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 80	99.70 38	100.00 1	99.73 109	100.00 1	99.89 41	99.79 22	99.88 235	99.98 1	100.00 1	99.98 5

test_fmvs2		99.72 54	99.85 17	99.34 292	99.91 31	98.08 383	99.48 109	100.00 1	99.90 50	99.99 7	99.91 31	99.50 61	99.98 27	99.98 1	99.99 16	99.96 13

test_fmvs3		99.83 21	99.93 2	99.53 226	99.96 7	98.62 338	99.67 53	100.00 1	99.95 32	100.00 1	99.95 16	99.85 14	99.99 8	99.98 1	99.99 16	99.98 5

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 3	99.98 3	99.76 71	99.12 241	100.00 1	100.00 1	99.99 7	99.91 31	99.98 1	100.00 1	99.97 4	100.00 1	99.99 2

test_vis1_n_1920		99.72 54	99.88 7	99.27 317	99.93 24	97.84 396	99.34 148	100.00 1	99.99 3	99.99 7	99.82 91	99.87 13	99.99 8	99.97 4	99.99 16	99.97 10

test_vis1_n		99.68 65	99.79 34	99.36 287	99.94 18	98.18 372	99.52 94	100.00 1	99.86 66	100.00 1	99.88 50	98.99 148	99.96 69	99.97 4	99.96 87	99.95 14

test_fmvs1_n		99.68 65	99.81 28	99.28 312	99.95 15	97.93 392	99.49 107	100.00 1	99.82 86	99.99 7	99.89 41	99.21 102	99.98 27	99.97 4	99.98 50	99.93 20

test_f		99.75 49	99.88 7	99.37 282	99.96 7	98.21 369	99.51 101	100.00 1	99.94 36	100.00 1	99.93 22	99.58 49	99.94 98	99.97 4	99.99 16	99.97 10

fmvsm_l_conf0.5_n_9		99.83 21	99.81 28	99.89 11	99.86 59	99.80 52	98.94 308	99.96 28	99.98 18	99.96 34	99.78 132	99.88 11	99.98 27	99.96 9	99.99 16	99.90 29

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 45	99.86 19	99.08 257	99.97 20	99.98 18	99.96 34	99.79 119	99.90 9	99.99 8	99.96 9	99.99 16	99.90 29

test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 88	99.01 281	99.99 11	99.99 3	99.98 14	99.88 50	99.97 2	99.99 8	99.96 9	100.00 1	99.98 5

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 54	99.88 45	99.55 170	99.17 216	99.98 12	99.99 3	99.96 34	99.84 77	99.96 3	99.99 8	99.96 9	99.99 16	99.88 40

test_cas_vis1_n_1920		99.76 46	99.86 13	99.45 252	99.93 24	98.40 357	99.30 165	99.98 12	99.94 36	99.99 7	99.89 41	99.80 21	99.97 44	99.96 9	99.97 73	99.97 10

fmvsm_s_conf0.5_n_10		99.77 44	99.73 55	99.88 19	99.81 106	99.75 80	99.06 263	99.85 82	99.99 3	99.97 24	99.84 77	99.12 119	99.98 27	99.95 14	99.99 16	99.90 29

fmvsm_s_conf0.5_n_7		99.73 52	99.78 39	99.60 191	99.74 178	98.93 304	98.85 322	99.96 28	99.96 28	99.97 24	99.76 149	99.82 18	99.96 69	99.95 14	99.98 50	99.90 29

fmvsm_l_conf0.5_n		99.80 30	99.78 39	99.85 32	99.88 45	99.66 121	99.11 246	99.91 52	99.98 18	99.96 34	99.64 236	99.60 43	99.99 8	99.95 14	99.99 16	99.88 40

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 41	99.82 94	99.70 109	99.17 216	99.97 20	99.99 3	99.96 34	99.82 91	99.94 4	100.00 1	99.95 14	100.00 1	99.80 65

test_fmvs1		99.48 130	99.65 74	98.97 359	99.54 283	97.16 423	99.11 246	99.98 12	99.78 103	99.96 34	99.81 98	98.72 191	99.97 44	99.95 14	99.97 73	99.79 73

mvsany_test3		99.85 12	99.88 7	99.75 97	99.95 15	99.37 222	99.53 92	99.98 12	99.77 107	99.99 7	99.95 16	99.85 14	99.94 98	99.95 14	99.98 50	99.94 17

fmvsm_s_conf0.5_n_9		99.82 24	99.82 25	99.82 46	99.83 85	99.59 157	98.97 299	99.92 43	99.99 3	99.97 24	99.84 77	99.90 9	99.94 98	99.94 20	99.99 16	99.92 24

fmvsm_s_conf0.1_n_2		99.81 28	99.78 39	99.89 11	99.93 24	99.76 71	98.92 312	99.98 12	99.99 3	99.99 7	99.88 50	99.43 66	99.94 98	99.94 20	99.99 16	99.99 2

fmvsm_l_conf0.5_n_a		99.80 30	99.79 34	99.84 38	99.88 45	99.64 133	99.12 241	99.91 52	99.98 18	99.95 45	99.67 220	99.67 34	99.99 8	99.94 20	99.99 16	99.88 40

MM		99.18 233	99.05 242	99.55 216	99.35 354	98.81 316	99.05 264	97.79 465	99.99 3	99.48 283	99.59 288	96.29 363	99.95 81	99.94 20	99.98 50	99.88 40

test_fmvsmconf_n		99.85 12	99.84 20	99.88 19	99.91 31	99.73 91	98.97 299	99.98 12	99.99 3	99.96 34	99.85 69	99.93 7	99.99 8	99.94 20	99.99 16	99.93 20

fmvsm_s_conf0.5_n_11		99.76 46	99.75 51	99.81 54	99.81 106	99.53 173	99.15 225	99.89 61	99.99 3	99.98 14	99.86 63	99.13 116	99.98 27	99.93 25	99.99 16	99.92 24

fmvsm_s_conf0.5_n_5		99.78 37	99.76 49	99.85 32	99.79 129	99.72 96	98.84 324	99.96 28	99.96 28	99.96 34	99.72 175	99.71 28	99.99 8	99.93 25	99.98 50	99.85 49

fmvsm_s_conf0.5_n_2		99.78 37	99.75 51	99.88 19	99.82 94	99.76 71	98.88 316	99.92 43	99.98 18	99.98 14	99.85 69	99.42 68	99.94 98	99.93 25	99.98 50	99.94 17

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 43	99.10 249	99.98 12	99.99 3	99.98 14	99.91 31	99.68 33	99.93 119	99.93 25	99.99 16	99.99 2

fmvsm_s_conf0.1_n		99.86 10	99.85 17	99.89 11	99.93 24	99.78 58	99.07 262	99.98 12	99.99 3	99.98 14	99.90 36	99.88 11	99.92 150	99.93 25	99.99 16	99.98 5

fmvsm_s_conf0.5_n_a		99.82 24	99.79 34	99.89 11	99.85 72	99.82 43	99.03 272	99.96 28	99.99 3	99.97 24	99.84 77	99.58 49	99.93 119	99.92 30	99.98 50	99.93 20

fmvsm_s_conf0.5_n		99.83 21	99.81 28	99.87 26	99.85 72	99.78 58	99.03 272	99.96 28	99.99 3	99.97 24	99.84 77	99.78 23	99.92 150	99.92 30	99.99 16	99.92 24

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 20	99.99 3	100.00 1	99.98 13	99.78 23	100.00 1	99.92 30	100.00 1	99.87 44

fmvsm_s_conf0.5_n_8		99.76 46	99.72 56	99.88 19	99.82 94	99.75 80	99.02 276	99.87 70	99.98 18	99.98 14	99.81 98	99.07 130	99.97 44	99.91 33	99.99 16	99.92 24

fmvsm_s_conf0.5_n_6		99.80 30	99.78 39	99.85 32	99.78 137	99.78 58	99.00 287	99.97 20	99.96 28	99.97 24	99.56 302	99.92 8	99.93 119	99.91 33	99.99 16	99.83 56

fmvsm_s_conf0.5_n_4		99.78 37	99.78 39	99.79 71	99.75 170	99.56 166	98.98 297	99.94 38	99.92 46	99.97 24	99.72 175	99.84 16	99.92 150	99.91 33	99.98 50	99.89 37

MVStest1		98.22 367	98.09 362	98.62 401	99.04 424	96.23 447	99.20 201	99.92 43	99.44 199	99.98 14	99.87 56	85.87 468	99.67 438	99.91 33	99.57 348	99.95 14

v1921920		99.56 105	99.57 103	99.55 216	99.75 170	99.11 277	99.05 264	99.61 245	99.15 261	99.88 83	99.71 185	99.08 127	99.87 250	99.90 37	99.97 73	99.66 147

v1240		99.56 105	99.58 98	99.51 232	99.80 115	99.00 291	99.00 287	99.65 223	99.15 261	99.90 68	99.75 157	99.09 123	99.88 235	99.90 37	99.96 87	99.67 133

v10		99.69 60	99.69 61	99.66 151	99.81 106	99.39 216	99.66 57	99.75 161	99.60 165	99.92 60	99.87 56	98.75 186	99.86 269	99.90 37	99.99 16	99.73 93

v1192		99.57 101	99.57 103	99.57 205	99.77 150	99.22 258	99.04 269	99.60 256	99.18 250	99.87 93	99.72 175	99.08 127	99.85 288	99.89 40	99.98 50	99.66 147

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 32	99.81 106	99.71 101	98.97 299	99.92 43	99.98 18	99.97 24	99.86 63	99.53 57	99.95 81	99.88 41	99.99 16	99.89 37

v144192		99.55 110	99.54 113	99.58 197	99.78 137	99.20 264	99.11 246	99.62 238	99.18 250	99.89 73	99.72 175	98.66 200	99.87 250	99.88 41	99.97 73	99.66 147

v8		99.68 65	99.69 61	99.65 158	99.80 115	99.40 213	99.66 57	99.76 156	99.64 149	99.93 53	99.85 69	98.66 200	99.84 304	99.88 41	99.99 16	99.71 102

mvs5depth		99.88 6	99.91 3	99.80 64	99.92 29	99.42 205	99.94 3	100.00 1	99.97 25	99.89 73	99.99 12	99.63 37	99.97 44	99.87 44	99.99 16	100.00 1

v1144		99.54 114	99.53 117	99.59 194	99.79 129	99.28 240	99.10 249	99.61 245	99.20 247	99.84 102	99.73 167	98.67 198	99.84 304	99.86 45	99.98 50	99.64 168

mmtdpeth		99.78 37	99.83 21	99.66 151	99.85 72	99.05 290	99.79 15	99.97 20	100.00 1	99.43 295	99.94 19	99.64 35	99.94 98	99.83 46	99.99 16	99.98 5

SSC-MVS		99.52 120	99.42 144	99.83 41	99.86 59	99.65 127	99.52 94	99.81 117	99.87 63	99.81 116	99.79 119	96.78 342	99.99 8	99.83 46	99.51 364	99.86 46

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

v2v482		99.50 123	99.47 129	99.58 197	99.78 137	99.25 248	99.14 229	99.58 271	99.25 238	99.81 116	99.62 260	98.24 259	99.84 304	99.83 46	99.97 73	99.64 168

test_vis1_rt		99.45 146	99.46 134	99.41 270	99.71 191	98.63 337	98.99 294	99.96 28	99.03 274	99.95 45	99.12 409	98.75 186	99.84 304	99.82 50	99.82 233	99.77 79

tt0805		99.63 85	99.57 103	99.81 54	99.87 54	99.88 12	99.58 82	98.70 426	99.72 113	99.91 63	99.60 278	99.43 66	99.81 357	99.81 51	99.53 360	99.73 93

VortexMVS		99.13 246	99.24 196	98.79 390	99.67 227	96.60 439	99.24 190	99.80 122	99.85 72	99.93 53	99.84 77	95.06 382	99.89 220	99.80 52	99.98 50	99.89 37

V42		99.56 105	99.54 113	99.63 172	99.79 129	99.46 190	99.39 129	99.59 262	99.24 240	99.86 96	99.70 195	98.55 215	99.82 341	99.79 53	99.95 111	99.60 204

SSC-MVS3.2		99.64 84	99.67 65	99.56 209	99.75 170	98.98 294	98.96 303	99.87 70	99.88 61	99.84 102	99.64 236	99.32 86	99.91 179	99.78 54	99.96 87	99.80 65

mvs_tets		99.90 2	99.90 4	99.90 8	99.96 7	99.79 55	99.72 33	99.88 66	99.92 46	99.98 14	99.93 22	99.94 4	99.98 27	99.77 55	100.00 1	99.92 24

WB-MVS		99.44 150	99.32 172	99.80 64	99.81 106	99.61 151	99.47 112	99.81 117	99.82 86	99.71 183	99.72 175	96.60 347	99.98 27	99.75 56	99.23 405	99.82 63

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 64	99.68 49	99.85 82	99.95 32	99.98 14	99.92 27	99.28 91	99.98 27	99.75 56	100.00 1	99.94 17

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 58	99.70 38	99.86 76	99.89 56	99.98 14	99.90 36	99.94 4	99.98 27	99.75 56	100.00 1	99.90 29

ANet_high		99.88 6	99.87 11	99.91 3	99.99 1	99.91 4	99.65 62	100.00 1	99.90 50	100.00 1	99.97 14	99.61 41	99.97 44	99.75 56	100.00 1	99.84 52

AstraMVS		99.15 243	99.06 237	99.42 262	99.85 72	98.59 341	99.13 236	97.26 473	99.84 76	99.87 93	99.77 142	96.11 366	99.93 119	99.71 60	99.96 87	99.74 89

Elysia		99.69 60	99.65 74	99.81 54	99.86 59	99.72 96	99.34 148	99.77 148	99.94 36	99.91 63	99.76 149	98.55 215	99.99 8	99.70 61	99.98 50	99.72 97

StellarMVS		99.69 60	99.65 74	99.81 54	99.86 59	99.72 96	99.34 148	99.77 148	99.94 36	99.91 63	99.76 149	98.55 215	99.99 8	99.70 61	99.98 50	99.72 97

tt0320-xc		99.82 24	99.82 25	99.82 46	99.82 94	99.84 27	99.82 10	99.92 43	99.94 36	99.94 48	99.93 22	99.34 83	99.92 150	99.70 61	99.96 87	99.70 105

reproduce_monomvs		97.40 405	97.46 394	97.20 455	99.05 421	91.91 483	99.20 201	99.18 398	99.84 76	99.86 96	99.75 157	80.67 476	99.83 324	99.69 64	99.95 111	99.85 49

SPE-MVS-test		99.68 65	99.70 58	99.64 165	99.57 267	99.83 35	99.78 17	99.97 20	99.92 46	99.50 280	99.38 355	99.57 51	99.95 81	99.69 64	99.90 160	99.15 377

guyue		99.12 249	99.02 251	99.41 270	99.84 77	98.56 342	99.19 207	98.30 451	99.82 86	99.84 102	99.75 157	94.84 385	99.92 150	99.68 66	99.94 127	99.74 89

tt0320		99.79 34	99.79 34	99.81 54	99.82 94	99.84 27	99.82 10	99.90 58	99.94 36	99.94 48	99.94 19	99.07 130	99.92 150	99.68 66	99.97 73	99.67 133

MGCNet		98.61 325	98.30 346	99.52 228	97.88 487	98.95 300	98.76 341	94.11 489	99.84 76	99.32 326	99.57 298	95.57 375	99.95 81	99.68 66	99.98 50	99.68 124

CS-MVS		99.67 76	99.70 58	99.58 197	99.53 290	99.84 27	99.79 15	99.96 28	99.90 50	99.61 236	99.41 345	99.51 60	99.95 81	99.66 69	99.89 174	98.96 419

mamv4		99.73 52	99.74 53	99.70 132	99.66 229	99.87 15	99.69 45	99.93 39	99.93 43	99.93 53	99.86 63	99.07 130	100.00 1	99.66 69	99.92 145	99.24 352

KinetiMVS		99.66 77	99.63 82	99.76 86	99.89 39	99.57 165	99.37 140	99.82 104	99.95 32	99.90 68	99.63 251	98.57 211	99.97 44	99.65 71	99.94 127	99.74 89

pmmvs6		99.86 10	99.86 13	99.83 41	99.94 18	99.90 7	99.83 7	99.91 52	99.85 72	99.94 48	99.95 16	99.73 27	99.90 198	99.65 71	99.97 73	99.69 117

MIMVSNet1		99.66 77	99.62 84	99.80 64	99.94 18	99.87 15	99.69 45	99.77 148	99.78 103	99.93 53	99.89 41	97.94 287	99.92 150	99.65 71	99.98 50	99.62 186

LuminaMVS		99.39 169	99.28 187	99.73 113	99.83 85	99.49 180	99.00 287	99.05 410	99.81 92	99.89 73	99.79 119	96.54 351	99.97 44	99.64 74	99.98 50	99.73 93

sc_t1		99.81 28	99.80 32	99.82 46	99.88 45	99.88 12	99.83 7	99.79 131	99.94 36	99.93 53	99.92 27	99.35 82	99.92 150	99.64 74	99.94 127	99.68 124

EC-MVSNet		99.69 60	99.69 61	99.68 140	99.71 191	99.91 4	99.76 23	99.96 28	99.86 66	99.51 277	99.39 353	99.57 51	99.93 119	99.64 74	99.86 205	99.20 365

K. test v3		98.87 300	98.60 311	99.69 138	99.93 24	99.46 190	99.74 27	94.97 484	99.78 103	99.88 83	99.88 50	93.66 400	99.97 44	99.61 77	99.95 111	99.64 168

KD-MVS_self_test		99.63 85	99.59 94	99.76 86	99.84 77	99.90 7	99.37 140	99.79 131	99.83 82	99.88 83	99.85 69	98.42 239	99.90 198	99.60 78	99.73 287	99.49 269

Anonymous20240521		99.44 150	99.42 144	99.49 238	99.89 39	98.96 299	99.62 67	99.76 156	99.85 72	99.82 109	99.88 50	96.39 358	99.97 44	99.59 79	99.98 50	99.55 229

TransMVSNet (Re)		99.78 37	99.77 45	99.81 54	99.91 31	99.85 22	99.75 25	99.86 76	99.70 124	99.91 63	99.89 41	99.60 43	99.87 250	99.59 79	99.74 281	99.71 102

OurMVSNet-221017-0		99.75 49	99.71 57	99.84 38	99.96 7	99.83 35	99.83 7	99.85 82	99.80 96	99.93 53	99.93 22	98.54 219	99.93 119	99.59 79	99.98 50	99.76 84

EU-MVSNet		99.39 169	99.62 84	98.72 395	99.88 45	96.44 441	99.56 87	99.85 82	99.90 50	99.90 68	99.85 69	98.09 275	99.83 324	99.58 82	99.95 111	99.90 29

mvs_anonymous		99.28 197	99.39 149	98.94 363	99.19 397	97.81 398	99.02 276	99.55 284	99.78 103	99.85 99	99.80 108	98.24 259	99.86 269	99.57 83	99.50 367	99.15 377

test1111		97.74 388	98.16 358	96.49 466	99.60 244	89.86 497	99.71 37	91.21 493	99.89 56	99.88 83	99.87 56	93.73 399	99.90 198	99.56 84	99.99 16	99.70 105

lessismore_v0				99.64 165	99.86 59	99.38 218		90.66 494		99.89 73	99.83 84	94.56 390	99.97 44	99.56 84	99.92 145	99.57 222

mvsany_test1		99.44 150	99.45 136	99.40 273	99.37 347	98.64 336	97.90 437	99.59 262	99.27 234	99.92 60	99.82 91	99.74 26	99.93 119	99.55 86	99.87 197	99.63 174

MVSMamba_PlusPlus		99.55 110	99.58 98	99.47 245	99.68 220	99.40 213	99.52 94	99.70 192	99.92 46	99.77 145	99.86 63	98.28 255	99.96 69	99.54 87	99.90 160	99.05 406

pm-mvs1		99.79 34	99.79 34	99.78 75	99.91 31	99.83 35	99.76 23	99.87 70	99.73 109	99.89 73	99.87 56	99.63 37	99.87 250	99.54 87	99.92 145	99.63 174

LTVRE_ROB	99.19 1	99.88 6	99.87 11	99.88 19	99.91 31	99.90 7	99.96 1	99.92 43	99.90 50	99.97 24	99.87 56	99.81 20	99.95 81	99.54 87	99.99 16	99.80 65
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DSMNet-mixed		99.48 130	99.65 74	98.95 362	99.71 191	97.27 420	99.50 102	99.82 104	99.59 167	99.41 304	99.85 69	99.62 40	100.00 1	99.53 90	99.89 174	99.59 211

test2506		94.73 456	94.59 456	95.15 473	99.59 250	85.90 499	99.75 25	74.01 501	99.89 56	99.71 183	99.86 63	79.00 486	99.90 198	99.52 91	99.99 16	99.65 156

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 183	99.93 43	99.95 45	99.89 41	99.71 28	99.96 69	99.51 92	99.97 73	99.84 52

FC-MVSNet-test		99.70 58	99.65 74	99.86 30	99.88 45	99.86 19	99.72 33	99.78 142	99.90 50	99.82 109	99.83 84	98.45 235	99.87 250	99.51 92	99.97 73	99.86 46

BP-MVS1		98.72 317	98.46 327	99.50 234	99.53 290	99.00 291	99.34 148	98.53 436	99.65 145	99.73 173	99.38 355	90.62 442	99.96 69	99.50 94	99.86 205	99.55 229

UA-Net		99.78 37	99.76 49	99.86 30	99.72 187	99.71 101	99.91 4	99.95 36	99.96 28	99.71 183	99.91 31	99.15 111	99.97 44	99.50 94	100.00 1	99.90 29

viewdifsd2359ckpt11		99.62 92	99.64 79	99.56 209	99.86 59	99.19 265	99.02 276	99.93 39	99.83 82	99.88 83	99.81 98	98.99 148	99.83 324	99.48 96	99.96 87	99.65 156

viewmsd2359difaftdt		99.62 92	99.64 79	99.56 209	99.86 59	99.19 265	99.02 276	99.93 39	99.83 82	99.88 83	99.81 98	98.99 148	99.83 324	99.48 96	99.96 87	99.65 156

PMMVS2		99.48 130	99.45 136	99.57 205	99.76 154	98.99 293	98.09 414	99.90 58	98.95 284	99.78 133	99.58 291	99.57 51	99.93 119	99.48 96	99.95 111	99.79 73

VPA-MVSNet		99.66 77	99.62 84	99.79 71	99.68 220	99.75 80	99.62 67	99.69 200	99.85 72	99.80 123	99.81 98	98.81 174	99.91 179	99.47 99	99.88 184	99.70 105

GDP-MVS		98.81 308	98.57 317	99.50 234	99.53 290	99.12 276	99.28 174	99.86 76	99.53 176	99.57 247	99.32 372	90.88 438	99.98 27	99.46 100	99.74 281	99.42 309

ECVR-MVS		97.73 389	98.04 365	96.78 459	99.59 250	90.81 492	99.72 33	90.43 495	99.89 56	99.86 96	99.86 63	93.60 401	99.89 220	99.46 100	99.99 16	99.65 156

nrg030		99.70 58	99.66 72	99.82 46	99.76 154	99.84 27	99.61 73	99.70 192	99.93 43	99.78 133	99.68 216	99.10 121	99.78 371	99.45 102	99.96 87	99.83 56

FE-MVSNET2		99.68 65	99.67 65	99.72 121	99.86 59	99.68 116	99.46 116	99.88 66	99.62 154	99.87 93	99.85 69	99.06 137	99.85 288	99.44 103	99.98 50	99.63 174

TAMVS		99.49 128	99.45 136	99.63 172	99.48 315	99.42 205	99.45 117	99.57 273	99.66 141	99.78 133	99.83 84	97.85 294	99.86 269	99.44 103	99.96 87	99.61 200

GeoE		99.69 60	99.66 72	99.78 75	99.76 154	99.76 71	99.60 79	99.82 104	99.46 194	99.75 158	99.56 302	99.63 37	99.95 81	99.43 105	99.88 184	99.62 186

new-patchmatchnet		99.35 182	99.57 103	98.71 399	99.82 94	96.62 437	98.55 369	99.75 161	99.50 181	99.88 83	99.87 56	99.31 87	99.88 235	99.43 105	100.00 1	99.62 186

test20.03		99.55 110	99.54 113	99.58 197	99.79 129	99.37 222	99.02 276	99.89 61	99.60 165	99.82 109	99.62 260	98.81 174	99.89 220	99.43 105	99.86 205	99.47 277

MVSFormer		99.41 163	99.44 140	99.31 304	99.57 267	98.40 357	99.77 19	99.80 122	99.73 109	99.63 220	99.30 377	98.02 280	99.98 27	99.43 105	99.69 307	99.55 229

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 27	99.77 19	99.80 122	99.73 109	99.97 24	99.92 27	99.77 25	99.98 27	99.43 105	100.00 1	99.90 29

SDMVSNet		99.77 44	99.77 45	99.76 86	99.80 115	99.65 127	99.63 64	99.86 76	99.97 25	99.89 73	99.89 41	99.52 59	99.99 8	99.42 110	99.96 87	99.65 156

Anonymous20231211		99.62 92	99.57 103	99.76 86	99.61 242	99.60 155	99.81 13	99.73 171	99.82 86	99.90 68	99.90 36	97.97 286	99.86 269	99.42 110	99.96 87	99.80 65

SixPastTwentyTwo		99.42 157	99.30 179	99.76 86	99.92 29	99.67 119	99.70 38	99.14 403	99.65 145	99.89 73	99.90 36	96.20 365	99.94 98	99.42 110	99.92 145	99.67 133

balanced_conf03		99.50 123	99.50 122	99.50 234	99.42 338	99.49 180	99.52 94	99.75 161	99.86 66	99.78 133	99.71 185	98.20 267	99.90 198	99.39 113	99.88 184	99.10 388

patch_mono-2		99.51 121	99.46 134	99.64 165	99.70 206	99.11 277	99.04 269	99.87 70	99.71 118	99.47 285	99.79 119	98.24 259	99.98 27	99.38 114	99.96 87	99.83 56

UGNet		99.38 172	99.34 166	99.49 238	98.90 436	98.90 308	99.70 38	99.35 356	99.86 66	98.57 420	99.81 98	98.50 230	99.93 119	99.38 114	99.98 50	99.66 147
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
XXY-MVS		99.71 57	99.67 65	99.81 54	99.89 39	99.72 96	99.59 80	99.82 104	99.39 215	99.82 109	99.84 77	99.38 74	99.91 179	99.38 114	99.93 139	99.80 65

FIs		99.65 83	99.58 98	99.84 38	99.84 77	99.85 22	99.66 57	99.75 161	99.86 66	99.74 168	99.79 119	98.27 257	99.85 288	99.37 117	99.93 139	99.83 56

sd_testset		99.78 37	99.78 39	99.80 64	99.80 115	99.76 71	99.80 14	99.79 131	99.97 25	99.89 73	99.89 41	99.53 57	99.99 8	99.36 118	99.96 87	99.65 156

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 82	99.70 124	99.92 60	99.93 22	99.45 62	99.97 44	99.36 118	100.00 1	99.85 49

casdiffmvs_mvg		99.68 65	99.68 64	99.69 138	99.81 106	99.59 157	99.29 172	99.90 58	99.71 118	99.79 129	99.73 167	99.54 54	99.84 304	99.36 118	99.96 87	99.65 156
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet		99.75 49	99.74 53	99.79 71	99.88 45	99.66 121	99.69 45	99.92 43	99.67 137	99.77 145	99.75 157	99.61 41	99.98 27	99.35 121	99.98 50	99.72 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dcpmvs_2		99.61 96	99.64 79	99.53 226	99.79 129	98.82 315	99.58 82	99.97 20	99.95 32	99.96 34	99.76 149	98.44 236	99.99 8	99.34 122	99.96 87	99.78 75

CHOSEN 1792x2688		99.39 169	99.30 179	99.65 158	99.88 45	99.25 248	98.78 339	99.88 66	98.66 327	99.96 34	99.79 119	97.45 316	99.93 119	99.34 122	99.99 16	99.78 75

CDS-MVSNet		99.22 219	99.13 212	99.50 234	99.35 354	99.11 277	98.96 303	99.54 290	99.46 194	99.61 236	99.70 195	96.31 361	99.83 324	99.34 122	99.88 184	99.55 229
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		99.00 280	99.16 205	98.51 407	99.75 170	95.90 453	98.07 417	99.84 89	99.84 76	99.89 73	99.73 167	96.01 369	99.99 8	99.33 125	100.00 1	99.63 174

HyFIR lowres test		98.91 293	98.64 308	99.73 113	99.85 72	99.47 184	98.07 417	99.83 98	98.64 330	99.89 73	99.60 278	92.57 414	100.00 1	99.33 125	99.97 73	99.72 97

pmmvs5		99.19 229	99.11 219	99.42 262	99.76 154	98.88 310	98.55 369	99.73 171	98.82 306	99.72 178	99.62 260	96.56 348	99.82 341	99.32 127	99.95 111	99.56 225

v148		99.40 165	99.41 147	99.39 276	99.76 154	98.94 301	99.09 254	99.59 262	99.17 255	99.81 116	99.61 270	98.41 240	99.69 421	99.32 127	99.94 127	99.53 245

baseline		99.63 85	99.62 84	99.66 151	99.80 115	99.62 141	99.44 119	99.80 122	99.71 118	99.72 178	99.69 204	99.15 111	99.83 324	99.32 127	99.94 127	99.53 245

CVMVSNet		98.61 325	98.88 285	97.80 438	99.58 257	93.60 476	99.26 183	99.64 231	99.66 141	99.72 178	99.67 220	93.26 405	99.93 119	99.30 130	99.81 243	99.87 44

PS-CasMVS		99.66 77	99.58 98	99.89 11	99.80 115	99.85 22	99.66 57	99.73 171	99.62 154	99.84 102	99.71 185	98.62 204	99.96 69	99.30 130	99.96 87	99.86 46

DTE-MVSNet		99.68 65	99.61 88	99.88 19	99.80 115	99.87 15	99.67 53	99.71 183	99.72 113	99.84 102	99.78 132	98.67 198	99.97 44	99.30 130	99.95 111	99.80 65

tmp_tt		95.75 448	95.42 443	96.76 460	89.90 500	94.42 470	98.86 320	97.87 463	78.01 491	99.30 336	99.69 204	97.70 302	95.89 493	99.29 133	98.14 463	99.95 14

PEN-MVS		99.66 77	99.59 94	99.89 11	99.83 85	99.87 15	99.66 57	99.73 171	99.70 124	99.84 102	99.73 167	98.56 214	99.96 69	99.29 133	99.94 127	99.83 56

WR-MVS_H		99.61 96	99.53 117	99.87 26	99.80 115	99.83 35	99.67 53	99.75 161	99.58 169	99.85 99	99.69 204	98.18 270	99.94 98	99.28 135	99.95 111	99.83 56

IterMVS		98.97 284	99.16 205	98.42 412	99.74 178	95.64 457	98.06 419	99.83 98	99.83 82	99.85 99	99.74 162	96.10 368	99.99 8	99.27 136	100.00 1	99.63 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NormalMVS		99.09 257	98.91 283	99.62 181	99.78 137	99.11 277	99.36 144	99.77 148	99.82 86	99.68 195	99.53 314	93.30 403	99.99 8	99.24 137	99.76 270	99.74 89

SymmetryMVS		99.01 277	98.82 293	99.58 197	99.65 234	99.11 277	99.36 144	99.20 396	99.82 86	99.68 195	99.53 314	93.30 403	99.99 8	99.24 137	99.63 328	99.64 168

WBMVS		97.50 401	97.18 405	98.48 409	98.85 444	95.89 454	98.44 386	99.52 305	99.53 176	99.52 270	99.42 344	80.10 479	99.86 269	99.24 137	99.95 111	99.68 124

h-mvs33		98.61 325	98.34 341	99.44 256	99.60 244	98.67 328	99.27 178	99.44 331	99.68 129	99.32 326	99.49 327	92.50 417	100.00 1	99.24 137	96.51 484	99.65 156

hse-mvs2		98.52 338	98.30 346	99.16 333	99.29 376	98.60 339	98.77 340	99.02 412	99.68 129	99.32 326	99.04 419	92.50 417	99.85 288	99.24 137	97.87 470	99.03 410

FMVSNet1		99.66 77	99.63 82	99.73 113	99.78 137	99.77 64	99.68 49	99.70 192	99.67 137	99.82 109	99.83 84	98.98 152	99.90 198	99.24 137	99.97 73	99.53 245

casdiffmvs		99.63 85	99.61 88	99.67 144	99.79 129	99.59 157	99.13 236	99.85 82	99.79 100	99.76 153	99.72 175	99.33 85	99.82 341	99.21 143	99.94 127	99.59 211
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CP-MVSNet		99.54 114	99.43 142	99.87 26	99.76 154	99.82 43	99.57 85	99.61 245	99.54 174	99.80 123	99.64 236	97.79 298	99.95 81	99.21 143	99.94 127	99.84 52

DELS-MVS		99.34 187	99.30 179	99.48 243	99.51 299	99.36 226	98.12 410	99.53 300	99.36 220	99.41 304	99.61 270	99.22 101	99.87 250	99.21 143	99.68 312	99.20 365
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
viewmambaseed2359dif		99.47 140	99.50 122	99.37 282	99.70 206	98.80 319	98.67 350	99.92 43	99.49 183	99.77 145	99.71 185	99.08 127	99.78 371	99.20 146	99.94 127	99.54 239

UniMVSNet (Re)		99.37 176	99.26 192	99.68 140	99.51 299	99.58 162	98.98 297	99.60 256	99.43 206	99.70 187	99.36 363	97.70 302	99.88 235	99.20 146	99.87 197	99.59 211

CANet		99.11 253	99.05 242	99.28 312	98.83 446	98.56 342	98.71 348	99.41 337	99.25 238	99.23 346	99.22 395	97.66 310	99.94 98	99.19 148	99.97 73	99.33 333

EI-MVSNet-UG-set		99.48 130	99.50 122	99.42 262	99.57 267	98.65 334	99.24 190	99.46 325	99.68 129	99.80 123	99.66 225	98.99 148	99.89 220	99.19 148	99.90 160	99.72 97

xiu_mvs_v1_base_debu		99.23 210	99.34 166	98.91 372	99.59 250	98.23 366	98.47 381	99.66 213	99.61 159	99.68 195	98.94 435	99.39 70	99.97 44	99.18 150	99.55 353	98.51 458

xiu_mvs_v1_base		99.23 210	99.34 166	98.91 372	99.59 250	98.23 366	98.47 381	99.66 213	99.61 159	99.68 195	98.94 435	99.39 70	99.97 44	99.18 150	99.55 353	98.51 458

xiu_mvs_v1_base_debi		99.23 210	99.34 166	98.91 372	99.59 250	98.23 366	98.47 381	99.66 213	99.61 159	99.68 195	98.94 435	99.39 70	99.97 44	99.18 150	99.55 353	98.51 458

VPNet		99.46 142	99.37 155	99.71 127	99.82 94	99.59 157	99.48 109	99.70 192	99.81 92	99.69 190	99.58 291	97.66 310	99.86 269	99.17 153	99.44 374	99.67 133

UniMVSNet_NR-MVSNet		99.37 176	99.25 194	99.72 121	99.47 321	99.56 166	98.97 299	99.61 245	99.43 206	99.67 202	99.28 381	97.85 294	99.95 81	99.17 153	99.81 243	99.65 156

DU-MVS		99.33 190	99.21 199	99.71 127	99.43 333	99.56 166	98.83 327	99.53 300	99.38 216	99.67 202	99.36 363	97.67 306	99.95 81	99.17 153	99.81 243	99.63 174

usedtu_dtu_shiyan2		99.44 150	99.33 171	99.78 75	99.86 59	99.76 71	99.54 90	99.79 131	99.66 141	99.66 208	99.79 119	96.76 343	99.96 69	99.15 156	99.72 293	99.62 186

EI-MVSNet-Vis-set		99.47 140	99.49 126	99.42 262	99.57 267	98.66 331	99.24 190	99.46 325	99.67 137	99.79 129	99.65 234	98.97 154	99.89 220	99.15 156	99.89 174	99.71 102

EI-MVSNet		99.38 172	99.44 140	99.21 327	99.58 257	98.09 380	99.26 183	99.46 325	99.62 154	99.75 158	99.67 220	98.54 219	99.85 288	99.15 156	99.92 145	99.68 124

VNet		99.18 233	99.06 237	99.56 209	99.24 387	99.36 226	99.33 154	99.31 370	99.67 137	99.47 285	99.57 298	96.48 352	99.84 304	99.15 156	99.30 393	99.47 277

EG-PatchMatch MVS		99.57 101	99.56 108	99.62 181	99.77 150	99.33 232	99.26 183	99.76 156	99.32 225	99.80 123	99.78 132	99.29 89	99.87 250	99.15 156	99.91 158	99.66 147

PVSNet_Blended_VisFu		99.40 165	99.38 152	99.44 256	99.90 37	98.66 331	98.94 308	99.91 52	97.97 391	99.79 129	99.73 167	99.05 139	99.97 44	99.15 156	99.99 16	99.68 124

IterMVS-LS		99.41 163	99.47 129	99.25 323	99.81 106	98.09 380	98.85 322	99.76 156	99.62 154	99.83 108	99.64 236	98.54 219	99.97 44	99.15 156	99.99 16	99.68 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TranMVSNet+NR-MVSNet		99.54 114	99.47 129	99.76 86	99.58 257	99.64 133	99.30 165	99.63 235	99.61 159	99.71 183	99.56 302	98.76 184	99.96 69	99.14 163	99.92 145	99.68 124

MVSTER		98.47 345	98.22 351	99.24 325	99.06 420	98.35 363	99.08 257	99.46 325	99.27 234	99.75 158	99.66 225	88.61 455	99.85 288	99.14 163	99.92 145	99.52 256

E5new		99.68 65	99.67 65	99.70 132	99.87 54	99.62 141	99.41 122	99.84 89	99.68 129	99.77 145	99.81 98	99.59 45	99.78 371	99.13 165	99.96 87	99.70 105

E6new		99.68 65	99.67 65	99.70 132	99.86 59	99.62 141	99.41 122	99.84 89	99.68 129	99.77 145	99.81 98	99.59 45	99.78 371	99.13 165	99.96 87	99.70 105

E6		99.68 65	99.67 65	99.70 132	99.86 59	99.62 141	99.41 122	99.84 89	99.68 129	99.77 145	99.81 98	99.59 45	99.78 371	99.13 165	99.96 87	99.70 105

E5		99.68 65	99.67 65	99.70 132	99.87 54	99.62 141	99.41 122	99.84 89	99.68 129	99.77 145	99.81 98	99.59 45	99.78 371	99.13 165	99.96 87	99.70 105

diffmvs_AUTHOR		99.48 130	99.48 127	99.47 245	99.80 115	98.89 309	98.71 348	99.82 104	99.79 100	99.66 208	99.63 251	98.87 170	99.88 235	99.13 165	99.95 111	99.62 186

Anonymous20231206		99.35 182	99.31 174	99.47 245	99.74 178	99.06 289	99.28 174	99.74 167	99.23 242	99.72 178	99.53 314	97.63 312	99.88 235	99.11 170	99.84 215	99.48 273

Syy-MVS		98.17 370	97.85 382	99.15 335	98.50 469	98.79 320	98.60 357	99.21 393	97.89 400	96.76 478	96.37 501	95.47 379	99.57 464	99.10 171	98.73 439	99.09 393

ttmdpeth		99.48 130	99.55 110	99.29 309	99.76 154	98.16 374	99.33 154	99.95 36	99.79 100	99.36 315	99.89 41	99.13 116	99.77 384	99.09 172	99.64 325	99.93 20

MVS_Test		99.28 197	99.31 174	99.19 330	99.35 354	98.79 320	99.36 144	99.49 318	99.17 255	99.21 351	99.67 220	98.78 181	99.66 443	99.09 172	99.66 321	99.10 388

usedtu_dtu_shiyan1		98.87 300	98.71 302	99.35 289	99.59 250	98.88 310	97.17 471	99.64 231	98.94 285	99.27 338	99.22 395	95.57 375	99.83 324	99.08 174	99.92 145	99.35 327

FE-MVSNET3		98.87 300	98.71 302	99.35 289	99.59 250	98.88 310	97.17 471	99.64 231	98.94 285	99.27 338	99.22 395	95.57 375	99.83 324	99.08 174	99.92 145	99.35 327

testgi		99.29 196	99.26 192	99.37 282	99.75 170	98.81 316	98.84 324	99.89 61	98.38 358	99.75 158	99.04 419	99.36 79	99.86 269	99.08 174	99.25 401	99.45 284

1112_ss		99.05 265	98.84 290	99.67 144	99.66 229	99.29 238	98.52 375	99.82 104	97.65 414	99.43 295	99.16 403	96.42 355	99.91 179	99.07 177	99.84 215	99.80 65

CANet_DTU		98.91 293	98.85 288	99.09 344	98.79 452	98.13 375	98.18 402	99.31 370	99.48 186	98.86 391	99.51 320	96.56 348	99.95 81	99.05 178	99.95 111	99.19 368

blended_shiyan8		97.82 383	97.45 396	98.92 367	98.06 483	97.45 414	97.73 443	99.35 356	97.96 394	98.35 431	97.34 483	92.76 413	99.84 304	99.04 179	96.49 486	99.47 277

blended_shiyan6		97.82 383	97.46 394	98.92 367	98.08 482	97.46 412	97.73 443	99.34 359	97.96 394	98.33 432	97.35 482	92.78 411	99.84 304	99.04 179	96.53 480	99.46 282

Baseline_NR-MVSNet		99.49 128	99.37 155	99.82 46	99.91 31	99.84 27	98.83 327	99.86 76	99.68 129	99.65 212	99.88 50	97.67 306	99.87 250	99.03 181	99.86 205	99.76 84

FMVSNet2		99.35 182	99.28 187	99.55 216	99.49 310	99.35 229	99.45 117	99.57 273	99.44 199	99.70 187	99.74 162	97.21 327	99.87 250	99.03 181	99.94 127	99.44 299

wanda-best-256-512		97.53 399	97.14 407	98.72 395	97.71 489	96.86 432	97.00 477	99.34 359	97.73 409	98.18 439	96.82 493	91.92 420	99.84 304	99.02 183	96.53 480	99.45 284

FE-blended-shiyan7		97.53 399	97.14 407	98.72 395	97.71 489	96.86 432	97.00 477	99.34 359	97.73 409	98.18 439	96.82 493	91.92 420	99.84 304	99.02 183	96.53 480	99.45 284

Test_1112_low_res		98.95 290	98.73 300	99.63 172	99.68 220	99.15 273	98.09 414	99.80 122	97.14 440	99.46 289	99.40 349	96.11 366	99.89 220	99.01 185	99.84 215	99.84 52

VDD-MVS		99.20 226	99.11 219	99.44 256	99.43 333	98.98 294	99.50 102	98.32 450	99.80 96	99.56 255	99.69 204	96.99 337	99.85 288	98.99 186	99.73 287	99.50 264

DeepC-MVS	98.90 4	99.62 92	99.61 88	99.67 144	99.72 187	99.44 198	99.24 190	99.71 183	99.27 234	99.93 53	99.90 36	99.70 31	99.93 119	98.99 186	99.99 16	99.64 168
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
pmmvs-eth3d		99.48 130	99.47 129	99.51 232	99.77 150	99.41 212	98.81 332	99.66 213	99.42 210	99.75 158	99.66 225	99.20 103	99.76 388	98.98 188	99.99 16	99.36 324

EPNet_dtu		97.62 394	97.79 385	97.11 458	96.67 495	92.31 481	98.51 376	98.04 457	99.24 240	95.77 487	99.47 334	93.78 398	99.66 443	98.98 188	99.62 330	99.37 321
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		99.34 187	99.32 172	99.39 276	99.67 227	98.77 322	98.57 366	99.81 117	99.61 159	99.48 283	99.41 345	98.47 231	99.86 269	98.97 190	99.90 160	99.53 245
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		99.40 165	99.31 174	99.68 140	99.43 333	99.55 170	99.73 30	99.50 314	99.46 194	99.88 83	99.36 363	97.54 313	99.87 250	98.97 190	99.87 197	99.63 174

TestfortrainingZip a		99.61 96	99.53 117	99.85 32	99.76 154	99.84 27	99.38 132	99.78 142	99.58 169	99.81 116	99.66 225	99.02 143	99.90 198	98.96 192	99.79 255	99.81 64

viewdifsd2359ckpt07		99.51 121	99.50 122	99.52 228	99.80 115	99.19 265	98.92 312	99.88 66	99.72 113	99.64 215	99.62 260	99.06 137	99.81 357	98.96 192	99.94 127	99.56 225

GBi-Net		99.42 157	99.31 174	99.73 113	99.49 310	99.77 64	99.68 49	99.70 192	99.44 199	99.62 230	99.83 84	97.21 327	99.90 198	98.96 192	99.90 160	99.53 245

FMVSNet5		97.80 386	97.25 403	99.42 262	98.83 446	98.97 297	99.38 132	99.80 122	98.87 298	99.25 342	99.69 204	80.60 478	99.91 179	98.96 192	99.90 160	99.38 318

test1		99.42 157	99.31 174	99.73 113	99.49 310	99.77 64	99.68 49	99.70 192	99.44 199	99.62 230	99.83 84	97.21 327	99.90 198	98.96 192	99.90 160	99.53 245

FMVSNet3		98.80 309	98.63 310	99.32 300	99.13 406	98.72 325	99.10 249	99.48 319	99.23 242	99.62 230	99.64 236	92.57 414	99.86 269	98.96 192	99.90 160	99.39 316

UnsupCasMVSNet_eth		98.83 305	98.57 317	99.59 194	99.68 220	99.45 196	98.99 294	99.67 208	99.48 186	99.55 260	99.36 363	94.92 383	99.86 269	98.95 198	96.57 479	99.45 284

CHOSEN 280x420		98.41 350	98.41 333	98.40 413	99.34 363	95.89 454	96.94 480	99.44 331	98.80 310	99.25 342	99.52 318	93.51 402	99.98 27	98.94 199	99.98 50	99.32 337

E4		99.61 96	99.59 94	99.66 151	99.84 77	99.53 173	99.08 257	99.84 89	99.65 145	99.74 168	99.80 108	99.45 62	99.77 384	98.93 200	99.95 111	99.69 117

TDRefinement		99.72 54	99.70 58	99.77 79	99.90 37	99.85 22	99.86 6	99.92 43	99.69 127	99.78 133	99.92 27	99.37 76	99.88 235	98.93 200	99.95 111	99.60 204

viewmacassd2359aftdt		99.63 85	99.61 88	99.68 140	99.84 77	99.61 151	99.14 229	99.87 70	99.71 118	99.75 158	99.77 142	99.54 54	99.72 405	98.91 202	99.96 87	99.70 105

alignmvs		98.28 360	97.96 371	99.25 323	99.12 408	98.93 304	99.03 272	98.42 443	99.64 149	98.72 406	97.85 474	90.86 439	99.62 455	98.88 203	99.13 407	99.19 368

testing3-2		96.51 427	96.43 422	96.74 462	99.36 350	91.38 489	99.10 249	97.87 463	99.48 186	98.57 420	98.71 450	76.65 489	99.66 443	98.87 204	99.26 400	99.18 370

MGCFI-Net		99.02 271	99.01 255	99.06 351	99.11 413	98.60 339	99.63 64	99.67 208	99.63 151	98.58 418	97.65 477	99.07 130	99.57 464	98.85 205	98.92 423	99.03 410

sss		98.90 295	98.77 299	99.27 317	99.48 315	98.44 354	98.72 346	99.32 366	97.94 397	99.37 314	99.35 368	96.31 361	99.91 179	98.85 205	99.63 328	99.47 277

xiu_mvs_v2_base		99.02 271	99.11 219	98.77 392	99.37 347	98.09 380	98.13 409	99.51 310	99.47 191	99.42 298	98.54 459	99.38 74	99.97 44	98.83 207	99.33 389	98.24 470

PS-MVSNAJ		99.00 280	99.08 231	98.76 393	99.37 347	98.10 379	98.00 425	99.51 310	99.47 191	99.41 304	98.50 461	99.28 91	99.97 44	98.83 207	99.34 388	98.20 474

E2		99.54 114	99.51 120	99.62 181	99.78 137	99.47 184	99.01 281	99.82 104	99.55 172	99.69 190	99.77 142	99.26 95	99.76 388	98.82 209	99.93 139	99.62 186

E3		99.54 114	99.51 120	99.62 181	99.78 137	99.47 184	99.01 281	99.82 104	99.55 172	99.69 190	99.77 142	99.25 99	99.76 388	98.82 209	99.93 139	99.62 186

D2MVS		99.22 219	99.19 202	99.29 309	99.69 212	98.74 324	98.81 332	99.41 337	98.55 339	99.68 195	99.69 204	98.13 272	99.87 250	98.82 209	99.98 50	99.24 352

PatchT		98.45 347	98.32 343	98.83 386	98.94 434	98.29 364	99.24 190	98.82 420	99.84 76	99.08 368	99.76 149	91.37 428	99.94 98	98.82 209	99.00 418	98.26 469

testf1		99.63 85	99.60 92	99.72 121	99.94 18	99.95 2	99.47 112	99.89 61	99.43 206	99.88 83	99.80 108	99.26 95	99.90 198	98.81 213	99.88 184	99.32 337

APD_test2		99.63 85	99.60 92	99.72 121	99.94 18	99.95 2	99.47 112	99.89 61	99.43 206	99.88 83	99.80 108	99.26 95	99.90 198	98.81 213	99.88 184	99.32 337

usedtu_blend_shiyan5		97.97 380	97.65 392	98.92 367	97.71 489	97.49 409	99.53 92	99.81 117	99.52 180	98.18 439	96.82 493	91.92 420	99.83 324	98.79 215	96.53 480	99.45 284

blend_shiyan4		95.04 455	93.76 459	98.88 381	97.92 485	97.49 409	97.72 445	99.34 359	97.93 398	97.65 467	97.11 487	77.69 487	99.83 324	98.79 215	79.72 494	99.33 333

sasdasda		99.02 271	99.00 259	99.09 344	99.10 415	98.70 326	99.61 73	99.66 213	99.63 151	98.64 412	97.65 477	99.04 140	99.54 468	98.79 215	98.92 423	99.04 408

Effi-MVS+		99.06 262	98.97 270	99.34 292	99.31 370	98.98 294	98.31 394	99.91 52	98.81 308	98.79 400	98.94 435	99.14 114	99.84 304	98.79 215	98.74 436	99.20 365

canonicalmvs		99.02 271	99.00 259	99.09 344	99.10 415	98.70 326	99.61 73	99.66 213	99.63 151	98.64 412	97.65 477	99.04 140	99.54 468	98.79 215	98.92 423	99.04 408

VDDNet		98.97 284	98.82 293	99.42 262	99.71 191	98.81 316	99.62 67	98.68 427	99.81 92	99.38 312	99.80 108	94.25 392	99.85 288	98.79 215	99.32 391	99.59 211

CR-MVSNet		98.35 357	98.20 353	98.83 386	99.05 421	98.12 376	99.30 165	99.67 208	97.39 428	99.16 357	99.79 119	91.87 425	99.91 179	98.78 221	98.77 432	98.44 463

test_method		91.72 457	92.32 460	89.91 476	93.49 499	70.18 502	90.28 491	99.56 278	61.71 494	95.39 489	99.52 318	93.90 394	99.94 98	98.76 222	98.27 456	99.62 186

RPMNet		98.60 328	98.53 323	98.83 386	99.05 421	98.12 376	99.30 165	99.62 238	99.86 66	99.16 357	99.74 162	92.53 416	99.92 150	98.75 223	98.77 432	98.44 463

mamba_0408		99.54 114	99.55 110	99.54 222	99.71 191	99.24 252	99.27 178	99.79 131	99.72 113	99.78 133	99.64 236	99.36 79	99.93 119	98.74 224	99.90 160	99.45 284

SSM_04072		99.55 110	99.55 110	99.55 216	99.71 191	99.24 252	99.27 178	99.79 131	99.72 113	99.78 133	99.64 236	99.36 79	99.97 44	98.74 224	99.90 160	99.45 284

SSM_0407		99.56 105	99.56 108	99.54 222	99.71 191	99.24 252	99.15 225	99.84 89	99.80 96	99.78 133	99.70 195	99.44 64	99.93 119	98.74 224	99.90 160	99.45 284

SSM_0404		99.57 101	99.58 98	99.54 222	99.76 154	99.28 240	99.19 207	99.84 89	99.80 96	99.78 133	99.70 195	99.44 64	99.93 119	98.74 224	99.95 111	99.41 310

pmmvs4		99.13 246	99.06 237	99.36 287	99.57 267	99.10 284	98.01 423	99.25 383	98.78 313	99.58 244	99.44 341	98.24 259	99.76 388	98.74 224	99.93 139	99.22 358

viewmanbaseed2359cas		99.50 123	99.47 129	99.61 187	99.73 182	99.52 177	99.03 272	99.83 98	99.49 183	99.65 212	99.64 236	99.18 105	99.71 410	98.73 229	99.92 145	99.58 216

tttt0517		97.62 394	97.20 404	98.90 378	99.76 154	97.40 417	99.48 109	94.36 486	99.06 272	99.70 187	99.49 327	84.55 471	99.94 98	98.73 229	99.65 323	99.36 324

viewcassd2359sk11		99.48 130	99.45 136	99.58 197	99.73 182	99.42 205	98.96 303	99.80 122	99.44 199	99.63 220	99.74 162	99.09 123	99.76 388	98.72 231	99.91 158	99.57 222

EPP-MVSNet		99.17 238	99.00 259	99.66 151	99.80 115	99.43 202	99.70 38	99.24 386	99.48 186	99.56 255	99.77 142	94.89 384	99.93 119	98.72 231	99.89 174	99.63 174

FE-MVSNET		99.45 146	99.36 160	99.71 127	99.84 77	99.64 133	99.16 222	99.91 52	98.65 328	99.73 173	99.73 167	98.54 219	99.82 341	98.71 233	99.96 87	99.67 133

Anonymous20240529		99.42 157	99.34 166	99.65 158	99.53 290	99.60 155	99.63 64	99.39 347	99.47 191	99.76 153	99.78 132	98.13 272	99.86 269	98.70 234	99.68 312	99.49 269

ACMH	98.42 6	99.59 100	99.54 113	99.72 121	99.86 59	99.62 141	99.56 87	99.79 131	98.77 315	99.80 123	99.85 69	99.64 35	99.85 288	98.70 234	99.89 174	99.70 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ab-mvs		99.33 190	99.28 187	99.47 245	99.57 267	99.39 216	99.78 17	99.43 334	98.87 298	99.57 247	99.82 91	98.06 278	99.87 250	98.69 236	99.73 287	99.15 377

LFMVS		98.46 346	98.19 356	99.26 320	99.24 387	98.52 350	99.62 67	96.94 475	99.87 63	99.31 331	99.58 291	91.04 433	99.81 357	98.68 237	99.42 378	99.45 284

WR-MVS		99.11 253	98.93 275	99.66 151	99.30 374	99.42 205	98.42 387	99.37 352	99.04 273	99.57 247	99.20 401	96.89 339	99.86 269	98.66 238	99.87 197	99.70 105

mvsmamba		99.08 258	98.95 273	99.45 252	99.36 350	99.18 270	99.39 129	98.81 421	99.37 217	99.35 317	99.70 195	96.36 360	99.94 98	98.66 238	99.59 344	99.22 358

viewdifsd2359ckpt13		99.42 157	99.37 155	99.57 205	99.72 187	99.46 190	99.01 281	99.80 122	99.20 247	99.51 277	99.60 278	98.92 161	99.70 414	98.65 240	99.90 160	99.55 229

RRT-MVS		99.08 258	99.00 259	99.33 295	99.27 381	98.65 334	99.62 67	99.93 39	99.66 141	99.67 202	99.82 91	95.27 381	99.93 119	98.64 241	99.09 411	99.41 310

E3new		99.42 157	99.37 155	99.56 209	99.68 220	99.38 218	98.93 311	99.79 131	99.30 229	99.55 260	99.69 204	98.88 168	99.76 388	98.63 242	99.89 174	99.53 245

Anonymous202405211		98.75 313	98.46 327	99.63 172	99.34 363	99.66 121	99.47 112	97.65 466	99.28 233	99.56 255	99.50 323	93.15 406	99.84 304	98.62 243	99.58 346	99.40 313

lecture		99.56 105	99.48 127	99.81 54	99.78 137	99.86 19	99.50 102	99.70 192	99.59 167	99.75 158	99.71 185	98.94 157	99.92 150	98.59 244	99.76 270	99.66 147

EPNet		98.13 371	97.77 386	99.18 332	94.57 498	97.99 386	99.24 190	97.96 459	99.74 108	97.29 471	99.62 260	93.13 407	99.97 44	98.59 244	99.83 223	99.58 216
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		99.05 265	99.09 229	98.91 372	99.21 392	98.36 362	98.82 331	99.47 322	98.85 301	98.90 386	99.56 302	98.78 181	99.09 484	98.57 246	99.68 312	99.26 349

Patchmatch-RL test		98.60 328	98.36 338	99.33 295	99.77 150	99.07 287	98.27 396	99.87 70	98.91 293	99.74 168	99.72 175	90.57 444	99.79 368	98.55 247	99.85 210	99.11 386

pmmvs3		98.08 374	97.80 383	98.91 372	99.41 340	97.69 404	97.87 438	99.66 213	95.87 459	99.50 280	99.51 320	90.35 446	99.97 44	98.55 247	99.47 371	99.08 399

ETV-MVS		99.18 233	99.18 203	99.16 333	99.34 363	99.28 240	99.12 241	99.79 131	99.48 186	98.93 380	98.55 458	99.40 69	99.93 119	98.51 249	99.52 363	98.28 468

viewdifsd2359ckpt09		99.24 208	99.16 205	99.49 238	99.70 206	99.22 258	98.88 316	99.81 117	98.70 323	99.38 312	99.37 358	98.22 264	99.76 388	98.48 250	99.88 184	99.51 258

jason		99.16 239	99.11 219	99.32 300	99.75 170	98.44 354	98.26 398	99.39 347	98.70 323	99.74 168	99.30 377	98.54 219	99.97 44	98.48 250	99.82 233	99.55 229
jason: jason.
APDe-MVS		99.48 130	99.36 160	99.85 32	99.55 281	99.81 48	99.50 102	99.69 200	98.99 277	99.75 158	99.71 185	98.79 179	99.93 119	98.46 252	99.85 210	99.80 65
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
icg_test_0407_2		99.30 194	99.29 184	99.31 304	99.71 191	98.55 344	98.17 404	99.71 183	99.41 211	99.73 173	99.60 278	99.17 107	99.92 150	98.45 253	99.70 299	99.45 284

IMVS_0407		99.38 172	99.42 144	99.28 312	99.71 191	98.55 344	99.27 178	99.71 183	99.41 211	99.73 173	99.60 278	99.17 107	99.83 324	98.45 253	99.70 299	99.45 284

IMVS_0404		99.23 210	99.20 200	99.32 300	99.71 191	98.55 344	98.57 366	99.71 183	99.41 211	99.52 270	99.60 278	98.12 274	99.95 81	98.45 253	99.70 299	99.45 284

IMVS_0403		99.37 176	99.39 149	99.28 312	99.71 191	98.55 344	99.19 207	99.71 183	99.41 211	99.67 202	99.60 278	99.12 119	99.84 304	98.45 253	99.70 299	99.45 284

CL-MVSNet_self_test		98.71 319	98.56 321	99.15 335	99.22 390	98.66 331	97.14 473	99.51 310	98.09 384	99.54 263	99.27 383	96.87 340	99.74 400	98.43 257	98.96 420	99.03 410

our_test_3		98.85 304	99.09 229	98.13 426	99.66 229	94.90 468	97.72 445	99.58 271	99.07 270	99.64 215	99.62 260	98.19 268	99.93 119	98.41 258	99.95 111	99.55 229

Gipuma		99.57 101	99.59 94	99.49 238	99.98 3	99.71 101	99.72 33	99.84 89	99.81 92	99.94 48	99.78 132	98.91 164	99.71 410	98.41 258	99.95 111	99.05 406
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		97.37 407	96.91 416	98.74 394	97.72 488	97.57 406	97.60 452	97.36 472	98.00 387	99.21 351	98.02 470	90.04 449	99.79 368	98.37 260	95.89 489	98.86 433

PM-MVS		99.36 180	99.29 184	99.58 197	99.83 85	99.66 121	98.95 306	99.86 76	98.85 301	99.81 116	99.73 167	98.40 244	99.92 150	98.36 261	99.83 223	99.17 373

baseline1		97.73 389	97.33 400	98.96 360	99.30 374	97.73 402	99.40 127	98.42 443	99.33 224	99.46 289	99.21 399	91.18 431	99.82 341	98.35 262	91.26 492	99.32 337

MVS-HIRNet		97.86 381	98.22 351	96.76 460	99.28 379	91.53 487	98.38 389	92.60 492	99.13 263	99.31 331	99.96 15	97.18 331	99.68 433	98.34 263	99.83 223	99.07 404

GA-MVS		97.99 379	97.68 389	98.93 366	99.52 297	98.04 384	97.19 470	99.05 410	98.32 371	98.81 396	98.97 431	89.89 451	99.41 479	98.33 264	99.05 414	99.34 332

Fast-Effi-MVS+		99.02 271	98.87 286	99.46 249	99.38 345	99.50 179	99.04 269	99.79 131	97.17 438	98.62 414	98.74 449	99.34 83	99.95 81	98.32 265	99.41 379	98.92 426

MDA-MVSNet_test_wron		98.95 290	98.99 266	98.85 382	99.64 235	97.16 423	98.23 400	99.33 364	98.93 290	99.56 255	99.66 225	97.39 320	99.83 324	98.29 266	99.88 184	99.55 229

N_pmnet		98.73 316	98.53 323	99.35 289	99.72 187	98.67 328	98.34 391	94.65 485	98.35 365	99.79 129	99.68 216	98.03 279	99.93 119	98.28 267	99.92 145	99.44 299

ET-MVSNet_ETH3D		96.78 419	96.07 429	98.91 372	99.26 384	97.92 393	97.70 448	96.05 480	97.96 394	92.37 493	98.43 462	87.06 459	99.90 198	98.27 268	97.56 473	98.91 427

thisisatest0530		97.45 402	96.95 413	98.94 363	99.68 220	97.73 402	99.09 254	94.19 488	98.61 335	99.56 255	99.30 377	84.30 473	99.93 119	98.27 268	99.54 358	99.16 375

YYNet1		98.95 290	98.99 266	98.84 384	99.64 235	97.14 425	98.22 401	99.32 366	98.92 292	99.59 242	99.66 225	97.40 318	99.83 324	98.27 268	99.90 160	99.55 229

reproduce_model		99.50 123	99.40 148	99.83 41	99.60 244	99.83 35	99.12 241	99.68 203	99.49 183	99.80 123	99.79 119	99.01 145	99.93 119	98.24 271	99.82 233	99.73 93

ACMM	98.09 11	99.46 142	99.38 152	99.72 121	99.80 115	99.69 113	99.13 236	99.65 223	98.99 277	99.64 215	99.72 175	99.39 70	99.86 269	98.23 272	99.81 243	99.60 204
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lupinMVS		98.96 287	98.87 286	99.24 325	99.57 267	98.40 357	98.12 410	99.18 398	98.28 373	99.63 220	99.13 405	98.02 280	99.97 44	98.22 273	99.69 307	99.35 327

3Dnovator	99.15 2	99.43 154	99.36 160	99.65 158	99.39 342	99.42 205	99.70 38	99.56 278	99.23 242	99.35 317	99.80 108	99.17 107	99.95 81	98.21 274	99.84 215	99.59 211

Fast-Effi-MVS+-dtu		99.20 226	99.12 216	99.43 260	99.25 385	99.69 113	99.05 264	99.82 104	99.50 181	98.97 376	99.05 417	98.98 152	99.98 27	98.20 275	99.24 403	98.62 448

MS-PatchMatch		99.00 280	98.97 270	99.09 344	99.11 413	98.19 370	98.76 341	99.33 364	98.49 348	99.44 291	99.58 291	98.21 265	99.69 421	98.20 275	99.62 330	99.39 316

TSAR-MVS + GP.		99.12 249	99.04 248	99.38 279	99.34 363	99.16 271	98.15 406	99.29 374	98.18 380	99.63 220	99.62 260	99.18 105	99.68 433	98.20 275	99.74 281	99.30 343

DP-MVS		99.48 130	99.39 149	99.74 102	99.57 267	99.62 141	99.29 172	99.61 245	99.87 63	99.74 168	99.76 149	98.69 194	99.87 250	98.20 275	99.80 250	99.75 87

MVP-Stereo		99.16 239	99.08 231	99.43 260	99.48 315	99.07 287	99.08 257	99.55 284	98.63 331	99.31 331	99.68 216	98.19 268	99.78 371	98.18 279	99.58 346	99.45 284
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HPM-MVS_fast		99.43 154	99.30 179	99.80 64	99.83 85	99.81 48	99.52 94	99.70 192	98.35 365	99.51 277	99.50 323	99.31 87	99.88 235	98.18 279	99.84 215	99.69 117

MDA-MVSNet-bldmvs		99.06 262	99.05 242	99.07 349	99.80 115	97.83 397	98.89 315	99.72 180	99.29 230	99.63 220	99.70 195	96.47 353	99.89 220	98.17 281	99.82 233	99.50 264

JIA-IIPM		98.06 375	97.92 378	98.50 408	98.59 465	97.02 427	98.80 335	98.51 438	99.88 61	97.89 455	99.87 56	91.89 424	99.90 198	98.16 282	97.68 472	98.59 451

EIA-MVS		99.12 249	99.01 255	99.45 252	99.36 350	99.62 141	99.34 148	99.79 131	98.41 354	98.84 393	98.89 439	98.75 186	99.84 304	98.15 283	99.51 364	98.89 430

miper_lstm_enhance		98.65 324	98.60 311	98.82 389	99.20 395	97.33 419	97.78 441	99.66 213	99.01 276	99.59 242	99.50 323	94.62 389	99.85 288	98.12 284	99.90 160	99.26 349

reproduce-ours		99.46 142	99.35 164	99.82 46	99.56 278	99.83 35	99.05 264	99.65 223	99.45 197	99.78 133	99.78 132	98.93 158	99.93 119	98.11 285	99.81 243	99.70 105

our_new_method		99.46 142	99.35 164	99.82 46	99.56 278	99.83 35	99.05 264	99.65 223	99.45 197	99.78 133	99.78 132	98.93 158	99.93 119	98.11 285	99.81 243	99.70 105

Effi-MVS+-dtu		99.07 261	98.92 279	99.52 228	98.89 439	99.78 58	99.15 225	99.66 213	99.34 221	98.92 383	99.24 393	97.69 304	99.98 27	98.11 285	99.28 396	98.81 437

tpm		97.15 411	96.95 413	97.75 440	98.91 435	94.24 471	99.32 157	97.96 459	97.71 412	98.29 433	99.32 372	86.72 465	99.92 150	98.10 288	96.24 487	99.09 393

DeepPCF-MVS	98.42 6	99.18 233	99.02 251	99.67 144	99.22 390	99.75 80	97.25 468	99.47 322	98.72 320	99.66 208	99.70 195	99.29 89	99.63 454	98.07 289	99.81 243	99.62 186

ppachtmachnet_test		98.89 298	99.12 216	98.20 424	99.66 229	95.24 464	97.63 450	99.68 203	99.08 268	99.78 133	99.62 260	98.65 202	99.88 235	98.02 290	99.96 87	99.48 273

tpmrst		97.73 389	98.07 364	96.73 463	98.71 461	92.00 482	99.10 249	98.86 417	98.52 344	98.92 383	99.54 312	91.90 423	99.82 341	98.02 290	99.03 416	98.37 465

CSCG		99.37 176	99.29 184	99.60 191	99.71 191	99.46 190	99.43 121	99.85 82	98.79 311	99.41 304	99.60 278	98.92 161	99.92 150	98.02 290	99.92 145	99.43 305

eth_miper_zixun_eth		98.68 322	98.71 302	98.60 403	99.10 415	96.84 434	97.52 458	99.54 290	98.94 285	99.58 244	99.48 330	96.25 364	99.76 388	98.01 293	99.93 139	99.21 361

Patchmtry		98.78 310	98.54 322	99.49 238	98.89 439	99.19 265	99.32 157	99.67 208	99.65 145	99.72 178	99.79 119	91.87 425	99.95 81	98.00 294	99.97 73	99.33 333

PVSNet_BlendedMVS		99.03 269	99.01 255	99.09 344	99.54 283	97.99 386	98.58 362	99.82 104	97.62 415	99.34 321	99.71 185	98.52 227	99.77 384	97.98 295	99.97 73	99.52 256

PVSNet_Blended		98.70 320	98.59 313	99.02 354	99.54 283	97.99 386	97.58 453	99.82 104	95.70 463	99.34 321	98.98 429	98.52 227	99.77 384	97.98 295	99.83 223	99.30 343

cl____		98.54 336	98.41 333	98.92 367	99.03 425	97.80 400	97.46 460	99.59 262	98.90 294	99.60 239	99.46 337	93.85 396	99.78 371	97.97 297	99.89 174	99.17 373

DIV-MVS_self_test		98.54 336	98.42 332	98.92 367	99.03 425	97.80 400	97.46 460	99.59 262	98.90 294	99.60 239	99.46 337	93.87 395	99.78 371	97.97 297	99.89 174	99.18 370

AUN-MVS		97.82 383	97.38 399	99.14 338	99.27 381	98.53 348	98.72 346	99.02 412	98.10 382	97.18 474	99.03 423	89.26 453	99.85 288	97.94 299	97.91 468	99.03 410

FA-MVS(test-final)		98.52 338	98.32 343	99.10 343	99.48 315	98.67 328	99.77 19	98.60 434	97.35 430	99.63 220	99.80 108	93.07 408	99.84 304	97.92 300	99.30 393	98.78 440

ambc				99.20 329	99.35 354	98.53 348	99.17 216	99.46 325		99.67 202	99.80 108	98.46 234	99.70 414	97.92 300	99.70 299	99.38 318

USDC		98.96 287	98.93 275	99.05 352	99.54 283	97.99 386	97.07 476	99.80 122	98.21 377	99.75 158	99.77 142	98.43 237	99.64 452	97.90 302	99.88 184	99.51 258

OPM-MVS		99.26 203	99.13 212	99.63 172	99.70 206	99.61 151	98.58 362	99.48 319	98.50 346	99.52 270	99.63 251	99.14 114	99.76 388	97.89 303	99.77 268	99.51 258
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DVP-MVS		99.32 192	99.17 204	99.77 79	99.69 212	99.80 52	99.14 229	99.31 370	99.16 257	99.62 230	99.61 270	98.35 248	99.91 179	97.88 304	99.72 293	99.61 200
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	99.70 206	99.79 55	99.14 229	99.61 245					99.92 150	97.88 304	99.72 293	99.77 79

c3_l		98.72 317	98.71 302	98.72 395	99.12 408	97.22 422	97.68 449	99.56 278	98.90 294	99.54 263	99.48 330	96.37 359	99.73 403	97.88 304	99.88 184	99.21 361

3Dnovator+	98.92 3	99.35 182	99.24 196	99.67 144	99.35 354	99.47 184	99.62 67	99.50 314	99.44 199	99.12 364	99.78 132	98.77 183	99.94 98	97.87 307	99.72 293	99.62 186

miper_ehance_all_eth		98.59 331	98.59 313	98.59 404	98.98 431	97.07 426	97.49 459	99.52 305	98.50 346	99.52 270	99.37 358	96.41 357	99.71 410	97.86 308	99.62 330	99.00 417

WTY-MVS		98.59 331	98.37 337	99.26 320	99.43 333	98.40 357	98.74 344	99.13 405	98.10 382	99.21 351	99.24 393	94.82 386	99.90 198	97.86 308	98.77 432	99.49 269

APD_test1		99.36 180	99.28 187	99.61 187	99.89 39	99.89 10	99.32 157	99.74 167	99.18 250	99.69 190	99.75 157	98.41 240	99.84 304	97.85 310	99.70 299	99.10 388

SED-MVS		99.40 165	99.28 187	99.77 79	99.69 212	99.82 43	99.20 201	99.54 290	99.13 263	99.82 109	99.63 251	98.91 164	99.92 150	97.85 310	99.70 299	99.58 216

test_241102_TWO								99.54 290	99.13 263	99.76 153	99.63 251	98.32 253	99.92 150	97.85 310	99.69 307	99.75 87

MVS_111021_HR		99.12 249	99.02 251	99.40 273	99.50 305	99.11 277	97.92 434	99.71 183	98.76 318	99.08 368	99.47 334	99.17 107	99.54 468	97.85 310	99.76 270	99.54 239

MTAPA		99.35 182	99.20 200	99.80 64	99.81 106	99.81 48	99.33 154	99.53 300	99.27 234	99.42 298	99.63 251	98.21 265	99.95 81	97.83 314	99.79 255	99.65 156

MSC_two_6792asdad				99.74 102	99.03 425	99.53 173		99.23 387					99.92 150	97.77 315	99.69 307	99.78 75

No_MVS				99.74 102	99.03 425	99.53 173		99.23 387					99.92 150	97.77 315	99.69 307	99.78 75

TESTMET0.1,1		96.24 434	95.84 435	97.41 449	98.24 476	93.84 474	97.38 462	95.84 481	98.43 351	97.81 461	98.56 457	79.77 482	99.89 220	97.77 315	98.77 432	98.52 457

ACMH+	98.40 8	99.50 123	99.43 142	99.71 127	99.86 59	99.76 71	99.32 157	99.77 148	99.53 176	99.77 145	99.76 149	99.26 95	99.78 371	97.77 315	99.88 184	99.60 204

IU-MVS					99.69 212	99.77 64		99.22 390	97.50 422	99.69 190				97.75 319	99.70 299	99.77 79

114514_t		98.49 343	98.11 361	99.64 165	99.73 182	99.58 162	99.24 190	99.76 156	89.94 486	99.42 298	99.56 302	97.76 301	99.86 269	97.74 320	99.82 233	99.47 277

DVP-MVS++		99.38 172	99.25 194	99.77 79	99.03 425	99.77 64	99.74 27	99.61 245	99.18 250	99.76 153	99.61 270	99.00 146	99.92 150	97.72 321	99.60 340	99.62 186

test_0728_THIRD									99.18 250	99.62 230	99.61 270	98.58 210	99.91 179	97.72 321	99.80 250	99.77 79

EGC-MVSNET		89.05 459	85.52 462	99.64 165	99.89 39	99.78 58	99.56 87	99.52 305	24.19 495	49.96 496	99.83 84	99.15 111	99.92 150	97.71 323	99.85 210	99.21 361

miper_enhance_ethall		98.03 376	97.94 376	98.32 418	98.27 475	96.43 442	96.95 479	99.41 337	96.37 454	99.43 295	98.96 433	94.74 387	99.69 421	97.71 323	99.62 330	98.83 436

TSAR-MVS + MP.		99.34 187	99.24 196	99.63 172	99.82 94	99.37 222	99.26 183	99.35 356	98.77 315	99.57 247	99.70 195	99.27 94	99.88 235	97.71 323	99.75 274	99.65 156
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
cl22		97.56 397	97.28 401	98.40 413	98.37 473	96.75 435	97.24 469	99.37 352	97.31 432	99.41 304	99.22 395	87.30 457	99.37 480	97.70 326	99.62 330	99.08 399

MP-MVS-pluss		99.14 244	98.92 279	99.80 64	99.83 85	99.83 35	98.61 355	99.63 235	96.84 447	99.44 291	99.58 291	98.81 174	99.91 179	97.70 326	99.82 233	99.67 133
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.28 197	99.11 219	99.79 71	99.75 170	99.81 48	98.95 306	99.53 300	98.27 374	99.53 268	99.73 167	98.75 186	99.87 250	97.70 326	99.83 223	99.68 124

UnsupCasMVSNet_bld		98.55 335	98.27 349	99.40 273	99.56 278	99.37 222	97.97 430	99.68 203	97.49 423	99.08 368	99.35 368	95.41 380	99.82 341	97.70 326	98.19 460	99.01 416

MVS_111021_LR		99.13 246	99.03 250	99.42 262	99.58 257	99.32 234	97.91 436	99.73 171	98.68 325	99.31 331	99.48 330	99.09 123	99.66 443	97.70 326	99.77 268	99.29 346

IS-MVSNet		99.03 269	98.85 288	99.55 216	99.80 115	99.25 248	99.73 30	99.15 402	99.37 217	99.61 236	99.71 185	94.73 388	99.81 357	97.70 326	99.88 184	99.58 216

MED-MVS test				99.74 102	99.76 154	99.65 127	99.38 132	99.78 142	99.58 169	99.81 116	99.66 225		99.90 198	97.69 332	99.79 255	99.67 133

MED-MVS		99.45 146	99.36 160	99.74 102	99.76 154	99.65 127	99.38 132	99.78 142	99.31 227	99.81 116	99.66 225	99.02 143	99.90 198	97.69 332	99.79 255	99.67 133

ME-MVS		99.26 203	99.10 227	99.73 113	99.60 244	99.65 127	98.75 343	99.45 330	99.31 227	99.65 212	99.66 225	98.00 285	99.86 269	97.69 332	99.79 255	99.67 133

test-LLR		97.15 411	96.95 413	97.74 441	98.18 478	95.02 466	97.38 462	96.10 477	98.00 387	97.81 461	98.58 454	90.04 449	99.91 179	97.69 332	98.78 430	98.31 466

test-mter		96.23 435	95.73 438	97.74 441	98.18 478	95.02 466	97.38 462	96.10 477	97.90 399	97.81 461	98.58 454	79.12 485	99.91 179	97.69 332	98.78 430	98.31 466

MonoMVSNet		98.23 365	98.32 343	97.99 429	98.97 432	96.62 437	99.49 107	98.42 443	99.62 154	99.40 309	99.79 119	95.51 378	98.58 491	97.68 337	95.98 488	98.76 443

XVS		99.27 201	99.11 219	99.75 97	99.71 191	99.71 101	99.37 140	99.61 245	99.29 230	98.76 403	99.47 334	98.47 231	99.88 235	97.62 338	99.73 287	99.67 133

X-MVStestdata		96.09 439	94.87 452	99.75 97	99.71 191	99.71 101	99.37 140	99.61 245	99.29 230	98.76 403	61.30 504	98.47 231	99.88 235	97.62 338	99.73 287	99.67 133

SMA-MVS		99.19 229	99.00 259	99.73 113	99.46 325	99.73 91	99.13 236	99.52 305	97.40 427	99.57 247	99.64 236	98.93 158	99.83 324	97.61 340	99.79 255	99.63 174
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CostFormer		96.71 422	96.79 421	96.46 467	98.90 436	90.71 493	99.41 122	98.68 427	94.69 476	98.14 446	99.34 371	86.32 467	99.80 365	97.60 341	98.07 466	98.88 431

PVSNet	97.47 15	98.42 349	98.44 330	98.35 415	99.46 325	96.26 446	96.70 483	99.34 359	97.68 413	99.00 375	99.13 405	97.40 318	99.72 405	97.59 342	99.68 312	99.08 399

new_pmnet		98.88 299	98.89 284	98.84 384	99.70 206	97.62 405	98.15 406	99.50 314	97.98 390	99.62 230	99.54 312	98.15 271	99.94 98	97.55 343	99.84 215	98.95 421

IB-MVS	95.41 20	95.30 454	94.46 458	97.84 437	98.76 457	95.33 462	97.33 465	96.07 479	96.02 458	95.37 490	97.41 481	76.17 490	99.96 69	97.54 344	95.44 491	98.22 471
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		99.24 208	99.11 219	99.61 187	98.38 472	99.79 55	99.57 85	99.68 203	99.61 159	99.15 359	99.71 185	98.70 193	99.91 179	97.54 344	99.68 312	99.13 385

ZNCC-MVS		99.22 219	99.04 248	99.77 79	99.76 154	99.73 91	99.28 174	99.56 278	98.19 379	99.14 361	99.29 380	98.84 173	99.92 150	97.53 346	99.80 250	99.64 168

CP-MVS		99.23 210	99.05 242	99.75 97	99.66 229	99.66 121	99.38 132	99.62 238	98.38 358	99.06 372	99.27 383	98.79 179	99.94 98	97.51 347	99.82 233	99.66 147

SD-MVS		99.01 277	99.30 179	98.15 425	99.50 305	99.40 213	98.94 308	99.61 245	99.22 246	99.75 158	99.82 91	99.54 54	95.51 495	97.48 348	99.87 197	99.54 239
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		98.49 343	98.29 348	99.11 341	98.96 433	98.42 356	97.54 454	99.32 366	97.53 420	98.47 426	98.15 469	97.88 291	99.82 341	97.46 349	99.24 403	99.09 393

DeepC-MVS_fast	98.47 5	99.23 210	99.12 216	99.56 209	99.28 379	99.22 258	98.99 294	99.40 344	99.08 268	99.58 244	99.64 236	98.90 167	99.83 324	97.44 350	99.75 274	99.63 174
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		99.25 205	99.08 231	99.76 86	99.73 182	99.70 109	99.31 162	99.59 262	98.36 360	99.36 315	99.37 358	98.80 178	99.91 179	97.43 351	99.75 274	99.68 124

ACMMPR		99.23 210	99.06 237	99.76 86	99.74 178	99.69 113	99.31 162	99.59 262	98.36 360	99.35 317	99.38 355	98.61 206	99.93 119	97.43 351	99.75 274	99.67 133

Vis-MVSNet (Re-imp)		98.77 311	98.58 316	99.34 292	99.78 137	98.88 310	99.61 73	99.56 278	99.11 267	99.24 345	99.56 302	93.00 410	99.78 371	97.43 351	99.89 174	99.35 327

MIMVSNet		98.43 348	98.20 353	99.11 341	99.53 290	98.38 361	99.58 82	98.61 432	98.96 281	99.33 323	99.76 149	90.92 435	99.81 357	97.38 354	99.76 270	99.15 377

WB-MVSnew		98.34 359	98.14 359	98.96 360	98.14 481	97.90 394	98.27 396	97.26 473	98.63 331	98.80 398	98.00 472	97.77 299	99.90 198	97.37 355	98.98 419	99.09 393

XVG-OURS-SEG-HR		99.16 239	98.99 266	99.66 151	99.84 77	99.64 133	98.25 399	99.73 171	98.39 357	99.63 220	99.43 342	99.70 31	99.90 198	97.34 356	98.64 443	99.44 299

COLMAP_ROB	98.06 12	99.45 146	99.37 155	99.70 132	99.83 85	99.70 109	99.38 132	99.78 142	99.53 176	99.67 202	99.78 132	99.19 104	99.86 269	97.32 357	99.87 197	99.55 229
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MCST-MVS		99.02 271	98.81 295	99.65 158	99.58 257	99.49 180	98.58 362	99.07 407	98.40 356	99.04 373	99.25 388	98.51 229	99.80 365	97.31 358	99.51 364	99.65 156

region2R		99.23 210	99.05 242	99.77 79	99.76 154	99.70 109	99.31 162	99.59 262	98.41 354	99.32 326	99.36 363	98.73 190	99.93 119	97.29 359	99.74 281	99.67 133

APD-MVS_3200maxsize		99.31 193	99.16 205	99.74 102	99.53 290	99.75 80	99.27 178	99.61 245	99.19 249	99.57 247	99.64 236	98.76 184	99.90 198	97.29 359	99.62 330	99.56 225

TAPA-MVS	97.92 13	98.03 376	97.55 393	99.46 249	99.47 321	99.44 198	98.50 377	99.62 238	86.79 487	99.07 371	99.26 386	98.26 258	99.62 455	97.28 361	99.73 287	99.31 341
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SR-MVS-dyc-post		99.27 201	99.11 219	99.73 113	99.54 283	99.74 88	99.26 183	99.62 238	99.16 257	99.52 270	99.64 236	98.41 240	99.91 179	97.27 362	99.61 337	99.54 239

RE-MVS-def			99.13 212		99.54 283	99.74 88	99.26 183	99.62 238	99.16 257	99.52 270	99.64 236	98.57 211		97.27 362	99.61 337	99.54 239

testing11		96.05 441	95.41 444	97.97 431	98.78 454	95.27 463	98.59 360	98.23 453	98.86 300	96.56 481	96.91 491	75.20 491	99.69 421	97.26 364	98.29 455	98.93 424

test_yl		98.25 362	97.95 372	99.13 339	99.17 401	98.47 351	99.00 287	98.67 429	98.97 279	99.22 349	99.02 424	91.31 429	99.69 421	97.26 364	98.93 421	99.24 352

DCV-MVSNet		98.25 362	97.95 372	99.13 339	99.17 401	98.47 351	99.00 287	98.67 429	98.97 279	99.22 349	99.02 424	91.31 429	99.69 421	97.26 364	98.93 421	99.24 352

PHI-MVS		99.11 253	98.95 273	99.59 194	99.13 406	99.59 157	99.17 216	99.65 223	97.88 402	99.25 342	99.46 337	98.97 154	99.80 365	97.26 364	99.82 233	99.37 321

tfpnnormal		99.43 154	99.38 152	99.60 191	99.87 54	99.75 80	99.59 80	99.78 142	99.71 118	99.90 68	99.69 204	98.85 172	99.90 198	97.25 368	99.78 264	99.15 377

PatchmatchNet		97.65 393	97.80 383	97.18 456	98.82 449	92.49 480	99.17 216	98.39 446	98.12 381	98.79 400	99.58 291	90.71 441	99.89 220	97.23 369	99.41 379	99.16 375
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNVR-MVS		98.99 283	98.80 297	99.56 209	99.25 385	99.43 202	98.54 372	99.27 378	98.58 337	98.80 398	99.43 342	98.53 224	99.70 414	97.22 370	99.59 344	99.54 239

testing3		96.48 428	95.63 440	99.01 355	99.23 389	97.81 398	98.90 314	99.10 406	98.72 320	97.84 460	97.92 473	72.44 495	99.85 288	97.21 371	99.33 389	99.35 327

HPM-MVS		99.25 205	99.07 235	99.78 75	99.81 106	99.75 80	99.61 73	99.67 208	97.72 411	99.35 317	99.25 388	99.23 100	99.92 150	97.21 371	99.82 233	99.67 133
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
mPP-MVS		99.19 229	99.00 259	99.76 86	99.76 154	99.68 116	99.38 132	99.54 290	98.34 369	99.01 374	99.50 323	98.53 224	99.93 119	97.18 373	99.78 264	99.66 147

ACMMP		99.25 205	99.08 231	99.74 102	99.79 129	99.68 116	99.50 102	99.65 223	98.07 385	99.52 270	99.69 204	98.57 211	99.92 150	97.18 373	99.79 255	99.63 174
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
myMVS_eth3d28		96.23 435	95.74 437	97.70 443	98.86 443	95.59 459	98.66 352	98.14 455	98.96 281	97.67 466	97.06 488	76.78 488	98.92 487	97.10 375	98.41 452	98.58 453

thisisatest0515		96.98 415	96.42 423	98.66 400	99.42 338	97.47 411	97.27 467	94.30 487	97.24 434	99.15 359	98.86 441	85.01 469	99.87 250	97.10 375	99.39 381	98.63 447

XVG-ACMP-BASELINE		99.23 210	99.10 227	99.63 172	99.82 94	99.58 162	98.83 327	99.72 180	98.36 360	99.60 239	99.71 185	98.92 161	99.91 179	97.08 377	99.84 215	99.40 313

MSDG		99.08 258	98.98 269	99.37 282	99.60 244	99.13 274	97.54 454	99.74 167	98.84 304	99.53 268	99.55 310	99.10 121	99.79 368	97.07 378	99.86 205	99.18 370

SteuartSystems-ACMMP		99.30 194	99.14 210	99.76 86	99.87 54	99.66 121	99.18 211	99.60 256	98.55 339	99.57 247	99.67 220	99.03 142	99.94 98	97.01 379	99.80 250	99.69 117
Skip Steuart: Steuart Systems R&D Blog.
UWE-MVS		96.21 437	95.78 436	97.49 445	98.53 467	93.83 475	98.04 420	93.94 490	98.96 281	98.46 427	98.17 468	79.86 480	99.87 250	96.99 380	99.06 412	98.78 440

EPMVS		96.53 425	96.32 424	97.17 457	98.18 478	92.97 479	99.39 129	89.95 496	98.21 377	98.61 415	99.59 288	86.69 466	99.72 405	96.99 380	99.23 405	98.81 437

MSP-MVS		99.04 268	98.79 298	99.81 54	99.78 137	99.73 91	99.35 147	99.57 273	98.54 342	99.54 263	98.99 426	96.81 341	99.93 119	96.97 382	99.53 360	99.77 79
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		98.96 287	98.70 306	99.74 102	99.52 297	99.71 101	98.86 320	99.19 397	98.47 350	98.59 417	99.06 416	98.08 277	99.91 179	96.94 383	99.60 340	99.60 204

SR-MVS		99.19 229	99.00 259	99.74 102	99.51 299	99.72 96	99.18 211	99.60 256	98.85 301	99.47 285	99.58 291	98.38 245	99.92 150	96.92 384	99.54 358	99.57 222

PGM-MVS		99.20 226	99.01 255	99.77 79	99.75 170	99.71 101	99.16 222	99.72 180	97.99 389	99.42 298	99.60 278	98.81 174	99.93 119	96.91 385	99.74 281	99.66 147

HY-MVS	98.23 9	98.21 369	97.95 372	98.99 356	99.03 425	98.24 365	99.61 73	98.72 425	96.81 448	98.73 405	99.51 320	94.06 393	99.86 269	96.91 385	98.20 458	98.86 433

MDTV_nov1_ep13			97.73 387		98.70 462	90.83 491	99.15 225	98.02 458	98.51 345	98.82 395	99.61 270	90.98 434	99.66 443	96.89 387	98.92 423

GST-MVS		99.16 239	98.96 272	99.75 97	99.73 182	99.73 91	99.20 201	99.55 284	98.22 376	99.32 326	99.35 368	98.65 202	99.91 179	96.86 388	99.74 281	99.62 186

test_post1							99.14 229				51.63 506	89.54 452	99.82 341	96.86 388

SCA		98.11 372	98.36 338	97.36 450	99.20 395	92.99 478	98.17 404	98.49 440	98.24 375	99.10 367	99.57 298	96.01 369	99.94 98	96.86 388	99.62 330	99.14 382

UBG		96.53 425	95.95 431	98.29 422	98.87 442	96.31 445	98.48 380	98.07 456	98.83 305	97.32 469	96.54 499	79.81 481	99.62 455	96.84 391	98.74 436	98.95 421

XVG-OURS		99.21 224	99.06 237	99.65 158	99.82 94	99.62 141	97.87 438	99.74 167	98.36 360	99.66 208	99.68 216	99.71 28	99.90 198	96.84 391	99.88 184	99.43 305

LCM-MVSNet-Re		99.28 197	99.15 209	99.67 144	99.33 368	99.76 71	99.34 148	99.97 20	98.93 290	99.91 63	99.79 119	98.68 195	99.93 119	96.80 393	99.56 349	99.30 343

RPSCF		99.18 233	99.02 251	99.64 165	99.83 85	99.85 22	99.44 119	99.82 104	98.33 370	99.50 280	99.78 132	97.90 289	99.65 450	96.78 394	99.83 223	99.44 299

旧先验2							97.94 432		95.33 467	98.94 379			99.88 235	96.75 395

MDTV_nov1_ep13_2view						91.44 488	99.14 229		97.37 429	99.21 351		91.78 427		96.75 395		99.03 410

CLD-MVS		98.76 312	98.57 317	99.33 295	99.57 267	98.97 297	97.53 456	99.55 284	96.41 452	99.27 338	99.13 405	99.07 130	99.78 371	96.73 397	99.89 174	99.23 356
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Patchmatch-test		98.10 373	97.98 370	98.48 409	99.27 381	96.48 440	99.40 127	99.07 407	98.81 308	99.23 346	99.57 298	90.11 448	99.87 250	96.69 398	99.64 325	99.09 393

baseline2		96.83 418	96.28 425	98.46 411	99.09 418	96.91 430	98.83 327	93.87 491	97.23 435	96.23 486	98.36 463	88.12 456	99.90 198	96.68 399	98.14 463	98.57 455

cascas		96.99 414	96.82 420	97.48 446	97.57 494	95.64 457	96.43 485	99.56 278	91.75 482	97.13 476	97.61 480	95.58 374	98.63 489	96.68 399	99.11 409	98.18 475

PC_three_1452									97.56 416	99.68 195	99.41 345	99.09 123	97.09 492	96.66 401	99.60 340	99.62 186

LPG-MVS_test		99.22 219	99.05 242	99.74 102	99.82 94	99.63 139	99.16 222	99.73 171	97.56 416	99.64 215	99.69 204	99.37 76	99.89 220	96.66 401	99.87 197	99.69 117

LGP-MVS_train				99.74 102	99.82 94	99.63 139		99.73 171	97.56 416	99.64 215	99.69 204	99.37 76	99.89 220	96.66 401	99.87 197	99.69 117

ETVMVS		96.14 438	95.22 449	98.89 379	98.80 450	98.01 385	98.66 352	98.35 449	98.71 322	97.18 474	96.31 503	74.23 494	99.75 397	96.64 404	98.13 465	98.90 428

TinyColmap		98.97 284	98.93 275	99.07 349	99.46 325	98.19 370	97.75 442	99.75 161	98.79 311	99.54 263	99.70 195	98.97 154	99.62 455	96.63 405	99.83 223	99.41 310

LF4IMVS		99.01 277	98.92 279	99.27 317	99.71 191	99.28 240	98.59 360	99.77 148	98.32 371	99.39 311	99.41 345	98.62 204	99.84 304	96.62 406	99.84 215	98.69 446

NCCC		98.82 306	98.57 317	99.58 197	99.21 392	99.31 235	98.61 355	99.25 383	98.65 328	98.43 428	99.26 386	97.86 292	99.81 357	96.55 407	99.27 399	99.61 200

OPU-MVS				99.29 309	99.12 408	99.44 198	99.20 201				99.40 349	99.00 146	98.84 488	96.54 408	99.60 340	99.58 216

F-COLMAP		98.74 314	98.45 329	99.62 181	99.57 267	99.47 184	98.84 324	99.65 223	96.31 455	98.93 380	99.19 402	97.68 305	99.87 250	96.52 409	99.37 384	99.53 245

testing99		95.86 446	95.19 450	97.87 435	98.76 457	95.03 465	98.62 354	98.44 442	98.68 325	96.67 480	96.66 498	74.31 493	99.69 421	96.51 410	98.03 467	98.90 428

ADS-MVSNet2		97.78 387	97.66 391	98.12 427	99.14 404	95.36 461	99.22 198	98.75 424	96.97 443	98.25 435	99.64 236	90.90 436	99.94 98	96.51 410	99.56 349	99.08 399

ADS-MVSNet		97.72 392	97.67 390	97.86 436	99.14 404	94.65 469	99.22 198	98.86 417	96.97 443	98.25 435	99.64 236	90.90 436	99.84 304	96.51 410	99.56 349	99.08 399

PatchMatch-RL		98.68 322	98.47 326	99.30 308	99.44 330	99.28 240	98.14 408	99.54 290	97.12 441	99.11 365	99.25 388	97.80 297	99.70 414	96.51 410	99.30 393	98.93 424

CMPMVS	77.52 23	98.50 341	98.19 356	99.41 270	98.33 474	99.56 166	99.01 281	99.59 262	95.44 465	99.57 247	99.80 108	95.64 372	99.46 478	96.47 414	99.92 145	99.21 361
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testing91		96.00 442	95.32 447	98.02 428	98.76 457	95.39 460	98.38 389	98.65 431	98.82 306	96.84 477	96.71 497	75.06 492	99.71 410	96.46 415	98.23 457	98.98 418

SF-MVS		99.10 256	98.93 275	99.62 181	99.58 257	99.51 178	99.13 236	99.65 223	97.97 391	99.42 298	99.61 270	98.86 171	99.87 250	96.45 416	99.68 312	99.49 269

FE-MVS		97.85 382	97.42 398	99.15 335	99.44 330	98.75 323	99.77 19	98.20 454	95.85 460	99.33 323	99.80 108	88.86 454	99.88 235	96.40 417	99.12 408	98.81 437

DPE-MVS		99.14 244	98.92 279	99.82 46	99.57 267	99.77 64	98.74 344	99.60 256	98.55 339	99.76 153	99.69 204	98.23 263	99.92 150	96.39 418	99.75 274	99.76 84
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
gm-plane-assit					97.59 492	89.02 498			93.47 478		98.30 464		99.84 304	96.38 419

AllTest		99.21 224	99.07 235	99.63 172	99.78 137	99.64 133	99.12 241	99.83 98	98.63 331	99.63 220	99.72 175	98.68 195	99.75 397	96.38 419	99.83 223	99.51 258

TestCases				99.63 172	99.78 137	99.64 133		99.83 98	98.63 331	99.63 220	99.72 175	98.68 195	99.75 397	96.38 419	99.83 223	99.51 258

testdata				99.42 262	99.51 299	98.93 304		99.30 373	96.20 456	98.87 390	99.40 349	98.33 252	99.89 220	96.29 422	99.28 396	99.44 299

dp		96.86 417	97.07 409	96.24 469	98.68 463	90.30 496	99.19 207	98.38 447	97.35 430	98.23 437	99.59 288	87.23 458	99.82 341	96.27 423	98.73 439	98.59 451

tpmvs		97.39 406	97.69 388	96.52 465	98.41 471	91.76 484	99.30 165	98.94 416	97.74 408	97.85 459	99.55 310	92.40 419	99.73 403	96.25 424	98.73 439	98.06 477

KD-MVS_2432*1600		95.89 443	95.41 444	97.31 453	94.96 496	93.89 472	97.09 474	99.22 390	97.23 435	98.88 387	99.04 419	79.23 483	99.54 468	96.24 425	96.81 477	98.50 461

miper_refine_blended		95.89 443	95.41 444	97.31 453	94.96 496	93.89 472	97.09 474	99.22 390	97.23 435	98.88 387	99.04 419	79.23 483	99.54 468	96.24 425	96.81 477	98.50 461

ACMP	97.51 14	99.05 265	98.84 290	99.67 144	99.78 137	99.55 170	98.88 316	99.66 213	97.11 442	99.47 285	99.60 278	99.07 130	99.89 220	96.18 427	99.85 210	99.58 216
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		98.90 295	98.72 301	99.44 256	99.39 342	99.42 205	98.58 362	99.64 231	97.31 432	99.44 291	99.62 260	98.59 208	99.69 421	96.17 428	99.79 255	99.22 358

DP-MVS Recon		98.50 341	98.23 350	99.31 304	99.49 310	99.46 190	98.56 368	99.63 235	94.86 474	98.85 392	99.37 358	97.81 296	99.59 462	96.08 429	99.44 374	98.88 431

tpm cat1		96.78 419	96.98 412	96.16 470	98.85 444	90.59 494	99.08 257	99.32 366	92.37 480	97.73 465	99.46 337	91.15 432	99.69 421	96.07 430	98.80 429	98.21 472

tpm2		96.35 431	96.22 426	96.73 463	98.88 441	91.75 485	99.21 200	98.51 438	93.27 479	97.89 455	99.21 399	84.83 470	99.70 414	96.04 431	98.18 461	98.75 444

dmvs_re		98.69 321	98.48 325	99.31 304	99.55 281	99.42 205	99.54 90	98.38 447	99.32 225	98.72 406	98.71 450	96.76 343	99.21 482	96.01 432	99.35 387	99.31 341

test_0402		99.22 219	99.14 210	99.45 252	99.79 129	99.43 202	99.28 174	99.68 203	99.54 174	99.40 309	99.56 302	99.07 130	99.82 341	96.01 432	99.96 87	99.11 386

ITE_SJBPF				99.38 279	99.63 237	99.44 198		99.73 171	98.56 338	99.33 323	99.53 314	98.88 168	99.68 433	96.01 432	99.65 323	99.02 415

test_prior2							97.95 431		97.87 403	98.05 448	99.05 417	97.90 289		95.99 435	99.49 369

testdata2													99.89 220	95.99 435

原ACMM1				99.37 282	99.47 321	98.87 314		99.27 378	96.74 450	98.26 434	99.32 372	97.93 288	99.82 341	95.96 437	99.38 382	99.43 305

新几何1				99.52 228	99.50 305	99.22 258		99.26 380	95.66 464	98.60 416	99.28 381	97.67 306	99.89 220	95.95 438	99.32 391	99.45 284

MP-MVS		99.06 262	98.83 292	99.76 86	99.76 154	99.71 101	99.32 157	99.50 314	98.35 365	98.97 376	99.48 330	98.37 246	99.92 150	95.95 438	99.75 274	99.63 174
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testing222		95.60 453	94.59 456	98.61 402	98.66 464	97.45 414	98.54 372	97.90 462	98.53 343	96.54 482	96.47 500	70.62 498	99.81 357	95.91 440	98.15 462	98.56 456

wuyk23d		97.58 396	99.13 212	92.93 474	99.69 212	99.49 180	99.52 94	99.77 148	97.97 391	99.96 34	99.79 119	99.84 16	99.94 98	95.85 441	99.82 233	79.36 492

HQP_MVS		98.90 295	98.68 307	99.55 216	99.58 257	99.24 252	98.80 335	99.54 290	98.94 285	99.14 361	99.25 388	97.24 325	99.82 341	95.84 442	99.78 264	99.60 204

plane_prior5								99.54 290					99.82 341	95.84 442	99.78 264	99.60 204

无先验							98.01 423	99.23 387	95.83 461				99.85 288	95.79 444		99.44 299

CPTT-MVS		98.74 314	98.44 330	99.64 165	99.61 242	99.38 218	99.18 211	99.55 284	96.49 451	99.27 338	99.37 358	97.11 333	99.92 150	95.74 445	99.67 318	99.62 186

PLC	97.35 16	98.36 354	97.99 368	99.48 243	99.32 369	99.24 252	98.50 377	99.51 310	95.19 470	98.58 418	98.96 433	96.95 338	99.83 324	95.63 446	99.25 401	99.37 321
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		98.57 333	98.34 341	99.28 312	99.18 400	99.10 284	98.34 391	99.41 337	98.48 349	98.52 423	98.98 429	97.05 335	99.78 371	95.59 447	99.50 367	98.96 419

1314		98.00 378	97.90 380	98.27 423	98.90 436	97.45 414	99.30 165	99.06 409	94.98 471	97.21 473	99.12 409	98.43 237	99.67 438	95.58 448	98.56 446	97.71 481

PVSNet_0	95.53 19	95.85 447	95.31 448	97.47 447	98.78 454	93.48 477	95.72 487	99.40 344	96.18 457	97.37 468	97.73 475	95.73 371	99.58 463	95.49 449	81.40 493	99.36 324

MAR-MVS		98.24 364	97.92 378	99.19 330	98.78 454	99.65 127	99.17 216	99.14 403	95.36 466	98.04 449	98.81 446	97.47 315	99.72 405	95.47 450	99.06 412	98.21 472
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	98.23 365	97.89 381	99.26 320	99.19 397	99.26 245	99.65 62	99.69 200	91.33 484	98.14 446	99.77 142	98.28 255	99.96 69	95.41 451	99.55 353	98.58 453

train_agg		98.35 357	97.95 372	99.57 205	99.35 354	99.35 229	98.11 412	99.41 337	94.90 472	97.92 453	98.99 426	98.02 280	99.85 288	95.38 452	99.44 374	99.50 264

9.14			98.64 308		99.45 329		98.81 332	99.60 256	97.52 421	99.28 337	99.56 302	98.53 224	99.83 324	95.36 453	99.64 325

APD-MVS		98.87 300	98.59 313	99.71 127	99.50 305	99.62 141	99.01 281	99.57 273	96.80 449	99.54 263	99.63 251	98.29 254	99.91 179	95.24 454	99.71 297	99.61 200
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
WAC-MVS						96.36 443								95.20 455

AdaColmap		98.60 328	98.35 340	99.38 279	99.12 408	99.22 258	98.67 350	99.42 336	97.84 406	98.81 396	99.27 383	97.32 323	99.81 357	95.14 456	99.53 360	99.10 388

test9_res														95.10 457	99.44 374	99.50 264

CDPH-MVS		98.56 334	98.20 353	99.61 187	99.50 305	99.46 190	98.32 393	99.41 337	95.22 468	99.21 351	99.10 413	98.34 250	99.82 341	95.09 458	99.66 321	99.56 225

BH-untuned		98.22 367	98.09 362	98.58 406	99.38 345	97.24 421	98.55 369	98.98 415	97.81 407	99.20 356	98.76 448	97.01 336	99.65 450	94.83 459	98.33 453	98.86 433

BP-MVS														94.73 460

HQP-MVS		98.36 354	98.02 367	99.39 276	99.31 370	98.94 301	97.98 427	99.37 352	97.45 424	98.15 442	98.83 443	96.67 345	99.70 414	94.73 460	99.67 318	99.53 245

QAPM		98.40 352	97.99 368	99.65 158	99.39 342	99.47 184	99.67 53	99.52 305	91.70 483	98.78 402	99.80 108	98.55 215	99.95 81	94.71 462	99.75 274	99.53 245

agg_prior2														94.58 463	99.46 373	99.50 264

myMVS_eth3d		95.63 451	94.73 453	98.34 417	98.50 469	96.36 443	98.60 357	99.21 393	97.89 400	96.76 478	96.37 501	72.10 496	99.57 464	94.38 464	98.73 439	99.09 393

BH-RMVSNet		98.41 350	98.14 359	99.21 327	99.21 392	98.47 351	98.60 357	98.26 452	98.35 365	98.93 380	99.31 375	97.20 330	99.66 443	94.32 465	99.10 410	99.51 258

E-PMN		97.14 413	97.43 397	96.27 468	98.79 452	91.62 486	95.54 488	99.01 414	99.44 199	98.88 387	99.12 409	92.78 411	99.68 433	94.30 466	99.03 416	97.50 482

MG-MVS		98.52 338	98.39 335	98.94 363	99.15 403	97.39 418	98.18 402	99.21 393	98.89 297	99.23 346	99.63 251	97.37 321	99.74 400	94.22 467	99.61 337	99.69 117

API-MVS		98.38 353	98.39 335	98.35 415	98.83 446	99.26 245	99.14 229	99.18 398	98.59 336	98.66 411	98.78 447	98.61 206	99.57 464	94.14 468	99.56 349	96.21 489

PAPM_NR		98.36 354	98.04 365	99.33 295	99.48 315	98.93 304	98.79 338	99.28 377	97.54 419	98.56 422	98.57 456	97.12 332	99.69 421	94.09 469	98.90 427	99.38 318

ZD-MVS					99.43 333	99.61 151		99.43 334	96.38 453	99.11 365	99.07 415	97.86 292	99.92 150	94.04 470	99.49 369

DPM-MVS		98.28 360	97.94 376	99.32 300	99.36 350	99.11 277	97.31 466	98.78 423	96.88 445	98.84 393	99.11 412	97.77 299	99.61 460	94.03 471	99.36 385	99.23 356

gg-mvs-nofinetune		95.87 445	95.17 451	97.97 431	98.19 477	96.95 428	99.69 45	89.23 497	99.89 56	96.24 485	99.94 19	81.19 475	99.51 474	93.99 472	98.20 458	97.44 483

PMVS	92.94 21	98.82 306	98.81 295	98.85 382	99.84 77	97.99 386	99.20 201	99.47 322	99.71 118	99.42 298	99.82 91	98.09 275	99.47 476	93.88 473	99.85 210	99.07 404
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		96.96 416	97.28 401	95.99 472	98.76 457	91.03 490	95.26 490	98.61 432	99.34 221	98.92 383	98.88 440	93.79 397	99.66 443	92.87 474	99.05 414	97.30 486

BH-w/o		97.20 410	97.01 411	97.76 439	99.08 419	95.69 456	98.03 422	98.52 437	95.76 462	97.96 452	98.02 470	95.62 373	99.47 476	92.82 475	97.25 476	98.12 476

TR-MVS		97.44 403	97.15 406	98.32 418	98.53 467	97.46 412	98.47 381	97.91 461	96.85 446	98.21 438	98.51 460	96.42 355	99.51 474	92.16 476	97.29 475	97.98 478

OpenMVS_ROB	97.31 17	97.36 408	96.84 418	98.89 379	99.29 376	99.45 196	98.87 319	99.48 319	86.54 489	99.44 291	99.74 162	97.34 322	99.86 269	91.61 477	99.28 396	97.37 485

GG-mvs-BLEND				97.36 450	97.59 492	96.87 431	99.70 38	88.49 498		94.64 491	97.26 486	80.66 477	99.12 483	91.50 478	96.50 485	96.08 491

DeepMVS_CX				97.98 430	99.69 212	96.95 428		99.26 380	75.51 492	95.74 488	98.28 465	96.47 353	99.62 455	91.23 479	97.89 469	97.38 484

PAPR		97.56 397	97.07 409	99.04 353	98.80 450	98.11 378	97.63 450	99.25 383	94.56 477	98.02 451	98.25 466	97.43 317	99.68 433	90.90 480	98.74 436	99.33 333

MVS		95.72 449	94.63 455	98.99 356	98.56 466	97.98 391	99.30 165	98.86 417	72.71 493	97.30 470	99.08 414	98.34 250	99.74 400	89.21 481	98.33 453	99.26 349

UWE-MVS-28		95.64 450	95.47 442	96.14 471	97.98 484	90.39 495	98.49 379	95.81 482	99.02 275	98.03 450	98.19 467	84.49 472	99.28 481	88.75 482	98.47 451	98.75 444

thres600view7		96.60 424	96.16 427	97.93 433	99.63 237	96.09 451	99.18 211	97.57 467	98.77 315	98.72 406	97.32 484	87.04 460	99.72 405	88.57 483	98.62 444	97.98 478

FPMVS		96.32 432	95.50 441	98.79 390	99.60 244	98.17 373	98.46 385	98.80 422	97.16 439	96.28 483	99.63 251	82.19 474	99.09 484	88.45 484	98.89 428	99.10 388

PCF-MVS	96.03 18	96.73 421	95.86 434	99.33 295	99.44 330	99.16 271	96.87 481	99.44 331	86.58 488	98.95 378	99.40 349	94.38 391	99.88 235	87.93 485	99.80 250	98.95 421
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres100view900		96.39 430	96.03 430	97.47 447	99.63 237	95.93 452	99.18 211	97.57 467	98.75 319	98.70 409	97.31 485	87.04 460	99.67 438	87.62 486	98.51 448	96.81 487

tfpn200view9		96.30 433	95.89 432	97.53 444	99.58 257	96.11 449	99.00 287	97.54 470	98.43 351	98.52 423	96.98 489	86.85 462	99.67 438	87.62 486	98.51 448	96.81 487

thres400		96.40 429	95.89 432	97.92 434	99.58 257	96.11 449	99.00 287	97.54 470	98.43 351	98.52 423	96.98 489	86.85 462	99.67 438	87.62 486	98.51 448	97.98 478

thres200		96.09 439	95.68 439	97.33 452	99.48 315	96.22 448	98.53 374	97.57 467	98.06 386	98.37 430	96.73 496	86.84 464	99.61 460	86.99 489	98.57 445	96.16 490

MVE	92.54 22	96.66 423	96.11 428	98.31 420	99.68 220	97.55 407	97.94 432	95.60 483	99.37 217	90.68 494	98.70 452	96.56 348	98.61 490	86.94 490	99.55 353	98.77 442
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dmvs_testset		97.27 409	96.83 419	98.59 404	99.46 325	97.55 407	99.25 189	96.84 476	98.78 313	97.24 472	97.67 476	97.11 333	98.97 486	86.59 491	98.54 447	99.27 347

PAPM		95.61 452	94.71 454	98.31 420	99.12 408	96.63 436	96.66 484	98.46 441	90.77 485	96.25 484	98.68 453	93.01 409	99.69 421	81.60 492	97.86 471	98.62 448

SD_0403		97.42 404	96.90 417	98.98 358	99.54 283	97.90 394	99.52 94	99.54 290	99.34 221	97.87 457	98.85 442	98.72 191	99.64 452	78.93 493	99.83 223	99.40 313

dongtai		89.37 458	88.91 461	90.76 475	99.19 397	77.46 500	95.47 489	87.82 499	92.28 481	94.17 492	98.82 445	71.22 497	95.54 494	63.85 494	97.34 474	99.27 347

kuosan		85.65 460	84.57 463	88.90 477	97.91 486	77.11 501	96.37 486	87.62 500	85.24 490	85.45 495	96.83 492	69.94 499	90.98 496	45.90 495	95.83 490	98.62 448

test123		29.31 461	33.05 466	18.08 478	25.93 502	12.24 503	97.53 456	10.93 503	11.78 496	24.21 497	50.08 508	21.04 500	8.60 497	23.51 496	32.43 496	33.39 493

testmvs		28.94 462	33.33 464	15.79 479	26.03 501	9.81 504	96.77 482	15.67 502	11.55 497	23.87 498	50.74 507	19.03 501	8.53 498	23.21 497	33.07 495	29.03 494

mmdepth		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

monomultidepth		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

test_blank		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uanet_test		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

DCPMVS		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

cdsmvs_eth3d_5k		24.88 463	33.17 465	0.00 480	0.00 503	0.00 505	0.00 492	99.62 238	0.00 498	0.00 499	99.13 405	99.82 18	0.00 499	0.00 498	0.00 497	0.00 495

pcd_1.5k_mvsjas		16.61 464	22.14 467	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	99.28 91	0.00 499	0.00 498	0.00 497	0.00 495

sosnet-low-res		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

sosnet		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uncertanet		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

Regformer		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

ab-mvs-re		8.26 475	11.02 478	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	99.16 403	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

uanet		8.33 465	11.11 468	0.00 480	0.00 503	0.00 505	0.00 492	0.00 504	0.00 498	0.00 499	100.00 1	0.00 502	0.00 499	0.00 498	0.00 497	0.00 495

TestfortrainingZip							99.38 132

FOURS1					99.83 85	99.89 10	99.74 27	99.71 183	99.69 127	99.63 220

test_one_0601					99.63 237	99.76 71		99.55 284	99.23 242	99.31 331	99.61 270	98.59 208

eth-test2					0.00 503

eth-test					0.00 503

test_241102_ONE					99.69 212	99.82 43		99.54 290	99.12 266	99.82 109	99.49 327	98.91 164	99.52 473

save fliter					99.53 290	99.25 248	98.29 395	99.38 351	99.07 270

test0726					99.69 212	99.80 52	99.24 190	99.57 273	99.16 257	99.73 173	99.65 234	98.35 248

GSMVS																99.14 382

test_part2					99.62 241	99.67 119				99.55 260

sam_mvs1												90.81 440				99.14 382

sam_mvs												90.52 445

MTGPA								99.53 300

test_post											52.41 505	90.25 447	99.86 269

patchmatchnet-post											99.62 260	90.58 443	99.94 98

MTMP							99.09 254	98.59 435

TEST9					99.35 354	99.35 229	98.11 412	99.41 337	94.83 475	97.92 453	98.99 426	98.02 280	99.85 288

test_8					99.34 363	99.31 235	98.08 416	99.40 344	94.90 472	97.87 457	98.97 431	98.02 280	99.84 304

agg_prior					99.35 354	99.36 226		99.39 347		97.76 464			99.85 288

test_prior4						99.19 265	98.00 425

test_prior				99.46 249	99.35 354	99.22 258		99.39 347					99.69 421			99.48 273

新几何2							98.04 420

旧先验1					99.49 310	99.29 238		99.26 380			99.39 353	97.67 306			99.36 385	99.46 282

原ACMM2							97.92 434

test222					99.51 299	99.08 286	97.83 440	99.29 374	95.21 469	98.68 410	99.31 375	97.28 324			99.38 382	99.43 305

segment_acmp												98.37 246

testdata1							97.72 445		97.86 405

test12				99.54 222	99.29 376	99.33 232		99.16 401		98.43 428		97.54 313	99.82 341		99.47 371	99.48 273

plane_prior7					99.58 257	99.38 218

plane_prior6					99.47 321	99.26 245						97.24 325

plane_prior4											99.25 388

plane_prior3						99.31 235			98.36 360	99.14 361

plane_prior2							98.80 335		98.94 285

plane_prior1					99.51 299

plane_prior						99.24 252	98.42 387		97.87 403						99.71 297

n2								0.00 504

nn								0.00 504

door-mid								99.83 98

test11								99.29 374

door								99.77 148

HQP5-MVS						98.94 301

HQP-NCC					99.31 370		97.98 427		97.45 424	98.15 442

ACMP_Plane					99.31 370		97.98 427		97.45 424	98.15 442

HQP4-MVS										98.15 442			99.70 414			99.53 245

HQP3-MVS								99.37 352							99.67 318

HQP2-MVS												96.67 345

NP-MVS					99.40 341	99.13 274					98.83 443

ACMMP++_ref															99.94 127

ACMMP++															99.79 255

Test By Simon												98.41 240

High-resolution multi-view benchmark