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	99.34 288	99.91 31	98.08 379	99.48 108	100.00 1	99.90 50	99.99 7	99.91 31	99.50 59	99.98 27	99.98 1	99.99 16	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 223	99.96 7	98.62 334	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	99.12 238	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	99.27 313	99.93 24	97.84 392	99.34 145	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	99.36 284	99.94 18	98.18 368	99.52 93	100.00 1	99.86 66	100.00 1	99.88 50	98.99 146	99.96 69	99.97 4	99.96 87	99.95 14

test_fmvs1_n		99.68 65	99.81 28	99.28 308	99.95 15	97.93 388	99.49 106	100.00 1	99.82 86	99.99 7	99.89 41	99.21 100	99.98 27	99.97 4	99.98 50	99.93 20

test_f		99.75 49	99.88 7	99.37 279	99.96 7	98.21 365	99.51 100	100.00 1	99.94 36	100.00 1	99.93 22	99.58 47	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	98.94 305	99.96 28	99.98 18	99.96 34	99.78 129	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	99.08 254	99.97 20	99.98 18	99.96 34	99.79 117	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	99.01 278	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	99.55 167	99.17 213	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	99.45 249	99.93 24	98.40 353	99.30 162	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	99.81 103	99.75 79	99.06 260	99.85 82	99.99 3	99.97 24	99.84 77	99.12 117	99.98 27	99.95 14	99.99 16	99.90 29

fmvsm_s_conf0.5_n_7		99.73 52	99.78 39	99.60 188	99.74 175	98.93 301	98.85 319	99.96 28	99.96 28	99.97 24	99.76 146	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	99.11 243	99.91 52	99.98 18	99.96 34	99.64 233	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 91	99.70 108	99.17 213	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		99.48 128	99.65 72	98.97 355	99.54 279	97.16 417	99.11 243	99.98 12	99.78 103	99.96 34	99.81 98	98.72 189	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	99.37 219	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 82	99.59 154	98.97 296	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	98.92 309	99.98 12	99.99 3	99.99 7	99.88 50	99.43 64	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	99.12 238	99.91 52	99.98 18	99.95 45	99.67 217	99.67 34	99.99 8	99.94 20	99.99 16	99.88 40

MM		99.18 230	99.05 239	99.55 213	99.35 350	98.81 312	99.05 261	97.79 457	99.99 3	99.48 280	99.59 285	96.29 360	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	98.97 296	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	99.81 103	99.53 170	99.15 222	99.89 61	99.99 3	99.98 14	99.86 63	99.13 114	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	99.79 126	99.72 95	98.84 321	99.96 28	99.96 28	99.96 34	99.72 172	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 91	99.76 71	98.88 313	99.92 43	99.98 18	99.98 14	99.85 69	99.42 66	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	99.10 246	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	99.07 259	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 69	99.82 43	99.03 269	99.96 28	99.99 3	99.97 24	99.84 77	99.58 47	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 69	99.78 58	99.03 269	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 91	99.75 79	99.02 273	99.87 70	99.98 18	99.98 14	99.81 98	99.07 128	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	99.78 134	99.78 58	99.00 284	99.97 20	99.96 28	99.97 24	99.56 299	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	99.75 167	99.56 163	98.98 294	99.94 38	99.92 46	99.97 24	99.72 172	99.84 16	99.92 149	99.91 33	99.98 50	99.89 37

MVStest1		98.22 363	98.09 358	98.62 393	99.04 420	96.23 439	99.20 198	99.92 43	99.44 196	99.98 14	99.87 56	85.87 460	99.67 430	99.91 33	99.57 344	99.95 14

v1921920		99.56 103	99.57 101	99.55 213	99.75 167	99.11 274	99.05 261	99.61 241	99.15 258	99.88 83	99.71 182	99.08 125	99.87 249	99.90 37	99.97 73	99.66 145

v1240		99.56 103	99.58 96	99.51 229	99.80 112	99.00 288	99.00 284	99.65 220	99.15 258	99.90 68	99.75 154	99.09 121	99.88 234	99.90 37	99.96 87	99.67 131

v10		99.69 60	99.69 61	99.66 148	99.81 103	99.39 213	99.66 57	99.75 158	99.60 162	99.92 60	99.87 56	98.75 184	99.86 268	99.90 37	99.99 16	99.73 93

v1192		99.57 99	99.57 101	99.57 202	99.77 147	99.22 255	99.04 266	99.60 252	99.18 247	99.87 93	99.72 172	99.08 125	99.85 287	99.89 40	99.98 50	99.66 145

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 32	99.81 103	99.71 100	98.97 296	99.92 43	99.98 18	99.97 24	99.86 63	99.53 55	99.95 80	99.88 41	99.99 16	99.89 37

v144192		99.55 108	99.54 111	99.58 194	99.78 134	99.20 261	99.11 243	99.62 234	99.18 247	99.89 73	99.72 172	98.66 198	99.87 249	99.88 41	99.97 73	99.66 145

v8		99.68 65	99.69 61	99.65 155	99.80 112	99.40 210	99.66 57	99.76 153	99.64 146	99.93 53	99.85 69	98.66 198	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	99.42 202	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		99.54 112	99.53 115	99.59 191	99.79 126	99.28 237	99.10 246	99.61 241	99.20 244	99.84 102	99.73 164	98.67 196	99.84 303	99.86 45	99.98 50	99.64 166

mmtdpeth		99.78 37	99.83 21	99.66 148	99.85 69	99.05 287	99.79 15	99.97 20	100.00 1	99.43 292	99.94 19	99.64 35	99.94 97	99.83 46	99.99 16	99.98 5

SSC-MVS		99.52 118	99.42 142	99.83 41	99.86 58	99.65 126	99.52 93	99.81 115	99.87 63	99.81 116	99.79 117	96.78 340	99.99 8	99.83 46	99.51 360	99.86 46

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 27	99.82 10	99.82 102	99.84 76	99.94 48	99.91 31	99.13 114	99.96 69	99.83 46	99.99 16	99.83 56

v2v482		99.50 121	99.47 127	99.58 194	99.78 134	99.25 245	99.14 226	99.58 267	99.25 235	99.81 116	99.62 257	98.24 257	99.84 303	99.83 46	99.97 73	99.64 166

test_vis1_rt		99.45 144	99.46 132	99.41 267	99.71 188	98.63 333	98.99 291	99.96 28	99.03 271	99.95 45	99.12 405	98.75 184	99.84 303	99.82 50	99.82 230	99.77 79

tt0805		99.63 83	99.57 101	99.81 54	99.87 54	99.88 12	99.58 82	98.70 418	99.72 113	99.91 63	99.60 275	99.43 64	99.81 351	99.81 51	99.53 356	99.73 93

VortexMVS		99.13 243	99.24 193	98.79 384	99.67 224	96.60 431	99.24 187	99.80 120	99.85 72	99.93 53	99.84 77	95.06 378	99.89 219	99.80 52	99.98 50	99.89 37

V42		99.56 103	99.54 111	99.63 169	99.79 126	99.46 187	99.39 126	99.59 258	99.24 237	99.86 96	99.70 192	98.55 213	99.82 335	99.79 53	99.95 109	99.60 201

SSC-MVS3.2		99.64 82	99.67 65	99.56 206	99.75 167	98.98 291	98.96 300	99.87 70	99.88 61	99.84 102	99.64 233	99.32 84	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		99.44 148	99.32 169	99.80 64	99.81 103	99.61 148	99.47 111	99.81 115	99.82 86	99.71 181	99.72 172	96.60 344	99.98 27	99.75 56	99.23 401	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 89	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		99.15 240	99.06 234	99.42 259	99.85 69	98.59 337	99.13 233	97.26 465	99.84 76	99.87 93	99.77 139	96.11 363	99.93 118	99.71 60	99.96 87	99.74 89

Elysia		99.69 60	99.65 72	99.81 54	99.86 58	99.72 95	99.34 145	99.77 145	99.94 36	99.91 63	99.76 146	98.55 213	99.99 8	99.70 61	99.98 50	99.72 97

StellarMVS		99.69 60	99.65 72	99.81 54	99.86 58	99.72 95	99.34 145	99.77 145	99.94 36	99.91 63	99.76 146	98.55 213	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 91	99.84 27	99.82 10	99.92 43	99.94 36	99.94 48	99.93 22	99.34 81	99.92 149	99.70 61	99.96 87	99.70 105

reproduce_monomvs		97.40 397	97.46 390	97.20 447	99.05 417	91.91 475	99.20 198	99.18 390	99.84 76	99.86 96	99.75 154	80.67 468	99.83 319	99.69 64	99.95 109	99.85 49

SPE-MVS-test		99.68 65	99.70 58	99.64 162	99.57 263	99.83 35	99.78 17	99.97 20	99.92 46	99.50 277	99.38 352	99.57 49	99.95 80	99.69 64	99.90 157	99.15 369

guyue		99.12 246	99.02 248	99.41 267	99.84 74	98.56 338	99.19 204	98.30 443	99.82 86	99.84 102	99.75 154	94.84 381	99.92 149	99.68 66	99.94 125	99.74 89

tt0320		99.79 34	99.79 34	99.81 54	99.82 91	99.84 27	99.82 10	99.90 58	99.94 36	99.94 48	99.94 19	99.07 128	99.92 149	99.68 66	99.97 73	99.67 131

MGCNet		98.61 321	98.30 342	99.52 225	97.88 481	98.95 297	98.76 338	94.11 481	99.84 76	99.32 323	99.57 295	95.57 372	99.95 80	99.68 66	99.98 50	99.68 122

CS-MVS		99.67 74	99.70 58	99.58 194	99.53 286	99.84 27	99.79 15	99.96 28	99.90 50	99.61 233	99.41 342	99.51 58	99.95 80	99.66 69	99.89 171	98.96 411

mamv4		99.73 52	99.74 53	99.70 131	99.66 226	99.87 15	99.69 45	99.93 39	99.93 43	99.93 53	99.86 63	99.07 128	100.00 1	99.66 69	99.92 143	99.24 344

KinetiMVS		99.66 75	99.63 80	99.76 85	99.89 39	99.57 162	99.37 137	99.82 102	99.95 32	99.90 68	99.63 248	98.57 209	99.97 44	99.65 71	99.94 125	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	99.69 115

MIMVSNet1		99.66 75	99.62 82	99.80 64	99.94 18	99.87 15	99.69 45	99.77 145	99.78 103	99.93 53	99.89 41	97.94 285	99.92 149	99.65 71	99.98 50	99.62 184

LuminaMVS		99.39 166	99.28 184	99.73 112	99.83 82	99.49 177	99.00 284	99.05 402	99.81 92	99.89 73	99.79 117	96.54 348	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	99.79 129	99.94 36	99.93 53	99.92 27	99.35 80	99.92 149	99.64 74	99.94 125	99.68 122

EC-MVSNet		99.69 60	99.69 61	99.68 137	99.71 188	99.91 4	99.76 23	99.96 28	99.86 66	99.51 274	99.39 350	99.57 49	99.93 118	99.64 74	99.86 202	99.20 357

K. test v3		98.87 297	98.60 307	99.69 135	99.93 24	99.46 187	99.74 27	94.97 476	99.78 103	99.88 83	99.88 50	93.66 396	99.97 44	99.61 77	99.95 109	99.64 166

KD-MVS_self_test		99.63 83	99.59 92	99.76 85	99.84 74	99.90 7	99.37 137	99.79 129	99.83 82	99.88 83	99.85 69	98.42 237	99.90 197	99.60 78	99.73 284	99.49 266

Anonymous20240521		99.44 148	99.42 142	99.49 235	99.89 39	98.96 296	99.62 67	99.76 153	99.85 72	99.82 109	99.88 50	96.39 355	99.97 44	99.59 79	99.98 50	99.55 226

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	99.74 278	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	98.54 217	99.93 118	99.59 79	99.98 50	99.76 84

EU-MVSNet		99.39 166	99.62 82	98.72 389	99.88 45	96.44 433	99.56 87	99.85 82	99.90 50	99.90 68	99.85 69	98.09 273	99.83 319	99.58 82	99.95 109	99.90 29

mvs_anonymous		99.28 194	99.39 147	98.94 359	99.19 393	97.81 394	99.02 273	99.55 280	99.78 103	99.85 99	99.80 106	98.24 257	99.86 268	99.57 83	99.50 363	99.15 369

test1111		97.74 382	98.16 354	96.49 458	99.60 241	89.86 489	99.71 37	91.21 485	99.89 56	99.88 83	99.87 56	93.73 395	99.90 197	99.56 84	99.99 16	99.70 105

lessismore_v0				99.64 162	99.86 58	99.38 215		90.66 486		99.89 73	99.83 84	94.56 386	99.97 44	99.56 84	99.92 143	99.57 219

mvsany_test1		99.44 148	99.45 134	99.40 270	99.37 343	98.64 332	97.90 434	99.59 258	99.27 231	99.92 60	99.82 91	99.74 26	99.93 118	99.55 86	99.87 194	99.63 172

MVSMamba_PlusPlus		99.55 108	99.58 96	99.47 242	99.68 217	99.40 210	99.52 93	99.70 189	99.92 46	99.77 145	99.86 63	98.28 253	99.96 69	99.54 87	99.90 157	99.05 398

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	99.92 143	99.63 172

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		99.48 128	99.65 72	98.95 358	99.71 188	97.27 414	99.50 101	99.82 102	99.59 164	99.41 301	99.85 69	99.62 40	100.00 1	99.53 90	99.89 171	99.59 208

test2506		94.73 448	94.59 448	95.15 465	99.59 247	85.90 491	99.75 25	74.01 493	99.89 56	99.71 181	99.86 63	79.00 478	99.90 197	99.52 91	99.99 16	99.65 154

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 180	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 72	99.86 30	99.88 45	99.86 19	99.72 33	99.78 139	99.90 50	99.82 109	99.83 84	98.45 233	99.87 249	99.51 92	99.97 73	99.86 46

BP-MVS1		98.72 313	98.46 323	99.50 231	99.53 286	99.00 288	99.34 145	98.53 428	99.65 142	99.73 171	99.38 352	90.62 434	99.96 69	99.50 94	99.86 202	99.55 226

UA-Net		99.78 37	99.76 49	99.86 30	99.72 184	99.71 100	99.91 4	99.95 36	99.96 28	99.71 181	99.91 31	99.15 109	99.97 44	99.50 94	100.00 1	99.90 29

viewdifsd2359ckpt11		99.62 90	99.64 77	99.56 206	99.86 58	99.19 262	99.02 273	99.93 39	99.83 82	99.88 83	99.81 98	98.99 146	99.83 319	99.48 96	99.96 87	99.65 154

viewmsd2359difaftdt		99.62 90	99.64 77	99.56 206	99.86 58	99.19 262	99.02 273	99.93 39	99.83 82	99.88 83	99.81 98	98.99 146	99.83 319	99.48 96	99.96 87	99.65 154

PMMVS2		99.48 128	99.45 134	99.57 202	99.76 151	98.99 290	98.09 411	99.90 58	98.95 281	99.78 133	99.58 288	99.57 49	99.93 118	99.48 96	99.95 109	99.79 73

VPA-MVSNet		99.66 75	99.62 82	99.79 71	99.68 217	99.75 79	99.62 67	99.69 197	99.85 72	99.80 123	99.81 98	98.81 172	99.91 178	99.47 99	99.88 181	99.70 105

GDP-MVS		98.81 304	98.57 313	99.50 231	99.53 286	99.12 273	99.28 171	99.86 76	99.53 173	99.57 244	99.32 369	90.88 430	99.98 27	99.46 100	99.74 278	99.42 302

ECVR-MVS		97.73 383	98.04 361	96.78 451	99.59 247	90.81 484	99.72 33	90.43 487	99.89 56	99.86 96	99.86 63	93.60 397	99.89 219	99.46 100	99.99 16	99.65 154

nrg030		99.70 58	99.66 70	99.82 46	99.76 151	99.84 27	99.61 73	99.70 189	99.93 43	99.78 133	99.68 213	99.10 119	99.78 365	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	99.62 151	99.87 93	99.85 69	99.06 135	99.85 287	99.44 103	99.98 50	99.63 172

TAMVS		99.49 126	99.45 134	99.63 169	99.48 311	99.42 202	99.45 116	99.57 269	99.66 139	99.78 133	99.83 84	97.85 292	99.86 268	99.44 103	99.96 87	99.61 197

GeoE		99.69 60	99.66 70	99.78 75	99.76 151	99.76 71	99.60 79	99.82 102	99.46 191	99.75 156	99.56 299	99.63 37	99.95 80	99.43 105	99.88 181	99.62 184

new-patchmatchnet		99.35 179	99.57 101	98.71 391	99.82 91	96.62 429	98.55 366	99.75 158	99.50 178	99.88 83	99.87 56	99.31 85	99.88 234	99.43 105	100.00 1	99.62 184

test20.03		99.55 108	99.54 111	99.58 194	99.79 126	99.37 219	99.02 273	99.89 61	99.60 162	99.82 109	99.62 257	98.81 172	99.89 219	99.43 105	99.86 202	99.47 274

MVSFormer		99.41 160	99.44 138	99.31 300	99.57 263	98.40 353	99.77 19	99.80 120	99.73 109	99.63 217	99.30 374	98.02 278	99.98 27	99.43 105	99.69 303	99.55 226

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 27	99.77 19	99.80 120	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	99.80 112	99.65 126	99.63 64	99.86 76	99.97 25	99.89 73	99.89 41	99.52 57	99.99 8	99.42 110	99.96 87	99.65 154

Anonymous20231211		99.62 90	99.57 101	99.76 85	99.61 239	99.60 152	99.81 13	99.73 168	99.82 86	99.90 68	99.90 36	97.97 284	99.86 268	99.42 110	99.96 87	99.80 65

SixPastTwentyTwo		99.42 154	99.30 176	99.76 85	99.92 29	99.67 118	99.70 38	99.14 395	99.65 142	99.89 73	99.90 36	96.20 362	99.94 97	99.42 110	99.92 143	99.67 131

balanced_conf03		99.50 121	99.50 120	99.50 231	99.42 334	99.49 177	99.52 93	99.75 158	99.86 66	99.78 133	99.71 182	98.20 265	99.90 197	99.39 113	99.88 181	99.10 380

patch_mono-2		99.51 119	99.46 132	99.64 162	99.70 203	99.11 274	99.04 266	99.87 70	99.71 118	99.47 282	99.79 117	98.24 257	99.98 27	99.38 114	99.96 87	99.83 56

UGNet		99.38 169	99.34 164	99.49 235	98.90 432	98.90 305	99.70 38	99.35 352	99.86 66	98.57 416	99.81 98	98.50 228	99.93 118	99.38 114	99.98 50	99.66 145
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	99.82 102	99.39 212	99.82 109	99.84 77	99.38 72	99.91 178	99.38 114	99.93 137	99.80 65

FIs		99.65 81	99.58 96	99.84 38	99.84 74	99.85 22	99.66 57	99.75 158	99.86 66	99.74 166	99.79 117	98.27 255	99.85 287	99.37 117	99.93 137	99.83 56

sd_testset		99.78 37	99.78 39	99.80 64	99.80 112	99.76 71	99.80 14	99.79 129	99.97 25	99.89 73	99.89 41	99.53 55	99.99 8	99.36 118	99.96 87	99.65 154

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 60	99.97 44	99.36 118	100.00 1	99.85 49

casdiffmvs_mvg		99.68 65	99.68 64	99.69 135	99.81 103	99.59 154	99.29 169	99.90 58	99.71 118	99.79 129	99.73 164	99.54 52	99.84 303	99.36 118	99.96 87	99.65 154
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	99.67 135	99.77 145	99.75 154	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 94	99.64 77	99.53 223	99.79 126	98.82 311	99.58 82	99.97 20	99.95 32	99.96 34	99.76 146	98.44 234	99.99 8	99.34 122	99.96 87	99.78 75

CHOSEN 1792x2688		99.39 166	99.30 176	99.65 155	99.88 45	99.25 245	98.78 336	99.88 66	98.66 323	99.96 34	99.79 117	97.45 314	99.93 118	99.34 122	99.99 16	99.78 75

CDS-MVSNet		99.22 216	99.13 209	99.50 231	99.35 350	99.11 274	98.96 300	99.54 286	99.46 191	99.61 233	99.70 192	96.31 358	99.83 319	99.34 122	99.88 181	99.55 226
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 277	99.16 202	98.51 399	99.75 167	95.90 445	98.07 414	99.84 89	99.84 76	99.89 73	99.73 164	96.01 366	99.99 8	99.33 125	100.00 1	99.63 172

HyFIR lowres test		98.91 290	98.64 304	99.73 112	99.85 69	99.47 181	98.07 414	99.83 96	98.64 326	99.89 73	99.60 275	92.57 408	100.00 1	99.33 125	99.97 73	99.72 97

pmmvs5		99.19 226	99.11 216	99.42 259	99.76 151	98.88 307	98.55 366	99.73 168	98.82 302	99.72 176	99.62 257	96.56 345	99.82 335	99.32 127	99.95 109	99.56 222

v148		99.40 162	99.41 145	99.39 273	99.76 151	98.94 298	99.09 251	99.59 258	99.17 252	99.81 116	99.61 267	98.41 238	99.69 413	99.32 127	99.94 125	99.53 242

baseline		99.63 83	99.62 82	99.66 148	99.80 112	99.62 140	99.44 118	99.80 120	99.71 118	99.72 176	99.69 201	99.15 109	99.83 319	99.32 127	99.94 125	99.53 242

CVMVSNet		98.61 321	98.88 282	97.80 430	99.58 253	93.60 468	99.26 180	99.64 228	99.66 139	99.72 176	99.67 217	93.26 401	99.93 118	99.30 130	99.81 240	99.87 44

PS-CasMVS		99.66 75	99.58 96	99.89 11	99.80 112	99.85 22	99.66 57	99.73 168	99.62 151	99.84 102	99.71 182	98.62 202	99.96 69	99.30 130	99.96 87	99.86 46

DTE-MVSNet		99.68 65	99.61 86	99.88 19	99.80 112	99.87 15	99.67 53	99.71 180	99.72 113	99.84 102	99.78 129	98.67 196	99.97 44	99.30 130	99.95 109	99.80 65

tmp_tt		95.75 440	95.42 435	96.76 452	89.90 492	94.42 462	98.86 317	97.87 455	78.01 483	99.30 333	99.69 201	97.70 300	95.89 485	99.29 133	98.14 459	99.95 14

PEN-MVS		99.66 75	99.59 92	99.89 11	99.83 82	99.87 15	99.66 57	99.73 168	99.70 124	99.84 102	99.73 164	98.56 212	99.96 69	99.29 133	99.94 125	99.83 56

WR-MVS_H		99.61 94	99.53 115	99.87 26	99.80 112	99.83 35	99.67 53	99.75 158	99.58 166	99.85 99	99.69 201	98.18 268	99.94 97	99.28 135	99.95 109	99.83 56

IterMVS		98.97 281	99.16 202	98.42 404	99.74 175	95.64 449	98.06 416	99.83 96	99.83 82	99.85 99	99.74 159	96.10 365	99.99 8	99.27 136	100.00 1	99.63 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NormalMVS		99.09 254	98.91 280	99.62 178	99.78 134	99.11 274	99.36 141	99.77 145	99.82 86	99.68 193	99.53 311	93.30 399	99.99 8	99.24 137	99.76 267	99.74 89

SymmetryMVS		99.01 274	98.82 290	99.58 194	99.65 231	99.11 274	99.36 141	99.20 388	99.82 86	99.68 193	99.53 311	93.30 399	99.99 8	99.24 137	99.63 324	99.64 166

WBMVS		97.50 393	97.18 399	98.48 401	98.85 440	95.89 446	98.44 383	99.52 301	99.53 173	99.52 267	99.42 341	80.10 471	99.86 268	99.24 137	99.95 109	99.68 122

h-mvs33		98.61 321	98.34 337	99.44 253	99.60 241	98.67 324	99.27 175	99.44 327	99.68 129	99.32 323	99.49 324	92.50 411	100.00 1	99.24 137	96.51 477	99.65 154

hse-mvs2		98.52 334	98.30 342	99.16 329	99.29 372	98.60 335	98.77 337	99.02 404	99.68 129	99.32 323	99.04 415	92.50 411	99.85 287	99.24 137	97.87 466	99.03 402

FMVSNet1		99.66 75	99.63 80	99.73 112	99.78 134	99.77 64	99.68 49	99.70 189	99.67 135	99.82 109	99.83 84	98.98 150	99.90 197	99.24 137	99.97 73	99.53 242

casdiffmvs		99.63 83	99.61 86	99.67 141	99.79 126	99.59 154	99.13 233	99.85 82	99.79 100	99.76 151	99.72 172	99.33 83	99.82 335	99.21 143	99.94 125	99.59 208
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		99.54 112	99.43 140	99.87 26	99.76 151	99.82 43	99.57 85	99.61 241	99.54 171	99.80 123	99.64 233	97.79 296	99.95 80	99.21 143	99.94 125	99.84 52

DELS-MVS		99.34 184	99.30 176	99.48 240	99.51 295	99.36 223	98.12 407	99.53 296	99.36 217	99.41 301	99.61 267	99.22 99	99.87 249	99.21 143	99.68 308	99.20 357
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		99.47 138	99.50 120	99.37 279	99.70 203	98.80 315	98.67 347	99.92 43	99.49 180	99.77 145	99.71 182	99.08 125	99.78 365	99.20 146	99.94 125	99.54 236

UniMVSNet (Re)		99.37 173	99.26 189	99.68 137	99.51 295	99.58 159	98.98 294	99.60 252	99.43 203	99.70 185	99.36 360	97.70 300	99.88 234	99.20 146	99.87 194	99.59 208

CANet		99.11 250	99.05 239	99.28 308	98.83 442	98.56 338	98.71 345	99.41 333	99.25 235	99.23 342	99.22 392	97.66 308	99.94 97	99.19 148	99.97 73	99.33 325

EI-MVSNet-UG-set		99.48 128	99.50 120	99.42 259	99.57 263	98.65 330	99.24 187	99.46 321	99.68 129	99.80 123	99.66 222	98.99 146	99.89 219	99.19 148	99.90 157	99.72 97

xiu_mvs_v1_base_debu		99.23 207	99.34 164	98.91 366	99.59 247	98.23 362	98.47 378	99.66 210	99.61 156	99.68 193	98.94 431	99.39 68	99.97 44	99.18 150	99.55 349	98.51 450

xiu_mvs_v1_base		99.23 207	99.34 164	98.91 366	99.59 247	98.23 362	98.47 378	99.66 210	99.61 156	99.68 193	98.94 431	99.39 68	99.97 44	99.18 150	99.55 349	98.51 450

xiu_mvs_v1_base_debi		99.23 207	99.34 164	98.91 366	99.59 247	98.23 362	98.47 378	99.66 210	99.61 156	99.68 193	98.94 431	99.39 68	99.97 44	99.18 150	99.55 349	98.51 450

VPNet		99.46 140	99.37 153	99.71 126	99.82 91	99.59 154	99.48 108	99.70 189	99.81 92	99.69 188	99.58 288	97.66 308	99.86 268	99.17 153	99.44 370	99.67 131

UniMVSNet_NR-MVSNet		99.37 173	99.25 191	99.72 120	99.47 317	99.56 163	98.97 296	99.61 241	99.43 203	99.67 200	99.28 378	97.85 292	99.95 80	99.17 153	99.81 240	99.65 154

DU-MVS		99.33 187	99.21 196	99.71 126	99.43 329	99.56 163	98.83 324	99.53 296	99.38 213	99.67 200	99.36 360	97.67 304	99.95 80	99.17 153	99.81 240	99.63 172

EI-MVSNet-Vis-set		99.47 138	99.49 124	99.42 259	99.57 263	98.66 327	99.24 187	99.46 321	99.67 135	99.79 129	99.65 231	98.97 152	99.89 219	99.15 156	99.89 171	99.71 102

EI-MVSNet		99.38 169	99.44 138	99.21 323	99.58 253	98.09 376	99.26 180	99.46 321	99.62 151	99.75 156	99.67 217	98.54 217	99.85 287	99.15 156	99.92 143	99.68 122

VNet		99.18 230	99.06 234	99.56 206	99.24 383	99.36 223	99.33 151	99.31 362	99.67 135	99.47 282	99.57 295	96.48 349	99.84 303	99.15 156	99.30 389	99.47 274

EG-PatchMatch MVS		99.57 99	99.56 106	99.62 178	99.77 147	99.33 229	99.26 180	99.76 153	99.32 222	99.80 123	99.78 129	99.29 87	99.87 249	99.15 156	99.91 155	99.66 145

PVSNet_Blended_VisFu		99.40 162	99.38 150	99.44 253	99.90 37	98.66 327	98.94 305	99.91 52	97.97 387	99.79 129	99.73 164	99.05 137	99.97 44	99.15 156	99.99 16	99.68 122

IterMVS-LS		99.41 160	99.47 127	99.25 319	99.81 103	98.09 376	98.85 319	99.76 153	99.62 151	99.83 108	99.64 233	98.54 217	99.97 44	99.15 156	99.99 16	99.68 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 112	99.47 127	99.76 85	99.58 253	99.64 132	99.30 162	99.63 231	99.61 156	99.71 181	99.56 299	98.76 182	99.96 69	99.14 162	99.92 143	99.68 122

MVSTER		98.47 341	98.22 347	99.24 321	99.06 416	98.35 359	99.08 254	99.46 321	99.27 231	99.75 156	99.66 222	88.61 447	99.85 287	99.14 162	99.92 143	99.52 253

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	99.78 365	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	99.78 365	99.13 164	99.96 87	99.70 105

diffmvs_AUTHOR		99.48 128	99.48 125	99.47 242	99.80 112	98.89 306	98.71 345	99.82 102	99.79 100	99.66 206	99.63 248	98.87 168	99.88 234	99.13 164	99.95 109	99.62 184

Anonymous20231206		99.35 179	99.31 171	99.47 242	99.74 175	99.06 286	99.28 171	99.74 164	99.23 239	99.72 176	99.53 311	97.63 310	99.88 234	99.11 167	99.84 212	99.48 270

Syy-MVS		98.17 366	97.85 378	99.15 331	98.50 465	98.79 316	98.60 354	99.21 385	97.89 394	96.76 470	96.37 493	95.47 375	99.57 456	99.10 168	98.73 435	99.09 385

ttmdpeth		99.48 128	99.55 108	99.29 305	99.76 151	98.16 370	99.33 151	99.95 36	99.79 100	99.36 312	99.89 41	99.13 114	99.77 376	99.09 169	99.64 321	99.93 20

MVS_Test		99.28 194	99.31 171	99.19 326	99.35 350	98.79 316	99.36 141	99.49 314	99.17 252	99.21 347	99.67 217	98.78 179	99.66 435	99.09 169	99.66 317	99.10 380

FE-MVSNET3		98.87 297	98.71 299	99.35 286	99.59 247	98.88 307	97.17 466	99.64 228	98.94 282	99.27 335	99.22 392	95.57 372	99.83 319	99.08 171	99.92 143	99.35 320

testgi		99.29 193	99.26 189	99.37 279	99.75 167	98.81 312	98.84 321	99.89 61	98.38 354	99.75 156	99.04 415	99.36 77	99.86 268	99.08 171	99.25 397	99.45 279

1112_ss		99.05 262	98.84 287	99.67 141	99.66 226	99.29 235	98.52 372	99.82 102	97.65 406	99.43 292	99.16 399	96.42 352	99.91 178	99.07 173	99.84 212	99.80 65

CANet_DTU		98.91 290	98.85 285	99.09 340	98.79 448	98.13 371	98.18 399	99.31 362	99.48 183	98.86 387	99.51 317	96.56 345	99.95 80	99.05 174	99.95 109	99.19 360

Baseline_NR-MVSNet		99.49 126	99.37 153	99.82 46	99.91 31	99.84 27	98.83 324	99.86 76	99.68 129	99.65 209	99.88 50	97.67 304	99.87 249	99.03 175	99.86 202	99.76 84

FMVSNet2		99.35 179	99.28 184	99.55 213	99.49 306	99.35 226	99.45 116	99.57 269	99.44 196	99.70 185	99.74 159	97.21 325	99.87 249	99.03 175	99.94 125	99.44 292

Test_1112_low_res		98.95 287	98.73 297	99.63 169	99.68 217	99.15 270	98.09 411	99.80 120	97.14 432	99.46 286	99.40 346	96.11 363	99.89 219	99.01 177	99.84 212	99.84 52

VDD-MVS		99.20 223	99.11 216	99.44 253	99.43 329	98.98 291	99.50 101	98.32 442	99.80 96	99.56 252	99.69 201	96.99 335	99.85 287	98.99 178	99.73 284	99.50 261

DeepC-MVS	98.90 4	99.62 90	99.61 86	99.67 141	99.72 184	99.44 195	99.24 187	99.71 180	99.27 231	99.93 53	99.90 36	99.70 31	99.93 118	98.99 178	99.99 16	99.64 166
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 128	99.47 127	99.51 229	99.77 147	99.41 209	98.81 329	99.66 210	99.42 207	99.75 156	99.66 222	99.20 101	99.76 380	98.98 180	99.99 16	99.36 317

EPNet_dtu		97.62 388	97.79 381	97.11 450	96.67 487	92.31 473	98.51 373	98.04 449	99.24 237	95.77 479	99.47 331	93.78 394	99.66 435	98.98 180	99.62 326	99.37 314
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 184	99.32 169	99.39 273	99.67 224	98.77 318	98.57 363	99.81 115	99.61 156	99.48 280	99.41 342	98.47 229	99.86 268	98.97 182	99.90 157	99.53 242
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		99.40 162	99.31 171	99.68 137	99.43 329	99.55 167	99.73 30	99.50 310	99.46 191	99.88 83	99.36 360	97.54 311	99.87 249	98.97 182	99.87 194	99.63 172

TestfortrainingZip a		99.61 94	99.53 115	99.85 32	99.76 151	99.84 27	99.38 129	99.78 139	99.58 166	99.81 116	99.66 222	99.02 141	99.90 197	98.96 184	99.79 252	99.81 64

viewdifsd2359ckpt07		99.51 119	99.50 120	99.52 225	99.80 112	99.19 262	98.92 309	99.88 66	99.72 113	99.64 212	99.62 257	99.06 135	99.81 351	98.96 184	99.94 125	99.56 222

GBi-Net		99.42 154	99.31 171	99.73 112	99.49 306	99.77 64	99.68 49	99.70 189	99.44 196	99.62 227	99.83 84	97.21 325	99.90 197	98.96 184	99.90 157	99.53 242

FMVSNet5		97.80 380	97.25 397	99.42 259	98.83 442	98.97 294	99.38 129	99.80 120	98.87 294	99.25 338	99.69 201	80.60 470	99.91 178	98.96 184	99.90 157	99.38 311

test1		99.42 154	99.31 171	99.73 112	99.49 306	99.77 64	99.68 49	99.70 189	99.44 196	99.62 227	99.83 84	97.21 325	99.90 197	98.96 184	99.90 157	99.53 242

FMVSNet3		98.80 305	98.63 306	99.32 296	99.13 402	98.72 321	99.10 246	99.48 315	99.23 239	99.62 227	99.64 233	92.57 408	99.86 268	98.96 184	99.90 157	99.39 309

UnsupCasMVSNet_eth		98.83 301	98.57 313	99.59 191	99.68 217	99.45 193	98.99 291	99.67 205	99.48 183	99.55 257	99.36 360	94.92 379	99.86 268	98.95 190	96.57 475	99.45 279

CHOSEN 280x420		98.41 346	98.41 329	98.40 405	99.34 359	95.89 446	96.94 472	99.44 327	98.80 306	99.25 338	99.52 315	93.51 398	99.98 27	98.94 191	99.98 50	99.32 329

E4		99.61 94	99.59 92	99.66 148	99.84 74	99.53 170	99.08 254	99.84 89	99.65 142	99.74 166	99.80 106	99.45 60	99.77 376	98.93 192	99.95 109	99.69 115

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 74	99.88 234	98.93 192	99.95 109	99.60 201

viewmacassd2359aftdt		99.63 83	99.61 86	99.68 137	99.84 74	99.61 148	99.14 226	99.87 70	99.71 118	99.75 156	99.77 139	99.54 52	99.72 397	98.91 194	99.96 87	99.70 105

alignmvs		98.28 356	97.96 367	99.25 319	99.12 404	98.93 301	99.03 269	98.42 435	99.64 146	98.72 402	97.85 470	90.86 431	99.62 447	98.88 195	99.13 403	99.19 360

testing3-2		96.51 419	96.43 414	96.74 454	99.36 346	91.38 481	99.10 246	97.87 455	99.48 183	98.57 416	98.71 446	76.65 481	99.66 435	98.87 196	99.26 396	99.18 362

MGCFI-Net		99.02 268	99.01 252	99.06 347	99.11 409	98.60 335	99.63 64	99.67 205	99.63 148	98.58 414	97.65 473	99.07 128	99.57 456	98.85 197	98.92 419	99.03 402

sss		98.90 292	98.77 296	99.27 313	99.48 311	98.44 350	98.72 343	99.32 358	97.94 391	99.37 311	99.35 365	96.31 358	99.91 178	98.85 197	99.63 324	99.47 274

xiu_mvs_v2_base		99.02 268	99.11 216	98.77 386	99.37 343	98.09 376	98.13 406	99.51 306	99.47 188	99.42 295	98.54 455	99.38 72	99.97 44	98.83 199	99.33 385	98.24 462

PS-MVSNAJ		99.00 277	99.08 228	98.76 387	99.37 343	98.10 375	98.00 422	99.51 306	99.47 188	99.41 301	98.50 457	99.28 89	99.97 44	98.83 199	99.34 384	98.20 466

E2		99.54 112	99.51 118	99.62 178	99.78 134	99.47 181	99.01 278	99.82 102	99.55 169	99.69 188	99.77 139	99.26 93	99.76 380	98.82 201	99.93 137	99.62 184

E3		99.54 112	99.51 118	99.62 178	99.78 134	99.47 181	99.01 278	99.82 102	99.55 169	99.69 188	99.77 139	99.25 97	99.76 380	98.82 201	99.93 137	99.62 184

D2MVS		99.22 216	99.19 199	99.29 305	99.69 209	98.74 320	98.81 329	99.41 333	98.55 335	99.68 193	99.69 201	98.13 270	99.87 249	98.82 201	99.98 50	99.24 344

PatchT		98.45 343	98.32 339	98.83 380	98.94 430	98.29 360	99.24 187	98.82 412	99.84 76	99.08 364	99.76 146	91.37 420	99.94 97	98.82 201	99.00 414	98.26 461

testf1		99.63 83	99.60 90	99.72 120	99.94 18	99.95 2	99.47 111	99.89 61	99.43 203	99.88 83	99.80 106	99.26 93	99.90 197	98.81 205	99.88 181	99.32 329

APD_test2		99.63 83	99.60 90	99.72 120	99.94 18	99.95 2	99.47 111	99.89 61	99.43 203	99.88 83	99.80 106	99.26 93	99.90 197	98.81 205	99.88 181	99.32 329

usedtu_blend_shiyan5		97.97 376	97.65 388	98.92 363	97.71 483	97.49 405	99.53 91	99.81 115	99.52 177	98.18 433	96.82 487	91.92 414	99.83 319	98.79 207	96.53 476	99.45 279

blend_shiyan4		95.04 447	93.76 451	98.88 375	97.92 479	97.49 405	97.72 440	99.34 354	97.93 392	97.65 459	97.11 481	77.69 479	99.83 319	98.79 207	79.72 486	99.33 325

sasdasda		99.02 268	99.00 256	99.09 340	99.10 411	98.70 322	99.61 73	99.66 210	99.63 148	98.64 408	97.65 473	99.04 138	99.54 460	98.79 207	98.92 419	99.04 400

Effi-MVS+		99.06 259	98.97 267	99.34 288	99.31 366	98.98 291	98.31 391	99.91 52	98.81 304	98.79 396	98.94 431	99.14 112	99.84 303	98.79 207	98.74 432	99.20 357

canonicalmvs		99.02 268	99.00 256	99.09 340	99.10 411	98.70 322	99.61 73	99.66 210	99.63 148	98.64 408	97.65 473	99.04 138	99.54 460	98.79 207	98.92 419	99.04 400

VDDNet		98.97 281	98.82 290	99.42 259	99.71 188	98.81 312	99.62 67	98.68 419	99.81 92	99.38 309	99.80 106	94.25 388	99.85 287	98.79 207	99.32 387	99.59 208

CR-MVSNet		98.35 353	98.20 349	98.83 380	99.05 417	98.12 372	99.30 162	99.67 205	97.39 420	99.16 353	99.79 117	91.87 417	99.91 178	98.78 213	98.77 428	98.44 455

test_method		91.72 449	92.32 452	89.91 468	93.49 491	70.18 494	90.28 483	99.56 274	61.71 486	95.39 481	99.52 315	93.90 390	99.94 97	98.76 214	98.27 452	99.62 184

RPMNet		98.60 324	98.53 319	98.83 380	99.05 417	98.12 372	99.30 162	99.62 234	99.86 66	99.16 353	99.74 159	92.53 410	99.92 149	98.75 215	98.77 428	98.44 455

mamba_0408		99.54 112	99.55 108	99.54 219	99.71 188	99.24 249	99.27 175	99.79 129	99.72 113	99.78 133	99.64 233	99.36 77	99.93 118	98.74 216	99.90 157	99.45 279

SSM_04072		99.55 108	99.55 108	99.55 213	99.71 188	99.24 249	99.27 175	99.79 129	99.72 113	99.78 133	99.64 233	99.36 77	99.97 44	98.74 216	99.90 157	99.45 279

SSM_0407		99.56 103	99.56 106	99.54 219	99.71 188	99.24 249	99.15 222	99.84 89	99.80 96	99.78 133	99.70 192	99.44 62	99.93 118	98.74 216	99.90 157	99.45 279

SSM_0404		99.57 99	99.58 96	99.54 219	99.76 151	99.28 237	99.19 204	99.84 89	99.80 96	99.78 133	99.70 192	99.44 62	99.93 118	98.74 216	99.95 109	99.41 303

pmmvs4		99.13 243	99.06 234	99.36 284	99.57 263	99.10 281	98.01 420	99.25 375	98.78 309	99.58 241	99.44 338	98.24 257	99.76 380	98.74 216	99.93 137	99.22 350

viewmanbaseed2359cas		99.50 121	99.47 127	99.61 184	99.73 179	99.52 174	99.03 269	99.83 96	99.49 180	99.65 209	99.64 233	99.18 103	99.71 402	98.73 221	99.92 143	99.58 213

tttt0517		97.62 388	97.20 398	98.90 372	99.76 151	97.40 411	99.48 108	94.36 478	99.06 269	99.70 185	99.49 324	84.55 463	99.94 97	98.73 221	99.65 319	99.36 317

viewcassd2359sk11		99.48 128	99.45 134	99.58 194	99.73 179	99.42 202	98.96 300	99.80 120	99.44 196	99.63 217	99.74 159	99.09 121	99.76 380	98.72 223	99.91 155	99.57 219

EPP-MVSNet		99.17 235	99.00 256	99.66 148	99.80 112	99.43 199	99.70 38	99.24 378	99.48 183	99.56 252	99.77 139	94.89 380	99.93 118	98.72 223	99.89 171	99.63 172

FE-MVSNET		99.45 144	99.36 158	99.71 126	99.84 74	99.64 132	99.16 219	99.91 52	98.65 324	99.73 171	99.73 164	98.54 217	99.82 335	98.71 225	99.96 87	99.67 131

Anonymous20240529		99.42 154	99.34 164	99.65 155	99.53 286	99.60 152	99.63 64	99.39 343	99.47 188	99.76 151	99.78 129	98.13 270	99.86 268	98.70 226	99.68 308	99.49 266

ACMH	98.42 6	99.59 98	99.54 111	99.72 120	99.86 58	99.62 140	99.56 87	99.79 129	98.77 311	99.80 123	99.85 69	99.64 35	99.85 287	98.70 226	99.89 171	99.70 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 187	99.28 184	99.47 242	99.57 263	99.39 213	99.78 17	99.43 330	98.87 294	99.57 244	99.82 91	98.06 276	99.87 249	98.69 228	99.73 284	99.15 369

LFMVS		98.46 342	98.19 352	99.26 316	99.24 383	98.52 346	99.62 67	96.94 467	99.87 63	99.31 328	99.58 288	91.04 425	99.81 351	98.68 229	99.42 374	99.45 279

WR-MVS		99.11 250	98.93 272	99.66 148	99.30 370	99.42 202	98.42 384	99.37 348	99.04 270	99.57 244	99.20 397	96.89 337	99.86 268	98.66 230	99.87 194	99.70 105

mvsmamba		99.08 255	98.95 270	99.45 249	99.36 346	99.18 267	99.39 126	98.81 413	99.37 214	99.35 314	99.70 192	96.36 357	99.94 97	98.66 230	99.59 340	99.22 350

viewdifsd2359ckpt13		99.42 154	99.37 153	99.57 202	99.72 184	99.46 187	99.01 278	99.80 120	99.20 244	99.51 274	99.60 275	98.92 159	99.70 406	98.65 232	99.90 157	99.55 226

RRT-MVS		99.08 255	99.00 256	99.33 291	99.27 377	98.65 330	99.62 67	99.93 39	99.66 139	99.67 200	99.82 91	95.27 377	99.93 118	98.64 233	99.09 407	99.41 303

E3new		99.42 154	99.37 153	99.56 206	99.68 217	99.38 215	98.93 308	99.79 129	99.30 226	99.55 257	99.69 201	98.88 166	99.76 380	98.63 234	99.89 171	99.53 242

Anonymous202405211		98.75 309	98.46 323	99.63 169	99.34 359	99.66 120	99.47 111	97.65 458	99.28 230	99.56 252	99.50 320	93.15 402	99.84 303	98.62 235	99.58 342	99.40 306

lecture		99.56 103	99.48 125	99.81 54	99.78 134	99.86 19	99.50 101	99.70 189	99.59 164	99.75 156	99.71 182	98.94 155	99.92 149	98.59 236	99.76 267	99.66 145

EPNet		98.13 367	97.77 382	99.18 328	94.57 490	97.99 382	99.24 187	97.96 451	99.74 108	97.29 463	99.62 257	93.13 403	99.97 44	98.59 236	99.83 220	99.58 213
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 262	99.09 226	98.91 366	99.21 388	98.36 358	98.82 328	99.47 318	98.85 297	98.90 382	99.56 299	98.78 179	99.09 476	98.57 238	99.68 308	99.26 341

Patchmatch-RL test		98.60 324	98.36 334	99.33 291	99.77 147	99.07 284	98.27 393	99.87 70	98.91 289	99.74 166	99.72 172	90.57 436	99.79 362	98.55 239	99.85 207	99.11 378

pmmvs3		98.08 370	97.80 379	98.91 366	99.41 336	97.69 400	97.87 435	99.66 210	95.87 451	99.50 277	99.51 317	90.35 438	99.97 44	98.55 239	99.47 367	99.08 391

ETV-MVS		99.18 230	99.18 200	99.16 329	99.34 359	99.28 237	99.12 238	99.79 129	99.48 183	98.93 376	98.55 454	99.40 67	99.93 118	98.51 241	99.52 359	98.28 460

viewdifsd2359ckpt09		99.24 205	99.16 202	99.49 235	99.70 203	99.22 255	98.88 313	99.81 115	98.70 319	99.38 309	99.37 355	98.22 262	99.76 380	98.48 242	99.88 181	99.51 255

jason		99.16 236	99.11 216	99.32 296	99.75 167	98.44 350	98.26 395	99.39 343	98.70 319	99.74 166	99.30 374	98.54 217	99.97 44	98.48 242	99.82 230	99.55 226
jason: jason.
APDe-MVS		99.48 128	99.36 158	99.85 32	99.55 277	99.81 48	99.50 101	99.69 197	98.99 274	99.75 156	99.71 182	98.79 177	99.93 118	98.46 244	99.85 207	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		99.30 191	99.29 181	99.31 300	99.71 188	98.55 340	98.17 401	99.71 180	99.41 208	99.73 171	99.60 275	99.17 105	99.92 149	98.45 245	99.70 295	99.45 279

IMVS_0407		99.38 169	99.42 142	99.28 308	99.71 188	98.55 340	99.27 175	99.71 180	99.41 208	99.73 171	99.60 275	99.17 105	99.83 319	98.45 245	99.70 295	99.45 279

IMVS_0404		99.23 207	99.20 197	99.32 296	99.71 188	98.55 340	98.57 363	99.71 180	99.41 208	99.52 267	99.60 275	98.12 272	99.95 80	98.45 245	99.70 295	99.45 279

IMVS_0403		99.37 173	99.39 147	99.28 308	99.71 188	98.55 340	99.19 204	99.71 180	99.41 208	99.67 200	99.60 275	99.12 117	99.84 303	98.45 245	99.70 295	99.45 279

CL-MVSNet_self_test		98.71 315	98.56 317	99.15 331	99.22 386	98.66 327	97.14 467	99.51 306	98.09 380	99.54 260	99.27 380	96.87 338	99.74 392	98.43 249	98.96 416	99.03 402

our_test_3		98.85 300	99.09 226	98.13 418	99.66 226	94.90 460	97.72 440	99.58 267	99.07 267	99.64 212	99.62 257	98.19 266	99.93 118	98.41 250	99.95 109	99.55 226

Gipuma		99.57 99	99.59 92	99.49 235	99.98 3	99.71 100	99.72 33	99.84 89	99.81 92	99.94 48	99.78 129	98.91 162	99.71 402	98.41 250	99.95 109	99.05 398
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 399	96.91 408	98.74 388	97.72 482	97.57 402	97.60 447	97.36 464	98.00 383	99.21 347	98.02 466	90.04 441	99.79 362	98.37 252	95.89 481	98.86 425

PM-MVS		99.36 177	99.29 181	99.58 194	99.83 82	99.66 120	98.95 303	99.86 76	98.85 297	99.81 116	99.73 164	98.40 242	99.92 149	98.36 253	99.83 220	99.17 365

baseline1		97.73 383	97.33 394	98.96 356	99.30 370	97.73 398	99.40 124	98.42 435	99.33 221	99.46 286	99.21 395	91.18 423	99.82 335	98.35 254	91.26 484	99.32 329

MVS-HIRNet		97.86 377	98.22 347	96.76 452	99.28 375	91.53 479	98.38 386	92.60 484	99.13 260	99.31 328	99.96 15	97.18 329	99.68 425	98.34 255	99.83 220	99.07 396

GA-MVS		97.99 375	97.68 385	98.93 362	99.52 293	98.04 380	97.19 465	99.05 402	98.32 367	98.81 392	98.97 427	89.89 443	99.41 471	98.33 256	99.05 410	99.34 324

Fast-Effi-MVS+		99.02 268	98.87 283	99.46 246	99.38 341	99.50 176	99.04 266	99.79 129	97.17 430	98.62 410	98.74 445	99.34 81	99.95 80	98.32 257	99.41 375	98.92 418

MDA-MVSNet_test_wron		98.95 287	98.99 263	98.85 376	99.64 232	97.16 417	98.23 397	99.33 356	98.93 286	99.56 252	99.66 222	97.39 318	99.83 319	98.29 258	99.88 181	99.55 226

N_pmnet		98.73 312	98.53 319	99.35 286	99.72 184	98.67 324	98.34 388	94.65 477	98.35 361	99.79 129	99.68 213	98.03 277	99.93 118	98.28 259	99.92 143	99.44 292

ET-MVSNet_ETH3D		96.78 411	96.07 421	98.91 366	99.26 380	97.92 389	97.70 443	96.05 472	97.96 390	92.37 485	98.43 458	87.06 451	99.90 197	98.27 260	97.56 469	98.91 419

thisisatest0530		97.45 394	96.95 405	98.94 359	99.68 217	97.73 398	99.09 251	94.19 480	98.61 331	99.56 252	99.30 374	84.30 465	99.93 118	98.27 260	99.54 354	99.16 367

YYNet1		98.95 287	98.99 263	98.84 378	99.64 232	97.14 419	98.22 398	99.32 358	98.92 288	99.59 239	99.66 222	97.40 316	99.83 319	98.27 260	99.90 157	99.55 226

reproduce_model		99.50 121	99.40 146	99.83 41	99.60 241	99.83 35	99.12 238	99.68 200	99.49 180	99.80 123	99.79 117	99.01 143	99.93 118	98.24 263	99.82 230	99.73 93

ACMM	98.09 11	99.46 140	99.38 150	99.72 120	99.80 112	99.69 112	99.13 233	99.65 220	98.99 274	99.64 212	99.72 172	99.39 68	99.86 268	98.23 264	99.81 240	99.60 201
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 284	98.87 283	99.24 321	99.57 263	98.40 353	98.12 407	99.18 390	98.28 369	99.63 217	99.13 401	98.02 278	99.97 44	98.22 265	99.69 303	99.35 320

3Dnovator	99.15 2	99.43 151	99.36 158	99.65 155	99.39 338	99.42 202	99.70 38	99.56 274	99.23 239	99.35 314	99.80 106	99.17 105	99.95 80	98.21 266	99.84 212	99.59 208

Fast-Effi-MVS+-dtu		99.20 223	99.12 213	99.43 257	99.25 381	99.69 112	99.05 261	99.82 102	99.50 178	98.97 372	99.05 413	98.98 150	99.98 27	98.20 267	99.24 399	98.62 440

MS-PatchMatch		99.00 277	98.97 267	99.09 340	99.11 409	98.19 366	98.76 338	99.33 356	98.49 344	99.44 288	99.58 288	98.21 263	99.69 413	98.20 267	99.62 326	99.39 309

TSAR-MVS + GP.		99.12 246	99.04 245	99.38 276	99.34 359	99.16 268	98.15 403	99.29 366	98.18 376	99.63 217	99.62 257	99.18 103	99.68 425	98.20 267	99.74 278	99.30 335

DP-MVS		99.48 128	99.39 147	99.74 101	99.57 263	99.62 140	99.29 169	99.61 241	99.87 63	99.74 166	99.76 146	98.69 192	99.87 249	98.20 267	99.80 247	99.75 87

MVP-Stereo		99.16 236	99.08 228	99.43 257	99.48 311	99.07 284	99.08 254	99.55 280	98.63 327	99.31 328	99.68 213	98.19 266	99.78 365	98.18 271	99.58 342	99.45 279
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 151	99.30 176	99.80 64	99.83 82	99.81 48	99.52 93	99.70 189	98.35 361	99.51 274	99.50 320	99.31 85	99.88 234	98.18 271	99.84 212	99.69 115

MDA-MVSNet-bldmvs		99.06 259	99.05 239	99.07 345	99.80 112	97.83 393	98.89 312	99.72 177	99.29 227	99.63 217	99.70 192	96.47 350	99.89 219	98.17 273	99.82 230	99.50 261

JIA-IIPM		98.06 371	97.92 374	98.50 400	98.59 461	97.02 421	98.80 332	98.51 430	99.88 61	97.89 447	99.87 56	91.89 416	99.90 197	98.16 274	97.68 468	98.59 443

EIA-MVS		99.12 246	99.01 252	99.45 249	99.36 346	99.62 140	99.34 145	99.79 129	98.41 350	98.84 389	98.89 435	98.75 184	99.84 303	98.15 275	99.51 360	98.89 422

miper_lstm_enhance		98.65 320	98.60 307	98.82 383	99.20 391	97.33 413	97.78 438	99.66 210	99.01 273	99.59 239	99.50 320	94.62 385	99.85 287	98.12 276	99.90 157	99.26 341

reproduce-ours		99.46 140	99.35 162	99.82 46	99.56 274	99.83 35	99.05 261	99.65 220	99.45 194	99.78 133	99.78 129	98.93 156	99.93 118	98.11 277	99.81 240	99.70 105

our_new_method		99.46 140	99.35 162	99.82 46	99.56 274	99.83 35	99.05 261	99.65 220	99.45 194	99.78 133	99.78 129	98.93 156	99.93 118	98.11 277	99.81 240	99.70 105

Effi-MVS+-dtu		99.07 258	98.92 276	99.52 225	98.89 435	99.78 58	99.15 222	99.66 210	99.34 218	98.92 379	99.24 390	97.69 302	99.98 27	98.11 277	99.28 392	98.81 429

tpm		97.15 403	96.95 405	97.75 432	98.91 431	94.24 463	99.32 154	97.96 451	97.71 404	98.29 427	99.32 369	86.72 457	99.92 149	98.10 280	96.24 479	99.09 385

DeepPCF-MVS	98.42 6	99.18 230	99.02 248	99.67 141	99.22 386	99.75 79	97.25 463	99.47 318	98.72 316	99.66 206	99.70 192	99.29 87	99.63 446	98.07 281	99.81 240	99.62 184

ppachtmachnet_test		98.89 295	99.12 213	98.20 416	99.66 226	95.24 456	97.63 445	99.68 200	99.08 265	99.78 133	99.62 257	98.65 200	99.88 234	98.02 282	99.96 87	99.48 270

tpmrst		97.73 383	98.07 360	96.73 455	98.71 457	92.00 474	99.10 246	98.86 409	98.52 340	98.92 379	99.54 309	91.90 415	99.82 335	98.02 282	99.03 412	98.37 457

CSCG		99.37 173	99.29 181	99.60 188	99.71 188	99.46 187	99.43 120	99.85 82	98.79 307	99.41 301	99.60 275	98.92 159	99.92 149	98.02 282	99.92 143	99.43 298

eth_miper_zixun_eth		98.68 318	98.71 299	98.60 395	99.10 411	96.84 426	97.52 453	99.54 286	98.94 282	99.58 241	99.48 327	96.25 361	99.76 380	98.01 285	99.93 137	99.21 353

Patchmtry		98.78 306	98.54 318	99.49 235	98.89 435	99.19 262	99.32 154	99.67 205	99.65 142	99.72 176	99.79 117	91.87 417	99.95 80	98.00 286	99.97 73	99.33 325

PVSNet_BlendedMVS		99.03 266	99.01 252	99.09 340	99.54 279	97.99 382	98.58 359	99.82 102	97.62 407	99.34 318	99.71 182	98.52 225	99.77 376	97.98 287	99.97 73	99.52 253

PVSNet_Blended		98.70 316	98.59 309	99.02 350	99.54 279	97.99 382	97.58 448	99.82 102	95.70 455	99.34 318	98.98 425	98.52 225	99.77 376	97.98 287	99.83 220	99.30 335

cl____		98.54 332	98.41 329	98.92 363	99.03 421	97.80 396	97.46 455	99.59 258	98.90 290	99.60 236	99.46 334	93.85 392	99.78 365	97.97 289	99.89 171	99.17 365

DIV-MVS_self_test		98.54 332	98.42 328	98.92 363	99.03 421	97.80 396	97.46 455	99.59 258	98.90 290	99.60 236	99.46 334	93.87 391	99.78 365	97.97 289	99.89 171	99.18 362

AUN-MVS		97.82 379	97.38 393	99.14 334	99.27 377	98.53 344	98.72 343	99.02 404	98.10 378	97.18 466	99.03 419	89.26 445	99.85 287	97.94 291	97.91 464	99.03 402

FA-MVS(test-final)		98.52 334	98.32 339	99.10 339	99.48 311	98.67 324	99.77 19	98.60 426	97.35 422	99.63 217	99.80 106	93.07 404	99.84 303	97.92 292	99.30 389	98.78 432

ambc				99.20 325	99.35 350	98.53 344	99.17 213	99.46 321		99.67 200	99.80 106	98.46 232	99.70 406	97.92 292	99.70 295	99.38 311

USDC		98.96 284	98.93 272	99.05 348	99.54 279	97.99 382	97.07 470	99.80 120	98.21 373	99.75 156	99.77 139	98.43 235	99.64 444	97.90 294	99.88 181	99.51 255

OPM-MVS		99.26 200	99.13 209	99.63 169	99.70 203	99.61 148	98.58 359	99.48 315	98.50 342	99.52 267	99.63 248	99.14 112	99.76 380	97.89 295	99.77 265	99.51 255
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 189	99.17 201	99.77 78	99.69 209	99.80 52	99.14 226	99.31 362	99.16 254	99.62 227	99.61 267	98.35 246	99.91 178	97.88 296	99.72 290	99.61 197
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	99.70 203	99.79 55	99.14 226	99.61 241					99.92 149	97.88 296	99.72 290	99.77 79

c3_l		98.72 313	98.71 299	98.72 389	99.12 404	97.22 416	97.68 444	99.56 274	98.90 290	99.54 260	99.48 327	96.37 356	99.73 395	97.88 296	99.88 181	99.21 353

3Dnovator+	98.92 3	99.35 179	99.24 193	99.67 141	99.35 350	99.47 181	99.62 67	99.50 310	99.44 196	99.12 360	99.78 129	98.77 181	99.94 97	97.87 299	99.72 290	99.62 184

miper_ehance_all_eth		98.59 327	98.59 309	98.59 396	98.98 427	97.07 420	97.49 454	99.52 301	98.50 342	99.52 267	99.37 355	96.41 354	99.71 402	97.86 300	99.62 326	99.00 409

WTY-MVS		98.59 327	98.37 333	99.26 316	99.43 329	98.40 353	98.74 341	99.13 397	98.10 378	99.21 347	99.24 390	94.82 382	99.90 197	97.86 300	98.77 428	99.49 266

APD_test1		99.36 177	99.28 184	99.61 184	99.89 39	99.89 10	99.32 154	99.74 164	99.18 247	99.69 188	99.75 154	98.41 238	99.84 303	97.85 302	99.70 295	99.10 380

SED-MVS		99.40 162	99.28 184	99.77 78	99.69 209	99.82 43	99.20 198	99.54 286	99.13 260	99.82 109	99.63 248	98.91 162	99.92 149	97.85 302	99.70 295	99.58 213

test_241102_TWO								99.54 286	99.13 260	99.76 151	99.63 248	98.32 251	99.92 149	97.85 302	99.69 303	99.75 87

MVS_111021_HR		99.12 246	99.02 248	99.40 270	99.50 301	99.11 274	97.92 431	99.71 180	98.76 314	99.08 364	99.47 331	99.17 105	99.54 460	97.85 302	99.76 267	99.54 236

MTAPA		99.35 179	99.20 197	99.80 64	99.81 103	99.81 48	99.33 151	99.53 296	99.27 231	99.42 295	99.63 248	98.21 263	99.95 80	97.83 306	99.79 252	99.65 154

MSC_two_6792asdad				99.74 101	99.03 421	99.53 170		99.23 379					99.92 149	97.77 307	99.69 303	99.78 75

No_MVS				99.74 101	99.03 421	99.53 170		99.23 379					99.92 149	97.77 307	99.69 303	99.78 75

TESTMET0.1,1		96.24 426	95.84 427	97.41 441	98.24 472	93.84 466	97.38 457	95.84 473	98.43 347	97.81 453	98.56 453	79.77 474	99.89 219	97.77 307	98.77 428	98.52 449

ACMH+	98.40 8	99.50 121	99.43 140	99.71 126	99.86 58	99.76 71	99.32 154	99.77 145	99.53 173	99.77 145	99.76 146	99.26 93	99.78 365	97.77 307	99.88 181	99.60 201

IU-MVS					99.69 209	99.77 64		99.22 382	97.50 414	99.69 188				97.75 311	99.70 295	99.77 79

114514_t		98.49 339	98.11 357	99.64 162	99.73 179	99.58 159	99.24 187	99.76 153	89.94 478	99.42 295	99.56 299	97.76 299	99.86 268	97.74 312	99.82 230	99.47 274

DVP-MVS++		99.38 169	99.25 191	99.77 78	99.03 421	99.77 64	99.74 27	99.61 241	99.18 247	99.76 151	99.61 267	99.00 144	99.92 149	97.72 313	99.60 336	99.62 184

test_0728_THIRD									99.18 247	99.62 227	99.61 267	98.58 208	99.91 178	97.72 313	99.80 247	99.77 79

EGC-MVSNET		89.05 451	85.52 454	99.64 162	99.89 39	99.78 58	99.56 87	99.52 301	24.19 487	49.96 488	99.83 84	99.15 109	99.92 149	97.71 315	99.85 207	99.21 353

miper_enhance_ethall		98.03 372	97.94 372	98.32 410	98.27 471	96.43 434	96.95 471	99.41 333	96.37 446	99.43 292	98.96 429	94.74 383	99.69 413	97.71 315	99.62 326	98.83 428

TSAR-MVS + MP.		99.34 184	99.24 193	99.63 169	99.82 91	99.37 219	99.26 180	99.35 352	98.77 311	99.57 244	99.70 192	99.27 92	99.88 234	97.71 315	99.75 271	99.65 154
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 391	97.28 395	98.40 405	98.37 469	96.75 427	97.24 464	99.37 348	97.31 424	99.41 301	99.22 392	87.30 449	99.37 472	97.70 318	99.62 326	99.08 391

MP-MVS-pluss		99.14 241	98.92 276	99.80 64	99.83 82	99.83 35	98.61 352	99.63 231	96.84 439	99.44 288	99.58 288	98.81 172	99.91 178	97.70 318	99.82 230	99.67 131
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 194	99.11 216	99.79 71	99.75 167	99.81 48	98.95 303	99.53 296	98.27 370	99.53 265	99.73 164	98.75 184	99.87 249	97.70 318	99.83 220	99.68 122

UnsupCasMVSNet_bld		98.55 331	98.27 345	99.40 270	99.56 274	99.37 219	97.97 427	99.68 200	97.49 415	99.08 364	99.35 365	95.41 376	99.82 335	97.70 318	98.19 456	99.01 408

MVS_111021_LR		99.13 243	99.03 247	99.42 259	99.58 253	99.32 231	97.91 433	99.73 168	98.68 321	99.31 328	99.48 327	99.09 121	99.66 435	97.70 318	99.77 265	99.29 338

IS-MVSNet		99.03 266	98.85 285	99.55 213	99.80 112	99.25 245	99.73 30	99.15 394	99.37 214	99.61 233	99.71 182	94.73 384	99.81 351	97.70 318	99.88 181	99.58 213

MED-MVS test				99.74 101	99.76 151	99.65 126	99.38 129	99.78 139	99.58 166	99.81 116	99.66 222		99.90 197	97.69 324	99.79 252	99.67 131

MED-MVS		99.45 144	99.36 158	99.74 101	99.76 151	99.65 126	99.38 129	99.78 139	99.31 224	99.81 116	99.66 222	99.02 141	99.90 197	97.69 324	99.79 252	99.67 131

ME-MVS		99.26 200	99.10 224	99.73 112	99.60 241	99.65 126	98.75 340	99.45 326	99.31 224	99.65 209	99.66 222	98.00 283	99.86 268	97.69 324	99.79 252	99.67 131

test-LLR		97.15 403	96.95 405	97.74 433	98.18 474	95.02 458	97.38 457	96.10 469	98.00 383	97.81 453	98.58 450	90.04 441	99.91 178	97.69 324	98.78 426	98.31 458

test-mter		96.23 427	95.73 430	97.74 433	98.18 474	95.02 458	97.38 457	96.10 469	97.90 393	97.81 453	98.58 450	79.12 477	99.91 178	97.69 324	98.78 426	98.31 458

MonoMVSNet		98.23 361	98.32 339	97.99 421	98.97 428	96.62 429	99.49 106	98.42 435	99.62 151	99.40 306	99.79 117	95.51 374	98.58 483	97.68 329	95.98 480	98.76 435

XVS		99.27 198	99.11 216	99.75 96	99.71 188	99.71 100	99.37 137	99.61 241	99.29 227	98.76 399	99.47 331	98.47 229	99.88 234	97.62 330	99.73 284	99.67 131

X-MVStestdata		96.09 431	94.87 444	99.75 96	99.71 188	99.71 100	99.37 137	99.61 241	99.29 227	98.76 399	61.30 496	98.47 229	99.88 234	97.62 330	99.73 284	99.67 131

SMA-MVS		99.19 226	99.00 256	99.73 112	99.46 321	99.73 90	99.13 233	99.52 301	97.40 419	99.57 244	99.64 233	98.93 156	99.83 319	97.61 332	99.79 252	99.63 172
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		96.71 414	96.79 413	96.46 459	98.90 432	90.71 485	99.41 121	98.68 419	94.69 468	98.14 438	99.34 368	86.32 459	99.80 359	97.60 333	98.07 462	98.88 423

PVSNet	97.47 15	98.42 345	98.44 326	98.35 407	99.46 321	96.26 438	96.70 475	99.34 354	97.68 405	99.00 371	99.13 401	97.40 316	99.72 397	97.59 334	99.68 308	99.08 391

new_pmnet		98.88 296	98.89 281	98.84 378	99.70 203	97.62 401	98.15 403	99.50 310	97.98 386	99.62 227	99.54 309	98.15 269	99.94 97	97.55 335	99.84 212	98.95 413

IB-MVS	95.41 20	95.30 446	94.46 450	97.84 429	98.76 453	95.33 454	97.33 460	96.07 471	96.02 450	95.37 482	97.41 477	76.17 482	99.96 69	97.54 336	95.44 483	98.22 463
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		99.24 205	99.11 216	99.61 184	98.38 468	99.79 55	99.57 85	99.68 200	99.61 156	99.15 355	99.71 182	98.70 191	99.91 178	97.54 336	99.68 308	99.13 377

ZNCC-MVS		99.22 216	99.04 245	99.77 78	99.76 151	99.73 90	99.28 171	99.56 274	98.19 375	99.14 357	99.29 377	98.84 171	99.92 149	97.53 338	99.80 247	99.64 166

CP-MVS		99.23 207	99.05 239	99.75 96	99.66 226	99.66 120	99.38 129	99.62 234	98.38 354	99.06 368	99.27 380	98.79 177	99.94 97	97.51 339	99.82 230	99.66 145

SD-MVS		99.01 274	99.30 176	98.15 417	99.50 301	99.40 210	98.94 305	99.61 241	99.22 243	99.75 156	99.82 91	99.54 52	95.51 487	97.48 340	99.87 194	99.54 236
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		98.49 339	98.29 344	99.11 337	98.96 429	98.42 352	97.54 449	99.32 358	97.53 412	98.47 422	98.15 465	97.88 289	99.82 335	97.46 341	99.24 399	99.09 385

DeepC-MVS_fast	98.47 5	99.23 207	99.12 213	99.56 206	99.28 375	99.22 255	98.99 291	99.40 340	99.08 265	99.58 241	99.64 233	98.90 165	99.83 319	97.44 342	99.75 271	99.63 172
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 202	99.08 228	99.76 85	99.73 179	99.70 108	99.31 159	99.59 258	98.36 356	99.36 312	99.37 355	98.80 176	99.91 178	97.43 343	99.75 271	99.68 122

ACMMPR		99.23 207	99.06 234	99.76 85	99.74 175	99.69 112	99.31 159	99.59 258	98.36 356	99.35 314	99.38 352	98.61 204	99.93 118	97.43 343	99.75 271	99.67 131

Vis-MVSNet (Re-imp)		98.77 307	98.58 312	99.34 288	99.78 134	98.88 307	99.61 73	99.56 274	99.11 264	99.24 341	99.56 299	93.00 406	99.78 365	97.43 343	99.89 171	99.35 320

MIMVSNet		98.43 344	98.20 349	99.11 337	99.53 286	98.38 357	99.58 82	98.61 424	98.96 278	99.33 320	99.76 146	90.92 427	99.81 351	97.38 346	99.76 267	99.15 369

WB-MVSnew		98.34 355	98.14 355	98.96 356	98.14 477	97.90 390	98.27 393	97.26 465	98.63 327	98.80 394	98.00 468	97.77 297	99.90 197	97.37 347	98.98 415	99.09 385

XVG-OURS-SEG-HR		99.16 236	98.99 263	99.66 148	99.84 74	99.64 132	98.25 396	99.73 168	98.39 353	99.63 217	99.43 339	99.70 31	99.90 197	97.34 348	98.64 439	99.44 292

COLMAP_ROB	98.06 12	99.45 144	99.37 153	99.70 131	99.83 82	99.70 108	99.38 129	99.78 139	99.53 173	99.67 200	99.78 129	99.19 102	99.86 268	97.32 349	99.87 194	99.55 226
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 268	98.81 292	99.65 155	99.58 253	99.49 177	98.58 359	99.07 399	98.40 352	99.04 369	99.25 385	98.51 227	99.80 359	97.31 350	99.51 360	99.65 154

region2R		99.23 207	99.05 239	99.77 78	99.76 151	99.70 108	99.31 159	99.59 258	98.41 350	99.32 323	99.36 360	98.73 188	99.93 118	97.29 351	99.74 278	99.67 131

APD-MVS_3200maxsize		99.31 190	99.16 202	99.74 101	99.53 286	99.75 79	99.27 175	99.61 241	99.19 246	99.57 244	99.64 233	98.76 182	99.90 197	97.29 351	99.62 326	99.56 222

TAPA-MVS	97.92 13	98.03 372	97.55 389	99.46 246	99.47 317	99.44 195	98.50 374	99.62 234	86.79 479	99.07 367	99.26 383	98.26 256	99.62 447	97.28 353	99.73 284	99.31 333
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 198	99.11 216	99.73 112	99.54 279	99.74 87	99.26 180	99.62 234	99.16 254	99.52 267	99.64 233	98.41 238	99.91 178	97.27 354	99.61 333	99.54 236

RE-MVS-def			99.13 209		99.54 279	99.74 87	99.26 180	99.62 234	99.16 254	99.52 267	99.64 233	98.57 209		97.27 354	99.61 333	99.54 236

testing11		96.05 433	95.41 436	97.97 423	98.78 450	95.27 455	98.59 357	98.23 445	98.86 296	96.56 473	96.91 485	75.20 483	99.69 413	97.26 356	98.29 451	98.93 416

test_yl		98.25 358	97.95 368	99.13 335	99.17 397	98.47 347	99.00 284	98.67 421	98.97 276	99.22 345	99.02 420	91.31 421	99.69 413	97.26 356	98.93 417	99.24 344

DCV-MVSNet		98.25 358	97.95 368	99.13 335	99.17 397	98.47 347	99.00 284	98.67 421	98.97 276	99.22 345	99.02 420	91.31 421	99.69 413	97.26 356	98.93 417	99.24 344

PHI-MVS		99.11 250	98.95 270	99.59 191	99.13 402	99.59 154	99.17 213	99.65 220	97.88 396	99.25 338	99.46 334	98.97 152	99.80 359	97.26 356	99.82 230	99.37 314

tfpnnormal		99.43 151	99.38 150	99.60 188	99.87 54	99.75 79	99.59 80	99.78 139	99.71 118	99.90 68	99.69 201	98.85 170	99.90 197	97.25 360	99.78 261	99.15 369

PatchmatchNet		97.65 387	97.80 379	97.18 448	98.82 445	92.49 472	99.17 213	98.39 438	98.12 377	98.79 396	99.58 288	90.71 433	99.89 219	97.23 361	99.41 375	99.16 367
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 280	98.80 294	99.56 206	99.25 381	99.43 199	98.54 369	99.27 370	98.58 333	98.80 394	99.43 339	98.53 222	99.70 406	97.22 362	99.59 340	99.54 236

testing3		96.48 420	95.63 432	99.01 351	99.23 385	97.81 394	98.90 311	99.10 398	98.72 316	97.84 452	97.92 469	72.44 487	99.85 287	97.21 363	99.33 385	99.35 320

HPM-MVS		99.25 202	99.07 232	99.78 75	99.81 103	99.75 79	99.61 73	99.67 205	97.72 403	99.35 314	99.25 385	99.23 98	99.92 149	97.21 363	99.82 230	99.67 131
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 226	99.00 256	99.76 85	99.76 151	99.68 115	99.38 129	99.54 286	98.34 365	99.01 370	99.50 320	98.53 222	99.93 118	97.18 365	99.78 261	99.66 145

ACMMP		99.25 202	99.08 228	99.74 101	99.79 126	99.68 115	99.50 101	99.65 220	98.07 381	99.52 267	99.69 201	98.57 209	99.92 149	97.18 365	99.79 252	99.63 172
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		96.23 427	95.74 429	97.70 435	98.86 439	95.59 451	98.66 349	98.14 447	98.96 278	97.67 458	97.06 482	76.78 480	98.92 479	97.10 367	98.41 448	98.58 445

thisisatest0515		96.98 407	96.42 415	98.66 392	99.42 334	97.47 407	97.27 462	94.30 479	97.24 426	99.15 355	98.86 437	85.01 461	99.87 249	97.10 367	99.39 377	98.63 439

XVG-ACMP-BASELINE		99.23 207	99.10 224	99.63 169	99.82 91	99.58 159	98.83 324	99.72 177	98.36 356	99.60 236	99.71 182	98.92 159	99.91 178	97.08 369	99.84 212	99.40 306

MSDG		99.08 255	98.98 266	99.37 279	99.60 241	99.13 271	97.54 449	99.74 164	98.84 300	99.53 265	99.55 307	99.10 119	99.79 362	97.07 370	99.86 202	99.18 362

SteuartSystems-ACMMP		99.30 191	99.14 207	99.76 85	99.87 54	99.66 120	99.18 208	99.60 252	98.55 335	99.57 244	99.67 217	99.03 140	99.94 97	97.01 371	99.80 247	99.69 115
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 429	95.78 428	97.49 437	98.53 463	93.83 467	98.04 417	93.94 482	98.96 278	98.46 423	98.17 464	79.86 472	99.87 249	96.99 372	99.06 408	98.78 432

EPMVS		96.53 417	96.32 416	97.17 449	98.18 474	92.97 471	99.39 126	89.95 488	98.21 373	98.61 411	99.59 285	86.69 458	99.72 397	96.99 372	99.23 401	98.81 429

MSP-MVS		99.04 265	98.79 295	99.81 54	99.78 134	99.73 90	99.35 144	99.57 269	98.54 338	99.54 260	98.99 422	96.81 339	99.93 118	96.97 374	99.53 356	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++		98.96 284	98.70 302	99.74 101	99.52 293	99.71 100	98.86 317	99.19 389	98.47 346	98.59 413	99.06 412	98.08 275	99.91 178	96.94 375	99.60 336	99.60 201

SR-MVS		99.19 226	99.00 256	99.74 101	99.51 295	99.72 95	99.18 208	99.60 252	98.85 297	99.47 282	99.58 288	98.38 243	99.92 149	96.92 376	99.54 354	99.57 219

PGM-MVS		99.20 223	99.01 252	99.77 78	99.75 167	99.71 100	99.16 219	99.72 177	97.99 385	99.42 295	99.60 275	98.81 172	99.93 118	96.91 377	99.74 278	99.66 145

HY-MVS	98.23 9	98.21 365	97.95 368	98.99 352	99.03 421	98.24 361	99.61 73	98.72 417	96.81 440	98.73 401	99.51 317	94.06 389	99.86 268	96.91 377	98.20 454	98.86 425

MDTV_nov1_ep13			97.73 383		98.70 458	90.83 483	99.15 222	98.02 450	98.51 341	98.82 391	99.61 267	90.98 426	99.66 435	96.89 379	98.92 419

GST-MVS		99.16 236	98.96 269	99.75 96	99.73 179	99.73 90	99.20 198	99.55 280	98.22 372	99.32 323	99.35 365	98.65 200	99.91 178	96.86 380	99.74 278	99.62 184

test_post1							99.14 226				51.63 498	89.54 444	99.82 335	96.86 380

SCA		98.11 368	98.36 334	97.36 442	99.20 391	92.99 470	98.17 401	98.49 432	98.24 371	99.10 363	99.57 295	96.01 366	99.94 97	96.86 380	99.62 326	99.14 374

UBG		96.53 417	95.95 423	98.29 414	98.87 438	96.31 437	98.48 377	98.07 448	98.83 301	97.32 461	96.54 491	79.81 473	99.62 447	96.84 383	98.74 432	98.95 413

XVG-OURS		99.21 221	99.06 234	99.65 155	99.82 91	99.62 140	97.87 435	99.74 164	98.36 356	99.66 206	99.68 213	99.71 28	99.90 197	96.84 383	99.88 181	99.43 298

LCM-MVSNet-Re		99.28 194	99.15 206	99.67 141	99.33 364	99.76 71	99.34 145	99.97 20	98.93 286	99.91 63	99.79 117	98.68 193	99.93 118	96.80 385	99.56 345	99.30 335

RPSCF		99.18 230	99.02 248	99.64 162	99.83 82	99.85 22	99.44 118	99.82 102	98.33 366	99.50 277	99.78 129	97.90 287	99.65 442	96.78 386	99.83 220	99.44 292

旧先验2							97.94 429		95.33 459	98.94 375			99.88 234	96.75 387

MDTV_nov1_ep13_2view						91.44 480	99.14 226		97.37 421	99.21 347		91.78 419		96.75 387		99.03 402

CLD-MVS		98.76 308	98.57 313	99.33 291	99.57 263	98.97 294	97.53 451	99.55 280	96.41 444	99.27 335	99.13 401	99.07 128	99.78 365	96.73 389	99.89 171	99.23 348
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 369	97.98 366	98.48 401	99.27 377	96.48 432	99.40 124	99.07 399	98.81 304	99.23 342	99.57 295	90.11 440	99.87 249	96.69 390	99.64 321	99.09 385

baseline2		96.83 410	96.28 417	98.46 403	99.09 414	96.91 424	98.83 324	93.87 483	97.23 427	96.23 478	98.36 459	88.12 448	99.90 197	96.68 391	98.14 459	98.57 447

cascas		96.99 406	96.82 412	97.48 438	97.57 486	95.64 449	96.43 477	99.56 274	91.75 474	97.13 468	97.61 476	95.58 371	98.63 481	96.68 391	99.11 405	98.18 467

PC_three_1452									97.56 408	99.68 193	99.41 342	99.09 121	97.09 484	96.66 393	99.60 336	99.62 184

LPG-MVS_test		99.22 216	99.05 239	99.74 101	99.82 91	99.63 138	99.16 219	99.73 168	97.56 408	99.64 212	99.69 201	99.37 74	99.89 219	96.66 393	99.87 194	99.69 115

LGP-MVS_train				99.74 101	99.82 91	99.63 138		99.73 168	97.56 408	99.64 212	99.69 201	99.37 74	99.89 219	96.66 393	99.87 194	99.69 115

ETVMVS		96.14 430	95.22 441	98.89 373	98.80 446	98.01 381	98.66 349	98.35 441	98.71 318	97.18 466	96.31 495	74.23 486	99.75 389	96.64 396	98.13 461	98.90 420

TinyColmap		98.97 281	98.93 272	99.07 345	99.46 321	98.19 366	97.75 439	99.75 158	98.79 307	99.54 260	99.70 192	98.97 152	99.62 447	96.63 397	99.83 220	99.41 303

LF4IMVS		99.01 274	98.92 276	99.27 313	99.71 188	99.28 237	98.59 357	99.77 145	98.32 367	99.39 308	99.41 342	98.62 202	99.84 303	96.62 398	99.84 212	98.69 438

NCCC		98.82 302	98.57 313	99.58 194	99.21 388	99.31 232	98.61 352	99.25 375	98.65 324	98.43 424	99.26 383	97.86 290	99.81 351	96.55 399	99.27 395	99.61 197

OPU-MVS				99.29 305	99.12 404	99.44 195	99.20 198				99.40 346	99.00 144	98.84 480	96.54 400	99.60 336	99.58 213

F-COLMAP		98.74 310	98.45 325	99.62 178	99.57 263	99.47 181	98.84 321	99.65 220	96.31 447	98.93 376	99.19 398	97.68 303	99.87 249	96.52 401	99.37 380	99.53 242

testing99		95.86 438	95.19 442	97.87 427	98.76 453	95.03 457	98.62 351	98.44 434	98.68 321	96.67 472	96.66 490	74.31 485	99.69 413	96.51 402	98.03 463	98.90 420

ADS-MVSNet2		97.78 381	97.66 387	98.12 419	99.14 400	95.36 453	99.22 195	98.75 416	96.97 435	98.25 429	99.64 233	90.90 428	99.94 97	96.51 402	99.56 345	99.08 391

ADS-MVSNet		97.72 386	97.67 386	97.86 428	99.14 400	94.65 461	99.22 195	98.86 409	96.97 435	98.25 429	99.64 233	90.90 428	99.84 303	96.51 402	99.56 345	99.08 391

PatchMatch-RL		98.68 318	98.47 322	99.30 304	99.44 326	99.28 237	98.14 405	99.54 286	97.12 433	99.11 361	99.25 385	97.80 295	99.70 406	96.51 402	99.30 389	98.93 416

CMPMVS	77.52 23	98.50 337	98.19 352	99.41 267	98.33 470	99.56 163	99.01 278	99.59 258	95.44 457	99.57 244	99.80 106	95.64 369	99.46 470	96.47 406	99.92 143	99.21 353
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 434	95.32 439	98.02 420	98.76 453	95.39 452	98.38 386	98.65 423	98.82 302	96.84 469	96.71 489	75.06 484	99.71 402	96.46 407	98.23 453	98.98 410

SF-MVS		99.10 253	98.93 272	99.62 178	99.58 253	99.51 175	99.13 233	99.65 220	97.97 387	99.42 295	99.61 267	98.86 169	99.87 249	96.45 408	99.68 308	99.49 266

FE-MVS		97.85 378	97.42 392	99.15 331	99.44 326	98.75 319	99.77 19	98.20 446	95.85 452	99.33 320	99.80 106	88.86 446	99.88 234	96.40 409	99.12 404	98.81 429

DPE-MVS		99.14 241	98.92 276	99.82 46	99.57 263	99.77 64	98.74 341	99.60 252	98.55 335	99.76 151	99.69 201	98.23 261	99.92 149	96.39 410	99.75 271	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					97.59 484	89.02 490			93.47 470		98.30 460		99.84 303	96.38 411

AllTest		99.21 221	99.07 232	99.63 169	99.78 134	99.64 132	99.12 238	99.83 96	98.63 327	99.63 217	99.72 172	98.68 193	99.75 389	96.38 411	99.83 220	99.51 255

TestCases				99.63 169	99.78 134	99.64 132		99.83 96	98.63 327	99.63 217	99.72 172	98.68 193	99.75 389	96.38 411	99.83 220	99.51 255

testdata				99.42 259	99.51 295	98.93 301		99.30 365	96.20 448	98.87 386	99.40 346	98.33 250	99.89 219	96.29 414	99.28 392	99.44 292

dp		96.86 409	97.07 401	96.24 461	98.68 459	90.30 488	99.19 204	98.38 439	97.35 422	98.23 431	99.59 285	87.23 450	99.82 335	96.27 415	98.73 435	98.59 443

tpmvs		97.39 398	97.69 384	96.52 457	98.41 467	91.76 476	99.30 162	98.94 408	97.74 402	97.85 451	99.55 307	92.40 413	99.73 395	96.25 416	98.73 435	98.06 469

KD-MVS_2432*1600		95.89 435	95.41 436	97.31 445	94.96 488	93.89 464	97.09 468	99.22 382	97.23 427	98.88 383	99.04 415	79.23 475	99.54 460	96.24 417	96.81 473	98.50 453

miper_refine_blended		95.89 435	95.41 436	97.31 445	94.96 488	93.89 464	97.09 468	99.22 382	97.23 427	98.88 383	99.04 415	79.23 475	99.54 460	96.24 417	96.81 473	98.50 453

ACMP	97.51 14	99.05 262	98.84 287	99.67 141	99.78 134	99.55 167	98.88 313	99.66 210	97.11 434	99.47 282	99.60 275	99.07 128	99.89 219	96.18 419	99.85 207	99.58 213
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 292	98.72 298	99.44 253	99.39 338	99.42 202	98.58 359	99.64 228	97.31 424	99.44 288	99.62 257	98.59 206	99.69 413	96.17 420	99.79 252	99.22 350

DP-MVS Recon		98.50 337	98.23 346	99.31 300	99.49 306	99.46 187	98.56 365	99.63 231	94.86 466	98.85 388	99.37 355	97.81 294	99.59 454	96.08 421	99.44 370	98.88 423

tpm cat1		96.78 411	96.98 404	96.16 462	98.85 440	90.59 486	99.08 254	99.32 358	92.37 472	97.73 457	99.46 334	91.15 424	99.69 413	96.07 422	98.80 425	98.21 464

tpm2		96.35 423	96.22 418	96.73 455	98.88 437	91.75 477	99.21 197	98.51 430	93.27 471	97.89 447	99.21 395	84.83 462	99.70 406	96.04 423	98.18 457	98.75 436

dmvs_re		98.69 317	98.48 321	99.31 300	99.55 277	99.42 202	99.54 90	98.38 439	99.32 222	98.72 402	98.71 446	96.76 341	99.21 474	96.01 424	99.35 383	99.31 333

test_0402		99.22 216	99.14 207	99.45 249	99.79 126	99.43 199	99.28 171	99.68 200	99.54 171	99.40 306	99.56 299	99.07 128	99.82 335	96.01 424	99.96 87	99.11 378

ITE_SJBPF				99.38 276	99.63 234	99.44 195		99.73 168	98.56 334	99.33 320	99.53 311	98.88 166	99.68 425	96.01 424	99.65 319	99.02 407

test_prior2							97.95 428		97.87 397	98.05 440	99.05 413	97.90 287		95.99 427	99.49 365

testdata2													99.89 219	95.99 427

原ACMM1				99.37 279	99.47 317	98.87 310		99.27 370	96.74 442	98.26 428	99.32 369	97.93 286	99.82 335	95.96 429	99.38 378	99.43 298

新几何1				99.52 225	99.50 301	99.22 255		99.26 372	95.66 456	98.60 412	99.28 378	97.67 304	99.89 219	95.95 430	99.32 387	99.45 279

MP-MVS		99.06 259	98.83 289	99.76 85	99.76 151	99.71 100	99.32 154	99.50 310	98.35 361	98.97 372	99.48 327	98.37 244	99.92 149	95.95 430	99.75 271	99.63 172
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 445	94.59 448	98.61 394	98.66 460	97.45 409	98.54 369	97.90 454	98.53 339	96.54 474	96.47 492	70.62 490	99.81 351	95.91 432	98.15 458	98.56 448

wuyk23d		97.58 390	99.13 209	92.93 466	99.69 209	99.49 177	99.52 93	99.77 145	97.97 387	99.96 34	99.79 117	99.84 16	99.94 97	95.85 433	99.82 230	79.36 484

HQP_MVS		98.90 292	98.68 303	99.55 213	99.58 253	99.24 249	98.80 332	99.54 286	98.94 282	99.14 357	99.25 385	97.24 323	99.82 335	95.84 434	99.78 261	99.60 201

plane_prior5								99.54 286					99.82 335	95.84 434	99.78 261	99.60 201

无先验							98.01 420	99.23 379	95.83 453				99.85 287	95.79 436		99.44 292

CPTT-MVS		98.74 310	98.44 326	99.64 162	99.61 239	99.38 215	99.18 208	99.55 280	96.49 443	99.27 335	99.37 355	97.11 331	99.92 149	95.74 437	99.67 314	99.62 184

PLC	97.35 16	98.36 350	97.99 364	99.48 240	99.32 365	99.24 249	98.50 374	99.51 306	95.19 462	98.58 414	98.96 429	96.95 336	99.83 319	95.63 438	99.25 397	99.37 314
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 329	98.34 337	99.28 308	99.18 396	99.10 281	98.34 388	99.41 333	98.48 345	98.52 419	98.98 425	97.05 333	99.78 365	95.59 439	99.50 363	98.96 411

1314		98.00 374	97.90 376	98.27 415	98.90 432	97.45 409	99.30 162	99.06 401	94.98 463	97.21 465	99.12 405	98.43 235	99.67 430	95.58 440	98.56 442	97.71 473

PVSNet_0	95.53 19	95.85 439	95.31 440	97.47 439	98.78 450	93.48 469	95.72 479	99.40 340	96.18 449	97.37 460	97.73 471	95.73 368	99.58 455	95.49 441	81.40 485	99.36 317

MAR-MVS		98.24 360	97.92 374	99.19 326	98.78 450	99.65 126	99.17 213	99.14 395	95.36 458	98.04 441	98.81 442	97.47 313	99.72 397	95.47 442	99.06 408	98.21 464
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	98.23 361	97.89 377	99.26 316	99.19 393	99.26 242	99.65 62	99.69 197	91.33 476	98.14 438	99.77 139	98.28 253	99.96 69	95.41 443	99.55 349	98.58 445

train_agg		98.35 353	97.95 368	99.57 202	99.35 350	99.35 226	98.11 409	99.41 333	94.90 464	97.92 445	98.99 422	98.02 278	99.85 287	95.38 444	99.44 370	99.50 261

9.14			98.64 304		99.45 325		98.81 329	99.60 252	97.52 413	99.28 334	99.56 299	98.53 222	99.83 319	95.36 445	99.64 321

APD-MVS		98.87 297	98.59 309	99.71 126	99.50 301	99.62 140	99.01 278	99.57 269	96.80 441	99.54 260	99.63 248	98.29 252	99.91 178	95.24 446	99.71 293	99.61 197
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 435								95.20 447

AdaColmap		98.60 324	98.35 336	99.38 276	99.12 404	99.22 255	98.67 347	99.42 332	97.84 400	98.81 392	99.27 380	97.32 321	99.81 351	95.14 448	99.53 356	99.10 380

test9_res														95.10 449	99.44 370	99.50 261

CDPH-MVS		98.56 330	98.20 349	99.61 184	99.50 301	99.46 187	98.32 390	99.41 333	95.22 460	99.21 347	99.10 409	98.34 248	99.82 335	95.09 450	99.66 317	99.56 222

BH-untuned		98.22 363	98.09 358	98.58 398	99.38 341	97.24 415	98.55 366	98.98 407	97.81 401	99.20 352	98.76 444	97.01 334	99.65 442	94.83 451	98.33 449	98.86 425

BP-MVS														94.73 452

HQP-MVS		98.36 350	98.02 363	99.39 273	99.31 366	98.94 298	97.98 424	99.37 348	97.45 416	98.15 434	98.83 439	96.67 342	99.70 406	94.73 452	99.67 314	99.53 242

QAPM		98.40 348	97.99 364	99.65 155	99.39 338	99.47 181	99.67 53	99.52 301	91.70 475	98.78 398	99.80 106	98.55 213	99.95 80	94.71 454	99.75 271	99.53 242

agg_prior2														94.58 455	99.46 369	99.50 261

myMVS_eth3d		95.63 443	94.73 445	98.34 409	98.50 465	96.36 435	98.60 354	99.21 385	97.89 394	96.76 470	96.37 493	72.10 488	99.57 456	94.38 456	98.73 435	99.09 385

BH-RMVSNet		98.41 346	98.14 355	99.21 323	99.21 388	98.47 347	98.60 354	98.26 444	98.35 361	98.93 376	99.31 372	97.20 328	99.66 435	94.32 457	99.10 406	99.51 255

E-PMN		97.14 405	97.43 391	96.27 460	98.79 448	91.62 478	95.54 480	99.01 406	99.44 196	98.88 383	99.12 405	92.78 407	99.68 425	94.30 458	99.03 412	97.50 474

MG-MVS		98.52 334	98.39 331	98.94 359	99.15 399	97.39 412	98.18 399	99.21 385	98.89 293	99.23 342	99.63 248	97.37 319	99.74 392	94.22 459	99.61 333	99.69 115

API-MVS		98.38 349	98.39 331	98.35 407	98.83 442	99.26 242	99.14 226	99.18 390	98.59 332	98.66 407	98.78 443	98.61 204	99.57 456	94.14 460	99.56 345	96.21 481

PAPM_NR		98.36 350	98.04 361	99.33 291	99.48 311	98.93 301	98.79 335	99.28 369	97.54 411	98.56 418	98.57 452	97.12 330	99.69 413	94.09 461	98.90 423	99.38 311

ZD-MVS					99.43 329	99.61 148		99.43 330	96.38 445	99.11 361	99.07 411	97.86 290	99.92 149	94.04 462	99.49 365

DPM-MVS		98.28 356	97.94 372	99.32 296	99.36 346	99.11 274	97.31 461	98.78 415	96.88 437	98.84 389	99.11 408	97.77 297	99.61 452	94.03 463	99.36 381	99.23 348

gg-mvs-nofinetune		95.87 437	95.17 443	97.97 423	98.19 473	96.95 422	99.69 45	89.23 489	99.89 56	96.24 477	99.94 19	81.19 467	99.51 466	93.99 464	98.20 454	97.44 475

PMVS	92.94 21	98.82 302	98.81 292	98.85 376	99.84 74	97.99 382	99.20 198	99.47 318	99.71 118	99.42 295	99.82 91	98.09 273	99.47 468	93.88 465	99.85 207	99.07 396
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 408	97.28 395	95.99 464	98.76 453	91.03 482	95.26 482	98.61 424	99.34 218	98.92 379	98.88 436	93.79 393	99.66 435	92.87 466	99.05 410	97.30 478

BH-w/o		97.20 402	97.01 403	97.76 431	99.08 415	95.69 448	98.03 419	98.52 429	95.76 454	97.96 444	98.02 466	95.62 370	99.47 468	92.82 467	97.25 472	98.12 468

TR-MVS		97.44 395	97.15 400	98.32 410	98.53 463	97.46 408	98.47 378	97.91 453	96.85 438	98.21 432	98.51 456	96.42 352	99.51 466	92.16 468	97.29 471	97.98 470

OpenMVS_ROB	97.31 17	97.36 400	96.84 410	98.89 373	99.29 372	99.45 193	98.87 316	99.48 315	86.54 481	99.44 288	99.74 159	97.34 320	99.86 268	91.61 469	99.28 392	97.37 477

GG-mvs-BLEND				97.36 442	97.59 484	96.87 425	99.70 38	88.49 490		94.64 483	97.26 480	80.66 469	99.12 475	91.50 470	96.50 478	96.08 483

DeepMVS_CX				97.98 422	99.69 209	96.95 422		99.26 372	75.51 484	95.74 480	98.28 461	96.47 350	99.62 447	91.23 471	97.89 465	97.38 476

PAPR		97.56 391	97.07 401	99.04 349	98.80 446	98.11 374	97.63 445	99.25 375	94.56 469	98.02 443	98.25 462	97.43 315	99.68 425	90.90 472	98.74 432	99.33 325

MVS		95.72 441	94.63 447	98.99 352	98.56 462	97.98 387	99.30 162	98.86 409	72.71 485	97.30 462	99.08 410	98.34 248	99.74 392	89.21 473	98.33 449	99.26 341

UWE-MVS-28		95.64 442	95.47 434	96.14 463	97.98 478	90.39 487	98.49 376	95.81 474	99.02 272	98.03 442	98.19 463	84.49 464	99.28 473	88.75 474	98.47 447	98.75 436

thres600view7		96.60 416	96.16 419	97.93 425	99.63 234	96.09 443	99.18 208	97.57 459	98.77 311	98.72 402	97.32 478	87.04 452	99.72 397	88.57 475	98.62 440	97.98 470

FPMVS		96.32 424	95.50 433	98.79 384	99.60 241	98.17 369	98.46 382	98.80 414	97.16 431	96.28 475	99.63 248	82.19 466	99.09 476	88.45 476	98.89 424	99.10 380

PCF-MVS	96.03 18	96.73 413	95.86 426	99.33 291	99.44 326	99.16 268	96.87 473	99.44 327	86.58 480	98.95 374	99.40 346	94.38 387	99.88 234	87.93 477	99.80 247	98.95 413
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 422	96.03 422	97.47 439	99.63 234	95.93 444	99.18 208	97.57 459	98.75 315	98.70 405	97.31 479	87.04 452	99.67 430	87.62 478	98.51 444	96.81 479

tfpn200view9		96.30 425	95.89 424	97.53 436	99.58 253	96.11 441	99.00 284	97.54 462	98.43 347	98.52 419	96.98 483	86.85 454	99.67 430	87.62 478	98.51 444	96.81 479

thres400		96.40 421	95.89 424	97.92 426	99.58 253	96.11 441	99.00 284	97.54 462	98.43 347	98.52 419	96.98 483	86.85 454	99.67 430	87.62 478	98.51 444	97.98 470

thres200		96.09 431	95.68 431	97.33 444	99.48 311	96.22 440	98.53 371	97.57 459	98.06 382	98.37 426	96.73 488	86.84 456	99.61 452	86.99 481	98.57 441	96.16 482

MVE	92.54 22	96.66 415	96.11 420	98.31 412	99.68 217	97.55 403	97.94 429	95.60 475	99.37 214	90.68 486	98.70 448	96.56 345	98.61 482	86.94 482	99.55 349	98.77 434
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 401	96.83 411	98.59 396	99.46 321	97.55 403	99.25 186	96.84 468	98.78 309	97.24 464	97.67 472	97.11 331	98.97 478	86.59 483	98.54 443	99.27 339

PAPM		95.61 444	94.71 446	98.31 412	99.12 404	96.63 428	96.66 476	98.46 433	90.77 477	96.25 476	98.68 449	93.01 405	99.69 413	81.60 484	97.86 467	98.62 440

SD_0403		97.42 396	96.90 409	98.98 354	99.54 279	97.90 390	99.52 93	99.54 286	99.34 218	97.87 449	98.85 438	98.72 189	99.64 444	78.93 485	99.83 220	99.40 306

dongtai		89.37 450	88.91 453	90.76 467	99.19 393	77.46 492	95.47 481	87.82 491	92.28 473	94.17 484	98.82 441	71.22 489	95.54 486	63.85 486	97.34 470	99.27 339

kuosan		85.65 452	84.57 455	88.90 469	97.91 480	77.11 493	96.37 478	87.62 492	85.24 482	85.45 487	96.83 486	69.94 491	90.98 488	45.90 487	95.83 482	98.62 440

test123		29.31 453	33.05 458	18.08 470	25.93 494	12.24 495	97.53 451	10.93 495	11.78 488	24.21 489	50.08 500	21.04 492	8.60 489	23.51 488	32.43 488	33.39 485

testmvs		28.94 454	33.33 456	15.79 471	26.03 493	9.81 496	96.77 474	15.67 494	11.55 489	23.87 490	50.74 499	19.03 493	8.53 490	23.21 489	33.07 487	29.03 486

mmdepth		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

monomultidepth		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

test_blank		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

uanet_test		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

DCPMVS		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

cdsmvs_eth3d_5k		24.88 455	33.17 457	0.00 472	0.00 495	0.00 497	0.00 484	99.62 234	0.00 490	0.00 491	99.13 401	99.82 18	0.00 491	0.00 490	0.00 489	0.00 487

pcd_1.5k_mvsjas		16.61 456	22.14 459	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	99.28 89	0.00 491	0.00 490	0.00 489	0.00 487

sosnet-low-res		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

sosnet		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

uncertanet		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

Regformer		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

ab-mvs-re		8.26 467	11.02 470	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	99.16 399	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

uanet		8.33 457	11.11 460	0.00 472	0.00 495	0.00 497	0.00 484	0.00 496	0.00 490	0.00 491	100.00 1	0.00 494	0.00 491	0.00 490	0.00 489	0.00 487

TestfortrainingZip							99.38 129

FOURS1					99.83 82	99.89 10	99.74 27	99.71 180	99.69 127	99.63 217

test_one_0601					99.63 234	99.76 71		99.55 280	99.23 239	99.31 328	99.61 267	98.59 206

eth-test2					0.00 495

eth-test					0.00 495

test_241102_ONE					99.69 209	99.82 43		99.54 286	99.12 263	99.82 109	99.49 324	98.91 162	99.52 465

save fliter					99.53 286	99.25 245	98.29 392	99.38 347	99.07 267

test0726					99.69 209	99.80 52	99.24 187	99.57 269	99.16 254	99.73 171	99.65 231	98.35 246

GSMVS																99.14 374

test_part2					99.62 238	99.67 118				99.55 257

sam_mvs1												90.81 432				99.14 374

sam_mvs												90.52 437

MTGPA								99.53 296

test_post											52.41 497	90.25 439	99.86 268

patchmatchnet-post											99.62 257	90.58 435	99.94 97

MTMP							99.09 251	98.59 427

TEST9					99.35 350	99.35 226	98.11 409	99.41 333	94.83 467	97.92 445	98.99 422	98.02 278	99.85 287

test_8					99.34 359	99.31 232	98.08 413	99.40 340	94.90 464	97.87 449	98.97 427	98.02 278	99.84 303

agg_prior					99.35 350	99.36 223		99.39 343		97.76 456			99.85 287

test_prior4						99.19 262	98.00 422

test_prior				99.46 246	99.35 350	99.22 255		99.39 343					99.69 413			99.48 270

新几何2							98.04 417

旧先验1					99.49 306	99.29 235		99.26 372			99.39 350	97.67 304			99.36 381	99.46 278

原ACMM2							97.92 431

test222					99.51 295	99.08 283	97.83 437	99.29 366	95.21 461	98.68 406	99.31 372	97.28 322			99.38 378	99.43 298

segment_acmp												98.37 244

testdata1							97.72 440		97.86 399

test12				99.54 219	99.29 372	99.33 229		99.16 393		98.43 424		97.54 311	99.82 335		99.47 367	99.48 270

plane_prior7					99.58 253	99.38 215

plane_prior6					99.47 317	99.26 242						97.24 323

plane_prior4											99.25 385

plane_prior3						99.31 232			98.36 356	99.14 357

plane_prior2							98.80 332		98.94 282

plane_prior1					99.51 295

plane_prior						99.24 249	98.42 384		97.87 397						99.71 293

n2								0.00 496

nn								0.00 496

door-mid								99.83 96

test11								99.29 366

door								99.77 145

HQP5-MVS						98.94 298

HQP-NCC					99.31 366		97.98 424		97.45 416	98.15 434

ACMP_Plane					99.31 366		97.98 424		97.45 416	98.15 434

HQP4-MVS										98.15 434			99.70 406			99.53 242

HQP3-MVS								99.37 348							99.67 314

HQP2-MVS												96.67 342

NP-MVS					99.40 337	99.13 271					98.83 439

ACMMP++_ref															99.94 125

ACMMP++															99.79 252

Test By Simon												98.41 238

High-resolution multi-view benchmark