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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 3	99.99 1	100.00 1	99.98 8	99.78 8	100.00 1	99.92 3	100.00 1	99.87 10

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 3	99.65 55	100.00 1	99.90 6	100.00 1	99.97 10	99.61 17	99.97 16	99.75 31	100.00 1	99.84 15

Gipuma		99.57 48	99.59 44	99.49 162	99.98 3	99.71 53	99.72 26	99.84 37	99.81 28	99.94 20	99.78 80	98.91 83	99.71 300	98.41 154	99.95 67	99.05 280
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		99.83 11	99.81 10	99.89 6	99.97 4	99.95 2	99.88 4	99.93 6	99.87 13	99.94 20	99.98 8	99.55 21	99.95 41	99.21 81	99.98 36	99.78 31

anonymousdsp		99.80 13	99.77 14	99.90 4	99.96 5	99.88 8	99.73 22	99.85 29	99.70 50	99.92 31	99.93 14	99.45 23	99.97 16	99.36 63	100.00 1	99.85 14

v7n		99.82 12	99.80 12	99.88 12	99.96 5	99.84 19	99.82 10	99.82 48	99.84 23	99.94 20	99.91 20	99.13 57	99.96 33	99.83 20	99.99 20	99.83 18

PS-MVSNAJss		99.84 9	99.82 9	99.89 6	99.96 5	99.77 38	99.68 42	99.85 29	99.95 3	99.98 3	99.92 17	99.28 39	99.98 7	99.75 31	100.00 1	99.94 2

jajsoiax		99.89 3	99.89 3	99.89 6	99.96 5	99.78 36	99.70 30	99.86 22	99.89 10	99.98 3	99.90 23	99.94 2	99.98 7	99.75 31	100.00 1	99.90 5

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 5	99.79 34	99.72 26	99.88 18	99.92 5	99.98 3	99.93 14	99.94 2	99.98 7	99.77 30	100.00 1	99.92 3

v52		99.85 7	99.84 7	99.89 6	99.96 5	99.89 6	99.87 5	99.81 56	99.85 19	99.96 8	99.90 23	99.27 42	99.95 41	99.93 1	99.99 20	99.82 23

V4		99.85 7	99.84 7	99.88 12	99.96 5	99.89 6	99.87 5	99.81 56	99.85 19	99.96 8	99.90 23	99.27 42	99.95 41	99.93 1	100.00 1	99.82 23

OurMVSNet-221017-0		99.75 19	99.71 25	99.84 21	99.96 5	99.83 23	99.83 8	99.85 29	99.80 31	99.93 26	99.93 14	98.54 141	99.93 66	99.59 39	99.98 36	99.76 38

pcd1.5k->3k		49.97 335	55.52 336	33.31 348	99.95 13	0.00 366	0.00 357	99.81 56	0.00 361	0.00 362	100.00 1	99.96 1	0.00 364	0.00 361	100.00 1	99.92 3

v748		99.76 17	99.74 21	99.84 21	99.95 13	99.83 23	99.82 10	99.80 60	99.82 27	99.95 16	99.87 37	98.72 112	99.93 66	99.72 34	99.98 36	99.75 41

pmmvs6		99.86 6	99.86 6	99.83 25	99.94 15	99.90 4	99.83 8	99.91 11	99.85 19	99.94 20	99.95 12	99.73 10	99.90 110	99.65 35	99.97 47	99.69 57

test_djsdf		99.84 9	99.81 10	99.91 2	99.94 15	99.84 19	99.77 14	99.80 60	99.73 42	99.97 6	99.92 17	99.77 9	99.98 7	99.43 54	100.00 1	99.90 5

MIMVSNet1		99.66 37	99.62 38	99.80 30	99.94 15	99.87 9	99.69 39	99.77 73	99.78 34	99.93 26	99.89 31	97.94 193	99.92 84	99.65 35	99.98 36	99.62 114

K. test v3		98.87 204	98.60 212	99.69 80	99.93 18	99.46 112	99.74 20	94.97 358	99.78 34	99.88 47	99.88 34	93.66 289	99.97 16	99.61 38	99.95 67	99.64 96

SixPastTwentyTwo		99.42 85	99.30 102	99.76 43	99.92 19	99.67 69	99.70 30	99.14 284	99.65 67	99.89 39	99.90 23	96.20 266	99.94 55	99.42 58	99.92 91	99.67 70

wuykxyi23d		99.65 42	99.64 36	99.69 80	99.92 19	99.20 188	98.89 216	99.99 2	98.73 200	99.95 16	99.80 64	99.84 4	99.99 4	99.64 37	99.98 36	99.89 9

Anonymous20240521		99.67 36	99.62 38	99.84 21	99.91 21	99.85 13	99.81 12	99.76 79	99.72 45	99.92 31	99.83 51	98.10 181	99.90 110	99.58 41	99.97 47	99.77 34

pm-mvs1		99.79 14	99.79 13	99.78 38	99.91 21	99.83 23	99.76 17	99.87 20	99.73 42	99.89 39	99.87 37	99.63 15	99.87 161	99.54 45	99.92 91	99.63 100

TransMVSNet (Re)		99.78 15	99.77 14	99.81 28	99.91 21	99.85 13	99.75 18	99.86 22	99.70 50	99.91 34	99.89 31	99.60 19	99.87 161	99.59 39	99.74 193	99.71 50

Baseline_NR-MVSNet		99.49 69	99.37 88	99.82 26	99.91 21	99.84 19	98.83 227	99.86 22	99.68 57	99.65 128	99.88 34	97.67 214	99.87 161	99.03 107	99.86 129	99.76 38

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 12	99.91 21	99.90 4	99.96 1	99.92 7	99.90 6	99.97 6	99.87 37	99.81 7	99.95 41	99.54 45	99.99 20	99.80 25
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
PVSNet_Blended_VisFu		99.40 91	99.38 85	99.44 176	99.90 26	98.66 239	98.94 214	99.91 11	97.97 256	99.79 80	99.73 99	99.05 69	99.97 16	99.15 94	99.99 20	99.68 63

TDRefinement		99.72 25	99.70 28	99.77 40	99.90 26	99.85 13	99.86 7	99.92 7	99.69 54	99.78 83	99.92 17	99.37 30	99.88 141	98.93 123	99.95 67	99.60 125

XXY-MVS		99.71 27	99.67 32	99.81 28	99.89 28	99.72 52	99.59 66	99.82 48	99.39 113	99.82 66	99.84 50	99.38 28	99.91 93	99.38 60	99.93 88	99.80 25

FC-MVSNet-test		99.70 28	99.65 34	99.86 18	99.88 29	99.86 12	99.72 26	99.78 70	99.90 6	99.82 66	99.83 51	98.45 154	99.87 161	99.51 48	99.97 47	99.86 12

EU-MVSNet		99.39 94	99.62 38	98.72 277	99.88 29	96.44 309	99.56 71	99.85 29	99.90 6	99.90 36	99.85 45	98.09 182	99.83 230	99.58 41	99.95 67	99.90 5

CHOSEN 1792x2688		99.39 94	99.30 102	99.65 98	99.88 29	99.25 175	98.78 236	99.88 18	98.66 204	99.96 8	99.79 71	97.45 225	99.93 66	99.34 65	99.99 20	99.78 31

Vis-MVSNet		99.75 19	99.74 21	99.79 35	99.88 29	99.66 72	99.69 39	99.92 7	99.67 59	99.77 88	99.75 93	99.61 17	99.98 7	99.35 64	99.98 36	99.72 47
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tfpnnormal		99.43 82	99.38 85	99.60 127	99.87 33	99.75 45	99.59 66	99.78 70	99.71 48	99.90 36	99.69 125	98.85 89	99.90 110	97.25 233	99.78 177	99.15 255

SteuartSystems-ACMMP		99.30 115	99.14 126	99.76 43	99.87 33	99.66 72	99.18 155	99.60 163	98.55 213	99.57 152	99.67 143	99.03 71	99.94 55	97.01 245	99.80 169	99.69 57
Skip Steuart: Steuart Systems R&D Blog.
no-one		99.28 118	99.23 118	99.45 174	99.87 33	99.08 203	98.95 211	99.52 204	98.88 178	99.77 88	99.83 51	97.78 206	99.90 110	98.46 152	99.99 20	99.38 216

v13		99.76 17	99.77 14	99.73 64	99.86 36	99.55 98	99.77 14	99.86 22	99.79 33	99.96 8	99.91 20	98.90 84	99.87 161	99.91 5	100.00 1	99.78 31

lessismore_v0				99.64 105	99.86 36	99.38 143		90.66 361		99.89 39	99.83 51	94.56 283	99.97 16	99.56 44	99.92 91	99.57 144

ACMH+	98.40 8	99.50 67	99.43 79	99.71 73	99.86 36	99.76 42	99.32 114	99.77 73	99.53 89	99.77 88	99.76 89	99.26 45	99.78 273	97.77 199	99.88 115	99.60 125

ACMH	98.42 6	99.59 46	99.54 54	99.72 69	99.86 36	99.62 85	99.56 71	99.79 68	98.77 192	99.80 75	99.85 45	99.64 14	99.85 197	98.70 139	99.89 109	99.70 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v12		99.75 19	99.77 14	99.72 69	99.85 40	99.53 101	99.75 18	99.86 22	99.78 34	99.96 8	99.90 23	98.88 87	99.86 181	99.91 5	100.00 1	99.77 34

v11		99.75 19	99.76 18	99.71 73	99.85 40	99.49 104	99.73 22	99.84 37	99.75 39	99.95 16	99.90 23	98.93 80	99.86 181	99.92 3	100.00 1	99.77 34

HyFIR lowres test		98.91 197	98.64 210	99.73 64	99.85 40	99.47 108	98.07 302	99.83 40	98.64 206	99.89 39	99.60 181	92.57 299	100.00 1	99.33 67	99.97 47	99.72 47

FIs		99.65 42	99.58 46	99.84 21	99.84 43	99.85 13	99.66 50	99.75 85	99.86 16	99.74 100	99.79 71	98.27 169	99.85 197	99.37 62	99.93 88	99.83 18

V9		99.74 23	99.75 20	99.71 73	99.84 43	99.50 102	99.74 20	99.86 22	99.76 38	99.96 8	99.90 23	98.83 90	99.85 197	99.91 5	100.00 1	99.77 34

XVG-OURS-SEG-HR		99.16 154	98.99 172	99.66 94	99.84 43	99.64 79	98.25 283	99.73 93	98.39 226	99.63 134	99.43 231	99.70 12	99.90 110	97.34 226	98.64 317	99.44 199

PMVS	92.94 21	98.82 210	98.81 198	98.85 262	99.84 43	97.99 279	99.20 153	99.47 218	99.71 48	99.42 187	99.82 59	98.09 182	99.47 349	93.88 335	99.85 132	99.07 278
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MP-MVS-pluss		99.14 157	98.92 182	99.80 30	99.83 47	99.83 23	98.61 245	99.63 142	96.84 302	99.44 181	99.58 189	98.81 91	99.91 93	97.70 203	99.82 155	99.67 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
V14		99.73 24	99.74 21	99.69 80	99.83 47	99.48 107	99.72 26	99.85 29	99.74 40	99.96 8	99.89 31	98.79 98	99.85 197	99.91 5	100.00 1	99.76 38

PM-MVS		99.36 101	99.29 107	99.58 133	99.83 47	99.66 72	98.95 211	99.86 22	98.85 181	99.81 72	99.73 99	98.40 159	99.92 84	98.36 158	99.83 146	99.17 253

PEN-MVS		99.66 37	99.59 44	99.89 6	99.83 47	99.87 9	99.66 50	99.73 93	99.70 50	99.84 61	99.73 99	98.56 135	99.96 33	99.29 76	99.94 80	99.83 18

HPM-MVS_fast		99.43 82	99.30 102	99.80 30	99.83 47	99.81 29	99.52 73	99.70 108	98.35 234	99.51 173	99.50 219	99.31 35	99.88 141	98.18 175	99.84 136	99.69 57

RPSCF		99.18 149	99.02 164	99.64 105	99.83 47	99.85 13	99.44 84	99.82 48	98.33 239	99.50 175	99.78 80	97.90 195	99.65 332	96.78 256	99.83 146	99.44 199

COLMAP_ROB	98.06 12	99.45 80	99.37 88	99.70 79	99.83 47	99.70 60	99.38 95	99.78 70	99.53 89	99.67 118	99.78 80	99.19 49	99.86 181	97.32 227	99.87 122	99.55 148
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TSAR-MVS + MP.		99.34 108	99.24 116	99.63 109	99.82 54	99.37 146	99.26 137	99.35 250	98.77 192	99.57 152	99.70 119	99.27 42	99.88 141	97.71 202	99.75 186	99.65 90

new-patchmatchnet		99.35 103	99.57 49	98.71 278	99.82 54	96.62 307	98.55 254	99.75 85	99.50 92	99.88 47	99.87 37	99.31 35	99.88 141	99.43 54	100.00 1	99.62 114

v15		99.72 25	99.73 24	99.68 83	99.82 54	99.44 119	99.70 30	99.85 29	99.72 45	99.95 16	99.88 34	98.76 105	99.84 213	99.90 9	100.00 1	99.75 41

VPNet		99.46 78	99.37 88	99.71 73	99.82 54	99.59 90	99.48 79	99.70 108	99.81 28	99.69 112	99.58 189	97.66 218	99.86 181	99.17 90	99.44 254	99.67 70

XVG-OURS		99.21 142	99.06 152	99.65 98	99.82 54	99.62 85	97.87 322	99.74 90	98.36 229	99.66 122	99.68 137	99.71 11	99.90 110	96.84 253	99.88 115	99.43 205

XVG-ACMP-BASELINE		99.23 130	99.10 142	99.63 109	99.82 54	99.58 92	98.83 227	99.72 102	98.36 229	99.60 148	99.71 112	98.92 81	99.91 93	97.08 242	99.84 136	99.40 210

LPG-MVS_test		99.22 139	99.05 157	99.74 56	99.82 54	99.63 83	99.16 168	99.73 93	97.56 279	99.64 130	99.69 125	99.37 30	99.89 126	96.66 263	99.87 122	99.69 57

LGP-MVS_train				99.74 56	99.82 54	99.63 83		99.73 93	97.56 279	99.64 130	99.69 125	99.37 30	99.89 126	96.66 263	99.87 122	99.69 57

zzz-MVS		99.30 115	99.14 126	99.80 30	99.81 62	99.81 29	98.73 241	99.53 194	99.27 126	99.42 187	99.63 162	98.21 174	99.95 41	97.83 196	99.79 172	99.65 90

MTAPA		99.35 103	99.20 122	99.80 30	99.81 62	99.81 29	99.33 111	99.53 194	99.27 126	99.42 187	99.63 162	98.21 174	99.95 41	97.83 196	99.79 172	99.65 90

testing_2		99.58 47	99.56 52	99.62 118	99.81 62	99.44 119	99.14 175	99.43 229	99.69 54	99.82 66	99.79 71	99.14 54	99.79 265	99.31 72	99.95 67	99.63 100

v17		99.70 28	99.71 25	99.67 86	99.81 62	99.44 119	99.70 30	99.83 40	99.69 54	99.94 20	99.87 37	98.70 113	99.84 213	99.88 14	99.99 20	99.73 44

v16		99.70 28	99.71 25	99.67 86	99.81 62	99.43 125	99.70 30	99.83 40	99.70 50	99.94 20	99.87 37	98.69 115	99.84 213	99.88 14	99.99 20	99.73 44

v10		99.69 32	99.69 29	99.66 94	99.81 62	99.39 137	99.66 50	99.75 85	99.60 82	99.92 31	99.87 37	98.75 108	99.86 181	99.90 9	99.99 20	99.73 44

HPM-MVS		99.25 125	99.07 150	99.78 38	99.81 62	99.75 45	99.61 61	99.67 121	97.72 269	99.35 209	99.25 269	99.23 46	99.92 84	97.21 237	99.82 155	99.67 70
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
IterMVS-LS		99.41 88	99.47 70	99.25 225	99.81 62	98.09 275	98.85 224	99.76 79	99.62 73	99.83 65	99.64 154	98.54 141	99.97 16	99.15 94	99.99 20	99.68 63
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1neww		99.55 55	99.54 54	99.61 121	99.80 70	99.39 137	99.32 114	99.61 149	99.18 143	99.87 52	99.69 125	98.64 127	99.82 238	99.79 26	99.94 80	99.60 125

v7new		99.55 55	99.54 54	99.61 121	99.80 70	99.39 137	99.32 114	99.61 149	99.18 143	99.87 52	99.69 125	98.64 127	99.82 238	99.79 26	99.94 80	99.60 125

v1240		99.56 51	99.58 46	99.51 158	99.80 70	99.00 208	99.00 201	99.65 134	99.15 150	99.90 36	99.75 93	99.09 61	99.88 141	99.90 9	99.96 60	99.67 70

v8		99.68 33	99.69 29	99.65 98	99.80 70	99.40 134	99.66 50	99.76 79	99.64 69	99.93 26	99.85 45	98.66 122	99.84 213	99.88 14	99.99 20	99.71 50

v7		99.56 51	99.54 54	99.61 121	99.80 70	99.39 137	99.30 124	99.59 167	99.14 152	99.82 66	99.72 105	98.75 108	99.84 213	99.83 20	99.94 80	99.61 119

v6		99.55 55	99.54 54	99.61 121	99.80 70	99.39 137	99.32 114	99.60 163	99.18 143	99.87 52	99.68 137	98.65 124	99.82 238	99.79 26	99.95 67	99.61 119

MDA-MVSNet-bldmvs		99.06 168	99.05 157	99.07 244	99.80 70	97.83 285	98.89 216	99.72 102	99.29 122	99.63 134	99.70 119	96.47 259	99.89 126	98.17 177	99.82 155	99.50 176

PS-CasMVS		99.66 37	99.58 46	99.89 6	99.80 70	99.85 13	99.66 50	99.73 93	99.62 73	99.84 61	99.71 112	98.62 129	99.96 33	99.30 73	99.96 60	99.86 12

DTE-MVSNet		99.68 33	99.61 42	99.88 12	99.80 70	99.87 9	99.67 47	99.71 105	99.72 45	99.84 61	99.78 80	98.67 120	99.97 16	99.30 73	99.95 67	99.80 25

WR-MVS_H		99.61 45	99.53 62	99.87 16	99.80 70	99.83 23	99.67 47	99.75 85	99.58 85	99.85 58	99.69 125	98.18 179	99.94 55	99.28 78	99.95 67	99.83 18

IS-MVSNet		99.03 174	98.85 191	99.55 148	99.80 70	99.25 175	99.73 22	99.15 283	99.37 115	99.61 146	99.71 112	94.73 281	99.81 257	97.70 203	99.88 115	99.58 140

EPP-MVSNet		99.17 152	99.00 169	99.66 94	99.80 70	99.43 125	99.70 30	99.24 275	99.48 94	99.56 159	99.77 86	94.89 279	99.93 66	98.72 138	99.89 109	99.63 100

ACMM	98.09 11	99.46 78	99.38 85	99.72 69	99.80 70	99.69 64	99.13 180	99.65 134	98.99 167	99.64 130	99.72 105	99.39 24	99.86 181	98.23 168	99.81 164	99.60 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1144		99.54 60	99.53 62	99.59 129	99.79 83	99.28 165	99.10 184	99.61 149	99.20 141	99.84 61	99.73 99	98.67 120	99.84 213	99.86 19	99.98 36	99.64 96

v18		99.68 33	99.69 29	99.65 98	99.79 83	99.40 134	99.68 42	99.83 40	99.66 64	99.93 26	99.85 45	98.65 124	99.84 213	99.87 18	99.99 20	99.71 50

V42		99.56 51	99.54 54	99.63 109	99.79 83	99.46 112	99.39 89	99.59 167	99.24 135	99.86 57	99.70 119	98.55 139	99.82 238	99.79 26	99.95 67	99.60 125

test20.03		99.55 55	99.54 54	99.58 133	99.79 83	99.37 146	99.02 197	99.89 15	99.60 82	99.82 66	99.62 169	98.81 91	99.89 126	99.43 54	99.86 129	99.47 188

test_0402		99.22 139	99.14 126	99.45 174	99.79 83	99.43 125	99.28 133	99.68 117	99.54 87	99.40 197	99.56 201	99.07 66	99.82 238	96.01 288	99.96 60	99.11 264

ACMMP		99.25 125	99.08 146	99.74 56	99.79 83	99.68 67	99.50 75	99.65 134	98.07 250	99.52 171	99.69 125	98.57 134	99.92 84	97.18 239	99.79 172	99.63 100
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
HSP-MVS		99.01 180	98.76 202	99.76 43	99.78 89	99.73 51	99.35 102	99.31 259	98.54 214	99.54 166	98.99 304	96.81 251	99.93 66	96.97 247	99.53 244	99.61 119

v144192		99.55 55	99.54 54	99.58 133	99.78 89	99.20 188	99.11 183	99.62 145	99.18 143	99.89 39	99.72 105	98.66 122	99.87 161	99.88 14	99.97 47	99.66 80

AllTest		99.21 142	99.07 150	99.63 109	99.78 89	99.64 79	99.12 182	99.83 40	98.63 207	99.63 134	99.72 105	98.68 117	99.75 287	96.38 274	99.83 146	99.51 170

TestCases				99.63 109	99.78 89	99.64 79		99.83 40	98.63 207	99.63 134	99.72 105	98.68 117	99.75 287	96.38 274	99.83 146	99.51 170

v1141		99.54 60	99.52 64	99.57 139	99.78 89	99.27 169	99.15 170	99.61 149	99.26 130	99.89 39	99.69 125	98.56 135	99.82 238	99.82 23	99.97 47	99.63 100

divwei89l23v2f112		99.54 60	99.52 64	99.57 139	99.78 89	99.27 169	99.15 170	99.61 149	99.26 130	99.89 39	99.69 125	98.56 135	99.82 238	99.82 23	99.96 60	99.63 100

v2v482		99.50 67	99.47 70	99.58 133	99.78 89	99.25 175	99.14 175	99.58 175	99.25 133	99.81 72	99.62 169	98.24 171	99.84 213	99.83 20	99.97 47	99.64 96

FMVSNet1		99.66 37	99.63 37	99.73 64	99.78 89	99.77 38	99.68 42	99.70 108	99.67 59	99.82 66	99.83 51	98.98 74	99.90 110	99.24 80	99.97 47	99.53 159

Vis-MVSNet (Re-imp)		98.77 216	98.58 215	99.34 203	99.78 89	98.88 225	99.61 61	99.56 182	99.11 155	99.24 230	99.56 201	93.00 297	99.78 273	97.43 222	99.89 109	99.35 225

ACMP	97.51 14	99.05 171	98.84 193	99.67 86	99.78 89	99.55 98	98.88 218	99.66 125	97.11 298	99.47 178	99.60 181	99.07 66	99.89 126	96.18 280	99.85 132	99.58 140
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs-eth3d		99.48 71	99.47 70	99.51 158	99.77 99	99.41 133	98.81 231	99.66 125	99.42 110	99.75 92	99.66 148	99.20 48	99.76 281	98.98 112	99.99 20	99.36 223

Patchmatch-RL test		98.60 227	98.36 236	99.33 205	99.77 99	99.07 205	98.27 281	99.87 20	98.91 176	99.74 100	99.72 105	90.57 319	99.79 265	98.55 148	99.85 132	99.11 264

v1192		99.57 48	99.57 49	99.57 139	99.77 99	99.22 182	99.04 194	99.60 163	99.18 143	99.87 52	99.72 105	99.08 64	99.85 197	99.89 13	99.98 36	99.66 80

v1		99.54 60	99.52 64	99.58 133	99.77 99	99.28 165	99.15 170	99.61 149	99.26 130	99.88 47	99.68 137	98.56 135	99.82 238	99.82 23	99.97 47	99.63 100

EG-PatchMatch MVS		99.57 48	99.56 52	99.62 118	99.77 99	99.33 156	99.26 137	99.76 79	99.32 121	99.80 75	99.78 80	99.29 37	99.87 161	99.15 94	99.91 101	99.66 80

pmmvs5		99.19 147	99.11 135	99.42 181	99.76 104	98.88 225	98.55 254	99.73 93	98.82 185	99.72 104	99.62 169	96.56 255	99.82 238	99.32 70	99.95 67	99.56 145

nrg030		99.70 28	99.66 33	99.82 26	99.76 104	99.84 19	99.61 61	99.70 108	99.93 4	99.78 83	99.68 137	99.10 59	99.78 273	99.45 52	99.96 60	99.83 18

v148		99.40 91	99.41 81	99.39 192	99.76 104	98.94 215	99.09 188	99.59 167	99.17 148	99.81 72	99.61 178	98.41 157	99.69 307	99.32 70	99.94 80	99.53 159

region2R		99.23 130	99.05 157	99.77 40	99.76 104	99.70 60	99.31 121	99.59 167	98.41 224	99.32 217	99.36 246	98.73 111	99.93 66	97.29 229	99.74 193	99.67 70

MP-MVS		99.06 168	98.83 196	99.76 43	99.76 104	99.71 53	99.32 114	99.50 209	98.35 234	98.97 259	99.48 222	98.37 162	99.92 84	95.95 294	99.75 186	99.63 100
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMMVS2		99.48 71	99.45 74	99.57 139	99.76 104	98.99 209	98.09 298	99.90 14	98.95 170	99.78 83	99.58 189	99.57 20	99.93 66	99.48 50	99.95 67	99.79 30

CP-MVSNet		99.54 60	99.43 79	99.87 16	99.76 104	99.82 28	99.57 69	99.61 149	99.54 87	99.80 75	99.64 154	97.79 205	99.95 41	99.21 81	99.94 80	99.84 15

mPP-MVS		99.19 147	99.00 169	99.76 43	99.76 104	99.68 67	99.38 95	99.54 189	98.34 238	99.01 256	99.50 219	98.53 145	99.93 66	97.18 239	99.78 177	99.66 80

semantic-postprocess				98.51 282	99.75 112	95.90 320		99.84 37	99.84 23	99.89 39	99.73 99	95.96 271	99.99 4	99.33 67	100.00 1	99.63 100

ACMMP_Plus		99.28 118	99.11 135	99.79 35	99.75 112	99.81 29	98.95 211	99.53 194	98.27 243	99.53 169	99.73 99	98.75 108	99.87 161	97.70 203	99.83 146	99.68 63

v1921920		99.56 51	99.57 49	99.55 148	99.75 112	99.11 197	99.05 192	99.61 149	99.15 150	99.88 47	99.71 112	99.08 64	99.87 161	99.90 9	99.97 47	99.66 80

testgi		99.29 117	99.26 113	99.37 198	99.75 112	98.81 232	98.84 225	99.89 15	98.38 227	99.75 92	99.04 303	99.36 33	99.86 181	99.08 104	99.25 282	99.45 194

PGM-MVS		99.20 144	99.01 167	99.77 40	99.75 112	99.71 53	99.16 168	99.72 102	97.99 254	99.42 187	99.60 181	98.81 91	99.93 66	96.91 249	99.74 193	99.66 80

jason		99.16 154	99.11 135	99.32 209	99.75 112	98.44 247	98.26 282	99.39 241	98.70 202	99.74 100	99.30 259	98.54 141	99.97 16	98.48 151	99.82 155	99.55 148
jason: jason.
Anonymous20231206		99.35 103	99.31 97	99.47 167	99.74 118	99.06 207	99.28 133	99.74 90	99.23 137	99.72 104	99.53 210	97.63 220	99.88 141	99.11 102	99.84 136	99.48 183

ACMMPR		99.23 130	99.06 152	99.76 43	99.74 118	99.69 64	99.31 121	99.59 167	98.36 229	99.35 209	99.38 240	98.61 131	99.93 66	97.43 222	99.75 186	99.67 70

IterMVS		98.97 186	99.16 123	98.42 287	99.74 118	95.64 327	98.06 303	99.83 40	99.83 26	99.85 58	99.74 95	96.10 269	99.99 4	99.27 79	100.00 1	99.63 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
conf0.01		97.19 292	96.74 298	98.51 282	99.73 121	98.35 255	99.35 102	95.78 349	96.54 306	99.39 198	99.08 291	86.57 344	99.88 141	95.69 302	98.57 320	97.30 347

conf0.002		97.19 292	96.74 298	98.51 282	99.73 121	98.35 255	99.35 102	95.78 349	96.54 306	99.39 198	99.08 291	86.57 344	99.88 141	95.69 302	98.57 320	97.30 347

thresconf0.02		97.25 287	96.74 298	98.75 272	99.73 121	98.35 255	99.35 102	95.78 349	96.54 306	99.39 198	99.08 291	86.57 344	99.88 141	95.69 302	98.57 320	98.02 330

tfpn_n400		97.25 287	96.74 298	98.75 272	99.73 121	98.35 255	99.35 102	95.78 349	96.54 306	99.39 198	99.08 291	86.57 344	99.88 141	95.69 302	98.57 320	98.02 330

tfpnconf		97.25 287	96.74 298	98.75 272	99.73 121	98.35 255	99.35 102	95.78 349	96.54 306	99.39 198	99.08 291	86.57 344	99.88 141	95.69 302	98.57 320	98.02 330

tfpnview11		97.25 287	96.74 298	98.75 272	99.73 121	98.35 255	99.35 102	95.78 349	96.54 306	99.39 198	99.08 291	86.57 344	99.88 141	95.69 302	98.57 320	98.02 330

HFP-MVS		99.25 125	99.08 146	99.76 43	99.73 121	99.70 60	99.31 121	99.59 167	98.36 229	99.36 207	99.37 241	98.80 95	99.91 93	97.43 222	99.75 186	99.68 63

#test#		99.12 160	98.90 185	99.76 43	99.73 121	99.70 60	99.10 184	99.59 167	97.60 277	99.36 207	99.37 241	98.80 95	99.91 93	96.84 253	99.75 186	99.68 63

testmv		99.53 66	99.51 67	99.59 129	99.73 121	99.31 159	98.48 263	99.92 7	99.57 86	99.87 52	99.79 71	99.12 58	99.91 93	99.16 93	99.99 20	99.55 148

114514_t		98.49 238	98.11 253	99.64 105	99.73 121	99.58 92	99.24 143	99.76 79	89.94 352	99.42 187	99.56 201	97.76 207	99.86 181	97.74 201	99.82 155	99.47 188

UA-Net		99.78 15	99.76 18	99.86 18	99.72 131	99.71 53	99.91 3	99.95 5	99.96 2	99.71 108	99.91 20	99.15 53	99.97 16	99.50 49	100.00 1	99.90 5

N_pmnet		98.73 221	98.53 221	99.35 202	99.72 131	98.67 238	98.34 277	94.65 359	98.35 234	99.79 80	99.68 137	98.03 186	99.93 66	98.28 166	99.92 91	99.44 199

DeepC-MVS	98.90 4	99.62 44	99.61 42	99.67 86	99.72 131	99.44 119	99.24 143	99.71 105	99.27 126	99.93 26	99.90 23	99.70 12	99.93 66	98.99 110	99.99 20	99.64 96
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		99.27 123	99.11 135	99.75 52	99.71 134	99.71 53	99.37 99	99.61 149	99.29 122	98.76 284	99.47 225	98.47 151	99.88 141	97.62 210	99.73 199	99.67 70

X-MVStestdata		96.09 323	94.87 331	99.75 52	99.71 134	99.71 53	99.37 99	99.61 149	99.29 122	98.76 284	61.30 365	98.47 151	99.88 141	97.62 210	99.73 199	99.67 70

VDDNet		98.97 186	98.82 197	99.42 181	99.71 134	98.81 232	99.62 57	98.68 305	99.81 28	99.38 205	99.80 64	94.25 285	99.85 197	98.79 131	99.32 273	99.59 136

abl_6		99.36 101	99.23 118	99.75 52	99.71 134	99.74 50	99.33 111	99.76 79	99.07 162	99.65 128	99.63 162	99.09 61	99.92 84	97.13 241	99.76 183	99.58 140

DSMNet-mixed		99.48 71	99.65 34	98.95 252	99.71 134	97.27 298	99.50 75	99.82 48	99.59 84	99.41 193	99.85 45	99.62 16	100.00 1	99.53 47	99.89 109	99.59 136

CSCG		99.37 98	99.29 107	99.60 127	99.71 134	99.46 112	99.43 85	99.85 29	98.79 189	99.41 193	99.60 181	98.92 81	99.92 84	98.02 184	99.92 91	99.43 205

LF4IMVS		99.01 180	98.92 182	99.27 216	99.71 134	99.28 165	98.59 248	99.77 73	98.32 240	99.39 198	99.41 235	98.62 129	99.84 213	96.62 266	99.84 136	98.69 301

OPM-MVS		99.26 124	99.13 129	99.63 109	99.70 141	99.61 89	98.58 249	99.48 214	98.50 217	99.52 171	99.63 162	99.14 54	99.76 281	97.89 192	99.77 181	99.51 170

new_pmnet		98.88 203	98.89 186	98.84 264	99.70 141	97.62 292	98.15 290	99.50 209	97.98 255	99.62 141	99.54 208	98.15 180	99.94 55	97.55 215	99.84 136	98.95 286

view600		96.86 302	96.52 305	97.88 307	99.69 143	95.87 322	99.39 89	97.68 330	99.11 155	98.96 261	97.82 347	87.40 330	99.79 265	89.78 343	98.83 301	97.98 334

view800		96.86 302	96.52 305	97.88 307	99.69 143	95.87 322	99.39 89	97.68 330	99.11 155	98.96 261	97.82 347	87.40 330	99.79 265	89.78 343	98.83 301	97.98 334

conf0.05thres1000		96.86 302	96.52 305	97.88 307	99.69 143	95.87 322	99.39 89	97.68 330	99.11 155	98.96 261	97.82 347	87.40 330	99.79 265	89.78 343	98.83 301	97.98 334

tfpn		96.86 302	96.52 305	97.88 307	99.69 143	95.87 322	99.39 89	97.68 330	99.11 155	98.96 261	97.82 347	87.40 330	99.79 265	89.78 343	98.83 301	97.98 334

PNet_i23d		97.02 297	97.87 270	94.49 344	99.69 143	84.81 363	95.18 356	99.85 29	97.83 266	99.32 217	99.57 196	95.53 276	99.47 349	96.09 282	97.74 348	99.18 251

wuyk23d		97.58 279	99.13 129	92.93 345	99.69 143	99.49 104	99.52 73	99.77 73	97.97 256	99.96 8	99.79 71	99.84 4	99.94 55	95.85 296	99.82 155	79.36 358

DeepMVS_CX				97.98 303	99.69 143	96.95 303		99.26 269	75.51 357	95.74 356	98.28 340	96.47 259	99.62 337	91.23 341	97.89 346	97.38 345

VPA-MVSNet		99.66 37	99.62 38	99.79 35	99.68 150	99.75 45	99.62 57	99.69 114	99.85 19	99.80 75	99.81 62	98.81 91	99.91 93	99.47 51	99.88 115	99.70 54

UnsupCasMVSNet_eth		98.83 208	98.57 217	99.59 129	99.68 150	99.45 117	98.99 204	99.67 121	99.48 94	99.55 163	99.36 246	94.92 278	99.86 181	98.95 121	96.57 353	99.45 194

Test_1112_low_res		98.95 192	98.73 203	99.63 109	99.68 150	99.15 194	98.09 298	99.80 60	97.14 295	99.46 180	99.40 236	96.11 268	99.89 126	99.01 109	99.84 136	99.84 15

MVE	92.54 22	96.66 310	96.11 314	98.31 294	99.68 150	97.55 294	97.94 317	95.60 355	99.37 115	90.68 359	98.70 329	96.56 255	98.61 359	86.94 357	99.55 239	98.77 299
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tfpn1000		97.28 286	96.83 295	98.64 279	99.67 154	97.68 291	99.41 86	95.47 356	97.14 295	99.43 185	99.07 298	85.87 351	99.88 141	96.78 256	98.67 316	98.34 316

our_test_3		98.85 207	99.09 144	98.13 300	99.66 155	94.90 336	97.72 326	99.58 175	99.07 162	99.64 130	99.62 169	98.19 177	99.93 66	98.41 154	99.95 67	99.55 148

ppachtmachnet_test		98.89 202	99.12 132	98.20 297	99.66 155	95.24 333	97.63 328	99.68 117	99.08 160	99.78 83	99.62 169	98.65 124	99.88 141	98.02 184	99.96 60	99.48 183

CP-MVS		99.23 130	99.05 157	99.75 52	99.66 155	99.66 72	99.38 95	99.62 145	98.38 227	99.06 253	99.27 265	98.79 98	99.94 55	97.51 218	99.82 155	99.66 80

1112_ss		99.05 171	98.84 193	99.67 86	99.66 155	99.29 163	98.52 259	99.82 48	97.65 274	99.43 185	99.16 282	96.42 261	99.91 93	99.07 105	99.84 136	99.80 25

1111		97.29 285	96.71 304	99.04 247	99.65 159	97.72 287	98.35 275	99.80 60	99.40 111	99.66 122	99.43 231	75.10 365	99.87 161	98.98 112	99.98 36	99.52 167

.test1245		85.84 334	89.27 335	75.54 347	99.65 159	97.72 287	98.35 275	99.80 60	99.40 111	99.66 122	99.43 231	75.10 365	99.87 161	98.98 112	33.07 359	29.03 360

YYNet1		98.95 192	98.99 172	98.84 264	99.64 161	97.14 301	98.22 285	99.32 255	98.92 175	99.59 149	99.66 148	97.40 227	99.83 230	98.27 167	99.90 103	99.55 148

MDA-MVSNet_test_wron		98.95 192	98.99 172	98.85 262	99.64 161	97.16 300	98.23 284	99.33 253	98.93 173	99.56 159	99.66 148	97.39 229	99.83 230	98.29 165	99.88 115	99.55 148

tfpn111		96.50 313	96.12 313	97.65 318	99.63 163	95.93 317	99.18 155	97.57 335	98.75 197	98.70 289	97.31 357	87.04 335	99.72 294	88.27 350	98.61 319	97.30 347

conf200view11		96.43 314	96.03 316	97.63 319	99.63 163	95.93 317	99.18 155	97.57 335	98.75 197	98.70 289	97.31 357	87.04 335	99.67 320	87.62 352	98.51 329	97.30 347

thres100view900		96.39 316	96.03 316	97.47 322	99.63 163	95.93 317	99.18 155	97.57 335	98.75 197	98.70 289	97.31 357	87.04 335	99.67 320	87.62 352	98.51 329	96.81 352

thres600view7		96.60 311	96.16 312	97.93 305	99.63 163	96.09 315	99.18 155	97.57 335	98.77 192	98.72 287	97.32 356	87.04 335	99.72 294	88.57 348	98.62 318	97.98 334

ITE_SJBPF				99.38 194	99.63 163	99.44 119		99.73 93	98.56 212	99.33 215	99.53 210	98.88 87	99.68 315	96.01 288	99.65 221	99.02 283

test_part2					99.62 168	99.67 69				99.55 163

ESAPD		98.87 204	98.58 215	99.74 56	99.62 168	99.67 69	98.74 238	99.53 194	97.71 270	99.55 163	99.57 196	98.40 159	99.90 110	94.47 327	99.68 210	99.66 80

CPTT-MVS		98.74 219	98.44 225	99.64 105	99.61 170	99.38 143	99.18 155	99.55 185	96.49 312	99.27 224	99.37 241	97.11 244	99.92 84	95.74 301	99.67 216	99.62 114

tfpn_ndepth		96.93 301	96.43 309	98.42 287	99.60 171	97.72 287	99.22 149	95.16 357	95.91 319	99.26 226	98.79 324	85.56 352	99.87 161	96.03 287	98.35 333	97.68 342

MSDG		99.08 166	98.98 175	99.37 198	99.60 171	99.13 195	97.54 332	99.74 90	98.84 184	99.53 169	99.55 206	99.10 59	99.79 265	97.07 243	99.86 129	99.18 251

FPMVS		96.32 318	95.50 325	98.79 269	99.60 171	98.17 269	98.46 268	98.80 299	97.16 294	96.28 351	99.63 162	82.19 356	99.09 355	88.45 349	98.89 300	99.10 268

xiu_mvs_v1_base_debu		99.23 130	99.34 93	98.91 256	99.59 174	98.23 264	98.47 264	99.66 125	99.61 77	99.68 114	98.94 315	99.39 24	99.97 16	99.18 87	99.55 239	98.51 309

xiu_mvs_v1_base		99.23 130	99.34 93	98.91 256	99.59 174	98.23 264	98.47 264	99.66 125	99.61 77	99.68 114	98.94 315	99.39 24	99.97 16	99.18 87	99.55 239	98.51 309

xiu_mvs_v1_base_debi		99.23 130	99.34 93	98.91 256	99.59 174	98.23 264	98.47 264	99.66 125	99.61 77	99.68 114	98.94 315	99.39 24	99.97 16	99.18 87	99.55 239	98.51 309

tfpn200view9		96.30 319	95.89 318	97.53 320	99.58 177	96.11 313	99.00 201	97.54 340	98.43 221	98.52 303	96.98 362	86.85 339	99.67 320	87.62 352	98.51 329	96.81 352

EI-MVSNet		99.38 96	99.44 76	99.21 230	99.58 177	98.09 275	99.26 137	99.46 221	99.62 73	99.75 92	99.67 143	98.54 141	99.85 197	99.15 94	99.92 91	99.68 63

CVMVSNet		98.61 226	98.88 187	97.80 313	99.58 177	93.60 341	99.26 137	99.64 139	99.66 64	99.72 104	99.67 143	93.26 292	99.93 66	99.30 73	99.81 164	99.87 10

thres400		96.40 315	95.89 318	97.92 306	99.58 177	96.11 313	99.00 201	97.54 340	98.43 221	98.52 303	96.98 362	86.85 339	99.67 320	87.62 352	98.51 329	97.98 334

MCST-MVS		99.02 176	98.81 198	99.65 98	99.58 177	99.49 104	98.58 249	99.07 287	98.40 225	99.04 254	99.25 269	98.51 149	99.80 262	97.31 228	99.51 246	99.65 90

HQP_MVS		98.90 199	98.68 207	99.55 148	99.58 177	99.24 178	98.80 232	99.54 189	98.94 171	99.14 244	99.25 269	97.24 235	99.82 238	95.84 297	99.78 177	99.60 125

plane_prior7					99.58 177	99.38 143

TranMVSNet+NR-MVSNet		99.54 60	99.47 70	99.76 43	99.58 177	99.64 79	99.30 124	99.63 142	99.61 77	99.71 108	99.56 201	98.76 105	99.96 33	99.14 100	99.92 91	99.68 63

MVS_111021_LR		99.13 158	99.03 163	99.42 181	99.58 177	99.32 158	97.91 321	99.73 93	98.68 203	99.31 219	99.48 222	99.09 61	99.66 325	97.70 203	99.77 181	99.29 237

EI-MVSNet-UG-set		99.48 71	99.50 68	99.42 181	99.57 186	98.65 241	99.24 143	99.46 221	99.68 57	99.80 75	99.66 148	98.99 73	99.89 126	99.19 85	99.90 103	99.72 47

EI-MVSNet-Vis-set		99.47 77	99.49 69	99.42 181	99.57 186	98.66 239	99.24 143	99.46 221	99.67 59	99.79 80	99.65 153	98.97 76	99.89 126	99.15 94	99.89 109	99.71 50

pmmvs4		99.13 158	99.06 152	99.36 201	99.57 186	99.10 200	98.01 306	99.25 272	98.78 191	99.58 150	99.44 230	98.24 171	99.76 281	98.74 136	99.93 88	99.22 241

DI_MVS_plusplus_test		98.80 213	98.65 209	99.27 216	99.57 186	98.90 222	98.44 270	97.95 326	99.02 166	99.51 173	99.23 277	96.18 267	99.76 281	98.52 150	99.42 261	99.14 259

MVSFormer		99.41 88	99.44 76	99.31 211	99.57 186	98.40 250	99.77 14	99.80 60	99.73 42	99.63 134	99.30 259	98.02 188	99.98 7	99.43 54	99.69 208	99.55 148

lupinMVS		98.96 189	98.87 188	99.24 227	99.57 186	98.40 250	98.12 294	99.18 280	98.28 242	99.63 134	99.13 284	98.02 188	99.97 16	98.22 169	99.69 208	99.35 225

Test4		98.65 224	98.44 225	99.27 216	99.57 186	98.86 228	98.43 271	99.41 232	98.85 181	99.57 152	98.95 314	93.05 295	99.75 287	98.57 146	99.56 233	99.19 248

ab-mvs		99.33 111	99.28 109	99.47 167	99.57 186	99.39 137	99.78 13	99.43 229	98.87 179	99.57 152	99.82 59	98.06 185	99.87 161	98.69 140	99.73 199	99.15 255

DP-MVS		99.48 71	99.39 83	99.74 56	99.57 186	99.62 85	99.29 132	99.61 149	99.87 13	99.74 100	99.76 89	98.69 115	99.87 161	98.20 171	99.80 169	99.75 41

F-COLMAP		98.74 219	98.45 224	99.62 118	99.57 186	99.47 108	98.84 225	99.65 134	96.31 314	98.93 267	99.19 281	97.68 213	99.87 161	96.52 269	99.37 268	99.53 159

CLD-MVS		98.76 217	98.57 217	99.33 205	99.57 186	98.97 212	97.53 334	99.55 185	96.41 313	99.27 224	99.13 284	99.07 66	99.78 273	96.73 260	99.89 109	99.23 240
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_normal		98.82 210	98.67 208	99.27 216	99.56 197	98.83 231	98.22 285	98.01 323	99.03 165	99.49 177	99.24 274	96.21 265	99.76 281	98.69 140	99.56 233	99.22 241

UnsupCasMVSNet_bld		98.55 233	98.27 242	99.40 189	99.56 197	99.37 146	97.97 314	99.68 117	97.49 284	99.08 249	99.35 251	95.41 277	99.82 238	97.70 203	98.19 339	99.01 284

APDe-MVS		99.48 71	99.36 91	99.85 20	99.55 199	99.81 29	99.50 75	99.69 114	98.99 167	99.75 92	99.71 112	98.79 98	99.93 66	98.46 152	99.85 132	99.80 25

PVSNet_BlendedMVS		99.03 174	99.01 167	99.09 240	99.54 200	97.99 279	98.58 249	99.82 48	97.62 275	99.34 213	99.71 112	98.52 147	99.77 279	97.98 188	99.97 47	99.52 167

PVSNet_Blended		98.70 222	98.59 213	99.02 249	99.54 200	97.99 279	97.58 331	99.82 48	95.70 325	99.34 213	98.98 307	98.52 147	99.77 279	97.98 188	99.83 146	99.30 234

USDC		98.96 189	98.93 179	99.05 246	99.54 200	97.99 279	97.07 344	99.80 60	98.21 245	99.75 92	99.77 86	98.43 155	99.64 334	97.90 191	99.88 115	99.51 170

APD-MVS_3200maxsize		99.31 114	99.16 123	99.74 56	99.53 203	99.75 45	99.27 136	99.61 149	99.19 142	99.57 152	99.64 154	98.76 105	99.90 110	97.29 229	99.62 224	99.56 145

MIMVSNet		98.43 243	98.20 247	99.11 238	99.53 203	98.38 253	99.58 68	98.61 307	98.96 169	99.33 215	99.76 89	90.92 312	99.81 257	97.38 225	99.76 183	99.15 255

Regformer-3		99.41 88	99.41 81	99.40 189	99.52 205	98.70 236	99.17 162	99.44 226	99.62 73	99.75 92	99.60 181	98.90 84	99.85 197	98.89 125	99.84 136	99.65 90

Regformer-4		99.45 80	99.44 76	99.50 160	99.52 205	98.94 215	99.17 162	99.53 194	99.64 69	99.76 91	99.60 181	98.96 79	99.90 110	98.91 124	99.84 136	99.67 70

HPM-MVS++		98.96 189	98.70 205	99.74 56	99.52 205	99.71 53	98.86 221	99.19 279	98.47 220	98.59 299	99.06 299	98.08 184	99.91 93	96.94 248	99.60 229	99.60 125

GA-MVS		97.99 271	97.68 278	98.93 255	99.52 205	98.04 278	97.19 343	99.05 290	98.32 240	98.81 278	98.97 310	89.89 326	99.41 353	98.33 161	99.05 292	99.34 227

test222					99.51 209	99.08 203	97.83 324	99.29 263	95.21 332	98.68 293	99.31 256	97.28 234			99.38 266	99.43 205

testdata				99.42 181	99.51 209	98.93 219		99.30 262	96.20 315	98.87 274	99.40 236	98.33 166	99.89 126	96.29 277	99.28 277	99.44 199

plane_prior1					99.51 209

UniMVSNet (Re)		99.37 98	99.26 113	99.68 83	99.51 209	99.58 92	98.98 208	99.60 163	99.43 108	99.70 110	99.36 246	97.70 209	99.88 141	99.20 84	99.87 122	99.59 136

DELS-MVS		99.34 108	99.30 102	99.48 165	99.51 209	99.36 149	98.12 294	99.53 194	99.36 117	99.41 193	99.61 178	99.22 47	99.87 161	99.21 81	99.68 210	99.20 245
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
新几何1				99.52 155	99.50 214	99.22 182		99.26 269	95.66 327	98.60 298	99.28 263	97.67 214	99.89 126	95.95 294	99.32 273	99.45 194

SD-MVS		99.01 180	99.30 102	98.15 299	99.50 214	99.40 134	98.94 214	99.61 149	99.22 140	99.75 92	99.82 59	99.54 22	95.51 361	97.48 219	99.87 122	99.54 156

CDPH-MVS		98.56 231	98.20 247	99.61 121	99.50 214	99.46 112	98.32 279	99.41 232	95.22 331	99.21 235	99.10 290	98.34 164	99.82 238	95.09 321	99.66 219	99.56 145

APD-MVS		98.87 204	98.59 213	99.71 73	99.50 214	99.62 85	99.01 199	99.57 179	96.80 304	99.54 166	99.63 162	98.29 167	99.91 93	95.24 318	99.71 205	99.61 119
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		99.12 160	99.02 164	99.40 189	99.50 214	99.11 197	97.92 319	99.71 105	98.76 195	99.08 249	99.47 225	99.17 51	99.54 345	97.85 195	99.76 183	99.54 156

旧先验1					99.49 219	99.29 163		99.26 269			99.39 239	97.67 214			99.36 269	99.46 192

1121		98.56 231	98.24 243	99.52 155	99.49 219	99.24 178	99.30 124	99.22 277	95.77 323	98.52 303	99.29 262	97.39 229	99.85 197	95.79 299	99.34 270	99.46 192

GBi-Net		99.42 85	99.31 97	99.73 64	99.49 219	99.77 38	99.68 42	99.70 108	99.44 103	99.62 141	99.83 51	97.21 238	99.90 110	98.96 117	99.90 103	99.53 159

test1		99.42 85	99.31 97	99.73 64	99.49 219	99.77 38	99.68 42	99.70 108	99.44 103	99.62 141	99.83 51	97.21 238	99.90 110	98.96 117	99.90 103	99.53 159

FMVSNet2		99.35 103	99.28 109	99.55 148	99.49 219	99.35 153	99.45 82	99.57 179	99.44 103	99.70 110	99.74 95	97.21 238	99.87 161	99.03 107	99.94 80	99.44 199

DP-MVS Recon		98.50 236	98.23 244	99.31 211	99.49 219	99.46 112	98.56 253	99.63 142	94.86 337	98.85 276	99.37 241	97.81 203	99.59 342	96.08 283	99.44 254	98.88 291

MVP-Stereo		99.16 154	99.08 146	99.43 179	99.48 225	99.07 205	99.08 189	99.55 185	98.63 207	99.31 219	99.68 137	98.19 177	99.78 273	98.18 175	99.58 231	99.45 194
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres200		96.09 323	95.68 324	97.33 326	99.48 225	96.22 312	98.53 258	97.57 335	98.06 251	98.37 311	96.73 364	86.84 341	99.61 341	86.99 356	98.57 320	96.16 355

sss		98.90 199	98.77 201	99.27 216	99.48 225	98.44 247	98.72 242	99.32 255	97.94 258	99.37 206	99.35 251	96.31 263	99.91 93	98.85 127	99.63 223	99.47 188

PAPM_NR		98.36 250	98.04 257	99.33 205	99.48 225	98.93 219	98.79 235	99.28 266	97.54 282	98.56 302	98.57 333	97.12 243	99.69 307	94.09 333	98.90 299	99.38 216

TAMVS		99.49 69	99.45 74	99.63 109	99.48 225	99.42 129	99.45 82	99.57 179	99.66 64	99.78 83	99.83 51	97.85 200	99.86 181	99.44 53	99.96 60	99.61 119

原ACMM1				99.37 198	99.47 230	98.87 227		99.27 267	96.74 305	98.26 314	99.32 254	97.93 194	99.82 238	95.96 293	99.38 266	99.43 205

plane_prior6					99.47 230	99.26 171						97.24 235

UniMVSNet_NR-MVSNet		99.37 98	99.25 115	99.72 69	99.47 230	99.56 95	98.97 209	99.61 149	99.43 108	99.67 118	99.28 263	97.85 200	99.95 41	99.17 90	99.81 164	99.65 90

TAPA-MVS	97.92 13	98.03 269	97.55 281	99.46 170	99.47 230	99.44 119	98.50 261	99.62 145	86.79 353	99.07 252	99.26 267	98.26 170	99.62 337	97.28 231	99.73 199	99.31 233
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SMA-MVS		99.23 130	99.06 152	99.74 56	99.46 234	99.76 42	99.13 180	99.58 175	97.62 275	99.68 114	99.64 154	99.02 72	99.83 230	97.61 212	99.82 155	99.63 100

test12356		98.43 243	98.39 232	98.55 281	99.46 234	96.36 310	97.32 341	99.81 56	97.60 277	99.62 141	99.37 241	94.57 282	99.89 126	97.80 198	99.92 91	99.40 210

test1235678		98.93 196	98.84 193	99.19 233	99.46 234	98.55 243	97.53 334	99.77 73	98.76 195	99.69 112	99.48 222	96.69 252	99.90 110	98.30 164	99.91 101	99.11 264

PVSNet	97.47 15	98.42 245	98.44 225	98.35 291	99.46 234	96.26 311	96.70 349	99.34 252	97.68 273	99.00 257	99.13 284	97.40 227	99.72 294	97.59 214	99.68 210	99.08 274

TinyColmap		98.97 186	98.93 179	99.07 244	99.46 234	98.19 267	97.75 325	99.75 85	98.79 189	99.54 166	99.70 119	98.97 76	99.62 337	96.63 265	99.83 146	99.41 209

PatchMatch-RL		98.68 223	98.47 222	99.30 213	99.44 239	99.28 165	98.14 292	99.54 189	97.12 297	99.11 247	99.25 269	97.80 204	99.70 301	96.51 270	99.30 275	98.93 288

PCF-MVS	96.03 18	96.73 308	95.86 320	99.33 205	99.44 239	99.16 192	96.87 346	99.44 226	86.58 354	98.95 265	99.40 236	94.38 284	99.88 141	87.93 351	99.80 169	98.95 286
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VDD-MVS		99.20 144	99.11 135	99.44 176	99.43 241	98.98 210	99.50 75	98.32 319	99.80 31	99.56 159	99.69 125	96.99 248	99.85 197	98.99 110	99.73 199	99.50 176

DU-MVS		99.33 111	99.21 121	99.71 73	99.43 241	99.56 95	98.83 227	99.53 194	99.38 114	99.67 118	99.36 246	97.67 214	99.95 41	99.17 90	99.81 164	99.63 100

NR-MVSNet		99.40 91	99.31 97	99.68 83	99.43 241	99.55 98	99.73 22	99.50 209	99.46 101	99.88 47	99.36 246	97.54 221	99.87 161	98.97 116	99.87 122	99.63 100

WTY-MVS		98.59 229	98.37 235	99.26 221	99.43 241	98.40 250	98.74 238	99.13 286	98.10 249	99.21 235	99.24 274	94.82 280	99.90 110	97.86 194	98.77 308	99.49 182

casdiffmvs		99.24 128	99.23 118	99.26 221	99.42 245	98.85 230	99.48 79	99.58 175	99.67 59	98.70 289	99.67 143	97.85 200	99.72 294	99.41 59	99.28 277	99.20 245

Regformer-1		99.32 113	99.27 111	99.47 167	99.41 246	98.95 214	98.99 204	99.48 214	99.48 94	99.66 122	99.52 212	98.78 101	99.87 161	98.36 158	99.74 193	99.60 125

Regformer-2		99.34 108	99.27 111	99.53 153	99.41 246	99.10 200	98.99 204	99.53 194	99.47 98	99.66 122	99.52 212	98.80 95	99.89 126	98.31 163	99.74 193	99.60 125

pmmvs3		98.08 267	97.80 272	98.91 256	99.41 246	97.69 290	97.87 322	99.66 125	95.87 320	99.50 175	99.51 216	90.35 321	99.97 16	98.55 148	99.47 251	99.08 274

NP-MVS					99.40 249	99.13 195					98.83 321

QAPM		98.40 248	97.99 259	99.65 98	99.39 250	99.47 108	99.67 47	99.52 204	91.70 349	98.78 283	99.80 64	98.55 139	99.95 41	94.71 325	99.75 186	99.53 159

OMC-MVS		98.90 199	98.72 204	99.44 176	99.39 250	99.42 129	98.58 249	99.64 139	97.31 291	99.44 181	99.62 169	98.59 133	99.69 307	96.17 281	99.79 172	99.22 241

3Dnovator	99.15 2	99.43 82	99.36 91	99.65 98	99.39 250	99.42 129	99.70 30	99.56 182	99.23 137	99.35 209	99.80 64	99.17 51	99.95 41	98.21 170	99.84 136	99.59 136

Fast-Effi-MVS+		99.02 176	98.87 188	99.46 170	99.38 253	99.50 102	99.04 194	99.79 68	97.17 293	98.62 296	98.74 328	99.34 34	99.95 41	98.32 162	99.41 263	98.92 289

BH-untuned		98.22 261	98.09 254	98.58 280	99.38 253	97.24 299	98.55 254	98.98 293	97.81 267	99.20 240	98.76 326	97.01 247	99.65 332	94.83 322	98.33 334	98.86 293

xiu_mvs_v2_base		99.02 176	99.11 135	98.77 270	99.37 255	98.09 275	98.13 293	99.51 206	99.47 98	99.42 187	98.54 335	99.38 28	99.97 16	98.83 128	99.33 272	98.24 321

PS-MVSNAJ		99.00 183	99.08 146	98.76 271	99.37 255	98.10 274	98.00 308	99.51 206	99.47 98	99.41 193	98.50 337	99.28 39	99.97 16	98.83 128	99.34 270	98.20 325

diffmvs		98.94 195	98.87 188	99.13 237	99.37 255	98.90 222	99.25 141	99.64 139	97.55 281	99.04 254	99.58 189	97.23 237	99.64 334	98.73 137	99.44 254	98.86 293

ambc				99.20 232	99.35 258	98.53 244	99.17 162	99.46 221		99.67 118	99.80 64	98.46 153	99.70 301	97.92 190	99.70 207	99.38 216

TEST9					99.35 258	99.35 153	98.11 296	99.41 232	94.83 339	97.92 330	98.99 304	98.02 188	99.85 197

train_agg		98.35 253	97.95 263	99.57 139	99.35 258	99.35 153	98.11 296	99.41 232	94.90 335	97.92 330	98.99 304	98.02 188	99.85 197	95.38 316	99.44 254	99.50 176

agg_prior1		98.33 256	97.92 265	99.57 139	99.35 258	99.36 149	97.99 310	99.39 241	94.85 338	97.76 340	98.98 307	98.03 186	99.85 197	95.49 311	99.44 254	99.51 170

agg_prior					99.35 258	99.36 149		99.39 241		97.76 340			99.85 197

test_prior3		98.62 225	98.34 238	99.46 170	99.35 258	99.22 182	97.95 315	99.39 241	97.87 261	98.05 325	99.05 300	97.90 195	99.69 307	95.99 290	99.49 249	99.48 183

test_prior				99.46 170	99.35 258	99.22 182		99.39 241					99.69 307			99.48 183

MVS_Test		99.28 118	99.31 97	99.19 233	99.35 258	98.79 234	99.36 101	99.49 213	99.17 148	99.21 235	99.67 143	98.78 101	99.66 325	99.09 103	99.66 219	99.10 268

CDS-MVSNet		99.22 139	99.13 129	99.50 160	99.35 258	99.11 197	98.96 210	99.54 189	99.46 101	99.61 146	99.70 119	96.31 263	99.83 230	99.34 65	99.88 115	99.55 148
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	98.92 3	99.35 103	99.24 116	99.67 86	99.35 258	99.47 108	99.62 57	99.50 209	99.44 103	99.12 246	99.78 80	98.77 104	99.94 55	97.87 193	99.72 204	99.62 114

Anonymous202405211		98.75 218	98.46 223	99.63 109	99.34 268	99.66 72	99.47 81	97.65 334	99.28 125	99.56 159	99.50 219	93.15 293	99.84 213	98.62 144	99.58 231	99.40 210

CHOSEN 280x420		98.41 246	98.41 230	98.40 289	99.34 268	95.89 321	96.94 345	99.44 226	98.80 188	99.25 227	99.52 212	93.51 290	99.98 7	98.94 122	99.98 36	99.32 232

test_8					99.34 268	99.31 159	98.08 301	99.40 238	94.90 335	97.87 334	98.97 310	98.02 188	99.84 213

agg_prior3		98.24 258	97.81 271	99.53 153	99.34 268	99.26 171	98.09 298	99.39 241	94.21 343	97.77 339	98.96 312	97.74 208	99.84 213	95.38 316	99.44 254	99.50 176

TSAR-MVS + GP.		99.12 160	99.04 162	99.38 194	99.34 268	99.16 192	98.15 290	99.29 263	98.18 247	99.63 134	99.62 169	99.18 50	99.68 315	98.20 171	99.74 193	99.30 234

LCM-MVSNet-Re		99.28 118	99.15 125	99.67 86	99.33 273	99.76 42	99.34 109	99.97 3	98.93 173	99.91 34	99.79 71	98.68 117	99.93 66	96.80 255	99.56 233	99.30 234

PLC	97.35 16	98.36 250	97.99 259	99.48 165	99.32 274	99.24 178	98.50 261	99.51 206	95.19 333	98.58 300	98.96 312	96.95 249	99.83 230	95.63 308	99.25 282	99.37 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Effi-MVS+		99.06 168	98.97 176	99.34 203	99.31 275	98.98 210	98.31 280	99.91 11	98.81 186	98.79 281	98.94 315	99.14 54	99.84 213	98.79 131	98.74 312	99.20 245

HQP-NCC					99.31 275		97.98 311		97.45 285	98.15 319

ACMP_Plane					99.31 275		97.98 311		97.45 285	98.15 319

HQP-MVS		98.36 250	98.02 258	99.39 192	99.31 275	98.94 215	97.98 311	99.37 247	97.45 285	98.15 319	98.83 321	96.67 253	99.70 301	94.73 323	99.67 216	99.53 159

WR-MVS		99.11 163	98.93 179	99.66 94	99.30 279	99.42 129	98.42 272	99.37 247	99.04 164	99.57 152	99.20 280	96.89 250	99.86 181	98.66 143	99.87 122	99.70 54

test12				99.54 152	99.29 280	99.33 156		99.16 282		98.43 309		97.54 221	99.82 238		99.47 251	99.48 183

OpenMVS_ROB	97.31 17	97.36 284	96.84 294	98.89 261	99.29 280	99.45 117	98.87 220	99.48 214	86.54 355	99.44 181	99.74 95	97.34 232	99.86 181	91.61 339	99.28 277	97.37 346

MVS-HIRNet		97.86 272	98.22 245	96.76 331	99.28 282	91.53 353	98.38 274	92.60 360	99.13 153	99.31 219	99.96 11	97.18 242	99.68 315	98.34 160	99.83 146	99.07 278

DeepC-MVS_fast	98.47 5	99.23 130	99.12 132	99.56 145	99.28 282	99.22 182	98.99 204	99.40 238	99.08 160	99.58 150	99.64 154	98.90 84	99.83 230	97.44 221	99.75 186	99.63 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Patchmatch-test		98.10 266	97.98 261	98.48 286	99.27 284	96.48 308	99.40 88	99.07 287	98.81 186	99.23 231	99.57 196	90.11 323	99.87 161	96.69 261	99.64 222	99.09 271

Fast-Effi-MVS+-dtu		99.20 144	99.12 132	99.43 179	99.25 285	99.69 64	99.05 192	99.82 48	99.50 92	98.97 259	99.05 300	98.98 74	99.98 7	98.20 171	99.24 284	98.62 302

CNVR-MVS		98.99 185	98.80 200	99.56 145	99.25 285	99.43 125	98.54 257	99.27 267	98.58 211	98.80 280	99.43 231	98.53 145	99.70 301	97.22 235	99.59 230	99.54 156

LFMVS		98.46 241	98.19 250	99.26 221	99.24 287	98.52 245	99.62 57	96.94 343	99.87 13	99.31 219	99.58 189	91.04 310	99.81 257	98.68 142	99.42 261	99.45 194

VNet		99.18 149	99.06 152	99.56 145	99.24 287	99.36 149	99.33 111	99.31 259	99.67 59	99.47 178	99.57 196	96.48 258	99.84 213	99.15 94	99.30 275	99.47 188

DeepPCF-MVS	98.42 6	99.18 149	99.02 164	99.67 86	99.22 289	99.75 45	97.25 342	99.47 218	98.72 201	99.66 122	99.70 119	99.29 37	99.63 336	98.07 183	99.81 164	99.62 114

MSLP-MVS++		99.05 171	99.09 144	98.91 256	99.21 290	98.36 254	98.82 230	99.47 218	98.85 181	98.90 272	99.56 201	98.78 101	99.09 355	98.57 146	99.68 210	99.26 238

NCCC		98.82 210	98.57 217	99.58 133	99.21 290	99.31 159	98.61 245	99.25 272	98.65 205	98.43 309	99.26 267	97.86 199	99.81 257	96.55 268	99.27 281	99.61 119

BH-RMVSNet		98.41 246	98.14 252	99.21 230	99.21 290	98.47 246	98.60 247	98.26 320	98.35 234	98.93 267	99.31 256	97.20 241	99.66 325	94.32 329	99.10 290	99.51 170

Patchmatch-test1		98.13 264	98.40 231	97.31 327	99.20 293	92.99 343	98.17 289	98.49 313	98.24 244	99.10 248	99.52 212	96.01 270	99.83 230	97.22 235	99.62 224	99.12 263

mvs_anonymous		99.28 118	99.39 83	98.94 253	99.19 294	97.81 286	99.02 197	99.55 185	99.78 34	99.85 58	99.80 64	98.24 171	99.86 181	99.57 43	99.50 247	99.15 255

OpenMVS	98.12 10	98.23 260	97.89 269	99.26 221	99.19 294	99.26 171	99.65 55	99.69 114	91.33 350	98.14 323	99.77 86	98.28 168	99.96 33	95.41 315	99.55 239	98.58 306

CNLPA		98.57 230	98.34 238	99.28 214	99.18 296	99.10 200	98.34 277	99.41 232	98.48 219	98.52 303	98.98 307	97.05 246	99.78 273	95.59 309	99.50 247	98.96 285

MG-MVS		98.52 235	98.39 232	98.94 253	99.15 297	97.39 297	98.18 287	99.21 278	98.89 177	99.23 231	99.63 162	97.37 231	99.74 291	94.22 331	99.61 228	99.69 57

ADS-MVSNet2		97.78 274	97.66 280	98.12 301	99.14 298	95.36 330	99.22 149	98.75 301	96.97 299	98.25 315	99.64 154	90.90 313	99.94 55	96.51 270	99.56 233	99.08 274

ADS-MVSNet		97.72 276	97.67 279	97.86 311	99.14 298	94.65 337	99.22 149	98.86 295	96.97 299	98.25 315	99.64 154	90.90 313	99.84 213	96.51 270	99.56 233	99.08 274

FMVSNet3		98.80 213	98.63 211	99.32 209	99.13 300	98.72 235	99.10 184	99.48 214	99.23 137	99.62 141	99.64 154	92.57 299	99.86 181	98.96 117	99.90 103	99.39 213

PHI-MVS		99.11 163	98.95 178	99.59 129	99.13 300	99.59 90	99.17 162	99.65 134	97.88 260	99.25 227	99.46 228	98.97 76	99.80 262	97.26 232	99.82 155	99.37 220

alignmvs		98.28 257	97.96 262	99.25 225	99.12 302	98.93 219	99.03 196	98.42 316	99.64 69	98.72 287	97.85 345	90.86 315	99.62 337	98.88 126	99.13 288	99.19 248

PAPM		95.61 331	94.71 332	98.31 294	99.12 302	96.63 306	96.66 350	98.46 314	90.77 351	96.25 352	98.68 330	93.01 296	99.69 307	81.60 358	97.86 347	98.62 302

AdaColmap		98.60 227	98.35 237	99.38 194	99.12 302	99.22 182	98.67 244	99.42 231	97.84 265	98.81 278	99.27 265	97.32 233	99.81 257	95.14 319	99.53 244	99.10 268

MS-PatchMatch		99.00 183	98.97 176	99.09 240	99.11 305	98.19 267	98.76 237	99.33 253	98.49 218	99.44 181	99.58 189	98.21 174	99.69 307	98.20 171	99.62 224	99.39 213

testus		98.15 263	98.06 256	98.40 289	99.11 305	95.95 316	96.77 347	99.89 15	95.83 321	99.23 231	98.47 338	97.50 223	99.84 213	96.58 267	99.20 287	99.39 213

canonicalmvs		99.02 176	99.00 169	99.09 240	99.10 307	98.70 236	99.61 61	99.66 125	99.63 72	98.64 295	97.65 352	99.04 70	99.54 345	98.79 131	98.92 297	99.04 281

MVS_0304		99.17 152	99.10 142	99.38 194	99.08 308	98.86 228	98.46 268	99.73 93	99.53 89	99.35 209	99.30 259	97.11 244	99.96 33	99.33 67	99.99 20	99.33 228

BH-w/o		97.20 291	97.01 289	97.76 314	99.08 308	95.69 326	98.03 305	98.52 310	95.76 324	97.96 329	98.02 343	95.62 274	99.47 349	92.82 337	97.25 351	98.12 327

MVSTER		98.47 240	98.22 245	99.24 227	99.06 310	98.35 255	99.08 189	99.46 221	99.27 126	99.75 92	99.66 148	88.61 329	99.85 197	99.14 100	99.92 91	99.52 167

CR-MVSNet		98.35 253	98.20 247	98.83 266	99.05 311	98.12 271	99.30 124	99.67 121	97.39 288	99.16 241	99.79 71	91.87 305	99.91 93	98.78 134	98.77 308	98.44 312

RPMNet		98.53 234	98.44 225	98.83 266	99.05 311	98.12 271	99.30 124	98.78 300	99.86 16	99.16 241	99.74 95	92.53 301	99.91 93	98.75 135	98.77 308	98.44 312

HY-MVS	98.23 9	98.21 262	97.95 263	98.99 250	99.03 313	98.24 263	99.61 61	98.72 303	96.81 303	98.73 286	99.51 216	94.06 286	99.86 181	96.91 249	98.20 337	98.86 293

PMMVS		98.49 238	98.29 241	99.11 238	98.96 314	98.42 249	97.54 332	99.32 255	97.53 283	98.47 308	98.15 342	97.88 198	99.82 238	97.46 220	99.24 284	99.09 271

PatchT		98.45 242	98.32 240	98.83 266	98.94 315	98.29 262	99.24 143	98.82 298	99.84 23	99.08 249	99.76 89	91.37 308	99.94 55	98.82 130	99.00 296	98.26 319

tpm		97.15 294	96.95 291	97.75 315	98.91 316	94.24 339	99.32 114	97.96 324	97.71 270	98.29 312	99.32 254	86.72 342	99.92 84	98.10 182	96.24 355	99.09 271

1314		98.00 270	97.90 268	98.27 296	98.90 317	97.45 296	99.30 124	99.06 289	94.98 334	97.21 347	99.12 288	98.43 155	99.67 320	95.58 310	98.56 327	97.71 341

tpmp4_e23		96.11 322	96.06 315	96.27 339	98.90 317	90.70 358	99.34 109	99.03 291	93.72 344	96.56 350	99.31 256	83.63 354	99.75 287	96.06 285	98.02 344	98.35 315

CostFormer		96.71 309	96.79 297	96.46 338	98.90 317	90.71 357	99.41 86	98.68 305	94.69 340	98.14 323	99.34 253	86.32 350	99.80 262	97.60 213	98.07 342	98.88 291

UGNet		99.38 96	99.34 93	99.49 162	98.90 317	98.90 222	99.70 30	99.35 250	99.86 16	98.57 301	99.81 62	98.50 150	99.93 66	99.38 60	99.98 36	99.66 80
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
Effi-MVS+-dtu		99.07 167	98.92 182	99.52 155	98.89 321	99.78 36	99.15 170	99.66 125	99.34 118	98.92 269	99.24 274	97.69 211	99.98 7	98.11 180	99.28 277	98.81 297

mvs-test1		98.83 208	98.70 205	99.22 229	98.89 321	99.65 77	98.88 218	99.66 125	99.34 118	98.29 312	98.94 315	97.69 211	99.96 33	98.11 180	98.54 328	98.04 329

Patchmtry		98.78 215	98.54 220	99.49 162	98.89 321	99.19 190	99.32 114	99.67 121	99.65 67	99.72 104	99.79 71	91.87 305	99.95 41	98.00 187	99.97 47	99.33 228

LP		98.34 255	98.44 225	98.05 302	98.88 324	95.31 332	99.28 133	98.74 302	99.12 154	98.98 258	99.79 71	93.40 291	99.93 66	98.38 156	99.41 263	98.90 290

tpm2		96.35 317	96.22 311	96.73 333	98.88 324	91.75 351	99.21 152	98.51 311	93.27 346	97.89 332	99.21 279	84.83 353	99.70 301	96.04 286	98.18 340	98.75 300

tpm cat1		96.78 307	96.98 290	96.16 342	98.85 326	90.59 359	99.08 189	99.32 255	92.37 347	97.73 342	99.46 228	91.15 309	99.69 307	96.07 284	98.80 305	98.21 323

CANet		99.11 163	99.05 157	99.28 214	98.83 327	98.56 242	98.71 243	99.41 232	99.25 133	99.23 231	99.22 278	97.66 218	99.94 55	99.19 85	99.97 47	99.33 228

FMVSNet5		97.80 273	97.25 284	99.42 181	98.83 327	98.97 212	99.38 95	99.80 60	98.87 179	99.25 227	99.69 125	80.60 361	99.91 93	98.96 117	99.90 103	99.38 216

API-MVS		98.38 249	98.39 232	98.35 291	98.83 327	99.26 171	99.14 175	99.18 280	98.59 210	98.66 294	98.78 325	98.61 131	99.57 344	94.14 332	99.56 233	96.21 354

PatchmatchNet		97.65 277	97.80 272	97.18 328	98.82 330	92.49 345	99.17 162	98.39 317	98.12 248	98.79 281	99.58 189	90.71 317	99.89 126	97.23 234	99.41 263	99.16 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PAPR		97.56 280	97.07 286	99.04 247	98.80 331	98.11 273	97.63 328	99.25 272	94.56 341	98.02 328	98.25 341	97.43 226	99.68 315	90.90 342	98.74 312	99.33 228

CANet_DTU		98.91 197	98.85 191	99.09 240	98.79 332	98.13 270	98.18 287	99.31 259	99.48 94	98.86 275	99.51 216	96.56 255	99.95 41	99.05 106	99.95 67	99.19 248

E-PMN		97.14 296	97.43 282	96.27 339	98.79 332	91.62 352	95.54 353	99.01 292	99.44 103	98.88 273	99.12 288	92.78 298	99.68 315	94.30 330	99.03 294	97.50 343

PVSNet_0	95.53 19	95.85 328	95.31 327	97.47 322	98.78 334	93.48 342	95.72 352	99.40 238	96.18 316	97.37 344	97.73 351	95.73 272	99.58 343	95.49 311	81.40 358	99.36 223

MAR-MVS		98.24 258	97.92 265	99.19 233	98.78 334	99.65 77	99.17 162	99.14 284	95.36 329	98.04 327	98.81 323	97.47 224	99.72 294	95.47 313	99.06 291	98.21 323
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
EMVS		96.96 299	97.28 283	95.99 343	98.76 336	91.03 355	95.26 355	98.61 307	99.34 118	98.92 269	98.88 320	93.79 287	99.66 325	92.87 336	99.05 292	97.30 347

PatchFormer-LS_test		96.95 300	97.07 286	96.62 336	98.76 336	91.85 349	99.18 155	98.45 315	97.29 292	97.73 342	97.22 361	88.77 328	99.76 281	98.13 179	98.04 343	98.25 320

IB-MVS	95.41 20	95.30 332	94.46 334	97.84 312	98.76 336	95.33 331	97.33 340	96.07 347	96.02 317	95.37 357	97.41 355	76.17 364	99.96 33	97.54 216	95.44 357	98.22 322
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
tpmrst		97.73 275	98.07 255	96.73 333	98.71 339	92.00 347	99.10 184	98.86 295	98.52 215	98.92 269	99.54 208	91.90 303	99.82 238	98.02 184	99.03 294	98.37 314

MDTV_nov1_ep13			97.73 276		98.70 340	90.83 356	99.15 170	98.02 322	98.51 216	98.82 277	99.61 178	90.98 311	99.66 325	96.89 251	98.92 297

dp		96.86 302	97.07 286	96.24 341	98.68 341	90.30 360	99.19 154	98.38 318	97.35 290	98.23 317	99.59 187	87.23 334	99.82 238	96.27 278	98.73 314	98.59 304

JIA-IIPM		98.06 268	97.92 265	98.50 285	98.59 342	97.02 302	98.80 232	98.51 311	99.88 12	97.89 332	99.87 37	91.89 304	99.90 110	98.16 178	97.68 349	98.59 304

MVS		95.72 330	94.63 333	98.99 250	98.56 343	97.98 284	99.30 124	98.86 295	72.71 358	97.30 345	99.08 291	98.34 164	99.74 291	89.21 347	98.33 334	99.26 238

TR-MVS		97.44 281	97.15 285	98.32 293	98.53 344	97.46 295	98.47 264	97.91 327	96.85 301	98.21 318	98.51 336	96.42 261	99.51 347	92.16 338	97.29 350	97.98 334

DWT-MVSNet_test		96.03 325	95.80 322	96.71 335	98.50 345	91.93 348	99.25 141	97.87 328	95.99 318	96.81 349	97.61 353	81.02 358	99.66 325	97.20 238	97.98 345	98.54 307

tpmvs		97.39 282	97.69 277	96.52 337	98.41 346	91.76 350	99.30 124	98.94 294	97.74 268	97.85 335	99.55 206	92.40 302	99.73 293	96.25 279	98.73 314	98.06 328

LS3D		99.24 128	99.11 135	99.61 121	98.38 347	99.79 34	99.57 69	99.68 117	99.61 77	99.15 243	99.71 112	98.70 113	99.91 93	97.54 216	99.68 210	99.13 262

CMPMVS	77.52 23	98.50 236	98.19 250	99.41 188	98.33 348	99.56 95	99.01 199	99.59 167	95.44 328	99.57 152	99.80 64	95.64 273	99.46 352	96.47 273	99.92 91	99.21 244
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TESTMET0.1,1		96.24 320	95.84 321	97.41 324	98.24 349	93.84 340	97.38 337	95.84 348	98.43 221	97.81 336	98.56 334	79.77 362	99.89 126	97.77 199	98.77 308	98.52 308

gg-mvs-nofinetune		95.87 327	95.17 330	97.97 304	98.19 350	96.95 303	99.69 39	89.23 363	99.89 10	96.24 353	99.94 13	81.19 357	99.51 347	93.99 334	98.20 337	97.44 344

test-LLR		97.15 294	96.95 291	97.74 316	98.18 351	95.02 334	97.38 337	96.10 345	98.00 252	97.81 336	98.58 331	90.04 324	99.91 93	97.69 208	98.78 306	98.31 317

test-mter		96.23 321	95.73 323	97.74 316	98.18 351	95.02 334	97.38 337	96.10 345	97.90 259	97.81 336	98.58 331	79.12 363	99.91 93	97.69 208	98.78 306	98.31 317

EPMVS		96.53 312	96.32 310	97.17 329	98.18 351	92.97 344	99.39 89	89.95 362	98.21 245	98.61 297	99.59 187	86.69 343	99.72 294	96.99 246	99.23 286	98.81 297

test0.0.03 1		97.37 283	96.91 293	98.74 276	97.72 354	97.57 293	97.60 330	97.36 342	98.00 252	99.21 235	98.02 343	90.04 324	99.79 265	98.37 157	95.89 356	98.86 293

GG-mvs-BLEND				97.36 325	97.59 355	96.87 305	99.70 30	88.49 364		94.64 358	97.26 360	80.66 360	99.12 354	91.50 340	96.50 354	96.08 356

gm-plane-assit					97.59 355	89.02 362			93.47 345		98.30 339		99.84 213	96.38 274

cascas		96.99 298	96.82 296	97.48 321	97.57 357	95.64 327	96.43 351	99.56 182	91.75 348	97.13 348	97.61 353	95.58 275	98.63 358	96.68 262	99.11 289	98.18 326

testpf		94.48 333	95.31 327	91.99 346	97.22 358	89.64 361	98.86 221	96.52 344	94.36 342	96.09 354	98.76 326	82.21 355	98.73 357	97.05 244	96.74 352	87.60 357

test2356		95.99 326	95.26 329	98.18 298	96.93 359	95.53 329	95.31 354	98.71 304	95.67 326	98.48 307	97.83 346	80.72 359	99.88 141	95.47 313	98.21 336	99.11 264

EPNet_dtu		97.62 278	97.79 274	97.11 330	96.67 360	92.31 346	98.51 260	98.04 321	99.24 135	95.77 355	99.47 225	93.78 288	99.66 325	98.98 112	99.62 224	99.37 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.13 264	97.77 275	99.18 236	94.57 361	97.99 279	99.24 143	97.96 324	99.74 40	97.29 346	99.62 169	93.13 294	99.97 16	98.59 145	99.83 146	99.58 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tmp_tt		95.75 329	95.42 326	96.76 331	89.90 362	94.42 338	98.86 221	97.87 328	78.01 356	99.30 223	99.69 125	97.70 209	95.89 360	99.29 76	98.14 341	99.95 1

testmvs		28.94 337	33.33 337	15.79 350	26.03 363	9.81 365	96.77 347	15.67 365	11.55 360	23.87 361	50.74 368	19.03 368	8.53 363	23.21 360	33.07 359	29.03 360

test123		29.31 336	33.05 339	18.08 349	25.93 364	12.24 364	97.53 334	10.93 366	11.78 359	24.21 360	50.08 369	21.04 367	8.60 362	23.51 359	32.43 361	33.39 359

cdsmvs_eth3d_5k		24.88 338	33.17 338	0.00 351	0.00 365	0.00 366	0.00 357	99.62 145	0.00 361	0.00 362	99.13 284	99.82 6	0.00 364	0.00 361	0.00 362	0.00 362

pcd_1.5k_mvsjas		16.61 339	22.14 340	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	100.00 1	99.28 39	0.00 364	0.00 361	0.00 362	0.00 362

sosnet-low-res		8.33 340	11.11 341	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	100.00 1	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

sosnet		8.33 340	11.11 341	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	100.00 1	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uncertanet		8.33 340	11.11 341	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	100.00 1	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

Regformer		8.33 340	11.11 341	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	100.00 1	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

ab-mvs-re		8.26 345	11.02 346	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	99.16 282	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

uanet		8.33 340	11.11 341	0.00 351	0.00 365	0.00 366	0.00 357	0.00 367	0.00 361	0.00 362	100.00 1	0.00 369	0.00 364	0.00 361	0.00 362	0.00 362

GSMVS																99.14 259

test_part3							98.74 238		97.71 270		99.57 196		99.90 110	94.47 327

test_part1								99.53 194				98.40 159			99.68 210	99.66 80

sam_mvs1												90.81 316				99.14 259

sam_mvs												90.52 320

MTGPA								99.53 194

test_post1							99.14 175				51.63 367	89.54 327	99.82 238	96.86 252

test_post											52.41 366	90.25 322	99.86 181

patchmatchnet-post											99.62 169	90.58 318	99.94 55

MTMP								98.59 309

test9_res														95.10 320	99.44 254	99.50 176

agg_prior2														94.58 326	99.46 253	99.50 176

test_prior4						99.19 190	98.00 308

test_prior2							97.95 315		97.87 261	98.05 325	99.05 300	97.90 195		95.99 290	99.49 249

旧先验2							97.94 317		95.33 330	98.94 266			99.88 141	96.75 258

新几何2							98.04 304

无先验							98.01 306	99.23 276	95.83 321				99.85 197	95.79 299		99.44 199

原ACMM2							97.92 319

testdata2													99.89 126	95.99 290

segment_acmp												98.37 162

testdata1							97.72 326		97.86 264

plane_prior5								99.54 189					99.82 238	95.84 297	99.78 177	99.60 125

plane_prior4											99.25 269

plane_prior3						99.31 159			98.36 229	99.14 244

plane_prior2							98.80 232		98.94 171

plane_prior						99.24 178	98.42 272		97.87 261						99.71 205

n2								0.00 367

nn								0.00 367

door-mid								99.83 40

test11								99.29 263

door								99.77 73

HQP5-MVS						98.94 215

BP-MVS														94.73 323

HQP4-MVS										98.15 319			99.70 301			99.53 159

HQP3-MVS								99.37 247							99.67 216

HQP2-MVS												96.67 253

MDTV_nov1_ep13_2view						91.44 354	99.14 175		97.37 289	99.21 235		91.78 307		96.75 258		99.03 282

ACMMP++_ref															99.94 80

ACMMP++															99.79 172

Test By Simon												98.41 157

High-resolution multi-view benchmark