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	99.73 111	100.00 1	99.89 41	99.79 22	99.88 238	99.98 1	100.00 1	99.98 5

test_fmvs2		99.72 53	99.85 17	99.34 300	99.91 31	98.08 412	99.48 109	100.00 1	99.90 49	99.99 7	99.91 31	99.50 62	99.98 26	99.98 1	99.99 19	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 229	99.96 7	98.62 364	99.67 53	100.00 1	99.95 32	100.00 1	99.95 16	99.85 14	99.99 7	99.98 1	99.99 19	99.98 5

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 3	99.98 3	99.76 70	99.12 243	100.00 1	100.00 1	99.99 7	99.91 31	99.98 1	100.00 1	99.97 4	100.00 1	99.99 2

test_vis1_n_1920		99.72 53	99.88 7	99.27 325	99.93 24	97.84 425	99.34 148	100.00 1	99.99 3	99.99 7	99.82 91	99.87 13	99.99 7	99.97 4	99.99 19	99.97 10

test_vis1_n		99.68 64	99.79 34	99.36 294	99.94 18	98.18 401	99.52 94	100.00 1	99.86 65	100.00 1	99.88 50	98.99 149	99.96 69	99.97 4	99.96 91	99.95 15

test_fmvs1_n		99.68 64	99.81 28	99.28 320	99.95 15	97.93 421	99.49 107	100.00 1	99.82 85	99.99 7	99.89 41	99.21 104	99.98 26	99.97 4	99.98 54	99.93 21

test_f		99.75 49	99.88 7	99.37 288	99.96 7	98.21 398	99.51 101	100.00 1	99.94 36	100.00 1	99.93 22	99.58 50	99.94 98	99.97 4	99.99 19	99.97 10

fmvsm_l_conf0.5_n_9		99.83 21	99.81 28	99.89 11	99.86 59	99.80 51	98.94 311	99.96 29	99.98 18	99.96 34	99.78 133	99.88 11	99.98 26	99.96 9	99.99 19	99.90 30

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 45	99.86 18	99.08 259	99.97 21	99.98 18	99.96 34	99.79 120	99.90 9	99.99 7	99.96 9	99.99 19	99.90 30

test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 87	99.01 283	99.99 12	99.99 3	99.98 14	99.88 50	99.97 2	99.99 7	99.96 9	100.00 1	99.98 5

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 54	99.88 45	99.55 171	99.17 218	99.98 13	99.99 3	99.96 34	99.84 76	99.96 3	99.99 7	99.96 9	99.99 19	99.88 41

test_cas_vis1_n_1920		99.76 46	99.86 13	99.45 255	99.93 24	98.40 386	99.30 166	99.98 13	99.94 36	99.99 7	99.89 41	99.80 21	99.97 44	99.96 9	99.97 77	99.97 10

fmvsm_s_conf0.5_n_10		99.77 44	99.73 54	99.88 19	99.81 109	99.75 79	99.06 265	99.85 91	99.99 3	99.97 24	99.84 76	99.12 122	99.98 26	99.95 14	99.99 19	99.90 30

fmvsm_s_conf0.5_n_7		99.73 52	99.78 39	99.60 192	99.74 184	98.93 321	98.85 326	99.96 29	99.96 28	99.97 24	99.76 155	99.82 18	99.96 69	99.95 14	99.98 54	99.90 30

fmvsm_l_conf0.5_n		99.80 30	99.78 39	99.85 32	99.88 45	99.66 121	99.11 248	99.91 55	99.98 18	99.96 34	99.64 244	99.60 44	99.99 7	99.95 14	99.99 19	99.88 41

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 41	99.82 97	99.70 108	99.17 218	99.97 21	99.99 3	99.96 34	99.82 91	99.94 4	100.00 1	99.95 14	100.00 1	99.80 67

test_fmvs1		99.48 133	99.65 74	98.97 370	99.54 302	97.16 455	99.11 248	99.98 13	99.78 102	99.96 34	99.81 98	98.72 192	99.97 44	99.95 14	99.97 77	99.79 75

mvsany_test3		99.85 12	99.88 7	99.75 98	99.95 15	99.37 227	99.53 92	99.98 13	99.77 106	99.99 7	99.95 16	99.85 14	99.94 98	99.95 14	99.98 54	99.94 18

fmvsm_s_conf0.5_n_9		99.82 24	99.82 25	99.82 46	99.83 88	99.59 158	98.97 302	99.92 45	99.99 3	99.97 24	99.84 76	99.90 9	99.94 98	99.94 20	99.99 19	99.92 25

fmvsm_s_conf0.1_n_2		99.81 28	99.78 39	99.89 11	99.93 24	99.76 70	98.92 315	99.98 13	99.99 3	99.99 7	99.88 50	99.43 67	99.94 98	99.94 20	99.99 19	99.99 2

fmvsm_l_conf0.5_n_a		99.80 30	99.79 34	99.84 38	99.88 45	99.64 134	99.12 243	99.91 55	99.98 18	99.95 45	99.67 230	99.67 34	99.99 7	99.94 20	99.99 19	99.88 41

MM		99.18 240	99.05 249	99.55 218	99.35 376	98.81 337	99.05 266	97.79 497	99.99 3	99.48 294	99.59 298	96.29 380	99.95 81	99.94 20	99.98 54	99.88 41

test_fmvsmconf_n		99.85 12	99.84 20	99.88 19	99.91 31	99.73 90	98.97 302	99.98 13	99.99 3	99.96 34	99.85 68	99.93 7	99.99 7	99.94 20	99.99 19	99.93 21

fmvsm_s_conf0.5_n_11		99.76 46	99.75 51	99.81 54	99.81 109	99.53 174	99.15 227	99.89 65	99.99 3	99.98 14	99.86 63	99.13 119	99.98 26	99.93 25	99.99 19	99.92 25

fmvsm_s_conf0.5_n_5		99.78 37	99.76 49	99.85 32	99.79 132	99.72 95	98.84 328	99.96 29	99.96 28	99.96 34	99.72 184	99.71 28	99.99 7	99.93 25	99.98 54	99.85 50

fmvsm_s_conf0.5_n_2		99.78 37	99.75 51	99.88 19	99.82 97	99.76 70	98.88 320	99.92 45	99.98 18	99.98 14	99.85 68	99.42 69	99.94 98	99.93 25	99.98 54	99.94 18

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 41	99.10 251	99.98 13	99.99 3	99.98 14	99.91 31	99.68 33	99.93 120	99.93 25	99.99 19	99.99 2

fmvsm_s_conf0.1_n		99.86 10	99.85 17	99.89 11	99.93 24	99.78 57	99.07 264	99.98 13	99.99 3	99.98 14	99.90 36	99.88 11	99.92 152	99.93 25	99.99 19	99.98 5

fmvsm_s_conf0.5_n_a		99.82 24	99.79 34	99.89 11	99.85 74	99.82 41	99.03 274	99.96 29	99.99 3	99.97 24	99.84 76	99.58 50	99.93 120	99.92 30	99.98 54	99.93 21

fmvsm_s_conf0.5_n		99.83 21	99.81 28	99.87 26	99.85 74	99.78 57	99.03 274	99.96 29	99.99 3	99.97 24	99.84 76	99.78 23	99.92 152	99.92 30	99.99 19	99.92 25

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 21	99.99 3	100.00 1	99.98 13	99.78 23	100.00 1	99.92 30	100.00 1	99.87 45

fmvsm_s_conf0.5_n_8		99.76 46	99.72 55	99.88 19	99.82 97	99.75 79	99.02 278	99.87 77	99.98 18	99.98 14	99.81 98	99.07 133	99.97 44	99.91 33	99.99 19	99.92 25

fmvsm_s_conf0.5_n_6		99.80 30	99.78 39	99.85 32	99.78 140	99.78 57	99.00 289	99.97 21	99.96 28	99.97 24	99.56 312	99.92 8	99.93 120	99.91 33	99.99 19	99.83 59

fmvsm_s_conf0.5_n_4		99.78 37	99.78 39	99.79 72	99.75 175	99.56 167	98.98 300	99.94 40	99.92 45	99.97 24	99.72 184	99.84 16	99.92 152	99.91 33	99.98 54	99.89 38

MVStest1		98.22 383	98.09 385	98.62 417	99.04 448	96.23 480	99.20 203	99.92 45	99.44 204	99.98 14	99.87 56	85.87 505	99.67 455	99.91 33	99.57 370	99.95 15

v1921920		99.56 105	99.57 104	99.55 218	99.75 175	99.11 288	99.05 266	99.61 259	99.15 268	99.88 82	99.71 194	99.08 130	99.87 254	99.90 37	99.97 77	99.66 149

v1240		99.56 105	99.58 99	99.51 235	99.80 118	99.00 305	99.00 289	99.65 236	99.15 268	99.90 67	99.75 163	99.09 126	99.88 238	99.90 37	99.96 91	99.67 135

v10		99.69 59	99.69 60	99.66 152	99.81 109	99.39 220	99.66 57	99.75 171	99.60 171	99.92 59	99.87 56	98.75 187	99.86 273	99.90 37	99.99 19	99.73 95

v1192		99.57 101	99.57 104	99.57 207	99.77 153	99.22 265	99.04 271	99.60 270	99.18 256	99.87 92	99.72 184	99.08 130	99.85 292	99.89 40	99.98 54	99.66 149

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 32	99.81 109	99.71 100	98.97 302	99.92 45	99.98 18	99.97 24	99.86 63	99.53 58	99.95 81	99.88 41	99.99 19	99.89 38

v144192		99.55 110	99.54 115	99.58 199	99.78 140	99.20 271	99.11 248	99.62 251	99.18 256	99.89 72	99.72 184	98.66 201	99.87 254	99.88 41	99.97 77	99.66 149

v8		99.68 64	99.69 60	99.65 159	99.80 118	99.40 216	99.66 57	99.76 166	99.64 154	99.93 53	99.85 68	98.66 201	99.84 309	99.88 41	99.99 19	99.71 104

mvs5depth		99.88 6	99.91 3	99.80 64	99.92 29	99.42 208	99.94 3	100.00 1	99.97 25	99.89 72	99.99 12	99.63 37	99.97 44	99.87 44	99.99 19	100.00 1

v1144		99.54 115	99.53 119	99.59 195	99.79 132	99.28 245	99.10 251	99.61 259	99.20 253	99.84 103	99.73 176	98.67 199	99.84 309	99.86 45	99.98 54	99.64 170

mmtdpeth		99.78 37	99.83 21	99.66 152	99.85 74	99.05 301	99.79 15	99.97 21	100.00 1	99.43 307	99.94 19	99.64 35	99.94 98	99.83 46	99.99 19	99.98 5

SSC-MVS		99.52 121	99.42 147	99.83 41	99.86 59	99.65 127	99.52 94	99.81 127	99.87 62	99.81 118	99.79 120	96.78 356	99.99 7	99.83 46	99.51 386	99.86 47

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 26	99.82 10	99.82 114	99.84 75	99.94 48	99.91 31	99.13 119	99.96 69	99.83 46	99.99 19	99.83 59

v2v482		99.50 126	99.47 130	99.58 199	99.78 140	99.25 254	99.14 231	99.58 285	99.25 244	99.81 118	99.62 270	98.24 262	99.84 309	99.83 46	99.97 77	99.64 170

test_vis1_rt		99.45 149	99.46 135	99.41 274	99.71 197	98.63 363	98.99 297	99.96 29	99.03 281	99.95 45	99.12 434	98.75 187	99.84 309	99.82 50	99.82 248	99.77 81

tt0805		99.63 86	99.57 104	99.81 54	99.87 54	99.88 12	99.58 82	98.70 452	99.72 115	99.91 62	99.60 288	99.43 67	99.81 365	99.81 51	99.53 382	99.73 95

VortexMVS		99.13 253	99.24 202	98.79 403	99.67 236	96.60 472	99.24 192	99.80 132	99.85 71	99.93 53	99.84 76	95.06 409	99.89 223	99.80 52	99.98 54	99.89 38

V42		99.56 105	99.54 115	99.63 173	99.79 132	99.46 193	99.39 129	99.59 276	99.24 246	99.86 96	99.70 204	98.55 216	99.82 349	99.79 53	99.95 116	99.60 207

SSC-MVS3.2		99.64 85	99.67 65	99.56 211	99.75 175	98.98 309	98.96 306	99.87 77	99.88 60	99.84 103	99.64 244	99.32 87	99.91 182	99.78 54	99.96 91	99.80 67

mvs_tets		99.90 2	99.90 4	99.90 8	99.96 7	99.79 54	99.72 33	99.88 71	99.92 45	99.98 14	99.93 22	99.94 4	99.98 26	99.77 55	100.00 1	99.92 25

WB-MVS		99.44 152	99.32 176	99.80 64	99.81 109	99.61 152	99.47 112	99.81 127	99.82 85	99.71 189	99.72 184	96.60 362	99.98 26	99.75 56	99.23 430	99.82 66

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 63	99.68 48	99.85 91	99.95 32	99.98 14	99.92 27	99.28 92	99.98 26	99.75 56	100.00 1	99.94 18

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 57	99.70 38	99.86 85	99.89 55	99.98 14	99.90 36	99.94 4	99.98 26	99.75 56	100.00 1	99.90 30

ANet_high		99.88 6	99.87 11	99.91 3	99.99 1	99.91 4	99.65 62	100.00 1	99.90 49	100.00 1	99.97 14	99.61 41	99.97 44	99.75 56	100.00 1	99.84 55

AstraMVS		99.15 250	99.06 244	99.42 266	99.85 74	98.59 367	99.13 238	97.26 506	99.84 75	99.87 92	99.77 145	96.11 385	99.93 120	99.71 60	99.96 91	99.74 91

Elysia		99.69 59	99.65 74	99.81 54	99.86 59	99.72 95	99.34 148	99.77 158	99.94 36	99.91 62	99.76 155	98.55 216	99.99 7	99.70 61	99.98 54	99.72 99

StellarMVS		99.69 59	99.65 74	99.81 54	99.86 59	99.72 95	99.34 148	99.77 158	99.94 36	99.91 62	99.76 155	98.55 216	99.99 7	99.70 61	99.98 54	99.72 99

tt0320-xc		99.82 24	99.82 25	99.82 46	99.82 97	99.84 26	99.82 10	99.92 45	99.94 36	99.94 48	99.93 22	99.34 84	99.92 152	99.70 61	99.96 91	99.70 107

reproduce_monomvs		97.40 432	97.46 420	97.20 484	99.05 445	91.91 522	99.20 203	99.18 419	99.84 75	99.86 96	99.75 163	80.67 513	99.83 330	99.69 64	99.95 116	99.85 50

SPE-MVS-test		99.68 64	99.70 57	99.64 166	99.57 282	99.83 33	99.78 17	99.97 21	99.92 45	99.50 291	99.38 371	99.57 52	99.95 81	99.69 64	99.90 171	99.15 386

guyue		99.12 256	99.02 258	99.41 274	99.84 80	98.56 370	99.19 209	98.30 480	99.82 85	99.84 103	99.75 163	94.84 412	99.92 152	99.68 66	99.94 133	99.74 91

tt0320		99.79 34	99.79 34	99.81 54	99.82 97	99.84 26	99.82 10	99.90 61	99.94 36	99.94 48	99.94 19	99.07 133	99.92 152	99.68 66	99.97 77	99.67 135

MGCNet		98.61 337	98.30 366	99.52 231	97.88 517	98.95 316	98.76 345	94.11 529	99.84 75	99.32 340	99.57 308	95.57 398	99.95 81	99.68 66	99.98 54	99.68 126

CS-MVS		99.67 76	99.70 57	99.58 199	99.53 311	99.84 26	99.79 15	99.96 29	99.90 49	99.61 247	99.41 359	99.51 61	99.95 81	99.66 69	99.89 186	98.96 430

KinetiMVS		99.66 77	99.63 82	99.76 87	99.89 39	99.57 166	99.37 140	99.82 114	99.95 32	99.90 67	99.63 260	98.57 212	99.97 44	99.65 70	99.94 133	99.74 91

pmmvs6		99.86 10	99.86 13	99.83 41	99.94 18	99.90 7	99.83 7	99.91 55	99.85 71	99.94 48	99.95 16	99.73 27	99.90 201	99.65 70	99.97 77	99.69 119

MIMVSNet1		99.66 77	99.62 85	99.80 64	99.94 18	99.87 15	99.69 45	99.77 158	99.78 102	99.93 53	99.89 41	97.94 293	99.92 152	99.65 70	99.98 54	99.62 188

LuminaMVS		99.39 172	99.28 191	99.73 113	99.83 88	99.49 182	99.00 289	99.05 432	99.81 91	99.89 72	99.79 120	96.54 366	99.97 44	99.64 73	99.98 54	99.73 95

sc_t1		99.81 28	99.80 32	99.82 46	99.88 45	99.88 12	99.83 7	99.79 141	99.94 36	99.93 53	99.92 27	99.35 83	99.92 152	99.64 73	99.94 133	99.68 126

EC-MVSNet		99.69 59	99.69 60	99.68 140	99.71 197	99.91 4	99.76 23	99.96 29	99.86 65	99.51 288	99.39 368	99.57 52	99.93 120	99.64 73	99.86 218	99.20 374

K. test v3		98.87 309	98.60 322	99.69 138	99.93 24	99.46 193	99.74 27	94.97 524	99.78 102	99.88 82	99.88 50	93.66 431	99.97 44	99.61 76	99.95 116	99.64 170

KD-MVS_self_test		99.63 86	99.59 95	99.76 87	99.84 80	99.90 7	99.37 140	99.79 141	99.83 81	99.88 82	99.85 68	98.42 241	99.90 201	99.60 77	99.73 306	99.49 274

Anonymous20240521		99.44 152	99.42 147	99.49 241	99.89 39	98.96 315	99.62 67	99.76 166	99.85 71	99.82 111	99.88 50	96.39 373	99.97 44	99.59 78	99.98 54	99.55 233

TransMVSNet (Re)		99.78 37	99.77 45	99.81 54	99.91 31	99.85 21	99.75 25	99.86 85	99.70 127	99.91 62	99.89 41	99.60 44	99.87 254	99.59 78	99.74 299	99.71 104

OurMVSNet-221017-0		99.75 49	99.71 56	99.84 38	99.96 7	99.83 33	99.83 7	99.85 91	99.80 95	99.93 53	99.93 22	98.54 220	99.93 120	99.59 78	99.98 54	99.76 86

EU-MVSNet		99.39 172	99.62 85	98.72 410	99.88 45	96.44 474	99.56 87	99.85 91	99.90 49	99.90 67	99.85 68	98.09 281	99.83 330	99.58 81	99.95 116	99.90 30

mvs_anonymous		99.28 203	99.39 153	98.94 374	99.19 419	97.81 427	99.02 278	99.55 299	99.78 102	99.85 100	99.80 108	98.24 262	99.86 273	99.57 82	99.50 389	99.15 386

test1111		97.74 413	98.16 380	96.49 503	99.60 257	89.86 537	99.71 37	91.21 533	99.89 55	99.88 82	99.87 56	93.73 430	99.90 201	99.56 83	99.99 19	99.70 107

lessismore_v0				99.64 166	99.86 59	99.38 222		90.66 534		99.89 72	99.83 83	94.56 418	99.97 44	99.56 83	99.92 154	99.57 225

mvsany_test1		99.44 152	99.45 137	99.40 277	99.37 369	98.64 361	97.90 451	99.59 276	99.27 239	99.92 59	99.82 91	99.74 26	99.93 120	99.55 85	99.87 210	99.63 176

MVSMamba_PlusPlus		99.55 110	99.58 99	99.47 248	99.68 229	99.40 216	99.52 94	99.70 204	99.92 45	99.77 150	99.86 63	98.28 258	99.96 69	99.54 86	99.90 171	99.05 416

pm-mvs1		99.79 34	99.79 34	99.78 76	99.91 31	99.83 33	99.76 23	99.87 77	99.73 111	99.89 72	99.87 56	99.63 37	99.87 254	99.54 86	99.92 154	99.63 176

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 45	99.90 49	99.97 24	99.87 56	99.81 20	99.95 81	99.54 86	99.99 19	99.80 67
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		99.48 133	99.65 74	98.95 373	99.71 197	97.27 452	99.50 102	99.82 114	99.59 173	99.41 316	99.85 68	99.62 40	100.00 1	99.53 89	99.89 186	99.59 214

test2506		94.73 491	94.59 491	95.15 511	99.59 263	85.90 539	99.75 25	74.01 541	99.89 55	99.71 189	99.86 63	79.00 523	99.90 201	99.52 90	99.99 19	99.65 158

balanced_ft_v1		99.37 179	99.36 164	99.38 283	99.10 438	99.38 222	99.68 48	99.72 191	99.72 115	99.36 327	99.77 145	97.66 317	99.94 98	99.52 90	99.73 306	98.83 448

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 195	99.93 43	99.95 45	99.89 41	99.71 28	99.96 69	99.51 92	99.97 77	99.84 55

FC-MVSNet-test		99.70 57	99.65 74	99.86 30	99.88 45	99.86 18	99.72 33	99.78 153	99.90 49	99.82 111	99.83 83	98.45 237	99.87 254	99.51 92	99.97 77	99.86 47

BP-MVS1		98.72 327	98.46 342	99.50 237	99.53 311	99.00 305	99.34 148	98.53 463	99.65 150	99.73 179	99.38 371	90.62 477	99.96 69	99.50 94	99.86 218	99.55 233

UA-Net		99.78 37	99.76 49	99.86 30	99.72 193	99.71 100	99.91 4	99.95 37	99.96 28	99.71 189	99.91 31	99.15 114	99.97 44	99.50 94	100.00 1	99.90 30

viewdifsd2359ckpt11		99.62 93	99.64 79	99.56 211	99.86 59	99.19 274	99.02 278	99.93 41	99.83 81	99.88 82	99.81 98	98.99 149	99.83 330	99.48 96	99.96 91	99.65 158

viewmsd2359difaftdt		99.62 93	99.64 79	99.56 211	99.86 59	99.19 274	99.02 278	99.93 41	99.83 81	99.88 82	99.81 98	98.99 149	99.83 330	99.48 96	99.96 91	99.65 158

PMMVS2		99.48 133	99.45 137	99.57 207	99.76 158	98.99 307	98.09 427	99.90 61	98.95 292	99.78 138	99.58 301	99.57 52	99.93 120	99.48 96	99.95 116	99.79 75

VPA-MVSNet		99.66 77	99.62 85	99.79 72	99.68 229	99.75 79	99.62 67	99.69 213	99.85 71	99.80 125	99.81 98	98.81 175	99.91 182	99.47 99	99.88 197	99.70 107

GDP-MVS		98.81 317	98.57 328	99.50 237	99.53 311	99.12 287	99.28 176	99.86 85	99.53 181	99.57 258	99.32 390	90.88 472	99.98 26	99.46 100	99.74 299	99.42 315

ECVR-MVS		97.73 414	98.04 388	96.78 495	99.59 263	90.81 531	99.72 33	90.43 535	99.89 55	99.86 96	99.86 63	93.60 432	99.89 223	99.46 100	99.99 19	99.65 158

nrg030		99.70 57	99.66 72	99.82 46	99.76 158	99.84 26	99.61 73	99.70 204	99.93 43	99.78 138	99.68 225	99.10 124	99.78 382	99.45 102	99.96 91	99.83 59

FE-MVSNET2		99.68 64	99.67 65	99.72 121	99.86 59	99.68 115	99.46 116	99.88 71	99.62 159	99.87 92	99.85 68	99.06 139	99.85 292	99.44 103	99.98 54	99.63 176

TAMVS		99.49 131	99.45 137	99.63 173	99.48 336	99.42 208	99.45 117	99.57 287	99.66 146	99.78 138	99.83 83	97.85 300	99.86 273	99.44 103	99.96 91	99.61 202

GeoE		99.69 59	99.66 72	99.78 76	99.76 158	99.76 70	99.60 79	99.82 114	99.46 199	99.75 164	99.56 312	99.63 37	99.95 81	99.43 105	99.88 197	99.62 188

new-patchmatchnet		99.35 186	99.57 104	98.71 414	99.82 97	96.62 470	98.55 378	99.75 171	99.50 186	99.88 82	99.87 56	99.31 88	99.88 238	99.43 105	100.00 1	99.62 188

test20.03		99.55 110	99.54 115	99.58 199	99.79 132	99.37 227	99.02 278	99.89 65	99.60 171	99.82 111	99.62 270	98.81 175	99.89 223	99.43 105	99.86 218	99.47 282

MVSFormer		99.41 166	99.44 142	99.31 312	99.57 282	98.40 386	99.77 19	99.80 132	99.73 111	99.63 230	99.30 396	98.02 286	99.98 26	99.43 105	99.69 328	99.55 233

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 26	99.77 19	99.80 132	99.73 111	99.97 24	99.92 27	99.77 25	99.98 26	99.43 105	100.00 1	99.90 30

SDMVSNet		99.77 44	99.77 45	99.76 87	99.80 118	99.65 127	99.63 64	99.86 85	99.97 25	99.89 72	99.89 41	99.52 60	99.99 7	99.42 110	99.96 91	99.65 158

Anonymous20231211		99.62 93	99.57 104	99.76 87	99.61 254	99.60 156	99.81 13	99.73 182	99.82 85	99.90 67	99.90 36	97.97 292	99.86 273	99.42 110	99.96 91	99.80 67

SixPastTwentyTwo		99.42 159	99.30 183	99.76 87	99.92 29	99.67 118	99.70 38	99.14 425	99.65 150	99.89 72	99.90 36	96.20 384	99.94 98	99.42 110	99.92 154	99.67 135

BridgeMVS		99.50 126	99.50 123	99.50 237	99.42 359	99.49 182	99.52 94	99.75 171	99.86 65	99.78 138	99.71 194	98.20 270	99.90 201	99.39 113	99.88 197	99.10 397

patch_mono-2		99.51 123	99.46 135	99.64 166	99.70 213	99.11 288	99.04 271	99.87 77	99.71 121	99.47 296	99.79 120	98.24 262	99.98 26	99.38 114	99.96 91	99.83 59

UGNet		99.38 175	99.34 170	99.49 241	98.90 461	98.90 326	99.70 38	99.35 375	99.86 65	98.57 443	99.81 98	98.50 232	99.93 120	99.38 114	99.98 54	99.66 149
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
XXY-MVS		99.71 56	99.67 65	99.81 54	99.89 39	99.72 95	99.59 80	99.82 114	99.39 220	99.82 111	99.84 76	99.38 75	99.91 182	99.38 114	99.93 147	99.80 67

FIs		99.65 83	99.58 99	99.84 38	99.84 80	99.85 21	99.66 57	99.75 171	99.86 65	99.74 174	99.79 120	98.27 260	99.85 292	99.37 117	99.93 147	99.83 59

sd_testset		99.78 37	99.78 39	99.80 64	99.80 118	99.76 70	99.80 14	99.79 141	99.97 25	99.89 72	99.89 41	99.53 58	99.99 7	99.36 118	99.96 91	99.65 158

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 91	99.70 127	99.92 59	99.93 22	99.45 63	99.97 44	99.36 118	100.00 1	99.85 50

casdiffmvs_mvg		99.68 64	99.68 63	99.69 138	99.81 109	99.59 158	99.29 174	99.90 61	99.71 121	99.79 132	99.73 176	99.54 55	99.84 309	99.36 118	99.96 91	99.65 158
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 72	99.88 45	99.66 121	99.69 45	99.92 45	99.67 140	99.77 150	99.75 163	99.61 41	99.98 26	99.35 121	99.98 54	99.72 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 97	99.64 79	99.53 229	99.79 132	98.82 336	99.58 82	99.97 21	99.95 32	99.96 34	99.76 155	98.44 238	99.99 7	99.34 122	99.96 91	99.78 77

CHOSEN 1792x2688		99.39 172	99.30 183	99.65 159	99.88 45	99.25 254	98.78 343	99.88 71	98.66 339	99.96 34	99.79 120	97.45 325	99.93 120	99.34 122	99.99 19	99.78 77

CDS-MVSNet		99.22 226	99.13 219	99.50 237	99.35 376	99.11 288	98.96 306	99.54 305	99.46 199	99.61 247	99.70 204	96.31 377	99.83 330	99.34 122	99.88 197	99.55 233
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 289	99.16 212	98.51 424	99.75 175	95.90 487	98.07 430	99.84 98	99.84 75	99.89 72	99.73 176	96.01 388	99.99 7	99.33 125	100.00 1	99.63 176

HyFIR lowres test		98.91 302	98.64 318	99.73 113	99.85 74	99.47 187	98.07 430	99.83 108	98.64 342	99.89 72	99.60 288	92.57 445	100.00 1	99.33 125	99.97 77	99.72 99

pmmvs5		99.19 236	99.11 226	99.42 266	99.76 158	98.88 329	98.55 378	99.73 182	98.82 317	99.72 184	99.62 270	96.56 363	99.82 349	99.32 127	99.95 116	99.56 229

v148		99.40 168	99.41 151	99.39 280	99.76 158	98.94 318	99.09 256	99.59 276	99.17 262	99.81 118	99.61 280	98.41 242	99.69 437	99.32 127	99.94 133	99.53 250

baseline		99.63 86	99.62 85	99.66 152	99.80 118	99.62 142	99.44 119	99.80 132	99.71 121	99.72 184	99.69 213	99.15 114	99.83 330	99.32 127	99.94 133	99.53 250

CVMVSNet		98.61 337	98.88 294	97.80 457	99.58 272	93.60 514	99.26 185	99.64 244	99.66 146	99.72 184	99.67 230	93.26 436	99.93 120	99.30 130	99.81 258	99.87 45

PS-CasMVS		99.66 77	99.58 99	99.89 11	99.80 118	99.85 21	99.66 57	99.73 182	99.62 159	99.84 103	99.71 194	98.62 205	99.96 69	99.30 130	99.96 91	99.86 47

DTE-MVSNet		99.68 64	99.61 89	99.88 19	99.80 118	99.87 15	99.67 53	99.71 195	99.72 115	99.84 103	99.78 133	98.67 199	99.97 44	99.30 130	99.95 116	99.80 67

tmp_tt		95.75 481	95.42 476	96.76 497	89.90 540	94.42 506	98.86 324	97.87 495	78.01 531	99.30 350	99.69 213	97.70 309	95.89 530	99.29 133	98.14 496	99.95 15

PEN-MVS		99.66 77	99.59 95	99.89 11	99.83 88	99.87 15	99.66 57	99.73 182	99.70 127	99.84 103	99.73 176	98.56 215	99.96 69	99.29 133	99.94 133	99.83 59

WR-MVS_H		99.61 97	99.53 119	99.87 26	99.80 118	99.83 33	99.67 53	99.75 171	99.58 175	99.85 100	99.69 213	98.18 273	99.94 98	99.28 135	99.95 116	99.83 59

IterMVS		98.97 293	99.16 212	98.42 429	99.74 184	95.64 491	98.06 432	99.83 108	99.83 81	99.85 100	99.74 171	96.10 387	99.99 7	99.27 136	100.00 1	99.63 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
hybridcas		99.65 83	99.63 82	99.70 132	99.85 74	99.67 118	99.30 166	99.87 77	99.67 140	99.81 118	99.77 145	99.21 104	99.81 365	99.24 137	99.94 133	99.61 202

NormalMVS		99.09 265	98.91 292	99.62 182	99.78 140	99.11 288	99.36 144	99.77 158	99.82 85	99.68 202	99.53 325	93.30 434	99.99 7	99.24 137	99.76 287	99.74 91

SymmetryMVS		99.01 286	98.82 302	99.58 199	99.65 243	99.11 288	99.36 144	99.20 417	99.82 85	99.68 202	99.53 325	93.30 434	99.99 7	99.24 137	99.63 349	99.64 170

WBMVS		97.50 428	97.18 434	98.48 426	98.85 469	95.89 488	98.44 396	99.52 321	99.53 181	99.52 281	99.42 358	80.10 516	99.86 273	99.24 137	99.95 116	99.68 126

h-mvs33		98.61 337	98.34 361	99.44 259	99.60 257	98.67 351	99.27 180	99.44 347	99.68 132	99.32 340	99.49 340	92.50 449	100.00 1	99.24 137	96.51 521	99.65 158

hse-mvs2		98.52 351	98.30 366	99.16 342	99.29 398	98.60 365	98.77 344	99.02 434	99.68 132	99.32 340	99.04 445	92.50 449	99.85 292	99.24 137	97.87 504	99.03 421

FMVSNet1		99.66 77	99.63 82	99.73 113	99.78 140	99.77 63	99.68 48	99.70 204	99.67 140	99.82 111	99.83 83	98.98 153	99.90 201	99.24 137	99.97 77	99.53 250

casdiffseed414692147		99.68 64	99.68 63	99.67 144	99.86 59	99.65 127	99.32 157	99.87 77	99.75 109	99.77 150	99.80 108	99.61 41	99.68 449	99.21 144	99.95 116	99.67 135

casdiffmvs		99.63 86	99.61 89	99.67 144	99.79 132	99.59 158	99.13 238	99.85 91	99.79 99	99.76 159	99.72 184	99.33 86	99.82 349	99.21 144	99.94 133	99.59 214
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		99.54 115	99.43 144	99.87 26	99.76 158	99.82 41	99.57 85	99.61 259	99.54 179	99.80 125	99.64 244	97.79 304	99.95 81	99.21 144	99.94 133	99.84 55

DELS-MVS		99.34 191	99.30 183	99.48 246	99.51 320	99.36 231	98.12 423	99.53 316	99.36 226	99.41 316	99.61 280	99.22 103	99.87 254	99.21 144	99.68 333	99.20 374
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		99.47 143	99.50 123	99.37 288	99.70 213	98.80 340	98.67 357	99.92 45	99.49 188	99.77 150	99.71 194	99.08 130	99.78 382	99.20 148	99.94 133	99.54 243

UniMVSNet (Re)		99.37 179	99.26 196	99.68 140	99.51 320	99.58 163	98.98 300	99.60 270	99.43 211	99.70 193	99.36 380	97.70 309	99.88 238	99.20 148	99.87 210	99.59 214

CANet		99.11 261	99.05 249	99.28 320	98.83 472	98.56 370	98.71 354	99.41 354	99.25 244	99.23 361	99.22 418	97.66 317	99.94 98	99.19 150	99.97 77	99.33 341

EI-MVSNet-UG-set		99.48 133	99.50 123	99.42 266	99.57 282	98.65 358	99.24 192	99.46 341	99.68 132	99.80 125	99.66 235	98.99 149	99.89 223	99.19 150	99.90 171	99.72 99

dtuplus		99.52 121	99.55 111	99.43 263	99.76 158	98.90 326	98.71 354	99.89 65	99.67 140	99.79 132	99.77 145	99.25 100	99.81 365	99.18 152	99.96 91	99.57 225

xiu_mvs_v1_base_debu		99.23 217	99.34 170	98.91 384	99.59 263	98.23 395	98.47 391	99.66 226	99.61 164	99.68 202	98.94 462	99.39 71	99.97 44	99.18 152	99.55 375	98.51 471

xiu_mvs_v1_base		99.23 217	99.34 170	98.91 384	99.59 263	98.23 395	98.47 391	99.66 226	99.61 164	99.68 202	98.94 462	99.39 71	99.97 44	99.18 152	99.55 375	98.51 471

xiu_mvs_v1_base_debi		99.23 217	99.34 170	98.91 384	99.59 263	98.23 395	98.47 391	99.66 226	99.61 164	99.68 202	98.94 462	99.39 71	99.97 44	99.18 152	99.55 375	98.51 471

VPNet		99.46 145	99.37 159	99.71 127	99.82 97	99.59 158	99.48 109	99.70 204	99.81 91	99.69 196	99.58 301	97.66 317	99.86 273	99.17 156	99.44 398	99.67 135

UniMVSNet_NR-MVSNet		99.37 179	99.25 200	99.72 121	99.47 342	99.56 167	98.97 302	99.61 259	99.43 211	99.67 210	99.28 401	97.85 300	99.95 81	99.17 156	99.81 258	99.65 158

DU-MVS		99.33 194	99.21 206	99.71 127	99.43 354	99.56 167	98.83 331	99.53 316	99.38 221	99.67 210	99.36 380	97.67 313	99.95 81	99.17 156	99.81 258	99.63 176

hybrid		99.42 159	99.43 144	99.37 288	99.75 175	98.77 343	98.72 351	99.84 98	99.61 164	99.65 220	99.68 225	98.53 225	99.79 378	99.16 159	99.94 133	99.54 243

usedtu_dtu_shiyan2		99.44 152	99.33 175	99.78 76	99.86 59	99.76 70	99.54 90	99.79 141	99.66 146	99.66 216	99.79 120	96.76 357	99.96 69	99.15 160	99.72 313	99.62 188

EI-MVSNet-Vis-set		99.47 143	99.49 127	99.42 266	99.57 282	98.66 354	99.24 192	99.46 341	99.67 140	99.79 132	99.65 242	98.97 155	99.89 223	99.15 160	99.89 186	99.71 104

EI-MVSNet		99.38 175	99.44 142	99.21 335	99.58 272	98.09 409	99.26 185	99.46 341	99.62 159	99.75 164	99.67 230	98.54 220	99.85 292	99.15 160	99.92 154	99.68 126

VNet		99.18 240	99.06 244	99.56 211	99.24 409	99.36 231	99.33 154	99.31 390	99.67 140	99.47 296	99.57 308	96.48 367	99.84 309	99.15 160	99.30 417	99.47 282

EG-PatchMatch MVS		99.57 101	99.56 109	99.62 182	99.77 153	99.33 237	99.26 185	99.76 166	99.32 231	99.80 125	99.78 133	99.29 90	99.87 254	99.15 160	99.91 167	99.66 149

PVSNet_Blended_VisFu		99.40 168	99.38 156	99.44 259	99.90 37	98.66 354	98.94 311	99.91 55	97.97 415	99.79 132	99.73 176	99.05 141	99.97 44	99.15 160	99.99 19	99.68 126

IterMVS-LS		99.41 166	99.47 130	99.25 331	99.81 109	98.09 409	98.85 326	99.76 166	99.62 159	99.83 109	99.64 244	98.54 220	99.97 44	99.15 160	99.99 19	99.68 126
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 115	99.47 130	99.76 87	99.58 272	99.64 134	99.30 166	99.63 248	99.61 164	99.71 189	99.56 312	98.76 185	99.96 69	99.14 167	99.92 154	99.68 126

MVSTER		98.47 358	98.22 373	99.24 333	99.06 444	98.35 392	99.08 259	99.46 341	99.27 239	99.75 164	99.66 235	88.61 491	99.85 292	99.14 167	99.92 154	99.52 261

E5new		99.68 64	99.67 65	99.70 132	99.87 54	99.62 142	99.41 122	99.84 98	99.68 132	99.77 150	99.81 98	99.59 46	99.78 382	99.13 169	99.96 91	99.70 107

E6new		99.68 64	99.67 65	99.70 132	99.86 59	99.62 142	99.41 122	99.84 98	99.68 132	99.77 150	99.81 98	99.59 46	99.78 382	99.13 169	99.96 91	99.70 107

E6		99.68 64	99.67 65	99.70 132	99.86 59	99.62 142	99.41 122	99.84 98	99.68 132	99.77 150	99.81 98	99.59 46	99.78 382	99.13 169	99.96 91	99.70 107

E5		99.68 64	99.67 65	99.70 132	99.87 54	99.62 142	99.41 122	99.84 98	99.68 132	99.77 150	99.81 98	99.59 46	99.78 382	99.13 169	99.96 91	99.70 107

diffmvs_AUTHOR		99.48 133	99.48 128	99.47 248	99.80 118	98.89 328	98.71 354	99.82 114	99.79 99	99.66 216	99.63 260	98.87 171	99.88 238	99.13 169	99.95 116	99.62 188

Anonymous20231206		99.35 186	99.31 178	99.47 248	99.74 184	99.06 300	99.28 176	99.74 177	99.23 248	99.72 184	99.53 325	97.63 320	99.88 238	99.11 174	99.84 230	99.48 278

Syy-MVS		98.17 389	97.85 405	99.15 344	98.50 496	98.79 341	98.60 365	99.21 414	97.89 425	96.76 510	96.37 538	95.47 403	99.57 481	99.10 175	98.73 470	99.09 403

ttmdpeth		99.48 133	99.55 111	99.29 317	99.76 158	98.16 403	99.33 154	99.95 37	99.79 99	99.36 327	99.89 41	99.13 119	99.77 395	99.09 176	99.64 346	99.93 21

MVS_Test		99.28 203	99.31 178	99.19 339	99.35 376	98.79 341	99.36 144	99.49 334	99.17 262	99.21 367	99.67 230	98.78 182	99.66 460	99.09 176	99.66 342	99.10 397

usedtu_dtu_shiyan1		98.87 309	98.71 311	99.35 297	99.59 263	98.88 329	97.17 488	99.64 244	98.94 293	99.27 352	99.22 418	95.57 398	99.83 330	99.08 178	99.92 154	99.35 334

FE-MVSNET3		98.87 309	98.71 311	99.35 297	99.59 263	98.88 329	97.17 488	99.64 244	98.94 293	99.27 352	99.22 418	95.57 398	99.83 330	99.08 178	99.92 154	99.35 334

testgi		99.29 201	99.26 196	99.37 288	99.75 175	98.81 337	98.84 328	99.89 65	98.38 374	99.75 164	99.04 445	99.36 80	99.86 273	99.08 178	99.25 426	99.45 289

1112_ss		99.05 273	98.84 299	99.67 144	99.66 239	99.29 243	98.52 385	99.82 114	97.65 440	99.43 307	99.16 427	96.42 370	99.91 182	99.07 181	99.84 230	99.80 67

CANet_DTU		98.91 302	98.85 297	99.09 354	98.79 478	98.13 404	98.18 414	99.31 390	99.48 191	98.86 413	99.51 333	96.56 363	99.95 81	99.05 182	99.95 116	99.19 377

blended_shiyan8		97.82 408	97.45 422	98.92 379	98.06 513	97.45 444	97.73 458	99.35 375	97.96 418	98.35 455	97.34 517	92.76 444	99.84 309	99.04 183	96.49 523	99.47 282

blended_shiyan6		97.82 408	97.46 420	98.92 379	98.08 512	97.46 442	97.73 458	99.34 379	97.96 418	98.33 456	97.35 516	92.78 442	99.84 309	99.04 183	96.53 517	99.46 287

ELoFTR		99.25 211	99.26 196	99.21 335	99.86 59	98.66 354	99.00 289	99.93 41	98.56 351	99.83 109	99.83 83	97.34 331	99.92 152	99.03 185	100.00 1	99.04 418

Baseline_NR-MVSNet		99.49 131	99.37 159	99.82 46	99.91 31	99.84 26	98.83 331	99.86 85	99.68 132	99.65 220	99.88 50	97.67 313	99.87 254	99.03 185	99.86 218	99.76 86

FMVSNet2		99.35 186	99.28 191	99.55 218	99.49 331	99.35 234	99.45 117	99.57 287	99.44 204	99.70 193	99.74 171	97.21 337	99.87 254	99.03 185	99.94 133	99.44 304

wanda-best-256-512		97.53 425	97.14 436	98.72 410	97.71 519	96.86 465	97.00 496	99.34 379	97.73 435	98.18 463	96.82 529	91.92 453	99.84 309	99.02 188	96.53 517	99.45 289

FE-blended-shiyan7		97.53 425	97.14 436	98.72 410	97.71 519	96.86 465	97.00 496	99.34 379	97.73 435	98.18 463	96.82 529	91.92 453	99.84 309	99.02 188	96.53 517	99.45 289

Test_1112_low_res		98.95 299	98.73 309	99.63 173	99.68 229	99.15 283	98.09 427	99.80 132	97.14 468	99.46 300	99.40 363	96.11 385	99.89 223	99.01 190	99.84 230	99.84 55

VDD-MVS		99.20 233	99.11 226	99.44 259	99.43 354	98.98 309	99.50 102	98.32 479	99.80 95	99.56 266	99.69 213	96.99 349	99.85 292	98.99 191	99.73 306	99.50 269

DeepC-MVS	98.90 4	99.62 93	99.61 89	99.67 144	99.72 193	99.44 201	99.24 192	99.71 195	99.27 239	99.93 53	99.90 36	99.70 31	99.93 120	98.99 191	99.99 19	99.64 170
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 133	99.47 130	99.51 235	99.77 153	99.41 215	98.81 336	99.66 226	99.42 215	99.75 164	99.66 235	99.20 106	99.76 401	98.98 193	99.99 19	99.36 331

EPNet_dtu		97.62 419	97.79 409	97.11 490	96.67 530	92.31 520	98.51 386	98.04 487	99.24 246	95.77 520	99.47 348	93.78 429	99.66 460	98.98 193	99.62 351	99.37 328
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 191	99.32 176	99.39 280	99.67 236	98.77 343	98.57 374	99.81 127	99.61 164	99.48 294	99.41 359	98.47 233	99.86 273	98.97 195	99.90 171	99.53 250
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		99.40 168	99.31 178	99.68 140	99.43 354	99.55 171	99.73 30	99.50 330	99.46 199	99.88 82	99.36 380	97.54 321	99.87 254	98.97 195	99.87 210	99.63 176

RoMa-SfM		99.32 196	99.23 205	99.59 195	99.77 153	99.53 174	98.89 318	99.88 71	98.78 324	99.65 220	99.52 329	97.78 305	99.90 201	98.96 197	99.86 218	99.35 334

TestfortrainingZip a		99.55 110	99.45 137	99.85 32	99.76 158	99.82 41	99.38 132	99.62 251	99.77 106	99.87 92	99.78 133	98.12 278	99.88 238	98.96 197	99.77 283	99.85 50

viewdifsd2359ckpt07		99.51 123	99.50 123	99.52 231	99.80 118	99.19 274	98.92 315	99.88 71	99.72 115	99.64 225	99.62 270	99.06 139	99.81 365	98.96 197	99.94 133	99.56 229

GBi-Net		99.42 159	99.31 178	99.73 113	99.49 331	99.77 63	99.68 48	99.70 204	99.44 204	99.62 240	99.83 83	97.21 337	99.90 201	98.96 197	99.90 171	99.53 250

FMVSNet5		97.80 411	97.25 431	99.42 266	98.83 472	98.97 312	99.38 132	99.80 132	98.87 307	99.25 357	99.69 213	80.60 515	99.91 182	98.96 197	99.90 171	99.38 325

test1		99.42 159	99.31 178	99.73 113	99.49 331	99.77 63	99.68 48	99.70 204	99.44 204	99.62 240	99.83 83	97.21 337	99.90 201	98.96 197	99.90 171	99.53 250

FMVSNet3		98.80 318	98.63 320	99.32 308	99.13 429	98.72 347	99.10 251	99.48 335	99.23 248	99.62 240	99.64 244	92.57 445	99.86 273	98.96 197	99.90 171	99.39 323

UnsupCasMVSNet_eth		98.83 314	98.57 328	99.59 195	99.68 229	99.45 199	98.99 297	99.67 221	99.48 191	99.55 271	99.36 380	94.92 410	99.86 273	98.95 204	96.57 516	99.45 289

CHOSEN 280x420		98.41 364	98.41 351	98.40 430	99.34 385	95.89 488	96.94 501	99.44 347	98.80 321	99.25 357	99.52 329	93.51 433	99.98 26	98.94 205	99.98 54	99.32 345

E4		99.61 97	99.59 95	99.66 152	99.84 80	99.53 174	99.08 259	99.84 98	99.65 150	99.74 174	99.80 108	99.45 63	99.77 395	98.93 206	99.95 116	99.69 119

TDRefinement		99.72 53	99.70 57	99.77 80	99.90 37	99.85 21	99.86 6	99.92 45	99.69 130	99.78 138	99.92 27	99.37 77	99.88 238	98.93 206	99.95 116	99.60 207

PDCNetPlus		98.55 347	98.50 338	98.69 415	99.64 245	96.12 482	97.67 465	100.00 1	98.34 386	99.79 132	99.75 163	92.45 451	99.98 26	98.92 208	99.99 19	99.96 13

viewmacassd2359aftdt		99.63 86	99.61 89	99.68 140	99.84 80	99.61 152	99.14 231	99.87 77	99.71 121	99.75 164	99.77 145	99.54 55	99.72 421	98.91 209	99.96 91	99.70 107

alignmvs		98.28 374	97.96 394	99.25 331	99.12 431	98.93 321	99.03 274	98.42 471	99.64 154	98.72 428	97.85 507	90.86 473	99.62 472	98.88 210	99.13 435	99.19 377

testing3-2		96.51 459	96.43 453	96.74 499	99.36 372	91.38 528	99.10 251	97.87 495	99.48 191	98.57 443	98.71 479	76.65 528	99.66 460	98.87 211	99.26 424	99.18 379

MGCFI-Net		99.02 280	99.01 263	99.06 362	99.11 436	98.60 365	99.63 64	99.67 221	99.63 156	98.58 441	97.65 511	99.07 133	99.57 481	98.85 212	98.92 453	99.03 421

sss		98.90 304	98.77 308	99.27 325	99.48 336	98.44 383	98.72 351	99.32 386	97.94 421	99.37 326	99.35 385	96.31 377	99.91 182	98.85 212	99.63 349	99.47 282

xiu_mvs_v2_base		99.02 280	99.11 226	98.77 406	99.37 369	98.09 409	98.13 422	99.51 326	99.47 196	99.42 310	98.54 491	99.38 75	99.97 44	98.83 214	99.33 413	98.24 485

PS-MVSNAJ		99.00 289	99.08 238	98.76 407	99.37 369	98.10 408	98.00 439	99.51 326	99.47 196	99.41 316	98.50 493	99.28 92	99.97 44	98.83 214	99.34 412	98.20 489

E2		99.54 115	99.51 121	99.62 182	99.78 140	99.47 187	99.01 283	99.82 114	99.55 177	99.69 196	99.77 145	99.26 96	99.76 401	98.82 216	99.93 147	99.62 188

E3		99.54 115	99.51 121	99.62 182	99.78 140	99.47 187	99.01 283	99.82 114	99.55 177	99.69 196	99.77 145	99.25 100	99.76 401	98.82 216	99.93 147	99.62 188

D2MVS		99.22 226	99.19 209	99.29 317	99.69 221	98.74 346	98.81 336	99.41 354	98.55 353	99.68 202	99.69 213	98.13 276	99.87 254	98.82 216	99.98 54	99.24 361

PatchT		98.45 361	98.32 363	98.83 399	98.94 459	98.29 393	99.24 192	98.82 445	99.84 75	99.08 387	99.76 155	91.37 462	99.94 98	98.82 216	99.00 447	98.26 483

testf1		99.63 86	99.60 93	99.72 121	99.94 18	99.95 2	99.47 112	99.89 65	99.43 211	99.88 82	99.80 108	99.26 96	99.90 201	98.81 220	99.88 197	99.32 345

APD_test2		99.63 86	99.60 93	99.72 121	99.94 18	99.95 2	99.47 112	99.89 65	99.43 211	99.88 82	99.80 108	99.26 96	99.90 201	98.81 220	99.88 197	99.32 345

gbinet_0.2-2-1-0.02		97.52 427	97.07 438	98.88 393	97.35 527	97.35 449	97.17 488	99.25 403	97.86 430	98.41 453	96.54 535	90.74 475	99.85 292	98.80 222	97.51 508	99.43 310

usedtu_blend_shiyan5		97.97 402	97.65 418	98.92 379	97.71 519	97.49 439	99.53 92	99.81 127	99.52 185	98.18 463	96.82 529	91.92 453	99.83 330	98.79 223	96.53 517	99.45 289

blend_shiyan4		95.04 489	93.76 495	98.88 393	97.92 515	97.49 439	97.72 460	99.34 379	97.93 422	97.65 495	97.11 522	77.69 526	99.83 330	98.79 223	79.72 534	99.33 341

sasdasda		99.02 280	99.00 267	99.09 354	99.10 438	98.70 349	99.61 73	99.66 226	99.63 156	98.64 434	97.65 511	99.04 142	99.54 486	98.79 223	98.92 453	99.04 418

Effi-MVS+		99.06 270	98.97 279	99.34 300	99.31 392	98.98 309	98.31 405	99.91 55	98.81 319	98.79 422	98.94 462	99.14 117	99.84 309	98.79 223	98.74 467	99.20 374

canonicalmvs		99.02 280	99.00 267	99.09 354	99.10 438	98.70 349	99.61 73	99.66 226	99.63 156	98.64 434	97.65 511	99.04 142	99.54 486	98.79 223	98.92 453	99.04 418

VDDNet		98.97 293	98.82 302	99.42 266	99.71 197	98.81 337	99.62 67	98.68 453	99.81 91	99.38 324	99.80 108	94.25 422	99.85 292	98.79 223	99.32 415	99.59 214

CR-MVSNet		98.35 371	98.20 375	98.83 399	99.05 445	98.12 405	99.30 166	99.67 221	97.39 455	99.16 374	99.79 120	91.87 458	99.91 182	98.78 229	98.77 462	98.44 476

test_method		91.72 496	92.32 496	89.91 516	93.49 539	70.18 542	90.28 529	99.56 292	61.71 534	95.39 522	99.52 329	93.90 425	99.94 98	98.76 230	98.27 489	99.62 188

RPMNet		98.60 340	98.53 334	98.83 399	99.05 445	98.12 405	99.30 166	99.62 251	99.86 65	99.16 374	99.74 171	92.53 447	99.92 152	98.75 231	98.77 462	98.44 476

mamba_0408		99.54 115	99.55 111	99.54 224	99.71 197	99.24 259	99.27 180	99.79 141	99.72 115	99.78 138	99.64 244	99.36 80	99.93 120	98.74 232	99.90 171	99.45 289

SSM_04072		99.55 110	99.55 111	99.55 218	99.71 197	99.24 259	99.27 180	99.79 141	99.72 115	99.78 138	99.64 244	99.36 80	99.97 44	98.74 232	99.90 171	99.45 289

SSM_0407		99.56 105	99.56 109	99.54 224	99.71 197	99.24 259	99.15 227	99.84 98	99.80 95	99.78 138	99.70 204	99.44 65	99.93 120	98.74 232	99.90 171	99.45 289

SSM_0404		99.57 101	99.58 99	99.54 224	99.76 158	99.28 245	99.19 209	99.84 98	99.80 95	99.78 138	99.70 204	99.44 65	99.93 120	98.74 232	99.95 116	99.41 316

pmmvs4		99.13 253	99.06 244	99.36 294	99.57 282	99.10 295	98.01 436	99.25 403	98.78 324	99.58 255	99.44 355	98.24 262	99.76 401	98.74 232	99.93 147	99.22 366

viewmanbaseed2359cas		99.50 126	99.47 130	99.61 188	99.73 188	99.52 179	99.03 274	99.83 108	99.49 188	99.65 220	99.64 244	99.18 108	99.71 426	98.73 237	99.92 154	99.58 219

tttt0517		97.62 419	97.20 433	98.90 390	99.76 158	97.40 447	99.48 109	94.36 526	99.06 279	99.70 193	99.49 340	84.55 508	99.94 98	98.73 237	99.65 344	99.36 331

viewcassd2359sk11		99.48 133	99.45 137	99.58 199	99.73 188	99.42 208	98.96 306	99.80 132	99.44 204	99.63 230	99.74 171	99.09 126	99.76 401	98.72 239	99.91 167	99.57 225

EPP-MVSNet		99.17 245	99.00 267	99.66 152	99.80 118	99.43 205	99.70 38	99.24 407	99.48 191	99.56 266	99.77 145	94.89 411	99.93 120	98.72 239	99.89 186	99.63 176

FE-MVSNET		99.45 149	99.36 164	99.71 127	99.84 80	99.64 134	99.16 224	99.91 55	98.65 340	99.73 179	99.73 176	98.54 220	99.82 349	98.71 241	99.96 91	99.67 135

Anonymous20240529		99.42 159	99.34 170	99.65 159	99.53 311	99.60 156	99.63 64	99.39 364	99.47 196	99.76 159	99.78 133	98.13 276	99.86 273	98.70 242	99.68 333	99.49 274

ACMH	98.42 6	99.59 100	99.54 115	99.72 121	99.86 59	99.62 142	99.56 87	99.79 141	98.77 327	99.80 125	99.85 68	99.64 35	99.85 292	98.70 242	99.89 186	99.70 107
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 194	99.28 191	99.47 248	99.57 282	99.39 220	99.78 17	99.43 351	98.87 307	99.57 258	99.82 91	98.06 284	99.87 254	98.69 244	99.73 306	99.15 386

LFMVS		98.46 360	98.19 378	99.26 328	99.24 409	98.52 378	99.62 67	96.94 509	99.87 62	99.31 345	99.58 301	91.04 467	99.81 365	98.68 245	99.42 402	99.45 289

DKM		99.12 256	98.98 277	99.54 224	99.71 197	99.48 186	98.53 383	99.88 71	99.18 256	98.99 397	99.64 244	96.25 381	99.75 411	98.66 246	99.93 147	99.40 319

WR-MVS		99.11 261	98.93 284	99.66 152	99.30 396	99.42 208	98.42 397	99.37 370	99.04 280	99.57 258	99.20 424	96.89 352	99.86 273	98.66 246	99.87 210	99.70 107

mvsmamba		99.08 266	98.95 282	99.45 255	99.36 372	99.18 280	99.39 129	98.81 447	99.37 222	99.35 331	99.70 204	96.36 375	99.94 98	98.66 246	99.59 366	99.22 366

viewdifsd2359ckpt13		99.42 159	99.37 159	99.57 207	99.72 193	99.46 193	99.01 283	99.80 132	99.20 253	99.51 288	99.60 288	98.92 162	99.70 430	98.65 249	99.90 171	99.55 233

RRT-MVS		99.08 266	99.00 267	99.33 303	99.27 403	98.65 358	99.62 67	99.93 41	99.66 146	99.67 210	99.82 91	95.27 407	99.93 120	98.64 250	99.09 440	99.41 316

E3new		99.42 159	99.37 159	99.56 211	99.68 229	99.38 222	98.93 314	99.79 141	99.30 234	99.55 271	99.69 213	98.88 169	99.76 401	98.63 251	99.89 186	99.53 250

Anonymous202405211		98.75 323	98.46 342	99.63 173	99.34 385	99.66 121	99.47 112	97.65 498	99.28 238	99.56 266	99.50 336	93.15 437	99.84 309	98.62 252	99.58 368	99.40 319

SP-SuperGlue		98.66 334	98.63 320	98.73 409	98.44 498	99.02 303	98.22 412	99.44 347	99.37 222	98.17 467	99.30 396	96.95 350	99.12 509	98.59 253	99.20 433	98.06 493

lecture		99.56 105	99.48 128	99.81 54	99.78 140	99.86 18	99.50 102	99.70 204	99.59 173	99.75 164	99.71 194	98.94 158	99.92 152	98.59 253	99.76 287	99.66 149

EPNet		98.13 391	97.77 411	99.18 341	94.57 538	97.99 415	99.24 192	97.96 490	99.74 110	97.29 502	99.62 270	93.13 438	99.97 44	98.59 253	99.83 238	99.58 219
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 273	99.09 236	98.91 384	99.21 414	98.36 391	98.82 335	99.47 338	98.85 310	98.90 408	99.56 312	98.78 182	99.09 512	98.57 256	99.68 333	99.26 358

Patchmatch-RL test		98.60 340	98.36 358	99.33 303	99.77 153	99.07 298	98.27 407	99.87 77	98.91 302	99.74 174	99.72 184	90.57 479	99.79 378	98.55 257	99.85 225	99.11 395

pmmvs3		98.08 394	97.80 407	98.91 384	99.41 361	97.69 433	97.87 452	99.66 226	95.87 492	99.50 291	99.51 333	90.35 481	99.97 44	98.55 257	99.47 394	99.08 409

LoFTR		99.29 201	99.26 196	99.36 294	99.70 213	99.05 301	98.66 359	99.95 37	98.85 310	99.86 96	99.75 163	98.14 275	99.93 120	98.54 259	99.91 167	99.10 397

SP-LightGlue		98.62 336	98.51 336	98.94 374	98.69 489	99.01 304	98.34 401	99.54 305	99.27 239	97.72 493	99.15 429	95.88 392	99.54 486	98.53 260	99.47 394	98.27 482

ETV-MVS		99.18 240	99.18 210	99.16 342	99.34 385	99.28 245	99.12 243	99.79 141	99.48 191	98.93 402	98.55 490	99.40 70	99.93 120	98.51 261	99.52 385	98.28 481

viewdifsd2359ckpt09		99.24 215	99.16 212	99.49 241	99.70 213	99.22 265	98.88 320	99.81 127	98.70 335	99.38 324	99.37 375	98.22 267	99.76 401	98.48 262	99.88 197	99.51 263

jason		99.16 246	99.11 226	99.32 308	99.75 175	98.44 383	98.26 409	99.39 364	98.70 335	99.74 174	99.30 396	98.54 220	99.97 44	98.48 262	99.82 248	99.55 233
jason: jason.
APDe-MVS		99.48 133	99.36 164	99.85 32	99.55 300	99.81 47	99.50 102	99.69 213	98.99 285	99.75 164	99.71 194	98.79 180	99.93 120	98.46 264	99.85 225	99.80 67
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
icg_test_0407_2		99.30 199	99.29 188	99.31 312	99.71 197	98.55 372	98.17 416	99.71 195	99.41 216	99.73 179	99.60 288	99.17 110	99.92 152	98.45 265	99.70 319	99.45 289

IMVS_0407		99.38 175	99.42 147	99.28 320	99.71 197	98.55 372	99.27 180	99.71 195	99.41 216	99.73 179	99.60 288	99.17 110	99.83 330	98.45 265	99.70 319	99.45 289

IMVS_0404		99.23 217	99.20 207	99.32 308	99.71 197	98.55 372	98.57 374	99.71 195	99.41 216	99.52 281	99.60 288	98.12 278	99.95 81	98.45 265	99.70 319	99.45 289

IMVS_0403		99.37 179	99.39 153	99.28 320	99.71 197	98.55 372	99.19 209	99.71 195	99.41 216	99.67 210	99.60 288	99.12 122	99.84 309	98.45 265	99.70 319	99.45 289

CL-MVSNet_self_test		98.71 329	98.56 332	99.15 344	99.22 412	98.66 354	97.14 491	99.51 326	98.09 406	99.54 274	99.27 403	96.87 353	99.74 416	98.43 269	98.96 449	99.03 421

our_test_3		98.85 313	99.09 236	98.13 444	99.66 239	94.90 504	97.72 460	99.58 285	99.07 277	99.64 225	99.62 270	98.19 271	99.93 120	98.41 270	99.95 116	99.55 233

Gipuma		99.57 101	99.59 95	99.49 241	99.98 3	99.71 100	99.72 33	99.84 98	99.81 91	99.94 48	99.78 133	98.91 165	99.71 426	98.41 270	99.95 116	99.05 416
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 434	96.91 446	98.74 408	97.72 518	97.57 436	97.60 468	97.36 504	98.00 411	99.21 367	98.02 503	90.04 485	99.79 378	98.37 272	95.89 526	98.86 445

PM-MVS		99.36 184	99.29 188	99.58 199	99.83 88	99.66 121	98.95 309	99.86 85	98.85 310	99.81 118	99.73 176	98.40 246	99.92 152	98.36 273	99.83 238	99.17 382

baseline1		97.73 414	97.33 427	98.96 371	99.30 396	97.73 431	99.40 127	98.42 471	99.33 230	99.46 300	99.21 422	91.18 465	99.82 349	98.35 274	91.26 529	99.32 345

MVS-HIRNet		97.86 406	98.22 373	96.76 497	99.28 401	91.53 526	98.38 399	92.60 532	99.13 270	99.31 345	99.96 15	97.18 341	99.68 449	98.34 275	99.83 238	99.07 414

GA-MVS		97.99 401	97.68 415	98.93 378	99.52 318	98.04 413	97.19 487	99.05 432	98.32 389	98.81 418	98.97 457	89.89 487	99.41 499	98.33 276	99.05 443	99.34 340

Fast-Effi-MVS+		99.02 280	98.87 295	99.46 252	99.38 366	99.50 181	99.04 271	99.79 141	97.17 466	98.62 437	98.74 477	99.34 84	99.95 81	98.32 277	99.41 403	98.92 437

MDA-MVSNet_test_wron		98.95 299	98.99 274	98.85 395	99.64 245	97.16 455	98.23 411	99.33 384	98.93 298	99.56 266	99.66 235	97.39 329	99.83 330	98.29 278	99.88 197	99.55 233

N_pmnet		98.73 326	98.53 334	99.35 297	99.72 193	98.67 351	98.34 401	94.65 525	98.35 382	99.79 132	99.68 225	98.03 285	99.93 120	98.28 279	99.92 154	99.44 304

ET-MVSNet_ETH3D		96.78 449	96.07 462	98.91 384	99.26 406	97.92 422	97.70 463	96.05 514	97.96 418	92.37 530	98.43 494	87.06 496	99.90 201	98.27 280	97.56 507	98.91 439

thisisatest0530		97.45 429	96.95 443	98.94 374	99.68 229	97.73 431	99.09 256	94.19 528	98.61 348	99.56 266	99.30 396	84.30 510	99.93 120	98.27 280	99.54 380	99.16 384

YYNet1		98.95 299	98.99 274	98.84 397	99.64 245	97.14 457	98.22 412	99.32 386	98.92 301	99.59 253	99.66 235	97.40 327	99.83 330	98.27 280	99.90 171	99.55 233

reproduce_model		99.50 126	99.40 152	99.83 41	99.60 257	99.83 33	99.12 243	99.68 216	99.49 188	99.80 125	99.79 120	99.01 146	99.93 120	98.24 283	99.82 248	99.73 95

ACMM	98.09 11	99.46 145	99.38 156	99.72 121	99.80 118	99.69 112	99.13 238	99.65 236	98.99 285	99.64 225	99.72 184	99.39 71	99.86 273	98.23 284	99.81 258	99.60 207
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 296	98.87 295	99.24 333	99.57 282	98.40 386	98.12 423	99.18 419	98.28 392	99.63 230	99.13 430	98.02 286	99.97 44	98.22 285	99.69 328	99.35 334

3Dnovator	99.15 2	99.43 156	99.36 164	99.65 159	99.39 363	99.42 208	99.70 38	99.56 292	99.23 248	99.35 331	99.80 108	99.17 110	99.95 81	98.21 286	99.84 230	99.59 214

Fast-Effi-MVS+-dtu		99.20 233	99.12 223	99.43 263	99.25 407	99.69 112	99.05 266	99.82 114	99.50 186	98.97 398	99.05 443	98.98 153	99.98 26	98.20 287	99.24 428	98.62 461

MS-PatchMatch		99.00 289	98.97 279	99.09 354	99.11 436	98.19 399	98.76 345	99.33 384	98.49 363	99.44 303	99.58 301	98.21 268	99.69 437	98.20 287	99.62 351	99.39 323

TSAR-MVS + GP.		99.12 256	99.04 255	99.38 283	99.34 385	99.16 281	98.15 419	99.29 394	98.18 399	99.63 230	99.62 270	99.18 108	99.68 449	98.20 287	99.74 299	99.30 352

DP-MVS		99.48 133	99.39 153	99.74 103	99.57 282	99.62 142	99.29 174	99.61 259	99.87 62	99.74 174	99.76 155	98.69 195	99.87 254	98.20 287	99.80 265	99.75 89

MVP-Stereo		99.16 246	99.08 238	99.43 263	99.48 336	99.07 298	99.08 259	99.55 299	98.63 343	99.31 345	99.68 225	98.19 271	99.78 382	98.18 291	99.58 368	99.45 289
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 156	99.30 183	99.80 64	99.83 88	99.81 47	99.52 94	99.70 204	98.35 382	99.51 288	99.50 336	99.31 88	99.88 238	98.18 291	99.84 230	99.69 119

MDA-MVSNet-bldmvs		99.06 270	99.05 249	99.07 360	99.80 118	97.83 426	98.89 318	99.72 191	99.29 235	99.63 230	99.70 204	96.47 368	99.89 223	98.17 293	99.82 248	99.50 269

JIA-IIPM		98.06 396	97.92 401	98.50 425	98.59 492	97.02 459	98.80 339	98.51 465	99.88 60	97.89 482	99.87 56	91.89 457	99.90 201	98.16 294	97.68 506	98.59 464

EIA-MVS		99.12 256	99.01 263	99.45 255	99.36 372	99.62 142	99.34 148	99.79 141	98.41 369	98.84 415	98.89 466	98.75 187	99.84 309	98.15 295	99.51 386	98.89 442

miper_lstm_enhance		98.65 335	98.60 322	98.82 402	99.20 417	97.33 450	97.78 456	99.66 226	99.01 283	99.59 253	99.50 336	94.62 417	99.85 292	98.12 296	99.90 171	99.26 358

reproduce-ours		99.46 145	99.35 168	99.82 46	99.56 296	99.83 33	99.05 266	99.65 236	99.45 202	99.78 138	99.78 133	98.93 159	99.93 120	98.11 297	99.81 258	99.70 107

our_new_method		99.46 145	99.35 168	99.82 46	99.56 296	99.83 33	99.05 266	99.65 236	99.45 202	99.78 138	99.78 133	98.93 159	99.93 120	98.11 297	99.81 258	99.70 107

Effi-MVS+-dtu		99.07 269	98.92 288	99.52 231	98.89 464	99.78 57	99.15 227	99.66 226	99.34 227	98.92 405	99.24 415	97.69 311	99.98 26	98.11 297	99.28 420	98.81 450

tpm		97.15 441	96.95 443	97.75 459	98.91 460	94.24 508	99.32 157	97.96 490	97.71 438	98.29 457	99.32 390	86.72 502	99.92 152	98.10 300	96.24 524	99.09 403

DeepPCF-MVS	98.42 6	99.18 240	99.02 258	99.67 144	99.22 412	99.75 79	97.25 485	99.47 338	98.72 332	99.66 216	99.70 204	99.29 90	99.63 471	98.07 301	99.81 258	99.62 188

ppachtmachnet_test		98.89 307	99.12 223	98.20 442	99.66 239	95.24 500	97.63 466	99.68 216	99.08 275	99.78 138	99.62 270	98.65 203	99.88 238	98.02 302	99.96 91	99.48 278

tpmrst		97.73 414	98.07 387	96.73 500	98.71 487	92.00 521	99.10 251	98.86 442	98.52 359	98.92 405	99.54 322	91.90 456	99.82 349	98.02 302	99.03 445	98.37 478

CSCG		99.37 179	99.29 188	99.60 192	99.71 197	99.46 193	99.43 121	99.85 91	98.79 322	99.41 316	99.60 288	98.92 162	99.92 152	98.02 302	99.92 154	99.43 310

eth_miper_zixun_eth		98.68 332	98.71 311	98.60 419	99.10 438	96.84 467	97.52 474	99.54 305	98.94 293	99.58 255	99.48 344	96.25 381	99.76 401	98.01 305	99.93 147	99.21 369

Patchmtry		98.78 319	98.54 333	99.49 241	98.89 464	99.19 274	99.32 157	99.67 221	99.65 150	99.72 184	99.79 120	91.87 458	99.95 81	98.00 306	99.97 77	99.33 341

PVSNet_BlendedMVS		99.03 277	99.01 263	99.09 354	99.54 302	97.99 415	98.58 370	99.82 114	97.62 441	99.34 335	99.71 194	98.52 229	99.77 395	97.98 307	99.97 77	99.52 261

PVSNet_Blended		98.70 330	98.59 324	99.02 365	99.54 302	97.99 415	97.58 469	99.82 114	95.70 497	99.34 335	98.98 455	98.52 229	99.77 395	97.98 307	99.83 238	99.30 352

cl____		98.54 349	98.41 351	98.92 379	99.03 449	97.80 429	97.46 476	99.59 276	98.90 303	99.60 250	99.46 351	93.85 427	99.78 382	97.97 309	99.89 186	99.17 382

DIV-MVS_self_test		98.54 349	98.42 350	98.92 379	99.03 449	97.80 429	97.46 476	99.59 276	98.90 303	99.60 250	99.46 351	93.87 426	99.78 382	97.97 309	99.89 186	99.18 379

AUN-MVS		97.82 408	97.38 425	99.14 347	99.27 403	98.53 376	98.72 351	99.02 434	98.10 404	97.18 505	99.03 449	89.26 489	99.85 292	97.94 311	97.91 502	99.03 421

FA-MVS(test-final)		98.52 351	98.32 363	99.10 353	99.48 336	98.67 351	99.77 19	98.60 461	97.35 457	99.63 230	99.80 108	93.07 439	99.84 309	97.92 312	99.30 417	98.78 453

ambc				99.20 338	99.35 376	98.53 376	99.17 218	99.46 341		99.67 210	99.80 108	98.46 236	99.70 430	97.92 312	99.70 319	99.38 325

USDC		98.96 296	98.93 284	99.05 363	99.54 302	97.99 415	97.07 495	99.80 132	98.21 396	99.75 164	99.77 145	98.43 239	99.64 469	97.90 314	99.88 197	99.51 263

OPM-MVS		99.26 209	99.13 219	99.63 173	99.70 213	99.61 152	98.58 370	99.48 335	98.50 361	99.52 281	99.63 260	99.14 117	99.76 401	97.89 315	99.77 283	99.51 263
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 196	99.17 211	99.77 80	99.69 221	99.80 51	99.14 231	99.31 390	99.16 264	99.62 240	99.61 280	98.35 250	99.91 182	97.88 316	99.72 313	99.61 202
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	99.70 213	99.79 54	99.14 231	99.61 259					99.92 152	97.88 316	99.72 313	99.77 81

c3_l		98.72 327	98.71 311	98.72 410	99.12 431	97.22 454	97.68 464	99.56 292	98.90 303	99.54 274	99.48 344	96.37 374	99.73 419	97.88 316	99.88 197	99.21 369

SIFT-ConvMatch		98.16 390	98.37 356	97.52 466	99.54 302	99.20 271	96.97 499	98.47 468	98.09 406	99.14 379	99.40 363	95.93 391	99.05 514	97.87 319	99.92 154	94.31 519

3Dnovator+	98.92 3	99.35 186	99.24 202	99.67 144	99.35 376	99.47 187	99.62 67	99.50 330	99.44 204	99.12 383	99.78 133	98.77 184	99.94 98	97.87 319	99.72 313	99.62 188

miper_ehance_all_eth		98.59 343	98.59 324	98.59 420	98.98 456	97.07 458	97.49 475	99.52 321	98.50 361	99.52 281	99.37 375	96.41 372	99.71 426	97.86 321	99.62 351	99.00 428

WTY-MVS		98.59 343	98.37 356	99.26 328	99.43 354	98.40 386	98.74 348	99.13 427	98.10 404	99.21 367	99.24 415	94.82 413	99.90 201	97.86 321	98.77 462	99.49 274

APD_test1		99.36 184	99.28 191	99.61 188	99.89 39	99.89 10	99.32 157	99.74 177	99.18 256	99.69 196	99.75 163	98.41 242	99.84 309	97.85 323	99.70 319	99.10 397

SED-MVS		99.40 168	99.28 191	99.77 80	99.69 221	99.82 41	99.20 203	99.54 305	99.13 270	99.82 111	99.63 260	98.91 165	99.92 152	97.85 323	99.70 319	99.58 219

test_241102_TWO								99.54 305	99.13 270	99.76 159	99.63 260	98.32 256	99.92 152	97.85 323	99.69 328	99.75 89

MVS_111021_HR		99.12 256	99.02 258	99.40 277	99.50 326	99.11 288	97.92 448	99.71 195	98.76 330	99.08 387	99.47 348	99.17 110	99.54 486	97.85 323	99.76 287	99.54 243

SIFT-PointCN		98.28 374	98.47 340	97.71 463	99.70 213	98.91 325	96.98 498	99.70 204	97.90 423	99.36 327	99.35 385	95.51 401	99.83 330	97.84 327	99.89 186	94.39 518

MTAPA		99.35 186	99.20 207	99.80 64	99.81 109	99.81 47	99.33 154	99.53 316	99.27 239	99.42 310	99.63 260	98.21 268	99.95 81	97.83 328	99.79 271	99.65 158

MSC_two_6792asdad				99.74 103	99.03 449	99.53 174		99.23 408					99.92 152	97.77 329	99.69 328	99.78 77

No_MVS				99.74 103	99.03 449	99.53 174		99.23 408					99.92 152	97.77 329	99.69 328	99.78 77

TESTMET0.1,1		96.24 467	95.84 468	97.41 474	98.24 505	93.84 511	97.38 478	95.84 519	98.43 366	97.81 488	98.56 489	79.77 519	99.89 223	97.77 329	98.77 462	98.52 470

ACMH+	98.40 8	99.50 126	99.43 144	99.71 127	99.86 59	99.76 70	99.32 157	99.77 158	99.53 181	99.77 150	99.76 155	99.26 96	99.78 382	97.77 329	99.88 197	99.60 207

IU-MVS					99.69 221	99.77 63		99.22 411	97.50 448	99.69 196				97.75 333	99.70 319	99.77 81

114514_t		98.49 356	98.11 384	99.64 166	99.73 188	99.58 163	99.24 192	99.76 166	89.94 526	99.42 310	99.56 312	97.76 308	99.86 273	97.74 334	99.82 248	99.47 282

DVP-MVS++		99.38 175	99.25 200	99.77 80	99.03 449	99.77 63	99.74 27	99.61 259	99.18 256	99.76 159	99.61 280	99.00 147	99.92 152	97.72 335	99.60 362	99.62 188

test_0728_THIRD									99.18 256	99.62 240	99.61 280	98.58 211	99.91 182	97.72 335	99.80 265	99.77 81

EGC-MVSNET		89.05 498	85.52 501	99.64 166	99.89 39	99.78 57	99.56 87	99.52 321	24.19 535	49.96 536	99.83 83	99.15 114	99.92 152	97.71 337	99.85 225	99.21 369

miper_enhance_ethall		98.03 397	97.94 399	98.32 435	98.27 504	96.43 475	96.95 500	99.41 354	96.37 487	99.43 307	98.96 459	94.74 414	99.69 437	97.71 337	99.62 351	98.83 448

TSAR-MVS + MP.		99.34 191	99.24 202	99.63 173	99.82 97	99.37 227	99.26 185	99.35 375	98.77 327	99.57 258	99.70 204	99.27 95	99.88 238	97.71 337	99.75 292	99.65 158
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 422	97.28 428	98.40 430	98.37 501	96.75 468	97.24 486	99.37 370	97.31 459	99.41 316	99.22 418	87.30 494	99.37 501	97.70 340	99.62 351	99.08 409

MP-MVS-pluss		99.14 251	98.92 288	99.80 64	99.83 88	99.83 33	98.61 363	99.63 248	96.84 479	99.44 303	99.58 301	98.81 175	99.91 182	97.70 340	99.82 248	99.67 135
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 203	99.11 226	99.79 72	99.75 175	99.81 47	98.95 309	99.53 316	98.27 393	99.53 279	99.73 176	98.75 187	99.87 254	97.70 340	99.83 238	99.68 126

UnsupCasMVSNet_bld		98.55 347	98.27 369	99.40 277	99.56 296	99.37 227	97.97 444	99.68 216	97.49 449	99.08 387	99.35 385	95.41 405	99.82 349	97.70 340	98.19 493	99.01 427

MVS_111021_LR		99.13 253	99.03 257	99.42 266	99.58 272	99.32 239	97.91 450	99.73 182	98.68 337	99.31 345	99.48 344	99.09 126	99.66 460	97.70 340	99.77 283	99.29 355

IS-MVSNet		99.03 277	98.85 297	99.55 218	99.80 118	99.25 254	99.73 30	99.15 423	99.37 222	99.61 247	99.71 194	94.73 415	99.81 365	97.70 340	99.88 197	99.58 219

MED-MVS test				99.74 103	99.76 158	99.65 127	99.38 132	99.78 153	99.58 175	99.81 118	99.66 235		99.90 201	97.69 346	99.79 271	99.67 135

MED-MVS		99.51 123	99.42 147	99.80 64	99.76 158	99.65 127	99.38 132	99.78 153	99.77 106	99.81 118	99.78 133	99.02 145	99.90 201	97.69 346	99.79 271	99.85 50

ME-MVS		99.26 209	99.10 234	99.73 113	99.60 257	99.65 127	98.75 347	99.45 346	99.31 233	99.65 220	99.66 235	98.00 291	99.86 273	97.69 346	99.79 271	99.67 135

test-LLR		97.15 441	96.95 443	97.74 460	98.18 508	95.02 502	97.38 478	96.10 511	98.00 411	97.81 488	98.58 486	90.04 485	99.91 182	97.69 346	98.78 460	98.31 479

test-mter		96.23 468	95.73 471	97.74 460	98.18 508	95.02 502	97.38 478	96.10 511	97.90 423	97.81 488	98.58 486	79.12 522	99.91 182	97.69 346	98.78 460	98.31 479

MonoMVSNet		98.23 381	98.32 363	97.99 447	98.97 457	96.62 470	99.49 107	98.42 471	99.62 159	99.40 321	99.79 120	95.51 401	98.58 523	97.68 351	95.98 525	98.76 456

SP-DiffGlue		98.47 358	98.43 349	98.59 420	97.44 526	98.59 367	98.01 436	99.36 374	99.00 284	99.06 391	99.20 424	97.01 347	99.25 505	97.64 352	99.15 434	97.92 501

SIFT-NCMNet		98.18 386	98.46 342	97.36 478	99.67 236	99.19 274	96.33 516	98.99 438	98.83 315	99.62 240	99.63 260	95.41 405	99.33 502	97.64 352	100.00 1	93.54 530

XVS		99.27 207	99.11 226	99.75 98	99.71 197	99.71 100	99.37 140	99.61 259	99.29 235	98.76 425	99.47 348	98.47 233	99.88 238	97.62 354	99.73 306	99.67 135

X-MVStestdata		96.09 472	94.87 487	99.75 98	99.71 197	99.71 100	99.37 140	99.61 259	99.29 235	98.76 425	61.30 544	98.47 233	99.88 238	97.62 354	99.73 306	99.67 135

SMA-MVS		99.19 236	99.00 267	99.73 113	99.46 346	99.73 90	99.13 238	99.52 321	97.40 454	99.57 258	99.64 244	98.93 159	99.83 330	97.61 356	99.79 271	99.63 176
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		96.71 452	96.79 451	96.46 504	98.90 461	90.71 532	99.41 122	98.68 453	94.69 511	98.14 472	99.34 389	86.32 504	99.80 375	97.60 357	98.07 500	98.88 443

PVSNet	97.47 15	98.42 363	98.44 347	98.35 432	99.46 346	96.26 479	96.70 510	99.34 379	97.68 439	99.00 396	99.13 430	97.40 327	99.72 421	97.59 358	99.68 333	99.08 409

SIFT-PCN-Cal		98.24 379	98.51 336	97.43 473	99.65 243	98.64 361	97.09 492	99.35 375	98.16 400	99.69 196	99.52 329	95.59 396	99.83 330	97.57 359	100.00 1	93.81 526

new_pmnet		98.88 308	98.89 293	98.84 397	99.70 213	97.62 435	98.15 419	99.50 330	97.98 414	99.62 240	99.54 322	98.15 274	99.94 98	97.55 360	99.84 230	98.95 432

IB-MVS	95.41 20	95.30 487	94.46 493	97.84 456	98.76 483	95.33 497	97.33 481	96.07 513	96.02 491	95.37 523	97.41 515	76.17 529	99.96 69	97.54 361	95.44 528	98.22 486
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		99.24 215	99.11 226	99.61 188	98.38 500	99.79 54	99.57 85	99.68 216	99.61 164	99.15 377	99.71 194	98.70 194	99.91 182	97.54 361	99.68 333	99.13 394

ZNCC-MVS		99.22 226	99.04 255	99.77 80	99.76 158	99.73 90	99.28 176	99.56 292	98.19 398	99.14 379	99.29 400	98.84 174	99.92 152	97.53 363	99.80 265	99.64 170

CP-MVS		99.23 217	99.05 249	99.75 98	99.66 239	99.66 121	99.38 132	99.62 251	98.38 374	99.06 391	99.27 403	98.79 180	99.94 98	97.51 364	99.82 248	99.66 149

SD-MVS		99.01 286	99.30 183	98.15 443	99.50 326	99.40 216	98.94 311	99.61 259	99.22 252	99.75 164	99.82 91	99.54 55	95.51 533	97.48 365	99.87 210	99.54 243
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
SIFT-NN-PointCN		97.97 402	98.24 371	97.14 489	99.59 263	98.71 348	96.75 507	99.56 292	97.02 473	97.91 481	99.27 403	96.85 354	98.39 524	97.47 366	99.76 287	94.31 519

PMMVS		98.49 356	98.29 368	99.11 351	98.96 458	98.42 385	97.54 470	99.32 386	97.53 446	98.47 449	98.15 502	97.88 297	99.82 349	97.46 367	99.24 428	99.09 403

DeepC-MVS_fast	98.47 5	99.23 217	99.12 223	99.56 211	99.28 401	99.22 265	98.99 297	99.40 361	99.08 275	99.58 255	99.64 244	98.90 168	99.83 330	97.44 368	99.75 292	99.63 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 211	99.08 238	99.76 87	99.73 188	99.70 108	99.31 163	99.59 276	98.36 376	99.36 327	99.37 375	98.80 179	99.91 182	97.43 369	99.75 292	99.68 126

ACMMPR		99.23 217	99.06 244	99.76 87	99.74 184	99.69 112	99.31 163	99.59 276	98.36 376	99.35 331	99.38 371	98.61 207	99.93 120	97.43 369	99.75 292	99.67 135

Vis-MVSNet (Re-imp)		98.77 321	98.58 327	99.34 300	99.78 140	98.88 329	99.61 73	99.56 292	99.11 274	99.24 360	99.56 312	93.00 441	99.78 382	97.43 369	99.89 186	99.35 334

MIMVSNet		98.43 362	98.20 375	99.11 351	99.53 311	98.38 390	99.58 82	98.61 458	98.96 289	99.33 337	99.76 155	90.92 469	99.81 365	97.38 372	99.76 287	99.15 386

WB-MVSnew		98.34 373	98.14 382	98.96 371	98.14 511	97.90 423	98.27 407	97.26 506	98.63 343	98.80 420	98.00 505	97.77 306	99.90 201	97.37 373	98.98 448	99.09 403

SP-MNN		97.94 405	97.82 406	98.31 437	98.30 503	97.67 434	97.81 455	97.93 492	98.14 401	97.16 507	98.64 485	96.31 377	99.21 507	97.34 374	98.75 466	98.05 495

XVG-OURS-SEG-HR		99.16 246	98.99 274	99.66 152	99.84 80	99.64 134	98.25 410	99.73 182	98.39 372	99.63 230	99.43 356	99.70 31	99.90 201	97.34 374	98.64 474	99.44 304

COLMAP_ROB	98.06 12	99.45 149	99.37 159	99.70 132	99.83 88	99.70 108	99.38 132	99.78 153	99.53 181	99.67 210	99.78 133	99.19 107	99.86 273	97.32 376	99.87 210	99.55 233
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
0.4-1-1-0.1		93.18 493	91.66 497	97.73 462	95.83 531	95.29 498	95.30 522	95.90 517	93.59 514	90.58 532	94.40 541	77.87 524	99.77 395	97.31 377	84.20 530	98.15 491

MCST-MVS		99.02 280	98.81 304	99.65 159	99.58 272	99.49 182	98.58 370	99.07 429	98.40 371	99.04 393	99.25 409	98.51 231	99.80 375	97.31 377	99.51 386	99.65 158

region2R		99.23 217	99.05 249	99.77 80	99.76 158	99.70 108	99.31 163	99.59 276	98.41 369	99.32 340	99.36 380	98.73 191	99.93 120	97.29 379	99.74 299	99.67 135

APD-MVS_3200maxsize		99.31 198	99.16 212	99.74 103	99.53 311	99.75 79	99.27 180	99.61 259	99.19 255	99.57 258	99.64 244	98.76 185	99.90 201	97.29 379	99.62 351	99.56 229

TAPA-MVS	97.92 13	98.03 397	97.55 419	99.46 252	99.47 342	99.44 201	98.50 387	99.62 251	86.79 527	99.07 390	99.26 407	98.26 261	99.62 472	97.28 381	99.73 306	99.31 350
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 207	99.11 226	99.73 113	99.54 302	99.74 87	99.26 185	99.62 251	99.16 264	99.52 281	99.64 244	98.41 242	99.91 182	97.27 382	99.61 359	99.54 243

RE-MVS-def			99.13 219		99.54 302	99.74 87	99.26 185	99.62 251	99.16 264	99.52 281	99.64 244	98.57 212		97.27 382	99.61 359	99.54 243

testing11		96.05 474	95.41 477	97.97 449	98.78 480	95.27 499	98.59 368	98.23 482	98.86 309	96.56 514	96.91 527	75.20 531	99.69 437	97.26 384	98.29 488	98.93 435

test_yl		98.25 377	97.95 395	99.13 349	99.17 423	98.47 379	99.00 289	98.67 455	98.97 287	99.22 365	99.02 450	91.31 463	99.69 437	97.26 384	98.93 451	99.24 361

DCV-MVSNet		98.25 377	97.95 395	99.13 349	99.17 423	98.47 379	99.00 289	98.67 455	98.97 287	99.22 365	99.02 450	91.31 463	99.69 437	97.26 384	98.93 451	99.24 361

PHI-MVS		99.11 261	98.95 282	99.59 195	99.13 429	99.59 158	99.17 218	99.65 236	97.88 427	99.25 357	99.46 351	98.97 155	99.80 375	97.26 384	99.82 248	99.37 328

tfpnnormal		99.43 156	99.38 156	99.60 192	99.87 54	99.75 79	99.59 80	99.78 153	99.71 121	99.90 67	99.69 213	98.85 173	99.90 201	97.25 388	99.78 279	99.15 386

PatchmatchNet		97.65 418	97.80 407	97.18 485	98.82 475	92.49 519	99.17 218	98.39 475	98.12 403	98.79 422	99.58 301	90.71 476	99.89 223	97.23 389	99.41 403	99.16 384
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 292	98.80 306	99.56 211	99.25 407	99.43 205	98.54 381	99.27 398	98.58 350	98.80 420	99.43 356	98.53 225	99.70 430	97.22 390	99.59 366	99.54 243

SIFT-UM-Cal		98.18 386	98.45 345	97.37 477	99.59 263	98.95 316	96.76 506	99.39 364	98.39 372	99.46 300	99.31 393	96.23 383	99.24 506	97.21 391	99.70 319	93.90 525

testing3		96.48 460	95.63 473	99.01 366	99.23 411	97.81 427	98.90 317	99.10 428	98.72 332	97.84 487	97.92 506	72.44 535	99.85 292	97.21 391	99.33 413	99.35 334

HPM-MVS		99.25 211	99.07 242	99.78 76	99.81 109	99.75 79	99.61 73	99.67 221	97.72 437	99.35 331	99.25 409	99.23 102	99.92 152	97.21 391	99.82 248	99.67 135
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
0.3-1-1-0.015		92.36 495	90.68 499	97.39 475	94.94 535	94.41 507	94.21 526	95.89 518	92.87 517	88.87 534	93.49 543	75.30 530	99.76 401	97.19 394	83.41 532	98.02 496

0.4-1-1-0.2		92.59 494	91.07 498	97.15 488	94.73 537	93.68 513	93.50 527	95.91 515	92.68 518	90.48 533	93.52 542	77.77 525	99.75 411	97.19 394	83.88 531	98.01 497

MatchFormer		99.03 277	99.02 258	99.08 359	99.56 296	98.47 379	98.57 374	99.90 61	98.13 402	99.80 125	99.75 163	98.34 252	99.84 309	97.18 396	99.90 171	98.92 437

mPP-MVS		99.19 236	99.00 267	99.76 87	99.76 158	99.68 115	99.38 132	99.54 305	98.34 386	99.01 395	99.50 336	98.53 225	99.93 120	97.18 396	99.78 279	99.66 149

ACMMP		99.25 211	99.08 238	99.74 103	99.79 132	99.68 115	99.50 102	99.65 236	98.07 408	99.52 281	99.69 213	98.57 212	99.92 152	97.18 396	99.79 271	99.63 176
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		96.23 468	95.74 470	97.70 464	98.86 468	95.59 494	98.66 359	98.14 484	98.96 289	97.67 494	97.06 523	76.78 527	98.92 517	97.10 399	98.41 485	98.58 466

thisisatest0515		96.98 445	96.42 454	98.66 416	99.42 359	97.47 441	97.27 483	94.30 527	97.24 462	99.15 377	98.86 468	85.01 506	99.87 254	97.10 399	99.39 405	98.63 460

XVG-ACMP-BASELINE		99.23 217	99.10 234	99.63 173	99.82 97	99.58 163	98.83 331	99.72 191	98.36 376	99.60 250	99.71 194	98.92 162	99.91 182	97.08 401	99.84 230	99.40 319

MSDG		99.08 266	98.98 277	99.37 288	99.60 257	99.13 285	97.54 470	99.74 177	98.84 314	99.53 279	99.55 320	99.10 124	99.79 378	97.07 402	99.86 218	99.18 379

SteuartSystems-ACMMP		99.30 199	99.14 217	99.76 87	99.87 54	99.66 121	99.18 213	99.60 270	98.55 353	99.57 258	99.67 230	99.03 144	99.94 98	97.01 403	99.80 265	99.69 119
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 470	95.78 469	97.49 467	98.53 494	93.83 512	98.04 433	93.94 530	98.96 289	98.46 450	98.17 501	79.86 517	99.87 254	96.99 404	99.06 441	98.78 453

EPMVS		96.53 457	96.32 455	97.17 487	98.18 508	92.97 517	99.39 129	89.95 536	98.21 396	98.61 438	99.59 298	86.69 503	99.72 421	96.99 404	99.23 430	98.81 450

SIFT-CM-Cal		97.96 404	98.15 381	97.39 475	99.61 254	99.15 283	96.75 507	98.41 474	98.04 410	99.03 394	99.54 322	95.24 408	99.41 499	96.97 406	99.80 265	93.61 529

MSP-MVS		99.04 276	98.79 307	99.81 54	99.78 140	99.73 90	99.35 147	99.57 287	98.54 356	99.54 274	98.99 452	96.81 355	99.93 120	96.97 406	99.53 382	99.77 81
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++		98.96 296	98.70 315	99.74 103	99.52 318	99.71 100	98.86 324	99.19 418	98.47 365	98.59 440	99.06 442	98.08 283	99.91 182	96.94 408	99.60 362	99.60 207

SR-MVS		99.19 236	99.00 267	99.74 103	99.51 320	99.72 95	99.18 213	99.60 270	98.85 310	99.47 296	99.58 301	98.38 247	99.92 152	96.92 409	99.54 380	99.57 225

PGM-MVS		99.20 233	99.01 263	99.77 80	99.75 175	99.71 100	99.16 224	99.72 191	97.99 413	99.42 310	99.60 288	98.81 175	99.93 120	96.91 410	99.74 299	99.66 149

HY-MVS	98.23 9	98.21 385	97.95 395	98.99 367	99.03 449	98.24 394	99.61 73	98.72 451	96.81 480	98.73 427	99.51 333	94.06 424	99.86 273	96.91 410	98.20 491	98.86 445

MDTV_nov1_ep13			97.73 413		98.70 488	90.83 530	99.15 227	98.02 488	98.51 360	98.82 417	99.61 280	90.98 468	99.66 460	96.89 412	98.92 453

SIFT-NCM-Cal		98.18 386	98.41 351	97.48 468	99.57 282	99.28 245	97.26 484	98.08 485	98.30 391	99.23 361	99.39 368	97.13 342	99.04 515	96.86 413	99.86 218	94.12 522

GST-MVS		99.16 246	98.96 281	99.75 98	99.73 188	99.73 90	99.20 203	99.55 299	98.22 395	99.32 340	99.35 385	98.65 203	99.91 182	96.86 413	99.74 299	99.62 188

test_post1							99.14 231				51.63 546	89.54 488	99.82 349	96.86 413

SCA		98.11 392	98.36 358	97.36 478	99.20 417	92.99 516	98.17 416	98.49 467	98.24 394	99.10 386	99.57 308	96.01 388	99.94 98	96.86 413	99.62 351	99.14 391

UBG		96.53 457	95.95 464	98.29 440	98.87 467	96.31 478	98.48 390	98.07 486	98.83 315	97.32 500	96.54 535	79.81 518	99.62 472	96.84 417	98.74 467	98.95 432

XVG-OURS		99.21 231	99.06 244	99.65 159	99.82 97	99.62 142	97.87 452	99.74 177	98.36 376	99.66 216	99.68 225	99.71 28	99.90 201	96.84 417	99.88 197	99.43 310

LCM-MVSNet-Re		99.28 203	99.15 216	99.67 144	99.33 390	99.76 70	99.34 148	99.97 21	98.93 298	99.91 62	99.79 120	98.68 196	99.93 120	96.80 419	99.56 371	99.30 352

RPSCF		99.18 240	99.02 258	99.64 166	99.83 88	99.85 21	99.44 119	99.82 114	98.33 388	99.50 291	99.78 133	97.90 295	99.65 467	96.78 420	99.83 238	99.44 304

旧先验2							97.94 446		95.33 502	98.94 401			99.88 238	96.75 421

MDTV_nov1_ep13_2view						91.44 527	99.14 231		97.37 456	99.21 367		91.78 460		96.75 421		99.03 421

CLD-MVS		98.76 322	98.57 328	99.33 303	99.57 282	98.97 312	97.53 472	99.55 299	96.41 485	99.27 352	99.13 430	99.07 133	99.78 382	96.73 423	99.89 186	99.23 364
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 393	97.98 393	98.48 426	99.27 403	96.48 473	99.40 127	99.07 429	98.81 319	99.23 361	99.57 308	90.11 484	99.87 254	96.69 424	99.64 346	99.09 403

baseline2		96.83 448	96.28 456	98.46 428	99.09 442	96.91 463	98.83 331	93.87 531	97.23 463	96.23 519	98.36 496	88.12 493	99.90 201	96.68 425	98.14 496	98.57 468

cascas		96.99 444	96.82 450	97.48 468	97.57 524	95.64 491	96.43 514	99.56 292	91.75 522	97.13 508	97.61 514	95.58 397	98.63 521	96.68 425	99.11 437	98.18 490

PC_three_1452									97.56 442	99.68 202	99.41 359	99.09 126	97.09 529	96.66 427	99.60 362	99.62 188

LPG-MVS_test		99.22 226	99.05 249	99.74 103	99.82 97	99.63 140	99.16 224	99.73 182	97.56 442	99.64 225	99.69 213	99.37 77	99.89 223	96.66 427	99.87 210	99.69 119

LGP-MVS_train				99.74 103	99.82 97	99.63 140		99.73 182	97.56 442	99.64 225	99.69 213	99.37 77	99.89 223	96.66 427	99.87 210	99.69 119

ETVMVS		96.14 471	95.22 482	98.89 391	98.80 476	98.01 414	98.66 359	98.35 478	98.71 334	97.18 505	96.31 540	74.23 534	99.75 411	96.64 430	98.13 499	98.90 440

SIFT-MNN		97.55 424	97.74 412	96.98 493	99.38 366	98.85 334	96.92 503	98.61 458	98.36 376	98.63 436	99.10 438	92.51 448	97.85 527	96.63 431	99.48 393	94.25 521

TinyColmap		98.97 293	98.93 284	99.07 360	99.46 346	98.19 399	97.75 457	99.75 171	98.79 322	99.54 274	99.70 204	98.97 155	99.62 472	96.63 431	99.83 238	99.41 316

LF4IMVS		99.01 286	98.92 288	99.27 325	99.71 197	99.28 245	98.59 368	99.77 158	98.32 389	99.39 323	99.41 359	98.62 205	99.84 309	96.62 433	99.84 230	98.69 459

NCCC		98.82 315	98.57 328	99.58 199	99.21 414	99.31 240	98.61 363	99.25 403	98.65 340	98.43 451	99.26 407	97.86 298	99.81 365	96.55 434	99.27 423	99.61 202

OPU-MVS				99.29 317	99.12 431	99.44 201	99.20 203				99.40 363	99.00 147	98.84 519	96.54 435	99.60 362	99.58 219

F-COLMAP		98.74 324	98.45 345	99.62 182	99.57 282	99.47 187	98.84 328	99.65 236	96.31 488	98.93 402	99.19 426	97.68 312	99.87 254	96.52 436	99.37 408	99.53 250

SIFT-UMatch		98.07 395	98.27 369	97.46 472	99.57 282	98.99 307	96.93 502	99.02 434	98.53 357	99.26 356	99.23 417	95.43 404	99.31 503	96.51 437	99.91 167	94.09 523

testing99		95.86 479	95.19 483	97.87 454	98.76 483	95.03 501	98.62 362	98.44 470	98.68 337	96.67 512	96.66 534	74.31 533	99.69 437	96.51 437	98.03 501	98.90 440

ADS-MVSNet2		97.78 412	97.66 417	98.12 445	99.14 427	95.36 496	99.22 200	98.75 450	96.97 474	98.25 459	99.64 244	90.90 470	99.94 98	96.51 437	99.56 371	99.08 409

ADS-MVSNet		97.72 417	97.67 416	97.86 455	99.14 427	94.65 505	99.22 200	98.86 442	96.97 474	98.25 459	99.64 244	90.90 470	99.84 309	96.51 437	99.56 371	99.08 409

PatchMatch-RL		98.68 332	98.47 340	99.30 316	99.44 351	99.28 245	98.14 421	99.54 305	97.12 469	99.11 384	99.25 409	97.80 303	99.70 430	96.51 437	99.30 417	98.93 435

CMPMVS	77.52 23	98.50 354	98.19 378	99.41 274	98.33 502	99.56 167	99.01 283	99.59 276	95.44 500	99.57 258	99.80 108	95.64 394	99.46 498	96.47 442	99.92 154	99.21 369
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 475	95.32 480	98.02 446	98.76 483	95.39 495	98.38 399	98.65 457	98.82 317	96.84 509	96.71 533	75.06 532	99.71 426	96.46 443	98.23 490	98.98 429

SF-MVS		99.10 264	98.93 284	99.62 182	99.58 272	99.51 180	99.13 238	99.65 236	97.97 415	99.42 310	99.61 280	98.86 172	99.87 254	96.45 444	99.68 333	99.49 274

FE-MVS		97.85 407	97.42 424	99.15 344	99.44 351	98.75 345	99.77 19	98.20 483	95.85 493	99.33 337	99.80 108	88.86 490	99.88 238	96.40 445	99.12 436	98.81 450

DPE-MVS		99.14 251	98.92 288	99.82 46	99.57 282	99.77 63	98.74 348	99.60 270	98.55 353	99.76 159	99.69 213	98.23 266	99.92 152	96.39 446	99.75 292	99.76 86
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 522	89.02 538			93.47 515		98.30 497		99.84 309	96.38 447

AllTest		99.21 231	99.07 242	99.63 173	99.78 140	99.64 134	99.12 243	99.83 108	98.63 343	99.63 230	99.72 184	98.68 196	99.75 411	96.38 447	99.83 238	99.51 263

TestCases				99.63 173	99.78 140	99.64 134		99.83 108	98.63 343	99.63 230	99.72 184	98.68 196	99.75 411	96.38 447	99.83 238	99.51 263

testdata				99.42 266	99.51 320	98.93 321		99.30 393	96.20 489	98.87 412	99.40 363	98.33 255	99.89 223	96.29 450	99.28 420	99.44 304

dp		96.86 447	97.07 438	96.24 506	98.68 490	90.30 536	99.19 209	98.38 476	97.35 457	98.23 461	99.59 298	87.23 495	99.82 349	96.27 451	98.73 470	98.59 464

SP-NN		96.37 463	96.23 458	96.77 496	96.83 528	96.95 460	96.47 513	97.07 508	96.75 482	93.41 529	97.75 508	94.13 423	95.69 531	96.25 452	97.43 509	97.68 504

tpmvs		97.39 433	97.69 414	96.52 502	98.41 499	91.76 523	99.30 166	98.94 440	97.74 434	97.85 486	99.55 320	92.40 452	99.73 419	96.25 452	98.73 470	98.06 493

KD-MVS_2432*1600		95.89 476	95.41 477	97.31 482	94.96 533	93.89 509	97.09 492	99.22 411	97.23 463	98.88 409	99.04 445	79.23 520	99.54 486	96.24 454	96.81 514	98.50 474

miper_refine_blended		95.89 476	95.41 477	97.31 482	94.96 533	93.89 509	97.09 492	99.22 411	97.23 463	98.88 409	99.04 445	79.23 520	99.54 486	96.24 454	96.81 514	98.50 474

ACMP	97.51 14	99.05 273	98.84 299	99.67 144	99.78 140	99.55 171	98.88 320	99.66 226	97.11 470	99.47 296	99.60 288	99.07 133	99.89 223	96.18 456	99.85 225	99.58 219
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 304	98.72 310	99.44 259	99.39 363	99.42 208	98.58 370	99.64 244	97.31 459	99.44 303	99.62 270	98.59 209	99.69 437	96.17 457	99.79 271	99.22 366

DP-MVS Recon		98.50 354	98.23 372	99.31 312	99.49 331	99.46 193	98.56 377	99.63 248	94.86 509	98.85 414	99.37 375	97.81 302	99.59 479	96.08 458	99.44 398	98.88 443

tpm cat1		96.78 449	96.98 442	96.16 507	98.85 469	90.59 533	99.08 259	99.32 386	92.37 519	97.73 492	99.46 351	91.15 466	99.69 437	96.07 459	98.80 459	98.21 487

tpm2		96.35 464	96.22 459	96.73 500	98.88 466	91.75 524	99.21 202	98.51 465	93.27 516	97.89 482	99.21 422	84.83 507	99.70 430	96.04 460	98.18 494	98.75 457

dmvs_re		98.69 331	98.48 339	99.31 312	99.55 300	99.42 208	99.54 90	98.38 476	99.32 231	98.72 428	98.71 479	96.76 357	99.21 507	96.01 461	99.35 411	99.31 350

test_0402		99.22 226	99.14 217	99.45 255	99.79 132	99.43 205	99.28 176	99.68 216	99.54 179	99.40 321	99.56 312	99.07 133	99.82 349	96.01 461	99.96 91	99.11 395

ITE_SJBPF				99.38 283	99.63 249	99.44 201		99.73 182	98.56 351	99.33 337	99.53 325	98.88 169	99.68 449	96.01 461	99.65 344	99.02 426

test_prior2							97.95 445		97.87 428	98.05 474	99.05 443	97.90 295		95.99 464	99.49 391

testdata2													99.89 223	95.99 464

原ACMM1				99.37 288	99.47 342	98.87 333		99.27 398	96.74 483	98.26 458	99.32 390	97.93 294	99.82 349	95.96 466	99.38 406	99.43 310

新几何1				99.52 231	99.50 326	99.22 265		99.26 400	95.66 498	98.60 439	99.28 401	97.67 313	99.89 223	95.95 467	99.32 415	99.45 289

MP-MVS		99.06 270	98.83 301	99.76 87	99.76 158	99.71 100	99.32 157	99.50 330	98.35 382	98.97 398	99.48 344	98.37 248	99.92 152	95.95 467	99.75 292	99.63 176
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ALIKED-LG		98.78 319	98.66 317	99.14 347	99.02 455	99.40 216	98.74 348	99.79 141	98.62 347	99.18 373	99.38 371	97.54 321	99.77 395	95.94 469	99.74 299	98.25 484

testing222		95.60 486	94.59 491	98.61 418	98.66 491	97.45 444	98.54 381	97.90 494	98.53 357	96.54 515	96.47 537	70.62 538	99.81 365	95.91 470	98.15 495	98.56 469

wuyk23d		97.58 421	99.13 219	92.93 514	99.69 221	99.49 182	99.52 94	99.77 158	97.97 415	99.96 34	99.79 120	99.84 16	99.94 98	95.85 471	99.82 248	79.36 532

HQP_MVS		98.90 304	98.68 316	99.55 218	99.58 272	99.24 259	98.80 339	99.54 305	98.94 293	99.14 379	99.25 409	97.24 335	99.82 349	95.84 472	99.78 279	99.60 207

plane_prior5								99.54 305					99.82 349	95.84 472	99.78 279	99.60 207

SIFT-NN-CMatch		97.30 436	97.34 426	97.18 485	99.54 302	98.85 334	96.02 518	95.77 522	97.05 472	97.55 496	98.70 481	96.35 376	98.75 520	95.82 474	99.26 424	93.95 524

无先验							98.01 436	99.23 408	95.83 494				99.85 292	95.79 475		99.44 304

SIFT-NN-UMatch		97.18 440	97.24 432	97.01 492	99.57 282	98.65 358	96.33 516	97.31 505	97.07 471	97.48 497	98.73 478	94.39 420	98.87 518	95.75 476	98.50 482	93.50 531

CPTT-MVS		98.74 324	98.44 347	99.64 166	99.61 254	99.38 222	99.18 213	99.55 299	96.49 484	99.27 352	99.37 375	97.11 344	99.92 152	95.74 477	99.67 339	99.62 188

PLC	97.35 16	98.36 368	97.99 391	99.48 246	99.32 391	99.24 259	98.50 387	99.51 326	95.19 505	98.58 441	98.96 459	96.95 350	99.83 330	95.63 478	99.25 426	99.37 328
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 345	98.34 361	99.28 320	99.18 422	99.10 295	98.34 401	99.41 354	98.48 364	98.52 446	98.98 455	97.05 346	99.78 382	95.59 479	99.50 389	98.96 430

1314		98.00 400	97.90 403	98.27 441	98.90 461	97.45 444	99.30 166	99.06 431	94.98 506	97.21 504	99.12 434	98.43 239	99.67 455	95.58 480	98.56 477	97.71 503

PVSNet_0	95.53 19	95.85 480	95.31 481	97.47 470	98.78 480	93.48 515	95.72 519	99.40 361	96.18 490	97.37 499	97.73 509	95.73 393	99.58 480	95.49 481	81.40 533	99.36 331

MAR-MVS		98.24 379	97.92 401	99.19 339	98.78 480	99.65 127	99.17 218	99.14 425	95.36 501	98.04 475	98.81 474	97.47 324	99.72 421	95.47 482	99.06 441	98.21 487
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	98.23 381	97.89 404	99.26 328	99.19 419	99.26 251	99.65 62	99.69 213	91.33 524	98.14 472	99.77 145	98.28 258	99.96 69	95.41 483	99.55 375	98.58 466

train_agg		98.35 371	97.95 395	99.57 207	99.35 376	99.35 234	98.11 425	99.41 354	94.90 507	97.92 479	98.99 452	98.02 286	99.85 292	95.38 484	99.44 398	99.50 269

9.14			98.64 318		99.45 350		98.81 336	99.60 270	97.52 447	99.28 351	99.56 312	98.53 225	99.83 330	95.36 485	99.64 346

APD-MVS		98.87 309	98.59 324	99.71 127	99.50 326	99.62 142	99.01 283	99.57 287	96.80 481	99.54 274	99.63 260	98.29 257	99.91 182	95.24 486	99.71 317	99.61 202
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 476								95.20 487

AdaColmap		98.60 340	98.35 360	99.38 283	99.12 431	99.22 265	98.67 357	99.42 353	97.84 432	98.81 418	99.27 403	97.32 333	99.81 365	95.14 488	99.53 382	99.10 397

test9_res														95.10 489	99.44 398	99.50 269

CDPH-MVS		98.56 346	98.20 375	99.61 188	99.50 326	99.46 193	98.32 404	99.41 354	95.22 503	99.21 367	99.10 438	98.34 252	99.82 349	95.09 490	99.66 342	99.56 229

SIFT-NN-NCMNet		97.22 438	97.27 430	97.07 491	99.64 245	99.20 271	96.53 512	95.91 515	96.91 476	97.38 498	98.95 461	96.01 388	98.29 525	94.87 491	99.21 432	93.73 528

BH-untuned		98.22 383	98.09 385	98.58 423	99.38 366	97.24 453	98.55 378	98.98 439	97.81 433	99.20 372	98.76 476	97.01 347	99.65 467	94.83 492	98.33 486	98.86 445

BP-MVS														94.73 493

HQP-MVS		98.36 368	98.02 390	99.39 280	99.31 392	98.94 318	97.98 441	99.37 370	97.45 450	98.15 468	98.83 471	96.67 359	99.70 430	94.73 493	99.67 339	99.53 250

QAPM		98.40 366	97.99 391	99.65 159	99.39 363	99.47 187	99.67 53	99.52 321	91.70 523	98.78 424	99.80 108	98.55 216	99.95 81	94.71 495	99.75 292	99.53 250

agg_prior2														94.58 496	99.46 397	99.50 269

myMVS_eth3d		95.63 484	94.73 488	98.34 434	98.50 496	96.36 476	98.60 365	99.21 414	97.89 425	96.76 510	96.37 538	72.10 536	99.57 481	94.38 497	98.73 470	99.09 403

BH-RMVSNet		98.41 364	98.14 382	99.21 335	99.21 414	98.47 379	98.60 365	98.26 481	98.35 382	98.93 402	99.31 393	97.20 340	99.66 460	94.32 498	99.10 438	99.51 263

E-PMN		97.14 443	97.43 423	96.27 505	98.79 478	91.62 525	95.54 520	99.01 437	99.44 204	98.88 409	99.12 434	92.78 442	99.68 449	94.30 499	99.03 445	97.50 505

MG-MVS		98.52 351	98.39 354	98.94 374	99.15 426	97.39 448	98.18 414	99.21 414	98.89 306	99.23 361	99.63 260	97.37 330	99.74 416	94.22 500	99.61 359	99.69 119

ALIKED-MNN		98.03 397	97.78 410	98.78 405	98.84 471	98.97 312	98.16 418	99.74 177	97.31 459	96.60 513	98.85 469	96.61 361	99.48 495	94.16 501	99.77 283	97.91 502

API-MVS		98.38 367	98.39 354	98.35 432	98.83 472	99.26 251	99.14 231	99.18 419	98.59 349	98.66 433	98.78 475	98.61 207	99.57 481	94.14 502	99.56 371	96.21 513

PAPM_NR		98.36 368	98.04 388	99.33 303	99.48 336	98.93 321	98.79 342	99.28 397	97.54 445	98.56 445	98.57 488	97.12 343	99.69 437	94.09 503	98.90 457	99.38 325

ZD-MVS					99.43 354	99.61 152		99.43 351	96.38 486	99.11 384	99.07 441	97.86 298	99.92 152	94.04 504	99.49 391

DPM-MVS		98.28 374	97.94 399	99.32 308	99.36 372	99.11 288	97.31 482	98.78 449	96.88 477	98.84 415	99.11 437	97.77 306	99.61 477	94.03 505	99.36 409	99.23 364

gg-mvs-nofinetune		95.87 478	95.17 484	97.97 449	98.19 507	96.95 460	99.69 45	89.23 537	99.89 55	96.24 518	99.94 19	81.19 512	99.51 493	93.99 506	98.20 491	97.44 506

XFeat-MNN		96.67 453	96.56 452	96.98 493	96.73 529	95.62 493	94.54 525	98.93 441	97.42 453	98.18 463	98.67 484	91.60 461	99.12 509	93.88 507	99.10 438	96.21 513

PMVS	92.94 21	98.82 315	98.81 304	98.85 395	99.84 80	97.99 415	99.20 203	99.47 338	99.71 121	99.42 310	99.82 91	98.09 281	99.47 496	93.88 507	99.85 225	99.07 414
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 446	97.28 428	95.99 509	98.76 483	91.03 529	95.26 523	98.61 458	99.34 227	98.92 405	98.88 467	93.79 428	99.66 460	92.87 509	99.05 443	97.30 509

BH-w/o		97.20 439	97.01 441	97.76 458	99.08 443	95.69 490	98.03 435	98.52 464	95.76 496	97.96 478	98.02 503	95.62 395	99.47 496	92.82 510	97.25 513	98.12 492

TR-MVS		97.44 430	97.15 435	98.32 435	98.53 494	97.46 442	98.47 391	97.91 493	96.85 478	98.21 462	98.51 492	96.42 370	99.51 493	92.16 511	97.29 512	97.98 498

ALIKED-NN		96.66 454	96.26 457	97.88 453	97.49 525	98.59 367	96.71 509	99.15 423	95.50 499	93.58 528	98.39 495	94.52 419	97.74 528	92.05 512	98.94 450	97.29 510

OpenMVS_ROB	97.31 17	97.36 435	96.84 448	98.89 391	99.29 398	99.45 199	98.87 323	99.48 335	86.54 529	99.44 303	99.74 171	97.34 331	99.86 273	91.61 513	99.28 420	97.37 508

GG-mvs-BLEND				97.36 478	97.59 522	96.87 464	99.70 38	88.49 538		94.64 526	97.26 520	80.66 514	99.12 509	91.50 514	96.50 522	96.08 516

DeepMVS_CX				97.98 448	99.69 221	96.95 460		99.26 400	75.51 532	95.74 521	98.28 498	96.47 368	99.62 472	91.23 515	97.89 503	97.38 507

PAPR		97.56 422	97.07 438	99.04 364	98.80 476	98.11 407	97.63 466	99.25 403	94.56 513	98.02 477	98.25 499	97.43 326	99.68 449	90.90 516	98.74 467	99.33 341

MVS		95.72 482	94.63 490	98.99 367	98.56 493	97.98 420	99.30 166	98.86 442	72.71 533	97.30 501	99.08 440	98.34 252	99.74 416	89.21 517	98.33 486	99.26 358

SIFT-NN		94.78 490	94.89 486	94.45 512	98.23 506	97.29 451	94.93 524	95.84 519	95.82 495	94.78 525	97.12 521	90.26 482	92.28 535	88.91 518	98.14 496	93.77 527

UWE-MVS-28		95.64 483	95.47 475	96.14 508	97.98 514	90.39 534	98.49 389	95.81 521	99.02 282	98.03 476	98.19 500	84.49 509	99.28 504	88.75 519	98.47 483	98.75 457

thres600view7		96.60 456	96.16 460	97.93 451	99.63 249	96.09 485	99.18 213	97.57 499	98.77 327	98.72 428	97.32 518	87.04 497	99.72 421	88.57 520	98.62 475	97.98 498

FPMVS		96.32 465	95.50 474	98.79 403	99.60 257	98.17 402	98.46 395	98.80 448	97.16 467	96.28 516	99.63 260	82.19 511	99.09 512	88.45 521	98.89 458	99.10 397

XFeat-NN		93.89 492	93.91 494	93.83 513	95.49 532	92.69 518	90.85 528	97.98 489	94.69 511	95.08 524	96.98 524	88.36 492	94.23 534	88.42 522	97.34 510	94.57 517

PCF-MVS	96.03 18	96.73 451	95.86 467	99.33 303	99.44 351	99.16 281	96.87 504	99.44 347	86.58 528	98.95 400	99.40 363	94.38 421	99.88 238	87.93 523	99.80 265	98.95 432
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 462	96.03 463	97.47 470	99.63 249	95.93 486	99.18 213	97.57 499	98.75 331	98.70 431	97.31 519	87.04 497	99.67 455	87.62 524	98.51 479	96.81 511

tfpn200view9		96.30 466	95.89 465	97.53 465	99.58 272	96.11 483	99.00 289	97.54 502	98.43 366	98.52 446	96.98 524	86.85 499	99.67 455	87.62 524	98.51 479	96.81 511

thres400		96.40 461	95.89 465	97.92 452	99.58 272	96.11 483	99.00 289	97.54 502	98.43 366	98.52 446	96.98 524	86.85 499	99.67 455	87.62 524	98.51 479	97.98 498

thres200		96.09 472	95.68 472	97.33 481	99.48 336	96.22 481	98.53 383	97.57 499	98.06 409	98.37 454	96.73 532	86.84 501	99.61 477	86.99 527	98.57 476	96.16 515

MVE	92.54 22	96.66 454	96.11 461	98.31 437	99.68 229	97.55 437	97.94 446	95.60 523	99.37 222	90.68 531	98.70 481	96.56 363	98.61 522	86.94 528	99.55 375	98.77 455
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 437	96.83 449	98.59 420	99.46 346	97.55 437	99.25 191	96.84 510	98.78 324	97.24 503	97.67 510	97.11 344	98.97 516	86.59 529	98.54 478	99.27 356

GLUNet-SfM		95.26 488	95.06 485	95.87 510	94.84 536	90.39 534	90.24 530	99.92 45	92.30 520	99.16 374	99.25 409	94.69 416	98.01 526	85.55 530	99.62 351	99.21 369

PAPM		95.61 485	94.71 489	98.31 437	99.12 431	96.63 469	96.66 511	98.46 469	90.77 525	96.25 517	98.68 483	93.01 440	99.69 437	81.60 531	97.86 505	98.62 461

SD_0403		97.42 431	96.90 447	98.98 369	99.54 302	97.90 423	99.52 94	99.54 305	99.34 227	97.87 484	98.85 469	98.72 192	99.64 469	78.93 532	99.83 238	99.40 319

dongtai		89.37 497	88.91 500	90.76 515	99.19 419	77.46 540	95.47 521	87.82 539	92.28 521	94.17 527	98.82 473	71.22 537	95.54 532	63.85 533	97.34 510	99.27 356

kuosan		85.65 499	84.57 502	88.90 517	97.91 516	77.11 541	96.37 515	87.62 540	85.24 530	85.45 535	96.83 528	69.94 539	90.98 536	45.90 534	95.83 527	98.62 461

test123		29.31 500	33.05 505	18.08 518	25.93 542	12.24 543	97.53 472	10.93 543	11.78 536	24.21 537	50.08 548	21.04 540	8.60 537	23.51 535	32.43 536	33.39 533

testmvs		28.94 501	33.33 503	15.79 519	26.03 541	9.81 544	96.77 505	15.67 542	11.55 537	23.87 538	50.74 547	19.03 541	8.53 538	23.21 536	33.07 535	29.03 534

mmdepth		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

monomultidepth		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

test_blank		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

uanet_test		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

DCPMVS		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

cdsmvs_eth3d_5k		24.88 502	33.17 504	0.00 520	0.00 543	0.00 545	0.00 531	99.62 251	0.00 538	0.00 539	99.13 430	99.82 18	0.00 539	0.00 537	0.00 537	0.00 535

pcd_1.5k_mvsjas		16.61 503	22.14 506	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	99.28 92	0.00 539	0.00 537	0.00 537	0.00 535

sosnet-low-res		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

sosnet		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

uncertanet		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

Regformer		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

ab-mvs-re		8.26 514	11.02 517	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	99.16 427	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

uanet		8.33 504	11.11 507	0.00 520	0.00 543	0.00 545	0.00 531	0.00 544	0.00 538	0.00 539	100.00 1	0.00 542	0.00 539	0.00 537	0.00 537	0.00 535

TestfortrainingZip				99.38 283	99.17 423	99.25 254	99.38 132	98.82 445	98.93 298	99.68 202	99.49 340	98.11 280	99.56 485		98.44 484	99.32 345

FOURS1					99.83 88	99.89 10	99.74 27	99.71 195	99.69 130	99.63 230

test_one_0601					99.63 249	99.76 70		99.55 299	99.23 248	99.31 345	99.61 280	98.59 209

eth-test2					0.00 543

eth-test					0.00 543

test_241102_ONE					99.69 221	99.82 41		99.54 305	99.12 273	99.82 111	99.49 340	98.91 165	99.52 492

save fliter					99.53 311	99.25 254	98.29 406	99.38 369	99.07 277

test0726					99.69 221	99.80 51	99.24 192	99.57 287	99.16 264	99.73 179	99.65 242	98.35 250

GSMVS																99.14 391

test_part2					99.62 253	99.67 118				99.55 271

sam_mvs1												90.81 474				99.14 391

sam_mvs												90.52 480

MTGPA								99.53 316

test_post											52.41 545	90.25 483	99.86 273

patchmatchnet-post											99.62 270	90.58 478	99.94 98

MTMP							99.09 256	98.59 462

TEST9					99.35 376	99.35 234	98.11 425	99.41 354	94.83 510	97.92 479	98.99 452	98.02 286	99.85 292

test_8					99.34 385	99.31 240	98.08 429	99.40 361	94.90 507	97.87 484	98.97 457	98.02 286	99.84 309

agg_prior					99.35 376	99.36 231		99.39 364		97.76 491			99.85 292

test_prior4						99.19 274	98.00 439

test_prior				99.46 252	99.35 376	99.22 265		99.39 364					99.69 437			99.48 278

新几何2							98.04 433

旧先验1					99.49 331	99.29 243		99.26 400			99.39 368	97.67 313			99.36 409	99.46 287

原ACMM2							97.92 448

test222					99.51 320	99.08 297	97.83 454	99.29 394	95.21 504	98.68 432	99.31 393	97.28 334			99.38 406	99.43 310

segment_acmp												98.37 248

testdata1							97.72 460		97.86 430

test12				99.54 224	99.29 398	99.33 237		99.16 422		98.43 451		97.54 321	99.82 349		99.47 394	99.48 278

plane_prior7					99.58 272	99.38 222

plane_prior6					99.47 342	99.26 251						97.24 335

plane_prior4											99.25 409

plane_prior3						99.31 240			98.36 376	99.14 379

plane_prior2							98.80 339		98.94 293

plane_prior1					99.51 320

plane_prior						99.24 259	98.42 397		97.87 428						99.71 317

n2								0.00 544

nn								0.00 544

door-mid								99.83 108

test11								99.29 394

door								99.77 158

HQP5-MVS						98.94 318

HQP-NCC					99.31 392		97.98 441		97.45 450	98.15 468

ACMP_Plane					99.31 392		97.98 441		97.45 450	98.15 468

HQP4-MVS										98.15 468			99.70 430			99.53 250

HQP3-MVS								99.37 370							99.67 339

HQP2-MVS												96.67 359

NP-MVS					99.40 362	99.13 285					98.83 471

ACMMP++_ref															99.94 133

ACMMP++															99.79 271

Test By Simon												98.41 242

High-resolution multi-view benchmark