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	99.73 109	100.00 1	99.89 41	99.79 22	99.88 234	99.98 1	100.00 1	99.98 5

test_fmvs2		99.72 54	99.85 17	99.34 289	99.91 31	98.08 380	99.48 108	100.00 1	99.90 50	99.99 7	99.91 31	99.50 60	99.98 27	99.98 1	99.99 16	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 224	99.96 7	98.62 335	99.67 53	100.00 1	99.95 32	100.00 1	99.95 16	99.85 14	99.99 8	99.98 1	99.99 16	99.98 5

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 3	99.98 3	99.76 71	99.12 239	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 54	99.88 7	99.27 314	99.93 24	97.84 393	99.34 146	100.00 1	99.99 3	99.99 7	99.82 91	99.87 13	99.99 8	99.97 4	99.99 16	99.97 10

test_vis1_n		99.68 65	99.79 34	99.36 285	99.94 18	98.18 369	99.52 93	100.00 1	99.86 66	100.00 1	99.88 50	98.99 147	99.96 69	99.97 4	99.96 87	99.95 14

test_fmvs1_n		99.68 65	99.81 28	99.28 309	99.95 15	97.93 389	99.49 106	100.00 1	99.82 86	99.99 7	99.89 41	99.21 101	99.98 27	99.97 4	99.98 50	99.93 20

test_f		99.75 49	99.88 7	99.37 280	99.96 7	98.21 366	99.51 100	100.00 1	99.94 36	100.00 1	99.93 22	99.58 48	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 58	99.80 52	98.94 306	99.96 28	99.98 18	99.96 34	99.78 130	99.88 11	99.98 27	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 19	99.08 255	99.97 20	99.98 18	99.96 34	99.79 118	99.90 9	99.99 8	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	99.01 279	99.99 11	99.99 3	99.98 14	99.88 50	99.97 2	99.99 8	99.96 9	100.00 1	99.98 5

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 54	99.88 45	99.55 168	99.17 214	99.98 12	99.99 3	99.96 34	99.84 77	99.96 3	99.99 8	99.96 9	99.99 16	99.88 40

test_cas_vis1_n_1920		99.76 46	99.86 13	99.45 250	99.93 24	98.40 354	99.30 163	99.98 12	99.94 36	99.99 7	99.89 41	99.80 21	99.97 44	99.96 9	99.97 73	99.97 10

fmvsm_s_conf0.5_n_10		99.77 44	99.73 55	99.88 19	99.81 104	99.75 79	99.06 261	99.85 82	99.99 3	99.97 24	99.84 77	99.12 118	99.98 27	99.95 14	99.99 16	99.90 29

fmvsm_s_conf0.5_n_7		99.73 52	99.78 39	99.60 189	99.74 176	98.93 302	98.85 320	99.96 28	99.96 28	99.97 24	99.76 147	99.82 18	99.96 69	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	99.11 244	99.91 52	99.98 18	99.96 34	99.64 234	99.60 43	99.99 8	99.95 14	99.99 16	99.88 40

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 41	99.82 92	99.70 108	99.17 214	99.97 20	99.99 3	99.96 34	99.82 91	99.94 4	100.00 1	99.95 14	100.00 1	99.80 65

test_fmvs1		99.48 129	99.65 73	98.97 356	99.54 280	97.16 419	99.11 244	99.98 12	99.78 103	99.96 34	99.81 98	98.72 190	99.97 44	99.95 14	99.97 73	99.79 73

mvsany_test3		99.85 12	99.88 7	99.75 96	99.95 15	99.37 220	99.53 91	99.98 12	99.77 107	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 83	99.59 155	98.97 297	99.92 43	99.99 3	99.97 24	99.84 77	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 71	98.92 310	99.98 12	99.99 3	99.99 7	99.88 50	99.43 65	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	99.12 239	99.91 52	99.98 18	99.95 45	99.67 218	99.67 34	99.99 8	99.94 20	99.99 16	99.88 40

MM		99.18 231	99.05 240	99.55 214	99.35 351	98.81 313	99.05 262	97.79 459	99.99 3	99.48 281	99.59 286	96.29 361	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	98.97 297	99.98 12	99.99 3	99.96 34	99.85 69	99.93 7	99.99 8	99.94 20	99.99 16	99.93 20

fmvsm_s_conf0.5_n_11		99.76 46	99.75 51	99.81 54	99.81 104	99.53 171	99.15 223	99.89 61	99.99 3	99.98 14	99.86 63	99.13 115	99.98 27	99.93 25	99.99 16	99.92 24

fmvsm_s_conf0.5_n_5		99.78 37	99.76 49	99.85 32	99.79 127	99.72 95	98.84 322	99.96 28	99.96 28	99.96 34	99.72 173	99.71 28	99.99 8	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 92	99.76 71	98.88 314	99.92 43	99.98 18	99.98 14	99.85 69	99.42 67	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 43	99.10 247	99.98 12	99.99 3	99.98 14	99.91 31	99.68 33	99.93 118	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 58	99.07 260	99.98 12	99.99 3	99.98 14	99.90 36	99.88 11	99.92 149	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 70	99.82 43	99.03 270	99.96 28	99.99 3	99.97 24	99.84 77	99.58 48	99.93 118	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 70	99.78 58	99.03 270	99.96 28	99.99 3	99.97 24	99.84 77	99.78 23	99.92 149	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 56	99.88 19	99.82 92	99.75 79	99.02 274	99.87 70	99.98 18	99.98 14	99.81 98	99.07 129	99.97 44	99.91 33	99.99 16	99.92 24

fmvsm_s_conf0.5_n_6		99.80 30	99.78 39	99.85 32	99.78 135	99.78 58	99.00 285	99.97 20	99.96 28	99.97 24	99.56 300	99.92 8	99.93 118	99.91 33	99.99 16	99.83 56

fmvsm_s_conf0.5_n_4		99.78 37	99.78 39	99.79 71	99.75 168	99.56 164	98.98 295	99.94 38	99.92 46	99.97 24	99.72 173	99.84 16	99.92 149	99.91 33	99.98 50	99.89 37

MVStest1		98.22 364	98.09 359	98.62 395	99.04 421	96.23 441	99.20 199	99.92 43	99.44 197	99.98 14	99.87 56	85.87 462	99.67 432	99.91 33	99.57 345	99.95 14

v1921920		99.56 104	99.57 102	99.55 214	99.75 168	99.11 275	99.05 262	99.61 242	99.15 259	99.88 83	99.71 183	99.08 126	99.87 249	99.90 37	99.97 73	99.66 146

v1240		99.56 104	99.58 97	99.51 230	99.80 113	99.00 289	99.00 285	99.65 221	99.15 259	99.90 68	99.75 155	99.09 122	99.88 234	99.90 37	99.96 87	99.67 132

v10		99.69 60	99.69 61	99.66 149	99.81 104	99.39 214	99.66 57	99.75 159	99.60 163	99.92 60	99.87 56	98.75 185	99.86 268	99.90 37	99.99 16	99.73 93

v1192		99.57 100	99.57 102	99.57 203	99.77 148	99.22 256	99.04 267	99.60 253	99.18 248	99.87 93	99.72 173	99.08 126	99.85 287	99.89 40	99.98 50	99.66 146

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 32	99.81 104	99.71 100	98.97 297	99.92 43	99.98 18	99.97 24	99.86 63	99.53 56	99.95 80	99.88 41	99.99 16	99.89 37

v144192		99.55 109	99.54 112	99.58 195	99.78 135	99.20 262	99.11 244	99.62 235	99.18 248	99.89 73	99.72 173	98.66 199	99.87 249	99.88 41	99.97 73	99.66 146

v8		99.68 65	99.69 61	99.65 156	99.80 113	99.40 211	99.66 57	99.76 154	99.64 147	99.93 53	99.85 69	98.66 199	99.84 303	99.88 41	99.99 16	99.71 102

mvs5depth		99.88 6	99.91 3	99.80 64	99.92 29	99.42 203	99.94 3	100.00 1	99.97 25	99.89 73	99.99 12	99.63 37	99.97 44	99.87 44	99.99 16	100.00 1

v1144		99.54 113	99.53 116	99.59 192	99.79 127	99.28 238	99.10 247	99.61 242	99.20 245	99.84 102	99.73 165	98.67 197	99.84 303	99.86 45	99.98 50	99.64 167

mmtdpeth		99.78 37	99.83 21	99.66 149	99.85 70	99.05 288	99.79 15	99.97 20	100.00 1	99.43 293	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 58	99.65 126	99.52 93	99.81 116	99.87 63	99.81 116	99.79 118	96.78 341	99.99 8	99.83 46	99.51 361	99.86 46

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 27	99.82 10	99.82 103	99.84 76	99.94 48	99.91 31	99.13 115	99.96 69	99.83 46	99.99 16	99.83 56

v2v482		99.50 122	99.47 128	99.58 195	99.78 135	99.25 246	99.14 227	99.58 268	99.25 236	99.81 116	99.62 258	98.24 258	99.84 303	99.83 46	99.97 73	99.64 167

test_vis1_rt		99.45 145	99.46 133	99.41 268	99.71 189	98.63 334	98.99 292	99.96 28	99.03 272	99.95 45	99.12 406	98.75 185	99.84 303	99.82 50	99.82 231	99.77 79

tt0805		99.63 84	99.57 102	99.81 54	99.87 54	99.88 12	99.58 82	98.70 420	99.72 113	99.91 63	99.60 276	99.43 65	99.81 352	99.81 51	99.53 357	99.73 93

VortexMVS		99.13 244	99.24 194	98.79 386	99.67 225	96.60 433	99.24 188	99.80 121	99.85 72	99.93 53	99.84 77	95.06 379	99.89 219	99.80 52	99.98 50	99.89 37

V42		99.56 104	99.54 112	99.63 170	99.79 127	99.46 188	99.39 127	99.59 259	99.24 238	99.86 96	99.70 193	98.55 214	99.82 336	99.79 53	99.95 110	99.60 202

SSC-MVS3.2		99.64 83	99.67 65	99.56 207	99.75 168	98.98 292	98.96 301	99.87 70	99.88 61	99.84 102	99.64 234	99.32 85	99.91 178	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 55	99.72 33	99.88 66	99.92 46	99.98 14	99.93 22	99.94 4	99.98 27	99.77 55	100.00 1	99.92 24

WB-MVS		99.44 149	99.32 170	99.80 64	99.81 104	99.61 149	99.47 111	99.81 116	99.82 86	99.71 182	99.72 173	96.60 345	99.98 27	99.75 56	99.23 402	99.82 63

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 64	99.68 49	99.85 82	99.95 32	99.98 14	99.92 27	99.28 90	99.98 27	99.75 56	100.00 1	99.94 17

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 58	99.70 38	99.86 76	99.89 56	99.98 14	99.90 36	99.94 4	99.98 27	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 50	100.00 1	99.97 14	99.61 41	99.97 44	99.75 56	100.00 1	99.84 52

AstraMVS		99.15 241	99.06 235	99.42 260	99.85 70	98.59 338	99.13 234	97.26 467	99.84 76	99.87 93	99.77 140	96.11 364	99.93 118	99.71 60	99.96 87	99.74 89

Elysia		99.69 60	99.65 73	99.81 54	99.86 58	99.72 95	99.34 146	99.77 146	99.94 36	99.91 63	99.76 147	98.55 214	99.99 8	99.70 61	99.98 50	99.72 97

StellarMVS		99.69 60	99.65 73	99.81 54	99.86 58	99.72 95	99.34 146	99.77 146	99.94 36	99.91 63	99.76 147	98.55 214	99.99 8	99.70 61	99.98 50	99.72 97

tt0320-xc		99.82 24	99.82 25	99.82 46	99.82 92	99.84 27	99.82 10	99.92 43	99.94 36	99.94 48	99.93 22	99.34 82	99.92 149	99.70 61	99.96 87	99.70 105

reproduce_monomvs		97.40 399	97.46 391	97.20 449	99.05 418	91.91 477	99.20 199	99.18 392	99.84 76	99.86 96	99.75 155	80.67 470	99.83 320	99.69 64	99.95 110	99.85 49

SPE-MVS-test		99.68 65	99.70 58	99.64 163	99.57 264	99.83 35	99.78 17	99.97 20	99.92 46	99.50 278	99.38 353	99.57 50	99.95 80	99.69 64	99.90 158	99.15 371

guyue		99.12 247	99.02 249	99.41 268	99.84 75	98.56 339	99.19 205	98.30 445	99.82 86	99.84 102	99.75 155	94.84 382	99.92 149	99.68 66	99.94 126	99.74 89

tt0320		99.79 34	99.79 34	99.81 54	99.82 92	99.84 27	99.82 10	99.90 58	99.94 36	99.94 48	99.94 19	99.07 129	99.92 149	99.68 66	99.97 73	99.67 132

MGCNet		98.61 322	98.30 343	99.52 226	97.88 483	98.95 298	98.76 339	94.11 483	99.84 76	99.32 324	99.57 296	95.57 373	99.95 80	99.68 66	99.98 50	99.68 123

CS-MVS		99.67 75	99.70 58	99.58 195	99.53 287	99.84 27	99.79 15	99.96 28	99.90 50	99.61 234	99.41 343	99.51 59	99.95 80	99.66 69	99.89 172	98.96 413

mamv4		99.73 52	99.74 53	99.70 131	99.66 227	99.87 15	99.69 45	99.93 39	99.93 43	99.93 53	99.86 63	99.07 129	100.00 1	99.66 69	99.92 144	99.24 346

KinetiMVS		99.66 76	99.63 81	99.76 85	99.89 39	99.57 163	99.37 138	99.82 103	99.95 32	99.90 68	99.63 249	98.57 210	99.97 44	99.65 71	99.94 126	99.74 89

pmmvs6		99.86 10	99.86 13	99.83 41	99.94 18	99.90 7	99.83 7	99.91 52	99.85 72	99.94 48	99.95 16	99.73 27	99.90 197	99.65 71	99.97 73	99.69 116

MIMVSNet1		99.66 76	99.62 83	99.80 64	99.94 18	99.87 15	99.69 45	99.77 146	99.78 103	99.93 53	99.89 41	97.94 286	99.92 149	99.65 71	99.98 50	99.62 185

LuminaMVS		99.39 167	99.28 185	99.73 112	99.83 83	99.49 178	99.00 285	99.05 404	99.81 92	99.89 73	99.79 118	96.54 349	99.97 44	99.64 74	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 81	99.92 149	99.64 74	99.94 126	99.68 123

EC-MVSNet		99.69 60	99.69 61	99.68 138	99.71 189	99.91 4	99.76 23	99.96 28	99.86 66	99.51 275	99.39 351	99.57 50	99.93 118	99.64 74	99.86 203	99.20 359

K. test v3		98.87 298	98.60 308	99.69 136	99.93 24	99.46 188	99.74 27	94.97 478	99.78 103	99.88 83	99.88 50	93.66 397	99.97 44	99.61 77	99.95 110	99.64 167

KD-MVS_self_test		99.63 84	99.59 93	99.76 85	99.84 75	99.90 7	99.37 138	99.79 130	99.83 82	99.88 83	99.85 69	98.42 238	99.90 197	99.60 78	99.73 285	99.49 267

Anonymous20240521		99.44 149	99.42 143	99.49 236	99.89 39	98.96 297	99.62 67	99.76 154	99.85 72	99.82 109	99.88 50	96.39 356	99.97 44	99.59 79	99.98 50	99.55 227

TransMVSNet (Re)		99.78 37	99.77 45	99.81 54	99.91 31	99.85 22	99.75 25	99.86 76	99.70 124	99.91 63	99.89 41	99.60 43	99.87 249	99.59 79	99.74 279	99.71 102

OurMVSNet-221017-0		99.75 49	99.71 57	99.84 38	99.96 7	99.83 35	99.83 7	99.85 82	99.80 96	99.93 53	99.93 22	98.54 218	99.93 118	99.59 79	99.98 50	99.76 84

EU-MVSNet		99.39 167	99.62 83	98.72 391	99.88 45	96.44 435	99.56 87	99.85 82	99.90 50	99.90 68	99.85 69	98.09 274	99.83 320	99.58 82	99.95 110	99.90 29

mvs_anonymous		99.28 195	99.39 148	98.94 360	99.19 394	97.81 395	99.02 274	99.55 281	99.78 103	99.85 99	99.80 107	98.24 258	99.86 268	99.57 83	99.50 364	99.15 371

test1111		97.74 384	98.16 355	96.49 460	99.60 242	89.86 491	99.71 37	91.21 487	99.89 56	99.88 83	99.87 56	93.73 396	99.90 197	99.56 84	99.99 16	99.70 105

lessismore_v0				99.64 163	99.86 58	99.38 216		90.66 488		99.89 73	99.83 84	94.56 387	99.97 44	99.56 84	99.92 144	99.57 220

mvsany_test1		99.44 149	99.45 135	99.40 271	99.37 344	98.64 333	97.90 435	99.59 259	99.27 232	99.92 60	99.82 91	99.74 26	99.93 118	99.55 86	99.87 195	99.63 173

MVSMamba_PlusPlus		99.55 109	99.58 97	99.47 243	99.68 218	99.40 211	99.52 93	99.70 190	99.92 46	99.77 145	99.86 63	98.28 254	99.96 69	99.54 87	99.90 158	99.05 400

pm-mvs1		99.79 34	99.79 34	99.78 75	99.91 31	99.83 35	99.76 23	99.87 70	99.73 109	99.89 73	99.87 56	99.63 37	99.87 249	99.54 87	99.92 144	99.63 173

LTVRE_ROB	99.19 1	99.88 6	99.87 11	99.88 19	99.91 31	99.90 7	99.96 1	99.92 43	99.90 50	99.97 24	99.87 56	99.81 20	99.95 80	99.54 87	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	98.95 359	99.71 189	97.27 416	99.50 101	99.82 103	99.59 165	99.41 302	99.85 69	99.62 40	100.00 1	99.53 90	99.89 172	99.59 209

test2506		94.73 450	94.59 450	95.15 467	99.59 248	85.90 493	99.75 25	74.01 495	99.89 56	99.71 182	99.86 63	79.00 480	99.90 197	99.52 91	99.99 16	99.65 155

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 181	99.93 43	99.95 45	99.89 41	99.71 28	99.96 69	99.51 92	99.97 73	99.84 52

FC-MVSNet-test		99.70 58	99.65 73	99.86 30	99.88 45	99.86 19	99.72 33	99.78 140	99.90 50	99.82 109	99.83 84	98.45 234	99.87 249	99.51 92	99.97 73	99.86 46

BP-MVS1		98.72 314	98.46 324	99.50 232	99.53 287	99.00 289	99.34 146	98.53 430	99.65 143	99.73 172	99.38 353	90.62 436	99.96 69	99.50 94	99.86 203	99.55 227

UA-Net		99.78 37	99.76 49	99.86 30	99.72 185	99.71 100	99.91 4	99.95 36	99.96 28	99.71 182	99.91 31	99.15 110	99.97 44	99.50 94	100.00 1	99.90 29

viewdifsd2359ckpt11		99.62 91	99.64 78	99.56 207	99.86 58	99.19 263	99.02 274	99.93 39	99.83 82	99.88 83	99.81 98	98.99 147	99.83 320	99.48 96	99.96 87	99.65 155

viewmsd2359difaftdt		99.62 91	99.64 78	99.56 207	99.86 58	99.19 263	99.02 274	99.93 39	99.83 82	99.88 83	99.81 98	98.99 147	99.83 320	99.48 96	99.96 87	99.65 155

PMMVS2		99.48 129	99.45 135	99.57 203	99.76 152	98.99 291	98.09 412	99.90 58	98.95 282	99.78 133	99.58 289	99.57 50	99.93 118	99.48 96	99.95 110	99.79 73

VPA-MVSNet		99.66 76	99.62 83	99.79 71	99.68 218	99.75 79	99.62 67	99.69 198	99.85 72	99.80 123	99.81 98	98.81 173	99.91 178	99.47 99	99.88 182	99.70 105

GDP-MVS		98.81 305	98.57 314	99.50 232	99.53 287	99.12 274	99.28 172	99.86 76	99.53 174	99.57 245	99.32 370	90.88 432	99.98 27	99.46 100	99.74 279	99.42 304

ECVR-MVS		97.73 385	98.04 362	96.78 453	99.59 248	90.81 486	99.72 33	90.43 489	99.89 56	99.86 96	99.86 63	93.60 398	99.89 219	99.46 100	99.99 16	99.65 155

nrg030		99.70 58	99.66 71	99.82 46	99.76 152	99.84 27	99.61 73	99.70 190	99.93 43	99.78 133	99.68 214	99.10 120	99.78 366	99.45 102	99.96 87	99.83 56

FE-MVSNET2		99.68 65	99.67 65	99.72 120	99.86 58	99.68 115	99.46 115	99.88 66	99.62 152	99.87 93	99.85 69	99.06 136	99.85 287	99.44 103	99.98 50	99.63 173

TAMVS		99.49 127	99.45 135	99.63 170	99.48 312	99.42 203	99.45 116	99.57 270	99.66 140	99.78 133	99.83 84	97.85 293	99.86 268	99.44 103	99.96 87	99.61 198

GeoE		99.69 60	99.66 71	99.78 75	99.76 152	99.76 71	99.60 79	99.82 103	99.46 192	99.75 157	99.56 300	99.63 37	99.95 80	99.43 105	99.88 182	99.62 185

new-patchmatchnet		99.35 180	99.57 102	98.71 393	99.82 92	96.62 431	98.55 367	99.75 159	99.50 179	99.88 83	99.87 56	99.31 86	99.88 234	99.43 105	100.00 1	99.62 185

test20.03		99.55 109	99.54 112	99.58 195	99.79 127	99.37 220	99.02 274	99.89 61	99.60 163	99.82 109	99.62 258	98.81 173	99.89 219	99.43 105	99.86 203	99.47 275

MVSFormer		99.41 161	99.44 139	99.31 301	99.57 264	98.40 354	99.77 19	99.80 121	99.73 109	99.63 218	99.30 375	98.02 279	99.98 27	99.43 105	99.69 304	99.55 227

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 27	99.77 19	99.80 121	99.73 109	99.97 24	99.92 27	99.77 25	99.98 27	99.43 105	100.00 1	99.90 29

SDMVSNet		99.77 44	99.77 45	99.76 85	99.80 113	99.65 126	99.63 64	99.86 76	99.97 25	99.89 73	99.89 41	99.52 58	99.99 8	99.42 110	99.96 87	99.65 155

Anonymous20231211		99.62 91	99.57 102	99.76 85	99.61 240	99.60 153	99.81 13	99.73 169	99.82 86	99.90 68	99.90 36	97.97 285	99.86 268	99.42 110	99.96 87	99.80 65

SixPastTwentyTwo		99.42 155	99.30 177	99.76 85	99.92 29	99.67 118	99.70 38	99.14 397	99.65 143	99.89 73	99.90 36	96.20 363	99.94 97	99.42 110	99.92 144	99.67 132

balanced_conf03		99.50 122	99.50 121	99.50 232	99.42 335	99.49 178	99.52 93	99.75 159	99.86 66	99.78 133	99.71 183	98.20 266	99.90 197	99.39 113	99.88 182	99.10 382

patch_mono-2		99.51 120	99.46 133	99.64 163	99.70 204	99.11 275	99.04 267	99.87 70	99.71 118	99.47 283	99.79 118	98.24 258	99.98 27	99.38 114	99.96 87	99.83 56

UGNet		99.38 170	99.34 165	99.49 236	98.90 433	98.90 306	99.70 38	99.35 353	99.86 66	98.57 417	99.81 98	98.50 229	99.93 118	99.38 114	99.98 50	99.66 146
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 57	99.67 65	99.81 54	99.89 39	99.72 95	99.59 80	99.82 103	99.39 213	99.82 109	99.84 77	99.38 73	99.91 178	99.38 114	99.93 138	99.80 65

FIs		99.65 82	99.58 97	99.84 38	99.84 75	99.85 22	99.66 57	99.75 159	99.86 66	99.74 167	99.79 118	98.27 256	99.85 287	99.37 117	99.93 138	99.83 56

sd_testset		99.78 37	99.78 39	99.80 64	99.80 113	99.76 71	99.80 14	99.79 130	99.97 25	99.89 73	99.89 41	99.53 56	99.99 8	99.36 118	99.96 87	99.65 155

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 82	99.70 124	99.92 60	99.93 22	99.45 61	99.97 44	99.36 118	100.00 1	99.85 49

casdiffmvs_mvg		99.68 65	99.68 64	99.69 136	99.81 104	99.59 155	99.29 170	99.90 58	99.71 118	99.79 129	99.73 165	99.54 53	99.84 303	99.36 118	99.96 87	99.65 155
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 43	99.67 136	99.77 145	99.75 155	99.61 41	99.98 27	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	99.53 224	99.79 127	98.82 312	99.58 82	99.97 20	99.95 32	99.96 34	99.76 147	98.44 235	99.99 8	99.34 122	99.96 87	99.78 75

CHOSEN 1792x2688		99.39 167	99.30 177	99.65 156	99.88 45	99.25 246	98.78 337	99.88 66	98.66 324	99.96 34	99.79 118	97.45 315	99.93 118	99.34 122	99.99 16	99.78 75

CDS-MVSNet		99.22 217	99.13 210	99.50 232	99.35 351	99.11 275	98.96 301	99.54 287	99.46 192	99.61 234	99.70 193	96.31 359	99.83 320	99.34 122	99.88 182	99.55 227
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 278	99.16 203	98.51 401	99.75 168	95.90 447	98.07 415	99.84 89	99.84 76	99.89 73	99.73 165	96.01 367	99.99 8	99.33 125	100.00 1	99.63 173

HyFIR lowres test		98.91 291	98.64 305	99.73 112	99.85 70	99.47 182	98.07 415	99.83 97	98.64 327	99.89 73	99.60 276	92.57 410	100.00 1	99.33 125	99.97 73	99.72 97

pmmvs5		99.19 227	99.11 217	99.42 260	99.76 152	98.88 308	98.55 367	99.73 169	98.82 303	99.72 177	99.62 258	96.56 346	99.82 336	99.32 127	99.95 110	99.56 223

v148		99.40 163	99.41 146	99.39 274	99.76 152	98.94 299	99.09 252	99.59 259	99.17 253	99.81 116	99.61 268	98.41 239	99.69 415	99.32 127	99.94 126	99.53 243

baseline		99.63 84	99.62 83	99.66 149	99.80 113	99.62 140	99.44 118	99.80 121	99.71 118	99.72 177	99.69 202	99.15 110	99.83 320	99.32 127	99.94 126	99.53 243

CVMVSNet		98.61 322	98.88 283	97.80 432	99.58 254	93.60 470	99.26 181	99.64 229	99.66 140	99.72 177	99.67 218	93.26 402	99.93 118	99.30 130	99.81 241	99.87 44

PS-CasMVS		99.66 76	99.58 97	99.89 11	99.80 113	99.85 22	99.66 57	99.73 169	99.62 152	99.84 102	99.71 183	98.62 203	99.96 69	99.30 130	99.96 87	99.86 46

DTE-MVSNet		99.68 65	99.61 87	99.88 19	99.80 113	99.87 15	99.67 53	99.71 181	99.72 113	99.84 102	99.78 130	98.67 197	99.97 44	99.30 130	99.95 110	99.80 65

tmp_tt		95.75 442	95.42 437	96.76 454	89.90 494	94.42 464	98.86 318	97.87 457	78.01 485	99.30 334	99.69 202	97.70 301	95.89 487	99.29 133	98.14 460	99.95 14

PEN-MVS		99.66 76	99.59 93	99.89 11	99.83 83	99.87 15	99.66 57	99.73 169	99.70 124	99.84 102	99.73 165	98.56 213	99.96 69	99.29 133	99.94 126	99.83 56

WR-MVS_H		99.61 95	99.53 116	99.87 26	99.80 113	99.83 35	99.67 53	99.75 159	99.58 167	99.85 99	99.69 202	98.18 269	99.94 97	99.28 135	99.95 110	99.83 56

IterMVS		98.97 282	99.16 203	98.42 406	99.74 176	95.64 451	98.06 417	99.83 97	99.83 82	99.85 99	99.74 160	96.10 366	99.99 8	99.27 136	100.00 1	99.63 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NormalMVS		99.09 255	98.91 281	99.62 179	99.78 135	99.11 275	99.36 142	99.77 146	99.82 86	99.68 194	99.53 312	93.30 400	99.99 8	99.24 137	99.76 268	99.74 89

SymmetryMVS		99.01 275	98.82 291	99.58 195	99.65 232	99.11 275	99.36 142	99.20 390	99.82 86	99.68 194	99.53 312	93.30 400	99.99 8	99.24 137	99.63 325	99.64 167

WBMVS		97.50 395	97.18 401	98.48 403	98.85 441	95.89 448	98.44 384	99.52 302	99.53 174	99.52 268	99.42 342	80.10 473	99.86 268	99.24 137	99.95 110	99.68 123

h-mvs33		98.61 322	98.34 338	99.44 254	99.60 242	98.67 325	99.27 176	99.44 328	99.68 129	99.32 324	99.49 325	92.50 413	100.00 1	99.24 137	96.51 479	99.65 155

hse-mvs2		98.52 335	98.30 343	99.16 330	99.29 373	98.60 336	98.77 338	99.02 406	99.68 129	99.32 324	99.04 416	92.50 413	99.85 287	99.24 137	97.87 467	99.03 404

FMVSNet1		99.66 76	99.63 81	99.73 112	99.78 135	99.77 64	99.68 49	99.70 190	99.67 136	99.82 109	99.83 84	98.98 151	99.90 197	99.24 137	99.97 73	99.53 243

casdiffmvs		99.63 84	99.61 87	99.67 142	99.79 127	99.59 155	99.13 234	99.85 82	99.79 100	99.76 152	99.72 173	99.33 84	99.82 336	99.21 143	99.94 126	99.59 209
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	99.76 152	99.82 43	99.57 85	99.61 242	99.54 172	99.80 123	99.64 234	97.79 297	99.95 80	99.21 143	99.94 126	99.84 52

DELS-MVS		99.34 185	99.30 177	99.48 241	99.51 296	99.36 224	98.12 408	99.53 297	99.36 218	99.41 302	99.61 268	99.22 100	99.87 249	99.21 143	99.68 309	99.20 359
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	99.37 280	99.70 204	98.80 316	98.67 348	99.92 43	99.49 181	99.77 145	99.71 183	99.08 126	99.78 366	99.20 146	99.94 126	99.54 237

UniMVSNet (Re)		99.37 174	99.26 190	99.68 138	99.51 296	99.58 160	98.98 295	99.60 253	99.43 204	99.70 186	99.36 361	97.70 301	99.88 234	99.20 146	99.87 195	99.59 209

CANet		99.11 251	99.05 240	99.28 309	98.83 443	98.56 339	98.71 346	99.41 334	99.25 236	99.23 343	99.22 393	97.66 309	99.94 97	99.19 148	99.97 73	99.33 327

EI-MVSNet-UG-set		99.48 129	99.50 121	99.42 260	99.57 264	98.65 331	99.24 188	99.46 322	99.68 129	99.80 123	99.66 223	98.99 147	99.89 219	99.19 148	99.90 158	99.72 97

xiu_mvs_v1_base_debu		99.23 208	99.34 165	98.91 368	99.59 248	98.23 363	98.47 379	99.66 211	99.61 157	99.68 194	98.94 432	99.39 69	99.97 44	99.18 150	99.55 350	98.51 452

xiu_mvs_v1_base		99.23 208	99.34 165	98.91 368	99.59 248	98.23 363	98.47 379	99.66 211	99.61 157	99.68 194	98.94 432	99.39 69	99.97 44	99.18 150	99.55 350	98.51 452

xiu_mvs_v1_base_debi		99.23 208	99.34 165	98.91 368	99.59 248	98.23 363	98.47 379	99.66 211	99.61 157	99.68 194	98.94 432	99.39 69	99.97 44	99.18 150	99.55 350	98.51 452

VPNet		99.46 141	99.37 154	99.71 126	99.82 92	99.59 155	99.48 108	99.70 190	99.81 92	99.69 189	99.58 289	97.66 309	99.86 268	99.17 153	99.44 371	99.67 132

UniMVSNet_NR-MVSNet		99.37 174	99.25 192	99.72 120	99.47 318	99.56 164	98.97 297	99.61 242	99.43 204	99.67 201	99.28 379	97.85 293	99.95 80	99.17 153	99.81 241	99.65 155

DU-MVS		99.33 188	99.21 197	99.71 126	99.43 330	99.56 164	98.83 325	99.53 297	99.38 214	99.67 201	99.36 361	97.67 305	99.95 80	99.17 153	99.81 241	99.63 173

EI-MVSNet-Vis-set		99.47 139	99.49 125	99.42 260	99.57 264	98.66 328	99.24 188	99.46 322	99.67 136	99.79 129	99.65 232	98.97 153	99.89 219	99.15 156	99.89 172	99.71 102

EI-MVSNet		99.38 170	99.44 139	99.21 324	99.58 254	98.09 377	99.26 181	99.46 322	99.62 152	99.75 157	99.67 218	98.54 218	99.85 287	99.15 156	99.92 144	99.68 123

VNet		99.18 231	99.06 235	99.56 207	99.24 384	99.36 224	99.33 152	99.31 364	99.67 136	99.47 283	99.57 296	96.48 350	99.84 303	99.15 156	99.30 390	99.47 275

EG-PatchMatch MVS		99.57 100	99.56 107	99.62 179	99.77 148	99.33 230	99.26 181	99.76 154	99.32 223	99.80 123	99.78 130	99.29 88	99.87 249	99.15 156	99.91 156	99.66 146

PVSNet_Blended_VisFu		99.40 163	99.38 151	99.44 254	99.90 37	98.66 328	98.94 306	99.91 52	97.97 388	99.79 129	99.73 165	99.05 138	99.97 44	99.15 156	99.99 16	99.68 123

IterMVS-LS		99.41 161	99.47 128	99.25 320	99.81 104	98.09 377	98.85 320	99.76 154	99.62 152	99.83 108	99.64 234	98.54 218	99.97 44	99.15 156	99.99 16	99.68 123
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 85	99.58 254	99.64 132	99.30 163	99.63 232	99.61 157	99.71 182	99.56 300	98.76 183	99.96 69	99.14 162	99.92 144	99.68 123

MVSTER		98.47 342	98.22 348	99.24 322	99.06 417	98.35 360	99.08 255	99.46 322	99.27 232	99.75 157	99.66 223	88.61 449	99.85 287	99.14 162	99.92 144	99.52 254

E6new		99.68 65	99.67 65	99.70 131	99.86 58	99.62 140	99.41 121	99.84 89	99.68 129	99.77 145	99.81 98	99.59 45	99.78 366	99.13 164	99.96 87	99.70 105

E6		99.68 65	99.67 65	99.70 131	99.86 58	99.62 140	99.41 121	99.84 89	99.68 129	99.77 145	99.81 98	99.59 45	99.78 366	99.13 164	99.96 87	99.70 105

E5		99.68 65	99.67 65	99.70 131	99.87 54	99.62 140	99.41 121	99.84 89	99.68 129	99.77 145	99.81 98	99.59 45	99.78 366	99.13 164	99.96 87	99.70 105

diffmvs_AUTHOR		99.48 129	99.48 126	99.47 243	99.80 113	98.89 307	98.71 346	99.82 103	99.79 100	99.66 207	99.63 249	98.87 169	99.88 234	99.13 164	99.95 110	99.62 185

Anonymous20231206		99.35 180	99.31 172	99.47 243	99.74 176	99.06 287	99.28 172	99.74 165	99.23 240	99.72 177	99.53 312	97.63 311	99.88 234	99.11 168	99.84 213	99.48 271

Syy-MVS		98.17 367	97.85 379	99.15 332	98.50 466	98.79 317	98.60 355	99.21 387	97.89 396	96.76 472	96.37 495	95.47 376	99.57 458	99.10 169	98.73 436	99.09 387

ttmdpeth		99.48 129	99.55 109	99.29 306	99.76 152	98.16 371	99.33 152	99.95 36	99.79 100	99.36 313	99.89 41	99.13 115	99.77 378	99.09 170	99.64 322	99.93 20

MVS_Test		99.28 195	99.31 172	99.19 327	99.35 351	98.79 317	99.36 142	99.49 315	99.17 253	99.21 348	99.67 218	98.78 180	99.66 437	99.09 170	99.66 318	99.10 382

FE-MVSNET3		98.87 298	98.71 300	99.35 287	99.59 248	98.88 308	97.17 468	99.64 229	98.94 283	99.27 336	99.22 393	95.57 373	99.83 320	99.08 172	99.92 144	99.35 322

testgi		99.29 194	99.26 190	99.37 280	99.75 168	98.81 313	98.84 322	99.89 61	98.38 355	99.75 157	99.04 416	99.36 78	99.86 268	99.08 172	99.25 398	99.45 281

1112_ss		99.05 263	98.84 288	99.67 142	99.66 227	99.29 236	98.52 373	99.82 103	97.65 408	99.43 293	99.16 400	96.42 353	99.91 178	99.07 174	99.84 213	99.80 65

CANet_DTU		98.91 291	98.85 286	99.09 341	98.79 449	98.13 372	98.18 400	99.31 364	99.48 184	98.86 388	99.51 318	96.56 346	99.95 80	99.05 175	99.95 110	99.19 362

blended_shiyan6		97.82 380	97.46 391	98.92 364	98.08 479	97.46 409	97.73 441	99.34 355	97.96 391	98.33 428	97.35 479	92.78 408	99.84 303	99.04 176	96.53 477	99.46 279

Baseline_NR-MVSNet		99.49 127	99.37 154	99.82 46	99.91 31	99.84 27	98.83 325	99.86 76	99.68 129	99.65 210	99.88 50	97.67 305	99.87 249	99.03 177	99.86 203	99.76 84

FMVSNet2		99.35 180	99.28 185	99.55 214	99.49 307	99.35 227	99.45 116	99.57 270	99.44 197	99.70 186	99.74 160	97.21 326	99.87 249	99.03 177	99.94 126	99.44 294

Test_1112_low_res		98.95 288	98.73 298	99.63 170	99.68 218	99.15 271	98.09 412	99.80 121	97.14 434	99.46 287	99.40 347	96.11 364	99.89 219	99.01 179	99.84 213	99.84 52

VDD-MVS		99.20 224	99.11 217	99.44 254	99.43 330	98.98 292	99.50 101	98.32 444	99.80 96	99.56 253	99.69 202	96.99 336	99.85 287	98.99 180	99.73 285	99.50 262

DeepC-MVS	98.90 4	99.62 91	99.61 87	99.67 142	99.72 185	99.44 196	99.24 188	99.71 181	99.27 232	99.93 53	99.90 36	99.70 31	99.93 118	98.99 180	99.99 16	99.64 167
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	99.51 230	99.77 148	99.41 210	98.81 330	99.66 211	99.42 208	99.75 157	99.66 223	99.20 102	99.76 382	98.98 182	99.99 16	99.36 319

EPNet_dtu		97.62 390	97.79 382	97.11 452	96.67 489	92.31 475	98.51 374	98.04 451	99.24 238	95.77 481	99.47 332	93.78 395	99.66 437	98.98 182	99.62 327	99.37 316
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 185	99.32 170	99.39 274	99.67 225	98.77 319	98.57 364	99.81 116	99.61 157	99.48 281	99.41 343	98.47 230	99.86 268	98.97 184	99.90 158	99.53 243
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		99.40 163	99.31 172	99.68 138	99.43 330	99.55 168	99.73 30	99.50 311	99.46 192	99.88 83	99.36 361	97.54 312	99.87 249	98.97 184	99.87 195	99.63 173

TestfortrainingZip a		99.61 95	99.53 116	99.85 32	99.76 152	99.84 27	99.38 130	99.78 140	99.58 167	99.81 116	99.66 223	99.02 142	99.90 197	98.96 186	99.79 253	99.81 64

viewdifsd2359ckpt07		99.51 120	99.50 121	99.52 226	99.80 113	99.19 263	98.92 310	99.88 66	99.72 113	99.64 213	99.62 258	99.06 136	99.81 352	98.96 186	99.94 126	99.56 223

GBi-Net		99.42 155	99.31 172	99.73 112	99.49 307	99.77 64	99.68 49	99.70 190	99.44 197	99.62 228	99.83 84	97.21 326	99.90 197	98.96 186	99.90 158	99.53 243

FMVSNet5		97.80 382	97.25 399	99.42 260	98.83 443	98.97 295	99.38 130	99.80 121	98.87 295	99.25 339	99.69 202	80.60 472	99.91 178	98.96 186	99.90 158	99.38 313

test1		99.42 155	99.31 172	99.73 112	99.49 307	99.77 64	99.68 49	99.70 190	99.44 197	99.62 228	99.83 84	97.21 326	99.90 197	98.96 186	99.90 158	99.53 243

FMVSNet3		98.80 306	98.63 307	99.32 297	99.13 403	98.72 322	99.10 247	99.48 316	99.23 240	99.62 228	99.64 234	92.57 410	99.86 268	98.96 186	99.90 158	99.39 311

UnsupCasMVSNet_eth		98.83 302	98.57 314	99.59 192	99.68 218	99.45 194	98.99 292	99.67 206	99.48 184	99.55 258	99.36 361	94.92 380	99.86 268	98.95 192	96.57 476	99.45 281

CHOSEN 280x420		98.41 347	98.41 330	98.40 407	99.34 360	95.89 448	96.94 474	99.44 328	98.80 307	99.25 339	99.52 316	93.51 399	99.98 27	98.94 193	99.98 50	99.32 331

E4		99.61 95	99.59 93	99.66 149	99.84 75	99.53 171	99.08 255	99.84 89	99.65 143	99.74 167	99.80 107	99.45 61	99.77 378	98.93 194	99.95 110	99.69 116

TDRefinement		99.72 54	99.70 58	99.77 78	99.90 37	99.85 22	99.86 6	99.92 43	99.69 127	99.78 133	99.92 27	99.37 75	99.88 234	98.93 194	99.95 110	99.60 202

viewmacassd2359aftdt		99.63 84	99.61 87	99.68 138	99.84 75	99.61 149	99.14 227	99.87 70	99.71 118	99.75 157	99.77 140	99.54 53	99.72 399	98.91 196	99.96 87	99.70 105

alignmvs		98.28 357	97.96 368	99.25 320	99.12 405	98.93 302	99.03 270	98.42 437	99.64 147	98.72 403	97.85 471	90.86 433	99.62 449	98.88 197	99.13 404	99.19 362

testing3-2		96.51 421	96.43 416	96.74 456	99.36 347	91.38 483	99.10 247	97.87 457	99.48 184	98.57 417	98.71 447	76.65 483	99.66 437	98.87 198	99.26 397	99.18 364

MGCFI-Net		99.02 269	99.01 253	99.06 348	99.11 410	98.60 336	99.63 64	99.67 206	99.63 149	98.58 415	97.65 474	99.07 129	99.57 458	98.85 199	98.92 420	99.03 404

sss		98.90 293	98.77 297	99.27 314	99.48 312	98.44 351	98.72 344	99.32 360	97.94 393	99.37 312	99.35 366	96.31 359	99.91 178	98.85 199	99.63 325	99.47 275

xiu_mvs_v2_base		99.02 269	99.11 217	98.77 388	99.37 344	98.09 377	98.13 407	99.51 307	99.47 189	99.42 296	98.54 456	99.38 73	99.97 44	98.83 201	99.33 386	98.24 464

PS-MVSNAJ		99.00 278	99.08 229	98.76 389	99.37 344	98.10 376	98.00 423	99.51 307	99.47 189	99.41 302	98.50 458	99.28 90	99.97 44	98.83 201	99.34 385	98.20 468

E2		99.54 113	99.51 119	99.62 179	99.78 135	99.47 182	99.01 279	99.82 103	99.55 170	99.69 189	99.77 140	99.26 94	99.76 382	98.82 203	99.93 138	99.62 185

E3		99.54 113	99.51 119	99.62 179	99.78 135	99.47 182	99.01 279	99.82 103	99.55 170	99.69 189	99.77 140	99.25 98	99.76 382	98.82 203	99.93 138	99.62 185

D2MVS		99.22 217	99.19 200	99.29 306	99.69 210	98.74 321	98.81 330	99.41 334	98.55 336	99.68 194	99.69 202	98.13 271	99.87 249	98.82 203	99.98 50	99.24 346

PatchT		98.45 344	98.32 340	98.83 382	98.94 431	98.29 361	99.24 188	98.82 414	99.84 76	99.08 365	99.76 147	91.37 422	99.94 97	98.82 203	99.00 415	98.26 463

testf1		99.63 84	99.60 91	99.72 120	99.94 18	99.95 2	99.47 111	99.89 61	99.43 204	99.88 83	99.80 107	99.26 94	99.90 197	98.81 207	99.88 182	99.32 331

APD_test2		99.63 84	99.60 91	99.72 120	99.94 18	99.95 2	99.47 111	99.89 61	99.43 204	99.88 83	99.80 107	99.26 94	99.90 197	98.81 207	99.88 182	99.32 331

usedtu_blend_shiyan5		97.97 377	97.65 389	98.92 364	97.71 485	97.49 406	99.53 91	99.81 116	99.52 178	98.18 435	96.82 489	91.92 416	99.83 320	98.79 209	96.53 477	99.45 281

blend_shiyan4		95.04 449	93.76 453	98.88 377	97.92 481	97.49 406	97.72 442	99.34 355	97.93 394	97.65 461	97.11 483	77.69 481	99.83 320	98.79 209	79.72 488	99.33 327

sasdasda		99.02 269	99.00 257	99.09 341	99.10 412	98.70 323	99.61 73	99.66 211	99.63 149	98.64 409	97.65 474	99.04 139	99.54 462	98.79 209	98.92 420	99.04 402

Effi-MVS+		99.06 260	98.97 268	99.34 289	99.31 367	98.98 292	98.31 392	99.91 52	98.81 305	98.79 397	98.94 432	99.14 113	99.84 303	98.79 209	98.74 433	99.20 359

canonicalmvs		99.02 269	99.00 257	99.09 341	99.10 412	98.70 323	99.61 73	99.66 211	99.63 149	98.64 409	97.65 474	99.04 139	99.54 462	98.79 209	98.92 420	99.04 402

VDDNet		98.97 282	98.82 291	99.42 260	99.71 189	98.81 313	99.62 67	98.68 421	99.81 92	99.38 310	99.80 107	94.25 389	99.85 287	98.79 209	99.32 388	99.59 209

CR-MVSNet		98.35 354	98.20 350	98.83 382	99.05 418	98.12 373	99.30 163	99.67 206	97.39 422	99.16 354	99.79 118	91.87 419	99.91 178	98.78 215	98.77 429	98.44 457

test_method		91.72 451	92.32 454	89.91 470	93.49 493	70.18 496	90.28 485	99.56 275	61.71 488	95.39 483	99.52 316	93.90 391	99.94 97	98.76 216	98.27 453	99.62 185

RPMNet		98.60 325	98.53 320	98.83 382	99.05 418	98.12 373	99.30 163	99.62 235	99.86 66	99.16 354	99.74 160	92.53 412	99.92 149	98.75 217	98.77 429	98.44 457

mamba_0408		99.54 113	99.55 109	99.54 220	99.71 189	99.24 250	99.27 176	99.79 130	99.72 113	99.78 133	99.64 234	99.36 78	99.93 118	98.74 218	99.90 158	99.45 281

SSM_04072		99.55 109	99.55 109	99.55 214	99.71 189	99.24 250	99.27 176	99.79 130	99.72 113	99.78 133	99.64 234	99.36 78	99.97 44	98.74 218	99.90 158	99.45 281

SSM_0407		99.56 104	99.56 107	99.54 220	99.71 189	99.24 250	99.15 223	99.84 89	99.80 96	99.78 133	99.70 193	99.44 63	99.93 118	98.74 218	99.90 158	99.45 281

SSM_0404		99.57 100	99.58 97	99.54 220	99.76 152	99.28 238	99.19 205	99.84 89	99.80 96	99.78 133	99.70 193	99.44 63	99.93 118	98.74 218	99.95 110	99.41 305

pmmvs4		99.13 244	99.06 235	99.36 285	99.57 264	99.10 282	98.01 421	99.25 377	98.78 310	99.58 242	99.44 339	98.24 258	99.76 382	98.74 218	99.93 138	99.22 352

viewmanbaseed2359cas		99.50 122	99.47 128	99.61 185	99.73 180	99.52 175	99.03 270	99.83 97	99.49 181	99.65 210	99.64 234	99.18 104	99.71 404	98.73 223	99.92 144	99.58 214

tttt0517		97.62 390	97.20 400	98.90 374	99.76 152	97.40 413	99.48 108	94.36 480	99.06 270	99.70 186	99.49 325	84.55 465	99.94 97	98.73 223	99.65 320	99.36 319

viewcassd2359sk11		99.48 129	99.45 135	99.58 195	99.73 180	99.42 203	98.96 301	99.80 121	99.44 197	99.63 218	99.74 160	99.09 122	99.76 382	98.72 225	99.91 156	99.57 220

EPP-MVSNet		99.17 236	99.00 257	99.66 149	99.80 113	99.43 200	99.70 38	99.24 380	99.48 184	99.56 253	99.77 140	94.89 381	99.93 118	98.72 225	99.89 172	99.63 173

FE-MVSNET		99.45 145	99.36 159	99.71 126	99.84 75	99.64 132	99.16 220	99.91 52	98.65 325	99.73 172	99.73 165	98.54 218	99.82 336	98.71 227	99.96 87	99.67 132

Anonymous20240529		99.42 155	99.34 165	99.65 156	99.53 287	99.60 153	99.63 64	99.39 344	99.47 189	99.76 152	99.78 130	98.13 271	99.86 268	98.70 228	99.68 309	99.49 267

ACMH	98.42 6	99.59 99	99.54 112	99.72 120	99.86 58	99.62 140	99.56 87	99.79 130	98.77 312	99.80 123	99.85 69	99.64 35	99.85 287	98.70 228	99.89 172	99.70 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 188	99.28 185	99.47 243	99.57 264	99.39 214	99.78 17	99.43 331	98.87 295	99.57 245	99.82 91	98.06 277	99.87 249	98.69 230	99.73 285	99.15 371

LFMVS		98.46 343	98.19 353	99.26 317	99.24 384	98.52 347	99.62 67	96.94 469	99.87 63	99.31 329	99.58 289	91.04 427	99.81 352	98.68 231	99.42 375	99.45 281

WR-MVS		99.11 251	98.93 273	99.66 149	99.30 371	99.42 203	98.42 385	99.37 349	99.04 271	99.57 245	99.20 398	96.89 338	99.86 268	98.66 232	99.87 195	99.70 105

mvsmamba		99.08 256	98.95 271	99.45 250	99.36 347	99.18 268	99.39 127	98.81 415	99.37 215	99.35 315	99.70 193	96.36 358	99.94 97	98.66 232	99.59 341	99.22 352

viewdifsd2359ckpt13		99.42 155	99.37 154	99.57 203	99.72 185	99.46 188	99.01 279	99.80 121	99.20 245	99.51 275	99.60 276	98.92 160	99.70 408	98.65 234	99.90 158	99.55 227

RRT-MVS		99.08 256	99.00 257	99.33 292	99.27 378	98.65 331	99.62 67	99.93 39	99.66 140	99.67 201	99.82 91	95.27 378	99.93 118	98.64 235	99.09 408	99.41 305

E3new		99.42 155	99.37 154	99.56 207	99.68 218	99.38 216	98.93 309	99.79 130	99.30 227	99.55 258	99.69 202	98.88 167	99.76 382	98.63 236	99.89 172	99.53 243

Anonymous202405211		98.75 310	98.46 324	99.63 170	99.34 360	99.66 120	99.47 111	97.65 460	99.28 231	99.56 253	99.50 321	93.15 403	99.84 303	98.62 237	99.58 343	99.40 308

lecture		99.56 104	99.48 126	99.81 54	99.78 135	99.86 19	99.50 101	99.70 190	99.59 165	99.75 157	99.71 183	98.94 156	99.92 149	98.59 238	99.76 268	99.66 146

EPNet		98.13 368	97.77 383	99.18 329	94.57 492	97.99 383	99.24 188	97.96 453	99.74 108	97.29 465	99.62 258	93.13 404	99.97 44	98.59 238	99.83 221	99.58 214
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 263	99.09 227	98.91 368	99.21 389	98.36 359	98.82 329	99.47 319	98.85 298	98.90 383	99.56 300	98.78 180	99.09 478	98.57 240	99.68 309	99.26 343

Patchmatch-RL test		98.60 325	98.36 335	99.33 292	99.77 148	99.07 285	98.27 394	99.87 70	98.91 290	99.74 167	99.72 173	90.57 438	99.79 363	98.55 241	99.85 208	99.11 380

pmmvs3		98.08 371	97.80 380	98.91 368	99.41 337	97.69 401	97.87 436	99.66 211	95.87 453	99.50 278	99.51 318	90.35 440	99.97 44	98.55 241	99.47 368	99.08 393

ETV-MVS		99.18 231	99.18 201	99.16 330	99.34 360	99.28 238	99.12 239	99.79 130	99.48 184	98.93 377	98.55 455	99.40 68	99.93 118	98.51 243	99.52 360	98.28 462

viewdifsd2359ckpt09		99.24 206	99.16 203	99.49 236	99.70 204	99.22 256	98.88 314	99.81 116	98.70 320	99.38 310	99.37 356	98.22 263	99.76 382	98.48 244	99.88 182	99.51 256

jason		99.16 237	99.11 217	99.32 297	99.75 168	98.44 351	98.26 396	99.39 344	98.70 320	99.74 167	99.30 375	98.54 218	99.97 44	98.48 244	99.82 231	99.55 227
jason: jason.
APDe-MVS		99.48 129	99.36 159	99.85 32	99.55 278	99.81 48	99.50 101	99.69 198	98.99 275	99.75 157	99.71 183	98.79 178	99.93 118	98.46 246	99.85 208	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		99.30 192	99.29 182	99.31 301	99.71 189	98.55 341	98.17 402	99.71 181	99.41 209	99.73 172	99.60 276	99.17 106	99.92 149	98.45 247	99.70 296	99.45 281

IMVS_0407		99.38 170	99.42 143	99.28 309	99.71 189	98.55 341	99.27 176	99.71 181	99.41 209	99.73 172	99.60 276	99.17 106	99.83 320	98.45 247	99.70 296	99.45 281

IMVS_0404		99.23 208	99.20 198	99.32 297	99.71 189	98.55 341	98.57 364	99.71 181	99.41 209	99.52 268	99.60 276	98.12 273	99.95 80	98.45 247	99.70 296	99.45 281

IMVS_0403		99.37 174	99.39 148	99.28 309	99.71 189	98.55 341	99.19 205	99.71 181	99.41 209	99.67 201	99.60 276	99.12 118	99.84 303	98.45 247	99.70 296	99.45 281

CL-MVSNet_self_test		98.71 316	98.56 318	99.15 332	99.22 387	98.66 328	97.14 469	99.51 307	98.09 381	99.54 261	99.27 381	96.87 339	99.74 394	98.43 251	98.96 417	99.03 404

our_test_3		98.85 301	99.09 227	98.13 420	99.66 227	94.90 462	97.72 442	99.58 268	99.07 268	99.64 213	99.62 258	98.19 267	99.93 118	98.41 252	99.95 110	99.55 227

Gipuma		99.57 100	99.59 93	99.49 236	99.98 3	99.71 100	99.72 33	99.84 89	99.81 92	99.94 48	99.78 130	98.91 163	99.71 404	98.41 252	99.95 110	99.05 400
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 401	96.91 410	98.74 390	97.72 484	97.57 403	97.60 449	97.36 466	98.00 384	99.21 348	98.02 467	90.04 443	99.79 363	98.37 254	95.89 483	98.86 427

PM-MVS		99.36 178	99.29 182	99.58 195	99.83 83	99.66 120	98.95 304	99.86 76	98.85 298	99.81 116	99.73 165	98.40 243	99.92 149	98.36 255	99.83 221	99.17 367

baseline1		97.73 385	97.33 396	98.96 357	99.30 371	97.73 399	99.40 125	98.42 437	99.33 222	99.46 287	99.21 396	91.18 425	99.82 336	98.35 256	91.26 486	99.32 331

MVS-HIRNet		97.86 378	98.22 348	96.76 454	99.28 376	91.53 481	98.38 387	92.60 486	99.13 261	99.31 329	99.96 15	97.18 330	99.68 427	98.34 257	99.83 221	99.07 398

GA-MVS		97.99 376	97.68 386	98.93 363	99.52 294	98.04 381	97.19 467	99.05 404	98.32 368	98.81 393	98.97 428	89.89 445	99.41 473	98.33 258	99.05 411	99.34 326

Fast-Effi-MVS+		99.02 269	98.87 284	99.46 247	99.38 342	99.50 177	99.04 267	99.79 130	97.17 432	98.62 411	98.74 446	99.34 82	99.95 80	98.32 259	99.41 376	98.92 420

MDA-MVSNet_test_wron		98.95 288	98.99 264	98.85 378	99.64 233	97.16 419	98.23 398	99.33 358	98.93 287	99.56 253	99.66 223	97.39 319	99.83 320	98.29 260	99.88 182	99.55 227

N_pmnet		98.73 313	98.53 320	99.35 287	99.72 185	98.67 325	98.34 389	94.65 479	98.35 362	99.79 129	99.68 214	98.03 278	99.93 118	98.28 261	99.92 144	99.44 294

ET-MVSNet_ETH3D		96.78 413	96.07 423	98.91 368	99.26 381	97.92 390	97.70 445	96.05 474	97.96 391	92.37 487	98.43 459	87.06 453	99.90 197	98.27 262	97.56 470	98.91 421

thisisatest0530		97.45 396	96.95 407	98.94 360	99.68 218	97.73 399	99.09 252	94.19 482	98.61 332	99.56 253	99.30 375	84.30 467	99.93 118	98.27 262	99.54 355	99.16 369

YYNet1		98.95 288	98.99 264	98.84 380	99.64 233	97.14 421	98.22 399	99.32 360	98.92 289	99.59 240	99.66 223	97.40 317	99.83 320	98.27 262	99.90 158	99.55 227

reproduce_model		99.50 122	99.40 147	99.83 41	99.60 242	99.83 35	99.12 239	99.68 201	99.49 181	99.80 123	99.79 118	99.01 144	99.93 118	98.24 265	99.82 231	99.73 93

ACMM	98.09 11	99.46 141	99.38 151	99.72 120	99.80 113	99.69 112	99.13 234	99.65 221	98.99 275	99.64 213	99.72 173	99.39 69	99.86 268	98.23 266	99.81 241	99.60 202
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 285	98.87 284	99.24 322	99.57 264	98.40 354	98.12 408	99.18 392	98.28 370	99.63 218	99.13 402	98.02 279	99.97 44	98.22 267	99.69 304	99.35 322

3Dnovator	99.15 2	99.43 152	99.36 159	99.65 156	99.39 339	99.42 203	99.70 38	99.56 275	99.23 240	99.35 315	99.80 107	99.17 106	99.95 80	98.21 268	99.84 213	99.59 209

Fast-Effi-MVS+-dtu		99.20 224	99.12 214	99.43 258	99.25 382	99.69 112	99.05 262	99.82 103	99.50 179	98.97 373	99.05 414	98.98 151	99.98 27	98.20 269	99.24 400	98.62 442

MS-PatchMatch		99.00 278	98.97 268	99.09 341	99.11 410	98.19 367	98.76 339	99.33 358	98.49 345	99.44 289	99.58 289	98.21 264	99.69 415	98.20 269	99.62 327	99.39 311

TSAR-MVS + GP.		99.12 247	99.04 246	99.38 277	99.34 360	99.16 269	98.15 404	99.29 368	98.18 377	99.63 218	99.62 258	99.18 104	99.68 427	98.20 269	99.74 279	99.30 337

DP-MVS		99.48 129	99.39 148	99.74 101	99.57 264	99.62 140	99.29 170	99.61 242	99.87 63	99.74 167	99.76 147	98.69 193	99.87 249	98.20 269	99.80 248	99.75 87

MVP-Stereo		99.16 237	99.08 229	99.43 258	99.48 312	99.07 285	99.08 255	99.55 281	98.63 328	99.31 329	99.68 214	98.19 267	99.78 366	98.18 273	99.58 343	99.45 281
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 152	99.30 177	99.80 64	99.83 83	99.81 48	99.52 93	99.70 190	98.35 362	99.51 275	99.50 321	99.31 86	99.88 234	98.18 273	99.84 213	99.69 116

MDA-MVSNet-bldmvs		99.06 260	99.05 240	99.07 346	99.80 113	97.83 394	98.89 313	99.72 178	99.29 228	99.63 218	99.70 193	96.47 351	99.89 219	98.17 275	99.82 231	99.50 262

JIA-IIPM		98.06 372	97.92 375	98.50 402	98.59 462	97.02 423	98.80 333	98.51 432	99.88 61	97.89 449	99.87 56	91.89 418	99.90 197	98.16 276	97.68 469	98.59 445

EIA-MVS		99.12 247	99.01 253	99.45 250	99.36 347	99.62 140	99.34 146	99.79 130	98.41 351	98.84 390	98.89 436	98.75 185	99.84 303	98.15 277	99.51 361	98.89 424

miper_lstm_enhance		98.65 321	98.60 308	98.82 385	99.20 392	97.33 415	97.78 439	99.66 211	99.01 274	99.59 240	99.50 321	94.62 386	99.85 287	98.12 278	99.90 158	99.26 343

reproduce-ours		99.46 141	99.35 163	99.82 46	99.56 275	99.83 35	99.05 262	99.65 221	99.45 195	99.78 133	99.78 130	98.93 157	99.93 118	98.11 279	99.81 241	99.70 105

our_new_method		99.46 141	99.35 163	99.82 46	99.56 275	99.83 35	99.05 262	99.65 221	99.45 195	99.78 133	99.78 130	98.93 157	99.93 118	98.11 279	99.81 241	99.70 105

Effi-MVS+-dtu		99.07 259	98.92 277	99.52 226	98.89 436	99.78 58	99.15 223	99.66 211	99.34 219	98.92 380	99.24 391	97.69 303	99.98 27	98.11 279	99.28 393	98.81 431

tpm		97.15 405	96.95 407	97.75 434	98.91 432	94.24 465	99.32 155	97.96 453	97.71 406	98.29 429	99.32 370	86.72 459	99.92 149	98.10 282	96.24 481	99.09 387

DeepPCF-MVS	98.42 6	99.18 231	99.02 249	99.67 142	99.22 387	99.75 79	97.25 465	99.47 319	98.72 317	99.66 207	99.70 193	99.29 88	99.63 448	98.07 283	99.81 241	99.62 185

ppachtmachnet_test		98.89 296	99.12 214	98.20 418	99.66 227	95.24 458	97.63 447	99.68 201	99.08 266	99.78 133	99.62 258	98.65 201	99.88 234	98.02 284	99.96 87	99.48 271

tpmrst		97.73 385	98.07 361	96.73 457	98.71 458	92.00 476	99.10 247	98.86 411	98.52 341	98.92 380	99.54 310	91.90 417	99.82 336	98.02 284	99.03 413	98.37 459

CSCG		99.37 174	99.29 182	99.60 189	99.71 189	99.46 188	99.43 120	99.85 82	98.79 308	99.41 302	99.60 276	98.92 160	99.92 149	98.02 284	99.92 144	99.43 300

eth_miper_zixun_eth		98.68 319	98.71 300	98.60 397	99.10 412	96.84 428	97.52 455	99.54 287	98.94 283	99.58 242	99.48 328	96.25 362	99.76 382	98.01 287	99.93 138	99.21 355

Patchmtry		98.78 307	98.54 319	99.49 236	98.89 436	99.19 263	99.32 155	99.67 206	99.65 143	99.72 177	99.79 118	91.87 419	99.95 80	98.00 288	99.97 73	99.33 327

PVSNet_BlendedMVS		99.03 267	99.01 253	99.09 341	99.54 280	97.99 383	98.58 360	99.82 103	97.62 409	99.34 319	99.71 183	98.52 226	99.77 378	97.98 289	99.97 73	99.52 254

PVSNet_Blended		98.70 317	98.59 310	99.02 351	99.54 280	97.99 383	97.58 450	99.82 103	95.70 457	99.34 319	98.98 426	98.52 226	99.77 378	97.98 289	99.83 221	99.30 337

cl____		98.54 333	98.41 330	98.92 364	99.03 422	97.80 397	97.46 457	99.59 259	98.90 291	99.60 237	99.46 335	93.85 393	99.78 366	97.97 291	99.89 172	99.17 367

DIV-MVS_self_test		98.54 333	98.42 329	98.92 364	99.03 422	97.80 397	97.46 457	99.59 259	98.90 291	99.60 237	99.46 335	93.87 392	99.78 366	97.97 291	99.89 172	99.18 364

AUN-MVS		97.82 380	97.38 395	99.14 335	99.27 378	98.53 345	98.72 344	99.02 406	98.10 379	97.18 468	99.03 420	89.26 447	99.85 287	97.94 293	97.91 465	99.03 404

FA-MVS(test-final)		98.52 335	98.32 340	99.10 340	99.48 312	98.67 325	99.77 19	98.60 428	97.35 424	99.63 218	99.80 107	93.07 405	99.84 303	97.92 294	99.30 390	98.78 434

ambc				99.20 326	99.35 351	98.53 345	99.17 214	99.46 322		99.67 201	99.80 107	98.46 233	99.70 408	97.92 294	99.70 296	99.38 313

USDC		98.96 285	98.93 273	99.05 349	99.54 280	97.99 383	97.07 472	99.80 121	98.21 374	99.75 157	99.77 140	98.43 236	99.64 446	97.90 296	99.88 182	99.51 256

OPM-MVS		99.26 201	99.13 210	99.63 170	99.70 204	99.61 149	98.58 360	99.48 316	98.50 343	99.52 268	99.63 249	99.14 113	99.76 382	97.89 297	99.77 266	99.51 256
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 190	99.17 202	99.77 78	99.69 210	99.80 52	99.14 227	99.31 364	99.16 255	99.62 228	99.61 268	98.35 247	99.91 178	97.88 298	99.72 291	99.61 198
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	99.70 204	99.79 55	99.14 227	99.61 242					99.92 149	97.88 298	99.72 291	99.77 79

c3_l		98.72 314	98.71 300	98.72 391	99.12 405	97.22 418	97.68 446	99.56 275	98.90 291	99.54 261	99.48 328	96.37 357	99.73 397	97.88 298	99.88 182	99.21 355

3Dnovator+	98.92 3	99.35 180	99.24 194	99.67 142	99.35 351	99.47 182	99.62 67	99.50 311	99.44 197	99.12 361	99.78 130	98.77 182	99.94 97	97.87 301	99.72 291	99.62 185

miper_ehance_all_eth		98.59 328	98.59 310	98.59 398	98.98 428	97.07 422	97.49 456	99.52 302	98.50 343	99.52 268	99.37 356	96.41 355	99.71 404	97.86 302	99.62 327	99.00 411

WTY-MVS		98.59 328	98.37 334	99.26 317	99.43 330	98.40 354	98.74 342	99.13 399	98.10 379	99.21 348	99.24 391	94.82 383	99.90 197	97.86 302	98.77 429	99.49 267

APD_test1		99.36 178	99.28 185	99.61 185	99.89 39	99.89 10	99.32 155	99.74 165	99.18 248	99.69 189	99.75 155	98.41 239	99.84 303	97.85 304	99.70 296	99.10 382

SED-MVS		99.40 163	99.28 185	99.77 78	99.69 210	99.82 43	99.20 199	99.54 287	99.13 261	99.82 109	99.63 249	98.91 163	99.92 149	97.85 304	99.70 296	99.58 214

test_241102_TWO								99.54 287	99.13 261	99.76 152	99.63 249	98.32 252	99.92 149	97.85 304	99.69 304	99.75 87

MVS_111021_HR		99.12 247	99.02 249	99.40 271	99.50 302	99.11 275	97.92 432	99.71 181	98.76 315	99.08 365	99.47 332	99.17 106	99.54 462	97.85 304	99.76 268	99.54 237

MTAPA		99.35 180	99.20 198	99.80 64	99.81 104	99.81 48	99.33 152	99.53 297	99.27 232	99.42 296	99.63 249	98.21 264	99.95 80	97.83 308	99.79 253	99.65 155

MSC_two_6792asdad				99.74 101	99.03 422	99.53 171		99.23 381					99.92 149	97.77 309	99.69 304	99.78 75

No_MVS				99.74 101	99.03 422	99.53 171		99.23 381					99.92 149	97.77 309	99.69 304	99.78 75

TESTMET0.1,1		96.24 428	95.84 429	97.41 443	98.24 473	93.84 468	97.38 459	95.84 475	98.43 348	97.81 455	98.56 454	79.77 476	99.89 219	97.77 309	98.77 429	98.52 451

ACMH+	98.40 8	99.50 122	99.43 141	99.71 126	99.86 58	99.76 71	99.32 155	99.77 146	99.53 174	99.77 145	99.76 147	99.26 94	99.78 366	97.77 309	99.88 182	99.60 202

IU-MVS					99.69 210	99.77 64		99.22 384	97.50 416	99.69 189				97.75 313	99.70 296	99.77 79

114514_t		98.49 340	98.11 358	99.64 163	99.73 180	99.58 160	99.24 188	99.76 154	89.94 480	99.42 296	99.56 300	97.76 300	99.86 268	97.74 314	99.82 231	99.47 275

DVP-MVS++		99.38 170	99.25 192	99.77 78	99.03 422	99.77 64	99.74 27	99.61 242	99.18 248	99.76 152	99.61 268	99.00 145	99.92 149	97.72 315	99.60 337	99.62 185

test_0728_THIRD									99.18 248	99.62 228	99.61 268	98.58 209	99.91 178	97.72 315	99.80 248	99.77 79

EGC-MVSNET		89.05 453	85.52 456	99.64 163	99.89 39	99.78 58	99.56 87	99.52 302	24.19 489	49.96 490	99.83 84	99.15 110	99.92 149	97.71 317	99.85 208	99.21 355

miper_enhance_ethall		98.03 373	97.94 373	98.32 412	98.27 472	96.43 436	96.95 473	99.41 334	96.37 448	99.43 293	98.96 430	94.74 384	99.69 415	97.71 317	99.62 327	98.83 430

TSAR-MVS + MP.		99.34 185	99.24 194	99.63 170	99.82 92	99.37 220	99.26 181	99.35 353	98.77 312	99.57 245	99.70 193	99.27 93	99.88 234	97.71 317	99.75 272	99.65 155
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 393	97.28 397	98.40 407	98.37 470	96.75 429	97.24 466	99.37 349	97.31 426	99.41 302	99.22 393	87.30 451	99.37 474	97.70 320	99.62 327	99.08 393

MP-MVS-pluss		99.14 242	98.92 277	99.80 64	99.83 83	99.83 35	98.61 353	99.63 232	96.84 441	99.44 289	99.58 289	98.81 173	99.91 178	97.70 320	99.82 231	99.67 132
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 195	99.11 217	99.79 71	99.75 168	99.81 48	98.95 304	99.53 297	98.27 371	99.53 266	99.73 165	98.75 185	99.87 249	97.70 320	99.83 221	99.68 123

UnsupCasMVSNet_bld		98.55 332	98.27 346	99.40 271	99.56 275	99.37 220	97.97 428	99.68 201	97.49 417	99.08 365	99.35 366	95.41 377	99.82 336	97.70 320	98.19 457	99.01 410

MVS_111021_LR		99.13 244	99.03 248	99.42 260	99.58 254	99.32 232	97.91 434	99.73 169	98.68 322	99.31 329	99.48 328	99.09 122	99.66 437	97.70 320	99.77 266	99.29 340

IS-MVSNet		99.03 267	98.85 286	99.55 214	99.80 113	99.25 246	99.73 30	99.15 396	99.37 215	99.61 234	99.71 183	94.73 385	99.81 352	97.70 320	99.88 182	99.58 214

MED-MVS test				99.74 101	99.76 152	99.65 126	99.38 130	99.78 140	99.58 167	99.81 116	99.66 223		99.90 197	97.69 326	99.79 253	99.67 132

MED-MVS		99.45 145	99.36 159	99.74 101	99.76 152	99.65 126	99.38 130	99.78 140	99.31 225	99.81 116	99.66 223	99.02 142	99.90 197	97.69 326	99.79 253	99.67 132

ME-MVS		99.26 201	99.10 225	99.73 112	99.60 242	99.65 126	98.75 341	99.45 327	99.31 225	99.65 210	99.66 223	98.00 284	99.86 268	97.69 326	99.79 253	99.67 132

test-LLR		97.15 405	96.95 407	97.74 435	98.18 475	95.02 460	97.38 459	96.10 471	98.00 384	97.81 455	98.58 451	90.04 443	99.91 178	97.69 326	98.78 427	98.31 460

test-mter		96.23 429	95.73 432	97.74 435	98.18 475	95.02 460	97.38 459	96.10 471	97.90 395	97.81 455	98.58 451	79.12 479	99.91 178	97.69 326	98.78 427	98.31 460

MonoMVSNet		98.23 362	98.32 340	97.99 423	98.97 429	96.62 431	99.49 106	98.42 437	99.62 152	99.40 307	99.79 118	95.51 375	98.58 485	97.68 331	95.98 482	98.76 437

XVS		99.27 199	99.11 217	99.75 96	99.71 189	99.71 100	99.37 138	99.61 242	99.29 228	98.76 400	99.47 332	98.47 230	99.88 234	97.62 332	99.73 285	99.67 132

X-MVStestdata		96.09 433	94.87 446	99.75 96	99.71 189	99.71 100	99.37 138	99.61 242	99.29 228	98.76 400	61.30 498	98.47 230	99.88 234	97.62 332	99.73 285	99.67 132

SMA-MVS		99.19 227	99.00 257	99.73 112	99.46 322	99.73 90	99.13 234	99.52 302	97.40 421	99.57 245	99.64 234	98.93 157	99.83 320	97.61 334	99.79 253	99.63 173
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		96.71 416	96.79 415	96.46 461	98.90 433	90.71 487	99.41 121	98.68 421	94.69 470	98.14 440	99.34 369	86.32 461	99.80 360	97.60 335	98.07 463	98.88 425

PVSNet	97.47 15	98.42 346	98.44 327	98.35 409	99.46 322	96.26 440	96.70 477	99.34 355	97.68 407	99.00 372	99.13 402	97.40 317	99.72 399	97.59 336	99.68 309	99.08 393

new_pmnet		98.88 297	98.89 282	98.84 380	99.70 204	97.62 402	98.15 404	99.50 311	97.98 387	99.62 228	99.54 310	98.15 270	99.94 97	97.55 337	99.84 213	98.95 415

IB-MVS	95.41 20	95.30 448	94.46 452	97.84 431	98.76 454	95.33 456	97.33 462	96.07 473	96.02 452	95.37 484	97.41 478	76.17 484	99.96 69	97.54 338	95.44 485	98.22 465
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		99.24 206	99.11 217	99.61 185	98.38 469	99.79 55	99.57 85	99.68 201	99.61 157	99.15 356	99.71 183	98.70 192	99.91 178	97.54 338	99.68 309	99.13 379

ZNCC-MVS		99.22 217	99.04 246	99.77 78	99.76 152	99.73 90	99.28 172	99.56 275	98.19 376	99.14 358	99.29 378	98.84 172	99.92 149	97.53 340	99.80 248	99.64 167

CP-MVS		99.23 208	99.05 240	99.75 96	99.66 227	99.66 120	99.38 130	99.62 235	98.38 355	99.06 369	99.27 381	98.79 178	99.94 97	97.51 341	99.82 231	99.66 146

SD-MVS		99.01 275	99.30 177	98.15 419	99.50 302	99.40 211	98.94 306	99.61 242	99.22 244	99.75 157	99.82 91	99.54 53	95.51 489	97.48 342	99.87 195	99.54 237
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		98.49 340	98.29 345	99.11 338	98.96 430	98.42 353	97.54 451	99.32 360	97.53 414	98.47 423	98.15 466	97.88 290	99.82 336	97.46 343	99.24 400	99.09 387

DeepC-MVS_fast	98.47 5	99.23 208	99.12 214	99.56 207	99.28 376	99.22 256	98.99 292	99.40 341	99.08 266	99.58 242	99.64 234	98.90 166	99.83 320	97.44 344	99.75 272	99.63 173
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 203	99.08 229	99.76 85	99.73 180	99.70 108	99.31 160	99.59 259	98.36 357	99.36 313	99.37 356	98.80 177	99.91 178	97.43 345	99.75 272	99.68 123

ACMMPR		99.23 208	99.06 235	99.76 85	99.74 176	99.69 112	99.31 160	99.59 259	98.36 357	99.35 315	99.38 353	98.61 205	99.93 118	97.43 345	99.75 272	99.67 132

Vis-MVSNet (Re-imp)		98.77 308	98.58 313	99.34 289	99.78 135	98.88 308	99.61 73	99.56 275	99.11 265	99.24 342	99.56 300	93.00 407	99.78 366	97.43 345	99.89 172	99.35 322

MIMVSNet		98.43 345	98.20 350	99.11 338	99.53 287	98.38 358	99.58 82	98.61 426	98.96 279	99.33 321	99.76 147	90.92 429	99.81 352	97.38 348	99.76 268	99.15 371

WB-MVSnew		98.34 356	98.14 356	98.96 357	98.14 478	97.90 391	98.27 394	97.26 467	98.63 328	98.80 395	98.00 469	97.77 298	99.90 197	97.37 349	98.98 416	99.09 387

XVG-OURS-SEG-HR		99.16 237	98.99 264	99.66 149	99.84 75	99.64 132	98.25 397	99.73 169	98.39 354	99.63 218	99.43 340	99.70 31	99.90 197	97.34 350	98.64 440	99.44 294

COLMAP_ROB	98.06 12	99.45 145	99.37 154	99.70 131	99.83 83	99.70 108	99.38 130	99.78 140	99.53 174	99.67 201	99.78 130	99.19 103	99.86 268	97.32 351	99.87 195	99.55 227
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 269	98.81 293	99.65 156	99.58 254	99.49 178	98.58 360	99.07 401	98.40 353	99.04 370	99.25 386	98.51 228	99.80 360	97.31 352	99.51 361	99.65 155

region2R		99.23 208	99.05 240	99.77 78	99.76 152	99.70 108	99.31 160	99.59 259	98.41 351	99.32 324	99.36 361	98.73 189	99.93 118	97.29 353	99.74 279	99.67 132

APD-MVS_3200maxsize		99.31 191	99.16 203	99.74 101	99.53 287	99.75 79	99.27 176	99.61 242	99.19 247	99.57 245	99.64 234	98.76 183	99.90 197	97.29 353	99.62 327	99.56 223

TAPA-MVS	97.92 13	98.03 373	97.55 390	99.46 247	99.47 318	99.44 196	98.50 375	99.62 235	86.79 481	99.07 368	99.26 384	98.26 257	99.62 449	97.28 355	99.73 285	99.31 335
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 199	99.11 217	99.73 112	99.54 280	99.74 87	99.26 181	99.62 235	99.16 255	99.52 268	99.64 234	98.41 239	99.91 178	97.27 356	99.61 334	99.54 237

RE-MVS-def			99.13 210		99.54 280	99.74 87	99.26 181	99.62 235	99.16 255	99.52 268	99.64 234	98.57 210		97.27 356	99.61 334	99.54 237

testing11		96.05 435	95.41 438	97.97 425	98.78 451	95.27 457	98.59 358	98.23 447	98.86 297	96.56 475	96.91 487	75.20 485	99.69 415	97.26 358	98.29 452	98.93 418

test_yl		98.25 359	97.95 369	99.13 336	99.17 398	98.47 348	99.00 285	98.67 423	98.97 277	99.22 346	99.02 421	91.31 423	99.69 415	97.26 358	98.93 418	99.24 346

DCV-MVSNet		98.25 359	97.95 369	99.13 336	99.17 398	98.47 348	99.00 285	98.67 423	98.97 277	99.22 346	99.02 421	91.31 423	99.69 415	97.26 358	98.93 418	99.24 346

PHI-MVS		99.11 251	98.95 271	99.59 192	99.13 403	99.59 155	99.17 214	99.65 221	97.88 398	99.25 339	99.46 335	98.97 153	99.80 360	97.26 358	99.82 231	99.37 316

tfpnnormal		99.43 152	99.38 151	99.60 189	99.87 54	99.75 79	99.59 80	99.78 140	99.71 118	99.90 68	99.69 202	98.85 171	99.90 197	97.25 362	99.78 262	99.15 371

PatchmatchNet		97.65 389	97.80 380	97.18 450	98.82 446	92.49 474	99.17 214	98.39 440	98.12 378	98.79 397	99.58 289	90.71 435	99.89 219	97.23 363	99.41 376	99.16 369
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 281	98.80 295	99.56 207	99.25 382	99.43 200	98.54 370	99.27 372	98.58 334	98.80 395	99.43 340	98.53 223	99.70 408	97.22 364	99.59 341	99.54 237

testing3		96.48 422	95.63 434	99.01 352	99.23 386	97.81 395	98.90 312	99.10 400	98.72 317	97.84 454	97.92 470	72.44 489	99.85 287	97.21 365	99.33 386	99.35 322

HPM-MVS		99.25 203	99.07 233	99.78 75	99.81 104	99.75 79	99.61 73	99.67 206	97.72 405	99.35 315	99.25 386	99.23 99	99.92 149	97.21 365	99.82 231	99.67 132
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 227	99.00 257	99.76 85	99.76 152	99.68 115	99.38 130	99.54 287	98.34 366	99.01 371	99.50 321	98.53 223	99.93 118	97.18 367	99.78 262	99.66 146

ACMMP		99.25 203	99.08 229	99.74 101	99.79 127	99.68 115	99.50 101	99.65 221	98.07 382	99.52 268	99.69 202	98.57 210	99.92 149	97.18 367	99.79 253	99.63 173
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		96.23 429	95.74 431	97.70 437	98.86 440	95.59 453	98.66 350	98.14 449	98.96 279	97.67 460	97.06 484	76.78 482	98.92 481	97.10 369	98.41 449	98.58 447

thisisatest0515		96.98 409	96.42 417	98.66 394	99.42 335	97.47 408	97.27 464	94.30 481	97.24 428	99.15 356	98.86 438	85.01 463	99.87 249	97.10 369	99.39 378	98.63 441

XVG-ACMP-BASELINE		99.23 208	99.10 225	99.63 170	99.82 92	99.58 160	98.83 325	99.72 178	98.36 357	99.60 237	99.71 183	98.92 160	99.91 178	97.08 371	99.84 213	99.40 308

MSDG		99.08 256	98.98 267	99.37 280	99.60 242	99.13 272	97.54 451	99.74 165	98.84 301	99.53 266	99.55 308	99.10 120	99.79 363	97.07 372	99.86 203	99.18 364

SteuartSystems-ACMMP		99.30 192	99.14 208	99.76 85	99.87 54	99.66 120	99.18 209	99.60 253	98.55 336	99.57 245	99.67 218	99.03 141	99.94 97	97.01 373	99.80 248	99.69 116
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 431	95.78 430	97.49 439	98.53 464	93.83 469	98.04 418	93.94 484	98.96 279	98.46 424	98.17 465	79.86 474	99.87 249	96.99 374	99.06 409	98.78 434

EPMVS		96.53 419	96.32 418	97.17 451	98.18 475	92.97 473	99.39 127	89.95 490	98.21 374	98.61 412	99.59 286	86.69 460	99.72 399	96.99 374	99.23 402	98.81 431

MSP-MVS		99.04 266	98.79 296	99.81 54	99.78 135	99.73 90	99.35 145	99.57 270	98.54 339	99.54 261	98.99 423	96.81 340	99.93 118	96.97 376	99.53 357	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++		98.96 285	98.70 303	99.74 101	99.52 294	99.71 100	98.86 318	99.19 391	98.47 347	98.59 414	99.06 413	98.08 276	99.91 178	96.94 377	99.60 337	99.60 202

SR-MVS		99.19 227	99.00 257	99.74 101	99.51 296	99.72 95	99.18 209	99.60 253	98.85 298	99.47 283	99.58 289	98.38 244	99.92 149	96.92 378	99.54 355	99.57 220

PGM-MVS		99.20 224	99.01 253	99.77 78	99.75 168	99.71 100	99.16 220	99.72 178	97.99 386	99.42 296	99.60 276	98.81 173	99.93 118	96.91 379	99.74 279	99.66 146

HY-MVS	98.23 9	98.21 366	97.95 369	98.99 353	99.03 422	98.24 362	99.61 73	98.72 419	96.81 442	98.73 402	99.51 318	94.06 390	99.86 268	96.91 379	98.20 455	98.86 427

MDTV_nov1_ep13			97.73 384		98.70 459	90.83 485	99.15 223	98.02 452	98.51 342	98.82 392	99.61 268	90.98 428	99.66 437	96.89 381	98.92 420

GST-MVS		99.16 237	98.96 270	99.75 96	99.73 180	99.73 90	99.20 199	99.55 281	98.22 373	99.32 324	99.35 366	98.65 201	99.91 178	96.86 382	99.74 279	99.62 185

test_post1							99.14 227				51.63 500	89.54 446	99.82 336	96.86 382

SCA		98.11 369	98.36 335	97.36 444	99.20 392	92.99 472	98.17 402	98.49 434	98.24 372	99.10 364	99.57 296	96.01 367	99.94 97	96.86 382	99.62 327	99.14 376

UBG		96.53 419	95.95 425	98.29 416	98.87 439	96.31 439	98.48 378	98.07 450	98.83 302	97.32 463	96.54 493	79.81 475	99.62 449	96.84 385	98.74 433	98.95 415

XVG-OURS		99.21 222	99.06 235	99.65 156	99.82 92	99.62 140	97.87 436	99.74 165	98.36 357	99.66 207	99.68 214	99.71 28	99.90 197	96.84 385	99.88 182	99.43 300

LCM-MVSNet-Re		99.28 195	99.15 207	99.67 142	99.33 365	99.76 71	99.34 146	99.97 20	98.93 287	99.91 63	99.79 118	98.68 194	99.93 118	96.80 387	99.56 346	99.30 337

RPSCF		99.18 231	99.02 249	99.64 163	99.83 83	99.85 22	99.44 118	99.82 103	98.33 367	99.50 278	99.78 130	97.90 288	99.65 444	96.78 388	99.83 221	99.44 294

旧先验2							97.94 430		95.33 461	98.94 376			99.88 234	96.75 389

MDTV_nov1_ep13_2view						91.44 482	99.14 227		97.37 423	99.21 348		91.78 421		96.75 389		99.03 404

CLD-MVS		98.76 309	98.57 314	99.33 292	99.57 264	98.97 295	97.53 453	99.55 281	96.41 446	99.27 336	99.13 402	99.07 129	99.78 366	96.73 391	99.89 172	99.23 350
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 370	97.98 367	98.48 403	99.27 378	96.48 434	99.40 125	99.07 401	98.81 305	99.23 343	99.57 296	90.11 442	99.87 249	96.69 392	99.64 322	99.09 387

baseline2		96.83 412	96.28 419	98.46 405	99.09 415	96.91 426	98.83 325	93.87 485	97.23 429	96.23 480	98.36 460	88.12 450	99.90 197	96.68 393	98.14 460	98.57 449

cascas		96.99 408	96.82 414	97.48 440	97.57 488	95.64 451	96.43 479	99.56 275	91.75 476	97.13 470	97.61 477	95.58 372	98.63 483	96.68 393	99.11 406	98.18 469

PC_three_1452									97.56 410	99.68 194	99.41 343	99.09 122	97.09 486	96.66 395	99.60 337	99.62 185

LPG-MVS_test		99.22 217	99.05 240	99.74 101	99.82 92	99.63 138	99.16 220	99.73 169	97.56 410	99.64 213	99.69 202	99.37 75	99.89 219	96.66 395	99.87 195	99.69 116

LGP-MVS_train				99.74 101	99.82 92	99.63 138		99.73 169	97.56 410	99.64 213	99.69 202	99.37 75	99.89 219	96.66 395	99.87 195	99.69 116

ETVMVS		96.14 432	95.22 443	98.89 375	98.80 447	98.01 382	98.66 350	98.35 443	98.71 319	97.18 468	96.31 497	74.23 488	99.75 391	96.64 398	98.13 462	98.90 422

TinyColmap		98.97 282	98.93 273	99.07 346	99.46 322	98.19 367	97.75 440	99.75 159	98.79 308	99.54 261	99.70 193	98.97 153	99.62 449	96.63 399	99.83 221	99.41 305

LF4IMVS		99.01 275	98.92 277	99.27 314	99.71 189	99.28 238	98.59 358	99.77 146	98.32 368	99.39 309	99.41 343	98.62 203	99.84 303	96.62 400	99.84 213	98.69 440

NCCC		98.82 303	98.57 314	99.58 195	99.21 389	99.31 233	98.61 353	99.25 377	98.65 325	98.43 425	99.26 384	97.86 291	99.81 352	96.55 401	99.27 396	99.61 198

OPU-MVS				99.29 306	99.12 405	99.44 196	99.20 199				99.40 347	99.00 145	98.84 482	96.54 402	99.60 337	99.58 214

F-COLMAP		98.74 311	98.45 326	99.62 179	99.57 264	99.47 182	98.84 322	99.65 221	96.31 449	98.93 377	99.19 399	97.68 304	99.87 249	96.52 403	99.37 381	99.53 243

testing99		95.86 440	95.19 444	97.87 429	98.76 454	95.03 459	98.62 352	98.44 436	98.68 322	96.67 474	96.66 492	74.31 487	99.69 415	96.51 404	98.03 464	98.90 422

ADS-MVSNet2		97.78 383	97.66 388	98.12 421	99.14 401	95.36 455	99.22 196	98.75 418	96.97 437	98.25 431	99.64 234	90.90 430	99.94 97	96.51 404	99.56 346	99.08 393

ADS-MVSNet		97.72 388	97.67 387	97.86 430	99.14 401	94.65 463	99.22 196	98.86 411	96.97 437	98.25 431	99.64 234	90.90 430	99.84 303	96.51 404	99.56 346	99.08 393

PatchMatch-RL		98.68 319	98.47 323	99.30 305	99.44 327	99.28 238	98.14 406	99.54 287	97.12 435	99.11 362	99.25 386	97.80 296	99.70 408	96.51 404	99.30 390	98.93 418

CMPMVS	77.52 23	98.50 338	98.19 353	99.41 268	98.33 471	99.56 164	99.01 279	99.59 259	95.44 459	99.57 245	99.80 107	95.64 370	99.46 472	96.47 408	99.92 144	99.21 355
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 436	95.32 441	98.02 422	98.76 454	95.39 454	98.38 387	98.65 425	98.82 303	96.84 471	96.71 491	75.06 486	99.71 404	96.46 409	98.23 454	98.98 412

SF-MVS		99.10 254	98.93 273	99.62 179	99.58 254	99.51 176	99.13 234	99.65 221	97.97 388	99.42 296	99.61 268	98.86 170	99.87 249	96.45 410	99.68 309	99.49 267

FE-MVS		97.85 379	97.42 394	99.15 332	99.44 327	98.75 320	99.77 19	98.20 448	95.85 454	99.33 321	99.80 107	88.86 448	99.88 234	96.40 411	99.12 405	98.81 431

DPE-MVS		99.14 242	98.92 277	99.82 46	99.57 264	99.77 64	98.74 342	99.60 253	98.55 336	99.76 152	99.69 202	98.23 262	99.92 149	96.39 412	99.75 272	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					97.59 486	89.02 492			93.47 472		98.30 461		99.84 303	96.38 413

AllTest		99.21 222	99.07 233	99.63 170	99.78 135	99.64 132	99.12 239	99.83 97	98.63 328	99.63 218	99.72 173	98.68 194	99.75 391	96.38 413	99.83 221	99.51 256

TestCases				99.63 170	99.78 135	99.64 132		99.83 97	98.63 328	99.63 218	99.72 173	98.68 194	99.75 391	96.38 413	99.83 221	99.51 256

testdata				99.42 260	99.51 296	98.93 302		99.30 367	96.20 450	98.87 387	99.40 347	98.33 251	99.89 219	96.29 416	99.28 393	99.44 294

dp		96.86 411	97.07 403	96.24 463	98.68 460	90.30 490	99.19 205	98.38 441	97.35 424	98.23 433	99.59 286	87.23 452	99.82 336	96.27 417	98.73 436	98.59 445

tpmvs		97.39 400	97.69 385	96.52 459	98.41 468	91.76 478	99.30 163	98.94 410	97.74 404	97.85 453	99.55 308	92.40 415	99.73 397	96.25 418	98.73 436	98.06 471

KD-MVS_2432*1600		95.89 437	95.41 438	97.31 447	94.96 490	93.89 466	97.09 470	99.22 384	97.23 429	98.88 384	99.04 416	79.23 477	99.54 462	96.24 419	96.81 474	98.50 455

miper_refine_blended		95.89 437	95.41 438	97.31 447	94.96 490	93.89 466	97.09 470	99.22 384	97.23 429	98.88 384	99.04 416	79.23 477	99.54 462	96.24 419	96.81 474	98.50 455

ACMP	97.51 14	99.05 263	98.84 288	99.67 142	99.78 135	99.55 168	98.88 314	99.66 211	97.11 436	99.47 283	99.60 276	99.07 129	99.89 219	96.18 421	99.85 208	99.58 214
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 293	98.72 299	99.44 254	99.39 339	99.42 203	98.58 360	99.64 229	97.31 426	99.44 289	99.62 258	98.59 207	99.69 415	96.17 422	99.79 253	99.22 352

DP-MVS Recon		98.50 338	98.23 347	99.31 301	99.49 307	99.46 188	98.56 366	99.63 232	94.86 468	98.85 389	99.37 356	97.81 295	99.59 456	96.08 423	99.44 371	98.88 425

tpm cat1		96.78 413	96.98 406	96.16 464	98.85 441	90.59 488	99.08 255	99.32 360	92.37 474	97.73 459	99.46 335	91.15 426	99.69 415	96.07 424	98.80 426	98.21 466

tpm2		96.35 425	96.22 420	96.73 457	98.88 438	91.75 479	99.21 198	98.51 432	93.27 473	97.89 449	99.21 396	84.83 464	99.70 408	96.04 425	98.18 458	98.75 438

dmvs_re		98.69 318	98.48 322	99.31 301	99.55 278	99.42 203	99.54 90	98.38 441	99.32 223	98.72 403	98.71 447	96.76 342	99.21 476	96.01 426	99.35 384	99.31 335

test_0402		99.22 217	99.14 208	99.45 250	99.79 127	99.43 200	99.28 172	99.68 201	99.54 172	99.40 307	99.56 300	99.07 129	99.82 336	96.01 426	99.96 87	99.11 380

ITE_SJBPF				99.38 277	99.63 235	99.44 196		99.73 169	98.56 335	99.33 321	99.53 312	98.88 167	99.68 427	96.01 426	99.65 320	99.02 409

test_prior2							97.95 429		97.87 399	98.05 442	99.05 414	97.90 288		95.99 429	99.49 366

testdata2													99.89 219	95.99 429

原ACMM1				99.37 280	99.47 318	98.87 311		99.27 372	96.74 444	98.26 430	99.32 370	97.93 287	99.82 336	95.96 431	99.38 379	99.43 300

新几何1				99.52 226	99.50 302	99.22 256		99.26 374	95.66 458	98.60 413	99.28 379	97.67 305	99.89 219	95.95 432	99.32 388	99.45 281

MP-MVS		99.06 260	98.83 290	99.76 85	99.76 152	99.71 100	99.32 155	99.50 311	98.35 362	98.97 373	99.48 328	98.37 245	99.92 149	95.95 432	99.75 272	99.63 173
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 447	94.59 450	98.61 396	98.66 461	97.45 411	98.54 370	97.90 456	98.53 340	96.54 476	96.47 494	70.62 492	99.81 352	95.91 434	98.15 459	98.56 450

wuyk23d		97.58 392	99.13 210	92.93 468	99.69 210	99.49 178	99.52 93	99.77 146	97.97 388	99.96 34	99.79 118	99.84 16	99.94 97	95.85 435	99.82 231	79.36 486

HQP_MVS		98.90 293	98.68 304	99.55 214	99.58 254	99.24 250	98.80 333	99.54 287	98.94 283	99.14 358	99.25 386	97.24 324	99.82 336	95.84 436	99.78 262	99.60 202

plane_prior5								99.54 287					99.82 336	95.84 436	99.78 262	99.60 202

无先验							98.01 421	99.23 381	95.83 455				99.85 287	95.79 438		99.44 294

CPTT-MVS		98.74 311	98.44 327	99.64 163	99.61 240	99.38 216	99.18 209	99.55 281	96.49 445	99.27 336	99.37 356	97.11 332	99.92 149	95.74 439	99.67 315	99.62 185

PLC	97.35 16	98.36 351	97.99 365	99.48 241	99.32 366	99.24 250	98.50 375	99.51 307	95.19 464	98.58 415	98.96 430	96.95 337	99.83 320	95.63 440	99.25 398	99.37 316
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 330	98.34 338	99.28 309	99.18 397	99.10 282	98.34 389	99.41 334	98.48 346	98.52 420	98.98 426	97.05 334	99.78 366	95.59 441	99.50 364	98.96 413

1314		98.00 375	97.90 377	98.27 417	98.90 433	97.45 411	99.30 163	99.06 403	94.98 465	97.21 467	99.12 406	98.43 236	99.67 432	95.58 442	98.56 443	97.71 475

PVSNet_0	95.53 19	95.85 441	95.31 442	97.47 441	98.78 451	93.48 471	95.72 481	99.40 341	96.18 451	97.37 462	97.73 472	95.73 369	99.58 457	95.49 443	81.40 487	99.36 319

MAR-MVS		98.24 361	97.92 375	99.19 327	98.78 451	99.65 126	99.17 214	99.14 397	95.36 460	98.04 443	98.81 443	97.47 314	99.72 399	95.47 444	99.06 409	98.21 466
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	98.23 362	97.89 378	99.26 317	99.19 394	99.26 243	99.65 62	99.69 198	91.33 478	98.14 440	99.77 140	98.28 254	99.96 69	95.41 445	99.55 350	98.58 447

train_agg		98.35 354	97.95 369	99.57 203	99.35 351	99.35 227	98.11 410	99.41 334	94.90 466	97.92 447	98.99 423	98.02 279	99.85 287	95.38 446	99.44 371	99.50 262

9.14			98.64 305		99.45 326		98.81 330	99.60 253	97.52 415	99.28 335	99.56 300	98.53 223	99.83 320	95.36 447	99.64 322

APD-MVS		98.87 298	98.59 310	99.71 126	99.50 302	99.62 140	99.01 279	99.57 270	96.80 443	99.54 261	99.63 249	98.29 253	99.91 178	95.24 448	99.71 294	99.61 198
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 437								95.20 449

AdaColmap		98.60 325	98.35 337	99.38 277	99.12 405	99.22 256	98.67 348	99.42 333	97.84 402	98.81 393	99.27 381	97.32 322	99.81 352	95.14 450	99.53 357	99.10 382

test9_res														95.10 451	99.44 371	99.50 262

CDPH-MVS		98.56 331	98.20 350	99.61 185	99.50 302	99.46 188	98.32 391	99.41 334	95.22 462	99.21 348	99.10 410	98.34 249	99.82 336	95.09 452	99.66 318	99.56 223

BH-untuned		98.22 364	98.09 359	98.58 400	99.38 342	97.24 417	98.55 367	98.98 409	97.81 403	99.20 353	98.76 445	97.01 335	99.65 444	94.83 453	98.33 450	98.86 427

BP-MVS														94.73 454

HQP-MVS		98.36 351	98.02 364	99.39 274	99.31 367	98.94 299	97.98 425	99.37 349	97.45 418	98.15 436	98.83 440	96.67 343	99.70 408	94.73 454	99.67 315	99.53 243

QAPM		98.40 349	97.99 365	99.65 156	99.39 339	99.47 182	99.67 53	99.52 302	91.70 477	98.78 399	99.80 107	98.55 214	99.95 80	94.71 456	99.75 272	99.53 243

agg_prior2														94.58 457	99.46 370	99.50 262

myMVS_eth3d		95.63 445	94.73 447	98.34 411	98.50 466	96.36 437	98.60 355	99.21 387	97.89 396	96.76 472	96.37 495	72.10 490	99.57 458	94.38 458	98.73 436	99.09 387

BH-RMVSNet		98.41 347	98.14 356	99.21 324	99.21 389	98.47 348	98.60 355	98.26 446	98.35 362	98.93 377	99.31 373	97.20 329	99.66 437	94.32 459	99.10 407	99.51 256

E-PMN		97.14 407	97.43 393	96.27 462	98.79 449	91.62 480	95.54 482	99.01 408	99.44 197	98.88 384	99.12 406	92.78 408	99.68 427	94.30 460	99.03 413	97.50 476

MG-MVS		98.52 335	98.39 332	98.94 360	99.15 400	97.39 414	98.18 400	99.21 387	98.89 294	99.23 343	99.63 249	97.37 320	99.74 394	94.22 461	99.61 334	99.69 116

API-MVS		98.38 350	98.39 332	98.35 409	98.83 443	99.26 243	99.14 227	99.18 392	98.59 333	98.66 408	98.78 444	98.61 205	99.57 458	94.14 462	99.56 346	96.21 483

PAPM_NR		98.36 351	98.04 362	99.33 292	99.48 312	98.93 302	98.79 336	99.28 371	97.54 413	98.56 419	98.57 453	97.12 331	99.69 415	94.09 463	98.90 424	99.38 313

ZD-MVS					99.43 330	99.61 149		99.43 331	96.38 447	99.11 362	99.07 412	97.86 291	99.92 149	94.04 464	99.49 366

DPM-MVS		98.28 357	97.94 373	99.32 297	99.36 347	99.11 275	97.31 463	98.78 417	96.88 439	98.84 390	99.11 409	97.77 298	99.61 454	94.03 465	99.36 382	99.23 350

gg-mvs-nofinetune		95.87 439	95.17 445	97.97 425	98.19 474	96.95 424	99.69 45	89.23 491	99.89 56	96.24 479	99.94 19	81.19 469	99.51 468	93.99 466	98.20 455	97.44 477

PMVS	92.94 21	98.82 303	98.81 293	98.85 378	99.84 75	97.99 383	99.20 199	99.47 319	99.71 118	99.42 296	99.82 91	98.09 274	99.47 470	93.88 467	99.85 208	99.07 398
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 410	97.28 397	95.99 466	98.76 454	91.03 484	95.26 484	98.61 426	99.34 219	98.92 380	98.88 437	93.79 394	99.66 437	92.87 468	99.05 411	97.30 480

BH-w/o		97.20 404	97.01 405	97.76 433	99.08 416	95.69 450	98.03 420	98.52 431	95.76 456	97.96 446	98.02 467	95.62 371	99.47 470	92.82 469	97.25 473	98.12 470

TR-MVS		97.44 397	97.15 402	98.32 412	98.53 464	97.46 409	98.47 379	97.91 455	96.85 440	98.21 434	98.51 457	96.42 353	99.51 468	92.16 470	97.29 472	97.98 472

OpenMVS_ROB	97.31 17	97.36 402	96.84 412	98.89 375	99.29 373	99.45 194	98.87 317	99.48 316	86.54 483	99.44 289	99.74 160	97.34 321	99.86 268	91.61 471	99.28 393	97.37 479

GG-mvs-BLEND				97.36 444	97.59 486	96.87 427	99.70 38	88.49 492		94.64 485	97.26 482	80.66 471	99.12 477	91.50 472	96.50 480	96.08 485

DeepMVS_CX				97.98 424	99.69 210	96.95 424		99.26 374	75.51 486	95.74 482	98.28 462	96.47 351	99.62 449	91.23 473	97.89 466	97.38 478

PAPR		97.56 393	97.07 403	99.04 350	98.80 447	98.11 375	97.63 447	99.25 377	94.56 471	98.02 445	98.25 463	97.43 316	99.68 427	90.90 474	98.74 433	99.33 327

MVS		95.72 443	94.63 449	98.99 353	98.56 463	97.98 388	99.30 163	98.86 411	72.71 487	97.30 464	99.08 411	98.34 249	99.74 394	89.21 475	98.33 450	99.26 343

UWE-MVS-28		95.64 444	95.47 436	96.14 465	97.98 480	90.39 489	98.49 377	95.81 476	99.02 273	98.03 444	98.19 464	84.49 466	99.28 475	88.75 476	98.47 448	98.75 438

thres600view7		96.60 418	96.16 421	97.93 427	99.63 235	96.09 445	99.18 209	97.57 461	98.77 312	98.72 403	97.32 480	87.04 454	99.72 399	88.57 477	98.62 441	97.98 472

FPMVS		96.32 426	95.50 435	98.79 386	99.60 242	98.17 370	98.46 383	98.80 416	97.16 433	96.28 477	99.63 249	82.19 468	99.09 478	88.45 478	98.89 425	99.10 382

PCF-MVS	96.03 18	96.73 415	95.86 428	99.33 292	99.44 327	99.16 269	96.87 475	99.44 328	86.58 482	98.95 375	99.40 347	94.38 388	99.88 234	87.93 479	99.80 248	98.95 415
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 424	96.03 424	97.47 441	99.63 235	95.93 446	99.18 209	97.57 461	98.75 316	98.70 406	97.31 481	87.04 454	99.67 432	87.62 480	98.51 445	96.81 481

tfpn200view9		96.30 427	95.89 426	97.53 438	99.58 254	96.11 443	99.00 285	97.54 464	98.43 348	98.52 420	96.98 485	86.85 456	99.67 432	87.62 480	98.51 445	96.81 481

thres400		96.40 423	95.89 426	97.92 428	99.58 254	96.11 443	99.00 285	97.54 464	98.43 348	98.52 420	96.98 485	86.85 456	99.67 432	87.62 480	98.51 445	97.98 472

thres200		96.09 433	95.68 433	97.33 446	99.48 312	96.22 442	98.53 372	97.57 461	98.06 383	98.37 427	96.73 490	86.84 458	99.61 454	86.99 483	98.57 442	96.16 484

MVE	92.54 22	96.66 417	96.11 422	98.31 414	99.68 218	97.55 404	97.94 430	95.60 477	99.37 215	90.68 488	98.70 449	96.56 346	98.61 484	86.94 484	99.55 350	98.77 436
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 403	96.83 413	98.59 398	99.46 322	97.55 404	99.25 187	96.84 470	98.78 310	97.24 466	97.67 473	97.11 332	98.97 480	86.59 485	98.54 444	99.27 341

PAPM		95.61 446	94.71 448	98.31 414	99.12 405	96.63 430	96.66 478	98.46 435	90.77 479	96.25 478	98.68 450	93.01 406	99.69 415	81.60 486	97.86 468	98.62 442

SD_0403		97.42 398	96.90 411	98.98 355	99.54 280	97.90 391	99.52 93	99.54 287	99.34 219	97.87 451	98.85 439	98.72 190	99.64 446	78.93 487	99.83 221	99.40 308

dongtai		89.37 452	88.91 455	90.76 469	99.19 394	77.46 494	95.47 483	87.82 493	92.28 475	94.17 486	98.82 442	71.22 491	95.54 488	63.85 488	97.34 471	99.27 341

kuosan		85.65 454	84.57 457	88.90 471	97.91 482	77.11 495	96.37 480	87.62 494	85.24 484	85.45 489	96.83 488	69.94 493	90.98 490	45.90 489	95.83 484	98.62 442

test123		29.31 455	33.05 460	18.08 472	25.93 496	12.24 497	97.53 453	10.93 497	11.78 490	24.21 491	50.08 502	21.04 494	8.60 491	23.51 490	32.43 490	33.39 487

testmvs		28.94 456	33.33 458	15.79 473	26.03 495	9.81 498	96.77 476	15.67 496	11.55 491	23.87 492	50.74 501	19.03 495	8.53 492	23.21 491	33.07 489	29.03 488

mmdepth		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

monomultidepth		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

test_blank		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uanet_test		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

DCPMVS		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

cdsmvs_eth3d_5k		24.88 457	33.17 459	0.00 474	0.00 497	0.00 499	0.00 486	99.62 235	0.00 492	0.00 493	99.13 402	99.82 18	0.00 493	0.00 492	0.00 491	0.00 489

pcd_1.5k_mvsjas		16.61 458	22.14 461	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	99.28 90	0.00 493	0.00 492	0.00 491	0.00 489

sosnet-low-res		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

sosnet		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uncertanet		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

Regformer		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

ab-mvs-re		8.26 469	11.02 472	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	99.16 400	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uanet		8.33 459	11.11 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	100.00 1	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

TestfortrainingZip							99.38 130

FOURS1					99.83 83	99.89 10	99.74 27	99.71 181	99.69 127	99.63 218

test_one_0601					99.63 235	99.76 71		99.55 281	99.23 240	99.31 329	99.61 268	98.59 207

eth-test2					0.00 497

eth-test					0.00 497

test_241102_ONE					99.69 210	99.82 43		99.54 287	99.12 264	99.82 109	99.49 325	98.91 163	99.52 467

save fliter					99.53 287	99.25 246	98.29 393	99.38 348	99.07 268

test0726					99.69 210	99.80 52	99.24 188	99.57 270	99.16 255	99.73 172	99.65 232	98.35 247

GSMVS																99.14 376

test_part2					99.62 239	99.67 118				99.55 258

sam_mvs1												90.81 434				99.14 376

sam_mvs												90.52 439

MTGPA								99.53 297

test_post											52.41 499	90.25 441	99.86 268

patchmatchnet-post											99.62 258	90.58 437	99.94 97

MTMP							99.09 252	98.59 429

TEST9					99.35 351	99.35 227	98.11 410	99.41 334	94.83 469	97.92 447	98.99 423	98.02 279	99.85 287

test_8					99.34 360	99.31 233	98.08 414	99.40 341	94.90 466	97.87 451	98.97 428	98.02 279	99.84 303

agg_prior					99.35 351	99.36 224		99.39 344		97.76 458			99.85 287

test_prior4						99.19 263	98.00 423

test_prior				99.46 247	99.35 351	99.22 256		99.39 344					99.69 415			99.48 271

新几何2							98.04 418

旧先验1					99.49 307	99.29 236		99.26 374			99.39 351	97.67 305			99.36 382	99.46 279

原ACMM2							97.92 432

test222					99.51 296	99.08 284	97.83 438	99.29 368	95.21 463	98.68 407	99.31 373	97.28 323			99.38 379	99.43 300

segment_acmp												98.37 245

testdata1							97.72 442		97.86 401

test12				99.54 220	99.29 373	99.33 230		99.16 395		98.43 425		97.54 312	99.82 336		99.47 368	99.48 271

plane_prior7					99.58 254	99.38 216

plane_prior6					99.47 318	99.26 243						97.24 324

plane_prior4											99.25 386

plane_prior3						99.31 233			98.36 357	99.14 358

plane_prior2							98.80 333		98.94 283

plane_prior1					99.51 296

plane_prior						99.24 250	98.42 385		97.87 399						99.71 294

n2								0.00 498

nn								0.00 498

door-mid								99.83 97

test11								99.29 368

door								99.77 146

HQP5-MVS						98.94 299

HQP-NCC					99.31 367		97.98 425		97.45 418	98.15 436

ACMP_Plane					99.31 367		97.98 425		97.45 418	98.15 436

HQP4-MVS										98.15 436			99.70 408			99.53 243

HQP3-MVS								99.37 349							99.67 315

HQP2-MVS												96.67 343

NP-MVS					99.40 338	99.13 272					98.83 440

ACMMP++_ref															99.94 126

ACMMP++															99.79 253

Test By Simon												98.41 239

High-resolution multi-view benchmark