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 25	99.98 3	99.75 78	99.70 38	100.00 1	99.73 107	100.00 1	99.89 41	99.79 22	99.88 229	99.98 1	100.00 1	99.98 5

test_fmvs2		99.72 52	99.85 17	99.34 271	99.91 31	98.08 362	99.48 107	100.00 1	99.90 48	99.99 7	99.91 31	99.50 57	99.98 27	99.98 1	99.99 16	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 209	99.96 7	98.62 317	99.67 53	100.00 1	99.95 30	100.00 1	99.95 16	99.85 14	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 70	99.12 229	100.00 1	100.00 1	99.99 7	99.91 31	99.98 1	100.00 1	99.97 4	100.00 1	99.99 2

test_vis1_n_1920		99.72 52	99.88 7	99.27 296	99.93 24	97.84 375	99.34 137	100.00 1	99.99 3	99.99 7	99.82 88	99.87 13	99.99 8	99.97 4	99.99 16	99.97 10

test_vis1_n		99.68 63	99.79 34	99.36 268	99.94 18	98.18 351	99.52 92	100.00 1	99.86 64	100.00 1	99.88 50	98.99 134	99.96 67	99.97 4	99.96 84	99.95 14

test_fmvs1_n		99.68 63	99.81 28	99.28 291	99.95 15	97.93 371	99.49 105	100.00 1	99.82 84	99.99 7	99.89 41	99.21 95	99.98 27	99.97 4	99.98 48	99.93 20

test_f		99.75 47	99.88 7	99.37 263	99.96 7	98.21 348	99.51 99	100.00 1	99.94 34	100.00 1	99.93 22	99.58 45	99.94 95	99.97 4	99.99 16	99.97 10

fmvsm_l_conf0.5_n_9		99.83 21	99.81 28	99.89 11	99.86 57	99.80 51	98.94 291	99.96 28	99.98 16	99.96 32	99.78 123	99.88 11	99.98 27	99.96 9	99.99 16	99.90 28

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 45	99.86 19	99.08 245	99.97 20	99.98 16	99.96 32	99.79 111	99.90 9	99.99 8	99.96 9	99.99 16	99.90 28

test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 85	99.01 267	99.99 11	99.99 3	99.98 14	99.88 50	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 52	99.88 45	99.55 159	99.17 205	99.98 12	99.99 3	99.96 32	99.84 75	99.96 3	99.99 8	99.96 9	99.99 16	99.88 38

test_cas_vis1_n_1920		99.76 45	99.86 13	99.45 233	99.93 24	98.40 336	99.30 154	99.98 12	99.94 34	99.99 7	99.89 41	99.80 21	99.97 42	99.96 9	99.97 70	99.97 10

fmvsm_s_conf0.5_n_7		99.73 50	99.78 39	99.60 176	99.74 163	98.93 285	98.85 302	99.96 28	99.96 26	99.97 23	99.76 138	99.82 18	99.96 67	99.95 14	99.98 48	99.90 28

fmvsm_l_conf0.5_n		99.80 30	99.78 39	99.85 31	99.88 45	99.66 117	99.11 234	99.91 52	99.98 16	99.96 32	99.64 219	99.60 43	99.99 8	99.95 14	99.99 16	99.88 38

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 39	99.82 87	99.70 106	99.17 205	99.97 20	99.99 3	99.96 32	99.82 88	99.94 4	100.00 1	99.95 14	100.00 1	99.80 62

test_fmvs1		99.48 118	99.65 67	98.97 338	99.54 262	97.16 398	99.11 234	99.98 12	99.78 101	99.96 32	99.81 95	98.72 176	99.97 42	99.95 14	99.97 70	99.79 70

mvsany_test3		99.85 12	99.88 7	99.75 93	99.95 15	99.37 205	99.53 91	99.98 12	99.77 105	99.99 7	99.95 16	99.85 14	99.94 95	99.95 14	99.98 48	99.94 17

fmvsm_s_conf0.5_n_9		99.82 24	99.82 25	99.82 44	99.83 78	99.59 146	98.97 283	99.92 43	99.99 3	99.97 23	99.84 75	99.90 9	99.94 95	99.94 19	99.99 16	99.92 24

fmvsm_s_conf0.1_n_2		99.81 28	99.78 39	99.89 11	99.93 24	99.76 70	98.92 294	99.98 12	99.99 3	99.99 7	99.88 50	99.43 61	99.94 95	99.94 19	99.99 16	99.99 2

fmvsm_l_conf0.5_n_a		99.80 30	99.79 34	99.84 36	99.88 45	99.64 126	99.12 229	99.91 52	99.98 16	99.95 43	99.67 207	99.67 34	99.99 8	99.94 19	99.99 16	99.88 38

MM		99.18 215	99.05 224	99.55 199	99.35 333	98.81 295	99.05 250	97.79 438	99.99 3	99.48 265	99.59 270	96.29 345	99.95 78	99.94 19	99.98 48	99.88 38

test_fmvsmconf_n		99.85 12	99.84 20	99.88 19	99.91 31	99.73 88	98.97 283	99.98 12	99.99 3	99.96 32	99.85 68	99.93 7	99.99 8	99.94 19	99.99 16	99.93 20

fmvsm_s_conf0.5_n_5		99.78 37	99.76 49	99.85 31	99.79 119	99.72 93	98.84 304	99.96 28	99.96 26	99.96 32	99.72 163	99.71 28	99.99 8	99.93 24	99.98 48	99.85 47

fmvsm_s_conf0.5_n_2		99.78 37	99.75 51	99.88 19	99.82 87	99.76 70	98.88 297	99.92 43	99.98 16	99.98 14	99.85 68	99.42 63	99.94 95	99.93 24	99.98 48	99.94 17

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 42	99.10 237	99.98 12	99.99 3	99.98 14	99.91 31	99.68 33	99.93 116	99.93 24	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 57	99.07 249	99.98 12	99.99 3	99.98 14	99.90 36	99.88 11	99.92 147	99.93 24	99.99 16	99.98 5

fmvsm_s_conf0.5_n_a		99.82 24	99.79 34	99.89 11	99.85 66	99.82 42	99.03 258	99.96 28	99.99 3	99.97 23	99.84 75	99.58 45	99.93 116	99.92 28	99.98 48	99.93 20

fmvsm_s_conf0.5_n		99.83 21	99.81 28	99.87 25	99.85 66	99.78 57	99.03 258	99.96 28	99.99 3	99.97 23	99.84 75	99.78 23	99.92 147	99.92 28	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 23	100.00 1	99.92 28	100.00 1	99.87 42

fmvsm_s_conf0.5_n_8		99.76 45	99.72 54	99.88 19	99.82 87	99.75 78	99.02 262	99.87 67	99.98 16	99.98 14	99.81 95	99.07 120	99.97 42	99.91 31	99.99 16	99.92 24

fmvsm_s_conf0.5_n_6		99.80 30	99.78 39	99.85 31	99.78 127	99.78 57	99.00 271	99.97 20	99.96 26	99.97 23	99.56 284	99.92 8	99.93 116	99.91 31	99.99 16	99.83 54

fmvsm_s_conf0.5_n_4		99.78 37	99.78 39	99.79 68	99.75 155	99.56 155	98.98 281	99.94 38	99.92 44	99.97 23	99.72 163	99.84 16	99.92 147	99.91 31	99.98 48	99.89 35

MVStest1		98.22 347	98.09 342	98.62 374	99.04 403	96.23 420	99.20 190	99.92 43	99.44 184	99.98 14	99.87 56	85.87 443	99.67 411	99.91 31	99.57 327	99.95 14

v1921920		99.56 96	99.57 95	99.55 199	99.75 155	99.11 258	99.05 250	99.61 224	99.15 242	99.88 81	99.71 173	99.08 117	99.87 244	99.90 35	99.97 70	99.66 136

v1240		99.56 96	99.58 90	99.51 214	99.80 106	99.00 272	99.00 271	99.65 204	99.15 242	99.90 66	99.75 146	99.09 114	99.88 229	99.90 35	99.96 84	99.67 125

v10		99.69 58	99.69 59	99.66 139	99.81 99	99.39 200	99.66 57	99.75 142	99.60 155	99.92 58	99.87 56	98.75 171	99.86 263	99.90 35	99.99 16	99.73 90

v1192		99.57 92	99.57 95	99.57 189	99.77 138	99.22 241	99.04 255	99.60 235	99.18 231	99.87 91	99.72 163	99.08 117	99.85 281	99.89 38	99.98 48	99.66 136

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 31	99.81 99	99.71 98	98.97 283	99.92 43	99.98 16	99.97 23	99.86 63	99.53 53	99.95 78	99.88 39	99.99 16	99.89 35

v144192		99.55 101	99.54 105	99.58 182	99.78 127	99.20 246	99.11 234	99.62 217	99.18 231	99.89 71	99.72 163	98.66 185	99.87 244	99.88 39	99.97 70	99.66 136

v8		99.68 63	99.69 59	99.65 145	99.80 106	99.40 197	99.66 57	99.76 137	99.64 140	99.93 51	99.85 68	98.66 185	99.84 296	99.88 39	99.99 16	99.71 99

mvs5depth		99.88 6	99.91 3	99.80 61	99.92 29	99.42 190	99.94 3	100.00 1	99.97 23	99.89 71	99.99 12	99.63 37	99.97 42	99.87 42	99.99 16	100.00 1

v1144		99.54 105	99.53 109	99.59 179	99.79 119	99.28 223	99.10 237	99.61 224	99.20 228	99.84 99	99.73 155	98.67 183	99.84 296	99.86 43	99.98 48	99.64 157

mmtdpeth		99.78 37	99.83 21	99.66 139	99.85 66	99.05 271	99.79 15	99.97 20	100.00 1	99.43 277	99.94 19	99.64 35	99.94 95	99.83 44	99.99 16	99.98 5

SSC-MVS		99.52 109	99.42 131	99.83 39	99.86 57	99.65 123	99.52 92	99.81 106	99.87 61	99.81 113	99.79 111	96.78 325	99.99 8	99.83 44	99.51 343	99.86 44

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 27	99.82 10	99.82 95	99.84 74	99.94 46	99.91 31	99.13 109	99.96 67	99.83 44	99.99 16	99.83 54

v2v482		99.50 111	99.47 117	99.58 182	99.78 127	99.25 231	99.14 217	99.58 250	99.25 219	99.81 113	99.62 243	98.24 244	99.84 296	99.83 44	99.97 70	99.64 157

test_vis1_rt		99.45 133	99.46 122	99.41 251	99.71 175	98.63 316	98.99 278	99.96 28	99.03 255	99.95 43	99.12 388	98.75 171	99.84 296	99.82 48	99.82 217	99.77 76

tt0805		99.63 78	99.57 95	99.81 52	99.87 54	99.88 12	99.58 82	98.70 399	99.72 111	99.91 61	99.60 260	99.43 61	99.81 341	99.81 49	99.53 339	99.73 90

VortexMVS		99.13 228	99.24 180	98.79 365	99.67 209	96.60 412	99.24 179	99.80 109	99.85 70	99.93 51	99.84 75	95.06 362	99.89 214	99.80 50	99.98 48	99.89 35

V42		99.56 96	99.54 105	99.63 159	99.79 119	99.46 175	99.39 122	99.59 241	99.24 221	99.86 93	99.70 183	98.55 200	99.82 325	99.79 51	99.95 104	99.60 189

SSC-MVS3.2		99.64 77	99.67 63	99.56 193	99.75 155	98.98 275	98.96 287	99.87 67	99.88 59	99.84 99	99.64 219	99.32 81	99.91 176	99.78 52	99.96 84	99.80 62

mvs_tets		99.90 2	99.90 4	99.90 8	99.96 7	99.79 54	99.72 33	99.88 65	99.92 44	99.98 14	99.93 22	99.94 4	99.98 27	99.77 53	100.00 1	99.92 24

WB-MVS		99.44 136	99.32 156	99.80 61	99.81 99	99.61 140	99.47 110	99.81 106	99.82 84	99.71 172	99.72 163	96.60 329	99.98 27	99.75 54	99.23 384	99.82 61

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 63	99.68 49	99.85 79	99.95 30	99.98 14	99.92 27	99.28 86	99.98 27	99.75 54	100.00 1	99.94 17

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 57	99.70 38	99.86 73	99.89 54	99.98 14	99.90 36	99.94 4	99.98 27	99.75 54	100.00 1	99.90 28

ANet_high		99.88 6	99.87 11	99.91 3	99.99 1	99.91 4	99.65 62	100.00 1	99.90 48	100.00 1	99.97 14	99.61 41	99.97 42	99.75 54	100.00 1	99.84 50

AstraMVS		99.15 225	99.06 219	99.42 243	99.85 66	98.59 320	99.13 224	97.26 446	99.84 74	99.87 91	99.77 133	96.11 348	99.93 116	99.71 58	99.96 84	99.74 86

Elysia		99.69 58	99.65 67	99.81 52	99.86 57	99.72 93	99.34 137	99.77 129	99.94 34	99.91 61	99.76 138	98.55 200	99.99 8	99.70 59	99.98 48	99.72 94

StellarMVS		99.69 58	99.65 67	99.81 52	99.86 57	99.72 93	99.34 137	99.77 129	99.94 34	99.91 61	99.76 138	98.55 200	99.99 8	99.70 59	99.98 48	99.72 94

tt0320-xc		99.82 24	99.82 25	99.82 44	99.82 87	99.84 27	99.82 10	99.92 43	99.94 34	99.94 46	99.93 22	99.34 78	99.92 147	99.70 59	99.96 84	99.70 102

reproduce_monomvs		97.40 380	97.46 373	97.20 428	99.05 400	91.91 456	99.20 190	99.18 371	99.84 74	99.86 93	99.75 146	80.67 451	99.83 312	99.69 62	99.95 104	99.85 47

SPE-MVS-test		99.68 63	99.70 56	99.64 152	99.57 246	99.83 34	99.78 17	99.97 20	99.92 44	99.50 262	99.38 337	99.57 47	99.95 78	99.69 62	99.90 146	99.15 350

guyue		99.12 231	99.02 233	99.41 251	99.84 71	98.56 321	99.19 196	98.30 424	99.82 84	99.84 99	99.75 146	94.84 365	99.92 147	99.68 64	99.94 119	99.74 86

tt0320		99.79 34	99.79 34	99.81 52	99.82 87	99.84 27	99.82 10	99.90 58	99.94 34	99.94 46	99.94 19	99.07 120	99.92 147	99.68 64	99.97 70	99.67 125

MVS_0304		98.61 305	98.30 326	99.52 211	97.88 463	98.95 281	98.76 321	94.11 462	99.84 74	99.32 307	99.57 280	95.57 357	99.95 78	99.68 64	99.98 48	99.68 116

CS-MVS		99.67 69	99.70 56	99.58 182	99.53 269	99.84 27	99.79 15	99.96 28	99.90 48	99.61 219	99.41 327	99.51 56	99.95 78	99.66 67	99.89 160	98.96 392

mamv4		99.73 50	99.74 52	99.70 124	99.66 211	99.87 15	99.69 45	99.93 39	99.93 41	99.93 51	99.86 63	99.07 120	100.00 1	99.66 67	99.92 134	99.24 325

KinetiMVS		99.66 70	99.63 75	99.76 82	99.89 39	99.57 154	99.37 129	99.82 95	99.95 30	99.90 66	99.63 234	98.57 196	99.97 42	99.65 69	99.94 119	99.74 86

pmmvs6		99.86 10	99.86 13	99.83 39	99.94 18	99.90 7	99.83 7	99.91 52	99.85 70	99.94 46	99.95 16	99.73 27	99.90 195	99.65 69	99.97 70	99.69 110

MIMVSNet1		99.66 70	99.62 77	99.80 61	99.94 18	99.87 15	99.69 45	99.77 129	99.78 101	99.93 51	99.89 41	97.94 270	99.92 147	99.65 69	99.98 48	99.62 174

LuminaMVS		99.39 153	99.28 171	99.73 107	99.83 78	99.49 167	99.00 271	99.05 383	99.81 90	99.89 71	99.79 111	96.54 333	99.97 42	99.64 72	99.98 48	99.73 90

sc_t1		99.81 28	99.80 32	99.82 44	99.88 45	99.88 12	99.83 7	99.79 117	99.94 34	99.93 51	99.92 27	99.35 77	99.92 147	99.64 72	99.94 119	99.68 116

EC-MVSNet		99.69 58	99.69 59	99.68 128	99.71 175	99.91 4	99.76 23	99.96 28	99.86 64	99.51 259	99.39 335	99.57 47	99.93 116	99.64 72	99.86 189	99.20 338

K. test v3		98.87 282	98.60 291	99.69 126	99.93 24	99.46 175	99.74 27	94.97 457	99.78 101	99.88 81	99.88 50	93.66 380	99.97 42	99.61 75	99.95 104	99.64 157

KD-MVS_self_test		99.63 78	99.59 87	99.76 82	99.84 71	99.90 7	99.37 129	99.79 117	99.83 80	99.88 81	99.85 68	98.42 224	99.90 195	99.60 76	99.73 267	99.49 250

Anonymous20240521		99.44 136	99.42 131	99.49 220	99.89 39	98.96 280	99.62 67	99.76 137	99.85 70	99.82 106	99.88 50	96.39 340	99.97 42	99.59 77	99.98 48	99.55 212

TransMVSNet (Re)		99.78 37	99.77 45	99.81 52	99.91 31	99.85 22	99.75 25	99.86 73	99.70 121	99.91 61	99.89 41	99.60 43	99.87 244	99.59 77	99.74 261	99.71 99

OurMVSNet-221017-0		99.75 47	99.71 55	99.84 36	99.96 7	99.83 34	99.83 7	99.85 79	99.80 94	99.93 51	99.93 22	98.54 204	99.93 116	99.59 77	99.98 48	99.76 81

EU-MVSNet		99.39 153	99.62 77	98.72 370	99.88 45	96.44 414	99.56 87	99.85 79	99.90 48	99.90 66	99.85 68	98.09 259	99.83 312	99.58 80	99.95 104	99.90 28

mvs_anonymous		99.28 181	99.39 136	98.94 342	99.19 376	97.81 377	99.02 262	99.55 263	99.78 101	99.85 96	99.80 101	98.24 244	99.86 263	99.57 81	99.50 346	99.15 350

test1111		97.74 365	98.16 338	96.49 439	99.60 226	89.86 470	99.71 37	91.21 466	99.89 54	99.88 81	99.87 56	93.73 379	99.90 195	99.56 82	99.99 16	99.70 102

lessismore_v0				99.64 152	99.86 57	99.38 202		90.66 467		99.89 71	99.83 81	94.56 370	99.97 42	99.56 82	99.92 134	99.57 207

mvsany_test1		99.44 136	99.45 124	99.40 254	99.37 326	98.64 315	97.90 416	99.59 241	99.27 215	99.92 58	99.82 88	99.74 26	99.93 116	99.55 84	99.87 181	99.63 163

MVSMamba_PlusPlus		99.55 101	99.58 90	99.47 226	99.68 203	99.40 197	99.52 92	99.70 173	99.92 44	99.77 139	99.86 63	98.28 240	99.96 67	99.54 85	99.90 146	99.05 379

pm-mvs1		99.79 34	99.79 34	99.78 72	99.91 31	99.83 34	99.76 23	99.87 67	99.73 107	99.89 71	99.87 56	99.63 37	99.87 244	99.54 85	99.92 134	99.63 163

LTVRE_ROB	99.19 1	99.88 6	99.87 11	99.88 19	99.91 31	99.90 7	99.96 1	99.92 43	99.90 48	99.97 23	99.87 56	99.81 20	99.95 78	99.54 85	99.99 16	99.80 62
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		99.48 118	99.65 67	98.95 341	99.71 175	97.27 395	99.50 100	99.82 95	99.59 157	99.41 286	99.85 68	99.62 40	100.00 1	99.53 88	99.89 160	99.59 196

test2506		94.73 430	94.59 431	95.15 446	99.59 231	85.90 472	99.75 25	74.01 474	99.89 54	99.71 172	99.86 63	79.00 461	99.90 195	99.52 89	99.99 16	99.65 145

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 164	99.93 41	99.95 43	99.89 41	99.71 28	99.96 67	99.51 90	99.97 70	99.84 50

FC-MVSNet-test		99.70 56	99.65 67	99.86 29	99.88 45	99.86 19	99.72 33	99.78 126	99.90 48	99.82 106	99.83 81	98.45 220	99.87 244	99.51 90	99.97 70	99.86 44

BP-MVS1		98.72 297	98.46 307	99.50 216	99.53 269	99.00 272	99.34 137	98.53 409	99.65 137	99.73 162	99.38 337	90.62 417	99.96 67	99.50 92	99.86 189	99.55 212

UA-Net		99.78 37	99.76 49	99.86 29	99.72 171	99.71 98	99.91 4	99.95 36	99.96 26	99.71 172	99.91 31	99.15 104	99.97 42	99.50 92	100.00 1	99.90 28

viewdifsd2359ckpt11		99.62 85	99.64 72	99.56 193	99.86 57	99.19 247	99.02 262	99.93 39	99.83 80	99.88 81	99.81 95	98.99 134	99.83 312	99.48 94	99.96 84	99.65 145

viewmsd2359difaftdt		99.62 85	99.64 72	99.56 193	99.86 57	99.19 247	99.02 262	99.93 39	99.83 80	99.88 81	99.81 95	98.99 134	99.83 312	99.48 94	99.96 84	99.65 145

PMMVS2		99.48 118	99.45 124	99.57 189	99.76 142	98.99 274	98.09 393	99.90 58	98.95 265	99.78 127	99.58 273	99.57 47	99.93 116	99.48 94	99.95 104	99.79 70

VPA-MVSNet		99.66 70	99.62 77	99.79 68	99.68 203	99.75 78	99.62 67	99.69 181	99.85 70	99.80 117	99.81 95	98.81 159	99.91 176	99.47 97	99.88 169	99.70 102

GDP-MVS		98.81 288	98.57 297	99.50 216	99.53 269	99.12 257	99.28 163	99.86 73	99.53 162	99.57 230	99.32 353	90.88 413	99.98 27	99.46 98	99.74 261	99.42 285

ECVR-MVS		97.73 366	98.04 345	96.78 432	99.59 231	90.81 465	99.72 33	90.43 468	99.89 54	99.86 93	99.86 63	93.60 381	99.89 214	99.46 98	99.99 16	99.65 145

nrg030		99.70 56	99.66 65	99.82 44	99.76 142	99.84 27	99.61 73	99.70 173	99.93 41	99.78 127	99.68 203	99.10 112	99.78 354	99.45 100	99.96 84	99.83 54

TAMVS		99.49 116	99.45 124	99.63 159	99.48 294	99.42 190	99.45 114	99.57 252	99.66 134	99.78 127	99.83 81	97.85 277	99.86 263	99.44 101	99.96 84	99.61 185

GeoE		99.69 58	99.66 65	99.78 72	99.76 142	99.76 70	99.60 79	99.82 95	99.46 179	99.75 148	99.56 284	99.63 37	99.95 78	99.43 102	99.88 169	99.62 174

new-patchmatchnet		99.35 166	99.57 95	98.71 372	99.82 87	96.62 410	98.55 348	99.75 142	99.50 166	99.88 81	99.87 56	99.31 82	99.88 229	99.43 102	100.00 1	99.62 174

test20.03		99.55 101	99.54 105	99.58 182	99.79 119	99.37 205	99.02 262	99.89 61	99.60 155	99.82 106	99.62 243	98.81 159	99.89 214	99.43 102	99.86 189	99.47 258

MVSFormer		99.41 147	99.44 127	99.31 283	99.57 246	98.40 336	99.77 19	99.80 109	99.73 107	99.63 204	99.30 358	98.02 264	99.98 27	99.43 102	99.69 286	99.55 212

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 27	99.77 19	99.80 109	99.73 107	99.97 23	99.92 27	99.77 25	99.98 27	99.43 102	100.00 1	99.90 28

SDMVSNet		99.77 44	99.77 45	99.76 82	99.80 106	99.65 123	99.63 64	99.86 73	99.97 23	99.89 71	99.89 41	99.52 55	99.99 8	99.42 107	99.96 84	99.65 145

Anonymous20231211		99.62 85	99.57 95	99.76 82	99.61 224	99.60 144	99.81 13	99.73 152	99.82 84	99.90 66	99.90 36	97.97 269	99.86 263	99.42 107	99.96 84	99.80 62

SixPastTwentyTwo		99.42 142	99.30 163	99.76 82	99.92 29	99.67 115	99.70 38	99.14 376	99.65 137	99.89 71	99.90 36	96.20 347	99.94 95	99.42 107	99.92 134	99.67 125

balanced_conf03		99.50 111	99.50 111	99.50 216	99.42 317	99.49 167	99.52 92	99.75 142	99.86 64	99.78 127	99.71 173	98.20 251	99.90 195	99.39 110	99.88 169	99.10 361

patch_mono-2		99.51 110	99.46 122	99.64 152	99.70 190	99.11 258	99.04 255	99.87 67	99.71 115	99.47 267	99.79 111	98.24 244	99.98 27	99.38 111	99.96 84	99.83 54

UGNet		99.38 156	99.34 151	99.49 220	98.90 415	98.90 289	99.70 38	99.35 334	99.86 64	98.57 399	99.81 95	98.50 215	99.93 116	99.38 111	99.98 48	99.66 136
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 55	99.67 63	99.81 52	99.89 39	99.72 93	99.59 80	99.82 95	99.39 199	99.82 106	99.84 75	99.38 69	99.91 176	99.38 111	99.93 130	99.80 62

FIs		99.65 76	99.58 90	99.84 36	99.84 71	99.85 22	99.66 57	99.75 142	99.86 64	99.74 158	99.79 111	98.27 242	99.85 281	99.37 114	99.93 130	99.83 54

sd_testset		99.78 37	99.78 39	99.80 61	99.80 106	99.76 70	99.80 14	99.79 117	99.97 23	99.89 71	99.89 41	99.53 53	99.99 8	99.36 115	99.96 84	99.65 145

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 79	99.70 121	99.92 58	99.93 22	99.45 58	99.97 42	99.36 115	100.00 1	99.85 47

casdiffmvs_mvg		99.68 63	99.68 62	99.69 126	99.81 99	99.59 146	99.29 161	99.90 58	99.71 115	99.79 123	99.73 155	99.54 50	99.84 296	99.36 115	99.96 84	99.65 145
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 47	99.74 52	99.79 68	99.88 45	99.66 117	99.69 45	99.92 43	99.67 130	99.77 139	99.75 146	99.61 41	99.98 27	99.35 118	99.98 48	99.72 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 89	99.64 72	99.53 209	99.79 119	98.82 294	99.58 82	99.97 20	99.95 30	99.96 32	99.76 138	98.44 221	99.99 8	99.34 119	99.96 84	99.78 72

CHOSEN 1792x2688		99.39 153	99.30 163	99.65 145	99.88 45	99.25 231	98.78 319	99.88 65	98.66 305	99.96 32	99.79 111	97.45 299	99.93 116	99.34 119	99.99 16	99.78 72

CDS-MVSNet		99.22 201	99.13 195	99.50 216	99.35 333	99.11 258	98.96 287	99.54 269	99.46 179	99.61 219	99.70 183	96.31 343	99.83 312	99.34 119	99.88 169	99.55 212
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 262	99.16 189	98.51 380	99.75 155	95.90 426	98.07 396	99.84 85	99.84 74	99.89 71	99.73 155	96.01 351	99.99 8	99.33 122	100.00 1	99.63 163

HyFIR lowres test		98.91 275	98.64 288	99.73 107	99.85 66	99.47 171	98.07 396	99.83 89	98.64 308	99.89 71	99.60 260	92.57 392	100.00 1	99.33 122	99.97 70	99.72 94

pmmvs5		99.19 211	99.11 202	99.42 243	99.76 142	98.88 291	98.55 348	99.73 152	98.82 285	99.72 167	99.62 243	96.56 330	99.82 325	99.32 124	99.95 104	99.56 209

v148		99.40 149	99.41 134	99.39 257	99.76 142	98.94 282	99.09 242	99.59 241	99.17 236	99.81 113	99.61 252	98.41 225	99.69 394	99.32 124	99.94 119	99.53 228

baseline		99.63 78	99.62 77	99.66 139	99.80 106	99.62 134	99.44 116	99.80 109	99.71 115	99.72 167	99.69 192	99.15 104	99.83 312	99.32 124	99.94 119	99.53 228

CVMVSNet		98.61 305	98.88 267	97.80 411	99.58 236	93.60 449	99.26 172	99.64 212	99.66 134	99.72 167	99.67 207	93.26 385	99.93 116	99.30 127	99.81 227	99.87 42

PS-CasMVS		99.66 70	99.58 90	99.89 11	99.80 106	99.85 22	99.66 57	99.73 152	99.62 145	99.84 99	99.71 173	98.62 189	99.96 67	99.30 127	99.96 84	99.86 44

DTE-MVSNet		99.68 63	99.61 81	99.88 19	99.80 106	99.87 15	99.67 53	99.71 164	99.72 111	99.84 99	99.78 123	98.67 183	99.97 42	99.30 127	99.95 104	99.80 62

tmp_tt		95.75 423	95.42 418	96.76 433	89.90 473	94.42 443	98.86 300	97.87 436	78.01 464	99.30 317	99.69 192	97.70 285	95.89 466	99.29 130	98.14 442	99.95 14

PEN-MVS		99.66 70	99.59 87	99.89 11	99.83 78	99.87 15	99.66 57	99.73 152	99.70 121	99.84 99	99.73 155	98.56 199	99.96 67	99.29 130	99.94 119	99.83 54

WR-MVS_H		99.61 89	99.53 109	99.87 25	99.80 106	99.83 34	99.67 53	99.75 142	99.58 159	99.85 96	99.69 192	98.18 254	99.94 95	99.28 132	99.95 104	99.83 54

IterMVS		98.97 266	99.16 189	98.42 385	99.74 163	95.64 430	98.06 398	99.83 89	99.83 80	99.85 96	99.74 151	96.10 350	99.99 8	99.27 133	100.00 1	99.63 163
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NormalMVS		99.09 239	98.91 265	99.62 168	99.78 127	99.11 258	99.36 133	99.77 129	99.82 84	99.68 182	99.53 296	93.30 383	99.99 8	99.24 134	99.76 250	99.74 86

SymmetryMVS		99.01 259	98.82 275	99.58 182	99.65 216	99.11 258	99.36 133	99.20 369	99.82 84	99.68 182	99.53 296	93.30 383	99.99 8	99.24 134	99.63 307	99.64 157

WBMVS		97.50 376	97.18 382	98.48 382	98.85 423	95.89 427	98.44 365	99.52 284	99.53 162	99.52 252	99.42 326	80.10 454	99.86 263	99.24 134	99.95 104	99.68 116

h-mvs33		98.61 305	98.34 321	99.44 237	99.60 226	98.67 307	99.27 167	99.44 309	99.68 126	99.32 307	99.49 309	92.50 395	100.00 1	99.24 134	96.51 459	99.65 145

hse-mvs2		98.52 318	98.30 326	99.16 312	99.29 355	98.60 318	98.77 320	99.02 385	99.68 126	99.32 307	99.04 398	92.50 395	99.85 281	99.24 134	97.87 449	99.03 383

FMVSNet1		99.66 70	99.63 75	99.73 107	99.78 127	99.77 63	99.68 49	99.70 173	99.67 130	99.82 106	99.83 81	98.98 138	99.90 195	99.24 134	99.97 70	99.53 228

casdiffmvs		99.63 78	99.61 81	99.67 132	99.79 119	99.59 146	99.13 224	99.85 79	99.79 98	99.76 143	99.72 163	99.33 80	99.82 325	99.21 140	99.94 119	99.59 196
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		99.54 105	99.43 129	99.87 25	99.76 142	99.82 42	99.57 85	99.61 224	99.54 160	99.80 117	99.64 219	97.79 281	99.95 78	99.21 140	99.94 119	99.84 50

DELS-MVS		99.34 171	99.30 163	99.48 224	99.51 278	99.36 209	98.12 389	99.53 279	99.36 204	99.41 286	99.61 252	99.22 94	99.87 244	99.21 140	99.68 291	99.20 338
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
viewmambaseed2359dif		99.47 127	99.50 111	99.37 263	99.70 190	98.80 298	98.67 329	99.92 43	99.49 168	99.77 139	99.71 173	99.08 117	99.78 354	99.20 143	99.94 119	99.54 222

UniMVSNet (Re)		99.37 160	99.26 176	99.68 128	99.51 278	99.58 151	98.98 281	99.60 235	99.43 190	99.70 176	99.36 344	97.70 285	99.88 229	99.20 143	99.87 181	99.59 196

CANet		99.11 235	99.05 224	99.28 291	98.83 425	98.56 321	98.71 327	99.41 315	99.25 219	99.23 325	99.22 376	97.66 293	99.94 95	99.19 145	99.97 70	99.33 307

EI-MVSNet-UG-set		99.48 118	99.50 111	99.42 243	99.57 246	98.65 313	99.24 179	99.46 304	99.68 126	99.80 117	99.66 212	98.99 134	99.89 214	99.19 145	99.90 146	99.72 94

xiu_mvs_v1_base_debu		99.23 192	99.34 151	98.91 348	99.59 231	98.23 345	98.47 360	99.66 194	99.61 149	99.68 182	98.94 414	99.39 65	99.97 42	99.18 147	99.55 332	98.51 431

xiu_mvs_v1_base		99.23 192	99.34 151	98.91 348	99.59 231	98.23 345	98.47 360	99.66 194	99.61 149	99.68 182	98.94 414	99.39 65	99.97 42	99.18 147	99.55 332	98.51 431

xiu_mvs_v1_base_debi		99.23 192	99.34 151	98.91 348	99.59 231	98.23 345	98.47 360	99.66 194	99.61 149	99.68 182	98.94 414	99.39 65	99.97 42	99.18 147	99.55 332	98.51 431

VPNet		99.46 129	99.37 142	99.71 119	99.82 87	99.59 146	99.48 107	99.70 173	99.81 90	99.69 179	99.58 273	97.66 293	99.86 263	99.17 150	99.44 353	99.67 125

UniMVSNet_NR-MVSNet		99.37 160	99.25 178	99.72 114	99.47 300	99.56 155	98.97 283	99.61 224	99.43 190	99.67 189	99.28 362	97.85 277	99.95 78	99.17 150	99.81 227	99.65 145

DU-MVS		99.33 174	99.21 183	99.71 119	99.43 312	99.56 155	98.83 307	99.53 279	99.38 200	99.67 189	99.36 344	97.67 289	99.95 78	99.17 150	99.81 227	99.63 163

EI-MVSNet-Vis-set		99.47 127	99.49 114	99.42 243	99.57 246	98.66 310	99.24 179	99.46 304	99.67 130	99.79 123	99.65 217	98.97 140	99.89 214	99.15 153	99.89 160	99.71 99

EI-MVSNet		99.38 156	99.44 127	99.21 306	99.58 236	98.09 359	99.26 172	99.46 304	99.62 145	99.75 148	99.67 207	98.54 204	99.85 281	99.15 153	99.92 134	99.68 116

VNet		99.18 215	99.06 219	99.56 193	99.24 366	99.36 209	99.33 143	99.31 343	99.67 130	99.47 267	99.57 280	96.48 334	99.84 296	99.15 153	99.30 372	99.47 258

EG-PatchMatch MVS		99.57 92	99.56 100	99.62 168	99.77 138	99.33 215	99.26 172	99.76 137	99.32 209	99.80 117	99.78 123	99.29 84	99.87 244	99.15 153	99.91 145	99.66 136

PVSNet_Blended_VisFu		99.40 149	99.38 139	99.44 237	99.90 37	98.66 310	98.94 291	99.91 52	97.97 369	99.79 123	99.73 155	99.05 127	99.97 42	99.15 153	99.99 16	99.68 116

IterMVS-LS		99.41 147	99.47 117	99.25 302	99.81 99	98.09 359	98.85 302	99.76 137	99.62 145	99.83 105	99.64 219	98.54 204	99.97 42	99.15 153	99.99 16	99.68 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 105	99.47 117	99.76 82	99.58 236	99.64 126	99.30 154	99.63 214	99.61 149	99.71 172	99.56 284	98.76 169	99.96 67	99.14 159	99.92 134	99.68 116

MVSTER		98.47 325	98.22 331	99.24 304	99.06 399	98.35 342	99.08 245	99.46 304	99.27 215	99.75 148	99.66 212	88.61 430	99.85 281	99.14 159	99.92 134	99.52 238

diffmvs_AUTHOR		99.48 118	99.48 115	99.47 226	99.80 106	98.89 290	98.71 327	99.82 95	99.79 98	99.66 195	99.63 234	98.87 155	99.88 229	99.13 161	99.95 104	99.62 174

Anonymous20231206		99.35 166	99.31 158	99.47 226	99.74 163	99.06 270	99.28 163	99.74 148	99.23 223	99.72 167	99.53 296	97.63 295	99.88 229	99.11 162	99.84 199	99.48 254

Syy-MVS		98.17 350	97.85 362	99.15 314	98.50 448	98.79 299	98.60 336	99.21 366	97.89 375	96.76 451	96.37 474	95.47 359	99.57 437	99.10 163	98.73 418	99.09 366

ttmdpeth		99.48 118	99.55 102	99.29 288	99.76 142	98.16 353	99.33 143	99.95 36	99.79 98	99.36 296	99.89 41	99.13 109	99.77 363	99.09 164	99.64 304	99.93 20

MVS_Test		99.28 181	99.31 158	99.19 309	99.35 333	98.79 299	99.36 133	99.49 297	99.17 236	99.21 330	99.67 207	98.78 166	99.66 416	99.09 164	99.66 300	99.10 361

testgi		99.29 180	99.26 176	99.37 263	99.75 155	98.81 295	98.84 304	99.89 61	98.38 336	99.75 148	99.04 398	99.36 74	99.86 263	99.08 166	99.25 380	99.45 263

1112_ss		99.05 247	98.84 272	99.67 132	99.66 211	99.29 221	98.52 354	99.82 95	97.65 387	99.43 277	99.16 382	96.42 337	99.91 176	99.07 167	99.84 199	99.80 62

CANet_DTU		98.91 275	98.85 270	99.09 323	98.79 431	98.13 354	98.18 381	99.31 343	99.48 171	98.86 370	99.51 302	96.56 330	99.95 78	99.05 168	99.95 104	99.19 341

Baseline_NR-MVSNet		99.49 116	99.37 142	99.82 44	99.91 31	99.84 27	98.83 307	99.86 73	99.68 126	99.65 198	99.88 50	97.67 289	99.87 244	99.03 169	99.86 189	99.76 81

FMVSNet2		99.35 166	99.28 171	99.55 199	99.49 289	99.35 212	99.45 114	99.57 252	99.44 184	99.70 176	99.74 151	97.21 310	99.87 244	99.03 169	99.94 119	99.44 275

Test_1112_low_res		98.95 272	98.73 282	99.63 159	99.68 203	99.15 254	98.09 393	99.80 109	97.14 413	99.46 271	99.40 331	96.11 348	99.89 214	99.01 171	99.84 199	99.84 50

VDD-MVS		99.20 208	99.11 202	99.44 237	99.43 312	98.98 275	99.50 100	98.32 423	99.80 94	99.56 238	99.69 192	96.99 320	99.85 281	98.99 172	99.73 267	99.50 245

DeepC-MVS	98.90 4	99.62 85	99.61 81	99.67 132	99.72 171	99.44 183	99.24 179	99.71 164	99.27 215	99.93 51	99.90 36	99.70 31	99.93 116	98.99 172	99.99 16	99.64 157
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 118	99.47 117	99.51 214	99.77 138	99.41 196	98.81 312	99.66 194	99.42 194	99.75 148	99.66 212	99.20 96	99.76 366	98.98 174	99.99 16	99.36 300

EPNet_dtu		97.62 371	97.79 365	97.11 431	96.67 468	92.31 454	98.51 355	98.04 430	99.24 221	95.77 460	99.47 316	93.78 378	99.66 416	98.98 174	99.62 309	99.37 297
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 171	99.32 156	99.39 257	99.67 209	98.77 301	98.57 345	99.81 106	99.61 149	99.48 265	99.41 327	98.47 216	99.86 263	98.97 176	99.90 146	99.53 228
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		99.40 149	99.31 158	99.68 128	99.43 312	99.55 159	99.73 30	99.50 293	99.46 179	99.88 81	99.36 344	97.54 296	99.87 244	98.97 176	99.87 181	99.63 163

GBi-Net		99.42 142	99.31 158	99.73 107	99.49 289	99.77 63	99.68 49	99.70 173	99.44 184	99.62 213	99.83 81	97.21 310	99.90 195	98.96 178	99.90 146	99.53 228

FMVSNet5		97.80 363	97.25 380	99.42 243	98.83 425	98.97 278	99.38 125	99.80 109	98.87 277	99.25 321	99.69 192	80.60 453	99.91 176	98.96 178	99.90 146	99.38 294

test1		99.42 142	99.31 158	99.73 107	99.49 289	99.77 63	99.68 49	99.70 173	99.44 184	99.62 213	99.83 81	97.21 310	99.90 195	98.96 178	99.90 146	99.53 228

FMVSNet3		98.80 289	98.63 290	99.32 279	99.13 385	98.72 304	99.10 237	99.48 298	99.23 223	99.62 213	99.64 219	92.57 392	99.86 263	98.96 178	99.90 146	99.39 292

UnsupCasMVSNet_eth		98.83 285	98.57 297	99.59 179	99.68 203	99.45 181	98.99 278	99.67 189	99.48 171	99.55 243	99.36 344	94.92 363	99.86 263	98.95 182	96.57 458	99.45 263

CHOSEN 280x420		98.41 330	98.41 313	98.40 386	99.34 342	95.89 427	96.94 452	99.44 309	98.80 289	99.25 321	99.52 300	93.51 382	99.98 27	98.94 183	99.98 48	99.32 310

TDRefinement		99.72 52	99.70 56	99.77 75	99.90 37	99.85 22	99.86 6	99.92 43	99.69 124	99.78 127	99.92 27	99.37 71	99.88 229	98.93 184	99.95 104	99.60 189

viewmacassd2359aftdt		99.63 78	99.61 81	99.68 128	99.84 71	99.61 140	99.14 217	99.87 67	99.71 115	99.75 148	99.77 133	99.54 50	99.72 378	98.91 185	99.96 84	99.70 102

alignmvs		98.28 340	97.96 351	99.25 302	99.12 387	98.93 285	99.03 258	98.42 416	99.64 140	98.72 385	97.85 453	90.86 414	99.62 428	98.88 186	99.13 386	99.19 341

testing3-2		96.51 402	96.43 397	96.74 435	99.36 329	91.38 462	99.10 237	97.87 436	99.48 171	98.57 399	98.71 429	76.65 463	99.66 416	98.87 187	99.26 379	99.18 343

MGCFI-Net		99.02 253	99.01 237	99.06 330	99.11 392	98.60 318	99.63 64	99.67 189	99.63 142	98.58 397	97.65 456	99.07 120	99.57 437	98.85 188	98.92 402	99.03 383

sss		98.90 277	98.77 281	99.27 296	99.48 294	98.44 333	98.72 325	99.32 339	97.94 373	99.37 295	99.35 349	96.31 343	99.91 176	98.85 188	99.63 307	99.47 258

xiu_mvs_v2_base		99.02 253	99.11 202	98.77 367	99.37 326	98.09 359	98.13 388	99.51 289	99.47 176	99.42 280	98.54 438	99.38 69	99.97 42	98.83 190	99.33 368	98.24 443

PS-MVSNAJ		99.00 262	99.08 213	98.76 368	99.37 326	98.10 358	98.00 404	99.51 289	99.47 176	99.41 286	98.50 440	99.28 86	99.97 42	98.83 190	99.34 367	98.20 447

D2MVS		99.22 201	99.19 186	99.29 288	99.69 195	98.74 303	98.81 312	99.41 315	98.55 317	99.68 182	99.69 192	98.13 256	99.87 244	98.82 192	99.98 48	99.24 325

PatchT		98.45 327	98.32 323	98.83 361	98.94 413	98.29 343	99.24 179	98.82 393	99.84 74	99.08 347	99.76 138	91.37 403	99.94 95	98.82 192	99.00 397	98.26 442

testf1		99.63 78	99.60 85	99.72 114	99.94 18	99.95 2	99.47 110	99.89 61	99.43 190	99.88 81	99.80 101	99.26 90	99.90 195	98.81 194	99.88 169	99.32 310

APD_test2		99.63 78	99.60 85	99.72 114	99.94 18	99.95 2	99.47 110	99.89 61	99.43 190	99.88 81	99.80 101	99.26 90	99.90 195	98.81 194	99.88 169	99.32 310

sasdasda		99.02 253	99.00 241	99.09 323	99.10 394	98.70 305	99.61 73	99.66 194	99.63 142	98.64 391	97.65 456	99.04 128	99.54 441	98.79 196	98.92 402	99.04 381

Effi-MVS+		99.06 244	98.97 252	99.34 271	99.31 349	98.98 275	98.31 373	99.91 52	98.81 287	98.79 379	98.94 414	99.14 107	99.84 296	98.79 196	98.74 415	99.20 338

canonicalmvs		99.02 253	99.00 241	99.09 323	99.10 394	98.70 305	99.61 73	99.66 194	99.63 142	98.64 391	97.65 456	99.04 128	99.54 441	98.79 196	98.92 402	99.04 381

VDDNet		98.97 266	98.82 275	99.42 243	99.71 175	98.81 295	99.62 67	98.68 400	99.81 90	99.38 294	99.80 101	94.25 372	99.85 281	98.79 196	99.32 370	99.59 196

CR-MVSNet		98.35 337	98.20 333	98.83 361	99.05 400	98.12 355	99.30 154	99.67 189	97.39 401	99.16 336	99.79 111	91.87 400	99.91 176	98.78 200	98.77 411	98.44 436

test_method		91.72 431	92.32 434	89.91 449	93.49 472	70.18 475	90.28 463	99.56 257	61.71 467	95.39 462	99.52 300	93.90 374	99.94 95	98.76 201	98.27 435	99.62 174

RPMNet		98.60 308	98.53 303	98.83 361	99.05 400	98.12 355	99.30 154	99.62 217	99.86 64	99.16 336	99.74 151	92.53 394	99.92 147	98.75 202	98.77 411	98.44 436

mamba_0408		99.54 105	99.55 102	99.54 205	99.71 175	99.24 235	99.27 167	99.79 117	99.72 111	99.78 127	99.64 219	99.36 74	99.93 116	98.74 203	99.90 146	99.45 263

SSM_04072		99.55 101	99.55 102	99.55 199	99.71 175	99.24 235	99.27 167	99.79 117	99.72 111	99.78 127	99.64 219	99.36 74	99.97 42	98.74 203	99.90 146	99.45 263

SSM_0407		99.56 96	99.56 100	99.54 205	99.71 175	99.24 235	99.15 214	99.84 85	99.80 94	99.78 127	99.70 183	99.44 59	99.93 116	98.74 203	99.90 146	99.45 263

SSM_0404		99.57 92	99.58 90	99.54 205	99.76 142	99.28 223	99.19 196	99.84 85	99.80 94	99.78 127	99.70 183	99.44 59	99.93 116	98.74 203	99.95 104	99.41 286

pmmvs4		99.13 228	99.06 219	99.36 268	99.57 246	99.10 265	98.01 402	99.25 356	98.78 292	99.58 227	99.44 323	98.24 244	99.76 366	98.74 203	99.93 130	99.22 331

viewmanbaseed2359cas		99.50 111	99.47 117	99.61 172	99.73 167	99.52 164	99.03 258	99.83 89	99.49 168	99.65 198	99.64 219	99.18 98	99.71 383	98.73 208	99.92 134	99.58 201

tttt0517		97.62 371	97.20 381	98.90 354	99.76 142	97.40 392	99.48 107	94.36 459	99.06 253	99.70 176	99.49 309	84.55 446	99.94 95	98.73 208	99.65 302	99.36 300

EPP-MVSNet		99.17 220	99.00 241	99.66 139	99.80 106	99.43 187	99.70 38	99.24 359	99.48 171	99.56 238	99.77 133	94.89 364	99.93 116	98.72 210	99.89 160	99.63 163

FE-MVSNET		99.45 133	99.36 146	99.71 119	99.84 71	99.64 126	99.16 211	99.91 52	98.65 306	99.73 162	99.73 155	98.54 204	99.82 325	98.71 211	99.96 84	99.67 125

Anonymous20240529		99.42 142	99.34 151	99.65 145	99.53 269	99.60 144	99.63 64	99.39 325	99.47 176	99.76 143	99.78 123	98.13 256	99.86 263	98.70 212	99.68 291	99.49 250

ACMH	98.42 6	99.59 91	99.54 105	99.72 114	99.86 57	99.62 134	99.56 87	99.79 117	98.77 294	99.80 117	99.85 68	99.64 35	99.85 281	98.70 212	99.89 160	99.70 102
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 174	99.28 171	99.47 226	99.57 246	99.39 200	99.78 17	99.43 312	98.87 277	99.57 230	99.82 88	98.06 262	99.87 244	98.69 214	99.73 267	99.15 350

LFMVS		98.46 326	98.19 336	99.26 299	99.24 366	98.52 329	99.62 67	96.94 448	99.87 61	99.31 312	99.58 273	91.04 408	99.81 341	98.68 215	99.42 357	99.45 263

WR-MVS		99.11 235	98.93 257	99.66 139	99.30 353	99.42 190	98.42 366	99.37 330	99.04 254	99.57 230	99.20 380	96.89 322	99.86 263	98.66 216	99.87 181	99.70 102

mvsmamba		99.08 240	98.95 255	99.45 233	99.36 329	99.18 251	99.39 122	98.81 394	99.37 201	99.35 298	99.70 183	96.36 342	99.94 95	98.66 216	99.59 323	99.22 331

viewdifsd2359ckpt13		99.42 142	99.37 142	99.57 189	99.72 171	99.46 175	99.01 267	99.80 109	99.20 228	99.51 259	99.60 260	98.92 147	99.70 387	98.65 218	99.90 146	99.55 212

RRT-MVS		99.08 240	99.00 241	99.33 274	99.27 360	98.65 313	99.62 67	99.93 39	99.66 134	99.67 189	99.82 88	95.27 361	99.93 116	98.64 219	99.09 390	99.41 286

Anonymous202405211		98.75 293	98.46 307	99.63 159	99.34 342	99.66 117	99.47 110	97.65 439	99.28 214	99.56 238	99.50 305	93.15 386	99.84 296	98.62 220	99.58 325	99.40 289

lecture		99.56 96	99.48 115	99.81 52	99.78 127	99.86 19	99.50 100	99.70 173	99.59 157	99.75 148	99.71 173	98.94 143	99.92 147	98.59 221	99.76 250	99.66 136

EPNet		98.13 351	97.77 366	99.18 311	94.57 471	97.99 365	99.24 179	97.96 432	99.74 106	97.29 444	99.62 243	93.13 387	99.97 42	98.59 221	99.83 207	99.58 201
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 247	99.09 211	98.91 348	99.21 371	98.36 341	98.82 311	99.47 301	98.85 280	98.90 365	99.56 284	98.78 166	99.09 457	98.57 223	99.68 291	99.26 322

Patchmatch-RL test		98.60 308	98.36 318	99.33 274	99.77 138	99.07 268	98.27 375	99.87 67	98.91 272	99.74 158	99.72 163	90.57 419	99.79 351	98.55 224	99.85 194	99.11 359

pmmvs3		98.08 354	97.80 363	98.91 348	99.41 319	97.69 383	97.87 417	99.66 194	95.87 432	99.50 262	99.51 302	90.35 421	99.97 42	98.55 224	99.47 350	99.08 372

ETV-MVS		99.18 215	99.18 187	99.16 312	99.34 342	99.28 223	99.12 229	99.79 117	99.48 171	98.93 359	98.55 437	99.40 64	99.93 116	98.51 226	99.52 342	98.28 441

jason		99.16 221	99.11 202	99.32 279	99.75 155	98.44 333	98.26 377	99.39 325	98.70 302	99.74 158	99.30 358	98.54 204	99.97 42	98.48 227	99.82 217	99.55 212
jason: jason.
APDe-MVS		99.48 118	99.36 146	99.85 31	99.55 260	99.81 47	99.50 100	99.69 181	98.99 258	99.75 148	99.71 173	98.79 164	99.93 116	98.46 228	99.85 194	99.80 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
icg_test_0407_2		99.30 178	99.29 168	99.31 283	99.71 175	98.55 323	98.17 383	99.71 164	99.41 195	99.73 162	99.60 260	99.17 100	99.92 147	98.45 229	99.70 278	99.45 263

IMVS_0407		99.38 156	99.42 131	99.28 291	99.71 175	98.55 323	99.27 167	99.71 164	99.41 195	99.73 162	99.60 260	99.17 100	99.83 312	98.45 229	99.70 278	99.45 263

IMVS_0404		99.23 192	99.20 184	99.32 279	99.71 175	98.55 323	98.57 345	99.71 164	99.41 195	99.52 252	99.60 260	98.12 258	99.95 78	98.45 229	99.70 278	99.45 263

IMVS_0403		99.37 160	99.39 136	99.28 291	99.71 175	98.55 323	99.19 196	99.71 164	99.41 195	99.67 189	99.60 260	99.12 111	99.84 296	98.45 229	99.70 278	99.45 263

CL-MVSNet_self_test		98.71 299	98.56 301	99.15 314	99.22 369	98.66 310	97.14 447	99.51 289	98.09 362	99.54 245	99.27 364	96.87 323	99.74 373	98.43 233	98.96 399	99.03 383

our_test_3		98.85 284	99.09 211	98.13 399	99.66 211	94.90 441	97.72 422	99.58 250	99.07 251	99.64 200	99.62 243	98.19 252	99.93 116	98.41 234	99.95 104	99.55 212

Gipuma		99.57 92	99.59 87	99.49 220	99.98 3	99.71 98	99.72 33	99.84 85	99.81 90	99.94 46	99.78 123	98.91 150	99.71 383	98.41 234	99.95 104	99.05 379
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 382	96.91 391	98.74 369	97.72 464	97.57 385	97.60 428	97.36 445	98.00 365	99.21 330	98.02 449	90.04 424	99.79 351	98.37 236	95.89 463	98.86 406

PM-MVS		99.36 164	99.29 168	99.58 182	99.83 78	99.66 117	98.95 289	99.86 73	98.85 280	99.81 113	99.73 155	98.40 229	99.92 147	98.36 237	99.83 207	99.17 346

baseline1		97.73 366	97.33 377	98.96 339	99.30 353	97.73 381	99.40 120	98.42 416	99.33 208	99.46 271	99.21 378	91.18 406	99.82 325	98.35 238	91.26 466	99.32 310

MVS-HIRNet		97.86 360	98.22 331	96.76 433	99.28 358	91.53 460	98.38 368	92.60 465	99.13 244	99.31 312	99.96 15	97.18 314	99.68 406	98.34 239	99.83 207	99.07 377

GA-MVS		97.99 359	97.68 369	98.93 345	99.52 276	98.04 363	97.19 446	99.05 383	98.32 349	98.81 375	98.97 410	89.89 426	99.41 452	98.33 240	99.05 393	99.34 306

Fast-Effi-MVS+		99.02 253	98.87 268	99.46 230	99.38 324	99.50 166	99.04 255	99.79 117	97.17 411	98.62 393	98.74 428	99.34 78	99.95 78	98.32 241	99.41 358	98.92 399

MDA-MVSNet_test_wron		98.95 272	98.99 248	98.85 357	99.64 217	97.16 398	98.23 379	99.33 337	98.93 269	99.56 238	99.66 212	97.39 303	99.83 312	98.29 242	99.88 169	99.55 212

N_pmnet		98.73 296	98.53 303	99.35 270	99.72 171	98.67 307	98.34 370	94.65 458	98.35 343	99.79 123	99.68 203	98.03 263	99.93 116	98.28 243	99.92 134	99.44 275

ET-MVSNet_ETH3D		96.78 394	96.07 404	98.91 348	99.26 363	97.92 372	97.70 424	96.05 453	97.96 372	92.37 466	98.43 441	87.06 434	99.90 195	98.27 244	97.56 452	98.91 400

thisisatest0530		97.45 377	96.95 388	98.94 342	99.68 203	97.73 381	99.09 242	94.19 461	98.61 313	99.56 238	99.30 358	84.30 448	99.93 116	98.27 244	99.54 337	99.16 348

YYNet1		98.95 272	98.99 248	98.84 359	99.64 217	97.14 400	98.22 380	99.32 339	98.92 271	99.59 225	99.66 212	97.40 301	99.83 312	98.27 244	99.90 146	99.55 212

reproduce_model		99.50 111	99.40 135	99.83 39	99.60 226	99.83 34	99.12 229	99.68 184	99.49 168	99.80 117	99.79 111	99.01 131	99.93 116	98.24 247	99.82 217	99.73 90

ACMM	98.09 11	99.46 129	99.38 139	99.72 114	99.80 106	99.69 110	99.13 224	99.65 204	98.99 258	99.64 200	99.72 163	99.39 65	99.86 263	98.23 248	99.81 227	99.60 189
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 269	98.87 268	99.24 304	99.57 246	98.40 336	98.12 389	99.18 371	98.28 351	99.63 204	99.13 384	98.02 264	99.97 42	98.22 249	99.69 286	99.35 303

3Dnovator	99.15 2	99.43 139	99.36 146	99.65 145	99.39 321	99.42 190	99.70 38	99.56 257	99.23 223	99.35 298	99.80 101	99.17 100	99.95 78	98.21 250	99.84 199	99.59 196

Fast-Effi-MVS+-dtu		99.20 208	99.12 199	99.43 241	99.25 364	99.69 110	99.05 250	99.82 95	99.50 166	98.97 355	99.05 396	98.98 138	99.98 27	98.20 251	99.24 382	98.62 421

MS-PatchMatch		99.00 262	98.97 252	99.09 323	99.11 392	98.19 349	98.76 321	99.33 337	98.49 326	99.44 273	99.58 273	98.21 249	99.69 394	98.20 251	99.62 309	99.39 292

TSAR-MVS + GP.		99.12 231	99.04 230	99.38 260	99.34 342	99.16 252	98.15 385	99.29 347	98.18 358	99.63 204	99.62 243	99.18 98	99.68 406	98.20 251	99.74 261	99.30 316

DP-MVS		99.48 118	99.39 136	99.74 98	99.57 246	99.62 134	99.29 161	99.61 224	99.87 61	99.74 158	99.76 138	98.69 179	99.87 244	98.20 251	99.80 234	99.75 84

MVP-Stereo		99.16 221	99.08 213	99.43 241	99.48 294	99.07 268	99.08 245	99.55 263	98.63 309	99.31 312	99.68 203	98.19 252	99.78 354	98.18 255	99.58 325	99.45 263
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 139	99.30 163	99.80 61	99.83 78	99.81 47	99.52 92	99.70 173	98.35 343	99.51 259	99.50 305	99.31 82	99.88 229	98.18 255	99.84 199	99.69 110

MDA-MVSNet-bldmvs		99.06 244	99.05 224	99.07 328	99.80 106	97.83 376	98.89 296	99.72 161	99.29 211	99.63 204	99.70 183	96.47 335	99.89 214	98.17 257	99.82 217	99.50 245

JIA-IIPM		98.06 355	97.92 358	98.50 381	98.59 444	97.02 402	98.80 315	98.51 411	99.88 59	97.89 429	99.87 56	91.89 399	99.90 195	98.16 258	97.68 451	98.59 424

EIA-MVS		99.12 231	99.01 237	99.45 233	99.36 329	99.62 134	99.34 137	99.79 117	98.41 332	98.84 372	98.89 418	98.75 171	99.84 296	98.15 259	99.51 343	98.89 403

miper_lstm_enhance		98.65 304	98.60 291	98.82 364	99.20 374	97.33 394	97.78 420	99.66 194	99.01 257	99.59 225	99.50 305	94.62 369	99.85 281	98.12 260	99.90 146	99.26 322

reproduce-ours		99.46 129	99.35 149	99.82 44	99.56 257	99.83 34	99.05 250	99.65 204	99.45 182	99.78 127	99.78 123	98.93 144	99.93 116	98.11 261	99.81 227	99.70 102

our_new_method		99.46 129	99.35 149	99.82 44	99.56 257	99.83 34	99.05 250	99.65 204	99.45 182	99.78 127	99.78 123	98.93 144	99.93 116	98.11 261	99.81 227	99.70 102

Effi-MVS+-dtu		99.07 243	98.92 261	99.52 211	98.89 418	99.78 57	99.15 214	99.66 194	99.34 205	98.92 362	99.24 374	97.69 287	99.98 27	98.11 261	99.28 375	98.81 410

tpm		97.15 386	96.95 388	97.75 413	98.91 414	94.24 444	99.32 146	97.96 432	97.71 385	98.29 410	99.32 353	86.72 440	99.92 147	98.10 264	96.24 461	99.09 366

DeepPCF-MVS	98.42 6	99.18 215	99.02 233	99.67 132	99.22 369	99.75 78	97.25 444	99.47 301	98.72 299	99.66 195	99.70 183	99.29 84	99.63 427	98.07 265	99.81 227	99.62 174

ppachtmachnet_test		98.89 280	99.12 199	98.20 397	99.66 211	95.24 437	97.63 426	99.68 184	99.08 249	99.78 127	99.62 243	98.65 187	99.88 229	98.02 266	99.96 84	99.48 254

tpmrst		97.73 366	98.07 344	96.73 436	98.71 440	92.00 455	99.10 237	98.86 390	98.52 322	98.92 362	99.54 294	91.90 398	99.82 325	98.02 266	99.03 395	98.37 438

CSCG		99.37 160	99.29 168	99.60 176	99.71 175	99.46 175	99.43 118	99.85 79	98.79 290	99.41 286	99.60 260	98.92 147	99.92 147	98.02 266	99.92 134	99.43 281

eth_miper_zixun_eth		98.68 302	98.71 284	98.60 376	99.10 394	96.84 407	97.52 434	99.54 269	98.94 266	99.58 227	99.48 312	96.25 346	99.76 366	98.01 269	99.93 130	99.21 334

Patchmtry		98.78 290	98.54 302	99.49 220	98.89 418	99.19 247	99.32 146	99.67 189	99.65 137	99.72 167	99.79 111	91.87 400	99.95 78	98.00 270	99.97 70	99.33 307

PVSNet_BlendedMVS		99.03 251	99.01 237	99.09 323	99.54 262	97.99 365	98.58 341	99.82 95	97.62 388	99.34 302	99.71 173	98.52 212	99.77 363	97.98 271	99.97 70	99.52 238

PVSNet_Blended		98.70 300	98.59 293	99.02 333	99.54 262	97.99 365	97.58 429	99.82 95	95.70 436	99.34 302	98.98 408	98.52 212	99.77 363	97.98 271	99.83 207	99.30 316

cl____		98.54 316	98.41 313	98.92 346	99.03 404	97.80 379	97.46 436	99.59 241	98.90 273	99.60 222	99.46 319	93.85 376	99.78 354	97.97 273	99.89 160	99.17 346

DIV-MVS_self_test		98.54 316	98.42 312	98.92 346	99.03 404	97.80 379	97.46 436	99.59 241	98.90 273	99.60 222	99.46 319	93.87 375	99.78 354	97.97 273	99.89 160	99.18 343

AUN-MVS		97.82 362	97.38 376	99.14 317	99.27 360	98.53 327	98.72 325	99.02 385	98.10 360	97.18 447	99.03 402	89.26 428	99.85 281	97.94 275	97.91 447	99.03 383

FA-MVS(test-final)		98.52 318	98.32 323	99.10 322	99.48 294	98.67 307	99.77 19	98.60 407	97.35 403	99.63 204	99.80 101	93.07 388	99.84 296	97.92 276	99.30 372	98.78 413

ambc				99.20 308	99.35 333	98.53 327	99.17 205	99.46 304		99.67 189	99.80 101	98.46 219	99.70 387	97.92 276	99.70 278	99.38 294

USDC		98.96 269	98.93 257	99.05 331	99.54 262	97.99 365	97.07 450	99.80 109	98.21 355	99.75 148	99.77 133	98.43 222	99.64 425	97.90 278	99.88 169	99.51 240

OPM-MVS		99.26 187	99.13 195	99.63 159	99.70 190	99.61 140	98.58 341	99.48 298	98.50 324	99.52 252	99.63 234	99.14 107	99.76 366	97.89 279	99.77 248	99.51 240
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 176	99.17 188	99.77 75	99.69 195	99.80 51	99.14 217	99.31 343	99.16 238	99.62 213	99.61 252	98.35 233	99.91 176	97.88 280	99.72 273	99.61 185
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 39	99.70 190	99.79 54	99.14 217	99.61 224					99.92 147	97.88 280	99.72 273	99.77 76

c3_l		98.72 297	98.71 284	98.72 370	99.12 387	97.22 397	97.68 425	99.56 257	98.90 273	99.54 245	99.48 312	96.37 341	99.73 376	97.88 280	99.88 169	99.21 334

3Dnovator+	98.92 3	99.35 166	99.24 180	99.67 132	99.35 333	99.47 171	99.62 67	99.50 293	99.44 184	99.12 343	99.78 123	98.77 168	99.94 95	97.87 283	99.72 273	99.62 174

miper_ehance_all_eth		98.59 311	98.59 293	98.59 377	98.98 410	97.07 401	97.49 435	99.52 284	98.50 324	99.52 252	99.37 340	96.41 339	99.71 383	97.86 284	99.62 309	99.00 390

WTY-MVS		98.59 311	98.37 317	99.26 299	99.43 312	98.40 336	98.74 323	99.13 378	98.10 360	99.21 330	99.24 374	94.82 366	99.90 195	97.86 284	98.77 411	99.49 250

APD_test1		99.36 164	99.28 171	99.61 172	99.89 39	99.89 10	99.32 146	99.74 148	99.18 231	99.69 179	99.75 146	98.41 225	99.84 296	97.85 286	99.70 278	99.10 361

SED-MVS		99.40 149	99.28 171	99.77 75	99.69 195	99.82 42	99.20 190	99.54 269	99.13 244	99.82 106	99.63 234	98.91 150	99.92 147	97.85 286	99.70 278	99.58 201

test_241102_TWO								99.54 269	99.13 244	99.76 143	99.63 234	98.32 238	99.92 147	97.85 286	99.69 286	99.75 84

MVS_111021_HR		99.12 231	99.02 233	99.40 254	99.50 284	99.11 258	97.92 413	99.71 164	98.76 297	99.08 347	99.47 316	99.17 100	99.54 441	97.85 286	99.76 250	99.54 222

MTAPA		99.35 166	99.20 184	99.80 61	99.81 99	99.81 47	99.33 143	99.53 279	99.27 215	99.42 280	99.63 234	98.21 249	99.95 78	97.83 290	99.79 239	99.65 145

MSC_two_6792asdad				99.74 98	99.03 404	99.53 162		99.23 360					99.92 147	97.77 291	99.69 286	99.78 72

No_MVS				99.74 98	99.03 404	99.53 162		99.23 360					99.92 147	97.77 291	99.69 286	99.78 72

TESTMET0.1,1		96.24 409	95.84 410	97.41 422	98.24 455	93.84 447	97.38 438	95.84 454	98.43 329	97.81 435	98.56 436	79.77 457	99.89 214	97.77 291	98.77 411	98.52 430

ACMH+	98.40 8	99.50 111	99.43 129	99.71 119	99.86 57	99.76 70	99.32 146	99.77 129	99.53 162	99.77 139	99.76 138	99.26 90	99.78 354	97.77 291	99.88 169	99.60 189

IU-MVS					99.69 195	99.77 63		99.22 363	97.50 395	99.69 179				97.75 295	99.70 278	99.77 76

114514_t		98.49 323	98.11 341	99.64 152	99.73 167	99.58 151	99.24 179	99.76 137	89.94 459	99.42 280	99.56 284	97.76 284	99.86 263	97.74 296	99.82 217	99.47 258

DVP-MVS++		99.38 156	99.25 178	99.77 75	99.03 404	99.77 63	99.74 27	99.61 224	99.18 231	99.76 143	99.61 252	99.00 132	99.92 147	97.72 297	99.60 319	99.62 174

test_0728_THIRD									99.18 231	99.62 213	99.61 252	98.58 195	99.91 176	97.72 297	99.80 234	99.77 76

EGC-MVSNET		89.05 433	85.52 436	99.64 152	99.89 39	99.78 57	99.56 87	99.52 284	24.19 468	49.96 469	99.83 81	99.15 104	99.92 147	97.71 299	99.85 194	99.21 334

miper_enhance_ethall		98.03 356	97.94 356	98.32 391	98.27 454	96.43 415	96.95 451	99.41 315	96.37 427	99.43 277	98.96 412	94.74 367	99.69 394	97.71 299	99.62 309	98.83 409

TSAR-MVS + MP.		99.34 171	99.24 180	99.63 159	99.82 87	99.37 205	99.26 172	99.35 334	98.77 294	99.57 230	99.70 183	99.27 89	99.88 229	97.71 299	99.75 254	99.65 145
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 374	97.28 378	98.40 386	98.37 452	96.75 408	97.24 445	99.37 330	97.31 405	99.41 286	99.22 376	87.30 432	99.37 453	97.70 302	99.62 309	99.08 372

MP-MVS-pluss		99.14 226	98.92 261	99.80 61	99.83 78	99.83 34	98.61 334	99.63 214	96.84 420	99.44 273	99.58 273	98.81 159	99.91 176	97.70 302	99.82 217	99.67 125
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 181	99.11 202	99.79 68	99.75 155	99.81 47	98.95 289	99.53 279	98.27 352	99.53 250	99.73 155	98.75 171	99.87 244	97.70 302	99.83 207	99.68 116

UnsupCasMVSNet_bld		98.55 315	98.27 329	99.40 254	99.56 257	99.37 205	97.97 409	99.68 184	97.49 396	99.08 347	99.35 349	95.41 360	99.82 325	97.70 302	98.19 439	99.01 389

MVS_111021_LR		99.13 228	99.03 232	99.42 243	99.58 236	99.32 217	97.91 415	99.73 152	98.68 303	99.31 312	99.48 312	99.09 114	99.66 416	97.70 302	99.77 248	99.29 319

IS-MVSNet		99.03 251	98.85 270	99.55 199	99.80 106	99.25 231	99.73 30	99.15 375	99.37 201	99.61 219	99.71 173	94.73 368	99.81 341	97.70 302	99.88 169	99.58 201

test-LLR		97.15 386	96.95 388	97.74 414	98.18 457	95.02 439	97.38 438	96.10 450	98.00 365	97.81 435	98.58 433	90.04 424	99.91 176	97.69 308	98.78 409	98.31 439

test-mter		96.23 410	95.73 413	97.74 414	98.18 457	95.02 439	97.38 438	96.10 450	97.90 374	97.81 435	98.58 433	79.12 460	99.91 176	97.69 308	98.78 409	98.31 439

MonoMVSNet		98.23 345	98.32 323	97.99 402	98.97 411	96.62 410	99.49 105	98.42 416	99.62 145	99.40 291	99.79 111	95.51 358	98.58 464	97.68 310	95.98 462	98.76 416

XVS		99.27 185	99.11 202	99.75 93	99.71 175	99.71 98	99.37 129	99.61 224	99.29 211	98.76 382	99.47 316	98.47 216	99.88 229	97.62 311	99.73 267	99.67 125

X-MVStestdata		96.09 414	94.87 427	99.75 93	99.71 175	99.71 98	99.37 129	99.61 224	99.29 211	98.76 382	61.30 477	98.47 216	99.88 229	97.62 311	99.73 267	99.67 125

SMA-MVS		99.19 211	99.00 241	99.73 107	99.46 304	99.73 88	99.13 224	99.52 284	97.40 400	99.57 230	99.64 219	98.93 144	99.83 312	97.61 313	99.79 239	99.63 163
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		96.71 397	96.79 396	96.46 440	98.90 415	90.71 466	99.41 119	98.68 400	94.69 449	98.14 420	99.34 352	86.32 442	99.80 348	97.60 314	98.07 445	98.88 404

PVSNet	97.47 15	98.42 329	98.44 310	98.35 388	99.46 304	96.26 419	96.70 455	99.34 336	97.68 386	99.00 354	99.13 384	97.40 301	99.72 378	97.59 315	99.68 291	99.08 372

new_pmnet		98.88 281	98.89 266	98.84 359	99.70 190	97.62 384	98.15 385	99.50 293	97.98 368	99.62 213	99.54 294	98.15 255	99.94 95	97.55 316	99.84 199	98.95 394

IB-MVS	95.41 20	95.30 429	94.46 433	97.84 410	98.76 436	95.33 435	97.33 441	96.07 452	96.02 431	95.37 463	97.41 460	76.17 464	99.96 67	97.54 317	95.44 465	98.22 444
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		99.24 191	99.11 202	99.61 172	98.38 451	99.79 54	99.57 85	99.68 184	99.61 149	99.15 338	99.71 173	98.70 178	99.91 176	97.54 317	99.68 291	99.13 358

ZNCC-MVS		99.22 201	99.04 230	99.77 75	99.76 142	99.73 88	99.28 163	99.56 257	98.19 357	99.14 340	99.29 361	98.84 158	99.92 147	97.53 319	99.80 234	99.64 157

CP-MVS		99.23 192	99.05 224	99.75 93	99.66 211	99.66 117	99.38 125	99.62 217	98.38 336	99.06 351	99.27 364	98.79 164	99.94 95	97.51 320	99.82 217	99.66 136

SD-MVS		99.01 259	99.30 163	98.15 398	99.50 284	99.40 197	98.94 291	99.61 224	99.22 227	99.75 148	99.82 88	99.54 50	95.51 468	97.48 321	99.87 181	99.54 222
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		98.49 323	98.29 328	99.11 320	98.96 412	98.42 335	97.54 430	99.32 339	97.53 393	98.47 405	98.15 448	97.88 274	99.82 325	97.46 322	99.24 382	99.09 366

DeepC-MVS_fast	98.47 5	99.23 192	99.12 199	99.56 193	99.28 358	99.22 241	98.99 278	99.40 322	99.08 249	99.58 227	99.64 219	98.90 153	99.83 312	97.44 323	99.75 254	99.63 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 188	99.08 213	99.76 82	99.73 167	99.70 106	99.31 151	99.59 241	98.36 338	99.36 296	99.37 340	98.80 163	99.91 176	97.43 324	99.75 254	99.68 116

ACMMPR		99.23 192	99.06 219	99.76 82	99.74 163	99.69 110	99.31 151	99.59 241	98.36 338	99.35 298	99.38 337	98.61 191	99.93 116	97.43 324	99.75 254	99.67 125

Vis-MVSNet (Re-imp)		98.77 291	98.58 296	99.34 271	99.78 127	98.88 291	99.61 73	99.56 257	99.11 248	99.24 324	99.56 284	93.00 390	99.78 354	97.43 324	99.89 160	99.35 303

MIMVSNet		98.43 328	98.20 333	99.11 320	99.53 269	98.38 340	99.58 82	98.61 405	98.96 262	99.33 304	99.76 138	90.92 410	99.81 341	97.38 327	99.76 250	99.15 350

WB-MVSnew		98.34 339	98.14 339	98.96 339	98.14 460	97.90 373	98.27 375	97.26 446	98.63 309	98.80 377	98.00 451	97.77 282	99.90 195	97.37 328	98.98 398	99.09 366

XVG-OURS-SEG-HR		99.16 221	98.99 248	99.66 139	99.84 71	99.64 126	98.25 378	99.73 152	98.39 335	99.63 204	99.43 324	99.70 31	99.90 195	97.34 329	98.64 422	99.44 275

COLMAP_ROB	98.06 12	99.45 133	99.37 142	99.70 124	99.83 78	99.70 106	99.38 125	99.78 126	99.53 162	99.67 189	99.78 123	99.19 97	99.86 263	97.32 330	99.87 181	99.55 212
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 253	98.81 277	99.65 145	99.58 236	99.49 167	98.58 341	99.07 380	98.40 334	99.04 352	99.25 369	98.51 214	99.80 348	97.31 331	99.51 343	99.65 145

region2R		99.23 192	99.05 224	99.77 75	99.76 142	99.70 106	99.31 151	99.59 241	98.41 332	99.32 307	99.36 344	98.73 175	99.93 116	97.29 332	99.74 261	99.67 125

APD-MVS_3200maxsize		99.31 177	99.16 189	99.74 98	99.53 269	99.75 78	99.27 167	99.61 224	99.19 230	99.57 230	99.64 219	98.76 169	99.90 195	97.29 332	99.62 309	99.56 209

TAPA-MVS	97.92 13	98.03 356	97.55 372	99.46 230	99.47 300	99.44 183	98.50 356	99.62 217	86.79 460	99.07 350	99.26 367	98.26 243	99.62 428	97.28 334	99.73 267	99.31 314
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 185	99.11 202	99.73 107	99.54 262	99.74 85	99.26 172	99.62 217	99.16 238	99.52 252	99.64 219	98.41 225	99.91 176	97.27 335	99.61 316	99.54 222

RE-MVS-def			99.13 195		99.54 262	99.74 85	99.26 172	99.62 217	99.16 238	99.52 252	99.64 219	98.57 196		97.27 335	99.61 316	99.54 222

testing11		96.05 416	95.41 419	97.97 404	98.78 433	95.27 436	98.59 339	98.23 426	98.86 279	96.56 454	96.91 467	75.20 465	99.69 394	97.26 337	98.29 434	98.93 397

test_yl		98.25 342	97.95 352	99.13 318	99.17 380	98.47 330	99.00 271	98.67 402	98.97 260	99.22 328	99.02 403	91.31 404	99.69 394	97.26 337	98.93 400	99.24 325

DCV-MVSNet		98.25 342	97.95 352	99.13 318	99.17 380	98.47 330	99.00 271	98.67 402	98.97 260	99.22 328	99.02 403	91.31 404	99.69 394	97.26 337	98.93 400	99.24 325

PHI-MVS		99.11 235	98.95 255	99.59 179	99.13 385	99.59 146	99.17 205	99.65 204	97.88 377	99.25 321	99.46 319	98.97 140	99.80 348	97.26 337	99.82 217	99.37 297

tfpnnormal		99.43 139	99.38 139	99.60 176	99.87 54	99.75 78	99.59 80	99.78 126	99.71 115	99.90 66	99.69 192	98.85 157	99.90 195	97.25 341	99.78 244	99.15 350

PatchmatchNet		97.65 370	97.80 363	97.18 429	98.82 428	92.49 453	99.17 205	98.39 419	98.12 359	98.79 379	99.58 273	90.71 416	99.89 214	97.23 342	99.41 358	99.16 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 265	98.80 279	99.56 193	99.25 364	99.43 187	98.54 351	99.27 351	98.58 315	98.80 377	99.43 324	98.53 209	99.70 387	97.22 343	99.59 323	99.54 222

testing3		96.48 403	95.63 415	99.01 334	99.23 368	97.81 377	98.90 295	99.10 379	98.72 299	97.84 434	97.92 452	72.44 469	99.85 281	97.21 344	99.33 368	99.35 303

HPM-MVS		99.25 188	99.07 217	99.78 72	99.81 99	99.75 78	99.61 73	99.67 189	97.72 384	99.35 298	99.25 369	99.23 93	99.92 147	97.21 344	99.82 217	99.67 125
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 211	99.00 241	99.76 82	99.76 142	99.68 113	99.38 125	99.54 269	98.34 347	99.01 353	99.50 305	98.53 209	99.93 116	97.18 346	99.78 244	99.66 136

ACMMP		99.25 188	99.08 213	99.74 98	99.79 119	99.68 113	99.50 100	99.65 204	98.07 363	99.52 252	99.69 192	98.57 196	99.92 147	97.18 346	99.79 239	99.63 163
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		96.23 410	95.74 412	97.70 416	98.86 422	95.59 432	98.66 331	98.14 428	98.96 262	97.67 440	97.06 464	76.78 462	98.92 460	97.10 348	98.41 431	98.58 426

thisisatest0515		96.98 390	96.42 398	98.66 373	99.42 317	97.47 388	97.27 443	94.30 460	97.24 407	99.15 338	98.86 420	85.01 444	99.87 244	97.10 348	99.39 360	98.63 420

XVG-ACMP-BASELINE		99.23 192	99.10 210	99.63 159	99.82 87	99.58 151	98.83 307	99.72 161	98.36 338	99.60 222	99.71 173	98.92 147	99.91 176	97.08 350	99.84 199	99.40 289

MSDG		99.08 240	98.98 251	99.37 263	99.60 226	99.13 255	97.54 430	99.74 148	98.84 283	99.53 250	99.55 292	99.10 112	99.79 351	97.07 351	99.86 189	99.18 343

SteuartSystems-ACMMP		99.30 178	99.14 193	99.76 82	99.87 54	99.66 117	99.18 200	99.60 235	98.55 317	99.57 230	99.67 207	99.03 130	99.94 95	97.01 352	99.80 234	99.69 110
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 412	95.78 411	97.49 418	98.53 446	93.83 448	98.04 399	93.94 463	98.96 262	98.46 406	98.17 447	79.86 455	99.87 244	96.99 353	99.06 391	98.78 413

EPMVS		96.53 400	96.32 399	97.17 430	98.18 457	92.97 452	99.39 122	89.95 469	98.21 355	98.61 394	99.59 270	86.69 441	99.72 378	96.99 353	99.23 384	98.81 410

MSP-MVS		99.04 250	98.79 280	99.81 52	99.78 127	99.73 88	99.35 136	99.57 252	98.54 320	99.54 245	98.99 405	96.81 324	99.93 116	96.97 355	99.53 339	99.77 76
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++		98.96 269	98.70 286	99.74 98	99.52 276	99.71 98	98.86 300	99.19 370	98.47 328	98.59 396	99.06 395	98.08 261	99.91 176	96.94 356	99.60 319	99.60 189

SR-MVS		99.19 211	99.00 241	99.74 98	99.51 278	99.72 93	99.18 200	99.60 235	98.85 280	99.47 267	99.58 273	98.38 230	99.92 147	96.92 357	99.54 337	99.57 207

PGM-MVS		99.20 208	99.01 237	99.77 75	99.75 155	99.71 98	99.16 211	99.72 161	97.99 367	99.42 280	99.60 260	98.81 159	99.93 116	96.91 358	99.74 261	99.66 136

HY-MVS	98.23 9	98.21 349	97.95 352	98.99 335	99.03 404	98.24 344	99.61 73	98.72 398	96.81 421	98.73 384	99.51 302	94.06 373	99.86 263	96.91 358	98.20 437	98.86 406

MDTV_nov1_ep13			97.73 367		98.70 441	90.83 464	99.15 214	98.02 431	98.51 323	98.82 374	99.61 252	90.98 409	99.66 416	96.89 360	98.92 402

GST-MVS		99.16 221	98.96 254	99.75 93	99.73 167	99.73 88	99.20 190	99.55 263	98.22 354	99.32 307	99.35 349	98.65 187	99.91 176	96.86 361	99.74 261	99.62 174

test_post1							99.14 217				51.63 479	89.54 427	99.82 325	96.86 361

SCA		98.11 352	98.36 318	97.36 423	99.20 374	92.99 451	98.17 383	98.49 413	98.24 353	99.10 346	99.57 280	96.01 351	99.94 95	96.86 361	99.62 309	99.14 355

UBG		96.53 400	95.95 406	98.29 395	98.87 421	96.31 418	98.48 359	98.07 429	98.83 284	97.32 442	96.54 472	79.81 456	99.62 428	96.84 364	98.74 415	98.95 394

XVG-OURS		99.21 206	99.06 219	99.65 145	99.82 87	99.62 134	97.87 417	99.74 148	98.36 338	99.66 195	99.68 203	99.71 28	99.90 195	96.84 364	99.88 169	99.43 281

LCM-MVSNet-Re		99.28 181	99.15 192	99.67 132	99.33 347	99.76 70	99.34 137	99.97 20	98.93 269	99.91 61	99.79 111	98.68 180	99.93 116	96.80 366	99.56 328	99.30 316

RPSCF		99.18 215	99.02 233	99.64 152	99.83 78	99.85 22	99.44 116	99.82 95	98.33 348	99.50 262	99.78 123	97.90 272	99.65 423	96.78 367	99.83 207	99.44 275

旧先验2							97.94 411		95.33 440	98.94 358			99.88 229	96.75 368

MDTV_nov1_ep13_2view						91.44 461	99.14 217		97.37 402	99.21 330		91.78 402		96.75 368		99.03 383

CLD-MVS		98.76 292	98.57 297	99.33 274	99.57 246	98.97 278	97.53 432	99.55 263	96.41 425	99.27 319	99.13 384	99.07 120	99.78 354	96.73 370	99.89 160	99.23 329
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 353	97.98 350	98.48 382	99.27 360	96.48 413	99.40 120	99.07 380	98.81 287	99.23 325	99.57 280	90.11 423	99.87 244	96.69 371	99.64 304	99.09 366

baseline2		96.83 393	96.28 400	98.46 384	99.09 397	96.91 405	98.83 307	93.87 464	97.23 408	96.23 459	98.36 442	88.12 431	99.90 195	96.68 372	98.14 442	98.57 428

cascas		96.99 389	96.82 395	97.48 419	97.57 467	95.64 430	96.43 457	99.56 257	91.75 455	97.13 449	97.61 459	95.58 356	98.63 462	96.68 372	99.11 388	98.18 448

PC_three_1452									97.56 389	99.68 182	99.41 327	99.09 114	97.09 465	96.66 374	99.60 319	99.62 174

LPG-MVS_test		99.22 201	99.05 224	99.74 98	99.82 87	99.63 132	99.16 211	99.73 152	97.56 389	99.64 200	99.69 192	99.37 71	99.89 214	96.66 374	99.87 181	99.69 110

LGP-MVS_train				99.74 98	99.82 87	99.63 132		99.73 152	97.56 389	99.64 200	99.69 192	99.37 71	99.89 214	96.66 374	99.87 181	99.69 110

ETVMVS		96.14 413	95.22 424	98.89 355	98.80 429	98.01 364	98.66 331	98.35 422	98.71 301	97.18 447	96.31 476	74.23 468	99.75 370	96.64 377	98.13 444	98.90 401

TinyColmap		98.97 266	98.93 257	99.07 328	99.46 304	98.19 349	97.75 421	99.75 142	98.79 290	99.54 245	99.70 183	98.97 140	99.62 428	96.63 378	99.83 207	99.41 286

LF4IMVS		99.01 259	98.92 261	99.27 296	99.71 175	99.28 223	98.59 339	99.77 129	98.32 349	99.39 293	99.41 327	98.62 189	99.84 296	96.62 379	99.84 199	98.69 419

NCCC		98.82 286	98.57 297	99.58 182	99.21 371	99.31 218	98.61 334	99.25 356	98.65 306	98.43 407	99.26 367	97.86 275	99.81 341	96.55 380	99.27 378	99.61 185

OPU-MVS				99.29 288	99.12 387	99.44 183	99.20 190				99.40 331	99.00 132	98.84 461	96.54 381	99.60 319	99.58 201

F-COLMAP		98.74 294	98.45 309	99.62 168	99.57 246	99.47 171	98.84 304	99.65 204	96.31 428	98.93 359	99.19 381	97.68 288	99.87 244	96.52 382	99.37 363	99.53 228

testing99		95.86 421	95.19 425	97.87 408	98.76 436	95.03 438	98.62 333	98.44 415	98.68 303	96.67 453	96.66 471	74.31 467	99.69 394	96.51 383	98.03 446	98.90 401

ADS-MVSNet2		97.78 364	97.66 371	98.12 400	99.14 383	95.36 434	99.22 187	98.75 397	96.97 416	98.25 412	99.64 219	90.90 411	99.94 95	96.51 383	99.56 328	99.08 372

ADS-MVSNet		97.72 369	97.67 370	97.86 409	99.14 383	94.65 442	99.22 187	98.86 390	96.97 416	98.25 412	99.64 219	90.90 411	99.84 296	96.51 383	99.56 328	99.08 372

PatchMatch-RL		98.68 302	98.47 306	99.30 287	99.44 309	99.28 223	98.14 387	99.54 269	97.12 414	99.11 344	99.25 369	97.80 280	99.70 387	96.51 383	99.30 372	98.93 397

CMPMVS	77.52 23	98.50 321	98.19 336	99.41 251	98.33 453	99.56 155	99.01 267	99.59 241	95.44 438	99.57 230	99.80 101	95.64 354	99.46 451	96.47 387	99.92 134	99.21 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 417	95.32 422	98.02 401	98.76 436	95.39 433	98.38 368	98.65 404	98.82 285	96.84 450	96.71 470	75.06 466	99.71 383	96.46 388	98.23 436	98.98 391

SF-MVS		99.10 238	98.93 257	99.62 168	99.58 236	99.51 165	99.13 224	99.65 204	97.97 369	99.42 280	99.61 252	98.86 156	99.87 244	96.45 389	99.68 291	99.49 250

FE-MVS		97.85 361	97.42 375	99.15 314	99.44 309	98.75 302	99.77 19	98.20 427	95.85 433	99.33 304	99.80 101	88.86 429	99.88 229	96.40 390	99.12 387	98.81 410

DPE-MVS		99.14 226	98.92 261	99.82 44	99.57 246	99.77 63	98.74 323	99.60 235	98.55 317	99.76 143	99.69 192	98.23 248	99.92 147	96.39 391	99.75 254	99.76 81
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 465	89.02 471			93.47 451		98.30 443		99.84 296	96.38 392

AllTest		99.21 206	99.07 217	99.63 159	99.78 127	99.64 126	99.12 229	99.83 89	98.63 309	99.63 204	99.72 163	98.68 180	99.75 370	96.38 392	99.83 207	99.51 240

TestCases				99.63 159	99.78 127	99.64 126		99.83 89	98.63 309	99.63 204	99.72 163	98.68 180	99.75 370	96.38 392	99.83 207	99.51 240

testdata				99.42 243	99.51 278	98.93 285		99.30 346	96.20 429	98.87 369	99.40 331	98.33 237	99.89 214	96.29 395	99.28 375	99.44 275

dp		96.86 392	97.07 384	96.24 442	98.68 442	90.30 469	99.19 196	98.38 420	97.35 403	98.23 414	99.59 270	87.23 433	99.82 325	96.27 396	98.73 418	98.59 424

tpmvs		97.39 381	97.69 368	96.52 438	98.41 450	91.76 457	99.30 154	98.94 389	97.74 383	97.85 433	99.55 292	92.40 397	99.73 376	96.25 397	98.73 418	98.06 450

KD-MVS_2432*1600		95.89 418	95.41 419	97.31 426	94.96 469	93.89 445	97.09 448	99.22 363	97.23 408	98.88 366	99.04 398	79.23 458	99.54 441	96.24 398	96.81 456	98.50 434

miper_refine_blended		95.89 418	95.41 419	97.31 426	94.96 469	93.89 445	97.09 448	99.22 363	97.23 408	98.88 366	99.04 398	79.23 458	99.54 441	96.24 398	96.81 456	98.50 434

ACMP	97.51 14	99.05 247	98.84 272	99.67 132	99.78 127	99.55 159	98.88 297	99.66 194	97.11 415	99.47 267	99.60 260	99.07 120	99.89 214	96.18 400	99.85 194	99.58 201
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 277	98.72 283	99.44 237	99.39 321	99.42 190	98.58 341	99.64 212	97.31 405	99.44 273	99.62 243	98.59 193	99.69 394	96.17 401	99.79 239	99.22 331

DP-MVS Recon		98.50 321	98.23 330	99.31 283	99.49 289	99.46 175	98.56 347	99.63 214	94.86 447	98.85 371	99.37 340	97.81 279	99.59 435	96.08 402	99.44 353	98.88 404

tpm cat1		96.78 394	96.98 387	96.16 443	98.85 423	90.59 467	99.08 245	99.32 339	92.37 453	97.73 439	99.46 319	91.15 407	99.69 394	96.07 403	98.80 408	98.21 445

tpm2		96.35 406	96.22 401	96.73 436	98.88 420	91.75 458	99.21 189	98.51 411	93.27 452	97.89 429	99.21 378	84.83 445	99.70 387	96.04 404	98.18 440	98.75 417

dmvs_re		98.69 301	98.48 305	99.31 283	99.55 260	99.42 190	99.54 90	98.38 420	99.32 209	98.72 385	98.71 429	96.76 326	99.21 455	96.01 405	99.35 366	99.31 314

test_0402		99.22 201	99.14 193	99.45 233	99.79 119	99.43 187	99.28 163	99.68 184	99.54 160	99.40 291	99.56 284	99.07 120	99.82 325	96.01 405	99.96 84	99.11 359

ITE_SJBPF				99.38 260	99.63 219	99.44 183		99.73 152	98.56 316	99.33 304	99.53 296	98.88 154	99.68 406	96.01 405	99.65 302	99.02 388

test_prior2							97.95 410		97.87 378	98.05 422	99.05 396	97.90 272		95.99 408	99.49 348

testdata2													99.89 214	95.99 408

原ACMM1				99.37 263	99.47 300	98.87 293		99.27 351	96.74 423	98.26 411	99.32 353	97.93 271	99.82 325	95.96 410	99.38 361	99.43 281

新几何1				99.52 211	99.50 284	99.22 241		99.26 353	95.66 437	98.60 395	99.28 362	97.67 289	99.89 214	95.95 411	99.32 370	99.45 263

MP-MVS		99.06 244	98.83 274	99.76 82	99.76 142	99.71 98	99.32 146	99.50 293	98.35 343	98.97 355	99.48 312	98.37 231	99.92 147	95.95 411	99.75 254	99.63 163
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 428	94.59 431	98.61 375	98.66 443	97.45 390	98.54 351	97.90 435	98.53 321	96.54 455	96.47 473	70.62 472	99.81 341	95.91 413	98.15 441	98.56 429

wuyk23d		97.58 373	99.13 195	92.93 447	99.69 195	99.49 167	99.52 92	99.77 129	97.97 369	99.96 32	99.79 111	99.84 16	99.94 95	95.85 414	99.82 217	79.36 465

HQP_MVS		98.90 277	98.68 287	99.55 199	99.58 236	99.24 235	98.80 315	99.54 269	98.94 266	99.14 340	99.25 369	97.24 308	99.82 325	95.84 415	99.78 244	99.60 189

plane_prior5								99.54 269					99.82 325	95.84 415	99.78 244	99.60 189

无先验							98.01 402	99.23 360	95.83 434				99.85 281	95.79 417		99.44 275

CPTT-MVS		98.74 294	98.44 310	99.64 152	99.61 224	99.38 202	99.18 200	99.55 263	96.49 424	99.27 319	99.37 340	97.11 316	99.92 147	95.74 418	99.67 297	99.62 174

PLC	97.35 16	98.36 334	97.99 348	99.48 224	99.32 348	99.24 235	98.50 356	99.51 289	95.19 443	98.58 397	98.96 412	96.95 321	99.83 312	95.63 419	99.25 380	99.37 297
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 313	98.34 321	99.28 291	99.18 379	99.10 265	98.34 370	99.41 315	98.48 327	98.52 402	98.98 408	97.05 318	99.78 354	95.59 420	99.50 346	98.96 392

1314		98.00 358	97.90 360	98.27 396	98.90 415	97.45 390	99.30 154	99.06 382	94.98 444	97.21 446	99.12 388	98.43 222	99.67 411	95.58 421	98.56 425	97.71 454

PVSNet_0	95.53 19	95.85 422	95.31 423	97.47 420	98.78 433	93.48 450	95.72 459	99.40 322	96.18 430	97.37 441	97.73 454	95.73 353	99.58 436	95.49 422	81.40 467	99.36 300

MAR-MVS		98.24 344	97.92 358	99.19 309	98.78 433	99.65 123	99.17 205	99.14 376	95.36 439	98.04 423	98.81 425	97.47 298	99.72 378	95.47 423	99.06 391	98.21 445
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	98.23 345	97.89 361	99.26 299	99.19 376	99.26 228	99.65 62	99.69 181	91.33 457	98.14 420	99.77 133	98.28 240	99.96 67	95.41 424	99.55 332	98.58 426

train_agg		98.35 337	97.95 352	99.57 189	99.35 333	99.35 212	98.11 391	99.41 315	94.90 445	97.92 427	98.99 405	98.02 264	99.85 281	95.38 425	99.44 353	99.50 245

9.14			98.64 288		99.45 308		98.81 312	99.60 235	97.52 394	99.28 318	99.56 284	98.53 209	99.83 312	95.36 426	99.64 304

APD-MVS		98.87 282	98.59 293	99.71 119	99.50 284	99.62 134	99.01 267	99.57 252	96.80 422	99.54 245	99.63 234	98.29 239	99.91 176	95.24 427	99.71 276	99.61 185
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 416								95.20 428

AdaColmap		98.60 308	98.35 320	99.38 260	99.12 387	99.22 241	98.67 329	99.42 314	97.84 381	98.81 375	99.27 364	97.32 306	99.81 341	95.14 429	99.53 339	99.10 361

test9_res														95.10 430	99.44 353	99.50 245

CDPH-MVS		98.56 314	98.20 333	99.61 172	99.50 284	99.46 175	98.32 372	99.41 315	95.22 441	99.21 330	99.10 392	98.34 235	99.82 325	95.09 431	99.66 300	99.56 209

BH-untuned		98.22 347	98.09 342	98.58 379	99.38 324	97.24 396	98.55 348	98.98 388	97.81 382	99.20 335	98.76 427	97.01 319	99.65 423	94.83 432	98.33 432	98.86 406

BP-MVS														94.73 433

HQP-MVS		98.36 334	98.02 347	99.39 257	99.31 349	98.94 282	97.98 406	99.37 330	97.45 397	98.15 416	98.83 422	96.67 327	99.70 387	94.73 433	99.67 297	99.53 228

QAPM		98.40 332	97.99 348	99.65 145	99.39 321	99.47 171	99.67 53	99.52 284	91.70 456	98.78 381	99.80 101	98.55 200	99.95 78	94.71 435	99.75 254	99.53 228

agg_prior2														94.58 436	99.46 352	99.50 245

myMVS_eth3d		95.63 426	94.73 428	98.34 390	98.50 448	96.36 416	98.60 336	99.21 366	97.89 375	96.76 451	96.37 474	72.10 470	99.57 437	94.38 437	98.73 418	99.09 366

BH-RMVSNet		98.41 330	98.14 339	99.21 306	99.21 371	98.47 330	98.60 336	98.26 425	98.35 343	98.93 359	99.31 356	97.20 313	99.66 416	94.32 438	99.10 389	99.51 240

E-PMN		97.14 388	97.43 374	96.27 441	98.79 431	91.62 459	95.54 460	99.01 387	99.44 184	98.88 366	99.12 388	92.78 391	99.68 406	94.30 439	99.03 395	97.50 455

MG-MVS		98.52 318	98.39 315	98.94 342	99.15 382	97.39 393	98.18 381	99.21 366	98.89 276	99.23 325	99.63 234	97.37 304	99.74 373	94.22 440	99.61 316	99.69 110

API-MVS		98.38 333	98.39 315	98.35 388	98.83 425	99.26 228	99.14 217	99.18 371	98.59 314	98.66 390	98.78 426	98.61 191	99.57 437	94.14 441	99.56 328	96.21 462

PAPM_NR		98.36 334	98.04 345	99.33 274	99.48 294	98.93 285	98.79 318	99.28 350	97.54 392	98.56 401	98.57 435	97.12 315	99.69 394	94.09 442	98.90 406	99.38 294

ZD-MVS					99.43 312	99.61 140		99.43 312	96.38 426	99.11 344	99.07 394	97.86 275	99.92 147	94.04 443	99.49 348

DPM-MVS		98.28 340	97.94 356	99.32 279	99.36 329	99.11 258	97.31 442	98.78 396	96.88 418	98.84 372	99.11 391	97.77 282	99.61 433	94.03 444	99.36 364	99.23 329

gg-mvs-nofinetune		95.87 420	95.17 426	97.97 404	98.19 456	96.95 403	99.69 45	89.23 470	99.89 54	96.24 458	99.94 19	81.19 450	99.51 447	93.99 445	98.20 437	97.44 456

PMVS	92.94 21	98.82 286	98.81 277	98.85 357	99.84 71	97.99 365	99.20 190	99.47 301	99.71 115	99.42 280	99.82 88	98.09 259	99.47 449	93.88 446	99.85 194	99.07 377
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 391	97.28 378	95.99 445	98.76 436	91.03 463	95.26 462	98.61 405	99.34 205	98.92 362	98.88 419	93.79 377	99.66 416	92.87 447	99.05 393	97.30 459

BH-w/o		97.20 385	97.01 386	97.76 412	99.08 398	95.69 429	98.03 401	98.52 410	95.76 435	97.96 426	98.02 449	95.62 355	99.47 449	92.82 448	97.25 455	98.12 449

TR-MVS		97.44 378	97.15 383	98.32 391	98.53 446	97.46 389	98.47 360	97.91 434	96.85 419	98.21 415	98.51 439	96.42 337	99.51 447	92.16 449	97.29 454	97.98 451

OpenMVS_ROB	97.31 17	97.36 383	96.84 393	98.89 355	99.29 355	99.45 181	98.87 299	99.48 298	86.54 462	99.44 273	99.74 151	97.34 305	99.86 263	91.61 450	99.28 375	97.37 458

GG-mvs-BLEND				97.36 423	97.59 465	96.87 406	99.70 38	88.49 471		94.64 464	97.26 463	80.66 452	99.12 456	91.50 451	96.50 460	96.08 464

DeepMVS_CX				97.98 403	99.69 195	96.95 403		99.26 353	75.51 465	95.74 461	98.28 444	96.47 335	99.62 428	91.23 452	97.89 448	97.38 457

PAPR		97.56 374	97.07 384	99.04 332	98.80 429	98.11 357	97.63 426	99.25 356	94.56 450	98.02 425	98.25 445	97.43 300	99.68 406	90.90 453	98.74 415	99.33 307

MVS		95.72 424	94.63 430	98.99 335	98.56 445	97.98 370	99.30 154	98.86 390	72.71 466	97.30 443	99.08 393	98.34 235	99.74 373	89.21 454	98.33 432	99.26 322

UWE-MVS-28		95.64 425	95.47 417	96.14 444	97.98 461	90.39 468	98.49 358	95.81 455	99.02 256	98.03 424	98.19 446	84.49 447	99.28 454	88.75 455	98.47 430	98.75 417

thres600view7		96.60 399	96.16 402	97.93 406	99.63 219	96.09 424	99.18 200	97.57 440	98.77 294	98.72 385	97.32 461	87.04 435	99.72 378	88.57 456	98.62 423	97.98 451

FPMVS		96.32 407	95.50 416	98.79 365	99.60 226	98.17 352	98.46 364	98.80 395	97.16 412	96.28 456	99.63 234	82.19 449	99.09 457	88.45 457	98.89 407	99.10 361

PCF-MVS	96.03 18	96.73 396	95.86 409	99.33 274	99.44 309	99.16 252	96.87 453	99.44 309	86.58 461	98.95 357	99.40 331	94.38 371	99.88 229	87.93 458	99.80 234	98.95 394
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 405	96.03 405	97.47 420	99.63 219	95.93 425	99.18 200	97.57 440	98.75 298	98.70 388	97.31 462	87.04 435	99.67 411	87.62 459	98.51 427	96.81 460

tfpn200view9		96.30 408	95.89 407	97.53 417	99.58 236	96.11 422	99.00 271	97.54 443	98.43 329	98.52 402	96.98 465	86.85 437	99.67 411	87.62 459	98.51 427	96.81 460

thres400		96.40 404	95.89 407	97.92 407	99.58 236	96.11 422	99.00 271	97.54 443	98.43 329	98.52 402	96.98 465	86.85 437	99.67 411	87.62 459	98.51 427	97.98 451

thres200		96.09 414	95.68 414	97.33 425	99.48 294	96.22 421	98.53 353	97.57 440	98.06 364	98.37 409	96.73 469	86.84 439	99.61 433	86.99 462	98.57 424	96.16 463

MVE	92.54 22	96.66 398	96.11 403	98.31 393	99.68 203	97.55 386	97.94 411	95.60 456	99.37 201	90.68 467	98.70 431	96.56 330	98.61 463	86.94 463	99.55 332	98.77 415
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 384	96.83 394	98.59 377	99.46 304	97.55 386	99.25 178	96.84 449	98.78 292	97.24 445	97.67 455	97.11 316	98.97 459	86.59 464	98.54 426	99.27 320

PAPM		95.61 427	94.71 429	98.31 393	99.12 387	96.63 409	96.66 456	98.46 414	90.77 458	96.25 457	98.68 432	93.01 389	99.69 394	81.60 465	97.86 450	98.62 421

SD_0403		97.42 379	96.90 392	98.98 337	99.54 262	97.90 373	99.52 92	99.54 269	99.34 205	97.87 431	98.85 421	98.72 176	99.64 425	78.93 466	99.83 207	99.40 289

dongtai		89.37 432	88.91 435	90.76 448	99.19 376	77.46 473	95.47 461	87.82 472	92.28 454	94.17 465	98.82 424	71.22 471	95.54 467	63.85 467	97.34 453	99.27 320

kuosan		85.65 434	84.57 437	88.90 450	97.91 462	77.11 474	96.37 458	87.62 473	85.24 463	85.45 468	96.83 468	69.94 473	90.98 469	45.90 468	95.83 464	98.62 421

test123		29.31 435	33.05 440	18.08 451	25.93 475	12.24 476	97.53 432	10.93 476	11.78 469	24.21 470	50.08 481	21.04 474	8.60 470	23.51 469	32.43 469	33.39 466

testmvs		28.94 436	33.33 438	15.79 452	26.03 474	9.81 477	96.77 454	15.67 475	11.55 470	23.87 471	50.74 480	19.03 475	8.53 471	23.21 470	33.07 468	29.03 467

mmdepth		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

monomultidepth		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

test_blank		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uanet_test		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

DCPMVS		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

cdsmvs_eth3d_5k		24.88 437	33.17 439	0.00 453	0.00 476	0.00 478	0.00 464	99.62 217	0.00 471	0.00 472	99.13 384	99.82 18	0.00 472	0.00 471	0.00 470	0.00 468

pcd_1.5k_mvsjas		16.61 438	22.14 441	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	99.28 86	0.00 472	0.00 471	0.00 470	0.00 468

sosnet-low-res		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

sosnet		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uncertanet		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

Regformer		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

ab-mvs-re		8.26 449	11.02 452	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	99.16 382	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

uanet		8.33 439	11.11 442	0.00 453	0.00 476	0.00 478	0.00 464	0.00 477	0.00 471	0.00 472	100.00 1	0.00 476	0.00 472	0.00 471	0.00 470	0.00 468

FOURS1					99.83 78	99.89 10	99.74 27	99.71 164	99.69 124	99.63 204

test_one_0601					99.63 219	99.76 70		99.55 263	99.23 223	99.31 312	99.61 252	98.59 193

eth-test2					0.00 476

eth-test					0.00 476

test_241102_ONE					99.69 195	99.82 42		99.54 269	99.12 247	99.82 106	99.49 309	98.91 150	99.52 446

save fliter					99.53 269	99.25 231	98.29 374	99.38 329	99.07 251

test0726					99.69 195	99.80 51	99.24 179	99.57 252	99.16 238	99.73 162	99.65 217	98.35 233

GSMVS																99.14 355

test_part2					99.62 223	99.67 115				99.55 243

sam_mvs1												90.81 415				99.14 355

sam_mvs												90.52 420

MTGPA								99.53 279

test_post											52.41 478	90.25 422	99.86 263

patchmatchnet-post											99.62 243	90.58 418	99.94 95

MTMP							99.09 242	98.59 408

TEST9					99.35 333	99.35 212	98.11 391	99.41 315	94.83 448	97.92 427	98.99 405	98.02 264	99.85 281

test_8					99.34 342	99.31 218	98.08 395	99.40 322	94.90 445	97.87 431	98.97 410	98.02 264	99.84 296

agg_prior					99.35 333	99.36 209		99.39 325		97.76 438			99.85 281

test_prior4						99.19 247	98.00 404

test_prior				99.46 230	99.35 333	99.22 241		99.39 325					99.69 394			99.48 254

新几何2							98.04 399

旧先验1					99.49 289	99.29 221		99.26 353			99.39 335	97.67 289			99.36 364	99.46 262

原ACMM2							97.92 413

test222					99.51 278	99.08 267	97.83 419	99.29 347	95.21 442	98.68 389	99.31 356	97.28 307			99.38 361	99.43 281

segment_acmp												98.37 231

testdata1							97.72 422		97.86 380

test12				99.54 205	99.29 355	99.33 215		99.16 374		98.43 407		97.54 296	99.82 325		99.47 350	99.48 254

plane_prior7					99.58 236	99.38 202

plane_prior6					99.47 300	99.26 228						97.24 308

plane_prior4											99.25 369

plane_prior3						99.31 218			98.36 338	99.14 340

plane_prior2							98.80 315		98.94 266

plane_prior1					99.51 278

plane_prior						99.24 235	98.42 366		97.87 378						99.71 276

n2								0.00 477

nn								0.00 477

door-mid								99.83 89

test11								99.29 347

door								99.77 129

HQP5-MVS						98.94 282

HQP-NCC					99.31 349		97.98 406		97.45 397	98.15 416

ACMP_Plane					99.31 349		97.98 406		97.45 397	98.15 416

HQP4-MVS										98.15 416			99.70 387			99.53 228

HQP3-MVS								99.37 330							99.67 297

HQP2-MVS												96.67 327

NP-MVS					99.40 320	99.13 255					98.83 422

ACMMP++_ref															99.94 119

ACMMP++															99.79 239

Test By Simon												98.41 225

High-resolution multi-view benchmark