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	99.34 290	99.91 31	98.08 381	99.48 108	100.00 1	99.90 50	99.99 7	99.91 31	99.50 61	99.98 27	99.98 1	99.99 16	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 225	99.96 7	98.62 336	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	99.12 240	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	99.27 315	99.93 24	97.84 394	99.34 147	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	99.36 286	99.94 18	98.18 370	99.52 93	100.00 1	99.86 66	100.00 1	99.88 50	98.99 148	99.96 69	99.97 4	99.96 87	99.95 14

test_fmvs1_n		99.68 65	99.81 28	99.28 310	99.95 15	97.93 390	99.49 106	100.00 1	99.82 86	99.99 7	99.89 41	99.21 102	99.98 27	99.97 4	99.98 50	99.93 20

test_f		99.75 49	99.88 7	99.37 281	99.96 7	98.21 367	99.51 100	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 52	98.94 307	99.96 28	99.98 18	99.96 34	99.78 131	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	99.08 256	99.97 20	99.98 18	99.96 34	99.79 119	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	99.01 280	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	99.55 169	99.17 215	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	99.45 251	99.93 24	98.40 355	99.30 164	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	99.81 105	99.75 79	99.06 262	99.85 82	99.99 3	99.97 24	99.84 77	99.12 119	99.98 27	99.95 14	99.99 16	99.90 29

fmvsm_s_conf0.5_n_7		99.73 52	99.78 39	99.60 190	99.74 177	98.93 303	98.85 321	99.96 28	99.96 28	99.97 24	99.76 148	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	99.11 245	99.91 52	99.98 18	99.96 34	99.64 235	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 93	99.70 108	99.17 215	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		99.48 130	99.65 74	98.97 357	99.54 281	97.16 421	99.11 245	99.98 12	99.78 103	99.96 34	99.81 98	98.72 191	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	99.37 221	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 84	99.59 156	98.97 298	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	98.92 311	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	99.12 240	99.91 52	99.98 18	99.95 45	99.67 219	99.67 34	99.99 8	99.94 20	99.99 16	99.88 40

MM		99.18 232	99.05 241	99.55 215	99.35 352	98.81 314	99.05 263	97.79 462	99.99 3	99.48 282	99.59 287	96.29 362	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	98.97 298	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	99.81 105	99.53 172	99.15 224	99.89 61	99.99 3	99.98 14	99.86 63	99.13 116	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	99.79 128	99.72 95	98.84 323	99.96 28	99.96 28	99.96 34	99.72 174	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 93	99.76 71	98.88 315	99.92 43	99.98 18	99.98 14	99.85 69	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 43	99.10 248	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	99.07 261	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 71	99.82 43	99.03 271	99.96 28	99.99 3	99.97 24	99.84 77	99.58 49	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 71	99.78 58	99.03 271	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 93	99.75 79	99.02 275	99.87 70	99.98 18	99.98 14	99.81 98	99.07 130	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	99.78 136	99.78 58	99.00 286	99.97 20	99.96 28	99.97 24	99.56 301	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	99.75 169	99.56 165	98.98 296	99.94 38	99.92 46	99.97 24	99.72 174	99.84 16	99.92 149	99.91 33	99.98 50	99.89 37

MVStest1		98.22 365	98.09 360	98.62 398	99.04 422	96.23 444	99.20 200	99.92 43	99.44 198	99.98 14	99.87 56	85.87 465	99.67 435	99.91 33	99.57 346	99.95 14

v1921920		99.56 105	99.57 103	99.55 215	99.75 169	99.11 276	99.05 263	99.61 243	99.15 260	99.88 83	99.71 184	99.08 127	99.87 249	99.90 37	99.97 73	99.66 147

v1240		99.56 105	99.58 98	99.51 231	99.80 114	99.00 290	99.00 286	99.65 222	99.15 260	99.90 68	99.75 156	99.09 123	99.88 234	99.90 37	99.96 87	99.67 133

v10		99.69 60	99.69 61	99.66 150	99.81 105	99.39 215	99.66 57	99.75 160	99.60 164	99.92 60	99.87 56	98.75 186	99.86 268	99.90 37	99.99 16	99.73 93

v1192		99.57 101	99.57 103	99.57 204	99.77 149	99.22 257	99.04 268	99.60 254	99.18 249	99.87 93	99.72 174	99.08 127	99.85 287	99.89 40	99.98 50	99.66 147

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 32	99.81 105	99.71 100	98.97 298	99.92 43	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		99.55 110	99.54 113	99.58 196	99.78 136	99.20 263	99.11 245	99.62 236	99.18 249	99.89 73	99.72 174	98.66 200	99.87 249	99.88 41	99.97 73	99.66 147

v8		99.68 65	99.69 61	99.65 157	99.80 114	99.40 212	99.66 57	99.76 155	99.64 148	99.93 53	99.85 69	98.66 200	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	99.42 204	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		99.54 114	99.53 117	99.59 193	99.79 128	99.28 239	99.10 248	99.61 243	99.20 246	99.84 102	99.73 166	98.67 198	99.84 303	99.86 45	99.98 50	99.64 168

mmtdpeth		99.78 37	99.83 21	99.66 150	99.85 71	99.05 289	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		99.52 120	99.42 144	99.83 41	99.86 59	99.65 126	99.52 93	99.81 117	99.87 63	99.81 116	99.79 119	96.78 342	99.99 8	99.83 46	99.51 362	99.86 46

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 27	99.82 10	99.82 104	99.84 76	99.94 48	99.91 31	99.13 116	99.96 69	99.83 46	99.99 16	99.83 56

v2v482		99.50 123	99.47 129	99.58 196	99.78 136	99.25 247	99.14 228	99.58 269	99.25 237	99.81 116	99.62 259	98.24 259	99.84 303	99.83 46	99.97 73	99.64 168

test_vis1_rt		99.45 146	99.46 134	99.41 269	99.71 190	98.63 335	98.99 293	99.96 28	99.03 273	99.95 45	99.12 407	98.75 186	99.84 303	99.82 50	99.82 232	99.77 79

tt0805		99.63 85	99.57 103	99.81 54	99.87 54	99.88 12	99.58 82	98.70 423	99.72 113	99.91 63	99.60 277	99.43 66	99.81 354	99.81 51	99.53 358	99.73 93

VortexMVS		99.13 245	99.24 195	98.79 388	99.67 226	96.60 436	99.24 189	99.80 122	99.85 72	99.93 53	99.84 77	95.06 380	99.89 219	99.80 52	99.98 50	99.89 37

V42		99.56 105	99.54 113	99.63 171	99.79 128	99.46 189	99.39 128	99.59 260	99.24 239	99.86 96	99.70 194	98.55 215	99.82 338	99.79 53	99.95 111	99.60 203

SSC-MVS3.2		99.64 84	99.67 65	99.56 208	99.75 169	98.98 293	98.96 302	99.87 70	99.88 61	99.84 102	99.64 235	99.32 86	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		99.44 150	99.32 171	99.80 64	99.81 105	99.61 150	99.47 111	99.81 117	99.82 86	99.71 183	99.72 174	96.60 346	99.98 27	99.75 56	99.23 403	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 91	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		99.15 242	99.06 236	99.42 261	99.85 71	98.59 339	99.13 235	97.26 470	99.84 76	99.87 93	99.77 141	96.11 365	99.93 118	99.71 60	99.96 87	99.74 89

Elysia		99.69 60	99.65 74	99.81 54	99.86 59	99.72 95	99.34 147	99.77 147	99.94 36	99.91 63	99.76 148	98.55 215	99.99 8	99.70 61	99.98 50	99.72 97

StellarMVS		99.69 60	99.65 74	99.81 54	99.86 59	99.72 95	99.34 147	99.77 147	99.94 36	99.91 63	99.76 148	98.55 215	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 93	99.84 27	99.82 10	99.92 43	99.94 36	99.94 48	99.93 22	99.34 83	99.92 149	99.70 61	99.96 87	99.70 105

reproduce_monomvs		97.40 402	97.46 392	97.20 452	99.05 419	91.91 480	99.20 200	99.18 395	99.84 76	99.86 96	99.75 156	80.67 473	99.83 322	99.69 64	99.95 111	99.85 49

SPE-MVS-test		99.68 65	99.70 58	99.64 164	99.57 265	99.83 35	99.78 17	99.97 20	99.92 46	99.50 279	99.38 354	99.57 51	99.95 80	99.69 64	99.90 159	99.15 374

guyue		99.12 248	99.02 250	99.41 269	99.84 76	98.56 340	99.19 206	98.30 448	99.82 86	99.84 102	99.75 156	94.84 383	99.92 149	99.68 66	99.94 127	99.74 89

tt0320		99.79 34	99.79 34	99.81 54	99.82 93	99.84 27	99.82 10	99.90 58	99.94 36	99.94 48	99.94 19	99.07 130	99.92 149	99.68 66	99.97 73	99.67 133

MGCNet		98.61 323	98.30 344	99.52 227	97.88 485	98.95 299	98.76 340	94.11 486	99.84 76	99.32 325	99.57 297	95.57 374	99.95 80	99.68 66	99.98 50	99.68 124

CS-MVS		99.67 76	99.70 58	99.58 196	99.53 288	99.84 27	99.79 15	99.96 28	99.90 50	99.61 235	99.41 344	99.51 60	99.95 80	99.66 69	99.89 173	98.96 416

mamv4		99.73 52	99.74 53	99.70 131	99.66 228	99.87 15	99.69 45	99.93 39	99.93 43	99.93 53	99.86 63	99.07 130	100.00 1	99.66 69	99.92 145	99.24 349

KinetiMVS		99.66 77	99.63 82	99.76 85	99.89 39	99.57 164	99.37 139	99.82 104	99.95 32	99.90 68	99.63 250	98.57 211	99.97 44	99.65 71	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 52	99.85 72	99.94 48	99.95 16	99.73 27	99.90 197	99.65 71	99.97 73	99.69 117

MIMVSNet1		99.66 77	99.62 84	99.80 64	99.94 18	99.87 15	99.69 45	99.77 147	99.78 103	99.93 53	99.89 41	97.94 287	99.92 149	99.65 71	99.98 50	99.62 186

LuminaMVS		99.39 168	99.28 186	99.73 112	99.83 84	99.49 179	99.00 286	99.05 407	99.81 92	99.89 73	99.79 119	96.54 350	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	99.79 131	99.94 36	99.93 53	99.92 27	99.35 82	99.92 149	99.64 74	99.94 127	99.68 124

EC-MVSNet		99.69 60	99.69 61	99.68 139	99.71 190	99.91 4	99.76 23	99.96 28	99.86 66	99.51 276	99.39 352	99.57 51	99.93 118	99.64 74	99.86 204	99.20 362

K. test v3		98.87 299	98.60 309	99.69 137	99.93 24	99.46 189	99.74 27	94.97 481	99.78 103	99.88 83	99.88 50	93.66 398	99.97 44	99.61 77	99.95 111	99.64 168

KD-MVS_self_test		99.63 85	99.59 94	99.76 85	99.84 76	99.90 7	99.37 139	99.79 131	99.83 82	99.88 83	99.85 69	98.42 239	99.90 197	99.60 78	99.73 286	99.49 268

Anonymous20240521		99.44 150	99.42 144	99.49 237	99.89 39	98.96 298	99.62 67	99.76 155	99.85 72	99.82 109	99.88 50	96.39 357	99.97 44	99.59 79	99.98 50	99.55 228

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	99.74 280	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	98.54 219	99.93 118	99.59 79	99.98 50	99.76 84

EU-MVSNet		99.39 168	99.62 84	98.72 393	99.88 45	96.44 438	99.56 87	99.85 82	99.90 50	99.90 68	99.85 69	98.09 275	99.83 322	99.58 82	99.95 111	99.90 29

mvs_anonymous		99.28 196	99.39 149	98.94 361	99.19 395	97.81 396	99.02 275	99.55 282	99.78 103	99.85 99	99.80 108	98.24 259	99.86 268	99.57 83	99.50 365	99.15 374

test1111		97.74 386	98.16 356	96.49 463	99.60 243	89.86 494	99.71 37	91.21 490	99.89 56	99.88 83	99.87 56	93.73 397	99.90 197	99.56 84	99.99 16	99.70 105

lessismore_v0				99.64 164	99.86 59	99.38 217		90.66 491		99.89 73	99.83 84	94.56 388	99.97 44	99.56 84	99.92 145	99.57 221

mvsany_test1		99.44 150	99.45 136	99.40 272	99.37 345	98.64 334	97.90 436	99.59 260	99.27 233	99.92 60	99.82 91	99.74 26	99.93 118	99.55 86	99.87 196	99.63 174

MVSMamba_PlusPlus		99.55 110	99.58 98	99.47 244	99.68 219	99.40 212	99.52 93	99.70 191	99.92 46	99.77 145	99.86 63	98.28 255	99.96 69	99.54 87	99.90 159	99.05 403

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	99.92 145	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 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		99.48 130	99.65 74	98.95 360	99.71 190	97.27 418	99.50 101	99.82 104	99.59 166	99.41 303	99.85 69	99.62 40	100.00 1	99.53 90	99.89 173	99.59 210

test2506		94.73 453	94.59 453	95.15 470	99.59 249	85.90 496	99.75 25	74.01 498	99.89 56	99.71 183	99.86 63	79.00 483	99.90 197	99.52 91	99.99 16	99.65 156

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 182	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 74	99.86 30	99.88 45	99.86 19	99.72 33	99.78 141	99.90 50	99.82 109	99.83 84	98.45 235	99.87 249	99.51 92	99.97 73	99.86 46

BP-MVS1		98.72 315	98.46 325	99.50 233	99.53 288	99.00 290	99.34 147	98.53 433	99.65 144	99.73 173	99.38 354	90.62 439	99.96 69	99.50 94	99.86 204	99.55 228

UA-Net		99.78 37	99.76 49	99.86 30	99.72 186	99.71 100	99.91 4	99.95 36	99.96 28	99.71 183	99.91 31	99.15 111	99.97 44	99.50 94	100.00 1	99.90 29

viewdifsd2359ckpt11		99.62 92	99.64 79	99.56 208	99.86 59	99.19 264	99.02 275	99.93 39	99.83 82	99.88 83	99.81 98	98.99 148	99.83 322	99.48 96	99.96 87	99.65 156

viewmsd2359difaftdt		99.62 92	99.64 79	99.56 208	99.86 59	99.19 264	99.02 275	99.93 39	99.83 82	99.88 83	99.81 98	98.99 148	99.83 322	99.48 96	99.96 87	99.65 156

PMMVS2		99.48 130	99.45 136	99.57 204	99.76 153	98.99 292	98.09 413	99.90 58	98.95 283	99.78 133	99.58 290	99.57 51	99.93 118	99.48 96	99.95 111	99.79 73

VPA-MVSNet		99.66 77	99.62 84	99.79 71	99.68 219	99.75 79	99.62 67	99.69 199	99.85 72	99.80 123	99.81 98	98.81 174	99.91 178	99.47 99	99.88 183	99.70 105

GDP-MVS		98.81 306	98.57 315	99.50 233	99.53 288	99.12 275	99.28 173	99.86 76	99.53 175	99.57 246	99.32 371	90.88 435	99.98 27	99.46 100	99.74 280	99.42 307

ECVR-MVS		97.73 387	98.04 363	96.78 456	99.59 249	90.81 489	99.72 33	90.43 492	99.89 56	99.86 96	99.86 63	93.60 399	99.89 219	99.46 100	99.99 16	99.65 156

nrg030		99.70 58	99.66 72	99.82 46	99.76 153	99.84 27	99.61 73	99.70 191	99.93 43	99.78 133	99.68 215	99.10 121	99.78 368	99.45 102	99.96 87	99.83 56

FE-MVSNET2		99.68 65	99.67 65	99.72 120	99.86 59	99.68 115	99.46 115	99.88 66	99.62 153	99.87 93	99.85 69	99.06 137	99.85 287	99.44 103	99.98 50	99.63 174

TAMVS		99.49 128	99.45 136	99.63 171	99.48 313	99.42 204	99.45 116	99.57 271	99.66 141	99.78 133	99.83 84	97.85 294	99.86 268	99.44 103	99.96 87	99.61 199

GeoE		99.69 60	99.66 72	99.78 75	99.76 153	99.76 71	99.60 79	99.82 104	99.46 193	99.75 158	99.56 301	99.63 37	99.95 80	99.43 105	99.88 183	99.62 186

new-patchmatchnet		99.35 181	99.57 103	98.71 396	99.82 93	96.62 434	98.55 368	99.75 160	99.50 180	99.88 83	99.87 56	99.31 87	99.88 234	99.43 105	100.00 1	99.62 186

test20.03		99.55 110	99.54 113	99.58 196	99.79 128	99.37 221	99.02 275	99.89 61	99.60 164	99.82 109	99.62 259	98.81 174	99.89 219	99.43 105	99.86 204	99.47 276

MVSFormer		99.41 162	99.44 140	99.31 302	99.57 265	98.40 355	99.77 19	99.80 122	99.73 109	99.63 219	99.30 376	98.02 280	99.98 27	99.43 105	99.69 305	99.55 228

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 27	99.77 19	99.80 122	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	99.80 114	99.65 126	99.63 64	99.86 76	99.97 25	99.89 73	99.89 41	99.52 59	99.99 8	99.42 110	99.96 87	99.65 156

Anonymous20231211		99.62 92	99.57 103	99.76 85	99.61 241	99.60 154	99.81 13	99.73 170	99.82 86	99.90 68	99.90 36	97.97 286	99.86 268	99.42 110	99.96 87	99.80 65

SixPastTwentyTwo		99.42 156	99.30 178	99.76 85	99.92 29	99.67 118	99.70 38	99.14 400	99.65 144	99.89 73	99.90 36	96.20 364	99.94 97	99.42 110	99.92 145	99.67 133

balanced_conf03		99.50 123	99.50 122	99.50 233	99.42 336	99.49 179	99.52 93	99.75 160	99.86 66	99.78 133	99.71 184	98.20 267	99.90 197	99.39 113	99.88 183	99.10 385

patch_mono-2		99.51 121	99.46 134	99.64 164	99.70 205	99.11 276	99.04 268	99.87 70	99.71 118	99.47 284	99.79 119	98.24 259	99.98 27	99.38 114	99.96 87	99.83 56

UGNet		99.38 171	99.34 166	99.49 237	98.90 434	98.90 307	99.70 38	99.35 354	99.86 66	98.57 418	99.81 98	98.50 230	99.93 118	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 57	99.67 65	99.81 54	99.89 39	99.72 95	99.59 80	99.82 104	99.39 214	99.82 109	99.84 77	99.38 74	99.91 178	99.38 114	99.93 139	99.80 65

FIs		99.65 83	99.58 98	99.84 38	99.84 76	99.85 22	99.66 57	99.75 160	99.86 66	99.74 168	99.79 119	98.27 257	99.85 287	99.37 117	99.93 139	99.83 56

sd_testset		99.78 37	99.78 39	99.80 64	99.80 114	99.76 71	99.80 14	99.79 131	99.97 25	99.89 73	99.89 41	99.53 57	99.99 8	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 82	99.70 124	99.92 60	99.93 22	99.45 62	99.97 44	99.36 118	100.00 1	99.85 49

casdiffmvs_mvg		99.68 65	99.68 64	99.69 137	99.81 105	99.59 156	99.29 171	99.90 58	99.71 118	99.79 129	99.73 166	99.54 54	99.84 303	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 43	99.67 137	99.77 145	99.75 156	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 96	99.64 79	99.53 225	99.79 128	98.82 313	99.58 82	99.97 20	99.95 32	99.96 34	99.76 148	98.44 236	99.99 8	99.34 122	99.96 87	99.78 75

CHOSEN 1792x2688		99.39 168	99.30 178	99.65 157	99.88 45	99.25 247	98.78 338	99.88 66	98.66 325	99.96 34	99.79 119	97.45 316	99.93 118	99.34 122	99.99 16	99.78 75

CDS-MVSNet		99.22 218	99.13 211	99.50 233	99.35 352	99.11 276	98.96 302	99.54 288	99.46 193	99.61 235	99.70 194	96.31 360	99.83 322	99.34 122	99.88 183	99.55 228
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 279	99.16 204	98.51 404	99.75 169	95.90 450	98.07 416	99.84 89	99.84 76	99.89 73	99.73 166	96.01 368	99.99 8	99.33 125	100.00 1	99.63 174

HyFIR lowres test		98.91 292	98.64 306	99.73 112	99.85 71	99.47 183	98.07 416	99.83 98	98.64 328	99.89 73	99.60 277	92.57 412	100.00 1	99.33 125	99.97 73	99.72 97

pmmvs5		99.19 228	99.11 218	99.42 261	99.76 153	98.88 309	98.55 368	99.73 170	98.82 304	99.72 178	99.62 259	96.56 347	99.82 338	99.32 127	99.95 111	99.56 224

v148		99.40 164	99.41 147	99.39 275	99.76 153	98.94 300	99.09 253	99.59 260	99.17 254	99.81 116	99.61 269	98.41 240	99.69 418	99.32 127	99.94 127	99.53 244

baseline		99.63 85	99.62 84	99.66 150	99.80 114	99.62 140	99.44 118	99.80 122	99.71 118	99.72 178	99.69 203	99.15 111	99.83 322	99.32 127	99.94 127	99.53 244

CVMVSNet		98.61 323	98.88 284	97.80 435	99.58 255	93.60 473	99.26 182	99.64 230	99.66 141	99.72 178	99.67 219	93.26 403	99.93 118	99.30 130	99.81 242	99.87 44

PS-CasMVS		99.66 77	99.58 98	99.89 11	99.80 114	99.85 22	99.66 57	99.73 170	99.62 153	99.84 102	99.71 184	98.62 204	99.96 69	99.30 130	99.96 87	99.86 46

DTE-MVSNet		99.68 65	99.61 88	99.88 19	99.80 114	99.87 15	99.67 53	99.71 182	99.72 113	99.84 102	99.78 131	98.67 198	99.97 44	99.30 130	99.95 111	99.80 65

tmp_tt		95.75 445	95.42 440	96.76 457	89.90 497	94.42 467	98.86 319	97.87 460	78.01 488	99.30 335	99.69 203	97.70 302	95.89 490	99.29 133	98.14 461	99.95 14

PEN-MVS		99.66 77	99.59 94	99.89 11	99.83 84	99.87 15	99.66 57	99.73 170	99.70 124	99.84 102	99.73 166	98.56 214	99.96 69	99.29 133	99.94 127	99.83 56

WR-MVS_H		99.61 96	99.53 117	99.87 26	99.80 114	99.83 35	99.67 53	99.75 160	99.58 168	99.85 99	99.69 203	98.18 270	99.94 97	99.28 135	99.95 111	99.83 56

IterMVS		98.97 283	99.16 204	98.42 409	99.74 177	95.64 454	98.06 418	99.83 98	99.83 82	99.85 99	99.74 161	96.10 367	99.99 8	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		99.09 256	98.91 282	99.62 180	99.78 136	99.11 276	99.36 143	99.77 147	99.82 86	99.68 195	99.53 313	93.30 401	99.99 8	99.24 137	99.76 269	99.74 89

SymmetryMVS		99.01 276	98.82 292	99.58 196	99.65 233	99.11 276	99.36 143	99.20 393	99.82 86	99.68 195	99.53 313	93.30 401	99.99 8	99.24 137	99.63 326	99.64 168

WBMVS		97.50 398	97.18 403	98.48 406	98.85 442	95.89 451	98.44 385	99.52 303	99.53 175	99.52 269	99.42 343	80.10 476	99.86 268	99.24 137	99.95 111	99.68 124

h-mvs33		98.61 323	98.34 339	99.44 255	99.60 243	98.67 326	99.27 177	99.44 329	99.68 129	99.32 325	99.49 326	92.50 415	100.00 1	99.24 137	96.51 481	99.65 156

hse-mvs2		98.52 336	98.30 344	99.16 331	99.29 374	98.60 337	98.77 339	99.02 409	99.68 129	99.32 325	99.04 417	92.50 415	99.85 287	99.24 137	97.87 468	99.03 407

FMVSNet1		99.66 77	99.63 82	99.73 112	99.78 136	99.77 64	99.68 49	99.70 191	99.67 137	99.82 109	99.83 84	98.98 152	99.90 197	99.24 137	99.97 73	99.53 244

casdiffmvs		99.63 85	99.61 88	99.67 143	99.79 128	99.59 156	99.13 235	99.85 82	99.79 100	99.76 153	99.72 174	99.33 85	99.82 338	99.21 143	99.94 127	99.59 210
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		99.54 114	99.43 142	99.87 26	99.76 153	99.82 43	99.57 85	99.61 243	99.54 173	99.80 123	99.64 235	97.79 298	99.95 80	99.21 143	99.94 127	99.84 52

DELS-MVS		99.34 186	99.30 178	99.48 242	99.51 297	99.36 225	98.12 409	99.53 298	99.36 219	99.41 303	99.61 269	99.22 101	99.87 249	99.21 143	99.68 310	99.20 362
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		99.47 140	99.50 122	99.37 281	99.70 205	98.80 317	98.67 349	99.92 43	99.49 182	99.77 145	99.71 184	99.08 127	99.78 368	99.20 146	99.94 127	99.54 238

UniMVSNet (Re)		99.37 175	99.26 191	99.68 139	99.51 297	99.58 161	98.98 296	99.60 254	99.43 205	99.70 187	99.36 362	97.70 302	99.88 234	99.20 146	99.87 196	99.59 210

CANet		99.11 252	99.05 241	99.28 310	98.83 444	98.56 340	98.71 347	99.41 335	99.25 237	99.23 344	99.22 394	97.66 310	99.94 97	99.19 148	99.97 73	99.33 330

EI-MVSNet-UG-set		99.48 130	99.50 122	99.42 261	99.57 265	98.65 332	99.24 189	99.46 323	99.68 129	99.80 123	99.66 224	98.99 148	99.89 219	99.19 148	99.90 159	99.72 97

xiu_mvs_v1_base_debu		99.23 209	99.34 166	98.91 370	99.59 249	98.23 364	98.47 380	99.66 212	99.61 158	99.68 195	98.94 433	99.39 70	99.97 44	99.18 150	99.55 351	98.51 455

xiu_mvs_v1_base		99.23 209	99.34 166	98.91 370	99.59 249	98.23 364	98.47 380	99.66 212	99.61 158	99.68 195	98.94 433	99.39 70	99.97 44	99.18 150	99.55 351	98.51 455

xiu_mvs_v1_base_debi		99.23 209	99.34 166	98.91 370	99.59 249	98.23 364	98.47 380	99.66 212	99.61 158	99.68 195	98.94 433	99.39 70	99.97 44	99.18 150	99.55 351	98.51 455

VPNet		99.46 142	99.37 155	99.71 126	99.82 93	99.59 156	99.48 108	99.70 191	99.81 92	99.69 190	99.58 290	97.66 310	99.86 268	99.17 153	99.44 372	99.67 133

UniMVSNet_NR-MVSNet		99.37 175	99.25 193	99.72 120	99.47 319	99.56 165	98.97 298	99.61 243	99.43 205	99.67 202	99.28 380	97.85 294	99.95 80	99.17 153	99.81 242	99.65 156

DU-MVS		99.33 189	99.21 198	99.71 126	99.43 331	99.56 165	98.83 326	99.53 298	99.38 215	99.67 202	99.36 362	97.67 306	99.95 80	99.17 153	99.81 242	99.63 174

EI-MVSNet-Vis-set		99.47 140	99.49 126	99.42 261	99.57 265	98.66 329	99.24 189	99.46 323	99.67 137	99.79 129	99.65 233	98.97 154	99.89 219	99.15 156	99.89 173	99.71 102

EI-MVSNet		99.38 171	99.44 140	99.21 325	99.58 255	98.09 378	99.26 182	99.46 323	99.62 153	99.75 158	99.67 219	98.54 219	99.85 287	99.15 156	99.92 145	99.68 124

VNet		99.18 232	99.06 236	99.56 208	99.24 385	99.36 225	99.33 153	99.31 367	99.67 137	99.47 284	99.57 297	96.48 351	99.84 303	99.15 156	99.30 391	99.47 276

EG-PatchMatch MVS		99.57 101	99.56 108	99.62 180	99.77 149	99.33 231	99.26 182	99.76 155	99.32 224	99.80 123	99.78 131	99.29 89	99.87 249	99.15 156	99.91 157	99.66 147

PVSNet_Blended_VisFu		99.40 164	99.38 152	99.44 255	99.90 37	98.66 329	98.94 307	99.91 52	97.97 389	99.79 129	99.73 166	99.05 139	99.97 44	99.15 156	99.99 16	99.68 124

IterMVS-LS		99.41 162	99.47 129	99.25 321	99.81 105	98.09 378	98.85 321	99.76 155	99.62 153	99.83 108	99.64 235	98.54 219	99.97 44	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		99.54 114	99.47 129	99.76 85	99.58 255	99.64 132	99.30 164	99.63 233	99.61 158	99.71 183	99.56 301	98.76 184	99.96 69	99.14 162	99.92 145	99.68 124

MVSTER		98.47 343	98.22 349	99.24 323	99.06 418	98.35 361	99.08 256	99.46 323	99.27 233	99.75 158	99.66 224	88.61 452	99.85 287	99.14 162	99.92 145	99.52 255

E5new		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	99.78 368	99.13 164	99.96 87	99.70 105

E6new		99.68 65	99.67 65	99.70 131	99.86 59	99.62 140	99.41 121	99.84 89	99.68 129	99.77 145	99.81 98	99.59 45	99.78 368	99.13 164	99.96 87	99.70 105

E6		99.68 65	99.67 65	99.70 131	99.86 59	99.62 140	99.41 121	99.84 89	99.68 129	99.77 145	99.81 98	99.59 45	99.78 368	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	99.78 368	99.13 164	99.96 87	99.70 105

diffmvs_AUTHOR		99.48 130	99.48 127	99.47 244	99.80 114	98.89 308	98.71 347	99.82 104	99.79 100	99.66 208	99.63 250	98.87 170	99.88 234	99.13 164	99.95 111	99.62 186

Anonymous20231206		99.35 181	99.31 173	99.47 244	99.74 177	99.06 288	99.28 173	99.74 166	99.23 241	99.72 178	99.53 313	97.63 312	99.88 234	99.11 169	99.84 214	99.48 272

Syy-MVS		98.17 368	97.85 380	99.15 333	98.50 467	98.79 318	98.60 356	99.21 390	97.89 398	96.76 475	96.37 498	95.47 377	99.57 461	99.10 170	98.73 437	99.09 390

ttmdpeth		99.48 130	99.55 110	99.29 307	99.76 153	98.16 372	99.33 153	99.95 36	99.79 100	99.36 314	99.89 41	99.13 116	99.77 381	99.09 171	99.64 323	99.93 20

MVS_Test		99.28 196	99.31 173	99.19 328	99.35 352	98.79 318	99.36 143	99.49 316	99.17 254	99.21 349	99.67 219	98.78 181	99.66 440	99.09 171	99.66 319	99.10 385

FE-MVSNET3		98.87 299	98.71 301	99.35 288	99.59 249	98.88 309	97.17 470	99.64 230	98.94 284	99.27 337	99.22 394	95.57 374	99.83 322	99.08 173	99.92 145	99.35 325

testgi		99.29 195	99.26 191	99.37 281	99.75 169	98.81 314	98.84 323	99.89 61	98.38 356	99.75 158	99.04 417	99.36 79	99.86 268	99.08 173	99.25 399	99.45 283

1112_ss		99.05 264	98.84 289	99.67 143	99.66 228	99.29 237	98.52 374	99.82 104	97.65 411	99.43 294	99.16 401	96.42 354	99.91 178	99.07 175	99.84 214	99.80 65

CANet_DTU		98.91 292	98.85 287	99.09 342	98.79 450	98.13 373	98.18 401	99.31 367	99.48 185	98.86 389	99.51 319	96.56 347	99.95 80	99.05 176	99.95 111	99.19 365

blended_shiyan8		97.82 381	97.45 394	98.92 365	98.06 481	97.45 412	97.73 442	99.35 354	97.96 392	98.35 429	97.34 481	92.76 411	99.84 303	99.04 177	96.49 483	99.47 276

blended_shiyan6		97.82 381	97.46 392	98.92 365	98.08 480	97.46 410	97.73 442	99.34 357	97.96 392	98.33 430	97.35 480	92.78 409	99.84 303	99.04 177	96.53 478	99.46 281

Baseline_NR-MVSNet		99.49 128	99.37 155	99.82 46	99.91 31	99.84 27	98.83 326	99.86 76	99.68 129	99.65 211	99.88 50	97.67 306	99.87 249	99.03 179	99.86 204	99.76 84

FMVSNet2		99.35 181	99.28 186	99.55 215	99.49 308	99.35 228	99.45 116	99.57 271	99.44 198	99.70 187	99.74 161	97.21 327	99.87 249	99.03 179	99.94 127	99.44 297

FE-blended-shiyan7		97.53 397	97.14 405	98.72 393	97.71 487	96.86 430	97.00 475	99.34 357	97.73 407	98.18 437	96.82 491	91.92 418	99.84 303	99.02 181	96.53 478	99.45 283

Test_1112_low_res		98.95 289	98.73 299	99.63 171	99.68 219	99.15 272	98.09 413	99.80 122	97.14 437	99.46 288	99.40 348	96.11 365	99.89 219	99.01 182	99.84 214	99.84 52

VDD-MVS		99.20 225	99.11 218	99.44 255	99.43 331	98.98 293	99.50 101	98.32 447	99.80 96	99.56 254	99.69 203	96.99 337	99.85 287	98.99 183	99.73 286	99.50 263

DeepC-MVS	98.90 4	99.62 92	99.61 88	99.67 143	99.72 186	99.44 197	99.24 189	99.71 182	99.27 233	99.93 53	99.90 36	99.70 31	99.93 118	98.99 183	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		99.48 130	99.47 129	99.51 231	99.77 149	99.41 211	98.81 331	99.66 212	99.42 209	99.75 158	99.66 224	99.20 103	99.76 385	98.98 185	99.99 16	99.36 322

EPNet_dtu		97.62 392	97.79 383	97.11 455	96.67 492	92.31 478	98.51 375	98.04 454	99.24 239	95.77 484	99.47 333	93.78 396	99.66 440	98.98 185	99.62 328	99.37 319
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 186	99.32 171	99.39 275	99.67 226	98.77 320	98.57 365	99.81 117	99.61 158	99.48 282	99.41 344	98.47 231	99.86 268	98.97 187	99.90 159	99.53 244
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	99.31 173	99.68 139	99.43 331	99.55 169	99.73 30	99.50 312	99.46 193	99.88 83	99.36 362	97.54 313	99.87 249	98.97 187	99.87 196	99.63 174

TestfortrainingZip a		99.61 96	99.53 117	99.85 32	99.76 153	99.84 27	99.38 131	99.78 141	99.58 168	99.81 116	99.66 224	99.02 143	99.90 197	98.96 189	99.79 254	99.81 64

viewdifsd2359ckpt07		99.51 121	99.50 122	99.52 227	99.80 114	99.19 264	98.92 311	99.88 66	99.72 113	99.64 214	99.62 259	99.06 137	99.81 354	98.96 189	99.94 127	99.56 224

GBi-Net		99.42 156	99.31 173	99.73 112	99.49 308	99.77 64	99.68 49	99.70 191	99.44 198	99.62 229	99.83 84	97.21 327	99.90 197	98.96 189	99.90 159	99.53 244

FMVSNet5		97.80 384	97.25 401	99.42 261	98.83 444	98.97 296	99.38 131	99.80 122	98.87 296	99.25 340	99.69 203	80.60 475	99.91 178	98.96 189	99.90 159	99.38 316

test1		99.42 156	99.31 173	99.73 112	99.49 308	99.77 64	99.68 49	99.70 191	99.44 198	99.62 229	99.83 84	97.21 327	99.90 197	98.96 189	99.90 159	99.53 244

FMVSNet3		98.80 307	98.63 308	99.32 298	99.13 404	98.72 323	99.10 248	99.48 317	99.23 241	99.62 229	99.64 235	92.57 412	99.86 268	98.96 189	99.90 159	99.39 314

UnsupCasMVSNet_eth		98.83 303	98.57 315	99.59 193	99.68 219	99.45 195	98.99 293	99.67 207	99.48 185	99.55 259	99.36 362	94.92 381	99.86 268	98.95 195	96.57 477	99.45 283

CHOSEN 280x420		98.41 348	98.41 331	98.40 410	99.34 361	95.89 451	96.94 477	99.44 329	98.80 308	99.25 340	99.52 317	93.51 400	99.98 27	98.94 196	99.98 50	99.32 334

E4		99.61 96	99.59 94	99.66 150	99.84 76	99.53 172	99.08 256	99.84 89	99.65 144	99.74 168	99.80 108	99.45 62	99.77 381	98.93 197	99.95 111	99.69 117

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 76	99.88 234	98.93 197	99.95 111	99.60 203

viewmacassd2359aftdt		99.63 85	99.61 88	99.68 139	99.84 76	99.61 150	99.14 228	99.87 70	99.71 118	99.75 158	99.77 141	99.54 54	99.72 402	98.91 199	99.96 87	99.70 105

alignmvs		98.28 358	97.96 369	99.25 321	99.12 406	98.93 303	99.03 271	98.42 440	99.64 148	98.72 404	97.85 472	90.86 436	99.62 452	98.88 200	99.13 405	99.19 365

testing3-2		96.51 424	96.43 419	96.74 459	99.36 348	91.38 486	99.10 248	97.87 460	99.48 185	98.57 418	98.71 448	76.65 486	99.66 440	98.87 201	99.26 398	99.18 367

MGCFI-Net		99.02 270	99.01 254	99.06 349	99.11 411	98.60 337	99.63 64	99.67 207	99.63 150	98.58 416	97.65 475	99.07 130	99.57 461	98.85 202	98.92 421	99.03 407

sss		98.90 294	98.77 298	99.27 315	99.48 313	98.44 352	98.72 345	99.32 363	97.94 395	99.37 313	99.35 367	96.31 360	99.91 178	98.85 202	99.63 326	99.47 276

xiu_mvs_v2_base		99.02 270	99.11 218	98.77 390	99.37 345	98.09 378	98.13 408	99.51 308	99.47 190	99.42 297	98.54 457	99.38 74	99.97 44	98.83 204	99.33 387	98.24 467

PS-MVSNAJ		99.00 279	99.08 230	98.76 391	99.37 345	98.10 377	98.00 424	99.51 308	99.47 190	99.41 303	98.50 459	99.28 91	99.97 44	98.83 204	99.34 386	98.20 471

E2		99.54 114	99.51 120	99.62 180	99.78 136	99.47 183	99.01 280	99.82 104	99.55 171	99.69 190	99.77 141	99.26 95	99.76 385	98.82 206	99.93 139	99.62 186

E3		99.54 114	99.51 120	99.62 180	99.78 136	99.47 183	99.01 280	99.82 104	99.55 171	99.69 190	99.77 141	99.25 99	99.76 385	98.82 206	99.93 139	99.62 186

D2MVS		99.22 218	99.19 201	99.29 307	99.69 211	98.74 322	98.81 331	99.41 335	98.55 337	99.68 195	99.69 203	98.13 272	99.87 249	98.82 206	99.98 50	99.24 349

PatchT		98.45 345	98.32 341	98.83 384	98.94 432	98.29 362	99.24 189	98.82 417	99.84 76	99.08 366	99.76 148	91.37 425	99.94 97	98.82 206	99.00 416	98.26 466

testf1		99.63 85	99.60 92	99.72 120	99.94 18	99.95 2	99.47 111	99.89 61	99.43 205	99.88 83	99.80 108	99.26 95	99.90 197	98.81 210	99.88 183	99.32 334

APD_test2		99.63 85	99.60 92	99.72 120	99.94 18	99.95 2	99.47 111	99.89 61	99.43 205	99.88 83	99.80 108	99.26 95	99.90 197	98.81 210	99.88 183	99.32 334

usedtu_blend_shiyan5		97.97 378	97.65 390	98.92 365	97.71 487	97.49 407	99.53 91	99.81 117	99.52 179	98.18 437	96.82 491	91.92 418	99.83 322	98.79 212	96.53 478	99.45 283

blend_shiyan4		95.04 452	93.76 456	98.88 379	97.92 483	97.49 407	97.72 444	99.34 357	97.93 396	97.65 464	97.11 485	77.69 484	99.83 322	98.79 212	79.72 491	99.33 330

sasdasda		99.02 270	99.00 258	99.09 342	99.10 413	98.70 324	99.61 73	99.66 212	99.63 150	98.64 410	97.65 475	99.04 140	99.54 465	98.79 212	98.92 421	99.04 405

Effi-MVS+		99.06 261	98.97 269	99.34 290	99.31 368	98.98 293	98.31 393	99.91 52	98.81 306	98.79 398	98.94 433	99.14 114	99.84 303	98.79 212	98.74 434	99.20 362

canonicalmvs		99.02 270	99.00 258	99.09 342	99.10 413	98.70 324	99.61 73	99.66 212	99.63 150	98.64 410	97.65 475	99.04 140	99.54 465	98.79 212	98.92 421	99.04 405

VDDNet		98.97 283	98.82 292	99.42 261	99.71 190	98.81 314	99.62 67	98.68 424	99.81 92	99.38 311	99.80 108	94.25 390	99.85 287	98.79 212	99.32 389	99.59 210

CR-MVSNet		98.35 355	98.20 351	98.83 384	99.05 419	98.12 374	99.30 164	99.67 207	97.39 425	99.16 355	99.79 119	91.87 422	99.91 178	98.78 218	98.77 430	98.44 460

test_method		91.72 454	92.32 457	89.91 473	93.49 496	70.18 499	90.28 488	99.56 276	61.71 491	95.39 486	99.52 317	93.90 392	99.94 97	98.76 219	98.27 454	99.62 186

RPMNet		98.60 326	98.53 321	98.83 384	99.05 419	98.12 374	99.30 164	99.62 236	99.86 66	99.16 355	99.74 161	92.53 414	99.92 149	98.75 220	98.77 430	98.44 460

mamba_0408		99.54 114	99.55 110	99.54 221	99.71 190	99.24 251	99.27 177	99.79 131	99.72 113	99.78 133	99.64 235	99.36 79	99.93 118	98.74 221	99.90 159	99.45 283

SSM_04072		99.55 110	99.55 110	99.55 215	99.71 190	99.24 251	99.27 177	99.79 131	99.72 113	99.78 133	99.64 235	99.36 79	99.97 44	98.74 221	99.90 159	99.45 283

SSM_0407		99.56 105	99.56 108	99.54 221	99.71 190	99.24 251	99.15 224	99.84 89	99.80 96	99.78 133	99.70 194	99.44 64	99.93 118	98.74 221	99.90 159	99.45 283

SSM_0404		99.57 101	99.58 98	99.54 221	99.76 153	99.28 239	99.19 206	99.84 89	99.80 96	99.78 133	99.70 194	99.44 64	99.93 118	98.74 221	99.95 111	99.41 308

pmmvs4		99.13 245	99.06 236	99.36 286	99.57 265	99.10 283	98.01 422	99.25 380	98.78 311	99.58 243	99.44 340	98.24 259	99.76 385	98.74 221	99.93 139	99.22 355

viewmanbaseed2359cas		99.50 123	99.47 129	99.61 186	99.73 181	99.52 176	99.03 271	99.83 98	99.49 182	99.65 211	99.64 235	99.18 105	99.71 407	98.73 226	99.92 145	99.58 215

tttt0517		97.62 392	97.20 402	98.90 376	99.76 153	97.40 415	99.48 108	94.36 483	99.06 271	99.70 187	99.49 326	84.55 468	99.94 97	98.73 226	99.65 321	99.36 322

viewcassd2359sk11		99.48 130	99.45 136	99.58 196	99.73 181	99.42 204	98.96 302	99.80 122	99.44 198	99.63 219	99.74 161	99.09 123	99.76 385	98.72 228	99.91 157	99.57 221

EPP-MVSNet		99.17 237	99.00 258	99.66 150	99.80 114	99.43 201	99.70 38	99.24 383	99.48 185	99.56 254	99.77 141	94.89 382	99.93 118	98.72 228	99.89 173	99.63 174

FE-MVSNET		99.45 146	99.36 160	99.71 126	99.84 76	99.64 132	99.16 221	99.91 52	98.65 326	99.73 173	99.73 166	98.54 219	99.82 338	98.71 230	99.96 87	99.67 133

Anonymous20240529		99.42 156	99.34 166	99.65 157	99.53 288	99.60 154	99.63 64	99.39 345	99.47 190	99.76 153	99.78 131	98.13 272	99.86 268	98.70 231	99.68 310	99.49 268

ACMH	98.42 6	99.59 100	99.54 113	99.72 120	99.86 59	99.62 140	99.56 87	99.79 131	98.77 313	99.80 123	99.85 69	99.64 35	99.85 287	98.70 231	99.89 173	99.70 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 189	99.28 186	99.47 244	99.57 265	99.39 215	99.78 17	99.43 332	98.87 296	99.57 246	99.82 91	98.06 278	99.87 249	98.69 233	99.73 286	99.15 374

LFMVS		98.46 344	98.19 354	99.26 318	99.24 385	98.52 348	99.62 67	96.94 472	99.87 63	99.31 330	99.58 290	91.04 430	99.81 354	98.68 234	99.42 376	99.45 283

WR-MVS		99.11 252	98.93 274	99.66 150	99.30 372	99.42 204	98.42 386	99.37 350	99.04 272	99.57 246	99.20 399	96.89 339	99.86 268	98.66 235	99.87 196	99.70 105

mvsmamba		99.08 257	98.95 272	99.45 251	99.36 348	99.18 269	99.39 128	98.81 418	99.37 216	99.35 316	99.70 194	96.36 359	99.94 97	98.66 235	99.59 342	99.22 355

viewdifsd2359ckpt13		99.42 156	99.37 155	99.57 204	99.72 186	99.46 189	99.01 280	99.80 122	99.20 246	99.51 276	99.60 277	98.92 161	99.70 411	98.65 237	99.90 159	99.55 228

RRT-MVS		99.08 257	99.00 258	99.33 293	99.27 379	98.65 332	99.62 67	99.93 39	99.66 141	99.67 202	99.82 91	95.27 379	99.93 118	98.64 238	99.09 409	99.41 308

E3new		99.42 156	99.37 155	99.56 208	99.68 219	99.38 217	98.93 310	99.79 131	99.30 228	99.55 259	99.69 203	98.88 168	99.76 385	98.63 239	99.89 173	99.53 244

Anonymous202405211		98.75 311	98.46 325	99.63 171	99.34 361	99.66 120	99.47 111	97.65 463	99.28 232	99.56 254	99.50 322	93.15 404	99.84 303	98.62 240	99.58 344	99.40 311

lecture		99.56 105	99.48 127	99.81 54	99.78 136	99.86 19	99.50 101	99.70 191	99.59 166	99.75 158	99.71 184	98.94 157	99.92 149	98.59 241	99.76 269	99.66 147

EPNet		98.13 369	97.77 384	99.18 330	94.57 495	97.99 384	99.24 189	97.96 456	99.74 108	97.29 468	99.62 259	93.13 405	99.97 44	98.59 241	99.83 222	99.58 215
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 264	99.09 228	98.91 370	99.21 390	98.36 360	98.82 330	99.47 320	98.85 299	98.90 384	99.56 301	98.78 181	99.09 481	98.57 243	99.68 310	99.26 346

Patchmatch-RL test		98.60 326	98.36 336	99.33 293	99.77 149	99.07 286	98.27 395	99.87 70	98.91 291	99.74 168	99.72 174	90.57 441	99.79 365	98.55 244	99.85 209	99.11 383

pmmvs3		98.08 372	97.80 381	98.91 370	99.41 338	97.69 402	97.87 437	99.66 212	95.87 456	99.50 279	99.51 319	90.35 443	99.97 44	98.55 244	99.47 369	99.08 396

ETV-MVS		99.18 232	99.18 202	99.16 331	99.34 361	99.28 239	99.12 240	99.79 131	99.48 185	98.93 378	98.55 456	99.40 69	99.93 118	98.51 246	99.52 361	98.28 465

viewdifsd2359ckpt09		99.24 207	99.16 204	99.49 237	99.70 205	99.22 257	98.88 315	99.81 117	98.70 321	99.38 311	99.37 357	98.22 264	99.76 385	98.48 247	99.88 183	99.51 257

jason		99.16 238	99.11 218	99.32 298	99.75 169	98.44 352	98.26 397	99.39 345	98.70 321	99.74 168	99.30 376	98.54 219	99.97 44	98.48 247	99.82 232	99.55 228
jason: jason.
APDe-MVS		99.48 130	99.36 160	99.85 32	99.55 279	99.81 48	99.50 101	99.69 199	98.99 276	99.75 158	99.71 184	98.79 179	99.93 118	98.46 249	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		99.30 193	99.29 183	99.31 302	99.71 190	98.55 342	98.17 403	99.71 182	99.41 210	99.73 173	99.60 277	99.17 107	99.92 149	98.45 250	99.70 297	99.45 283

IMVS_0407		99.38 171	99.42 144	99.28 310	99.71 190	98.55 342	99.27 177	99.71 182	99.41 210	99.73 173	99.60 277	99.17 107	99.83 322	98.45 250	99.70 297	99.45 283

IMVS_0404		99.23 209	99.20 199	99.32 298	99.71 190	98.55 342	98.57 365	99.71 182	99.41 210	99.52 269	99.60 277	98.12 274	99.95 80	98.45 250	99.70 297	99.45 283

IMVS_0403		99.37 175	99.39 149	99.28 310	99.71 190	98.55 342	99.19 206	99.71 182	99.41 210	99.67 202	99.60 277	99.12 119	99.84 303	98.45 250	99.70 297	99.45 283

CL-MVSNet_self_test		98.71 317	98.56 319	99.15 333	99.22 388	98.66 329	97.14 471	99.51 308	98.09 382	99.54 262	99.27 382	96.87 340	99.74 397	98.43 254	98.96 418	99.03 407

our_test_3		98.85 302	99.09 228	98.13 423	99.66 228	94.90 465	97.72 444	99.58 269	99.07 269	99.64 214	99.62 259	98.19 268	99.93 118	98.41 255	99.95 111	99.55 228

Gipuma		99.57 101	99.59 94	99.49 237	99.98 3	99.71 100	99.72 33	99.84 89	99.81 92	99.94 48	99.78 131	98.91 164	99.71 407	98.41 255	99.95 111	99.05 403
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 404	96.91 413	98.74 392	97.72 486	97.57 404	97.60 451	97.36 469	98.00 385	99.21 349	98.02 468	90.04 446	99.79 365	98.37 257	95.89 486	98.86 430

PM-MVS		99.36 179	99.29 183	99.58 196	99.83 84	99.66 120	98.95 305	99.86 76	98.85 299	99.81 116	99.73 166	98.40 244	99.92 149	98.36 258	99.83 222	99.17 370

baseline1		97.73 387	97.33 398	98.96 358	99.30 372	97.73 400	99.40 126	98.42 440	99.33 223	99.46 288	99.21 397	91.18 428	99.82 338	98.35 259	91.26 489	99.32 334

MVS-HIRNet		97.86 379	98.22 349	96.76 457	99.28 377	91.53 484	98.38 388	92.60 489	99.13 262	99.31 330	99.96 15	97.18 331	99.68 430	98.34 260	99.83 222	99.07 401

GA-MVS		97.99 377	97.68 387	98.93 364	99.52 295	98.04 382	97.19 469	99.05 407	98.32 369	98.81 394	98.97 429	89.89 448	99.41 476	98.33 261	99.05 412	99.34 329

Fast-Effi-MVS+		99.02 270	98.87 285	99.46 248	99.38 343	99.50 178	99.04 268	99.79 131	97.17 435	98.62 412	98.74 447	99.34 83	99.95 80	98.32 262	99.41 377	98.92 423

MDA-MVSNet_test_wron		98.95 289	98.99 265	98.85 380	99.64 234	97.16 421	98.23 399	99.33 361	98.93 288	99.56 254	99.66 224	97.39 320	99.83 322	98.29 263	99.88 183	99.55 228

N_pmnet		98.73 314	98.53 321	99.35 288	99.72 186	98.67 326	98.34 390	94.65 482	98.35 363	99.79 129	99.68 215	98.03 279	99.93 118	98.28 264	99.92 145	99.44 297

ET-MVSNet_ETH3D		96.78 416	96.07 426	98.91 370	99.26 382	97.92 391	97.70 447	96.05 477	97.96 392	92.37 490	98.43 460	87.06 456	99.90 197	98.27 265	97.56 471	98.91 424

thisisatest0530		97.45 399	96.95 410	98.94 361	99.68 219	97.73 400	99.09 253	94.19 485	98.61 333	99.56 254	99.30 376	84.30 470	99.93 118	98.27 265	99.54 356	99.16 372

YYNet1		98.95 289	98.99 265	98.84 382	99.64 234	97.14 423	98.22 400	99.32 363	98.92 290	99.59 241	99.66 224	97.40 318	99.83 322	98.27 265	99.90 159	99.55 228

reproduce_model		99.50 123	99.40 148	99.83 41	99.60 243	99.83 35	99.12 240	99.68 202	99.49 182	99.80 123	99.79 119	99.01 145	99.93 118	98.24 268	99.82 232	99.73 93

ACMM	98.09 11	99.46 142	99.38 152	99.72 120	99.80 114	99.69 112	99.13 235	99.65 222	98.99 276	99.64 214	99.72 174	99.39 70	99.86 268	98.23 269	99.81 242	99.60 203
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 286	98.87 285	99.24 323	99.57 265	98.40 355	98.12 409	99.18 395	98.28 371	99.63 219	99.13 403	98.02 280	99.97 44	98.22 270	99.69 305	99.35 325

3Dnovator	99.15 2	99.43 153	99.36 160	99.65 157	99.39 340	99.42 204	99.70 38	99.56 276	99.23 241	99.35 316	99.80 108	99.17 107	99.95 80	98.21 271	99.84 214	99.59 210

Fast-Effi-MVS+-dtu		99.20 225	99.12 215	99.43 259	99.25 383	99.69 112	99.05 263	99.82 104	99.50 180	98.97 374	99.05 415	98.98 152	99.98 27	98.20 272	99.24 401	98.62 445

MS-PatchMatch		99.00 279	98.97 269	99.09 342	99.11 411	98.19 368	98.76 340	99.33 361	98.49 346	99.44 290	99.58 290	98.21 265	99.69 418	98.20 272	99.62 328	99.39 314

TSAR-MVS + GP.		99.12 248	99.04 247	99.38 278	99.34 361	99.16 270	98.15 405	99.29 371	98.18 378	99.63 219	99.62 259	99.18 105	99.68 430	98.20 272	99.74 280	99.30 340

DP-MVS		99.48 130	99.39 149	99.74 101	99.57 265	99.62 140	99.29 171	99.61 243	99.87 63	99.74 168	99.76 148	98.69 194	99.87 249	98.20 272	99.80 249	99.75 87

MVP-Stereo		99.16 238	99.08 230	99.43 259	99.48 313	99.07 286	99.08 256	99.55 282	98.63 329	99.31 330	99.68 215	98.19 268	99.78 368	98.18 276	99.58 344	99.45 283
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 153	99.30 178	99.80 64	99.83 84	99.81 48	99.52 93	99.70 191	98.35 363	99.51 276	99.50 322	99.31 87	99.88 234	98.18 276	99.84 214	99.69 117

MDA-MVSNet-bldmvs		99.06 261	99.05 241	99.07 347	99.80 114	97.83 395	98.89 314	99.72 179	99.29 229	99.63 219	99.70 194	96.47 352	99.89 219	98.17 278	99.82 232	99.50 263

JIA-IIPM		98.06 373	97.92 376	98.50 405	98.59 463	97.02 425	98.80 334	98.51 435	99.88 61	97.89 452	99.87 56	91.89 421	99.90 197	98.16 279	97.68 470	98.59 448

EIA-MVS		99.12 248	99.01 254	99.45 251	99.36 348	99.62 140	99.34 147	99.79 131	98.41 352	98.84 391	98.89 437	98.75 186	99.84 303	98.15 280	99.51 362	98.89 427

miper_lstm_enhance		98.65 322	98.60 309	98.82 387	99.20 393	97.33 417	97.78 440	99.66 212	99.01 275	99.59 241	99.50 322	94.62 387	99.85 287	98.12 281	99.90 159	99.26 346

reproduce-ours		99.46 142	99.35 164	99.82 46	99.56 276	99.83 35	99.05 263	99.65 222	99.45 196	99.78 133	99.78 131	98.93 158	99.93 118	98.11 282	99.81 242	99.70 105

our_new_method		99.46 142	99.35 164	99.82 46	99.56 276	99.83 35	99.05 263	99.65 222	99.45 196	99.78 133	99.78 131	98.93 158	99.93 118	98.11 282	99.81 242	99.70 105

Effi-MVS+-dtu		99.07 260	98.92 278	99.52 227	98.89 437	99.78 58	99.15 224	99.66 212	99.34 220	98.92 381	99.24 392	97.69 304	99.98 27	98.11 282	99.28 394	98.81 434

tpm		97.15 408	96.95 410	97.75 437	98.91 433	94.24 468	99.32 156	97.96 456	97.71 409	98.29 431	99.32 371	86.72 462	99.92 149	98.10 285	96.24 484	99.09 390

DeepPCF-MVS	98.42 6	99.18 232	99.02 250	99.67 143	99.22 388	99.75 79	97.25 467	99.47 320	98.72 318	99.66 208	99.70 194	99.29 89	99.63 451	98.07 286	99.81 242	99.62 186

ppachtmachnet_test		98.89 297	99.12 215	98.20 421	99.66 228	95.24 461	97.63 449	99.68 202	99.08 267	99.78 133	99.62 259	98.65 202	99.88 234	98.02 287	99.96 87	99.48 272

tpmrst		97.73 387	98.07 362	96.73 460	98.71 459	92.00 479	99.10 248	98.86 414	98.52 342	98.92 381	99.54 311	91.90 420	99.82 338	98.02 287	99.03 414	98.37 462

CSCG		99.37 175	99.29 183	99.60 190	99.71 190	99.46 189	99.43 120	99.85 82	98.79 309	99.41 303	99.60 277	98.92 161	99.92 149	98.02 287	99.92 145	99.43 303

eth_miper_zixun_eth		98.68 320	98.71 301	98.60 400	99.10 413	96.84 431	97.52 457	99.54 288	98.94 284	99.58 243	99.48 329	96.25 363	99.76 385	98.01 290	99.93 139	99.21 358

Patchmtry		98.78 308	98.54 320	99.49 237	98.89 437	99.19 264	99.32 156	99.67 207	99.65 144	99.72 178	99.79 119	91.87 422	99.95 80	98.00 291	99.97 73	99.33 330

PVSNet_BlendedMVS		99.03 268	99.01 254	99.09 342	99.54 281	97.99 384	98.58 361	99.82 104	97.62 412	99.34 320	99.71 184	98.52 227	99.77 381	97.98 292	99.97 73	99.52 255

PVSNet_Blended		98.70 318	98.59 311	99.02 352	99.54 281	97.99 384	97.58 452	99.82 104	95.70 460	99.34 320	98.98 427	98.52 227	99.77 381	97.98 292	99.83 222	99.30 340

cl____		98.54 334	98.41 331	98.92 365	99.03 423	97.80 398	97.46 459	99.59 260	98.90 292	99.60 238	99.46 336	93.85 394	99.78 368	97.97 294	99.89 173	99.17 370

DIV-MVS_self_test		98.54 334	98.42 330	98.92 365	99.03 423	97.80 398	97.46 459	99.59 260	98.90 292	99.60 238	99.46 336	93.87 393	99.78 368	97.97 294	99.89 173	99.18 367

AUN-MVS		97.82 381	97.38 397	99.14 336	99.27 379	98.53 346	98.72 345	99.02 409	98.10 380	97.18 471	99.03 421	89.26 450	99.85 287	97.94 296	97.91 466	99.03 407

FA-MVS(test-final)		98.52 336	98.32 341	99.10 341	99.48 313	98.67 326	99.77 19	98.60 431	97.35 427	99.63 219	99.80 108	93.07 406	99.84 303	97.92 297	99.30 391	98.78 437

ambc				99.20 327	99.35 352	98.53 346	99.17 215	99.46 323		99.67 202	99.80 108	98.46 234	99.70 411	97.92 297	99.70 297	99.38 316

USDC		98.96 286	98.93 274	99.05 350	99.54 281	97.99 384	97.07 474	99.80 122	98.21 375	99.75 158	99.77 141	98.43 237	99.64 449	97.90 299	99.88 183	99.51 257

OPM-MVS		99.26 202	99.13 211	99.63 171	99.70 205	99.61 150	98.58 361	99.48 317	98.50 344	99.52 269	99.63 250	99.14 114	99.76 385	97.89 300	99.77 267	99.51 257
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 191	99.17 203	99.77 78	99.69 211	99.80 52	99.14 228	99.31 367	99.16 256	99.62 229	99.61 269	98.35 248	99.91 178	97.88 301	99.72 292	99.61 199
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	99.70 205	99.79 55	99.14 228	99.61 243					99.92 149	97.88 301	99.72 292	99.77 79

c3_l		98.72 315	98.71 301	98.72 393	99.12 406	97.22 420	97.68 448	99.56 276	98.90 292	99.54 262	99.48 329	96.37 358	99.73 400	97.88 301	99.88 183	99.21 358

3Dnovator+	98.92 3	99.35 181	99.24 195	99.67 143	99.35 352	99.47 183	99.62 67	99.50 312	99.44 198	99.12 362	99.78 131	98.77 183	99.94 97	97.87 304	99.72 292	99.62 186

miper_ehance_all_eth		98.59 329	98.59 311	98.59 401	98.98 429	97.07 424	97.49 458	99.52 303	98.50 344	99.52 269	99.37 357	96.41 356	99.71 407	97.86 305	99.62 328	99.00 414

WTY-MVS		98.59 329	98.37 335	99.26 318	99.43 331	98.40 355	98.74 343	99.13 402	98.10 380	99.21 349	99.24 392	94.82 384	99.90 197	97.86 305	98.77 430	99.49 268

APD_test1		99.36 179	99.28 186	99.61 186	99.89 39	99.89 10	99.32 156	99.74 166	99.18 249	99.69 190	99.75 156	98.41 240	99.84 303	97.85 307	99.70 297	99.10 385

SED-MVS		99.40 164	99.28 186	99.77 78	99.69 211	99.82 43	99.20 200	99.54 288	99.13 262	99.82 109	99.63 250	98.91 164	99.92 149	97.85 307	99.70 297	99.58 215

test_241102_TWO								99.54 288	99.13 262	99.76 153	99.63 250	98.32 253	99.92 149	97.85 307	99.69 305	99.75 87

MVS_111021_HR		99.12 248	99.02 250	99.40 272	99.50 303	99.11 276	97.92 433	99.71 182	98.76 316	99.08 366	99.47 333	99.17 107	99.54 465	97.85 307	99.76 269	99.54 238

MTAPA		99.35 181	99.20 199	99.80 64	99.81 105	99.81 48	99.33 153	99.53 298	99.27 233	99.42 297	99.63 250	98.21 265	99.95 80	97.83 311	99.79 254	99.65 156

MSC_two_6792asdad				99.74 101	99.03 423	99.53 172		99.23 384					99.92 149	97.77 312	99.69 305	99.78 75

No_MVS				99.74 101	99.03 423	99.53 172		99.23 384					99.92 149	97.77 312	99.69 305	99.78 75

TESTMET0.1,1		96.24 431	95.84 432	97.41 446	98.24 474	93.84 471	97.38 461	95.84 478	98.43 349	97.81 458	98.56 455	79.77 479	99.89 219	97.77 312	98.77 430	98.52 454

ACMH+	98.40 8	99.50 123	99.43 142	99.71 126	99.86 59	99.76 71	99.32 156	99.77 147	99.53 175	99.77 145	99.76 148	99.26 95	99.78 368	97.77 312	99.88 183	99.60 203

IU-MVS					99.69 211	99.77 64		99.22 387	97.50 419	99.69 190				97.75 316	99.70 297	99.77 79

114514_t		98.49 341	98.11 359	99.64 164	99.73 181	99.58 161	99.24 189	99.76 155	89.94 483	99.42 297	99.56 301	97.76 301	99.86 268	97.74 317	99.82 232	99.47 276

DVP-MVS++		99.38 171	99.25 193	99.77 78	99.03 423	99.77 64	99.74 27	99.61 243	99.18 249	99.76 153	99.61 269	99.00 146	99.92 149	97.72 318	99.60 338	99.62 186

test_0728_THIRD									99.18 249	99.62 229	99.61 269	98.58 210	99.91 178	97.72 318	99.80 249	99.77 79

EGC-MVSNET		89.05 456	85.52 459	99.64 164	99.89 39	99.78 58	99.56 87	99.52 303	24.19 492	49.96 493	99.83 84	99.15 111	99.92 149	97.71 320	99.85 209	99.21 358

miper_enhance_ethall		98.03 374	97.94 374	98.32 415	98.27 473	96.43 439	96.95 476	99.41 335	96.37 451	99.43 294	98.96 431	94.74 385	99.69 418	97.71 320	99.62 328	98.83 433

TSAR-MVS + MP.		99.34 186	99.24 195	99.63 171	99.82 93	99.37 221	99.26 182	99.35 354	98.77 313	99.57 246	99.70 194	99.27 94	99.88 234	97.71 320	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		97.56 395	97.28 399	98.40 410	98.37 471	96.75 432	97.24 468	99.37 350	97.31 429	99.41 303	99.22 394	87.30 454	99.37 477	97.70 323	99.62 328	99.08 396

MP-MVS-pluss		99.14 243	98.92 278	99.80 64	99.83 84	99.83 35	98.61 354	99.63 233	96.84 444	99.44 290	99.58 290	98.81 174	99.91 178	97.70 323	99.82 232	99.67 133
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 196	99.11 218	99.79 71	99.75 169	99.81 48	98.95 305	99.53 298	98.27 372	99.53 267	99.73 166	98.75 186	99.87 249	97.70 323	99.83 222	99.68 124

UnsupCasMVSNet_bld		98.55 333	98.27 347	99.40 272	99.56 276	99.37 221	97.97 429	99.68 202	97.49 420	99.08 366	99.35 367	95.41 378	99.82 338	97.70 323	98.19 458	99.01 413

MVS_111021_LR		99.13 245	99.03 249	99.42 261	99.58 255	99.32 233	97.91 435	99.73 170	98.68 323	99.31 330	99.48 329	99.09 123	99.66 440	97.70 323	99.77 267	99.29 343

IS-MVSNet		99.03 268	98.85 287	99.55 215	99.80 114	99.25 247	99.73 30	99.15 399	99.37 216	99.61 235	99.71 184	94.73 386	99.81 354	97.70 323	99.88 183	99.58 215

MED-MVS test				99.74 101	99.76 153	99.65 126	99.38 131	99.78 141	99.58 168	99.81 116	99.66 224		99.90 197	97.69 329	99.79 254	99.67 133

MED-MVS		99.45 146	99.36 160	99.74 101	99.76 153	99.65 126	99.38 131	99.78 141	99.31 226	99.81 116	99.66 224	99.02 143	99.90 197	97.69 329	99.79 254	99.67 133

ME-MVS		99.26 202	99.10 226	99.73 112	99.60 243	99.65 126	98.75 342	99.45 328	99.31 226	99.65 211	99.66 224	98.00 285	99.86 268	97.69 329	99.79 254	99.67 133

test-LLR		97.15 408	96.95 410	97.74 438	98.18 476	95.02 463	97.38 461	96.10 474	98.00 385	97.81 458	98.58 452	90.04 446	99.91 178	97.69 329	98.78 428	98.31 463

test-mter		96.23 432	95.73 435	97.74 438	98.18 476	95.02 463	97.38 461	96.10 474	97.90 397	97.81 458	98.58 452	79.12 482	99.91 178	97.69 329	98.78 428	98.31 463

MonoMVSNet		98.23 363	98.32 341	97.99 426	98.97 430	96.62 434	99.49 106	98.42 440	99.62 153	99.40 308	99.79 119	95.51 376	98.58 488	97.68 334	95.98 485	98.76 440

XVS		99.27 200	99.11 218	99.75 96	99.71 190	99.71 100	99.37 139	99.61 243	99.29 229	98.76 401	99.47 333	98.47 231	99.88 234	97.62 335	99.73 286	99.67 133

X-MVStestdata		96.09 436	94.87 449	99.75 96	99.71 190	99.71 100	99.37 139	99.61 243	99.29 229	98.76 401	61.30 501	98.47 231	99.88 234	97.62 335	99.73 286	99.67 133

SMA-MVS		99.19 228	99.00 258	99.73 112	99.46 323	99.73 90	99.13 235	99.52 303	97.40 424	99.57 246	99.64 235	98.93 158	99.83 322	97.61 337	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		96.71 419	96.79 418	96.46 464	98.90 434	90.71 490	99.41 121	98.68 424	94.69 473	98.14 443	99.34 370	86.32 464	99.80 362	97.60 338	98.07 464	98.88 428

PVSNet	97.47 15	98.42 347	98.44 328	98.35 412	99.46 323	96.26 443	96.70 480	99.34 357	97.68 410	99.00 373	99.13 403	97.40 318	99.72 402	97.59 339	99.68 310	99.08 396

new_pmnet		98.88 298	98.89 283	98.84 382	99.70 205	97.62 403	98.15 405	99.50 312	97.98 388	99.62 229	99.54 311	98.15 271	99.94 97	97.55 340	99.84 214	98.95 418

IB-MVS	95.41 20	95.30 451	94.46 455	97.84 434	98.76 455	95.33 459	97.33 464	96.07 476	96.02 455	95.37 487	97.41 479	76.17 487	99.96 69	97.54 341	95.44 488	98.22 468
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		99.24 207	99.11 218	99.61 186	98.38 470	99.79 55	99.57 85	99.68 202	99.61 158	99.15 357	99.71 184	98.70 193	99.91 178	97.54 341	99.68 310	99.13 382

ZNCC-MVS		99.22 218	99.04 247	99.77 78	99.76 153	99.73 90	99.28 173	99.56 276	98.19 377	99.14 359	99.29 379	98.84 173	99.92 149	97.53 343	99.80 249	99.64 168

CP-MVS		99.23 209	99.05 241	99.75 96	99.66 228	99.66 120	99.38 131	99.62 236	98.38 356	99.06 370	99.27 382	98.79 179	99.94 97	97.51 344	99.82 232	99.66 147

SD-MVS		99.01 276	99.30 178	98.15 422	99.50 303	99.40 212	98.94 307	99.61 243	99.22 245	99.75 158	99.82 91	99.54 54	95.51 492	97.48 345	99.87 196	99.54 238
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		98.49 341	98.29 346	99.11 339	98.96 431	98.42 354	97.54 453	99.32 363	97.53 417	98.47 424	98.15 467	97.88 291	99.82 338	97.46 346	99.24 401	99.09 390

DeepC-MVS_fast	98.47 5	99.23 209	99.12 215	99.56 208	99.28 377	99.22 257	98.99 293	99.40 342	99.08 267	99.58 243	99.64 235	98.90 167	99.83 322	97.44 347	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		99.25 204	99.08 230	99.76 85	99.73 181	99.70 108	99.31 161	99.59 260	98.36 358	99.36 314	99.37 357	98.80 178	99.91 178	97.43 348	99.75 273	99.68 124

ACMMPR		99.23 209	99.06 236	99.76 85	99.74 177	99.69 112	99.31 161	99.59 260	98.36 358	99.35 316	99.38 354	98.61 206	99.93 118	97.43 348	99.75 273	99.67 133

Vis-MVSNet (Re-imp)		98.77 309	98.58 314	99.34 290	99.78 136	98.88 309	99.61 73	99.56 276	99.11 266	99.24 343	99.56 301	93.00 408	99.78 368	97.43 348	99.89 173	99.35 325

MIMVSNet		98.43 346	98.20 351	99.11 339	99.53 288	98.38 359	99.58 82	98.61 429	98.96 280	99.33 322	99.76 148	90.92 432	99.81 354	97.38 351	99.76 269	99.15 374

WB-MVSnew		98.34 357	98.14 357	98.96 358	98.14 479	97.90 392	98.27 395	97.26 470	98.63 329	98.80 396	98.00 470	97.77 299	99.90 197	97.37 352	98.98 417	99.09 390

XVG-OURS-SEG-HR		99.16 238	98.99 265	99.66 150	99.84 76	99.64 132	98.25 398	99.73 170	98.39 355	99.63 219	99.43 341	99.70 31	99.90 197	97.34 353	98.64 441	99.44 297

COLMAP_ROB	98.06 12	99.45 146	99.37 155	99.70 131	99.83 84	99.70 108	99.38 131	99.78 141	99.53 175	99.67 202	99.78 131	99.19 104	99.86 268	97.32 354	99.87 196	99.55 228
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 270	98.81 294	99.65 157	99.58 255	99.49 179	98.58 361	99.07 404	98.40 354	99.04 371	99.25 387	98.51 229	99.80 362	97.31 355	99.51 362	99.65 156

region2R		99.23 209	99.05 241	99.77 78	99.76 153	99.70 108	99.31 161	99.59 260	98.41 352	99.32 325	99.36 362	98.73 190	99.93 118	97.29 356	99.74 280	99.67 133

APD-MVS_3200maxsize		99.31 192	99.16 204	99.74 101	99.53 288	99.75 79	99.27 177	99.61 243	99.19 248	99.57 246	99.64 235	98.76 184	99.90 197	97.29 356	99.62 328	99.56 224

TAPA-MVS	97.92 13	98.03 374	97.55 391	99.46 248	99.47 319	99.44 197	98.50 376	99.62 236	86.79 484	99.07 369	99.26 385	98.26 258	99.62 452	97.28 358	99.73 286	99.31 338
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 200	99.11 218	99.73 112	99.54 281	99.74 87	99.26 182	99.62 236	99.16 256	99.52 269	99.64 235	98.41 240	99.91 178	97.27 359	99.61 335	99.54 238

RE-MVS-def			99.13 211		99.54 281	99.74 87	99.26 182	99.62 236	99.16 256	99.52 269	99.64 235	98.57 211		97.27 359	99.61 335	99.54 238

testing11		96.05 438	95.41 441	97.97 428	98.78 452	95.27 460	98.59 359	98.23 450	98.86 298	96.56 478	96.91 489	75.20 488	99.69 418	97.26 361	98.29 453	98.93 421

test_yl		98.25 360	97.95 370	99.13 337	99.17 399	98.47 349	99.00 286	98.67 426	98.97 278	99.22 347	99.02 422	91.31 426	99.69 418	97.26 361	98.93 419	99.24 349

DCV-MVSNet		98.25 360	97.95 370	99.13 337	99.17 399	98.47 349	99.00 286	98.67 426	98.97 278	99.22 347	99.02 422	91.31 426	99.69 418	97.26 361	98.93 419	99.24 349

PHI-MVS		99.11 252	98.95 272	99.59 193	99.13 404	99.59 156	99.17 215	99.65 222	97.88 400	99.25 340	99.46 336	98.97 154	99.80 362	97.26 361	99.82 232	99.37 319

tfpnnormal		99.43 153	99.38 152	99.60 190	99.87 54	99.75 79	99.59 80	99.78 141	99.71 118	99.90 68	99.69 203	98.85 172	99.90 197	97.25 365	99.78 263	99.15 374

PatchmatchNet		97.65 391	97.80 381	97.18 453	98.82 447	92.49 477	99.17 215	98.39 443	98.12 379	98.79 398	99.58 290	90.71 438	99.89 219	97.23 366	99.41 377	99.16 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 282	98.80 296	99.56 208	99.25 383	99.43 201	98.54 371	99.27 375	98.58 335	98.80 396	99.43 341	98.53 224	99.70 411	97.22 367	99.59 342	99.54 238

testing3		96.48 425	95.63 437	99.01 353	99.23 387	97.81 396	98.90 313	99.10 403	98.72 318	97.84 457	97.92 471	72.44 492	99.85 287	97.21 368	99.33 387	99.35 325

HPM-MVS		99.25 204	99.07 234	99.78 75	99.81 105	99.75 79	99.61 73	99.67 207	97.72 408	99.35 316	99.25 387	99.23 100	99.92 149	97.21 368	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
mPP-MVS		99.19 228	99.00 258	99.76 85	99.76 153	99.68 115	99.38 131	99.54 288	98.34 367	99.01 372	99.50 322	98.53 224	99.93 118	97.18 370	99.78 263	99.66 147

ACMMP		99.25 204	99.08 230	99.74 101	99.79 128	99.68 115	99.50 101	99.65 222	98.07 383	99.52 269	99.69 203	98.57 211	99.92 149	97.18 370	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		96.23 432	95.74 434	97.70 440	98.86 441	95.59 456	98.66 351	98.14 452	98.96 280	97.67 463	97.06 486	76.78 485	98.92 484	97.10 372	98.41 450	98.58 450

thisisatest0515		96.98 412	96.42 420	98.66 397	99.42 336	97.47 409	97.27 466	94.30 484	97.24 431	99.15 357	98.86 439	85.01 466	99.87 249	97.10 372	99.39 379	98.63 444

XVG-ACMP-BASELINE		99.23 209	99.10 226	99.63 171	99.82 93	99.58 161	98.83 326	99.72 179	98.36 358	99.60 238	99.71 184	98.92 161	99.91 178	97.08 374	99.84 214	99.40 311

MSDG		99.08 257	98.98 268	99.37 281	99.60 243	99.13 273	97.54 453	99.74 166	98.84 302	99.53 267	99.55 309	99.10 121	99.79 365	97.07 375	99.86 204	99.18 367

SteuartSystems-ACMMP		99.30 193	99.14 209	99.76 85	99.87 54	99.66 120	99.18 210	99.60 254	98.55 337	99.57 246	99.67 219	99.03 142	99.94 97	97.01 376	99.80 249	99.69 117
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 434	95.78 433	97.49 442	98.53 465	93.83 472	98.04 419	93.94 487	98.96 280	98.46 425	98.17 466	79.86 477	99.87 249	96.99 377	99.06 410	98.78 437

EPMVS		96.53 422	96.32 421	97.17 454	98.18 476	92.97 476	99.39 128	89.95 493	98.21 375	98.61 413	99.59 287	86.69 463	99.72 402	96.99 377	99.23 403	98.81 434

MSP-MVS		99.04 267	98.79 297	99.81 54	99.78 136	99.73 90	99.35 146	99.57 271	98.54 340	99.54 262	98.99 424	96.81 341	99.93 118	96.97 379	99.53 358	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++		98.96 286	98.70 304	99.74 101	99.52 295	99.71 100	98.86 319	99.19 394	98.47 348	98.59 415	99.06 414	98.08 277	99.91 178	96.94 380	99.60 338	99.60 203

SR-MVS		99.19 228	99.00 258	99.74 101	99.51 297	99.72 95	99.18 210	99.60 254	98.85 299	99.47 284	99.58 290	98.38 245	99.92 149	96.92 381	99.54 356	99.57 221

PGM-MVS		99.20 225	99.01 254	99.77 78	99.75 169	99.71 100	99.16 221	99.72 179	97.99 387	99.42 297	99.60 277	98.81 174	99.93 118	96.91 382	99.74 280	99.66 147

HY-MVS	98.23 9	98.21 367	97.95 370	98.99 354	99.03 423	98.24 363	99.61 73	98.72 422	96.81 445	98.73 403	99.51 319	94.06 391	99.86 268	96.91 382	98.20 456	98.86 430

MDTV_nov1_ep13			97.73 385		98.70 460	90.83 488	99.15 224	98.02 455	98.51 343	98.82 393	99.61 269	90.98 431	99.66 440	96.89 384	98.92 421

GST-MVS		99.16 238	98.96 271	99.75 96	99.73 181	99.73 90	99.20 200	99.55 282	98.22 374	99.32 325	99.35 367	98.65 202	99.91 178	96.86 385	99.74 280	99.62 186

test_post1							99.14 228				51.63 503	89.54 449	99.82 338	96.86 385

SCA		98.11 370	98.36 336	97.36 447	99.20 393	92.99 475	98.17 403	98.49 437	98.24 373	99.10 365	99.57 297	96.01 368	99.94 97	96.86 385	99.62 328	99.14 379

UBG		96.53 422	95.95 428	98.29 419	98.87 440	96.31 442	98.48 379	98.07 453	98.83 303	97.32 466	96.54 496	79.81 478	99.62 452	96.84 388	98.74 434	98.95 418

XVG-OURS		99.21 223	99.06 236	99.65 157	99.82 93	99.62 140	97.87 437	99.74 166	98.36 358	99.66 208	99.68 215	99.71 28	99.90 197	96.84 388	99.88 183	99.43 303

LCM-MVSNet-Re		99.28 196	99.15 208	99.67 143	99.33 366	99.76 71	99.34 147	99.97 20	98.93 288	99.91 63	99.79 119	98.68 195	99.93 118	96.80 390	99.56 347	99.30 340

RPSCF		99.18 232	99.02 250	99.64 164	99.83 84	99.85 22	99.44 118	99.82 104	98.33 368	99.50 279	99.78 131	97.90 289	99.65 447	96.78 391	99.83 222	99.44 297

旧先验2							97.94 431		95.33 464	98.94 377			99.88 234	96.75 392

MDTV_nov1_ep13_2view						91.44 485	99.14 228		97.37 426	99.21 349		91.78 424		96.75 392		99.03 407

CLD-MVS		98.76 310	98.57 315	99.33 293	99.57 265	98.97 296	97.53 455	99.55 282	96.41 449	99.27 337	99.13 403	99.07 130	99.78 368	96.73 394	99.89 173	99.23 353
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 371	97.98 368	98.48 406	99.27 379	96.48 437	99.40 126	99.07 404	98.81 306	99.23 344	99.57 297	90.11 445	99.87 249	96.69 395	99.64 323	99.09 390

baseline2		96.83 415	96.28 422	98.46 408	99.09 416	96.91 428	98.83 326	93.87 488	97.23 432	96.23 483	98.36 461	88.12 453	99.90 197	96.68 396	98.14 461	98.57 452

cascas		96.99 411	96.82 417	97.48 443	97.57 491	95.64 454	96.43 482	99.56 276	91.75 479	97.13 473	97.61 478	95.58 373	98.63 486	96.68 396	99.11 407	98.18 472

PC_three_1452									97.56 413	99.68 195	99.41 344	99.09 123	97.09 489	96.66 398	99.60 338	99.62 186

LPG-MVS_test		99.22 218	99.05 241	99.74 101	99.82 93	99.63 138	99.16 221	99.73 170	97.56 413	99.64 214	99.69 203	99.37 76	99.89 219	96.66 398	99.87 196	99.69 117

LGP-MVS_train				99.74 101	99.82 93	99.63 138		99.73 170	97.56 413	99.64 214	99.69 203	99.37 76	99.89 219	96.66 398	99.87 196	99.69 117

ETVMVS		96.14 435	95.22 446	98.89 377	98.80 448	98.01 383	98.66 351	98.35 446	98.71 320	97.18 471	96.31 500	74.23 491	99.75 394	96.64 401	98.13 463	98.90 425

TinyColmap		98.97 283	98.93 274	99.07 347	99.46 323	98.19 368	97.75 441	99.75 160	98.79 309	99.54 262	99.70 194	98.97 154	99.62 452	96.63 402	99.83 222	99.41 308

LF4IMVS		99.01 276	98.92 278	99.27 315	99.71 190	99.28 239	98.59 359	99.77 147	98.32 369	99.39 310	99.41 344	98.62 204	99.84 303	96.62 403	99.84 214	98.69 443

NCCC		98.82 304	98.57 315	99.58 196	99.21 390	99.31 234	98.61 354	99.25 380	98.65 326	98.43 426	99.26 385	97.86 292	99.81 354	96.55 404	99.27 397	99.61 199

OPU-MVS				99.29 307	99.12 406	99.44 197	99.20 200				99.40 348	99.00 146	98.84 485	96.54 405	99.60 338	99.58 215

F-COLMAP		98.74 312	98.45 327	99.62 180	99.57 265	99.47 183	98.84 323	99.65 222	96.31 452	98.93 378	99.19 400	97.68 305	99.87 249	96.52 406	99.37 382	99.53 244

testing99		95.86 443	95.19 447	97.87 432	98.76 455	95.03 462	98.62 353	98.44 439	98.68 323	96.67 477	96.66 495	74.31 490	99.69 418	96.51 407	98.03 465	98.90 425

ADS-MVSNet2		97.78 385	97.66 389	98.12 424	99.14 402	95.36 458	99.22 197	98.75 421	96.97 440	98.25 433	99.64 235	90.90 433	99.94 97	96.51 407	99.56 347	99.08 396

ADS-MVSNet		97.72 390	97.67 388	97.86 433	99.14 402	94.65 466	99.22 197	98.86 414	96.97 440	98.25 433	99.64 235	90.90 433	99.84 303	96.51 407	99.56 347	99.08 396

PatchMatch-RL		98.68 320	98.47 324	99.30 306	99.44 328	99.28 239	98.14 407	99.54 288	97.12 438	99.11 363	99.25 387	97.80 297	99.70 411	96.51 407	99.30 391	98.93 421

CMPMVS	77.52 23	98.50 339	98.19 354	99.41 269	98.33 472	99.56 165	99.01 280	99.59 260	95.44 462	99.57 246	99.80 108	95.64 371	99.46 475	96.47 411	99.92 145	99.21 358
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 439	95.32 444	98.02 425	98.76 455	95.39 457	98.38 388	98.65 428	98.82 304	96.84 474	96.71 494	75.06 489	99.71 407	96.46 412	98.23 455	98.98 415

SF-MVS		99.10 255	98.93 274	99.62 180	99.58 255	99.51 177	99.13 235	99.65 222	97.97 389	99.42 297	99.61 269	98.86 171	99.87 249	96.45 413	99.68 310	99.49 268

FE-MVS		97.85 380	97.42 396	99.15 333	99.44 328	98.75 321	99.77 19	98.20 451	95.85 457	99.33 322	99.80 108	88.86 451	99.88 234	96.40 414	99.12 406	98.81 434

DPE-MVS		99.14 243	98.92 278	99.82 46	99.57 265	99.77 64	98.74 343	99.60 254	98.55 337	99.76 153	99.69 203	98.23 263	99.92 149	96.39 415	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					97.59 489	89.02 495			93.47 475		98.30 462		99.84 303	96.38 416

AllTest		99.21 223	99.07 234	99.63 171	99.78 136	99.64 132	99.12 240	99.83 98	98.63 329	99.63 219	99.72 174	98.68 195	99.75 394	96.38 416	99.83 222	99.51 257

TestCases				99.63 171	99.78 136	99.64 132		99.83 98	98.63 329	99.63 219	99.72 174	98.68 195	99.75 394	96.38 416	99.83 222	99.51 257

testdata				99.42 261	99.51 297	98.93 303		99.30 370	96.20 453	98.87 388	99.40 348	98.33 252	99.89 219	96.29 419	99.28 394	99.44 297

dp		96.86 414	97.07 406	96.24 466	98.68 461	90.30 493	99.19 206	98.38 444	97.35 427	98.23 435	99.59 287	87.23 455	99.82 338	96.27 420	98.73 437	98.59 448

tpmvs		97.39 403	97.69 386	96.52 462	98.41 469	91.76 481	99.30 164	98.94 413	97.74 406	97.85 456	99.55 309	92.40 417	99.73 400	96.25 421	98.73 437	98.06 474

KD-MVS_2432*1600		95.89 440	95.41 441	97.31 450	94.96 493	93.89 469	97.09 472	99.22 387	97.23 432	98.88 385	99.04 417	79.23 480	99.54 465	96.24 422	96.81 475	98.50 458

miper_refine_blended		95.89 440	95.41 441	97.31 450	94.96 493	93.89 469	97.09 472	99.22 387	97.23 432	98.88 385	99.04 417	79.23 480	99.54 465	96.24 422	96.81 475	98.50 458

ACMP	97.51 14	99.05 264	98.84 289	99.67 143	99.78 136	99.55 169	98.88 315	99.66 212	97.11 439	99.47 284	99.60 277	99.07 130	99.89 219	96.18 424	99.85 209	99.58 215
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 294	98.72 300	99.44 255	99.39 340	99.42 204	98.58 361	99.64 230	97.31 429	99.44 290	99.62 259	98.59 208	99.69 418	96.17 425	99.79 254	99.22 355

DP-MVS Recon		98.50 339	98.23 348	99.31 302	99.49 308	99.46 189	98.56 367	99.63 233	94.86 471	98.85 390	99.37 357	97.81 296	99.59 459	96.08 426	99.44 372	98.88 428

tpm cat1		96.78 416	96.98 409	96.16 467	98.85 442	90.59 491	99.08 256	99.32 363	92.37 477	97.73 462	99.46 336	91.15 429	99.69 418	96.07 427	98.80 427	98.21 469

tpm2		96.35 428	96.22 423	96.73 460	98.88 439	91.75 482	99.21 199	98.51 435	93.27 476	97.89 452	99.21 397	84.83 467	99.70 411	96.04 428	98.18 459	98.75 441

dmvs_re		98.69 319	98.48 323	99.31 302	99.55 279	99.42 204	99.54 90	98.38 444	99.32 224	98.72 404	98.71 448	96.76 343	99.21 479	96.01 429	99.35 385	99.31 338

test_0402		99.22 218	99.14 209	99.45 251	99.79 128	99.43 201	99.28 173	99.68 202	99.54 173	99.40 308	99.56 301	99.07 130	99.82 338	96.01 429	99.96 87	99.11 383

ITE_SJBPF				99.38 278	99.63 236	99.44 197		99.73 170	98.56 336	99.33 322	99.53 313	98.88 168	99.68 430	96.01 429	99.65 321	99.02 412

test_prior2							97.95 430		97.87 401	98.05 445	99.05 415	97.90 289		95.99 432	99.49 367

testdata2													99.89 219	95.99 432

原ACMM1				99.37 281	99.47 319	98.87 312		99.27 375	96.74 447	98.26 432	99.32 371	97.93 288	99.82 338	95.96 434	99.38 380	99.43 303

新几何1				99.52 227	99.50 303	99.22 257		99.26 377	95.66 461	98.60 414	99.28 380	97.67 306	99.89 219	95.95 435	99.32 389	99.45 283

MP-MVS		99.06 261	98.83 291	99.76 85	99.76 153	99.71 100	99.32 156	99.50 312	98.35 363	98.97 374	99.48 329	98.37 246	99.92 149	95.95 435	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		95.60 450	94.59 453	98.61 399	98.66 462	97.45 412	98.54 371	97.90 459	98.53 341	96.54 479	96.47 497	70.62 495	99.81 354	95.91 437	98.15 460	98.56 453

wuyk23d		97.58 394	99.13 211	92.93 471	99.69 211	99.49 179	99.52 93	99.77 147	97.97 389	99.96 34	99.79 119	99.84 16	99.94 97	95.85 438	99.82 232	79.36 489

HQP_MVS		98.90 294	98.68 305	99.55 215	99.58 255	99.24 251	98.80 334	99.54 288	98.94 284	99.14 359	99.25 387	97.24 325	99.82 338	95.84 439	99.78 263	99.60 203

plane_prior5								99.54 288					99.82 338	95.84 439	99.78 263	99.60 203

无先验							98.01 422	99.23 384	95.83 458				99.85 287	95.79 441		99.44 297

CPTT-MVS		98.74 312	98.44 328	99.64 164	99.61 241	99.38 217	99.18 210	99.55 282	96.49 448	99.27 337	99.37 357	97.11 333	99.92 149	95.74 442	99.67 316	99.62 186

PLC	97.35 16	98.36 352	97.99 366	99.48 242	99.32 367	99.24 251	98.50 376	99.51 308	95.19 467	98.58 416	98.96 431	96.95 338	99.83 322	95.63 443	99.25 399	99.37 319
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 331	98.34 339	99.28 310	99.18 398	99.10 283	98.34 390	99.41 335	98.48 347	98.52 421	98.98 427	97.05 335	99.78 368	95.59 444	99.50 365	98.96 416

1314		98.00 376	97.90 378	98.27 420	98.90 434	97.45 412	99.30 164	99.06 406	94.98 468	97.21 470	99.12 407	98.43 237	99.67 435	95.58 445	98.56 444	97.71 478

PVSNet_0	95.53 19	95.85 444	95.31 445	97.47 444	98.78 452	93.48 474	95.72 484	99.40 342	96.18 454	97.37 465	97.73 473	95.73 370	99.58 460	95.49 446	81.40 490	99.36 322

MAR-MVS		98.24 362	97.92 376	99.19 328	98.78 452	99.65 126	99.17 215	99.14 400	95.36 463	98.04 446	98.81 444	97.47 315	99.72 402	95.47 447	99.06 410	98.21 469
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	98.23 363	97.89 379	99.26 318	99.19 395	99.26 244	99.65 62	99.69 199	91.33 481	98.14 443	99.77 141	98.28 255	99.96 69	95.41 448	99.55 351	98.58 450

train_agg		98.35 355	97.95 370	99.57 204	99.35 352	99.35 228	98.11 411	99.41 335	94.90 469	97.92 450	98.99 424	98.02 280	99.85 287	95.38 449	99.44 372	99.50 263

9.14			98.64 306		99.45 327		98.81 331	99.60 254	97.52 418	99.28 336	99.56 301	98.53 224	99.83 322	95.36 450	99.64 323

APD-MVS		98.87 299	98.59 311	99.71 126	99.50 303	99.62 140	99.01 280	99.57 271	96.80 446	99.54 262	99.63 250	98.29 254	99.91 178	95.24 451	99.71 295	99.61 199
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 440								95.20 452

AdaColmap		98.60 326	98.35 338	99.38 278	99.12 406	99.22 257	98.67 349	99.42 334	97.84 404	98.81 394	99.27 382	97.32 323	99.81 354	95.14 453	99.53 358	99.10 385

test9_res														95.10 454	99.44 372	99.50 263

CDPH-MVS		98.56 332	98.20 351	99.61 186	99.50 303	99.46 189	98.32 392	99.41 335	95.22 465	99.21 349	99.10 411	98.34 250	99.82 338	95.09 455	99.66 319	99.56 224

BH-untuned		98.22 365	98.09 360	98.58 403	99.38 343	97.24 419	98.55 368	98.98 412	97.81 405	99.20 354	98.76 446	97.01 336	99.65 447	94.83 456	98.33 451	98.86 430

BP-MVS														94.73 457

HQP-MVS		98.36 352	98.02 365	99.39 275	99.31 368	98.94 300	97.98 426	99.37 350	97.45 421	98.15 439	98.83 441	96.67 344	99.70 411	94.73 457	99.67 316	99.53 244

QAPM		98.40 350	97.99 366	99.65 157	99.39 340	99.47 183	99.67 53	99.52 303	91.70 480	98.78 400	99.80 108	98.55 215	99.95 80	94.71 459	99.75 273	99.53 244

agg_prior2														94.58 460	99.46 371	99.50 263

myMVS_eth3d		95.63 448	94.73 450	98.34 414	98.50 467	96.36 440	98.60 356	99.21 390	97.89 398	96.76 475	96.37 498	72.10 493	99.57 461	94.38 461	98.73 437	99.09 390

BH-RMVSNet		98.41 348	98.14 357	99.21 325	99.21 390	98.47 349	98.60 356	98.26 449	98.35 363	98.93 378	99.31 374	97.20 330	99.66 440	94.32 462	99.10 408	99.51 257

E-PMN		97.14 410	97.43 395	96.27 465	98.79 450	91.62 483	95.54 485	99.01 411	99.44 198	98.88 385	99.12 407	92.78 409	99.68 430	94.30 463	99.03 414	97.50 479

MG-MVS		98.52 336	98.39 333	98.94 361	99.15 401	97.39 416	98.18 401	99.21 390	98.89 295	99.23 344	99.63 250	97.37 321	99.74 397	94.22 464	99.61 335	99.69 117

API-MVS		98.38 351	98.39 333	98.35 412	98.83 444	99.26 244	99.14 228	99.18 395	98.59 334	98.66 409	98.78 445	98.61 206	99.57 461	94.14 465	99.56 347	96.21 486

PAPM_NR		98.36 352	98.04 363	99.33 293	99.48 313	98.93 303	98.79 337	99.28 374	97.54 416	98.56 420	98.57 454	97.12 332	99.69 418	94.09 466	98.90 425	99.38 316

ZD-MVS					99.43 331	99.61 150		99.43 332	96.38 450	99.11 363	99.07 413	97.86 292	99.92 149	94.04 467	99.49 367

DPM-MVS		98.28 358	97.94 374	99.32 298	99.36 348	99.11 276	97.31 465	98.78 420	96.88 442	98.84 391	99.11 410	97.77 299	99.61 457	94.03 468	99.36 383	99.23 353

gg-mvs-nofinetune		95.87 442	95.17 448	97.97 428	98.19 475	96.95 426	99.69 45	89.23 494	99.89 56	96.24 482	99.94 19	81.19 472	99.51 471	93.99 469	98.20 456	97.44 480

PMVS	92.94 21	98.82 304	98.81 294	98.85 380	99.84 76	97.99 384	99.20 200	99.47 320	99.71 118	99.42 297	99.82 91	98.09 275	99.47 473	93.88 470	99.85 209	99.07 401
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 413	97.28 399	95.99 469	98.76 455	91.03 487	95.26 487	98.61 429	99.34 220	98.92 381	98.88 438	93.79 395	99.66 440	92.87 471	99.05 412	97.30 483

BH-w/o		97.20 407	97.01 408	97.76 436	99.08 417	95.69 453	98.03 421	98.52 434	95.76 459	97.96 449	98.02 468	95.62 372	99.47 473	92.82 472	97.25 474	98.12 473

TR-MVS		97.44 400	97.15 404	98.32 415	98.53 465	97.46 410	98.47 380	97.91 458	96.85 443	98.21 436	98.51 458	96.42 354	99.51 471	92.16 473	97.29 473	97.98 475

OpenMVS_ROB	97.31 17	97.36 405	96.84 415	98.89 377	99.29 374	99.45 195	98.87 318	99.48 317	86.54 486	99.44 290	99.74 161	97.34 322	99.86 268	91.61 474	99.28 394	97.37 482

GG-mvs-BLEND				97.36 447	97.59 489	96.87 429	99.70 38	88.49 495		94.64 488	97.26 484	80.66 474	99.12 480	91.50 475	96.50 482	96.08 488

DeepMVS_CX				97.98 427	99.69 211	96.95 426		99.26 377	75.51 489	95.74 485	98.28 463	96.47 352	99.62 452	91.23 476	97.89 467	97.38 481

PAPR		97.56 395	97.07 406	99.04 351	98.80 448	98.11 376	97.63 449	99.25 380	94.56 474	98.02 448	98.25 464	97.43 317	99.68 430	90.90 477	98.74 434	99.33 330

MVS		95.72 446	94.63 452	98.99 354	98.56 464	97.98 389	99.30 164	98.86 414	72.71 490	97.30 467	99.08 412	98.34 250	99.74 397	89.21 478	98.33 451	99.26 346

UWE-MVS-28		95.64 447	95.47 439	96.14 468	97.98 482	90.39 492	98.49 378	95.81 479	99.02 274	98.03 447	98.19 465	84.49 469	99.28 478	88.75 479	98.47 449	98.75 441

thres600view7		96.60 421	96.16 424	97.93 430	99.63 236	96.09 448	99.18 210	97.57 464	98.77 313	98.72 404	97.32 482	87.04 457	99.72 402	88.57 480	98.62 442	97.98 475

FPMVS		96.32 429	95.50 438	98.79 388	99.60 243	98.17 371	98.46 384	98.80 419	97.16 436	96.28 480	99.63 250	82.19 471	99.09 481	88.45 481	98.89 426	99.10 385

PCF-MVS	96.03 18	96.73 418	95.86 431	99.33 293	99.44 328	99.16 270	96.87 478	99.44 329	86.58 485	98.95 376	99.40 348	94.38 389	99.88 234	87.93 482	99.80 249	98.95 418
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 427	96.03 427	97.47 444	99.63 236	95.93 449	99.18 210	97.57 464	98.75 317	98.70 407	97.31 483	87.04 457	99.67 435	87.62 483	98.51 446	96.81 484

tfpn200view9		96.30 430	95.89 429	97.53 441	99.58 255	96.11 446	99.00 286	97.54 467	98.43 349	98.52 421	96.98 487	86.85 459	99.67 435	87.62 483	98.51 446	96.81 484

thres400		96.40 426	95.89 429	97.92 431	99.58 255	96.11 446	99.00 286	97.54 467	98.43 349	98.52 421	96.98 487	86.85 459	99.67 435	87.62 483	98.51 446	97.98 475

thres200		96.09 436	95.68 436	97.33 449	99.48 313	96.22 445	98.53 373	97.57 464	98.06 384	98.37 428	96.73 493	86.84 461	99.61 457	86.99 486	98.57 443	96.16 487

MVE	92.54 22	96.66 420	96.11 425	98.31 417	99.68 219	97.55 405	97.94 431	95.60 480	99.37 216	90.68 491	98.70 450	96.56 347	98.61 487	86.94 487	99.55 351	98.77 439
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 406	96.83 416	98.59 401	99.46 323	97.55 405	99.25 188	96.84 473	98.78 311	97.24 469	97.67 474	97.11 333	98.97 483	86.59 488	98.54 445	99.27 344

PAPM		95.61 449	94.71 451	98.31 417	99.12 406	96.63 433	96.66 481	98.46 438	90.77 482	96.25 481	98.68 451	93.01 407	99.69 418	81.60 489	97.86 469	98.62 445

SD_0403		97.42 401	96.90 414	98.98 356	99.54 281	97.90 392	99.52 93	99.54 288	99.34 220	97.87 454	98.85 440	98.72 191	99.64 449	78.93 490	99.83 222	99.40 311

dongtai		89.37 455	88.91 458	90.76 472	99.19 395	77.46 497	95.47 486	87.82 496	92.28 478	94.17 489	98.82 443	71.22 494	95.54 491	63.85 491	97.34 472	99.27 344

kuosan		85.65 457	84.57 460	88.90 474	97.91 484	77.11 498	96.37 483	87.62 497	85.24 487	85.45 492	96.83 490	69.94 496	90.98 493	45.90 492	95.83 487	98.62 445

test123		29.31 458	33.05 463	18.08 475	25.93 499	12.24 500	97.53 455	10.93 500	11.78 493	24.21 494	50.08 505	21.04 497	8.60 494	23.51 493	32.43 493	33.39 490

testmvs		28.94 459	33.33 461	15.79 476	26.03 498	9.81 501	96.77 479	15.67 499	11.55 494	23.87 495	50.74 504	19.03 498	8.53 495	23.21 494	33.07 492	29.03 491

mmdepth		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

monomultidepth		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

test_blank		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet_test		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

DCPMVS		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

cdsmvs_eth3d_5k		24.88 460	33.17 462	0.00 477	0.00 500	0.00 502	0.00 489	99.62 236	0.00 495	0.00 496	99.13 403	99.82 18	0.00 496	0.00 495	0.00 494	0.00 492

pcd_1.5k_mvsjas		16.61 461	22.14 464	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	99.28 91	0.00 496	0.00 495	0.00 494	0.00 492

sosnet-low-res		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

sosnet		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uncertanet		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

Regformer		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

ab-mvs-re		8.26 472	11.02 475	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	99.16 401	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet		8.33 462	11.11 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	100.00 1	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

TestfortrainingZip							99.38 131

FOURS1					99.83 84	99.89 10	99.74 27	99.71 182	99.69 127	99.63 219

test_one_0601					99.63 236	99.76 71		99.55 282	99.23 241	99.31 330	99.61 269	98.59 208

eth-test2					0.00 500

eth-test					0.00 500

test_241102_ONE					99.69 211	99.82 43		99.54 288	99.12 265	99.82 109	99.49 326	98.91 164	99.52 470

save fliter					99.53 288	99.25 247	98.29 394	99.38 349	99.07 269

test0726					99.69 211	99.80 52	99.24 189	99.57 271	99.16 256	99.73 173	99.65 233	98.35 248

GSMVS																99.14 379

test_part2					99.62 240	99.67 118				99.55 259

sam_mvs1												90.81 437				99.14 379

sam_mvs												90.52 442

MTGPA								99.53 298

test_post											52.41 502	90.25 444	99.86 268

patchmatchnet-post											99.62 259	90.58 440	99.94 97

MTMP							99.09 253	98.59 432

TEST9					99.35 352	99.35 228	98.11 411	99.41 335	94.83 472	97.92 450	98.99 424	98.02 280	99.85 287

test_8					99.34 361	99.31 234	98.08 415	99.40 342	94.90 469	97.87 454	98.97 429	98.02 280	99.84 303

agg_prior					99.35 352	99.36 225		99.39 345		97.76 461			99.85 287

test_prior4						99.19 264	98.00 424

test_prior				99.46 248	99.35 352	99.22 257		99.39 345					99.69 418			99.48 272

新几何2							98.04 419

旧先验1					99.49 308	99.29 237		99.26 377			99.39 352	97.67 306			99.36 383	99.46 281

原ACMM2							97.92 433

test222					99.51 297	99.08 285	97.83 439	99.29 371	95.21 466	98.68 408	99.31 374	97.28 324			99.38 380	99.43 303

segment_acmp												98.37 246

testdata1							97.72 444		97.86 403

test12				99.54 221	99.29 374	99.33 231		99.16 398		98.43 426		97.54 313	99.82 338		99.47 369	99.48 272

plane_prior7					99.58 255	99.38 217

plane_prior6					99.47 319	99.26 244						97.24 325

plane_prior4											99.25 387

plane_prior3						99.31 234			98.36 358	99.14 359

plane_prior2							98.80 334		98.94 284

plane_prior1					99.51 297

plane_prior						99.24 251	98.42 386		97.87 401						99.71 295

n2								0.00 501

nn								0.00 501

door-mid								99.83 98

test11								99.29 371

door								99.77 147

HQP5-MVS						98.94 300

HQP-NCC					99.31 368		97.98 426		97.45 421	98.15 439

ACMP_Plane					99.31 368		97.98 426		97.45 421	98.15 439

HQP4-MVS										98.15 439			99.70 411			99.53 244

HQP3-MVS								99.37 350							99.67 316

HQP2-MVS												96.67 344

NP-MVS					99.40 339	99.13 273					98.83 441

ACMMP++_ref															99.94 127

ACMMP++															99.79 254

Test By Simon												98.41 240

High-resolution multi-view benchmark