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 24	99.98 3	99.75 77	99.70 38	100.00 1	99.73 101	100.00 1	99.89 41	99.79 21	99.88 222	99.98 1	100.00 1	99.98 5

test_fmvs2		99.72 51	99.85 17	99.34 258	99.91 31	98.08 345	99.48 107	100.00 1	99.90 47	99.99 7	99.91 31	99.50 55	99.98 27	99.98 1	99.99 16	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 198	99.96 7	98.62 304	99.67 53	100.00 1	99.95 29	100.00 1	99.95 16	99.85 13	99.99 8	99.98 1	99.99 16	99.98 5

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 3	99.98 3	99.76 69	99.12 221	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 51	99.88 7	99.27 279	99.93 24	97.84 358	99.34 137	100.00 1	99.99 3	99.99 7	99.82 88	99.87 12	99.99 8	99.97 4	99.99 16	99.97 10

test_vis1_n		99.68 62	99.79 33	99.36 255	99.94 18	98.18 334	99.52 92	100.00 1	99.86 63	100.00 1	99.88 50	98.99 123	99.96 65	99.97 4	99.96 83	99.95 14

test_fmvs1_n		99.68 62	99.81 28	99.28 276	99.95 15	97.93 354	99.49 105	100.00 1	99.82 81	99.99 7	99.89 41	99.21 89	99.98 27	99.97 4	99.98 47	99.93 20

test_f		99.75 46	99.88 7	99.37 251	99.96 7	98.21 331	99.51 99	100.00 1	99.94 33	100.00 1	99.93 22	99.58 44	99.94 92	99.97 4	99.99 16	99.97 10

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 45	99.86 19	99.08 237	99.97 20	99.98 16	99.96 32	99.79 109	99.90 9	99.99 8	99.96 9	99.99 16	99.90 28

test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 84	99.01 256	99.99 11	99.99 3	99.98 14	99.88 50	99.97 2	99.99 8	99.96 9	100.00 1	99.98 5

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 51	99.88 45	99.55 156	99.17 200	99.98 12	99.99 3	99.96 32	99.84 75	99.96 3	99.99 8	99.96 9	99.99 16	99.88 37

test_cas_vis1_n_1920		99.76 44	99.86 13	99.45 221	99.93 24	98.40 319	99.30 154	99.98 12	99.94 33	99.99 7	99.89 41	99.80 20	99.97 41	99.96 9	99.97 69	99.97 10

fmvsm_s_conf0.5_n_7		99.73 49	99.78 38	99.60 172	99.74 156	98.93 274	98.85 289	99.96 28	99.96 25	99.97 23	99.76 134	99.82 17	99.96 65	99.95 13	99.98 47	99.90 28

fmvsm_l_conf0.5_n		99.80 29	99.78 38	99.85 30	99.88 45	99.66 116	99.11 226	99.91 48	99.98 16	99.96 32	99.64 211	99.60 42	99.99 8	99.95 13	99.99 16	99.88 37

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 38	99.82 82	99.70 105	99.17 200	99.97 20	99.99 3	99.96 32	99.82 88	99.94 4	100.00 1	99.95 13	100.00 1	99.80 61

test_fmvs1		99.48 109	99.65 66	98.97 321	99.54 245	97.16 381	99.11 226	99.98 12	99.78 95	99.96 32	99.81 95	98.72 161	99.97 41	99.95 13	99.97 69	99.79 69

mvsany_test3		99.85 12	99.88 7	99.75 92	99.95 15	99.37 200	99.53 91	99.98 12	99.77 99	99.99 7	99.95 16	99.85 13	99.94 92	99.95 13	99.98 47	99.94 17

fmvsm_s_conf0.5_n_9		99.82 23	99.82 25	99.82 43	99.83 73	99.59 143	98.97 271	99.92 40	99.99 3	99.97 23	99.84 75	99.90 9	99.94 92	99.94 18	99.99 16	99.92 24

fmvsm_s_conf0.1_n_2		99.81 27	99.78 38	99.89 11	99.93 24	99.76 69	98.92 281	99.98 12	99.99 3	99.99 7	99.88 50	99.43 57	99.94 92	99.94 18	99.99 16	99.99 2

fmvsm_l_conf0.5_n_a		99.80 29	99.79 33	99.84 35	99.88 45	99.64 125	99.12 221	99.91 48	99.98 16	99.95 42	99.67 199	99.67 33	99.99 8	99.94 18	99.99 16	99.88 37

MM		99.18 198	99.05 207	99.55 192	99.35 316	98.81 283	99.05 242	97.79 421	99.99 3	99.48 248	99.59 253	96.29 328	99.95 76	99.94 18	99.98 47	99.88 37

test_fmvsmconf_n		99.85 12	99.84 20	99.88 18	99.91 31	99.73 87	98.97 271	99.98 12	99.99 3	99.96 32	99.85 68	99.93 7	99.99 8	99.94 18	99.99 16	99.93 20

fmvsm_s_conf0.5_n_5		99.78 36	99.76 48	99.85 30	99.79 113	99.72 92	98.84 291	99.96 28	99.96 25	99.96 32	99.72 158	99.71 27	99.99 8	99.93 23	99.98 47	99.85 46

fmvsm_s_conf0.5_n_2		99.78 36	99.75 50	99.88 18	99.82 82	99.76 69	98.88 284	99.92 40	99.98 16	99.98 14	99.85 68	99.42 59	99.94 92	99.93 23	99.98 47	99.94 17

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 42	99.10 229	99.98 12	99.99 3	99.98 14	99.91 31	99.68 32	99.93 113	99.93 23	99.99 16	99.99 2

fmvsm_s_conf0.1_n		99.86 10	99.85 17	99.89 11	99.93 24	99.78 56	99.07 241	99.98 12	99.99 3	99.98 14	99.90 36	99.88 11	99.92 141	99.93 23	99.99 16	99.98 5

fmvsm_s_conf0.5_n_a		99.82 23	99.79 33	99.89 11	99.85 63	99.82 42	99.03 250	99.96 28	99.99 3	99.97 23	99.84 75	99.58 44	99.93 113	99.92 27	99.98 47	99.93 20

fmvsm_s_conf0.5_n		99.83 21	99.81 28	99.87 24	99.85 63	99.78 56	99.03 250	99.96 28	99.99 3	99.97 23	99.84 75	99.78 22	99.92 141	99.92 27	99.99 16	99.92 24

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 20	99.99 3	100.00 1	99.98 13	99.78 22	100.00 1	99.92 27	100.00 1	99.87 41

fmvsm_s_conf0.5_n_8		99.76 44	99.72 53	99.88 18	99.82 82	99.75 77	99.02 253	99.87 62	99.98 16	99.98 14	99.81 95	99.07 109	99.97 41	99.91 30	99.99 16	99.92 24

fmvsm_s_conf0.5_n_6		99.80 29	99.78 38	99.85 30	99.78 121	99.78 56	99.00 259	99.97 20	99.96 25	99.97 23	99.56 267	99.92 8	99.93 113	99.91 30	99.99 16	99.83 53

fmvsm_s_conf0.5_n_4		99.78 36	99.78 38	99.79 67	99.75 148	99.56 152	98.98 269	99.94 37	99.92 43	99.97 23	99.72 158	99.84 15	99.92 141	99.91 30	99.98 47	99.89 34

MVStest1		98.22 330	98.09 325	98.62 357	99.04 386	96.23 403	99.20 187	99.92 40	99.44 173	99.98 14	99.87 56	85.87 426	99.67 394	99.91 30	99.57 310	99.95 14

v1921920		99.56 91	99.57 90	99.55 192	99.75 148	99.11 247	99.05 242	99.61 207	99.15 226	99.88 80	99.71 168	99.08 107	99.87 236	99.90 34	99.97 69	99.66 133

v1240		99.56 91	99.58 86	99.51 203	99.80 101	99.00 261	99.00 259	99.65 187	99.15 226	99.90 65	99.75 142	99.09 104	99.88 222	99.90 34	99.96 83	99.67 123

v10		99.69 57	99.69 58	99.66 136	99.81 94	99.39 195	99.66 57	99.75 129	99.60 146	99.92 57	99.87 56	98.75 156	99.86 255	99.90 34	99.99 16	99.73 89

v1192		99.57 88	99.57 90	99.57 185	99.77 132	99.22 232	99.04 247	99.60 218	99.18 215	99.87 88	99.72 158	99.08 107	99.85 273	99.89 37	99.98 47	99.66 133

fmvsm_s_conf0.5_n_3		99.79 33	99.77 44	99.85 30	99.81 94	99.71 97	98.97 271	99.92 40	99.98 16	99.97 23	99.86 63	99.53 51	99.95 76	99.88 38	99.99 16	99.89 34

v144192		99.55 95	99.54 97	99.58 178	99.78 121	99.20 237	99.11 226	99.62 200	99.18 215	99.89 70	99.72 158	98.66 170	99.87 236	99.88 38	99.97 69	99.66 133

v8		99.68 62	99.69 58	99.65 142	99.80 101	99.40 192	99.66 57	99.76 124	99.64 131	99.93 50	99.85 68	98.66 170	99.84 288	99.88 38	99.99 16	99.71 98

mvs5depth		99.88 6	99.91 3	99.80 60	99.92 29	99.42 185	99.94 3	100.00 1	99.97 22	99.89 70	99.99 12	99.63 36	99.97 41	99.87 41	99.99 16	100.00 1

v1144		99.54 98	99.53 101	99.59 175	99.79 113	99.28 218	99.10 229	99.61 207	99.20 213	99.84 96	99.73 151	98.67 168	99.84 288	99.86 42	99.98 47	99.64 152

mmtdpeth		99.78 36	99.83 21	99.66 136	99.85 63	99.05 260	99.79 15	99.97 20	100.00 1	99.43 260	99.94 19	99.64 34	99.94 92	99.83 43	99.99 16	99.98 5

SSC-MVS		99.52 101	99.42 120	99.83 38	99.86 57	99.65 122	99.52 92	99.81 96	99.87 60	99.81 110	99.79 109	96.78 308	99.99 8	99.83 43	99.51 326	99.86 43

v7n		99.82 23	99.80 31	99.88 18	99.96 7	99.84 27	99.82 10	99.82 86	99.84 73	99.94 45	99.91 31	99.13 100	99.96 65	99.83 43	99.99 16	99.83 53

v2v482		99.50 103	99.47 107	99.58 178	99.78 121	99.25 225	99.14 210	99.58 233	99.25 204	99.81 110	99.62 231	98.24 228	99.84 288	99.83 43	99.97 69	99.64 152

test_vis1_rt		99.45 122	99.46 111	99.41 239	99.71 166	98.63 303	98.99 266	99.96 28	99.03 239	99.95 42	99.12 371	98.75 156	99.84 288	99.82 47	99.82 204	99.77 75

tt0805		99.63 77	99.57 90	99.81 51	99.87 54	99.88 12	99.58 82	98.70 382	99.72 105	99.91 60	99.60 248	99.43 57	99.81 328	99.81 48	99.53 322	99.73 89

VortexMVS		99.13 211	99.24 164	98.79 348	99.67 192	96.60 395	99.24 176	99.80 99	99.85 69	99.93 50	99.84 75	95.06 345	99.89 207	99.80 49	99.98 47	99.89 34

V42		99.56 91	99.54 97	99.63 156	99.79 113	99.46 171	99.39 122	99.59 224	99.24 206	99.86 90	99.70 177	98.55 185	99.82 313	99.79 50	99.95 99	99.60 183

SSC-MVS3.2		99.64 76	99.67 62	99.56 188	99.75 148	98.98 264	98.96 275	99.87 62	99.88 58	99.84 96	99.64 211	99.32 75	99.91 169	99.78 51	99.96 83	99.80 61

mvs_tets		99.90 2	99.90 4	99.90 8	99.96 7	99.79 53	99.72 33	99.88 60	99.92 43	99.98 14	99.93 22	99.94 4	99.98 27	99.77 52	100.00 1	99.92 24

WB-MVS		99.44 124	99.32 141	99.80 60	99.81 94	99.61 138	99.47 110	99.81 96	99.82 81	99.71 160	99.72 158	96.60 312	99.98 27	99.75 53	99.23 367	99.82 60

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 62	99.68 49	99.85 73	99.95 29	99.98 14	99.92 27	99.28 80	99.98 27	99.75 53	100.00 1	99.94 17

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 56	99.70 38	99.86 67	99.89 53	99.98 14	99.90 36	99.94 4	99.98 27	99.75 53	100.00 1	99.90 28

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 47	100.00 1	99.97 14	99.61 40	99.97 41	99.75 53	100.00 1	99.84 49

AstraMVS		99.15 208	99.06 202	99.42 231	99.85 63	98.59 307	99.13 216	97.26 429	99.84 73	99.87 88	99.77 130	96.11 331	99.93 113	99.71 57	99.96 83	99.74 85

Elysia		99.69 57	99.65 66	99.81 51	99.86 57	99.72 92	99.34 137	99.77 116	99.94 33	99.91 60	99.76 134	98.55 185	99.99 8	99.70 58	99.98 47	99.72 93

StellarMVS		99.69 57	99.65 66	99.81 51	99.86 57	99.72 92	99.34 137	99.77 116	99.94 33	99.91 60	99.76 134	98.55 185	99.99 8	99.70 58	99.98 47	99.72 93

tt0320-xc		99.82 23	99.82 25	99.82 43	99.82 82	99.84 27	99.82 10	99.92 40	99.94 33	99.94 45	99.93 22	99.34 72	99.92 141	99.70 58	99.96 83	99.70 101

reproduce_monomvs		97.40 363	97.46 356	97.20 411	99.05 383	91.91 439	99.20 187	99.18 354	99.84 73	99.86 90	99.75 142	80.67 434	99.83 303	99.69 61	99.95 99	99.85 46

SPE-MVS-test		99.68 62	99.70 55	99.64 149	99.57 229	99.83 34	99.78 17	99.97 20	99.92 43	99.50 245	99.38 320	99.57 46	99.95 76	99.69 61	99.90 137	99.15 333

guyue		99.12 214	99.02 216	99.41 239	99.84 68	98.56 308	99.19 193	98.30 407	99.82 81	99.84 96	99.75 142	94.84 348	99.92 141	99.68 63	99.94 112	99.74 85

tt0320		99.79 33	99.79 33	99.81 51	99.82 82	99.84 27	99.82 10	99.90 53	99.94 33	99.94 45	99.94 19	99.07 109	99.92 141	99.68 63	99.97 69	99.67 123

MVS_0304		98.61 288	98.30 309	99.52 200	97.88 446	98.95 270	98.76 308	94.11 445	99.84 73	99.32 290	99.57 263	95.57 340	99.95 76	99.68 63	99.98 47	99.68 114

CS-MVS		99.67 68	99.70 55	99.58 178	99.53 252	99.84 27	99.79 15	99.96 28	99.90 47	99.61 204	99.41 310	99.51 54	99.95 76	99.66 66	99.89 147	98.96 375

mamv4		99.73 49	99.74 51	99.70 122	99.66 194	99.87 15	99.69 45	99.93 38	99.93 40	99.93 50	99.86 63	99.07 109	100.00 1	99.66 66	99.92 126	99.24 308

KinetiMVS		99.66 69	99.63 72	99.76 81	99.89 39	99.57 151	99.37 129	99.82 86	99.95 29	99.90 65	99.63 223	98.57 181	99.97 41	99.65 68	99.94 112	99.74 85

pmmvs6		99.86 10	99.86 13	99.83 38	99.94 18	99.90 7	99.83 7	99.91 48	99.85 69	99.94 45	99.95 16	99.73 26	99.90 188	99.65 68	99.97 69	99.69 108

MIMVSNet1		99.66 69	99.62 74	99.80 60	99.94 18	99.87 15	99.69 45	99.77 116	99.78 95	99.93 50	99.89 41	97.94 253	99.92 141	99.65 68	99.98 47	99.62 169

LuminaMVS		99.39 140	99.28 155	99.73 106	99.83 73	99.49 163	99.00 259	99.05 366	99.81 87	99.89 70	99.79 109	96.54 316	99.97 41	99.64 71	99.98 47	99.73 89

sc_t1		99.81 27	99.80 31	99.82 43	99.88 45	99.88 12	99.83 7	99.79 106	99.94 33	99.93 50	99.92 27	99.35 71	99.92 141	99.64 71	99.94 112	99.68 114

EC-MVSNet		99.69 57	99.69 58	99.68 126	99.71 166	99.91 4	99.76 23	99.96 28	99.86 63	99.51 243	99.39 318	99.57 46	99.93 113	99.64 71	99.86 176	99.20 321

K. test v3		98.87 265	98.60 274	99.69 124	99.93 24	99.46 171	99.74 27	94.97 440	99.78 95	99.88 80	99.88 50	93.66 363	99.97 41	99.61 74	99.95 99	99.64 152

KD-MVS_self_test		99.63 77	99.59 83	99.76 81	99.84 68	99.90 7	99.37 129	99.79 106	99.83 79	99.88 80	99.85 68	98.42 208	99.90 188	99.60 75	99.73 254	99.49 241

Anonymous20240521		99.44 124	99.42 120	99.49 209	99.89 39	98.96 269	99.62 67	99.76 124	99.85 69	99.82 103	99.88 50	96.39 323	99.97 41	99.59 76	99.98 47	99.55 205

TransMVSNet (Re)		99.78 36	99.77 44	99.81 51	99.91 31	99.85 22	99.75 25	99.86 67	99.70 112	99.91 60	99.89 41	99.60 42	99.87 236	99.59 76	99.74 248	99.71 98

OurMVSNet-221017-0		99.75 46	99.71 54	99.84 35	99.96 7	99.83 34	99.83 7	99.85 73	99.80 91	99.93 50	99.93 22	98.54 189	99.93 113	99.59 76	99.98 47	99.76 80

EU-MVSNet		99.39 140	99.62 74	98.72 353	99.88 45	96.44 397	99.56 87	99.85 73	99.90 47	99.90 65	99.85 68	98.09 242	99.83 303	99.58 79	99.95 99	99.90 28

mvs_anonymous		99.28 165	99.39 124	98.94 325	99.19 359	97.81 360	99.02 253	99.55 246	99.78 95	99.85 93	99.80 99	98.24 228	99.86 255	99.57 80	99.50 329	99.15 333

test1111		97.74 348	98.16 321	96.49 422	99.60 209	89.86 453	99.71 37	91.21 449	99.89 53	99.88 80	99.87 56	93.73 362	99.90 188	99.56 81	99.99 16	99.70 101

lessismore_v0				99.64 149	99.86 57	99.38 197		90.66 450		99.89 70	99.83 81	94.56 353	99.97 41	99.56 81	99.92 126	99.57 200

mvsany_test1		99.44 124	99.45 113	99.40 242	99.37 309	98.64 302	97.90 399	99.59 224	99.27 200	99.92 57	99.82 88	99.74 25	99.93 113	99.55 83	99.87 168	99.63 158

MVSMamba_PlusPlus		99.55 95	99.58 86	99.47 215	99.68 186	99.40 192	99.52 92	99.70 156	99.92 43	99.77 132	99.86 63	98.28 224	99.96 65	99.54 84	99.90 137	99.05 362

pm-mvs1		99.79 33	99.79 33	99.78 71	99.91 31	99.83 34	99.76 23	99.87 62	99.73 101	99.89 70	99.87 56	99.63 36	99.87 236	99.54 84	99.92 126	99.63 158

LTVRE_ROB	99.19 1	99.88 6	99.87 11	99.88 18	99.91 31	99.90 7	99.96 1	99.92 40	99.90 47	99.97 23	99.87 56	99.81 19	99.95 76	99.54 84	99.99 16	99.80 61
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		99.48 109	99.65 66	98.95 324	99.71 166	97.27 378	99.50 100	99.82 86	99.59 148	99.41 269	99.85 68	99.62 39	100.00 1	99.53 87	99.89 147	99.59 190

test2506		94.73 413	94.59 414	95.15 429	99.59 214	85.90 455	99.75 25	74.01 457	99.89 53	99.71 160	99.86 63	79.00 444	99.90 188	99.52 88	99.99 16	99.65 142

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 151	99.93 40	99.95 42	99.89 41	99.71 27	99.96 65	99.51 89	99.97 69	99.84 49

FC-MVSNet-test		99.70 55	99.65 66	99.86 28	99.88 45	99.86 19	99.72 33	99.78 113	99.90 47	99.82 103	99.83 81	98.45 204	99.87 236	99.51 89	99.97 69	99.86 43

BP-MVS1		98.72 280	98.46 290	99.50 205	99.53 252	99.00 261	99.34 137	98.53 392	99.65 128	99.73 153	99.38 320	90.62 400	99.96 65	99.50 91	99.86 176	99.55 205

UA-Net		99.78 36	99.76 48	99.86 28	99.72 163	99.71 97	99.91 4	99.95 35	99.96 25	99.71 160	99.91 31	99.15 95	99.97 41	99.50 91	100.00 1	99.90 28

PMMVS2		99.48 109	99.45 113	99.57 185	99.76 136	98.99 263	98.09 376	99.90 53	98.95 249	99.78 124	99.58 256	99.57 46	99.93 113	99.48 93	99.95 99	99.79 69

VPA-MVSNet		99.66 69	99.62 74	99.79 67	99.68 186	99.75 77	99.62 67	99.69 164	99.85 69	99.80 114	99.81 95	98.81 144	99.91 169	99.47 94	99.88 156	99.70 101

GDP-MVS		98.81 271	98.57 280	99.50 205	99.53 252	99.12 246	99.28 163	99.86 67	99.53 153	99.57 215	99.32 336	90.88 396	99.98 27	99.46 95	99.74 248	99.42 269

ECVR-MVS		97.73 349	98.04 328	96.78 415	99.59 214	90.81 448	99.72 33	90.43 451	99.89 53	99.86 90	99.86 63	93.60 364	99.89 207	99.46 95	99.99 16	99.65 142

nrg030		99.70 55	99.66 64	99.82 43	99.76 136	99.84 27	99.61 73	99.70 156	99.93 40	99.78 124	99.68 195	99.10 102	99.78 341	99.45 97	99.96 83	99.83 53

TAMVS		99.49 107	99.45 113	99.63 156	99.48 277	99.42 185	99.45 114	99.57 235	99.66 125	99.78 124	99.83 81	97.85 260	99.86 255	99.44 98	99.96 83	99.61 179

GeoE		99.69 57	99.66 64	99.78 71	99.76 136	99.76 69	99.60 79	99.82 86	99.46 168	99.75 140	99.56 267	99.63 36	99.95 76	99.43 99	99.88 156	99.62 169

new-patchmatchnet		99.35 151	99.57 90	98.71 355	99.82 82	96.62 393	98.55 332	99.75 129	99.50 157	99.88 80	99.87 56	99.31 76	99.88 222	99.43 99	100.00 1	99.62 169

test20.03		99.55 95	99.54 97	99.58 178	99.79 113	99.37 200	99.02 253	99.89 56	99.60 146	99.82 103	99.62 231	98.81 144	99.89 207	99.43 99	99.86 176	99.47 249

MVSFormer		99.41 134	99.44 116	99.31 269	99.57 229	98.40 319	99.77 19	99.80 99	99.73 101	99.63 189	99.30 341	98.02 247	99.98 27	99.43 99	99.69 269	99.55 205

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 27	99.77 19	99.80 99	99.73 101	99.97 23	99.92 27	99.77 24	99.98 27	99.43 99	100.00 1	99.90 28

SDMVSNet		99.77 43	99.77 44	99.76 81	99.80 101	99.65 122	99.63 64	99.86 67	99.97 22	99.89 70	99.89 41	99.52 53	99.99 8	99.42 104	99.96 83	99.65 142

Anonymous20231211		99.62 83	99.57 90	99.76 81	99.61 207	99.60 141	99.81 13	99.73 139	99.82 81	99.90 65	99.90 36	97.97 252	99.86 255	99.42 104	99.96 83	99.80 61

SixPastTwentyTwo		99.42 130	99.30 148	99.76 81	99.92 29	99.67 114	99.70 38	99.14 359	99.65 128	99.89 70	99.90 36	96.20 330	99.94 92	99.42 104	99.92 126	99.67 123

balanced_conf03		99.50 103	99.50 103	99.50 205	99.42 300	99.49 163	99.52 92	99.75 129	99.86 63	99.78 124	99.71 168	98.20 235	99.90 188	99.39 107	99.88 156	99.10 344

patch_mono-2		99.51 102	99.46 111	99.64 149	99.70 174	99.11 247	99.04 247	99.87 62	99.71 107	99.47 250	99.79 109	98.24 228	99.98 27	99.38 108	99.96 83	99.83 53

UGNet		99.38 143	99.34 136	99.49 209	98.90 398	98.90 278	99.70 38	99.35 317	99.86 63	98.57 382	99.81 95	98.50 199	99.93 113	99.38 108	99.98 47	99.66 133
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 54	99.67 62	99.81 51	99.89 39	99.72 92	99.59 80	99.82 86	99.39 184	99.82 103	99.84 75	99.38 65	99.91 169	99.38 108	99.93 122	99.80 61

FIs		99.65 75	99.58 86	99.84 35	99.84 68	99.85 22	99.66 57	99.75 129	99.86 63	99.74 149	99.79 109	98.27 226	99.85 273	99.37 111	99.93 122	99.83 53

sd_testset		99.78 36	99.78 38	99.80 60	99.80 101	99.76 69	99.80 14	99.79 106	99.97 22	99.89 70	99.89 41	99.53 51	99.99 8	99.36 112	99.96 83	99.65 142

anonymousdsp		99.80 29	99.77 44	99.90 8	99.96 7	99.88 12	99.73 30	99.85 73	99.70 112	99.92 57	99.93 22	99.45 56	99.97 41	99.36 112	100.00 1	99.85 46

casdiffmvs_mvg		99.68 62	99.68 61	99.69 124	99.81 94	99.59 143	99.29 161	99.90 53	99.71 107	99.79 120	99.73 151	99.54 49	99.84 288	99.36 112	99.96 83	99.65 142
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 46	99.74 51	99.79 67	99.88 45	99.66 116	99.69 45	99.92 40	99.67 121	99.77 132	99.75 142	99.61 40	99.98 27	99.35 115	99.98 47	99.72 93
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 85	99.64 71	99.53 198	99.79 113	98.82 282	99.58 82	99.97 20	99.95 29	99.96 32	99.76 134	98.44 205	99.99 8	99.34 116	99.96 83	99.78 71

CHOSEN 1792x2688		99.39 140	99.30 148	99.65 142	99.88 45	99.25 225	98.78 306	99.88 60	98.66 289	99.96 32	99.79 109	97.45 282	99.93 113	99.34 116	99.99 16	99.78 71

CDS-MVSNet		99.22 184	99.13 178	99.50 205	99.35 316	99.11 247	98.96 275	99.54 252	99.46 168	99.61 204	99.70 177	96.31 326	99.83 303	99.34 116	99.88 156	99.55 205
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 245	99.16 172	98.51 363	99.75 148	95.90 409	98.07 379	99.84 79	99.84 73	99.89 70	99.73 151	96.01 334	99.99 8	99.33 119	100.00 1	99.63 158

HyFIR lowres test		98.91 258	98.64 271	99.73 106	99.85 63	99.47 167	98.07 379	99.83 81	98.64 291	99.89 70	99.60 248	92.57 375	100.00 1	99.33 119	99.97 69	99.72 93

pmmvs5		99.19 194	99.11 185	99.42 231	99.76 136	98.88 279	98.55 332	99.73 139	98.82 269	99.72 155	99.62 231	96.56 313	99.82 313	99.32 121	99.95 99	99.56 202

v148		99.40 136	99.41 122	99.39 245	99.76 136	98.94 271	99.09 234	99.59 224	99.17 220	99.81 110	99.61 240	98.41 209	99.69 377	99.32 121	99.94 112	99.53 219

baseline		99.63 77	99.62 74	99.66 136	99.80 101	99.62 132	99.44 116	99.80 99	99.71 107	99.72 155	99.69 184	99.15 95	99.83 303	99.32 121	99.94 112	99.53 219

CVMVSNet		98.61 288	98.88 250	97.80 394	99.58 219	93.60 432	99.26 169	99.64 195	99.66 125	99.72 155	99.67 199	93.26 368	99.93 113	99.30 124	99.81 214	99.87 41

PS-CasMVS		99.66 69	99.58 86	99.89 11	99.80 101	99.85 22	99.66 57	99.73 139	99.62 136	99.84 96	99.71 168	98.62 174	99.96 65	99.30 124	99.96 83	99.86 43

DTE-MVSNet		99.68 62	99.61 78	99.88 18	99.80 101	99.87 15	99.67 53	99.71 151	99.72 105	99.84 96	99.78 121	98.67 168	99.97 41	99.30 124	99.95 99	99.80 61

tmp_tt		95.75 406	95.42 401	96.76 416	89.90 456	94.42 426	98.86 287	97.87 419	78.01 447	99.30 300	99.69 184	97.70 268	95.89 449	99.29 127	98.14 425	99.95 14

PEN-MVS		99.66 69	99.59 83	99.89 11	99.83 73	99.87 15	99.66 57	99.73 139	99.70 112	99.84 96	99.73 151	98.56 184	99.96 65	99.29 127	99.94 112	99.83 53

WR-MVS_H		99.61 85	99.53 101	99.87 24	99.80 101	99.83 34	99.67 53	99.75 129	99.58 150	99.85 93	99.69 184	98.18 238	99.94 92	99.28 129	99.95 99	99.83 53

IterMVS		98.97 249	99.16 172	98.42 368	99.74 156	95.64 413	98.06 381	99.83 81	99.83 79	99.85 93	99.74 147	96.10 333	99.99 8	99.27 130	100.00 1	99.63 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NormalMVS		99.09 222	98.91 248	99.62 165	99.78 121	99.11 247	99.36 133	99.77 116	99.82 81	99.68 170	99.53 279	93.30 366	99.99 8	99.24 131	99.76 237	99.74 85

SymmetryMVS		99.01 242	98.82 258	99.58 178	99.65 199	99.11 247	99.36 133	99.20 352	99.82 81	99.68 170	99.53 279	93.30 366	99.99 8	99.24 131	99.63 290	99.64 152

WBMVS		97.50 359	97.18 365	98.48 365	98.85 406	95.89 410	98.44 349	99.52 267	99.53 153	99.52 237	99.42 309	80.10 437	99.86 255	99.24 131	99.95 99	99.68 114

h-mvs33		98.61 288	98.34 304	99.44 225	99.60 209	98.67 294	99.27 167	99.44 292	99.68 117	99.32 290	99.49 292	92.50 378	100.00 1	99.24 131	96.51 442	99.65 142

hse-mvs2		98.52 301	98.30 309	99.16 295	99.29 338	98.60 305	98.77 307	99.02 368	99.68 117	99.32 290	99.04 381	92.50 378	99.85 273	99.24 131	97.87 432	99.03 366

FMVSNet1		99.66 69	99.63 72	99.73 106	99.78 121	99.77 62	99.68 49	99.70 156	99.67 121	99.82 103	99.83 81	98.98 125	99.90 188	99.24 131	99.97 69	99.53 219

casdiffmvs		99.63 77	99.61 78	99.67 129	99.79 113	99.59 143	99.13 216	99.85 73	99.79 93	99.76 135	99.72 158	99.33 74	99.82 313	99.21 137	99.94 112	99.59 190
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 98	99.43 118	99.87 24	99.76 136	99.82 42	99.57 85	99.61 207	99.54 151	99.80 114	99.64 211	97.79 264	99.95 76	99.21 137	99.94 112	99.84 49

DELS-MVS		99.34 156	99.30 148	99.48 213	99.51 261	99.36 204	98.12 372	99.53 262	99.36 189	99.41 269	99.61 240	99.22 88	99.87 236	99.21 137	99.68 274	99.20 321
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
UniMVSNet (Re)		99.37 146	99.26 160	99.68 126	99.51 261	99.58 148	98.98 269	99.60 218	99.43 179	99.70 164	99.36 327	97.70 268	99.88 222	99.20 140	99.87 168	99.59 190

CANet		99.11 218	99.05 207	99.28 276	98.83 408	98.56 308	98.71 314	99.41 298	99.25 204	99.23 308	99.22 359	97.66 276	99.94 92	99.19 141	99.97 69	99.33 290

EI-MVSNet-UG-set		99.48 109	99.50 103	99.42 231	99.57 229	98.65 300	99.24 176	99.46 287	99.68 117	99.80 114	99.66 204	98.99 123	99.89 207	99.19 141	99.90 137	99.72 93

xiu_mvs_v1_base_debu		99.23 176	99.34 136	98.91 331	99.59 214	98.23 328	98.47 344	99.66 177	99.61 140	99.68 170	98.94 397	99.39 61	99.97 41	99.18 143	99.55 315	98.51 414

xiu_mvs_v1_base		99.23 176	99.34 136	98.91 331	99.59 214	98.23 328	98.47 344	99.66 177	99.61 140	99.68 170	98.94 397	99.39 61	99.97 41	99.18 143	99.55 315	98.51 414

xiu_mvs_v1_base_debi		99.23 176	99.34 136	98.91 331	99.59 214	98.23 328	98.47 344	99.66 177	99.61 140	99.68 170	98.94 397	99.39 61	99.97 41	99.18 143	99.55 315	98.51 414

VPNet		99.46 118	99.37 129	99.71 118	99.82 82	99.59 143	99.48 107	99.70 156	99.81 87	99.69 167	99.58 256	97.66 276	99.86 255	99.17 146	99.44 336	99.67 123

UniMVSNet_NR-MVSNet		99.37 146	99.25 162	99.72 113	99.47 283	99.56 152	98.97 271	99.61 207	99.43 179	99.67 177	99.28 345	97.85 260	99.95 76	99.17 146	99.81 214	99.65 142

DU-MVS		99.33 159	99.21 167	99.71 118	99.43 295	99.56 152	98.83 294	99.53 262	99.38 185	99.67 177	99.36 327	97.67 272	99.95 76	99.17 146	99.81 214	99.63 158

EI-MVSNet-Vis-set		99.47 117	99.49 105	99.42 231	99.57 229	98.66 297	99.24 176	99.46 287	99.67 121	99.79 120	99.65 209	98.97 127	99.89 207	99.15 149	99.89 147	99.71 98

EI-MVSNet		99.38 143	99.44 116	99.21 289	99.58 219	98.09 342	99.26 169	99.46 287	99.62 136	99.75 140	99.67 199	98.54 189	99.85 273	99.15 149	99.92 126	99.68 114

VNet		99.18 198	99.06 202	99.56 188	99.24 349	99.36 204	99.33 143	99.31 326	99.67 121	99.47 250	99.57 263	96.48 317	99.84 288	99.15 149	99.30 355	99.47 249

EG-PatchMatch MVS		99.57 88	99.56 95	99.62 165	99.77 132	99.33 210	99.26 169	99.76 124	99.32 194	99.80 114	99.78 121	99.29 78	99.87 236	99.15 149	99.91 136	99.66 133

PVSNet_Blended_VisFu		99.40 136	99.38 126	99.44 225	99.90 37	98.66 297	98.94 279	99.91 48	97.97 352	99.79 120	99.73 151	99.05 116	99.97 41	99.15 149	99.99 16	99.68 114

IterMVS-LS		99.41 134	99.47 107	99.25 285	99.81 94	98.09 342	98.85 289	99.76 124	99.62 136	99.83 102	99.64 211	98.54 189	99.97 41	99.15 149	99.99 16	99.68 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 98	99.47 107	99.76 81	99.58 219	99.64 125	99.30 154	99.63 197	99.61 140	99.71 160	99.56 267	98.76 154	99.96 65	99.14 155	99.92 126	99.68 114

MVSTER		98.47 308	98.22 314	99.24 287	99.06 382	98.35 325	99.08 237	99.46 287	99.27 200	99.75 140	99.66 204	88.61 413	99.85 273	99.14 155	99.92 126	99.52 229

Anonymous20231206		99.35 151	99.31 143	99.47 215	99.74 156	99.06 259	99.28 163	99.74 135	99.23 208	99.72 155	99.53 279	97.63 278	99.88 222	99.11 157	99.84 186	99.48 245

Syy-MVS		98.17 333	97.85 345	99.15 297	98.50 431	98.79 286	98.60 321	99.21 349	97.89 358	96.76 434	96.37 457	95.47 342	99.57 420	99.10 158	98.73 401	99.09 349

ttmdpeth		99.48 109	99.55 96	99.29 273	99.76 136	98.16 336	99.33 143	99.95 35	99.79 93	99.36 279	99.89 41	99.13 100	99.77 349	99.09 159	99.64 287	99.93 20

MVS_Test		99.28 165	99.31 143	99.19 292	99.35 316	98.79 286	99.36 133	99.49 280	99.17 220	99.21 313	99.67 199	98.78 151	99.66 399	99.09 159	99.66 283	99.10 344

testgi		99.29 164	99.26 160	99.37 251	99.75 148	98.81 283	98.84 291	99.89 56	98.38 319	99.75 140	99.04 381	99.36 70	99.86 255	99.08 161	99.25 363	99.45 254

1112_ss		99.05 230	98.84 255	99.67 129	99.66 194	99.29 216	98.52 338	99.82 86	97.65 370	99.43 260	99.16 365	96.42 320	99.91 169	99.07 162	99.84 186	99.80 61

CANet_DTU		98.91 258	98.85 253	99.09 306	98.79 414	98.13 337	98.18 365	99.31 326	99.48 160	98.86 353	99.51 285	96.56 313	99.95 76	99.05 163	99.95 99	99.19 324

Baseline_NR-MVSNet		99.49 107	99.37 129	99.82 43	99.91 31	99.84 27	98.83 294	99.86 67	99.68 117	99.65 184	99.88 50	97.67 272	99.87 236	99.03 164	99.86 176	99.76 80

FMVSNet2		99.35 151	99.28 155	99.55 192	99.49 272	99.35 207	99.45 114	99.57 235	99.44 173	99.70 164	99.74 147	97.21 293	99.87 236	99.03 164	99.94 112	99.44 259

Test_1112_low_res		98.95 255	98.73 265	99.63 156	99.68 186	99.15 243	98.09 376	99.80 99	97.14 396	99.46 254	99.40 314	96.11 331	99.89 207	99.01 166	99.84 186	99.84 49

VDD-MVS		99.20 191	99.11 185	99.44 225	99.43 295	98.98 264	99.50 100	98.32 406	99.80 91	99.56 223	99.69 184	96.99 303	99.85 273	98.99 167	99.73 254	99.50 236

DeepC-MVS	98.90 4	99.62 83	99.61 78	99.67 129	99.72 163	99.44 178	99.24 176	99.71 151	99.27 200	99.93 50	99.90 36	99.70 30	99.93 113	98.99 167	99.99 16	99.64 152
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 109	99.47 107	99.51 203	99.77 132	99.41 191	98.81 299	99.66 177	99.42 183	99.75 140	99.66 204	99.20 90	99.76 352	98.98 169	99.99 16	99.36 283

EPNet_dtu		97.62 354	97.79 348	97.11 414	96.67 451	92.31 437	98.51 339	98.04 413	99.24 206	95.77 443	99.47 299	93.78 361	99.66 399	98.98 169	99.62 292	99.37 280
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 156	99.32 141	99.39 245	99.67 192	98.77 288	98.57 330	99.81 96	99.61 140	99.48 248	99.41 310	98.47 200	99.86 255	98.97 171	99.90 137	99.53 219
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		99.40 136	99.31 143	99.68 126	99.43 295	99.55 156	99.73 30	99.50 276	99.46 168	99.88 80	99.36 327	97.54 279	99.87 236	98.97 171	99.87 168	99.63 158

GBi-Net		99.42 130	99.31 143	99.73 106	99.49 272	99.77 62	99.68 49	99.70 156	99.44 173	99.62 198	99.83 81	97.21 293	99.90 188	98.96 173	99.90 137	99.53 219

FMVSNet5		97.80 346	97.25 363	99.42 231	98.83 408	98.97 267	99.38 125	99.80 99	98.87 261	99.25 304	99.69 184	80.60 436	99.91 169	98.96 173	99.90 137	99.38 277

test1		99.42 130	99.31 143	99.73 106	99.49 272	99.77 62	99.68 49	99.70 156	99.44 173	99.62 198	99.83 81	97.21 293	99.90 188	98.96 173	99.90 137	99.53 219

FMVSNet3		98.80 272	98.63 273	99.32 266	99.13 368	98.72 291	99.10 229	99.48 281	99.23 208	99.62 198	99.64 211	92.57 375	99.86 255	98.96 173	99.90 137	99.39 275

UnsupCasMVSNet_eth		98.83 268	98.57 280	99.59 175	99.68 186	99.45 176	98.99 266	99.67 172	99.48 160	99.55 228	99.36 327	94.92 346	99.86 255	98.95 177	96.57 441	99.45 254

CHOSEN 280x420		98.41 313	98.41 296	98.40 369	99.34 325	95.89 410	96.94 435	99.44 292	98.80 273	99.25 304	99.52 283	93.51 365	99.98 27	98.94 178	99.98 47	99.32 293

TDRefinement		99.72 51	99.70 55	99.77 74	99.90 37	99.85 22	99.86 6	99.92 40	99.69 115	99.78 124	99.92 27	99.37 67	99.88 222	98.93 179	99.95 99	99.60 183

alignmvs		98.28 323	97.96 334	99.25 285	99.12 370	98.93 274	99.03 250	98.42 399	99.64 131	98.72 368	97.85 436	90.86 397	99.62 411	98.88 180	99.13 369	99.19 324

testing3-2		96.51 385	96.43 380	96.74 418	99.36 312	91.38 445	99.10 229	97.87 419	99.48 160	98.57 382	98.71 412	76.65 446	99.66 399	98.87 181	99.26 362	99.18 326

MGCFI-Net		99.02 236	99.01 220	99.06 313	99.11 375	98.60 305	99.63 64	99.67 172	99.63 133	98.58 380	97.65 439	99.07 109	99.57 420	98.85 182	98.92 385	99.03 366

sss		98.90 260	98.77 264	99.27 279	99.48 277	98.44 316	98.72 312	99.32 322	97.94 356	99.37 278	99.35 332	96.31 326	99.91 169	98.85 182	99.63 290	99.47 249

xiu_mvs_v2_base		99.02 236	99.11 185	98.77 350	99.37 309	98.09 342	98.13 371	99.51 272	99.47 165	99.42 263	98.54 421	99.38 65	99.97 41	98.83 184	99.33 351	98.24 426

PS-MVSNAJ		99.00 245	99.08 196	98.76 351	99.37 309	98.10 341	98.00 387	99.51 272	99.47 165	99.41 269	98.50 423	99.28 80	99.97 41	98.83 184	99.34 350	98.20 430

D2MVS		99.22 184	99.19 169	99.29 273	99.69 178	98.74 290	98.81 299	99.41 298	98.55 300	99.68 170	99.69 184	98.13 240	99.87 236	98.82 186	99.98 47	99.24 308

PatchT		98.45 310	98.32 306	98.83 344	98.94 396	98.29 326	99.24 176	98.82 376	99.84 73	99.08 330	99.76 134	91.37 386	99.94 92	98.82 186	99.00 380	98.26 425

testf1		99.63 77	99.60 81	99.72 113	99.94 18	99.95 2	99.47 110	99.89 56	99.43 179	99.88 80	99.80 99	99.26 84	99.90 188	98.81 188	99.88 156	99.32 293

APD_test2		99.63 77	99.60 81	99.72 113	99.94 18	99.95 2	99.47 110	99.89 56	99.43 179	99.88 80	99.80 99	99.26 84	99.90 188	98.81 188	99.88 156	99.32 293

sasdasda		99.02 236	99.00 224	99.09 306	99.10 377	98.70 292	99.61 73	99.66 177	99.63 133	98.64 374	97.65 439	99.04 117	99.54 424	98.79 190	98.92 385	99.04 364

Effi-MVS+		99.06 227	98.97 235	99.34 258	99.31 332	98.98 264	98.31 357	99.91 48	98.81 271	98.79 362	98.94 397	99.14 98	99.84 288	98.79 190	98.74 398	99.20 321

canonicalmvs		99.02 236	99.00 224	99.09 306	99.10 377	98.70 292	99.61 73	99.66 177	99.63 133	98.64 374	97.65 439	99.04 117	99.54 424	98.79 190	98.92 385	99.04 364

VDDNet		98.97 249	98.82 258	99.42 231	99.71 166	98.81 283	99.62 67	98.68 383	99.81 87	99.38 277	99.80 99	94.25 355	99.85 273	98.79 190	99.32 353	99.59 190

CR-MVSNet		98.35 320	98.20 316	98.83 344	99.05 383	98.12 338	99.30 154	99.67 172	97.39 384	99.16 319	99.79 109	91.87 383	99.91 169	98.78 194	98.77 394	98.44 419

test_method		91.72 414	92.32 417	89.91 432	93.49 455	70.18 458	90.28 446	99.56 240	61.71 450	95.39 445	99.52 283	93.90 357	99.94 92	98.76 195	98.27 418	99.62 169

RPMNet		98.60 291	98.53 286	98.83 344	99.05 383	98.12 338	99.30 154	99.62 200	99.86 63	99.16 319	99.74 147	92.53 377	99.92 141	98.75 196	98.77 394	98.44 419

pmmvs4		99.13 211	99.06 202	99.36 255	99.57 229	99.10 254	98.01 385	99.25 339	98.78 276	99.58 212	99.44 306	98.24 228	99.76 352	98.74 197	99.93 122	99.22 314

tttt0517		97.62 354	97.20 364	98.90 337	99.76 136	97.40 375	99.48 107	94.36 442	99.06 237	99.70 164	99.49 292	84.55 429	99.94 92	98.73 198	99.65 285	99.36 283

EPP-MVSNet		99.17 203	99.00 224	99.66 136	99.80 101	99.43 182	99.70 38	99.24 342	99.48 160	99.56 223	99.77 130	94.89 347	99.93 113	98.72 199	99.89 147	99.63 158

Anonymous20240529		99.42 130	99.34 136	99.65 142	99.53 252	99.60 141	99.63 64	99.39 308	99.47 165	99.76 135	99.78 121	98.13 240	99.86 255	98.70 200	99.68 274	99.49 241

ACMH	98.42 6	99.59 87	99.54 97	99.72 113	99.86 57	99.62 132	99.56 87	99.79 106	98.77 278	99.80 114	99.85 68	99.64 34	99.85 273	98.70 200	99.89 147	99.70 101
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 159	99.28 155	99.47 215	99.57 229	99.39 195	99.78 17	99.43 295	98.87 261	99.57 215	99.82 88	98.06 245	99.87 236	98.69 202	99.73 254	99.15 333

LFMVS		98.46 309	98.19 319	99.26 282	99.24 349	98.52 312	99.62 67	96.94 431	99.87 60	99.31 295	99.58 256	91.04 391	99.81 328	98.68 203	99.42 340	99.45 254

WR-MVS		99.11 218	98.93 240	99.66 136	99.30 336	99.42 185	98.42 350	99.37 313	99.04 238	99.57 215	99.20 363	96.89 305	99.86 255	98.66 204	99.87 168	99.70 101

mvsmamba		99.08 223	98.95 238	99.45 221	99.36 312	99.18 240	99.39 122	98.81 377	99.37 186	99.35 281	99.70 177	96.36 325	99.94 92	98.66 204	99.59 306	99.22 314

RRT-MVS		99.08 223	99.00 224	99.33 261	99.27 343	98.65 300	99.62 67	99.93 38	99.66 125	99.67 177	99.82 88	95.27 344	99.93 113	98.64 206	99.09 373	99.41 270

Anonymous202405211		98.75 276	98.46 290	99.63 156	99.34 325	99.66 116	99.47 110	97.65 422	99.28 199	99.56 223	99.50 288	93.15 369	99.84 288	98.62 207	99.58 308	99.40 272

lecture		99.56 91	99.48 106	99.81 51	99.78 121	99.86 19	99.50 100	99.70 156	99.59 148	99.75 140	99.71 168	98.94 130	99.92 141	98.59 208	99.76 237	99.66 133

EPNet		98.13 334	97.77 349	99.18 294	94.57 454	97.99 348	99.24 176	97.96 415	99.74 100	97.29 427	99.62 231	93.13 370	99.97 41	98.59 208	99.83 194	99.58 195
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 230	99.09 194	98.91 331	99.21 354	98.36 324	98.82 298	99.47 284	98.85 264	98.90 348	99.56 267	98.78 151	99.09 440	98.57 210	99.68 274	99.26 305

Patchmatch-RL test		98.60 291	98.36 301	99.33 261	99.77 132	99.07 257	98.27 359	99.87 62	98.91 256	99.74 149	99.72 158	90.57 402	99.79 338	98.55 211	99.85 181	99.11 342

pmmvs3		98.08 337	97.80 346	98.91 331	99.41 302	97.69 366	97.87 400	99.66 177	95.87 415	99.50 245	99.51 285	90.35 404	99.97 41	98.55 211	99.47 333	99.08 355

ETV-MVS		99.18 198	99.18 170	99.16 295	99.34 325	99.28 218	99.12 221	99.79 106	99.48 160	98.93 342	98.55 420	99.40 60	99.93 113	98.51 213	99.52 325	98.28 424

jason		99.16 204	99.11 185	99.32 266	99.75 148	98.44 316	98.26 361	99.39 308	98.70 286	99.74 149	99.30 341	98.54 189	99.97 41	98.48 214	99.82 204	99.55 205
jason: jason.
APDe-MVS		99.48 109	99.36 132	99.85 30	99.55 243	99.81 47	99.50 100	99.69 164	98.99 242	99.75 140	99.71 168	98.79 149	99.93 113	98.46 215	99.85 181	99.80 61
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CL-MVSNet_self_test		98.71 282	98.56 284	99.15 297	99.22 352	98.66 297	97.14 430	99.51 272	98.09 345	99.54 230	99.27 347	96.87 306	99.74 359	98.43 216	98.96 382	99.03 366

our_test_3		98.85 267	99.09 194	98.13 382	99.66 194	94.90 424	97.72 405	99.58 233	99.07 235	99.64 185	99.62 231	98.19 236	99.93 113	98.41 217	99.95 99	99.55 205

Gipuma		99.57 88	99.59 83	99.49 209	99.98 3	99.71 97	99.72 33	99.84 79	99.81 87	99.94 45	99.78 121	98.91 136	99.71 368	98.41 217	99.95 99	99.05 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 365	96.91 374	98.74 352	97.72 447	97.57 368	97.60 411	97.36 428	98.00 348	99.21 313	98.02 432	90.04 407	99.79 338	98.37 219	95.89 446	98.86 389

PM-MVS		99.36 149	99.29 153	99.58 178	99.83 73	99.66 116	98.95 277	99.86 67	98.85 264	99.81 110	99.73 151	98.40 213	99.92 141	98.36 220	99.83 194	99.17 329

baseline1		97.73 349	97.33 360	98.96 322	99.30 336	97.73 364	99.40 120	98.42 399	99.33 193	99.46 254	99.21 361	91.18 389	99.82 313	98.35 221	91.26 449	99.32 293

MVS-HIRNet		97.86 343	98.22 314	96.76 416	99.28 341	91.53 443	98.38 352	92.60 448	99.13 228	99.31 295	99.96 15	97.18 297	99.68 389	98.34 222	99.83 194	99.07 360

GA-MVS		97.99 342	97.68 352	98.93 328	99.52 259	98.04 346	97.19 429	99.05 366	98.32 332	98.81 358	98.97 393	89.89 409	99.41 435	98.33 223	99.05 376	99.34 289

Fast-Effi-MVS+		99.02 236	98.87 251	99.46 218	99.38 307	99.50 162	99.04 247	99.79 106	97.17 394	98.62 376	98.74 411	99.34 72	99.95 76	98.32 224	99.41 341	98.92 382

MDA-MVSNet_test_wron		98.95 255	98.99 231	98.85 340	99.64 200	97.16 381	98.23 363	99.33 320	98.93 253	99.56 223	99.66 204	97.39 286	99.83 303	98.29 225	99.88 156	99.55 205

N_pmnet		98.73 279	98.53 286	99.35 257	99.72 163	98.67 294	98.34 354	94.65 441	98.35 326	99.79 120	99.68 195	98.03 246	99.93 113	98.28 226	99.92 126	99.44 259

ET-MVSNet_ETH3D		96.78 377	96.07 387	98.91 331	99.26 346	97.92 355	97.70 407	96.05 436	97.96 355	92.37 449	98.43 424	87.06 417	99.90 188	98.27 227	97.56 435	98.91 383

thisisatest0530		97.45 360	96.95 371	98.94 325	99.68 186	97.73 364	99.09 234	94.19 444	98.61 296	99.56 223	99.30 341	84.30 431	99.93 113	98.27 227	99.54 320	99.16 331

YYNet1		98.95 255	98.99 231	98.84 342	99.64 200	97.14 383	98.22 364	99.32 322	98.92 255	99.59 210	99.66 204	97.40 284	99.83 303	98.27 227	99.90 137	99.55 205

reproduce_model		99.50 103	99.40 123	99.83 38	99.60 209	99.83 34	99.12 221	99.68 167	99.49 159	99.80 114	99.79 109	99.01 120	99.93 113	98.24 230	99.82 204	99.73 89

ACMM	98.09 11	99.46 118	99.38 126	99.72 113	99.80 101	99.69 109	99.13 216	99.65 187	98.99 242	99.64 185	99.72 158	99.39 61	99.86 255	98.23 231	99.81 214	99.60 183
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 252	98.87 251	99.24 287	99.57 229	98.40 319	98.12 372	99.18 354	98.28 334	99.63 189	99.13 367	98.02 247	99.97 41	98.22 232	99.69 269	99.35 286

3Dnovator	99.15 2	99.43 127	99.36 132	99.65 142	99.39 304	99.42 185	99.70 38	99.56 240	99.23 208	99.35 281	99.80 99	99.17 93	99.95 76	98.21 233	99.84 186	99.59 190

Fast-Effi-MVS+-dtu		99.20 191	99.12 182	99.43 229	99.25 347	99.69 109	99.05 242	99.82 86	99.50 157	98.97 338	99.05 379	98.98 125	99.98 27	98.20 234	99.24 365	98.62 404

MS-PatchMatch		99.00 245	98.97 235	99.09 306	99.11 375	98.19 332	98.76 308	99.33 320	98.49 309	99.44 256	99.58 256	98.21 233	99.69 377	98.20 234	99.62 292	99.39 275

TSAR-MVS + GP.		99.12 214	99.04 213	99.38 248	99.34 325	99.16 241	98.15 368	99.29 330	98.18 341	99.63 189	99.62 231	99.18 92	99.68 389	98.20 234	99.74 248	99.30 299

DP-MVS		99.48 109	99.39 124	99.74 97	99.57 229	99.62 132	99.29 161	99.61 207	99.87 60	99.74 149	99.76 134	98.69 164	99.87 236	98.20 234	99.80 221	99.75 83

MVP-Stereo		99.16 204	99.08 196	99.43 229	99.48 277	99.07 257	99.08 237	99.55 246	98.63 292	99.31 295	99.68 195	98.19 236	99.78 341	98.18 238	99.58 308	99.45 254
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 127	99.30 148	99.80 60	99.83 73	99.81 47	99.52 92	99.70 156	98.35 326	99.51 243	99.50 288	99.31 76	99.88 222	98.18 238	99.84 186	99.69 108

MDA-MVSNet-bldmvs		99.06 227	99.05 207	99.07 311	99.80 101	97.83 359	98.89 283	99.72 148	99.29 196	99.63 189	99.70 177	96.47 318	99.89 207	98.17 240	99.82 204	99.50 236

JIA-IIPM		98.06 338	97.92 341	98.50 364	98.59 427	97.02 385	98.80 302	98.51 394	99.88 58	97.89 412	99.87 56	91.89 382	99.90 188	98.16 241	97.68 434	98.59 407

EIA-MVS		99.12 214	99.01 220	99.45 221	99.36 312	99.62 132	99.34 137	99.79 106	98.41 315	98.84 355	98.89 401	98.75 156	99.84 288	98.15 242	99.51 326	98.89 386

miper_lstm_enhance		98.65 287	98.60 274	98.82 347	99.20 357	97.33 377	97.78 403	99.66 177	99.01 241	99.59 210	99.50 288	94.62 352	99.85 273	98.12 243	99.90 137	99.26 305

reproduce-ours		99.46 118	99.35 134	99.82 43	99.56 240	99.83 34	99.05 242	99.65 187	99.45 171	99.78 124	99.78 121	98.93 131	99.93 113	98.11 244	99.81 214	99.70 101

our_new_method		99.46 118	99.35 134	99.82 43	99.56 240	99.83 34	99.05 242	99.65 187	99.45 171	99.78 124	99.78 121	98.93 131	99.93 113	98.11 244	99.81 214	99.70 101

Effi-MVS+-dtu		99.07 226	98.92 244	99.52 200	98.89 401	99.78 56	99.15 208	99.66 177	99.34 190	98.92 345	99.24 357	97.69 270	99.98 27	98.11 244	99.28 358	98.81 393

tpm		97.15 369	96.95 371	97.75 396	98.91 397	94.24 427	99.32 146	97.96 415	97.71 368	98.29 393	99.32 336	86.72 423	99.92 141	98.10 247	96.24 444	99.09 349

DeepPCF-MVS	98.42 6	99.18 198	99.02 216	99.67 129	99.22 352	99.75 77	97.25 427	99.47 284	98.72 283	99.66 182	99.70 177	99.29 78	99.63 410	98.07 248	99.81 214	99.62 169

ppachtmachnet_test		98.89 263	99.12 182	98.20 380	99.66 194	95.24 420	97.63 409	99.68 167	99.08 233	99.78 124	99.62 231	98.65 172	99.88 222	98.02 249	99.96 83	99.48 245

tpmrst		97.73 349	98.07 327	96.73 419	98.71 423	92.00 438	99.10 229	98.86 373	98.52 305	98.92 345	99.54 277	91.90 381	99.82 313	98.02 249	99.03 378	98.37 421

CSCG		99.37 146	99.29 153	99.60 172	99.71 166	99.46 171	99.43 118	99.85 73	98.79 274	99.41 269	99.60 248	98.92 134	99.92 141	98.02 249	99.92 126	99.43 265

eth_miper_zixun_eth		98.68 285	98.71 267	98.60 359	99.10 377	96.84 390	97.52 417	99.54 252	98.94 250	99.58 212	99.48 295	96.25 329	99.76 352	98.01 252	99.93 122	99.21 317

Patchmtry		98.78 273	98.54 285	99.49 209	98.89 401	99.19 238	99.32 146	99.67 172	99.65 128	99.72 155	99.79 109	91.87 383	99.95 76	98.00 253	99.97 69	99.33 290

PVSNet_BlendedMVS		99.03 234	99.01 220	99.09 306	99.54 245	97.99 348	98.58 326	99.82 86	97.62 371	99.34 285	99.71 168	98.52 196	99.77 349	97.98 254	99.97 69	99.52 229

PVSNet_Blended		98.70 283	98.59 276	99.02 316	99.54 245	97.99 348	97.58 412	99.82 86	95.70 419	99.34 285	98.98 391	98.52 196	99.77 349	97.98 254	99.83 194	99.30 299

cl____		98.54 299	98.41 296	98.92 329	99.03 387	97.80 362	97.46 419	99.59 224	98.90 257	99.60 207	99.46 302	93.85 359	99.78 341	97.97 256	99.89 147	99.17 329

DIV-MVS_self_test		98.54 299	98.42 295	98.92 329	99.03 387	97.80 362	97.46 419	99.59 224	98.90 257	99.60 207	99.46 302	93.87 358	99.78 341	97.97 256	99.89 147	99.18 326

AUN-MVS		97.82 345	97.38 359	99.14 300	99.27 343	98.53 310	98.72 312	99.02 368	98.10 343	97.18 430	99.03 385	89.26 411	99.85 273	97.94 258	97.91 430	99.03 366

FA-MVS(test-final)		98.52 301	98.32 306	99.10 305	99.48 277	98.67 294	99.77 19	98.60 390	97.35 386	99.63 189	99.80 99	93.07 371	99.84 288	97.92 259	99.30 355	98.78 396

ambc				99.20 291	99.35 316	98.53 310	99.17 200	99.46 287		99.67 177	99.80 99	98.46 203	99.70 371	97.92 259	99.70 265	99.38 277

USDC		98.96 252	98.93 240	99.05 314	99.54 245	97.99 348	97.07 433	99.80 99	98.21 338	99.75 140	99.77 130	98.43 206	99.64 408	97.90 261	99.88 156	99.51 231

OPM-MVS		99.26 171	99.13 178	99.63 156	99.70 174	99.61 138	98.58 326	99.48 281	98.50 307	99.52 237	99.63 223	99.14 98	99.76 352	97.89 262	99.77 235	99.51 231
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 161	99.17 171	99.77 74	99.69 178	99.80 51	99.14 210	99.31 326	99.16 222	99.62 198	99.61 240	98.35 217	99.91 169	97.88 263	99.72 260	99.61 179
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 38	99.70 174	99.79 53	99.14 210	99.61 207					99.92 141	97.88 263	99.72 260	99.77 75

c3_l		98.72 280	98.71 267	98.72 353	99.12 370	97.22 380	97.68 408	99.56 240	98.90 257	99.54 230	99.48 295	96.37 324	99.73 362	97.88 263	99.88 156	99.21 317

3Dnovator+	98.92 3	99.35 151	99.24 164	99.67 129	99.35 316	99.47 167	99.62 67	99.50 276	99.44 173	99.12 326	99.78 121	98.77 153	99.94 92	97.87 266	99.72 260	99.62 169

miper_ehance_all_eth		98.59 294	98.59 276	98.59 360	98.98 393	97.07 384	97.49 418	99.52 267	98.50 307	99.52 237	99.37 323	96.41 322	99.71 368	97.86 267	99.62 292	99.00 373

WTY-MVS		98.59 294	98.37 300	99.26 282	99.43 295	98.40 319	98.74 310	99.13 361	98.10 343	99.21 313	99.24 357	94.82 349	99.90 188	97.86 267	98.77 394	99.49 241

APD_test1		99.36 149	99.28 155	99.61 169	99.89 39	99.89 10	99.32 146	99.74 135	99.18 215	99.69 167	99.75 142	98.41 209	99.84 288	97.85 269	99.70 265	99.10 344

SED-MVS		99.40 136	99.28 155	99.77 74	99.69 178	99.82 42	99.20 187	99.54 252	99.13 228	99.82 103	99.63 223	98.91 136	99.92 141	97.85 269	99.70 265	99.58 195

test_241102_TWO								99.54 252	99.13 228	99.76 135	99.63 223	98.32 222	99.92 141	97.85 269	99.69 269	99.75 83

MVS_111021_HR		99.12 214	99.02 216	99.40 242	99.50 267	99.11 247	97.92 396	99.71 151	98.76 281	99.08 330	99.47 299	99.17 93	99.54 424	97.85 269	99.76 237	99.54 214

MTAPA		99.35 151	99.20 168	99.80 60	99.81 94	99.81 47	99.33 143	99.53 262	99.27 200	99.42 263	99.63 223	98.21 233	99.95 76	97.83 273	99.79 226	99.65 142

MSC_two_6792asdad				99.74 97	99.03 387	99.53 159		99.23 343					99.92 141	97.77 274	99.69 269	99.78 71

No_MVS				99.74 97	99.03 387	99.53 159		99.23 343					99.92 141	97.77 274	99.69 269	99.78 71

TESTMET0.1,1		96.24 392	95.84 393	97.41 405	98.24 438	93.84 430	97.38 421	95.84 437	98.43 312	97.81 418	98.56 419	79.77 440	99.89 207	97.77 274	98.77 394	98.52 413

ACMH+	98.40 8	99.50 103	99.43 118	99.71 118	99.86 57	99.76 69	99.32 146	99.77 116	99.53 153	99.77 132	99.76 134	99.26 84	99.78 341	97.77 274	99.88 156	99.60 183

IU-MVS					99.69 178	99.77 62		99.22 346	97.50 378	99.69 167				97.75 278	99.70 265	99.77 75

114514_t		98.49 306	98.11 324	99.64 149	99.73 160	99.58 148	99.24 176	99.76 124	89.94 442	99.42 263	99.56 267	97.76 267	99.86 255	97.74 279	99.82 204	99.47 249

DVP-MVS++		99.38 143	99.25 162	99.77 74	99.03 387	99.77 62	99.74 27	99.61 207	99.18 215	99.76 135	99.61 240	99.00 121	99.92 141	97.72 280	99.60 302	99.62 169

test_0728_THIRD									99.18 215	99.62 198	99.61 240	98.58 180	99.91 169	97.72 280	99.80 221	99.77 75

EGC-MVSNET		89.05 416	85.52 419	99.64 149	99.89 39	99.78 56	99.56 87	99.52 267	24.19 451	49.96 452	99.83 81	99.15 95	99.92 141	97.71 282	99.85 181	99.21 317

miper_enhance_ethall		98.03 339	97.94 339	98.32 374	98.27 437	96.43 398	96.95 434	99.41 298	96.37 410	99.43 260	98.96 395	94.74 350	99.69 377	97.71 282	99.62 292	98.83 392

TSAR-MVS + MP.		99.34 156	99.24 164	99.63 156	99.82 82	99.37 200	99.26 169	99.35 317	98.77 278	99.57 215	99.70 177	99.27 83	99.88 222	97.71 282	99.75 241	99.65 142
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 357	97.28 361	98.40 369	98.37 435	96.75 391	97.24 428	99.37 313	97.31 388	99.41 269	99.22 359	87.30 415	99.37 436	97.70 285	99.62 292	99.08 355

MP-MVS-pluss		99.14 209	98.92 244	99.80 60	99.83 73	99.83 34	98.61 319	99.63 197	96.84 403	99.44 256	99.58 256	98.81 144	99.91 169	97.70 285	99.82 204	99.67 123
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 165	99.11 185	99.79 67	99.75 148	99.81 47	98.95 277	99.53 262	98.27 335	99.53 235	99.73 151	98.75 156	99.87 236	97.70 285	99.83 194	99.68 114

UnsupCasMVSNet_bld		98.55 298	98.27 312	99.40 242	99.56 240	99.37 200	97.97 392	99.68 167	97.49 379	99.08 330	99.35 332	95.41 343	99.82 313	97.70 285	98.19 422	99.01 372

MVS_111021_LR		99.13 211	99.03 215	99.42 231	99.58 219	99.32 212	97.91 398	99.73 139	98.68 287	99.31 295	99.48 295	99.09 104	99.66 399	97.70 285	99.77 235	99.29 302

IS-MVSNet		99.03 234	98.85 253	99.55 192	99.80 101	99.25 225	99.73 30	99.15 358	99.37 186	99.61 204	99.71 168	94.73 351	99.81 328	97.70 285	99.88 156	99.58 195

test-LLR		97.15 369	96.95 371	97.74 397	98.18 440	95.02 422	97.38 421	96.10 433	98.00 348	97.81 418	98.58 416	90.04 407	99.91 169	97.69 291	98.78 392	98.31 422

test-mter		96.23 393	95.73 396	97.74 397	98.18 440	95.02 422	97.38 421	96.10 433	97.90 357	97.81 418	98.58 416	79.12 443	99.91 169	97.69 291	98.78 392	98.31 422

MonoMVSNet		98.23 328	98.32 306	97.99 385	98.97 394	96.62 393	99.49 105	98.42 399	99.62 136	99.40 274	99.79 109	95.51 341	98.58 447	97.68 293	95.98 445	98.76 399

XVS		99.27 169	99.11 185	99.75 92	99.71 166	99.71 97	99.37 129	99.61 207	99.29 196	98.76 365	99.47 299	98.47 200	99.88 222	97.62 294	99.73 254	99.67 123

X-MVStestdata		96.09 397	94.87 410	99.75 92	99.71 166	99.71 97	99.37 129	99.61 207	99.29 196	98.76 365	61.30 460	98.47 200	99.88 222	97.62 294	99.73 254	99.67 123

SMA-MVS		99.19 194	99.00 224	99.73 106	99.46 287	99.73 87	99.13 216	99.52 267	97.40 383	99.57 215	99.64 211	98.93 131	99.83 303	97.61 296	99.79 226	99.63 158
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		96.71 380	96.79 379	96.46 423	98.90 398	90.71 449	99.41 119	98.68 383	94.69 432	98.14 403	99.34 335	86.32 425	99.80 335	97.60 297	98.07 428	98.88 387

PVSNet	97.47 15	98.42 312	98.44 293	98.35 371	99.46 287	96.26 402	96.70 438	99.34 319	97.68 369	99.00 337	99.13 367	97.40 284	99.72 364	97.59 298	99.68 274	99.08 355

new_pmnet		98.88 264	98.89 249	98.84 342	99.70 174	97.62 367	98.15 368	99.50 276	97.98 351	99.62 198	99.54 277	98.15 239	99.94 92	97.55 299	99.84 186	98.95 377

IB-MVS	95.41 20	95.30 412	94.46 416	97.84 393	98.76 419	95.33 418	97.33 424	96.07 435	96.02 414	95.37 446	97.41 443	76.17 447	99.96 65	97.54 300	95.44 448	98.22 427
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		99.24 175	99.11 185	99.61 169	98.38 434	99.79 53	99.57 85	99.68 167	99.61 140	99.15 321	99.71 168	98.70 163	99.91 169	97.54 300	99.68 274	99.13 341

ZNCC-MVS		99.22 184	99.04 213	99.77 74	99.76 136	99.73 87	99.28 163	99.56 240	98.19 340	99.14 323	99.29 344	98.84 143	99.92 141	97.53 302	99.80 221	99.64 152

CP-MVS		99.23 176	99.05 207	99.75 92	99.66 194	99.66 116	99.38 125	99.62 200	98.38 319	99.06 334	99.27 347	98.79 149	99.94 92	97.51 303	99.82 204	99.66 133

SD-MVS		99.01 242	99.30 148	98.15 381	99.50 267	99.40 192	98.94 279	99.61 207	99.22 212	99.75 140	99.82 88	99.54 49	95.51 451	97.48 304	99.87 168	99.54 214
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
PMMVS		98.49 306	98.29 311	99.11 303	98.96 395	98.42 318	97.54 413	99.32 322	97.53 376	98.47 388	98.15 431	97.88 257	99.82 313	97.46 305	99.24 365	99.09 349

DeepC-MVS_fast	98.47 5	99.23 176	99.12 182	99.56 188	99.28 341	99.22 232	98.99 266	99.40 305	99.08 233	99.58 212	99.64 211	98.90 139	99.83 303	97.44 306	99.75 241	99.63 158
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 172	99.08 196	99.76 81	99.73 160	99.70 105	99.31 151	99.59 224	98.36 321	99.36 279	99.37 323	98.80 148	99.91 169	97.43 307	99.75 241	99.68 114

ACMMPR		99.23 176	99.06 202	99.76 81	99.74 156	99.69 109	99.31 151	99.59 224	98.36 321	99.35 281	99.38 320	98.61 176	99.93 113	97.43 307	99.75 241	99.67 123

Vis-MVSNet (Re-imp)		98.77 274	98.58 279	99.34 258	99.78 121	98.88 279	99.61 73	99.56 240	99.11 232	99.24 307	99.56 267	93.00 373	99.78 341	97.43 307	99.89 147	99.35 286

MIMVSNet		98.43 311	98.20 316	99.11 303	99.53 252	98.38 323	99.58 82	98.61 388	98.96 246	99.33 287	99.76 134	90.92 393	99.81 328	97.38 310	99.76 237	99.15 333

WB-MVSnew		98.34 322	98.14 322	98.96 322	98.14 443	97.90 356	98.27 359	97.26 429	98.63 292	98.80 360	98.00 434	97.77 265	99.90 188	97.37 311	98.98 381	99.09 349

XVG-OURS-SEG-HR		99.16 204	98.99 231	99.66 136	99.84 68	99.64 125	98.25 362	99.73 139	98.39 318	99.63 189	99.43 307	99.70 30	99.90 188	97.34 312	98.64 405	99.44 259

COLMAP_ROB	98.06 12	99.45 122	99.37 129	99.70 122	99.83 73	99.70 105	99.38 125	99.78 113	99.53 153	99.67 177	99.78 121	99.19 91	99.86 255	97.32 313	99.87 168	99.55 205
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 236	98.81 260	99.65 142	99.58 219	99.49 163	98.58 326	99.07 363	98.40 317	99.04 335	99.25 352	98.51 198	99.80 335	97.31 314	99.51 326	99.65 142

region2R		99.23 176	99.05 207	99.77 74	99.76 136	99.70 105	99.31 151	99.59 224	98.41 315	99.32 290	99.36 327	98.73 160	99.93 113	97.29 315	99.74 248	99.67 123

APD-MVS_3200maxsize		99.31 162	99.16 172	99.74 97	99.53 252	99.75 77	99.27 167	99.61 207	99.19 214	99.57 215	99.64 211	98.76 154	99.90 188	97.29 315	99.62 292	99.56 202

TAPA-MVS	97.92 13	98.03 339	97.55 355	99.46 218	99.47 283	99.44 178	98.50 340	99.62 200	86.79 443	99.07 333	99.26 350	98.26 227	99.62 411	97.28 317	99.73 254	99.31 297
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 169	99.11 185	99.73 106	99.54 245	99.74 84	99.26 169	99.62 200	99.16 222	99.52 237	99.64 211	98.41 209	99.91 169	97.27 318	99.61 299	99.54 214

RE-MVS-def			99.13 178		99.54 245	99.74 84	99.26 169	99.62 200	99.16 222	99.52 237	99.64 211	98.57 181		97.27 318	99.61 299	99.54 214

testing11		96.05 399	95.41 402	97.97 387	98.78 416	95.27 419	98.59 324	98.23 409	98.86 263	96.56 437	96.91 450	75.20 448	99.69 377	97.26 320	98.29 417	98.93 380

test_yl		98.25 325	97.95 335	99.13 301	99.17 363	98.47 313	99.00 259	98.67 385	98.97 244	99.22 311	99.02 386	91.31 387	99.69 377	97.26 320	98.93 383	99.24 308

DCV-MVSNet		98.25 325	97.95 335	99.13 301	99.17 363	98.47 313	99.00 259	98.67 385	98.97 244	99.22 311	99.02 386	91.31 387	99.69 377	97.26 320	98.93 383	99.24 308

PHI-MVS		99.11 218	98.95 238	99.59 175	99.13 368	99.59 143	99.17 200	99.65 187	97.88 360	99.25 304	99.46 302	98.97 127	99.80 335	97.26 320	99.82 204	99.37 280

tfpnnormal		99.43 127	99.38 126	99.60 172	99.87 54	99.75 77	99.59 80	99.78 113	99.71 107	99.90 65	99.69 184	98.85 142	99.90 188	97.25 324	99.78 231	99.15 333

PatchmatchNet		97.65 353	97.80 346	97.18 412	98.82 411	92.49 436	99.17 200	98.39 402	98.12 342	98.79 362	99.58 256	90.71 399	99.89 207	97.23 325	99.41 341	99.16 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 248	98.80 262	99.56 188	99.25 347	99.43 182	98.54 335	99.27 334	98.58 298	98.80 360	99.43 307	98.53 193	99.70 371	97.22 326	99.59 306	99.54 214

testing3		96.48 386	95.63 398	99.01 317	99.23 351	97.81 360	98.90 282	99.10 362	98.72 283	97.84 417	97.92 435	72.44 452	99.85 273	97.21 327	99.33 351	99.35 286

HPM-MVS		99.25 172	99.07 200	99.78 71	99.81 94	99.75 77	99.61 73	99.67 172	97.72 367	99.35 281	99.25 352	99.23 87	99.92 141	97.21 327	99.82 204	99.67 123
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 194	99.00 224	99.76 81	99.76 136	99.68 112	99.38 125	99.54 252	98.34 330	99.01 336	99.50 288	98.53 193	99.93 113	97.18 329	99.78 231	99.66 133

ACMMP		99.25 172	99.08 196	99.74 97	99.79 113	99.68 112	99.50 100	99.65 187	98.07 346	99.52 237	99.69 184	98.57 181	99.92 141	97.18 329	99.79 226	99.63 158
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		96.23 393	95.74 395	97.70 399	98.86 405	95.59 415	98.66 316	98.14 411	98.96 246	97.67 423	97.06 447	76.78 445	98.92 443	97.10 331	98.41 414	98.58 409

thisisatest0515		96.98 373	96.42 381	98.66 356	99.42 300	97.47 371	97.27 426	94.30 443	97.24 390	99.15 321	98.86 403	85.01 427	99.87 236	97.10 331	99.39 343	98.63 403

XVG-ACMP-BASELINE		99.23 176	99.10 193	99.63 156	99.82 82	99.58 148	98.83 294	99.72 148	98.36 321	99.60 207	99.71 168	98.92 134	99.91 169	97.08 333	99.84 186	99.40 272

MSDG		99.08 223	98.98 234	99.37 251	99.60 209	99.13 244	97.54 413	99.74 135	98.84 267	99.53 235	99.55 275	99.10 102	99.79 338	97.07 334	99.86 176	99.18 326

SteuartSystems-ACMMP		99.30 163	99.14 176	99.76 81	99.87 54	99.66 116	99.18 195	99.60 218	98.55 300	99.57 215	99.67 199	99.03 119	99.94 92	97.01 335	99.80 221	99.69 108
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 395	95.78 394	97.49 401	98.53 429	93.83 431	98.04 382	93.94 446	98.96 246	98.46 389	98.17 430	79.86 438	99.87 236	96.99 336	99.06 374	98.78 396

EPMVS		96.53 383	96.32 382	97.17 413	98.18 440	92.97 435	99.39 122	89.95 452	98.21 338	98.61 377	99.59 253	86.69 424	99.72 364	96.99 336	99.23 367	98.81 393

MSP-MVS		99.04 233	98.79 263	99.81 51	99.78 121	99.73 87	99.35 136	99.57 235	98.54 303	99.54 230	98.99 388	96.81 307	99.93 113	96.97 338	99.53 322	99.77 75
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++		98.96 252	98.70 269	99.74 97	99.52 259	99.71 97	98.86 287	99.19 353	98.47 311	98.59 379	99.06 378	98.08 244	99.91 169	96.94 339	99.60 302	99.60 183

SR-MVS		99.19 194	99.00 224	99.74 97	99.51 261	99.72 92	99.18 195	99.60 218	98.85 264	99.47 250	99.58 256	98.38 214	99.92 141	96.92 340	99.54 320	99.57 200

PGM-MVS		99.20 191	99.01 220	99.77 74	99.75 148	99.71 97	99.16 206	99.72 148	97.99 350	99.42 263	99.60 248	98.81 144	99.93 113	96.91 341	99.74 248	99.66 133

HY-MVS	98.23 9	98.21 332	97.95 335	98.99 318	99.03 387	98.24 327	99.61 73	98.72 381	96.81 404	98.73 367	99.51 285	94.06 356	99.86 255	96.91 341	98.20 420	98.86 389

MDTV_nov1_ep13			97.73 350		98.70 424	90.83 447	99.15 208	98.02 414	98.51 306	98.82 357	99.61 240	90.98 392	99.66 399	96.89 343	98.92 385

GST-MVS		99.16 204	98.96 237	99.75 92	99.73 160	99.73 87	99.20 187	99.55 246	98.22 337	99.32 290	99.35 332	98.65 172	99.91 169	96.86 344	99.74 248	99.62 169

test_post1							99.14 210				51.63 462	89.54 410	99.82 313	96.86 344

SCA		98.11 335	98.36 301	97.36 406	99.20 357	92.99 434	98.17 367	98.49 396	98.24 336	99.10 329	99.57 263	96.01 334	99.94 92	96.86 344	99.62 292	99.14 338

UBG		96.53 383	95.95 389	98.29 378	98.87 404	96.31 401	98.48 343	98.07 412	98.83 268	97.32 425	96.54 455	79.81 439	99.62 411	96.84 347	98.74 398	98.95 377

XVG-OURS		99.21 189	99.06 202	99.65 142	99.82 82	99.62 132	97.87 400	99.74 135	98.36 321	99.66 182	99.68 195	99.71 27	99.90 188	96.84 347	99.88 156	99.43 265

LCM-MVSNet-Re		99.28 165	99.15 175	99.67 129	99.33 330	99.76 69	99.34 137	99.97 20	98.93 253	99.91 60	99.79 109	98.68 165	99.93 113	96.80 349	99.56 311	99.30 299

RPSCF		99.18 198	99.02 216	99.64 149	99.83 73	99.85 22	99.44 116	99.82 86	98.33 331	99.50 245	99.78 121	97.90 255	99.65 406	96.78 350	99.83 194	99.44 259

旧先验2							97.94 394		95.33 423	98.94 341			99.88 222	96.75 351

MDTV_nov1_ep13_2view						91.44 444	99.14 210		97.37 385	99.21 313		91.78 385		96.75 351		99.03 366

CLD-MVS		98.76 275	98.57 280	99.33 261	99.57 229	98.97 267	97.53 415	99.55 246	96.41 408	99.27 302	99.13 367	99.07 109	99.78 341	96.73 353	99.89 147	99.23 312
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 336	97.98 333	98.48 365	99.27 343	96.48 396	99.40 120	99.07 363	98.81 271	99.23 308	99.57 263	90.11 406	99.87 236	96.69 354	99.64 287	99.09 349

baseline2		96.83 376	96.28 383	98.46 367	99.09 380	96.91 388	98.83 294	93.87 447	97.23 391	96.23 442	98.36 425	88.12 414	99.90 188	96.68 355	98.14 425	98.57 411

cascas		96.99 372	96.82 378	97.48 402	97.57 450	95.64 413	96.43 440	99.56 240	91.75 438	97.13 432	97.61 442	95.58 339	98.63 445	96.68 355	99.11 371	98.18 431

PC_three_1452									97.56 372	99.68 170	99.41 310	99.09 104	97.09 448	96.66 357	99.60 302	99.62 169

LPG-MVS_test		99.22 184	99.05 207	99.74 97	99.82 82	99.63 130	99.16 206	99.73 139	97.56 372	99.64 185	99.69 184	99.37 67	99.89 207	96.66 357	99.87 168	99.69 108

LGP-MVS_train				99.74 97	99.82 82	99.63 130		99.73 139	97.56 372	99.64 185	99.69 184	99.37 67	99.89 207	96.66 357	99.87 168	99.69 108

ETVMVS		96.14 396	95.22 407	98.89 338	98.80 412	98.01 347	98.66 316	98.35 405	98.71 285	97.18 430	96.31 459	74.23 451	99.75 356	96.64 360	98.13 427	98.90 384

TinyColmap		98.97 249	98.93 240	99.07 311	99.46 287	98.19 332	97.75 404	99.75 129	98.79 274	99.54 230	99.70 177	98.97 127	99.62 411	96.63 361	99.83 194	99.41 270

LF4IMVS		99.01 242	98.92 244	99.27 279	99.71 166	99.28 218	98.59 324	99.77 116	98.32 332	99.39 276	99.41 310	98.62 174	99.84 288	96.62 362	99.84 186	98.69 402

NCCC		98.82 269	98.57 280	99.58 178	99.21 354	99.31 213	98.61 319	99.25 339	98.65 290	98.43 390	99.26 350	97.86 258	99.81 328	96.55 363	99.27 361	99.61 179

OPU-MVS				99.29 273	99.12 370	99.44 178	99.20 187				99.40 314	99.00 121	98.84 444	96.54 364	99.60 302	99.58 195

F-COLMAP		98.74 277	98.45 292	99.62 165	99.57 229	99.47 167	98.84 291	99.65 187	96.31 411	98.93 342	99.19 364	97.68 271	99.87 236	96.52 365	99.37 346	99.53 219

testing99		95.86 404	95.19 408	97.87 391	98.76 419	95.03 421	98.62 318	98.44 398	98.68 287	96.67 436	96.66 454	74.31 450	99.69 377	96.51 366	98.03 429	98.90 384

ADS-MVSNet2		97.78 347	97.66 354	98.12 383	99.14 366	95.36 417	99.22 184	98.75 380	96.97 399	98.25 395	99.64 211	90.90 394	99.94 92	96.51 366	99.56 311	99.08 355

ADS-MVSNet		97.72 352	97.67 353	97.86 392	99.14 366	94.65 425	99.22 184	98.86 373	96.97 399	98.25 395	99.64 211	90.90 394	99.84 288	96.51 366	99.56 311	99.08 355

PatchMatch-RL		98.68 285	98.47 289	99.30 272	99.44 292	99.28 218	98.14 370	99.54 252	97.12 397	99.11 327	99.25 352	97.80 263	99.70 371	96.51 366	99.30 355	98.93 380

CMPMVS	77.52 23	98.50 304	98.19 319	99.41 239	98.33 436	99.56 152	99.01 256	99.59 224	95.44 421	99.57 215	99.80 99	95.64 337	99.46 434	96.47 370	99.92 126	99.21 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 400	95.32 405	98.02 384	98.76 419	95.39 416	98.38 352	98.65 387	98.82 269	96.84 433	96.71 453	75.06 449	99.71 368	96.46 371	98.23 419	98.98 374

SF-MVS		99.10 221	98.93 240	99.62 165	99.58 219	99.51 161	99.13 216	99.65 187	97.97 352	99.42 263	99.61 240	98.86 141	99.87 236	96.45 372	99.68 274	99.49 241

FE-MVS		97.85 344	97.42 358	99.15 297	99.44 292	98.75 289	99.77 19	98.20 410	95.85 416	99.33 287	99.80 99	88.86 412	99.88 222	96.40 373	99.12 370	98.81 393

DPE-MVS		99.14 209	98.92 244	99.82 43	99.57 229	99.77 62	98.74 310	99.60 218	98.55 300	99.76 135	99.69 184	98.23 232	99.92 141	96.39 374	99.75 241	99.76 80
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 448	89.02 454			93.47 434		98.30 426		99.84 288	96.38 375

AllTest		99.21 189	99.07 200	99.63 156	99.78 121	99.64 125	99.12 221	99.83 81	98.63 292	99.63 189	99.72 158	98.68 165	99.75 356	96.38 375	99.83 194	99.51 231

TestCases				99.63 156	99.78 121	99.64 125		99.83 81	98.63 292	99.63 189	99.72 158	98.68 165	99.75 356	96.38 375	99.83 194	99.51 231

testdata				99.42 231	99.51 261	98.93 274		99.30 329	96.20 412	98.87 352	99.40 314	98.33 221	99.89 207	96.29 378	99.28 358	99.44 259

dp		96.86 375	97.07 367	96.24 425	98.68 425	90.30 452	99.19 193	98.38 403	97.35 386	98.23 397	99.59 253	87.23 416	99.82 313	96.27 379	98.73 401	98.59 407

tpmvs		97.39 364	97.69 351	96.52 421	98.41 433	91.76 440	99.30 154	98.94 372	97.74 366	97.85 416	99.55 275	92.40 380	99.73 362	96.25 380	98.73 401	98.06 433

KD-MVS_2432*1600		95.89 401	95.41 402	97.31 409	94.96 452	93.89 428	97.09 431	99.22 346	97.23 391	98.88 349	99.04 381	79.23 441	99.54 424	96.24 381	96.81 439	98.50 417

miper_refine_blended		95.89 401	95.41 402	97.31 409	94.96 452	93.89 428	97.09 431	99.22 346	97.23 391	98.88 349	99.04 381	79.23 441	99.54 424	96.24 381	96.81 439	98.50 417

ACMP	97.51 14	99.05 230	98.84 255	99.67 129	99.78 121	99.55 156	98.88 284	99.66 177	97.11 398	99.47 250	99.60 248	99.07 109	99.89 207	96.18 383	99.85 181	99.58 195
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 260	98.72 266	99.44 225	99.39 304	99.42 185	98.58 326	99.64 195	97.31 388	99.44 256	99.62 231	98.59 178	99.69 377	96.17 384	99.79 226	99.22 314

DP-MVS Recon		98.50 304	98.23 313	99.31 269	99.49 272	99.46 171	98.56 331	99.63 197	94.86 430	98.85 354	99.37 323	97.81 262	99.59 418	96.08 385	99.44 336	98.88 387

tpm cat1		96.78 377	96.98 370	96.16 426	98.85 406	90.59 450	99.08 237	99.32 322	92.37 436	97.73 422	99.46 302	91.15 390	99.69 377	96.07 386	98.80 391	98.21 428

tpm2		96.35 389	96.22 384	96.73 419	98.88 403	91.75 441	99.21 186	98.51 394	93.27 435	97.89 412	99.21 361	84.83 428	99.70 371	96.04 387	98.18 423	98.75 400

dmvs_re		98.69 284	98.48 288	99.31 269	99.55 243	99.42 185	99.54 90	98.38 403	99.32 194	98.72 368	98.71 412	96.76 309	99.21 438	96.01 388	99.35 349	99.31 297

test_0402		99.22 184	99.14 176	99.45 221	99.79 113	99.43 182	99.28 163	99.68 167	99.54 151	99.40 274	99.56 267	99.07 109	99.82 313	96.01 388	99.96 83	99.11 342

ITE_SJBPF				99.38 248	99.63 202	99.44 178		99.73 139	98.56 299	99.33 287	99.53 279	98.88 140	99.68 389	96.01 388	99.65 285	99.02 371

test_prior2							97.95 393		97.87 361	98.05 405	99.05 379	97.90 255		95.99 391	99.49 331

testdata2													99.89 207	95.99 391

原ACMM1				99.37 251	99.47 283	98.87 281		99.27 334	96.74 406	98.26 394	99.32 336	97.93 254	99.82 313	95.96 393	99.38 344	99.43 265

新几何1				99.52 200	99.50 267	99.22 232		99.26 336	95.66 420	98.60 378	99.28 345	97.67 272	99.89 207	95.95 394	99.32 353	99.45 254

MP-MVS		99.06 227	98.83 257	99.76 81	99.76 136	99.71 97	99.32 146	99.50 276	98.35 326	98.97 338	99.48 295	98.37 215	99.92 141	95.95 394	99.75 241	99.63 158
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 411	94.59 414	98.61 358	98.66 426	97.45 373	98.54 335	97.90 418	98.53 304	96.54 438	96.47 456	70.62 455	99.81 328	95.91 396	98.15 424	98.56 412

wuyk23d		97.58 356	99.13 178	92.93 430	99.69 178	99.49 163	99.52 92	99.77 116	97.97 352	99.96 32	99.79 109	99.84 15	99.94 92	95.85 397	99.82 204	79.36 448

HQP_MVS		98.90 260	98.68 270	99.55 192	99.58 219	99.24 229	98.80 302	99.54 252	98.94 250	99.14 323	99.25 352	97.24 291	99.82 313	95.84 398	99.78 231	99.60 183

plane_prior5								99.54 252					99.82 313	95.84 398	99.78 231	99.60 183

无先验							98.01 385	99.23 343	95.83 417				99.85 273	95.79 400		99.44 259

CPTT-MVS		98.74 277	98.44 293	99.64 149	99.61 207	99.38 197	99.18 195	99.55 246	96.49 407	99.27 302	99.37 323	97.11 299	99.92 141	95.74 401	99.67 280	99.62 169

PLC	97.35 16	98.36 317	97.99 331	99.48 213	99.32 331	99.24 229	98.50 340	99.51 272	95.19 426	98.58 380	98.96 395	96.95 304	99.83 303	95.63 402	99.25 363	99.37 280
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 296	98.34 304	99.28 276	99.18 362	99.10 254	98.34 354	99.41 298	98.48 310	98.52 385	98.98 391	97.05 301	99.78 341	95.59 403	99.50 329	98.96 375

1314		98.00 341	97.90 343	98.27 379	98.90 398	97.45 373	99.30 154	99.06 365	94.98 427	97.21 429	99.12 371	98.43 206	99.67 394	95.58 404	98.56 408	97.71 437

PVSNet_0	95.53 19	95.85 405	95.31 406	97.47 403	98.78 416	93.48 433	95.72 442	99.40 305	96.18 413	97.37 424	97.73 437	95.73 336	99.58 419	95.49 405	81.40 450	99.36 283

MAR-MVS		98.24 327	97.92 341	99.19 292	98.78 416	99.65 122	99.17 200	99.14 359	95.36 422	98.04 406	98.81 408	97.47 281	99.72 364	95.47 406	99.06 374	98.21 428
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	98.23 328	97.89 344	99.26 282	99.19 359	99.26 222	99.65 62	99.69 164	91.33 440	98.14 403	99.77 130	98.28 224	99.96 65	95.41 407	99.55 315	98.58 409

train_agg		98.35 320	97.95 335	99.57 185	99.35 316	99.35 207	98.11 374	99.41 298	94.90 428	97.92 410	98.99 388	98.02 247	99.85 273	95.38 408	99.44 336	99.50 236

9.14			98.64 271		99.45 291		98.81 299	99.60 218	97.52 377	99.28 301	99.56 267	98.53 193	99.83 303	95.36 409	99.64 287

APD-MVS		98.87 265	98.59 276	99.71 118	99.50 267	99.62 132	99.01 256	99.57 235	96.80 405	99.54 230	99.63 223	98.29 223	99.91 169	95.24 410	99.71 263	99.61 179
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 399								95.20 411

AdaColmap		98.60 291	98.35 303	99.38 248	99.12 370	99.22 232	98.67 315	99.42 297	97.84 364	98.81 358	99.27 347	97.32 289	99.81 328	95.14 412	99.53 322	99.10 344

test9_res														95.10 413	99.44 336	99.50 236

CDPH-MVS		98.56 297	98.20 316	99.61 169	99.50 267	99.46 171	98.32 356	99.41 298	95.22 424	99.21 313	99.10 375	98.34 219	99.82 313	95.09 414	99.66 283	99.56 202

BH-untuned		98.22 330	98.09 325	98.58 362	99.38 307	97.24 379	98.55 332	98.98 371	97.81 365	99.20 318	98.76 410	97.01 302	99.65 406	94.83 415	98.33 415	98.86 389

BP-MVS														94.73 416

HQP-MVS		98.36 317	98.02 330	99.39 245	99.31 332	98.94 271	97.98 389	99.37 313	97.45 380	98.15 399	98.83 405	96.67 310	99.70 371	94.73 416	99.67 280	99.53 219

QAPM		98.40 315	97.99 331	99.65 142	99.39 304	99.47 167	99.67 53	99.52 267	91.70 439	98.78 364	99.80 99	98.55 185	99.95 76	94.71 418	99.75 241	99.53 219

agg_prior2														94.58 419	99.46 335	99.50 236

myMVS_eth3d		95.63 409	94.73 411	98.34 373	98.50 431	96.36 399	98.60 321	99.21 349	97.89 358	96.76 434	96.37 457	72.10 453	99.57 420	94.38 420	98.73 401	99.09 349

BH-RMVSNet		98.41 313	98.14 322	99.21 289	99.21 354	98.47 313	98.60 321	98.26 408	98.35 326	98.93 342	99.31 339	97.20 296	99.66 399	94.32 421	99.10 372	99.51 231

E-PMN		97.14 371	97.43 357	96.27 424	98.79 414	91.62 442	95.54 443	99.01 370	99.44 173	98.88 349	99.12 371	92.78 374	99.68 389	94.30 422	99.03 378	97.50 438

MG-MVS		98.52 301	98.39 298	98.94 325	99.15 365	97.39 376	98.18 365	99.21 349	98.89 260	99.23 308	99.63 223	97.37 287	99.74 359	94.22 423	99.61 299	99.69 108

API-MVS		98.38 316	98.39 298	98.35 371	98.83 408	99.26 222	99.14 210	99.18 354	98.59 297	98.66 373	98.78 409	98.61 176	99.57 420	94.14 424	99.56 311	96.21 445

PAPM_NR		98.36 317	98.04 328	99.33 261	99.48 277	98.93 274	98.79 305	99.28 333	97.54 375	98.56 384	98.57 418	97.12 298	99.69 377	94.09 425	98.90 389	99.38 277

ZD-MVS					99.43 295	99.61 138		99.43 295	96.38 409	99.11 327	99.07 377	97.86 258	99.92 141	94.04 426	99.49 331

DPM-MVS		98.28 323	97.94 339	99.32 266	99.36 312	99.11 247	97.31 425	98.78 379	96.88 401	98.84 355	99.11 374	97.77 265	99.61 416	94.03 427	99.36 347	99.23 312

gg-mvs-nofinetune		95.87 403	95.17 409	97.97 387	98.19 439	96.95 386	99.69 45	89.23 453	99.89 53	96.24 441	99.94 19	81.19 433	99.51 430	93.99 428	98.20 420	97.44 439

PMVS	92.94 21	98.82 269	98.81 260	98.85 340	99.84 68	97.99 348	99.20 187	99.47 284	99.71 107	99.42 263	99.82 88	98.09 242	99.47 432	93.88 429	99.85 181	99.07 360
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 374	97.28 361	95.99 428	98.76 419	91.03 446	95.26 445	98.61 388	99.34 190	98.92 345	98.88 402	93.79 360	99.66 399	92.87 430	99.05 376	97.30 442

BH-w/o		97.20 368	97.01 369	97.76 395	99.08 381	95.69 412	98.03 384	98.52 393	95.76 418	97.96 409	98.02 432	95.62 338	99.47 432	92.82 431	97.25 438	98.12 432

TR-MVS		97.44 361	97.15 366	98.32 374	98.53 429	97.46 372	98.47 344	97.91 417	96.85 402	98.21 398	98.51 422	96.42 320	99.51 430	92.16 432	97.29 437	97.98 434

OpenMVS_ROB	97.31 17	97.36 366	96.84 376	98.89 338	99.29 338	99.45 176	98.87 286	99.48 281	86.54 445	99.44 256	99.74 147	97.34 288	99.86 255	91.61 433	99.28 358	97.37 441

GG-mvs-BLEND				97.36 406	97.59 448	96.87 389	99.70 38	88.49 454		94.64 447	97.26 446	80.66 435	99.12 439	91.50 434	96.50 443	96.08 447

DeepMVS_CX				97.98 386	99.69 178	96.95 386		99.26 336	75.51 448	95.74 444	98.28 427	96.47 318	99.62 411	91.23 435	97.89 431	97.38 440

PAPR		97.56 357	97.07 367	99.04 315	98.80 412	98.11 340	97.63 409	99.25 339	94.56 433	98.02 408	98.25 428	97.43 283	99.68 389	90.90 436	98.74 398	99.33 290

MVS		95.72 407	94.63 413	98.99 318	98.56 428	97.98 353	99.30 154	98.86 373	72.71 449	97.30 426	99.08 376	98.34 219	99.74 359	89.21 437	98.33 415	99.26 305

UWE-MVS-28		95.64 408	95.47 400	96.14 427	97.98 444	90.39 451	98.49 342	95.81 438	99.02 240	98.03 407	98.19 429	84.49 430	99.28 437	88.75 438	98.47 413	98.75 400

thres600view7		96.60 382	96.16 385	97.93 389	99.63 202	96.09 407	99.18 195	97.57 423	98.77 278	98.72 368	97.32 444	87.04 418	99.72 364	88.57 439	98.62 406	97.98 434

FPMVS		96.32 390	95.50 399	98.79 348	99.60 209	98.17 335	98.46 348	98.80 378	97.16 395	96.28 439	99.63 223	82.19 432	99.09 440	88.45 440	98.89 390	99.10 344

PCF-MVS	96.03 18	96.73 379	95.86 392	99.33 261	99.44 292	99.16 241	96.87 436	99.44 292	86.58 444	98.95 340	99.40 314	94.38 354	99.88 222	87.93 441	99.80 221	98.95 377
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 388	96.03 388	97.47 403	99.63 202	95.93 408	99.18 195	97.57 423	98.75 282	98.70 371	97.31 445	87.04 418	99.67 394	87.62 442	98.51 410	96.81 443

tfpn200view9		96.30 391	95.89 390	97.53 400	99.58 219	96.11 405	99.00 259	97.54 426	98.43 312	98.52 385	96.98 448	86.85 420	99.67 394	87.62 442	98.51 410	96.81 443

thres400		96.40 387	95.89 390	97.92 390	99.58 219	96.11 405	99.00 259	97.54 426	98.43 312	98.52 385	96.98 448	86.85 420	99.67 394	87.62 442	98.51 410	97.98 434

thres200		96.09 397	95.68 397	97.33 408	99.48 277	96.22 404	98.53 337	97.57 423	98.06 347	98.37 392	96.73 452	86.84 422	99.61 416	86.99 445	98.57 407	96.16 446

MVE	92.54 22	96.66 381	96.11 386	98.31 376	99.68 186	97.55 369	97.94 394	95.60 439	99.37 186	90.68 450	98.70 414	96.56 313	98.61 446	86.94 446	99.55 315	98.77 398
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 367	96.83 377	98.59 360	99.46 287	97.55 369	99.25 175	96.84 432	98.78 276	97.24 428	97.67 438	97.11 299	98.97 442	86.59 447	98.54 409	99.27 303

PAPM		95.61 410	94.71 412	98.31 376	99.12 370	96.63 392	96.66 439	98.46 397	90.77 441	96.25 440	98.68 415	93.01 372	99.69 377	81.60 448	97.86 433	98.62 404

SD_0403		97.42 362	96.90 375	98.98 320	99.54 245	97.90 356	99.52 92	99.54 252	99.34 190	97.87 414	98.85 404	98.72 161	99.64 408	78.93 449	99.83 194	99.40 272

dongtai		89.37 415	88.91 418	90.76 431	99.19 359	77.46 456	95.47 444	87.82 455	92.28 437	94.17 448	98.82 407	71.22 454	95.54 450	63.85 450	97.34 436	99.27 303

kuosan		85.65 417	84.57 420	88.90 433	97.91 445	77.11 457	96.37 441	87.62 456	85.24 446	85.45 451	96.83 451	69.94 456	90.98 452	45.90 451	95.83 447	98.62 404

test123		29.31 418	33.05 423	18.08 434	25.93 458	12.24 459	97.53 415	10.93 459	11.78 452	24.21 453	50.08 464	21.04 457	8.60 453	23.51 452	32.43 452	33.39 449

testmvs		28.94 419	33.33 421	15.79 435	26.03 457	9.81 460	96.77 437	15.67 458	11.55 453	23.87 454	50.74 463	19.03 458	8.53 454	23.21 453	33.07 451	29.03 450

mmdepth		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

monomultidepth		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

test_blank		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

uanet_test		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

DCPMVS		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

cdsmvs_eth3d_5k		24.88 420	33.17 422	0.00 436	0.00 459	0.00 461	0.00 447	99.62 200	0.00 454	0.00 455	99.13 367	99.82 17	0.00 455	0.00 454	0.00 453	0.00 451

pcd_1.5k_mvsjas		16.61 421	22.14 424	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	99.28 80	0.00 455	0.00 454	0.00 453	0.00 451

sosnet-low-res		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

sosnet		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

uncertanet		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

Regformer		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

ab-mvs-re		8.26 432	11.02 435	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	99.16 365	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

uanet		8.33 422	11.11 425	0.00 436	0.00 459	0.00 461	0.00 447	0.00 460	0.00 454	0.00 455	100.00 1	0.00 459	0.00 455	0.00 454	0.00 453	0.00 451

FOURS1					99.83 73	99.89 10	99.74 27	99.71 151	99.69 115	99.63 189

test_one_0601					99.63 202	99.76 69		99.55 246	99.23 208	99.31 295	99.61 240	98.59 178

eth-test2					0.00 459

eth-test					0.00 459

test_241102_ONE					99.69 178	99.82 42		99.54 252	99.12 231	99.82 103	99.49 292	98.91 136	99.52 429

save fliter					99.53 252	99.25 225	98.29 358	99.38 312	99.07 235

test0726					99.69 178	99.80 51	99.24 176	99.57 235	99.16 222	99.73 153	99.65 209	98.35 217

GSMVS																99.14 338

test_part2					99.62 206	99.67 114				99.55 228

sam_mvs1												90.81 398				99.14 338

sam_mvs												90.52 403

MTGPA								99.53 262

test_post											52.41 461	90.25 405	99.86 255

patchmatchnet-post											99.62 231	90.58 401	99.94 92

MTMP							99.09 234	98.59 391

TEST9					99.35 316	99.35 207	98.11 374	99.41 298	94.83 431	97.92 410	98.99 388	98.02 247	99.85 273

test_8					99.34 325	99.31 213	98.08 378	99.40 305	94.90 428	97.87 414	98.97 393	98.02 247	99.84 288

agg_prior					99.35 316	99.36 204		99.39 308		97.76 421			99.85 273

test_prior4						99.19 238	98.00 387

test_prior				99.46 218	99.35 316	99.22 232		99.39 308					99.69 377			99.48 245

新几何2							98.04 382

旧先验1					99.49 272	99.29 216		99.26 336			99.39 318	97.67 272			99.36 347	99.46 253

原ACMM2							97.92 396

test222					99.51 261	99.08 256	97.83 402	99.29 330	95.21 425	98.68 372	99.31 339	97.28 290			99.38 344	99.43 265

segment_acmp												98.37 215

testdata1							97.72 405		97.86 363

test12				99.54 197	99.29 338	99.33 210		99.16 357		98.43 390		97.54 279	99.82 313		99.47 333	99.48 245

plane_prior7					99.58 219	99.38 197

plane_prior6					99.47 283	99.26 222						97.24 291

plane_prior4											99.25 352

plane_prior3						99.31 213			98.36 321	99.14 323

plane_prior2							98.80 302		98.94 250

plane_prior1					99.51 261

plane_prior						99.24 229	98.42 350		97.87 361						99.71 263

n2								0.00 460

nn								0.00 460

door-mid								99.83 81

test11								99.29 330

door								99.77 116

HQP5-MVS						98.94 271

HQP-NCC					99.31 332		97.98 389		97.45 380	98.15 399

ACMP_Plane					99.31 332		97.98 389		97.45 380	98.15 399

HQP4-MVS										98.15 399			99.70 371			99.53 219

HQP3-MVS								99.37 313							99.67 280

HQP2-MVS												96.67 310

NP-MVS					99.40 303	99.13 244					98.83 405

ACMMP++_ref															99.94 112

ACMMP++															99.79 226

Test By Simon												98.41 209

High-resolution multi-view benchmark