 97	2.86 93	8.93 95	7.52 99	11.20 102	6.11 103	24.24 95	6.83 94	11.08 97	11.21 104	1.11 76	5.71 92	15.30 97	21.90 106	17.71 94	34.49 105

MeshStereo	13.21 98	10.28 99	5.53 92	24.29 105	14.71 104	17.26 101	7.30 92	12.03 93	7.47 84	4.66 91	9.13 91	38.56 100	29.80 99	0.45 51	9.43 97	7.37 97	3.76 77	2.44 82	18.85 84	2.41 73	12.93 104	7.18 84	1.21 77	5.79 93	22.04 103	16.38 98	33.15 104	32.34 103
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 99	3.23 77	1.83 67	7.63 88	7.22 92	10.44 94	4.19 79	5.68 83	8.64 87	4.20 86	3.75 62	72.37 115	26.68 96	4.43 103	10.42 101	10.25 108	10.56 100	8.02 107	58.50 113	8.44 99	11.36 99	8.93 98	16.95 111	10.34 107	18.62 102	15.80 97	14.33 90	21.91 86

MFMNet_re	16.26 100	13.95 103	7.70 98	24.27 104	9.34 98	33.99 111	17.43 103	12.42 97	19.62 109	10.23 106	13.58 100	39.02 102	13.47 83	1.40 85	10.73 104	10.12 107	29.43 113	12.22 109	31.31 99	10.07 102	17.19 108	9.15 99	8.44 103	9.02 104	8.97 89	5.68 64	24.08 99	36.04 107

STStereo	19.12 101	25.20 111	16.96 113	40.13 111	26.93 111	32.21 109	21.77 110	19.35 109	16.49 103	7.75 103	17.60 107	46.83 106	36.96 107	1.06 80	8.10 84	2.99 61	3.81 79	1.57 69	31.85 100	5.18 89	6.77 84	5.31 68	3.04 91	9.71 106	29.57 106	23.01 110	40.22 109	35.81 106

ELAS_RVC	19.26 102	19.85 109	14.31 108	33.92 108	22.40 107	33.39 110	19.24 107	15.07 100	10.89 91	8.20 104	17.30 106	54.84 112	32.79 104	3.23 97	8.72 93	3.46 74	11.25 103	6.19 104	19.97 86	7.86 97	12.63 103	12.02 107	9.45 106	10.70 108	30.22 108	22.17 107	41.23 111	38.75 110
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
SAMSARA	19.29 103	2.55 71	5.07 90	4.09 80	3.44 75	3.26 71	4.38 81	15.32 101	9.58 89	4.44 87	5.63 75	8.73 66	24.82 94	3.30 100	10.45 102	6.81 95	5.49 87	3.46 88	21.26 89	4.77 88	17.96 110	24.74 115	0.82 72	4.77 86	80.12 118	91.25 118	77.91 117	76.45 117

MADNet+	19.30 104	7.92 97	15.32 110	17.51 99	11.42 101	8.09 91	18.41 105	28.03 114	17.21 106	12.46 109	12.08 96	33.63 95	30.37 100	9.62 110	18.64 113	14.89 112	25.78 111	10.10 108	40.05 107	16.50 110	17.34 109	12.76 108	9.48 107	18.60 113	35.02 114	20.22 103	21.19 96	38.41 108

ELAS	19.48 105	22.90 110	14.50 109	35.26 110	24.25 110	31.48 108	19.80 109	16.72 104	15.91 102	7.71 102	15.62 105	48.88 110	30.47 101	3.20 96	9.19 96	3.39 72	10.97 101	5.79 100	21.49 90	6.53 93	11.69 101	11.90 106	9.30 104	9.63 105	32.86 109	24.17 111	41.19 110	41.31 111
A. Geiger, M. Roser, R. Urtasun: Efficient large-scale stereo matching. ACCV 2010
MANE	19.83 106	17.14 108	15.43 111	34.46 109	23.74 109	25.27 106	17.85 104	19.50 110	17.04 105	6.13 99	12.40 97	41.03 103	35.23 106	3.36 101	7.87 80	8.98 102	7.09 95	29.77 115	21.81 91	3.71 83	9.51 94	5.54 73	2.50 86	8.83 103	34.12 111	27.95 113	51.25 112	47.85 113

SPS-STEREO	20.13 107	14.92 104	11.88 104	19.84 101	20.39 105	18.24 102	9.27 97	16.06 103	19.74 110	19.91 113	20.06 112	33.66 96	37.68 109	7.23 106	13.17 110	16.66 114	26.75 112	16.01 113	32.30 101	23.65 114	30.25 115	22.84 114	13.67 109	13.47 111	16.56 99	15.26 96	29.55 102	24.45 94
K. Yamaguchi, D. McAllester, R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014
SGM+DAISY	20.91 108	16.10 106	12.43 105	22.81 103	21.80 106	24.45 105	18.63 106	17.89 105	12.77 93	18.42 112	19.55 111	30.39 93	40.48 111	11.67 112	12.56 109	15.10 113	25.73 110	12.69 110	34.93 104	21.56 113	26.12 114	22.15 113	21.16 112	12.73 110	17.44 100	13.30 90	33.35 106	28.26 98

LE_ROB	22.46 109	10.87 102	12.67 106	32.44 107	37.93 112	31.36 107	33.43 112	23.22 112	21.14 113	6.73 101	17.81 108	52.15 111	50.94 116	1.56 89	6.43 71	2.92 58	3.14 69	1.46 66	30.14 98	8.43 98	7.23 87	4.28 61	7.08 101	25.69 114	34.50 113	36.15 114	53.30 113	53.30 114

LSM0	22.48 110	6.09 93	4.65 86	14.89 96	10.39 100	16.43 99	13.29 102	23.38 113	15.69 99	14.75 111	18.69 109	35.79 97	33.70 105	0.93 75	73.52 117	85.94 117	13.36 105	5.62 99	55.67 112	16.83 111	21.95 112	18.82 112	4.28 96	8.01 101	16.44 98	16.43 99	28.65 100	32.66 104

BEATNet-Init1	23.62 111	27.86 112	13.22 107	44.78 112	23.54 108	38.58 112	23.96 111	18.57 107	18.15 108	9.17 105	13.26 99	47.63 107	48.00 115	3.29 99	12.31 108	9.22 103	7.35 96	5.48 98	48.70 110	19.34 112	12.33 102	11.24 105	7.11 102	11.28 109	33.76 110	21.63 105	60.52 115	47.50 112

DispFullNet	26.27 112	39.81 113	17.89 114	52.75 113	38.74 113	45.03 113	55.21 114	13.74 99	12.89 95	13.75 110	9.88 94	43.75 105	32.46 103	86.51 117	11.01 105	13.68 111	29.48 114	16.23 114	51.78 111	14.64 106	13.87 105	10.46 103	14.59 110	6.22 95	8.62 86	8.03 78	16.08 92	32.10 102

PSMNet_ROB	31.36 113	48.75 114	16.32 112	64.48 117	46.97 115	60.77 116	61.30 115	19.02 108	20.70 112	27.57 115	24.90 114	41.43 104	42.18 112	83.15 116	13.95 111	4.06 76	17.90 108	5.94 102	42.51 108	15.37 107	11.21 98	9.54 100	57.95 116	14.27 112	29.64 107	19.74 101	29.38 101	17.81 80

PWCK	38.25 114	63.47 117	23.98 115	73.60 118	41.23 114	60.98 117	51.31 113	54.00 116	35.86 115	26.51 114	39.23 115	62.00 114	46.09 113	26.43 113	14.46 112	9.53 104	30.80 115	12.71 111	83.96 116	28.46 115	23.42 113	13.60 111	47.98 114	30.51 115	34.48 112	27.91 112	31.53 103	38.61 109

DPSimNet_ROB	58.77 115	55.98 115	59.21 117	62.88 116	73.92 116	60.20 115	64.84 117	28.34 115	91.63 118	80.59 117	52.65 116	74.49 116	47.24 114	90.64 118	55.91 116	45.99 116	54.62 116	42.30 116	68.03 114	59.25 116	51.18 116	59.54 117	44.54 113	45.19 116	44.85 115	61.56 115	56.15 114	55.05 115

CC-Net-ROB	70.08 116	57.59 116	33.20 116	55.61 114	76.68 117	84.28 118	62.79 116	83.71 118	83.30 117	76.21 116	74.67 117	74.97 117	61.84 117	62.54 115	41.03 115	41.52 115	86.04 117	88.34 118	89.47 117	90.97 117	75.66 117	54.62 116	53.92 115	50.83 117	74.81 117	79.04 117	83.52 118	94.87 119

MADNet++	76.54 117	72.15 118	83.11 118	61.53 115	81.21 118	58.42 114	69.47 118	68.24 117	78.58 116	85.63 118	92.37 118	80.60 118	85.04 118	36.71 114	81.19 118	89.82 118	88.15 118	69.64 117	82.54 115	91.39 118	92.32 118	93.48 118	65.75 117	90.26 119	59.09 116	74.29 116	74.25 116	61.32 116

MEDIAN_ROB	97.56 118	99.08 119	98.39 119	100.00 119	99.99 119	100.00 121	100.00 121	99.49 121	98.31 119	100.00 121	99.95 121	97.97 121	96.05 119	99.97 119	99.41 119	98.43 119	98.53 119	98.66 119	99.88 120	97.95 119	93.38 119	96.45 119	100.00 118	97.23 120	86.96 119	92.33 119	89.80 119	95.90 120

AVERAGE_ROB	98.95 119	100.00 122	99.79 120	100.00 119	100.00 120	100.00 121	100.00 121	100.00 122	100.00 120	100.00 121	100.00 122	100.00 124	100.00 122	100.00 120	99.77 120	99.85 120	100.00 122	100.00 122	100.00 121	100.00 122	98.12 120	99.51 120	100.00 118	100.00 121	93.43 120	93.75 120	94.35 120	93.16 118

DGTPSM_ROB	99.52 120	99.77 120	100.00 121	100.00 119	100.00 120	100.00 121	100.00 121	99.17 119	100.00 120	99.31 119	99.64 120	91.36 119	99.13 120	100.00 120	99.94 121	100.00 121	99.82 120	99.99 120	99.53 118	99.99 120	99.80 122	99.66 121	100.00 118	100.00 121	100.00 121	100.00 121	100.00 121	100.00 121

DPSMNet_ROB	99.53 121	99.78 121	100.00 121

Low-res two-view benchmark