High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

v7n		99.89 2	99.86 4	99.93 1	99.97 1	99.83 2	99.93 1	99.96 14	99.77 5	99.89 18	99.99 1	99.86 70	99.84 5	99.89 9	99.81 10	99.97 1	99.88 9

v52		99.89 2	99.85 6	99.92 4	99.97 1	99.80 11	99.92 2	99.97 1	99.78 3	99.90 14	99.96 5	99.85 76	99.82 7	99.88 12	99.82 6	99.96 3	99.89 5

V4		99.89 2	99.85 6	99.92 4	99.97 1	99.80 11	99.92 2	99.97 1	99.78 3	99.90 14	99.96 5	99.84 78	99.82 7	99.88 12	99.82 6	99.96 3	99.89 5

SixPastTwentyTwo		99.89 2	99.85 6	99.93 1	99.97 1	99.88 1	99.92 2	99.97 1	99.66 14	99.94 3	99.94 15	99.74 96	99.81 9	99.97 2	99.89 1	99.96 3	99.89 5

v748		99.89 2	99.87 1	99.92 4	99.96 8	99.80 11	99.91 5	99.95 28	99.77 5	99.92 8	99.96 5	99.93 39	99.81 9	99.92 7	99.82 6	99.96 3	99.90 2

LTVRE_ROB	99.39 1	99.90 1	99.87 1	99.93 1	99.97 1	99.82 6	99.91 5	99.92 43	99.75 7	99.93 4	99.89 42	100.00 1	99.87 2	99.93 4	99.82 6	99.96 3	99.90 2
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
anonymousdsp		99.87 8	99.86 4	99.88 15	99.95 11	99.75 23	99.90 7	99.96 14	99.69 10	99.83 54	99.96 5	99.99 3	99.74 26	99.95 3	99.83 3	99.91 24	99.88 9

pmmvs6		99.88 7	99.87 1	99.89 12	99.97 1	99.76 18	99.89 8	99.96 14	99.82 2	99.90 14	99.92 26	99.95 22	99.68 39	99.93 4	99.88 2	99.95 9	99.86 12

TDRefinement		99.81 10	99.76 11	99.86 18	99.83 101	99.53 69	99.89 8	99.91 48	99.73 8	99.88 23	99.83 62	99.96 13	99.76 19	99.91 8	99.81 10	99.86 54	99.59 78

EU-MVSNet		99.76 15	99.74 13	99.78 49	99.82 106	99.81 9	99.88 10	99.87 63	99.31 64	99.75 86	99.91 35	99.76 95	99.78 15	99.84 19	99.74 17	99.56 157	99.81 19

TransMVSNet (Re)		99.72 25	99.59 33	99.88 15	99.95 11	99.76 18	99.88 10	99.94 31	99.58 31	99.92 8	99.90 39	98.55 177	99.65 54	99.89 9	99.76 14	99.95 9	99.70 53

WR-MVS		99.79 11	99.68 16	99.91 8	99.95 11	99.83 2	99.87 12	99.96 14	99.39 57	99.93 4	99.87 50	99.29 156	99.77 17	99.83 20	99.72 20	99.97 1	99.82 16

PEN-MVS		99.77 13	99.65 19	99.91 8	99.95 11	99.80 11	99.86 13	99.97 1	99.08 94	99.89 18	99.69 81	99.68 104	99.84 5	99.81 24	99.64 27	99.95 9	99.81 19

DTE-MVSNet		99.75 17	99.61 27	99.92 4	99.95 11	99.81 9	99.86 13	99.96 14	99.18 81	99.92 8	99.66 84	99.45 138	99.85 3	99.80 25	99.56 33	99.96 3	99.79 25

Gipuma		99.55 68	99.23 94	99.91 8	99.87 52	99.52 75	99.86 13	99.93 35	99.87 1	99.96 2	96.72 226	99.55 122	99.97 1	99.77 30	99.46 53	99.87 48	99.74 41
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PS-CasMVS		99.73 20	99.59 33	99.90 11	99.95 11	99.80 11	99.85 16	99.97 1	98.95 112	99.86 34	99.73 72	99.36 147	99.81 9	99.83 20	99.67 24	99.95 9	99.83 15

FC-MVSNet-test		99.84 9	99.80 9	99.89 12	99.96 8	99.83 2	99.84 17	99.95 28	99.37 58	99.77 76	99.95 10	99.96 13	99.85 3	99.93 4	99.83 3	99.95 9	99.72 48

pm-mvs1		99.77 13	99.69 15	99.86 18	99.94 21	99.68 37	99.84 17	99.93 35	99.59 28	99.87 29	99.92 26	99.21 159	99.65 54	99.88 12	99.77 13	99.93 18	99.78 28

MIMVSNet1		99.79 11	99.75 12	99.84 24	99.89 40	99.83 2	99.84 17	99.89 57	99.31 64	99.93 4	99.92 26	99.97 9	99.68 39	99.89 9	99.64 27	99.82 77	99.66 61

CP-MVSNet		99.68 34	99.51 45	99.89 12	99.95 11	99.76 18	99.83 20	99.96 14	98.83 129	99.84 45	99.65 86	99.09 163	99.80 13	99.78 28	99.62 31	99.95 9	99.82 16

WR-MVS_H		99.73 20	99.61 27	99.88 15	99.95 11	99.82 6	99.83 20	99.96 14	99.01 105	99.84 45	99.71 79	99.41 144	99.74 26	99.77 30	99.70 22	99.95 9	99.82 16

Vis-MVSNet		99.76 15	99.78 10	99.75 63	99.92 27	99.77 17	99.83 20	99.85 82	99.43 50	99.85 41	99.84 60	100.00 1	99.13 137	99.83 20	99.66 25	99.90 27	99.90 2
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH	99.11 4	99.72 25	99.63 22	99.84 24	99.87 52	99.59 53	99.83 20	99.88 61	99.46 47	99.87 29	99.66 84	99.95 22	99.76 19	99.73 35	99.47 51	99.84 62	99.52 109
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		99.70 30	99.67 17	99.74 69	99.94 21	99.71 29	99.82 24	99.91 48	99.14 90	99.53 145	99.70 80	99.88 64	99.33 108	99.88 12	99.61 32	99.94 16	99.77 30

v1240		99.58 55	99.38 76	99.82 30	99.89 40	99.49 85	99.82 24	99.83 103	99.63 19	99.86 34	99.96 5	98.92 171	99.75 21	99.15 134	98.96 118	99.76 94	99.56 93

v1921920		99.59 51	99.40 66	99.82 30	99.88 45	99.45 91	99.81 26	99.83 103	99.65 15	99.86 34	99.95 10	99.29 156	99.75 21	98.98 160	98.86 135	99.78 88	99.59 78

v1192		99.60 49	99.41 63	99.82 30	99.89 40	99.43 97	99.81 26	99.84 95	99.63 19	99.85 41	99.95 10	99.35 150	99.72 34	99.01 154	98.90 126	99.82 77	99.58 88

v11		99.72 25	99.62 25	99.85 21	99.87 52	99.71 29	99.81 26	99.96 14	99.63 19	99.83 54	99.97 4	99.58 118	99.75 21	99.33 87	99.33 63	99.87 48	99.79 25

tfpnnormal		99.74 18	99.63 22	99.86 18	99.93 24	99.75 23	99.80 29	99.89 57	99.31 64	99.88 23	99.43 120	99.66 107	99.77 17	99.80 25	99.71 21	99.92 22	99.76 34

v13		99.73 20	99.63 22	99.85 21	99.87 52	99.71 29	99.80 29	99.96 14	99.62 22	99.83 54	99.93 19	99.66 107	99.75 21	99.41 73	99.26 70	99.89 32	99.80 24

v12		99.72 25	99.61 27	99.85 21	99.86 69	99.70 34	99.79 31	99.96 14	99.61 23	99.83 54	99.93 19	99.61 111	99.74 26	99.38 75	99.22 72	99.89 32	99.79 25

CHOSEN 1792x2688		99.65 38	99.55 39	99.77 53	99.93 24	99.60 49	99.79 31	99.92 43	99.73 8	99.74 92	99.93 19	99.98 4	99.80 13	98.83 188	99.01 108	99.45 171	99.76 34

DeepC-MVS	99.05 5	99.74 18	99.64 20	99.84 24	99.90 35	99.39 105	99.79 31	99.81 122	99.69 10	99.90 14	99.87 50	99.98 4	99.81 9	99.62 51	99.32 65	99.83 73	99.65 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
V14		99.69 32	99.56 38	99.84 24	99.86 69	99.68 37	99.78 34	99.96 14	99.60 27	99.83 54	99.93 19	99.58 118	99.72 34	99.28 102	99.11 93	99.88 37	99.77 30

V9		99.71 29	99.59 33	99.84 24	99.86 69	99.69 36	99.78 34	99.96 14	99.61 23	99.84 45	99.93 19	99.61 111	99.73 30	99.34 85	99.17 78	99.88 37	99.78 28

new-patchmatchnet		98.49 189	97.60 198	99.53 114	99.90 35	99.55 64	99.77 36	99.48 208	99.67 12	99.86 34	99.98 3	99.98 4	99.50 85	96.90 230	91.52 231	98.67 216	95.62 231

v144192		99.58 55	99.39 70	99.80 39	99.87 52	99.44 93	99.77 36	99.84 95	99.64 17	99.86 34	99.93 19	99.35 150	99.72 34	98.92 168	98.82 141	99.74 101	99.66 61

MDA-MVSNet-bldmvs		99.11 141	99.11 123	99.12 187	99.91 32	99.38 110	99.77 36	98.72 233	99.31 64	99.85 41	99.43 120	98.26 186	99.48 93	99.85 18	98.47 174	96.99 228	99.08 180

v1144		99.61 43	99.43 59	99.82 30	99.88 45	99.41 102	99.76 39	99.86 67	99.64 17	99.84 45	99.95 10	99.49 134	99.74 26	99.00 156	98.93 123	99.84 62	99.58 88

v148		99.58 55	99.43 59	99.76 57	99.87 52	99.40 104	99.76 39	99.85 82	99.48 45	99.83 54	99.82 64	99.83 81	99.51 81	99.20 121	98.82 141	99.75 97	99.45 123

v15		99.67 36	99.54 41	99.83 29	99.86 69	99.67 40	99.76 39	99.95 28	99.59 28	99.83 54	99.93 19	99.55 122	99.71 38	99.23 111	99.05 101	99.87 48	99.75 37

no-one		99.73 20	99.70 14	99.76 57	99.77 132	99.58 55	99.76 39	99.90 56	99.08 94	99.86 34	99.90 39	99.98 4	99.66 51	99.98 1	99.73 18	99.59 150	99.67 59

HyFIR lowres test		99.50 72	99.26 89	99.80 39	99.95 11	99.62 44	99.76 39	99.97 1	99.67 12	99.56 142	99.94 15	98.40 181	99.78 15	98.84 187	98.59 169	99.76 94	99.72 48

COLMAP_ROB	99.18 2	99.70 30	99.60 31	99.81 34	99.84 90	99.37 116	99.76 39	99.84 95	99.54 40	99.82 61	99.64 87	99.95 22	99.75 21	99.79 27	99.56 33	99.83 73	99.37 149
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs-eth3d		99.61 43	99.48 48	99.75 63	99.87 52	99.30 137	99.75 45	99.89 57	99.23 71	99.85 41	99.88 48	99.97 9	99.49 89	99.46 64	99.01 108	99.68 116	99.52 109

v7		99.61 43	99.46 55	99.79 46	99.83 101	99.37 116	99.75 45	99.84 95	99.56 36	99.76 79	99.94 15	99.60 115	99.73 30	99.11 140	99.01 108	99.85 58	99.63 69

v10		99.65 38	99.51 45	99.81 34	99.83 101	99.61 47	99.75 45	99.94 31	99.56 36	99.76 79	99.94 15	99.60 115	99.73 30	99.11 140	99.01 108	99.85 58	99.74 41

v2v482		99.56 66	99.35 79	99.81 34	99.87 52	99.35 123	99.75 45	99.85 82	99.56 36	99.87 29	99.95 10	99.44 140	99.66 51	98.91 171	98.76 150	99.86 54	99.45 123

APDe-MVS		99.60 49	99.48 48	99.73 71	99.85 82	99.51 82	99.75 45	99.85 82	99.17 82	99.81 64	99.56 99	99.94 33	99.44 97	99.42 72	99.22 72	99.67 118	99.54 99

N_pmnet		98.64 184	98.23 189	99.11 190	99.78 125	99.25 148	99.75 45	99.39 219	99.65 15	99.70 109	99.78 69	99.89 59	98.81 165	97.60 223	94.28 226	97.24 227	97.15 226

MDTV_nov1_ep13_2view		98.73 179	98.31 183	99.22 176	99.75 141	99.24 153	99.75 45	99.93 35	99.31 64	99.84 45	99.86 56	99.81 84	99.31 113	97.40 227	94.77 225	96.73 230	97.81 219

ACMH+	98.94 6	99.69 32	99.59 33	99.81 34	99.88 45	99.41 102	99.75 45	99.86 67	99.43 50	99.80 66	99.54 102	99.97 9	99.73 30	99.82 23	99.52 45	99.85 58	99.43 130

our_test_3					99.75 141	99.11 175	99.74 53

v1141		99.58 55	99.39 70	99.80 39	99.87 52	99.39 105	99.74 53	99.85 82	99.58 31	99.84 45	99.92 26	99.49 134	99.68 39	98.98 160	98.83 138	99.84 62	99.52 109

divwei89l23v2f112		99.58 55	99.39 70	99.80 39	99.87 52	99.39 105	99.74 53	99.85 82	99.57 34	99.84 45	99.92 26	99.48 136	99.67 43	98.98 160	98.83 138	99.84 62	99.52 109

v1		99.58 55	99.39 70	99.80 39	99.87 52	99.39 105	99.74 53	99.85 82	99.58 31	99.84 45	99.92 26	99.51 129	99.67 43	98.98 160	98.82 141	99.84 62	99.52 109

HSP-MVS		99.27 122	99.07 128	99.50 122	99.76 137	99.54 67	99.73 57	99.72 163	98.94 114	99.23 194	98.96 165	99.96 13	98.91 158	98.72 197	97.71 210	99.63 134	99.66 61

TSAR-MVS + MP.		99.56 66	99.54 41	99.58 103	99.69 165	99.14 167	99.73 57	99.45 211	99.50 41	99.35 187	99.60 95	99.93 39	99.50 85	99.56 54	99.37 62	99.77 92	99.64 68

v17		99.62 41	99.48 48	99.79 46	99.80 111	99.60 49	99.73 57	99.94 31	99.46 47	99.73 98	99.88 48	99.52 127	99.67 43	99.16 133	98.96 118	99.84 62	99.75 37

v8		99.61 43	99.45 56	99.79 46	99.80 111	99.59 53	99.73 57	99.93 35	99.48 45	99.77 76	99.90 39	99.48 136	99.67 43	99.11 140	98.89 127	99.84 62	99.73 44

V42		99.57 62	99.41 63	99.75 63	99.84 90	99.37 116	99.73 57	99.83 103	99.41 53	99.75 86	99.89 42	99.42 142	99.60 64	99.15 134	98.96 118	99.76 94	99.65 65

v1neww		99.57 62	99.40 66	99.77 53	99.80 111	99.34 126	99.72 62	99.82 111	99.49 42	99.76 79	99.89 42	99.50 131	99.67 43	99.10 148	98.89 127	99.84 62	99.59 78

v7new		99.57 62	99.40 66	99.77 53	99.80 111	99.34 126	99.72 62	99.82 111	99.49 42	99.76 79	99.89 42	99.50 131	99.67 43	99.10 148	98.89 127	99.84 62	99.59 78

XVS					99.86 69	99.30 137	99.72 62			99.69 111		99.93 39				99.60 144

X-MVStestdata					99.86 69	99.30 137	99.72 62			99.69 111		99.93 39				99.60 144

v16		99.61 43	99.47 52	99.78 49	99.79 119	99.60 49	99.72 62	99.94 31	99.45 49	99.70 109	99.85 57	99.54 125	99.67 43	99.15 134	98.96 118	99.83 73	99.76 34

v6		99.57 62	99.40 66	99.77 53	99.80 111	99.34 126	99.72 62	99.82 111	99.49 42	99.76 79	99.89 42	99.52 127	99.67 43	99.10 148	98.89 127	99.84 62	99.59 78

LGP-MVS_train		99.46 83	99.18 109	99.78 49	99.87 52	99.25 148	99.71 68	99.87 63	98.02 198	99.79 69	98.90 170	99.96 13	99.66 51	99.49 58	99.17 78	99.79 87	99.49 115

v18		99.59 51	99.44 58	99.76 57	99.78 125	99.57 57	99.70 69	99.93 35	99.43 50	99.69 111	99.85 57	99.51 129	99.65 54	99.08 151	98.87 132	99.82 77	99.74 41

gm-plane-assit		96.82 221	94.84 228	99.13 185	99.95 11	99.78 16	99.69 70	99.92 43	99.19 79	99.84 45	99.92 26	72.93 246	96.44 225	98.21 215	97.01 219	98.92 210	96.87 227

thisisatest0515		99.73 20	99.67 17	99.81 34	99.93 24	99.74 25	99.68 71	99.91 48	99.59 28	99.88 23	99.73 72	99.81 84	99.55 71	99.59 52	99.53 43	99.89 32	99.70 53

test20.03		99.68 34	99.60 31	99.76 57	99.91 32	99.70 34	99.68 71	99.87 63	99.05 101	99.88 23	99.92 26	99.88 64	99.50 85	99.77 30	99.42 58	99.75 97	99.49 115

ACMM	98.37 12	99.47 79	99.23 94	99.74 69	99.86 69	99.19 161	99.68 71	99.86 67	99.16 86	99.71 107	98.52 189	99.95 22	99.62 61	99.35 82	99.02 106	99.74 101	99.42 134
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
conf0.05thres1000		98.36 194	97.28 204	99.63 93	99.92 27	99.74 25	99.66 74	99.88 61	98.68 143	98.92 211	97.30 223	86.02 238	99.49 89	99.77 30	99.73 18	99.93 18	99.69 55

EPP-MVSNet		99.34 107	99.10 124	99.62 98	99.94 21	99.74 25	99.66 74	99.80 128	99.07 97	98.93 210	99.61 92	96.13 198	99.49 89	99.67 42	99.63 29	99.92 22	99.86 12

SteuartSystems-ACMMP		99.47 79	99.22 97	99.76 57	99.88 45	99.36 119	99.65 76	99.84 95	98.47 167	99.80 66	98.68 181	99.96 13	99.68 39	99.37 77	99.06 98	99.72 109	99.66 61
Skip Steuart: Steuart Systems R&D Blog.
Anonymous20240521		99.43 86	99.23 94	99.67 83	99.92 27	99.76 18	99.64 77	99.93 35	99.06 99	99.68 118	97.77 211	98.97 168	98.97 154	99.72 36	99.54 41	99.88 37	99.81 19

TranMVSNet+NR-MVSNet		99.59 51	99.42 62	99.80 39	99.87 52	99.55 64	99.64 77	99.86 67	99.05 101	99.88 23	99.72 76	99.33 153	99.64 57	99.47 62	99.14 83	99.91 24	99.67 59

ACMP	98.32 13	99.44 85	99.18 109	99.75 63	99.83 101	99.18 162	99.64 77	99.83 103	98.81 131	99.79 69	98.42 195	99.96 13	99.64 57	99.46 64	98.98 115	99.74 101	99.44 126
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tfpn_n400		99.08 145	98.56 170	99.70 76	99.85 82	99.56 62	99.63 80	99.86 67	99.21 74	99.37 181	98.95 166	94.24 204	99.55 71	99.20 121	99.29 67	99.93 18	99.44 126

tfpnconf		99.08 145	98.56 170	99.70 76	99.85 82	99.56 62	99.63 80	99.86 67	99.21 74	99.37 181	98.95 166	94.24 204	99.55 71	99.20 121	99.29 67	99.93 18	99.44 126

Anonymous20231206		99.48 76	99.31 83	99.69 80	99.79 119	99.57 57	99.63 80	99.79 131	98.88 121	99.91 12	99.72 76	99.93 39	99.59 65	99.24 108	98.63 165	99.43 176	99.18 168

ACMMP		99.36 102	99.06 129	99.71 73	99.86 69	99.36 119	99.63 80	99.85 82	98.33 182	99.72 102	97.73 213	99.94 33	99.53 77	99.37 77	99.13 89	99.65 122	99.56 93
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
1111		96.83 220	95.02 227	98.95 200	99.90 35	99.57 57	99.62 84	99.97 1	98.58 159	98.06 239	99.87 50	69.04 249	96.43 226	99.36 80	99.14 83	99.73 105	99.54 99

.test1245		79.44 239	75.07 242	84.53 241	99.90 35	99.57 57	99.62 84	99.97 1	98.58 159	98.06 239	99.87 50	69.04 249	96.43 226	99.36 80	24.74 243	13.21 247	34.30 243

DI_MVS_plusplus_trai		98.74 176	98.08 194	99.51 120	99.79 119	99.29 141	99.61 86	99.60 188	99.20 76	99.46 163	99.09 157	92.93 211	98.97 154	98.27 213	98.35 182	99.65 122	99.45 123

ACMMPR		99.51 70	99.32 82	99.72 72	99.87 52	99.33 130	99.61 86	99.85 82	99.19 79	99.73 98	98.73 178	99.95 22	99.61 62	99.35 82	99.14 83	99.66 120	99.58 88

PVSNet_Blended_VisFu		99.66 37	99.64 20	99.67 83	99.91 32	99.71 29	99.61 86	99.79 131	99.41 53	99.91 12	99.85 57	99.61 111	99.00 147	99.67 42	99.42 58	99.81 81	99.81 19

UGNet		99.40 95	99.61 27	99.16 183	99.88 45	99.64 43	99.61 86	99.77 143	99.31 64	99.63 127	99.33 131	99.93 39	96.46 224	99.63 48	99.53 43	99.63 134	99.89 5
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
FMVSNet1		99.50 72	99.57 37	99.42 136	99.67 173	99.65 42	99.60 90	99.91 48	99.40 55	99.39 177	99.83 62	99.27 158	98.14 188	99.68 39	99.50 46	99.81 81	99.68 56

MP-MVS		99.35 105	99.09 126	99.65 87	99.84 90	99.22 156	99.59 91	99.78 137	98.13 191	99.67 120	98.44 193	99.93 39	99.43 99	99.31 91	99.09 95	99.60 144	99.49 115
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		99.32 112	98.99 139	99.71 73	99.86 69	99.31 135	99.59 91	99.86 67	97.51 213	99.75 86	98.23 199	99.94 33	99.53 77	99.29 97	99.08 96	99.65 122	99.54 99

CDPH-MVS		99.05 151	98.63 166	99.54 113	99.75 141	98.78 197	99.59 91	99.68 176	97.79 206	99.37 181	98.20 202	99.86 70	99.14 135	98.58 202	98.01 202	99.68 116	99.16 174

Baseline_NR-MVSNet		99.62 41	99.48 48	99.78 49	99.85 82	99.76 18	99.59 91	99.82 111	98.84 127	99.88 23	99.91 35	99.04 165	99.61 62	99.46 64	99.78 12	99.94 16	99.60 76

pmmvs5		99.58 55	99.47 52	99.70 76	99.84 90	99.50 83	99.58 95	99.80 128	98.98 110	99.73 98	99.92 26	99.81 84	99.49 89	99.28 102	99.05 101	99.77 92	99.73 44

IterMVS-LS		99.16 135	98.82 159	99.57 107	99.87 52	99.71 29	99.58 95	99.92 43	99.24 70	99.71 107	99.73 72	95.79 199	98.91 158	98.82 189	98.66 161	99.43 176	99.77 30
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		98.11 196	97.12 205	99.26 169	99.65 179	98.34 223	99.57 97	99.97 1	97.48 215	99.43 167	99.04 162	90.84 222	98.15 186	98.04 217	97.78 205	98.82 213	98.30 209

tfpnview11		99.04 153	98.49 178	99.68 81	99.84 90	99.58 55	99.56 98	99.86 67	98.86 123	99.37 181	98.95 166	94.24 204	99.54 76	98.87 177	99.54 41	99.91 24	99.39 144

ACMMP_Plus		99.47 79	99.33 81	99.63 93	99.85 82	99.28 142	99.56 98	99.83 103	98.75 135	99.48 159	99.03 163	99.95 22	99.47 96	99.48 59	99.19 75	99.57 154	99.59 78

CHOSEN 280x420		98.99 158	98.91 147	99.07 194	99.77 132	99.26 145	99.55 100	99.92 43	98.62 153	98.67 222	99.62 91	97.20 196	98.44 179	99.50 57	99.18 76	98.08 222	98.99 192

NR-MVSNet		99.52 69	99.29 85	99.80 39	99.96 8	99.38 110	99.55 100	99.81 122	98.86 123	99.87 29	99.51 113	98.81 173	99.72 34	99.86 17	99.04 104	99.89 32	99.54 99

IS_MVSNet		99.15 137	99.12 121	99.19 181	99.92 27	99.73 28	99.55 100	99.86 67	98.45 172	96.91 245	98.74 177	98.33 184	99.02 146	99.54 56	99.47 51	99.88 37	99.61 75

LS3D		99.39 97	99.28 87	99.52 118	99.77 132	99.39 105	99.55 100	99.82 111	98.93 116	99.64 125	98.52 189	99.67 106	98.58 175	99.74 34	99.63 29	99.75 97	99.06 183

zzz-MVS		99.51 70	99.36 77	99.68 81	99.88 45	99.38 110	99.53 104	99.84 95	99.11 93	99.59 136	98.93 169	99.95 22	99.58 68	99.44 70	99.21 74	99.65 122	99.52 109

PM-MVS		99.49 75	99.43 59	99.57 107	99.76 137	99.34 126	99.53 104	99.77 143	98.93 116	99.75 86	99.46 117	99.83 81	99.11 139	99.72 36	99.29 67	99.49 167	99.46 122

DeepC-MVS_fast	98.69 9	99.32 112	99.13 119	99.53 114	99.63 181	98.78 197	99.53 104	99.33 223	99.08 94	99.77 76	99.18 147	99.89 59	99.29 116	99.00 156	98.70 157	99.65 122	99.30 157
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
IterMVS		99.08 145	98.90 148	99.29 161	99.87 52	99.53 69	99.52 107	99.77 143	98.94 114	99.75 86	99.91 35	97.52 194	98.72 170	98.86 181	98.14 193	98.09 221	99.43 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CSCG		99.61 43	99.52 44	99.71 73	99.89 40	99.62 44	99.52 107	99.76 151	99.61 23	99.69 111	99.73 72	99.96 13	99.57 69	99.27 105	98.62 166	99.81 81	99.85 14

3Dnovator+	98.92 7	99.31 115	99.03 134	99.63 93	99.77 132	98.90 188	99.52 107	99.81 122	99.37 58	99.72 102	98.03 207	99.73 99	99.32 111	98.99 159	98.81 146	99.67 118	99.36 150

OPM-MVS		99.39 97	99.22 97	99.59 101	99.76 137	98.82 194	99.51 110	99.79 131	99.17 82	99.53 145	99.31 135	99.95 22	99.35 103	99.22 114	98.79 149	99.60 144	99.27 161

pmmvs4		99.34 107	99.15 116	99.57 107	99.77 132	98.90 188	99.51 110	99.77 143	99.07 97	99.73 98	99.72 76	99.84 78	99.07 141	98.85 183	98.39 180	99.55 161	99.27 161

UniMVSNet (Re)		99.50 72	99.29 85	99.75 63	99.86 69	99.47 88	99.51 110	99.82 111	98.90 119	99.89 18	99.64 87	99.00 166	99.55 71	99.32 89	99.08 96	99.90 27	99.59 78

QAPM		99.41 91	99.21 101	99.64 92	99.78 125	99.16 164	99.51 110	99.85 82	99.20 76	99.72 102	99.43 120	99.81 84	99.25 120	98.87 177	98.71 155	99.71 112	99.30 157

Anonymous202405211			99.14 117		99.87 52	99.55 64	99.50 114	99.70 168	98.55 163		98.61 184	98.46 178	98.76 168	99.66 44	99.50 46	99.85 58	99.63 69

Anonymous20231211		99.47 79	99.39 70	99.57 107	99.89 40	99.60 49	99.50 114	99.69 171	98.91 118	99.62 128	99.17 148	99.35 150	98.86 163	99.63 48	99.46 53	99.84 62	99.62 73

HFP-MVS		99.46 83	99.30 84	99.65 87	99.82 106	99.25 148	99.50 114	99.82 111	99.23 71	99.58 140	98.86 171	99.94 33	99.56 70	99.14 137	99.12 92	99.63 134	99.56 93

PMMVS2		99.23 125	99.22 97	99.24 170	99.80 111	99.14 167	99.50 114	99.82 111	99.12 92	98.41 234	99.91 35	99.98 4	98.51 176	99.48 59	98.76 150	99.38 182	98.14 214

UniMVSNet_NR-MVSNet		99.41 91	99.12 121	99.76 57	99.86 69	99.48 86	99.50 114	99.81 122	98.84 127	99.89 18	99.45 118	98.32 185	99.59 65	99.22 114	98.89 127	99.90 27	99.63 69

DU-MVS		99.48 76	99.26 89	99.75 63	99.85 82	99.38 110	99.50 114	99.81 122	98.86 123	99.89 18	99.51 113	98.98 167	99.59 65	99.46 64	98.97 116	99.87 48	99.63 69

3Dnovator	99.16 3	99.42 89	99.22 97	99.65 87	99.78 125	99.13 170	99.50 114	99.85 82	99.40 55	99.80 66	98.59 185	99.79 92	99.30 115	99.20 121	99.06 98	99.71 112	99.35 152

X-MVS		99.30 117	98.99 139	99.66 85	99.85 82	99.30 137	99.49 121	99.82 111	98.32 183	99.69 111	97.31 222	99.93 39	99.50 85	99.37 77	99.16 80	99.60 144	99.53 104

LP		97.43 217	96.28 220	98.77 210	99.69 165	98.92 187	99.49 121	99.70 168	98.53 164	99.82 61	99.12 153	95.67 201	97.30 209	94.65 234	91.76 229	96.65 232	95.34 233

tfpn1000		98.73 179	98.07 195	99.50 122	99.84 90	99.61 47	99.48 123	99.84 95	98.71 141	98.74 218	98.71 180	91.70 220	99.17 125	98.81 190	99.55 39	99.90 27	99.43 130

CDS-MVSNet		99.15 137	99.10 124	99.21 178	99.59 195	99.22 156	99.48 123	99.47 209	98.89 120	99.41 173	99.84 60	98.11 188	97.76 200	99.26 107	99.01 108	99.57 154	99.38 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
USDC		99.29 121	98.98 141	99.65 87	99.72 155	98.87 192	99.47 125	99.66 182	99.35 61	99.87 29	99.58 97	99.87 69	99.51 81	98.85 183	97.93 204	99.65 122	98.38 206

TinyColmap		99.21 127	98.89 149	99.59 101	99.61 187	98.61 210	99.47 125	99.67 178	99.02 104	99.82 61	99.15 149	99.74 96	99.35 103	99.17 131	98.33 184	99.63 134	98.22 212

ESAPD		99.41 91	99.36 77	99.47 128	99.66 174	99.48 86	99.46 127	99.75 159	98.65 146	99.41 173	99.67 82	99.95 22	98.82 164	99.21 117	99.14 83	99.72 109	99.40 142

tfpn		96.77 223	94.47 230	99.45 131	99.88 45	99.62 44	99.46 127	99.83 103	97.61 211	98.27 237	94.22 234	71.45 248	99.34 107	99.32 89	99.46 53	99.90 27	99.58 88

Fast-Effi-MVS+		99.39 97	99.18 109	99.63 93	99.86 69	99.28 142	99.45 129	99.91 48	98.47 167	99.61 130	99.50 115	99.57 120	99.17 125	99.24 108	98.66 161	99.78 88	99.59 78

HPM-MVS++		99.23 125	98.98 141	99.53 114	99.75 141	99.02 181	99.44 130	99.77 143	98.65 146	99.52 151	98.72 179	99.92 48	99.33 108	98.77 195	98.40 179	99.40 180	99.36 150

CP-MVS		99.41 91	99.20 102	99.65 87	99.80 111	99.23 155	99.44 130	99.75 159	98.60 157	99.74 92	98.66 182	99.93 39	99.48 93	99.33 87	99.16 80	99.73 105	99.48 118

view800		97.89 201	96.56 211	99.45 131	99.86 69	99.57 57	99.42 132	99.80 128	97.50 214	98.40 235	93.78 235	86.63 237	99.31 113	99.24 108	99.68 23	99.89 32	99.54 99

CVMVSNet		99.06 150	98.88 152	99.28 166	99.52 202	99.53 69	99.42 132	99.69 171	98.74 136	98.27 237	99.89 42	95.48 203	99.44 97	99.46 64	99.33 63	99.32 190	99.75 37

DELS-MVS		99.42 89	99.53 43	99.29 161	99.52 202	99.43 97	99.42 132	99.28 225	99.16 86	99.72 102	99.82 64	99.97 9	98.17 185	99.56 54	99.16 80	99.65 122	99.59 78
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CANet		99.36 102	99.39 70	99.34 157	99.80 111	99.35 123	99.41 135	99.47 209	99.20 76	99.74 92	99.54 102	99.68 104	98.05 194	99.23 111	98.97 116	99.57 154	99.73 44

thresconf0.02		98.10 197	96.83 208	99.58 103	99.71 158	99.28 142	99.40 136	99.72 163	98.65 146	99.39 177	98.23 199	86.73 236	99.43 99	97.73 222	98.17 191	99.86 54	99.05 185

MVS_0304		99.36 102	99.35 79	99.37 149	99.85 82	99.36 119	99.39 137	99.56 195	99.36 60	99.75 86	99.23 141	99.90 56	97.97 197	99.00 156	98.83 138	99.69 115	99.77 30

EG-PatchMatch MVS		99.59 51	99.49 47	99.70 76	99.82 106	99.26 145	99.39 137	99.83 103	98.99 107	99.93 4	99.54 102	99.92 48	99.51 81	99.78 28	99.50 46	99.73 105	99.41 135

MIMVSNet		99.00 156	99.03 134	98.97 199	99.32 228	99.32 134	99.39 137	99.91 48	98.41 177	98.76 217	99.24 139	99.17 161	97.13 211	99.30 92	98.80 147	99.29 191	99.01 189

PMVS	94.32 17	99.27 122	99.55 39	98.94 201	99.60 191	99.43 97	99.39 137	99.54 198	98.99 107	99.69 111	99.60 95	99.81 84	95.68 232	99.88 12	99.83 3	99.73 105	99.31 155
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
view600		97.88 202	96.55 213	99.44 134	99.84 90	99.52 75	99.38 141	99.76 151	97.36 217	98.50 229	93.29 236	87.31 233	99.26 119	99.13 138	99.76 14	99.88 37	99.48 118

test1235678		99.39 97	99.20 102	99.62 98	99.84 90	99.38 110	99.38 141	99.86 67	98.47 167	99.79 69	99.82 64	99.41 144	99.63 59	99.30 92	98.71 155	99.21 199	99.28 159

pmmvs3		98.85 170	98.60 167	99.13 185	99.66 174	98.72 203	99.37 143	99.06 230	98.44 173	99.76 79	99.74 70	99.55 122	99.15 133	99.04 152	96.00 224	97.80 223	98.72 198

testmv		99.39 97	99.19 106	99.62 98	99.84 90	99.38 110	99.37 143	99.86 67	98.47 167	99.79 69	99.82 64	99.39 146	99.63 59	99.30 92	98.70 157	99.21 199	99.28 159

thres600view7		97.86 204	96.53 217	99.41 139	99.84 90	99.52 75	99.36 145	99.76 151	97.32 218	98.38 236	93.24 237	87.25 234	99.23 122	99.11 140	99.75 16	99.88 37	99.48 118

OpenMVS	98.82 8	99.17 132	98.85 155	99.53 114	99.75 141	99.06 178	99.36 145	99.82 111	98.28 185	99.76 79	98.47 191	99.61 111	98.91 158	98.80 191	98.70 157	99.60 144	99.04 188

MSDG		99.32 112	99.09 126	99.58 103	99.75 141	98.74 201	99.36 145	99.54 198	99.14 90	99.72 102	99.24 139	99.89 59	99.51 81	99.30 92	98.76 150	99.62 140	98.54 202

CLD-MVS		99.30 117	99.01 138	99.63 93	99.75 141	98.89 191	99.35 148	99.60 188	98.53 164	99.86 34	99.57 98	99.94 33	99.52 80	98.96 164	98.10 197	99.70 114	99.08 180
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
APD-MVS		99.17 132	98.92 145	99.46 129	99.78 125	99.24 153	99.34 149	99.78 137	97.79 206	99.48 159	98.25 198	99.88 64	98.77 167	99.18 129	98.92 124	99.63 134	99.18 168
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TAMVS		99.05 151	99.02 137	99.08 193	99.69 165	99.22 156	99.33 150	99.32 224	99.16 86	98.97 208	99.87 50	97.36 195	97.76 200	99.21 117	99.00 113	99.44 173	99.33 153

ADS-MVSNet		97.29 219	96.17 222	98.59 215	99.59 195	98.70 205	99.32 151	99.86 67	98.47 167	99.56 142	99.08 158	98.16 187	97.34 208	92.92 235	91.17 232	95.91 234	94.72 235

EPMVS		96.76 224	95.30 226	98.46 220	99.42 219	98.47 217	99.32 151	99.91 48	98.42 176	99.51 155	99.07 160	92.81 215	97.12 212	92.39 238	91.71 230	95.51 236	94.20 237

train_agg		98.89 167	98.48 179	99.38 145	99.69 165	98.76 200	99.31 153	99.60 188	97.71 208	98.98 207	97.89 209	99.89 59	99.29 116	98.32 208	97.59 213	99.42 179	99.16 174

GBi-Net		98.96 160	99.05 130	98.85 207	99.02 239	99.53 69	99.31 153	99.78 137	98.13 191	98.48 230	99.43 120	97.58 191	96.92 218	99.68 39	99.50 46	99.61 141	99.53 104

test1		98.96 160	99.05 130	98.85 207	99.02 239	99.53 69	99.31 153	99.78 137	98.13 191	98.48 230	99.43 120	97.58 191	96.92 218	99.68 39	99.50 46	99.61 141	99.53 104

FMVSNet2		99.07 149	99.19 106	98.93 203	99.02 239	99.53 69	99.31 153	99.84 95	98.86 123	98.88 213	99.64 87	98.44 180	96.92 218	99.35 82	99.00 113	99.61 141	99.53 104

Vis-MVSNet (Re-imp)		99.40 95	99.28 87	99.55 112	99.92 27	99.68 37	99.31 153	99.87 63	98.69 142	99.16 198	99.08 158	98.64 176	99.20 124	99.65 46	99.46 53	99.83 73	99.72 48

testgi		99.43 86	99.47 52	99.38 145	99.90 35	99.67 40	99.30 158	99.73 161	98.64 152	99.53 145	99.52 111	99.90 56	98.08 191	99.65 46	99.40 61	99.75 97	99.55 98

CPTT-MVS		99.21 127	98.89 149	99.58 103	99.72 155	99.12 173	99.30 158	99.76 151	98.62 153	99.66 123	97.51 216	99.89 59	99.48 93	99.01 154	98.64 163	99.58 152	99.40 142

tfpn_ndepth		98.67 183	98.03 196	99.42 136	99.65 179	99.50 83	99.29 160	99.78 137	98.17 190	99.04 204	98.36 196	93.29 209	98.88 161	98.46 206	99.26 70	99.88 37	99.14 177

thres400		97.82 207	96.47 218	99.40 140	99.81 110	99.44 93	99.29 160	99.69 171	97.15 221	98.57 226	92.82 242	87.96 231	99.16 129	98.96 164	99.55 39	99.86 54	99.41 135

MVS_111021_HR		99.30 117	99.14 117	99.48 126	99.58 198	99.25 148	99.27 162	99.61 186	98.74 136	99.66 123	99.02 164	99.84 78	99.33 108	99.20 121	98.76 150	99.44 173	99.18 168

PHI-MVS		99.33 109	99.19 106	99.49 125	99.69 165	99.25 148	99.27 162	99.59 192	98.44 173	99.78 75	99.15 149	99.92 48	98.95 157	99.39 74	99.04 104	99.64 132	99.18 168

MDTV_nov1_ep13		97.41 218	96.26 221	98.76 211	99.47 212	98.43 219	99.26 164	99.82 111	98.06 197	99.23 194	99.22 142	92.86 214	98.05 194	95.33 233	93.66 228	96.73 230	96.26 228

MCST-MVS		99.17 132	98.82 159	99.57 107	99.75 141	98.70 205	99.25 165	99.69 171	98.62 153	99.59 136	98.54 187	99.79 92	99.53 77	98.48 205	98.15 192	99.64 132	99.43 130

MVS_111021_LR		99.25 124	99.13 119	99.39 141	99.50 209	99.14 167	99.23 166	99.50 206	98.67 144	99.61 130	99.12 153	99.81 84	99.16 129	99.28 102	98.67 160	99.35 186	99.21 166

PatchmatchNet		96.81 222	95.41 224	98.43 221	99.43 218	98.30 224	99.23 166	99.93 35	98.19 188	99.64 125	98.81 175	93.50 208	97.43 207	92.89 237	90.78 234	94.94 240	95.41 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TSAR-MVS + ACMM		99.31 115	99.26 89	99.37 149	99.66 174	98.97 185	99.20 168	99.56 195	99.33 62	99.19 197	99.54 102	99.91 54	99.32 111	99.12 139	98.34 183	99.29 191	99.65 65

OMC-MVS		99.11 141	98.95 143	99.29 161	99.37 223	98.57 212	99.19 169	99.20 227	98.87 122	99.58 140	99.13 151	99.88 64	99.00 147	99.19 126	98.46 175	99.43 176	98.57 200

CANet_DTU		99.03 154	99.18 109	98.87 206	99.58 198	99.03 179	99.18 170	99.41 215	98.65 146	99.74 92	99.55 101	99.71 101	96.13 230	99.19 126	98.92 124	99.17 203	99.18 168

casdiffmvs1		99.33 109	99.20 102	99.48 126	99.75 141	99.35 123	99.18 170	99.86 67	99.16 86	99.67 120	99.64 87	99.07 164	98.78 166	98.71 198	98.64 163	99.65 122	99.81 19

tfpn111		98.25 195	97.29 203	99.37 149	99.74 150	99.52 75	99.17 172	99.76 151	96.10 240	98.65 224	98.23 199	89.10 226	99.00 147	99.11 140	99.56 33	99.88 37	99.41 135

conf0.01		96.70 226	94.44 232	99.34 157	99.71 158	99.46 89	99.17 172	99.73 161	96.10 240	98.53 227	91.96 243	75.75 244	99.00 147	98.85 183	99.56 33	99.87 48	99.38 145

conf0.002		96.39 229	93.87 234	99.33 159	99.70 163	99.45 91	99.17 172	99.71 166	96.10 240	98.51 228	91.88 244	72.65 247	99.00 147	98.80 191	98.82 141	99.87 48	99.38 145

conf200view11		97.85 205	96.54 214	99.37 149	99.74 150	99.52 75	99.17 172	99.76 151	96.10 240	98.65 224	92.99 238	89.10 226	99.00 147	99.11 140	99.56 33	99.88 37	99.41 135

tfpn200view9		97.85 205	96.54 214	99.38 145	99.74 150	99.52 75	99.17 172	99.76 151	96.10 240	98.70 220	92.99 238	89.10 226	99.00 147	99.11 140	99.56 33	99.88 37	99.41 135

CNVR-MVS		99.08 145	98.83 156	99.37 149	99.61 187	98.74 201	99.15 177	99.54 198	98.59 158	99.37 181	98.15 203	99.88 64	99.08 140	98.91 171	98.46 175	99.48 168	99.06 183

new_pmnet		98.91 166	98.89 149	98.94 201	99.51 207	98.27 226	99.15 177	98.66 234	99.17 82	99.48 159	99.79 68	99.80 90	98.49 178	99.23 111	98.20 189	98.34 219	97.74 222

UA-Net		99.64 40	99.62 25	99.66 85	99.97 1	99.82 6	99.14 179	99.96 14	98.95 112	99.52 151	99.38 128	99.86 70	99.55 71	99.72 36	99.66 25	99.80 84	99.94 1

thres200		97.87 203	96.56 211	99.39 141	99.76 137	99.52 75	99.13 180	99.76 151	96.88 232	98.66 223	92.87 241	88.77 230	99.16 129	99.11 140	99.42 58	99.88 37	99.33 153

RPSCF		99.48 76	99.45 56	99.52 118	99.73 154	99.33 130	99.13 180	99.77 143	99.33 62	99.47 162	99.39 127	99.92 48	99.36 102	99.63 48	99.13 89	99.63 134	99.41 135

AdaColmap		98.93 164	98.53 173	99.39 141	99.52 202	98.65 208	99.11 182	99.59 192	98.08 195	99.44 165	97.46 219	99.45 138	99.24 121	98.92 168	98.44 178	99.44 173	98.73 196

test12356		99.12 140	99.03 134	99.23 171	99.78 125	98.95 186	99.10 183	99.72 163	98.26 186	99.81 64	99.87 50	99.20 160	98.06 192	99.47 62	98.80 147	98.91 211	98.67 199

NCCC		98.88 168	98.42 180	99.42 136	99.62 182	98.81 195	99.10 183	99.54 198	98.76 133	99.53 145	95.97 229	99.80 90	99.16 129	98.49 204	98.06 200	99.55 161	99.05 185

thisisatest0530		98.78 174	98.26 184	99.39 141	99.78 125	99.43 97	99.07 185	99.64 184	98.44 173	99.42 171	99.22 142	92.68 217	98.63 173	99.30 92	99.14 83	99.80 84	99.60 76

tttt0517		98.77 175	98.25 186	99.38 145	99.79 119	99.46 89	99.07 185	99.64 184	98.40 180	99.38 179	99.21 145	92.54 218	98.63 173	99.34 85	99.14 83	99.80 84	99.62 73

FPMVS		98.48 190	98.83 156	98.07 230	99.09 237	97.98 236	99.07 185	98.04 242	98.99 107	99.22 196	98.85 172	99.43 141	93.79 239	99.66 44	99.11 93	99.24 196	97.76 220

TSAR-MVS + COLMAP		98.74 176	98.58 169	98.93 203	99.29 230	98.23 227	99.04 188	99.24 226	98.79 132	98.80 216	99.37 129	99.71 101	98.06 192	98.02 219	97.46 215	99.16 204	98.48 204

MVS_Test		99.09 143	98.92 145	99.29 161	99.61 187	99.07 177	99.04 188	99.81 122	98.58 159	99.37 181	99.74 70	98.87 172	98.41 180	98.61 201	98.01 202	99.50 166	99.57 92

PVSNet_BlendedMVS		99.20 129	99.17 113	99.23 171	99.69 165	99.33 130	99.04 188	99.13 228	98.41 177	99.79 69	99.33 131	99.36 147	98.10 189	99.29 97	98.87 132	99.65 122	99.56 93

PVSNet_Blended		99.20 129	99.17 113	99.23 171	99.69 165	99.33 130	99.04 188	99.13 228	98.41 177	99.79 69	99.33 131	99.36 147	98.10 189	99.29 97	98.87 132	99.65 122	99.56 93

Effi-MVS+-dtu		99.01 155	99.05 130	98.98 197	99.60 191	99.13 170	99.03 192	99.61 186	98.52 166	99.01 205	98.53 188	99.83 81	96.95 217	99.48 59	98.59 169	99.66 120	99.25 165

SMA-MVS		99.43 86	99.41 63	99.45 131	99.82 106	99.31 135	99.02 193	99.59 192	99.06 99	99.34 190	99.53 109	99.96 13	99.38 101	99.29 97	99.13 89	99.53 163	99.59 78

v1.0		91.57 238	84.95 241	99.29 161	99.79 119	99.44 93	99.02 193	99.79 131	97.96 201	99.12 202	99.22 142	99.95 22	98.50 177	99.21 117	98.84 137	99.56 157	0.00 246

FMVSNet3		98.63 186	98.75 162	98.49 218	98.10 246	99.44 93	99.02 193	99.78 137	98.13 191	98.48 230	99.43 120	97.58 191	96.16 229	98.85 183	98.39 180	99.40 180	99.41 135

TAPA-MVS	98.54 10	99.30 117	99.24 93	99.36 155	99.44 216	98.77 199	99.00 196	99.41 215	99.23 71	99.60 134	99.50 115	99.86 70	99.15 133	99.29 97	98.95 122	99.56 157	99.08 180
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DWT-MVSNet_training		94.92 236	92.14 239	98.15 227	99.37 223	98.43 219	98.99 197	98.51 235	96.76 234	99.52 151	97.35 221	77.20 243	97.08 214	89.76 242	90.38 236	95.43 237	95.13 234

MS-PatchMatch		98.94 163	98.71 164	99.21 178	99.52 202	98.22 230	98.97 198	99.53 203	98.76 133	99.50 157	98.59 185	99.56 121	98.68 171	98.63 200	98.45 177	99.05 208	98.73 196

diffmvs1		99.15 137	99.04 133	99.27 168	99.66 174	99.17 163	98.97 198	99.86 67	99.03 103	99.41 173	99.54 102	99.33 153	98.40 181	98.36 207	98.12 194	99.33 188	99.75 37

Fast-Effi-MVS+-dtu		98.82 171	98.80 161	98.84 209	99.51 207	98.90 188	98.96 200	99.91 48	98.29 184	99.11 203	98.47 191	99.63 110	96.03 231	99.21 117	98.12 194	99.52 164	99.01 189

tpmrst		96.18 231	94.47 230	98.18 225	99.52 202	97.89 238	98.96 200	99.79 131	98.07 196	99.16 198	99.30 138	92.69 216	96.69 221	90.76 240	88.85 239	94.96 239	93.69 239

PCF-MVS	97.86 15	98.95 162	98.53 173	99.44 134	99.70 163	98.80 196	98.96 200	99.69 171	98.65 146	99.59 136	99.33 131	99.94 33	99.12 138	98.01 220	97.11 216	99.59 150	97.83 218
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		99.09 143	98.86 154	99.36 155	99.71 158	99.21 159	98.95 203	99.85 82	98.65 146	99.68 118	99.56 99	98.38 182	98.36 182	98.25 214	98.24 186	99.58 152	99.73 44

RPMNet		97.70 210	96.54 214	99.06 195	99.57 201	98.23 227	98.95 203	99.97 1	96.89 230	99.49 158	99.13 151	89.63 224	97.09 213	96.68 231	97.02 218	99.26 194	98.19 213

canonicalmvs		99.00 156	98.68 165	99.37 149	99.68 172	99.42 101	98.94 205	99.89 57	99.00 106	98.99 206	98.43 194	95.69 200	98.96 156	99.18 129	99.18 76	99.74 101	99.88 9

SD-MVS		99.35 105	99.26 89	99.46 129	99.66 174	99.15 166	98.92 206	99.67 178	99.55 39	99.35 187	98.83 173	99.91 54	99.35 103	99.19 126	98.53 171	99.78 88	99.68 56

CR-MVSNet		97.91 200	96.80 209	99.22 176	99.60 191	98.23 227	98.91 207	99.97 1	96.89 230	99.43 167	99.10 156	89.24 225	98.15 186	98.04 217	97.78 205	99.26 194	98.30 209

Patchmtry						98.19 232	98.91 207	99.97 1		99.43 167

abl_6				99.21 178	99.49 210	98.62 209	98.90 209	99.44 213	97.08 224	99.61 130	97.19 224	99.73 99	98.35 183			99.45 171	98.84 194

FMVSNet5		97.69 211	96.98 206	98.53 217	98.53 244	99.36 119	98.90 209	99.54 198	96.38 236	98.44 233	95.38 231	90.08 223	97.05 216	99.46 64	99.06 98	98.73 215	99.12 179

test0.0.03 1		98.41 192	98.41 181	98.40 222	99.62 182	99.16 164	98.87 211	99.41 215	97.15 221	96.60 247	99.31 135	97.00 197	96.55 223	98.91 171	98.51 173	99.37 183	98.82 195

dps		95.59 233	93.46 236	98.08 228	99.33 226	98.22 230	98.87 211	99.70 168	96.17 238	98.87 214	97.75 212	86.85 235	96.60 222	91.24 239	89.62 237	95.10 238	94.34 236

DeepPCF-MVS	98.38 11	99.16 135	99.20 102	99.12 187	99.20 234	98.71 204	98.85 213	99.06 230	99.17 82	98.96 209	99.61 92	99.86 70	99.29 116	99.17 131	98.72 154	99.36 184	99.15 176

ambc			98.83 156		99.72 155	98.52 214	98.84 214		98.96 111	99.92 8	99.34 130	99.74 96	99.04 145	98.68 199	97.57 214	99.46 169	98.99 192

GA-MVS		98.59 187	98.15 191	99.09 192	99.59 195	99.13 170	98.84 214	99.52 204	98.61 156	99.35 187	99.67 82	93.03 210	97.73 202	98.90 175	98.26 185	99.51 165	99.48 118

TSAR-MVS + GP.		99.33 109	99.17 113	99.51 120	99.71 158	99.00 182	98.84 214	99.71 166	98.23 187	99.74 92	99.53 109	99.90 56	99.35 103	99.38 75	98.85 136	99.72 109	99.31 155

Effi-MVS+		99.20 129	98.93 144	99.50 122	99.79 119	99.26 145	98.82 217	99.96 14	98.37 181	99.60 134	99.12 153	98.36 183	99.05 144	98.93 166	98.82 141	99.78 88	99.68 56

thres100view900		97.69 211	96.37 219	99.23 171	99.74 150	99.21 159	98.81 218	99.43 214	96.10 240	98.70 220	92.99 238	89.10 226	98.88 161	98.58 202	99.31 66	99.82 77	99.27 161

tpm		96.56 227	94.68 229	98.74 212	99.12 235	97.90 237	98.79 219	99.93 35	96.79 233	99.69 111	99.19 146	81.48 241	97.56 204	95.46 232	93.97 227	97.37 226	97.99 215

gg-mvs-nofinetune		98.40 193	98.26 184	98.57 216	99.83 101	98.86 193	98.77 220	99.97 1	99.57 34	99.99 1	99.99 1	93.81 207	93.50 240	98.91 171	98.20 189	99.33 188	98.52 203

tpmp4_e23		95.42 235	92.99 238	98.27 223	99.32 228	97.77 241	98.74 221	99.79 131	97.11 223	99.61 130	97.47 218	80.64 242	96.36 228	92.92 235	88.79 240	95.80 235	96.19 229

CNLPA		98.82 171	98.52 175	99.18 182	99.21 233	98.50 216	98.73 222	99.34 222	98.73 138	99.56 142	97.55 215	99.42 142	99.06 143	98.93 166	98.10 197	99.21 199	98.38 206

HQP-MVS		98.70 182	98.19 190	99.28 166	99.61 187	98.52 214	98.71 223	99.35 220	97.97 200	99.53 145	97.38 220	99.85 76	99.14 135	97.53 224	96.85 221	99.36 184	99.26 164

diffmvs		98.99 158	98.88 152	99.11 190	99.62 182	99.12 173	98.70 224	99.86 67	98.72 140	99.43 167	99.44 119	99.14 162	97.87 198	98.31 209	97.73 209	99.18 202	99.72 48

tpm cat1		95.52 234	93.49 235	97.88 233	99.28 231	97.87 239	98.65 225	99.77 143	97.27 219	99.46 163	98.04 206	90.99 221	95.46 233	88.57 244	88.14 242	94.64 243	93.54 240

CostFormer		95.61 232	93.35 237	98.24 224	99.48 211	98.03 234	98.65 225	99.83 103	96.93 228	99.42 171	98.83 173	83.65 240	97.08 214	90.39 241	89.54 238	94.94 240	96.11 230

PLC	97.83 16	98.88 168	98.52 175	99.30 160	99.45 214	98.60 211	98.65 225	99.49 207	98.66 145	99.59 136	96.33 227	99.59 117	99.17 125	98.87 177	98.53 171	99.46 169	99.05 185
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchMatch-RL		98.80 173	98.52 175	99.12 187	99.38 222	98.70 205	98.56 228	99.55 197	97.81 205	99.34 190	97.57 214	99.31 155	98.67 172	99.27 105	98.62 166	99.22 198	98.35 208

MAR-MVS		98.54 188	98.15 191	98.98 197	99.37 223	98.09 233	98.56 228	99.65 183	96.11 239	99.27 192	97.16 225	99.50 131	98.03 196	98.87 177	98.23 187	99.01 209	99.13 178
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVS-HIRNet		98.45 191	98.25 186	98.69 213	99.12 235	97.81 240	98.55 230	99.85 82	98.58 159	99.67 120	99.61 92	99.86 70	97.46 206	97.95 221	96.37 223	97.49 225	97.56 223

PMMVS		98.71 181	98.55 172	98.90 205	99.28 231	98.45 218	98.53 231	99.45 211	97.67 210	99.15 201	98.76 176	99.54 125	97.79 199	98.77 195	98.23 187	99.16 204	98.46 205

EPNet		98.06 199	98.11 193	98.00 231	99.60 191	98.99 184	98.38 232	99.68 176	98.18 189	98.85 215	97.89 209	95.60 202	92.72 242	98.30 210	98.10 197	98.76 214	99.72 48
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		98.92 165	98.73 163	99.14 184	99.44 216	99.00 182	98.36 233	99.35 220	98.82 130	99.38 179	96.06 228	99.79 92	99.07 141	98.88 176	99.05 101	99.27 193	99.53 104

test-LLR		97.74 209	97.46 200	98.08 228	99.62 182	98.37 221	98.26 234	99.41 215	97.03 225	97.38 243	99.54 102	92.89 212	95.12 235	98.78 193	97.68 211	98.65 217	97.90 216

TESTMET0.1,1		97.62 215	97.46 200	97.81 234	99.07 238	98.37 221	98.26 234	98.35 238	97.03 225	97.38 243	99.54 102	92.89 212	95.12 235	98.78 193	97.68 211	98.65 217	97.90 216

MVSTER		97.55 216	96.75 210	98.48 219	99.46 213	99.54 67	98.24 236	99.77 143	97.56 212	99.41 173	99.31 135	84.86 239	94.66 237	98.86 181	97.75 207	99.34 187	99.38 145

IB-MVS	98.10 14	97.76 208	97.40 202	98.18 225	99.62 182	99.11 175	98.24 236	98.35 238	96.56 235	99.44 165	91.28 245	98.96 170	93.84 238	98.09 216	98.62 166	99.56 157	99.18 168
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
EPNet_dtu		98.09 198	98.25 186	97.91 232	99.58 198	98.02 235	98.19 238	99.67 178	97.94 202	99.74 92	99.07 160	98.71 175	93.40 241	97.50 225	97.09 217	96.89 229	99.44 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testus		98.74 176	98.33 182	99.23 171	99.71 158	99.03 179	98.17 239	99.60 188	97.18 220	99.52 151	98.07 205	98.45 179	99.21 123	98.30 210	98.06 200	99.14 206	99.21 166

test-mter		97.65 214	97.57 199	97.75 236	98.90 242	98.56 213	98.15 240	98.45 237	96.92 229	96.84 246	99.52 111	92.53 219	95.24 234	99.04 152	98.12 194	98.90 212	98.29 211

test2356		96.34 230	94.05 233	99.00 196	99.39 221	98.28 225	98.15 240	99.51 205	96.23 237	99.16 198	97.95 208	73.39 245	98.75 169	97.07 229	96.86 220	99.06 207	98.57 200

E-PMN		96.72 225	95.78 223	97.81 234	99.45 214	95.46 245	98.14 242	98.33 240	97.99 199	98.73 219	98.09 204	98.97 168	97.54 205	97.45 226	91.09 233	94.70 242	91.40 241

CMPMVS	76.62 19	98.64 184	98.60 167	98.68 214	99.33 226	97.07 242	98.11 243	98.50 236	97.69 209	99.26 193	98.35 197	99.66 107	97.62 203	99.43 71	99.02 106	99.24 196	99.01 189
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EMVS		96.47 228	95.38 225	97.74 237	99.42 219	95.37 246	98.07 244	98.27 241	97.85 204	98.90 212	97.48 217	98.73 174	97.20 210	97.21 228	90.39 235	94.59 244	90.65 242

MVE	91.08 18	97.68 213	97.65 197	97.71 238	98.46 245	91.62 249	97.92 245	98.86 232	98.73 138	97.99 241	98.64 183	99.96 13	99.17 125	99.59 52	97.75 207	93.87 245	97.27 224
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX						96.39 244	97.15 246	88.89 243	97.94 202	99.51 155	95.71 230	97.88 189	98.19 184	98.92 168		97.73 224	97.75 221

testpf		93.65 237	91.79 240	95.82 239	98.71 243	93.25 247	96.38 247	99.67 178	95.38 246	97.83 242	94.48 233	87.69 232	89.61 244	88.96 243	88.79 240	92.71 246	93.97 238

tmp_tt				88.14 240	96.68 247	91.91 248	93.70 248	61.38 244	99.61 23	90.51 248	99.40 126	99.71 101	90.32 243	99.22 114	99.44 57	96.25 233

GG-mvs-BLEND		70.44 240	96.91 207	39.57 242	3.32 250	96.51 243	91.01 249	4.05 247	97.03 225	33.20 249	94.67 232	97.75 190	7.59 247	98.28 212	96.85 221	98.24 220	97.26 225

Patchmatch-RL test							65.75 250

test123		21.52 242	28.47 244	13.42 244	7.29 249	10.12 251	15.70 251	8.31 245	31.54 248	19.34 251	36.33 247	37.40 251	17.14 245	27.45 246	23.17 245	12.73 249	33.30 245

testmvs		22.33 241	29.66 243	13.79 243	8.97 248	10.35 250	15.53 252	8.09 246	32.51 247	19.87 250	45.18 246	30.56 252	17.05 246	29.96 245	24.74 243	13.21 247	34.30 243

sosnet-low-res		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

sosnet		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

MTAPA										99.62 128		99.95 22

MTMP										99.53 145		99.92 48

mPP-MVS					99.84 90							99.92 48

NP-MVS									97.37 216

High-resolution multi-view benchmark