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	99.73 104	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	99.34 265	99.91 31	98.08 356	99.48 107	100.00 1	99.90 48	99.99 7	99.91 31	99.50 56	99.98 27	99.98 1	99.99 16	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 204	99.96 7	98.62 311	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	99.12 227	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	99.27 290	99.93 24	97.84 369	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	99.36 262	99.94 18	98.18 345	99.52 92	100.00 1	99.86 64	100.00 1	99.88 50	98.99 133	99.96 67	99.97 4	99.96 84	99.95 14

test_fmvs1_n		99.68 63	99.81 28	99.28 285	99.95 15	97.93 365	99.49 105	100.00 1	99.82 82	99.99 7	99.89 41	99.21 94	99.98 27	99.97 4	99.98 48	99.93 20

test_f		99.75 47	99.88 7	99.37 257	99.96 7	98.21 342	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	98.94 286	99.96 28	99.98 16	99.96 32	99.78 121	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	99.08 243	99.97 20	99.98 16	99.96 32	99.79 109	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	99.01 263	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	99.55 157	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	99.45 227	99.93 24	98.40 330	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	99.60 174	99.74 158	98.93 280	98.85 297	99.96 28	99.96 26	99.97 23	99.76 135	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	99.11 232	99.91 50	99.98 16	99.96 32	99.64 215	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 83	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		99.48 115	99.65 67	98.97 332	99.54 256	97.16 392	99.11 232	99.98 12	99.78 98	99.96 32	99.81 95	98.72 171	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	99.37 202	99.53 91	99.98 12	99.77 102	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 74	99.59 144	98.97 278	99.92 41	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	98.92 289	99.98 12	99.99 3	99.99 7	99.88 50	99.43 60	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	99.12 227	99.91 50	99.98 16	99.95 43	99.67 203	99.67 34	99.99 8	99.94 19	99.99 16	99.88 38

MM		99.18 209	99.05 218	99.55 194	99.35 327	98.81 289	99.05 248	97.79 432	99.99 3	99.48 259	99.59 264	96.29 339	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	98.97 278	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	99.79 114	99.72 93	98.84 299	99.96 28	99.96 26	99.96 32	99.72 159	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 83	99.76 70	98.88 292	99.92 41	99.98 16	99.98 14	99.85 68	99.42 62	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	99.10 235	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	99.07 247	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 64	99.82 42	99.03 256	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 64	99.78 57	99.03 256	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 83	99.75 78	99.02 260	99.87 64	99.98 16	99.98 14	99.81 95	99.07 119	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	99.78 122	99.78 57	99.00 266	99.97 20	99.96 26	99.97 23	99.56 278	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	99.75 150	99.56 153	98.98 276	99.94 38	99.92 44	99.97 23	99.72 159	99.84 16	99.92 147	99.91 31	99.98 48	99.89 35

MVStest1		98.22 341	98.09 336	98.62 368	99.04 397	96.23 414	99.20 190	99.92 41	99.44 180	99.98 14	99.87 56	85.87 437	99.67 405	99.91 31	99.57 321	99.95 14

v1921920		99.56 93	99.57 92	99.55 194	99.75 150	99.11 253	99.05 248	99.61 218	99.15 237	99.88 81	99.71 169	99.08 116	99.87 243	99.90 35	99.97 70	99.66 134

v1240		99.56 93	99.58 87	99.51 209	99.80 102	99.00 267	99.00 266	99.65 198	99.15 237	99.90 66	99.75 143	99.09 113	99.88 229	99.90 35	99.96 84	99.67 124

v10		99.69 58	99.69 59	99.66 137	99.81 95	99.39 197	99.66 57	99.75 136	99.60 151	99.92 58	99.87 56	98.75 166	99.86 262	99.90 35	99.99 16	99.73 90

v1192		99.57 89	99.57 92	99.57 187	99.77 133	99.22 238	99.04 253	99.60 229	99.18 226	99.87 89	99.72 159	99.08 116	99.85 280	99.89 38	99.98 48	99.66 134

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 31	99.81 95	99.71 98	98.97 278	99.92 41	99.98 16	99.97 23	99.86 63	99.53 52	99.95 78	99.88 39	99.99 16	99.89 35

v144192		99.55 98	99.54 102	99.58 180	99.78 122	99.20 243	99.11 232	99.62 211	99.18 226	99.89 71	99.72 159	98.66 180	99.87 243	99.88 39	99.97 70	99.66 134

v8		99.68 63	99.69 59	99.65 143	99.80 102	99.40 194	99.66 57	99.76 131	99.64 136	99.93 51	99.85 68	98.66 180	99.84 295	99.88 39	99.99 16	99.71 99

mvs5depth		99.88 6	99.91 3	99.80 61	99.92 29	99.42 187	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		99.54 102	99.53 106	99.59 177	99.79 114	99.28 220	99.10 235	99.61 218	99.20 224	99.84 97	99.73 152	98.67 178	99.84 295	99.86 43	99.98 48	99.64 153

mmtdpeth		99.78 37	99.83 21	99.66 137	99.85 64	99.05 266	99.79 15	99.97 20	100.00 1	99.43 271	99.94 19	99.64 35	99.94 95	99.83 44	99.99 16	99.98 5

SSC-MVS		99.52 106	99.42 127	99.83 39	99.86 57	99.65 123	99.52 92	99.81 101	99.87 61	99.81 111	99.79 109	96.78 319	99.99 8	99.83 44	99.51 337	99.86 44

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 27	99.82 10	99.82 91	99.84 74	99.94 46	99.91 31	99.13 108	99.96 67	99.83 44	99.99 16	99.83 54

v2v482		99.50 108	99.47 113	99.58 180	99.78 122	99.25 228	99.14 216	99.58 244	99.25 215	99.81 111	99.62 238	98.24 238	99.84 295	99.83 44	99.97 70	99.64 153

test_vis1_rt		99.45 129	99.46 118	99.41 245	99.71 169	98.63 310	98.99 273	99.96 28	99.03 250	99.95 43	99.12 382	98.75 166	99.84 295	99.82 48	99.82 211	99.77 76

tt0805		99.63 78	99.57 92	99.81 52	99.87 54	99.88 12	99.58 82	98.70 393	99.72 108	99.91 61	99.60 255	99.43 60	99.81 337	99.81 49	99.53 333	99.73 90

VortexMVS		99.13 222	99.24 174	98.79 359	99.67 203	96.60 406	99.24 179	99.80 104	99.85 70	99.93 51	99.84 75	95.06 356	99.89 214	99.80 50	99.98 48	99.89 35

V42		99.56 93	99.54 102	99.63 157	99.79 114	99.46 173	99.39 122	99.59 235	99.24 217	99.86 91	99.70 179	98.55 195	99.82 322	99.79 51	99.95 100	99.60 184

SSC-MVS3.2		99.64 77	99.67 63	99.56 190	99.75 150	98.98 270	98.96 282	99.87 64	99.88 59	99.84 97	99.64 215	99.32 80	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 62	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		99.44 131	99.32 150	99.80 61	99.81 95	99.61 139	99.47 110	99.81 101	99.82 82	99.71 168	99.72 159	96.60 323	99.98 27	99.75 54	99.23 378	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 75	99.95 30	99.98 14	99.92 27	99.28 85	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 69	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		99.15 219	99.06 213	99.42 237	99.85 64	98.59 314	99.13 222	97.26 440	99.84 74	99.87 89	99.77 131	96.11 342	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	99.77 123	99.94 34	99.91 61	99.76 135	98.55 195	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	99.77 123	99.94 34	99.91 61	99.76 135	98.55 195	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 83	99.84 27	99.82 10	99.92 41	99.94 34	99.94 46	99.93 22	99.34 77	99.92 147	99.70 59	99.96 84	99.70 102

reproduce_monomvs		97.40 374	97.46 367	97.20 422	99.05 394	91.91 450	99.20 190	99.18 365	99.84 74	99.86 91	99.75 143	80.67 445	99.83 311	99.69 62	99.95 100	99.85 47

SPE-MVS-test		99.68 63	99.70 56	99.64 150	99.57 240	99.83 34	99.78 17	99.97 20	99.92 44	99.50 256	99.38 331	99.57 47	99.95 78	99.69 62	99.90 141	99.15 344

guyue		99.12 225	99.02 227	99.41 245	99.84 69	98.56 315	99.19 196	98.30 418	99.82 82	99.84 97	99.75 143	94.84 359	99.92 147	99.68 64	99.94 114	99.74 86

tt0320		99.79 34	99.79 34	99.81 52	99.82 83	99.84 27	99.82 10	99.90 55	99.94 34	99.94 46	99.94 19	99.07 119	99.92 147	99.68 64	99.97 70	99.67 124

MVS_0304		98.61 299	98.30 320	99.52 206	97.88 457	98.95 276	98.76 316	94.11 456	99.84 74	99.32 301	99.57 274	95.57 351	99.95 78	99.68 64	99.98 48	99.68 115

CS-MVS		99.67 69	99.70 56	99.58 180	99.53 263	99.84 27	99.79 15	99.96 28	99.90 48	99.61 214	99.41 321	99.51 55	99.95 78	99.66 67	99.89 154	98.96 386

mamv4		99.73 50	99.74 52	99.70 123	99.66 205	99.87 15	99.69 45	99.93 39	99.93 41	99.93 51	99.86 63	99.07 119	100.00 1	99.66 67	99.92 129	99.24 319

KinetiMVS		99.66 70	99.63 73	99.76 82	99.89 39	99.57 152	99.37 129	99.82 91	99.95 30	99.90 66	99.63 230	98.57 191	99.97 42	99.65 69	99.94 114	99.74 86

pmmvs6		99.86 10	99.86 13	99.83 39	99.94 18	99.90 7	99.83 7	99.91 50	99.85 70	99.94 46	99.95 16	99.73 27	99.90 195	99.65 69	99.97 70	99.69 109

MIMVSNet1		99.66 70	99.62 75	99.80 61	99.94 18	99.87 15	99.69 45	99.77 123	99.78 98	99.93 51	99.89 41	97.94 264	99.92 147	99.65 69	99.98 48	99.62 170

LuminaMVS		99.39 147	99.28 165	99.73 107	99.83 74	99.49 165	99.00 266	99.05 377	99.81 88	99.89 71	99.79 109	96.54 327	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	99.79 111	99.94 34	99.93 51	99.92 27	99.35 76	99.92 147	99.64 72	99.94 114	99.68 115

EC-MVSNet		99.69 58	99.69 59	99.68 127	99.71 169	99.91 4	99.76 23	99.96 28	99.86 64	99.51 254	99.39 329	99.57 47	99.93 116	99.64 72	99.86 183	99.20 332

K. test v3		98.87 276	98.60 285	99.69 125	99.93 24	99.46 173	99.74 27	94.97 451	99.78 98	99.88 81	99.88 50	93.66 374	99.97 42	99.61 75	99.95 100	99.64 153

KD-MVS_self_test		99.63 78	99.59 84	99.76 82	99.84 69	99.90 7	99.37 129	99.79 111	99.83 80	99.88 81	99.85 68	98.42 218	99.90 195	99.60 76	99.73 261	99.49 244

Anonymous20240521		99.44 131	99.42 127	99.49 215	99.89 39	98.96 275	99.62 67	99.76 131	99.85 70	99.82 104	99.88 50	96.39 334	99.97 42	99.59 77	99.98 48	99.55 207

TransMVSNet (Re)		99.78 37	99.77 45	99.81 52	99.91 31	99.85 22	99.75 25	99.86 69	99.70 117	99.91 61	99.89 41	99.60 43	99.87 243	99.59 77	99.74 255	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 75	99.80 92	99.93 51	99.93 22	98.54 199	99.93 116	99.59 77	99.98 48	99.76 81

EU-MVSNet		99.39 147	99.62 75	98.72 364	99.88 45	96.44 408	99.56 87	99.85 75	99.90 48	99.90 66	99.85 68	98.09 253	99.83 311	99.58 80	99.95 100	99.90 28

mvs_anonymous		99.28 175	99.39 132	98.94 336	99.19 370	97.81 371	99.02 260	99.55 257	99.78 98	99.85 94	99.80 99	98.24 238	99.86 262	99.57 81	99.50 340	99.15 344

test1111		97.74 359	98.16 332	96.49 433	99.60 220	89.86 464	99.71 37	91.21 460	99.89 54	99.88 81	99.87 56	93.73 373	99.90 195	99.56 82	99.99 16	99.70 102

lessismore_v0				99.64 150	99.86 57	99.38 199		90.66 461		99.89 71	99.83 81	94.56 364	99.97 42	99.56 82	99.92 129	99.57 202

mvsany_test1		99.44 131	99.45 120	99.40 248	99.37 320	98.64 309	97.90 410	99.59 235	99.27 211	99.92 58	99.82 88	99.74 26	99.93 116	99.55 84	99.87 175	99.63 159

MVSMamba_PlusPlus		99.55 98	99.58 87	99.47 221	99.68 197	99.40 194	99.52 92	99.70 167	99.92 44	99.77 137	99.86 63	98.28 234	99.96 67	99.54 85	99.90 141	99.05 373

pm-mvs1		99.79 34	99.79 34	99.78 72	99.91 31	99.83 34	99.76 23	99.87 64	99.73 104	99.89 71	99.87 56	99.63 37	99.87 243	99.54 85	99.92 129	99.63 159

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 41	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		99.48 115	99.65 67	98.95 335	99.71 169	97.27 389	99.50 100	99.82 91	99.59 153	99.41 280	99.85 68	99.62 40	100.00 1	99.53 88	99.89 154	99.59 191

test2506		94.73 424	94.59 425	95.15 440	99.59 225	85.90 466	99.75 25	74.01 468	99.89 54	99.71 168	99.86 63	79.00 455	99.90 195	99.52 89	99.99 16	99.65 143

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 158	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	99.78 120	99.90 48	99.82 104	99.83 81	98.45 214	99.87 243	99.51 90	99.97 70	99.86 44

BP-MVS1		98.72 291	98.46 301	99.50 211	99.53 263	99.00 267	99.34 137	98.53 403	99.65 133	99.73 159	99.38 331	90.62 411	99.96 67	99.50 92	99.86 183	99.55 207

UA-Net		99.78 37	99.76 49	99.86 29	99.72 166	99.71 98	99.91 4	99.95 36	99.96 26	99.71 168	99.91 31	99.15 103	99.97 42	99.50 92	100.00 1	99.90 28

PMMVS2		99.48 115	99.45 120	99.57 187	99.76 137	98.99 269	98.09 387	99.90 55	98.95 260	99.78 125	99.58 267	99.57 47	99.93 116	99.48 94	99.95 100	99.79 70

VPA-MVSNet		99.66 70	99.62 75	99.79 68	99.68 197	99.75 78	99.62 67	99.69 175	99.85 70	99.80 115	99.81 95	98.81 154	99.91 176	99.47 95	99.88 163	99.70 102

GDP-MVS		98.81 282	98.57 291	99.50 211	99.53 263	99.12 252	99.28 163	99.86 69	99.53 158	99.57 225	99.32 347	90.88 407	99.98 27	99.46 96	99.74 255	99.42 279

ECVR-MVS		97.73 360	98.04 339	96.78 426	99.59 225	90.81 459	99.72 33	90.43 462	99.89 54	99.86 91	99.86 63	93.60 375	99.89 214	99.46 96	99.99 16	99.65 143

nrg030		99.70 56	99.66 65	99.82 44	99.76 137	99.84 27	99.61 73	99.70 167	99.93 41	99.78 125	99.68 199	99.10 111	99.78 350	99.45 98	99.96 84	99.83 54

TAMVS		99.49 113	99.45 120	99.63 157	99.48 288	99.42 187	99.45 114	99.57 246	99.66 130	99.78 125	99.83 81	97.85 271	99.86 262	99.44 99	99.96 84	99.61 180

GeoE		99.69 58	99.66 65	99.78 72	99.76 137	99.76 70	99.60 79	99.82 91	99.46 175	99.75 146	99.56 278	99.63 37	99.95 78	99.43 100	99.88 163	99.62 170

new-patchmatchnet		99.35 160	99.57 92	98.71 366	99.82 83	96.62 404	98.55 342	99.75 136	99.50 162	99.88 81	99.87 56	99.31 81	99.88 229	99.43 100	100.00 1	99.62 170

test20.03		99.55 98	99.54 102	99.58 180	99.79 114	99.37 202	99.02 260	99.89 58	99.60 151	99.82 104	99.62 238	98.81 154	99.89 214	99.43 100	99.86 183	99.47 252

MVSFormer		99.41 141	99.44 123	99.31 277	99.57 240	98.40 330	99.77 19	99.80 104	99.73 104	99.63 199	99.30 352	98.02 258	99.98 27	99.43 100	99.69 280	99.55 207

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 27	99.77 19	99.80 104	99.73 104	99.97 23	99.92 27	99.77 25	99.98 27	99.43 100	100.00 1	99.90 28

SDMVSNet		99.77 44	99.77 45	99.76 82	99.80 102	99.65 123	99.63 64	99.86 69	99.97 23	99.89 71	99.89 41	99.52 54	99.99 8	99.42 105	99.96 84	99.65 143

Anonymous20231211		99.62 84	99.57 92	99.76 82	99.61 218	99.60 142	99.81 13	99.73 146	99.82 82	99.90 66	99.90 36	97.97 263	99.86 262	99.42 105	99.96 84	99.80 62

SixPastTwentyTwo		99.42 137	99.30 157	99.76 82	99.92 29	99.67 115	99.70 38	99.14 370	99.65 133	99.89 71	99.90 36	96.20 341	99.94 95	99.42 105	99.92 129	99.67 124

balanced_conf03		99.50 108	99.50 108	99.50 211	99.42 311	99.49 165	99.52 92	99.75 136	99.86 64	99.78 125	99.71 169	98.20 245	99.90 195	99.39 108	99.88 163	99.10 355

patch_mono-2		99.51 107	99.46 118	99.64 150	99.70 184	99.11 253	99.04 253	99.87 64	99.71 112	99.47 261	99.79 109	98.24 238	99.98 27	99.38 109	99.96 84	99.83 54

UGNet		99.38 150	99.34 145	99.49 215	98.90 409	98.90 284	99.70 38	99.35 328	99.86 64	98.57 393	99.81 95	98.50 209	99.93 116	99.38 109	99.98 48	99.66 134
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 91	99.39 195	99.82 104	99.84 75	99.38 68	99.91 176	99.38 109	99.93 125	99.80 62

FIs		99.65 76	99.58 87	99.84 36	99.84 69	99.85 22	99.66 57	99.75 136	99.86 64	99.74 155	99.79 109	98.27 236	99.85 280	99.37 112	99.93 125	99.83 54

sd_testset		99.78 37	99.78 39	99.80 61	99.80 102	99.76 70	99.80 14	99.79 111	99.97 23	99.89 71	99.89 41	99.53 52	99.99 8	99.36 113	99.96 84	99.65 143

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 75	99.70 117	99.92 58	99.93 22	99.45 57	99.97 42	99.36 113	100.00 1	99.85 47

casdiffmvs_mvg		99.68 63	99.68 62	99.69 125	99.81 95	99.59 144	99.29 161	99.90 55	99.71 112	99.79 121	99.73 152	99.54 50	99.84 295	99.36 113	99.96 84	99.65 143
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 41	99.67 126	99.77 137	99.75 143	99.61 41	99.98 27	99.35 116	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 86	99.64 72	99.53 204	99.79 114	98.82 288	99.58 82	99.97 20	99.95 30	99.96 32	99.76 135	98.44 215	99.99 8	99.34 117	99.96 84	99.78 72

CHOSEN 1792x2688		99.39 147	99.30 157	99.65 143	99.88 45	99.25 228	98.78 314	99.88 62	98.66 300	99.96 32	99.79 109	97.45 293	99.93 116	99.34 117	99.99 16	99.78 72

CDS-MVSNet		99.22 195	99.13 189	99.50 211	99.35 327	99.11 253	98.96 282	99.54 263	99.46 175	99.61 214	99.70 179	96.31 337	99.83 311	99.34 117	99.88 163	99.55 207
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 256	99.16 183	98.51 374	99.75 150	95.90 420	98.07 390	99.84 81	99.84 74	99.89 71	99.73 152	96.01 345	99.99 8	99.33 120	100.00 1	99.63 159

HyFIR lowres test		98.91 269	98.64 282	99.73 107	99.85 64	99.47 169	98.07 390	99.83 85	98.64 302	99.89 71	99.60 255	92.57 386	100.00 1	99.33 120	99.97 70	99.72 94

pmmvs5		99.19 205	99.11 196	99.42 237	99.76 137	98.88 285	98.55 342	99.73 146	98.82 280	99.72 163	99.62 238	96.56 324	99.82 322	99.32 122	99.95 100	99.56 204

v148		99.40 143	99.41 130	99.39 251	99.76 137	98.94 277	99.09 240	99.59 235	99.17 231	99.81 111	99.61 247	98.41 219	99.69 388	99.32 122	99.94 114	99.53 222

baseline		99.63 78	99.62 75	99.66 137	99.80 102	99.62 133	99.44 116	99.80 104	99.71 112	99.72 163	99.69 188	99.15 103	99.83 311	99.32 122	99.94 114	99.53 222

CVMVSNet		98.61 299	98.88 261	97.80 405	99.58 230	93.60 443	99.26 172	99.64 206	99.66 130	99.72 163	99.67 203	93.26 379	99.93 116	99.30 125	99.81 221	99.87 42

PS-CasMVS		99.66 70	99.58 87	99.89 11	99.80 102	99.85 22	99.66 57	99.73 146	99.62 141	99.84 97	99.71 169	98.62 184	99.96 67	99.30 125	99.96 84	99.86 44

DTE-MVSNet		99.68 63	99.61 79	99.88 19	99.80 102	99.87 15	99.67 53	99.71 158	99.72 108	99.84 97	99.78 121	98.67 178	99.97 42	99.30 125	99.95 100	99.80 62

tmp_tt		95.75 417	95.42 412	96.76 427	89.90 467	94.42 437	98.86 295	97.87 430	78.01 458	99.30 311	99.69 188	97.70 279	95.89 460	99.29 128	98.14 436	99.95 14

PEN-MVS		99.66 70	99.59 84	99.89 11	99.83 74	99.87 15	99.66 57	99.73 146	99.70 117	99.84 97	99.73 152	98.56 194	99.96 67	99.29 128	99.94 114	99.83 54

WR-MVS_H		99.61 86	99.53 106	99.87 25	99.80 102	99.83 34	99.67 53	99.75 136	99.58 155	99.85 94	99.69 188	98.18 248	99.94 95	99.28 130	99.95 100	99.83 54

IterMVS		98.97 260	99.16 183	98.42 379	99.74 158	95.64 424	98.06 392	99.83 85	99.83 80	99.85 94	99.74 148	96.10 344	99.99 8	99.27 131	100.00 1	99.63 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NormalMVS		99.09 233	98.91 259	99.62 166	99.78 122	99.11 253	99.36 133	99.77 123	99.82 82	99.68 178	99.53 290	93.30 377	99.99 8	99.24 132	99.76 244	99.74 86

SymmetryMVS		99.01 253	98.82 269	99.58 180	99.65 210	99.11 253	99.36 133	99.20 363	99.82 82	99.68 178	99.53 290	93.30 377	99.99 8	99.24 132	99.63 301	99.64 153

WBMVS		97.50 370	97.18 376	98.48 376	98.85 417	95.89 421	98.44 359	99.52 278	99.53 158	99.52 247	99.42 320	80.10 448	99.86 262	99.24 132	99.95 100	99.68 115

h-mvs33		98.61 299	98.34 315	99.44 231	99.60 220	98.67 301	99.27 167	99.44 303	99.68 122	99.32 301	99.49 303	92.50 389	100.00 1	99.24 132	96.51 453	99.65 143

hse-mvs2		98.52 312	98.30 320	99.16 306	99.29 349	98.60 312	98.77 315	99.02 379	99.68 122	99.32 301	99.04 392	92.50 389	99.85 280	99.24 132	97.87 443	99.03 377

FMVSNet1		99.66 70	99.63 73	99.73 107	99.78 122	99.77 63	99.68 49	99.70 167	99.67 126	99.82 104	99.83 81	98.98 135	99.90 195	99.24 132	99.97 70	99.53 222

casdiffmvs		99.63 78	99.61 79	99.67 130	99.79 114	99.59 144	99.13 222	99.85 75	99.79 96	99.76 141	99.72 159	99.33 79	99.82 322	99.21 138	99.94 114	99.59 191
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		99.54 102	99.43 125	99.87 25	99.76 137	99.82 42	99.57 85	99.61 218	99.54 156	99.80 115	99.64 215	97.79 275	99.95 78	99.21 138	99.94 114	99.84 50

DELS-MVS		99.34 165	99.30 157	99.48 219	99.51 272	99.36 206	98.12 383	99.53 273	99.36 200	99.41 280	99.61 247	99.22 93	99.87 243	99.21 138	99.68 285	99.20 332
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		99.47 123	99.50 108	99.37 257	99.70 184	98.80 292	98.67 323	99.92 41	99.49 164	99.77 137	99.71 169	99.08 116	99.78 350	99.20 141	99.94 114	99.54 216

UniMVSNet (Re)		99.37 154	99.26 170	99.68 127	99.51 272	99.58 149	98.98 276	99.60 229	99.43 186	99.70 172	99.36 338	97.70 279	99.88 229	99.20 141	99.87 175	99.59 191

CANet		99.11 229	99.05 218	99.28 285	98.83 419	98.56 315	98.71 322	99.41 309	99.25 215	99.23 319	99.22 370	97.66 287	99.94 95	99.19 143	99.97 70	99.33 301

EI-MVSNet-UG-set		99.48 115	99.50 108	99.42 237	99.57 240	98.65 307	99.24 179	99.46 298	99.68 122	99.80 115	99.66 208	98.99 133	99.89 214	99.19 143	99.90 141	99.72 94

xiu_mvs_v1_base_debu		99.23 186	99.34 145	98.91 342	99.59 225	98.23 339	98.47 354	99.66 188	99.61 145	99.68 178	98.94 408	99.39 64	99.97 42	99.18 145	99.55 326	98.51 425

xiu_mvs_v1_base		99.23 186	99.34 145	98.91 342	99.59 225	98.23 339	98.47 354	99.66 188	99.61 145	99.68 178	98.94 408	99.39 64	99.97 42	99.18 145	99.55 326	98.51 425

xiu_mvs_v1_base_debi		99.23 186	99.34 145	98.91 342	99.59 225	98.23 339	98.47 354	99.66 188	99.61 145	99.68 178	98.94 408	99.39 64	99.97 42	99.18 145	99.55 326	98.51 425

VPNet		99.46 125	99.37 138	99.71 119	99.82 83	99.59 144	99.48 107	99.70 167	99.81 88	99.69 175	99.58 267	97.66 287	99.86 262	99.17 148	99.44 347	99.67 124

UniMVSNet_NR-MVSNet		99.37 154	99.25 172	99.72 114	99.47 294	99.56 153	98.97 278	99.61 218	99.43 186	99.67 185	99.28 356	97.85 271	99.95 78	99.17 148	99.81 221	99.65 143

DU-MVS		99.33 168	99.21 177	99.71 119	99.43 306	99.56 153	98.83 302	99.53 273	99.38 196	99.67 185	99.36 338	97.67 283	99.95 78	99.17 148	99.81 221	99.63 159

EI-MVSNet-Vis-set		99.47 123	99.49 111	99.42 237	99.57 240	98.66 304	99.24 179	99.46 298	99.67 126	99.79 121	99.65 213	98.97 137	99.89 214	99.15 151	99.89 154	99.71 99

EI-MVSNet		99.38 150	99.44 123	99.21 300	99.58 230	98.09 353	99.26 172	99.46 298	99.62 141	99.75 146	99.67 203	98.54 199	99.85 280	99.15 151	99.92 129	99.68 115

VNet		99.18 209	99.06 213	99.56 190	99.24 360	99.36 206	99.33 143	99.31 337	99.67 126	99.47 261	99.57 274	96.48 328	99.84 295	99.15 151	99.30 366	99.47 252

EG-PatchMatch MVS		99.57 89	99.56 97	99.62 166	99.77 133	99.33 212	99.26 172	99.76 131	99.32 205	99.80 115	99.78 121	99.29 83	99.87 243	99.15 151	99.91 140	99.66 134

PVSNet_Blended_VisFu		99.40 143	99.38 135	99.44 231	99.90 37	98.66 304	98.94 286	99.91 50	97.97 363	99.79 121	99.73 152	99.05 126	99.97 42	99.15 151	99.99 16	99.68 115

IterMVS-LS		99.41 141	99.47 113	99.25 296	99.81 95	98.09 353	98.85 297	99.76 131	99.62 141	99.83 103	99.64 215	98.54 199	99.97 42	99.15 151	99.99 16	99.68 115
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 102	99.47 113	99.76 82	99.58 230	99.64 126	99.30 154	99.63 208	99.61 145	99.71 168	99.56 278	98.76 164	99.96 67	99.14 157	99.92 129	99.68 115

MVSTER		98.47 319	98.22 325	99.24 298	99.06 393	98.35 336	99.08 243	99.46 298	99.27 211	99.75 146	99.66 208	88.61 424	99.85 280	99.14 157	99.92 129	99.52 232

Anonymous20231206		99.35 160	99.31 152	99.47 221	99.74 158	99.06 265	99.28 163	99.74 142	99.23 219	99.72 163	99.53 290	97.63 289	99.88 229	99.11 159	99.84 193	99.48 248

Syy-MVS		98.17 344	97.85 356	99.15 308	98.50 442	98.79 293	98.60 330	99.21 360	97.89 369	96.76 445	96.37 468	95.47 353	99.57 431	99.10 160	98.73 412	99.09 360

ttmdpeth		99.48 115	99.55 99	99.29 282	99.76 137	98.16 347	99.33 143	99.95 36	99.79 96	99.36 290	99.89 41	99.13 108	99.77 359	99.09 161	99.64 298	99.93 20

MVS_Test		99.28 175	99.31 152	99.19 303	99.35 327	98.79 293	99.36 133	99.49 291	99.17 231	99.21 324	99.67 203	98.78 161	99.66 410	99.09 161	99.66 294	99.10 355

testgi		99.29 174	99.26 170	99.37 257	99.75 150	98.81 289	98.84 299	99.89 58	98.38 330	99.75 146	99.04 392	99.36 73	99.86 262	99.08 163	99.25 374	99.45 257

1112_ss		99.05 241	98.84 266	99.67 130	99.66 205	99.29 218	98.52 348	99.82 91	97.65 381	99.43 271	99.16 376	96.42 331	99.91 176	99.07 164	99.84 193	99.80 62

CANet_DTU		98.91 269	98.85 264	99.09 317	98.79 425	98.13 348	98.18 375	99.31 337	99.48 167	98.86 364	99.51 296	96.56 324	99.95 78	99.05 165	99.95 100	99.19 335

Baseline_NR-MVSNet		99.49 113	99.37 138	99.82 44	99.91 31	99.84 27	98.83 302	99.86 69	99.68 122	99.65 193	99.88 50	97.67 283	99.87 243	99.03 166	99.86 183	99.76 81

FMVSNet2		99.35 160	99.28 165	99.55 194	99.49 283	99.35 209	99.45 114	99.57 246	99.44 180	99.70 172	99.74 148	97.21 304	99.87 243	99.03 166	99.94 114	99.44 269

Test_1112_low_res		98.95 266	98.73 276	99.63 157	99.68 197	99.15 249	98.09 387	99.80 104	97.14 407	99.46 265	99.40 325	96.11 342	99.89 214	99.01 168	99.84 193	99.84 50

VDD-MVS		99.20 202	99.11 196	99.44 231	99.43 306	98.98 270	99.50 100	98.32 417	99.80 92	99.56 233	99.69 188	96.99 314	99.85 280	98.99 169	99.73 261	99.50 239

DeepC-MVS	98.90 4	99.62 84	99.61 79	99.67 130	99.72 166	99.44 180	99.24 179	99.71 158	99.27 211	99.93 51	99.90 36	99.70 31	99.93 116	98.99 169	99.99 16	99.64 153
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 115	99.47 113	99.51 209	99.77 133	99.41 193	98.81 307	99.66 188	99.42 190	99.75 146	99.66 208	99.20 95	99.76 362	98.98 171	99.99 16	99.36 294

EPNet_dtu		97.62 365	97.79 359	97.11 425	96.67 462	92.31 448	98.51 349	98.04 424	99.24 217	95.77 454	99.47 310	93.78 372	99.66 410	98.98 171	99.62 303	99.37 291
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 165	99.32 150	99.39 251	99.67 203	98.77 295	98.57 339	99.81 101	99.61 145	99.48 259	99.41 321	98.47 210	99.86 262	98.97 173	99.90 141	99.53 222
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		99.40 143	99.31 152	99.68 127	99.43 306	99.55 157	99.73 30	99.50 287	99.46 175	99.88 81	99.36 338	97.54 290	99.87 243	98.97 173	99.87 175	99.63 159

GBi-Net		99.42 137	99.31 152	99.73 107	99.49 283	99.77 63	99.68 49	99.70 167	99.44 180	99.62 208	99.83 81	97.21 304	99.90 195	98.96 175	99.90 141	99.53 222

FMVSNet5		97.80 357	97.25 374	99.42 237	98.83 419	98.97 273	99.38 125	99.80 104	98.87 272	99.25 315	99.69 188	80.60 447	99.91 176	98.96 175	99.90 141	99.38 288

test1		99.42 137	99.31 152	99.73 107	99.49 283	99.77 63	99.68 49	99.70 167	99.44 180	99.62 208	99.83 81	97.21 304	99.90 195	98.96 175	99.90 141	99.53 222

FMVSNet3		98.80 283	98.63 284	99.32 273	99.13 379	98.72 298	99.10 235	99.48 292	99.23 219	99.62 208	99.64 215	92.57 386	99.86 262	98.96 175	99.90 141	99.39 286

UnsupCasMVSNet_eth		98.83 279	98.57 291	99.59 177	99.68 197	99.45 178	98.99 273	99.67 183	99.48 167	99.55 238	99.36 338	94.92 357	99.86 262	98.95 179	96.57 452	99.45 257

CHOSEN 280x420		98.41 324	98.41 307	98.40 380	99.34 336	95.89 421	96.94 446	99.44 303	98.80 284	99.25 315	99.52 294	93.51 376	99.98 27	98.94 180	99.98 48	99.32 304

TDRefinement		99.72 52	99.70 56	99.77 75	99.90 37	99.85 22	99.86 6	99.92 41	99.69 120	99.78 125	99.92 27	99.37 70	99.88 229	98.93 181	99.95 100	99.60 184

alignmvs		98.28 334	97.96 345	99.25 296	99.12 381	98.93 280	99.03 256	98.42 410	99.64 136	98.72 379	97.85 447	90.86 408	99.62 422	98.88 182	99.13 380	99.19 335

testing3-2		96.51 396	96.43 391	96.74 429	99.36 323	91.38 456	99.10 235	97.87 430	99.48 167	98.57 393	98.71 423	76.65 457	99.66 410	98.87 183	99.26 373	99.18 337

MGCFI-Net		99.02 247	99.01 231	99.06 324	99.11 386	98.60 312	99.63 64	99.67 183	99.63 138	98.58 391	97.65 450	99.07 119	99.57 431	98.85 184	98.92 396	99.03 377

sss		98.90 271	98.77 275	99.27 290	99.48 288	98.44 327	98.72 320	99.32 333	97.94 367	99.37 289	99.35 343	96.31 337	99.91 176	98.85 184	99.63 301	99.47 252

xiu_mvs_v2_base		99.02 247	99.11 196	98.77 361	99.37 320	98.09 353	98.13 382	99.51 283	99.47 172	99.42 274	98.54 432	99.38 68	99.97 42	98.83 186	99.33 362	98.24 437

PS-MVSNAJ		99.00 256	99.08 207	98.76 362	99.37 320	98.10 352	98.00 398	99.51 283	99.47 172	99.41 280	98.50 434	99.28 85	99.97 42	98.83 186	99.34 361	98.20 441

D2MVS		99.22 195	99.19 180	99.29 282	99.69 189	98.74 297	98.81 307	99.41 309	98.55 311	99.68 178	99.69 188	98.13 250	99.87 243	98.82 188	99.98 48	99.24 319

PatchT		98.45 321	98.32 317	98.83 355	98.94 407	98.29 337	99.24 179	98.82 387	99.84 74	99.08 341	99.76 135	91.37 397	99.94 95	98.82 188	99.00 391	98.26 436

testf1		99.63 78	99.60 82	99.72 114	99.94 18	99.95 2	99.47 110	99.89 58	99.43 186	99.88 81	99.80 99	99.26 89	99.90 195	98.81 190	99.88 163	99.32 304

APD_test2		99.63 78	99.60 82	99.72 114	99.94 18	99.95 2	99.47 110	99.89 58	99.43 186	99.88 81	99.80 99	99.26 89	99.90 195	98.81 190	99.88 163	99.32 304

sasdasda		99.02 247	99.00 235	99.09 317	99.10 388	98.70 299	99.61 73	99.66 188	99.63 138	98.64 385	97.65 450	99.04 127	99.54 435	98.79 192	98.92 396	99.04 375

Effi-MVS+		99.06 238	98.97 246	99.34 265	99.31 343	98.98 270	98.31 367	99.91 50	98.81 282	98.79 373	98.94 408	99.14 106	99.84 295	98.79 192	98.74 409	99.20 332

canonicalmvs		99.02 247	99.00 235	99.09 317	99.10 388	98.70 299	99.61 73	99.66 188	99.63 138	98.64 385	97.65 450	99.04 127	99.54 435	98.79 192	98.92 396	99.04 375

VDDNet		98.97 260	98.82 269	99.42 237	99.71 169	98.81 289	99.62 67	98.68 394	99.81 88	99.38 288	99.80 99	94.25 366	99.85 280	98.79 192	99.32 364	99.59 191

CR-MVSNet		98.35 331	98.20 327	98.83 355	99.05 394	98.12 349	99.30 154	99.67 183	97.39 395	99.16 330	99.79 109	91.87 394	99.91 176	98.78 196	98.77 405	98.44 430

test_method		91.72 425	92.32 428	89.91 443	93.49 466	70.18 469	90.28 457	99.56 251	61.71 461	95.39 456	99.52 294	93.90 368	99.94 95	98.76 197	98.27 429	99.62 170

RPMNet		98.60 302	98.53 297	98.83 355	99.05 394	98.12 349	99.30 154	99.62 211	99.86 64	99.16 330	99.74 148	92.53 388	99.92 147	98.75 198	98.77 405	98.44 430

mamba_0408		99.54 102	99.55 99	99.54 200	99.71 169	99.24 232	99.27 167	99.79 111	99.72 108	99.78 125	99.64 215	99.36 73	99.93 116	98.74 199	99.90 141	99.45 257

mamba_test_0407_2		99.55 98	99.55 99	99.55 194	99.71 169	99.24 232	99.27 167	99.79 111	99.72 108	99.78 125	99.64 215	99.36 73	99.97 42	98.74 199	99.90 141	99.45 257

mamba_test_0407		99.56 93	99.56 97	99.54 200	99.71 169	99.24 232	99.15 213	99.84 81	99.80 92	99.78 125	99.70 179	99.44 58	99.93 116	98.74 199	99.90 141	99.45 257

mamba_0404		99.57 89	99.58 87	99.54 200	99.76 137	99.28 220	99.19 196	99.84 81	99.80 92	99.78 125	99.70 179	99.44 58	99.93 116	98.74 199	99.95 100	99.41 280

pmmvs4		99.13 222	99.06 213	99.36 262	99.57 240	99.10 260	98.01 396	99.25 350	98.78 287	99.58 222	99.44 317	98.24 238	99.76 362	98.74 199	99.93 125	99.22 325

viewmanbaseed2359cas		99.50 108	99.47 113	99.61 170	99.73 162	99.52 162	99.03 256	99.83 85	99.49 164	99.65 193	99.64 215	99.18 97	99.71 378	98.73 204	99.92 129	99.58 196

tttt0517		97.62 365	97.20 375	98.90 348	99.76 137	97.40 386	99.48 107	94.36 453	99.06 248	99.70 172	99.49 303	84.55 440	99.94 95	98.73 204	99.65 296	99.36 294

EPP-MVSNet		99.17 214	99.00 235	99.66 137	99.80 102	99.43 184	99.70 38	99.24 353	99.48 167	99.56 233	99.77 131	94.89 358	99.93 116	98.72 206	99.89 154	99.63 159

Anonymous20240529		99.42 137	99.34 145	99.65 143	99.53 263	99.60 142	99.63 64	99.39 319	99.47 172	99.76 141	99.78 121	98.13 250	99.86 262	98.70 207	99.68 285	99.49 244

ACMH	98.42 6	99.59 88	99.54 102	99.72 114	99.86 57	99.62 133	99.56 87	99.79 111	98.77 289	99.80 115	99.85 68	99.64 35	99.85 280	98.70 207	99.89 154	99.70 102
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 168	99.28 165	99.47 221	99.57 240	99.39 197	99.78 17	99.43 306	98.87 272	99.57 225	99.82 88	98.06 256	99.87 243	98.69 209	99.73 261	99.15 344

LFMVS		98.46 320	98.19 330	99.26 293	99.24 360	98.52 323	99.62 67	96.94 442	99.87 61	99.31 306	99.58 267	91.04 402	99.81 337	98.68 210	99.42 351	99.45 257

WR-MVS		99.11 229	98.93 251	99.66 137	99.30 347	99.42 187	98.42 360	99.37 324	99.04 249	99.57 225	99.20 374	96.89 316	99.86 262	98.66 211	99.87 175	99.70 102

mvsmamba		99.08 234	98.95 249	99.45 227	99.36 323	99.18 246	99.39 122	98.81 388	99.37 197	99.35 292	99.70 179	96.36 336	99.94 95	98.66 211	99.59 317	99.22 325

RRT-MVS		99.08 234	99.00 235	99.33 268	99.27 354	98.65 307	99.62 67	99.93 39	99.66 130	99.67 185	99.82 88	95.27 355	99.93 116	98.64 213	99.09 384	99.41 280

Anonymous202405211		98.75 287	98.46 301	99.63 157	99.34 336	99.66 117	99.47 110	97.65 433	99.28 210	99.56 233	99.50 299	93.15 380	99.84 295	98.62 214	99.58 319	99.40 283

lecture		99.56 93	99.48 112	99.81 52	99.78 122	99.86 19	99.50 100	99.70 167	99.59 153	99.75 146	99.71 169	98.94 140	99.92 147	98.59 215	99.76 244	99.66 134

EPNet		98.13 345	97.77 360	99.18 305	94.57 465	97.99 359	99.24 179	97.96 426	99.74 103	97.29 438	99.62 238	93.13 381	99.97 42	98.59 215	99.83 201	99.58 196
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 241	99.09 205	98.91 342	99.21 365	98.36 335	98.82 306	99.47 295	98.85 275	98.90 359	99.56 278	98.78 161	99.09 451	98.57 217	99.68 285	99.26 316

Patchmatch-RL test		98.60 302	98.36 312	99.33 268	99.77 133	99.07 263	98.27 369	99.87 64	98.91 267	99.74 155	99.72 159	90.57 413	99.79 347	98.55 218	99.85 188	99.11 353

pmmvs3		98.08 348	97.80 357	98.91 342	99.41 313	97.69 377	97.87 411	99.66 188	95.87 426	99.50 256	99.51 296	90.35 415	99.97 42	98.55 218	99.47 344	99.08 366

ETV-MVS		99.18 209	99.18 181	99.16 306	99.34 336	99.28 220	99.12 227	99.79 111	99.48 167	98.93 353	98.55 431	99.40 63	99.93 116	98.51 220	99.52 336	98.28 435

jason		99.16 215	99.11 196	99.32 273	99.75 150	98.44 327	98.26 371	99.39 319	98.70 297	99.74 155	99.30 352	98.54 199	99.97 42	98.48 221	99.82 211	99.55 207
jason: jason.
APDe-MVS		99.48 115	99.36 141	99.85 31	99.55 254	99.81 47	99.50 100	99.69 175	98.99 253	99.75 146	99.71 169	98.79 159	99.93 116	98.46 222	99.85 188	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		99.30 172	99.29 162	99.31 277	99.71 169	98.55 317	98.17 377	99.71 158	99.41 191	99.73 159	99.60 255	99.17 99	99.92 147	98.45 223	99.70 272	99.45 257

icg_test_0407		99.38 150	99.42 127	99.28 285	99.71 169	98.55 317	99.27 167	99.71 158	99.41 191	99.73 159	99.60 255	99.17 99	99.83 311	98.45 223	99.70 272	99.45 257

ICG_test_0404		99.23 186	99.20 178	99.32 273	99.71 169	98.55 317	98.57 339	99.71 158	99.41 191	99.52 247	99.60 255	98.12 252	99.95 78	98.45 223	99.70 272	99.45 257

icg_test_0403		99.37 154	99.39 132	99.28 285	99.71 169	98.55 317	99.19 196	99.71 158	99.41 191	99.67 185	99.60 255	99.12 110	99.84 295	98.45 223	99.70 272	99.45 257

CL-MVSNet_self_test		98.71 293	98.56 295	99.15 308	99.22 363	98.66 304	97.14 441	99.51 283	98.09 356	99.54 240	99.27 358	96.87 317	99.74 369	98.43 227	98.96 393	99.03 377

our_test_3		98.85 278	99.09 205	98.13 393	99.66 205	94.90 435	97.72 416	99.58 244	99.07 246	99.64 195	99.62 238	98.19 246	99.93 116	98.41 228	99.95 100	99.55 207

Gipuma		99.57 89	99.59 84	99.49 215	99.98 3	99.71 98	99.72 33	99.84 81	99.81 88	99.94 46	99.78 121	98.91 146	99.71 378	98.41 228	99.95 100	99.05 373
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 376	96.91 385	98.74 363	97.72 458	97.57 379	97.60 422	97.36 439	98.00 359	99.21 324	98.02 443	90.04 418	99.79 347	98.37 230	95.89 457	98.86 400

PM-MVS		99.36 158	99.29 162	99.58 180	99.83 74	99.66 117	98.95 284	99.86 69	98.85 275	99.81 111	99.73 152	98.40 223	99.92 147	98.36 231	99.83 201	99.17 340

baseline1		97.73 360	97.33 371	98.96 333	99.30 347	97.73 375	99.40 120	98.42 410	99.33 204	99.46 265	99.21 372	91.18 400	99.82 322	98.35 232	91.26 460	99.32 304

MVS-HIRNet		97.86 354	98.22 325	96.76 427	99.28 352	91.53 454	98.38 362	92.60 459	99.13 239	99.31 306	99.96 15	97.18 308	99.68 400	98.34 233	99.83 201	99.07 371

GA-MVS		97.99 353	97.68 363	98.93 339	99.52 270	98.04 357	97.19 440	99.05 377	98.32 343	98.81 369	98.97 404	89.89 420	99.41 446	98.33 234	99.05 387	99.34 300

Fast-Effi-MVS+		99.02 247	98.87 262	99.46 224	99.38 318	99.50 164	99.04 253	99.79 111	97.17 405	98.62 387	98.74 422	99.34 77	99.95 78	98.32 235	99.41 352	98.92 393

MDA-MVSNet_test_wron		98.95 266	98.99 242	98.85 351	99.64 211	97.16 392	98.23 373	99.33 331	98.93 264	99.56 233	99.66 208	97.39 297	99.83 311	98.29 236	99.88 163	99.55 207

N_pmnet		98.73 290	98.53 297	99.35 264	99.72 166	98.67 301	98.34 364	94.65 452	98.35 337	99.79 121	99.68 199	98.03 257	99.93 116	98.28 237	99.92 129	99.44 269

ET-MVSNet_ETH3D		96.78 388	96.07 398	98.91 342	99.26 357	97.92 366	97.70 418	96.05 447	97.96 366	92.37 460	98.43 435	87.06 428	99.90 195	98.27 238	97.56 446	98.91 394

thisisatest0530		97.45 371	96.95 382	98.94 336	99.68 197	97.73 375	99.09 240	94.19 455	98.61 307	99.56 233	99.30 352	84.30 442	99.93 116	98.27 238	99.54 331	99.16 342

YYNet1		98.95 266	98.99 242	98.84 353	99.64 211	97.14 394	98.22 374	99.32 333	98.92 266	99.59 220	99.66 208	97.40 295	99.83 311	98.27 238	99.90 141	99.55 207

reproduce_model		99.50 108	99.40 131	99.83 39	99.60 220	99.83 34	99.12 227	99.68 178	99.49 164	99.80 115	99.79 109	99.01 130	99.93 116	98.24 241	99.82 211	99.73 90

ACMM	98.09 11	99.46 125	99.38 135	99.72 114	99.80 102	99.69 110	99.13 222	99.65 198	98.99 253	99.64 195	99.72 159	99.39 64	99.86 262	98.23 242	99.81 221	99.60 184
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 263	98.87 262	99.24 298	99.57 240	98.40 330	98.12 383	99.18 365	98.28 345	99.63 199	99.13 378	98.02 258	99.97 42	98.22 243	99.69 280	99.35 297

3Dnovator	99.15 2	99.43 134	99.36 141	99.65 143	99.39 315	99.42 187	99.70 38	99.56 251	99.23 219	99.35 292	99.80 99	99.17 99	99.95 78	98.21 244	99.84 193	99.59 191

Fast-Effi-MVS+-dtu		99.20 202	99.12 193	99.43 235	99.25 358	99.69 110	99.05 248	99.82 91	99.50 162	98.97 349	99.05 390	98.98 135	99.98 27	98.20 245	99.24 376	98.62 415

MS-PatchMatch		99.00 256	98.97 246	99.09 317	99.11 386	98.19 343	98.76 316	99.33 331	98.49 320	99.44 267	99.58 267	98.21 243	99.69 388	98.20 245	99.62 303	99.39 286

TSAR-MVS + GP.		99.12 225	99.04 224	99.38 254	99.34 336	99.16 247	98.15 379	99.29 341	98.18 352	99.63 199	99.62 238	99.18 97	99.68 400	98.20 245	99.74 255	99.30 310

DP-MVS		99.48 115	99.39 132	99.74 98	99.57 240	99.62 133	99.29 161	99.61 218	99.87 61	99.74 155	99.76 135	98.69 174	99.87 243	98.20 245	99.80 228	99.75 84

MVP-Stereo		99.16 215	99.08 207	99.43 235	99.48 288	99.07 263	99.08 243	99.55 257	98.63 303	99.31 306	99.68 199	98.19 246	99.78 350	98.18 249	99.58 319	99.45 257
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 134	99.30 157	99.80 61	99.83 74	99.81 47	99.52 92	99.70 167	98.35 337	99.51 254	99.50 299	99.31 81	99.88 229	98.18 249	99.84 193	99.69 109

MDA-MVSNet-bldmvs		99.06 238	99.05 218	99.07 322	99.80 102	97.83 370	98.89 291	99.72 155	99.29 207	99.63 199	99.70 179	96.47 329	99.89 214	98.17 251	99.82 211	99.50 239

JIA-IIPM		98.06 349	97.92 352	98.50 375	98.59 438	97.02 396	98.80 310	98.51 405	99.88 59	97.89 423	99.87 56	91.89 393	99.90 195	98.16 252	97.68 445	98.59 418

EIA-MVS		99.12 225	99.01 231	99.45 227	99.36 323	99.62 133	99.34 137	99.79 111	98.41 326	98.84 366	98.89 412	98.75 166	99.84 295	98.15 253	99.51 337	98.89 397

miper_lstm_enhance		98.65 298	98.60 285	98.82 358	99.20 368	97.33 388	97.78 414	99.66 188	99.01 252	99.59 220	99.50 299	94.62 363	99.85 280	98.12 254	99.90 141	99.26 316

reproduce-ours		99.46 125	99.35 143	99.82 44	99.56 251	99.83 34	99.05 248	99.65 198	99.45 178	99.78 125	99.78 121	98.93 141	99.93 116	98.11 255	99.81 221	99.70 102

our_new_method		99.46 125	99.35 143	99.82 44	99.56 251	99.83 34	99.05 248	99.65 198	99.45 178	99.78 125	99.78 121	98.93 141	99.93 116	98.11 255	99.81 221	99.70 102

Effi-MVS+-dtu		99.07 237	98.92 255	99.52 206	98.89 412	99.78 57	99.15 213	99.66 188	99.34 201	98.92 356	99.24 368	97.69 281	99.98 27	98.11 255	99.28 369	98.81 404

tpm		97.15 380	96.95 382	97.75 407	98.91 408	94.24 438	99.32 146	97.96 426	97.71 379	98.29 404	99.32 347	86.72 434	99.92 147	98.10 258	96.24 455	99.09 360

DeepPCF-MVS	98.42 6	99.18 209	99.02 227	99.67 130	99.22 363	99.75 78	97.25 438	99.47 295	98.72 294	99.66 191	99.70 179	99.29 83	99.63 421	98.07 259	99.81 221	99.62 170

ppachtmachnet_test		98.89 274	99.12 193	98.20 391	99.66 205	95.24 431	97.63 420	99.68 178	99.08 244	99.78 125	99.62 238	98.65 182	99.88 229	98.02 260	99.96 84	99.48 248

tpmrst		97.73 360	98.07 338	96.73 430	98.71 434	92.00 449	99.10 235	98.86 384	98.52 316	98.92 356	99.54 288	91.90 392	99.82 322	98.02 260	99.03 389	98.37 432

CSCG		99.37 154	99.29 162	99.60 174	99.71 169	99.46 173	99.43 118	99.85 75	98.79 285	99.41 280	99.60 255	98.92 144	99.92 147	98.02 260	99.92 129	99.43 275

eth_miper_zixun_eth		98.68 296	98.71 278	98.60 370	99.10 388	96.84 401	97.52 428	99.54 263	98.94 261	99.58 222	99.48 306	96.25 340	99.76 362	98.01 263	99.93 125	99.21 328

Patchmtry		98.78 284	98.54 296	99.49 215	98.89 412	99.19 244	99.32 146	99.67 183	99.65 133	99.72 163	99.79 109	91.87 394	99.95 78	98.00 264	99.97 70	99.33 301

PVSNet_BlendedMVS		99.03 245	99.01 231	99.09 317	99.54 256	97.99 359	98.58 335	99.82 91	97.62 382	99.34 296	99.71 169	98.52 206	99.77 359	97.98 265	99.97 70	99.52 232

PVSNet_Blended		98.70 294	98.59 287	99.02 327	99.54 256	97.99 359	97.58 423	99.82 91	95.70 430	99.34 296	98.98 402	98.52 206	99.77 359	97.98 265	99.83 201	99.30 310

cl____		98.54 310	98.41 307	98.92 340	99.03 398	97.80 373	97.46 430	99.59 235	98.90 268	99.60 217	99.46 313	93.85 370	99.78 350	97.97 267	99.89 154	99.17 340

DIV-MVS_self_test		98.54 310	98.42 306	98.92 340	99.03 398	97.80 373	97.46 430	99.59 235	98.90 268	99.60 217	99.46 313	93.87 369	99.78 350	97.97 267	99.89 154	99.18 337

AUN-MVS		97.82 356	97.38 370	99.14 311	99.27 354	98.53 321	98.72 320	99.02 379	98.10 354	97.18 441	99.03 396	89.26 422	99.85 280	97.94 269	97.91 441	99.03 377

FA-MVS(test-final)		98.52 312	98.32 317	99.10 316	99.48 288	98.67 301	99.77 19	98.60 401	97.35 397	99.63 199	99.80 99	93.07 382	99.84 295	97.92 270	99.30 366	98.78 407

ambc				99.20 302	99.35 327	98.53 321	99.17 205	99.46 298		99.67 185	99.80 99	98.46 213	99.70 382	97.92 270	99.70 272	99.38 288

USDC		98.96 263	98.93 251	99.05 325	99.54 256	97.99 359	97.07 444	99.80 104	98.21 349	99.75 146	99.77 131	98.43 216	99.64 419	97.90 272	99.88 163	99.51 234

OPM-MVS		99.26 181	99.13 189	99.63 157	99.70 184	99.61 139	98.58 335	99.48 292	98.50 318	99.52 247	99.63 230	99.14 106	99.76 362	97.89 273	99.77 242	99.51 234
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 170	99.17 182	99.77 75	99.69 189	99.80 51	99.14 216	99.31 337	99.16 233	99.62 208	99.61 247	98.35 227	99.91 176	97.88 274	99.72 267	99.61 180
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	99.70 184	99.79 54	99.14 216	99.61 218					99.92 147	97.88 274	99.72 267	99.77 76

c3_l		98.72 291	98.71 278	98.72 364	99.12 381	97.22 391	97.68 419	99.56 251	98.90 268	99.54 240	99.48 306	96.37 335	99.73 372	97.88 274	99.88 163	99.21 328

3Dnovator+	98.92 3	99.35 160	99.24 174	99.67 130	99.35 327	99.47 169	99.62 67	99.50 287	99.44 180	99.12 337	99.78 121	98.77 163	99.94 95	97.87 277	99.72 267	99.62 170

miper_ehance_all_eth		98.59 305	98.59 287	98.59 371	98.98 404	97.07 395	97.49 429	99.52 278	98.50 318	99.52 247	99.37 334	96.41 333	99.71 378	97.86 278	99.62 303	99.00 384

WTY-MVS		98.59 305	98.37 311	99.26 293	99.43 306	98.40 330	98.74 318	99.13 372	98.10 354	99.21 324	99.24 368	94.82 360	99.90 195	97.86 278	98.77 405	99.49 244

APD_test1		99.36 158	99.28 165	99.61 170	99.89 39	99.89 10	99.32 146	99.74 142	99.18 226	99.69 175	99.75 143	98.41 219	99.84 295	97.85 280	99.70 272	99.10 355

SED-MVS		99.40 143	99.28 165	99.77 75	99.69 189	99.82 42	99.20 190	99.54 263	99.13 239	99.82 104	99.63 230	98.91 146	99.92 147	97.85 280	99.70 272	99.58 196

test_241102_TWO								99.54 263	99.13 239	99.76 141	99.63 230	98.32 232	99.92 147	97.85 280	99.69 280	99.75 84

MVS_111021_HR		99.12 225	99.02 227	99.40 248	99.50 278	99.11 253	97.92 407	99.71 158	98.76 292	99.08 341	99.47 310	99.17 99	99.54 435	97.85 280	99.76 244	99.54 216

MTAPA		99.35 160	99.20 178	99.80 61	99.81 95	99.81 47	99.33 143	99.53 273	99.27 211	99.42 274	99.63 230	98.21 243	99.95 78	97.83 284	99.79 233	99.65 143

MSC_two_6792asdad				99.74 98	99.03 398	99.53 160		99.23 354					99.92 147	97.77 285	99.69 280	99.78 72

No_MVS				99.74 98	99.03 398	99.53 160		99.23 354					99.92 147	97.77 285	99.69 280	99.78 72

TESTMET0.1,1		96.24 403	95.84 404	97.41 416	98.24 449	93.84 441	97.38 432	95.84 448	98.43 323	97.81 429	98.56 430	79.77 451	99.89 214	97.77 285	98.77 405	98.52 424

ACMH+	98.40 8	99.50 108	99.43 125	99.71 119	99.86 57	99.76 70	99.32 146	99.77 123	99.53 158	99.77 137	99.76 135	99.26 89	99.78 350	97.77 285	99.88 163	99.60 184

IU-MVS					99.69 189	99.77 63		99.22 357	97.50 389	99.69 175				97.75 289	99.70 272	99.77 76

114514_t		98.49 317	98.11 335	99.64 150	99.73 162	99.58 149	99.24 179	99.76 131	89.94 453	99.42 274	99.56 278	97.76 278	99.86 262	97.74 290	99.82 211	99.47 252

DVP-MVS++		99.38 150	99.25 172	99.77 75	99.03 398	99.77 63	99.74 27	99.61 218	99.18 226	99.76 141	99.61 247	99.00 131	99.92 147	97.72 291	99.60 313	99.62 170

test_0728_THIRD									99.18 226	99.62 208	99.61 247	98.58 190	99.91 176	97.72 291	99.80 228	99.77 76

EGC-MVSNET		89.05 427	85.52 430	99.64 150	99.89 39	99.78 57	99.56 87	99.52 278	24.19 462	49.96 463	99.83 81	99.15 103	99.92 147	97.71 293	99.85 188	99.21 328

miper_enhance_ethall		98.03 350	97.94 350	98.32 385	98.27 448	96.43 409	96.95 445	99.41 309	96.37 421	99.43 271	98.96 406	94.74 361	99.69 388	97.71 293	99.62 303	98.83 403

TSAR-MVS + MP.		99.34 165	99.24 174	99.63 157	99.82 83	99.37 202	99.26 172	99.35 328	98.77 289	99.57 225	99.70 179	99.27 88	99.88 229	97.71 293	99.75 248	99.65 143
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 368	97.28 372	98.40 380	98.37 446	96.75 402	97.24 439	99.37 324	97.31 399	99.41 280	99.22 370	87.30 426	99.37 447	97.70 296	99.62 303	99.08 366

MP-MVS-pluss		99.14 220	98.92 255	99.80 61	99.83 74	99.83 34	98.61 328	99.63 208	96.84 414	99.44 267	99.58 267	98.81 154	99.91 176	97.70 296	99.82 211	99.67 124
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 175	99.11 196	99.79 68	99.75 150	99.81 47	98.95 284	99.53 273	98.27 346	99.53 245	99.73 152	98.75 166	99.87 243	97.70 296	99.83 201	99.68 115

UnsupCasMVSNet_bld		98.55 309	98.27 323	99.40 248	99.56 251	99.37 202	97.97 403	99.68 178	97.49 390	99.08 341	99.35 343	95.41 354	99.82 322	97.70 296	98.19 433	99.01 383

MVS_111021_LR		99.13 222	99.03 226	99.42 237	99.58 230	99.32 214	97.91 409	99.73 146	98.68 298	99.31 306	99.48 306	99.09 113	99.66 410	97.70 296	99.77 242	99.29 313

IS-MVSNet		99.03 245	98.85 264	99.55 194	99.80 102	99.25 228	99.73 30	99.15 369	99.37 197	99.61 214	99.71 169	94.73 362	99.81 337	97.70 296	99.88 163	99.58 196

test-LLR		97.15 380	96.95 382	97.74 408	98.18 451	95.02 433	97.38 432	96.10 444	98.00 359	97.81 429	98.58 427	90.04 418	99.91 176	97.69 302	98.78 403	98.31 433

test-mter		96.23 404	95.73 407	97.74 408	98.18 451	95.02 433	97.38 432	96.10 444	97.90 368	97.81 429	98.58 427	79.12 454	99.91 176	97.69 302	98.78 403	98.31 433

MonoMVSNet		98.23 339	98.32 317	97.99 396	98.97 405	96.62 404	99.49 105	98.42 410	99.62 141	99.40 285	99.79 109	95.51 352	98.58 458	97.68 304	95.98 456	98.76 410

XVS		99.27 179	99.11 196	99.75 93	99.71 169	99.71 98	99.37 129	99.61 218	99.29 207	98.76 376	99.47 310	98.47 210	99.88 229	97.62 305	99.73 261	99.67 124

X-MVStestdata		96.09 408	94.87 421	99.75 93	99.71 169	99.71 98	99.37 129	99.61 218	99.29 207	98.76 376	61.30 471	98.47 210	99.88 229	97.62 305	99.73 261	99.67 124

SMA-MVS		99.19 205	99.00 235	99.73 107	99.46 298	99.73 88	99.13 222	99.52 278	97.40 394	99.57 225	99.64 215	98.93 141	99.83 311	97.61 307	99.79 233	99.63 159
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		96.71 391	96.79 390	96.46 434	98.90 409	90.71 460	99.41 119	98.68 394	94.69 443	98.14 414	99.34 346	86.32 436	99.80 344	97.60 308	98.07 439	98.88 398

PVSNet	97.47 15	98.42 323	98.44 304	98.35 382	99.46 298	96.26 413	96.70 449	99.34 330	97.68 380	99.00 348	99.13 378	97.40 295	99.72 374	97.59 309	99.68 285	99.08 366

new_pmnet		98.88 275	98.89 260	98.84 353	99.70 184	97.62 378	98.15 379	99.50 287	97.98 362	99.62 208	99.54 288	98.15 249	99.94 95	97.55 310	99.84 193	98.95 388

IB-MVS	95.41 20	95.30 423	94.46 427	97.84 404	98.76 430	95.33 429	97.33 435	96.07 446	96.02 425	95.37 457	97.41 454	76.17 458	99.96 67	97.54 311	95.44 459	98.22 438
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		99.24 185	99.11 196	99.61 170	98.38 445	99.79 54	99.57 85	99.68 178	99.61 145	99.15 332	99.71 169	98.70 173	99.91 176	97.54 311	99.68 285	99.13 352

ZNCC-MVS		99.22 195	99.04 224	99.77 75	99.76 137	99.73 88	99.28 163	99.56 251	98.19 351	99.14 334	99.29 355	98.84 153	99.92 147	97.53 313	99.80 228	99.64 153

CP-MVS		99.23 186	99.05 218	99.75 93	99.66 205	99.66 117	99.38 125	99.62 211	98.38 330	99.06 345	99.27 358	98.79 159	99.94 95	97.51 314	99.82 211	99.66 134

SD-MVS		99.01 253	99.30 157	98.15 392	99.50 278	99.40 194	98.94 286	99.61 218	99.22 223	99.75 146	99.82 88	99.54 50	95.51 462	97.48 315	99.87 175	99.54 216
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		98.49 317	98.29 322	99.11 314	98.96 406	98.42 329	97.54 424	99.32 333	97.53 387	98.47 399	98.15 442	97.88 268	99.82 322	97.46 316	99.24 376	99.09 360

DeepC-MVS_fast	98.47 5	99.23 186	99.12 193	99.56 190	99.28 352	99.22 238	98.99 273	99.40 316	99.08 244	99.58 222	99.64 215	98.90 149	99.83 311	97.44 317	99.75 248	99.63 159
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 182	99.08 207	99.76 82	99.73 162	99.70 106	99.31 151	99.59 235	98.36 332	99.36 290	99.37 334	98.80 158	99.91 176	97.43 318	99.75 248	99.68 115

ACMMPR		99.23 186	99.06 213	99.76 82	99.74 158	99.69 110	99.31 151	99.59 235	98.36 332	99.35 292	99.38 331	98.61 186	99.93 116	97.43 318	99.75 248	99.67 124

Vis-MVSNet (Re-imp)		98.77 285	98.58 290	99.34 265	99.78 122	98.88 285	99.61 73	99.56 251	99.11 243	99.24 318	99.56 278	93.00 384	99.78 350	97.43 318	99.89 154	99.35 297

MIMVSNet		98.43 322	98.20 327	99.11 314	99.53 263	98.38 334	99.58 82	98.61 399	98.96 257	99.33 298	99.76 135	90.92 404	99.81 337	97.38 321	99.76 244	99.15 344

WB-MVSnew		98.34 333	98.14 333	98.96 333	98.14 454	97.90 367	98.27 369	97.26 440	98.63 303	98.80 371	98.00 445	97.77 276	99.90 195	97.37 322	98.98 392	99.09 360

XVG-OURS-SEG-HR		99.16 215	98.99 242	99.66 137	99.84 69	99.64 126	98.25 372	99.73 146	98.39 329	99.63 199	99.43 318	99.70 31	99.90 195	97.34 323	98.64 416	99.44 269

COLMAP_ROB	98.06 12	99.45 129	99.37 138	99.70 123	99.83 74	99.70 106	99.38 125	99.78 120	99.53 158	99.67 185	99.78 121	99.19 96	99.86 262	97.32 324	99.87 175	99.55 207
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 247	98.81 271	99.65 143	99.58 230	99.49 165	98.58 335	99.07 374	98.40 328	99.04 346	99.25 363	98.51 208	99.80 344	97.31 325	99.51 337	99.65 143

region2R		99.23 186	99.05 218	99.77 75	99.76 137	99.70 106	99.31 151	99.59 235	98.41 326	99.32 301	99.36 338	98.73 170	99.93 116	97.29 326	99.74 255	99.67 124

APD-MVS_3200maxsize		99.31 171	99.16 183	99.74 98	99.53 263	99.75 78	99.27 167	99.61 218	99.19 225	99.57 225	99.64 215	98.76 164	99.90 195	97.29 326	99.62 303	99.56 204

TAPA-MVS	97.92 13	98.03 350	97.55 366	99.46 224	99.47 294	99.44 180	98.50 350	99.62 211	86.79 454	99.07 344	99.26 361	98.26 237	99.62 422	97.28 328	99.73 261	99.31 308
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 179	99.11 196	99.73 107	99.54 256	99.74 85	99.26 172	99.62 211	99.16 233	99.52 247	99.64 215	98.41 219	99.91 176	97.27 329	99.61 310	99.54 216

RE-MVS-def			99.13 189		99.54 256	99.74 85	99.26 172	99.62 211	99.16 233	99.52 247	99.64 215	98.57 191		97.27 329	99.61 310	99.54 216

testing11		96.05 410	95.41 413	97.97 398	98.78 427	95.27 430	98.59 333	98.23 420	98.86 274	96.56 448	96.91 461	75.20 459	99.69 388	97.26 331	98.29 428	98.93 391

test_yl		98.25 336	97.95 346	99.13 312	99.17 374	98.47 324	99.00 266	98.67 396	98.97 255	99.22 322	99.02 397	91.31 398	99.69 388	97.26 331	98.93 394	99.24 319

DCV-MVSNet		98.25 336	97.95 346	99.13 312	99.17 374	98.47 324	99.00 266	98.67 396	98.97 255	99.22 322	99.02 397	91.31 398	99.69 388	97.26 331	98.93 394	99.24 319

PHI-MVS		99.11 229	98.95 249	99.59 177	99.13 379	99.59 144	99.17 205	99.65 198	97.88 371	99.25 315	99.46 313	98.97 137	99.80 344	97.26 331	99.82 211	99.37 291

tfpnnormal		99.43 134	99.38 135	99.60 174	99.87 54	99.75 78	99.59 80	99.78 120	99.71 112	99.90 66	99.69 188	98.85 152	99.90 195	97.25 335	99.78 238	99.15 344

PatchmatchNet		97.65 364	97.80 357	97.18 423	98.82 422	92.49 447	99.17 205	98.39 413	98.12 353	98.79 373	99.58 267	90.71 410	99.89 214	97.23 336	99.41 352	99.16 342
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 259	98.80 273	99.56 190	99.25 358	99.43 184	98.54 345	99.27 345	98.58 309	98.80 371	99.43 318	98.53 203	99.70 382	97.22 337	99.59 317	99.54 216

testing3		96.48 397	95.63 409	99.01 328	99.23 362	97.81 371	98.90 290	99.10 373	98.72 294	97.84 428	97.92 446	72.44 463	99.85 280	97.21 338	99.33 362	99.35 297

HPM-MVS		99.25 182	99.07 211	99.78 72	99.81 95	99.75 78	99.61 73	99.67 183	97.72 378	99.35 292	99.25 363	99.23 92	99.92 147	97.21 338	99.82 211	99.67 124
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 205	99.00 235	99.76 82	99.76 137	99.68 113	99.38 125	99.54 263	98.34 341	99.01 347	99.50 299	98.53 203	99.93 116	97.18 340	99.78 238	99.66 134

ACMMP		99.25 182	99.08 207	99.74 98	99.79 114	99.68 113	99.50 100	99.65 198	98.07 357	99.52 247	99.69 188	98.57 191	99.92 147	97.18 340	99.79 233	99.63 159
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		96.23 404	95.74 406	97.70 410	98.86 416	95.59 426	98.66 325	98.14 422	98.96 257	97.67 434	97.06 458	76.78 456	98.92 454	97.10 342	98.41 425	98.58 420

thisisatest0515		96.98 384	96.42 392	98.66 367	99.42 311	97.47 382	97.27 437	94.30 454	97.24 401	99.15 332	98.86 414	85.01 438	99.87 243	97.10 342	99.39 354	98.63 414

XVG-ACMP-BASELINE		99.23 186	99.10 204	99.63 157	99.82 83	99.58 149	98.83 302	99.72 155	98.36 332	99.60 217	99.71 169	98.92 144	99.91 176	97.08 344	99.84 193	99.40 283

MSDG		99.08 234	98.98 245	99.37 257	99.60 220	99.13 250	97.54 424	99.74 142	98.84 278	99.53 245	99.55 286	99.10 111	99.79 347	97.07 345	99.86 183	99.18 337

SteuartSystems-ACMMP		99.30 172	99.14 187	99.76 82	99.87 54	99.66 117	99.18 200	99.60 229	98.55 311	99.57 225	99.67 203	99.03 129	99.94 95	97.01 346	99.80 228	99.69 109
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 406	95.78 405	97.49 412	98.53 440	93.83 442	98.04 393	93.94 457	98.96 257	98.46 400	98.17 441	79.86 449	99.87 243	96.99 347	99.06 385	98.78 407

EPMVS		96.53 394	96.32 393	97.17 424	98.18 451	92.97 446	99.39 122	89.95 463	98.21 349	98.61 388	99.59 264	86.69 435	99.72 374	96.99 347	99.23 378	98.81 404

MSP-MVS		99.04 244	98.79 274	99.81 52	99.78 122	99.73 88	99.35 136	99.57 246	98.54 314	99.54 240	98.99 399	96.81 318	99.93 116	96.97 349	99.53 333	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++		98.96 263	98.70 280	99.74 98	99.52 270	99.71 98	98.86 295	99.19 364	98.47 322	98.59 390	99.06 389	98.08 255	99.91 176	96.94 350	99.60 313	99.60 184

SR-MVS		99.19 205	99.00 235	99.74 98	99.51 272	99.72 93	99.18 200	99.60 229	98.85 275	99.47 261	99.58 267	98.38 224	99.92 147	96.92 351	99.54 331	99.57 202

PGM-MVS		99.20 202	99.01 231	99.77 75	99.75 150	99.71 98	99.16 211	99.72 155	97.99 361	99.42 274	99.60 255	98.81 154	99.93 116	96.91 352	99.74 255	99.66 134

HY-MVS	98.23 9	98.21 343	97.95 346	98.99 329	99.03 398	98.24 338	99.61 73	98.72 392	96.81 415	98.73 378	99.51 296	94.06 367	99.86 262	96.91 352	98.20 431	98.86 400

MDTV_nov1_ep13			97.73 361		98.70 435	90.83 458	99.15 213	98.02 425	98.51 317	98.82 368	99.61 247	90.98 403	99.66 410	96.89 354	98.92 396

GST-MVS		99.16 215	98.96 248	99.75 93	99.73 162	99.73 88	99.20 190	99.55 257	98.22 348	99.32 301	99.35 343	98.65 182	99.91 176	96.86 355	99.74 255	99.62 170

test_post1							99.14 216				51.63 473	89.54 421	99.82 322	96.86 355

SCA		98.11 346	98.36 312	97.36 417	99.20 368	92.99 445	98.17 377	98.49 407	98.24 347	99.10 340	99.57 274	96.01 345	99.94 95	96.86 355	99.62 303	99.14 349

UBG		96.53 394	95.95 400	98.29 389	98.87 415	96.31 412	98.48 353	98.07 423	98.83 279	97.32 436	96.54 466	79.81 450	99.62 422	96.84 358	98.74 409	98.95 388

XVG-OURS		99.21 200	99.06 213	99.65 143	99.82 83	99.62 133	97.87 411	99.74 142	98.36 332	99.66 191	99.68 199	99.71 28	99.90 195	96.84 358	99.88 163	99.43 275

LCM-MVSNet-Re		99.28 175	99.15 186	99.67 130	99.33 341	99.76 70	99.34 137	99.97 20	98.93 264	99.91 61	99.79 109	98.68 175	99.93 116	96.80 360	99.56 322	99.30 310

RPSCF		99.18 209	99.02 227	99.64 150	99.83 74	99.85 22	99.44 116	99.82 91	98.33 342	99.50 256	99.78 121	97.90 266	99.65 417	96.78 361	99.83 201	99.44 269

旧先验2							97.94 405		95.33 434	98.94 352			99.88 229	96.75 362

MDTV_nov1_ep13_2view						91.44 455	99.14 216		97.37 396	99.21 324		91.78 396		96.75 362		99.03 377

CLD-MVS		98.76 286	98.57 291	99.33 268	99.57 240	98.97 273	97.53 426	99.55 257	96.41 419	99.27 313	99.13 378	99.07 119	99.78 350	96.73 364	99.89 154	99.23 323
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 347	97.98 344	98.48 376	99.27 354	96.48 407	99.40 120	99.07 374	98.81 282	99.23 319	99.57 274	90.11 417	99.87 243	96.69 365	99.64 298	99.09 360

baseline2		96.83 387	96.28 394	98.46 378	99.09 391	96.91 399	98.83 302	93.87 458	97.23 402	96.23 453	98.36 436	88.12 425	99.90 195	96.68 366	98.14 436	98.57 422

cascas		96.99 383	96.82 389	97.48 413	97.57 461	95.64 424	96.43 451	99.56 251	91.75 449	97.13 443	97.61 453	95.58 350	98.63 456	96.68 366	99.11 382	98.18 442

PC_three_1452									97.56 383	99.68 178	99.41 321	99.09 113	97.09 459	96.66 368	99.60 313	99.62 170

LPG-MVS_test		99.22 195	99.05 218	99.74 98	99.82 83	99.63 131	99.16 211	99.73 146	97.56 383	99.64 195	99.69 188	99.37 70	99.89 214	96.66 368	99.87 175	99.69 109

LGP-MVS_train				99.74 98	99.82 83	99.63 131		99.73 146	97.56 383	99.64 195	99.69 188	99.37 70	99.89 214	96.66 368	99.87 175	99.69 109

ETVMVS		96.14 407	95.22 418	98.89 349	98.80 423	98.01 358	98.66 325	98.35 416	98.71 296	97.18 441	96.31 470	74.23 462	99.75 366	96.64 371	98.13 438	98.90 395

TinyColmap		98.97 260	98.93 251	99.07 322	99.46 298	98.19 343	97.75 415	99.75 136	98.79 285	99.54 240	99.70 179	98.97 137	99.62 422	96.63 372	99.83 201	99.41 280

LF4IMVS		99.01 253	98.92 255	99.27 290	99.71 169	99.28 220	98.59 333	99.77 123	98.32 343	99.39 287	99.41 321	98.62 184	99.84 295	96.62 373	99.84 193	98.69 413

NCCC		98.82 280	98.57 291	99.58 180	99.21 365	99.31 215	98.61 328	99.25 350	98.65 301	98.43 401	99.26 361	97.86 269	99.81 337	96.55 374	99.27 372	99.61 180

OPU-MVS				99.29 282	99.12 381	99.44 180	99.20 190				99.40 325	99.00 131	98.84 455	96.54 375	99.60 313	99.58 196

F-COLMAP		98.74 288	98.45 303	99.62 166	99.57 240	99.47 169	98.84 299	99.65 198	96.31 422	98.93 353	99.19 375	97.68 282	99.87 243	96.52 376	99.37 357	99.53 222

testing99		95.86 415	95.19 419	97.87 402	98.76 430	95.03 432	98.62 327	98.44 409	98.68 298	96.67 447	96.66 465	74.31 461	99.69 388	96.51 377	98.03 440	98.90 395

ADS-MVSNet2		97.78 358	97.66 365	98.12 394	99.14 377	95.36 428	99.22 187	98.75 391	96.97 410	98.25 406	99.64 215	90.90 405	99.94 95	96.51 377	99.56 322	99.08 366

ADS-MVSNet		97.72 363	97.67 364	97.86 403	99.14 377	94.65 436	99.22 187	98.86 384	96.97 410	98.25 406	99.64 215	90.90 405	99.84 295	96.51 377	99.56 322	99.08 366

PatchMatch-RL		98.68 296	98.47 300	99.30 281	99.44 303	99.28 220	98.14 381	99.54 263	97.12 408	99.11 338	99.25 363	97.80 274	99.70 382	96.51 377	99.30 366	98.93 391

CMPMVS	77.52 23	98.50 315	98.19 330	99.41 245	98.33 447	99.56 153	99.01 263	99.59 235	95.44 432	99.57 225	99.80 99	95.64 348	99.46 445	96.47 381	99.92 129	99.21 328
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 411	95.32 416	98.02 395	98.76 430	95.39 427	98.38 362	98.65 398	98.82 280	96.84 444	96.71 464	75.06 460	99.71 378	96.46 382	98.23 430	98.98 385

SF-MVS		99.10 232	98.93 251	99.62 166	99.58 230	99.51 163	99.13 222	99.65 198	97.97 363	99.42 274	99.61 247	98.86 151	99.87 243	96.45 383	99.68 285	99.49 244

FE-MVS		97.85 355	97.42 369	99.15 308	99.44 303	98.75 296	99.77 19	98.20 421	95.85 427	99.33 298	99.80 99	88.86 423	99.88 229	96.40 384	99.12 381	98.81 404

DPE-MVS		99.14 220	98.92 255	99.82 44	99.57 240	99.77 63	98.74 318	99.60 229	98.55 311	99.76 141	99.69 188	98.23 242	99.92 147	96.39 385	99.75 248	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					97.59 459	89.02 465			93.47 445		98.30 437		99.84 295	96.38 386

AllTest		99.21 200	99.07 211	99.63 157	99.78 122	99.64 126	99.12 227	99.83 85	98.63 303	99.63 199	99.72 159	98.68 175	99.75 366	96.38 386	99.83 201	99.51 234

TestCases				99.63 157	99.78 122	99.64 126		99.83 85	98.63 303	99.63 199	99.72 159	98.68 175	99.75 366	96.38 386	99.83 201	99.51 234

testdata				99.42 237	99.51 272	98.93 280		99.30 340	96.20 423	98.87 363	99.40 325	98.33 231	99.89 214	96.29 389	99.28 369	99.44 269

dp		96.86 386	97.07 378	96.24 436	98.68 436	90.30 463	99.19 196	98.38 414	97.35 397	98.23 408	99.59 264	87.23 427	99.82 322	96.27 390	98.73 412	98.59 418

tpmvs		97.39 375	97.69 362	96.52 432	98.41 444	91.76 451	99.30 154	98.94 383	97.74 377	97.85 427	99.55 286	92.40 391	99.73 372	96.25 391	98.73 412	98.06 444

KD-MVS_2432*1600		95.89 412	95.41 413	97.31 420	94.96 463	93.89 439	97.09 442	99.22 357	97.23 402	98.88 360	99.04 392	79.23 452	99.54 435	96.24 392	96.81 450	98.50 428

miper_refine_blended		95.89 412	95.41 413	97.31 420	94.96 463	93.89 439	97.09 442	99.22 357	97.23 402	98.88 360	99.04 392	79.23 452	99.54 435	96.24 392	96.81 450	98.50 428

ACMP	97.51 14	99.05 241	98.84 266	99.67 130	99.78 122	99.55 157	98.88 292	99.66 188	97.11 409	99.47 261	99.60 255	99.07 119	99.89 214	96.18 394	99.85 188	99.58 196
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 271	98.72 277	99.44 231	99.39 315	99.42 187	98.58 335	99.64 206	97.31 399	99.44 267	99.62 238	98.59 188	99.69 388	96.17 395	99.79 233	99.22 325

DP-MVS Recon		98.50 315	98.23 324	99.31 277	99.49 283	99.46 173	98.56 341	99.63 208	94.86 441	98.85 365	99.37 334	97.81 273	99.59 429	96.08 396	99.44 347	98.88 398

tpm cat1		96.78 388	96.98 381	96.16 437	98.85 417	90.59 461	99.08 243	99.32 333	92.37 447	97.73 433	99.46 313	91.15 401	99.69 388	96.07 397	98.80 402	98.21 439

tpm2		96.35 400	96.22 395	96.73 430	98.88 414	91.75 452	99.21 189	98.51 405	93.27 446	97.89 423	99.21 372	84.83 439	99.70 382	96.04 398	98.18 434	98.75 411

dmvs_re		98.69 295	98.48 299	99.31 277	99.55 254	99.42 187	99.54 90	98.38 414	99.32 205	98.72 379	98.71 423	96.76 320	99.21 449	96.01 399	99.35 360	99.31 308

test_0402		99.22 195	99.14 187	99.45 227	99.79 114	99.43 184	99.28 163	99.68 178	99.54 156	99.40 285	99.56 278	99.07 119	99.82 322	96.01 399	99.96 84	99.11 353

ITE_SJBPF				99.38 254	99.63 213	99.44 180		99.73 146	98.56 310	99.33 298	99.53 290	98.88 150	99.68 400	96.01 399	99.65 296	99.02 382

test_prior2							97.95 404		97.87 372	98.05 416	99.05 390	97.90 266		95.99 402	99.49 342

testdata2													99.89 214	95.99 402

原ACMM1				99.37 257	99.47 294	98.87 287		99.27 345	96.74 417	98.26 405	99.32 347	97.93 265	99.82 322	95.96 404	99.38 355	99.43 275

新几何1				99.52 206	99.50 278	99.22 238		99.26 347	95.66 431	98.60 389	99.28 356	97.67 283	99.89 214	95.95 405	99.32 364	99.45 257

MP-MVS		99.06 238	98.83 268	99.76 82	99.76 137	99.71 98	99.32 146	99.50 287	98.35 337	98.97 349	99.48 306	98.37 225	99.92 147	95.95 405	99.75 248	99.63 159
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 422	94.59 425	98.61 369	98.66 437	97.45 384	98.54 345	97.90 429	98.53 315	96.54 449	96.47 467	70.62 466	99.81 337	95.91 407	98.15 435	98.56 423

wuyk23d		97.58 367	99.13 189	92.93 441	99.69 189	99.49 165	99.52 92	99.77 123	97.97 363	99.96 32	99.79 109	99.84 16	99.94 95	95.85 408	99.82 211	79.36 459

HQP_MVS		98.90 271	98.68 281	99.55 194	99.58 230	99.24 232	98.80 310	99.54 263	98.94 261	99.14 334	99.25 363	97.24 302	99.82 322	95.84 409	99.78 238	99.60 184

plane_prior5								99.54 263					99.82 322	95.84 409	99.78 238	99.60 184

无先验							98.01 396	99.23 354	95.83 428				99.85 280	95.79 411		99.44 269

CPTT-MVS		98.74 288	98.44 304	99.64 150	99.61 218	99.38 199	99.18 200	99.55 257	96.49 418	99.27 313	99.37 334	97.11 310	99.92 147	95.74 412	99.67 291	99.62 170

PLC	97.35 16	98.36 328	97.99 342	99.48 219	99.32 342	99.24 232	98.50 350	99.51 283	95.19 437	98.58 391	98.96 406	96.95 315	99.83 311	95.63 413	99.25 374	99.37 291
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 307	98.34 315	99.28 285	99.18 373	99.10 260	98.34 364	99.41 309	98.48 321	98.52 396	98.98 402	97.05 312	99.78 350	95.59 414	99.50 340	98.96 386

1314		98.00 352	97.90 354	98.27 390	98.90 409	97.45 384	99.30 154	99.06 376	94.98 438	97.21 440	99.12 382	98.43 216	99.67 405	95.58 415	98.56 419	97.71 448

PVSNet_0	95.53 19	95.85 416	95.31 417	97.47 414	98.78 427	93.48 444	95.72 453	99.40 316	96.18 424	97.37 435	97.73 448	95.73 347	99.58 430	95.49 416	81.40 461	99.36 294

MAR-MVS		98.24 338	97.92 352	99.19 303	98.78 427	99.65 123	99.17 205	99.14 370	95.36 433	98.04 417	98.81 419	97.47 292	99.72 374	95.47 417	99.06 385	98.21 439
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	98.23 339	97.89 355	99.26 293	99.19 370	99.26 225	99.65 62	99.69 175	91.33 451	98.14 414	99.77 131	98.28 234	99.96 67	95.41 418	99.55 326	98.58 420

train_agg		98.35 331	97.95 346	99.57 187	99.35 327	99.35 209	98.11 385	99.41 309	94.90 439	97.92 421	98.99 399	98.02 258	99.85 280	95.38 419	99.44 347	99.50 239

9.14			98.64 282		99.45 302		98.81 307	99.60 229	97.52 388	99.28 312	99.56 278	98.53 203	99.83 311	95.36 420	99.64 298

APD-MVS		98.87 276	98.59 287	99.71 119	99.50 278	99.62 133	99.01 263	99.57 246	96.80 416	99.54 240	99.63 230	98.29 233	99.91 176	95.24 421	99.71 270	99.61 180
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 410								95.20 422

AdaColmap		98.60 302	98.35 314	99.38 254	99.12 381	99.22 238	98.67 323	99.42 308	97.84 375	98.81 369	99.27 358	97.32 300	99.81 337	95.14 423	99.53 333	99.10 355

test9_res														95.10 424	99.44 347	99.50 239

CDPH-MVS		98.56 308	98.20 327	99.61 170	99.50 278	99.46 173	98.32 366	99.41 309	95.22 435	99.21 324	99.10 386	98.34 229	99.82 322	95.09 425	99.66 294	99.56 204

BH-untuned		98.22 341	98.09 336	98.58 373	99.38 318	97.24 390	98.55 342	98.98 382	97.81 376	99.20 329	98.76 421	97.01 313	99.65 417	94.83 426	98.33 426	98.86 400

BP-MVS														94.73 427

HQP-MVS		98.36 328	98.02 341	99.39 251	99.31 343	98.94 277	97.98 400	99.37 324	97.45 391	98.15 410	98.83 416	96.67 321	99.70 382	94.73 427	99.67 291	99.53 222

QAPM		98.40 326	97.99 342	99.65 143	99.39 315	99.47 169	99.67 53	99.52 278	91.70 450	98.78 375	99.80 99	98.55 195	99.95 78	94.71 429	99.75 248	99.53 222

agg_prior2														94.58 430	99.46 346	99.50 239

myMVS_eth3d		95.63 420	94.73 422	98.34 384	98.50 442	96.36 410	98.60 330	99.21 360	97.89 369	96.76 445	96.37 468	72.10 464	99.57 431	94.38 431	98.73 412	99.09 360

BH-RMVSNet		98.41 324	98.14 333	99.21 300	99.21 365	98.47 324	98.60 330	98.26 419	98.35 337	98.93 353	99.31 350	97.20 307	99.66 410	94.32 432	99.10 383	99.51 234

E-PMN		97.14 382	97.43 368	96.27 435	98.79 425	91.62 453	95.54 454	99.01 381	99.44 180	98.88 360	99.12 382	92.78 385	99.68 400	94.30 433	99.03 389	97.50 449

MG-MVS		98.52 312	98.39 309	98.94 336	99.15 376	97.39 387	98.18 375	99.21 360	98.89 271	99.23 319	99.63 230	97.37 298	99.74 369	94.22 434	99.61 310	99.69 109

API-MVS		98.38 327	98.39 309	98.35 382	98.83 419	99.26 225	99.14 216	99.18 365	98.59 308	98.66 384	98.78 420	98.61 186	99.57 431	94.14 435	99.56 322	96.21 456

PAPM_NR		98.36 328	98.04 339	99.33 268	99.48 288	98.93 280	98.79 313	99.28 344	97.54 386	98.56 395	98.57 429	97.12 309	99.69 388	94.09 436	98.90 400	99.38 288

ZD-MVS					99.43 306	99.61 139		99.43 306	96.38 420	99.11 338	99.07 388	97.86 269	99.92 147	94.04 437	99.49 342

DPM-MVS		98.28 334	97.94 350	99.32 273	99.36 323	99.11 253	97.31 436	98.78 390	96.88 412	98.84 366	99.11 385	97.77 276	99.61 427	94.03 438	99.36 358	99.23 323

gg-mvs-nofinetune		95.87 414	95.17 420	97.97 398	98.19 450	96.95 397	99.69 45	89.23 464	99.89 54	96.24 452	99.94 19	81.19 444	99.51 441	93.99 439	98.20 431	97.44 450

PMVS	92.94 21	98.82 280	98.81 271	98.85 351	99.84 69	97.99 359	99.20 190	99.47 295	99.71 112	99.42 274	99.82 88	98.09 253	99.47 443	93.88 440	99.85 188	99.07 371
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 385	97.28 372	95.99 439	98.76 430	91.03 457	95.26 456	98.61 399	99.34 201	98.92 356	98.88 413	93.79 371	99.66 410	92.87 441	99.05 387	97.30 453

BH-w/o		97.20 379	97.01 380	97.76 406	99.08 392	95.69 423	98.03 395	98.52 404	95.76 429	97.96 420	98.02 443	95.62 349	99.47 443	92.82 442	97.25 449	98.12 443

TR-MVS		97.44 372	97.15 377	98.32 385	98.53 440	97.46 383	98.47 354	97.91 428	96.85 413	98.21 409	98.51 433	96.42 331	99.51 441	92.16 443	97.29 448	97.98 445

OpenMVS_ROB	97.31 17	97.36 377	96.84 387	98.89 349	99.29 349	99.45 178	98.87 294	99.48 292	86.54 456	99.44 267	99.74 148	97.34 299	99.86 262	91.61 444	99.28 369	97.37 452

GG-mvs-BLEND				97.36 417	97.59 459	96.87 400	99.70 38	88.49 465		94.64 458	97.26 457	80.66 446	99.12 450	91.50 445	96.50 454	96.08 458

DeepMVS_CX				97.98 397	99.69 189	96.95 397		99.26 347	75.51 459	95.74 455	98.28 438	96.47 329	99.62 422	91.23 446	97.89 442	97.38 451

PAPR		97.56 368	97.07 378	99.04 326	98.80 423	98.11 351	97.63 420	99.25 350	94.56 444	98.02 419	98.25 439	97.43 294	99.68 400	90.90 447	98.74 409	99.33 301

MVS		95.72 418	94.63 424	98.99 329	98.56 439	97.98 364	99.30 154	98.86 384	72.71 460	97.30 437	99.08 387	98.34 229	99.74 369	89.21 448	98.33 426	99.26 316

UWE-MVS-28		95.64 419	95.47 411	96.14 438	97.98 455	90.39 462	98.49 352	95.81 449	99.02 251	98.03 418	98.19 440	84.49 441	99.28 448	88.75 449	98.47 424	98.75 411

thres600view7		96.60 393	96.16 396	97.93 400	99.63 213	96.09 418	99.18 200	97.57 434	98.77 289	98.72 379	97.32 455	87.04 429	99.72 374	88.57 450	98.62 417	97.98 445

FPMVS		96.32 401	95.50 410	98.79 359	99.60 220	98.17 346	98.46 358	98.80 389	97.16 406	96.28 450	99.63 230	82.19 443	99.09 451	88.45 451	98.89 401	99.10 355

PCF-MVS	96.03 18	96.73 390	95.86 403	99.33 268	99.44 303	99.16 247	96.87 447	99.44 303	86.58 455	98.95 351	99.40 325	94.38 365	99.88 229	87.93 452	99.80 228	98.95 388
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 399	96.03 399	97.47 414	99.63 213	95.93 419	99.18 200	97.57 434	98.75 293	98.70 382	97.31 456	87.04 429	99.67 405	87.62 453	98.51 421	96.81 454

tfpn200view9		96.30 402	95.89 401	97.53 411	99.58 230	96.11 416	99.00 266	97.54 437	98.43 323	98.52 396	96.98 459	86.85 431	99.67 405	87.62 453	98.51 421	96.81 454

thres400		96.40 398	95.89 401	97.92 401	99.58 230	96.11 416	99.00 266	97.54 437	98.43 323	98.52 396	96.98 459	86.85 431	99.67 405	87.62 453	98.51 421	97.98 445

thres200		96.09 408	95.68 408	97.33 419	99.48 288	96.22 415	98.53 347	97.57 434	98.06 358	98.37 403	96.73 463	86.84 433	99.61 427	86.99 456	98.57 418	96.16 457

MVE	92.54 22	96.66 392	96.11 397	98.31 387	99.68 197	97.55 380	97.94 405	95.60 450	99.37 197	90.68 461	98.70 425	96.56 324	98.61 457	86.94 457	99.55 326	98.77 409
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 378	96.83 388	98.59 371	99.46 298	97.55 380	99.25 178	96.84 443	98.78 287	97.24 439	97.67 449	97.11 310	98.97 453	86.59 458	98.54 420	99.27 314

PAPM		95.61 421	94.71 423	98.31 387	99.12 381	96.63 403	96.66 450	98.46 408	90.77 452	96.25 451	98.68 426	93.01 383	99.69 388	81.60 459	97.86 444	98.62 415

SD_0403		97.42 373	96.90 386	98.98 331	99.54 256	97.90 367	99.52 92	99.54 263	99.34 201	97.87 425	98.85 415	98.72 171	99.64 419	78.93 460	99.83 201	99.40 283

dongtai		89.37 426	88.91 429	90.76 442	99.19 370	77.46 467	95.47 455	87.82 466	92.28 448	94.17 459	98.82 418	71.22 465	95.54 461	63.85 461	97.34 447	99.27 314

kuosan		85.65 428	84.57 431	88.90 444	97.91 456	77.11 468	96.37 452	87.62 467	85.24 457	85.45 462	96.83 462	69.94 467	90.98 463	45.90 462	95.83 458	98.62 415

test123		29.31 429	33.05 434	18.08 445	25.93 469	12.24 470	97.53 426	10.93 470	11.78 463	24.21 464	50.08 475	21.04 468	8.60 464	23.51 463	32.43 463	33.39 460

testmvs		28.94 430	33.33 432	15.79 446	26.03 468	9.81 471	96.77 448	15.67 469	11.55 464	23.87 465	50.74 474	19.03 469	8.53 465	23.21 464	33.07 462	29.03 461

mmdepth		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

monomultidepth		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

test_blank		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uanet_test		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

DCPMVS		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

cdsmvs_eth3d_5k		24.88 431	33.17 433	0.00 447	0.00 470	0.00 472	0.00 458	99.62 211	0.00 465	0.00 466	99.13 378	99.82 18	0.00 466	0.00 465	0.00 464	0.00 462

pcd_1.5k_mvsjas		16.61 432	22.14 435	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	99.28 85	0.00 466	0.00 465	0.00 464	0.00 462

sosnet-low-res		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

sosnet		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uncertanet		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

Regformer		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

ab-mvs-re		8.26 443	11.02 446	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	99.16 376	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uanet		8.33 433	11.11 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	100.00 1	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

FOURS1					99.83 74	99.89 10	99.74 27	99.71 158	99.69 120	99.63 199

test_one_0601					99.63 213	99.76 70		99.55 257	99.23 219	99.31 306	99.61 247	98.59 188

eth-test2					0.00 470

eth-test					0.00 470

test_241102_ONE					99.69 189	99.82 42		99.54 263	99.12 242	99.82 104	99.49 303	98.91 146	99.52 440

save fliter					99.53 263	99.25 228	98.29 368	99.38 323	99.07 246

test0726					99.69 189	99.80 51	99.24 179	99.57 246	99.16 233	99.73 159	99.65 213	98.35 227

GSMVS																99.14 349

test_part2					99.62 217	99.67 115				99.55 238

sam_mvs1												90.81 409				99.14 349

sam_mvs												90.52 414

MTGPA								99.53 273

test_post											52.41 472	90.25 416	99.86 262

patchmatchnet-post											99.62 238	90.58 412	99.94 95

MTMP							99.09 240	98.59 402

TEST9					99.35 327	99.35 209	98.11 385	99.41 309	94.83 442	97.92 421	98.99 399	98.02 258	99.85 280

test_8					99.34 336	99.31 215	98.08 389	99.40 316	94.90 439	97.87 425	98.97 404	98.02 258	99.84 295

agg_prior					99.35 327	99.36 206		99.39 319		97.76 432			99.85 280

test_prior4						99.19 244	98.00 398

test_prior				99.46 224	99.35 327	99.22 238		99.39 319					99.69 388			99.48 248

新几何2							98.04 393

旧先验1					99.49 283	99.29 218		99.26 347			99.39 329	97.67 283			99.36 358	99.46 256

原ACMM2							97.92 407

test222					99.51 272	99.08 262	97.83 413	99.29 341	95.21 436	98.68 383	99.31 350	97.28 301			99.38 355	99.43 275

segment_acmp												98.37 225

testdata1							97.72 416		97.86 374

test12				99.54 200	99.29 349	99.33 212		99.16 368		98.43 401		97.54 290	99.82 322		99.47 344	99.48 248

plane_prior7					99.58 230	99.38 199

plane_prior6					99.47 294	99.26 225						97.24 302

plane_prior4											99.25 363

plane_prior3						99.31 215			98.36 332	99.14 334

plane_prior2							98.80 310		98.94 261

plane_prior1					99.51 272

plane_prior						99.24 232	98.42 360		97.87 372						99.71 270

n2								0.00 471

nn								0.00 471

door-mid								99.83 85

test11								99.29 341

door								99.77 123

HQP5-MVS						98.94 277

HQP-NCC					99.31 343		97.98 400		97.45 391	98.15 410

ACMP_Plane					99.31 343		97.98 400		97.45 391	98.15 410

HQP4-MVS										98.15 410			99.70 382			99.53 222

HQP3-MVS								99.37 324							99.67 291

HQP2-MVS												96.67 321

NP-MVS					99.40 314	99.13 250					98.83 416

ACMMP++_ref															99.94 114

ACMMP++															99.79 233

Test By Simon												98.41 219

High-resolution multi-view benchmark