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.

gg-mvs-nofinetune		98.40 187	98.26 179	98.57 209	99.83 98	98.86 185	98.77 213	99.97 1	99.57 34	99.99 1	99.99 1	93.81 201	93.50 232	98.91 166	98.20 183	99.33 181	98.52 196

v7n		99.89 2	99.86 5	99.93 1	99.97 2	99.83 3	99.93 1	99.96 15	99.77 6	99.89 18	99.99 1	99.86 70	99.84 5	99.89 9	99.81 10	99.97 1	99.88 9

new-patchmatchnet		98.49 183	97.60 191	99.53 112	99.90 34	99.55 62	99.77 37	99.48 200	99.67 13	99.86 33	99.98 3	99.98 5	99.50 85	96.90 222	91.52 223	98.67 208	95.62 224

v11		99.72 25	99.62 25	99.85 21	99.87 50	99.71 28	99.81 27	99.96 15	99.63 20	99.83 53	99.97 4	99.58 117	99.75 21	99.33 85	99.33 60	99.87 47	99.79 24

anonymousdsp		99.87 8	99.86 5	99.88 15	99.95 12	99.75 23	99.90 8	99.96 15	99.69 11	99.83 53	99.96 5	99.99 4	99.74 26	99.95 3	99.83 3	99.91 25	99.88 9

v748		99.89 2	99.87 1	99.92 4	99.96 9	99.80 12	99.91 6	99.95 29	99.77 6	99.92 8	99.96 5	99.93 39	99.81 9	99.92 7	99.82 6	99.96 3	99.90 2

v1240		99.58 55	99.38 75	99.82 31	99.89 39	99.49 82	99.82 25	99.83 98	99.63 20	99.86 33	99.96 5	98.92 165	99.75 21	99.15 129	98.96 113	99.76 89	99.56 85

v52		99.89 2	99.85 7	99.92 4	99.97 2	99.80 12	99.92 2	99.97 1	99.78 4	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 7	99.92 4	99.97 2	99.80 12	99.92 2	99.97 1	99.78 4	99.90 14	99.96 5	99.84 78	99.82 7	99.88 12	99.82 6	99.96 3	99.89 5

v1921920		99.59 51	99.40 66	99.82 31	99.88 43	99.45 86	99.81 27	99.83 98	99.65 16	99.86 33	99.95 10	99.29 153	99.75 21	98.98 155	98.86 130	99.78 83	99.59 71

v1192		99.60 49	99.41 64	99.82 31	99.89 39	99.43 92	99.81 27	99.84 90	99.63 20	99.85 40	99.95 10	99.35 149	99.72 34	99.01 149	98.90 121	99.82 74	99.58 80

FC-MVSNet-test		99.84 10	99.80 10	99.89 12	99.96 9	99.83 3	99.84 18	99.95 29	99.37 58	99.77 75	99.95 10	99.96 14	99.85 3	99.93 4	99.83 3	99.95 10	99.72 46

v1144		99.61 43	99.43 60	99.82 31	99.88 43	99.41 97	99.76 40	99.86 66	99.64 18	99.84 44	99.95 10	99.49 133	99.74 26	99.00 151	98.93 118	99.84 60	99.58 80

v2v482		99.56 66	99.35 77	99.81 35	99.87 50	99.35 118	99.75 46	99.85 78	99.56 36	99.87 28	99.95 10	99.44 139	99.66 52	98.91 166	98.76 145	99.86 53	99.45 115

v7		99.61 43	99.46 55	99.79 46	99.83 98	99.37 111	99.75 46	99.84 90	99.56 36	99.76 78	99.94 15	99.60 114	99.73 30	99.11 135	99.01 103	99.85 57	99.63 65

v10		99.65 38	99.51 45	99.81 35	99.83 98	99.61 46	99.75 46	99.94 32	99.56 36	99.76 78	99.94 15	99.60 114	99.73 30	99.11 135	99.01 103	99.85 57	99.74 39

SixPastTwentyTwo		99.89 2	99.85 7	99.93 1	99.97 2	99.88 2	99.92 2	99.97 1	99.66 15	99.94 3	99.94 15	99.74 95	99.81 9	99.97 2	99.89 1	99.96 3	99.89 5

HyFIR lowres test		99.50 72	99.26 87	99.80 39	99.95 12	99.62 43	99.76 40	99.97 1	99.67 13	99.56 138	99.94 15	98.40 174	99.78 15	98.84 182	98.59 163	99.76 89	99.72 46

v144192		99.58 55	99.39 70	99.80 39	99.87 50	99.44 88	99.77 37	99.84 90	99.64 18	99.86 33	99.93 19	99.35 149	99.72 34	98.92 163	98.82 136	99.74 97	99.66 57

v15		99.67 36	99.54 41	99.83 30	99.86 66	99.67 39	99.76 40	99.95 29	99.59 29	99.83 53	99.93 19	99.55 121	99.71 38	99.23 107	99.05 96	99.87 47	99.75 36

v13		99.73 21	99.63 22	99.85 21	99.87 50	99.71 28	99.80 30	99.96 15	99.62 23	99.83 53	99.93 19	99.66 106	99.75 21	99.41 70	99.26 67	99.89 33	99.80 23

v12		99.72 25	99.61 27	99.85 21	99.86 66	99.70 33	99.79 32	99.96 15	99.61 24	99.83 53	99.93 19	99.61 110	99.74 26	99.38 74	99.22 69	99.89 33	99.79 24

V14		99.69 32	99.56 38	99.84 24	99.86 66	99.68 36	99.78 35	99.96 15	99.60 28	99.83 53	99.93 19	99.58 117	99.72 34	99.28 98	99.11 88	99.88 37	99.77 29

V9		99.71 29	99.59 33	99.84 24	99.86 66	99.69 35	99.78 35	99.96 15	99.61 24	99.84 44	99.93 19	99.61 110	99.73 30	99.34 84	99.17 76	99.88 37	99.78 27

CHOSEN 1792x2688		99.65 38	99.55 39	99.77 53	99.93 25	99.60 48	99.79 32	99.92 43	99.73 9	99.74 91	99.93 19	99.98 5	99.80 13	98.83 183	99.01 103	99.45 164	99.76 33

pmmvs6		99.88 7	99.87 1	99.89 12	99.97 2	99.76 19	99.89 9	99.96 15	99.82 3	99.90 14	99.92 26	99.95 23	99.68 40	99.93 4	99.88 2	99.95 10	99.86 13

pmmvs5		99.58 55	99.47 52	99.70 76	99.84 87	99.50 80	99.58 94	99.80 124	98.98 108	99.73 97	99.92 26	99.81 84	99.49 89	99.28 98	99.05 96	99.77 87	99.73 42

gm-plane-assit		96.82 215	94.84 221	99.13 179	99.95 12	99.78 17	99.69 71	99.92 43	99.19 79	99.84 44	99.92 26	72.93 238	96.44 217	98.21 207	97.01 211	98.92 202	96.87 220

v1141		99.58 55	99.39 70	99.80 39	99.87 50	99.39 100	99.74 54	99.85 78	99.58 31	99.84 44	99.92 26	99.49 133	99.68 40	98.98 155	98.83 133	99.84 60	99.52 101

pm-mvs1		99.77 14	99.69 16	99.86 18	99.94 22	99.68 36	99.84 18	99.93 36	99.59 29	99.87 28	99.92 26	99.21 156	99.65 55	99.88 12	99.77 14	99.93 19	99.78 27

divwei89l23v2f112		99.58 55	99.39 70	99.80 39	99.87 50	99.39 100	99.74 54	99.85 78	99.57 34	99.84 44	99.92 26	99.48 135	99.67 44	98.98 155	98.83 133	99.84 60	99.52 101

v1		99.58 55	99.39 70	99.80 39	99.87 50	99.39 100	99.74 54	99.85 78	99.58 31	99.84 44	99.92 26	99.51 128	99.67 44	98.98 155	98.82 136	99.84 60	99.52 101

test20.03		99.68 34	99.60 31	99.76 57	99.91 31	99.70 33	99.68 72	99.87 62	99.05 100	99.88 23	99.92 26	99.88 64	99.50 85	99.77 31	99.42 55	99.75 92	99.49 107

MIMVSNet1		99.79 12	99.75 13	99.84 24	99.89 39	99.83 3	99.84 18	99.89 56	99.31 64	99.93 4	99.92 26	99.97 10	99.68 40	99.89 9	99.64 28	99.82 74	99.66 57

EU-MVSNet		99.76 16	99.74 14	99.78 49	99.82 104	99.81 10	99.88 11	99.87 62	99.31 64	99.75 85	99.91 35	99.76 94	99.78 15	99.84 20	99.74 18	99.56 151	99.81 20

PMMVS2		99.23 121	99.22 94	99.24 163	99.80 108	99.14 159	99.50 113	99.82 106	99.12 92	98.41 227	99.91 35	99.98 5	98.51 170	99.48 56	98.76 145	99.38 175	98.14 207

Baseline_NR-MVSNet		99.62 41	99.48 48	99.78 49	99.85 79	99.76 19	99.59 90	99.82 106	98.84 124	99.88 23	99.91 35	99.04 160	99.61 63	99.46 61	99.78 13	99.94 17	99.60 70

IterMVS		99.08 141	98.90 143	99.29 155	99.87 50	99.53 66	99.52 106	99.77 139	98.94 112	99.75 85	99.91 35	97.52 187	98.72 165	98.86 176	98.14 187	98.09 213	99.43 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v8		99.61 43	99.45 56	99.79 46	99.80 108	99.59 51	99.73 58	99.93 36	99.48 45	99.77 75	99.90 39	99.48 135	99.67 44	99.11 135	98.89 122	99.84 60	99.73 42

TransMVSNet (Re)		99.72 25	99.59 33	99.88 15	99.95 12	99.76 19	99.88 11	99.94 32	99.58 31	99.92 8	99.90 39	98.55 171	99.65 55	99.89 9	99.76 15	99.95 10	99.70 50

no-one		99.73 21	99.70 15	99.76 57	99.77 127	99.58 53	99.76 40	99.90 55	99.08 95	99.86 33	99.90 39	99.98 5	99.66 52	99.98 1	99.73 19	99.59 144	99.67 55

v1neww		99.57 62	99.40 66	99.77 53	99.80 108	99.34 120	99.72 63	99.82 106	99.49 42	99.76 78	99.89 42	99.50 130	99.67 44	99.10 143	98.89 122	99.84 60	99.59 71

v7new		99.57 62	99.40 66	99.77 53	99.80 108	99.34 120	99.72 63	99.82 106	99.49 42	99.76 78	99.89 42	99.50 130	99.67 44	99.10 143	98.89 122	99.84 60	99.59 71

v6		99.57 62	99.40 66	99.77 53	99.80 108	99.34 120	99.72 63	99.82 106	99.49 42	99.76 78	99.89 42	99.52 126	99.67 44	99.10 143	98.89 122	99.84 60	99.59 71

V42		99.57 62	99.41 64	99.75 63	99.84 87	99.37 111	99.73 58	99.83 98	99.41 53	99.75 85	99.89 42	99.42 141	99.60 65	99.15 129	98.96 113	99.76 89	99.65 61

CVMVSNet		99.06 146	98.88 147	99.28 160	99.52 194	99.53 66	99.42 128	99.69 165	98.74 133	98.27 230	99.89 42	95.48 196	99.44 98	99.46 61	99.33 60	99.32 182	99.75 36

LTVRE_ROB	99.39 1	99.90 1	99.87 1	99.93 1	99.97 2	99.82 7	99.91 6	99.92 43	99.75 8	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
pmmvs-eth3d		99.61 43	99.48 48	99.75 63	99.87 50	99.30 131	99.75 46	99.89 56	99.23 71	99.85 40	99.88 48	99.97 10	99.49 89	99.46 61	99.01 103	99.68 111	99.52 101

v17		99.62 41	99.48 48	99.79 46	99.80 108	99.60 48	99.73 58	99.94 32	99.46 47	99.73 97	99.88 48	99.52 126	99.67 44	99.16 128	98.96 113	99.84 60	99.75 36

1111		96.83 214	95.02 220	98.95 193	99.90 34	99.57 55	99.62 83	99.97 1	98.58 153	98.06 232	99.87 50	69.04 241	96.43 218	99.36 79	99.14 82	99.73 101	99.54 91

.test1245		79.44 232	75.07 234	84.53 234	99.90 34	99.57 55	99.62 83	99.97 1	98.58 153	98.06 232	99.87 50	69.04 241	96.43 218	99.36 79	24.74 235	13.21 239	34.30 236

test12356		99.12 136	99.03 129	99.23 164	99.78 121	98.95 178	99.10 181	99.72 158	98.26 177	99.81 63	99.87 50	99.20 157	98.06 185	99.47 59	98.80 142	98.91 203	98.67 192

WR-MVS		99.79 12	99.68 17	99.91 8	99.95 12	99.83 3	99.87 13	99.96 15	99.39 57	99.93 4	99.87 50	99.29 153	99.77 17	99.83 21	99.72 21	99.97 1	99.82 17

TAMVS		99.05 147	99.02 132	99.08 186	99.69 159	99.22 150	99.33 146	99.32 216	99.16 86	98.97 201	99.87 50	97.36 188	97.76 192	99.21 113	99.00 108	99.44 166	99.33 146

DeepC-MVS	99.05 5	99.74 19	99.64 20	99.84 24	99.90 34	99.39 100	99.79 32	99.81 118	99.69 11	99.90 14	99.87 50	99.98 5	99.81 9	99.62 49	99.32 62	99.83 70	99.65 61
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MDTV_nov1_ep13_2view		98.73 173	98.31 178	99.22 169	99.75 136	99.24 147	99.75 46	99.93 36	99.31 64	99.84 44	99.86 56	99.81 84	99.31 113	97.40 219	94.77 217	96.73 222	97.81 212

v18		99.59 51	99.44 59	99.76 57	99.78 121	99.57 55	99.70 70	99.93 36	99.43 50	99.69 110	99.85 57	99.51 128	99.65 55	99.08 146	98.87 127	99.82 74	99.74 39

v16		99.61 43	99.47 52	99.78 49	99.79 116	99.60 48	99.72 63	99.94 32	99.45 49	99.70 108	99.85 57	99.54 124	99.67 44	99.15 129	98.96 113	99.83 70	99.76 33

PVSNet_Blended_VisFu		99.66 37	99.64 20	99.67 83	99.91 31	99.71 28	99.61 85	99.79 127	99.41 53	99.91 12	99.85 57	99.61 110	99.00 147	99.67 42	99.42 55	99.81 78	99.81 20

CDS-MVSNet		99.15 134	99.10 120	99.21 171	99.59 187	99.22 150	99.48 120	99.47 201	98.89 117	99.41 169	99.84 60	98.11 180	97.76 192	99.26 103	99.01 103	99.57 148	99.38 137
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		99.76 16	99.78 11	99.75 63	99.92 27	99.77 18	99.83 21	99.85 78	99.43 50	99.85 40	99.84 60	100.00 1	99.13 137	99.83 21	99.66 26	99.90 28	99.90 2
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FMVSNet1		99.50 72	99.57 37	99.42 133	99.67 167	99.65 41	99.60 89	99.91 48	99.40 55	99.39 171	99.83 62	99.27 155	98.14 180	99.68 39	99.50 45	99.81 78	99.68 52

TDRefinement		99.81 11	99.76 12	99.86 18	99.83 98	99.53 66	99.89 9	99.91 48	99.73 9	99.88 23	99.83 62	99.96 14	99.76 19	99.91 8	99.81 10	99.86 53	99.59 71

Anonymous20231211		99.86 9	99.87 1	99.84 24	99.98 1	99.91 1	99.92 2	99.97 1	99.86 2	99.49 155	99.82 64	100.00 1	99.70 39	99.86 17	99.79 12	99.96 3	99.87 12

v148		99.58 55	99.43 60	99.76 57	99.87 50	99.40 99	99.76 40	99.85 78	99.48 45	99.83 53	99.82 64	99.83 81	99.51 81	99.20 116	98.82 136	99.75 92	99.45 115

testmv		99.39 94	99.19 102	99.62 97	99.84 87	99.38 105	99.37 139	99.86 66	98.47 160	99.79 68	99.82 64	99.39 145	99.63 60	99.30 90	98.70 152	99.21 191	99.28 152

test1235678		99.39 94	99.20 99	99.62 97	99.84 87	99.38 105	99.38 137	99.86 66	98.47 160	99.79 68	99.82 64	99.41 143	99.63 60	99.30 90	98.71 150	99.21 191	99.28 152

DELS-MVS		99.42 87	99.53 43	99.29 155	99.52 194	99.43 92	99.42 128	99.28 217	99.16 86	99.72 101	99.82 64	99.97 10	98.17 177	99.56 51	99.16 78	99.65 117	99.59 71
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
new_pmnet		98.91 161	98.89 144	98.94 194	99.51 199	98.27 218	99.15 174	98.66 226	99.17 82	99.48 157	99.79 69	99.80 89	98.49 172	99.23 107	98.20 183	98.34 211	97.74 215

N_pmnet		98.64 178	98.23 182	99.11 184	99.78 121	99.25 142	99.75 46	99.39 211	99.65 16	99.70 108	99.78 70	99.89 59	98.81 162	97.60 215	94.28 218	97.24 219	97.15 219

pmmvs3		98.85 165	98.60 161	99.13 179	99.66 168	98.72 195	99.37 139	99.06 222	98.44 166	99.76 78	99.74 71	99.55 121	99.15 133	99.04 147	96.00 216	97.80 215	98.72 191

MVS_Test		99.09 140	98.92 140	99.29 155	99.61 179	99.07 168	99.04 184	99.81 118	98.58 153	99.37 174	99.74 71	98.87 166	98.41 174	98.61 195	98.01 195	99.50 159	99.57 84

PS-CasMVS		99.73 21	99.59 33	99.90 11	99.95 12	99.80 12	99.85 17	99.97 1	98.95 110	99.86 33	99.73 73	99.36 146	99.81 9	99.83 21	99.67 25	99.95 10	99.83 16

IterMVS-LS		99.16 132	98.82 152	99.57 106	99.87 50	99.71 28	99.58 94	99.92 43	99.24 70	99.71 106	99.73 73	95.79 192	98.91 157	98.82 184	98.66 156	99.43 169	99.77 29
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 39	99.62 43	99.52 106	99.76 147	99.61 24	99.69 110	99.73 73	99.96 14	99.57 70	99.27 101	98.62 160	99.81 78	99.85 15

Anonymous20231206		99.48 76	99.31 81	99.69 80	99.79 116	99.57 55	99.63 79	99.79 127	98.88 118	99.91 12	99.72 76	99.93 39	99.59 66	99.24 104	98.63 159	99.43 169	99.18 161

pmmvs4		99.34 104	99.15 112	99.57 106	99.77 127	98.90 180	99.51 109	99.77 139	99.07 98	99.73 97	99.72 76	99.84 78	99.07 141	98.85 178	98.39 174	99.55 155	99.27 154

TranMVSNet+NR-MVSNet		99.59 51	99.42 63	99.80 39	99.87 50	99.55 62	99.64 77	99.86 66	99.05 100	99.88 23	99.72 76	99.33 151	99.64 58	99.47 59	99.14 82	99.91 25	99.67 55

WR-MVS_H		99.73 21	99.61 27	99.88 15	99.95 12	99.82 7	99.83 21	99.96 15	99.01 103	99.84 44	99.71 79	99.41 143	99.74 26	99.77 31	99.70 23	99.95 10	99.82 17

FC-MVSNet-train		99.70 30	99.67 18	99.74 69	99.94 22	99.71 28	99.82 25	99.91 48	99.14 90	99.53 141	99.70 80	99.88 64	99.33 108	99.88 12	99.61 33	99.94 17	99.77 29

PEN-MVS		99.77 14	99.65 19	99.91 8	99.95 12	99.80 12	99.86 14	99.97 1	99.08 95	99.89 18	99.69 81	99.68 103	99.84 5	99.81 25	99.64 28	99.95 10	99.81 20

GA-MVS		98.59 181	98.15 184	99.09 185	99.59 187	99.13 162	98.84 207	99.52 196	98.61 150	99.35 181	99.67 82	93.03 204	97.73 194	98.90 170	98.26 179	99.51 158	99.48 110

diffmvs		98.83 166	98.51 172	99.19 174	99.62 174	98.98 176	99.18 167	99.82 106	99.15 89	99.51 151	99.66 83	95.37 197	98.07 184	98.49 198	98.22 182	98.96 201	99.73 42

DTE-MVSNet		99.75 18	99.61 27	99.92 4	99.95 12	99.81 10	99.86 14	99.96 15	99.18 81	99.92 8	99.66 83	99.45 137	99.85 3	99.80 26	99.56 34	99.96 3	99.79 24

ACMH	99.11 4	99.72 25	99.63 22	99.84 24	99.87 50	99.59 51	99.83 21	99.88 60	99.46 47	99.87 28	99.66 83	99.95 23	99.76 19	99.73 36	99.47 49	99.84 60	99.52 101
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		99.68 34	99.51 45	99.89 12	99.95 12	99.76 19	99.83 21	99.96 15	98.83 126	99.84 44	99.65 86	99.09 159	99.80 13	99.78 29	99.62 32	99.95 10	99.82 17

UniMVSNet (Re)		99.50 72	99.29 83	99.75 63	99.86 66	99.47 84	99.51 109	99.82 106	98.90 116	99.89 18	99.64 87	99.00 161	99.55 72	99.32 87	99.08 91	99.90 28	99.59 71

FMVSNet2		99.07 145	99.19 102	98.93 196	99.02 231	99.53 66	99.31 149	99.84 90	98.86 120	98.88 206	99.64 87	98.44 173	96.92 210	99.35 81	99.00 108	99.61 135	99.53 96

COLMAP_ROB	99.18 2	99.70 30	99.60 31	99.81 35	99.84 87	99.37 111	99.76 40	99.84 90	99.54 40	99.82 60	99.64 87	99.95 23	99.75 21	99.79 28	99.56 34	99.83 70	99.37 141
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CHOSEN 280x420		98.99 154	98.91 142	99.07 187	99.77 127	99.26 139	99.55 99	99.92 43	98.62 147	98.67 215	99.62 90	97.20 189	98.44 173	99.50 54	99.18 74	98.08 214	98.99 185

MVS-HIRNet		98.45 185	98.25 180	98.69 206	99.12 227	97.81 232	98.55 222	99.85 78	98.58 153	99.67 117	99.61 91	99.86 70	97.46 198	97.95 213	96.37 215	97.49 217	97.56 216

EPP-MVSNet		99.34 104	99.10 120	99.62 97	99.94 22	99.74 25	99.66 74	99.80 124	99.07 98	98.93 203	99.61 91	96.13 191	99.49 89	99.67 42	99.63 30	99.92 23	99.86 13

DeepPCF-MVS	98.38 11	99.16 132	99.20 99	99.12 181	99.20 226	98.71 196	98.85 206	99.06 222	99.17 82	98.96 202	99.61 91	99.86 70	99.29 116	99.17 126	98.72 149	99.36 177	99.15 169

SMA-MVS		99.47 79	99.45 56	99.50 120	99.83 98	99.34 120	99.14 176	99.60 180	99.09 94	99.36 180	99.60 94	99.96 14	99.46 97	99.41 70	99.16 78	99.59 144	99.61 68

TSAR-MVS + MP.		99.56 66	99.54 41	99.58 102	99.69 159	99.14 159	99.73 58	99.45 203	99.50 41	99.35 181	99.60 94	99.93 39	99.50 85	99.56 51	99.37 59	99.77 87	99.64 64

PMVS	94.32 17	99.27 118	99.55 39	98.94 194	99.60 183	99.43 92	99.39 133	99.54 190	98.99 105	99.69 110	99.60 94	99.81 84	95.68 224	99.88 12	99.83 3	99.73 101	99.31 148
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
USDC		99.29 117	98.98 136	99.65 86	99.72 150	98.87 184	99.47 122	99.66 175	99.35 61	99.87 28	99.58 97	99.87 69	99.51 81	98.85 178	97.93 197	99.65 117	98.38 199

CLD-MVS		99.30 113	99.01 133	99.63 92	99.75 136	98.89 183	99.35 144	99.60 180	98.53 157	99.86 33	99.57 98	99.94 33	99.52 80	98.96 159	98.10 190	99.70 109	99.08 173
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
APDe-MVS		99.60 49	99.48 48	99.73 71	99.85 79	99.51 79	99.75 46	99.85 78	99.17 82	99.81 63	99.56 99	99.94 33	99.44 98	99.42 69	99.22 69	99.67 113	99.54 91

CANet_DTU		99.03 150	99.18 105	98.87 199	99.58 190	99.03 170	99.18 167	99.41 207	98.65 142	99.74 91	99.55 100	99.71 100	96.13 222	99.19 121	98.92 119	99.17 194	99.18 161

CANet		99.36 99	99.39 70	99.34 151	99.80 108	99.35 118	99.41 131	99.47 201	99.20 76	99.74 91	99.54 101	99.68 103	98.05 187	99.23 107	98.97 111	99.57 148	99.73 42

TSAR-MVS + ACMM		99.31 111	99.26 87	99.37 144	99.66 168	98.97 177	99.20 165	99.56 187	99.33 62	99.19 190	99.54 101	99.91 54	99.32 111	99.12 134	98.34 177	99.29 183	99.65 61

test-LLR		97.74 203	97.46 193	98.08 221	99.62 174	98.37 213	98.26 226	99.41 207	97.03 217	97.38 236	99.54 101	92.89 206	95.12 227	98.78 188	97.68 203	98.65 209	97.90 209

TESTMET0.1,1		97.62 209	97.46 193	97.81 227	99.07 230	98.37 213	98.26 226	98.35 230	97.03 217	97.38 236	99.54 101	92.89 206	95.12 227	98.78 188	97.68 203	98.65 209	97.90 209

EG-PatchMatch MVS		99.59 51	99.49 47	99.70 76	99.82 104	99.26 139	99.39 133	99.83 98	98.99 105	99.93 4	99.54 101	99.92 48	99.51 81	99.78 29	99.50 45	99.73 101	99.41 128

ACMH+	98.94 6	99.69 32	99.59 33	99.81 35	99.88 43	99.41 97	99.75 46	99.86 66	99.43 50	99.80 65	99.54 101	99.97 10	99.73 30	99.82 24	99.52 44	99.85 57	99.43 122

TSAR-MVS + GP.		99.33 106	99.17 109	99.51 118	99.71 153	99.00 173	98.84 207	99.71 161	98.23 178	99.74 91	99.53 107	99.90 56	99.35 103	99.38 74	98.85 131	99.72 105	99.31 148

test-mter		97.65 208	97.57 192	97.75 229	98.90 234	98.56 205	98.15 232	98.45 229	96.92 221	96.84 239	99.52 108	92.53 211	95.24 226	99.04 147	98.12 188	98.90 204	98.29 204

testgi		99.43 86	99.47 52	99.38 141	99.90 34	99.67 39	99.30 155	99.73 156	98.64 146	99.53 141	99.52 108	99.90 56	98.08 183	99.65 45	99.40 58	99.75 92	99.55 90

DU-MVS		99.48 76	99.26 87	99.75 63	99.85 79	99.38 105	99.50 113	99.81 118	98.86 120	99.89 18	99.51 110	98.98 162	99.59 66	99.46 61	98.97 111	99.87 47	99.63 65

NR-MVSNet		99.52 69	99.29 83	99.80 39	99.96 9	99.38 105	99.55 99	99.81 118	98.86 120	99.87 28	99.51 110	98.81 167	99.72 34	99.86 17	99.04 99	99.89 33	99.54 91

Fast-Effi-MVS+		99.39 94	99.18 105	99.63 92	99.86 66	99.28 136	99.45 125	99.91 48	98.47 160	99.61 125	99.50 112	99.57 119	99.17 125	99.24 104	98.66 156	99.78 83	99.59 71

TAPA-MVS	98.54 10	99.30 113	99.24 91	99.36 150	99.44 208	98.77 191	99.00 191	99.41 207	99.23 71	99.60 129	99.50 112	99.86 70	99.15 133	99.29 94	98.95 117	99.56 151	99.08 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PM-MVS		99.49 75	99.43 60	99.57 106	99.76 132	99.34 120	99.53 103	99.77 139	98.93 114	99.75 85	99.46 114	99.83 81	99.11 139	99.72 37	99.29 64	99.49 160	99.46 114

UniMVSNet_NR-MVSNet		99.41 89	99.12 117	99.76 57	99.86 66	99.48 83	99.50 113	99.81 118	98.84 124	99.89 18	99.45 115	98.32 177	99.59 66	99.22 110	98.89 122	99.90 28	99.63 65

tfpnnormal		99.74 19	99.63 22	99.86 18	99.93 25	99.75 23	99.80 30	99.89 56	99.31 64	99.88 23	99.43 116	99.66 106	99.77 17	99.80 26	99.71 22	99.92 23	99.76 33

MDA-MVSNet-bldmvs		99.11 137	99.11 119	99.12 181	99.91 31	99.38 105	99.77 37	98.72 225	99.31 64	99.85 40	99.43 116	98.26 178	99.48 93	99.85 19	98.47 168	96.99 220	99.08 173

GBi-Net		98.96 155	99.05 126	98.85 200	99.02 231	99.53 66	99.31 149	99.78 133	98.13 182	98.48 223	99.43 116	97.58 184	96.92 210	99.68 39	99.50 45	99.61 135	99.53 96

test1		98.96 155	99.05 126	98.85 200	99.02 231	99.53 66	99.31 149	99.78 133	98.13 182	98.48 223	99.43 116	97.58 184	96.92 210	99.68 39	99.50 45	99.61 135	99.53 96

FMVSNet3		98.63 180	98.75 156	98.49 211	98.10 238	99.44 88	99.02 189	99.78 133	98.13 182	98.48 223	99.43 116	97.58 184	96.16 221	98.85 178	98.39 174	99.40 173	99.41 128

QAPM		99.41 89	99.21 98	99.64 91	99.78 121	99.16 156	99.51 109	99.85 78	99.20 76	99.72 101	99.43 116	99.81 84	99.25 120	98.87 172	98.71 150	99.71 107	99.30 150

tmp_tt				88.14 233	96.68 239	91.91 240	93.70 240	61.38 236	99.61 24	90.51 241	99.40 122	99.71 100	90.32 235	99.22 110	99.44 54	96.25 225

RPSCF		99.48 76	99.45 56	99.52 116	99.73 148	99.33 125	99.13 178	99.77 139	99.33 62	99.47 160	99.39 123	99.92 48	99.36 102	99.63 47	99.13 85	99.63 128	99.41 128

UA-Net		99.64 40	99.62 25	99.66 84	99.97 2	99.82 7	99.14 176	99.96 15	98.95 110	99.52 147	99.38 124	99.86 70	99.55 72	99.72 37	99.66 26	99.80 81	99.94 1

TSAR-MVS + COLMAP		98.74 170	98.58 163	98.93 196	99.29 222	98.23 219	99.04 184	99.24 218	98.79 129	98.80 209	99.37 125	99.71 100	98.06 185	98.02 211	97.46 207	99.16 195	98.48 197

ambc			98.83 149		99.72 150	98.52 206	98.84 207		98.96 109	99.92 8	99.34 126	99.74 95	99.04 145	98.68 193	97.57 206	99.46 162	98.99 185

PVSNet_BlendedMVS		99.20 126	99.17 109	99.23 164	99.69 159	99.33 125	99.04 184	99.13 220	98.41 169	99.79 68	99.33 127	99.36 146	98.10 181	99.29 94	98.87 127	99.65 117	99.56 85

PVSNet_Blended		99.20 126	99.17 109	99.23 164	99.69 159	99.33 125	99.04 184	99.13 220	98.41 169	99.79 68	99.33 127	99.36 146	98.10 181	99.29 94	98.87 127	99.65 117	99.56 85

UGNet		99.40 92	99.61 27	99.16 177	99.88 43	99.64 42	99.61 85	99.77 139	99.31 64	99.63 123	99.33 127	99.93 39	96.46 216	99.63 47	99.53 43	99.63 128	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
PCF-MVS	97.86 15	98.95 157	98.53 167	99.44 131	99.70 157	98.80 188	98.96 194	99.69 165	98.65 142	99.59 131	99.33 127	99.94 33	99.12 138	98.01 212	97.11 208	99.59 144	97.83 211
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OPM-MVS		99.39 94	99.22 94	99.59 100	99.76 132	98.82 186	99.51 109	99.79 127	99.17 82	99.53 141	99.31 131	99.95 23	99.35 103	99.22 110	98.79 144	99.60 138	99.27 154

test0.0.03 1		98.41 186	98.41 176	98.40 215	99.62 174	99.16 156	98.87 204	99.41 207	97.15 213	96.60 240	99.31 131	97.00 190	96.55 215	98.91 166	98.51 167	99.37 176	98.82 188

MVSTER		97.55 210	96.75 203	98.48 212	99.46 205	99.54 64	98.24 228	99.77 139	97.56 204	99.41 169	99.31 131	84.86 231	94.66 229	98.86 176	97.75 200	99.34 180	99.38 137

tpmrst		96.18 225	94.47 223	98.18 218	99.52 194	97.89 230	98.96 194	99.79 127	98.07 187	99.16 191	99.30 134	92.69 210	96.69 213	90.76 232	88.85 231	94.96 231	93.69 232

MIMVSNet		99.00 152	99.03 129	98.97 192	99.32 220	99.32 129	99.39 133	99.91 48	98.41 169	98.76 210	99.24 135	99.17 158	97.13 203	99.30 90	98.80 142	99.29 183	99.01 182

MSDG		99.32 108	99.09 122	99.58 102	99.75 136	98.74 193	99.36 141	99.54 190	99.14 90	99.72 101	99.24 135	99.89 59	99.51 81	99.30 90	98.76 145	99.62 134	98.54 195

MVS_0304		99.36 99	99.35 77	99.37 144	99.85 79	99.36 114	99.39 133	99.56 187	99.36 60	99.75 85	99.23 137	99.90 56	97.97 190	99.00 151	98.83 133	99.69 110	99.77 29

ESAPD		99.21 123	99.14 113	99.29 155	99.79 116	99.44 88	99.02 189	99.79 127	97.96 192	99.12 195	99.22 138	99.95 23	98.50 171	99.21 113	98.84 132	99.56 151	99.34 145

MDTV_nov1_ep13		97.41 212	96.26 214	98.76 204	99.47 204	98.43 211	99.26 161	99.82 106	98.06 188	99.23 187	99.22 138	92.86 208	98.05 187	95.33 225	93.66 220	96.73 222	96.26 221

tpm		96.56 221	94.68 222	98.74 205	99.12 227	97.90 229	98.79 212	99.93 36	96.79 225	99.69 110	99.19 140	81.48 233	97.56 196	95.46 224	93.97 219	97.37 218	97.99 208

DeepC-MVS_fast	98.69 9	99.32 108	99.13 115	99.53 112	99.63 173	98.78 189	99.53 103	99.33 215	99.08 95	99.77 75	99.18 141	99.89 59	99.29 116	99.00 151	98.70 152	99.65 117	99.30 150
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		99.33 106	99.19 102	99.49 124	99.69 159	99.25 142	99.27 159	99.59 185	98.44 166	99.78 74	99.15 142	99.92 48	98.95 156	99.39 73	99.04 99	99.64 126	99.18 161

TinyColmap		99.21 123	98.89 144	99.59 100	99.61 179	98.61 202	99.47 122	99.67 171	99.02 102	99.82 60	99.15 142	99.74 95	99.35 103	99.17 126	98.33 178	99.63 128	98.22 205

RPMNet		97.70 204	96.54 207	99.06 188	99.57 193	98.23 219	98.95 197	99.97 1	96.89 222	99.49 155	99.13 144	89.63 216	97.09 205	96.68 223	97.02 210	99.26 186	98.19 206

OMC-MVS		99.11 137	98.95 138	99.29 155	99.37 215	98.57 204	99.19 166	99.20 219	98.87 119	99.58 135	99.13 144	99.88 64	99.00 147	99.19 121	98.46 169	99.43 169	98.57 193

Effi-MVS+		99.20 126	98.93 139	99.50 120	99.79 116	99.26 139	98.82 210	99.96 15	98.37 172	99.60 129	99.12 146	98.36 175	99.05 144	98.93 161	98.82 136	99.78 83	99.68 52

LP		97.43 211	96.28 213	98.77 203	99.69 159	98.92 179	99.49 118	99.70 163	98.53 157	99.82 60	99.12 146	95.67 194	97.30 201	94.65 226	91.76 221	96.65 224	95.34 226

MVS_111021_LR		99.25 120	99.13 115	99.39 138	99.50 201	99.14 159	99.23 163	99.50 198	98.67 140	99.61 125	99.12 146	99.81 84	99.16 129	99.28 98	98.67 155	99.35 179	99.21 159

CR-MVSNet		97.91 194	96.80 202	99.22 169	99.60 183	98.23 219	98.91 200	99.97 1	96.89 222	99.43 165	99.10 149	89.24 217	98.15 178	98.04 209	97.78 198	99.26 186	98.30 202

DI_MVS_plusplus_trai		98.74 170	98.08 187	99.51 118	99.79 116	99.29 135	99.61 85	99.60 180	99.20 76	99.46 161	99.09 150	92.93 205	98.97 154	98.27 206	98.35 176	99.65 117	99.45 115

ADS-MVSNet		97.29 213	96.17 215	98.59 208	99.59 187	98.70 197	99.32 147	99.86 66	98.47 160	99.56 138	99.08 151	98.16 179	97.34 200	92.92 227	91.17 224	95.91 226	94.72 228

Vis-MVSNet (Re-imp)		99.40 92	99.28 85	99.55 110	99.92 27	99.68 36	99.31 149	99.87 62	98.69 138	99.16 191	99.08 151	98.64 170	99.20 124	99.65 45	99.46 51	99.83 70	99.72 46

EPNet_dtu		98.09 192	98.25 180	97.91 225	99.58 190	98.02 227	98.19 230	99.67 171	97.94 194	99.74 91	99.07 153	98.71 169	93.40 233	97.50 217	97.09 209	96.89 221	99.44 118
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		96.76 218	95.30 219	98.46 213	99.42 211	98.47 209	99.32 147	99.91 48	98.42 168	99.51 151	99.07 153	92.81 209	97.12 204	92.39 230	91.71 222	95.51 228	94.20 230

PatchT		98.11 190	97.12 198	99.26 162	99.65 171	98.34 215	99.57 96	99.97 1	97.48 207	99.43 165	99.04 155	90.84 214	98.15 178	98.04 209	97.78 198	98.82 205	98.30 202

ACMMP_Plus		99.47 79	99.33 79	99.63 92	99.85 79	99.28 136	99.56 97	99.83 98	98.75 132	99.48 157	99.03 156	99.95 23	99.47 96	99.48 56	99.19 73	99.57 148	99.59 71

MVS_111021_HR		99.30 113	99.14 113	99.48 125	99.58 190	99.25 142	99.27 159	99.61 178	98.74 133	99.66 119	99.02 157	99.84 78	99.33 108	99.20 116	98.76 145	99.44 166	99.18 161

HSP-MVS		99.27 118	99.07 124	99.50 120	99.76 132	99.54 64	99.73 58	99.72 158	98.94 112	99.23 187	98.96 158	99.96 14	98.91 157	98.72 192	97.71 202	99.63 128	99.66 57

tfpn_n400		99.08 141	98.56 164	99.70 76	99.85 79	99.56 60	99.63 79	99.86 66	99.21 74	99.37 174	98.95 159	94.24 198	99.55 72	99.20 116	99.29 64	99.93 19	99.44 118

tfpnconf		99.08 141	98.56 164	99.70 76	99.85 79	99.56 60	99.63 79	99.86 66	99.21 74	99.37 174	98.95 159	94.24 198	99.55 72	99.20 116	99.29 64	99.93 19	99.44 118

tfpnview11		99.04 149	98.49 173	99.68 81	99.84 87	99.58 53	99.56 97	99.86 66	98.86 120	99.37 174	98.95 159	94.24 198	99.54 76	98.87 172	99.54 42	99.91 25	99.39 136

zzz-MVS		99.51 70	99.36 76	99.68 81	99.88 43	99.38 105	99.53 103	99.84 90	99.11 93	99.59 131	98.93 162	99.95 23	99.58 69	99.44 67	99.21 71	99.65 117	99.52 101

LGP-MVS_train		99.46 83	99.18 105	99.78 49	99.87 50	99.25 142	99.71 69	99.87 62	98.02 189	99.79 68	98.90 163	99.96 14	99.66 52	99.49 55	99.17 76	99.79 82	99.49 107

HFP-MVS		99.46 83	99.30 82	99.65 86	99.82 104	99.25 142	99.50 113	99.82 106	99.23 71	99.58 135	98.86 164	99.94 33	99.56 71	99.14 132	99.12 87	99.63 128	99.56 85

FPMVS		98.48 184	98.83 149	98.07 223	99.09 229	97.98 228	99.07 183	98.04 234	98.99 105	99.22 189	98.85 165	99.43 140	93.79 231	99.66 44	99.11 88	99.24 188	97.76 213

SD-MVS		99.35 102	99.26 87	99.46 126	99.66 168	99.15 158	98.92 199	99.67 171	99.55 39	99.35 181	98.83 166	99.91 54	99.35 103	99.19 121	98.53 165	99.78 83	99.68 52

CostFormer		95.61 226	93.35 230	98.24 217	99.48 203	98.03 226	98.65 217	99.83 98	96.93 220	99.42 168	98.83 166	83.65 232	97.08 206	90.39 233	89.54 230	94.94 232	96.11 223

PatchmatchNet		96.81 216	95.41 217	98.43 214	99.43 210	98.30 216	99.23 163	99.93 36	98.19 179	99.64 121	98.81 168	93.50 202	97.43 199	92.89 229	90.78 226	94.94 232	95.41 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PMMVS		98.71 175	98.55 166	98.90 198	99.28 223	98.45 210	98.53 223	99.45 203	97.67 202	99.15 194	98.76 169	99.54 124	97.79 191	98.77 190	98.23 180	99.16 195	98.46 198

IS_MVSNet		99.15 134	99.12 117	99.19 174	99.92 27	99.73 27	99.55 99	99.86 66	98.45 165	96.91 238	98.74 170	98.33 176	99.02 146	99.54 53	99.47 49	99.88 37	99.61 68

ACMMPR		99.51 70	99.32 80	99.72 72	99.87 50	99.33 125	99.61 85	99.85 78	99.19 79	99.73 97	98.73 171	99.95 23	99.61 63	99.35 81	99.14 82	99.66 115	99.58 80

HPM-MVS++		99.23 121	98.98 136	99.53 112	99.75 136	99.02 172	99.44 126	99.77 139	98.65 142	99.52 147	98.72 172	99.92 48	99.33 108	98.77 190	98.40 173	99.40 173	99.36 142

tfpn1000		98.73 173	98.07 188	99.50 120	99.84 87	99.61 46	99.48 120	99.84 90	98.71 137	98.74 211	98.71 173	91.70 212	99.17 125	98.81 185	99.55 40	99.90 28	99.43 122

SteuartSystems-ACMMP		99.47 79	99.22 94	99.76 57	99.88 43	99.36 114	99.65 76	99.84 90	98.47 160	99.80 65	98.68 174	99.96 14	99.68 40	99.37 76	99.06 93	99.72 105	99.66 57
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		99.41 89	99.20 99	99.65 86	99.80 108	99.23 149	99.44 126	99.75 155	98.60 151	99.74 91	98.66 175	99.93 39	99.48 93	99.33 85	99.16 78	99.73 101	99.48 110

MVE	91.08 18	97.68 207	97.65 190	97.71 231	98.46 237	91.62 241	97.92 237	98.86 224	98.73 135	97.99 234	98.64 176	99.96 14	99.17 125	99.59 50	97.75 200	93.87 237	97.27 217
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MS-PatchMatch		98.94 158	98.71 158	99.21 171	99.52 194	98.22 222	98.97 193	99.53 195	98.76 130	99.50 154	98.59 177	99.56 120	98.68 167	98.63 194	98.45 171	99.05 199	98.73 189

3Dnovator	99.16 3	99.42 87	99.22 94	99.65 86	99.78 121	99.13 162	99.50 113	99.85 78	99.40 55	99.80 65	98.59 177	99.79 91	99.30 115	99.20 116	99.06 93	99.71 107	99.35 144

MCST-MVS		99.17 129	98.82 152	99.57 106	99.75 136	98.70 197	99.25 162	99.69 165	98.62 147	99.59 131	98.54 179	99.79 91	99.53 77	98.48 200	98.15 186	99.64 126	99.43 122

Effi-MVS+-dtu		99.01 151	99.05 126	98.98 190	99.60 183	99.13 162	99.03 188	99.61 178	98.52 159	99.01 198	98.53 180	99.83 81	96.95 209	99.48 56	98.59 163	99.66 115	99.25 158

ACMM	98.37 12	99.47 79	99.23 92	99.74 69	99.86 66	99.19 154	99.68 72	99.86 66	99.16 86	99.71 106	98.52 181	99.95 23	99.62 62	99.35 81	99.02 101	99.74 97	99.42 126
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		99.39 94	99.28 85	99.52 116	99.77 127	99.39 100	99.55 99	99.82 106	98.93 114	99.64 121	98.52 181	99.67 105	98.58 169	99.74 35	99.63 30	99.75 92	99.06 176

Fast-Effi-MVS+-dtu		98.82 167	98.80 154	98.84 202	99.51 199	98.90 180	98.96 194	99.91 48	98.29 175	99.11 196	98.47 183	99.63 109	96.03 223	99.21 113	98.12 188	99.52 157	99.01 182

OpenMVS	98.82 8	99.17 129	98.85 148	99.53 112	99.75 136	99.06 169	99.36 141	99.82 106	98.28 176	99.76 78	98.47 183	99.61 110	98.91 157	98.80 186	98.70 152	99.60 138	99.04 181

MP-MVS		99.35 102	99.09 122	99.65 86	99.84 87	99.22 150	99.59 90	99.78 133	98.13 182	99.67 117	98.44 185	99.93 39	99.43 100	99.31 89	99.09 90	99.60 138	99.49 107
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
canonicalmvs		99.00 152	98.68 159	99.37 144	99.68 166	99.42 95	98.94 198	99.89 56	99.00 104	98.99 199	98.43 186	95.69 193	98.96 155	99.18 124	99.18 74	99.74 97	99.88 9

ACMP	98.32 13	99.44 85	99.18 105	99.75 63	99.83 98	99.18 155	99.64 77	99.83 98	98.81 128	99.79 68	98.42 187	99.96 14	99.64 58	99.46 61	98.98 110	99.74 97	99.44 118
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tfpn_ndepth		98.67 177	98.03 189	99.42 133	99.65 171	99.50 80	99.29 157	99.78 133	98.17 181	99.04 197	98.36 188	93.29 203	98.88 160	98.46 201	99.26 67	99.88 37	99.14 170

CMPMVS	76.62 19	98.64 178	98.60 161	98.68 207	99.33 218	97.07 234	98.11 235	98.50 228	97.69 201	99.26 186	98.35 189	99.66 106	97.62 195	99.43 68	99.02 101	99.24 188	99.01 182
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20240521		99.10 139	98.79 155	99.45 128	99.73 148	99.42 95	99.31 149	99.65 176	97.95 193	99.58 135	98.31 190	97.82 182	98.69 166	99.40 72	99.20 72	99.75 92	99.42 126

APD-MVS		99.17 129	98.92 140	99.46 126	99.78 121	99.24 147	99.34 145	99.78 133	97.79 198	99.48 157	98.25 191	99.88 64	98.77 163	99.18 124	98.92 119	99.63 128	99.18 161
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
tfpn111		98.25 189	97.29 196	99.37 144	99.74 144	99.52 72	99.17 169	99.76 147	96.10 232	98.65 217	98.23 192	89.10 218	99.00 147	99.11 135	99.56 34	99.88 37	99.41 128

thresconf0.02		98.10 191	96.83 201	99.58 102	99.71 153	99.28 136	99.40 132	99.72 158	98.65 142	99.39 171	98.23 192	86.73 228	99.43 100	97.73 214	98.17 185	99.86 53	99.05 178

PGM-MVS		99.32 108	98.99 134	99.71 73	99.86 66	99.31 130	99.59 90	99.86 66	97.51 205	99.75 85	98.23 192	99.94 33	99.53 77	99.29 94	99.08 91	99.65 117	99.54 91

CDPH-MVS		99.05 147	98.63 160	99.54 111	99.75 136	98.78 189	99.59 90	99.68 169	97.79 198	99.37 174	98.20 195	99.86 70	99.14 135	98.58 196	98.01 195	99.68 111	99.16 167

CNVR-MVS		99.08 141	98.83 149	99.37 144	99.61 179	98.74 193	99.15 174	99.54 190	98.59 152	99.37 174	98.15 196	99.88 64	99.08 140	98.91 166	98.46 169	99.48 161	99.06 176

E-PMN		96.72 219	95.78 216	97.81 227	99.45 206	95.46 237	98.14 234	98.33 232	97.99 190	98.73 212	98.09 197	98.97 163	97.54 197	97.45 218	91.09 225	94.70 234	91.40 234

testus		98.74 170	98.33 177	99.23 164	99.71 153	99.03 170	98.17 231	99.60 180	97.18 212	99.52 147	98.07 198	98.45 172	99.21 123	98.30 203	98.06 193	99.14 197	99.21 159

tpm cat1		95.52 228	93.49 228	97.88 226	99.28 223	97.87 231	98.65 217	99.77 139	97.27 211	99.46 161	98.04 199	90.99 213	95.46 225	88.57 236	88.14 234	94.64 235	93.54 233

3Dnovator+	98.92 7	99.31 111	99.03 129	99.63 92	99.77 127	98.90 180	99.52 106	99.81 118	99.37 58	99.72 101	98.03 200	99.73 98	99.32 111	98.99 154	98.81 141	99.67 113	99.36 142

test2356		96.34 224	94.05 226	99.00 189	99.39 213	98.28 217	98.15 232	99.51 197	96.23 229	99.16 191	97.95 201	73.39 237	98.75 164	97.07 221	96.86 212	99.06 198	98.57 193

train_agg		98.89 162	98.48 174	99.38 141	99.69 159	98.76 192	99.31 149	99.60 180	97.71 200	98.98 200	97.89 202	99.89 59	99.29 116	98.32 202	97.59 205	99.42 172	99.16 167

EPNet		98.06 193	98.11 186	98.00 224	99.60 183	98.99 175	98.38 224	99.68 169	98.18 180	98.85 208	97.89 202	95.60 195	92.72 234	98.30 203	98.10 190	98.76 206	99.72 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dps		95.59 227	93.46 229	98.08 221	99.33 218	98.22 222	98.87 204	99.70 163	96.17 230	98.87 207	97.75 204	86.85 227	96.60 214	91.24 231	89.62 229	95.10 230	94.34 229

ACMMP		99.36 99	99.06 125	99.71 73	99.86 66	99.36 114	99.63 79	99.85 78	98.33 173	99.72 101	97.73 205	99.94 33	99.53 77	99.37 76	99.13 85	99.65 117	99.56 85
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
PatchMatch-RL		98.80 169	98.52 169	99.12 181	99.38 214	98.70 197	98.56 220	99.55 189	97.81 197	99.34 184	97.57 206	99.31 152	98.67 168	99.27 101	98.62 160	99.22 190	98.35 201

CNLPA		98.82 167	98.52 169	99.18 176	99.21 225	98.50 208	98.73 215	99.34 214	98.73 135	99.56 138	97.55 207	99.42 141	99.06 143	98.93 161	98.10 190	99.21 191	98.38 199

CPTT-MVS		99.21 123	98.89 144	99.58 102	99.72 150	99.12 165	99.30 155	99.76 147	98.62 147	99.66 119	97.51 208	99.89 59	99.48 93	99.01 149	98.64 158	99.58 147	99.40 135

EMVS		96.47 222	95.38 218	97.74 230	99.42 211	95.37 238	98.07 236	98.27 233	97.85 196	98.90 205	97.48 209	98.73 168	97.20 202	97.21 220	90.39 227	94.59 236	90.65 235

tpmp4_e23		95.42 229	92.99 231	98.27 216	99.32 220	97.77 233	98.74 214	99.79 127	97.11 215	99.61 125	97.47 210	80.64 234	96.36 220	92.92 227	88.79 232	95.80 227	96.19 222

AdaColmap		98.93 159	98.53 167	99.39 138	99.52 194	98.65 200	99.11 180	99.59 185	98.08 186	99.44 163	97.46 211	99.45 137	99.24 121	98.92 163	98.44 172	99.44 166	98.73 189

HQP-MVS		98.70 176	98.19 183	99.28 160	99.61 179	98.52 206	98.71 216	99.35 212	97.97 191	99.53 141	97.38 212	99.85 76	99.14 135	97.53 216	96.85 213	99.36 177	99.26 157

DWT-MVSNet_training		94.92 230	92.14 232	98.15 220	99.37 215	98.43 211	98.99 192	98.51 227	96.76 226	99.52 147	97.35 213	77.20 235	97.08 206	89.76 234	90.38 228	95.43 229	95.13 227

X-MVS		99.30 113	98.99 134	99.66 84	99.85 79	99.30 131	99.49 118	99.82 106	98.32 174	99.69 110	97.31 214	99.93 39	99.50 85	99.37 76	99.16 78	99.60 138	99.53 96

conf0.05thres1000		98.36 188	97.28 197	99.63 92	99.92 27	99.74 25	99.66 74	99.88 60	98.68 139	98.92 204	97.30 215	86.02 230	99.49 89	99.77 31	99.73 19	99.93 19	99.69 51

abl_6				99.21 171	99.49 202	98.62 201	98.90 202	99.44 205	97.08 216	99.61 125	97.19 216	99.73 98	98.35 175			99.45 164	98.84 187

MAR-MVS		98.54 182	98.15 184	98.98 190	99.37 215	98.09 225	98.56 220	99.65 176	96.11 231	99.27 185	97.16 217	99.50 130	98.03 189	98.87 172	98.23 180	99.01 200	99.13 171
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
Gipuma		99.55 68	99.23 92	99.91 8	99.87 50	99.52 72	99.86 14	99.93 36	99.87 1	99.96 2	96.72 218	99.55 121	99.97 1	99.77 31	99.46 51	99.87 47	99.74 39
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PLC	97.83 16	98.88 163	98.52 169	99.30 154	99.45 206	98.60 203	98.65 217	99.49 199	98.66 141	99.59 131	96.33 219	99.59 116	99.17 125	98.87 172	98.53 165	99.46 162	99.05 178
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSLP-MVS++		98.92 160	98.73 157	99.14 178	99.44 208	99.00 173	98.36 225	99.35 212	98.82 127	99.38 173	96.06 220	99.79 91	99.07 141	98.88 171	99.05 96	99.27 185	99.53 96

NCCC		98.88 163	98.42 175	99.42 133	99.62 174	98.81 187	99.10 181	99.54 190	98.76 130	99.53 141	95.97 221	99.80 89	99.16 129	98.49 198	98.06 193	99.55 155	99.05 178

DeepMVS_CX						96.39 236	97.15 238	88.89 235	97.94 194	99.51 151	95.71 222	97.88 181	98.19 176	98.92 163		97.73 216	97.75 214

FMVSNet5		97.69 205	96.98 199	98.53 210	98.53 236	99.36 114	98.90 202	99.54 190	96.38 228	98.44 226	95.38 223	90.08 215	97.05 208	99.46 61	99.06 93	98.73 207	99.12 172

GG-mvs-BLEND		70.44 233	96.91 200	39.57 235	3.32 242	96.51 235	91.01 241	4.05 239	97.03 217	33.20 242	94.67 224	97.75 183	7.59 239	98.28 205	96.85 213	98.24 212	97.26 218

testpf		93.65 231	91.79 233	95.82 232	98.71 235	93.25 239	96.38 239	99.67 171	95.38 238	97.83 235	94.48 225	87.69 224	89.61 236	88.96 235	88.79 232	92.71 238	93.97 231

tfpn		96.77 217	94.47 223	99.45 128	99.88 43	99.62 43	99.46 124	99.83 98	97.61 203	98.27 230	94.22 226	71.45 240	99.34 107	99.32 87	99.46 51	99.90 28	99.58 80

view800		97.89 195	96.56 204	99.45 128	99.86 66	99.57 55	99.42 128	99.80 124	97.50 206	98.40 228	93.78 227	86.63 229	99.31 113	99.24 104	99.68 24	99.89 33	99.54 91

view600		97.88 196	96.55 206	99.44 131	99.84 87	99.52 72	99.38 137	99.76 147	97.36 209	98.50 222	93.29 228	87.31 225	99.26 119	99.13 133	99.76 15	99.88 37	99.48 110

thres600view7		97.86 198	96.53 210	99.41 136	99.84 87	99.52 72	99.36 141	99.76 147	97.32 210	98.38 229	93.24 229	87.25 226	99.23 122	99.11 135	99.75 17	99.88 37	99.48 110

conf200view11		97.85 199	96.54 207	99.37 144	99.74 144	99.52 72	99.17 169	99.76 147	96.10 232	98.65 217	92.99 230	89.10 218	99.00 147	99.11 135	99.56 34	99.88 37	99.41 128

thres100view900		97.69 205	96.37 212	99.23 164	99.74 144	99.21 153	98.81 211	99.43 206	96.10 232	98.70 213	92.99 230	89.10 218	98.88 160	98.58 196	99.31 63	99.82 74	99.27 154

tfpn200view9		97.85 199	96.54 207	99.38 141	99.74 144	99.52 72	99.17 169	99.76 147	96.10 232	98.70 213	92.99 230	89.10 218	99.00 147	99.11 135	99.56 34	99.88 37	99.41 128

thres200		97.87 197	96.56 204	99.39 138	99.76 132	99.52 72	99.13 178	99.76 147	96.88 224	98.66 216	92.87 233	88.77 222	99.16 129	99.11 135	99.42 55	99.88 37	99.33 146

thres400		97.82 201	96.47 211	99.40 137	99.81 107	99.44 88	99.29 157	99.69 165	97.15 213	98.57 219	92.82 234	87.96 223	99.16 129	98.96 159	99.55 40	99.86 53	99.41 128

conf0.01		96.70 220	94.44 225	99.34 151	99.71 153	99.46 85	99.17 169	99.73 156	96.10 232	98.53 220	91.96 235	75.75 236	99.00 147	98.85 178	99.56 34	99.87 47	99.38 137

conf0.002		96.39 223	93.87 227	99.33 153	99.70 157	99.45 86	99.17 169	99.71 161	96.10 232	98.51 221	91.88 236	72.65 239	99.00 147	98.80 186	98.82 136	99.87 47	99.38 137

IB-MVS	98.10 14	97.76 202	97.40 195	98.18 218	99.62 174	99.11 166	98.24 228	98.35 230	96.56 227	99.44 163	91.28 237	98.96 164	93.84 230	98.09 208	98.62 160	99.56 151	99.18 161
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
testmvs		22.33 234	29.66 235	13.79 236	8.97 240	10.35 242	15.53 244	8.09 238	32.51 239	19.87 243	45.18 238	30.56 244	17.05 238	29.96 237	24.74 235	13.21 239	34.30 236

test123		21.52 235	28.47 236	13.42 237	7.29 241	10.12 243	15.70 243	8.31 237	31.54 240	19.34 244	36.33 239	37.40 243	17.14 237	27.45 238	23.17 237	12.73 241	33.30 238

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

our_test_3					99.75 136	99.11 166	99.74 54

MTAPA										99.62 124		99.95 23

MTMP										99.53 141		99.92 48

Patchmatch-RL test							65.75 242

XVS					99.86 66	99.30 131	99.72 63			99.69 110		99.93 39				99.60 138

X-MVStestdata					99.86 66	99.30 131	99.72 63			99.69 110		99.93 39				99.60 138

mPP-MVS					99.84 87							99.92 48

NP-MVS									97.37 208

Patchmtry						98.19 224	98.91 200	99.97 1		99.43 165

High-resolution multi-view benchmark