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	97.16 423	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	97.79 465	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	98.62 401	99.04 424	96.23 447	99.20 201	99.92 42	99.44 199	99.98 14	99.87 56	85.87 468	99.67 441	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	99.84 304	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	99.84 304	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	99.84 304	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	99.84 304	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	98.70 426	99.72 112	99.91 62	99.60 278	99.43 66	99.81 357	99.81 51	99.53 360	99.73 93

VortexMVS		99.13 246	99.24 196	98.79 390	99.67 227	96.60 439	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	99.82 341	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	97.26 473	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		97.40 405	97.46 394	97.20 457	99.05 421	91.91 486	99.20 201	99.18 398	99.84 75	99.86 95	99.75 157	80.67 476	99.83 324	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	99.15 376

guyue		99.12 249	99.02 251	99.41 269	99.84 77	98.56 342	99.19 207	98.30 451	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	94.11 492	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	98.96 418

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	99.05 410	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	99.20 364

K. test v3		98.87 300	98.60 311	99.69 137	99.93 24	99.46 189	99.74 27	94.97 487	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	98.72 395	99.88 45	96.44 441	99.56 87	99.85 81	99.90 49	99.90 67	99.85 68	98.09 274	99.83 324	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	99.15 376

test1111		97.74 388	98.16 358	96.49 469	99.60 243	89.86 500	99.71 37	91.21 496	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		90.66 497		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	99.05 405

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	97.27 420	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		94.73 456	94.59 456	95.15 476	99.59 249	85.90 502	99.75 25	74.01 504	99.89 55	99.71 182	99.86 63	79.00 486	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	98.83 435

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	98.53 436	99.65 145	99.73 172	99.38 355	90.62 442	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	99.83 324	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	99.83 324	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	99.42 309

ECVR-MVS		97.73 389	98.04 365	96.78 462	99.59 249	90.81 495	99.72 33	90.43 498	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	99.78 371	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	98.71 399	99.82 94	96.62 437	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	99.14 403	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	99.10 387

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	99.84 304	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	99.83 324	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	98.51 407	99.75 170	95.90 453	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	99.82 341	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	99.69 424	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	99.83 324	99.32 127	99.94 127	99.53 245

CVMVSNet		98.61 325	98.88 285	97.80 438	99.58 256	93.60 479	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		95.75 448	95.42 443	96.76 463	89.90 503	94.42 471	98.86 320	97.87 463	78.01 494	99.30 336	99.69 204	97.70 301	95.89 496	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	98.42 412	99.74 178	95.64 457	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	99.20 396	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		97.50 401	97.18 405	98.48 409	98.85 444	95.89 454	98.44 386	99.52 305	99.53 176	99.52 269	99.42 344	80.10 479	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	96.51 484	99.65 156

hse-mvs2		98.52 338	98.30 346	99.16 333	99.29 375	98.60 339	98.77 340	99.02 412	99.68 129	99.32 326	99.04 419	92.50 417	99.85 288	99.24 137	97.87 470	99.03 409

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	99.82 341	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	99.20 364
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	99.78 371	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	99.33 333

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	98.51 458

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	98.51 458

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	98.51 458

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	99.84 304	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	88.61 455	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	99.78 371	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	99.78 371	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	99.78 371	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	99.78 371	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	99.21 393	97.89 400	96.76 478	96.37 501	95.47 379	99.57 467	99.10 171	98.73 439	99.09 392

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	99.77 384	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	99.66 446	99.09 172	99.66 321	99.10 387

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	99.83 324	99.08 174	99.92 145	99.35 327

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	99.83 324	99.08 174	99.92 145	99.35 327

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	97.65 414	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	99.19 367

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	98.35 431	97.34 483	92.76 413	99.84 304	99.04 179	96.49 486	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	98.33 432	97.35 482	92.78 411	99.84 304	99.04 179	96.53 480	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	98.72 395	97.71 489	96.86 432	97.00 477	99.34 359	97.73 409	98.18 439	96.82 493	91.92 420	99.84 304	99.02 183	96.53 480	99.45 284

FE-blended-shiyan7		97.53 399	97.14 407	98.72 395	97.71 489	96.86 432	97.00 477	99.34 359	97.73 409	98.18 439	96.82 493	91.92 420	99.84 304	99.02 183	96.53 480	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	97.14 440	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	98.32 450	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	99.76 389	98.98 188	99.99 16	99.36 324

EPNet_dtu		97.62 394	97.79 385	97.11 461	96.67 495	92.31 484	98.51 376	98.04 457	99.24 240	95.77 487	99.47 334	93.78 398	99.66 446	98.98 188	99.62 330	99.37 321
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	99.81 357	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	80.60 478	99.91 179	98.96 192	99.90 159	99.38 318

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	99.39 316

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	96.57 479	99.45 284

CHOSEN 280x420		98.41 350	98.41 333	98.40 413	99.34 362	95.89 454	96.94 480	99.44 331	98.80 310	99.25 342	99.52 318	93.51 402	99.98 26	98.94 199	99.98 50	99.32 337

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	99.77 384	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	99.72 408	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	98.42 443	99.64 149	98.72 406	97.85 474	90.86 439	99.62 458	98.88 203	99.13 407	99.19 367

testing3-2		96.51 427	96.43 422	96.74 465	99.36 349	91.38 492	99.10 249	97.87 463	99.48 186	98.57 420	98.71 450	76.65 491	99.66 446	98.87 204	99.26 400	99.18 369

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	99.57 467	98.85 205	98.92 423	99.03 409

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	98.77 392	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	98.24 470

PS-MVSNAJ		99.00 280	99.08 231	98.76 393	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	98.20 474

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	99.76 389	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	99.76 389	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	99.24 352

PatchT		98.45 347	98.32 343	98.83 386	98.94 434	98.29 364	99.24 190	98.82 420	99.84 75	99.08 368	99.76 149	91.37 428	99.94 97	98.82 209	99.00 418	98.26 469

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	99.32 337

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	99.32 337

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	98.18 439	96.82 493	91.92 420	99.83 324	98.79 215	96.53 480	99.45 284

blend_shiyan4		95.04 455	93.76 459	98.88 381	97.92 485	97.49 409	97.72 445	99.34 359	97.93 398	97.65 467	97.11 487	77.69 489	99.83 324	98.79 215	79.72 497	99.33 333

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	99.54 471	98.79 215	98.92 423	99.04 407

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	99.84 304	98.79 215	98.74 436	99.20 364

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	99.54 471	98.79 215	98.92 423	99.04 407

VDDNet		98.97 284	98.82 293	99.42 261	99.71 191	98.81 316	99.62 67	98.68 427	99.81 91	99.38 311	99.80 107	94.25 392	99.85 288	98.79 215	99.32 391	99.59 211

CR-MVSNet		98.35 357	98.20 353	98.83 386	99.05 421	98.12 376	99.30 165	99.67 208	97.39 428	99.16 357	99.79 118	91.87 425	99.91 179	98.78 221	98.77 432	98.44 463

test_method		91.72 460	92.32 460	89.91 479	93.49 502	70.18 505	90.28 494	99.56 278	61.71 497	95.39 489	99.52 318	93.90 394	99.94 97	98.76 222	98.27 456	99.62 186

RPMNet		98.60 328	98.53 323	98.83 386	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	98.75 223	98.77 432	98.44 463

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	98.74 224	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	98.74 224	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	98.74 224	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	98.74 224	99.95 111	99.41 310

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	99.76 389	98.74 224	99.93 139	99.22 357

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	99.71 413	98.73 229	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	94.36 489	99.06 272	99.70 186	99.49 327	84.55 471	99.94 97	98.73 229	99.65 323	99.36 324

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	99.76 389	98.72 231	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	99.24 386	99.48 186	99.56 254	99.77 141	94.89 384	99.93 119	98.72 231	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	99.82 341	98.71 233	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	98.70 234	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	98.70 234	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	98.69 236	99.73 286	99.15 376

LFMVS		98.46 346	98.19 356	99.26 320	99.24 386	98.52 350	99.62 67	96.94 475	99.87 62	99.31 331	99.58 291	91.04 433	99.81 357	98.68 237	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	98.66 238	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	98.81 421	99.37 217	99.35 317	99.70 195	96.36 360	99.94 97	98.66 238	99.59 344	99.22 357

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	99.70 417	98.65 240	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	98.64 241	99.09 411	99.41 310

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	99.76 389	98.63 242	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	97.65 466	99.28 233	99.56 254	99.50 323	93.15 406	99.84 304	98.62 243	99.58 346	99.40 313

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	98.59 244	99.76 269	99.66 147

EPNet		98.13 371	97.77 386	99.18 332	94.57 501	97.99 386	99.24 190	97.96 459	99.74 107	97.29 471	99.62 260	93.13 407	99.97 43	98.59 244	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	99.09 487	98.57 246	99.68 312	99.26 349

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	90.57 444	99.79 368	98.55 247	99.85 209	99.11 385

pmmvs3		98.08 374	97.80 383	98.91 372	99.41 339	97.69 404	97.87 438	99.66 213	95.87 459	99.50 279	99.51 320	90.35 446	99.97 43	98.55 247	99.47 371	99.08 398

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	98.51 249	99.52 363	98.28 468

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	99.76 389	98.48 250	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	98.48 250	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	98.46 252	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	98.45 253	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	99.83 324	98.45 253	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	98.45 253	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	99.84 304	98.45 253	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	97.14 473	99.51 310	98.09 384	99.54 262	99.27 383	96.87 340	99.74 403	98.43 257	98.96 420	99.03 409

our_test_3		98.85 304	99.09 229	98.13 426	99.66 229	94.90 469	97.72 445	99.58 271	99.07 270	99.64 214	99.62 260	98.19 267	99.93 119	98.41 258	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	99.71 413	98.41 258	99.95 111	99.05 405
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 407	96.91 416	98.74 394	97.72 488	97.57 406	97.60 452	97.36 472	98.00 387	99.21 351	98.02 470	90.04 449	99.79 368	98.37 260	95.89 489	98.86 432

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	98.36 261	99.83 222	99.17 372

baseline1		97.73 389	97.33 400	98.96 360	99.30 373	97.73 402	99.40 127	98.42 443	99.33 224	99.46 288	99.21 399	91.18 431	99.82 341	98.35 262	91.26 492	99.32 337

MVS-HIRNet		97.86 381	98.22 351	96.76 463	99.28 378	91.53 490	98.38 389	92.60 495	99.13 263	99.31 331	99.96 15	97.18 331	99.68 436	98.34 263	99.83 222	99.07 403

GA-MVS		97.99 379	97.68 389	98.93 366	99.52 296	98.04 384	97.19 470	99.05 410	98.32 371	98.81 396	98.97 431	89.89 451	99.41 482	98.33 264	99.05 414	99.34 332

Fast-Effi-MVS+		99.02 271	98.87 286	99.46 248	99.38 344	99.50 178	99.04 269	99.79 130	97.17 438	98.62 414	98.74 449	99.34 83	99.95 80	98.32 265	99.41 379	98.92 425

MDA-MVSNet_test_wron		98.95 290	98.99 266	98.85 382	99.64 234	97.16 423	98.23 400	99.33 364	98.93 290	99.56 254	99.66 225	97.39 320	99.83 324	98.29 266	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	94.65 488	98.35 365	99.79 128	99.68 216	98.03 278	99.93 119	98.28 267	99.92 145	99.44 299

ET-MVSNet_ETH3D		96.78 419	96.07 429	98.91 372	99.26 383	97.92 393	97.70 448	96.05 480	97.96 394	92.37 493	98.43 462	87.06 459	99.90 198	98.27 268	97.56 473	98.91 426

thisisatest0530		97.45 402	96.95 413	98.94 363	99.68 220	97.73 402	99.09 254	94.19 491	98.61 335	99.56 254	99.30 377	84.30 473	99.93 119	98.27 268	99.54 358	99.16 374

YYNet1		98.95 290	98.99 266	98.84 384	99.64 234	97.14 425	98.22 401	99.32 366	98.92 292	99.59 241	99.66 225	97.40 318	99.83 324	98.27 268	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	98.24 271	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	98.23 272	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	99.18 398	98.28 373	99.63 219	99.13 405	98.02 279	99.97 43	98.22 273	99.69 307	99.35 327

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	98.21 274	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	98.20 275	99.24 403	98.62 448

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	99.69 424	98.20 275	99.62 330	99.39 316

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	99.68 436	98.20 275	99.74 280	99.30 343

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	98.20 275	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	99.78 371	98.18 279	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	98.18 279	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	98.17 281	99.82 232	99.50 264

JIA-IIPM		98.06 375	97.92 378	98.50 408	98.59 465	97.02 427	98.80 335	98.51 438	99.88 60	97.89 455	99.87 56	91.89 424	99.90 198	98.16 282	97.68 472	98.59 451

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	99.84 304	98.15 283	99.51 364	98.89 429

miper_lstm_enhance		98.65 324	98.60 311	98.82 389	99.20 394	97.33 419	97.78 441	99.66 213	99.01 276	99.59 241	99.50 323	94.62 389	99.85 288	98.12 284	99.90 159	99.26 349

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	98.11 285	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	98.11 285	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	98.11 285	99.28 396	98.81 437

tpm		97.15 411	96.95 413	97.75 440	98.91 435	94.24 473	99.32 157	97.96 459	97.71 412	98.29 433	99.32 372	86.72 465	99.92 150	98.10 288	96.24 487	99.09 392

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	99.63 457	98.07 289	99.81 242	99.62 186

ppachtmachnet_test		98.89 298	99.12 216	98.20 424	99.66 229	95.24 465	97.63 450	99.68 203	99.08 268	99.78 132	99.62 260	98.65 201	99.88 235	98.02 290	99.96 87	99.48 273

tpmrst		97.73 389	98.07 364	96.73 466	98.71 461	92.00 485	99.10 249	98.86 417	98.52 344	98.92 383	99.54 312	91.90 423	99.82 341	98.02 290	99.03 416	98.37 465

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	98.02 290	99.92 145	99.43 305

eth_miper_zixun_eth		98.68 322	98.71 302	98.60 403	99.10 414	96.84 434	97.52 458	99.54 290	98.94 285	99.58 243	99.48 330	96.25 364	99.76 389	98.01 293	99.93 139	99.21 360

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	98.00 294	99.97 73	99.33 333

PVSNet_BlendedMVS		99.03 269	99.01 255	99.09 344	99.54 282	97.99 386	98.58 362	99.82 103	97.62 415	99.34 321	99.71 185	98.52 226	99.77 384	97.98 295	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	95.70 463	99.34 321	98.98 429	98.52 226	99.77 384	97.98 295	99.83 222	99.30 343

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	99.78 371	97.97 297	99.89 173	99.17 372

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	99.78 371	97.97 297	99.89 173	99.18 369

AUN-MVS		97.82 383	97.38 399	99.14 338	99.27 380	98.53 348	98.72 346	99.02 412	98.10 382	97.18 474	99.03 423	89.26 453	99.85 288	97.94 299	97.91 468	99.03 409

FA-MVS(test-final)		98.52 338	98.32 343	99.10 343	99.48 314	98.67 328	99.77 19	98.60 434	97.35 430	99.63 219	99.80 107	93.07 408	99.84 304	97.92 300	99.30 393	98.78 440

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	99.70 417	97.92 300	99.70 299	99.38 318

USDC		98.96 287	98.93 275	99.05 352	99.54 282	97.99 386	97.07 476	99.80 121	98.21 377	99.75 157	99.77 141	98.43 236	99.64 455	97.90 302	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	99.76 389	97.89 303	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	97.88 304	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	97.88 304	99.72 293	99.77 79

c3_l		98.72 317	98.71 302	98.72 395	99.12 407	97.22 422	97.68 449	99.56 278	98.90 294	99.54 262	99.48 330	96.37 359	99.73 406	97.88 304	99.88 183	99.21 360

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	97.87 307	99.72 293	99.62 186

miper_ehance_all_eth		98.59 331	98.59 313	98.59 404	98.98 431	97.07 426	97.49 459	99.52 305	98.50 346	99.52 269	99.37 358	96.41 357	99.71 413	97.86 308	99.62 330	99.00 416

WTY-MVS		98.59 331	98.37 337	99.26 320	99.43 332	98.40 357	98.74 344	99.13 405	98.10 382	99.21 351	99.24 393	94.82 386	99.90 198	97.86 308	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	99.84 304	97.85 310	99.70 299	99.10 387

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	97.85 310	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	97.85 310	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	99.54 471	97.85 310	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	97.83 314	99.79 254	99.65 156

MSC_two_6792asdad				99.74 102	99.03 425	99.53 172		99.23 387					99.92 150	97.77 315	99.69 307	99.78 75

No_MVS				99.74 102	99.03 425	99.53 172		99.23 387					99.92 150	97.77 315	99.69 307	99.78 75

TESTMET0.1,1		96.24 434	95.84 435	97.41 450	98.24 476	93.84 476	97.38 462	95.84 484	98.43 351	97.81 461	98.56 457	79.77 482	99.89 220	97.77 315	98.77 432	98.52 457

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	99.78 371	97.77 315	99.88 183	99.60 204

IU-MVS					99.69 212	99.77 63		99.22 390	97.50 422	99.69 189				97.75 319	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	89.94 489	99.42 297	99.56 302	97.76 300	99.86 269	97.74 320	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	97.72 321	99.60 340	99.62 186

test_0728_THIRD									99.18 250	99.62 229	99.61 270	98.58 209	99.91 179	97.72 321	99.80 249	99.77 79

EGC-MVSNET		89.05 462	85.52 465	99.64 164	99.89 39	99.78 57	99.56 87	99.52 305	24.19 498	49.96 499	99.83 83	99.15 111	99.92 150	97.71 323	99.85 209	99.21 360

miper_enhance_ethall		98.03 376	97.94 376	98.32 418	98.27 475	96.43 442	96.95 479	99.41 337	96.37 454	99.43 294	98.96 433	94.74 387	99.69 424	97.71 323	99.62 330	98.83 435

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	97.71 323	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	98.40 413	98.37 473	96.75 435	97.24 469	99.37 352	97.31 432	99.41 303	99.22 395	87.30 457	99.37 483	97.70 326	99.62 330	99.08 398

MP-MVS-pluss		99.14 244	98.92 279	99.80 64	99.83 85	99.83 34	98.61 355	99.63 235	96.84 447	99.44 290	99.58 291	98.81 173	99.91 179	97.70 326	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	97.70 326	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	97.49 423	99.08 368	99.35 368	95.41 380	99.82 341	97.70 326	98.19 460	99.01 415

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	99.66 446	97.70 326	99.77 267	99.29 346

IS-MVSNet		99.03 269	98.85 288	99.55 215	99.80 115	99.25 248	99.73 30	99.15 402	99.37 217	99.61 235	99.71 185	94.73 388	99.81 357	97.70 326	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	97.69 332	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	97.69 332	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	97.69 332	99.79 254	99.67 133

test-LLR		97.15 411	96.95 413	97.74 441	98.18 478	95.02 467	97.38 462	96.10 477	98.00 387	97.81 461	98.58 454	90.04 449	99.91 179	97.69 332	98.78 430	98.31 466

test-mter		96.23 435	95.73 438	97.74 441	98.18 478	95.02 467	97.38 462	96.10 477	97.90 399	97.81 461	98.58 454	79.12 485	99.91 179	97.69 332	98.78 430	98.31 466

MonoMVSNet		98.23 365	98.32 343	97.99 429	98.97 432	96.62 437	99.49 107	98.42 443	99.62 154	99.40 308	99.79 118	95.51 378	98.58 494	97.68 337	95.98 488	98.76 443

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	97.62 338	99.73 286	99.67 133

X-MVStestdata		96.09 439	94.87 452	99.75 97	99.71 191	99.71 100	99.37 140	99.61 245	99.29 230	98.76 403	61.30 507	98.47 230	99.88 235	97.62 338	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	97.40 427	99.57 246	99.64 236	98.93 157	99.83 324	97.61 340	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		96.71 422	96.79 421	96.46 470	98.90 436	90.71 496	99.41 122	98.68 427	94.69 476	98.14 446	99.34 371	86.32 467	99.80 365	97.60 341	98.07 466	98.88 430

PVSNet	97.47 15	98.42 349	98.44 330	98.35 415	99.46 324	96.26 446	96.70 483	99.34 359	97.68 413	99.00 375	99.13 405	97.40 318	99.72 408	97.59 342	99.68 312	99.08 398

new_pmnet		98.88 299	98.89 284	98.84 384	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	97.55 343	99.84 214	98.95 420

IB-MVS	95.41 20	95.30 454	94.46 458	97.84 437	98.76 457	95.33 462	97.33 465	96.07 479	96.02 458	95.37 490	97.41 481	76.17 492	99.96 68	97.54 344	95.44 491	98.22 471
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	97.54 344	99.68 312	99.13 384

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	97.53 346	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	97.51 347	99.82 232	99.66 147

SD-MVS		99.01 277	99.30 179	98.15 425	99.50 304	99.40 212	98.94 308	99.61 245	99.22 246	99.75 157	99.82 90	99.54 54	95.51 498	97.48 348	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	97.53 420	98.47 426	98.15 469	97.88 290	99.82 341	97.46 349	99.24 403	99.09 392

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	99.83 324	97.44 350	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	97.43 351	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	97.43 351	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	99.78 371	97.43 351	99.89 173	99.35 327

MIMVSNet		98.43 348	98.20 353	99.11 341	99.53 289	98.38 361	99.58 82	98.61 432	98.96 281	99.33 323	99.76 149	90.92 435	99.81 357	97.38 354	99.76 269	99.15 376

WB-MVSnew		98.34 359	98.14 359	98.96 360	98.14 481	97.90 394	98.27 396	97.26 473	98.63 331	98.80 398	98.00 472	97.77 298	99.90 198	97.37 355	98.98 419	99.09 392

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	97.34 356	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	97.32 357	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		93.18 457	91.66 461	97.73 443	95.83 496	95.29 463	95.30 490	95.90 482	93.59 478	90.58 495	94.40 504	77.87 487	99.77 384	97.31 358	84.20 493	98.15 476

MCST-MVS		99.02 271	98.81 295	99.65 157	99.58 256	99.49 179	98.58 362	99.07 407	98.40 356	99.04 373	99.25 388	98.51 228	99.80 365	97.31 358	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	97.29 360	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	97.29 360	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	86.79 490	99.07 371	99.26 386	98.26 257	99.62 458	97.28 362	99.73 286	99.31 341
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	97.27 363	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		97.27 363	99.61 337	99.54 239

testing11		96.05 441	95.41 444	97.97 431	98.78 454	95.27 464	98.59 360	98.23 453	98.86 300	96.56 481	96.91 491	75.20 494	99.69 424	97.26 365	98.29 455	98.93 423

test_yl		98.25 362	97.95 372	99.13 339	99.17 400	98.47 351	99.00 287	98.67 429	98.97 279	99.22 349	99.02 424	91.31 429	99.69 424	97.26 365	98.93 421	99.24 352

DCV-MVSNet		98.25 362	97.95 372	99.13 339	99.17 400	98.47 351	99.00 287	98.67 429	98.97 279	99.22 349	99.02 424	91.31 429	99.69 424	97.26 365	98.93 421	99.24 352

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	99.80 365	97.26 365	99.82 232	99.37 321

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	97.25 369	99.78 263	99.15 376

PatchmatchNet		97.65 393	97.80 383	97.18 458	98.82 449	92.49 483	99.17 216	98.39 446	98.12 381	98.79 400	99.58 291	90.71 441	99.89 220	97.23 370	99.41 379	99.16 374
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	99.70 417	97.22 371	99.59 344	99.54 239

testing3		96.48 428	95.63 440	99.01 355	99.23 388	97.81 398	98.90 314	99.10 406	98.72 320	97.84 460	97.92 473	72.44 498	99.85 288	97.21 372	99.33 389	99.35 327

HPM-MVS		99.25 205	99.07 235	99.78 75	99.81 106	99.75 79	99.61 73	99.67 208	97.72 411	99.35 317	99.25 388	99.23 100	99.92 150	97.21 372	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		92.36 459	90.68 463	97.39 451	94.94 499	94.41 472	94.21 492	95.89 483	92.87 481	88.87 497	93.49 506	75.30 493	99.76 389	97.19 374	83.41 495	98.02 479

0.4-1-1-0.2		92.59 458	91.07 462	97.15 460	94.73 500	93.68 478	93.50 493	95.91 481	92.68 482	90.48 496	93.52 505	77.77 488	99.75 399	97.19 374	83.88 494	98.01 480

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	97.18 376	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	97.18 376	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		96.23 435	95.74 437	97.70 444	98.86 443	95.59 459	98.66 352	98.14 455	98.96 281	97.67 466	97.06 488	76.78 490	98.92 490	97.10 378	98.41 452	98.58 453

thisisatest0515		96.98 415	96.42 423	98.66 400	99.42 337	97.47 411	97.27 467	94.30 490	97.24 434	99.15 359	98.86 441	85.01 469	99.87 250	97.10 378	99.39 381	98.63 447

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	97.08 380	99.84 214	99.40 313

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	99.79 368	97.07 381	99.86 204	99.18 369

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	97.01 382	99.80 249	99.69 117
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 437	95.78 436	97.49 446	98.53 467	93.83 477	98.04 420	93.94 493	98.96 281	98.46 427	98.17 468	79.86 480	99.87 250	96.99 383	99.06 412	98.78 440

EPMVS		96.53 425	96.32 424	97.17 459	98.18 478	92.97 482	99.39 129	89.95 499	98.21 377	98.61 415	99.59 288	86.69 466	99.72 408	96.99 383	99.23 405	98.81 437

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	96.97 385	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	99.19 397	98.47 350	98.59 417	99.06 416	98.08 276	99.91 179	96.94 386	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	96.92 387	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	96.91 388	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	98.72 425	96.81 448	98.73 405	99.51 320	94.06 393	99.86 269	96.91 388	98.20 458	98.86 432

MDTV_nov1_ep13			97.73 387		98.70 462	90.83 494	99.15 225	98.02 458	98.51 345	98.82 395	99.61 270	90.98 434	99.66 446	96.89 390	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	96.86 391	99.74 280	99.62 186

test_post1							99.14 229				51.63 509	89.54 452	99.82 341	96.86 391

SCA		98.11 372	98.36 338	97.36 452	99.20 394	92.99 481	98.17 404	98.49 440	98.24 375	99.10 367	99.57 298	96.01 369	99.94 97	96.86 391	99.62 330	99.14 381

UBG		96.53 425	95.95 431	98.29 422	98.87 442	96.31 445	98.48 380	98.07 456	98.83 305	97.32 469	96.54 499	79.81 481	99.62 458	96.84 394	98.74 436	98.95 420

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	96.84 394	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	96.80 396	99.56 349	99.30 343

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	99.65 453	96.78 397	99.83 222	99.44 299

旧先验2							97.94 432		95.33 467	98.94 379			99.88 235	96.75 398

MDTV_nov1_ep13_2view						91.44 491	99.14 229		97.37 429	99.21 351		91.78 427		96.75 398		99.03 409

CLD-MVS		98.76 312	98.57 317	99.33 295	99.57 266	98.97 297	97.53 456	99.55 284	96.41 452	99.27 338	99.13 405	99.07 130	99.78 371	96.73 400	99.89 173	99.23 355
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	98.48 409	99.27 380	96.48 440	99.40 127	99.07 407	98.81 308	99.23 346	99.57 298	90.11 448	99.87 250	96.69 401	99.64 325	99.09 392

baseline2		96.83 418	96.28 425	98.46 411	99.09 418	96.91 430	98.83 327	93.87 494	97.23 435	96.23 486	98.36 463	88.12 456	99.90 198	96.68 402	98.14 463	98.57 455

cascas		96.99 414	96.82 420	97.48 447	97.57 494	95.64 457	96.43 485	99.56 278	91.75 485	97.13 476	97.61 480	95.58 374	98.63 492	96.68 402	99.11 409	98.18 475

PC_three_1452									97.56 416	99.68 194	99.41 345	99.09 123	97.09 495	96.66 404	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	97.56 416	99.64 214	99.69 204	99.37 76	99.89 220	96.66 404	99.87 196	99.69 117

LGP-MVS_train				99.74 102	99.82 94	99.63 138		99.73 170	97.56 416	99.64 214	99.69 204	99.37 76	99.89 220	96.66 404	99.87 196	99.69 117

ETVMVS		96.14 438	95.22 449	98.89 379	98.80 450	98.01 385	98.66 352	98.35 449	98.71 322	97.18 474	96.31 503	74.23 497	99.75 399	96.64 407	98.13 465	98.90 427

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	99.62 458	96.63 408	99.83 222	99.41 310

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	99.84 304	96.62 409	99.84 214	98.69 446

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	99.81 357	96.55 410	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	98.84 491	96.54 411	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	96.31 455	98.93 380	99.19 402	97.68 304	99.87 250	96.52 412	99.37 384	99.53 245

testing99		95.86 446	95.19 450	97.87 435	98.76 457	95.03 466	98.62 354	98.44 442	98.68 325	96.67 480	96.66 498	74.31 496	99.69 424	96.51 413	98.03 467	98.90 427

ADS-MVSNet2		97.78 387	97.66 391	98.12 427	99.14 403	95.36 461	99.22 198	98.75 424	96.97 443	98.25 435	99.64 236	90.90 436	99.94 97	96.51 413	99.56 349	99.08 398

ADS-MVSNet		97.72 392	97.67 390	97.86 436	99.14 403	94.65 470	99.22 198	98.86 417	96.97 443	98.25 435	99.64 236	90.90 436	99.84 304	96.51 413	99.56 349	99.08 398

PatchMatch-RL		98.68 322	98.47 326	99.30 308	99.44 329	99.28 240	98.14 408	99.54 290	97.12 441	99.11 365	99.25 388	97.80 296	99.70 417	96.51 413	99.30 393	98.93 423

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	95.44 465	99.57 246	99.80 107	95.64 372	99.46 481	96.47 417	99.92 145	99.21 360
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 442	95.32 447	98.02 428	98.76 457	95.39 460	98.38 389	98.65 431	98.82 306	96.84 477	96.71 497	75.06 495	99.71 413	96.46 418	98.23 457	98.98 417

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	96.45 419	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	98.20 454	95.85 460	99.33 323	99.80 107	88.86 454	99.88 235	96.40 420	99.12 408	98.81 437

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	96.39 421	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	89.02 501			93.47 479		98.30 464		99.84 304	96.38 422

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	99.75 399	96.38 422	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	99.75 399	96.38 422	99.83 222	99.51 258

testdata				99.42 261	99.51 298	98.93 304		99.30 373	96.20 456	98.87 390	99.40 349	98.33 251	99.89 220	96.29 425	99.28 396	99.44 299

dp		96.86 417	97.07 409	96.24 472	98.68 463	90.30 499	99.19 207	98.38 447	97.35 430	98.23 437	99.59 288	87.23 458	99.82 341	96.27 426	98.73 439	98.59 451

tpmvs		97.39 406	97.69 388	96.52 468	98.41 471	91.76 487	99.30 165	98.94 416	97.74 408	97.85 459	99.55 310	92.40 419	99.73 406	96.25 427	98.73 439	98.06 478

KD-MVS_2432*1600		95.89 443	95.41 444	97.31 455	94.96 497	93.89 474	97.09 474	99.22 390	97.23 435	98.88 387	99.04 419	79.23 483	99.54 471	96.24 428	96.81 477	98.50 461

miper_refine_blended		95.89 443	95.41 444	97.31 455	94.96 497	93.89 474	97.09 474	99.22 390	97.23 435	98.88 387	99.04 419	79.23 483	99.54 471	96.24 428	96.81 477	98.50 461

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	97.11 442	99.47 284	99.60 278	99.07 130	99.89 220	96.18 430	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	97.31 432	99.44 290	99.62 260	98.59 207	99.69 424	96.17 431	99.79 254	99.22 357

DP-MVS Recon		98.50 341	98.23 350	99.31 304	99.49 309	99.46 189	98.56 368	99.63 235	94.86 474	98.85 392	99.37 358	97.81 295	99.59 465	96.08 432	99.44 374	98.88 430

tpm cat1		96.78 419	96.98 412	96.16 473	98.85 444	90.59 497	99.08 257	99.32 366	92.37 483	97.73 465	99.46 337	91.15 432	99.69 424	96.07 433	98.80 429	98.21 472

tpm2		96.35 431	96.22 426	96.73 466	98.88 441	91.75 488	99.21 200	98.51 438	93.27 480	97.89 455	99.21 399	84.83 470	99.70 417	96.04 434	98.18 461	98.75 444

dmvs_re		98.69 321	98.48 325	99.31 304	99.55 280	99.42 204	99.54 90	98.38 447	99.32 225	98.72 406	98.71 450	96.76 343	99.21 485	96.01 435	99.35 387	99.31 341

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	99.82 341	96.01 435	99.96 87	99.11 385

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	99.68 436	96.01 435	99.65 323	99.02 414

test_prior2							97.95 431		97.87 403	98.05 448	99.05 417	97.90 288		95.99 438	99.49 369

testdata2													99.89 220	95.99 438

原ACMM1				99.37 282	99.47 320	98.87 314		99.27 378	96.74 450	98.26 434	99.32 372	97.93 287	99.82 341	95.96 440	99.38 382	99.43 305

新几何1				99.52 227	99.50 304	99.22 258		99.26 380	95.66 464	98.60 416	99.28 381	97.67 305	99.89 220	95.95 441	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	95.95 441	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		95.60 453	94.59 456	98.61 402	98.66 464	97.45 414	98.54 372	97.90 462	98.53 343	96.54 482	96.47 500	70.62 501	99.81 357	95.91 443	98.15 462	98.56 456

wuyk23d		97.58 396	99.13 212	92.93 477	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	95.85 444	99.82 232	79.36 495

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	99.82 341	95.84 445	99.78 263	99.60 204

plane_prior5								99.54 290					99.82 341	95.84 445	99.78 263	99.60 204

无先验							98.01 423	99.23 387	95.83 461				99.85 288	95.79 447		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	96.49 451	99.27 338	99.37 358	97.11 333	99.92 150	95.74 448	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	95.19 470	98.58 418	98.96 433	96.95 338	99.83 324	95.63 449	99.25 401	99.37 321
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	99.78 371	95.59 450	99.50 367	98.96 418

1314		98.00 378	97.90 380	98.27 423	98.90 436	97.45 414	99.30 165	99.06 409	94.98 471	97.21 473	99.12 409	98.43 236	99.67 441	95.58 451	98.56 446	97.71 484

PVSNet_0	95.53 19	95.85 447	95.31 448	97.47 448	98.78 454	93.48 480	95.72 487	99.40 344	96.18 457	97.37 468	97.73 475	95.73 371	99.58 466	95.49 452	81.40 496	99.36 324

MAR-MVS		98.24 364	97.92 378	99.19 330	98.78 454	99.65 126	99.17 216	99.14 403	95.36 466	98.04 449	98.81 446	97.47 315	99.72 408	95.47 453	99.06 412	98.21 472
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	91.33 487	98.14 446	99.77 141	98.28 254	99.96 68	95.41 454	99.55 353	98.58 453

train_agg		98.35 357	97.95 372	99.57 204	99.35 353	99.35 229	98.11 412	99.41 337	94.90 472	97.92 453	98.99 426	98.02 279	99.85 288	95.38 455	99.44 374	99.50 264

9.14			98.64 308		99.45 328		98.81 332	99.60 256	97.52 421	99.28 337	99.56 302	98.53 223	99.83 324	95.36 456	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	96.80 449	99.54 262	99.63 251	98.29 253	99.91 179	95.24 457	99.71 297	99.61 200
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 443								95.20 458

AdaColmap		98.60 328	98.35 340	99.38 278	99.12 407	99.22 258	98.67 350	99.42 336	97.84 406	98.81 396	99.27 383	97.32 323	99.81 357	95.14 459	99.53 360	99.10 387

test9_res														95.10 460	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	95.22 468	99.21 351	99.10 413	98.34 249	99.82 341	95.09 461	99.66 321	99.56 225

BH-untuned		98.22 367	98.09 362	98.58 406	99.38 344	97.24 421	98.55 369	98.98 415	97.81 407	99.20 356	98.76 448	97.01 336	99.65 453	94.83 462	98.33 453	98.86 432

BP-MVS														94.73 463

HQP-MVS		98.36 354	98.02 367	99.39 275	99.31 369	98.94 301	97.98 427	99.37 352	97.45 424	98.15 442	98.83 443	96.67 345	99.70 417	94.73 463	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	91.70 486	98.78 402	99.80 107	98.55 214	99.95 80	94.71 465	99.75 273	99.53 245

agg_prior2														94.58 466	99.46 373	99.50 264

myMVS_eth3d		95.63 451	94.73 453	98.34 417	98.50 469	96.36 443	98.60 357	99.21 393	97.89 400	96.76 478	96.37 501	72.10 499	99.57 467	94.38 467	98.73 439	99.09 392

BH-RMVSNet		98.41 350	98.14 359	99.21 327	99.21 391	98.47 351	98.60 357	98.26 452	98.35 365	98.93 380	99.31 375	97.20 330	99.66 446	94.32 468	99.10 410	99.51 258

E-PMN		97.14 413	97.43 397	96.27 471	98.79 452	91.62 489	95.54 488	99.01 414	99.44 199	98.88 387	99.12 409	92.78 411	99.68 436	94.30 469	99.03 416	97.50 485

MG-MVS		98.52 338	98.39 335	98.94 363	99.15 402	97.39 418	98.18 402	99.21 393	98.89 297	99.23 346	99.63 251	97.37 321	99.74 403	94.22 470	99.61 337	99.69 117

API-MVS		98.38 353	98.39 335	98.35 415	98.83 446	99.26 245	99.14 229	99.18 398	98.59 336	98.66 411	98.78 447	98.61 205	99.57 467	94.14 471	99.56 349	96.21 492

PAPM_NR		98.36 354	98.04 365	99.33 295	99.48 314	98.93 304	98.79 338	99.28 377	97.54 419	98.56 422	98.57 456	97.12 332	99.69 424	94.09 472	98.90 427	99.38 318

ZD-MVS					99.43 332	99.61 150		99.43 334	96.38 453	99.11 365	99.07 415	97.86 291	99.92 150	94.04 473	99.49 369

DPM-MVS		98.28 360	97.94 376	99.32 300	99.36 349	99.11 277	97.31 466	98.78 423	96.88 445	98.84 393	99.11 412	97.77 298	99.61 463	94.03 474	99.36 385	99.23 355

gg-mvs-nofinetune		95.87 445	95.17 451	97.97 431	98.19 477	96.95 428	99.69 45	89.23 500	99.89 55	96.24 485	99.94 19	81.19 475	99.51 477	93.99 475	98.20 458	97.44 486

PMVS	92.94 21	98.82 306	98.81 295	98.85 382	99.84 77	97.99 386	99.20 201	99.47 322	99.71 118	99.42 297	99.82 90	98.09 274	99.47 479	93.88 476	99.85 209	99.07 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 416	97.28 401	95.99 475	98.76 457	91.03 493	95.26 491	98.61 432	99.34 221	98.92 383	98.88 440	93.79 397	99.66 446	92.87 477	99.05 414	97.30 489

BH-w/o		97.20 410	97.01 411	97.76 439	99.08 419	95.69 456	98.03 422	98.52 437	95.76 462	97.96 452	98.02 470	95.62 373	99.47 479	92.82 478	97.25 476	98.12 477

TR-MVS		97.44 403	97.15 406	98.32 418	98.53 467	97.46 412	98.47 381	97.91 461	96.85 446	98.21 438	98.51 460	96.42 355	99.51 477	92.16 479	97.29 475	97.98 481

OpenMVS_ROB	97.31 17	97.36 408	96.84 418	98.89 379	99.29 375	99.45 195	98.87 319	99.48 319	86.54 492	99.44 290	99.74 162	97.34 322	99.86 269	91.61 480	99.28 396	97.37 488

GG-mvs-BLEND				97.36 452	97.59 492	96.87 431	99.70 38	88.49 501		94.64 491	97.26 486	80.66 477	99.12 486	91.50 481	96.50 485	96.08 494

DeepMVS_CX				97.98 430	99.69 212	96.95 428		99.26 380	75.51 495	95.74 488	98.28 465	96.47 353	99.62 458	91.23 482	97.89 469	97.38 487

PAPR		97.56 397	97.07 409	99.04 353	98.80 450	98.11 378	97.63 450	99.25 383	94.56 477	98.02 451	98.25 466	97.43 317	99.68 436	90.90 483	98.74 436	99.33 333

MVS		95.72 449	94.63 455	98.99 356	98.56 466	97.98 391	99.30 165	98.86 417	72.71 496	97.30 470	99.08 414	98.34 249	99.74 403	89.21 484	98.33 453	99.26 349

UWE-MVS-28		95.64 450	95.47 442	96.14 474	97.98 484	90.39 498	98.49 379	95.81 485	99.02 275	98.03 450	98.19 467	84.49 472	99.28 484	88.75 485	98.47 451	98.75 444

thres600view7		96.60 424	96.16 427	97.93 433	99.63 236	96.09 451	99.18 211	97.57 467	98.77 315	98.72 406	97.32 484	87.04 460	99.72 408	88.57 486	98.62 444	97.98 481

FPMVS		96.32 432	95.50 441	98.79 390	99.60 243	98.17 373	98.46 385	98.80 422	97.16 439	96.28 483	99.63 251	82.19 474	99.09 487	88.45 487	98.89 428	99.10 387

PCF-MVS	96.03 18	96.73 421	95.86 434	99.33 295	99.44 329	99.16 271	96.87 481	99.44 331	86.58 491	98.95 378	99.40 349	94.38 391	99.88 235	87.93 488	99.80 249	98.95 420
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 430	96.03 430	97.47 448	99.63 236	95.93 452	99.18 211	97.57 467	98.75 319	98.70 409	97.31 485	87.04 460	99.67 441	87.62 489	98.51 448	96.81 490

tfpn200view9		96.30 433	95.89 432	97.53 445	99.58 256	96.11 449	99.00 287	97.54 470	98.43 351	98.52 423	96.98 489	86.85 462	99.67 441	87.62 489	98.51 448	96.81 490

thres400		96.40 429	95.89 432	97.92 434	99.58 256	96.11 449	99.00 287	97.54 470	98.43 351	98.52 423	96.98 489	86.85 462	99.67 441	87.62 489	98.51 448	97.98 481

thres200		96.09 439	95.68 439	97.33 454	99.48 314	96.22 448	98.53 374	97.57 467	98.06 386	98.37 430	96.73 496	86.84 464	99.61 463	86.99 492	98.57 445	96.16 493

MVE	92.54 22	96.66 423	96.11 428	98.31 420	99.68 220	97.55 407	97.94 432	95.60 486	99.37 217	90.68 494	98.70 452	96.56 348	98.61 493	86.94 493	99.55 353	98.77 442
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 409	96.83 419	98.59 404	99.46 324	97.55 407	99.25 189	96.84 476	98.78 313	97.24 472	97.67 476	97.11 333	98.97 489	86.59 494	98.54 447	99.27 347

PAPM		95.61 452	94.71 454	98.31 420	99.12 407	96.63 436	96.66 484	98.46 441	90.77 488	96.25 484	98.68 453	93.01 409	99.69 424	81.60 495	97.86 471	98.62 448

SD_0403		97.42 404	96.90 417	98.98 358	99.54 282	97.90 394	99.52 94	99.54 290	99.34 221	97.87 457	98.85 442	98.72 190	99.64 455	78.93 496	99.83 222	99.40 313

dongtai		89.37 461	88.91 464	90.76 478	99.19 396	77.46 503	95.47 489	87.82 502	92.28 484	94.17 492	98.82 445	71.22 500	95.54 497	63.85 497	97.34 474	99.27 347

kuosan		85.65 463	84.57 466	88.90 480	97.91 486	77.11 504	96.37 486	87.62 503	85.24 493	85.45 498	96.83 492	69.94 502	90.98 499	45.90 498	95.83 490	98.62 448

test123		29.31 464	33.05 469	18.08 481	25.93 505	12.24 506	97.53 456	10.93 506	11.78 499	24.21 500	50.08 511	21.04 503	8.60 500	23.51 499	32.43 499	33.39 496

testmvs		28.94 465	33.33 467	15.79 482	26.03 504	9.81 507	96.77 482	15.67 505	11.55 500	23.87 501	50.74 510	19.03 504	8.53 501	23.21 500	33.07 498	29.03 497

mmdepth		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

monomultidepth		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

test_blank		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

uanet_test		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

DCPMVS		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

cdsmvs_eth3d_5k		24.88 466	33.17 468	0.00 483	0.00 506	0.00 508	0.00 495	99.62 238	0.00 501	0.00 502	99.13 405	99.82 18	0.00 502	0.00 501	0.00 500	0.00 498

pcd_1.5k_mvsjas		16.61 467	22.14 470	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	99.28 91	0.00 502	0.00 501	0.00 500	0.00 498

sosnet-low-res		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

sosnet		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

uncertanet		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

Regformer		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

ab-mvs-re		8.26 478	11.02 481	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	99.16 403	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

uanet		8.33 468	11.11 471	0.00 483	0.00 506	0.00 508	0.00 495	0.00 507	0.00 501	0.00 502	100.00 1	0.00 505	0.00 502	0.00 501	0.00 500	0.00 498

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					0.00 506

eth-test					0.00 506

test_241102_ONE					99.69 212	99.82 42		99.54 290	99.12 266	99.82 108	99.49 327	98.91 163	99.52 476

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																99.14 381

test_part2					99.62 240	99.67 118				99.55 259

sam_mvs1												90.81 440				99.14 381

sam_mvs												90.52 445

MTGPA								99.53 300

test_post											52.41 508	90.25 447	99.86 269

patchmatchnet-post											99.62 260	90.58 443	99.94 97

MTMP							99.09 254	98.59 435

TEST9					99.35 353	99.35 229	98.11 412	99.41 337	94.83 475	97.92 453	98.99 426	98.02 279	99.85 288

test_8					99.34 362	99.31 235	98.08 416	99.40 344	94.90 472	97.87 457	98.97 431	98.02 279	99.84 304

agg_prior					99.35 353	99.36 226		99.39 347		97.76 464			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					99.69 424			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	95.21 469	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		99.16 401		98.43 428		97.54 313	99.82 341		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								0.00 507

nn								0.00 507

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		97.45 424	98.15 442

ACMP_Plane					99.31 369		97.98 427		97.45 424	98.15 442

HQP4-MVS										98.15 442			99.70 417			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