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.

test_vis3_rt		99.89 3	99.90 4	99.87 23	99.98 3	99.75 73	99.70 35	100.00 1	99.73 87	100.00 1	99.89 38	99.79 20	99.88 207	99.98 1	100.00 1	99.98 5

test_fmvs2		99.72 46	99.85 17	99.34 244	99.91 31	98.08 331	99.48 102	100.00 1	99.90 39	99.99 7	99.91 28	99.50 54	99.98 23	99.98 1	99.99 16	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 186	99.96 7	98.62 292	99.67 50	100.00 1	99.95 27	100.00 1	99.95 16	99.85 12	99.99 8	99.98 1	99.99 16	99.98 5

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 3	99.98 3	99.76 65	99.12 208	100.00 1	100.00 1	99.99 7	99.91 28	99.98 1	100.00 1	99.97 4	100.00 1	99.99 2

test_vis1_n_1920		99.72 46	99.88 7	99.27 265	99.93 24	97.84 343	99.34 129	100.00 1	99.99 3	99.99 7	99.82 83	99.87 11	99.99 8	99.97 4	99.99 16	99.97 10

test_vis1_n		99.68 55	99.79 30	99.36 241	99.94 18	98.18 320	99.52 89	100.00 1	99.86 55	100.00 1	99.88 47	98.99 118	99.96 58	99.97 4	99.96 76	99.95 14

test_fmvs1_n		99.68 55	99.81 26	99.28 262	99.95 15	97.93 340	99.49 100	100.00 1	99.82 71	99.99 7	99.89 38	99.21 86	99.98 23	99.97 4	99.98 45	99.93 20

test_f		99.75 41	99.88 7	99.37 237	99.96 7	98.21 317	99.51 95	100.00 1	99.94 30	100.00 1	99.93 21	99.58 43	99.94 85	99.97 4	99.99 16	99.97 10

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 44	99.86 18	99.08 224	99.97 20	99.98 15	99.96 30	99.79 103	99.90 9	99.99 8	99.96 9	99.99 16	99.90 26

test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 79	99.01 242	99.99 11	99.99 3	99.98 14	99.88 47	99.97 2	99.99 8	99.96 9	100.00 1	99.98 5

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 47	99.88 44	99.55 147	99.17 188	99.98 12	99.99 3	99.96 30	99.84 72	99.96 3	99.99 8	99.96 9	99.99 16	99.88 34

test_cas_vis1_n_1920		99.76 40	99.86 13	99.45 209	99.93 24	98.40 305	99.30 144	99.98 12	99.94 30	99.99 7	99.89 38	99.80 19	99.97 37	99.96 9	99.97 63	99.97 10

fmvsm_s_conf0.5_n_7		99.73 44	99.78 34	99.60 161	99.74 143	98.93 262	98.85 273	99.96 28	99.96 23	99.97 22	99.76 126	99.82 16	99.96 58	99.95 13	99.98 45	99.90 26

fmvsm_l_conf0.5_n		99.80 26	99.78 34	99.85 29	99.88 44	99.66 109	99.11 213	99.91 46	99.98 15	99.96 30	99.64 199	99.60 41	99.99 8	99.95 13	99.99 16	99.88 34

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 37	99.82 74	99.70 98	99.17 188	99.97 20	99.99 3	99.96 30	99.82 83	99.94 4	100.00 1	99.95 13	100.00 1	99.80 58

test_fmvs1		99.48 100	99.65 61	98.97 306	99.54 230	97.16 366	99.11 213	99.98 12	99.78 81	99.96 30	99.81 90	98.72 155	99.97 37	99.95 13	99.97 63	99.79 66

mvsany_test3		99.85 12	99.88 7	99.75 83	99.95 15	99.37 190	99.53 88	99.98 12	99.77 85	99.99 7	99.95 16	99.85 12	99.94 85	99.95 13	99.98 45	99.94 17

fmvsm_s_conf0.1_n_2		99.81 25	99.78 34	99.89 11	99.93 24	99.76 65	98.92 265	99.98 12	99.99 3	99.99 7	99.88 47	99.43 56	99.94 85	99.94 18	99.99 16	99.99 2

fmvsm_l_conf0.5_n_a		99.80 26	99.79 30	99.84 34	99.88 44	99.64 118	99.12 208	99.91 46	99.98 15	99.95 40	99.67 187	99.67 32	99.99 8	99.94 18	99.99 16	99.88 34

MM		99.18 188	99.05 195	99.55 180	99.35 300	98.81 271	99.05 229	97.79 406	99.99 3	99.48 233	99.59 240	96.29 317	99.95 69	99.94 18	99.98 45	99.88 34

test_fmvsmconf_n		99.85 12	99.84 20	99.88 18	99.91 31	99.73 82	98.97 256	99.98 12	99.99 3	99.96 30	99.85 65	99.93 7	99.99 8	99.94 18	99.99 16	99.93 20

fmvsm_s_conf0.5_n_5		99.78 32	99.76 44	99.85 29	99.79 102	99.72 87	98.84 275	99.96 28	99.96 23	99.96 30	99.72 147	99.71 26	99.99 8	99.93 22	99.98 45	99.85 43

fmvsm_s_conf0.5_n_2		99.78 32	99.75 46	99.88 18	99.82 74	99.76 65	98.88 268	99.92 40	99.98 15	99.98 14	99.85 65	99.42 58	99.94 85	99.93 22	99.98 45	99.94 17

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 38	99.10 216	99.98 12	99.99 3	99.98 14	99.91 28	99.68 31	99.93 105	99.93 22	99.99 16	99.99 2

fmvsm_s_conf0.1_n		99.86 10	99.85 17	99.89 11	99.93 24	99.78 52	99.07 228	99.98 12	99.99 3	99.98 14	99.90 33	99.88 10	99.92 132	99.93 22	99.99 16	99.98 5

fmvsm_s_conf0.5_n_a		99.82 23	99.79 30	99.89 11	99.85 59	99.82 38	99.03 237	99.96 28	99.99 3	99.97 22	99.84 72	99.58 43	99.93 105	99.92 26	99.98 45	99.93 20

fmvsm_s_conf0.5_n		99.83 21	99.81 26	99.87 23	99.85 59	99.78 52	99.03 237	99.96 28	99.99 3	99.97 22	99.84 72	99.78 21	99.92 132	99.92 26	99.99 16	99.92 24

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 20	99.99 3	100.00 1	99.98 13	99.78 21	100.00 1	99.92 26	100.00 1	99.87 38

fmvsm_s_conf0.5_n_6		99.80 26	99.78 34	99.85 29	99.78 110	99.78 52	99.00 245	99.97 20	99.96 23	99.97 22	99.56 254	99.92 8	99.93 105	99.91 29	99.99 16	99.83 50

fmvsm_s_conf0.5_n_4		99.78 32	99.78 34	99.79 59	99.75 135	99.56 143	98.98 254	99.94 37	99.92 35	99.97 22	99.72 147	99.84 14	99.92 132	99.91 29	99.98 45	99.89 32

MVStest1		98.22 315	98.09 310	98.62 341	99.04 370	96.23 387	99.20 176	99.92 40	99.44 158	99.98 14	99.87 53	85.87 410	99.67 379	99.91 29	99.57 294	99.95 14

v1921920		99.56 83	99.57 82	99.55 180	99.75 135	99.11 237	99.05 229	99.61 196	99.15 210	99.88 70	99.71 157	99.08 104	99.87 221	99.90 32	99.97 63	99.66 120

v1240		99.56 83	99.58 78	99.51 191	99.80 90	99.00 249	99.00 245	99.65 176	99.15 210	99.90 57	99.75 132	99.09 101	99.88 207	99.90 32	99.96 76	99.67 111

v10		99.69 52	99.69 53	99.66 126	99.81 83	99.39 185	99.66 54	99.75 119	99.60 132	99.92 51	99.87 53	98.75 150	99.86 240	99.90 32	99.99 16	99.73 82

v1192		99.57 80	99.57 82	99.57 173	99.77 119	99.22 222	99.04 234	99.60 207	99.18 199	99.87 78	99.72 147	99.08 104	99.85 258	99.89 35	99.98 45	99.66 120

fmvsm_s_conf0.5_n_3		99.79 30	99.77 40	99.85 29	99.81 83	99.71 90	98.97 256	99.92 40	99.98 15	99.97 22	99.86 60	99.53 50	99.95 69	99.88 36	99.99 16	99.89 32

v144192		99.55 86	99.54 89	99.58 167	99.78 110	99.20 227	99.11 213	99.62 189	99.18 199	99.89 61	99.72 147	98.66 163	99.87 221	99.88 36	99.97 63	99.66 120

v8		99.68 55	99.69 53	99.65 132	99.80 90	99.40 182	99.66 54	99.76 114	99.64 117	99.93 46	99.85 65	98.66 163	99.84 273	99.88 36	99.99 16	99.71 88

mvs5depth		99.88 6	99.91 3	99.80 52	99.92 29	99.42 175	99.94 3	100.00 1	99.97 20	99.89 61	99.99 12	99.63 35	99.97 37	99.87 39	99.99 16	100.00 1

v1144		99.54 89	99.53 93	99.59 164	99.79 102	99.28 208	99.10 216	99.61 196	99.20 197	99.84 85	99.73 140	98.67 161	99.84 273	99.86 40	99.98 45	99.64 138

mmtdpeth		99.78 32	99.83 21	99.66 126	99.85 59	99.05 248	99.79 12	99.97 20	100.00 1	99.43 245	99.94 19	99.64 33	99.94 85	99.83 41	99.99 16	99.98 5

SSC-MVS		99.52 92	99.42 111	99.83 37	99.86 55	99.65 115	99.52 89	99.81 91	99.87 52	99.81 98	99.79 103	96.78 298	99.99 8	99.83 41	99.51 310	99.86 40

v7n		99.82 23	99.80 29	99.88 18	99.96 7	99.84 25	99.82 9	99.82 82	99.84 64	99.94 43	99.91 28	99.13 97	99.96 58	99.83 41	99.99 16	99.83 50

v2v482		99.50 94	99.47 98	99.58 167	99.78 110	99.25 215	99.14 198	99.58 222	99.25 188	99.81 98	99.62 218	98.24 218	99.84 273	99.83 41	99.97 63	99.64 138

test_vis1_rt		99.45 113	99.46 102	99.41 226	99.71 153	98.63 291	98.99 251	99.96 28	99.03 223	99.95 40	99.12 356	98.75 150	99.84 273	99.82 45	99.82 191	99.77 72

tt0805		99.63 69	99.57 82	99.81 47	99.87 52	99.88 12	99.58 79	98.70 368	99.72 91	99.91 54	99.60 235	99.43 56	99.81 313	99.81 46	99.53 306	99.73 82

V42		99.56 83	99.54 89	99.63 146	99.79 102	99.46 161	99.39 117	99.59 213	99.24 190	99.86 79	99.70 165	98.55 177	99.82 298	99.79 47	99.95 90	99.60 168

SSC-MVS3.2		99.64 68	99.67 57	99.56 176	99.75 135	98.98 252	98.96 259	99.87 59	99.88 50	99.84 85	99.64 199	99.32 72	99.91 155	99.78 48	99.96 76	99.80 58

mvs_tets		99.90 2	99.90 4	99.90 8	99.96 7	99.79 49	99.72 30	99.88 57	99.92 35	99.98 14	99.93 21	99.94 4	99.98 23	99.77 49	100.00 1	99.92 24

WB-MVS		99.44 115	99.32 132	99.80 52	99.81 83	99.61 131	99.47 105	99.81 91	99.82 71	99.71 147	99.72 147	96.60 302	99.98 23	99.75 50	99.23 351	99.82 57

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 58	99.68 46	99.85 69	99.95 27	99.98 14	99.92 25	99.28 77	99.98 23	99.75 50	100.00 1	99.94 17

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 52	99.70 35	99.86 63	99.89 45	99.98 14	99.90 33	99.94 4	99.98 23	99.75 50	100.00 1	99.90 26

ANet_high		99.88 6	99.87 11	99.91 3	99.99 1	99.91 4	99.65 59	100.00 1	99.90 39	100.00 1	99.97 14	99.61 39	99.97 37	99.75 50	100.00 1	99.84 46

reproduce_monomvs		97.40 347	97.46 341	97.20 395	99.05 367	91.91 423	99.20 176	99.18 341	99.84 64	99.86 79	99.75 132	80.67 418	99.83 288	99.69 54	99.95 90	99.85 43

SPE-MVS-test		99.68 55	99.70 50	99.64 139	99.57 214	99.83 30	99.78 14	99.97 20	99.92 35	99.50 230	99.38 305	99.57 45	99.95 69	99.69 54	99.90 125	99.15 317

MVS_0304		98.61 273	98.30 294	99.52 188	97.88 430	98.95 258	98.76 292	94.11 429	99.84 64	99.32 275	99.57 250	95.57 328	99.95 69	99.68 56	99.98 45	99.68 103

CS-MVS		99.67 61	99.70 50	99.58 167	99.53 236	99.84 25	99.79 12	99.96 28	99.90 39	99.61 189	99.41 295	99.51 53	99.95 69	99.66 57	99.89 135	98.96 359

mamv4		99.73 44	99.74 47	99.70 112	99.66 180	99.87 14	99.69 42	99.93 38	99.93 32	99.93 46	99.86 60	99.07 106	100.00 1	99.66 57	99.92 114	99.24 292

pmmvs6		99.86 10	99.86 13	99.83 37	99.94 18	99.90 7	99.83 7	99.91 46	99.85 61	99.94 43	99.95 16	99.73 25	99.90 174	99.65 59	99.97 63	99.69 97

MIMVSNet1		99.66 62	99.62 66	99.80 52	99.94 18	99.87 14	99.69 42	99.77 109	99.78 81	99.93 46	99.89 38	97.94 243	99.92 132	99.65 59	99.98 45	99.62 154

EC-MVSNet		99.69 52	99.69 53	99.68 116	99.71 153	99.91 4	99.76 20	99.96 28	99.86 55	99.51 228	99.39 303	99.57 45	99.93 105	99.64 61	99.86 164	99.20 305

K. test v3		98.87 250	98.60 259	99.69 114	99.93 24	99.46 161	99.74 24	94.97 424	99.78 81	99.88 70	99.88 47	93.66 349	99.97 37	99.61 62	99.95 90	99.64 138

KD-MVS_self_test		99.63 69	99.59 75	99.76 73	99.84 63	99.90 7	99.37 124	99.79 100	99.83 69	99.88 70	99.85 65	98.42 198	99.90 174	99.60 63	99.73 239	99.49 226

Anonymous20240521		99.44 115	99.42 111	99.49 197	99.89 39	98.96 257	99.62 64	99.76 114	99.85 61	99.82 91	99.88 47	96.39 312	99.97 37	99.59 64	99.98 45	99.55 190

TransMVSNet (Re)		99.78 32	99.77 40	99.81 47	99.91 31	99.85 20	99.75 22	99.86 63	99.70 98	99.91 54	99.89 38	99.60 41	99.87 221	99.59 64	99.74 233	99.71 88

OurMVSNet-221017-0		99.75 41	99.71 49	99.84 34	99.96 7	99.83 30	99.83 7	99.85 69	99.80 77	99.93 46	99.93 21	98.54 179	99.93 105	99.59 64	99.98 45	99.76 77

EU-MVSNet		99.39 131	99.62 66	98.72 337	99.88 44	96.44 381	99.56 84	99.85 69	99.90 39	99.90 57	99.85 65	98.09 232	99.83 288	99.58 67	99.95 90	99.90 26

mvs_anonymous		99.28 155	99.39 115	98.94 310	99.19 343	97.81 345	99.02 240	99.55 235	99.78 81	99.85 82	99.80 93	98.24 218	99.86 240	99.57 68	99.50 313	99.15 317

test1111		97.74 333	98.16 306	96.49 406	99.60 194	89.86 437	99.71 34	91.21 433	99.89 45	99.88 70	99.87 53	93.73 348	99.90 174	99.56 69	99.99 16	99.70 91

lessismore_v0				99.64 139	99.86 55	99.38 187		90.66 434		99.89 61	99.83 76	94.56 339	99.97 37	99.56 69	99.92 114	99.57 185

mvsany_test1		99.44 115	99.45 104	99.40 228	99.37 293	98.64 290	97.90 383	99.59 213	99.27 184	99.92 51	99.82 83	99.74 24	99.93 105	99.55 71	99.87 156	99.63 143

MVSMamba_PlusPlus		99.55 86	99.58 78	99.47 203	99.68 173	99.40 182	99.52 89	99.70 146	99.92 35	99.77 120	99.86 60	98.28 214	99.96 58	99.54 72	99.90 125	99.05 346

pm-mvs1		99.79 30	99.79 30	99.78 63	99.91 31	99.83 30	99.76 20	99.87 59	99.73 87	99.89 61	99.87 53	99.63 35	99.87 221	99.54 72	99.92 114	99.63 143

LTVRE_ROB	99.19 1	99.88 6	99.87 11	99.88 18	99.91 31	99.90 7	99.96 1	99.92 40	99.90 39	99.97 22	99.87 53	99.81 18	99.95 69	99.54 72	99.99 16	99.80 58
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
DSMNet-mixed		99.48 100	99.65 61	98.95 309	99.71 153	97.27 363	99.50 96	99.82 82	99.59 134	99.41 254	99.85 65	99.62 38	100.00 1	99.53 75	99.89 135	99.59 175

test2506		94.73 397	94.59 398	95.15 413	99.59 199	85.90 439	99.75 22	74.01 441	99.89 45	99.71 147	99.86 60	79.00 428	99.90 174	99.52 76	99.99 16	99.65 128

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 141	99.93 32	99.95 40	99.89 38	99.71 26	99.96 58	99.51 77	99.97 63	99.84 46

FC-MVSNet-test		99.70 50	99.65 61	99.86 27	99.88 44	99.86 18	99.72 30	99.78 106	99.90 39	99.82 91	99.83 76	98.45 194	99.87 221	99.51 77	99.97 63	99.86 40

BP-MVS1		98.72 265	98.46 275	99.50 193	99.53 236	99.00 249	99.34 129	98.53 378	99.65 114	99.73 140	99.38 305	90.62 384	99.96 58	99.50 79	99.86 164	99.55 190

UA-Net		99.78 32	99.76 44	99.86 27	99.72 150	99.71 90	99.91 4	99.95 35	99.96 23	99.71 147	99.91 28	99.15 92	99.97 37	99.50 79	100.00 1	99.90 26

PMMVS2		99.48 100	99.45 104	99.57 173	99.76 123	98.99 251	98.09 360	99.90 51	98.95 233	99.78 112	99.58 243	99.57 45	99.93 105	99.48 81	99.95 90	99.79 66

VPA-MVSNet		99.66 62	99.62 66	99.79 59	99.68 173	99.75 73	99.62 64	99.69 153	99.85 61	99.80 102	99.81 90	98.81 138	99.91 155	99.47 82	99.88 144	99.70 91

GDP-MVS		98.81 256	98.57 265	99.50 193	99.53 236	99.12 236	99.28 153	99.86 63	99.53 138	99.57 200	99.32 321	90.88 380	99.98 23	99.46 83	99.74 233	99.42 254

ECVR-MVS		97.73 334	98.04 313	96.78 399	99.59 199	90.81 432	99.72 30	90.43 435	99.89 45	99.86 79	99.86 60	93.60 350	99.89 193	99.46 83	99.99 16	99.65 128

nrg030		99.70 50	99.66 59	99.82 42	99.76 123	99.84 25	99.61 70	99.70 146	99.93 32	99.78 112	99.68 183	99.10 99	99.78 326	99.45 85	99.96 76	99.83 50

TAMVS		99.49 98	99.45 104	99.63 146	99.48 261	99.42 175	99.45 109	99.57 224	99.66 111	99.78 112	99.83 76	97.85 250	99.86 240	99.44 86	99.96 76	99.61 164

GeoE		99.69 52	99.66 59	99.78 63	99.76 123	99.76 65	99.60 76	99.82 82	99.46 153	99.75 128	99.56 254	99.63 35	99.95 69	99.43 87	99.88 144	99.62 154

new-patchmatchnet		99.35 141	99.57 82	98.71 339	99.82 74	96.62 378	98.55 316	99.75 119	99.50 142	99.88 70	99.87 53	99.31 73	99.88 207	99.43 87	100.00 1	99.62 154

test20.03		99.55 86	99.54 89	99.58 167	99.79 102	99.37 190	99.02 240	99.89 53	99.60 132	99.82 91	99.62 218	98.81 138	99.89 193	99.43 87	99.86 164	99.47 234

MVSFormer		99.41 125	99.44 107	99.31 255	99.57 214	98.40 305	99.77 16	99.80 94	99.73 87	99.63 174	99.30 326	98.02 237	99.98 23	99.43 87	99.69 254	99.55 190

test_djsdf		99.84 17	99.81 26	99.91 3	99.94 18	99.84 25	99.77 16	99.80 94	99.73 87	99.97 22	99.92 25	99.77 23	99.98 23	99.43 87	100.00 1	99.90 26

SDMVSNet		99.77 39	99.77 40	99.76 73	99.80 90	99.65 115	99.63 61	99.86 63	99.97 20	99.89 61	99.89 38	99.52 52	99.99 8	99.42 92	99.96 76	99.65 128

Anonymous20231211		99.62 75	99.57 82	99.76 73	99.61 192	99.60 134	99.81 10	99.73 129	99.82 71	99.90 57	99.90 33	97.97 242	99.86 240	99.42 92	99.96 76	99.80 58

SixPastTwentyTwo		99.42 121	99.30 139	99.76 73	99.92 29	99.67 107	99.70 35	99.14 346	99.65 114	99.89 61	99.90 33	96.20 319	99.94 85	99.42 92	99.92 114	99.67 111

balanced_conf03		99.50 94	99.50 95	99.50 193	99.42 284	99.49 154	99.52 89	99.75 119	99.86 55	99.78 112	99.71 157	98.20 225	99.90 174	99.39 95	99.88 144	99.10 328

patch_mono-2		99.51 93	99.46 102	99.64 139	99.70 161	99.11 237	99.04 234	99.87 59	99.71 93	99.47 235	99.79 103	98.24 218	99.98 23	99.38 96	99.96 76	99.83 50

UGNet		99.38 133	99.34 127	99.49 197	98.90 382	98.90 266	99.70 35	99.35 305	99.86 55	98.57 367	99.81 90	98.50 189	99.93 105	99.38 96	99.98 45	99.66 120
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
XXY-MVS		99.71 49	99.67 57	99.81 47	99.89 39	99.72 87	99.59 77	99.82 82	99.39 169	99.82 91	99.84 72	99.38 64	99.91 155	99.38 96	99.93 110	99.80 58

FIs		99.65 67	99.58 78	99.84 34	99.84 63	99.85 20	99.66 54	99.75 119	99.86 55	99.74 136	99.79 103	98.27 216	99.85 258	99.37 99	99.93 110	99.83 50

sd_testset		99.78 32	99.78 34	99.80 52	99.80 90	99.76 65	99.80 11	99.79 100	99.97 20	99.89 61	99.89 38	99.53 50	99.99 8	99.36 100	99.96 76	99.65 128

anonymousdsp		99.80 26	99.77 40	99.90 8	99.96 7	99.88 12	99.73 27	99.85 69	99.70 98	99.92 51	99.93 21	99.45 55	99.97 37	99.36 100	100.00 1	99.85 43

casdiffmvs_mvg		99.68 55	99.68 56	99.69 114	99.81 83	99.59 136	99.29 151	99.90 51	99.71 93	99.79 108	99.73 140	99.54 48	99.84 273	99.36 100	99.96 76	99.65 128
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		99.75 41	99.74 47	99.79 59	99.88 44	99.66 109	99.69 42	99.92 40	99.67 107	99.77 120	99.75 132	99.61 39	99.98 23	99.35 103	99.98 45	99.72 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 77	99.64 64	99.53 186	99.79 102	98.82 270	99.58 79	99.97 20	99.95 27	99.96 30	99.76 126	98.44 195	99.99 8	99.34 104	99.96 76	99.78 68

CHOSEN 1792x2688		99.39 131	99.30 139	99.65 132	99.88 44	99.25 215	98.78 290	99.88 57	98.66 273	99.96 30	99.79 103	97.45 272	99.93 105	99.34 104	99.99 16	99.78 68

CDS-MVSNet		99.22 174	99.13 167	99.50 193	99.35 300	99.11 237	98.96 259	99.54 241	99.46 153	99.61 189	99.70 165	96.31 315	99.83 288	99.34 104	99.88 144	99.55 190
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 230	99.16 161	98.51 347	99.75 135	95.90 393	98.07 363	99.84 75	99.84 64	99.89 61	99.73 140	96.01 322	99.99 8	99.33 107	100.00 1	99.63 143

HyFIR lowres test		98.91 243	98.64 256	99.73 97	99.85 59	99.47 157	98.07 363	99.83 77	98.64 275	99.89 61	99.60 235	92.57 359	100.00 1	99.33 107	99.97 63	99.72 85

pmmvs5		99.19 184	99.11 174	99.42 219	99.76 123	98.88 267	98.55 316	99.73 129	98.82 253	99.72 142	99.62 218	96.56 303	99.82 298	99.32 109	99.95 90	99.56 187

v148		99.40 127	99.41 113	99.39 231	99.76 123	98.94 259	99.09 221	99.59 213	99.17 204	99.81 98	99.61 227	98.41 199	99.69 362	99.32 109	99.94 103	99.53 204

baseline		99.63 69	99.62 66	99.66 126	99.80 90	99.62 125	99.44 111	99.80 94	99.71 93	99.72 142	99.69 172	99.15 92	99.83 288	99.32 109	99.94 103	99.53 204

CVMVSNet		98.61 273	98.88 236	97.80 378	99.58 204	93.60 416	99.26 159	99.64 184	99.66 111	99.72 142	99.67 187	93.26 352	99.93 105	99.30 112	99.81 201	99.87 38

PS-CasMVS		99.66 62	99.58 78	99.89 11	99.80 90	99.85 20	99.66 54	99.73 129	99.62 122	99.84 85	99.71 157	98.62 167	99.96 58	99.30 112	99.96 76	99.86 40

DTE-MVSNet		99.68 55	99.61 70	99.88 18	99.80 90	99.87 14	99.67 50	99.71 141	99.72 91	99.84 85	99.78 114	98.67 161	99.97 37	99.30 112	99.95 90	99.80 58

tmp_tt		95.75 390	95.42 385	96.76 400	89.90 440	94.42 410	98.86 271	97.87 404	78.01 431	99.30 285	99.69 172	97.70 258	95.89 433	99.29 115	98.14 409	99.95 14

PEN-MVS		99.66 62	99.59 75	99.89 11	99.83 67	99.87 14	99.66 54	99.73 129	99.70 98	99.84 85	99.73 140	98.56 176	99.96 58	99.29 115	99.94 103	99.83 50

WR-MVS_H		99.61 77	99.53 93	99.87 23	99.80 90	99.83 30	99.67 50	99.75 119	99.58 135	99.85 82	99.69 172	98.18 228	99.94 85	99.28 117	99.95 90	99.83 50

IterMVS		98.97 234	99.16 161	98.42 352	99.74 143	95.64 397	98.06 365	99.83 77	99.83 69	99.85 82	99.74 136	96.10 321	99.99 8	99.27 118	100.00 1	99.63 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WBMVS		97.50 344	97.18 350	98.48 349	98.85 390	95.89 394	98.44 333	99.52 255	99.53 138	99.52 222	99.42 294	80.10 421	99.86 240	99.24 119	99.95 90	99.68 103

h-mvs33		98.61 273	98.34 289	99.44 213	99.60 194	98.67 282	99.27 157	99.44 280	99.68 103	99.32 275	99.49 277	92.50 362	100.00 1	99.24 119	96.51 426	99.65 128

hse-mvs2		98.52 286	98.30 294	99.16 281	99.29 322	98.60 293	98.77 291	99.02 354	99.68 103	99.32 275	99.04 366	92.50 362	99.85 258	99.24 119	97.87 416	99.03 350

FMVSNet1		99.66 62	99.63 65	99.73 97	99.78 110	99.77 58	99.68 46	99.70 146	99.67 107	99.82 91	99.83 76	98.98 120	99.90 174	99.24 119	99.97 63	99.53 204

casdiffmvs		99.63 69	99.61 70	99.67 119	99.79 102	99.59 136	99.13 204	99.85 69	99.79 79	99.76 123	99.72 147	99.33 71	99.82 298	99.21 123	99.94 103	99.59 175
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CP-MVSNet		99.54 89	99.43 109	99.87 23	99.76 123	99.82 38	99.57 82	99.61 196	99.54 136	99.80 102	99.64 199	97.79 254	99.95 69	99.21 123	99.94 103	99.84 46

DELS-MVS		99.34 146	99.30 139	99.48 201	99.51 245	99.36 194	98.12 356	99.53 250	99.36 174	99.41 254	99.61 227	99.22 85	99.87 221	99.21 123	99.68 259	99.20 305
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
UniMVSNet (Re)		99.37 136	99.26 150	99.68 116	99.51 245	99.58 140	98.98 254	99.60 207	99.43 164	99.70 151	99.36 312	97.70 258	99.88 207	99.20 126	99.87 156	99.59 175

CANet		99.11 205	99.05 195	99.28 262	98.83 392	98.56 295	98.71 298	99.41 286	99.25 188	99.23 293	99.22 344	97.66 266	99.94 85	99.19 127	99.97 63	99.33 274

EI-MVSNet-UG-set		99.48 100	99.50 95	99.42 219	99.57 214	98.65 288	99.24 166	99.46 275	99.68 103	99.80 102	99.66 192	98.99 118	99.89 193	99.19 127	99.90 125	99.72 85

xiu_mvs_v1_base_debu		99.23 166	99.34 127	98.91 316	99.59 199	98.23 314	98.47 328	99.66 166	99.61 126	99.68 157	98.94 382	99.39 60	99.97 37	99.18 129	99.55 299	98.51 398

xiu_mvs_v1_base		99.23 166	99.34 127	98.91 316	99.59 199	98.23 314	98.47 328	99.66 166	99.61 126	99.68 157	98.94 382	99.39 60	99.97 37	99.18 129	99.55 299	98.51 398

xiu_mvs_v1_base_debi		99.23 166	99.34 127	98.91 316	99.59 199	98.23 314	98.47 328	99.66 166	99.61 126	99.68 157	98.94 382	99.39 60	99.97 37	99.18 129	99.55 299	98.51 398

VPNet		99.46 109	99.37 120	99.71 108	99.82 74	99.59 136	99.48 102	99.70 146	99.81 74	99.69 154	99.58 243	97.66 266	99.86 240	99.17 132	99.44 320	99.67 111

UniMVSNet_NR-MVSNet		99.37 136	99.25 152	99.72 103	99.47 267	99.56 143	98.97 256	99.61 196	99.43 164	99.67 162	99.28 330	97.85 250	99.95 69	99.17 132	99.81 201	99.65 128

DU-MVS		99.33 149	99.21 156	99.71 108	99.43 279	99.56 143	98.83 278	99.53 250	99.38 170	99.67 162	99.36 312	97.67 262	99.95 69	99.17 132	99.81 201	99.63 143

EI-MVSNet-Vis-set		99.47 108	99.49 97	99.42 219	99.57 214	98.66 285	99.24 166	99.46 275	99.67 107	99.79 108	99.65 197	98.97 122	99.89 193	99.15 135	99.89 135	99.71 88

EI-MVSNet		99.38 133	99.44 107	99.21 275	99.58 204	98.09 328	99.26 159	99.46 275	99.62 122	99.75 128	99.67 187	98.54 179	99.85 258	99.15 135	99.92 114	99.68 103

VNet		99.18 188	99.06 191	99.56 176	99.24 333	99.36 194	99.33 133	99.31 314	99.67 107	99.47 235	99.57 250	96.48 306	99.84 273	99.15 135	99.30 339	99.47 234

EG-PatchMatch MVS		99.57 80	99.56 87	99.62 155	99.77 119	99.33 200	99.26 159	99.76 114	99.32 178	99.80 102	99.78 114	99.29 75	99.87 221	99.15 135	99.91 124	99.66 120

PVSNet_Blended_VisFu		99.40 127	99.38 117	99.44 213	99.90 37	98.66 285	98.94 263	99.91 46	97.97 336	99.79 108	99.73 140	99.05 111	99.97 37	99.15 135	99.99 16	99.68 103

IterMVS-LS		99.41 125	99.47 98	99.25 271	99.81 83	98.09 328	98.85 273	99.76 114	99.62 122	99.83 90	99.64 199	98.54 179	99.97 37	99.15 135	99.99 16	99.68 103
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 89	99.47 98	99.76 73	99.58 204	99.64 118	99.30 144	99.63 186	99.61 126	99.71 147	99.56 254	98.76 148	99.96 58	99.14 141	99.92 114	99.68 103

MVSTER		98.47 293	98.22 299	99.24 273	99.06 366	98.35 311	99.08 224	99.46 275	99.27 184	99.75 128	99.66 192	88.61 397	99.85 258	99.14 141	99.92 114	99.52 214

Anonymous20231206		99.35 141	99.31 134	99.47 203	99.74 143	99.06 247	99.28 153	99.74 125	99.23 192	99.72 142	99.53 266	97.63 268	99.88 207	99.11 143	99.84 174	99.48 230

Syy-MVS		98.17 318	97.85 330	99.15 283	98.50 415	98.79 274	98.60 305	99.21 337	97.89 342	96.76 418	96.37 441	95.47 330	99.57 404	99.10 144	98.73 385	99.09 333

ttmdpeth		99.48 100	99.55 88	99.29 259	99.76 123	98.16 322	99.33 133	99.95 35	99.79 79	99.36 264	99.89 38	99.13 97	99.77 334	99.09 145	99.64 272	99.93 20

MVS_Test		99.28 155	99.31 134	99.19 278	99.35 300	98.79 274	99.36 127	99.49 268	99.17 204	99.21 298	99.67 187	98.78 145	99.66 384	99.09 145	99.66 268	99.10 328

testgi		99.29 154	99.26 150	99.37 237	99.75 135	98.81 271	98.84 275	99.89 53	98.38 303	99.75 128	99.04 366	99.36 69	99.86 240	99.08 147	99.25 347	99.45 239

1112_ss		99.05 216	98.84 241	99.67 119	99.66 180	99.29 206	98.52 322	99.82 82	97.65 354	99.43 245	99.16 350	96.42 309	99.91 155	99.07 148	99.84 174	99.80 58

CANet_DTU		98.91 243	98.85 239	99.09 292	98.79 398	98.13 323	98.18 349	99.31 314	99.48 145	98.86 338	99.51 270	96.56 303	99.95 69	99.05 149	99.95 90	99.19 308

Baseline_NR-MVSNet		99.49 98	99.37 120	99.82 42	99.91 31	99.84 25	98.83 278	99.86 63	99.68 103	99.65 169	99.88 47	97.67 262	99.87 221	99.03 150	99.86 164	99.76 77

FMVSNet2		99.35 141	99.28 146	99.55 180	99.49 256	99.35 197	99.45 109	99.57 224	99.44 158	99.70 151	99.74 136	97.21 283	99.87 221	99.03 150	99.94 103	99.44 244

Test_1112_low_res		98.95 240	98.73 250	99.63 146	99.68 173	99.15 233	98.09 360	99.80 94	97.14 380	99.46 239	99.40 299	96.11 320	99.89 193	99.01 152	99.84 174	99.84 46

VDD-MVS		99.20 181	99.11 174	99.44 213	99.43 279	98.98 252	99.50 96	98.32 392	99.80 77	99.56 208	99.69 172	96.99 293	99.85 258	98.99 153	99.73 239	99.50 221

DeepC-MVS	98.90 4	99.62 75	99.61 70	99.67 119	99.72 150	99.44 168	99.24 166	99.71 141	99.27 184	99.93 46	99.90 33	99.70 29	99.93 105	98.99 153	99.99 16	99.64 138
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 100	99.47 98	99.51 191	99.77 119	99.41 181	98.81 283	99.66 166	99.42 168	99.75 128	99.66 192	99.20 87	99.76 337	98.98 155	99.99 16	99.36 267

EPNet_dtu		97.62 339	97.79 333	97.11 398	96.67 435	92.31 421	98.51 323	98.04 398	99.24 190	95.77 427	99.47 284	93.78 347	99.66 384	98.98 155	99.62 276	99.37 264
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 146	99.32 132	99.39 231	99.67 179	98.77 276	98.57 314	99.81 91	99.61 126	99.48 233	99.41 295	98.47 190	99.86 240	98.97 157	99.90 125	99.53 204
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
NR-MVSNet		99.40 127	99.31 134	99.68 116	99.43 279	99.55 147	99.73 27	99.50 264	99.46 153	99.88 70	99.36 312	97.54 269	99.87 221	98.97 157	99.87 156	99.63 143

GBi-Net		99.42 121	99.31 134	99.73 97	99.49 256	99.77 58	99.68 46	99.70 146	99.44 158	99.62 183	99.83 76	97.21 283	99.90 174	98.96 159	99.90 125	99.53 204

FMVSNet5		97.80 331	97.25 348	99.42 219	98.83 392	98.97 255	99.38 120	99.80 94	98.87 245	99.25 289	99.69 172	80.60 420	99.91 155	98.96 159	99.90 125	99.38 261

test1		99.42 121	99.31 134	99.73 97	99.49 256	99.77 58	99.68 46	99.70 146	99.44 158	99.62 183	99.83 76	97.21 283	99.90 174	98.96 159	99.90 125	99.53 204

FMVSNet3		98.80 257	98.63 258	99.32 252	99.13 352	98.72 279	99.10 216	99.48 269	99.23 192	99.62 183	99.64 199	92.57 359	99.86 240	98.96 159	99.90 125	99.39 259

UnsupCasMVSNet_eth		98.83 253	98.57 265	99.59 164	99.68 173	99.45 166	98.99 251	99.67 161	99.48 145	99.55 213	99.36 312	94.92 333	99.86 240	98.95 163	96.57 425	99.45 239

CHOSEN 280x420		98.41 298	98.41 281	98.40 353	99.34 309	95.89 394	96.94 419	99.44 280	98.80 257	99.25 289	99.52 268	93.51 351	99.98 23	98.94 164	99.98 45	99.32 277

TDRefinement		99.72 46	99.70 50	99.77 66	99.90 37	99.85 20	99.86 6	99.92 40	99.69 101	99.78 112	99.92 25	99.37 66	99.88 207	98.93 165	99.95 90	99.60 168

alignmvs		98.28 308	97.96 319	99.25 271	99.12 354	98.93 262	99.03 237	98.42 385	99.64 117	98.72 353	97.85 420	90.86 381	99.62 395	98.88 166	99.13 353	99.19 308

testing3-2		96.51 369	96.43 364	96.74 402	99.36 296	91.38 429	99.10 216	97.87 404	99.48 145	98.57 367	98.71 396	76.65 430	99.66 384	98.87 167	99.26 346	99.18 310

MGCFI-Net		99.02 222	99.01 207	99.06 299	99.11 359	98.60 293	99.63 61	99.67 161	99.63 119	98.58 365	97.65 423	99.07 106	99.57 404	98.85 168	98.92 369	99.03 350

sss		98.90 245	98.77 249	99.27 265	99.48 261	98.44 302	98.72 296	99.32 310	97.94 340	99.37 263	99.35 317	96.31 315	99.91 155	98.85 168	99.63 275	99.47 234

xiu_mvs_v2_base		99.02 222	99.11 174	98.77 334	99.37 293	98.09 328	98.13 355	99.51 260	99.47 150	99.42 248	98.54 405	99.38 64	99.97 37	98.83 170	99.33 335	98.24 410

PS-MVSNAJ		99.00 230	99.08 185	98.76 335	99.37 293	98.10 327	98.00 371	99.51 260	99.47 150	99.41 254	98.50 407	99.28 77	99.97 37	98.83 170	99.34 334	98.20 414

D2MVS		99.22 174	99.19 158	99.29 259	99.69 165	98.74 278	98.81 283	99.41 286	98.55 284	99.68 157	99.69 172	98.13 230	99.87 221	98.82 172	99.98 45	99.24 292

PatchT		98.45 295	98.32 291	98.83 329	98.94 380	98.29 312	99.24 166	98.82 362	99.84 64	99.08 315	99.76 126	91.37 370	99.94 85	98.82 172	99.00 364	98.26 409

testf1		99.63 69	99.60 73	99.72 103	99.94 18	99.95 2	99.47 105	99.89 53	99.43 164	99.88 70	99.80 93	99.26 81	99.90 174	98.81 174	99.88 144	99.32 277

APD_test2		99.63 69	99.60 73	99.72 103	99.94 18	99.95 2	99.47 105	99.89 53	99.43 164	99.88 70	99.80 93	99.26 81	99.90 174	98.81 174	99.88 144	99.32 277

sasdasda		99.02 222	99.00 211	99.09 292	99.10 361	98.70 280	99.61 70	99.66 166	99.63 119	98.64 359	97.65 423	99.04 112	99.54 408	98.79 176	98.92 369	99.04 348

Effi-MVS+		99.06 213	98.97 222	99.34 244	99.31 316	98.98 252	98.31 341	99.91 46	98.81 255	98.79 347	98.94 382	99.14 95	99.84 273	98.79 176	98.74 382	99.20 305

canonicalmvs		99.02 222	99.00 211	99.09 292	99.10 361	98.70 280	99.61 70	99.66 166	99.63 119	98.64 359	97.65 423	99.04 112	99.54 408	98.79 176	98.92 369	99.04 348

VDDNet		98.97 234	98.82 244	99.42 219	99.71 153	98.81 271	99.62 64	98.68 369	99.81 74	99.38 262	99.80 93	94.25 341	99.85 258	98.79 176	99.32 337	99.59 175

CR-MVSNet		98.35 305	98.20 301	98.83 329	99.05 367	98.12 324	99.30 144	99.67 161	97.39 368	99.16 304	99.79 103	91.87 367	99.91 155	98.78 180	98.77 378	98.44 403

test_method		91.72 398	92.32 401	89.91 416	93.49 439	70.18 442	90.28 430	99.56 229	61.71 434	95.39 429	99.52 268	93.90 343	99.94 85	98.76 181	98.27 402	99.62 154

RPMNet		98.60 276	98.53 271	98.83 329	99.05 367	98.12 324	99.30 144	99.62 189	99.86 55	99.16 304	99.74 136	92.53 361	99.92 132	98.75 182	98.77 378	98.44 403

pmmvs4		99.13 200	99.06 191	99.36 241	99.57 214	99.10 242	98.01 369	99.25 327	98.78 260	99.58 197	99.44 291	98.24 218	99.76 337	98.74 183	99.93 110	99.22 298

tttt0517		97.62 339	97.20 349	98.90 322	99.76 123	97.40 360	99.48 102	94.36 426	99.06 221	99.70 151	99.49 277	84.55 413	99.94 85	98.73 184	99.65 270	99.36 267

EPP-MVSNet		99.17 193	99.00 211	99.66 126	99.80 90	99.43 172	99.70 35	99.24 330	99.48 145	99.56 208	99.77 123	94.89 334	99.93 105	98.72 185	99.89 135	99.63 143

Anonymous20240529		99.42 121	99.34 127	99.65 132	99.53 236	99.60 134	99.63 61	99.39 296	99.47 150	99.76 123	99.78 114	98.13 230	99.86 240	98.70 186	99.68 259	99.49 226

ACMH	98.42 6	99.59 79	99.54 89	99.72 103	99.86 55	99.62 125	99.56 84	99.79 100	98.77 262	99.80 102	99.85 65	99.64 33	99.85 258	98.70 186	99.89 135	99.70 91
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 149	99.28 146	99.47 203	99.57 214	99.39 185	99.78 14	99.43 283	98.87 245	99.57 200	99.82 83	98.06 235	99.87 221	98.69 188	99.73 239	99.15 317

LFMVS		98.46 294	98.19 304	99.26 268	99.24 333	98.52 298	99.62 64	96.94 415	99.87 52	99.31 280	99.58 243	91.04 375	99.81 313	98.68 189	99.42 324	99.45 239

WR-MVS		99.11 205	98.93 227	99.66 126	99.30 320	99.42 175	98.42 334	99.37 301	99.04 222	99.57 200	99.20 348	96.89 295	99.86 240	98.66 190	99.87 156	99.70 91

mvsmamba		99.08 209	98.95 225	99.45 209	99.36 296	99.18 230	99.39 117	98.81 363	99.37 171	99.35 266	99.70 165	96.36 314	99.94 85	98.66 190	99.59 290	99.22 298

RRT-MVS		99.08 209	99.00 211	99.33 247	99.27 327	98.65 288	99.62 64	99.93 38	99.66 111	99.67 162	99.82 83	95.27 332	99.93 105	98.64 192	99.09 357	99.41 255

Anonymous202405211		98.75 261	98.46 275	99.63 146	99.34 309	99.66 109	99.47 105	97.65 407	99.28 183	99.56 208	99.50 273	93.15 353	99.84 273	98.62 193	99.58 292	99.40 257

EPNet		98.13 319	97.77 334	99.18 280	94.57 438	97.99 334	99.24 166	97.96 400	99.74 86	97.29 411	99.62 218	93.13 354	99.97 37	98.59 194	99.83 182	99.58 180
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 216	99.09 183	98.91 316	99.21 338	98.36 310	98.82 282	99.47 272	98.85 248	98.90 333	99.56 254	98.78 145	99.09 424	98.57 195	99.68 259	99.26 289

Patchmatch-RL test		98.60 276	98.36 286	99.33 247	99.77 119	99.07 245	98.27 343	99.87 59	98.91 240	99.74 136	99.72 147	90.57 386	99.79 323	98.55 196	99.85 169	99.11 326

pmmvs3		98.08 322	97.80 331	98.91 316	99.41 286	97.69 351	97.87 384	99.66 166	95.87 399	99.50 230	99.51 270	90.35 388	99.97 37	98.55 196	99.47 317	99.08 339

ETV-MVS		99.18 188	99.18 159	99.16 281	99.34 309	99.28 208	99.12 208	99.79 100	99.48 145	98.93 327	98.55 404	99.40 59	99.93 105	98.51 198	99.52 309	98.28 408

jason		99.16 194	99.11 174	99.32 252	99.75 135	98.44 302	98.26 345	99.39 296	98.70 270	99.74 136	99.30 326	98.54 179	99.97 37	98.48 199	99.82 191	99.55 190
jason: jason.
APDe-MVS		99.48 100	99.36 123	99.85 29	99.55 228	99.81 43	99.50 96	99.69 153	98.99 226	99.75 128	99.71 157	98.79 143	99.93 105	98.46 200	99.85 169	99.80 58
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CL-MVSNet_self_test		98.71 267	98.56 269	99.15 283	99.22 336	98.66 285	97.14 414	99.51 260	98.09 329	99.54 215	99.27 332	96.87 296	99.74 344	98.43 201	98.96 366	99.03 350

our_test_3		98.85 252	99.09 183	98.13 366	99.66 180	94.90 408	97.72 389	99.58 222	99.07 219	99.64 170	99.62 218	98.19 226	99.93 105	98.41 202	99.95 90	99.55 190

Gipuma		99.57 80	99.59 75	99.49 197	99.98 3	99.71 90	99.72 30	99.84 75	99.81 74	99.94 43	99.78 114	98.91 130	99.71 353	98.41 202	99.95 90	99.05 346
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 349	96.91 359	98.74 336	97.72 431	97.57 353	97.60 395	97.36 413	98.00 332	99.21 298	98.02 416	90.04 391	99.79 323	98.37 204	95.89 430	98.86 373

PM-MVS		99.36 139	99.29 144	99.58 167	99.83 67	99.66 109	98.95 261	99.86 63	98.85 248	99.81 98	99.73 140	98.40 203	99.92 132	98.36 205	99.83 182	99.17 313

baseline1		97.73 334	97.33 345	98.96 307	99.30 320	97.73 349	99.40 115	98.42 385	99.33 177	99.46 239	99.21 346	91.18 373	99.82 298	98.35 206	91.26 433	99.32 277

MVS-HIRNet		97.86 328	98.22 299	96.76 400	99.28 325	91.53 427	98.38 336	92.60 432	99.13 212	99.31 280	99.96 15	97.18 287	99.68 374	98.34 207	99.83 182	99.07 344

GA-MVS		97.99 327	97.68 337	98.93 313	99.52 243	98.04 332	97.19 413	99.05 353	98.32 316	98.81 343	98.97 378	89.89 393	99.41 419	98.33 208	99.05 360	99.34 273

Fast-Effi-MVS+		99.02 222	98.87 237	99.46 206	99.38 291	99.50 153	99.04 234	99.79 100	97.17 378	98.62 361	98.74 395	99.34 70	99.95 69	98.32 209	99.41 325	98.92 366

MDA-MVSNet_test_wron		98.95 240	98.99 218	98.85 325	99.64 185	97.16 366	98.23 347	99.33 308	98.93 237	99.56 208	99.66 192	97.39 276	99.83 288	98.29 210	99.88 144	99.55 190

N_pmnet		98.73 264	98.53 271	99.35 243	99.72 150	98.67 282	98.34 338	94.65 425	98.35 310	99.79 108	99.68 183	98.03 236	99.93 105	98.28 211	99.92 114	99.44 244

ET-MVSNet_ETH3D		96.78 361	96.07 371	98.91 316	99.26 330	97.92 341	97.70 391	96.05 420	97.96 339	92.37 433	98.43 408	87.06 401	99.90 174	98.27 212	97.56 419	98.91 367

thisisatest0530		97.45 345	96.95 356	98.94 310	99.68 173	97.73 349	99.09 221	94.19 428	98.61 280	99.56 208	99.30 326	84.30 415	99.93 105	98.27 212	99.54 304	99.16 315

YYNet1		98.95 240	98.99 218	98.84 327	99.64 185	97.14 368	98.22 348	99.32 310	98.92 239	99.59 195	99.66 192	97.40 274	99.83 288	98.27 212	99.90 125	99.55 190

reproduce_model		99.50 94	99.40 114	99.83 37	99.60 194	99.83 30	99.12 208	99.68 156	99.49 144	99.80 102	99.79 103	99.01 115	99.93 105	98.24 215	99.82 191	99.73 82

ACMM	98.09 11	99.46 109	99.38 117	99.72 103	99.80 90	99.69 102	99.13 204	99.65 176	98.99 226	99.64 170	99.72 147	99.39 60	99.86 240	98.23 216	99.81 201	99.60 168
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 237	98.87 237	99.24 273	99.57 214	98.40 305	98.12 356	99.18 341	98.28 318	99.63 174	99.13 352	98.02 237	99.97 37	98.22 217	99.69 254	99.35 270

3Dnovator	99.15 2	99.43 118	99.36 123	99.65 132	99.39 288	99.42 175	99.70 35	99.56 229	99.23 192	99.35 266	99.80 93	99.17 90	99.95 69	98.21 218	99.84 174	99.59 175

Fast-Effi-MVS+-dtu		99.20 181	99.12 171	99.43 217	99.25 331	99.69 102	99.05 229	99.82 82	99.50 142	98.97 323	99.05 364	98.98 120	99.98 23	98.20 219	99.24 349	98.62 388

MS-PatchMatch		99.00 230	98.97 222	99.09 292	99.11 359	98.19 318	98.76 292	99.33 308	98.49 293	99.44 241	99.58 243	98.21 223	99.69 362	98.20 219	99.62 276	99.39 259

TSAR-MVS + GP.		99.12 202	99.04 201	99.38 234	99.34 309	99.16 231	98.15 352	99.29 318	98.18 325	99.63 174	99.62 218	99.18 89	99.68 374	98.20 219	99.74 233	99.30 283

DP-MVS		99.48 100	99.39 115	99.74 88	99.57 214	99.62 125	99.29 151	99.61 196	99.87 52	99.74 136	99.76 126	98.69 157	99.87 221	98.20 219	99.80 208	99.75 80

MVP-Stereo		99.16 194	99.08 185	99.43 217	99.48 261	99.07 245	99.08 224	99.55 235	98.63 276	99.31 280	99.68 183	98.19 226	99.78 326	98.18 223	99.58 292	99.45 239
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 118	99.30 139	99.80 52	99.83 67	99.81 43	99.52 89	99.70 146	98.35 310	99.51 228	99.50 273	99.31 73	99.88 207	98.18 223	99.84 174	99.69 97

MDA-MVSNet-bldmvs		99.06 213	99.05 195	99.07 297	99.80 90	97.83 344	98.89 267	99.72 138	99.29 180	99.63 174	99.70 165	96.47 307	99.89 193	98.17 225	99.82 191	99.50 221

JIA-IIPM		98.06 323	97.92 326	98.50 348	98.59 411	97.02 370	98.80 286	98.51 380	99.88 50	97.89 397	99.87 53	91.89 366	99.90 174	98.16 226	97.68 418	98.59 391

EIA-MVS		99.12 202	99.01 207	99.45 209	99.36 296	99.62 125	99.34 129	99.79 100	98.41 299	98.84 340	98.89 386	98.75 150	99.84 273	98.15 227	99.51 310	98.89 370

miper_lstm_enhance		98.65 272	98.60 259	98.82 332	99.20 341	97.33 362	97.78 387	99.66 166	99.01 225	99.59 195	99.50 273	94.62 338	99.85 258	98.12 228	99.90 125	99.26 289

reproduce-ours		99.46 109	99.35 125	99.82 42	99.56 225	99.83 30	99.05 229	99.65 176	99.45 156	99.78 112	99.78 114	98.93 125	99.93 105	98.11 229	99.81 201	99.70 91

our_new_method		99.46 109	99.35 125	99.82 42	99.56 225	99.83 30	99.05 229	99.65 176	99.45 156	99.78 112	99.78 114	98.93 125	99.93 105	98.11 229	99.81 201	99.70 91

Effi-MVS+-dtu		99.07 212	98.92 231	99.52 188	98.89 385	99.78 52	99.15 196	99.66 166	99.34 175	98.92 330	99.24 342	97.69 260	99.98 23	98.11 229	99.28 342	98.81 377

tpm		97.15 353	96.95 356	97.75 380	98.91 381	94.24 411	99.32 136	97.96 400	97.71 352	98.29 378	99.32 321	86.72 407	99.92 132	98.10 232	96.24 428	99.09 333

DeepPCF-MVS	98.42 6	99.18 188	99.02 204	99.67 119	99.22 336	99.75 73	97.25 411	99.47 272	98.72 267	99.66 167	99.70 165	99.29 75	99.63 394	98.07 233	99.81 201	99.62 154

ppachtmachnet_test		98.89 248	99.12 171	98.20 364	99.66 180	95.24 404	97.63 393	99.68 156	99.08 217	99.78 112	99.62 218	98.65 165	99.88 207	98.02 234	99.96 76	99.48 230

tpmrst		97.73 334	98.07 312	96.73 403	98.71 407	92.00 422	99.10 216	98.86 359	98.52 289	98.92 330	99.54 264	91.90 365	99.82 298	98.02 234	99.03 362	98.37 405

CSCG		99.37 136	99.29 144	99.60 161	99.71 153	99.46 161	99.43 113	99.85 69	98.79 258	99.41 254	99.60 235	98.92 128	99.92 132	98.02 234	99.92 114	99.43 250

eth_miper_zixun_eth		98.68 270	98.71 252	98.60 343	99.10 361	96.84 375	97.52 401	99.54 241	98.94 234	99.58 197	99.48 280	96.25 318	99.76 337	98.01 237	99.93 110	99.21 301

Patchmtry		98.78 258	98.54 270	99.49 197	98.89 385	99.19 228	99.32 136	99.67 161	99.65 114	99.72 142	99.79 103	91.87 367	99.95 69	98.00 238	99.97 63	99.33 274

PVSNet_BlendedMVS		99.03 220	99.01 207	99.09 292	99.54 230	97.99 334	98.58 310	99.82 82	97.62 355	99.34 270	99.71 157	98.52 186	99.77 334	97.98 239	99.97 63	99.52 214

PVSNet_Blended		98.70 268	98.59 261	99.02 302	99.54 230	97.99 334	97.58 396	99.82 82	95.70 403	99.34 270	98.98 376	98.52 186	99.77 334	97.98 239	99.83 182	99.30 283

cl____		98.54 284	98.41 281	98.92 314	99.03 371	97.80 347	97.46 403	99.59 213	98.90 241	99.60 192	99.46 287	93.85 345	99.78 326	97.97 241	99.89 135	99.17 313

DIV-MVS_self_test		98.54 284	98.42 280	98.92 314	99.03 371	97.80 347	97.46 403	99.59 213	98.90 241	99.60 192	99.46 287	93.87 344	99.78 326	97.97 241	99.89 135	99.18 310

AUN-MVS		97.82 330	97.38 344	99.14 286	99.27 327	98.53 296	98.72 296	99.02 354	98.10 327	97.18 414	99.03 370	89.26 395	99.85 258	97.94 243	97.91 414	99.03 350

FA-MVS(test-final)		98.52 286	98.32 291	99.10 291	99.48 261	98.67 282	99.77 16	98.60 376	97.35 370	99.63 174	99.80 93	93.07 355	99.84 273	97.92 244	99.30 339	98.78 380

ambc				99.20 277	99.35 300	98.53 296	99.17 188	99.46 275		99.67 162	99.80 93	98.46 193	99.70 356	97.92 244	99.70 250	99.38 261

USDC		98.96 237	98.93 227	99.05 300	99.54 230	97.99 334	97.07 417	99.80 94	98.21 322	99.75 128	99.77 123	98.43 196	99.64 393	97.90 246	99.88 144	99.51 216

OPM-MVS		99.26 161	99.13 167	99.63 146	99.70 161	99.61 131	98.58 310	99.48 269	98.50 291	99.52 222	99.63 211	99.14 95	99.76 337	97.89 247	99.77 222	99.51 216
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 151	99.17 160	99.77 66	99.69 165	99.80 47	99.14 198	99.31 314	99.16 206	99.62 183	99.61 227	98.35 207	99.91 155	97.88 248	99.72 245	99.61 164
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.83 37	99.70 161	99.79 49	99.14 198	99.61 196					99.92 132	97.88 248	99.72 245	99.77 72

c3_l		98.72 265	98.71 252	98.72 337	99.12 354	97.22 365	97.68 392	99.56 229	98.90 241	99.54 215	99.48 280	96.37 313	99.73 347	97.88 248	99.88 144	99.21 301

3Dnovator+	98.92 3	99.35 141	99.24 154	99.67 119	99.35 300	99.47 157	99.62 64	99.50 264	99.44 158	99.12 311	99.78 114	98.77 147	99.94 85	97.87 251	99.72 245	99.62 154

miper_ehance_all_eth		98.59 279	98.59 261	98.59 344	98.98 377	97.07 369	97.49 402	99.52 255	98.50 291	99.52 222	99.37 308	96.41 311	99.71 353	97.86 252	99.62 276	99.00 357

WTY-MVS		98.59 279	98.37 285	99.26 268	99.43 279	98.40 305	98.74 294	99.13 348	98.10 327	99.21 298	99.24 342	94.82 335	99.90 174	97.86 252	98.77 378	99.49 226

APD_test1		99.36 139	99.28 146	99.61 158	99.89 39	99.89 10	99.32 136	99.74 125	99.18 199	99.69 154	99.75 132	98.41 199	99.84 273	97.85 254	99.70 250	99.10 328

SED-MVS		99.40 127	99.28 146	99.77 66	99.69 165	99.82 38	99.20 176	99.54 241	99.13 212	99.82 91	99.63 211	98.91 130	99.92 132	97.85 254	99.70 250	99.58 180

test_241102_TWO								99.54 241	99.13 212	99.76 123	99.63 211	98.32 212	99.92 132	97.85 254	99.69 254	99.75 80

MVS_111021_HR		99.12 202	99.02 204	99.40 228	99.50 251	99.11 237	97.92 380	99.71 141	98.76 265	99.08 315	99.47 284	99.17 90	99.54 408	97.85 254	99.76 224	99.54 199

MTAPA		99.35 141	99.20 157	99.80 52	99.81 83	99.81 43	99.33 133	99.53 250	99.27 184	99.42 248	99.63 211	98.21 223	99.95 69	97.83 258	99.79 213	99.65 128

MSC_two_6792asdad				99.74 88	99.03 371	99.53 150		99.23 331					99.92 132	97.77 259	99.69 254	99.78 68

No_MVS				99.74 88	99.03 371	99.53 150		99.23 331					99.92 132	97.77 259	99.69 254	99.78 68

TESTMET0.1,1		96.24 376	95.84 377	97.41 389	98.24 422	93.84 414	97.38 405	95.84 421	98.43 296	97.81 402	98.56 403	79.77 424	99.89 193	97.77 259	98.77 378	98.52 397

ACMH+	98.40 8	99.50 94	99.43 109	99.71 108	99.86 55	99.76 65	99.32 136	99.77 109	99.53 138	99.77 120	99.76 126	99.26 81	99.78 326	97.77 259	99.88 144	99.60 168

IU-MVS					99.69 165	99.77 58		99.22 334	97.50 362	99.69 154				97.75 263	99.70 250	99.77 72

114514_t		98.49 291	98.11 309	99.64 139	99.73 147	99.58 140	99.24 166	99.76 114	89.94 426	99.42 248	99.56 254	97.76 257	99.86 240	97.74 264	99.82 191	99.47 234

DVP-MVS++		99.38 133	99.25 152	99.77 66	99.03 371	99.77 58	99.74 24	99.61 196	99.18 199	99.76 123	99.61 227	99.00 116	99.92 132	97.72 265	99.60 286	99.62 154

test_0728_THIRD									99.18 199	99.62 183	99.61 227	98.58 173	99.91 155	97.72 265	99.80 208	99.77 72

EGC-MVSNET		89.05 400	85.52 403	99.64 139	99.89 39	99.78 52	99.56 84	99.52 255	24.19 435	49.96 436	99.83 76	99.15 92	99.92 132	97.71 267	99.85 169	99.21 301

miper_enhance_ethall		98.03 324	97.94 324	98.32 358	98.27 421	96.43 382	96.95 418	99.41 286	96.37 394	99.43 245	98.96 380	94.74 336	99.69 362	97.71 267	99.62 276	98.83 376

TSAR-MVS + MP.		99.34 146	99.24 154	99.63 146	99.82 74	99.37 190	99.26 159	99.35 305	98.77 262	99.57 200	99.70 165	99.27 80	99.88 207	97.71 267	99.75 226	99.65 128
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 342	97.28 346	98.40 353	98.37 419	96.75 376	97.24 412	99.37 301	97.31 372	99.41 254	99.22 344	87.30 399	99.37 420	97.70 270	99.62 276	99.08 339

MP-MVS-pluss		99.14 198	98.92 231	99.80 52	99.83 67	99.83 30	98.61 303	99.63 186	96.84 387	99.44 241	99.58 243	98.81 138	99.91 155	97.70 270	99.82 191	99.67 111
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 155	99.11 174	99.79 59	99.75 135	99.81 43	98.95 261	99.53 250	98.27 319	99.53 220	99.73 140	98.75 150	99.87 221	97.70 270	99.83 182	99.68 103

UnsupCasMVSNet_bld		98.55 283	98.27 297	99.40 228	99.56 225	99.37 190	97.97 376	99.68 156	97.49 363	99.08 315	99.35 317	95.41 331	99.82 298	97.70 270	98.19 406	99.01 356

MVS_111021_LR		99.13 200	99.03 203	99.42 219	99.58 204	99.32 202	97.91 382	99.73 129	98.68 271	99.31 280	99.48 280	99.09 101	99.66 384	97.70 270	99.77 222	99.29 286

IS-MVSNet		99.03 220	98.85 239	99.55 180	99.80 90	99.25 215	99.73 27	99.15 345	99.37 171	99.61 189	99.71 157	94.73 337	99.81 313	97.70 270	99.88 144	99.58 180

test-LLR		97.15 353	96.95 356	97.74 381	98.18 424	95.02 406	97.38 405	96.10 417	98.00 332	97.81 402	98.58 400	90.04 391	99.91 155	97.69 276	98.78 376	98.31 406

test-mter		96.23 377	95.73 380	97.74 381	98.18 424	95.02 406	97.38 405	96.10 417	97.90 341	97.81 402	98.58 400	79.12 427	99.91 155	97.69 276	98.78 376	98.31 406

MonoMVSNet		98.23 313	98.32 291	97.99 369	98.97 378	96.62 378	99.49 100	98.42 385	99.62 122	99.40 259	99.79 103	95.51 329	98.58 431	97.68 278	95.98 429	98.76 383

XVS		99.27 159	99.11 174	99.75 83	99.71 153	99.71 90	99.37 124	99.61 196	99.29 180	98.76 350	99.47 284	98.47 190	99.88 207	97.62 279	99.73 239	99.67 111

X-MVStestdata		96.09 381	94.87 394	99.75 83	99.71 153	99.71 90	99.37 124	99.61 196	99.29 180	98.76 350	61.30 444	98.47 190	99.88 207	97.62 279	99.73 239	99.67 111

SMA-MVS		99.19 184	99.00 211	99.73 97	99.46 271	99.73 82	99.13 204	99.52 255	97.40 367	99.57 200	99.64 199	98.93 125	99.83 288	97.61 281	99.79 213	99.63 143
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CostFormer		96.71 364	96.79 363	96.46 407	98.90 382	90.71 433	99.41 114	98.68 369	94.69 416	98.14 388	99.34 320	86.32 409	99.80 320	97.60 282	98.07 412	98.88 371

PVSNet	97.47 15	98.42 297	98.44 278	98.35 355	99.46 271	96.26 386	96.70 422	99.34 307	97.68 353	99.00 322	99.13 352	97.40 274	99.72 349	97.59 283	99.68 259	99.08 339

new_pmnet		98.88 249	98.89 235	98.84 327	99.70 161	97.62 352	98.15 352	99.50 264	97.98 335	99.62 183	99.54 264	98.15 229	99.94 85	97.55 284	99.84 174	98.95 361

IB-MVS	95.41 20	95.30 396	94.46 400	97.84 377	98.76 403	95.33 402	97.33 408	96.07 419	96.02 398	95.37 430	97.41 427	76.17 431	99.96 58	97.54 285	95.44 432	98.22 411
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
LS3D		99.24 165	99.11 174	99.61 158	98.38 418	99.79 49	99.57 82	99.68 156	99.61 126	99.15 306	99.71 157	98.70 156	99.91 155	97.54 285	99.68 259	99.13 325

ZNCC-MVS		99.22 174	99.04 201	99.77 66	99.76 123	99.73 82	99.28 153	99.56 229	98.19 324	99.14 308	99.29 329	98.84 137	99.92 132	97.53 287	99.80 208	99.64 138

CP-MVS		99.23 166	99.05 195	99.75 83	99.66 180	99.66 109	99.38 120	99.62 189	98.38 303	99.06 319	99.27 332	98.79 143	99.94 85	97.51 288	99.82 191	99.66 120

SD-MVS		99.01 228	99.30 139	98.15 365	99.50 251	99.40 182	98.94 263	99.61 196	99.22 196	99.75 128	99.82 83	99.54 48	95.51 435	97.48 289	99.87 156	99.54 199
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
PMMVS		98.49 291	98.29 296	99.11 289	98.96 379	98.42 304	97.54 397	99.32 310	97.53 360	98.47 373	98.15 415	97.88 247	99.82 298	97.46 290	99.24 349	99.09 333

DeepC-MVS_fast	98.47 5	99.23 166	99.12 171	99.56 176	99.28 325	99.22 222	98.99 251	99.40 293	99.08 217	99.58 197	99.64 199	98.90 133	99.83 288	97.44 291	99.75 226	99.63 143
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 162	99.08 185	99.76 73	99.73 147	99.70 98	99.31 141	99.59 213	98.36 305	99.36 264	99.37 308	98.80 142	99.91 155	97.43 292	99.75 226	99.68 103

ACMMPR		99.23 166	99.06 191	99.76 73	99.74 143	99.69 102	99.31 141	99.59 213	98.36 305	99.35 266	99.38 305	98.61 169	99.93 105	97.43 292	99.75 226	99.67 111

Vis-MVSNet (Re-imp)		98.77 259	98.58 264	99.34 244	99.78 110	98.88 267	99.61 70	99.56 229	99.11 216	99.24 292	99.56 254	93.00 357	99.78 326	97.43 292	99.89 135	99.35 270

MIMVSNet		98.43 296	98.20 301	99.11 289	99.53 236	98.38 309	99.58 79	98.61 374	98.96 230	99.33 272	99.76 126	90.92 377	99.81 313	97.38 295	99.76 224	99.15 317

WB-MVSnew		98.34 307	98.14 307	98.96 307	98.14 427	97.90 342	98.27 343	97.26 414	98.63 276	98.80 345	98.00 418	97.77 255	99.90 174	97.37 296	98.98 365	99.09 333

XVG-OURS-SEG-HR		99.16 194	98.99 218	99.66 126	99.84 63	99.64 118	98.25 346	99.73 129	98.39 302	99.63 174	99.43 292	99.70 29	99.90 174	97.34 297	98.64 389	99.44 244

COLMAP_ROB	98.06 12	99.45 113	99.37 120	99.70 112	99.83 67	99.70 98	99.38 120	99.78 106	99.53 138	99.67 162	99.78 114	99.19 88	99.86 240	97.32 298	99.87 156	99.55 190
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 222	98.81 245	99.65 132	99.58 204	99.49 154	98.58 310	99.07 350	98.40 301	99.04 320	99.25 337	98.51 188	99.80 320	97.31 299	99.51 310	99.65 128

region2R		99.23 166	99.05 195	99.77 66	99.76 123	99.70 98	99.31 141	99.59 213	98.41 299	99.32 275	99.36 312	98.73 154	99.93 105	97.29 300	99.74 233	99.67 111

APD-MVS_3200maxsize		99.31 152	99.16 161	99.74 88	99.53 236	99.75 73	99.27 157	99.61 196	99.19 198	99.57 200	99.64 199	98.76 148	99.90 174	97.29 300	99.62 276	99.56 187

TAPA-MVS	97.92 13	98.03 324	97.55 340	99.46 206	99.47 267	99.44 168	98.50 324	99.62 189	86.79 427	99.07 318	99.26 335	98.26 217	99.62 395	97.28 302	99.73 239	99.31 281
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 159	99.11 174	99.73 97	99.54 230	99.74 79	99.26 159	99.62 189	99.16 206	99.52 222	99.64 199	98.41 199	99.91 155	97.27 303	99.61 283	99.54 199

RE-MVS-def			99.13 167		99.54 230	99.74 79	99.26 159	99.62 189	99.16 206	99.52 222	99.64 199	98.57 174		97.27 303	99.61 283	99.54 199

testing11		96.05 383	95.41 386	97.97 371	98.78 400	95.27 403	98.59 308	98.23 394	98.86 247	96.56 421	96.91 434	75.20 432	99.69 362	97.26 305	98.29 401	98.93 364

test_yl		98.25 310	97.95 320	99.13 287	99.17 347	98.47 299	99.00 245	98.67 371	98.97 228	99.22 296	99.02 371	91.31 371	99.69 362	97.26 305	98.93 367	99.24 292

DCV-MVSNet		98.25 310	97.95 320	99.13 287	99.17 347	98.47 299	99.00 245	98.67 371	98.97 228	99.22 296	99.02 371	91.31 371	99.69 362	97.26 305	98.93 367	99.24 292

PHI-MVS		99.11 205	98.95 225	99.59 164	99.13 352	99.59 136	99.17 188	99.65 176	97.88 344	99.25 289	99.46 287	98.97 122	99.80 320	97.26 305	99.82 191	99.37 264

tfpnnormal		99.43 118	99.38 117	99.60 161	99.87 52	99.75 73	99.59 77	99.78 106	99.71 93	99.90 57	99.69 172	98.85 136	99.90 174	97.25 309	99.78 218	99.15 317

PatchmatchNet		97.65 338	97.80 331	97.18 396	98.82 395	92.49 420	99.17 188	98.39 388	98.12 326	98.79 347	99.58 243	90.71 383	99.89 193	97.23 310	99.41 325	99.16 315
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 233	98.80 247	99.56 176	99.25 331	99.43 172	98.54 319	99.27 322	98.58 282	98.80 345	99.43 292	98.53 183	99.70 356	97.22 311	99.59 290	99.54 199

testing3		96.48 370	95.63 382	99.01 303	99.23 335	97.81 345	98.90 266	99.10 349	98.72 267	97.84 401	97.92 419	72.44 436	99.85 258	97.21 312	99.33 335	99.35 270

HPM-MVS		99.25 162	99.07 189	99.78 63	99.81 83	99.75 73	99.61 70	99.67 161	97.72 351	99.35 266	99.25 337	99.23 84	99.92 132	97.21 312	99.82 191	99.67 111
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 184	99.00 211	99.76 73	99.76 123	99.68 105	99.38 120	99.54 241	98.34 314	99.01 321	99.50 273	98.53 183	99.93 105	97.18 314	99.78 218	99.66 120

ACMMP		99.25 162	99.08 185	99.74 88	99.79 102	99.68 105	99.50 96	99.65 176	98.07 330	99.52 222	99.69 172	98.57 174	99.92 132	97.18 314	99.79 213	99.63 143
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
myMVS_eth3d28		96.23 377	95.74 379	97.70 383	98.86 389	95.59 399	98.66 300	98.14 396	98.96 230	97.67 407	97.06 431	76.78 429	98.92 427	97.10 316	98.41 398	98.58 393

thisisatest0515		96.98 357	96.42 365	98.66 340	99.42 284	97.47 356	97.27 410	94.30 427	97.24 374	99.15 306	98.86 388	85.01 411	99.87 221	97.10 316	99.39 327	98.63 387

XVG-ACMP-BASELINE		99.23 166	99.10 182	99.63 146	99.82 74	99.58 140	98.83 278	99.72 138	98.36 305	99.60 192	99.71 157	98.92 128	99.91 155	97.08 318	99.84 174	99.40 257

MSDG		99.08 209	98.98 221	99.37 237	99.60 194	99.13 234	97.54 397	99.74 125	98.84 251	99.53 220	99.55 262	99.10 99	99.79 323	97.07 319	99.86 164	99.18 310

SteuartSystems-ACMMP		99.30 153	99.14 165	99.76 73	99.87 52	99.66 109	99.18 183	99.60 207	98.55 284	99.57 200	99.67 187	99.03 114	99.94 85	97.01 320	99.80 208	99.69 97
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 379	95.78 378	97.49 385	98.53 413	93.83 415	98.04 366	93.94 430	98.96 230	98.46 374	98.17 414	79.86 422	99.87 221	96.99 321	99.06 358	98.78 380

EPMVS		96.53 367	96.32 366	97.17 397	98.18 424	92.97 419	99.39 117	89.95 436	98.21 322	98.61 362	99.59 240	86.69 408	99.72 349	96.99 321	99.23 351	98.81 377

MSP-MVS		99.04 219	98.79 248	99.81 47	99.78 110	99.73 82	99.35 128	99.57 224	98.54 287	99.54 215	98.99 373	96.81 297	99.93 105	96.97 323	99.53 306	99.77 72
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		98.96 237	98.70 254	99.74 88	99.52 243	99.71 90	98.86 271	99.19 340	98.47 295	98.59 364	99.06 363	98.08 234	99.91 155	96.94 324	99.60 286	99.60 168

SR-MVS		99.19 184	99.00 211	99.74 88	99.51 245	99.72 87	99.18 183	99.60 207	98.85 248	99.47 235	99.58 243	98.38 204	99.92 132	96.92 325	99.54 304	99.57 185

PGM-MVS		99.20 181	99.01 207	99.77 66	99.75 135	99.71 90	99.16 194	99.72 138	97.99 334	99.42 248	99.60 235	98.81 138	99.93 105	96.91 326	99.74 233	99.66 120

HY-MVS	98.23 9	98.21 317	97.95 320	98.99 304	99.03 371	98.24 313	99.61 70	98.72 367	96.81 388	98.73 352	99.51 270	94.06 342	99.86 240	96.91 326	98.20 404	98.86 373

MDTV_nov1_ep13			97.73 335		98.70 408	90.83 431	99.15 196	98.02 399	98.51 290	98.82 342	99.61 227	90.98 376	99.66 384	96.89 328	98.92 369

GST-MVS		99.16 194	98.96 224	99.75 83	99.73 147	99.73 82	99.20 176	99.55 235	98.22 321	99.32 275	99.35 317	98.65 165	99.91 155	96.86 329	99.74 233	99.62 154

test_post1							99.14 198				51.63 446	89.54 394	99.82 298	96.86 329

SCA		98.11 320	98.36 286	97.36 390	99.20 341	92.99 418	98.17 351	98.49 382	98.24 320	99.10 314	99.57 250	96.01 322	99.94 85	96.86 329	99.62 276	99.14 322

UBG		96.53 367	95.95 373	98.29 362	98.87 388	96.31 385	98.48 327	98.07 397	98.83 252	97.32 409	96.54 439	79.81 423	99.62 395	96.84 332	98.74 382	98.95 361

XVG-OURS		99.21 179	99.06 191	99.65 132	99.82 74	99.62 125	97.87 384	99.74 125	98.36 305	99.66 167	99.68 183	99.71 26	99.90 174	96.84 332	99.88 144	99.43 250

LCM-MVSNet-Re		99.28 155	99.15 164	99.67 119	99.33 314	99.76 65	99.34 129	99.97 20	98.93 237	99.91 54	99.79 103	98.68 158	99.93 105	96.80 334	99.56 295	99.30 283

RPSCF		99.18 188	99.02 204	99.64 139	99.83 67	99.85 20	99.44 111	99.82 82	98.33 315	99.50 230	99.78 114	97.90 245	99.65 391	96.78 335	99.83 182	99.44 244

旧先验2							97.94 378		95.33 407	98.94 326			99.88 207	96.75 336

MDTV_nov1_ep13_2view						91.44 428	99.14 198		97.37 369	99.21 298		91.78 369		96.75 336		99.03 350

CLD-MVS		98.76 260	98.57 265	99.33 247	99.57 214	98.97 255	97.53 399	99.55 235	96.41 392	99.27 287	99.13 352	99.07 106	99.78 326	96.73 338	99.89 135	99.23 296
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 321	97.98 318	98.48 349	99.27 327	96.48 380	99.40 115	99.07 350	98.81 255	99.23 293	99.57 250	90.11 390	99.87 221	96.69 339	99.64 272	99.09 333

baseline2		96.83 360	96.28 367	98.46 351	99.09 364	96.91 373	98.83 278	93.87 431	97.23 375	96.23 426	98.36 409	88.12 398	99.90 174	96.68 340	98.14 409	98.57 395

cascas		96.99 356	96.82 362	97.48 386	97.57 434	95.64 397	96.43 424	99.56 229	91.75 422	97.13 416	97.61 426	95.58 327	98.63 429	96.68 340	99.11 355	98.18 415

PC_three_1452									97.56 356	99.68 157	99.41 295	99.09 101	97.09 432	96.66 342	99.60 286	99.62 154

LPG-MVS_test		99.22 174	99.05 195	99.74 88	99.82 74	99.63 123	99.16 194	99.73 129	97.56 356	99.64 170	99.69 172	99.37 66	99.89 193	96.66 342	99.87 156	99.69 97

LGP-MVS_train				99.74 88	99.82 74	99.63 123		99.73 129	97.56 356	99.64 170	99.69 172	99.37 66	99.89 193	96.66 342	99.87 156	99.69 97

ETVMVS		96.14 380	95.22 391	98.89 323	98.80 396	98.01 333	98.66 300	98.35 391	98.71 269	97.18 414	96.31 443	74.23 435	99.75 341	96.64 345	98.13 411	98.90 368

TinyColmap		98.97 234	98.93 227	99.07 297	99.46 271	98.19 318	97.75 388	99.75 119	98.79 258	99.54 215	99.70 165	98.97 122	99.62 395	96.63 346	99.83 182	99.41 255

LF4IMVS		99.01 228	98.92 231	99.27 265	99.71 153	99.28 208	98.59 308	99.77 109	98.32 316	99.39 261	99.41 295	98.62 167	99.84 273	96.62 347	99.84 174	98.69 386

NCCC		98.82 254	98.57 265	99.58 167	99.21 338	99.31 203	98.61 303	99.25 327	98.65 274	98.43 375	99.26 335	97.86 248	99.81 313	96.55 348	99.27 345	99.61 164

OPU-MVS				99.29 259	99.12 354	99.44 168	99.20 176				99.40 299	99.00 116	98.84 428	96.54 349	99.60 286	99.58 180

F-COLMAP		98.74 262	98.45 277	99.62 155	99.57 214	99.47 157	98.84 275	99.65 176	96.31 395	98.93 327	99.19 349	97.68 261	99.87 221	96.52 350	99.37 330	99.53 204

testing99		95.86 388	95.19 392	97.87 375	98.76 403	95.03 405	98.62 302	98.44 384	98.68 271	96.67 420	96.66 438	74.31 434	99.69 362	96.51 351	98.03 413	98.90 368

ADS-MVSNet2		97.78 332	97.66 339	98.12 367	99.14 350	95.36 401	99.22 173	98.75 366	96.97 383	98.25 380	99.64 199	90.90 378	99.94 85	96.51 351	99.56 295	99.08 339

ADS-MVSNet		97.72 337	97.67 338	97.86 376	99.14 350	94.65 409	99.22 173	98.86 359	96.97 383	98.25 380	99.64 199	90.90 378	99.84 273	96.51 351	99.56 295	99.08 339

PatchMatch-RL		98.68 270	98.47 274	99.30 258	99.44 276	99.28 208	98.14 354	99.54 241	97.12 381	99.11 312	99.25 337	97.80 253	99.70 356	96.51 351	99.30 339	98.93 364

CMPMVS	77.52 23	98.50 289	98.19 304	99.41 226	98.33 420	99.56 143	99.01 242	99.59 213	95.44 405	99.57 200	99.80 93	95.64 325	99.46 418	96.47 355	99.92 114	99.21 301
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 384	95.32 389	98.02 368	98.76 403	95.39 400	98.38 336	98.65 373	98.82 253	96.84 417	96.71 437	75.06 433	99.71 353	96.46 356	98.23 403	98.98 358

SF-MVS		99.10 208	98.93 227	99.62 155	99.58 204	99.51 152	99.13 204	99.65 176	97.97 336	99.42 248	99.61 227	98.86 135	99.87 221	96.45 357	99.68 259	99.49 226

FE-MVS		97.85 329	97.42 343	99.15 283	99.44 276	98.75 277	99.77 16	98.20 395	95.85 400	99.33 272	99.80 93	88.86 396	99.88 207	96.40 358	99.12 354	98.81 377

DPE-MVS		99.14 198	98.92 231	99.82 42	99.57 214	99.77 58	98.74 294	99.60 207	98.55 284	99.76 123	99.69 172	98.23 222	99.92 132	96.39 359	99.75 226	99.76 77
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 432	89.02 438			93.47 418		98.30 410		99.84 273	96.38 360

AllTest		99.21 179	99.07 189	99.63 146	99.78 110	99.64 118	99.12 208	99.83 77	98.63 276	99.63 174	99.72 147	98.68 158	99.75 341	96.38 360	99.83 182	99.51 216

TestCases				99.63 146	99.78 110	99.64 118		99.83 77	98.63 276	99.63 174	99.72 147	98.68 158	99.75 341	96.38 360	99.83 182	99.51 216

testdata				99.42 219	99.51 245	98.93 262		99.30 317	96.20 396	98.87 337	99.40 299	98.33 211	99.89 193	96.29 363	99.28 342	99.44 244

dp		96.86 359	97.07 352	96.24 409	98.68 409	90.30 436	99.19 182	98.38 389	97.35 370	98.23 382	99.59 240	87.23 400	99.82 298	96.27 364	98.73 385	98.59 391

tpmvs		97.39 348	97.69 336	96.52 405	98.41 417	91.76 424	99.30 144	98.94 358	97.74 350	97.85 400	99.55 262	92.40 364	99.73 347	96.25 365	98.73 385	98.06 417

KD-MVS_2432*1600		95.89 385	95.41 386	97.31 393	94.96 436	93.89 412	97.09 415	99.22 334	97.23 375	98.88 334	99.04 366	79.23 425	99.54 408	96.24 366	96.81 423	98.50 401

miper_refine_blended		95.89 385	95.41 386	97.31 393	94.96 436	93.89 412	97.09 415	99.22 334	97.23 375	98.88 334	99.04 366	79.23 425	99.54 408	96.24 366	96.81 423	98.50 401

ACMP	97.51 14	99.05 216	98.84 241	99.67 119	99.78 110	99.55 147	98.88 268	99.66 166	97.11 382	99.47 235	99.60 235	99.07 106	99.89 193	96.18 368	99.85 169	99.58 180
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 245	98.72 251	99.44 213	99.39 288	99.42 175	98.58 310	99.64 184	97.31 372	99.44 241	99.62 218	98.59 171	99.69 362	96.17 369	99.79 213	99.22 298

DP-MVS Recon		98.50 289	98.23 298	99.31 255	99.49 256	99.46 161	98.56 315	99.63 186	94.86 414	98.85 339	99.37 308	97.81 252	99.59 402	96.08 370	99.44 320	98.88 371

tpm cat1		96.78 361	96.98 355	96.16 410	98.85 390	90.59 434	99.08 224	99.32 310	92.37 420	97.73 406	99.46 287	91.15 374	99.69 362	96.07 371	98.80 375	98.21 412

tpm2		96.35 373	96.22 368	96.73 403	98.88 387	91.75 425	99.21 175	98.51 380	93.27 419	97.89 397	99.21 346	84.83 412	99.70 356	96.04 372	98.18 407	98.75 384

dmvs_re		98.69 269	98.48 273	99.31 255	99.55 228	99.42 175	99.54 87	98.38 389	99.32 178	98.72 353	98.71 396	96.76 299	99.21 422	96.01 373	99.35 333	99.31 281

test_0402		99.22 174	99.14 165	99.45 209	99.79 102	99.43 172	99.28 153	99.68 156	99.54 136	99.40 259	99.56 254	99.07 106	99.82 298	96.01 373	99.96 76	99.11 326

ITE_SJBPF				99.38 234	99.63 187	99.44 168		99.73 129	98.56 283	99.33 272	99.53 266	98.88 134	99.68 374	96.01 373	99.65 270	99.02 355

test_prior2							97.95 377		97.87 345	98.05 390	99.05 364	97.90 245		95.99 376	99.49 315

testdata2													99.89 193	95.99 376

原ACMM1				99.37 237	99.47 267	98.87 269		99.27 322	96.74 390	98.26 379	99.32 321	97.93 244	99.82 298	95.96 378	99.38 328	99.43 250

新几何1				99.52 188	99.50 251	99.22 222		99.26 324	95.66 404	98.60 363	99.28 330	97.67 262	99.89 193	95.95 379	99.32 337	99.45 239

MP-MVS		99.06 213	98.83 243	99.76 73	99.76 123	99.71 90	99.32 136	99.50 264	98.35 310	98.97 323	99.48 280	98.37 205	99.92 132	95.95 379	99.75 226	99.63 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 395	94.59 398	98.61 342	98.66 410	97.45 358	98.54 319	97.90 403	98.53 288	96.54 422	96.47 440	70.62 439	99.81 313	95.91 381	98.15 408	98.56 396

wuyk23d		97.58 341	99.13 167	92.93 414	99.69 165	99.49 154	99.52 89	99.77 109	97.97 336	99.96 30	99.79 103	99.84 14	99.94 85	95.85 382	99.82 191	79.36 432

HQP_MVS		98.90 245	98.68 255	99.55 180	99.58 204	99.24 219	98.80 286	99.54 241	98.94 234	99.14 308	99.25 337	97.24 281	99.82 298	95.84 383	99.78 218	99.60 168

plane_prior5								99.54 241					99.82 298	95.84 383	99.78 218	99.60 168

无先验							98.01 369	99.23 331	95.83 401				99.85 258	95.79 385		99.44 244

CPTT-MVS		98.74 262	98.44 278	99.64 139	99.61 192	99.38 187	99.18 183	99.55 235	96.49 391	99.27 287	99.37 308	97.11 289	99.92 132	95.74 386	99.67 265	99.62 154

PLC	97.35 16	98.36 302	97.99 316	99.48 201	99.32 315	99.24 219	98.50 324	99.51 260	95.19 410	98.58 365	98.96 380	96.95 294	99.83 288	95.63 387	99.25 347	99.37 264
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 281	98.34 289	99.28 262	99.18 346	99.10 242	98.34 338	99.41 286	98.48 294	98.52 370	98.98 376	97.05 291	99.78 326	95.59 388	99.50 313	98.96 359

1314		98.00 326	97.90 328	98.27 363	98.90 382	97.45 358	99.30 144	99.06 352	94.98 411	97.21 413	99.12 356	98.43 196	99.67 379	95.58 389	98.56 392	97.71 421

PVSNet_0	95.53 19	95.85 389	95.31 390	97.47 387	98.78 400	93.48 417	95.72 426	99.40 293	96.18 397	97.37 408	97.73 421	95.73 324	99.58 403	95.49 390	81.40 434	99.36 267

MAR-MVS		98.24 312	97.92 326	99.19 278	98.78 400	99.65 115	99.17 188	99.14 346	95.36 406	98.04 391	98.81 392	97.47 271	99.72 349	95.47 391	99.06 358	98.21 412
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
OpenMVS	98.12 10	98.23 313	97.89 329	99.26 268	99.19 343	99.26 212	99.65 59	99.69 153	91.33 424	98.14 388	99.77 123	98.28 214	99.96 58	95.41 392	99.55 299	98.58 393

train_agg		98.35 305	97.95 320	99.57 173	99.35 300	99.35 197	98.11 358	99.41 286	94.90 412	97.92 395	98.99 373	98.02 237	99.85 258	95.38 393	99.44 320	99.50 221

9.14			98.64 256		99.45 275		98.81 283	99.60 207	97.52 361	99.28 286	99.56 254	98.53 183	99.83 288	95.36 394	99.64 272

APD-MVS		98.87 250	98.59 261	99.71 108	99.50 251	99.62 125	99.01 242	99.57 224	96.80 389	99.54 215	99.63 211	98.29 213	99.91 155	95.24 395	99.71 248	99.61 164
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 383								95.20 396

AdaColmap		98.60 276	98.35 288	99.38 234	99.12 354	99.22 222	98.67 299	99.42 285	97.84 348	98.81 343	99.27 332	97.32 279	99.81 313	95.14 397	99.53 306	99.10 328

test9_res														95.10 398	99.44 320	99.50 221

CDPH-MVS		98.56 282	98.20 301	99.61 158	99.50 251	99.46 161	98.32 340	99.41 286	95.22 408	99.21 298	99.10 360	98.34 209	99.82 298	95.09 399	99.66 268	99.56 187

BH-untuned		98.22 315	98.09 310	98.58 346	99.38 291	97.24 364	98.55 316	98.98 357	97.81 349	99.20 303	98.76 394	97.01 292	99.65 391	94.83 400	98.33 399	98.86 373

BP-MVS														94.73 401

HQP-MVS		98.36 302	98.02 315	99.39 231	99.31 316	98.94 259	97.98 373	99.37 301	97.45 364	98.15 384	98.83 389	96.67 300	99.70 356	94.73 401	99.67 265	99.53 204

QAPM		98.40 300	97.99 316	99.65 132	99.39 288	99.47 157	99.67 50	99.52 255	91.70 423	98.78 349	99.80 93	98.55 177	99.95 69	94.71 403	99.75 226	99.53 204

agg_prior2														94.58 404	99.46 319	99.50 221

myMVS_eth3d		95.63 393	94.73 395	98.34 357	98.50 415	96.36 383	98.60 305	99.21 337	97.89 342	96.76 418	96.37 441	72.10 437	99.57 404	94.38 405	98.73 385	99.09 333

BH-RMVSNet		98.41 298	98.14 307	99.21 275	99.21 338	98.47 299	98.60 305	98.26 393	98.35 310	98.93 327	99.31 324	97.20 286	99.66 384	94.32 406	99.10 356	99.51 216

E-PMN		97.14 355	97.43 342	96.27 408	98.79 398	91.62 426	95.54 427	99.01 356	99.44 158	98.88 334	99.12 356	92.78 358	99.68 374	94.30 407	99.03 362	97.50 422

MG-MVS		98.52 286	98.39 283	98.94 310	99.15 349	97.39 361	98.18 349	99.21 337	98.89 244	99.23 293	99.63 211	97.37 277	99.74 344	94.22 408	99.61 283	99.69 97

API-MVS		98.38 301	98.39 283	98.35 355	98.83 392	99.26 212	99.14 198	99.18 341	98.59 281	98.66 358	98.78 393	98.61 169	99.57 404	94.14 409	99.56 295	96.21 429

PAPM_NR		98.36 302	98.04 313	99.33 247	99.48 261	98.93 262	98.79 289	99.28 321	97.54 359	98.56 369	98.57 402	97.12 288	99.69 362	94.09 410	98.90 373	99.38 261

ZD-MVS					99.43 279	99.61 131		99.43 283	96.38 393	99.11 312	99.07 362	97.86 248	99.92 132	94.04 411	99.49 315

DPM-MVS		98.28 308	97.94 324	99.32 252	99.36 296	99.11 237	97.31 409	98.78 365	96.88 385	98.84 340	99.11 359	97.77 255	99.61 400	94.03 412	99.36 331	99.23 296

gg-mvs-nofinetune		95.87 387	95.17 393	97.97 371	98.19 423	96.95 371	99.69 42	89.23 437	99.89 45	96.24 425	99.94 19	81.19 417	99.51 414	93.99 413	98.20 404	97.44 423

PMVS	92.94 21	98.82 254	98.81 245	98.85 325	99.84 63	97.99 334	99.20 176	99.47 272	99.71 93	99.42 248	99.82 83	98.09 232	99.47 416	93.88 414	99.85 169	99.07 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 358	97.28 346	95.99 412	98.76 403	91.03 430	95.26 429	98.61 374	99.34 175	98.92 330	98.88 387	93.79 346	99.66 384	92.87 415	99.05 360	97.30 426

BH-w/o		97.20 352	97.01 354	97.76 379	99.08 365	95.69 396	98.03 368	98.52 379	95.76 402	97.96 394	98.02 416	95.62 326	99.47 416	92.82 416	97.25 422	98.12 416

TR-MVS		97.44 346	97.15 351	98.32 358	98.53 413	97.46 357	98.47 328	97.91 402	96.85 386	98.21 383	98.51 406	96.42 309	99.51 414	92.16 417	97.29 421	97.98 418

OpenMVS_ROB	97.31 17	97.36 350	96.84 360	98.89 323	99.29 322	99.45 166	98.87 270	99.48 269	86.54 429	99.44 241	99.74 136	97.34 278	99.86 240	91.61 418	99.28 342	97.37 425

GG-mvs-BLEND				97.36 390	97.59 432	96.87 374	99.70 35	88.49 438		94.64 431	97.26 430	80.66 419	99.12 423	91.50 419	96.50 427	96.08 431

DeepMVS_CX				97.98 370	99.69 165	96.95 371		99.26 324	75.51 432	95.74 428	98.28 411	96.47 307	99.62 395	91.23 420	97.89 415	97.38 424

PAPR		97.56 342	97.07 352	99.04 301	98.80 396	98.11 326	97.63 393	99.25 327	94.56 417	98.02 393	98.25 412	97.43 273	99.68 374	90.90 421	98.74 382	99.33 274

MVS		95.72 391	94.63 397	98.99 304	98.56 412	97.98 339	99.30 144	98.86 359	72.71 433	97.30 410	99.08 361	98.34 209	99.74 344	89.21 422	98.33 399	99.26 289

UWE-MVS-28		95.64 392	95.47 384	96.14 411	97.98 428	90.39 435	98.49 326	95.81 422	99.02 224	98.03 392	98.19 413	84.49 414	99.28 421	88.75 423	98.47 397	98.75 384

thres600view7		96.60 366	96.16 369	97.93 373	99.63 187	96.09 391	99.18 183	97.57 408	98.77 262	98.72 353	97.32 428	87.04 402	99.72 349	88.57 424	98.62 390	97.98 418

FPMVS		96.32 374	95.50 383	98.79 333	99.60 194	98.17 321	98.46 332	98.80 364	97.16 379	96.28 423	99.63 211	82.19 416	99.09 424	88.45 425	98.89 374	99.10 328

PCF-MVS	96.03 18	96.73 363	95.86 376	99.33 247	99.44 276	99.16 231	96.87 420	99.44 280	86.58 428	98.95 325	99.40 299	94.38 340	99.88 207	87.93 426	99.80 208	98.95 361
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 372	96.03 372	97.47 387	99.63 187	95.93 392	99.18 183	97.57 408	98.75 266	98.70 356	97.31 429	87.04 402	99.67 379	87.62 427	98.51 394	96.81 427

tfpn200view9		96.30 375	95.89 374	97.53 384	99.58 204	96.11 389	99.00 245	97.54 411	98.43 296	98.52 370	96.98 432	86.85 404	99.67 379	87.62 427	98.51 394	96.81 427

thres400		96.40 371	95.89 374	97.92 374	99.58 204	96.11 389	99.00 245	97.54 411	98.43 296	98.52 370	96.98 432	86.85 404	99.67 379	87.62 427	98.51 394	97.98 418

thres200		96.09 381	95.68 381	97.33 392	99.48 261	96.22 388	98.53 321	97.57 408	98.06 331	98.37 377	96.73 436	86.84 406	99.61 400	86.99 430	98.57 391	96.16 430

MVE	92.54 22	96.66 365	96.11 370	98.31 360	99.68 173	97.55 354	97.94 378	95.60 423	99.37 171	90.68 434	98.70 398	96.56 303	98.61 430	86.94 431	99.55 299	98.77 382
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 351	96.83 361	98.59 344	99.46 271	97.55 354	99.25 165	96.84 416	98.78 260	97.24 412	97.67 422	97.11 289	98.97 426	86.59 432	98.54 393	99.27 287

PAPM		95.61 394	94.71 396	98.31 360	99.12 354	96.63 377	96.66 423	98.46 383	90.77 425	96.25 424	98.68 399	93.01 356	99.69 362	81.60 433	97.86 417	98.62 388

dongtai		89.37 399	88.91 402	90.76 415	99.19 343	77.46 440	95.47 428	87.82 439	92.28 421	94.17 432	98.82 391	71.22 438	95.54 434	63.85 434	97.34 420	99.27 287

kuosan		85.65 401	84.57 404	88.90 417	97.91 429	77.11 441	96.37 425	87.62 440	85.24 430	85.45 435	96.83 435	69.94 440	90.98 436	45.90 435	95.83 431	98.62 388

test123		29.31 402	33.05 407	18.08 418	25.93 442	12.24 443	97.53 399	10.93 443	11.78 436	24.21 437	50.08 448	21.04 441	8.60 437	23.51 436	32.43 436	33.39 433

testmvs		28.94 403	33.33 405	15.79 419	26.03 441	9.81 444	96.77 421	15.67 442	11.55 437	23.87 438	50.74 447	19.03 442	8.53 438	23.21 437	33.07 435	29.03 434

mmdepth		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

monomultidepth		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

test_blank		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

uanet_test		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

DCPMVS		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

cdsmvs_eth3d_5k		24.88 404	33.17 406	0.00 420	0.00 443	0.00 445	0.00 431	99.62 189	0.00 438	0.00 439	99.13 352	99.82 16	0.00 439	0.00 438	0.00 437	0.00 435

pcd_1.5k_mvsjas		16.61 405	22.14 408	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	99.28 77	0.00 439	0.00 438	0.00 437	0.00 435

sosnet-low-res		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

sosnet		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

uncertanet		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

Regformer		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

ab-mvs-re		8.26 416	11.02 419	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	99.16 350	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

uanet		8.33 406	11.11 409	0.00 420	0.00 443	0.00 445	0.00 431	0.00 444	0.00 438	0.00 439	100.00 1	0.00 443	0.00 439	0.00 438	0.00 437	0.00 435

FOURS1					99.83 67	99.89 10	99.74 24	99.71 141	99.69 101	99.63 174

test_one_0601					99.63 187	99.76 65		99.55 235	99.23 192	99.31 280	99.61 227	98.59 171

eth-test2					0.00 443

eth-test					0.00 443

test_241102_ONE					99.69 165	99.82 38		99.54 241	99.12 215	99.82 91	99.49 277	98.91 130	99.52 413

save fliter					99.53 236	99.25 215	98.29 342	99.38 300	99.07 219

test0726					99.69 165	99.80 47	99.24 166	99.57 224	99.16 206	99.73 140	99.65 197	98.35 207

GSMVS																99.14 322

test_part2					99.62 191	99.67 107				99.55 213

sam_mvs1												90.81 382				99.14 322

sam_mvs												90.52 387

MTGPA								99.53 250

test_post											52.41 445	90.25 389	99.86 240

patchmatchnet-post											99.62 218	90.58 385	99.94 85

MTMP							99.09 221	98.59 377

TEST9					99.35 300	99.35 197	98.11 358	99.41 286	94.83 415	97.92 395	98.99 373	98.02 237	99.85 258

test_8					99.34 309	99.31 203	98.08 362	99.40 293	94.90 412	97.87 399	98.97 378	98.02 237	99.84 273

agg_prior					99.35 300	99.36 194		99.39 296		97.76 405			99.85 258

test_prior4						99.19 228	98.00 371

test_prior				99.46 206	99.35 300	99.22 222		99.39 296					99.69 362			99.48 230

新几何2							98.04 366

旧先验1					99.49 256	99.29 206		99.26 324			99.39 303	97.67 262			99.36 331	99.46 238

原ACMM2							97.92 380

test222					99.51 245	99.08 244	97.83 386	99.29 318	95.21 409	98.68 357	99.31 324	97.28 280			99.38 328	99.43 250

segment_acmp												98.37 205

testdata1							97.72 389		97.86 347

test12				99.54 185	99.29 322	99.33 200		99.16 344		98.43 375		97.54 269	99.82 298		99.47 317	99.48 230

plane_prior7					99.58 204	99.38 187

plane_prior6					99.47 267	99.26 212						97.24 281

plane_prior4											99.25 337

plane_prior3						99.31 203			98.36 305	99.14 308

plane_prior2							98.80 286		98.94 234

plane_prior1					99.51 245

plane_prior						99.24 219	98.42 334		97.87 345						99.71 248

n2								0.00 444

nn								0.00 444

door-mid								99.83 77

test11								99.29 318

door								99.77 109

HQP5-MVS						98.94 259

HQP-NCC					99.31 316		97.98 373		97.45 364	98.15 384

ACMP_Plane					99.31 316		97.98 373		97.45 364	98.15 384

HQP4-MVS										98.15 384			99.70 356			99.53 204

HQP3-MVS								99.37 301							99.67 265

HQP2-MVS												96.67 300

NP-MVS					99.40 287	99.13 234					98.83 389

ACMMP++_ref															99.94 103

ACMMP++															99.79 213

Test By Simon												98.41 199

High-resolution multi-view benchmark