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	99.73 113	100.00 1	99.89 41	99.79 22	99.88 241	99.98 1	100.00 1	99.98 5

test_fmvs2		99.72 53	99.85 17	99.34 309	99.91 31	98.08 425	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	99.53 232	99.96 7	98.62 376	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	99.12 245	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	99.27 334	99.93 24	97.84 438	99.34 149	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	99.36 301	99.94 18	98.18 413	99.52 94	100.00 1	99.86 66	100.00 1	99.88 50	98.99 151	99.96 69	99.97 4	99.96 91	99.95 15

test_fmvs1_n		99.68 64	99.81 28	99.28 329	99.95 15	97.93 434	99.49 107	100.00 1	99.82 86	99.99 7	99.89 41	99.21 105	99.98 26	99.97 4	99.98 54	99.93 21

test_f		99.75 49	99.88 7	99.37 295	99.96 7	98.21 410	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 60	99.80 51	98.94 314	99.96 30	99.98 18	99.96 34	99.78 134	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 46	99.86 18	99.08 262	99.97 21	99.98 18	99.96 34	99.79 121	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	99.01 286	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 46	99.55 173	99.17 220	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	99.45 259	99.93 24	98.40 398	99.30 167	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	99.81 112	99.75 79	99.06 268	99.85 95	99.99 3	99.97 24	99.84 76	99.12 123	99.98 26	99.95 14	99.99 19	99.90 30

fmvsm_s_conf0.5_n_7		99.73 52	99.78 39	99.60 195	99.74 193	98.93 329	98.85 329	99.96 30	99.96 28	99.97 24	99.76 156	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 46	99.66 123	99.11 250	99.91 57	99.98 18	99.96 34	99.64 250	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 99	99.70 109	99.17 220	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		99.48 135	99.65 74	98.97 381	99.54 316	97.16 469	99.11 250	99.98 13	99.78 103	99.96 34	99.81 98	98.72 195	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	99.37 231	99.53 92	99.98 13	99.77 108	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 90	99.59 160	98.97 305	99.92 47	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	98.92 318	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 46	99.64 136	99.12 245	99.91 57	99.98 18	99.95 45	99.67 235	99.67 34	99.99 7	99.94 20	99.99 19	99.88 41

MM		99.18 246	99.05 259	99.55 221	99.35 390	98.81 349	99.05 269	97.79 513	99.99 3	99.48 305	99.59 306	96.29 394	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	98.97 305	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	99.81 112	99.53 176	99.15 229	99.89 68	99.99 3	99.98 14	99.86 63	99.13 120	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	99.79 137	99.72 95	98.84 332	99.96 30	99.96 28	99.96 34	99.72 187	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 99	99.76 70	98.88 323	99.92 47	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	99.10 254	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	99.07 267	99.98 13	99.99 3	99.98 14	99.90 36	99.88 11	99.92 153	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 75	99.82 41	99.03 277	99.96 30	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 75	99.78 57	99.03 277	99.96 30	99.99 3	99.97 24	99.84 76	99.78 23	99.92 153	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 99	99.75 79	99.02 281	99.87 80	99.98 18	99.98 14	99.81 98	99.07 134	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	99.78 146	99.78 57	99.00 292	99.97 21	99.96 28	99.97 24	99.56 321	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	99.75 182	99.56 169	98.98 303	99.94 41	99.92 45	99.97 24	99.72 187	99.84 16	99.92 153	99.91 33	99.98 54	99.89 38

MVStest1		98.22 398	98.09 400	98.62 429	99.04 464	96.23 494	99.20 205	99.92 47	99.44 210	99.98 14	99.87 56	85.87 521	99.67 471	99.91 33	99.57 384	99.95 15

v1921920		99.56 106	99.57 105	99.55 221	99.75 182	99.11 295	99.05 269	99.61 273	99.15 277	99.88 82	99.71 197	99.08 131	99.87 257	99.90 37	99.97 77	99.66 149

v1240		99.56 106	99.58 100	99.51 238	99.80 123	99.00 313	99.00 292	99.65 250	99.15 277	99.90 67	99.75 164	99.09 127	99.88 241	99.90 37	99.96 91	99.67 135

v10		99.69 59	99.69 60	99.66 153	99.81 112	99.39 224	99.66 57	99.75 183	99.60 177	99.92 59	99.87 56	98.75 190	99.86 277	99.90 37	99.99 19	99.73 95

v1192		99.57 102	99.57 105	99.57 210	99.77 159	99.22 270	99.04 274	99.60 285	99.18 263	99.87 92	99.72 187	99.08 131	99.85 296	99.89 40	99.98 54	99.66 149

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 32	99.81 112	99.71 101	98.97 305	99.92 47	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		99.55 111	99.54 116	99.58 202	99.78 146	99.20 277	99.11 250	99.62 265	99.18 263	99.89 72	99.72 187	98.66 204	99.87 257	99.88 41	99.97 77	99.66 149

v8		99.68 64	99.69 60	99.65 160	99.80 123	99.40 220	99.66 57	99.76 178	99.64 160	99.93 53	99.85 68	98.66 204	99.84 313	99.88 41	99.99 19	99.71 104

mvs5depth		99.88 6	99.91 3	99.80 64	99.92 29	99.42 212	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		99.54 116	99.53 120	99.59 198	99.79 137	99.28 250	99.10 254	99.61 273	99.20 260	99.84 104	99.73 177	98.67 202	99.84 313	99.86 45	99.98 54	99.64 170

mmtdpeth		99.78 37	99.83 21	99.66 153	99.85 75	99.05 308	99.79 15	99.97 21	100.00 1	99.43 318	99.94 19	99.64 35	99.94 98	99.83 46	99.99 19	99.98 5

SSC-MVS		99.52 122	99.42 152	99.83 41	99.86 60	99.65 129	99.52 94	99.81 135	99.87 63	99.81 119	99.79 121	96.78 370	99.99 7	99.83 46	99.51 400	99.86 47

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 26	99.82 10	99.82 122	99.84 76	99.94 48	99.91 31	99.13 120	99.96 69	99.83 46	99.99 19	99.83 59

v2v482		99.50 127	99.47 132	99.58 202	99.78 146	99.25 259	99.14 233	99.58 300	99.25 251	99.81 119	99.62 276	98.24 271	99.84 313	99.83 46	99.97 77	99.64 170

test_vis1_rt		99.45 151	99.46 138	99.41 280	99.71 207	98.63 375	98.99 300	99.96 30	99.03 292	99.95 45	99.12 449	98.75 190	99.84 313	99.82 50	99.82 255	99.77 81

tt0805		99.63 86	99.57 105	99.81 54	99.87 55	99.88 12	99.58 82	98.70 468	99.72 117	99.91 62	99.60 296	99.43 67	99.81 376	99.81 51	99.53 396	99.73 95

VortexMVS		99.13 262	99.24 208	98.79 415	99.67 247	96.60 486	99.24 194	99.80 143	99.85 72	99.93 53	99.84 76	95.06 424	99.89 226	99.80 52	99.98 54	99.89 38

V42		99.56 106	99.54 116	99.63 175	99.79 137	99.46 197	99.39 129	99.59 291	99.24 253	99.86 96	99.70 207	98.55 220	99.82 359	99.79 53	99.95 116	99.60 208

SSC-MVS3.2		99.64 85	99.67 65	99.56 214	99.75 182	98.98 317	98.96 309	99.87 80	99.88 61	99.84 104	99.64 250	99.32 88	99.91 185	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 74	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		99.44 155	99.32 181	99.80 64	99.81 112	99.61 154	99.47 112	99.81 135	99.82 86	99.71 193	99.72 187	96.60 376	99.98 26	99.75 56	99.23 445	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 95	99.95 32	99.98 14	99.92 27	99.28 93	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 89	99.89 56	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		99.15 257	99.06 252	99.42 270	99.85 75	98.59 379	99.13 240	97.26 522	99.84 76	99.87 92	99.77 146	96.11 400	99.93 120	99.71 60	99.96 91	99.74 91

Elysia		99.69 59	99.65 74	99.81 54	99.86 60	99.72 95	99.34 149	99.77 170	99.94 36	99.91 62	99.76 156	98.55 220	99.99 7	99.70 61	99.98 54	99.72 99

StellarMVS		99.69 59	99.65 74	99.81 54	99.86 60	99.72 95	99.34 149	99.77 170	99.94 36	99.91 62	99.76 156	98.55 220	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 99	99.84 26	99.82 10	99.92 47	99.94 36	99.94 48	99.93 22	99.34 85	99.92 153	99.70 61	99.96 91	99.70 107

reproduce_monomvs		97.40 447	97.46 435	97.20 499	99.05 461	91.91 538	99.20 205	99.18 435	99.84 76	99.86 96	99.75 164	80.67 529	99.83 336	99.69 64	99.95 116	99.85 50

SPE-MVS-test		99.68 64	99.70 57	99.64 167	99.57 296	99.83 33	99.78 17	99.97 21	99.92 45	99.50 300	99.38 385	99.57 52	99.95 81	99.69 64	99.90 175	99.15 395

guyue		99.12 265	99.02 268	99.41 280	99.84 81	98.56 382	99.19 211	98.30 496	99.82 86	99.84 104	99.75 164	94.84 428	99.92 153	99.68 66	99.94 135	99.74 91

tt0320		99.79 34	99.79 34	99.81 54	99.82 99	99.84 26	99.82 10	99.90 64	99.94 36	99.94 48	99.94 19	99.07 134	99.92 153	99.68 66	99.97 77	99.67 135

MGCNet		98.61 351	98.30 381	99.52 234	97.88 533	98.95 324	98.76 349	94.11 545	99.84 76	99.32 351	99.57 317	95.57 413	99.95 81	99.68 66	99.98 54	99.68 126

CS-MVS		99.67 76	99.70 57	99.58 202	99.53 325	99.84 26	99.79 15	99.96 30	99.90 49	99.61 255	99.41 371	99.51 61	99.95 81	99.66 69	99.89 191	98.96 442

KinetiMVS		99.66 77	99.63 82	99.76 87	99.89 40	99.57 168	99.37 140	99.82 122	99.95 32	99.90 67	99.63 266	98.57 216	99.97 44	99.65 70	99.94 135	99.74 91

pmmvs6		99.86 10	99.86 13	99.83 41	99.94 18	99.90 7	99.83 7	99.91 57	99.85 72	99.94 48	99.95 16	99.73 27	99.90 204	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	99.77 170	99.78 103	99.93 53	99.89 41	97.94 302	99.92 153	99.65 70	99.98 54	99.62 188

LuminaMVS		99.39 176	99.28 197	99.73 113	99.83 90	99.49 184	99.00 292	99.05 448	99.81 92	99.89 72	99.79 121	96.54 380	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 46	99.88 12	99.83 7	99.79 152	99.94 36	99.93 53	99.92 27	99.35 84	99.92 153	99.64 73	99.94 135	99.68 126

EC-MVSNet		99.69 59	99.69 60	99.68 141	99.71 207	99.91 4	99.76 23	99.96 30	99.86 66	99.51 297	99.39 382	99.57 52	99.93 120	99.64 73	99.86 224	99.20 383

K. test v3		98.87 323	98.60 336	99.69 139	99.93 24	99.46 197	99.74 27	94.97 540	99.78 103	99.88 82	99.88 50	93.66 447	99.97 44	99.61 76	99.95 116	99.64 170

KD-MVS_self_test		99.63 86	99.59 96	99.76 87	99.84 81	99.90 7	99.37 140	99.79 152	99.83 82	99.88 82	99.85 68	98.42 248	99.90 204	99.60 77	99.73 317	99.49 282

Anonymous20240521		99.44 155	99.42 152	99.49 244	99.89 40	98.96 323	99.62 67	99.76 178	99.85 72	99.82 112	99.88 50	96.39 387	99.97 44	99.59 78	99.98 54	99.55 236

TransMVSNet (Re)		99.78 37	99.77 45	99.81 54	99.91 31	99.85 21	99.75 25	99.86 89	99.70 130	99.91 62	99.89 41	99.60 44	99.87 257	99.59 78	99.74 310	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 95	99.80 96	99.93 53	99.93 22	98.54 225	99.93 120	99.59 78	99.98 54	99.76 86

EU-MVSNet		99.39 176	99.62 85	98.72 422	99.88 46	96.44 488	99.56 87	99.85 95	99.90 49	99.90 67	99.85 68	98.09 290	99.83 336	99.58 81	99.95 116	99.90 30

mvs_anonymous		99.28 208	99.39 158	98.94 386	99.19 434	97.81 440	99.02 281	99.55 315	99.78 103	99.85 101	99.80 109	98.24 271	99.86 277	99.57 82	99.50 403	99.15 395

test1111		97.74 428	98.16 395	96.49 518	99.60 270	89.86 553	99.71 37	91.21 549	99.89 56	99.88 82	99.87 56	93.73 446	99.90 204	99.56 83	99.99 19	99.70 107

lessismore_v0				99.64 167	99.86 60	99.38 226		90.66 550		99.89 72	99.83 83	94.56 434	99.97 44	99.56 83	99.92 158	99.57 228

dtuonlycased		99.24 220	99.47 132	98.56 436	99.90 37	96.17 496	97.62 480	99.85 95	99.66 151	99.86 96	99.50 346	99.39 71	99.93 120	99.55 85	99.85 231	99.59 215

mvsany_test1		99.44 155	99.45 141	99.40 283	99.37 383	98.64 373	97.90 463	99.59 291	99.27 246	99.92 59	99.82 91	99.74 26	99.93 120	99.55 85	99.87 216	99.63 176

MVSMamba_PlusPlus		99.55 111	99.58 100	99.47 252	99.68 240	99.40 220	99.52 94	99.70 216	99.92 45	99.77 151	99.86 63	98.28 267	99.96 69	99.54 87	99.90 175	99.05 427

pm-mvs1		99.79 34	99.79 34	99.78 76	99.91 31	99.83 33	99.76 23	99.87 80	99.73 113	99.89 72	99.87 56	99.63 37	99.87 257	99.54 87	99.92 158	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 47	99.90 49	99.97 24	99.87 56	99.81 20	99.95 81	99.54 87	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		99.48 135	99.65 74	98.95 384	99.71 207	97.27 466	99.50 102	99.82 122	99.59 179	99.41 327	99.85 68	99.62 40	100.00 1	99.53 90	99.89 191	99.59 215

test2506		94.73 506	94.59 506	95.15 526	99.59 276	85.90 555	99.75 25	74.01 557	99.89 56	99.71 193	99.86 63	79.00 539	99.90 204	99.52 91	99.99 19	99.65 158

balanced_ft_v1		99.37 184	99.36 169	99.38 290	99.10 454	99.38 226	99.68 48	99.72 203	99.72 117	99.36 338	99.77 146	97.66 327	99.94 98	99.52 91	99.73 317	98.83 461

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 40	99.91 4	99.89 5	99.71 207	99.93 43	99.95 45	99.89 41	99.71 28	99.96 69	99.51 93	99.97 77	99.84 55

FC-MVSNet-test		99.70 57	99.65 74	99.86 30	99.88 46	99.86 18	99.72 33	99.78 165	99.90 49	99.82 112	99.83 83	98.45 244	99.87 257	99.51 93	99.97 77	99.86 47

BP-MVS1		98.72 341	98.46 357	99.50 240	99.53 325	99.00 313	99.34 149	98.53 479	99.65 156	99.73 182	99.38 385	90.62 493	99.96 69	99.50 95	99.86 224	99.55 236

UA-Net		99.78 37	99.76 49	99.86 30	99.72 202	99.71 101	99.91 4	99.95 38	99.96 28	99.71 193	99.91 31	99.15 115	99.97 44	99.50 95	100.00 1	99.90 30

viewdifsd2359ckpt11		99.62 94	99.64 79	99.56 214	99.86 60	99.19 280	99.02 281	99.93 43	99.83 82	99.88 82	99.81 98	98.99 151	99.83 336	99.48 97	99.96 91	99.65 158

viewmsd2359difaftdt		99.62 94	99.64 79	99.56 214	99.86 60	99.19 280	99.02 281	99.93 43	99.83 82	99.88 82	99.81 98	98.99 151	99.83 336	99.48 97	99.96 91	99.65 158

PMMVS2		99.48 135	99.45 141	99.57 210	99.76 164	98.99 315	98.09 439	99.90 64	98.95 304	99.78 139	99.58 309	99.57 52	99.93 120	99.48 97	99.95 116	99.79 75

VPA-MVSNet		99.66 77	99.62 85	99.79 72	99.68 240	99.75 79	99.62 67	99.69 225	99.85 72	99.80 126	99.81 98	98.81 177	99.91 185	99.47 100	99.88 202	99.70 107

GDP-MVS		98.81 331	98.57 342	99.50 240	99.53 325	99.12 294	99.28 177	99.86 89	99.53 187	99.57 266	99.32 404	90.88 488	99.98 26	99.46 101	99.74 310	99.42 323

ECVR-MVS		97.73 429	98.04 403	96.78 510	99.59 276	90.81 547	99.72 33	90.43 551	99.89 56	99.86 96	99.86 63	93.60 448	99.89 226	99.46 101	99.99 19	99.65 158

nrg030		99.70 57	99.66 72	99.82 46	99.76 164	99.84 26	99.61 73	99.70 216	99.93 43	99.78 139	99.68 229	99.10 125	99.78 394	99.45 103	99.96 91	99.83 59

FE-MVSNET2		99.68 64	99.67 65	99.72 122	99.86 60	99.68 117	99.46 116	99.88 74	99.62 165	99.87 92	99.85 68	99.06 141	99.85 296	99.44 104	99.98 54	99.63 176

TAMVS		99.49 132	99.45 141	99.63 175	99.48 350	99.42 212	99.45 117	99.57 303	99.66 151	99.78 139	99.83 83	97.85 309	99.86 277	99.44 104	99.96 91	99.61 203

GeoE		99.69 59	99.66 72	99.78 76	99.76 164	99.76 70	99.60 79	99.82 122	99.46 205	99.75 165	99.56 321	99.63 37	99.95 81	99.43 106	99.88 202	99.62 188

new-patchmatchnet		99.35 191	99.57 105	98.71 426	99.82 99	96.62 484	98.55 384	99.75 183	99.50 192	99.88 82	99.87 56	99.31 89	99.88 241	99.43 106	100.00 1	99.62 188

test20.03		99.55 111	99.54 116	99.58 202	99.79 137	99.37 231	99.02 281	99.89 68	99.60 177	99.82 112	99.62 276	98.81 177	99.89 226	99.43 106	99.86 224	99.47 290

MVSFormer		99.41 170	99.44 146	99.31 321	99.57 296	98.40 398	99.77 19	99.80 143	99.73 113	99.63 238	99.30 410	98.02 295	99.98 26	99.43 106	99.69 341	99.55 236

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 26	99.77 19	99.80 143	99.73 113	99.97 24	99.92 27	99.77 25	99.98 26	99.43 106	100.00 1	99.90 30

SDMVSNet		99.77 44	99.77 45	99.76 87	99.80 123	99.65 129	99.63 64	99.86 89	99.97 25	99.89 72	99.89 41	99.52 60	99.99 7	99.42 111	99.96 91	99.65 158

Anonymous20231211		99.62 94	99.57 105	99.76 87	99.61 267	99.60 158	99.81 13	99.73 194	99.82 86	99.90 67	99.90 36	97.97 301	99.86 277	99.42 111	99.96 91	99.80 67

SixPastTwentyTwo		99.42 163	99.30 188	99.76 87	99.92 29	99.67 120	99.70 38	99.14 441	99.65 156	99.89 72	99.90 36	96.20 398	99.94 98	99.42 111	99.92 158	99.67 135

BridgeMVS		99.50 127	99.50 125	99.50 240	99.42 373	99.49 184	99.52 94	99.75 183	99.86 66	99.78 139	99.71 197	98.20 279	99.90 204	99.39 114	99.88 202	99.10 407

patch_mono-2		99.51 124	99.46 138	99.64 167	99.70 223	99.11 295	99.04 274	99.87 80	99.71 123	99.47 307	99.79 121	98.24 271	99.98 26	99.38 115	99.96 91	99.83 59

UGNet		99.38 179	99.34 175	99.49 244	98.90 477	98.90 334	99.70 38	99.35 391	99.86 66	98.57 458	99.81 98	98.50 237	99.93 120	99.38 115	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 40	99.72 95	99.59 80	99.82 122	99.39 227	99.82 112	99.84 76	99.38 76	99.91 185	99.38 115	99.93 149	99.80 67

FIs		99.65 83	99.58 100	99.84 38	99.84 81	99.85 21	99.66 57	99.75 183	99.86 66	99.74 176	99.79 121	98.27 269	99.85 296	99.37 118	99.93 149	99.83 59

sd_testset		99.78 37	99.78 39	99.80 64	99.80 123	99.76 70	99.80 14	99.79 152	99.97 25	99.89 72	99.89 41	99.53 58	99.99 7	99.36 119	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 95	99.70 130	99.92 59	99.93 22	99.45 63	99.97 44	99.36 119	100.00 1	99.85 50

casdiffmvs_mvg		99.68 64	99.68 63	99.69 139	99.81 112	99.59 160	99.29 175	99.90 64	99.71 123	99.79 133	99.73 177	99.54 55	99.84 313	99.36 119	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 46	99.66 123	99.69 45	99.92 47	99.67 144	99.77 151	99.75 164	99.61 41	99.98 26	99.35 122	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 98	99.64 79	99.53 232	99.79 137	98.82 348	99.58 82	99.97 21	99.95 32	99.96 34	99.76 156	98.44 245	99.99 7	99.34 123	99.96 91	99.78 77

CHOSEN 1792x2688		99.39 176	99.30 188	99.65 160	99.88 46	99.25 259	98.78 347	99.88 74	98.66 353	99.96 34	99.79 121	97.45 337	99.93 120	99.34 123	99.99 19	99.78 77

CDS-MVSNet		99.22 232	99.13 226	99.50 240	99.35 390	99.11 295	98.96 309	99.54 321	99.46 205	99.61 255	99.70 207	96.31 391	99.83 336	99.34 123	99.88 202	99.55 236
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 300	99.16 219	98.51 437	99.75 182	95.90 502	98.07 442	99.84 105	99.84 76	99.89 72	99.73 177	96.01 403	99.99 7	99.33 126	100.00 1	99.63 176

HyFIR lowres test		98.91 315	98.64 332	99.73 113	99.85 75	99.47 189	98.07 442	99.83 115	98.64 356	99.89 72	99.60 296	92.57 461	100.00 1	99.33 126	99.97 77	99.72 99

pmmvs5		99.19 242	99.11 233	99.42 270	99.76 164	98.88 339	98.55 384	99.73 194	98.82 329	99.72 188	99.62 276	96.56 377	99.82 359	99.32 128	99.95 116	99.56 232

v148		99.40 172	99.41 156	99.39 286	99.76 164	98.94 326	99.09 259	99.59 291	99.17 270	99.81 119	99.61 286	98.41 249	99.69 453	99.32 128	99.94 135	99.53 257

baseline		99.63 86	99.62 85	99.66 153	99.80 123	99.62 144	99.44 119	99.80 143	99.71 123	99.72 188	99.69 216	99.15 115	99.83 336	99.32 128	99.94 135	99.53 257

CVMVSNet		98.61 351	98.88 306	97.80 472	99.58 286	93.60 530	99.26 187	99.64 258	99.66 151	99.72 188	99.67 235	93.26 452	99.93 120	99.30 131	99.81 265	99.87 45

PS-CasMVS		99.66 77	99.58 100	99.89 11	99.80 123	99.85 21	99.66 57	99.73 194	99.62 165	99.84 104	99.71 197	98.62 208	99.96 69	99.30 131	99.96 91	99.86 47

DTE-MVSNet		99.68 64	99.61 89	99.88 19	99.80 123	99.87 15	99.67 53	99.71 207	99.72 117	99.84 104	99.78 134	98.67 202	99.97 44	99.30 131	99.95 116	99.80 67

tmp_tt		95.75 496	95.42 491	96.76 512	89.90 556	94.42 522	98.86 327	97.87 511	78.01 546	99.30 361	99.69 216	97.70 319	95.89 546	99.29 134	98.14 512	99.95 15

PEN-MVS		99.66 77	99.59 96	99.89 11	99.83 90	99.87 15	99.66 57	99.73 194	99.70 130	99.84 104	99.73 177	98.56 219	99.96 69	99.29 134	99.94 135	99.83 59

viewmamba		99.49 132	99.51 122	99.42 270	99.75 182	98.90 334	98.85 329	99.85 95	99.69 133	99.73 182	99.67 235	98.79 182	99.82 359	99.28 136	99.95 116	99.54 248

WR-MVS_H		99.61 98	99.53 120	99.87 26	99.80 123	99.83 33	99.67 53	99.75 183	99.58 181	99.85 101	99.69 216	98.18 282	99.94 98	99.28 136	99.95 116	99.83 59

IterMVS		98.97 304	99.16 219	98.42 442	99.74 193	95.64 507	98.06 444	99.83 115	99.83 82	99.85 101	99.74 172	96.10 402	99.99 7	99.27 138	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	99.70 133	99.85 75	99.67 120	99.30 167	99.87 80	99.67 144	99.81 119	99.77 146	99.21 105	99.81 376	99.24 139	99.94 135	99.61 203

NormalMVS		99.09 274	98.91 304	99.62 184	99.78 146	99.11 295	99.36 144	99.77 170	99.82 86	99.68 208	99.53 335	93.30 450	99.99 7	99.24 139	99.76 295	99.74 91

SymmetryMVS		99.01 297	98.82 314	99.58 202	99.65 254	99.11 295	99.36 144	99.20 433	99.82 86	99.68 208	99.53 335	93.30 450	99.99 7	99.24 139	99.63 363	99.64 170

WBMVS		97.50 443	97.18 449	98.48 439	98.85 485	95.89 503	98.44 404	99.52 337	99.53 187	99.52 290	99.42 369	80.10 532	99.86 277	99.24 139	99.95 116	99.68 126

h-mvs33		98.61 351	98.34 376	99.44 263	99.60 270	98.67 363	99.27 182	99.44 363	99.68 136	99.32 351	99.49 351	92.50 465	100.00 1	99.24 139	96.51 537	99.65 158

hse-mvs2		98.52 365	98.30 381	99.16 353	99.29 413	98.60 377	98.77 348	99.02 450	99.68 136	99.32 351	99.04 460	92.50 465	99.85 296	99.24 139	97.87 520	99.03 432

FMVSNet1		99.66 77	99.63 82	99.73 113	99.78 146	99.77 63	99.68 48	99.70 216	99.67 144	99.82 112	99.83 83	98.98 155	99.90 204	99.24 139	99.97 77	99.53 257

casdiffseed414692147		99.68 64	99.68 63	99.67 145	99.86 60	99.65 129	99.32 158	99.87 80	99.75 111	99.77 151	99.80 109	99.61 41	99.68 465	99.21 146	99.95 116	99.67 135

casdiffmvs		99.63 86	99.61 89	99.67 145	99.79 137	99.59 160	99.13 240	99.85 95	99.79 100	99.76 160	99.72 187	99.33 87	99.82 359	99.21 146	99.94 135	99.59 215
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		99.54 116	99.43 149	99.87 26	99.76 164	99.82 41	99.57 85	99.61 273	99.54 185	99.80 126	99.64 250	97.79 313	99.95 81	99.21 146	99.94 135	99.84 55

DELS-MVS		99.34 196	99.30 188	99.48 250	99.51 334	99.36 235	98.12 435	99.53 332	99.36 233	99.41 327	99.61 286	99.22 104	99.87 257	99.21 146	99.68 346	99.20 383
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		99.47 145	99.50 125	99.37 295	99.70 223	98.80 352	98.67 363	99.92 47	99.49 194	99.77 151	99.71 197	99.08 131	99.78 394	99.20 150	99.94 135	99.54 248

UniMVSNet (Re)		99.37 184	99.26 202	99.68 141	99.51 334	99.58 165	98.98 303	99.60 285	99.43 217	99.70 197	99.36 394	97.70 319	99.88 241	99.20 150	99.87 216	99.59 215

RoMa-HiRes		99.38 179	99.30 188	99.64 167	99.81 112	99.47 189	99.11 250	99.94 41	99.03 292	99.55 279	99.56 321	97.71 318	99.92 153	99.19 152	99.77 290	99.54 248

CANet		99.11 270	99.05 259	99.28 329	98.83 488	98.56 382	98.71 360	99.41 370	99.25 251	99.23 373	99.22 433	97.66 327	99.94 98	99.19 152	99.97 77	99.33 350

EI-MVSNet-UG-set		99.48 135	99.50 125	99.42 270	99.57 296	98.65 370	99.24 194	99.46 357	99.68 136	99.80 126	99.66 241	98.99 151	99.89 226	99.19 152	99.90 175	99.72 99

dtuplus		99.52 122	99.55 112	99.43 267	99.76 164	98.90 334	98.71 360	99.89 68	99.67 144	99.79 133	99.77 146	99.25 101	99.81 376	99.18 155	99.96 91	99.57 228

xiu_mvs_v1_base_debu		99.23 223	99.34 175	98.91 396	99.59 276	98.23 407	98.47 398	99.66 240	99.61 170	99.68 208	98.94 477	99.39 71	99.97 44	99.18 155	99.55 389	98.51 485

xiu_mvs_v1_base		99.23 223	99.34 175	98.91 396	99.59 276	98.23 407	98.47 398	99.66 240	99.61 170	99.68 208	98.94 477	99.39 71	99.97 44	99.18 155	99.55 389	98.51 485

xiu_mvs_v1_base_debi		99.23 223	99.34 175	98.91 396	99.59 276	98.23 407	98.47 398	99.66 240	99.61 170	99.68 208	98.94 477	99.39 71	99.97 44	99.18 155	99.55 389	98.51 485

onestephybrid01		99.45 151	99.46 138	99.42 270	99.69 231	98.88 339	98.76 349	99.81 135	99.78 103	99.67 216	99.73 177	98.61 210	99.84 313	99.17 159	99.93 149	99.52 268

VPNet		99.46 147	99.37 164	99.71 128	99.82 99	99.59 160	99.48 109	99.70 216	99.81 92	99.69 201	99.58 309	97.66 327	99.86 277	99.17 159	99.44 412	99.67 135

UniMVSNet_NR-MVSNet		99.37 184	99.25 206	99.72 122	99.47 356	99.56 169	98.97 305	99.61 273	99.43 217	99.67 216	99.28 416	97.85 309	99.95 81	99.17 159	99.81 265	99.65 158

DU-MVS		99.33 199	99.21 213	99.71 128	99.43 368	99.56 169	98.83 335	99.53 332	99.38 228	99.67 216	99.36 394	97.67 323	99.95 81	99.17 159	99.81 265	99.63 176

hybrid		99.42 163	99.43 149	99.37 295	99.75 182	98.77 355	98.72 357	99.84 105	99.61 170	99.65 227	99.68 229	98.53 230	99.79 390	99.16 163	99.94 135	99.54 248

usedtu_dtu_shiyan2		99.44 155	99.33 180	99.78 76	99.86 60	99.76 70	99.54 90	99.79 152	99.66 151	99.66 223	99.79 121	96.76 371	99.96 69	99.15 164	99.72 325	99.62 188

EI-MVSNet-Vis-set		99.47 145	99.49 129	99.42 270	99.57 296	98.66 366	99.24 194	99.46 357	99.67 144	99.79 133	99.65 248	98.97 157	99.89 226	99.15 164	99.89 191	99.71 104

EI-MVSNet		99.38 179	99.44 146	99.21 346	99.58 286	98.09 421	99.26 187	99.46 357	99.62 165	99.75 165	99.67 235	98.54 225	99.85 296	99.15 164	99.92 158	99.68 126

VNet		99.18 246	99.06 252	99.56 214	99.24 424	99.36 235	99.33 155	99.31 406	99.67 144	99.47 307	99.57 317	96.48 381	99.84 313	99.15 164	99.30 432	99.47 290

EG-PatchMatch MVS		99.57 102	99.56 110	99.62 184	99.77 159	99.33 241	99.26 187	99.76 178	99.32 238	99.80 126	99.78 134	99.29 91	99.87 257	99.15 164	99.91 171	99.66 149

PVSNet_Blended_VisFu		99.40 172	99.38 161	99.44 263	99.90 37	98.66 366	98.94 314	99.91 57	97.97 429	99.79 133	99.73 177	99.05 143	99.97 44	99.15 164	99.99 19	99.68 126

IterMVS-LS		99.41 170	99.47 132	99.25 341	99.81 112	98.09 421	98.85 329	99.76 178	99.62 165	99.83 110	99.64 250	98.54 225	99.97 44	99.15 164	99.99 19	99.68 126
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Casviewmamba		99.63 86	99.60 93	99.73 113	99.84 81	99.72 95	99.36 144	99.87 80	99.67 144	99.74 176	99.73 177	99.07 134	99.83 336	99.14 171	99.93 149	99.62 188

TranMVSNet+NR-MVSNet		99.54 116	99.47 132	99.76 87	99.58 286	99.64 136	99.30 167	99.63 262	99.61 170	99.71 193	99.56 321	98.76 188	99.96 69	99.14 171	99.92 158	99.68 126

MVSTER		98.47 372	98.22 388	99.24 343	99.06 460	98.35 404	99.08 262	99.46 357	99.27 246	99.75 165	99.66 241	88.61 507	99.85 296	99.14 171	99.92 158	99.52 268

hybridnocas07		99.43 159	99.44 146	99.39 286	99.75 182	98.85 345	98.76 349	99.85 95	99.71 123	99.70 197	99.68 229	98.47 239	99.77 407	99.13 174	99.95 116	99.55 236

E5new		99.68 64	99.67 65	99.70 133	99.87 55	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 394	99.13 174	99.96 91	99.70 107

E6new		99.68 64	99.67 65	99.70 133	99.86 60	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 394	99.13 174	99.96 91	99.70 107

E6		99.68 64	99.67 65	99.70 133	99.86 60	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 394	99.13 174	99.96 91	99.70 107

E5		99.68 64	99.67 65	99.70 133	99.87 55	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 394	99.13 174	99.96 91	99.70 107

diffmvs_AUTHOR		99.48 135	99.48 130	99.47 252	99.80 123	98.89 337	98.71 360	99.82 122	99.79 100	99.66 223	99.63 266	98.87 173	99.88 241	99.13 174	99.95 116	99.62 188

Anonymous20231206		99.35 191	99.31 183	99.47 252	99.74 193	99.06 307	99.28 177	99.74 189	99.23 255	99.72 188	99.53 335	97.63 332	99.88 241	99.11 180	99.84 237	99.48 286

Syy-MVS		98.17 404	97.85 420	99.15 355	98.50 512	98.79 353	98.60 371	99.21 430	97.89 439	96.76 526	96.37 553	95.47 418	99.57 497	99.10 181	98.73 486	99.09 413

ttmdpeth		99.48 135	99.55 112	99.29 326	99.76 164	98.16 415	99.33 155	99.95 38	99.79 100	99.36 338	99.89 41	99.13 120	99.77 407	99.09 182	99.64 359	99.93 21

MVS_Test		99.28 208	99.31 183	99.19 350	99.35 390	98.79 353	99.36 144	99.49 350	99.17 270	99.21 379	99.67 235	98.78 185	99.66 476	99.09 182	99.66 355	99.10 407

usedtu_dtu_shiyan1		98.87 323	98.71 325	99.35 305	99.59 276	98.88 339	97.17 502	99.64 258	98.94 305	99.27 363	99.22 433	95.57 413	99.83 336	99.08 184	99.92 158	99.35 343

FE-MVSNET3		98.87 323	98.71 325	99.35 305	99.59 276	98.88 339	97.17 502	99.64 258	98.94 305	99.27 363	99.22 433	95.57 413	99.83 336	99.08 184	99.92 158	99.35 343

testgi		99.29 206	99.26 202	99.37 295	99.75 182	98.81 349	98.84 332	99.89 68	98.38 388	99.75 165	99.04 460	99.36 81	99.86 277	99.08 184	99.25 441	99.45 297

1112_ss		99.05 284	98.84 311	99.67 145	99.66 250	99.29 248	98.52 391	99.82 122	97.65 454	99.43 318	99.16 442	96.42 384	99.91 185	99.07 187	99.84 237	99.80 67

CANet_DTU		98.91 315	98.85 309	99.09 365	98.79 494	98.13 416	98.18 424	99.31 406	99.48 197	98.86 428	99.51 343	96.56 377	99.95 81	99.05 188	99.95 116	99.19 386

blended_shiyan8		97.82 423	97.45 437	98.92 391	98.06 529	97.45 457	97.73 470	99.35 391	97.96 432	98.35 470	97.34 532	92.76 460	99.84 313	99.04 189	96.49 539	99.47 290

blended_shiyan6		97.82 423	97.46 435	98.92 391	98.08 528	97.46 455	97.73 470	99.34 395	97.96 432	98.33 471	97.35 531	92.78 458	99.84 313	99.04 189	96.53 533	99.46 295

ELoFTR		99.25 216	99.26 202	99.21 346	99.86 60	98.66 366	99.00 292	99.93 43	98.56 365	99.83 110	99.83 83	97.34 343	99.92 153	99.03 191	100.00 1	99.04 429

Baseline_NR-MVSNet		99.49 132	99.37 164	99.82 46	99.91 31	99.84 26	98.83 335	99.86 89	99.68 136	99.65 227	99.88 50	97.67 323	99.87 257	99.03 191	99.86 224	99.76 86

FMVSNet2		99.35 191	99.28 197	99.55 221	99.49 345	99.35 238	99.45 117	99.57 303	99.44 210	99.70 197	99.74 172	97.21 350	99.87 257	99.03 191	99.94 135	99.44 312

wanda-best-256-512		97.53 440	97.14 451	98.72 422	97.71 535	96.86 479	97.00 511	99.34 395	97.73 449	98.18 478	96.82 544	91.92 469	99.84 313	99.02 194	96.53 533	99.45 297

FE-blended-shiyan7		97.53 440	97.14 451	98.72 422	97.71 535	96.86 479	97.00 511	99.34 395	97.73 449	98.18 478	96.82 544	91.92 469	99.84 313	99.02 194	96.53 533	99.45 297

Test_1112_low_res		98.95 310	98.73 322	99.63 175	99.68 240	99.15 289	98.09 439	99.80 143	97.14 482	99.46 311	99.40 377	96.11 400	99.89 226	99.01 196	99.84 237	99.84 55

VDD-MVS		99.20 239	99.11 233	99.44 263	99.43 368	98.98 317	99.50 102	98.32 495	99.80 96	99.56 274	99.69 216	96.99 363	99.85 296	98.99 197	99.73 317	99.50 277

DeepC-MVS	98.90 4	99.62 94	99.61 89	99.67 145	99.72 202	99.44 205	99.24 194	99.71 207	99.27 246	99.93 53	99.90 36	99.70 31	99.93 120	98.99 197	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		99.48 135	99.47 132	99.51 238	99.77 159	99.41 219	98.81 340	99.66 240	99.42 221	99.75 165	99.66 241	99.20 107	99.76 414	98.98 199	99.99 19	99.36 340

EPNet_dtu		97.62 434	97.79 424	97.11 505	96.67 546	92.31 536	98.51 392	98.04 503	99.24 253	95.77 536	99.47 359	93.78 445	99.66 476	98.98 199	99.62 365	99.37 337
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dtuonly		98.93 314	99.11 233	98.38 445	99.72 202	95.75 505	97.07 509	99.91 57	99.04 290	99.65 227	99.41 371	98.32 263	99.83 336	98.97 201	99.90 175	99.55 236

diffmvs		99.34 196	99.32 181	99.39 286	99.67 247	98.77 355	98.57 380	99.81 135	99.61 170	99.48 305	99.41 371	98.47 239	99.86 277	98.97 201	99.90 175	99.53 257
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		99.40 172	99.31 183	99.68 141	99.43 368	99.55 173	99.73 30	99.50 346	99.46 205	99.88 82	99.36 394	97.54 333	99.87 257	98.97 201	99.87 216	99.63 176

RoMa-SfM		99.32 201	99.23 211	99.59 198	99.77 159	99.53 176	98.89 321	99.88 74	98.78 337	99.65 227	99.52 339	97.78 314	99.90 204	98.96 204	99.86 224	99.35 343

TestfortrainingZip a		99.55 111	99.45 141	99.85 32	99.76 164	99.82 41	99.38 132	99.62 265	99.77 108	99.87 92	99.78 134	98.12 287	99.88 241	98.96 204	99.77 290	99.85 50

viewdifsd2359ckpt07		99.51 124	99.50 125	99.52 234	99.80 123	99.19 280	98.92 318	99.88 74	99.72 117	99.64 233	99.62 276	99.06 141	99.81 376	98.96 204	99.94 135	99.56 232

GBi-Net		99.42 163	99.31 183	99.73 113	99.49 345	99.77 63	99.68 48	99.70 216	99.44 210	99.62 248	99.83 83	97.21 350	99.90 204	98.96 204	99.90 175	99.53 257

FMVSNet5		97.80 426	97.25 446	99.42 270	98.83 488	98.97 320	99.38 132	99.80 143	98.87 319	99.25 369	99.69 216	80.60 531	99.91 185	98.96 204	99.90 175	99.38 333

test1		99.42 163	99.31 183	99.73 113	99.49 345	99.77 63	99.68 48	99.70 216	99.44 210	99.62 248	99.83 83	97.21 350	99.90 204	98.96 204	99.90 175	99.53 257

FMVSNet3		98.80 332	98.63 334	99.32 317	99.13 444	98.72 359	99.10 254	99.48 351	99.23 255	99.62 248	99.64 250	92.57 461	99.86 277	98.96 204	99.90 175	99.39 331

UnsupCasMVSNet_eth		98.83 328	98.57 342	99.59 198	99.68 240	99.45 203	98.99 300	99.67 235	99.48 197	99.55 279	99.36 394	94.92 426	99.86 277	98.95 211	96.57 532	99.45 297

CHOSEN 280x420		98.41 379	98.41 366	98.40 443	99.34 399	95.89 503	96.94 516	99.44 363	98.80 333	99.25 369	99.52 339	93.51 449	99.98 26	98.94 212	99.98 54	99.32 354

E4		99.61 98	99.59 96	99.66 153	99.84 81	99.53 176	99.08 262	99.84 105	99.65 156	99.74 176	99.80 109	99.45 63	99.77 407	98.93 213	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 47	99.69 133	99.78 139	99.92 27	99.37 78	99.88 241	98.93 213	99.95 116	99.60 208

PDCNetPlus		98.55 361	98.50 353	98.69 427	99.64 256	96.12 497	97.67 477	100.00 1	98.34 400	99.79 133	99.75 164	92.45 467	99.98 26	98.92 215	99.99 19	99.96 13

viewmacassd2359aftdt		99.63 86	99.61 89	99.68 141	99.84 81	99.61 154	99.14 233	99.87 80	99.71 123	99.75 165	99.77 146	99.54 55	99.72 437	98.91 216	99.96 91	99.70 107

DKM-HiRes		98.95 310	98.73 322	99.62 184	99.82 99	99.47 189	98.50 393	99.81 135	99.41 222	97.76 506	99.58 309	95.04 425	99.83 336	98.89 217	99.76 295	99.58 221

alignmvs		98.28 389	97.96 409	99.25 341	99.12 446	98.93 329	99.03 277	98.42 487	99.64 160	98.72 443	97.85 522	90.86 489	99.62 488	98.88 218	99.13 450	99.19 386

testing3-2		96.51 474	96.43 468	96.74 514	99.36 386	91.38 544	99.10 254	97.87 511	99.48 197	98.57 458	98.71 494	76.65 544	99.66 476	98.87 219	99.26 439	99.18 388

MGCFI-Net		99.02 291	99.01 274	99.06 373	99.11 451	98.60 377	99.63 64	99.67 235	99.63 162	98.58 456	97.65 526	99.07 134	99.57 497	98.85 220	98.92 469	99.03 432

sss		98.90 318	98.77 321	99.27 334	99.48 350	98.44 395	98.72 357	99.32 402	97.94 435	99.37 337	99.35 399	96.31 391	99.91 185	98.85 220	99.63 363	99.47 290

PRO-TEST		99.15 257	99.22 212	98.95 384	99.11 451	98.09 421	99.28 177	99.69 225	99.90 49	99.11 397	99.81 98	97.64 330	99.92 153	98.84 222	99.64 359	98.83 461

xiu_mvs_v2_base		99.02 291	99.11 233	98.77 418	99.37 383	98.09 421	98.13 433	99.51 342	99.47 202	99.42 321	98.54 506	99.38 76	99.97 44	98.83 223	99.33 428	98.24 499

PS-MVSNAJ		99.00 300	99.08 246	98.76 419	99.37 383	98.10 420	98.00 451	99.51 342	99.47 202	99.41 327	98.50 508	99.28 93	99.97 44	98.83 223	99.34 427	98.20 503

E2		99.54 116	99.51 122	99.62 184	99.78 146	99.47 189	99.01 286	99.82 122	99.55 183	99.69 201	99.77 146	99.26 97	99.76 414	98.82 225	99.93 149	99.62 188

E3		99.54 116	99.51 122	99.62 184	99.78 146	99.47 189	99.01 286	99.82 122	99.55 183	99.69 201	99.77 146	99.25 101	99.76 414	98.82 225	99.93 149	99.62 188

D2MVS		99.22 232	99.19 216	99.29 326	99.69 231	98.74 358	98.81 340	99.41 370	98.55 367	99.68 208	99.69 216	98.13 285	99.87 257	98.82 225	99.98 54	99.24 370

PatchT		98.45 375	98.32 378	98.83 411	98.94 475	98.29 405	99.24 194	98.82 461	99.84 76	99.08 401	99.76 156	91.37 478	99.94 98	98.82 225	99.00 463	98.26 497

testf1		99.63 86	99.60 93	99.72 122	99.94 18	99.95 2	99.47 112	99.89 68	99.43 217	99.88 82	99.80 109	99.26 97	99.90 204	98.81 229	99.88 202	99.32 354

APD_test2		99.63 86	99.60 93	99.72 122	99.94 18	99.95 2	99.47 112	99.89 68	99.43 217	99.88 82	99.80 109	99.26 97	99.90 204	98.81 229	99.88 202	99.32 354

gbinet_0.2-2-1-0.02		97.52 442	97.07 453	98.88 405	97.35 543	97.35 463	97.17 502	99.25 419	97.86 444	98.41 468	96.54 550	90.74 491	99.85 296	98.80 231	97.51 524	99.43 318

usedtu_blend_shiyan5		97.97 417	97.65 433	98.92 391	97.71 535	97.49 452	99.53 92	99.81 135	99.52 191	98.18 478	96.82 544	91.92 469	99.83 336	98.79 232	96.53 533	99.45 297

blend_shiyan4		95.04 504	93.76 510	98.88 405	97.92 531	97.49 452	97.72 472	99.34 395	97.93 436	97.65 511	97.11 537	77.69 542	99.83 336	98.79 232	79.72 550	99.33 350

sasdasda		99.02 291	99.00 278	99.09 365	99.10 454	98.70 361	99.61 73	99.66 240	99.63 162	98.64 449	97.65 526	99.04 144	99.54 502	98.79 232	98.92 469	99.04 429

Effi-MVS+		99.06 280	98.97 290	99.34 309	99.31 407	98.98 317	98.31 415	99.91 57	98.81 331	98.79 437	98.94 477	99.14 118	99.84 313	98.79 232	98.74 483	99.20 383

canonicalmvs		99.02 291	99.00 278	99.09 365	99.10 454	98.70 361	99.61 73	99.66 240	99.63 162	98.64 449	97.65 526	99.04 144	99.54 502	98.79 232	98.92 469	99.04 429

VDDNet		98.97 304	98.82 314	99.42 270	99.71 207	98.81 349	99.62 67	98.68 469	99.81 92	99.38 335	99.80 109	94.25 438	99.85 296	98.79 232	99.32 430	99.59 215

CR-MVSNet		98.35 386	98.20 390	98.83 411	99.05 461	98.12 417	99.30 167	99.67 235	97.39 469	99.16 387	99.79 121	91.87 474	99.91 185	98.78 238	98.77 478	98.44 490

test_method		91.72 511	92.32 511	89.91 531	93.49 555	70.18 558	90.28 544	99.56 308	61.71 549	95.39 538	99.52 339	93.90 441	99.94 98	98.76 239	98.27 505	99.62 188

RPMNet		98.60 354	98.53 348	98.83 411	99.05 461	98.12 417	99.30 167	99.62 265	99.86 66	99.16 387	99.74 172	92.53 463	99.92 153	98.75 240	98.77 478	98.44 490

mamba_0408		99.54 116	99.55 112	99.54 227	99.71 207	99.24 264	99.27 182	99.79 152	99.72 117	99.78 139	99.64 250	99.36 81	99.93 120	98.74 241	99.90 175	99.45 297

SSM_04072		99.55 111	99.55 112	99.55 221	99.71 207	99.24 264	99.27 182	99.79 152	99.72 117	99.78 139	99.64 250	99.36 81	99.97 44	98.74 241	99.90 175	99.45 297

SSM_0407		99.56 106	99.56 110	99.54 227	99.71 207	99.24 264	99.15 229	99.84 105	99.80 96	99.78 139	99.70 207	99.44 65	99.93 120	98.74 241	99.90 175	99.45 297

SSM_0404		99.57 102	99.58 100	99.54 227	99.76 164	99.28 250	99.19 211	99.84 105	99.80 96	99.78 139	99.70 207	99.44 65	99.93 120	98.74 241	99.95 116	99.41 324

pmmvs4		99.13 262	99.06 252	99.36 301	99.57 296	99.10 302	98.01 448	99.25 419	98.78 337	99.58 263	99.44 366	98.24 271	99.76 414	98.74 241	99.93 149	99.22 375

viewmanbaseed2359cas		99.50 127	99.47 132	99.61 191	99.73 197	99.52 181	99.03 277	99.83 115	99.49 194	99.65 227	99.64 250	99.18 109	99.71 442	98.73 246	99.92 158	99.58 221

tttt0517		97.62 434	97.20 448	98.90 402	99.76 164	97.40 461	99.48 109	94.36 542	99.06 289	99.70 197	99.49 351	84.55 524	99.94 98	98.73 246	99.65 357	99.36 340

viewcassd2359sk11		99.48 135	99.45 141	99.58 202	99.73 197	99.42 212	98.96 309	99.80 143	99.44 210	99.63 238	99.74 172	99.09 127	99.76 414	98.72 248	99.91 171	99.57 228

EPP-MVSNet		99.17 251	99.00 278	99.66 153	99.80 123	99.43 209	99.70 38	99.24 423	99.48 197	99.56 274	99.77 146	94.89 427	99.93 120	98.72 248	99.89 191	99.63 176

PMatch-SfM		98.91 315	98.81 316	99.22 345	99.79 137	98.89 337	98.18 424	99.61 273	99.18 263	99.03 408	99.61 286	96.13 399	99.80 386	98.71 250	99.04 460	98.99 440

FE-MVSNET		99.45 151	99.36 169	99.71 128	99.84 81	99.64 136	99.16 226	99.91 57	98.65 354	99.73 182	99.73 177	98.54 225	99.82 359	98.71 250	99.96 91	99.67 135

Anonymous20240529		99.42 163	99.34 175	99.65 160	99.53 325	99.60 158	99.63 64	99.39 380	99.47 202	99.76 160	99.78 134	98.13 285	99.86 277	98.70 252	99.68 346	99.49 282

ACMH	98.42 6	99.59 101	99.54 116	99.72 122	99.86 60	99.62 144	99.56 87	99.79 152	98.77 340	99.80 126	99.85 68	99.64 35	99.85 296	98.70 252	99.89 191	99.70 107
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 199	99.28 197	99.47 252	99.57 296	99.39 224	99.78 17	99.43 367	98.87 319	99.57 266	99.82 91	98.06 293	99.87 257	98.69 254	99.73 317	99.15 395

test-260524					99.64 256	99.70 109		99.58 300		99.69 201		97.64 330	99.87 257	98.68 255	99.76 295

LFMVS		98.46 374	98.19 393	99.26 338	99.24 424	98.52 390	99.62 67	96.94 525	99.87 63	99.31 356	99.58 309	91.04 483	99.81 376	98.68 255	99.42 417	99.45 297

PMatch-Up-SfM		99.08 275	99.02 268	99.27 334	99.81 112	99.04 310	98.13 433	99.83 115	99.16 272	99.26 367	99.69 216	97.22 349	99.83 336	98.67 257	99.43 416	98.94 447

DKM		99.12 265	98.98 288	99.54 227	99.71 207	99.48 188	98.53 389	99.88 74	99.18 263	98.99 412	99.64 250	96.25 395	99.75 425	98.66 258	99.93 149	99.40 327

WR-MVS		99.11 270	98.93 296	99.66 153	99.30 411	99.42 212	98.42 406	99.37 386	99.04 290	99.57 266	99.20 439	96.89 366	99.86 277	98.66 258	99.87 216	99.70 107

mvsmamba		99.08 275	98.95 294	99.45 259	99.36 386	99.18 286	99.39 129	98.81 463	99.37 229	99.35 342	99.70 207	96.36 389	99.94 98	98.66 258	99.59 380	99.22 375

viewdifsd2359ckpt13		99.42 163	99.37 164	99.57 210	99.72 202	99.46 197	99.01 286	99.80 143	99.20 260	99.51 297	99.60 296	98.92 164	99.70 446	98.65 261	99.90 175	99.55 236

RRT-MVS		99.08 275	99.00 278	99.33 312	99.27 418	98.65 370	99.62 67	99.93 43	99.66 151	99.67 216	99.82 91	95.27 422	99.93 120	98.64 262	99.09 455	99.41 324

E3new		99.42 163	99.37 164	99.56 214	99.68 240	99.38 226	98.93 317	99.79 152	99.30 241	99.55 279	99.69 216	98.88 171	99.76 414	98.63 263	99.89 191	99.53 257

Anonymous202405211		98.75 337	98.46 357	99.63 175	99.34 399	99.66 123	99.47 112	97.65 514	99.28 245	99.56 274	99.50 346	93.15 453	99.84 313	98.62 264	99.58 382	99.40 327

SP-SuperGlue		98.66 348	98.63 334	98.73 421	98.44 514	99.02 311	98.22 422	99.44 363	99.37 229	98.17 482	99.30 410	96.95 364	99.12 525	98.59 265	99.20 448	98.06 507

lecture		99.56 106	99.48 130	99.81 54	99.78 146	99.86 18	99.50 102	99.70 216	99.59 179	99.75 165	99.71 197	98.94 160	99.92 153	98.59 265	99.76 295	99.66 149

EPNet		98.13 406	97.77 426	99.18 352	94.57 554	97.99 428	99.24 194	97.96 506	99.74 112	97.29 518	99.62 276	93.13 454	99.97 44	98.59 265	99.83 245	99.58 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 284	99.09 244	98.91 396	99.21 429	98.36 403	98.82 339	99.47 354	98.85 322	98.90 423	99.56 321	98.78 185	99.09 528	98.57 268	99.68 346	99.26 367

DenseAffine		99.17 251	99.06 252	99.49 244	99.76 164	99.33 241	98.43 405	99.97 21	99.11 283	99.17 386	99.61 286	97.05 359	99.76 414	98.56 269	99.88 202	99.38 333

Patchmatch-RL test		98.60 354	98.36 373	99.33 312	99.77 159	99.07 305	98.27 417	99.87 80	98.91 314	99.74 176	99.72 187	90.57 495	99.79 390	98.55 270	99.85 231	99.11 404

pmmvs3		98.08 409	97.80 422	98.91 396	99.41 375	97.69 446	97.87 464	99.66 240	95.87 506	99.50 300	99.51 343	90.35 497	99.97 44	98.55 270	99.47 408	99.08 419

LoFTR		99.29 206	99.26 202	99.36 301	99.70 223	99.05 308	98.66 365	99.95 38	98.85 322	99.86 96	99.75 164	98.14 284	99.93 120	98.54 272	99.91 171	99.10 407

SP-LightGlue		98.62 350	98.51 350	98.94 386	98.69 505	99.01 312	98.34 410	99.54 321	99.27 246	97.72 509	99.15 444	95.88 407	99.54 502	98.53 273	99.47 408	98.27 496

ETV-MVS		99.18 246	99.18 217	99.16 353	99.34 399	99.28 250	99.12 245	99.79 152	99.48 197	98.93 417	98.55 505	99.40 70	99.93 120	98.51 274	99.52 399	98.28 495

viewdifsd2359ckpt09		99.24 220	99.16 219	99.49 244	99.70 223	99.22 270	98.88 323	99.81 135	98.70 348	99.38 335	99.37 389	98.22 276	99.76 414	98.48 275	99.88 202	99.51 271

jason		99.16 253	99.11 233	99.32 317	99.75 182	98.44 395	98.26 419	99.39 380	98.70 348	99.74 176	99.30 410	98.54 225	99.97 44	98.48 275	99.82 255	99.55 236
jason: jason.
APDe-MVS		99.48 135	99.36 169	99.85 32	99.55 314	99.81 47	99.50 102	99.69 225	98.99 297	99.75 165	99.71 197	98.79 182	99.93 120	98.46 277	99.85 231	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		99.30 204	99.29 194	99.31 321	99.71 207	98.55 384	98.17 427	99.71 207	99.41 222	99.73 182	99.60 296	99.17 111	99.92 153	98.45 278	99.70 332	99.45 297

IMVS_0407		99.38 179	99.42 152	99.28 329	99.71 207	98.55 384	99.27 182	99.71 207	99.41 222	99.73 182	99.60 296	99.17 111	99.83 336	98.45 278	99.70 332	99.45 297

IMVS_0404		99.23 223	99.20 214	99.32 317	99.71 207	98.55 384	98.57 380	99.71 207	99.41 222	99.52 290	99.60 296	98.12 287	99.95 81	98.45 278	99.70 332	99.45 297

IMVS_0403		99.37 184	99.39 158	99.28 329	99.71 207	98.55 384	99.19 211	99.71 207	99.41 222	99.67 216	99.60 296	99.12 123	99.84 313	98.45 278	99.70 332	99.45 297

CL-MVSNet_self_test		98.71 343	98.56 346	99.15 355	99.22 427	98.66 366	97.14 505	99.51 342	98.09 420	99.54 283	99.27 418	96.87 367	99.74 432	98.43 282	98.96 465	99.03 432

our_test_3		98.85 327	99.09 244	98.13 459	99.66 250	94.90 520	97.72 472	99.58 300	99.07 287	99.64 233	99.62 276	98.19 280	99.93 120	98.41 283	99.95 116	99.55 236

Gipuma		99.57 102	99.59 96	99.49 244	99.98 3	99.71 101	99.72 33	99.84 105	99.81 92	99.94 48	99.78 134	98.91 167	99.71 442	98.41 283	99.95 116	99.05 427
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 449	96.91 461	98.74 420	97.72 534	97.57 449	97.60 481	97.36 520	98.00 425	99.21 379	98.02 518	90.04 501	99.79 390	98.37 285	95.89 542	98.86 458

PM-MVS		99.36 189	99.29 194	99.58 202	99.83 90	99.66 123	98.95 312	99.86 89	98.85 322	99.81 119	99.73 177	98.40 253	99.92 153	98.36 286	99.83 245	99.17 391

baseline1		97.73 429	97.33 442	98.96 382	99.30 411	97.73 444	99.40 127	98.42 487	99.33 237	99.46 311	99.21 437	91.18 481	99.82 359	98.35 287	91.26 545	99.32 354

MVS-HIRNet		97.86 421	98.22 388	96.76 512	99.28 416	91.53 542	98.38 408	92.60 548	99.13 279	99.31 356	99.96 15	97.18 354	99.68 465	98.34 288	99.83 245	99.07 425

GA-MVS		97.99 416	97.68 430	98.93 390	99.52 332	98.04 426	97.19 501	99.05 448	98.32 403	98.81 433	98.97 472	89.89 503	99.41 515	98.33 289	99.05 458	99.34 349

Fast-Effi-MVS+		99.02 291	98.87 307	99.46 256	99.38 380	99.50 183	99.04 274	99.79 152	97.17 480	98.62 452	98.74 492	99.34 85	99.95 81	98.32 290	99.41 418	98.92 450

MDA-MVSNet_test_wron		98.95 310	98.99 285	98.85 407	99.64 256	97.16 469	98.23 421	99.33 400	98.93 310	99.56 274	99.66 241	97.39 341	99.83 336	98.29 291	99.88 202	99.55 236

N_pmnet		98.73 340	98.53 348	99.35 305	99.72 202	98.67 363	98.34 410	94.65 541	98.35 396	99.79 133	99.68 229	98.03 294	99.93 120	98.28 292	99.92 158	99.44 312

ET-MVSNet_ETH3D		96.78 464	96.07 477	98.91 396	99.26 421	97.92 435	97.70 475	96.05 530	97.96 432	92.37 546	98.43 509	87.06 512	99.90 204	98.27 293	97.56 523	98.91 452

thisisatest0530		97.45 444	96.95 458	98.94 386	99.68 240	97.73 444	99.09 259	94.19 544	98.61 362	99.56 274	99.30 410	84.30 526	99.93 120	98.27 293	99.54 394	99.16 393

YYNet1		98.95 310	98.99 285	98.84 409	99.64 256	97.14 471	98.22 422	99.32 402	98.92 313	99.59 261	99.66 241	97.40 339	99.83 336	98.27 293	99.90 175	99.55 236

reproduce_model		99.50 127	99.40 157	99.83 41	99.60 270	99.83 33	99.12 245	99.68 230	99.49 194	99.80 126	99.79 121	99.01 148	99.93 120	98.24 296	99.82 255	99.73 95

ACMM	98.09 11	99.46 147	99.38 161	99.72 122	99.80 123	99.69 114	99.13 240	99.65 250	98.99 297	99.64 233	99.72 187	99.39 71	99.86 277	98.23 297	99.81 265	99.60 208
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 307	98.87 307	99.24 343	99.57 296	98.40 398	98.12 435	99.18 435	98.28 406	99.63 238	99.13 445	98.02 295	99.97 44	98.22 298	99.69 341	99.35 343

3Dnovator	99.15 2	99.43 159	99.36 169	99.65 160	99.39 377	99.42 212	99.70 38	99.56 308	99.23 255	99.35 342	99.80 109	99.17 111	99.95 81	98.21 299	99.84 237	99.59 215

Fast-Effi-MVS+-dtu		99.20 239	99.12 230	99.43 267	99.25 422	99.69 114	99.05 269	99.82 122	99.50 192	98.97 413	99.05 458	98.98 155	99.98 26	98.20 300	99.24 443	98.62 475

MS-PatchMatch		99.00 300	98.97 290	99.09 365	99.11 451	98.19 411	98.76 349	99.33 400	98.49 377	99.44 314	99.58 309	98.21 277	99.69 453	98.20 300	99.62 365	99.39 331

TSAR-MVS + GP.		99.12 265	99.04 265	99.38 290	99.34 399	99.16 287	98.15 430	99.29 410	98.18 413	99.63 238	99.62 276	99.18 109	99.68 465	98.20 300	99.74 310	99.30 361

DP-MVS		99.48 135	99.39 158	99.74 103	99.57 296	99.62 144	99.29 175	99.61 273	99.87 63	99.74 176	99.76 156	98.69 198	99.87 257	98.20 300	99.80 272	99.75 89

MVP-Stereo		99.16 253	99.08 246	99.43 267	99.48 350	99.07 305	99.08 262	99.55 315	98.63 357	99.31 356	99.68 229	98.19 280	99.78 394	98.18 304	99.58 382	99.45 297
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 159	99.30 188	99.80 64	99.83 90	99.81 47	99.52 94	99.70 216	98.35 396	99.51 297	99.50 346	99.31 89	99.88 241	98.18 304	99.84 237	99.69 119

MDA-MVSNet-bldmvs		99.06 280	99.05 259	99.07 371	99.80 123	97.83 439	98.89 321	99.72 203	99.29 242	99.63 238	99.70 207	96.47 382	99.89 226	98.17 306	99.82 255	99.50 277

JIA-IIPM		98.06 411	97.92 416	98.50 438	98.59 508	97.02 473	98.80 343	98.51 481	99.88 61	97.89 497	99.87 56	91.89 473	99.90 204	98.16 307	97.68 522	98.59 478

EIA-MVS		99.12 265	99.01 274	99.45 259	99.36 386	99.62 144	99.34 149	99.79 152	98.41 383	98.84 430	98.89 481	98.75 190	99.84 313	98.15 308	99.51 400	98.89 455

miper_lstm_enhance		98.65 349	98.60 336	98.82 414	99.20 432	97.33 464	97.78 468	99.66 240	99.01 295	99.59 261	99.50 346	94.62 433	99.85 296	98.12 309	99.90 175	99.26 367

reproduce-ours		99.46 147	99.35 173	99.82 46	99.56 310	99.83 33	99.05 269	99.65 250	99.45 208	99.78 139	99.78 134	98.93 161	99.93 120	98.11 310	99.81 265	99.70 107

our_new_method		99.46 147	99.35 173	99.82 46	99.56 310	99.83 33	99.05 269	99.65 250	99.45 208	99.78 139	99.78 134	98.93 161	99.93 120	98.11 310	99.81 265	99.70 107

Effi-MVS+-dtu		99.07 279	98.92 300	99.52 234	98.89 480	99.78 57	99.15 229	99.66 240	99.34 234	98.92 420	99.24 430	97.69 321	99.98 26	98.11 310	99.28 435	98.81 464

tpm		97.15 456	96.95 458	97.75 474	98.91 476	94.24 524	99.32 158	97.96 506	97.71 452	98.29 472	99.32 404	86.72 518	99.92 153	98.10 313	96.24 540	99.09 413

DeepPCF-MVS	98.42 6	99.18 246	99.02 268	99.67 145	99.22 427	99.75 79	97.25 499	99.47 354	98.72 345	99.66 223	99.70 207	99.29 91	99.63 487	98.07 314	99.81 265	99.62 188

ppachtmachnet_test		98.89 321	99.12 230	98.20 457	99.66 250	95.24 516	97.63 478	99.68 230	99.08 285	99.78 139	99.62 276	98.65 206	99.88 241	98.02 315	99.96 91	99.48 286

tpmrst		97.73 429	98.07 402	96.73 515	98.71 503	92.00 537	99.10 254	98.86 458	98.52 373	98.92 420	99.54 332	91.90 472	99.82 359	98.02 315	99.03 461	98.37 492

CSCG		99.37 184	99.29 194	99.60 195	99.71 207	99.46 197	99.43 121	99.85 95	98.79 335	99.41 327	99.60 296	98.92 164	99.92 153	98.02 315	99.92 158	99.43 318

eth_miper_zixun_eth		98.68 346	98.71 325	98.60 431	99.10 454	96.84 481	97.52 487	99.54 321	98.94 305	99.58 263	99.48 355	96.25 395	99.76 414	98.01 318	99.93 149	99.21 378

Patchmtry		98.78 333	98.54 347	99.49 244	98.89 480	99.19 280	99.32 158	99.67 235	99.65 156	99.72 188	99.79 121	91.87 474	99.95 81	98.00 319	99.97 77	99.33 350

PVSNet_BlendedMVS		99.03 288	99.01 274	99.09 365	99.54 316	97.99 428	98.58 376	99.82 122	97.62 455	99.34 346	99.71 197	98.52 234	99.77 407	97.98 320	99.97 77	99.52 268

PVSNet_Blended		98.70 344	98.59 338	99.02 376	99.54 316	97.99 428	97.58 482	99.82 122	95.70 511	99.34 346	98.98 470	98.52 234	99.77 407	97.98 320	99.83 245	99.30 361

cl____		98.54 363	98.41 366	98.92 391	99.03 465	97.80 442	97.46 489	99.59 291	98.90 315	99.60 258	99.46 362	93.85 443	99.78 394	97.97 322	99.89 191	99.17 391

DIV-MVS_self_test		98.54 363	98.42 365	98.92 391	99.03 465	97.80 442	97.46 489	99.59 291	98.90 315	99.60 258	99.46 362	93.87 442	99.78 394	97.97 322	99.89 191	99.18 388

AUN-MVS		97.82 423	97.38 440	99.14 358	99.27 418	98.53 388	98.72 357	99.02 450	98.10 418	97.18 521	99.03 464	89.26 505	99.85 296	97.94 324	97.91 518	99.03 432

FA-MVS(test-final)		98.52 365	98.32 378	99.10 364	99.48 350	98.67 363	99.77 19	98.60 477	97.35 471	99.63 238	99.80 109	93.07 455	99.84 313	97.92 325	99.30 432	98.78 467

ambc				99.20 349	99.35 390	98.53 388	99.17 220	99.46 357		99.67 216	99.80 109	98.46 243	99.70 446	97.92 325	99.70 332	99.38 333

USDC		98.96 307	98.93 296	99.05 374	99.54 316	97.99 428	97.07 509	99.80 143	98.21 410	99.75 165	99.77 146	98.43 246	99.64 485	97.90 327	99.88 202	99.51 271

OPM-MVS		99.26 214	99.13 226	99.63 175	99.70 223	99.61 154	98.58 376	99.48 351	98.50 375	99.52 290	99.63 266	99.14 118	99.76 414	97.89 328	99.77 290	99.51 271
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 201	99.17 218	99.77 80	99.69 231	99.80 51	99.14 233	99.31 406	99.16 272	99.62 248	99.61 286	98.35 257	99.91 185	97.88 329	99.72 325	99.61 203
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	99.70 223	99.79 54	99.14 233	99.61 273					99.92 153	97.88 329	99.72 325	99.77 81

c3_l		98.72 341	98.71 325	98.72 422	99.12 446	97.22 468	97.68 476	99.56 308	98.90 315	99.54 283	99.48 355	96.37 388	99.73 435	97.88 329	99.88 202	99.21 378

SIFT-ConvMatch		98.16 405	98.37 371	97.52 481	99.54 316	99.20 277	96.97 514	98.47 484	98.09 420	99.14 392	99.40 377	95.93 406	99.05 530	97.87 332	99.92 158	94.31 534

3Dnovator+	98.92 3	99.35 191	99.24 208	99.67 145	99.35 390	99.47 189	99.62 67	99.50 346	99.44 210	99.12 396	99.78 134	98.77 187	99.94 98	97.87 332	99.72 325	99.62 188

miper_ehance_all_eth		98.59 357	98.59 338	98.59 432	98.98 472	97.07 472	97.49 488	99.52 337	98.50 375	99.52 290	99.37 389	96.41 386	99.71 442	97.86 334	99.62 365	99.00 439

WTY-MVS		98.59 357	98.37 371	99.26 338	99.43 368	98.40 398	98.74 354	99.13 443	98.10 418	99.21 379	99.24 430	94.82 429	99.90 204	97.86 334	98.77 478	99.49 282

ArgMatch-SfM		99.14 259	99.06 252	99.36 301	99.59 276	99.14 291	98.45 403	99.81 135	98.67 352	99.50 300	99.42 369	98.55 220	99.84 313	97.85 336	99.73 317	99.11 404

APD_test1		99.36 189	99.28 197	99.61 191	99.89 40	99.89 10	99.32 158	99.74 189	99.18 263	99.69 201	99.75 164	98.41 249	99.84 313	97.85 336	99.70 332	99.10 407

SED-MVS		99.40 172	99.28 197	99.77 80	99.69 231	99.82 41	99.20 205	99.54 321	99.13 279	99.82 112	99.63 266	98.91 167	99.92 153	97.85 336	99.70 332	99.58 221

test_241102_TWO								99.54 321	99.13 279	99.76 160	99.63 266	98.32 263	99.92 153	97.85 336	99.69 341	99.75 89

MVS_111021_HR		99.12 265	99.02 268	99.40 283	99.50 340	99.11 295	97.92 460	99.71 207	98.76 343	99.08 401	99.47 359	99.17 111	99.54 502	97.85 336	99.76 295	99.54 248

SIFT-PointCN		98.28 389	98.47 355	97.71 478	99.70 223	98.91 333	96.98 513	99.70 216	97.90 437	99.36 338	99.35 399	95.51 416	99.83 336	97.84 341	99.89 191	94.39 533

MTAPA		99.35 191	99.20 214	99.80 64	99.81 112	99.81 47	99.33 155	99.53 332	99.27 246	99.42 321	99.63 266	98.21 277	99.95 81	97.83 342	99.79 278	99.65 158

MSC_two_6792asdad				99.74 103	99.03 465	99.53 176		99.23 424					99.92 153	97.77 343	99.69 341	99.78 77

No_MVS				99.74 103	99.03 465	99.53 176		99.23 424					99.92 153	97.77 343	99.69 341	99.78 77

TESTMET0.1,1		96.24 482	95.84 483	97.41 489	98.24 521	93.84 527	97.38 492	95.84 535	98.43 380	97.81 503	98.56 504	79.77 535	99.89 226	97.77 343	98.77 478	98.52 484

ACMH+	98.40 8	99.50 127	99.43 149	99.71 128	99.86 60	99.76 70	99.32 158	99.77 170	99.53 187	99.77 151	99.76 156	99.26 97	99.78 394	97.77 343	99.88 202	99.60 208

IU-MVS					99.69 231	99.77 63		99.22 427	97.50 462	99.69 201				97.75 347	99.70 332	99.77 81

114514_t		98.49 370	98.11 399	99.64 167	99.73 197	99.58 165	99.24 194	99.76 178	89.94 541	99.42 321	99.56 321	97.76 317	99.86 277	97.74 348	99.82 255	99.47 290

MASt3R-SfM		98.45 375	98.51 350	98.26 456	99.32 405	97.43 460	97.43 491	99.69 225	94.97 521	99.75 165	99.41 371	98.49 238	99.75 425	97.73 349	99.79 278	97.61 519

DVP-MVS++		99.38 179	99.25 206	99.77 80	99.03 465	99.77 63	99.74 27	99.61 273	99.18 263	99.76 160	99.61 286	99.00 149	99.92 153	97.72 350	99.60 376	99.62 188

test_0728_THIRD									99.18 263	99.62 248	99.61 286	98.58 215	99.91 185	97.72 350	99.80 272	99.77 81

EGC-MVSNET		89.05 513	85.52 516	99.64 167	99.89 40	99.78 57	99.56 87	99.52 337	24.19 550	49.96 552	99.83 83	99.15 115	99.92 153	97.71 352	99.85 231	99.21 378

miper_enhance_ethall		98.03 412	97.94 414	98.32 449	98.27 520	96.43 489	96.95 515	99.41 370	96.37 501	99.43 318	98.96 474	94.74 430	99.69 453	97.71 352	99.62 365	98.83 461

TSAR-MVS + MP.		99.34 196	99.24 208	99.63 175	99.82 99	99.37 231	99.26 187	99.35 391	98.77 340	99.57 266	99.70 207	99.27 96	99.88 241	97.71 352	99.75 303	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		97.56 437	97.28 443	98.40 443	98.37 517	96.75 482	97.24 500	99.37 386	97.31 473	99.41 327	99.22 433	87.30 510	99.37 517	97.70 355	99.62 365	99.08 419

MP-MVS-pluss		99.14 259	98.92 300	99.80 64	99.83 90	99.83 33	98.61 369	99.63 262	96.84 493	99.44 314	99.58 309	98.81 177	99.91 185	97.70 355	99.82 255	99.67 135
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 208	99.11 233	99.79 72	99.75 182	99.81 47	98.95 312	99.53 332	98.27 407	99.53 288	99.73 177	98.75 190	99.87 257	97.70 355	99.83 245	99.68 126

UnsupCasMVSNet_bld		98.55 361	98.27 384	99.40 283	99.56 310	99.37 231	97.97 456	99.68 230	97.49 463	99.08 401	99.35 399	95.41 420	99.82 359	97.70 355	98.19 509	99.01 438

MVS_111021_LR		99.13 262	99.03 267	99.42 270	99.58 286	99.32 244	97.91 462	99.73 194	98.68 350	99.31 356	99.48 355	99.09 127	99.66 476	97.70 355	99.77 290	99.29 364

IS-MVSNet		99.03 288	98.85 309	99.55 221	99.80 123	99.25 259	99.73 30	99.15 439	99.37 229	99.61 255	99.71 197	94.73 431	99.81 376	97.70 355	99.88 202	99.58 221

aaatest				99.74 103	99.76 164	99.65 129	99.38 132	99.78 165	99.58 181	99.81 119	99.66 241		99.90 204	97.69 361	99.79 278	99.67 135

MED-MVS		99.51 124	99.42 152	99.80 64	99.76 164	99.65 129	99.38 132	99.78 165	99.77 108	99.81 119	99.78 134	99.02 147	99.90 204	97.69 361	99.76 295	99.85 50

aaEdge-Enhanced		99.26 214	99.10 242	99.73 113	99.60 270	99.65 129	98.75 353	99.45 362	99.31 240	99.65 227	99.66 241	98.00 300	99.86 277	97.69 361	99.79 278	99.67 135

test-LLR		97.15 456	96.95 458	97.74 475	98.18 524	95.02 518	97.38 492	96.10 527	98.00 425	97.81 503	98.58 501	90.04 501	99.91 185	97.69 361	98.78 476	98.31 493

test-mter		96.23 483	95.73 486	97.74 475	98.18 524	95.02 518	97.38 492	96.10 527	97.90 437	97.81 503	98.58 501	79.12 538	99.91 185	97.69 361	98.78 476	98.31 493

MonoMVSNet		98.23 396	98.32 378	97.99 462	98.97 473	96.62 484	99.49 107	98.42 487	99.62 165	99.40 332	99.79 121	95.51 416	98.58 539	97.68 366	95.98 541	98.76 470

SP-DiffGlue		98.47 372	98.43 364	98.59 432	97.44 542	98.59 379	98.01 448	99.36 390	99.00 296	99.06 405	99.20 439	97.01 361	99.25 521	97.64 367	99.15 449	97.92 515

SIFT-NCMNet		98.18 401	98.46 357	97.36 493	99.67 247	99.19 280	96.33 531	98.99 454	98.83 327	99.62 248	99.63 266	95.41 420	99.33 518	97.64 367	100.00 1	93.54 545

XVS		99.27 212	99.11 233	99.75 98	99.71 207	99.71 101	99.37 140	99.61 273	99.29 242	98.76 440	99.47 359	98.47 239	99.88 241	97.62 369	99.73 317	99.67 135

X-MVStestdata		96.09 487	94.87 502	99.75 98	99.71 207	99.71 101	99.37 140	99.61 273	99.29 242	98.76 440	61.30 559	98.47 239	99.88 241	97.62 369	99.73 317	99.67 135

SMA-MVS		99.19 242	99.00 278	99.73 113	99.46 360	99.73 90	99.13 240	99.52 337	97.40 468	99.57 266	99.64 250	98.93 161	99.83 336	97.61 371	99.79 278	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		96.71 467	96.79 466	96.46 519	98.90 477	90.71 548	99.41 122	98.68 469	94.69 526	98.14 487	99.34 403	86.32 520	99.80 386	97.60 372	98.07 516	98.88 456

PVSNet	97.47 15	98.42 378	98.44 362	98.35 446	99.46 360	96.26 493	96.70 525	99.34 395	97.68 453	99.00 411	99.13 445	97.40 339	99.72 437	97.59 373	99.68 346	99.08 419

SIFT-PCN-Cal		98.24 394	98.51 350	97.43 488	99.65 254	98.64 373	97.09 506	99.35 391	98.16 414	99.69 201	99.52 339	95.59 411	99.83 336	97.57 374	100.00 1	93.81 541

new_pmnet		98.88 322	98.89 305	98.84 409	99.70 223	97.62 448	98.15 430	99.50 346	97.98 428	99.62 248	99.54 332	98.15 283	99.94 98	97.55 375	99.84 237	98.95 444

IB-MVS	95.41 20	95.30 502	94.46 508	97.84 471	98.76 499	95.33 513	97.33 495	96.07 529	96.02 505	95.37 539	97.41 530	76.17 545	99.96 69	97.54 376	95.44 544	98.22 500
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		99.24 220	99.11 233	99.61 191	98.38 516	99.79 54	99.57 85	99.68 230	99.61 170	99.15 390	99.71 197	98.70 197	99.91 185	97.54 376	99.68 346	99.13 403

ZNCC-MVS		99.22 232	99.04 265	99.77 80	99.76 164	99.73 90	99.28 177	99.56 308	98.19 412	99.14 392	99.29 414	98.84 176	99.92 153	97.53 378	99.80 272	99.64 170

CP-MVS		99.23 223	99.05 259	99.75 98	99.66 250	99.66 123	99.38 132	99.62 265	98.38 388	99.06 405	99.27 418	98.79 182	99.94 98	97.51 379	99.82 255	99.66 149

SD-MVS		99.01 297	99.30 188	98.15 458	99.50 340	99.40 220	98.94 314	99.61 273	99.22 259	99.75 165	99.82 91	99.54 55	95.51 549	97.48 380	99.87 216	99.54 248
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		97.97 417	98.24 386	97.14 504	99.59 276	98.71 360	96.75 522	99.56 308	97.02 487	97.91 496	99.27 418	96.85 368	98.39 540	97.47 381	99.76 295	94.31 534

PMMVS		98.49 370	98.29 383	99.11 362	98.96 474	98.42 397	97.54 483	99.32 402	97.53 460	98.47 464	98.15 517	97.88 306	99.82 359	97.46 382	99.24 443	99.09 413

DeepC-MVS_fast	98.47 5	99.23 223	99.12 230	99.56 214	99.28 416	99.22 270	98.99 300	99.40 377	99.08 285	99.58 263	99.64 250	98.90 170	99.83 336	97.44 383	99.75 303	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		99.25 216	99.08 246	99.76 87	99.73 197	99.70 109	99.31 164	99.59 291	98.36 390	99.36 338	99.37 389	98.80 181	99.91 185	97.43 384	99.75 303	99.68 126

ACMMPR		99.23 223	99.06 252	99.76 87	99.74 193	99.69 114	99.31 164	99.59 291	98.36 390	99.35 342	99.38 385	98.61 210	99.93 120	97.43 384	99.75 303	99.67 135

Vis-MVSNet (Re-imp)		98.77 335	98.58 341	99.34 309	99.78 146	98.88 339	99.61 73	99.56 308	99.11 283	99.24 372	99.56 321	93.00 457	99.78 394	97.43 384	99.89 191	99.35 343

MIMVSNet		98.43 377	98.20 390	99.11 362	99.53 325	98.38 402	99.58 82	98.61 474	98.96 301	99.33 348	99.76 156	90.92 485	99.81 376	97.38 387	99.76 295	99.15 395

WB-MVSnew		98.34 388	98.14 397	98.96 382	98.14 527	97.90 436	98.27 417	97.26 522	98.63 357	98.80 435	98.00 520	97.77 315	99.90 204	97.37 388	98.98 464	99.09 413

SP-MNN		97.94 420	97.82 421	98.31 451	98.30 519	97.67 447	97.81 467	97.93 508	98.14 415	97.16 523	98.64 500	96.31 391	99.21 523	97.34 389	98.75 482	98.05 509

XVG-OURS-SEG-HR		99.16 253	98.99 285	99.66 153	99.84 81	99.64 136	98.25 420	99.73 194	98.39 386	99.63 238	99.43 367	99.70 31	99.90 204	97.34 389	98.64 490	99.44 312

COLMAP_ROB	98.06 12	99.45 151	99.37 164	99.70 133	99.83 90	99.70 109	99.38 132	99.78 165	99.53 187	99.67 216	99.78 134	99.19 108	99.86 277	97.32 391	99.87 216	99.55 236
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		93.18 508	91.66 512	97.73 477	95.83 547	95.29 514	95.30 537	95.90 533	93.59 529	90.58 548	94.40 556	77.87 540	99.77 407	97.31 392	84.20 546	98.15 505

MCST-MVS		99.02 291	98.81 316	99.65 160	99.58 286	99.49 184	98.58 376	99.07 445	98.40 385	99.04 407	99.25 424	98.51 236	99.80 386	97.31 392	99.51 400	99.65 158

ArgMatch-Sym		99.06 280	98.96 292	99.35 305	99.62 265	99.22 270	98.34 410	99.79 152	98.80 333	99.50 300	99.29 414	98.30 265	99.75 425	97.30 394	99.71 329	99.08 419

region2R		99.23 223	99.05 259	99.77 80	99.76 164	99.70 109	99.31 164	99.59 291	98.41 383	99.32 351	99.36 394	98.73 194	99.93 120	97.29 395	99.74 310	99.67 135

APD-MVS_3200maxsize		99.31 203	99.16 219	99.74 103	99.53 325	99.75 79	99.27 182	99.61 273	99.19 262	99.57 266	99.64 250	98.76 188	99.90 204	97.29 395	99.62 365	99.56 232

TAPA-MVS	97.92 13	98.03 412	97.55 434	99.46 256	99.47 356	99.44 205	98.50 393	99.62 265	86.79 542	99.07 404	99.26 422	98.26 270	99.62 488	97.28 397	99.73 317	99.31 359
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 212	99.11 233	99.73 113	99.54 316	99.74 87	99.26 187	99.62 265	99.16 272	99.52 290	99.64 250	98.41 249	99.91 185	97.27 398	99.61 373	99.54 248

RE-MVS-def			99.13 226		99.54 316	99.74 87	99.26 187	99.62 265	99.16 272	99.52 290	99.64 250	98.57 216		97.27 398	99.61 373	99.54 248

testing11		96.05 489	95.41 492	97.97 464	98.78 496	95.27 515	98.59 374	98.23 498	98.86 321	96.56 530	96.91 542	75.20 547	99.69 453	97.26 400	98.29 504	98.93 448

test_yl		98.25 392	97.95 410	99.13 360	99.17 438	98.47 391	99.00 292	98.67 471	98.97 299	99.22 377	99.02 465	91.31 479	99.69 453	97.26 400	98.93 467	99.24 370

DCV-MVSNet		98.25 392	97.95 410	99.13 360	99.17 438	98.47 391	99.00 292	98.67 471	98.97 299	99.22 377	99.02 465	91.31 479	99.69 453	97.26 400	98.93 467	99.24 370

PHI-MVS		99.11 270	98.95 294	99.59 198	99.13 444	99.59 160	99.17 220	99.65 250	97.88 441	99.25 369	99.46 362	98.97 157	99.80 386	97.26 400	99.82 255	99.37 337

tfpnnormal		99.43 159	99.38 161	99.60 195	99.87 55	99.75 79	99.59 80	99.78 165	99.71 123	99.90 67	99.69 216	98.85 175	99.90 204	97.25 404	99.78 286	99.15 395

PatchmatchNet		97.65 433	97.80 422	97.18 500	98.82 491	92.49 535	99.17 220	98.39 491	98.12 417	98.79 437	99.58 309	90.71 492	99.89 226	97.23 405	99.41 418	99.16 393
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 303	98.80 319	99.56 214	99.25 422	99.43 209	98.54 387	99.27 414	98.58 364	98.80 435	99.43 367	98.53 230	99.70 446	97.22 406	99.59 380	99.54 248

SIFT-UM-Cal		98.18 401	98.45 360	97.37 492	99.59 276	98.95 324	96.76 521	99.39 380	98.39 386	99.46 311	99.31 407	96.23 397	99.24 522	97.21 407	99.70 332	93.90 540

testing3		96.48 475	95.63 488	99.01 377	99.23 426	97.81 440	98.90 320	99.10 444	98.72 345	97.84 502	97.92 521	72.44 551	99.85 296	97.21 407	99.33 428	99.35 343

HPM-MVS		99.25 216	99.07 250	99.78 76	99.81 112	99.75 79	99.61 73	99.67 235	97.72 451	99.35 342	99.25 424	99.23 103	99.92 153	97.21 407	99.82 255	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		92.36 510	90.68 514	97.39 490	94.94 551	94.41 523	94.21 541	95.89 534	92.87 532	88.87 550	93.49 558	75.30 546	99.76 414	97.19 410	83.41 548	98.02 510

0.4-1-1-0.2		92.59 509	91.07 513	97.15 503	94.73 553	93.68 529	93.50 542	95.91 531	92.68 533	90.48 549	93.52 557	77.77 541	99.75 425	97.19 410	83.88 547	98.01 511

MatchFormer		99.03 288	99.02 268	99.08 370	99.56 310	98.47 391	98.57 380	99.90 64	98.13 416	99.80 126	99.75 164	98.34 259	99.84 313	97.18 412	99.90 175	98.92 450

mPP-MVS		99.19 242	99.00 278	99.76 87	99.76 164	99.68 117	99.38 132	99.54 321	98.34 400	99.01 410	99.50 346	98.53 230	99.93 120	97.18 412	99.78 286	99.66 149

ACMMP		99.25 216	99.08 246	99.74 103	99.79 137	99.68 117	99.50 102	99.65 250	98.07 422	99.52 290	99.69 216	98.57 216	99.92 153	97.18 412	99.79 278	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		96.23 483	95.74 485	97.70 479	98.86 484	95.59 510	98.66 365	98.14 500	98.96 301	97.67 510	97.06 538	76.78 543	98.92 533	97.10 415	98.41 501	98.58 480

thisisatest0515		96.98 460	96.42 469	98.66 428	99.42 373	97.47 454	97.27 497	94.30 543	97.24 476	99.15 390	98.86 483	85.01 522	99.87 257	97.10 415	99.39 420	98.63 474

XVG-ACMP-BASELINE		99.23 223	99.10 242	99.63 175	99.82 99	99.58 165	98.83 335	99.72 203	98.36 390	99.60 258	99.71 197	98.92 164	99.91 185	97.08 417	99.84 237	99.40 327

MSDG		99.08 275	98.98 288	99.37 295	99.60 270	99.13 292	97.54 483	99.74 189	98.84 326	99.53 288	99.55 330	99.10 125	99.79 390	97.07 418	99.86 224	99.18 388

SteuartSystems-ACMMP		99.30 204	99.14 224	99.76 87	99.87 55	99.66 123	99.18 215	99.60 285	98.55 367	99.57 266	99.67 235	99.03 146	99.94 98	97.01 419	99.80 272	99.69 119
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 485	95.78 484	97.49 482	98.53 510	93.83 528	98.04 445	93.94 546	98.96 301	98.46 465	98.17 516	79.86 533	99.87 257	96.99 420	99.06 456	98.78 467

EPMVS		96.53 472	96.32 470	97.17 502	98.18 524	92.97 533	99.39 129	89.95 552	98.21 410	98.61 453	99.59 306	86.69 519	99.72 437	96.99 420	99.23 445	98.81 464

SIFT-CM-Cal		97.96 419	98.15 396	97.39 490	99.61 267	99.15 289	96.75 522	98.41 490	98.04 424	99.03 408	99.54 332	95.24 423	99.41 515	96.97 422	99.80 272	93.61 544

MSP-MVS		99.04 287	98.79 320	99.81 54	99.78 146	99.73 90	99.35 148	99.57 303	98.54 370	99.54 283	98.99 467	96.81 369	99.93 120	96.97 422	99.53 396	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++		98.96 307	98.70 329	99.74 103	99.52 332	99.71 101	98.86 327	99.19 434	98.47 379	98.59 455	99.06 457	98.08 292	99.91 185	96.94 424	99.60 376	99.60 208

SR-MVS		99.19 242	99.00 278	99.74 103	99.51 334	99.72 95	99.18 215	99.60 285	98.85 322	99.47 307	99.58 309	98.38 254	99.92 153	96.92 425	99.54 394	99.57 228

PGM-MVS		99.20 239	99.01 274	99.77 80	99.75 182	99.71 101	99.16 226	99.72 203	97.99 427	99.42 321	99.60 296	98.81 177	99.93 120	96.91 426	99.74 310	99.66 149

HY-MVS	98.23 9	98.21 400	97.95 410	98.99 378	99.03 465	98.24 406	99.61 73	98.72 467	96.81 494	98.73 442	99.51 343	94.06 440	99.86 277	96.91 426	98.20 507	98.86 458

MDTV_nov1_ep13			97.73 428		98.70 504	90.83 546	99.15 229	98.02 504	98.51 374	98.82 432	99.61 286	90.98 484	99.66 476	96.89 428	98.92 469

SIFT-NCM-Cal		98.18 401	98.41 366	97.48 483	99.57 296	99.28 250	97.26 498	98.08 501	98.30 405	99.23 373	99.39 382	97.13 355	99.04 531	96.86 429	99.86 224	94.12 537

GST-MVS		99.16 253	98.96 292	99.75 98	99.73 197	99.73 90	99.20 205	99.55 315	98.22 409	99.32 351	99.35 399	98.65 206	99.91 185	96.86 429	99.74 310	99.62 188

test_post1							99.14 233				51.63 561	89.54 504	99.82 359	96.86 429

SCA		98.11 407	98.36 373	97.36 493	99.20 432	92.99 532	98.17 427	98.49 483	98.24 408	99.10 400	99.57 317	96.01 403	99.94 98	96.86 429	99.62 365	99.14 400

UBG		96.53 472	95.95 479	98.29 454	98.87 483	96.31 492	98.48 397	98.07 502	98.83 327	97.32 516	96.54 550	79.81 534	99.62 488	96.84 433	98.74 483	98.95 444

XVG-OURS		99.21 237	99.06 252	99.65 160	99.82 99	99.62 144	97.87 464	99.74 189	98.36 390	99.66 223	99.68 229	99.71 28	99.90 204	96.84 433	99.88 202	99.43 318

LCM-MVSNet-Re		99.28 208	99.15 223	99.67 145	99.33 404	99.76 70	99.34 149	99.97 21	98.93 310	99.91 62	99.79 121	98.68 199	99.93 120	96.80 435	99.56 385	99.30 361

RPSCF		99.18 246	99.02 268	99.64 167	99.83 90	99.85 21	99.44 119	99.82 122	98.33 402	99.50 300	99.78 134	97.90 304	99.65 483	96.78 436	99.83 245	99.44 312

旧先验2							97.94 458		95.33 516	98.94 416			99.88 241	96.75 437

MDTV_nov1_ep13_2view						91.44 543	99.14 233		97.37 470	99.21 379		91.78 476		96.75 437		99.03 432

CLD-MVS		98.76 336	98.57 342	99.33 312	99.57 296	98.97 320	97.53 485	99.55 315	96.41 499	99.27 363	99.13 445	99.07 134	99.78 394	96.73 439	99.89 191	99.23 373
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 408	97.98 408	98.48 439	99.27 418	96.48 487	99.40 127	99.07 445	98.81 331	99.23 373	99.57 317	90.11 500	99.87 257	96.69 440	99.64 359	99.09 413

baseline2		96.83 463	96.28 471	98.46 441	99.09 458	96.91 477	98.83 335	93.87 547	97.23 477	96.23 535	98.36 511	88.12 509	99.90 204	96.68 441	98.14 512	98.57 482

cascas		96.99 459	96.82 465	97.48 483	97.57 540	95.64 507	96.43 529	99.56 308	91.75 537	97.13 524	97.61 529	95.58 412	98.63 537	96.68 441	99.11 452	98.18 504

PC_three_1452									97.56 456	99.68 208	99.41 371	99.09 127	97.09 545	96.66 443	99.60 376	99.62 188

LPG-MVS_test		99.22 232	99.05 259	99.74 103	99.82 99	99.63 142	99.16 226	99.73 194	97.56 456	99.64 233	99.69 216	99.37 78	99.89 226	96.66 443	99.87 216	99.69 119

LGP-MVS_train				99.74 103	99.82 99	99.63 142		99.73 194	97.56 456	99.64 233	99.69 216	99.37 78	99.89 226	96.66 443	99.87 216	99.69 119

ETVMVS		96.14 486	95.22 497	98.89 403	98.80 492	98.01 427	98.66 365	98.35 494	98.71 347	97.18 521	96.31 555	74.23 550	99.75 425	96.64 446	98.13 515	98.90 453

SIFT-MNN		97.55 439	97.74 427	96.98 508	99.38 380	98.85 345	96.92 518	98.61 474	98.36 390	98.63 451	99.10 453	92.51 464	97.85 543	96.63 447	99.48 407	94.25 536

TinyColmap		98.97 304	98.93 296	99.07 371	99.46 360	98.19 411	97.75 469	99.75 183	98.79 335	99.54 283	99.70 207	98.97 157	99.62 488	96.63 447	99.83 245	99.41 324

LF4IMVS		99.01 297	98.92 300	99.27 334	99.71 207	99.28 250	98.59 374	99.77 170	98.32 403	99.39 334	99.41 371	98.62 208	99.84 313	96.62 449	99.84 237	98.69 473

NCCC		98.82 329	98.57 342	99.58 202	99.21 429	99.31 245	98.61 369	99.25 419	98.65 354	98.43 466	99.26 422	97.86 307	99.81 376	96.55 450	99.27 438	99.61 203

OPU-MVS				99.29 326	99.12 446	99.44 205	99.20 205				99.40 377	99.00 149	98.84 535	96.54 451	99.60 376	99.58 221

F-COLMAP		98.74 338	98.45 360	99.62 184	99.57 296	99.47 189	98.84 332	99.65 250	96.31 502	98.93 417	99.19 441	97.68 322	99.87 257	96.52 452	99.37 423	99.53 257

SIFT-UMatch		98.07 410	98.27 384	97.46 487	99.57 296	98.99 315	96.93 517	99.02 450	98.53 371	99.26 367	99.23 432	95.43 419	99.31 519	96.51 453	99.91 171	94.09 538

testing99		95.86 494	95.19 498	97.87 469	98.76 499	95.03 517	98.62 368	98.44 486	98.68 350	96.67 528	96.66 549	74.31 549	99.69 453	96.51 453	98.03 517	98.90 453

ADS-MVSNet2		97.78 427	97.66 432	98.12 460	99.14 442	95.36 512	99.22 202	98.75 466	96.97 488	98.25 474	99.64 250	90.90 486	99.94 98	96.51 453	99.56 385	99.08 419

ADS-MVSNet		97.72 432	97.67 431	97.86 470	99.14 442	94.65 521	99.22 202	98.86 458	96.97 488	98.25 474	99.64 250	90.90 486	99.84 313	96.51 453	99.56 385	99.08 419

PatchMatch-RL		98.68 346	98.47 355	99.30 325	99.44 365	99.28 250	98.14 432	99.54 321	97.12 483	99.11 397	99.25 424	97.80 312	99.70 446	96.51 453	99.30 432	98.93 448

CMPMVS	77.52 23	98.50 368	98.19 393	99.41 280	98.33 518	99.56 169	99.01 286	99.59 291	95.44 514	99.57 266	99.80 109	95.64 409	99.46 514	96.47 458	99.92 158	99.21 378
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 490	95.32 495	98.02 461	98.76 499	95.39 511	98.38 408	98.65 473	98.82 329	96.84 525	96.71 548	75.06 548	99.71 442	96.46 459	98.23 506	98.98 441

SF-MVS		99.10 273	98.93 296	99.62 184	99.58 286	99.51 182	99.13 240	99.65 250	97.97 429	99.42 321	99.61 286	98.86 174	99.87 257	96.45 460	99.68 346	99.49 282

FE-MVS		97.85 422	97.42 439	99.15 355	99.44 365	98.75 357	99.77 19	98.20 499	95.85 507	99.33 348	99.80 109	88.86 506	99.88 241	96.40 461	99.12 451	98.81 464

DPE-MVS		99.14 259	98.92 300	99.82 46	99.57 296	99.77 63	98.74 354	99.60 285	98.55 367	99.76 160	99.69 216	98.23 275	99.92 153	96.39 462	99.75 303	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					97.59 538	89.02 554			93.47 530		98.30 512		99.84 313	96.38 463

AllTest		99.21 237	99.07 250	99.63 175	99.78 146	99.64 136	99.12 245	99.83 115	98.63 357	99.63 238	99.72 187	98.68 199	99.75 425	96.38 463	99.83 245	99.51 271

TestCases				99.63 175	99.78 146	99.64 136		99.83 115	98.63 357	99.63 238	99.72 187	98.68 199	99.75 425	96.38 463	99.83 245	99.51 271

testdata				99.42 270	99.51 334	98.93 329		99.30 409	96.20 503	98.87 427	99.40 377	98.33 262	99.89 226	96.29 466	99.28 435	99.44 312

dp		96.86 462	97.07 453	96.24 521	98.68 506	90.30 552	99.19 211	98.38 492	97.35 471	98.23 476	99.59 306	87.23 511	99.82 359	96.27 467	98.73 486	98.59 478

SP-NN		96.37 478	96.23 473	96.77 511	96.83 544	96.95 474	96.47 528	97.07 524	96.75 496	93.41 545	97.75 523	94.13 439	95.69 547	96.25 468	97.43 525	97.68 518

tpmvs		97.39 448	97.69 429	96.52 517	98.41 515	91.76 539	99.30 167	98.94 456	97.74 448	97.85 501	99.55 330	92.40 468	99.73 435	96.25 468	98.73 486	98.06 507

KD-MVS_2432*1600		95.89 491	95.41 492	97.31 497	94.96 549	93.89 525	97.09 506	99.22 427	97.23 477	98.88 424	99.04 460	79.23 536	99.54 502	96.24 470	96.81 530	98.50 488

miper_refine_blended		95.89 491	95.41 492	97.31 497	94.96 549	93.89 525	97.09 506	99.22 427	97.23 477	98.88 424	99.04 460	79.23 536	99.54 502	96.24 470	96.81 530	98.50 488

ACMP	97.51 14	99.05 284	98.84 311	99.67 145	99.78 146	99.55 173	98.88 323	99.66 240	97.11 484	99.47 307	99.60 296	99.07 134	99.89 226	96.18 472	99.85 231	99.58 221
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 318	98.72 324	99.44 263	99.39 377	99.42 212	98.58 376	99.64 258	97.31 473	99.44 314	99.62 276	98.59 213	99.69 453	96.17 473	99.79 278	99.22 375

DP-MVS Recon		98.50 368	98.23 387	99.31 321	99.49 345	99.46 197	98.56 383	99.63 262	94.86 524	98.85 429	99.37 389	97.81 311	99.59 495	96.08 474	99.44 412	98.88 456

tpm cat1		96.78 464	96.98 457	96.16 522	98.85 485	90.59 549	99.08 262	99.32 402	92.37 534	97.73 508	99.46 362	91.15 482	99.69 453	96.07 475	98.80 475	98.21 501

tpm2		96.35 479	96.22 474	96.73 515	98.88 482	91.75 540	99.21 204	98.51 481	93.27 531	97.89 497	99.21 437	84.83 523	99.70 446	96.04 476	98.18 510	98.75 471

dmvs_re		98.69 345	98.48 354	99.31 321	99.55 314	99.42 212	99.54 90	98.38 492	99.32 238	98.72 443	98.71 494	96.76 371	99.21 523	96.01 477	99.35 426	99.31 359

test_0402		99.22 232	99.14 224	99.45 259	99.79 137	99.43 209	99.28 177	99.68 230	99.54 185	99.40 332	99.56 321	99.07 134	99.82 359	96.01 477	99.96 91	99.11 404

ITE_SJBPF				99.38 290	99.63 261	99.44 205		99.73 194	98.56 365	99.33 348	99.53 335	98.88 171	99.68 465	96.01 477	99.65 357	99.02 437

test_prior2							97.95 457		97.87 442	98.05 489	99.05 458	97.90 304		95.99 480	99.49 405

testdata2													99.89 226	95.99 480

原ACMM1				99.37 295	99.47 356	98.87 344		99.27 414	96.74 497	98.26 473	99.32 404	97.93 303	99.82 359	95.96 482	99.38 421	99.43 318

新几何1				99.52 234	99.50 340	99.22 270		99.26 416	95.66 512	98.60 454	99.28 416	97.67 323	99.89 226	95.95 483	99.32 430	99.45 297

MP-MVS		99.06 280	98.83 313	99.76 87	99.76 164	99.71 101	99.32 158	99.50 346	98.35 396	98.97 413	99.48 355	98.37 255	99.92 153	95.95 483	99.75 303	99.63 176
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ALIKED-LG		98.78 333	98.66 331	99.14 358	99.02 471	99.40 220	98.74 354	99.79 152	98.62 361	99.18 385	99.38 385	97.54 333	99.77 407	95.94 485	99.74 310	98.25 498

testing222		95.60 501	94.59 506	98.61 430	98.66 507	97.45 457	98.54 387	97.90 510	98.53 371	96.54 531	96.47 552	70.62 554	99.81 376	95.91 486	98.15 511	98.56 483

wuyk23d		97.58 436	99.13 226	92.93 529	99.69 231	99.49 184	99.52 94	99.77 170	97.97 429	99.96 34	99.79 121	99.84 16	99.94 98	95.85 487	99.82 255	79.36 547

HQP_MVS		98.90 318	98.68 330	99.55 221	99.58 286	99.24 264	98.80 343	99.54 321	98.94 305	99.14 392	99.25 424	97.24 347	99.82 359	95.84 488	99.78 286	99.60 208

plane_prior5								99.54 321					99.82 359	95.84 488	99.78 286	99.60 208

SIFT-NN-CMatch		97.30 451	97.34 441	97.18 500	99.54 316	98.85 345	96.02 533	95.77 538	97.05 486	97.55 512	98.70 496	96.35 390	98.75 536	95.82 490	99.26 439	93.95 539

无先验							98.01 448	99.23 424	95.83 508				99.85 296	95.79 491		99.44 312

SIFT-NN-UMatch		97.18 455	97.24 447	97.01 507	99.57 296	98.65 370	96.33 531	97.31 521	97.07 485	97.48 513	98.73 493	94.39 436	98.87 534	95.75 492	98.50 498	93.50 546

CPTT-MVS		98.74 338	98.44 362	99.64 167	99.61 267	99.38 226	99.18 215	99.55 315	96.49 498	99.27 363	99.37 389	97.11 357	99.92 153	95.74 493	99.67 352	99.62 188

PLC	97.35 16	98.36 383	97.99 406	99.48 250	99.32 405	99.24 264	98.50 393	99.51 342	95.19 519	98.58 456	98.96 474	96.95 364	99.83 336	95.63 494	99.25 441	99.37 337
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 359	98.34 376	99.28 329	99.18 437	99.10 302	98.34 410	99.41 370	98.48 378	98.52 461	98.98 470	97.05 359	99.78 394	95.59 495	99.50 403	98.96 442

1314		98.00 415	97.90 418	98.27 455	98.90 477	97.45 457	99.30 167	99.06 447	94.98 520	97.21 520	99.12 449	98.43 246	99.67 471	95.58 496	98.56 493	97.71 517

PVSNet_0	95.53 19	95.85 495	95.31 496	97.47 485	98.78 496	93.48 531	95.72 534	99.40 377	96.18 504	97.37 515	97.73 524	95.73 408	99.58 496	95.49 497	81.40 549	99.36 340

MAR-MVS		98.24 394	97.92 416	99.19 350	98.78 496	99.65 129	99.17 220	99.14 441	95.36 515	98.04 490	98.81 489	97.47 336	99.72 437	95.47 498	99.06 456	98.21 501
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	98.23 396	97.89 419	99.26 338	99.19 434	99.26 256	99.65 62	99.69 225	91.33 539	98.14 487	99.77 146	98.28 267	99.96 69	95.41 499	99.55 389	98.58 480

train_agg		98.35 386	97.95 410	99.57 210	99.35 390	99.35 238	98.11 437	99.41 370	94.90 522	97.92 494	98.99 467	98.02 295	99.85 296	95.38 500	99.44 412	99.50 277

9.14			98.64 332		99.45 364		98.81 340	99.60 285	97.52 461	99.28 362	99.56 321	98.53 230	99.83 336	95.36 501	99.64 359

APD-MVS		98.87 323	98.59 338	99.71 128	99.50 340	99.62 144	99.01 286	99.57 303	96.80 495	99.54 283	99.63 266	98.29 266	99.91 185	95.24 502	99.71 329	99.61 203
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 490								95.20 503

AdaColmap		98.60 354	98.35 375	99.38 290	99.12 446	99.22 270	98.67 363	99.42 369	97.84 446	98.81 433	99.27 418	97.32 345	99.81 376	95.14 504	99.53 396	99.10 407

test9_res														95.10 505	99.44 412	99.50 277

CDPH-MVS		98.56 360	98.20 390	99.61 191	99.50 340	99.46 197	98.32 414	99.41 370	95.22 517	99.21 379	99.10 453	98.34 259	99.82 359	95.09 506	99.66 355	99.56 232

SIFT-NN-NCMNet		97.22 453	97.27 445	97.07 506	99.64 256	99.20 277	96.53 527	95.91 531	96.91 490	97.38 514	98.95 476	96.01 403	98.29 541	94.87 507	99.21 447	93.73 543

BH-untuned		98.22 398	98.09 400	98.58 435	99.38 380	97.24 467	98.55 384	98.98 455	97.81 447	99.20 384	98.76 491	97.01 361	99.65 483	94.83 508	98.33 502	98.86 458

BP-MVS														94.73 509

HQP-MVS		98.36 383	98.02 405	99.39 286	99.31 407	98.94 326	97.98 453	99.37 386	97.45 464	98.15 483	98.83 486	96.67 373	99.70 446	94.73 509	99.67 352	99.53 257

QAPM		98.40 381	97.99 406	99.65 160	99.39 377	99.47 189	99.67 53	99.52 337	91.70 538	98.78 439	99.80 109	98.55 220	99.95 81	94.71 511	99.75 303	99.53 257

agg_prior2														94.58 512	99.46 411	99.50 277

myMVS_eth3d		95.63 499	94.73 503	98.34 448	98.50 512	96.36 490	98.60 371	99.21 430	97.89 439	96.76 526	96.37 553	72.10 552	99.57 497	94.38 513	98.73 486	99.09 413

BH-RMVSNet		98.41 379	98.14 397	99.21 346	99.21 429	98.47 391	98.60 371	98.26 497	98.35 396	98.93 417	99.31 407	97.20 353	99.66 476	94.32 514	99.10 453	99.51 271

E-PMN		97.14 458	97.43 438	96.27 520	98.79 494	91.62 541	95.54 535	99.01 453	99.44 210	98.88 424	99.12 449	92.78 458	99.68 465	94.30 515	99.03 461	97.50 520

MG-MVS		98.52 365	98.39 369	98.94 386	99.15 441	97.39 462	98.18 424	99.21 430	98.89 318	99.23 373	99.63 266	97.37 342	99.74 432	94.22 516	99.61 373	99.69 119

ALIKED-MNN		98.03 412	97.78 425	98.78 417	98.84 487	98.97 320	98.16 429	99.74 189	97.31 473	96.60 529	98.85 484	96.61 375	99.48 511	94.16 517	99.77 290	97.91 516

API-MVS		98.38 382	98.39 369	98.35 446	98.83 488	99.26 256	99.14 233	99.18 435	98.59 363	98.66 448	98.78 490	98.61 210	99.57 497	94.14 518	99.56 385	96.21 528

PAPM_NR		98.36 383	98.04 403	99.33 312	99.48 350	98.93 329	98.79 346	99.28 413	97.54 459	98.56 460	98.57 503	97.12 356	99.69 453	94.09 519	98.90 473	99.38 333

ZD-MVS					99.43 368	99.61 154		99.43 367	96.38 500	99.11 397	99.07 456	97.86 307	99.92 153	94.04 520	99.49 405

DPM-MVS		98.28 389	97.94 414	99.32 317	99.36 386	99.11 295	97.31 496	98.78 465	96.88 491	98.84 430	99.11 452	97.77 315	99.61 493	94.03 521	99.36 424	99.23 373

gg-mvs-nofinetune		95.87 493	95.17 499	97.97 464	98.19 523	96.95 474	99.69 45	89.23 553	99.89 56	96.24 534	99.94 19	81.19 528	99.51 509	93.99 522	98.20 507	97.44 521

XFeat-MNN		96.67 468	96.56 467	96.98 508	96.73 545	95.62 509	94.54 540	98.93 457	97.42 467	98.18 478	98.67 499	91.60 477	99.12 525	93.88 523	99.10 453	96.21 528

PMVS	92.94 21	98.82 329	98.81 316	98.85 407	99.84 81	97.99 428	99.20 205	99.47 354	99.71 123	99.42 321	99.82 91	98.09 290	99.47 512	93.88 523	99.85 231	99.07 425
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 461	97.28 443	95.99 524	98.76 499	91.03 545	95.26 538	98.61 474	99.34 234	98.92 420	98.88 482	93.79 444	99.66 476	92.87 525	99.05 458	97.30 524

BH-w/o		97.20 454	97.01 456	97.76 473	99.08 459	95.69 506	98.03 447	98.52 480	95.76 510	97.96 493	98.02 518	95.62 410	99.47 512	92.82 526	97.25 529	98.12 506

TR-MVS		97.44 445	97.15 450	98.32 449	98.53 510	97.46 455	98.47 398	97.91 509	96.85 492	98.21 477	98.51 507	96.42 384	99.51 509	92.16 527	97.29 528	97.98 512

ALIKED-NN		96.66 469	96.26 472	97.88 468	97.49 541	98.59 379	96.71 524	99.15 439	95.50 513	93.58 544	98.39 510	94.52 435	97.74 544	92.05 528	98.94 466	97.29 525

OpenMVS_ROB	97.31 17	97.36 450	96.84 463	98.89 403	99.29 413	99.45 203	98.87 326	99.48 351	86.54 544	99.44 314	99.74 172	97.34 343	99.86 277	91.61 529	99.28 435	97.37 523

GG-mvs-BLEND				97.36 493	97.59 538	96.87 478	99.70 38	88.49 554		94.64 542	97.26 535	80.66 530	99.12 525	91.50 530	96.50 538	96.08 531

DeepMVS_CX				97.98 463	99.69 231	96.95 474		99.26 416	75.51 547	95.74 537	98.28 513	96.47 382	99.62 488	91.23 531	97.89 519	97.38 522

PAPR		97.56 437	97.07 453	99.04 375	98.80 492	98.11 419	97.63 478	99.25 419	94.56 528	98.02 492	98.25 514	97.43 338	99.68 465	90.90 532	98.74 483	99.33 350

MVS		95.72 497	94.63 505	98.99 378	98.56 509	97.98 433	99.30 167	98.86 458	72.71 548	97.30 517	99.08 455	98.34 259	99.74 432	89.21 533	98.33 502	99.26 367

SIFT-NN		94.78 505	94.89 501	94.45 527	98.23 522	97.29 465	94.93 539	95.84 535	95.82 509	94.78 541	97.12 536	90.26 498	92.28 551	88.91 534	98.14 512	93.77 542

UWE-MVS-28		95.64 498	95.47 490	96.14 523	97.98 530	90.39 550	98.49 396	95.81 537	99.02 294	98.03 491	98.19 515	84.49 525	99.28 520	88.75 535	98.47 499	98.75 471

thres600view7		96.60 471	96.16 475	97.93 466	99.63 261	96.09 500	99.18 215	97.57 515	98.77 340	98.72 443	97.32 533	87.04 513	99.72 437	88.57 536	98.62 491	97.98 512

FPMVS		96.32 480	95.50 489	98.79 415	99.60 270	98.17 414	98.46 402	98.80 464	97.16 481	96.28 532	99.63 266	82.19 527	99.09 528	88.45 537	98.89 474	99.10 407

XFeat-NN		93.89 507	93.91 509	93.83 528	95.49 548	92.69 534	90.85 543	97.98 505	94.69 526	95.08 540	96.98 539	88.36 508	94.23 550	88.42 538	97.34 526	94.57 532

PCF-MVS	96.03 18	96.73 466	95.86 482	99.33 312	99.44 365	99.16 287	96.87 519	99.44 363	86.58 543	98.95 415	99.40 377	94.38 437	99.88 241	87.93 539	99.80 272	98.95 444
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 477	96.03 478	97.47 485	99.63 261	95.93 501	99.18 215	97.57 515	98.75 344	98.70 446	97.31 534	87.04 513	99.67 471	87.62 540	98.51 495	96.81 526

tfpn200view9		96.30 481	95.89 480	97.53 480	99.58 286	96.11 498	99.00 292	97.54 518	98.43 380	98.52 461	96.98 539	86.85 515	99.67 471	87.62 540	98.51 495	96.81 526

thres400		96.40 476	95.89 480	97.92 467	99.58 286	96.11 498	99.00 292	97.54 518	98.43 380	98.52 461	96.98 539	86.85 515	99.67 471	87.62 540	98.51 495	97.98 512

thres200		96.09 487	95.68 487	97.33 496	99.48 350	96.22 495	98.53 389	97.57 515	98.06 423	98.37 469	96.73 547	86.84 517	99.61 493	86.99 543	98.57 492	96.16 530

MVE	92.54 22	96.66 469	96.11 476	98.31 451	99.68 240	97.55 450	97.94 458	95.60 539	99.37 229	90.68 547	98.70 496	96.56 377	98.61 538	86.94 544	99.55 389	98.77 469
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 452	96.83 464	98.59 432	99.46 360	97.55 450	99.25 193	96.84 526	98.78 337	97.24 519	97.67 525	97.11 357	98.97 532	86.59 545	98.54 494	99.27 365

GLUNet-SfM		95.26 503	95.06 500	95.87 525	94.84 552	90.39 550	90.24 545	99.92 47	92.30 535	99.16 387	99.25 424	94.69 432	98.01 542	85.55 546	99.62 365	99.21 378

PAPM		95.61 500	94.71 504	98.31 451	99.12 446	96.63 483	96.66 526	98.46 485	90.77 540	96.25 533	98.68 498	93.01 456	99.69 453	81.60 547	97.86 521	98.62 475

SD_0403		97.42 446	96.90 462	98.98 380	99.54 316	97.90 436	99.52 94	99.54 321	99.34 234	97.87 499	98.85 484	98.72 195	99.64 485	78.93 548	99.83 245	99.40 327

dongtai		89.37 512	88.91 515	90.76 530	99.19 434	77.46 556	95.47 536	87.82 555	92.28 536	94.17 543	98.82 488	71.22 553	95.54 548	63.85 549	97.34 526	99.27 365

kuosan		85.65 514	84.57 517	88.90 532	97.91 532	77.11 557	96.37 530	87.62 556	85.24 545	85.45 551	96.83 543	69.94 555	90.98 552	45.90 550	95.83 543	98.62 475

test123		29.31 515	33.05 520	18.08 533	25.93 558	12.24 559	97.53 485	10.93 559	11.78 551	24.21 553	50.08 563	21.04 556	8.60 553	23.51 551	32.43 552	33.39 548

testmvs		28.94 516	33.33 518	15.79 534	26.03 557	9.81 560	96.77 520	15.67 558	11.55 552	23.87 554	50.74 562	19.03 557	8.53 554	23.21 552	33.07 551	29.03 549

mmdepth		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

monomultidepth		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

test_blank		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uanet_test		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

DCPMVS		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

cdsmvs_eth3d_5k		24.88 517	33.17 519	0.00 535	0.00 559	0.00 561	0.00 546	99.62 265	0.00 553	0.00 555	99.13 445	99.82 18	0.00 555	0.00 553	0.00 553	0.00 550

pcd_1.5k_mvsjas		16.61 518	22.14 521	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	99.28 93	0.00 555	0.00 553	0.00 553	0.00 550

sosnet-low-res		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

sosnet		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uncertanet		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

Regformer		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

ab-mvs-re		8.26 529	11.02 532	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	99.16 442	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uanet		8.33 519	11.11 522	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	100.00 1	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

TestfortrainingZip				99.38 290	99.17 438	99.25 259	99.38 132	98.82 461	98.93 310	99.68 208	99.49 351	98.11 289	99.56 501		98.44 500	99.32 354

FOURS1					99.83 90	99.89 10	99.74 27	99.71 207	99.69 133	99.63 238

test_one_0601					99.63 261	99.76 70		99.55 315	99.23 255	99.31 356	99.61 286	98.59 213

eth-test2					0.00 559

eth-test					0.00 559

test_241102_ONE					99.69 231	99.82 41		99.54 321	99.12 282	99.82 112	99.49 351	98.91 167	99.52 508

save fliter					99.53 325	99.25 259	98.29 416	99.38 385	99.07 287

test0726					99.69 231	99.80 51	99.24 194	99.57 303	99.16 272	99.73 182	99.65 248	98.35 257

GSMVS																99.14 400

test_part2					99.62 265	99.67 120				99.55 279

sam_mvs1												90.81 490				99.14 400

sam_mvs												90.52 496

MTGPA								99.53 332

test_post											52.41 560	90.25 499	99.86 277

patchmatchnet-post											99.62 276	90.58 494	99.94 98

MTMP							99.09 259	98.59 478

TEST9					99.35 390	99.35 238	98.11 437	99.41 370	94.83 525	97.92 494	98.99 467	98.02 295	99.85 296

test_8					99.34 399	99.31 245	98.08 441	99.40 377	94.90 522	97.87 499	98.97 472	98.02 295	99.84 313

agg_prior					99.35 390	99.36 235		99.39 380		97.76 506			99.85 296

test_prior4						99.19 280	98.00 451

test_prior				99.46 256	99.35 390	99.22 270		99.39 380					99.69 453			99.48 286

新几何2							98.04 445

旧先验1					99.49 345	99.29 248		99.26 416			99.39 382	97.67 323			99.36 424	99.46 295

原ACMM2							97.92 460

test222					99.51 334	99.08 304	97.83 466	99.29 410	95.21 518	98.68 447	99.31 407	97.28 346			99.38 421	99.43 318

segment_acmp												98.37 255

testdata1							97.72 472		97.86 444

test12				99.54 227	99.29 413	99.33 241		99.16 438		98.43 466		97.54 333	99.82 359		99.47 408	99.48 286

plane_prior7					99.58 286	99.38 226

plane_prior6					99.47 356	99.26 256						97.24 347

plane_prior4											99.25 424

plane_prior3						99.31 245			98.36 390	99.14 392

plane_prior2							98.80 343		98.94 305

plane_prior1					99.51 334

plane_prior						99.24 264	98.42 406		97.87 442						99.71 329

n2								0.00 560

nn								0.00 560

door-mid								99.83 115

test11								99.29 410

door								99.77 170

HQP5-MVS						98.94 326

HQP-NCC					99.31 407		97.98 453		97.45 464	98.15 483

ACMP_Plane					99.31 407		97.98 453		97.45 464	98.15 483

HQP4-MVS										98.15 483			99.70 446			99.53 257

HQP3-MVS								99.37 386							99.67 352

HQP2-MVS												96.67 373

NP-MVS					99.40 376	99.13 292					98.83 486

ACMMP++_ref															99.94 135

ACMMP++															99.79 278

Test By Simon												98.41 249

High-resolution multi-view benchmark