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 26	99.98 3	99.75 79	99.70 38	100.00 1	99.73 108	100.00 1	99.89 41	99.79 22	99.88 235	99.98 1	100.00 1	99.98 5

test_fmvs2		99.72 53	99.85 17	99.34 292	99.91 31	98.08 383	99.48 109	100.00 1	99.90 49	99.99 7	99.91 31	99.50 61	99.98 26	99.98 1	99.99 16	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 225	99.96 7	98.62 338	99.67 53	100.00 1	99.95 32	100.00 1	99.95 16	99.85 14	99.99 7	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	99.12 241	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 53	99.88 7	99.27 317	99.93 24	97.84 396	99.34 148	100.00 1	99.99 3	99.99 7	99.82 90	99.87 13	99.99 7	99.97 4	99.99 16	99.97 10

test_vis1_n		99.68 64	99.79 34	99.36 287	99.94 18	98.18 372	99.52 94	100.00 1	99.86 65	100.00 1	99.88 50	98.99 147	99.96 68	99.97 4	99.96 87	99.95 14

test_fmvs1_n		99.68 64	99.81 28	99.28 312	99.95 15	97.93 392	99.49 107	100.00 1	99.82 85	99.99 7	99.89 41	99.21 102	99.98 26	99.97 4	99.98 50	99.93 20

test_f		99.75 49	99.88 7	99.37 282	99.96 7	98.21 369	99.51 101	100.00 1	99.94 36	100.00 1	99.93 22	99.58 49	99.94 97	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 59	99.80 51	98.94 308	99.96 28	99.98 18	99.96 34	99.78 131	99.88 11	99.98 26	99.96 9	99.99 16	99.90 29

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 45	99.86 18	99.08 257	99.97 20	99.98 18	99.96 34	99.79 118	99.90 9	99.99 7	99.96 9	99.99 16	99.90 29

test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 87	99.01 281	99.99 11	99.99 3	99.98 14	99.88 50	99.97 2	99.99 7	99.96 9	100.00 1	99.98 5

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 54	99.88 45	99.55 169	99.17 216	99.98 12	99.99 3	99.96 34	99.84 76	99.96 3	99.99 7	99.96 9	99.99 16	99.88 40

test_cas_vis1_n_1920		99.76 46	99.86 13	99.45 251	99.93 24	98.40 357	99.30 165	99.98 12	99.94 36	99.99 7	99.89 41	99.80 21	99.97 43	99.96 9	99.97 73	99.97 10

fmvsm_s_conf0.5_n_10		99.77 44	99.73 54	99.88 19	99.81 106	99.75 79	99.06 263	99.85 81	99.99 3	99.97 24	99.84 76	99.12 119	99.98 26	99.95 14	99.99 16	99.90 29

fmvsm_s_conf0.5_n_7		99.73 52	99.78 39	99.60 190	99.74 178	98.93 304	98.85 322	99.96 28	99.96 28	99.97 24	99.76 149	99.82 18	99.96 68	99.95 14	99.98 50	99.90 29

fmvsm_l_conf0.5_n		99.80 30	99.78 39	99.85 32	99.88 45	99.66 120	99.11 246	99.91 51	99.98 18	99.96 34	99.64 236	99.60 43	99.99 7	99.95 14	99.99 16	99.88 40

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 41	99.82 94	99.70 108	99.17 216	99.97 20	99.99 3	99.96 34	99.82 90	99.94 4	100.00 1	99.95 14	100.00 1	99.80 65

test_fmvs1		99.48 129	99.65 73	98.97 359	99.54 282	97.16 424	99.11 246	99.98 12	99.78 102	99.96 34	99.81 97	98.72 190	99.97 43	99.95 14	99.97 73	99.79 73

mvsany_test3		99.85 12	99.88 7	99.75 97	99.95 15	99.37 222	99.53 92	99.98 12	99.77 106	99.99 7	99.95 16	99.85 14	99.94 97	99.95 14	99.98 50	99.94 17

fmvsm_s_conf0.5_n_9		99.82 24	99.82 25	99.82 46	99.83 85	99.59 156	98.97 299	99.92 42	99.99 3	99.97 24	99.84 76	99.90 9	99.94 97	99.94 20	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	98.92 312	99.98 12	99.99 3	99.99 7	99.88 50	99.43 66	99.94 97	99.94 20	99.99 16	99.99 2

fmvsm_l_conf0.5_n_a		99.80 30	99.79 34	99.84 38	99.88 45	99.64 132	99.12 241	99.91 51	99.98 18	99.95 45	99.67 220	99.67 34	99.99 7	99.94 20	99.99 16	99.88 40

MM		99.18 233	99.05 242	99.55 215	99.35 353	98.81 316	99.05 264	97.79 466	99.99 3	99.48 282	99.59 288	96.29 363	99.95 80	99.94 20	99.98 50	99.88 40

test_fmvsmconf_n		99.85 12	99.84 20	99.88 19	99.91 31	99.73 90	98.97 299	99.98 12	99.99 3	99.96 34	99.85 68	99.93 7	99.99 7	99.94 20	99.99 16	99.93 20

fmvsm_s_conf0.5_n_11		99.76 46	99.75 51	99.81 54	99.81 106	99.53 172	99.15 225	99.89 60	99.99 3	99.98 14	99.86 63	99.13 116	99.98 26	99.93 25	99.99 16	99.92 24

fmvsm_s_conf0.5_n_5		99.78 37	99.76 49	99.85 32	99.79 129	99.72 95	98.84 324	99.96 28	99.96 28	99.96 34	99.72 175	99.71 28	99.99 7	99.93 25	99.98 50	99.85 49

fmvsm_s_conf0.5_n_2		99.78 37	99.75 51	99.88 19	99.82 94	99.76 70	98.88 316	99.92 42	99.98 18	99.98 14	99.85 68	99.42 68	99.94 97	99.93 25	99.98 50	99.94 17

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 42	99.10 249	99.98 12	99.99 3	99.98 14	99.91 31	99.68 33	99.93 119	99.93 25	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	99.07 262	99.98 12	99.99 3	99.98 14	99.90 36	99.88 11	99.92 150	99.93 25	99.99 16	99.98 5

fmvsm_s_conf0.5_n_a		99.82 24	99.79 34	99.89 11	99.85 72	99.82 42	99.03 272	99.96 28	99.99 3	99.97 24	99.84 76	99.58 49	99.93 119	99.92 30	99.98 50	99.93 20

fmvsm_s_conf0.5_n		99.83 21	99.81 28	99.87 26	99.85 72	99.78 57	99.03 272	99.96 28	99.99 3	99.97 24	99.84 76	99.78 23	99.92 150	99.92 30	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 30	100.00 1	99.87 44

fmvsm_s_conf0.5_n_8		99.76 46	99.72 55	99.88 19	99.82 94	99.75 79	99.02 276	99.87 69	99.98 18	99.98 14	99.81 97	99.07 130	99.97 43	99.91 33	99.99 16	99.92 24

fmvsm_s_conf0.5_n_6		99.80 30	99.78 39	99.85 32	99.78 137	99.78 57	99.00 287	99.97 20	99.96 28	99.97 24	99.56 302	99.92 8	99.93 119	99.91 33	99.99 16	99.83 56

fmvsm_s_conf0.5_n_4		99.78 37	99.78 39	99.79 71	99.75 170	99.56 165	98.98 297	99.94 38	99.92 45	99.97 24	99.72 175	99.84 16	99.92 150	99.91 33	99.98 50	99.89 37

MVStest1		98.22 367	98.09 362	98.62 402	99.04 424	96.23 448	99.20 201	99.92 42	99.44 199	99.98 14	99.87 56	85.87 469	99.67 442	99.91 33	99.57 348	99.95 14

v1921920		99.56 104	99.57 102	99.55 215	99.75 170	99.11 277	99.05 264	99.61 245	99.15 261	99.88 82	99.71 185	99.08 127	99.87 250	99.90 37	99.97 73	99.66 147

v1240		99.56 104	99.58 97	99.51 231	99.80 115	99.00 291	99.00 287	99.65 223	99.15 261	99.90 67	99.75 157	99.09 123	99.88 235	99.90 37	99.96 87	99.67 133

v10		99.69 59	99.69 60	99.66 150	99.81 106	99.39 215	99.66 57	99.75 160	99.60 165	99.92 59	99.87 56	98.75 185	99.86 269	99.90 37	99.99 16	99.73 93

v1192		99.57 100	99.57 102	99.57 204	99.77 150	99.22 258	99.04 269	99.60 256	99.18 250	99.87 92	99.72 175	99.08 127	99.85 288	99.89 40	99.98 50	99.66 147

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 32	99.81 106	99.71 100	98.97 299	99.92 42	99.98 18	99.97 24	99.86 63	99.53 57	99.95 80	99.88 41	99.99 16	99.89 37

v144192		99.55 109	99.54 112	99.58 196	99.78 137	99.20 264	99.11 246	99.62 238	99.18 250	99.89 72	99.72 175	98.66 199	99.87 250	99.88 41	99.97 73	99.66 147

v8		99.68 64	99.69 60	99.65 157	99.80 115	99.40 212	99.66 57	99.76 155	99.64 149	99.93 53	99.85 68	98.66 199	99.84 305	99.88 41	99.99 16	99.71 102

mvs5depth		99.88 6	99.91 3	99.80 64	99.92 29	99.42 204	99.94 3	100.00 1	99.97 25	99.89 72	99.99 12	99.63 37	99.97 43	99.87 44	99.99 16	100.00 1

v1144		99.54 113	99.53 116	99.59 193	99.79 129	99.28 240	99.10 249	99.61 245	99.20 247	99.84 101	99.73 167	98.67 197	99.84 305	99.86 45	99.98 50	99.64 168

mmtdpeth		99.78 37	99.83 21	99.66 150	99.85 72	99.05 290	99.79 15	99.97 20	100.00 1	99.43 294	99.94 19	99.64 35	99.94 97	99.83 46	99.99 16	99.98 5

SSC-MVS		99.52 119	99.42 143	99.83 41	99.86 59	99.65 126	99.52 94	99.81 116	99.87 62	99.81 115	99.79 118	96.78 342	99.99 7	99.83 46	99.51 364	99.86 46

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 26	99.82 10	99.82 103	99.84 75	99.94 48	99.91 31	99.13 116	99.96 68	99.83 46	99.99 16	99.83 56

v2v482		99.50 122	99.47 128	99.58 196	99.78 137	99.25 248	99.14 229	99.58 271	99.25 238	99.81 115	99.62 260	98.24 258	99.84 305	99.83 46	99.97 73	99.64 168

test_vis1_rt		99.45 145	99.46 133	99.41 269	99.71 191	98.63 337	98.99 294	99.96 28	99.03 274	99.95 45	99.12 409	98.75 185	99.84 305	99.82 50	99.82 232	99.77 79

tt0805		99.63 84	99.57 102	99.81 54	99.87 54	99.88 12	99.58 82	98.70 427	99.72 112	99.91 62	99.60 278	99.43 66	99.81 358	99.81 51	99.53 360	99.73 93

VortexMVS		99.13 246	99.24 196	98.79 391	99.67 227	96.60 440	99.24 190	99.80 121	99.85 71	99.93 53	99.84 76	95.06 382	99.89 220	99.80 52	99.98 50	99.89 37

V42		99.56 104	99.54 112	99.63 171	99.79 129	99.46 189	99.39 129	99.59 262	99.24 240	99.86 95	99.70 195	98.55 214	99.82 342	99.79 53	99.95 111	99.60 204

SSC-MVS3.2		99.64 83	99.67 64	99.56 208	99.75 170	98.98 294	98.96 303	99.87 69	99.88 60	99.84 101	99.64 236	99.32 86	99.91 179	99.78 54	99.96 87	99.80 65

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 45	99.98 14	99.93 22	99.94 4	99.98 26	99.77 55	100.00 1	99.92 24

WB-MVS		99.44 149	99.32 172	99.80 64	99.81 106	99.61 150	99.47 112	99.81 116	99.82 85	99.71 182	99.72 175	96.60 347	99.98 26	99.75 56	99.23 405	99.82 63

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 63	99.68 48	99.85 81	99.95 32	99.98 14	99.92 27	99.28 91	99.98 26	99.75 56	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 75	99.89 55	99.98 14	99.90 36	99.94 4	99.98 26	99.75 56	100.00 1	99.90 29

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 49	100.00 1	99.97 14	99.61 41	99.97 43	99.75 56	100.00 1	99.84 52

AstraMVS		99.15 243	99.06 237	99.42 261	99.85 72	98.59 341	99.13 236	97.26 474	99.84 75	99.87 92	99.77 141	96.11 366	99.93 119	99.71 60	99.96 87	99.74 89

Elysia		99.69 59	99.65 73	99.81 54	99.86 59	99.72 95	99.34 148	99.77 147	99.94 36	99.91 62	99.76 149	98.55 214	99.99 7	99.70 61	99.98 50	99.72 97

StellarMVS		99.69 59	99.65 73	99.81 54	99.86 59	99.72 95	99.34 148	99.77 147	99.94 36	99.91 62	99.76 149	98.55 214	99.99 7	99.70 61	99.98 50	99.72 97

tt0320-xc		99.82 24	99.82 25	99.82 46	99.82 94	99.84 26	99.82 10	99.92 42	99.94 36	99.94 48	99.93 22	99.34 83	99.92 150	99.70 61	99.96 87	99.70 105

reproduce_monomvs		97.40 406	97.46 394	97.20 458	99.05 421	91.91 487	99.20 201	99.18 399	99.84 75	99.86 95	99.75 157	80.67 477	99.83 325	99.69 64	99.95 111	99.85 49

SPE-MVS-test		99.68 64	99.70 57	99.64 164	99.57 266	99.83 34	99.78 17	99.97 20	99.92 45	99.50 279	99.38 355	99.57 51	99.95 80	99.69 64	99.90 159	99.15 377

guyue		99.12 249	99.02 251	99.41 269	99.84 77	98.56 342	99.19 207	98.30 452	99.82 85	99.84 101	99.75 157	94.84 385	99.92 150	99.68 66	99.94 127	99.74 89

tt0320		99.79 34	99.79 34	99.81 54	99.82 94	99.84 26	99.82 10	99.90 57	99.94 36	99.94 48	99.94 19	99.07 130	99.92 150	99.68 66	99.97 73	99.67 133

MGCNet		98.61 325	98.30 346	99.52 227	97.88 487	98.95 300	98.76 341	94.11 493	99.84 75	99.32 326	99.57 298	95.57 375	99.95 80	99.68 66	99.98 50	99.68 124

CS-MVS		99.67 75	99.70 57	99.58 196	99.53 289	99.84 26	99.79 15	99.96 28	99.90 49	99.61 235	99.41 345	99.51 60	99.95 80	99.66 69	99.89 173	98.96 419

KinetiMVS		99.66 76	99.63 81	99.76 86	99.89 39	99.57 164	99.37 140	99.82 103	99.95 32	99.90 67	99.63 251	98.57 210	99.97 43	99.65 70	99.94 127	99.74 89

pmmvs6		99.86 10	99.86 13	99.83 41	99.94 18	99.90 7	99.83 7	99.91 51	99.85 71	99.94 48	99.95 16	99.73 27	99.90 198	99.65 70	99.97 73	99.69 117

MIMVSNet1		99.66 76	99.62 83	99.80 64	99.94 18	99.87 15	99.69 45	99.77 147	99.78 102	99.93 53	99.89 41	97.94 286	99.92 150	99.65 70	99.98 50	99.62 186

LuminaMVS		99.39 168	99.28 187	99.73 113	99.83 85	99.49 179	99.00 287	99.05 411	99.81 91	99.89 72	99.79 118	96.54 351	99.97 43	99.64 73	99.98 50	99.73 93

sc_t1		99.81 28	99.80 32	99.82 46	99.88 45	99.88 12	99.83 7	99.79 130	99.94 36	99.93 53	99.92 27	99.35 82	99.92 150	99.64 73	99.94 127	99.68 124

EC-MVSNet		99.69 59	99.69 60	99.68 139	99.71 191	99.91 4	99.76 23	99.96 28	99.86 65	99.51 276	99.39 353	99.57 51	99.93 119	99.64 73	99.86 204	99.20 365

K. test v3		98.87 300	98.60 311	99.69 137	99.93 24	99.46 189	99.74 27	94.97 488	99.78 102	99.88 82	99.88 50	93.66 400	99.97 43	99.61 76	99.95 111	99.64 168

KD-MVS_self_test		99.63 84	99.59 93	99.76 86	99.84 77	99.90 7	99.37 140	99.79 130	99.83 81	99.88 82	99.85 68	98.42 238	99.90 198	99.60 77	99.73 286	99.49 269

Anonymous20240521		99.44 149	99.42 143	99.49 237	99.89 39	98.96 299	99.62 67	99.76 155	99.85 71	99.82 108	99.88 50	96.39 358	99.97 43	99.59 78	99.98 50	99.55 229

TransMVSNet (Re)		99.78 37	99.77 45	99.81 54	99.91 31	99.85 21	99.75 25	99.86 75	99.70 124	99.91 62	99.89 41	99.60 43	99.87 250	99.59 78	99.74 280	99.71 102

OurMVSNet-221017-0		99.75 49	99.71 56	99.84 38	99.96 7	99.83 34	99.83 7	99.85 81	99.80 95	99.93 53	99.93 22	98.54 218	99.93 119	99.59 78	99.98 50	99.76 84

EU-MVSNet		99.39 168	99.62 83	98.72 396	99.88 45	96.44 442	99.56 87	99.85 81	99.90 49	99.90 67	99.85 68	98.09 274	99.83 325	99.58 81	99.95 111	99.90 29

mvs_anonymous		99.28 197	99.39 148	98.94 363	99.19 396	97.81 398	99.02 276	99.55 284	99.78 102	99.85 98	99.80 107	98.24 258	99.86 269	99.57 82	99.50 367	99.15 377

test1111		97.74 388	98.16 358	96.49 470	99.60 243	89.86 501	99.71 37	91.21 497	99.89 55	99.88 82	99.87 56	93.73 399	99.90 198	99.56 83	99.99 16	99.70 105

lessismore_v0				99.64 164	99.86 59	99.38 217		90.66 498		99.89 72	99.83 83	94.56 390	99.97 43	99.56 83	99.92 145	99.57 222

mvsany_test1		99.44 149	99.45 135	99.40 272	99.37 346	98.64 336	97.90 437	99.59 262	99.27 234	99.92 59	99.82 90	99.74 26	99.93 119	99.55 85	99.87 196	99.63 174

MVSMamba_PlusPlus		99.55 109	99.58 97	99.47 244	99.68 220	99.40 212	99.52 94	99.70 192	99.92 45	99.77 144	99.86 63	98.28 254	99.96 68	99.54 86	99.90 159	99.05 406

pm-mvs1		99.79 34	99.79 34	99.78 75	99.91 31	99.83 34	99.76 23	99.87 69	99.73 108	99.89 72	99.87 56	99.63 37	99.87 250	99.54 86	99.92 145	99.63 174

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 42	99.90 49	99.97 24	99.87 56	99.81 20	99.95 80	99.54 86	99.99 16	99.80 65
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		99.48 129	99.65 73	98.95 362	99.71 191	97.27 421	99.50 102	99.82 103	99.59 167	99.41 303	99.85 68	99.62 40	100.00 1	99.53 89	99.89 173	99.59 211

test2506		94.73 457	94.59 457	95.15 477	99.59 249	85.90 503	99.75 25	74.01 505	99.89 55	99.71 182	99.86 63	79.00 487	99.90 198	99.52 90	99.99 16	99.65 156

balanced_ft_v1		99.37 175	99.36 159	99.38 278	99.10 414	99.38 217	99.68 48	99.72 179	99.72 112	99.36 314	99.77 141	97.66 309	99.94 97	99.52 90	99.73 286	98.83 436

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 183	99.93 43	99.95 45	99.89 41	99.71 28	99.96 68	99.51 92	99.97 73	99.84 52

FC-MVSNet-test		99.70 57	99.65 73	99.86 30	99.88 45	99.86 18	99.72 33	99.78 141	99.90 49	99.82 108	99.83 83	98.45 234	99.87 250	99.51 92	99.97 73	99.86 46

BP-MVS1		98.72 317	98.46 327	99.50 233	99.53 289	99.00 291	99.34 148	98.53 437	99.65 145	99.73 172	99.38 355	90.62 443	99.96 68	99.50 94	99.86 204	99.55 229

UA-Net		99.78 37	99.76 49	99.86 30	99.72 187	99.71 100	99.91 4	99.95 36	99.96 28	99.71 182	99.91 31	99.15 111	99.97 43	99.50 94	100.00 1	99.90 29

viewdifsd2359ckpt11		99.62 91	99.64 78	99.56 208	99.86 59	99.19 265	99.02 276	99.93 39	99.83 81	99.88 82	99.81 97	98.99 147	99.83 325	99.48 96	99.96 87	99.65 156

viewmsd2359difaftdt		99.62 91	99.64 78	99.56 208	99.86 59	99.19 265	99.02 276	99.93 39	99.83 81	99.88 82	99.81 97	98.99 147	99.83 325	99.48 96	99.96 87	99.65 156

PMMVS2		99.48 129	99.45 135	99.57 204	99.76 154	98.99 293	98.09 414	99.90 57	98.95 284	99.78 132	99.58 291	99.57 51	99.93 119	99.48 96	99.95 111	99.79 73

VPA-MVSNet		99.66 76	99.62 83	99.79 71	99.68 220	99.75 79	99.62 67	99.69 200	99.85 71	99.80 122	99.81 97	98.81 173	99.91 179	99.47 99	99.88 183	99.70 105

GDP-MVS		98.81 308	98.57 317	99.50 233	99.53 289	99.12 276	99.28 174	99.86 75	99.53 176	99.57 246	99.32 372	90.88 438	99.98 26	99.46 100	99.74 280	99.42 310

ECVR-MVS		97.73 389	98.04 365	96.78 463	99.59 249	90.81 496	99.72 33	90.43 499	99.89 55	99.86 95	99.86 63	93.60 401	99.89 220	99.46 100	99.99 16	99.65 156

nrg030		99.70 57	99.66 71	99.82 46	99.76 154	99.84 26	99.61 73	99.70 192	99.93 43	99.78 132	99.68 216	99.10 121	99.78 372	99.45 102	99.96 87	99.83 56

FE-MVSNET2		99.68 64	99.67 64	99.72 121	99.86 59	99.68 115	99.46 116	99.88 65	99.62 154	99.87 92	99.85 68	99.06 136	99.85 288	99.44 103	99.98 50	99.63 174

TAMVS		99.49 127	99.45 135	99.63 171	99.48 314	99.42 204	99.45 117	99.57 273	99.66 141	99.78 132	99.83 83	97.85 293	99.86 269	99.44 103	99.96 87	99.61 200

GeoE		99.69 59	99.66 71	99.78 75	99.76 154	99.76 70	99.60 79	99.82 103	99.46 194	99.75 157	99.56 302	99.63 37	99.95 80	99.43 105	99.88 183	99.62 186

new-patchmatchnet		99.35 182	99.57 102	98.71 400	99.82 94	96.62 438	98.55 369	99.75 160	99.50 181	99.88 82	99.87 56	99.31 87	99.88 235	99.43 105	100.00 1	99.62 186

test20.03		99.55 109	99.54 112	99.58 196	99.79 129	99.37 222	99.02 276	99.89 60	99.60 165	99.82 108	99.62 260	98.81 173	99.89 220	99.43 105	99.86 204	99.47 277

MVSFormer		99.41 162	99.44 139	99.31 304	99.57 266	98.40 357	99.77 19	99.80 121	99.73 108	99.63 219	99.30 377	98.02 279	99.98 26	99.43 105	99.69 307	99.55 229

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 26	99.77 19	99.80 121	99.73 108	99.97 24	99.92 27	99.77 25	99.98 26	99.43 105	100.00 1	99.90 29

SDMVSNet		99.77 44	99.77 45	99.76 86	99.80 115	99.65 126	99.63 64	99.86 75	99.97 25	99.89 72	99.89 41	99.52 59	99.99 7	99.42 110	99.96 87	99.65 156

Anonymous20231211		99.62 91	99.57 102	99.76 86	99.61 241	99.60 154	99.81 13	99.73 170	99.82 85	99.90 67	99.90 36	97.97 285	99.86 269	99.42 110	99.96 87	99.80 65

SixPastTwentyTwo		99.42 156	99.30 179	99.76 86	99.92 29	99.67 118	99.70 38	99.14 404	99.65 145	99.89 72	99.90 36	96.20 365	99.94 97	99.42 110	99.92 145	99.67 133

balanced_conf03		99.50 122	99.50 121	99.50 233	99.42 337	99.49 179	99.52 94	99.75 160	99.86 65	99.78 132	99.71 185	98.20 266	99.90 198	99.39 113	99.88 183	99.10 388

patch_mono-2		99.51 120	99.46 133	99.64 164	99.70 206	99.11 277	99.04 269	99.87 69	99.71 118	99.47 284	99.79 118	98.24 258	99.98 26	99.38 114	99.96 87	99.83 56

UGNet		99.38 171	99.34 166	99.49 237	98.90 436	98.90 308	99.70 38	99.35 356	99.86 65	98.57 420	99.81 97	98.50 229	99.93 119	99.38 114	99.98 50	99.66 147
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 56	99.67 64	99.81 54	99.89 39	99.72 95	99.59 80	99.82 103	99.39 215	99.82 108	99.84 76	99.38 74	99.91 179	99.38 114	99.93 139	99.80 65

FIs		99.65 82	99.58 97	99.84 38	99.84 77	99.85 21	99.66 57	99.75 160	99.86 65	99.74 167	99.79 118	98.27 256	99.85 288	99.37 117	99.93 139	99.83 56

sd_testset		99.78 37	99.78 39	99.80 64	99.80 115	99.76 70	99.80 14	99.79 130	99.97 25	99.89 72	99.89 41	99.53 57	99.99 7	99.36 118	99.96 87	99.65 156

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 81	99.70 124	99.92 59	99.93 22	99.45 62	99.97 43	99.36 118	100.00 1	99.85 49

casdiffmvs_mvg		99.68 64	99.68 63	99.69 137	99.81 106	99.59 156	99.29 172	99.90 57	99.71 118	99.79 128	99.73 167	99.54 54	99.84 305	99.36 118	99.96 87	99.65 156
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 49	99.74 53	99.79 71	99.88 45	99.66 120	99.69 45	99.92 42	99.67 137	99.77 144	99.75 157	99.61 41	99.98 26	99.35 121	99.98 50	99.72 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 95	99.64 78	99.53 225	99.79 129	98.82 315	99.58 82	99.97 20	99.95 32	99.96 34	99.76 149	98.44 235	99.99 7	99.34 122	99.96 87	99.78 75

CHOSEN 1792x2688		99.39 168	99.30 179	99.65 157	99.88 45	99.25 248	98.78 339	99.88 65	98.66 327	99.96 34	99.79 118	97.45 316	99.93 119	99.34 122	99.99 16	99.78 75

CDS-MVSNet		99.22 219	99.13 212	99.50 233	99.35 353	99.11 277	98.96 303	99.54 290	99.46 194	99.61 235	99.70 195	96.31 361	99.83 325	99.34 122	99.88 183	99.55 229
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 280	99.16 205	98.51 408	99.75 170	95.90 454	98.07 417	99.84 88	99.84 75	99.89 72	99.73 167	96.01 369	99.99 7	99.33 125	100.00 1	99.63 174

HyFIR lowres test		98.91 293	98.64 308	99.73 113	99.85 72	99.47 183	98.07 417	99.83 97	98.64 330	99.89 72	99.60 278	92.57 414	100.00 1	99.33 125	99.97 73	99.72 97

pmmvs5		99.19 229	99.11 219	99.42 261	99.76 154	98.88 310	98.55 369	99.73 170	98.82 306	99.72 177	99.62 260	96.56 348	99.82 342	99.32 127	99.95 111	99.56 225

v148		99.40 164	99.41 146	99.39 275	99.76 154	98.94 301	99.09 254	99.59 262	99.17 255	99.81 115	99.61 270	98.41 239	99.69 425	99.32 127	99.94 127	99.53 245

baseline		99.63 84	99.62 83	99.66 150	99.80 115	99.62 140	99.44 119	99.80 121	99.71 118	99.72 177	99.69 204	99.15 111	99.83 325	99.32 127	99.94 127	99.53 245

CVMVSNet		98.61 325	98.88 285	97.80 439	99.58 256	93.60 480	99.26 183	99.64 231	99.66 141	99.72 177	99.67 220	93.26 405	99.93 119	99.30 130	99.81 242	99.87 44

PS-CasMVS		99.66 76	99.58 97	99.89 11	99.80 115	99.85 21	99.66 57	99.73 170	99.62 154	99.84 101	99.71 185	98.62 203	99.96 68	99.30 130	99.96 87	99.86 46

DTE-MVSNet		99.68 64	99.61 87	99.88 19	99.80 115	99.87 15	99.67 53	99.71 183	99.72 112	99.84 101	99.78 131	98.67 197	99.97 43	99.30 130	99.95 111	99.80 65

tmp_tt		95.75 449	95.42 444	96.76 464	89.90 504	94.42 472	98.86 320	97.87 464	78.01 495	99.30 336	99.69 204	97.70 301	95.89 497	99.29 133	98.14 463	99.95 14

PEN-MVS		99.66 76	99.59 93	99.89 11	99.83 85	99.87 15	99.66 57	99.73 170	99.70 124	99.84 101	99.73 167	98.56 213	99.96 68	99.29 133	99.94 127	99.83 56

WR-MVS_H		99.61 95	99.53 116	99.87 26	99.80 115	99.83 34	99.67 53	99.75 160	99.58 169	99.85 98	99.69 204	98.18 269	99.94 97	99.28 135	99.95 111	99.83 56

IterMVS		98.97 284	99.16 205	98.42 413	99.74 178	95.64 458	98.06 419	99.83 97	99.83 81	99.85 98	99.74 162	96.10 368	99.99 7	99.27 136	100.00 1	99.63 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NormalMVS		99.09 257	98.91 283	99.62 180	99.78 137	99.11 277	99.36 144	99.77 147	99.82 85	99.68 194	99.53 314	93.30 403	99.99 7	99.24 137	99.76 269	99.74 89

SymmetryMVS		99.01 277	98.82 293	99.58 196	99.65 233	99.11 277	99.36 144	99.20 397	99.82 85	99.68 194	99.53 314	93.30 403	99.99 7	99.24 137	99.63 328	99.64 168

WBMVS		97.50 402	97.18 405	98.48 410	98.85 444	95.89 455	98.44 386	99.52 305	99.53 176	99.52 269	99.42 344	80.10 480	99.86 269	99.24 137	99.95 111	99.68 124

h-mvs33		98.61 325	98.34 341	99.44 255	99.60 243	98.67 328	99.27 178	99.44 331	99.68 129	99.32 326	99.49 327	92.50 417	100.00 1	99.24 137	96.51 485	99.65 156

hse-mvs2		98.52 338	98.30 346	99.16 333	99.29 375	98.60 339	98.77 340	99.02 413	99.68 129	99.32 326	99.04 419	92.50 417	99.85 288	99.24 137	97.87 470	99.03 410

FMVSNet1		99.66 76	99.63 81	99.73 113	99.78 137	99.77 63	99.68 48	99.70 192	99.67 137	99.82 108	99.83 83	98.98 151	99.90 198	99.24 137	99.97 73	99.53 245

casdiffmvs		99.63 84	99.61 87	99.67 143	99.79 129	99.59 156	99.13 236	99.85 81	99.79 99	99.76 152	99.72 175	99.33 85	99.82 342	99.21 143	99.94 127	99.59 211
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		99.54 113	99.43 141	99.87 26	99.76 154	99.82 42	99.57 85	99.61 245	99.54 174	99.80 122	99.64 236	97.79 297	99.95 80	99.21 143	99.94 127	99.84 52

DELS-MVS		99.34 187	99.30 179	99.48 242	99.51 298	99.36 226	98.12 410	99.53 300	99.36 220	99.41 303	99.61 270	99.22 101	99.87 250	99.21 143	99.68 312	99.20 365
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		99.47 139	99.50 121	99.37 282	99.70 206	98.80 319	98.67 350	99.92 42	99.49 183	99.77 144	99.71 185	99.08 127	99.78 372	99.20 146	99.94 127	99.54 239

UniMVSNet (Re)		99.37 175	99.26 192	99.68 139	99.51 298	99.58 161	98.98 297	99.60 256	99.43 206	99.70 186	99.36 363	97.70 301	99.88 235	99.20 146	99.87 196	99.59 211

CANet		99.11 253	99.05 242	99.28 312	98.83 446	98.56 342	98.71 348	99.41 337	99.25 238	99.23 346	99.22 395	97.66 309	99.94 97	99.19 148	99.97 73	99.33 334

EI-MVSNet-UG-set		99.48 129	99.50 121	99.42 261	99.57 266	98.65 334	99.24 190	99.46 325	99.68 129	99.80 122	99.66 225	98.99 147	99.89 220	99.19 148	99.90 159	99.72 97

xiu_mvs_v1_base_debu		99.23 210	99.34 166	98.91 372	99.59 249	98.23 366	98.47 381	99.66 213	99.61 159	99.68 194	98.94 435	99.39 70	99.97 43	99.18 150	99.55 353	98.51 459

xiu_mvs_v1_base		99.23 210	99.34 166	98.91 372	99.59 249	98.23 366	98.47 381	99.66 213	99.61 159	99.68 194	98.94 435	99.39 70	99.97 43	99.18 150	99.55 353	98.51 459

xiu_mvs_v1_base_debi		99.23 210	99.34 166	98.91 372	99.59 249	98.23 366	98.47 381	99.66 213	99.61 159	99.68 194	98.94 435	99.39 70	99.97 43	99.18 150	99.55 353	98.51 459

VPNet		99.46 141	99.37 154	99.71 127	99.82 94	99.59 156	99.48 109	99.70 192	99.81 91	99.69 189	99.58 291	97.66 309	99.86 269	99.17 153	99.44 374	99.67 133

UniMVSNet_NR-MVSNet		99.37 175	99.25 194	99.72 121	99.47 320	99.56 165	98.97 299	99.61 245	99.43 206	99.67 201	99.28 381	97.85 293	99.95 80	99.17 153	99.81 242	99.65 156

DU-MVS		99.33 190	99.21 199	99.71 127	99.43 332	99.56 165	98.83 327	99.53 300	99.38 216	99.67 201	99.36 363	97.67 305	99.95 80	99.17 153	99.81 242	99.63 174

usedtu_dtu_shiyan2		99.44 149	99.33 171	99.78 75	99.86 59	99.76 70	99.54 90	99.79 130	99.66 141	99.66 207	99.79 118	96.76 343	99.96 68	99.15 156	99.72 293	99.62 186

EI-MVSNet-Vis-set		99.47 139	99.49 125	99.42 261	99.57 266	98.66 331	99.24 190	99.46 325	99.67 137	99.79 128	99.65 234	98.97 153	99.89 220	99.15 156	99.89 173	99.71 102

EI-MVSNet		99.38 171	99.44 139	99.21 327	99.58 256	98.09 380	99.26 183	99.46 325	99.62 154	99.75 157	99.67 220	98.54 218	99.85 288	99.15 156	99.92 145	99.68 124

VNet		99.18 233	99.06 237	99.56 208	99.24 386	99.36 226	99.33 154	99.31 370	99.67 137	99.47 284	99.57 298	96.48 352	99.84 305	99.15 156	99.30 393	99.47 277

EG-PatchMatch MVS		99.57 100	99.56 107	99.62 180	99.77 150	99.33 232	99.26 183	99.76 155	99.32 225	99.80 122	99.78 131	99.29 89	99.87 250	99.15 156	99.91 157	99.66 147

PVSNet_Blended_VisFu		99.40 164	99.38 151	99.44 255	99.90 37	98.66 331	98.94 308	99.91 51	97.97 391	99.79 128	99.73 167	99.05 138	99.97 43	99.15 156	99.99 16	99.68 124

IterMVS-LS		99.41 162	99.47 128	99.25 323	99.81 106	98.09 380	98.85 322	99.76 155	99.62 154	99.83 107	99.64 236	98.54 218	99.97 43	99.15 156	99.99 16	99.68 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 113	99.47 128	99.76 86	99.58 256	99.64 132	99.30 165	99.63 235	99.61 159	99.71 182	99.56 302	98.76 183	99.96 68	99.14 163	99.92 145	99.68 124

MVSTER		98.47 345	98.22 351	99.24 325	99.06 420	98.35 363	99.08 257	99.46 325	99.27 234	99.75 157	99.66 225	88.61 456	99.85 288	99.14 163	99.92 145	99.52 256

E5new		99.68 64	99.67 64	99.70 132	99.87 54	99.62 140	99.41 122	99.84 88	99.68 129	99.77 144	99.81 97	99.59 45	99.78 372	99.13 165	99.96 87	99.70 105

E6new		99.68 64	99.67 64	99.70 132	99.86 59	99.62 140	99.41 122	99.84 88	99.68 129	99.77 144	99.81 97	99.59 45	99.78 372	99.13 165	99.96 87	99.70 105

E6		99.68 64	99.67 64	99.70 132	99.86 59	99.62 140	99.41 122	99.84 88	99.68 129	99.77 144	99.81 97	99.59 45	99.78 372	99.13 165	99.96 87	99.70 105

E5		99.68 64	99.67 64	99.70 132	99.87 54	99.62 140	99.41 122	99.84 88	99.68 129	99.77 144	99.81 97	99.59 45	99.78 372	99.13 165	99.96 87	99.70 105

diffmvs_AUTHOR		99.48 129	99.48 126	99.47 244	99.80 115	98.89 309	98.71 348	99.82 103	99.79 99	99.66 207	99.63 251	98.87 169	99.88 235	99.13 165	99.95 111	99.62 186

Anonymous20231206		99.35 182	99.31 174	99.47 244	99.74 178	99.06 289	99.28 174	99.74 166	99.23 242	99.72 177	99.53 314	97.63 312	99.88 235	99.11 170	99.84 214	99.48 273

Syy-MVS		98.17 370	97.85 382	99.15 335	98.50 469	98.79 320	98.60 357	99.21 394	97.89 400	96.76 479	96.37 502	95.47 379	99.57 468	99.10 171	98.73 439	99.09 393

ttmdpeth		99.48 129	99.55 109	99.29 309	99.76 154	98.16 374	99.33 154	99.95 36	99.79 99	99.36 314	99.89 41	99.13 116	99.77 385	99.09 172	99.64 325	99.93 20

MVS_Test		99.28 197	99.31 174	99.19 330	99.35 353	98.79 320	99.36 144	99.49 318	99.17 255	99.21 351	99.67 220	98.78 180	99.66 447	99.09 172	99.66 321	99.10 388

usedtu_dtu_shiyan1		98.87 300	98.71 302	99.35 289	99.59 249	98.88 310	97.17 471	99.64 231	98.94 285	99.27 338	99.22 395	95.57 375	99.83 325	99.08 174	99.92 145	99.35 328

FE-MVSNET3		98.87 300	98.71 302	99.35 289	99.59 249	98.88 310	97.17 471	99.64 231	98.94 285	99.27 338	99.22 395	95.57 375	99.83 325	99.08 174	99.92 145	99.35 328

testgi		99.29 196	99.26 192	99.37 282	99.75 170	98.81 316	98.84 324	99.89 60	98.38 358	99.75 157	99.04 419	99.36 79	99.86 269	99.08 174	99.25 401	99.45 284

1112_ss		99.05 265	98.84 290	99.67 143	99.66 229	99.29 238	98.52 375	99.82 103	97.65 415	99.43 294	99.16 403	96.42 355	99.91 179	99.07 177	99.84 214	99.80 65

CANet_DTU		98.91 293	98.85 288	99.09 344	98.79 452	98.13 375	98.18 402	99.31 370	99.48 186	98.86 391	99.51 320	96.56 348	99.95 80	99.05 178	99.95 111	99.19 368

blended_shiyan8		97.82 383	97.45 396	98.92 367	98.06 483	97.45 414	97.73 443	99.35 356	97.96 394	98.35 432	97.34 483	92.76 413	99.84 305	99.04 179	96.49 487	99.47 277

blended_shiyan6		97.82 383	97.46 394	98.92 367	98.08 482	97.46 412	97.73 443	99.34 359	97.96 394	98.33 433	97.35 482	92.78 411	99.84 305	99.04 179	96.53 481	99.46 282

Baseline_NR-MVSNet		99.49 127	99.37 154	99.82 46	99.91 31	99.84 26	98.83 327	99.86 75	99.68 129	99.65 211	99.88 50	97.67 305	99.87 250	99.03 181	99.86 204	99.76 84

FMVSNet2		99.35 182	99.28 187	99.55 215	99.49 309	99.35 229	99.45 117	99.57 273	99.44 199	99.70 186	99.74 162	97.21 327	99.87 250	99.03 181	99.94 127	99.44 299

wanda-best-256-512		97.53 399	97.14 407	98.72 396	97.71 489	96.86 433	97.00 478	99.34 359	97.73 410	98.18 440	96.82 493	91.92 420	99.84 305	99.02 183	96.53 481	99.45 284

FE-blended-shiyan7		97.53 399	97.14 407	98.72 396	97.71 489	96.86 433	97.00 478	99.34 359	97.73 410	98.18 440	96.82 493	91.92 420	99.84 305	99.02 183	96.53 481	99.45 284

Test_1112_low_res		98.95 290	98.73 300	99.63 171	99.68 220	99.15 273	98.09 414	99.80 121	97.14 441	99.46 288	99.40 349	96.11 366	99.89 220	99.01 185	99.84 214	99.84 52

VDD-MVS		99.20 226	99.11 219	99.44 255	99.43 332	98.98 294	99.50 102	98.32 451	99.80 95	99.56 254	99.69 204	96.99 337	99.85 288	98.99 186	99.73 286	99.50 264

DeepC-MVS	98.90 4	99.62 91	99.61 87	99.67 143	99.72 187	99.44 197	99.24 190	99.71 183	99.27 234	99.93 53	99.90 36	99.70 31	99.93 119	98.99 186	99.99 16	99.64 168
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 129	99.47 128	99.51 231	99.77 150	99.41 211	98.81 332	99.66 213	99.42 210	99.75 157	99.66 225	99.20 103	99.76 390	98.98 188	99.99 16	99.36 325

EPNet_dtu		97.62 394	97.79 385	97.11 462	96.67 496	92.31 485	98.51 376	98.04 458	99.24 240	95.77 488	99.47 334	93.78 398	99.66 447	98.98 188	99.62 330	99.37 322
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 187	99.32 172	99.39 275	99.67 227	98.77 322	98.57 366	99.81 116	99.61 159	99.48 282	99.41 345	98.47 230	99.86 269	98.97 190	99.90 159	99.53 245
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		99.40 164	99.31 174	99.68 139	99.43 332	99.55 169	99.73 30	99.50 314	99.46 194	99.88 82	99.36 363	97.54 313	99.87 250	98.97 190	99.87 196	99.63 174

TestfortrainingZip a		99.61 95	99.53 116	99.85 32	99.76 154	99.84 26	99.38 132	99.78 141	99.58 169	99.81 115	99.66 225	99.02 142	99.90 198	98.96 192	99.79 254	99.81 64

viewdifsd2359ckpt07		99.51 120	99.50 121	99.52 227	99.80 115	99.19 265	98.92 312	99.88 65	99.72 112	99.64 214	99.62 260	99.06 136	99.81 358	98.96 192	99.94 127	99.56 225

GBi-Net		99.42 156	99.31 174	99.73 113	99.49 309	99.77 63	99.68 48	99.70 192	99.44 199	99.62 229	99.83 83	97.21 327	99.90 198	98.96 192	99.90 159	99.53 245

FMVSNet5		97.80 386	97.25 403	99.42 261	98.83 446	98.97 297	99.38 132	99.80 121	98.87 298	99.25 342	99.69 204	80.60 479	99.91 179	98.96 192	99.90 159	99.38 319

test1		99.42 156	99.31 174	99.73 113	99.49 309	99.77 63	99.68 48	99.70 192	99.44 199	99.62 229	99.83 83	97.21 327	99.90 198	98.96 192	99.90 159	99.53 245

FMVSNet3		98.80 309	98.63 310	99.32 300	99.13 405	98.72 325	99.10 249	99.48 319	99.23 242	99.62 229	99.64 236	92.57 414	99.86 269	98.96 192	99.90 159	99.39 317

UnsupCasMVSNet_eth		98.83 305	98.57 317	99.59 193	99.68 220	99.45 195	98.99 294	99.67 208	99.48 186	99.55 259	99.36 363	94.92 383	99.86 269	98.95 198	96.57 480	99.45 284

CHOSEN 280x420		98.41 350	98.41 333	98.40 414	99.34 362	95.89 455	96.94 481	99.44 331	98.80 310	99.25 342	99.52 318	93.51 402	99.98 26	98.94 199	99.98 50	99.32 338

E4		99.61 95	99.59 93	99.66 150	99.84 77	99.53 172	99.08 257	99.84 88	99.65 145	99.74 167	99.80 107	99.45 62	99.77 385	98.93 200	99.95 111	99.69 117

TDRefinement		99.72 53	99.70 57	99.77 79	99.90 37	99.85 21	99.86 6	99.92 42	99.69 127	99.78 132	99.92 27	99.37 76	99.88 235	98.93 200	99.95 111	99.60 204

viewmacassd2359aftdt		99.63 84	99.61 87	99.68 139	99.84 77	99.61 150	99.14 229	99.87 69	99.71 118	99.75 157	99.77 141	99.54 54	99.72 409	98.91 202	99.96 87	99.70 105

alignmvs		98.28 360	97.96 371	99.25 323	99.12 407	98.93 304	99.03 272	98.42 444	99.64 149	98.72 406	97.85 474	90.86 439	99.62 459	98.88 203	99.13 407	99.19 368

testing3-2		96.51 428	96.43 423	96.74 466	99.36 349	91.38 493	99.10 249	97.87 464	99.48 186	98.57 420	98.71 450	76.65 492	99.66 447	98.87 204	99.26 400	99.18 370

MGCFI-Net		99.02 271	99.01 255	99.06 351	99.11 412	98.60 339	99.63 64	99.67 208	99.63 151	98.58 418	97.65 477	99.07 130	99.57 468	98.85 205	98.92 423	99.03 410

sss		98.90 295	98.77 299	99.27 317	99.48 314	98.44 354	98.72 346	99.32 366	97.94 397	99.37 313	99.35 368	96.31 361	99.91 179	98.85 205	99.63 328	99.47 277

xiu_mvs_v2_base		99.02 271	99.11 219	98.77 393	99.37 346	98.09 380	98.13 409	99.51 310	99.47 191	99.42 297	98.54 459	99.38 74	99.97 43	98.83 207	99.33 389	98.24 471

PS-MVSNAJ		99.00 280	99.08 231	98.76 394	99.37 346	98.10 379	98.00 425	99.51 310	99.47 191	99.41 303	98.50 461	99.28 91	99.97 43	98.83 207	99.34 388	98.20 475

E2		99.54 113	99.51 119	99.62 180	99.78 137	99.47 183	99.01 281	99.82 103	99.55 172	99.69 189	99.77 141	99.26 95	99.76 390	98.82 209	99.93 139	99.62 186

E3		99.54 113	99.51 119	99.62 180	99.78 137	99.47 183	99.01 281	99.82 103	99.55 172	99.69 189	99.77 141	99.25 99	99.76 390	98.82 209	99.93 139	99.62 186

D2MVS		99.22 219	99.19 202	99.29 309	99.69 212	98.74 324	98.81 332	99.41 337	98.55 339	99.68 194	99.69 204	98.13 271	99.87 250	98.82 209	99.98 50	99.24 353

PatchT		98.45 347	98.32 343	98.83 387	98.94 434	98.29 364	99.24 190	98.82 421	99.84 75	99.08 368	99.76 149	91.37 428	99.94 97	98.82 209	99.00 418	98.26 470

testf1		99.63 84	99.60 91	99.72 121	99.94 18	99.95 2	99.47 112	99.89 60	99.43 206	99.88 82	99.80 107	99.26 95	99.90 198	98.81 213	99.88 183	99.32 338

APD_test2		99.63 84	99.60 91	99.72 121	99.94 18	99.95 2	99.47 112	99.89 60	99.43 206	99.88 82	99.80 107	99.26 95	99.90 198	98.81 213	99.88 183	99.32 338

gbinet_0.2-2-1-0.02		97.52 401	97.07 409	98.88 381	97.35 495	97.35 419	97.17 471	99.25 383	97.86 405	98.41 430	96.54 499	90.74 441	99.85 288	98.80 215	97.51 474	99.43 305

usedtu_blend_shiyan5		97.97 380	97.65 392	98.92 367	97.71 489	97.49 409	99.53 92	99.81 116	99.52 180	98.18 440	96.82 493	91.92 420	99.83 325	98.79 216	96.53 481	99.45 284

blend_shiyan4		95.04 456	93.76 460	98.88 381	97.92 485	97.49 409	97.72 445	99.34 359	97.93 398	97.65 468	97.11 487	77.69 490	99.83 325	98.79 216	79.72 498	99.33 334

sasdasda		99.02 271	99.00 259	99.09 344	99.10 414	98.70 326	99.61 73	99.66 213	99.63 151	98.64 412	97.65 477	99.04 139	99.54 472	98.79 216	98.92 423	99.04 408

Effi-MVS+		99.06 262	98.97 270	99.34 292	99.31 369	98.98 294	98.31 394	99.91 51	98.81 308	98.79 400	98.94 435	99.14 114	99.84 305	98.79 216	98.74 436	99.20 365

canonicalmvs		99.02 271	99.00 259	99.09 344	99.10 414	98.70 326	99.61 73	99.66 213	99.63 151	98.64 412	97.65 477	99.04 139	99.54 472	98.79 216	98.92 423	99.04 408

VDDNet		98.97 284	98.82 293	99.42 261	99.71 191	98.81 316	99.62 67	98.68 428	99.81 91	99.38 311	99.80 107	94.25 392	99.85 288	98.79 216	99.32 391	99.59 211

CR-MVSNet		98.35 357	98.20 353	98.83 387	99.05 421	98.12 376	99.30 165	99.67 208	97.39 429	99.16 357	99.79 118	91.87 425	99.91 179	98.78 222	98.77 432	98.44 464

test_method		91.72 461	92.32 461	89.91 480	93.49 503	70.18 506	90.28 495	99.56 278	61.71 498	95.39 490	99.52 318	93.90 394	99.94 97	98.76 223	98.27 456	99.62 186

RPMNet		98.60 328	98.53 323	98.83 387	99.05 421	98.12 376	99.30 165	99.62 238	99.86 65	99.16 357	99.74 162	92.53 416	99.92 150	98.75 224	98.77 432	98.44 464

mamba_0408		99.54 113	99.55 109	99.54 221	99.71 191	99.24 252	99.27 178	99.79 130	99.72 112	99.78 132	99.64 236	99.36 79	99.93 119	98.74 225	99.90 159	99.45 284

SSM_04072		99.55 109	99.55 109	99.55 215	99.71 191	99.24 252	99.27 178	99.79 130	99.72 112	99.78 132	99.64 236	99.36 79	99.97 43	98.74 225	99.90 159	99.45 284

SSM_0407		99.56 104	99.56 107	99.54 221	99.71 191	99.24 252	99.15 225	99.84 88	99.80 95	99.78 132	99.70 195	99.44 64	99.93 119	98.74 225	99.90 159	99.45 284

SSM_0404		99.57 100	99.58 97	99.54 221	99.76 154	99.28 240	99.19 207	99.84 88	99.80 95	99.78 132	99.70 195	99.44 64	99.93 119	98.74 225	99.95 111	99.41 311

pmmvs4		99.13 246	99.06 237	99.36 287	99.57 266	99.10 284	98.01 423	99.25 383	98.78 313	99.58 243	99.44 341	98.24 258	99.76 390	98.74 225	99.93 139	99.22 358

viewmanbaseed2359cas		99.50 122	99.47 128	99.61 186	99.73 182	99.52 176	99.03 272	99.83 97	99.49 183	99.65 211	99.64 236	99.18 105	99.71 414	98.73 230	99.92 145	99.58 216

tttt0517		97.62 394	97.20 404	98.90 378	99.76 154	97.40 417	99.48 109	94.36 490	99.06 272	99.70 186	99.49 327	84.55 472	99.94 97	98.73 230	99.65 323	99.36 325

viewcassd2359sk11		99.48 129	99.45 135	99.58 196	99.73 182	99.42 204	98.96 303	99.80 121	99.44 199	99.63 219	99.74 162	99.09 123	99.76 390	98.72 232	99.91 157	99.57 222

EPP-MVSNet		99.17 238	99.00 259	99.66 150	99.80 115	99.43 201	99.70 38	99.24 387	99.48 186	99.56 254	99.77 141	94.89 384	99.93 119	98.72 232	99.89 173	99.63 174

FE-MVSNET		99.45 145	99.36 159	99.71 127	99.84 77	99.64 132	99.16 222	99.91 51	98.65 328	99.73 172	99.73 167	98.54 218	99.82 342	98.71 234	99.96 87	99.67 133

Anonymous20240529		99.42 156	99.34 166	99.65 157	99.53 289	99.60 154	99.63 64	99.39 347	99.47 191	99.76 152	99.78 131	98.13 271	99.86 269	98.70 235	99.68 312	99.49 269

ACMH	98.42 6	99.59 99	99.54 112	99.72 121	99.86 59	99.62 140	99.56 87	99.79 130	98.77 315	99.80 122	99.85 68	99.64 35	99.85 288	98.70 235	99.89 173	99.70 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 190	99.28 187	99.47 244	99.57 266	99.39 215	99.78 17	99.43 334	98.87 298	99.57 246	99.82 90	98.06 277	99.87 250	98.69 237	99.73 286	99.15 377

LFMVS		98.46 346	98.19 356	99.26 320	99.24 386	98.52 350	99.62 67	96.94 476	99.87 62	99.31 331	99.58 291	91.04 433	99.81 358	98.68 238	99.42 378	99.45 284

WR-MVS		99.11 253	98.93 275	99.66 150	99.30 373	99.42 204	98.42 387	99.37 352	99.04 273	99.57 246	99.20 401	96.89 339	99.86 269	98.66 239	99.87 196	99.70 105

mvsmamba		99.08 258	98.95 273	99.45 251	99.36 349	99.18 270	99.39 129	98.81 422	99.37 217	99.35 317	99.70 195	96.36 360	99.94 97	98.66 239	99.59 344	99.22 358

viewdifsd2359ckpt13		99.42 156	99.37 154	99.57 204	99.72 187	99.46 189	99.01 281	99.80 121	99.20 247	99.51 276	99.60 278	98.92 160	99.70 418	98.65 241	99.90 159	99.55 229

RRT-MVS		99.08 258	99.00 259	99.33 295	99.27 380	98.65 334	99.62 67	99.93 39	99.66 141	99.67 201	99.82 90	95.27 381	99.93 119	98.64 242	99.09 411	99.41 311

E3new		99.42 156	99.37 154	99.56 208	99.68 220	99.38 217	98.93 311	99.79 130	99.30 229	99.55 259	99.69 204	98.88 167	99.76 390	98.63 243	99.89 173	99.53 245

Anonymous202405211		98.75 313	98.46 327	99.63 171	99.34 362	99.66 120	99.47 112	97.65 467	99.28 233	99.56 254	99.50 323	93.15 406	99.84 305	98.62 244	99.58 346	99.40 314

lecture		99.56 104	99.48 126	99.81 54	99.78 137	99.86 18	99.50 102	99.70 192	99.59 167	99.75 157	99.71 185	98.94 156	99.92 150	98.59 245	99.76 269	99.66 147

EPNet		98.13 371	97.77 386	99.18 332	94.57 502	97.99 386	99.24 190	97.96 460	99.74 107	97.29 472	99.62 260	93.13 407	99.97 43	98.59 245	99.83 222	99.58 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 265	99.09 229	98.91 372	99.21 391	98.36 362	98.82 331	99.47 322	98.85 301	98.90 386	99.56 302	98.78 180	99.09 488	98.57 247	99.68 312	99.26 350

Patchmatch-RL test		98.60 328	98.36 338	99.33 295	99.77 150	99.07 287	98.27 396	99.87 69	98.91 293	99.74 167	99.72 175	90.57 445	99.79 369	98.55 248	99.85 209	99.11 386

pmmvs3		98.08 374	97.80 383	98.91 372	99.41 339	97.69 404	97.87 438	99.66 213	95.87 460	99.50 279	99.51 320	90.35 447	99.97 43	98.55 248	99.47 371	99.08 399

ETV-MVS		99.18 233	99.18 203	99.16 333	99.34 362	99.28 240	99.12 241	99.79 130	99.48 186	98.93 380	98.55 458	99.40 69	99.93 119	98.51 250	99.52 363	98.28 469

viewdifsd2359ckpt09		99.24 208	99.16 205	99.49 237	99.70 206	99.22 258	98.88 316	99.81 116	98.70 323	99.38 311	99.37 358	98.22 263	99.76 390	98.48 251	99.88 183	99.51 258

jason		99.16 239	99.11 219	99.32 300	99.75 170	98.44 354	98.26 398	99.39 347	98.70 323	99.74 167	99.30 377	98.54 218	99.97 43	98.48 251	99.82 232	99.55 229
jason: jason.
APDe-MVS		99.48 129	99.36 159	99.85 32	99.55 280	99.81 47	99.50 102	99.69 200	98.99 277	99.75 157	99.71 185	98.79 178	99.93 119	98.46 253	99.85 209	99.80 65
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
icg_test_0407_2		99.30 194	99.29 184	99.31 304	99.71 191	98.55 344	98.17 404	99.71 183	99.41 211	99.73 172	99.60 278	99.17 107	99.92 150	98.45 254	99.70 299	99.45 284

IMVS_0407		99.38 171	99.42 143	99.28 312	99.71 191	98.55 344	99.27 178	99.71 183	99.41 211	99.73 172	99.60 278	99.17 107	99.83 325	98.45 254	99.70 299	99.45 284

IMVS_0404		99.23 210	99.20 200	99.32 300	99.71 191	98.55 344	98.57 366	99.71 183	99.41 211	99.52 269	99.60 278	98.12 273	99.95 80	98.45 254	99.70 299	99.45 284

IMVS_0403		99.37 175	99.39 148	99.28 312	99.71 191	98.55 344	99.19 207	99.71 183	99.41 211	99.67 201	99.60 278	99.12 119	99.84 305	98.45 254	99.70 299	99.45 284

CL-MVSNet_self_test		98.71 319	98.56 321	99.15 335	99.22 389	98.66 331	97.14 474	99.51 310	98.09 384	99.54 262	99.27 383	96.87 340	99.74 404	98.43 258	98.96 420	99.03 410

our_test_3		98.85 304	99.09 229	98.13 427	99.66 229	94.90 470	97.72 445	99.58 271	99.07 270	99.64 214	99.62 260	98.19 267	99.93 119	98.41 259	99.95 111	99.55 229

Gipuma		99.57 100	99.59 93	99.49 237	99.98 3	99.71 100	99.72 33	99.84 88	99.81 91	99.94 48	99.78 131	98.91 163	99.71 414	98.41 259	99.95 111	99.05 406
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 408	96.91 417	98.74 395	97.72 488	97.57 406	97.60 452	97.36 473	98.00 387	99.21 351	98.02 470	90.04 450	99.79 369	98.37 261	95.89 490	98.86 433

PM-MVS		99.36 180	99.29 184	99.58 196	99.83 85	99.66 120	98.95 306	99.86 75	98.85 301	99.81 115	99.73 167	98.40 243	99.92 150	98.36 262	99.83 222	99.17 373

baseline1		97.73 389	97.33 400	98.96 360	99.30 373	97.73 402	99.40 127	98.42 444	99.33 224	99.46 288	99.21 399	91.18 431	99.82 342	98.35 263	91.26 493	99.32 338

MVS-HIRNet		97.86 381	98.22 351	96.76 464	99.28 378	91.53 491	98.38 389	92.60 496	99.13 263	99.31 331	99.96 15	97.18 331	99.68 437	98.34 264	99.83 222	99.07 404

GA-MVS		97.99 379	97.68 389	98.93 366	99.52 296	98.04 384	97.19 470	99.05 411	98.32 371	98.81 396	98.97 431	89.89 452	99.41 483	98.33 265	99.05 414	99.34 333

Fast-Effi-MVS+		99.02 271	98.87 286	99.46 248	99.38 344	99.50 178	99.04 269	99.79 130	97.17 439	98.62 414	98.74 449	99.34 83	99.95 80	98.32 266	99.41 379	98.92 426

MDA-MVSNet_test_wron		98.95 290	98.99 266	98.85 383	99.64 234	97.16 424	98.23 400	99.33 364	98.93 290	99.56 254	99.66 225	97.39 320	99.83 325	98.29 267	99.88 183	99.55 229

N_pmnet		98.73 316	98.53 323	99.35 289	99.72 187	98.67 328	98.34 391	94.65 489	98.35 365	99.79 128	99.68 216	98.03 278	99.93 119	98.28 268	99.92 145	99.44 299

ET-MVSNet_ETH3D		96.78 420	96.07 430	98.91 372	99.26 383	97.92 393	97.70 448	96.05 481	97.96 394	92.37 494	98.43 462	87.06 460	99.90 198	98.27 269	97.56 473	98.91 427

thisisatest0530		97.45 403	96.95 414	98.94 363	99.68 220	97.73 402	99.09 254	94.19 492	98.61 335	99.56 254	99.30 377	84.30 474	99.93 119	98.27 269	99.54 358	99.16 375

YYNet1		98.95 290	98.99 266	98.84 385	99.64 234	97.14 426	98.22 401	99.32 366	98.92 292	99.59 241	99.66 225	97.40 318	99.83 325	98.27 269	99.90 159	99.55 229

reproduce_model		99.50 122	99.40 147	99.83 41	99.60 243	99.83 34	99.12 241	99.68 203	99.49 183	99.80 122	99.79 118	99.01 144	99.93 119	98.24 272	99.82 232	99.73 93

ACMM	98.09 11	99.46 141	99.38 151	99.72 121	99.80 115	99.69 112	99.13 236	99.65 223	98.99 277	99.64 214	99.72 175	99.39 70	99.86 269	98.23 273	99.81 242	99.60 204
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 287	98.87 286	99.24 325	99.57 266	98.40 357	98.12 410	99.18 399	98.28 373	99.63 219	99.13 405	98.02 279	99.97 43	98.22 274	99.69 307	99.35 328

3Dnovator	99.15 2	99.43 153	99.36 159	99.65 157	99.39 341	99.42 204	99.70 38	99.56 278	99.23 242	99.35 317	99.80 107	99.17 107	99.95 80	98.21 275	99.84 214	99.59 211

Fast-Effi-MVS+-dtu		99.20 226	99.12 216	99.43 259	99.25 384	99.69 112	99.05 264	99.82 103	99.50 181	98.97 376	99.05 417	98.98 151	99.98 26	98.20 276	99.24 403	98.62 449

MS-PatchMatch		99.00 280	98.97 270	99.09 344	99.11 412	98.19 370	98.76 341	99.33 364	98.49 348	99.44 290	99.58 291	98.21 264	99.69 425	98.20 276	99.62 330	99.39 317

TSAR-MVS + GP.		99.12 249	99.04 248	99.38 278	99.34 362	99.16 271	98.15 406	99.29 374	98.18 380	99.63 219	99.62 260	99.18 105	99.68 437	98.20 276	99.74 280	99.30 344

DP-MVS		99.48 129	99.39 148	99.74 102	99.57 266	99.62 140	99.29 172	99.61 245	99.87 62	99.74 167	99.76 149	98.69 193	99.87 250	98.20 276	99.80 249	99.75 87

MVP-Stereo		99.16 239	99.08 231	99.43 259	99.48 314	99.07 287	99.08 257	99.55 284	98.63 331	99.31 331	99.68 216	98.19 267	99.78 372	98.18 280	99.58 346	99.45 284
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 153	99.30 179	99.80 64	99.83 85	99.81 47	99.52 94	99.70 192	98.35 365	99.51 276	99.50 323	99.31 87	99.88 235	98.18 280	99.84 214	99.69 117

MDA-MVSNet-bldmvs		99.06 262	99.05 242	99.07 349	99.80 115	97.83 397	98.89 315	99.72 179	99.29 230	99.63 219	99.70 195	96.47 353	99.89 220	98.17 282	99.82 232	99.50 264

JIA-IIPM		98.06 375	97.92 378	98.50 409	98.59 465	97.02 428	98.80 335	98.51 439	99.88 60	97.89 456	99.87 56	91.89 424	99.90 198	98.16 283	97.68 472	98.59 452

EIA-MVS		99.12 249	99.01 255	99.45 251	99.36 349	99.62 140	99.34 148	99.79 130	98.41 354	98.84 393	98.89 439	98.75 185	99.84 305	98.15 284	99.51 364	98.89 430

miper_lstm_enhance		98.65 324	98.60 311	98.82 390	99.20 394	97.33 420	97.78 441	99.66 213	99.01 276	99.59 241	99.50 323	94.62 389	99.85 288	98.12 285	99.90 159	99.26 350

reproduce-ours		99.46 141	99.35 164	99.82 46	99.56 277	99.83 34	99.05 264	99.65 223	99.45 197	99.78 132	99.78 131	98.93 157	99.93 119	98.11 286	99.81 242	99.70 105

our_new_method		99.46 141	99.35 164	99.82 46	99.56 277	99.83 34	99.05 264	99.65 223	99.45 197	99.78 132	99.78 131	98.93 157	99.93 119	98.11 286	99.81 242	99.70 105

Effi-MVS+-dtu		99.07 261	98.92 279	99.52 227	98.89 439	99.78 57	99.15 225	99.66 213	99.34 221	98.92 383	99.24 393	97.69 303	99.98 26	98.11 286	99.28 396	98.81 438

tpm		97.15 412	96.95 414	97.75 441	98.91 435	94.24 474	99.32 157	97.96 460	97.71 413	98.29 434	99.32 372	86.72 466	99.92 150	98.10 289	96.24 488	99.09 393

DeepPCF-MVS	98.42 6	99.18 233	99.02 251	99.67 143	99.22 389	99.75 79	97.25 468	99.47 322	98.72 320	99.66 207	99.70 195	99.29 89	99.63 458	98.07 290	99.81 242	99.62 186

ppachtmachnet_test		98.89 298	99.12 216	98.20 425	99.66 229	95.24 466	97.63 450	99.68 203	99.08 268	99.78 132	99.62 260	98.65 201	99.88 235	98.02 291	99.96 87	99.48 273

tpmrst		97.73 389	98.07 364	96.73 467	98.71 461	92.00 486	99.10 249	98.86 418	98.52 344	98.92 383	99.54 312	91.90 423	99.82 342	98.02 291	99.03 416	98.37 466

CSCG		99.37 175	99.29 184	99.60 190	99.71 191	99.46 189	99.43 121	99.85 81	98.79 311	99.41 303	99.60 278	98.92 160	99.92 150	98.02 291	99.92 145	99.43 305

eth_miper_zixun_eth		98.68 322	98.71 302	98.60 404	99.10 414	96.84 435	97.52 458	99.54 290	98.94 285	99.58 243	99.48 330	96.25 364	99.76 390	98.01 294	99.93 139	99.21 361

Patchmtry		98.78 310	98.54 322	99.49 237	98.89 439	99.19 265	99.32 157	99.67 208	99.65 145	99.72 177	99.79 118	91.87 425	99.95 80	98.00 295	99.97 73	99.33 334

PVSNet_BlendedMVS		99.03 269	99.01 255	99.09 344	99.54 282	97.99 386	98.58 362	99.82 103	97.62 416	99.34 321	99.71 185	98.52 226	99.77 385	97.98 296	99.97 73	99.52 256

PVSNet_Blended		98.70 320	98.59 313	99.02 354	99.54 282	97.99 386	97.58 453	99.82 103	95.70 464	99.34 321	98.98 429	98.52 226	99.77 385	97.98 296	99.83 222	99.30 344

cl____		98.54 336	98.41 333	98.92 367	99.03 425	97.80 400	97.46 460	99.59 262	98.90 294	99.60 238	99.46 337	93.85 396	99.78 372	97.97 298	99.89 173	99.17 373

DIV-MVS_self_test		98.54 336	98.42 332	98.92 367	99.03 425	97.80 400	97.46 460	99.59 262	98.90 294	99.60 238	99.46 337	93.87 395	99.78 372	97.97 298	99.89 173	99.18 370

AUN-MVS		97.82 383	97.38 399	99.14 338	99.27 380	98.53 348	98.72 346	99.02 413	98.10 382	97.18 475	99.03 423	89.26 454	99.85 288	97.94 300	97.91 468	99.03 410

FA-MVS(test-final)		98.52 338	98.32 343	99.10 343	99.48 314	98.67 328	99.77 19	98.60 435	97.35 431	99.63 219	99.80 107	93.07 408	99.84 305	97.92 301	99.30 393	98.78 441

ambc				99.20 329	99.35 353	98.53 348	99.17 216	99.46 325		99.67 201	99.80 107	98.46 233	99.70 418	97.92 301	99.70 299	99.38 319

USDC		98.96 287	98.93 275	99.05 352	99.54 282	97.99 386	97.07 477	99.80 121	98.21 377	99.75 157	99.77 141	98.43 236	99.64 456	97.90 303	99.88 183	99.51 258

OPM-MVS		99.26 203	99.13 212	99.63 171	99.70 206	99.61 150	98.58 362	99.48 319	98.50 346	99.52 269	99.63 251	99.14 114	99.76 390	97.89 304	99.77 267	99.51 258
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 192	99.17 204	99.77 79	99.69 212	99.80 51	99.14 229	99.31 370	99.16 257	99.62 229	99.61 270	98.35 247	99.91 179	97.88 305	99.72 293	99.61 200
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 41	99.70 206	99.79 54	99.14 229	99.61 245					99.92 150	97.88 305	99.72 293	99.77 79

c3_l		98.72 317	98.71 302	98.72 396	99.12 407	97.22 423	97.68 449	99.56 278	98.90 294	99.54 262	99.48 330	96.37 359	99.73 407	97.88 305	99.88 183	99.21 361

3Dnovator+	98.92 3	99.35 182	99.24 196	99.67 143	99.35 353	99.47 183	99.62 67	99.50 314	99.44 199	99.12 364	99.78 131	98.77 182	99.94 97	97.87 308	99.72 293	99.62 186

miper_ehance_all_eth		98.59 331	98.59 313	98.59 405	98.98 431	97.07 427	97.49 459	99.52 305	98.50 346	99.52 269	99.37 358	96.41 357	99.71 414	97.86 309	99.62 330	99.00 417

WTY-MVS		98.59 331	98.37 337	99.26 320	99.43 332	98.40 357	98.74 344	99.13 406	98.10 382	99.21 351	99.24 393	94.82 386	99.90 198	97.86 309	98.77 432	99.49 269

APD_test1		99.36 180	99.28 187	99.61 186	99.89 39	99.89 10	99.32 157	99.74 166	99.18 250	99.69 189	99.75 157	98.41 239	99.84 305	97.85 311	99.70 299	99.10 388

SED-MVS		99.40 164	99.28 187	99.77 79	99.69 212	99.82 42	99.20 201	99.54 290	99.13 263	99.82 108	99.63 251	98.91 163	99.92 150	97.85 311	99.70 299	99.58 216

test_241102_TWO								99.54 290	99.13 263	99.76 152	99.63 251	98.32 252	99.92 150	97.85 311	99.69 307	99.75 87

MVS_111021_HR		99.12 249	99.02 251	99.40 272	99.50 304	99.11 277	97.92 434	99.71 183	98.76 318	99.08 368	99.47 334	99.17 107	99.54 472	97.85 311	99.76 269	99.54 239

MTAPA		99.35 182	99.20 200	99.80 64	99.81 106	99.81 47	99.33 154	99.53 300	99.27 234	99.42 297	99.63 251	98.21 264	99.95 80	97.83 315	99.79 254	99.65 156

MSC_two_6792asdad				99.74 102	99.03 425	99.53 172		99.23 388					99.92 150	97.77 316	99.69 307	99.78 75

No_MVS				99.74 102	99.03 425	99.53 172		99.23 388					99.92 150	97.77 316	99.69 307	99.78 75

TESTMET0.1,1		96.24 435	95.84 436	97.41 451	98.24 476	93.84 477	97.38 462	95.84 485	98.43 351	97.81 462	98.56 457	79.77 483	99.89 220	97.77 316	98.77 432	98.52 458

ACMH+	98.40 8	99.50 122	99.43 141	99.71 127	99.86 59	99.76 70	99.32 157	99.77 147	99.53 176	99.77 144	99.76 149	99.26 95	99.78 372	97.77 316	99.88 183	99.60 204

IU-MVS					99.69 212	99.77 63		99.22 391	97.50 423	99.69 189				97.75 320	99.70 299	99.77 79

114514_t		98.49 343	98.11 361	99.64 164	99.73 182	99.58 161	99.24 190	99.76 155	89.94 490	99.42 297	99.56 302	97.76 300	99.86 269	97.74 321	99.82 232	99.47 277

DVP-MVS++		99.38 171	99.25 194	99.77 79	99.03 425	99.77 63	99.74 27	99.61 245	99.18 250	99.76 152	99.61 270	99.00 145	99.92 150	97.72 322	99.60 340	99.62 186

test_0728_THIRD									99.18 250	99.62 229	99.61 270	98.58 209	99.91 179	97.72 322	99.80 249	99.77 79

EGC-MVSNET		89.05 463	85.52 466	99.64 164	99.89 39	99.78 57	99.56 87	99.52 305	24.19 499	49.96 500	99.83 83	99.15 111	99.92 150	97.71 324	99.85 209	99.21 361

miper_enhance_ethall		98.03 376	97.94 376	98.32 419	98.27 475	96.43 443	96.95 480	99.41 337	96.37 455	99.43 294	98.96 433	94.74 387	99.69 425	97.71 324	99.62 330	98.83 436

TSAR-MVS + MP.		99.34 187	99.24 196	99.63 171	99.82 94	99.37 222	99.26 183	99.35 356	98.77 315	99.57 246	99.70 195	99.27 94	99.88 235	97.71 324	99.75 273	99.65 156
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 397	97.28 401	98.40 414	98.37 473	96.75 436	97.24 469	99.37 352	97.31 433	99.41 303	99.22 395	87.30 458	99.37 484	97.70 327	99.62 330	99.08 399

MP-MVS-pluss		99.14 244	98.92 279	99.80 64	99.83 85	99.83 34	98.61 355	99.63 235	96.84 448	99.44 290	99.58 291	98.81 173	99.91 179	97.70 327	99.82 232	99.67 133
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 197	99.11 219	99.79 71	99.75 170	99.81 47	98.95 306	99.53 300	98.27 374	99.53 267	99.73 167	98.75 185	99.87 250	97.70 327	99.83 222	99.68 124

UnsupCasMVSNet_bld		98.55 335	98.27 349	99.40 272	99.56 277	99.37 222	97.97 430	99.68 203	97.49 424	99.08 368	99.35 368	95.41 380	99.82 342	97.70 327	98.19 460	99.01 416

MVS_111021_LR		99.13 246	99.03 250	99.42 261	99.58 256	99.32 234	97.91 436	99.73 170	98.68 325	99.31 331	99.48 330	99.09 123	99.66 447	97.70 327	99.77 267	99.29 347

IS-MVSNet		99.03 269	98.85 288	99.55 215	99.80 115	99.25 248	99.73 30	99.15 403	99.37 217	99.61 235	99.71 185	94.73 388	99.81 358	97.70 327	99.88 183	99.58 216

MED-MVS test				99.74 102	99.76 154	99.65 126	99.38 132	99.78 141	99.58 169	99.81 115	99.66 225		99.90 198	97.69 333	99.79 254	99.67 133

MED-MVS		99.45 145	99.36 159	99.74 102	99.76 154	99.65 126	99.38 132	99.78 141	99.31 227	99.81 115	99.66 225	99.02 142	99.90 198	97.69 333	99.79 254	99.67 133

ME-MVS		99.26 203	99.10 227	99.73 113	99.60 243	99.65 126	98.75 343	99.45 330	99.31 227	99.65 211	99.66 225	98.00 284	99.86 269	97.69 333	99.79 254	99.67 133

test-LLR		97.15 412	96.95 414	97.74 442	98.18 478	95.02 468	97.38 462	96.10 478	98.00 387	97.81 462	98.58 454	90.04 450	99.91 179	97.69 333	98.78 430	98.31 467

test-mter		96.23 436	95.73 439	97.74 442	98.18 478	95.02 468	97.38 462	96.10 478	97.90 399	97.81 462	98.58 454	79.12 486	99.91 179	97.69 333	98.78 430	98.31 467

MonoMVSNet		98.23 365	98.32 343	97.99 430	98.97 432	96.62 438	99.49 107	98.42 444	99.62 154	99.40 308	99.79 118	95.51 378	98.58 495	97.68 338	95.98 489	98.76 444

XVS		99.27 201	99.11 219	99.75 97	99.71 191	99.71 100	99.37 140	99.61 245	99.29 230	98.76 403	99.47 334	98.47 230	99.88 235	97.62 339	99.73 286	99.67 133

X-MVStestdata		96.09 440	94.87 453	99.75 97	99.71 191	99.71 100	99.37 140	99.61 245	99.29 230	98.76 403	61.30 508	98.47 230	99.88 235	97.62 339	99.73 286	99.67 133

SMA-MVS		99.19 229	99.00 259	99.73 113	99.46 324	99.73 90	99.13 236	99.52 305	97.40 428	99.57 246	99.64 236	98.93 157	99.83 325	97.61 341	99.79 254	99.63 174
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		96.71 423	96.79 422	96.46 471	98.90 436	90.71 497	99.41 122	98.68 428	94.69 477	98.14 447	99.34 371	86.32 468	99.80 366	97.60 342	98.07 466	98.88 431

PVSNet	97.47 15	98.42 349	98.44 330	98.35 416	99.46 324	96.26 447	96.70 484	99.34 359	97.68 414	99.00 375	99.13 405	97.40 318	99.72 409	97.59 343	99.68 312	99.08 399

new_pmnet		98.88 299	98.89 284	98.84 385	99.70 206	97.62 405	98.15 406	99.50 314	97.98 390	99.62 229	99.54 312	98.15 270	99.94 97	97.55 344	99.84 214	98.95 421

IB-MVS	95.41 20	95.30 455	94.46 459	97.84 438	98.76 457	95.33 463	97.33 465	96.07 480	96.02 459	95.37 491	97.41 481	76.17 493	99.96 68	97.54 345	95.44 492	98.22 472
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		99.24 208	99.11 219	99.61 186	98.38 472	99.79 54	99.57 85	99.68 203	99.61 159	99.15 359	99.71 185	98.70 192	99.91 179	97.54 345	99.68 312	99.13 385

ZNCC-MVS		99.22 219	99.04 248	99.77 79	99.76 154	99.73 90	99.28 174	99.56 278	98.19 379	99.14 361	99.29 380	98.84 172	99.92 150	97.53 347	99.80 249	99.64 168

CP-MVS		99.23 210	99.05 242	99.75 97	99.66 229	99.66 120	99.38 132	99.62 238	98.38 358	99.06 372	99.27 383	98.79 178	99.94 97	97.51 348	99.82 232	99.66 147

SD-MVS		99.01 277	99.30 179	98.15 426	99.50 304	99.40 212	98.94 308	99.61 245	99.22 246	99.75 157	99.82 90	99.54 54	95.51 499	97.48 349	99.87 196	99.54 239
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		98.49 343	98.29 348	99.11 341	98.96 433	98.42 356	97.54 454	99.32 366	97.53 421	98.47 426	98.15 469	97.88 290	99.82 342	97.46 350	99.24 403	99.09 393

DeepC-MVS_fast	98.47 5	99.23 210	99.12 216	99.56 208	99.28 378	99.22 258	98.99 294	99.40 344	99.08 268	99.58 243	99.64 236	98.90 166	99.83 325	97.44 351	99.75 273	99.63 174
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 205	99.08 231	99.76 86	99.73 182	99.70 108	99.31 162	99.59 262	98.36 360	99.36 314	99.37 358	98.80 177	99.91 179	97.43 352	99.75 273	99.68 124

ACMMPR		99.23 210	99.06 237	99.76 86	99.74 178	99.69 112	99.31 162	99.59 262	98.36 360	99.35 317	99.38 355	98.61 205	99.93 119	97.43 352	99.75 273	99.67 133

Vis-MVSNet (Re-imp)		98.77 311	98.58 316	99.34 292	99.78 137	98.88 310	99.61 73	99.56 278	99.11 267	99.24 345	99.56 302	93.00 410	99.78 372	97.43 352	99.89 173	99.35 328

MIMVSNet		98.43 348	98.20 353	99.11 341	99.53 289	98.38 361	99.58 82	98.61 433	98.96 281	99.33 323	99.76 149	90.92 435	99.81 358	97.38 355	99.76 269	99.15 377

WB-MVSnew		98.34 359	98.14 359	98.96 360	98.14 481	97.90 394	98.27 396	97.26 474	98.63 331	98.80 398	98.00 472	97.77 298	99.90 198	97.37 356	98.98 419	99.09 393

XVG-OURS-SEG-HR		99.16 239	98.99 266	99.66 150	99.84 77	99.64 132	98.25 399	99.73 170	98.39 357	99.63 219	99.43 342	99.70 31	99.90 198	97.34 357	98.64 443	99.44 299

COLMAP_ROB	98.06 12	99.45 145	99.37 154	99.70 132	99.83 85	99.70 108	99.38 132	99.78 141	99.53 176	99.67 201	99.78 131	99.19 104	99.86 269	97.32 358	99.87 196	99.55 229
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
0.4-1-1-0.1		93.18 458	91.66 462	97.73 444	95.83 497	95.29 464	95.30 491	95.90 483	93.59 479	90.58 496	94.40 505	77.87 488	99.77 385	97.31 359	84.20 494	98.15 477

MCST-MVS		99.02 271	98.81 295	99.65 157	99.58 256	99.49 179	98.58 362	99.07 408	98.40 356	99.04 373	99.25 388	98.51 228	99.80 366	97.31 359	99.51 364	99.65 156

region2R		99.23 210	99.05 242	99.77 79	99.76 154	99.70 108	99.31 162	99.59 262	98.41 354	99.32 326	99.36 363	98.73 189	99.93 119	97.29 361	99.74 280	99.67 133

APD-MVS_3200maxsize		99.31 193	99.16 205	99.74 102	99.53 289	99.75 79	99.27 178	99.61 245	99.19 249	99.57 246	99.64 236	98.76 183	99.90 198	97.29 361	99.62 330	99.56 225

TAPA-MVS	97.92 13	98.03 376	97.55 393	99.46 248	99.47 320	99.44 197	98.50 377	99.62 238	86.79 491	99.07 371	99.26 386	98.26 257	99.62 459	97.28 363	99.73 286	99.31 342
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 201	99.11 219	99.73 113	99.54 282	99.74 87	99.26 183	99.62 238	99.16 257	99.52 269	99.64 236	98.41 239	99.91 179	97.27 364	99.61 337	99.54 239

RE-MVS-def			99.13 212		99.54 282	99.74 87	99.26 183	99.62 238	99.16 257	99.52 269	99.64 236	98.57 210		97.27 364	99.61 337	99.54 239

testing11		96.05 442	95.41 445	97.97 432	98.78 454	95.27 465	98.59 360	98.23 454	98.86 300	96.56 482	96.91 491	75.20 495	99.69 425	97.26 366	98.29 455	98.93 424

test_yl		98.25 362	97.95 372	99.13 339	99.17 400	98.47 351	99.00 287	98.67 430	98.97 279	99.22 349	99.02 424	91.31 429	99.69 425	97.26 366	98.93 421	99.24 353

DCV-MVSNet		98.25 362	97.95 372	99.13 339	99.17 400	98.47 351	99.00 287	98.67 430	98.97 279	99.22 349	99.02 424	91.31 429	99.69 425	97.26 366	98.93 421	99.24 353

PHI-MVS		99.11 253	98.95 273	99.59 193	99.13 405	99.59 156	99.17 216	99.65 223	97.88 402	99.25 342	99.46 337	98.97 153	99.80 366	97.26 366	99.82 232	99.37 322

tfpnnormal		99.43 153	99.38 151	99.60 190	99.87 54	99.75 79	99.59 80	99.78 141	99.71 118	99.90 67	99.69 204	98.85 171	99.90 198	97.25 370	99.78 263	99.15 377

PatchmatchNet		97.65 393	97.80 383	97.18 459	98.82 449	92.49 484	99.17 216	98.39 447	98.12 381	98.79 400	99.58 291	90.71 442	99.89 220	97.23 371	99.41 379	99.16 375
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 283	98.80 297	99.56 208	99.25 384	99.43 201	98.54 372	99.27 378	98.58 337	98.80 398	99.43 342	98.53 223	99.70 418	97.22 372	99.59 344	99.54 239

testing3		96.48 429	95.63 441	99.01 355	99.23 388	97.81 398	98.90 314	99.10 407	98.72 320	97.84 461	97.92 473	72.44 499	99.85 288	97.21 373	99.33 389	99.35 328

HPM-MVS		99.25 205	99.07 235	99.78 75	99.81 106	99.75 79	99.61 73	99.67 208	97.72 412	99.35 317	99.25 388	99.23 100	99.92 150	97.21 373	99.82 232	99.67 133
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
0.3-1-1-0.015		92.36 460	90.68 464	97.39 452	94.94 500	94.41 473	94.21 493	95.89 484	92.87 482	88.87 498	93.49 507	75.30 494	99.76 390	97.19 375	83.41 496	98.02 480

0.4-1-1-0.2		92.59 459	91.07 463	97.15 461	94.73 501	93.68 479	93.50 494	95.91 482	92.68 483	90.48 497	93.52 506	77.77 489	99.75 400	97.19 375	83.88 495	98.01 481

mPP-MVS		99.19 229	99.00 259	99.76 86	99.76 154	99.68 115	99.38 132	99.54 290	98.34 369	99.01 374	99.50 323	98.53 223	99.93 119	97.18 377	99.78 263	99.66 147

ACMMP		99.25 205	99.08 231	99.74 102	99.79 129	99.68 115	99.50 102	99.65 223	98.07 385	99.52 269	99.69 204	98.57 210	99.92 150	97.18 377	99.79 254	99.63 174
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		96.23 436	95.74 438	97.70 445	98.86 443	95.59 460	98.66 352	98.14 456	98.96 281	97.67 467	97.06 488	76.78 491	98.92 491	97.10 379	98.41 452	98.58 454

thisisatest0515		96.98 416	96.42 424	98.66 401	99.42 337	97.47 411	97.27 467	94.30 491	97.24 435	99.15 359	98.86 441	85.01 470	99.87 250	97.10 379	99.39 381	98.63 448

XVG-ACMP-BASELINE		99.23 210	99.10 227	99.63 171	99.82 94	99.58 161	98.83 327	99.72 179	98.36 360	99.60 238	99.71 185	98.92 160	99.91 179	97.08 381	99.84 214	99.40 314

MSDG		99.08 258	98.98 269	99.37 282	99.60 243	99.13 274	97.54 454	99.74 166	98.84 304	99.53 267	99.55 310	99.10 121	99.79 369	97.07 382	99.86 204	99.18 370

SteuartSystems-ACMMP		99.30 194	99.14 210	99.76 86	99.87 54	99.66 120	99.18 211	99.60 256	98.55 339	99.57 246	99.67 220	99.03 141	99.94 97	97.01 383	99.80 249	99.69 117
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 438	95.78 437	97.49 447	98.53 467	93.83 478	98.04 420	93.94 494	98.96 281	98.46 427	98.17 468	79.86 481	99.87 250	96.99 384	99.06 412	98.78 441

EPMVS		96.53 426	96.32 425	97.17 460	98.18 478	92.97 483	99.39 129	89.95 500	98.21 377	98.61 415	99.59 288	86.69 467	99.72 409	96.99 384	99.23 405	98.81 438

MSP-MVS		99.04 268	98.79 298	99.81 54	99.78 137	99.73 90	99.35 147	99.57 273	98.54 342	99.54 262	98.99 426	96.81 341	99.93 119	96.97 386	99.53 360	99.77 79
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++		98.96 287	98.70 306	99.74 102	99.52 296	99.71 100	98.86 320	99.19 398	98.47 350	98.59 417	99.06 416	98.08 276	99.91 179	96.94 387	99.60 340	99.60 204

SR-MVS		99.19 229	99.00 259	99.74 102	99.51 298	99.72 95	99.18 211	99.60 256	98.85 301	99.47 284	99.58 291	98.38 244	99.92 150	96.92 388	99.54 358	99.57 222

PGM-MVS		99.20 226	99.01 255	99.77 79	99.75 170	99.71 100	99.16 222	99.72 179	97.99 389	99.42 297	99.60 278	98.81 173	99.93 119	96.91 389	99.74 280	99.66 147

HY-MVS	98.23 9	98.21 369	97.95 372	98.99 356	99.03 425	98.24 365	99.61 73	98.72 426	96.81 449	98.73 405	99.51 320	94.06 393	99.86 269	96.91 389	98.20 458	98.86 433

MDTV_nov1_ep13			97.73 387		98.70 462	90.83 495	99.15 225	98.02 459	98.51 345	98.82 395	99.61 270	90.98 434	99.66 447	96.89 391	98.92 423

GST-MVS		99.16 239	98.96 272	99.75 97	99.73 182	99.73 90	99.20 201	99.55 284	98.22 376	99.32 326	99.35 368	98.65 201	99.91 179	96.86 392	99.74 280	99.62 186

test_post1							99.14 229				51.63 510	89.54 453	99.82 342	96.86 392

SCA		98.11 372	98.36 338	97.36 453	99.20 394	92.99 482	98.17 404	98.49 441	98.24 375	99.10 367	99.57 298	96.01 369	99.94 97	96.86 392	99.62 330	99.14 382

UBG		96.53 426	95.95 432	98.29 423	98.87 442	96.31 446	98.48 380	98.07 457	98.83 305	97.32 470	96.54 499	79.81 482	99.62 459	96.84 395	98.74 436	98.95 421

XVG-OURS		99.21 224	99.06 237	99.65 157	99.82 94	99.62 140	97.87 438	99.74 166	98.36 360	99.66 207	99.68 216	99.71 28	99.90 198	96.84 395	99.88 183	99.43 305

LCM-MVSNet-Re		99.28 197	99.15 209	99.67 143	99.33 367	99.76 70	99.34 148	99.97 20	98.93 290	99.91 62	99.79 118	98.68 194	99.93 119	96.80 397	99.56 349	99.30 344

RPSCF		99.18 233	99.02 251	99.64 164	99.83 85	99.85 21	99.44 119	99.82 103	98.33 370	99.50 279	99.78 131	97.90 288	99.65 454	96.78 398	99.83 222	99.44 299

旧先验2							97.94 432		95.33 468	98.94 379			99.88 235	96.75 399

MDTV_nov1_ep13_2view						91.44 492	99.14 229		97.37 430	99.21 351		91.78 427		96.75 399		99.03 410

CLD-MVS		98.76 312	98.57 317	99.33 295	99.57 266	98.97 297	97.53 456	99.55 284	96.41 453	99.27 338	99.13 405	99.07 130	99.78 372	96.73 401	99.89 173	99.23 356
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 373	97.98 370	98.48 410	99.27 380	96.48 441	99.40 127	99.07 408	98.81 308	99.23 346	99.57 298	90.11 449	99.87 250	96.69 402	99.64 325	99.09 393

baseline2		96.83 419	96.28 426	98.46 412	99.09 418	96.91 431	98.83 327	93.87 495	97.23 436	96.23 487	98.36 463	88.12 457	99.90 198	96.68 403	98.14 463	98.57 456

cascas		96.99 415	96.82 421	97.48 448	97.57 494	95.64 458	96.43 486	99.56 278	91.75 486	97.13 477	97.61 480	95.58 374	98.63 493	96.68 403	99.11 409	98.18 476

PC_three_1452									97.56 417	99.68 194	99.41 345	99.09 123	97.09 496	96.66 405	99.60 340	99.62 186

LPG-MVS_test		99.22 219	99.05 242	99.74 102	99.82 94	99.63 138	99.16 222	99.73 170	97.56 417	99.64 214	99.69 204	99.37 76	99.89 220	96.66 405	99.87 196	99.69 117

LGP-MVS_train				99.74 102	99.82 94	99.63 138		99.73 170	97.56 417	99.64 214	99.69 204	99.37 76	99.89 220	96.66 405	99.87 196	99.69 117

ETVMVS		96.14 439	95.22 450	98.89 379	98.80 450	98.01 385	98.66 352	98.35 450	98.71 322	97.18 475	96.31 504	74.23 498	99.75 400	96.64 408	98.13 465	98.90 428

TinyColmap		98.97 284	98.93 275	99.07 349	99.46 324	98.19 370	97.75 442	99.75 160	98.79 311	99.54 262	99.70 195	98.97 153	99.62 459	96.63 409	99.83 222	99.41 311

LF4IMVS		99.01 277	98.92 279	99.27 317	99.71 191	99.28 240	98.59 360	99.77 147	98.32 371	99.39 310	99.41 345	98.62 203	99.84 305	96.62 410	99.84 214	98.69 447

NCCC		98.82 306	98.57 317	99.58 196	99.21 391	99.31 235	98.61 355	99.25 383	98.65 328	98.43 428	99.26 386	97.86 291	99.81 358	96.55 411	99.27 399	99.61 200

OPU-MVS				99.29 309	99.12 407	99.44 197	99.20 201				99.40 349	99.00 145	98.84 492	96.54 412	99.60 340	99.58 216

F-COLMAP		98.74 314	98.45 329	99.62 180	99.57 266	99.47 183	98.84 324	99.65 223	96.31 456	98.93 380	99.19 402	97.68 304	99.87 250	96.52 413	99.37 384	99.53 245

testing99		95.86 447	95.19 451	97.87 436	98.76 457	95.03 467	98.62 354	98.44 443	98.68 325	96.67 481	96.66 498	74.31 497	99.69 425	96.51 414	98.03 467	98.90 428

ADS-MVSNet2		97.78 387	97.66 391	98.12 428	99.14 403	95.36 462	99.22 198	98.75 425	96.97 444	98.25 436	99.64 236	90.90 436	99.94 97	96.51 414	99.56 349	99.08 399

ADS-MVSNet		97.72 392	97.67 390	97.86 437	99.14 403	94.65 471	99.22 198	98.86 418	96.97 444	98.25 436	99.64 236	90.90 436	99.84 305	96.51 414	99.56 349	99.08 399

PatchMatch-RL		98.68 322	98.47 326	99.30 308	99.44 329	99.28 240	98.14 408	99.54 290	97.12 442	99.11 365	99.25 388	97.80 296	99.70 418	96.51 414	99.30 393	98.93 424

CMPMVS	77.52 23	98.50 341	98.19 356	99.41 269	98.33 474	99.56 165	99.01 281	99.59 262	95.44 466	99.57 246	99.80 107	95.64 372	99.46 482	96.47 418	99.92 145	99.21 361
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 443	95.32 448	98.02 429	98.76 457	95.39 461	98.38 389	98.65 432	98.82 306	96.84 478	96.71 497	75.06 496	99.71 414	96.46 419	98.23 457	98.98 418

SF-MVS		99.10 256	98.93 275	99.62 180	99.58 256	99.51 177	99.13 236	99.65 223	97.97 391	99.42 297	99.61 270	98.86 170	99.87 250	96.45 420	99.68 312	99.49 269

FE-MVS		97.85 382	97.42 398	99.15 335	99.44 329	98.75 323	99.77 19	98.20 455	95.85 461	99.33 323	99.80 107	88.86 455	99.88 235	96.40 421	99.12 408	98.81 438

DPE-MVS		99.14 244	98.92 279	99.82 46	99.57 266	99.77 63	98.74 344	99.60 256	98.55 339	99.76 152	99.69 204	98.23 262	99.92 150	96.39 422	99.75 273	99.76 84
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 492	89.02 502			93.47 480		98.30 464		99.84 305	96.38 423

AllTest		99.21 224	99.07 235	99.63 171	99.78 137	99.64 132	99.12 241	99.83 97	98.63 331	99.63 219	99.72 175	98.68 194	99.75 400	96.38 423	99.83 222	99.51 258

TestCases				99.63 171	99.78 137	99.64 132		99.83 97	98.63 331	99.63 219	99.72 175	98.68 194	99.75 400	96.38 423	99.83 222	99.51 258

testdata				99.42 261	99.51 298	98.93 304		99.30 373	96.20 457	98.87 390	99.40 349	98.33 251	99.89 220	96.29 426	99.28 396	99.44 299

dp		96.86 418	97.07 409	96.24 473	98.68 463	90.30 500	99.19 207	98.38 448	97.35 431	98.23 438	99.59 288	87.23 459	99.82 342	96.27 427	98.73 439	98.59 452

tpmvs		97.39 407	97.69 388	96.52 469	98.41 471	91.76 488	99.30 165	98.94 417	97.74 409	97.85 460	99.55 310	92.40 419	99.73 407	96.25 428	98.73 439	98.06 479

KD-MVS_2432*1600		95.89 444	95.41 445	97.31 456	94.96 498	93.89 475	97.09 475	99.22 391	97.23 436	98.88 387	99.04 419	79.23 484	99.54 472	96.24 429	96.81 478	98.50 462

miper_refine_blended		95.89 444	95.41 445	97.31 456	94.96 498	93.89 475	97.09 475	99.22 391	97.23 436	98.88 387	99.04 419	79.23 484	99.54 472	96.24 429	96.81 478	98.50 462

ACMP	97.51 14	99.05 265	98.84 290	99.67 143	99.78 137	99.55 169	98.88 316	99.66 213	97.11 443	99.47 284	99.60 278	99.07 130	99.89 220	96.18 431	99.85 209	99.58 216
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 295	98.72 301	99.44 255	99.39 341	99.42 204	98.58 362	99.64 231	97.31 433	99.44 290	99.62 260	98.59 207	99.69 425	96.17 432	99.79 254	99.22 358

DP-MVS Recon		98.50 341	98.23 350	99.31 304	99.49 309	99.46 189	98.56 368	99.63 235	94.86 475	98.85 392	99.37 358	97.81 295	99.59 466	96.08 433	99.44 374	98.88 431

tpm cat1		96.78 420	96.98 413	96.16 474	98.85 444	90.59 498	99.08 257	99.32 366	92.37 484	97.73 466	99.46 337	91.15 432	99.69 425	96.07 434	98.80 429	98.21 473

tpm2		96.35 432	96.22 427	96.73 467	98.88 441	91.75 489	99.21 200	98.51 439	93.27 481	97.89 456	99.21 399	84.83 471	99.70 418	96.04 435	98.18 461	98.75 445

dmvs_re		98.69 321	98.48 325	99.31 304	99.55 280	99.42 204	99.54 90	98.38 448	99.32 225	98.72 406	98.71 450	96.76 343	99.21 486	96.01 436	99.35 387	99.31 342

test_0402		99.22 219	99.14 210	99.45 251	99.79 129	99.43 201	99.28 174	99.68 203	99.54 174	99.40 308	99.56 302	99.07 130	99.82 342	96.01 436	99.96 87	99.11 386

ITE_SJBPF				99.38 278	99.63 236	99.44 197		99.73 170	98.56 338	99.33 323	99.53 314	98.88 167	99.68 437	96.01 436	99.65 323	99.02 415

test_prior2							97.95 431		97.87 403	98.05 449	99.05 417	97.90 288		95.99 439	99.49 369

testdata2													99.89 220	95.99 439

原ACMM1				99.37 282	99.47 320	98.87 314		99.27 378	96.74 451	98.26 435	99.32 372	97.93 287	99.82 342	95.96 441	99.38 382	99.43 305

新几何1				99.52 227	99.50 304	99.22 258		99.26 380	95.66 465	98.60 416	99.28 381	97.67 305	99.89 220	95.95 442	99.32 391	99.45 284

MP-MVS		99.06 262	98.83 292	99.76 86	99.76 154	99.71 100	99.32 157	99.50 314	98.35 365	98.97 376	99.48 330	98.37 245	99.92 150	95.95 442	99.75 273	99.63 174
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 454	94.59 457	98.61 403	98.66 464	97.45 414	98.54 372	97.90 463	98.53 343	96.54 483	96.47 501	70.62 502	99.81 358	95.91 444	98.15 462	98.56 457

wuyk23d		97.58 396	99.13 212	92.93 478	99.69 212	99.49 179	99.52 94	99.77 147	97.97 391	99.96 34	99.79 118	99.84 16	99.94 97	95.85 445	99.82 232	79.36 496

HQP_MVS		98.90 295	98.68 307	99.55 215	99.58 256	99.24 252	98.80 335	99.54 290	98.94 285	99.14 361	99.25 388	97.24 325	99.82 342	95.84 446	99.78 263	99.60 204

plane_prior5								99.54 290					99.82 342	95.84 446	99.78 263	99.60 204

无先验							98.01 423	99.23 388	95.83 462				99.85 288	95.79 448		99.44 299

CPTT-MVS		98.74 314	98.44 330	99.64 164	99.61 241	99.38 217	99.18 211	99.55 284	96.49 452	99.27 338	99.37 358	97.11 333	99.92 150	95.74 449	99.67 318	99.62 186

PLC	97.35 16	98.36 354	97.99 368	99.48 242	99.32 368	99.24 252	98.50 377	99.51 310	95.19 471	98.58 418	98.96 433	96.95 338	99.83 325	95.63 450	99.25 401	99.37 322
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 333	98.34 341	99.28 312	99.18 399	99.10 284	98.34 391	99.41 337	98.48 349	98.52 423	98.98 429	97.05 335	99.78 372	95.59 451	99.50 367	98.96 419

1314		98.00 378	97.90 380	98.27 424	98.90 436	97.45 414	99.30 165	99.06 410	94.98 472	97.21 474	99.12 409	98.43 236	99.67 442	95.58 452	98.56 446	97.71 485

PVSNet_0	95.53 19	95.85 448	95.31 449	97.47 449	98.78 454	93.48 481	95.72 488	99.40 344	96.18 458	97.37 469	97.73 475	95.73 371	99.58 467	95.49 453	81.40 497	99.36 325

MAR-MVS		98.24 364	97.92 378	99.19 330	98.78 454	99.65 126	99.17 216	99.14 404	95.36 467	98.04 450	98.81 446	97.47 315	99.72 409	95.47 454	99.06 412	98.21 473
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	98.23 365	97.89 381	99.26 320	99.19 396	99.26 245	99.65 62	99.69 200	91.33 488	98.14 447	99.77 141	98.28 254	99.96 68	95.41 455	99.55 353	98.58 454

train_agg		98.35 357	97.95 372	99.57 204	99.35 353	99.35 229	98.11 412	99.41 337	94.90 473	97.92 454	98.99 426	98.02 279	99.85 288	95.38 456	99.44 374	99.50 264

9.14			98.64 308		99.45 328		98.81 332	99.60 256	97.52 422	99.28 337	99.56 302	98.53 223	99.83 325	95.36 457	99.64 325

APD-MVS		98.87 300	98.59 313	99.71 127	99.50 304	99.62 140	99.01 281	99.57 273	96.80 450	99.54 262	99.63 251	98.29 253	99.91 179	95.24 458	99.71 297	99.61 200
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 444								95.20 459

AdaColmap		98.60 328	98.35 340	99.38 278	99.12 407	99.22 258	98.67 350	99.42 336	97.84 407	98.81 396	99.27 383	97.32 323	99.81 358	95.14 460	99.53 360	99.10 388

test9_res														95.10 461	99.44 374	99.50 264

CDPH-MVS		98.56 334	98.20 353	99.61 186	99.50 304	99.46 189	98.32 393	99.41 337	95.22 469	99.21 351	99.10 413	98.34 249	99.82 342	95.09 462	99.66 321	99.56 225

BH-untuned		98.22 367	98.09 362	98.58 407	99.38 344	97.24 422	98.55 369	98.98 416	97.81 408	99.20 356	98.76 448	97.01 336	99.65 454	94.83 463	98.33 453	98.86 433

BP-MVS														94.73 464

HQP-MVS		98.36 354	98.02 367	99.39 275	99.31 369	98.94 301	97.98 427	99.37 352	97.45 425	98.15 443	98.83 443	96.67 345	99.70 418	94.73 464	99.67 318	99.53 245

QAPM		98.40 352	97.99 368	99.65 157	99.39 341	99.47 183	99.67 53	99.52 305	91.70 487	98.78 402	99.80 107	98.55 214	99.95 80	94.71 466	99.75 273	99.53 245

agg_prior2														94.58 467	99.46 373	99.50 264

myMVS_eth3d		95.63 452	94.73 454	98.34 418	98.50 469	96.36 444	98.60 357	99.21 394	97.89 400	96.76 479	96.37 502	72.10 500	99.57 468	94.38 468	98.73 439	99.09 393

BH-RMVSNet		98.41 350	98.14 359	99.21 327	99.21 391	98.47 351	98.60 357	98.26 453	98.35 365	98.93 380	99.31 375	97.20 330	99.66 447	94.32 469	99.10 410	99.51 258

E-PMN		97.14 414	97.43 397	96.27 472	98.79 452	91.62 490	95.54 489	99.01 415	99.44 199	98.88 387	99.12 409	92.78 411	99.68 437	94.30 470	99.03 416	97.50 486

MG-MVS		98.52 338	98.39 335	98.94 363	99.15 402	97.39 418	98.18 402	99.21 394	98.89 297	99.23 346	99.63 251	97.37 321	99.74 404	94.22 471	99.61 337	99.69 117

API-MVS		98.38 353	98.39 335	98.35 416	98.83 446	99.26 245	99.14 229	99.18 399	98.59 336	98.66 411	98.78 447	98.61 205	99.57 468	94.14 472	99.56 349	96.21 493

PAPM_NR		98.36 354	98.04 365	99.33 295	99.48 314	98.93 304	98.79 338	99.28 377	97.54 420	98.56 422	98.57 456	97.12 332	99.69 425	94.09 473	98.90 427	99.38 319

ZD-MVS					99.43 332	99.61 150		99.43 334	96.38 454	99.11 365	99.07 415	97.86 291	99.92 150	94.04 474	99.49 369

DPM-MVS		98.28 360	97.94 376	99.32 300	99.36 349	99.11 277	97.31 466	98.78 424	96.88 446	98.84 393	99.11 412	97.77 298	99.61 464	94.03 475	99.36 385	99.23 356

gg-mvs-nofinetune		95.87 446	95.17 452	97.97 432	98.19 477	96.95 429	99.69 45	89.23 501	99.89 55	96.24 486	99.94 19	81.19 476	99.51 478	93.99 476	98.20 458	97.44 487

PMVS	92.94 21	98.82 306	98.81 295	98.85 383	99.84 77	97.99 386	99.20 201	99.47 322	99.71 118	99.42 297	99.82 90	98.09 274	99.47 480	93.88 477	99.85 209	99.07 404
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 417	97.28 401	95.99 476	98.76 457	91.03 494	95.26 492	98.61 433	99.34 221	98.92 383	98.88 440	93.79 397	99.66 447	92.87 478	99.05 414	97.30 490

BH-w/o		97.20 411	97.01 412	97.76 440	99.08 419	95.69 457	98.03 422	98.52 438	95.76 463	97.96 453	98.02 470	95.62 373	99.47 480	92.82 479	97.25 477	98.12 478

TR-MVS		97.44 404	97.15 406	98.32 419	98.53 467	97.46 412	98.47 381	97.91 462	96.85 447	98.21 439	98.51 460	96.42 355	99.51 478	92.16 480	97.29 476	97.98 482

OpenMVS_ROB	97.31 17	97.36 409	96.84 419	98.89 379	99.29 375	99.45 195	98.87 319	99.48 319	86.54 493	99.44 290	99.74 162	97.34 322	99.86 269	91.61 481	99.28 396	97.37 489

GG-mvs-BLEND				97.36 453	97.59 492	96.87 432	99.70 38	88.49 502		94.64 492	97.26 486	80.66 478	99.12 487	91.50 482	96.50 486	96.08 495

DeepMVS_CX				97.98 431	99.69 212	96.95 429		99.26 380	75.51 496	95.74 489	98.28 465	96.47 353	99.62 459	91.23 483	97.89 469	97.38 488

PAPR		97.56 397	97.07 409	99.04 353	98.80 450	98.11 378	97.63 450	99.25 383	94.56 478	98.02 452	98.25 466	97.43 317	99.68 437	90.90 484	98.74 436	99.33 334

MVS		95.72 450	94.63 456	98.99 356	98.56 466	97.98 391	99.30 165	98.86 418	72.71 497	97.30 471	99.08 414	98.34 249	99.74 404	89.21 485	98.33 453	99.26 350

UWE-MVS-28		95.64 451	95.47 443	96.14 475	97.98 484	90.39 499	98.49 379	95.81 486	99.02 275	98.03 451	98.19 467	84.49 473	99.28 485	88.75 486	98.47 451	98.75 445

thres600view7		96.60 425	96.16 428	97.93 434	99.63 236	96.09 452	99.18 211	97.57 468	98.77 315	98.72 406	97.32 484	87.04 461	99.72 409	88.57 487	98.62 444	97.98 482

FPMVS		96.32 433	95.50 442	98.79 391	99.60 243	98.17 373	98.46 385	98.80 423	97.16 440	96.28 484	99.63 251	82.19 475	99.09 488	88.45 488	98.89 428	99.10 388

PCF-MVS	96.03 18	96.73 422	95.86 435	99.33 295	99.44 329	99.16 271	96.87 482	99.44 331	86.58 492	98.95 378	99.40 349	94.38 391	99.88 235	87.93 489	99.80 249	98.95 421
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 431	96.03 431	97.47 449	99.63 236	95.93 453	99.18 211	97.57 468	98.75 319	98.70 409	97.31 485	87.04 461	99.67 442	87.62 490	98.51 448	96.81 491

tfpn200view9		96.30 434	95.89 433	97.53 446	99.58 256	96.11 450	99.00 287	97.54 471	98.43 351	98.52 423	96.98 489	86.85 463	99.67 442	87.62 490	98.51 448	96.81 491

thres400		96.40 430	95.89 433	97.92 435	99.58 256	96.11 450	99.00 287	97.54 471	98.43 351	98.52 423	96.98 489	86.85 463	99.67 442	87.62 490	98.51 448	97.98 482

thres200		96.09 440	95.68 440	97.33 455	99.48 314	96.22 449	98.53 374	97.57 468	98.06 386	98.37 431	96.73 496	86.84 465	99.61 464	86.99 493	98.57 445	96.16 494

MVE	92.54 22	96.66 424	96.11 429	98.31 421	99.68 220	97.55 407	97.94 432	95.60 487	99.37 217	90.68 495	98.70 452	96.56 348	98.61 494	86.94 494	99.55 353	98.77 443
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 410	96.83 420	98.59 405	99.46 324	97.55 407	99.25 189	96.84 477	98.78 313	97.24 473	97.67 476	97.11 333	98.97 490	86.59 495	98.54 447	99.27 348

PAPM		95.61 453	94.71 455	98.31 421	99.12 407	96.63 437	96.66 485	98.46 442	90.77 489	96.25 485	98.68 453	93.01 409	99.69 425	81.60 496	97.86 471	98.62 449

SD_0403		97.42 405	96.90 418	98.98 358	99.54 282	97.90 394	99.52 94	99.54 290	99.34 221	97.87 458	98.85 442	98.72 190	99.64 456	78.93 497	99.83 222	99.40 314

dongtai		89.37 462	88.91 465	90.76 479	99.19 396	77.46 504	95.47 490	87.82 503	92.28 485	94.17 493	98.82 445	71.22 501	95.54 498	63.85 498	97.34 475	99.27 348

kuosan		85.65 464	84.57 467	88.90 481	97.91 486	77.11 505	96.37 487	87.62 504	85.24 494	85.45 499	96.83 492	69.94 503	90.98 500	45.90 499	95.83 491	98.62 449

test123		29.31 465	33.05 470	18.08 482	25.93 506	12.24 507	97.53 456	10.93 507	11.78 500	24.21 501	50.08 512	21.04 504	8.60 501	23.51 500	32.43 500	33.39 497

testmvs		28.94 466	33.33 468	15.79 483	26.03 505	9.81 508	96.77 483	15.67 506	11.55 501	23.87 502	50.74 511	19.03 505	8.53 502	23.21 501	33.07 499	29.03 498

mmdepth		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

monomultidepth		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

test_blank		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

uanet_test		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

DCPMVS		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

cdsmvs_eth3d_5k		24.88 467	33.17 469	0.00 484	0.00 507	0.00 509	0.00 496	99.62 238	0.00 502	0.00 503	99.13 405	99.82 18	0.00 503	0.00 502	0.00 501	0.00 499

pcd_1.5k_mvsjas		16.61 468	22.14 471	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	99.28 91	0.00 503	0.00 502	0.00 501	0.00 499

sosnet-low-res		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

sosnet		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

uncertanet		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

Regformer		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

ab-mvs-re		8.26 479	11.02 482	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	99.16 403	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

uanet		8.33 469	11.11 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	100.00 1	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

TestfortrainingZip							99.38 132

FOURS1					99.83 85	99.89 10	99.74 27	99.71 183	99.69 127	99.63 219

test_one_0601					99.63 236	99.76 70		99.55 284	99.23 242	99.31 331	99.61 270	98.59 207

eth-test2					0.00 507

eth-test					0.00 507

test_241102_ONE					99.69 212	99.82 42		99.54 290	99.12 266	99.82 108	99.49 327	98.91 163	99.52 477

save fliter					99.53 289	99.25 248	98.29 395	99.38 351	99.07 270

test0726					99.69 212	99.80 51	99.24 190	99.57 273	99.16 257	99.73 172	99.65 234	98.35 247

GSMVS																99.14 382

test_part2					99.62 240	99.67 118				99.55 259

sam_mvs1												90.81 440				99.14 382

sam_mvs												90.52 446

MTGPA								99.53 300

test_post											52.41 509	90.25 448	99.86 269

patchmatchnet-post											99.62 260	90.58 444	99.94 97

MTMP							99.09 254	98.59 436

TEST9					99.35 353	99.35 229	98.11 412	99.41 337	94.83 476	97.92 454	98.99 426	98.02 279	99.85 288

test_8					99.34 362	99.31 235	98.08 416	99.40 344	94.90 473	97.87 458	98.97 431	98.02 279	99.84 305

agg_prior					99.35 353	99.36 226		99.39 347		97.76 465			99.85 288

test_prior4						99.19 265	98.00 425

test_prior				99.46 248	99.35 353	99.22 258		99.39 347					99.69 425			99.48 273

新几何2							98.04 420

旧先验1					99.49 309	99.29 238		99.26 380			99.39 353	97.67 305			99.36 385	99.46 282

原ACMM2							97.92 434

test222					99.51 298	99.08 286	97.83 440	99.29 374	95.21 470	98.68 410	99.31 375	97.28 324			99.38 382	99.43 305

segment_acmp												98.37 245

testdata1							97.72 445		97.86 405

test12				99.54 221	99.29 375	99.33 232		99.16 402		98.43 428		97.54 313	99.82 342		99.47 371	99.48 273

plane_prior7					99.58 256	99.38 217

plane_prior6					99.47 320	99.26 245						97.24 325

plane_prior4											99.25 388

plane_prior3						99.31 235			98.36 360	99.14 361

plane_prior2							98.80 335		98.94 285

plane_prior1					99.51 298

plane_prior						99.24 252	98.42 387		97.87 403						99.71 297

n2								0.00 508

nn								0.00 508

door-mid								99.83 97

test11								99.29 374

door								99.77 147

HQP5-MVS						98.94 301

HQP-NCC					99.31 369		97.98 427		97.45 425	98.15 443

ACMP_Plane					99.31 369		97.98 427		97.45 425	98.15 443

HQP4-MVS										98.15 443			99.70 418			99.53 245

HQP3-MVS								99.37 352							99.67 318

HQP2-MVS												96.67 345

NP-MVS					99.40 340	99.13 274					98.83 443

ACMMP++_ref															99.94 127

ACMMP++															99.79 254

Test By Simon												98.41 239

High-resolution multi-view benchmark