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_fmvs3		99.12 69	99.41 26	98.25 305	99.76 30	95.07 390	99.05 68	99.94 3	97.78 251	99.82 34	99.84 3	98.56 73	99.71 312	99.96 1	99.96 28	99.97 4

test_fmvs2		98.70 147	98.97 98	97.89 346	99.54 123	94.05 428	98.55 126	99.92 8	96.78 352	99.72 47	99.78 13	96.60 254	99.67 348	99.91 2	99.90 88	99.94 10

test_fmvsmvis_n_1920		99.26 39	99.49 16	98.54 265	99.66 70	96.97 285	98.00 215	99.85 19	99.24 77	99.92 8	99.50 68	99.39 12	99.95 25	99.89 3	99.98 12	98.71 409

fmvsm_s_conf0.5_n_10		99.15 57	99.27 47	98.78 204	99.47 161	96.56 313	97.75 261	99.71 48	99.60 35	99.74 46	99.44 85	97.96 139	99.95 25	99.86 4	99.94 51	99.82 36

fmvsm_l_conf0.5_n		99.21 47	99.28 46	99.02 151	99.64 77	97.28 257	97.82 246	99.76 39	98.73 151	99.82 34	99.09 197	98.81 39	99.95 25	99.86 4	99.96 28	99.83 33

fmvsm_s_conf0.5_n_9		99.17 52	99.38 28	98.53 267	99.51 134	95.82 349	97.62 281	99.78 36	99.72 14	99.90 14	99.48 75	98.66 59	99.89 97	99.85 6	99.93 57	99.89 16

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 167	99.65 71	97.05 280	97.80 250	99.76 39	98.70 159	99.78 39	99.11 188	98.79 43	99.95 25	99.85 6	99.96 28	99.83 33

test_fmvsm_n_1920		99.33 30	99.45 23	98.99 156	99.57 103	97.73 214	97.93 230	99.83 26	99.22 80	99.93 6	99.30 125	99.42 11	99.96 13	99.85 6	99.99 5	99.29 284

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 92	98.21 146	97.82 246	99.84 23	99.41 57	99.92 8	99.41 94	99.51 8	99.95 25	99.84 9	99.97 21	99.87 22

fmvsm_l_conf0.5_n_9		99.32 32	99.43 24	98.98 160	99.59 92	97.18 271	97.44 312	99.83 26	99.56 39	99.91 12	99.34 115	99.36 13	99.93 53	99.83 10	99.98 12	99.85 30

fmvsm_s_conf0.1_n_2		99.20 50	99.38 28	98.65 234	99.69 61	96.08 336	97.49 303	99.90 12	99.53 41	99.88 21	99.64 37	98.51 76	99.90 81	99.83 10	99.98 12	99.97 4

fmvsm_s_conf0.5_n_2		99.14 62	99.31 41	98.63 240	99.49 150	96.08 336	97.38 317	99.81 32	99.48 44	99.84 30	99.57 49	98.46 82	99.89 97	99.82 12	99.97 21	99.91 13

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 74	99.87 12	98.13 152	98.08 196	99.95 2	99.45 50	99.98 2	99.75 16	99.80 1	99.97 6	99.82 12	99.99 5	99.99 2

test_fmvs1_n		98.09 259	98.28 219	97.52 393	99.68 64	93.47 456	98.63 116	99.93 6	95.41 429	99.68 57	99.64 37	91.88 415	99.48 443	99.82 12	99.87 100	99.62 92

test_f		98.67 161	98.87 111	98.05 333	99.72 45	95.59 355	98.51 135	99.81 32	96.30 378	99.78 39	99.82 5	96.14 280	98.63 512	99.82 12	99.93 57	99.95 9

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 14	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 16	100.00 1	99.85 30

fmvsm_s_conf0.5_n_4		99.01 90	99.22 54	98.38 289	99.31 209	95.48 365	97.56 292	99.73 45	98.87 140	99.75 44	99.27 131	98.80 41	99.86 144	99.80 17	99.90 88	99.81 41

MM		98.22 240	97.99 260	98.91 175	98.66 384	96.97 285	97.89 237	94.44 517	99.54 40	98.95 224	99.14 180	93.50 379	99.92 65	99.80 17	99.96 28	99.85 30

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 154	97.77 255	99.90 12	99.33 66	99.97 3	99.66 32	99.71 3	99.96 13	99.79 19	99.99 5	99.96 8

test_vis1_n		98.31 227	98.50 175	97.73 366	99.76 30	94.17 423	98.68 109	99.91 10	96.31 376	99.79 38	99.57 49	92.85 396	99.42 459	99.79 19	99.84 114	99.60 102

test_fmvs1		97.72 301	97.94 268	97.07 420	98.66 384	92.39 477	97.68 270	99.81 32	95.20 436	99.54 79	99.44 85	91.56 419	99.41 460	99.78 21	99.77 172	99.40 231

fmvsm_s_conf0.5_n_6		99.08 79	99.21 57	98.69 227	99.36 194	96.51 315	97.62 281	99.68 64	98.43 183	99.85 27	99.10 191	99.12 23	99.88 115	99.77 22	99.92 71	99.67 78

fmvsm_s_conf0.5_n_11		99.21 47	99.34 35	98.80 197	99.48 158	96.56 313	97.97 228	99.69 57	99.63 28	99.84 30	99.54 62	98.21 115	99.94 41	99.76 23	99.95 39	99.88 20

test_vis1_n_1920		98.40 207	98.92 102	96.81 436	99.74 37	90.76 509	98.15 184	99.91 10	98.33 190	99.89 18	99.55 56	95.07 329	99.88 115	99.76 23	99.93 57	99.79 47

test_vis3_rt		99.14 62	99.17 60	99.07 138	99.78 24	98.38 124	98.92 83	99.94 3	97.80 247	99.91 12	99.67 30	97.15 212	98.91 504	99.76 23	99.56 293	99.92 12

fmvsm_s_conf0.5_n_3		99.22 46	99.37 31	98.78 204	99.46 164	96.58 311	97.65 276	99.72 46	99.47 47	99.86 24	99.50 68	98.94 31	99.89 97	99.75 26	99.97 21	99.86 28

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 77	98.10 157	97.68 270	99.84 23	99.29 72	99.92 8	99.57 49	99.60 5	99.96 13	99.74 27	99.98 12	99.89 16

fmvsm_s_conf0.1_n_a		99.17 52	99.30 44	98.80 197	99.75 34	96.59 308	97.97 228	99.86 17	98.22 203	99.88 21	99.71 22	98.59 67	99.84 179	99.73 28	99.98 12	99.98 3

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 112	99.45 14	99.61 92	99.66 23	99.68 57	99.66 32	98.44 84	99.95 25	99.73 28	99.96 28	99.75 62

fmvsm_s_conf0.5_n_8		99.13 66	99.26 50	98.74 217	99.51 134	96.44 321	97.65 276	99.65 77	99.66 23	99.78 39	99.48 75	97.92 142	99.93 53	99.72 30	99.95 39	99.87 22

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 236	99.71 49	96.10 331	97.87 241	99.85 19	98.56 177	99.90 14	99.68 25	98.69 57	99.85 158	99.72 30	99.98 12	99.97 4

fmvsm_s_conf0.5_n_5		99.07 82	99.10 80	98.99 156	99.47 161	97.22 264	97.40 314	99.83 26	97.61 266	99.85 27	99.30 125	98.80 41	99.95 25	99.71 32	99.90 88	99.78 50

fmvsm_s_conf0.5_n_a		99.10 72	99.20 58	98.78 204	99.55 117	96.59 308	97.79 251	99.82 31	98.21 205	99.81 36	99.53 64	98.46 82	99.84 179	99.70 33	99.97 21	99.90 15

v10		98.97 99	99.11 74	98.55 260	99.44 171	96.21 330	98.90 84	99.55 126	98.73 151	99.48 96	99.60 45	96.63 253	99.83 197	99.70 33	99.99 5	99.61 100

fmvsm_s_conf0.5_n		99.09 73	99.26 50	98.61 246	99.55 117	96.09 334	97.74 263	99.81 32	98.55 178	99.85 27	99.55 56	98.60 66	99.84 179	99.69 35	99.98 12	99.89 16

mvs5depth		99.30 33	99.59 12	98.44 281	99.65 71	95.35 374	99.82 3	99.94 3	99.83 7	99.42 112	99.94 2	98.13 125	99.96 13	99.63 36	99.96 28	100.00 1

v1240		98.55 184	98.62 154	98.32 296	99.22 239	95.58 357	97.51 300	99.45 180	97.16 324	99.45 106	99.24 144	96.12 284	99.85 158	99.60 37	99.88 95	99.55 137

v8		99.01 90	99.16 62	98.57 253	99.47 161	96.31 326	98.90 84	99.47 171	99.03 121	99.52 87	99.57 49	96.93 228	99.81 226	99.60 37	99.98 12	99.60 102

v1921920		98.54 187	98.60 159	98.38 289	99.20 245	95.76 353	97.56 292	99.36 222	97.23 318	99.38 121	99.17 168	96.02 287	99.84 179	99.57 39	99.90 88	99.54 143

v1192		98.60 173	98.66 146	98.41 285	99.27 221	95.88 345	97.52 298	99.36 222	97.41 293	99.33 138	99.20 156	96.37 269	99.82 209	99.57 39	99.92 71	99.55 137

fmvsm_s_conf0.5_n_7		98.83 122	99.04 87	98.20 312	99.30 213	94.83 401	97.23 335	99.36 222	98.64 161	99.84 30	99.43 88	98.10 127	99.91 74	99.56 41	99.96 28	99.87 22

mmtdpeth		99.30 33	99.42 25	98.92 173	99.58 94	96.89 293	99.48 13	99.92 8	99.92 2	98.26 342	99.80 11	98.33 96	99.91 74	99.56 41	99.95 39	99.97 4

mvs_tets		99.63 6	99.67 6	99.49 55	99.88 9	98.61 104	99.34 23	99.71 48	99.27 74	99.90 14	99.74 18	99.68 4	99.97 6	99.55 43	99.99 5	99.88 20

Elysia		99.15 57	99.14 68	99.18 113	99.63 83	97.92 186	98.50 137	99.43 194	99.67 20	99.70 51	99.13 182	96.66 249	99.98 4	99.54 44	99.96 28	99.64 86

StellarMVS		99.15 57	99.14 68	99.18 113	99.63 83	97.92 186	98.50 137	99.43 194	99.67 20	99.70 51	99.13 182	96.66 249	99.98 4	99.54 44	99.96 28	99.64 86

tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 45	98.98 72	99.51 10	99.85 19	99.86 6	99.88 21	99.82 5	99.02 26	99.90 81	99.54 44	99.95 39	99.61 100

tt0320		99.61 8	99.65 9	99.48 57	99.71 49	98.94 79	99.54 8	99.83 26	99.87 5	99.89 18	99.82 5	98.75 47	99.90 81	99.54 44	99.95 39	99.59 109

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 149	99.20 49	99.65 77	99.48 44	99.92 8	99.71 22	98.07 128	99.96 13	99.53 48	100.00 1	99.93 11

test_cas_vis1_n_1920		98.33 222	98.68 141	97.27 408	99.69 61	92.29 480	98.03 207	99.85 19	97.62 263	99.96 4	99.62 40	93.98 368	99.74 292	99.52 49	99.86 107	99.79 47

v144192		98.54 187	98.57 163	98.45 279	99.21 241	95.98 339	97.63 280	99.36 222	97.15 326	99.32 144	99.18 163	95.84 301	99.84 179	99.50 50	99.91 80	99.54 143

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 104	99.28 40	99.66 71	99.09 110	99.89 18	99.68 25	99.53 7	99.97 6	99.50 50	99.99 5	99.87 22

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 82	99.88 4	99.86 24	99.80 11	99.03 24	99.89 97	99.48 52	99.93 57	99.60 102

v1144		98.60 173	98.66 146	98.41 285	99.36 194	95.90 343	97.58 290	99.34 234	97.51 279	99.27 153	99.15 176	96.34 271	99.80 235	99.47 53	99.93 57	99.51 165

OurMVSNet-221017-0		99.37 28	99.31 41	99.53 38	99.91 3	98.98 72	99.63 7	99.58 103	99.44 52	99.78 39	99.76 15	96.39 265	99.92 65	99.44 54	99.92 71	99.68 73

KinetiMVS		99.03 88	99.02 90	99.03 148	99.70 57	97.48 235	98.43 148	99.29 263	99.70 15	99.60 71	99.07 199	96.13 282	99.94 41	99.42 55	99.87 100	99.68 73

tt0805		98.69 151	98.62 154	98.90 178	99.75 34	99.30 21	99.15 57	96.97 470	98.86 142	98.87 249	97.62 438	98.63 63	98.96 500	99.41 56	98.29 461	98.45 436

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 26	99.53 9	99.76 39	99.64 26	99.84 30	99.83 4	99.50 9	99.87 135	99.36 57	99.92 71	99.64 86

MVStest1		95.86 422	95.60 415	96.63 443	95.87 538	91.70 486	97.93 230	98.94 344	98.03 227	99.56 74	99.66 32	71.83 526	98.26 517	99.35 58	99.24 373	99.91 13

v2v482		98.56 180	98.62 154	98.37 292	99.42 178	95.81 350	97.58 290	99.16 305	97.90 239	99.28 151	99.01 225	95.98 294	99.79 249	99.33 59	99.90 88	99.51 165

VortexMVS		97.98 272	98.31 215	97.02 422	98.88 333	91.45 491	98.03 207	99.47 171	98.65 160	99.55 77	99.47 78	91.49 421	99.81 226	99.32 60	99.91 80	99.80 45

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 158	99.14 58	99.93 6	99.82 8	99.93 6	99.81 8	99.17 20	99.94 41	99.31 61	100.00 1	99.82 36

MGCNet		97.44 324	97.01 344	98.72 221	96.42 529	96.74 303	97.20 340	91.97 540	98.46 182	98.30 336	98.79 287	92.74 398	99.91 74	99.30 62	99.94 51	99.52 161

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 26	99.85 16	99.11 64	99.90 1	99.78 36	99.63 28	99.78 39	99.67 30	99.48 10	99.81 226	99.30 62	99.97 21	99.77 53
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
MVSMamba_PlusPlus		98.83 122	98.98 97	98.36 293	99.32 207	96.58 311	98.90 84	99.41 205	99.75 10	98.72 275	99.50 68	96.17 279	99.94 41	99.27 64	99.78 164	98.57 429

MVSFormer		98.26 235	98.43 189	97.77 357	98.88 333	93.89 444	99.39 20	99.56 121	99.11 100	98.16 348	98.13 395	93.81 372	99.97 6	99.26 65	99.57 289	99.43 214

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 92	99.39 20	99.56 121	99.11 100	99.70 51	99.73 20	99.00 27	99.97 6	99.26 65	99.98 12	99.89 16

Anonymous20240521		98.69 151	98.87 111	98.16 318	99.77 27	95.11 389	99.08 62	99.44 188	99.34 65	99.33 138	99.55 56	94.10 367	99.94 41	99.25 67	99.96 28	99.42 219

K. test v3		98.00 268	97.66 298	99.03 148	99.79 23	97.56 228	99.19 53	92.47 533	99.62 32	99.52 87	99.66 32	89.61 441	99.96 13	99.25 67	99.81 140	99.56 130

KD-MVS_self_test		99.25 40	99.18 59	99.44 65	99.63 83	99.06 70	98.69 108	99.54 132	99.31 69	99.62 69	99.53 64	97.36 198	99.86 144	99.24 69	99.71 217	99.39 232

AstraMVS		98.16 253	98.07 253	98.41 285	99.51 134	95.86 346	98.00 215	95.14 511	98.97 127	99.43 108	99.24 144	93.25 383	99.84 179	99.21 70	99.87 100	99.54 143

Anonymous20231211		99.27 37	99.27 47	99.26 101	99.29 215	98.18 147	99.49 12	99.51 144	99.70 15	99.80 37	99.68 25	96.84 232	99.83 197	99.21 70	99.91 80	99.77 53

V42		98.78 133	98.78 126	98.76 211	99.44 171	97.04 281	98.27 170	99.19 294	97.87 241	99.25 165	99.16 170	96.84 232	99.78 261	99.21 70	99.84 114	99.46 200

MIMVSNet1		99.38 27	99.32 39	99.55 28	99.86 14	99.19 41	99.41 17	99.59 100	99.59 36	99.71 49	99.57 49	97.12 214	99.90 81	99.21 70	99.87 100	99.54 143

nrg030		99.40 25	99.35 33	99.54 31	99.58 94	99.13 60	98.98 76	99.48 159	99.68 19	99.46 101	99.26 137	98.62 64	99.73 299	99.17 74	99.92 71	99.76 58

LuminaMVS		98.39 214	98.20 232	98.98 160	99.50 141	97.49 232	97.78 252	97.69 442	98.75 150	99.49 94	99.25 142	92.30 406	99.94 41	99.14 75	99.88 95	99.50 169

SSC-MVS3.2		98.53 189	98.79 124	97.74 363	99.46 164	93.62 454	96.45 397	99.34 234	99.33 66	98.93 233	98.70 311	97.90 143	99.90 81	99.12 76	99.92 71	99.69 72

SSC-MVS		98.71 142	98.74 128	98.62 242	99.72 45	96.08 336	98.74 99	98.64 397	99.74 12	99.67 59	99.24 144	94.57 346	99.95 25	99.11 77	99.24 373	99.82 36

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 57	98.93 132	99.65 63	99.72 21	98.93 33	99.95 25	99.11 77	100.00 1	99.82 36

VPA-MVSNet		99.30 33	99.30 44	99.28 96	99.49 150	98.36 129	99.00 73	99.45 180	99.63 28	99.52 87	99.44 85	98.25 107	99.88 115	99.09 79	99.84 114	99.62 92

guyue		98.01 267	97.93 270	98.26 303	99.45 169	95.48 365	98.08 196	96.24 489	98.89 138	99.34 135	99.14 180	91.32 424	99.82 209	99.07 80	99.83 126	99.48 188

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 101	99.29 36	99.63 82	99.30 71	99.65 63	99.60 45	99.16 22	99.82 209	99.07 80	99.83 126	99.56 130

TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 107	99.27 42	99.57 111	99.39 58	99.75 44	99.62 40	99.17 20	99.83 197	99.06 82	99.62 267	99.66 80

EC-MVSNet		99.09 73	99.05 86	99.20 110	99.28 218	98.93 80	99.24 44	99.84 23	99.08 114	98.12 353	98.37 367	98.72 50	99.90 81	99.05 83	99.77 172	98.77 402

SixPastTwentyTwo		98.75 138	98.62 154	99.16 118	99.83 18	97.96 181	99.28 40	98.20 426	99.37 60	99.70 51	99.65 36	92.65 400	99.93 53	99.04 84	99.84 114	99.60 102

CS-MVS		99.13 66	99.10 80	99.24 106	99.06 287	99.15 52	99.36 22	99.88 15	99.36 63	98.21 344	98.46 357	98.68 58	99.93 53	99.03 85	99.85 109	98.64 421

FC-MVSNet-test		99.27 37	99.25 52	99.34 83	99.77 27	98.37 126	99.30 35	99.57 111	99.61 34	99.40 117	99.50 68	97.12 214	99.85 158	99.02 86	99.94 51	99.80 45

UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 103	99.90 3	99.86 24	99.78 13	99.58 6	99.95 25	99.00 87	99.95 39	99.78 50

lessismore_v0				98.97 162	99.73 38	97.53 231		86.71 551		99.37 125	99.52 67	89.93 436	99.92 65	98.99 88	99.72 208	99.44 210

BP-MVS1		97.40 328	96.97 346	98.71 223	99.07 282	96.81 298	98.34 163	97.18 462	98.58 172	98.17 345	98.61 335	84.01 490	99.94 41	98.97 89	99.78 164	99.37 244

mvsany_test3		98.87 112	98.92 102	98.74 217	99.38 187	96.94 289	98.58 123	99.10 316	96.49 367	99.96 4	99.81 8	98.18 118	99.45 453	98.97 89	99.79 159	99.83 33

Vis-MVSNet		99.34 29	99.36 32	99.27 99	99.73 38	98.26 138	99.17 54	99.78 36	99.11 100	99.27 153	99.48 75	98.82 38	99.95 25	98.94 91	99.93 57	99.59 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FE-MVSNET2		99.15 57	99.22 54	98.94 167	99.70 57	97.49 232	98.62 118	99.67 70	98.85 145	99.34 135	99.54 62	98.47 77	99.81 226	98.93 92	99.91 80	99.51 165

SPE-MVS-test		99.13 66	99.09 82	99.26 101	99.13 270	98.97 74	99.31 30	99.88 15	99.44 52	98.16 348	98.51 348	98.64 61	99.93 53	98.91 93	99.85 109	98.88 383

mvs_anonymous		97.83 294	98.16 242	96.87 432	98.18 439	91.89 484	97.31 327	98.90 354	97.37 298	98.83 256	99.46 80	96.28 274	99.79 249	98.90 94	98.16 468	98.95 368

WR-MVS_H		99.33 30	99.22 54	99.65 8	99.71 49	99.24 29	99.32 26	99.55 126	99.46 49	99.50 93	99.34 115	97.30 201	99.93 53	98.90 94	99.93 57	99.77 53

reproduce_monomvs		95.00 451	95.25 435	94.22 509	97.51 493	83.34 546	97.86 242	98.44 412	98.51 179	99.29 149	99.30 125	67.68 535	99.56 410	98.89 96	99.81 140	99.77 53

PS-CasMVS		99.40 25	99.33 37	99.62 9	99.71 49	99.10 65	99.29 36	99.53 136	99.53 41	99.46 101	99.41 94	98.23 110	99.95 25	98.89 96	99.95 39	99.81 41

UA-Net		99.47 16	99.40 27	99.70 2	99.49 150	99.29 23	99.80 4	99.72 46	99.82 8	99.04 203	99.81 8	98.05 131	99.96 13	98.85 98	99.99 5	99.86 28

new-patchmatchnet		98.35 217	98.74 128	97.18 412	99.24 233	92.23 482	96.42 401	99.48 159	98.30 194	99.69 55	99.53 64	97.44 193	99.82 209	98.84 99	99.77 172	99.49 177

test1111		96.49 388	96.82 359	95.52 490	99.42 178	87.08 532	99.22 46	87.14 550	99.11 100	99.46 101	99.58 47	88.69 447	99.86 144	98.80 100	99.95 39	99.62 92

GDP-MVS		97.50 316	97.11 339	98.67 230	99.02 303	96.85 296	98.16 183	99.71 48	98.32 192	98.52 315	98.54 343	83.39 494	99.95 25	98.79 101	99.56 293	99.19 320

PEN-MVS		99.41 24	99.34 35	99.62 9	99.73 38	99.14 57	99.29 36	99.54 132	99.62 32	99.56 74	99.42 89	98.16 122	99.96 13	98.78 102	99.93 57	99.77 53

DTE-MVSNet		99.43 22	99.35 33	99.66 7	99.71 49	99.30 21	99.31 30	99.51 144	99.64 26	99.56 74	99.46 80	98.23 110	99.97 6	98.78 102	99.93 57	99.72 64

EG-PatchMatch MVS		98.99 94	99.01 92	98.94 167	99.50 141	97.47 236	98.04 205	99.59 100	98.15 221	99.40 117	99.36 110	98.58 72	99.76 273	98.78 102	99.68 240	99.59 109

BridgeMVS		98.63 167	98.72 132	98.38 289	98.66 384	96.68 307	98.90 84	99.42 201	98.99 124	98.97 218	99.19 159	95.81 302	99.85 158	98.77 105	99.77 172	98.60 425

EI-MVSNet-UG-set		98.69 151	98.71 135	98.62 242	99.10 275	96.37 323	97.23 335	98.87 360	99.20 84	99.19 176	98.99 231	97.30 201	99.85 158	98.77 105	99.79 159	99.65 85

test_vis1_rt		97.75 299	97.72 291	97.83 351	98.81 348	96.35 324	97.30 328	99.69 57	94.61 451	97.87 375	98.05 404	96.26 276	98.32 516	98.74 107	98.18 465	98.82 389

CP-MVSNet		99.21 47	99.09 82	99.56 26	99.65 71	98.96 78	99.13 59	99.34 234	99.42 55	99.33 138	99.26 137	97.01 223	99.94 41	98.74 107	99.93 57	99.79 47

EI-MVSNet-Vis-set		98.68 157	98.70 138	98.63 240	99.09 278	96.40 322	97.23 335	98.86 365	99.20 84	99.18 181	98.97 238	97.29 203	99.85 158	98.72 109	99.78 164	99.64 86

test2506		92.39 495	91.89 497	93.89 515	99.38 187	82.28 551	99.32 26	66.03 559	99.08 114	98.77 268	99.57 49	66.26 539	99.84 179	98.71 110	99.95 39	99.54 143

baseline		98.96 101	99.02 90	98.76 211	99.38 187	97.26 259	98.49 140	99.50 149	98.86 142	99.19 176	99.06 200	98.23 110	99.69 331	98.71 110	99.76 188	99.33 268

FIs		99.14 62	99.09 82	99.29 95	99.70 57	98.28 136	99.13 59	99.52 142	99.48 44	99.24 167	99.41 94	96.79 239	99.82 209	98.69 112	99.88 95	99.76 58

casdiffmvs_mvg		99.12 69	99.16 62	98.99 156	99.43 176	97.73 214	98.00 215	99.62 89	99.22 80	99.55 77	99.22 152	98.93 33	99.75 285	98.66 113	99.81 140	99.50 169
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WB-MVS		98.52 193	98.55 165	98.43 282	99.65 71	95.59 355	98.52 130	98.77 380	99.65 25	99.52 87	99.00 229	94.34 356	99.93 53	98.65 114	98.83 424	99.76 58

IterMVS-SCA-FT		97.85 291	98.18 238	96.87 432	99.27 221	91.16 501	95.53 455	99.25 278	99.10 107	99.41 114	99.35 111	93.10 389	99.96 13	98.65 114	99.94 51	99.49 177

UniMVSNet (Re)		98.87 112	98.71 135	99.35 80	99.24 233	98.73 95	97.73 265	99.38 214	98.93 132	99.12 185	98.73 300	96.77 240	99.86 144	98.63 116	99.80 152	99.46 200

balanced_ft_v1		98.28 232	98.35 207	98.10 323	98.08 450	96.23 328	99.23 45	99.26 276	98.34 188	97.46 409	99.42 89	95.38 320	99.88 115	98.60 117	99.34 352	98.17 457

hybridcas		99.08 79	99.13 70	98.92 173	99.54 123	97.61 226	98.22 177	99.66 71	99.27 74	99.40 117	99.24 144	98.47 77	99.70 321	98.59 118	99.80 152	99.46 200

EI-MVSNet		98.40 207	98.51 172	98.04 334	99.10 275	94.73 406	97.20 340	98.87 360	98.97 127	99.06 193	99.02 213	96.00 289	99.80 235	98.58 119	99.82 133	99.60 102

IterMVS-LS		98.55 184	98.70 138	98.09 325	99.48 158	94.73 406	97.22 339	99.39 212	98.97 127	99.38 121	99.31 124	96.00 289	99.93 53	98.58 119	99.97 21	99.60 102
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_Test		98.18 248	98.36 204	97.67 371	98.48 406	94.73 406	98.18 179	99.02 334	97.69 257	98.04 362	99.11 188	97.22 208	99.56 410	98.57 121	98.90 422	98.71 409

UniMVSNet_NR-MVSNet		98.86 116	98.68 141	99.40 71	99.17 259	98.74 92	97.68 270	99.40 210	99.14 98	99.06 193	98.59 338	96.71 247	99.93 53	98.57 121	99.77 172	99.53 157

DU-MVS		98.82 125	98.63 152	99.39 72	99.16 261	98.74 92	97.54 296	99.25 278	98.84 148	99.06 193	98.76 296	96.76 242	99.93 53	98.57 121	99.77 172	99.50 169

UGNet		98.53 189	98.45 186	98.79 201	97.94 459	96.96 287	99.08 62	98.54 406	99.10 107	96.82 450	99.47 78	96.55 257	99.84 179	98.56 124	99.94 51	99.55 137
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
viewdifsd2359ckpt11		98.84 119	99.04 87	98.24 307	99.56 111	95.51 360	97.38 317	99.70 54	99.16 94	99.57 72	99.40 97	98.26 105	99.71 312	98.55 125	99.82 133	99.50 169

viewmsd2359difaftdt		98.84 119	99.04 87	98.24 307	99.56 111	95.51 360	97.38 317	99.70 54	99.16 94	99.57 72	99.40 97	98.26 105	99.71 312	98.55 125	99.82 133	99.50 169

ECVR-MVS		96.42 394	96.61 378	95.85 479	99.38 187	88.18 527	99.22 46	86.00 552	99.08 114	99.36 128	99.57 49	88.47 452	99.82 209	98.52 127	99.95 39	99.54 143

IterMVS		97.73 300	98.11 247	96.57 445	99.24 233	90.28 512	95.52 457	99.21 288	98.86 142	99.33 138	99.33 118	93.11 388	99.94 41	98.49 128	99.94 51	99.48 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Casviewmamba		99.12 69	99.12 71	99.09 134	99.53 127	98.08 162	98.34 163	99.66 71	99.35 64	99.35 130	99.23 150	98.39 88	99.72 310	98.46 129	99.81 140	99.47 197

casdiffmvs		98.95 102	99.00 94	98.81 194	99.38 187	97.33 247	97.82 246	99.57 111	99.17 93	99.35 130	99.17 168	98.35 94	99.69 331	98.46 129	99.73 199	99.41 222
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVSTER		96.86 371	96.55 382	97.79 355	97.91 461	94.21 421	97.56 292	98.87 360	97.49 282	99.06 193	99.05 207	80.72 503	99.80 235	98.44 131	99.82 133	99.37 244

ACMH	96.65 7	99.25 40	99.24 53	99.26 101	99.72 45	98.38 124	99.07 65	99.55 126	98.30 194	99.65 63	99.45 84	99.22 17	99.76 273	98.44 131	99.77 172	99.64 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
E5new		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 100	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

E6new		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 100	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

E6		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 100	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

E5		99.05 83	99.11 74	98.85 182	99.60 88	97.30 251	98.42 151	99.63 82	98.73 151	99.26 157	99.39 100	98.71 51	99.70 321	98.43 133	99.84 114	99.54 143

FMVSNet1		99.17 52	99.17 60	99.17 115	99.55 117	98.24 140	99.20 49	99.44 188	99.21 82	99.43 108	99.55 56	97.82 154	99.86 144	98.42 137	99.89 94	99.41 222

RoMa-HiRes		98.68 157	98.52 170	99.16 118	99.50 141	98.35 130	98.01 213	99.71 48	96.94 336	99.35 130	98.66 321	96.38 267	99.63 376	98.39 138	99.71 217	99.48 188

TestfortrainingZip a		99.09 73	98.92 102	99.61 13	99.58 94	99.17 43	98.68 109	99.27 270	98.85 145	99.61 70	99.16 170	97.14 213	99.86 144	98.39 138	99.57 289	99.81 41

v148		98.45 201	98.60 159	98.00 337	99.44 171	94.98 392	97.44 312	99.06 322	98.30 194	99.32 144	98.97 238	96.65 251	99.62 381	98.37 140	99.85 109	99.39 232

GeoE		99.05 83	98.99 96	99.25 104	99.44 171	98.35 130	98.73 103	99.56 121	98.42 184	98.91 236	98.81 284	98.94 31	99.91 74	98.35 141	99.73 199	99.49 177

VDD-MVS		98.56 180	98.39 196	99.07 138	99.13 270	98.07 165	98.59 122	97.01 467	99.59 36	99.11 186	99.27 131	94.82 336	99.79 249	98.34 142	99.63 263	99.34 262

TranMVSNet+NR-MVSNet		99.17 52	99.07 85	99.46 63	99.37 193	98.87 85	98.39 157	99.42 201	99.42 55	99.36 128	99.06 200	98.38 89	99.95 25	98.34 142	99.90 88	99.57 124

pmmvs5		97.64 308	97.49 311	98.08 328	99.14 267	95.12 388	96.70 375	99.05 326	93.77 476	98.62 295	98.83 278	93.23 384	99.75 285	98.33 144	99.76 188	99.36 252

patch_mono-2		98.51 194	98.63 152	98.17 315	99.38 187	94.78 403	97.36 322	99.69 57	98.16 216	98.49 317	99.29 128	97.06 217	99.97 6	98.29 145	99.91 80	99.76 58

viewmamba		98.57 178	98.66 146	98.31 298	99.20 245	95.89 344	96.92 361	99.57 111	98.71 158	99.02 207	99.04 209	97.48 190	99.71 312	98.28 146	99.70 228	99.35 258

EU-MVSNet		97.66 307	98.50 175	95.13 499	99.63 83	85.84 535	98.35 161	98.21 425	98.23 201	99.54 79	99.46 80	95.02 330	99.68 343	98.24 147	99.87 100	99.87 22

TDRefinement		99.42 23	99.38 28	99.55 28	99.76 30	99.33 20	99.68 6	99.71 48	99.38 59	99.53 83	99.61 43	98.64 61	99.80 235	98.24 147	99.84 114	99.52 161

lecture		99.25 40	99.12 71	99.62 9	99.64 77	99.40 11	98.89 88	99.51 144	99.19 89	99.37 125	99.25 142	98.36 90	99.88 115	98.23 149	99.67 246	99.59 109

diffmvs_AUTHOR		98.50 195	98.59 161	98.23 310	99.35 199	95.48 365	96.61 386	99.60 94	98.37 185	98.90 237	99.00 229	97.37 197	99.76 273	98.22 150	99.85 109	99.46 200

DELS-MVS		98.27 233	98.20 232	98.48 276	98.86 336	96.70 305	95.60 453	99.20 290	97.73 254	98.45 323	98.71 304	97.50 186	99.82 209	98.21 151	99.59 280	98.93 373
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
XXY-MVS		99.14 62	99.15 67	99.10 130	99.76 30	97.74 212	98.85 93	99.62 89	98.48 181	99.37 125	99.49 74	98.75 47	99.86 144	98.20 152	99.80 152	99.71 65

MGCFI-Net		98.34 218	98.28 219	98.51 270	98.47 407	97.59 227	98.96 78	99.48 159	99.18 92	97.40 416	95.50 500	98.66 59	99.50 435	98.18 153	98.71 435	98.44 439

alignmvs		97.35 333	96.88 354	98.78 204	98.54 401	98.09 158	97.71 266	97.69 442	99.20 84	97.59 397	95.90 491	88.12 456	99.55 415	98.18 153	98.96 417	98.70 412

Syy-MVS		96.04 411	95.56 419	97.49 396	97.10 505	94.48 413	96.18 420	96.58 483	95.65 413	94.77 509	92.29 539	91.27 425	99.36 466	98.17 155	98.05 476	98.63 422

casdiffseed414692147		99.09 73	99.12 71	99.01 153	99.55 117	97.91 188	98.30 165	99.68 64	99.04 119	99.19 176	99.37 104	98.98 28	99.61 389	98.13 156	99.83 126	99.50 169

VNet		98.42 203	98.30 216	98.79 201	98.79 354	97.29 256	98.23 173	98.66 394	99.31 69	98.85 251	98.80 285	94.80 339	99.78 261	98.13 156	99.13 393	99.31 278

h-mvs33		97.77 298	97.33 323	99.10 130	99.21 241	97.84 196	98.35 161	98.57 403	99.11 100	98.58 304	99.02 213	88.65 450	99.96 13	98.11 158	96.34 518	99.49 177

hse-mvs2		97.46 321	97.07 340	98.64 236	98.73 359	97.33 247	97.45 310	97.64 447	99.11 100	98.58 304	97.98 410	88.65 450	99.79 249	98.11 158	97.39 498	98.81 394

test-260524					99.33 205	99.02 71		99.25 278		99.23 169		96.59 255	99.85 158	98.10 160	99.62 267

VPNet		98.87 112	98.83 120	99.01 153	99.70 57	97.62 225	98.43 148	99.35 228	99.47 47	99.28 151	99.05 207	96.72 246	99.82 209	98.09 161	99.36 347	99.59 109

sasdasda		98.34 218	98.26 225	98.58 250	98.46 409	97.82 202	98.96 78	99.46 176	99.19 89	97.46 409	95.46 503	98.59 67	99.46 450	98.08 162	98.71 435	98.46 433

canonicalmvs		98.34 218	98.26 225	98.58 250	98.46 409	97.82 202	98.96 78	99.46 176	99.19 89	97.46 409	95.46 503	98.59 67	99.46 450	98.08 162	98.71 435	98.46 433

Baseline_NR-MVSNet		98.98 98	98.86 115	99.36 74	99.82 19	98.55 109	97.47 308	99.57 111	99.37 60	99.21 174	99.61 43	96.76 242	99.83 197	98.06 164	99.83 126	99.71 65

DeepC-MVS	97.60 4	98.97 99	98.93 101	99.10 130	99.35 199	97.98 177	98.01 213	99.46 176	97.56 272	99.54 79	99.50 68	98.97 29	99.84 179	98.06 164	99.92 71	99.49 177
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
usedtu_dtu_shiyan2		98.99 94	98.86 115	99.39 72	99.73 38	98.71 98	99.05 68	99.47 171	99.16 94	99.49 94	99.12 186	96.34 271	99.93 53	98.05 166	99.36 347	99.54 143

NormalMVS		98.26 235	97.97 264	99.15 123	99.64 77	97.83 197	98.28 167	99.43 194	99.24 77	98.80 263	98.85 271	89.76 439	99.94 41	98.04 167	99.67 246	99.68 73

SymmetryMVS		98.05 263	97.71 293	99.09 134	99.29 215	97.83 197	98.28 167	97.64 447	99.24 77	98.80 263	98.85 271	89.76 439	99.94 41	98.04 167	99.50 319	99.49 177

xiu_mvs_v1_base_debu		97.86 285	98.17 239	96.92 429	98.98 311	93.91 441	96.45 397	99.17 302	97.85 243	98.41 327	97.14 465	98.47 77	99.92 65	98.02 169	99.05 400	96.92 505

xiu_mvs_v1_base		97.86 285	98.17 239	96.92 429	98.98 311	93.91 441	96.45 397	99.17 302	97.85 243	98.41 327	97.14 465	98.47 77	99.92 65	98.02 169	99.05 400	96.92 505

xiu_mvs_v1_base_debi		97.86 285	98.17 239	96.92 429	98.98 311	93.91 441	96.45 397	99.17 302	97.85 243	98.41 327	97.14 465	98.47 77	99.92 65	98.02 169	99.05 400	96.92 505

hybridnocas07		98.32 223	98.37 202	98.17 315	99.14 267	95.51 360	96.67 379	99.56 121	97.85 243	98.75 271	98.95 246	96.65 251	99.63 376	98.00 172	99.78 164	99.37 244

dcpmvs_2		98.78 133	99.11 74	97.78 356	99.56 111	93.67 451	99.06 66	99.86 17	99.50 43	99.66 60	99.26 137	97.21 209	99.99 2	98.00 172	99.91 80	99.68 73

NR-MVSNet		98.95 102	98.82 121	99.36 74	99.16 261	98.72 97	99.22 46	99.20 290	99.10 107	99.72 47	98.76 296	96.38 267	99.86 144	98.00 172	99.82 133	99.50 169

E4		98.87 112	98.88 108	98.81 194	99.52 131	97.23 261	97.62 281	99.61 92	98.58 172	99.18 181	99.33 118	98.29 99	99.69 331	97.99 175	99.83 126	99.52 161

viewmacassd2359aftdt		98.86 116	98.87 111	98.83 190	99.53 127	97.32 250	97.70 268	99.64 79	98.22 203	99.25 165	99.27 131	98.40 86	99.61 389	97.98 176	99.87 100	99.55 137

SDMVSNet		99.23 45	99.32 39	98.96 164	99.68 64	97.35 245	98.84 95	99.48 159	99.69 17	99.63 66	99.68 25	99.03 24	99.96 13	97.97 177	99.92 71	99.57 124

FMVSNet2		98.49 196	98.40 193	98.75 213	98.90 327	97.14 276	98.61 120	99.13 312	98.59 169	99.19 176	99.28 129	94.14 363	99.82 209	97.97 177	99.80 152	99.29 284

diffmvs		98.22 240	98.24 229	98.17 315	99.00 307	95.44 369	96.38 403	99.58 103	97.79 250	98.53 313	98.50 352	96.76 242	99.74 292	97.95 179	99.64 258	99.34 262
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt07		98.71 142	98.86 115	98.26 303	99.43 176	95.65 354	97.20 340	99.66 71	99.20 84	99.29 149	99.01 225	98.29 99	99.73 299	97.92 180	99.75 192	99.39 232

Anonymous20240529		98.93 104	98.87 111	99.12 126	99.19 249	98.22 145	99.01 71	98.99 340	99.25 76	99.54 79	99.37 104	97.04 218	99.80 235	97.89 181	99.52 308	99.35 258

pmmvs-eth3d		98.47 198	98.34 208	98.86 181	99.30 213	97.76 210	97.16 345	99.28 267	95.54 420	99.42 112	99.19 159	97.27 204	99.63 376	97.89 181	99.97 21	99.20 314

Patchmatch-RL test		97.26 341	97.02 343	97.99 339	99.52 131	95.53 359	96.13 423	99.71 48	97.47 283	99.27 153	99.16 170	84.30 488	99.62 381	97.89 181	99.77 172	98.81 394

VDDNet		98.21 243	97.95 265	99.01 153	99.58 94	97.74 212	99.01 71	97.29 458	99.67 20	98.97 218	99.50 68	90.45 433	99.80 235	97.88 184	99.20 382	99.48 188

APDe-MVS		98.99 94	98.79 124	99.60 16	99.21 241	99.15 52	98.87 89	99.48 159	97.57 270	99.35 130	99.24 144	97.83 151	99.89 97	97.88 184	99.70 228	99.75 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SSM_0407		98.86 116	98.96 100	98.55 260	99.27 221	96.50 316	98.04 205	99.66 71	99.09 110	99.22 171	99.02 213	98.79 43	99.87 135	97.87 186	99.72 208	99.27 291

SSM_0404		98.90 108	99.01 92	98.57 253	99.42 178	96.59 308	98.13 186	99.66 71	99.09 110	99.30 148	99.02 213	98.79 43	99.89 97	97.87 186	99.80 152	99.23 304

CANet		97.87 284	97.76 285	98.19 314	97.75 471	95.51 360	96.76 370	99.05 326	97.74 253	96.93 438	98.21 389	95.59 311	99.89 97	97.86 188	99.93 57	99.19 320

hybrid		98.22 240	98.27 222	98.08 328	99.13 270	95.24 380	96.61 386	99.53 136	97.43 292	98.46 321	98.97 238	96.75 245	99.65 368	97.84 189	99.69 234	99.35 258

SP-LightGlue		97.22 346	97.01 344	97.88 347	97.33 499	97.19 268	96.38 403	99.08 320	97.28 308	96.53 465	97.50 446	92.36 403	98.70 511	97.84 189	98.76 430	97.74 483

testf1		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 57	98.90 136	99.43 108	99.35 111	98.86 35	99.67 348	97.81 191	99.81 140	99.24 302

APD_test2		99.25 40	99.16 62	99.51 49	99.89 6	99.63 3	98.71 106	99.69 57	98.90 136	99.43 108	99.35 111	98.86 35	99.67 348	97.81 191	99.81 140	99.24 302

PM-MVS		98.82 125	98.72 132	99.12 126	99.64 77	98.54 112	97.98 224	99.68 64	97.62 263	99.34 135	99.18 163	97.54 180	99.77 267	97.79 193	99.74 195	99.04 350

DKM-HiRes		98.14 254	97.80 282	99.16 118	99.51 134	98.40 121	96.70 375	99.63 82	97.55 274	97.45 412	98.74 298	93.27 382	99.54 421	97.78 194	99.55 298	99.53 157

RoMa-SfM		98.46 199	98.27 222	99.02 151	99.35 199	98.32 133	97.56 292	99.70 54	95.88 400	99.38 121	98.65 324	96.41 263	99.46 450	97.78 194	99.71 217	99.28 287

reproduce_model		99.15 57	98.97 98	99.67 4	99.33 205	99.44 9	98.15 184	99.47 171	99.12 99	99.52 87	99.32 123	98.31 97	99.90 81	97.78 194	99.73 199	99.66 80

tttt0517		95.64 430	94.98 443	97.64 378	99.36 194	93.81 446	98.72 104	90.47 544	98.08 226	98.67 284	98.34 371	73.88 524	99.92 65	97.77 197	99.51 311	99.20 314

GBi-Net		98.65 163	98.47 183	99.17 115	98.90 327	98.24 140	99.20 49	99.44 188	98.59 169	98.95 224	99.55 56	94.14 363	99.86 144	97.77 197	99.69 234	99.41 222

test1		98.65 163	98.47 183	99.17 115	98.90 327	98.24 140	99.20 49	99.44 188	98.59 169	98.95 224	99.55 56	94.14 363	99.86 144	97.77 197	99.69 234	99.41 222

FMVSNet3		97.50 316	97.24 328	98.29 301	98.08 450	95.83 348	97.86 242	98.91 353	97.89 240	98.95 224	98.95 246	87.06 459	99.81 226	97.77 197	99.69 234	99.23 304

SP-SuperGlue		97.31 336	97.23 329	97.57 389	96.96 511	97.24 260	96.26 414	98.76 382	97.68 258	96.88 447	97.85 421	94.32 357	98.01 521	97.76 201	98.57 449	97.45 495

UnsupCasMVSNet_eth		97.89 279	97.60 304	98.75 213	99.31 209	97.17 273	97.62 281	99.35 228	98.72 157	98.76 270	98.68 315	92.57 401	99.74 292	97.76 201	95.60 532	99.34 262

mamba_0408		98.80 129	98.88 108	98.55 260	99.27 221	96.50 316	98.00 215	99.60 94	98.93 132	99.22 171	98.84 276	98.59 67	99.89 97	97.74 203	99.72 208	99.27 291

SSM_04072		98.80 129	98.88 108	98.56 258	99.27 221	96.50 316	98.00 215	99.60 94	98.93 132	99.22 171	98.84 276	98.59 67	99.90 81	97.74 203	99.72 208	99.27 291

onestephybrid01		98.40 207	98.39 196	98.42 283	99.05 290	96.23 328	96.73 373	99.41 205	98.18 212	98.65 287	99.02 213	97.02 221	99.69 331	97.73 205	99.70 228	99.33 268

E2		98.70 147	98.68 141	98.73 219	99.40 183	97.10 278	97.48 304	99.57 111	98.09 224	99.00 209	99.20 156	97.90 143	99.67 348	97.73 205	99.77 172	99.43 214

E3		98.69 151	98.68 141	98.73 219	99.40 183	97.10 278	97.48 304	99.57 111	98.09 224	99.00 209	99.20 156	97.90 143	99.67 348	97.73 205	99.77 172	99.43 214

test20.03		98.78 133	98.77 127	98.78 204	99.46 164	97.20 267	97.78 252	99.24 284	99.04 119	99.41 114	98.90 257	97.65 165	99.76 273	97.70 208	99.79 159	99.39 232

Gipuma		99.03 88	99.16 62	98.64 236	99.94 2	98.51 114	99.32 26	99.75 43	99.58 38	98.60 300	99.62 40	98.22 113	99.51 434	97.70 208	99.73 199	97.89 472
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PatchT		96.65 379	96.35 391	97.54 391	97.40 496	95.32 377	97.98 224	96.64 482	99.33 66	96.89 445	99.42 89	84.32 487	99.81 226	97.69 210	97.49 492	97.48 493

blended_shiyan8		95.98 416	95.33 430	97.94 342	97.05 509	94.87 400	95.34 464	98.59 400	96.17 381	97.09 430	92.39 537	87.62 458	99.76 273	97.65 211	96.05 529	99.20 314

blended_shiyan6		95.99 415	95.33 430	97.95 341	97.06 507	94.89 398	95.34 464	98.58 401	96.17 381	97.06 432	92.41 536	87.64 457	99.76 273	97.64 212	96.09 523	99.19 320

RRT-MVS		97.88 282	97.98 261	97.61 381	98.15 443	93.77 448	98.97 77	99.64 79	99.16 94	98.69 280	99.42 89	91.60 416	99.89 97	97.63 213	98.52 452	99.16 334

viewmambaseed2359dif		98.19 246	98.26 225	97.99 339	99.02 303	95.03 391	96.59 389	99.53 136	96.21 380	99.00 209	98.99 231	97.62 170	99.61 389	97.62 214	99.72 208	99.33 268

reproduce-ours		99.09 73	98.90 105	99.67 4	99.27 221	99.49 5	98.00 215	99.42 201	99.05 117	99.48 96	99.27 131	98.29 99	99.89 97	97.61 215	99.71 217	99.62 92

our_new_method		99.09 73	98.90 105	99.67 4	99.27 221	99.49 5	98.00 215	99.42 201	99.05 117	99.48 96	99.27 131	98.29 99	99.89 97	97.61 215	99.71 217	99.62 92

mvsany_test1		97.60 310	97.54 306	97.77 357	97.72 472	95.35 374	95.36 463	97.13 465	94.13 467	99.71 49	99.33 118	97.93 141	99.30 477	97.60 217	98.94 419	98.67 420

viewmanbaseed2359cas		98.58 177	98.54 167	98.70 225	99.28 218	97.13 277	97.47 308	99.55 126	97.55 274	98.96 223	98.92 251	97.77 157	99.59 398	97.59 218	99.77 172	99.39 232

wanda-best-256-512		95.48 436	94.74 450	97.68 369	96.53 523	94.12 425	94.17 505	98.57 403	95.84 402	96.71 454	91.16 542	86.05 469	99.76 273	97.57 219	96.09 523	99.17 328

FE-blended-shiyan7		95.48 436	94.74 450	97.68 369	96.53 523	94.12 425	94.17 505	98.57 403	95.84 402	96.71 454	91.16 542	86.05 469	99.76 273	97.57 219	96.09 523	99.17 328

D2MVS		97.84 292	97.84 280	97.83 351	99.14 267	94.74 405	96.94 357	98.88 358	95.84 402	98.89 240	98.96 242	94.40 353	99.69 331	97.55 221	99.95 39	99.05 346

MSLP-MVS++		98.02 265	98.14 245	97.64 378	98.58 396	95.19 385	97.48 304	99.23 286	97.47 283	97.90 372	98.62 333	97.04 218	98.81 507	97.55 221	99.41 340	98.94 372

WR-MVS		98.40 207	98.19 236	99.03 148	99.00 307	97.65 221	96.85 364	98.94 344	98.57 174	98.89 240	98.50 352	95.60 310	99.85 158	97.54 223	99.85 109	99.59 109

HPM-MVS_fast		99.01 90	98.82 121	99.57 21	99.71 49	99.35 16	99.00 73	99.50 149	97.33 301	98.94 232	98.86 268	98.75 47	99.82 209	97.53 224	99.71 217	99.56 130

RPMNet		97.02 362	96.93 348	97.30 406	97.71 475	94.22 419	98.11 191	99.30 255	99.37 60	96.91 441	99.34 115	86.72 461	99.87 135	97.53 224	97.36 501	97.81 477

viewcassd2359sk11		98.55 184	98.51 172	98.67 230	99.29 215	96.99 284	97.39 315	99.54 132	97.73 254	98.81 261	99.08 198	97.55 178	99.66 361	97.52 226	99.67 246	99.36 252

icg_test_0407_2		98.20 245	98.38 200	97.65 375	99.03 295	94.03 431	95.78 447	99.45 180	98.16 216	99.06 193	98.71 304	98.27 103	99.68 343	97.50 227	99.45 328	99.22 309

IMVS_0407		98.39 214	98.64 150	97.66 373	99.03 295	94.03 431	98.10 193	99.45 180	98.16 216	99.06 193	98.71 304	98.27 103	99.71 312	97.50 227	99.45 328	99.22 309

IMVS_0404		98.07 261	98.20 232	97.69 368	99.03 295	94.03 431	96.67 379	99.45 180	98.16 216	98.03 363	98.71 304	96.80 238	99.82 209	97.50 227	99.45 328	99.22 309

IMVS_0403		98.34 218	98.56 164	97.66 373	99.03 295	94.03 431	97.98 224	99.45 180	98.16 216	98.89 240	98.71 304	97.90 143	99.74 292	97.50 227	99.45 328	99.22 309

PMMVS2		98.07 261	98.08 251	98.04 334	99.41 181	94.59 412	94.59 491	99.40 210	97.50 280	98.82 259	98.83 278	96.83 234	99.84 179	97.50 227	99.81 140	99.71 65

usedtu_dtu_shiyan1		97.37 330	97.13 337	98.11 321	99.03 295	95.40 371	94.47 494	98.99 340	96.87 345	97.97 367	97.81 424	92.12 409	99.75 285	97.49 232	99.43 337	99.16 334

FE-MVSNET3		97.37 330	97.13 337	98.11 321	99.03 295	95.40 371	94.47 494	98.99 340	96.87 345	97.97 367	97.81 424	92.12 409	99.75 285	97.49 232	99.43 337	99.16 334

LFMVS		97.20 348	96.72 366	98.64 236	98.72 361	96.95 288	98.93 82	94.14 525	99.74 12	98.78 265	99.01 225	84.45 485	99.73 299	97.44 234	99.27 367	99.25 298

ACMM	96.08 12	98.91 106	98.73 130	99.48 57	99.55 117	99.14 57	98.07 200	99.37 218	97.62 263	99.04 203	98.96 242	98.84 37	99.79 249	97.43 235	99.65 256	99.49 177
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		95.51 435	95.47 421	95.65 487	98.25 431	88.27 526	93.25 527	98.88 358	93.53 479	94.65 512	97.15 464	86.17 466	99.93 53	97.41 236	99.93 57	98.73 408

CR-MVSNet		96.28 401	95.95 403	97.28 407	97.71 475	94.22 419	98.11 191	98.92 351	92.31 499	96.91 441	99.37 104	85.44 477	99.81 226	97.39 237	97.36 501	97.81 477

Anonymous202405211		97.90 277	97.50 310	99.08 136	98.90 327	98.25 139	98.53 129	96.16 490	98.87 140	99.11 186	98.86 268	90.40 434	99.78 261	97.36 238	99.31 359	99.19 320

E3new		98.41 204	98.34 208	98.62 242	99.19 249	96.90 292	97.32 325	99.50 149	97.40 295	98.63 291	98.92 251	97.21 209	99.65 368	97.34 239	99.52 308	99.31 278

CANet_DTU		97.26 341	97.06 341	97.84 350	97.57 483	94.65 410	96.19 418	98.79 377	97.23 318	95.14 502	98.24 386	93.22 385	99.84 179	97.34 239	99.84 114	99.04 350

gbinet_0.2-2-1-0.02		95.44 438	94.55 453	98.14 319	95.99 537	95.34 376	94.71 482	98.29 421	96.00 394	96.05 482	90.50 546	84.99 479	99.79 249	97.33 241	97.07 509	99.28 287

FE-MVSNET		98.59 175	98.50 175	98.87 179	99.58 94	97.30 251	98.08 196	99.74 44	96.94 336	98.97 218	99.10 191	96.94 227	99.74 292	97.33 241	99.86 107	99.55 137

DKM		98.18 248	97.95 265	98.85 182	99.35 199	98.31 134	96.68 377	99.69 57	96.90 342	98.61 297	98.77 291	94.41 351	98.93 502	97.32 243	99.84 114	99.32 273

Anonymous20231206		98.21 243	98.21 231	98.20 312	99.51 134	95.43 370	98.13 186	99.32 242	96.16 385	98.93 233	98.82 281	96.00 289	99.83 197	97.32 243	99.73 199	99.36 252

MP-MVS-pluss		98.57 178	98.23 230	99.60 16	99.69 61	99.35 16	97.16 345	99.38 214	94.87 444	98.97 218	98.99 231	98.01 133	99.88 115	97.29 245	99.70 228	99.58 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FMVSNet5		96.01 413	95.20 439	98.41 285	97.53 488	96.10 331	98.74 99	99.50 149	97.22 321	98.03 363	99.04 209	69.80 529	99.88 115	97.27 246	99.71 217	99.25 298

our_test_3		97.39 329	97.73 290	96.34 453	98.70 369	89.78 518	94.61 490	98.97 343	96.50 366	99.04 203	98.85 271	95.98 294	99.84 179	97.26 247	99.67 246	99.41 222

sd_testset		99.28 36	99.31 41	99.19 112	99.68 64	98.06 168	99.41 17	99.30 255	99.69 17	99.63 66	99.68 25	99.25 16	99.96 13	97.25 248	99.92 71	99.57 124

ELoFTR		97.81 296	97.74 287	98.04 334	99.39 185	95.79 351	97.28 333	99.58 103	94.13 467	99.38 121	99.37 104	93.31 381	99.60 393	97.23 249	99.96 28	98.74 407

jason		97.45 323	97.35 321	97.76 360	99.24 233	93.93 440	95.86 442	98.42 415	94.24 463	98.50 316	98.13 395	94.82 336	99.91 74	97.22 250	99.73 199	99.43 214
jason: jason.
viewdifsd2359ckpt13		98.39 214	98.29 218	98.70 225	99.26 230	97.19 268	97.51 300	99.48 159	96.94 336	98.58 304	98.82 281	97.47 192	99.55 415	97.21 251	99.33 354	99.34 262

dtuplus		98.32 223	98.39 196	98.10 323	99.15 265	95.29 378	96.68 377	99.51 144	97.32 303	99.18 181	99.15 176	97.61 172	99.62 381	97.19 252	99.74 195	99.38 241

miper_lstm_enhance		97.18 350	97.16 333	97.25 410	98.16 442	92.85 468	95.15 472	99.31 247	97.25 312	98.74 274	98.78 289	90.07 435	99.78 261	97.19 252	99.80 152	99.11 341

DP-MVS		98.93 104	98.81 123	99.28 96	99.21 241	98.45 118	98.46 145	99.33 240	99.63 28	99.48 96	99.15 176	97.23 207	99.75 285	97.17 254	99.66 254	99.63 91

MTAPA		98.88 111	98.64 150	99.61 13	99.67 68	99.36 15	98.43 148	99.20 290	98.83 149	98.89 240	98.90 257	96.98 225	99.92 65	97.16 255	99.70 228	99.56 130

TSAR-MVS + GP.		98.18 248	97.98 261	98.77 209	98.71 365	97.88 192	96.32 408	98.66 394	96.33 374	99.23 169	98.51 348	97.48 190	99.40 461	97.16 255	99.46 326	99.02 353

3Dnovator	98.27 2	98.81 127	98.73 130	99.05 145	98.76 355	97.81 205	99.25 43	99.30 255	98.57 174	98.55 310	99.33 118	97.95 140	99.90 81	97.16 255	99.67 246	99.44 210

PMatch-Up-SfM		97.79 297	97.48 314	98.72 221	99.03 295	97.78 207	96.05 429	99.48 159	96.90 342	98.72 275	99.18 163	92.00 413	99.71 312	97.15 258	98.77 428	98.69 413

PMatch-SfM		97.89 279	97.64 300	98.66 232	99.26 230	97.44 241	96.08 427	99.51 144	96.72 355	98.47 320	99.13 182	93.62 378	99.70 321	97.14 259	98.80 427	98.83 387

MSC_two_6792asdad				99.32 91	98.43 414	98.37 126		98.86 365					99.89 97	97.14 259	99.60 276	99.71 65

No_MVS				99.32 91	98.43 414	98.37 126		98.86 365					99.89 97	97.14 259	99.60 276	99.71 65

ACMMP_NAP		98.75 138	98.48 181	99.57 21	99.58 94	99.29 23	97.82 246	99.25 278	96.94 336	98.78 265	99.12 186	98.02 132	99.84 179	97.13 262	99.67 246	99.59 109

PVSNet_Blended_VisFu		98.17 251	98.15 243	98.22 311	99.73 38	95.15 386	97.36 322	99.68 64	94.45 458	98.99 213	99.27 131	96.87 231	99.94 41	97.13 262	99.91 80	99.57 124

HyFIR lowres test		97.19 349	96.60 380	98.96 164	99.62 87	97.28 257	95.17 470	99.50 149	94.21 464	99.01 208	98.32 376	86.61 462	99.99 2	97.10 264	99.84 114	99.60 102

EGC-MVSNET		85.24 512	80.54 515	99.34 83	99.77 27	99.20 38	99.08 62	99.29 263	12.08 555	20.84 558	99.42 89	97.55 178	99.85 158	97.08 265	99.72 208	98.96 367

DVP-MVS++		98.90 108	98.70 138	99.51 49	98.43 414	99.15 52	99.43 15	99.32 242	98.17 213	99.26 157	99.02 213	98.18 118	99.88 115	97.07 266	99.45 328	99.49 177

test_0728_THIRD									98.17 213	99.08 191	99.02 213	97.89 147	99.88 115	97.07 266	99.71 217	99.70 70

eth_miper_zixun_eth		97.23 345	97.25 327	97.17 414	98.00 455	92.77 470	94.71 482	99.18 298	97.27 310	98.56 308	98.74 298	91.89 414	99.69 331	97.06 268	99.81 140	99.05 346

viewdifsd2359ckpt09		98.13 255	97.92 271	98.77 209	99.18 257	97.35 245	97.29 329	99.53 136	95.81 407	98.09 356	98.47 356	96.34 271	99.66 361	97.02 269	99.51 311	99.29 284

MDA-MVSNet_test_wron		97.60 310	97.66 298	97.41 403	99.04 292	93.09 459	95.27 466	98.42 415	97.26 311	98.88 244	98.95 246	95.43 318	99.73 299	97.02 269	98.72 433	99.41 222

cl____		97.02 362	96.83 358	97.58 384	97.82 467	94.04 430	94.66 487	99.16 305	97.04 330	98.63 291	98.71 304	88.68 449	99.69 331	97.00 271	99.81 140	99.00 358

DIV-MVS_self_test		97.02 362	96.84 357	97.58 384	97.82 467	94.03 431	94.66 487	99.16 305	97.04 330	98.63 291	98.71 304	88.69 447	99.69 331	97.00 271	99.81 140	99.01 355

DVP-MVS		98.77 136	98.52 170	99.52 44	99.50 141	99.21 32	98.02 210	98.84 369	97.97 231	99.08 191	99.02 213	97.61 172	99.88 115	96.99 273	99.63 263	99.48 188
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				99.60 16	99.50 141	99.23 30	98.02 210	99.32 242					99.88 115	96.99 273	99.63 263	99.68 73

YYNet1		97.60 310	97.67 295	97.39 404	99.04 292	93.04 463	95.27 466	98.38 418	97.25 312	98.92 235	98.95 246	95.48 316	99.73 299	96.99 273	98.74 431	99.41 222

dtuonly		96.49 388	97.28 324	94.10 511	98.80 351	83.27 547	93.66 519	99.48 159	95.10 437	97.87 375	98.30 378	95.61 309	99.68 343	96.98 276	99.75 192	99.33 268

pmmvs4		97.58 313	97.28 324	98.51 270	98.84 340	96.93 290	95.40 462	98.52 409	93.60 478	98.61 297	98.65 324	95.10 328	99.60 393	96.97 277	99.79 159	98.99 359

TAMVS		98.24 239	98.05 254	98.80 197	99.07 282	97.18 271	97.88 238	98.81 374	96.66 360	99.17 184	99.21 154	94.81 338	99.77 267	96.96 278	99.88 95	99.44 210

PatchmatchNet1														96.95 279	99.71 217	99.28 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
c3_l		97.36 332	97.37 319	97.31 405	98.09 449	93.25 458	95.01 475	99.16 305	97.05 329	98.77 268	98.72 302	92.88 394	99.64 373	96.93 280	99.76 188	99.05 346

SED-MVS		98.91 106	98.72 132	99.49 55	99.49 150	99.17 43	98.10 193	99.31 247	98.03 227	99.66 60	99.02 213	98.36 90	99.88 115	96.91 281	99.62 267	99.41 222

test_241102_TWO								99.30 255	98.03 227	99.26 157	99.02 213	97.51 185	99.88 115	96.91 281	99.60 276	99.66 80

ET-MVSNet_ETH3D		94.30 461	93.21 473	97.58 384	98.14 444	94.47 414	94.78 481	93.24 532	94.72 448	89.56 543	95.87 492	78.57 516	99.81 226	96.91 281	97.11 508	98.46 433

N_pmnet		97.63 309	97.17 332	98.99 156	99.27 221	97.86 194	95.98 432	93.41 530	95.25 433	99.47 100	98.90 257	95.63 308	99.85 158	96.91 281	99.73 199	99.27 291

1112_ss		97.29 340	96.86 355	98.58 250	99.34 204	96.32 325	96.75 371	99.58 103	93.14 485	96.89 445	97.48 448	92.11 411	99.86 144	96.91 281	99.54 301	99.57 124

thisisatest0530		95.27 443	94.45 455	97.74 363	99.19 249	94.37 416	97.86 242	90.20 545	97.17 323	98.22 343	97.65 435	73.53 525	99.90 81	96.90 286	99.35 350	98.95 368

Fast-Effi-MVS+-dtu		98.27 233	98.09 248	98.81 194	98.43 414	98.11 154	97.61 286	99.50 149	98.64 161	97.39 418	97.52 445	98.12 126	99.95 25	96.90 286	98.71 435	98.38 446

TSAR-MVS + MP.		98.63 167	98.49 180	99.06 144	99.64 77	97.90 190	98.51 135	98.94 344	96.96 334	99.24 167	98.89 263	97.83 151	99.81 226	96.88 288	99.49 323	99.48 188
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MVS_111021_HR		98.25 238	98.08 251	98.75 213	99.09 278	97.46 238	95.97 433	99.27 270	97.60 268	97.99 366	98.25 384	98.15 124	99.38 465	96.87 289	99.57 289	99.42 219

EPP-MVSNet		98.30 228	98.04 255	99.07 138	99.56 111	97.83 197	99.29 36	98.07 433	99.03 121	98.59 302	99.13 182	92.16 408	99.90 81	96.87 289	99.68 240	99.49 177

ZNCC-MVS		98.68 157	98.40 193	99.54 31	99.57 103	99.21 32	98.46 145	99.29 263	97.28 308	98.11 354	98.39 364	98.00 134	99.87 135	96.86 291	99.64 258	99.55 137

MS-PatchMatch		97.68 305	97.75 286	97.45 400	98.23 436	93.78 447	97.29 329	98.84 369	96.10 388	98.64 290	98.65 324	96.04 286	99.36 466	96.84 292	99.14 391	99.20 314

3Dnovator+	97.89 3	98.69 151	98.51 172	99.24 106	98.81 348	98.40 121	99.02 70	99.19 294	98.99 124	98.07 358	99.28 129	97.11 216	99.84 179	96.84 292	99.32 357	99.47 197

miper_ehance_all_eth		97.06 359	97.03 342	97.16 416	97.83 466	93.06 460	94.66 487	99.09 318	95.99 395	98.69 280	98.45 358	92.73 399	99.61 389	96.79 294	99.03 404	98.82 389

XVS		98.72 141	98.45 186	99.53 38	99.46 164	99.21 32	98.65 114	99.34 234	98.62 166	97.54 402	98.63 330	97.50 186	99.83 197	96.79 294	99.53 305	99.56 130

X-MVStestdata		94.32 459	92.59 482	99.53 38	99.46 164	99.21 32	98.65 114	99.34 234	98.62 166	97.54 402	45.85 554	97.50 186	99.83 197	96.79 294	99.53 305	99.56 130

lupinMVS		97.06 359	96.86 355	97.65 375	98.88 333	93.89 444	95.48 458	97.97 435	93.53 479	98.16 348	97.58 439	93.81 372	99.91 74	96.77 297	99.57 289	99.17 328

DenseAffine		98.10 256	97.86 278	98.84 188	99.32 207	97.93 185	96.62 385	99.76 39	96.68 359	98.65 287	98.72 302	94.46 349	99.33 472	96.76 298	99.75 192	99.25 298

IU-MVS					99.49 150	99.15 52		98.87 360	92.97 489	99.41 114				96.76 298	99.62 267	99.66 80

CHOSEN 1792x2688		97.49 319	97.14 336	98.54 265	99.68 64	96.09 334	96.50 394	99.62 89	91.58 506	98.84 254	98.97 238	92.36 403	99.88 115	96.76 298	99.95 39	99.67 78

ppachtmachnet_test		97.50 316	97.74 287	96.78 439	98.70 369	91.23 500	94.55 492	99.05 326	96.36 373	99.21 174	98.79 287	96.39 265	99.78 261	96.74 301	99.82 133	99.34 262

DeepPCF-MVS	96.93 5	98.32 223	98.01 258	99.23 108	98.39 419	98.97 74	95.03 474	99.18 298	96.88 344	99.33 138	98.78 289	98.16 122	99.28 481	96.74 301	99.62 267	99.44 210

EIA-MVS		98.00 268	97.74 287	98.80 197	98.72 361	98.09 158	98.05 203	99.60 94	97.39 296	96.63 459	95.55 498	97.68 162	99.80 235	96.73 303	99.27 367	98.52 431

CDS-MVSNet		97.69 304	97.35 321	98.69 227	98.73 359	97.02 283	96.92 361	98.75 386	95.89 399	98.59 302	98.67 317	92.08 412	99.74 292	96.72 304	99.81 140	99.32 273
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CSCG		98.68 157	98.50 175	99.20 110	99.45 169	98.63 101	98.56 125	99.57 111	97.87 241	98.85 251	98.04 405	97.66 164	99.84 179	96.72 304	99.81 140	99.13 339

ACMH+	96.62 9	99.08 79	99.00 94	99.33 89	99.71 49	98.83 87	98.60 121	99.58 103	99.11 100	99.53 83	99.18 163	98.81 39	99.67 348	96.71 306	99.77 172	99.50 169

usedtu_blend_shiyan5		96.20 407	95.62 413	97.94 342	96.53 523	94.93 395	98.83 96	99.59 100	98.89 138	96.71 454	91.16 542	86.05 469	99.73 299	96.70 307	96.09 523	99.17 328

blend_shiyan4		92.09 501	90.16 508	97.88 347	96.78 517	94.93 395	95.24 468	98.58 401	96.22 379	96.07 480	91.42 541	63.46 550	99.73 299	96.70 307	76.98 551	98.98 360

MVS_111021_LR		98.30 228	98.12 246	98.83 190	99.16 261	98.03 170	96.09 426	99.30 255	97.58 269	98.10 355	98.24 386	98.25 107	99.34 470	96.69 309	99.65 256	99.12 340

PRO-TEST		97.86 285	97.88 276	97.81 353	98.01 454	94.96 393	97.99 222	99.48 159	97.80 247	97.83 380	97.76 428	96.27 275	99.80 235	96.68 310	99.07 399	98.69 413

OPM-MVS		98.56 180	98.32 214	99.25 104	99.41 181	98.73 95	97.13 347	99.18 298	97.10 327	98.75 271	98.92 251	98.18 118	99.65 368	96.68 310	99.56 293	99.37 244
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
aaatest				99.45 64	99.58 94	98.93 80	98.68 109	99.60 94	96.46 370	99.53 83	98.77 291		99.83 197	96.67 312	99.64 258	99.58 117

MED-MVS		99.01 90	98.84 119	99.52 44	99.58 94	98.93 80	98.68 109	99.60 94	98.85 145	99.53 83	99.16 170	97.87 149	99.83 197	96.67 312	99.62 267	99.81 41

aaEdge-Enhanced		98.61 171	98.33 213	99.44 65	99.24 233	98.93 80	97.45 310	99.06 322	98.14 222	99.06 193	98.77 291	96.97 226	99.82 209	96.67 312	99.64 258	99.58 117

Effi-MVS+-dtu		98.26 235	97.90 274	99.35 80	98.02 453	99.49 5	98.02 210	99.16 305	98.29 197	97.64 392	97.99 409	96.44 262	99.95 25	96.66 315	98.93 420	98.60 425

testing3-2		93.78 472	93.91 462	93.39 522	98.82 345	81.72 553	97.76 258	95.28 509	98.60 168	96.54 464	96.66 474	65.85 542	99.62 381	96.65 316	98.99 412	98.82 389

Effi-MVS+		98.02 265	97.82 281	98.62 242	98.53 403	97.19 268	97.33 324	99.68 64	97.30 306	96.68 457	97.46 451	98.56 73	99.80 235	96.63 317	98.20 464	98.86 385

WBMVS		95.18 446	94.78 448	96.37 452	97.68 480	89.74 519	95.80 446	98.73 389	97.54 277	98.30 336	98.44 359	70.06 528	99.82 209	96.62 318	99.87 100	99.54 143

mvsmamba		97.57 314	97.26 326	98.51 270	98.69 374	96.73 304	98.74 99	97.25 459	97.03 332	97.88 374	99.23 150	90.95 427	99.87 135	96.61 319	99.00 410	98.91 378

MDA-MVSNet-bldmvs		97.94 275	97.91 273	98.06 331	99.44 171	94.96 393	96.63 384	99.15 310	98.35 187	98.83 256	99.11 188	94.31 358	99.85 158	96.60 320	98.72 433	99.37 244

Test_1112_low_res		96.99 366	96.55 382	98.31 298	99.35 199	95.47 368	95.84 445	99.53 136	91.51 508	96.80 451	98.48 355	91.36 423	99.83 197	96.58 321	99.53 305	99.62 92

LS3D		98.63 167	98.38 200	99.36 74	97.25 501	99.38 12	99.12 61	99.32 242	99.21 82	98.44 324	98.88 265	97.31 200	99.80 235	96.58 321	99.34 352	98.92 374

APD_test1		98.83 122	98.66 146	99.34 83	99.78 24	99.47 8	98.42 151	99.45 180	98.28 199	98.98 214	99.19 159	97.76 158	99.58 405	96.57 323	99.55 298	98.97 364

HFP-MVS		98.71 142	98.44 188	99.51 49	99.49 150	99.16 48	98.52 130	99.31 247	97.47 283	98.58 304	98.50 352	97.97 138	99.85 158	96.57 323	99.59 280	99.53 157

ACMMPR		98.70 147	98.42 191	99.54 31	99.52 131	99.14 57	98.52 130	99.31 247	97.47 283	98.56 308	98.54 343	97.75 159	99.88 115	96.57 323	99.59 280	99.58 117

sss		97.21 347	96.93 348	98.06 331	98.83 342	95.22 384	96.75 371	98.48 411	94.49 453	97.27 422	97.90 417	92.77 397	99.80 235	96.57 323	99.32 357	99.16 334

SR-MVS-dyc-post		98.81 127	98.55 165	99.57 21	99.20 245	99.38 12	98.48 143	99.30 255	98.64 161	98.95 224	98.96 242	97.49 189	99.86 144	96.56 327	99.39 343	99.45 206

RE-MVS-def			98.58 162		99.20 245	99.38 12	98.48 143	99.30 255	98.64 161	98.95 224	98.96 242	97.75 159		96.56 327	99.39 343	99.45 206

SD-MVS		98.40 207	98.68 141	97.54 391	98.96 315	97.99 174	97.88 238	99.36 222	98.20 209	99.63 66	99.04 209	98.76 46	95.33 547	96.56 327	99.74 195	99.31 278
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
ambc				98.24 307	98.82 345	95.97 341	98.62 118	99.00 339		99.27 153	99.21 154	96.99 224	99.50 435	96.55 330	99.50 319	99.26 297

APD-MVS_3200maxsize		98.84 119	98.61 158	99.53 38	99.19 249	99.27 26	98.49 140	99.33 240	98.64 161	99.03 206	98.98 236	97.89 147	99.85 158	96.54 331	99.42 339	99.46 200

CP-MVS		98.70 147	98.42 191	99.52 44	99.36 194	99.12 62	98.72 104	99.36 222	97.54 277	98.30 336	98.40 363	97.86 150	99.89 97	96.53 332	99.72 208	99.56 130

MVP-Stereo		98.08 260	97.92 271	98.57 253	98.96 315	96.79 299	97.90 236	99.18 298	96.41 372	98.46 321	98.95 246	95.93 298	99.60 393	96.51 333	98.98 415	99.31 278
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
testgi		98.32 223	98.39 196	98.13 320	99.57 103	95.54 358	97.78 252	99.49 157	97.37 298	99.19 176	97.65 435	98.96 30	99.49 439	96.50 334	98.99 412	99.34 262

HPM-MVS		98.79 131	98.53 169	99.59 20	99.65 71	99.29 23	99.16 55	99.43 194	96.74 354	98.61 297	98.38 366	98.62 64	99.87 135	96.47 335	99.67 246	99.59 109
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
region2R		98.69 151	98.40 193	99.54 31	99.53 127	99.17 43	98.52 130	99.31 247	97.46 288	98.44 324	98.51 348	97.83 151	99.88 115	96.46 336	99.58 285	99.58 117

LoFTR		97.97 273	97.79 283	98.53 267	98.80 351	97.47 236	97.01 351	99.55 126	95.55 418	99.46 101	99.22 152	94.22 361	99.44 455	96.45 337	99.82 133	98.68 418

SMA-MVS		98.40 207	98.03 256	99.51 49	99.16 261	99.21 32	98.05 203	99.22 287	94.16 466	98.98 214	99.10 191	97.52 184	99.79 249	96.45 337	99.64 258	99.53 157
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
CNVR-MVS		98.17 251	97.87 277	99.07 138	98.67 379	98.24 140	97.01 351	98.93 347	97.25 312	97.62 394	98.34 371	97.27 204	99.57 407	96.42 339	99.33 354	99.39 232

ttmdpeth		97.91 276	98.02 257	97.58 384	98.69 374	94.10 427	98.13 186	98.90 354	97.95 233	97.32 421	99.58 47	95.95 297	98.75 509	96.41 340	99.22 377	99.87 22

CL-MVSNet_self_test		97.44 324	97.22 330	98.08 328	98.57 398	95.78 352	94.30 500	98.79 377	96.58 363	98.60 300	98.19 391	94.74 342	99.64 373	96.41 340	98.84 423	98.82 389

cl22		95.79 425	95.39 427	96.98 425	96.77 518	92.79 469	94.40 497	98.53 407	94.59 452	97.89 373	98.17 392	82.82 499	99.24 483	96.37 342	99.03 404	98.92 374

PS-MVSNAJ		97.08 357	97.39 317	96.16 466	98.56 399	92.46 475	95.24 468	98.85 368	97.25 312	97.49 407	95.99 488	98.07 128	99.90 81	96.37 342	98.67 441	96.12 522

CVMVSNet		96.25 404	97.21 331	93.38 523	99.10 275	80.56 555	97.20 340	98.19 428	96.94 336	99.00 209	99.02 213	89.50 443	99.80 235	96.36 344	99.59 280	99.78 50

xiu_mvs_v2_base		97.16 352	97.49 311	96.17 464	98.54 401	92.46 475	95.45 459	98.84 369	97.25 312	97.48 408	96.49 477	98.31 97	99.90 81	96.34 345	98.68 440	96.15 521

AUN-MVS		96.24 406	95.45 423	98.60 248	98.70 369	97.22 264	97.38 317	97.65 445	95.95 397	95.53 496	97.96 415	82.11 502	99.79 249	96.31 346	97.44 495	98.80 399

miper_enhance_ethall		96.01 413	95.74 408	96.81 436	96.41 530	92.27 481	93.69 518	98.89 357	91.14 513	98.30 336	97.35 458	90.58 432	99.58 405	96.31 346	99.03 404	98.60 425

ArgMatch-SfM		97.96 274	97.72 291	98.66 232	99.02 303	97.33 247	96.49 395	99.52 142	95.46 424	98.71 279	98.29 381	96.14 280	99.69 331	96.30 348	99.56 293	98.97 364

SP-MNN		96.46 392	96.24 399	97.10 417	96.71 519	95.98 339	96.00 431	97.33 456	95.82 406	94.93 506	97.10 468	93.70 376	98.01 521	96.30 348	98.30 460	97.30 499

ACMMP		98.75 138	98.50 175	99.52 44	99.56 111	99.16 48	98.87 89	99.37 218	97.16 324	98.82 259	99.01 225	97.71 161	99.87 135	96.29 350	99.69 234	99.54 143
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
ETV-MVS		98.03 264	97.86 278	98.56 258	98.69 374	98.07 165	97.51 300	99.50 149	98.10 223	97.50 406	95.51 499	98.41 85	99.88 115	96.27 351	99.24 373	97.71 486

XVG-OURS-SEG-HR		98.49 196	98.28 219	99.14 124	99.49 150	98.83 87	96.54 390	99.48 159	97.32 303	99.11 186	98.61 335	99.33 15	99.30 477	96.23 352	98.38 455	99.28 287

GA-MVS		95.86 422	95.32 432	97.49 396	98.60 391	94.15 424	93.83 516	97.93 436	95.49 422	96.68 457	97.42 453	83.21 495	99.30 477	96.22 353	98.55 450	99.01 355

mPP-MVS		98.64 165	98.34 208	99.54 31	99.54 123	99.17 43	98.63 116	99.24 284	97.47 283	98.09 356	98.68 315	97.62 170	99.89 97	96.22 353	99.62 267	99.57 124

Fast-Effi-MVS+		97.67 306	97.38 318	98.57 253	98.71 365	97.43 242	97.23 335	99.45 180	94.82 446	96.13 477	96.51 476	98.52 75	99.91 74	96.19 355	98.83 424	98.37 448

pmmvs3		95.03 449	94.40 457	96.93 428	97.70 477	92.53 474	95.08 473	97.71 441	88.57 531	97.71 388	98.08 402	79.39 510	99.82 209	96.19 355	99.11 397	98.43 441

MCST-MVS		98.00 268	97.63 302	99.10 130	99.24 233	98.17 148	96.89 363	98.73 389	95.66 412	97.92 370	97.70 433	97.17 211	99.66 361	96.18 357	99.23 376	99.47 197

SteuartSystems-ACMMP		98.79 131	98.54 167	99.54 31	99.73 38	99.16 48	98.23 173	99.31 247	97.92 237	98.90 237	98.90 257	98.00 134	99.88 115	96.15 358	99.72 208	99.58 117
Skip Steuart: Steuart Systems R&D Blog.
SR-MVS		98.71 142	98.43 189	99.57 21	99.18 257	99.35 16	98.36 160	99.29 263	98.29 197	98.88 244	98.85 271	97.53 182	99.87 135	96.14 359	99.31 359	99.48 188

MSP-MVS		98.40 207	98.00 259	99.61 13	99.57 103	99.25 28	98.57 124	99.35 228	97.55 274	99.31 147	97.71 431	94.61 345	99.88 115	96.14 359	99.19 385	99.70 70
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
FA-MVS(test-final)		96.99 366	96.82 359	97.50 395	98.70 369	94.78 403	99.34 23	96.99 468	95.07 438	98.48 319	99.33 118	88.41 453	99.65 368	96.13 361	98.92 421	98.07 463

SP-DiffGlue		96.87 370	96.76 363	97.21 411	95.17 540	96.88 295	96.12 424	98.93 347	96.51 364	98.37 333	97.55 441	93.65 377	97.83 524	96.11 362	98.45 454	96.92 505

DeepC-MVS_fast	96.85 6	98.30 228	98.15 243	98.75 213	98.61 389	97.23 261	97.76 258	99.09 318	97.31 305	98.75 271	98.66 321	97.56 177	99.64 373	96.10 363	99.55 298	99.39 232
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
GST-MVS		98.61 171	98.30 216	99.52 44	99.51 134	99.20 38	98.26 171	99.25 278	97.44 291	98.67 284	98.39 364	97.68 162	99.85 158	96.00 364	99.51 311	99.52 161

EPNet		96.14 408	95.44 424	98.25 305	90.76 554	95.50 364	97.92 233	94.65 514	98.97 127	92.98 530	98.85 271	89.12 445	99.87 135	95.99 365	99.68 240	99.39 232
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
COLMAP_ROB	96.50 10	98.99 94	98.85 118	99.41 69	99.58 94	99.10 65	98.74 99	99.56 121	99.09 110	99.33 138	99.19 159	98.40 86	99.72 310	95.98 366	99.76 188	99.42 219
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmtry		97.35 333	96.97 346	98.50 274	97.31 500	96.47 319	98.18 179	98.92 351	98.95 131	98.78 265	99.37 104	85.44 477	99.85 158	95.96 367	99.83 126	99.17 328

tfpnnormal		98.90 108	98.90 105	98.91 175	99.67 68	97.82 202	99.00 73	99.44 188	99.45 50	99.51 92	99.24 144	98.20 117	99.86 144	95.92 368	99.69 234	99.04 350

XVG-ACMP-BASELINE		98.56 180	98.34 208	99.22 109	99.54 123	98.59 106	97.71 266	99.46 176	97.25 312	98.98 214	98.99 231	97.54 180	99.84 179	95.88 369	99.74 195	99.23 304

tpm		94.67 454	94.34 459	95.66 486	97.68 480	88.42 524	97.88 238	94.90 512	94.46 455	96.03 484	98.56 342	78.66 514	99.79 249	95.88 369	95.01 535	98.78 401

ab-mvs		98.41 204	98.36 204	98.59 249	99.19 249	97.23 261	99.32 26	98.81 374	97.66 260	98.62 295	99.40 97	96.82 235	99.80 235	95.88 369	99.51 311	98.75 405

test-LLR		93.90 470	93.85 463	94.04 512	96.53 523	84.62 541	94.05 510	92.39 534	96.17 381	94.12 518	95.07 508	82.30 500	99.67 348	95.87 372	98.18 465	97.82 475

test-mter		92.33 498	91.76 499	94.04 512	96.53 523	84.62 541	94.05 510	92.39 534	94.00 474	94.12 518	95.07 508	65.63 543	99.67 348	95.87 372	98.18 465	97.82 475

PGM-MVS		98.66 162	98.37 202	99.55 28	99.53 127	99.18 42	98.23 173	99.49 157	97.01 333	98.69 280	98.88 265	98.00 134	99.89 97	95.87 372	99.59 280	99.58 117

USDC		97.41 327	97.40 316	97.44 401	98.94 317	93.67 451	95.17 470	99.53 136	94.03 472	98.97 218	99.10 191	95.29 321	99.34 470	95.84 375	99.73 199	99.30 282

HPM-MVS++		98.10 256	97.64 300	99.48 57	99.09 278	99.13 60	97.52 298	98.75 386	97.46 288	96.90 444	97.83 423	96.01 288	99.84 179	95.82 376	99.35 350	99.46 200

TESTMET0.1,1		92.19 500	91.77 498	93.46 519	96.48 528	82.80 550	94.05 510	91.52 542	94.45 458	94.00 523	94.88 514	66.65 537	99.56 410	95.78 377	98.11 471	98.02 465

DSMNet-mixed		97.42 326	97.60 304	96.87 432	99.15 265	91.46 490	98.54 128	99.12 313	92.87 493	97.58 398	99.63 39	96.21 278	99.90 81	95.74 378	99.54 301	99.27 291

XVG-OURS		98.53 189	98.34 208	99.11 128	99.50 141	98.82 89	95.97 433	99.50 149	97.30 306	99.05 201	98.98 236	99.35 14	99.32 474	95.72 379	99.68 240	99.18 324

RPSCF		98.62 170	98.36 204	99.42 67	99.65 71	99.42 10	98.55 126	99.57 111	97.72 256	98.90 237	99.26 137	96.12 284	99.52 428	95.72 379	99.71 217	99.32 273

PHI-MVS		98.29 231	97.95 265	99.34 83	98.44 412	99.16 48	98.12 190	99.38 214	96.01 393	98.06 359	98.43 360	97.80 155	99.67 348	95.69 381	99.58 285	99.20 314

SF-MVS		98.53 189	98.27 222	99.32 91	99.31 209	98.75 91	98.19 178	99.41 205	96.77 353	98.83 256	98.90 257	97.80 155	99.82 209	95.68 382	99.52 308	99.38 241

dtuonlycased		97.70 303	98.19 236	96.24 458	99.75 34	89.51 520	94.69 486	99.64 79	98.23 201	99.46 101	98.57 340	98.25 107	99.85 158	95.65 383	99.44 335	99.36 252

PDCNetPlus		95.22 445	94.73 452	96.70 442	97.85 464	91.14 502	93.94 513	99.97 1	93.06 488	98.95 224	98.89 263	74.32 523	99.14 492	95.63 384	99.93 57	99.82 36

test_0402		98.76 137	98.71 135	98.93 170	99.56 111	98.14 151	98.45 147	99.34 234	99.28 73	98.95 224	98.91 254	98.34 95	99.79 249	95.63 384	99.91 80	98.86 385

tpmrst		95.07 448	95.46 422	93.91 514	97.11 504	84.36 543	97.62 281	96.96 471	94.98 440	96.35 474	98.80 285	85.46 476	99.59 398	95.60 386	96.23 520	97.79 480

PMMVS		96.51 385	95.98 401	98.09 325	97.53 488	95.84 347	94.92 477	98.84 369	91.58 506	96.05 482	95.58 497	95.68 307	99.66 361	95.59 387	98.09 472	98.76 404

LPG-MVS_test		98.71 142	98.46 185	99.47 61	99.57 103	98.97 74	98.23 173	99.48 159	96.60 361	99.10 189	99.06 200	98.71 51	99.83 197	95.58 388	99.78 164	99.62 92

LGP-MVS_train				99.47 61	99.57 103	98.97 74		99.48 159	96.60 361	99.10 189	99.06 200	98.71 51	99.83 197	95.58 388	99.78 164	99.62 92

ArgMatch-Sym		97.83 294	97.54 306	98.71 223	98.98 311	97.65 221	96.25 415	99.43 194	95.60 415	98.85 251	97.98 410	95.72 305	99.56 410	95.54 390	99.50 319	98.92 374

IS-MVSNet		98.19 246	97.90 274	99.08 136	99.57 103	97.97 178	99.31 30	98.32 419	99.01 123	98.98 214	99.03 212	91.59 417	99.79 249	95.49 391	99.80 152	99.48 188

baseline1		95.96 419	95.44 424	97.52 393	98.51 405	93.99 438	98.39 157	96.09 494	98.21 205	98.40 332	97.76 428	86.88 460	99.63 376	95.42 392	89.27 545	98.95 368

DPE-MVS		98.59 175	98.26 225	99.57 21	99.27 221	99.15 52	97.01 351	99.39 212	97.67 259	99.44 107	98.99 231	97.53 182	99.89 97	95.40 393	99.68 240	99.66 80
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MASt3R-SfM		96.02 412	95.82 406	96.60 444	97.03 510	94.90 397	94.26 503	98.53 407	88.40 533	98.41 327	98.67 317	92.39 402	97.62 529	95.31 394	99.41 340	97.29 500

NCCC		97.86 285	97.47 315	99.05 145	98.61 389	98.07 165	96.98 354	98.90 354	97.63 262	97.04 434	97.93 416	95.99 293	99.66 361	95.31 394	98.82 426	99.43 214

testing3		93.51 476	92.09 489	97.75 361	98.60 391	94.40 415	97.32 325	95.26 510	97.56 272	96.79 452	95.50 500	53.57 555	99.77 267	95.26 396	98.97 416	99.08 342

SP-NN		94.67 454	94.44 456	95.36 496	95.12 541	95.23 383	94.27 502	96.10 493	94.46 455	90.91 540	95.76 495	91.47 422	93.87 549	95.23 397	96.62 515	97.00 504

PC_three_1452									93.27 482	99.40 117	98.54 343	98.22 113	97.00 536	95.17 398	99.45 328	99.49 177

Patchmatch-test		96.55 384	96.34 392	97.17 414	98.35 421	93.06 460	98.40 156	97.79 438	97.33 301	98.41 327	98.67 317	83.68 493	99.69 331	95.16 399	99.31 359	98.77 402

EPMVS		93.72 474	93.27 472	95.09 501	96.04 535	87.76 528	98.13 186	85.01 553	94.69 449	96.92 439	98.64 328	78.47 518	99.31 475	95.04 400	96.46 517	98.20 455

MonoMVSNet		96.25 404	96.53 384	95.39 494	96.57 522	91.01 503	98.82 97	97.68 444	98.57 174	98.03 363	99.37 104	90.92 428	97.78 526	94.99 401	93.88 540	97.38 497

UnsupCasMVSNet_bld		97.30 338	96.92 350	98.45 279	99.28 218	96.78 302	96.20 417	99.27 270	95.42 426	98.28 340	98.30 378	93.16 386	99.71 312	94.99 401	97.37 499	98.87 384

PatchmatchNet		95.58 432	95.67 412	95.30 498	97.34 498	87.32 531	97.65 276	96.65 481	95.30 430	97.07 431	98.69 313	84.77 482	99.75 285	94.97 403	98.64 442	98.83 387
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		94.93 452	94.78 448	95.38 495	93.58 545	87.68 529	96.78 368	95.69 505	97.35 300	89.14 545	98.09 401	88.15 455	99.49 439	94.95 404	99.30 363	98.98 360
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_yl		96.69 376	96.29 395	97.90 344	98.28 428	95.24 380	97.29 329	97.36 452	98.21 205	98.17 345	97.86 419	86.27 464	99.55 415	94.87 405	98.32 457	98.89 380

DCV-MVSNet		96.69 376	96.29 395	97.90 344	98.28 428	95.24 380	97.29 329	97.36 452	98.21 205	98.17 345	97.86 419	86.27 464	99.55 415	94.87 405	98.32 457	98.89 380

ACMP	95.32 15	98.41 204	98.09 248	99.36 74	99.51 134	98.79 90	97.68 270	99.38 214	95.76 410	98.81 261	98.82 281	98.36 90	99.82 209	94.75 407	99.77 172	99.48 188
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		97.55 315	97.53 308	97.60 382	98.92 323	93.77 448	96.64 383	99.43 194	94.49 453	97.62 394	99.18 163	96.82 235	99.67 348	94.73 408	99.93 57	99.36 252

PVSNet_Blended		96.88 369	96.68 369	97.47 399	98.92 323	93.77 448	94.71 482	99.43 194	90.98 515	97.62 394	97.36 457	96.82 235	99.67 348	94.73 408	99.56 293	98.98 360

MP-MVS		98.46 199	98.09 248	99.54 31	99.57 103	99.22 31	98.50 137	99.19 294	97.61 266	97.58 398	98.66 321	97.40 195	99.88 115	94.72 410	99.60 276	99.54 143
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
OPU-MVS				98.82 192	98.59 394	98.30 135	98.10 193				98.52 347	98.18 118	98.75 509	94.62 411	99.48 324	99.41 222

LF4IMVS		97.90 277	97.69 294	98.52 269	99.17 259	97.66 219	97.19 344	99.47 171	96.31 376	97.85 379	98.20 390	96.71 247	99.52 428	94.62 411	99.72 208	98.38 446

CostFormer		93.97 469	93.78 465	94.51 506	97.53 488	85.83 536	97.98 224	95.96 496	89.29 526	94.99 505	98.63 330	78.63 515	99.62 381	94.54 413	96.50 516	98.09 462

thisisatest0515		94.12 466	93.16 474	96.97 426	98.60 391	92.90 466	93.77 517	90.61 543	94.10 469	96.91 441	95.87 492	74.99 522	99.80 235	94.52 414	99.12 396	98.20 455

旧先验2							95.76 448		88.56 532	97.52 404			99.66 361	94.48 415

CLD-MVS		97.49 319	97.16 333	98.48 276	99.07 282	97.03 282	94.71 482	99.21 288	94.46 455	98.06 359	97.16 463	97.57 176	99.48 443	94.46 416	99.78 164	98.95 368
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
AllTest		98.44 202	98.20 232	99.16 118	99.50 141	98.55 109	98.25 172	99.58 103	96.80 350	98.88 244	99.06 200	97.65 165	99.57 407	94.45 417	99.61 274	99.37 244

TestCases				99.16 118	99.50 141	98.55 109		99.58 103	96.80 350	98.88 244	99.06 200	97.65 165	99.57 407	94.45 417	99.61 274	99.37 244

nomal-1		94.03 467	93.02 477	97.07 420	97.95 457	92.86 467	96.66 382	95.37 508	96.16 385	94.89 507	94.68 518	69.16 531	99.73 299	94.43 419	97.86 483	98.62 424

HQP_MVS		97.99 271	97.67 295	98.93 170	99.19 249	97.65 221	97.77 255	99.27 270	98.20 209	97.79 384	97.98 410	94.90 332	99.70 321	94.42 420	99.51 311	99.45 206

plane_prior5								99.27 270					99.70 321	94.42 420	99.51 311	99.45 206

JIA-IIPM		95.52 434	95.03 442	97.00 423	96.85 515	94.03 431	96.93 359	95.82 500	99.20 84	94.63 513	99.71 22	83.09 496	99.60 393	94.42 420	94.64 536	97.36 498

cascas		94.79 453	94.33 460	96.15 468	96.02 536	92.36 479	92.34 534	99.26 276	85.34 540	95.08 504	94.96 513	92.96 393	98.53 514	94.41 423	98.59 447	97.56 491

TinyColmap		97.89 279	97.98 261	97.60 382	98.86 336	94.35 417	96.21 416	99.44 188	97.45 290	99.06 193	98.88 265	97.99 137	99.28 481	94.38 424	99.58 285	99.18 324

9.14			97.78 284		99.07 282		97.53 297	99.32 242	95.53 421	98.54 312	98.70 311	97.58 175	99.76 273	94.32 425	99.46 326

test_post1							97.59 289				20.48 558	83.07 497	99.66 361	94.16 426

SCA		96.41 395	96.66 373	95.67 485	98.24 433	88.35 525	95.85 444	96.88 476	96.11 387	97.67 391	98.67 317	93.10 389	99.85 158	94.16 426	99.22 377	98.81 394

test_prior2							95.74 449		96.48 368	96.11 478	97.63 437	95.92 299		94.16 426	99.20 382

tpmvs		95.02 450	95.25 435	94.33 507	96.39 531	85.87 534	98.08 196	96.83 478	95.46 424	95.51 497	98.69 313	85.91 472	99.53 424	94.16 426	96.23 520	97.58 490

LCM-MVSNet-Re		98.64 165	98.48 181	99.11 128	98.85 339	98.51 114	98.49 140	99.83 26	98.37 185	99.69 55	99.46 80	98.21 115	99.92 65	94.13 430	99.30 363	98.91 378

MSDG		97.71 302	97.52 309	98.28 302	98.91 326	96.82 297	94.42 496	99.37 218	97.65 261	98.37 333	98.29 381	97.40 195	99.33 472	94.09 431	99.22 377	98.68 418

MVS-HIRNet		94.32 459	95.62 413	90.42 530	98.46 409	75.36 556	96.29 410	89.13 547	95.25 433	95.38 498	99.75 16	92.88 394	99.19 487	94.07 432	99.39 343	96.72 512

DP-MVS Recon		97.33 335	96.92 350	98.57 253	99.09 278	97.99 174	96.79 366	99.35 228	93.18 484	97.71 388	98.07 403	95.00 331	99.31 475	93.97 433	99.13 393	98.42 443

new_pmnet		96.99 366	96.76 363	97.67 371	98.72 361	94.89 398	95.95 437	98.20 426	92.62 496	98.55 310	98.54 343	94.88 335	99.52 428	93.96 434	99.44 335	98.59 428

MDTV_nov1_ep13			95.22 437		97.06 507	83.20 548	97.74 263	96.16 490	94.37 461	96.99 437	98.83 278	83.95 491	99.53 424	93.90 435	97.95 481

WTY-MVS		96.67 378	96.27 397	97.87 349	98.81 348	94.61 411	96.77 369	97.92 437	94.94 442	97.12 427	97.74 430	91.11 426	99.82 209	93.89 436	98.15 469	99.18 324

Vis-MVSNet (Re-imp)		97.46 321	97.16 333	98.34 295	99.55 117	96.10 331	98.94 81	98.44 412	98.32 192	98.16 348	98.62 333	88.76 446	99.73 299	93.88 437	99.79 159	99.18 324

ITE_SJBPF				98.87 179	99.22 239	98.48 116		99.35 228	97.50 280	98.28 340	98.60 337	97.64 168	99.35 469	93.86 438	99.27 367	98.79 400

CPTT-MVS		97.84 292	97.36 320	99.27 99	99.31 209	98.46 117	98.29 166	99.27 270	94.90 443	97.83 380	98.37 367	94.90 332	99.84 179	93.85 439	99.54 301	99.51 165

APD-MVS		98.10 256	97.67 295	99.42 67	99.11 273	98.93 80	97.76 258	99.28 267	94.97 441	98.72 275	98.77 291	97.04 218	99.85 158	93.79 440	99.54 301	99.49 177
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testing11		93.08 486	92.02 491	96.26 457	97.56 484	90.83 507	96.32 408	95.70 503	96.47 369	92.66 533	93.73 523	64.36 546	99.59 398	93.77 441	97.57 488	98.37 448

SIFT-MNN		95.92 420	95.97 402	95.74 484	98.18 439	98.00 172	94.17 505	96.99 468	95.74 411	97.16 426	97.90 417	90.71 430	95.79 544	93.71 442	99.21 380	93.44 534

train_agg		97.10 354	96.45 389	99.07 138	98.71 365	98.08 162	95.96 435	99.03 331	91.64 504	95.85 485	97.53 442	96.47 260	99.76 273	93.67 443	99.16 388	99.36 252

SIFT-NCM-Cal		96.56 383	96.68 369	96.20 462	98.27 430	98.44 119	94.40 497	96.67 480	95.29 431	97.63 393	98.17 392	96.40 264	96.59 542	93.61 444	99.66 254	93.57 533

PVSNet	93.40 17	95.67 428	95.70 410	95.57 488	98.83 342	88.57 523	92.50 532	97.72 440	92.69 495	96.49 472	96.44 480	93.72 375	99.43 457	93.61 444	99.28 366	98.71 409

test0.0.03 1		94.51 456	93.69 466	96.99 424	96.05 534	93.61 455	94.97 476	93.49 529	96.17 381	97.57 400	94.88 514	82.30 500	99.01 499	93.60 446	94.17 539	98.37 448

testdata				98.09 325	98.93 319	95.40 371		98.80 376	90.08 521	97.45 412	98.37 367	95.26 322	99.70 321	93.58 447	98.95 418	99.17 328

SIFT-NCMNet		96.30 399	96.40 390	96.03 472	97.80 469	97.68 218	92.34 534	96.94 473	95.55 418	98.84 254	98.63 330	94.17 362	97.63 528	93.57 448	99.71 217	92.77 544

MDTV_nov1_ep13_2view						74.92 557	97.69 269		90.06 522	97.75 387		85.78 473		93.52 449		98.69 413

TAPA-MVS	96.21 11	96.63 380	95.95 403	98.65 234	98.93 319	98.09 158	96.93 359	99.28 267	83.58 542	98.13 352	97.78 426	96.13 282	99.40 461	93.52 449	99.29 365	98.45 436
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SIFT-PointCN		96.45 393	96.47 386	96.39 451	98.13 447	97.54 230	93.31 526	97.23 461	94.67 450	98.68 283	98.32 376	94.64 344	97.81 525	93.50 451	99.77 172	93.83 530

OMC-MVS		97.88 282	97.49 311	99.04 147	98.89 332	98.63 101	96.94 357	99.25 278	95.02 439	98.53 313	98.51 348	97.27 204	99.47 446	93.50 451	99.51 311	99.01 355

PatchMatch-RL		97.24 344	96.78 362	98.61 246	99.03 295	97.83 197	96.36 405	99.06 322	93.49 481	97.36 420	97.78 426	95.75 303	99.49 439	93.44 453	98.77 428	98.52 431

114514_t		96.50 387	95.77 407	98.69 227	99.48 158	97.43 242	97.84 245	99.55 126	81.42 545	96.51 469	98.58 339	95.53 312	99.67 348	93.41 454	99.58 285	98.98 360

dp		93.47 477	93.59 468	93.13 525	96.64 521	81.62 554	97.66 274	96.42 487	92.80 494	96.11 478	98.64 328	78.55 517	99.59 398	93.31 455	92.18 544	98.16 458

test9_res														93.28 456	99.15 390	99.38 241

0.4-1-1-0.1		88.42 508	85.91 511	95.94 474	93.08 546	91.54 488	90.99 538	92.04 538	89.96 523	84.83 550	83.25 548	63.75 548	99.52 428	93.25 457	82.07 546	96.75 510

testing99		93.04 487	91.98 494	96.23 460	97.53 488	90.70 510	96.35 406	95.94 497	96.87 345	93.41 529	93.43 528	63.84 547	99.59 398	93.24 458	97.19 504	98.40 444

SIFT-ConvMatch		96.57 382	96.62 376	96.43 449	98.20 437	98.27 137	93.88 514	96.88 476	95.29 431	98.88 244	98.25 384	95.18 325	97.43 531	93.22 459	99.83 126	93.59 532

IB-MVS	91.63 19	92.24 499	90.90 503	96.27 456	97.22 502	91.24 499	94.36 499	93.33 531	92.37 498	92.24 537	94.58 520	66.20 540	99.89 97	93.16 460	94.63 537	97.66 487
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
SIFT-UMatch		96.33 397	96.47 386	95.89 477	98.29 426	97.95 182	93.84 515	97.24 460	95.78 409	98.72 275	98.04 405	93.45 380	96.81 538	93.14 461	99.73 199	92.91 542

testing91		93.32 480	92.27 486	96.47 448	97.54 486	91.25 498	96.17 422	96.76 479	97.18 322	93.65 528	93.50 526	65.11 545	99.63 376	93.04 462	97.45 494	98.53 430

0.3-1-1-0.015		87.27 510	84.50 514	95.57 488	91.70 549	90.77 508	89.41 544	92.04 538	88.98 527	82.46 552	81.35 549	60.36 552	99.50 435	92.96 463	81.23 548	96.45 515

baseline2		93.73 473	92.83 480	96.42 450	97.70 477	91.28 497	96.84 365	89.77 546	93.96 475	92.44 535	95.93 490	79.14 511	99.77 267	92.94 464	96.76 514	98.21 454

0.4-1-1-0.2		87.49 509	84.89 512	95.31 497	91.33 552	90.08 516	88.47 545	92.07 537	88.70 530	84.06 551	81.08 550	63.62 549	99.49 439	92.93 465	81.71 547	96.37 516

OpenMVS	96.65 7	97.09 356	96.68 369	98.32 296	98.32 423	97.16 274	98.86 92	99.37 218	89.48 524	96.29 475	99.15 176	96.56 256	99.90 81	92.90 466	99.20 382	97.89 472

ADS-MVSNet2		95.43 439	94.98 443	96.76 440	98.14 444	91.74 485	97.92 233	97.76 439	90.23 517	96.51 469	98.91 254	85.61 474	99.85 158	92.88 467	96.90 510	98.69 413

ADS-MVSNet		95.24 444	94.93 446	96.18 463	98.14 444	90.10 515	97.92 233	97.32 457	90.23 517	96.51 469	98.91 254	85.61 474	99.74 292	92.88 467	96.90 510	98.69 413

BP-MVS														92.82 469

HQP-MVS		97.00 365	96.49 385	98.55 260	98.67 379	96.79 299	96.29 410	99.04 329	96.05 389	95.55 492	96.84 469	93.84 370	99.54 421	92.82 469	99.26 371	99.32 273

testdata2													99.79 249	92.80 471

MatchFormer		97.07 358	96.92 350	97.49 396	98.44 412	95.92 342	96.79 366	99.14 311	93.08 487	99.32 144	99.10 191	93.89 369	99.03 495	92.78 472	99.78 164	97.52 492

CDPH-MVS		97.26 341	96.66 373	99.07 138	99.00 307	98.15 149	96.03 430	99.01 337	91.21 512	97.79 384	97.85 421	96.89 230	99.69 331	92.75 473	99.38 346	99.39 232

新几何1				98.91 175	98.94 317	97.76 210		98.76 382	87.58 536	96.75 453	98.10 399	94.80 339	99.78 261	92.73 474	99.00 410	99.20 314

ZD-MVS					99.01 306	98.84 86		99.07 321	94.10 469	98.05 361	98.12 397	96.36 270	99.86 144	92.70 475	99.19 385

F-COLMAP		97.30 338	96.68 369	99.14 124	99.19 249	98.39 123	97.27 334	99.30 255	92.93 490	96.62 460	98.00 408	95.73 304	99.68 343	92.62 476	98.46 453	99.35 258

原ACMM1				98.35 294	98.90 327	96.25 327		98.83 373	92.48 497	96.07 480	98.10 399	95.39 319	99.71 312	92.61 477	98.99 412	99.08 342

SIFT-CM-Cal		96.28 401	96.31 394	96.16 466	98.39 419	98.11 154	93.46 524	96.47 486	94.81 447	98.49 317	98.43 360	94.48 348	97.34 533	92.60 478	99.70 228	93.02 540

SIFT-NN-PointCN		96.06 409	96.11 400	95.91 476	97.88 462	97.73 214	93.49 522	97.51 449	93.22 483	96.57 462	98.26 383	96.23 277	96.60 541	92.54 479	99.27 367	93.40 535

agg_prior2														92.50 480	99.16 388	99.37 244

SIFT-NN-UMatch		95.38 441	95.26 434	95.75 482	98.25 431	97.78 207	93.24 528	95.66 507	94.01 473	95.10 503	97.47 450	93.12 387	96.78 539	92.42 481	98.04 478	92.69 545

FE-MVS		95.66 429	94.95 445	97.77 357	98.53 403	95.28 379	99.40 19	96.09 494	93.11 486	97.96 369	99.26 137	79.10 512	99.77 267	92.40 482	98.71 435	98.27 453

无先验							95.74 449	98.74 388	89.38 525				99.73 299	92.38 483		99.22 309

SIFT-UM-Cal		96.49 388	96.62 376	96.12 469	98.13 447	97.89 191	93.35 525	98.44 412	95.48 423	98.63 291	98.34 371	95.45 317	97.45 530	92.22 484	99.50 319	93.02 540

SIFT-PCN-Cal		96.34 396	96.46 388	96.01 473	98.17 441	96.89 293	93.48 523	97.35 455	94.84 445	99.35 130	98.30 378	94.70 343	97.92 523	92.03 485	99.88 95	93.21 539

SIFT-NN-CMatch		95.63 431	95.48 420	96.08 470	98.24 433	98.00 172	92.71 530	94.29 520	94.20 465	95.85 485	97.26 460	95.72 305	97.01 535	91.99 486	99.02 407	93.23 537

CMPMVS	75.91 23	96.29 400	95.44 424	98.84 188	96.25 532	98.69 99	97.02 350	99.12 313	88.90 528	97.83 380	98.86 268	89.51 442	98.90 505	91.92 487	99.51 311	98.92 374
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
BH-untuned		96.83 372	96.75 365	97.08 418	98.74 358	93.33 457	96.71 374	98.26 422	96.72 355	98.44 324	97.37 456	95.20 323	99.47 446	91.89 488	97.43 496	98.44 439

UWE-MVS		92.38 496	91.76 499	94.21 510	97.16 503	84.65 540	95.42 461	88.45 548	95.96 396	96.17 476	95.84 494	66.36 538	99.71 312	91.87 489	98.64 442	98.28 451

myMVS_eth3d28		92.92 490	92.31 485	94.77 502	97.84 465	87.59 530	96.19 418	96.11 492	97.08 328	94.27 515	93.49 527	66.07 541	98.78 508	91.78 490	97.93 482	97.92 471

gm-plane-assit					94.83 542	81.97 552			88.07 535		94.99 511		99.60 393	91.76 491

CNLPA		97.17 351	96.71 367	98.55 260	98.56 399	98.05 169	96.33 407	98.93 347	96.91 341	97.06 432	97.39 454	94.38 354	99.45 453	91.66 492	99.18 387	98.14 459

MIMVSNet		96.62 381	96.25 398	97.71 367	99.04 292	94.66 409	99.16 55	96.92 475	97.23 318	97.87 375	99.10 191	86.11 468	99.65 368	91.65 493	99.21 380	98.82 389

1314		95.74 426	95.60 415	96.17 464	97.53 488	92.75 471	98.07 200	98.31 420	91.22 511	94.25 516	96.68 473	95.53 312	99.03 495	91.64 494	97.18 505	96.74 511

PMVS	91.26 20	97.86 285	97.94 268	97.65 375	99.71 49	97.94 184	98.52 130	98.68 392	98.99 124	97.52 404	99.35 111	97.41 194	98.18 519	91.59 495	99.67 246	96.82 509
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tpm cat1		93.29 481	93.13 476	93.75 516	97.39 497	84.74 539	97.39 315	97.65 445	83.39 543	94.16 517	98.41 362	82.86 498	99.39 463	91.56 496	95.35 534	97.14 503

test_method		79.78 513	79.50 516	80.62 531	80.21 557	45.76 562	70.82 548	98.41 417	31.08 553	80.89 553	97.71 431	84.85 481	97.37 532	91.51 497	80.03 549	98.75 405

DPM-MVS		96.32 398	95.59 417	98.51 270	98.76 355	97.21 266	94.54 493	98.26 422	91.94 503	96.37 473	97.25 461	93.06 391	99.43 457	91.42 498	98.74 431	98.89 380

WAC-MVS						90.90 505								91.37 499

KD-MVS_2432*1600		92.87 491	91.99 492	95.51 491	91.37 550	89.27 521	94.07 508	98.14 429	95.42 426	97.25 423	96.44 480	67.86 533	99.24 483	91.28 500	96.08 527	98.02 465

miper_refine_blended		92.87 491	91.99 492	95.51 491	91.37 550	89.27 521	94.07 508	98.14 429	95.42 426	97.25 423	96.44 480	67.86 533	99.24 483	91.28 500	96.08 527	98.02 465

HY-MVS	95.94 13	95.90 421	95.35 429	97.55 390	97.95 457	94.79 402	98.81 98	96.94 473	92.28 500	95.17 501	98.57 340	89.90 437	99.75 285	91.20 502	97.33 503	98.10 461

MG-MVS		96.77 375	96.61 378	97.26 409	98.31 424	93.06 460	95.93 438	98.12 431	96.45 371	97.92 370	98.73 300	93.77 374	99.39 463	91.19 503	99.04 403	99.33 268

WB-MVSnew		95.73 427	95.57 418	96.23 460	96.70 520	90.70 510	96.07 428	93.86 527	95.60 415	97.04 434	95.45 507	96.00 289	99.55 415	91.04 504	98.31 459	98.43 441

SIFT-NN-NCMNet		95.39 440	95.22 437	95.92 475	98.29 426	98.34 132	93.58 521	94.60 516	94.07 471	94.84 508	97.53 442	94.37 355	96.62 540	91.01 505	98.64 442	92.80 543

AdaColmap		97.14 353	96.71 367	98.46 278	98.34 422	97.80 206	96.95 356	98.93 347	95.58 417	96.92 439	97.66 434	95.87 300	99.53 424	90.97 506	99.14 391	98.04 464

FBQ-MVS		93.12 484	91.90 496	96.81 436	97.80 469	92.96 464	97.12 348	95.93 498	95.83 405	94.07 520	93.03 532	65.21 544	99.18 488	90.94 507	97.13 506	98.28 451

PLC	94.65 16	96.51 385	95.73 409	98.85 182	98.75 357	97.91 188	96.42 401	99.06 322	90.94 516	95.59 489	97.38 455	94.41 351	99.59 398	90.93 508	98.04 478	99.05 346
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tpm2		93.09 485	92.58 483	94.62 505	97.56 484	86.53 533	97.66 274	95.79 502	86.15 538	94.07 520	98.23 388	75.95 520	99.53 424	90.91 509	96.86 513	97.81 477

QAPM		97.31 336	96.81 361	98.82 192	98.80 351	97.49 232	99.06 66	99.19 294	90.22 519	97.69 390	99.16 170	96.91 229	99.90 81	90.89 510	99.41 340	99.07 344

ALIKED-LG		97.10 354	96.63 375	98.50 274	97.96 456	98.68 100	97.75 261	99.68 64	95.86 401	98.36 335	98.33 375	91.58 418	99.04 494	90.87 511	99.31 359	97.77 481

PAPM_NR		96.82 374	96.32 393	98.30 300	99.07 282	96.69 306	97.48 304	98.76 382	95.81 407	96.61 461	96.47 479	94.12 366	99.17 489	90.82 512	97.78 484	99.06 345

UBG		93.25 482	92.32 484	96.04 471	97.72 472	90.16 513	95.92 440	95.91 499	96.03 392	93.95 525	93.04 531	69.60 530	99.52 428	90.72 513	97.98 480	98.45 436

BH-RMVSNet		96.83 372	96.58 381	97.58 384	98.47 407	94.05 428	96.67 379	97.36 452	96.70 358	97.87 375	97.98 410	95.14 327	99.44 455	90.47 514	98.58 448	99.25 298

API-MVS		97.04 361	96.91 353	97.42 402	97.88 462	98.23 144	98.18 179	98.50 410	97.57 270	97.39 418	96.75 472	96.77 240	99.15 491	90.16 515	99.02 407	94.88 528

ALIKED-MNN		95.97 418	95.30 433	98.00 337	97.66 482	98.12 153	96.98 354	99.41 205	91.11 514	94.04 522	97.30 459	91.56 419	98.61 513	89.99 516	99.63 263	97.28 501

E-PMN		94.17 464	94.37 458	93.58 518	96.86 514	85.71 537	90.11 541	97.07 466	98.17 213	97.82 383	97.19 462	84.62 484	98.94 501	89.77 517	97.68 487	96.09 523

MAR-MVS		96.47 391	95.70 410	98.79 201	97.92 460	99.12 62	98.28 167	98.60 399	92.16 501	95.54 495	96.17 485	94.77 341	99.52 428	89.62 518	98.23 462	97.72 485
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
XFeat-MNN		93.41 479	92.98 479	94.68 504	92.63 547	92.92 465	89.72 543	95.81 501	92.10 502	97.23 425	96.29 484	84.95 480	97.31 534	89.60 519	98.54 451	93.81 531

myMVS_eth3d		91.92 503	90.45 504	96.30 454	97.10 505	90.90 505	96.18 420	96.58 483	95.65 413	94.77 509	92.29 539	53.88 554	99.36 466	89.59 520	98.05 476	98.63 422

wuyk23d		96.06 409	97.62 303	91.38 527	98.65 388	98.57 108	98.85 93	96.95 472	96.86 348	99.90 14	99.16 170	99.18 19	98.40 515	89.23 521	99.77 172	77.18 550

OpenMVS_ROB	95.38 14	95.84 424	95.18 440	97.81 353	98.41 418	97.15 275	97.37 321	98.62 398	83.86 541	98.65 287	98.37 367	94.29 359	99.68 343	88.41 522	98.62 446	96.60 513

dmvs_re		95.98 416	95.39 427	97.74 363	98.86 336	97.45 239	98.37 159	95.69 505	97.95 233	96.56 463	95.95 489	90.70 431	97.68 527	88.32 523	96.13 522	98.11 460

BH-w/o		95.13 447	94.89 447	95.86 478	98.20 437	91.31 495	95.65 451	97.37 451	93.64 477	96.52 468	95.70 496	93.04 392	99.02 497	88.10 524	95.82 530	97.24 502

EMVS		93.83 471	94.02 461	93.23 524	96.83 516	84.96 538	89.77 542	96.32 488	97.92 237	97.43 415	96.36 483	86.17 466	98.93 502	87.68 525	97.73 486	95.81 524

gg-mvs-nofinetune		92.37 497	91.20 501	95.85 479	95.80 539	92.38 478	99.31 30	81.84 555	99.75 10	91.83 538	99.74 18	68.29 532	99.02 497	87.15 526	97.12 507	96.16 520

ETVMVS		92.60 493	91.08 502	97.18 412	97.70 477	93.65 453	96.54 390	95.70 503	96.51 364	94.68 511	92.39 537	61.80 551	99.50 435	86.97 527	97.41 497	98.40 444

testing222		91.96 502	90.37 505	96.72 441	97.47 495	92.59 472	96.11 425	94.76 513	96.83 349	92.90 531	92.87 533	57.92 553	99.55 415	86.93 528	97.52 490	98.00 468

TR-MVS		95.55 433	95.12 441	96.86 435	97.54 486	93.94 439	96.49 395	96.53 485	94.36 462	97.03 436	96.61 475	94.26 360	99.16 490	86.91 529	96.31 519	97.47 494

ALIKED-NN		94.29 462	93.41 471	96.94 427	96.18 533	97.66 219	94.90 478	98.68 392	88.85 529	90.43 541	96.81 471	89.82 438	96.59 542	86.67 530	98.33 456	96.58 514

PVSNet_0	89.98 21	91.15 505	90.30 507	93.70 517	97.72 472	84.34 544	90.24 539	97.42 450	90.20 520	93.79 526	93.09 530	90.90 429	98.89 506	86.57 531	72.76 553	97.87 474

tmp_tt		78.77 514	78.73 517	78.90 532	58.45 559	74.76 558	94.20 504	78.26 557	39.16 552	86.71 547	92.82 534	80.50 504	75.19 554	86.16 532	92.29 543	86.74 547

SIFT-NN		92.96 488	92.79 481	93.46 519	96.92 512	96.45 320	91.89 536	94.39 518	92.91 491	92.54 534	95.46 503	88.26 454	90.71 552	85.22 533	97.52 490	93.22 538

PAPR		95.29 442	94.47 454	97.75 361	97.50 494	95.14 387	94.89 479	98.71 391	91.39 510	95.35 499	95.48 502	94.57 346	99.14 492	84.95 534	97.37 499	98.97 364

thres600view7		94.45 457	93.83 464	96.29 455	99.06 287	91.53 489	97.99 222	94.24 523	98.34 188	97.44 414	95.01 510	79.84 506	99.67 348	84.33 535	98.23 462	97.66 487

MVS		93.19 483	92.09 489	96.50 447	96.91 513	94.03 431	98.07 200	98.06 434	68.01 549	94.56 514	96.48 478	95.96 296	99.30 477	83.84 536	96.89 512	96.17 519

XFeat-NN		89.63 507	89.13 510	91.14 528	90.93 553	90.02 517	84.90 546	94.05 526	88.10 534	92.89 532	93.33 529	78.74 513	90.89 551	83.46 537	95.72 531	92.52 546

thres100view900		94.19 463	93.67 467	95.75 482	99.06 287	91.35 494	98.03 207	94.24 523	98.33 190	97.40 416	94.98 512	79.84 506	99.62 381	83.05 538	98.08 473	96.29 517

tfpn200view9		94.03 467	93.44 469	95.78 481	98.93 319	91.44 492	97.60 287	94.29 520	97.94 235	97.10 428	94.31 521	79.67 508	99.62 381	83.05 538	98.08 473	96.29 517

thres400		94.14 465	93.44 469	96.24 458	98.93 319	91.44 492	97.60 287	94.29 520	97.94 235	97.10 428	94.31 521	79.67 508	99.62 381	83.05 538	98.08 473	97.66 487

thres200		93.72 474	93.14 475	95.46 493	98.66 384	91.29 496	96.61 386	94.63 515	97.39 296	96.83 449	93.71 524	79.88 505	99.56 410	82.40 541	98.13 470	95.54 526

GG-mvs-BLEND				94.76 503	94.54 543	92.13 483	99.31 30	80.47 556		88.73 546	91.01 545	67.59 536	98.16 520	82.30 542	94.53 538	93.98 529

MVE	83.40 22	92.50 494	91.92 495	94.25 508	98.83 342	91.64 487	92.71 530	83.52 554	95.92 398	86.46 548	95.46 503	95.20 323	95.40 546	80.51 543	98.64 442	95.73 525
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PCF-MVS	92.86 18	94.36 458	93.00 478	98.42 283	98.70 369	97.56 228	93.16 529	99.11 315	79.59 546	97.55 401	97.43 452	92.19 407	99.73 299	79.85 544	99.45 328	97.97 469
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FPMVS		93.44 478	92.23 487	97.08 418	99.25 232	97.86 194	95.61 452	97.16 464	92.90 492	93.76 527	98.65 324	75.94 521	95.66 545	79.30 545	97.49 492	97.73 484

DeepMVS_CX				93.44 521	98.24 433	94.21 421		94.34 519	64.28 550	91.34 539	94.87 516	89.45 444	92.77 550	77.54 546	93.14 541	93.35 536

SD_0403		96.28 401	95.83 405	97.64 378	98.72 361	94.30 418	98.87 89	98.77 380	97.80 247	96.53 465	98.02 407	97.34 199	99.47 446	76.93 547	99.48 324	99.16 334

dmvs_testset		92.94 489	92.21 488	95.13 499	98.59 394	90.99 504	97.65 276	92.09 536	96.95 335	94.00 523	93.55 525	92.34 405	96.97 537	72.20 548	92.52 542	97.43 496

UWE-MVS-28		90.22 506	89.28 509	93.02 526	94.50 544	82.87 549	96.52 393	87.51 549	95.21 435	92.36 536	96.04 486	71.57 527	98.25 518	72.04 549	97.77 485	97.94 470

PAPM		91.88 504	90.34 506	96.51 446	98.06 452	92.56 473	92.44 533	97.17 463	86.35 537	90.38 542	96.01 487	86.61 462	99.21 486	70.65 550	95.43 533	97.75 482

GLUNet-SfM		86.26 511	84.68 513	91.01 529	80.58 556	83.56 545	78.04 547	93.59 528	76.70 547	95.29 500	94.72 517	77.51 519	94.26 548	66.39 551	99.33 354	95.20 527

MVS_clip		56.94 518	60.93 520	44.97 536	71.47 558	51.70 561	61.73 549	21.77 562	28.88 554	86.09 549	92.75 535	48.89 557	27.00 557	61.70 552	75.08 552	56.23 551

VLMVS_CLIP		57.57 517	58.80 521	53.85 535	47.22 560	42.89 563	60.06 550	76.87 558	39.44 551	65.76 555	80.47 551	36.24 559	64.75 556	58.06 553	65.11 555	53.91 552

dongtai		76.24 515	75.95 518	77.12 533	92.39 548	67.91 559	90.16 540	59.44 561	82.04 544	89.42 544	94.67 519	49.68 556	81.74 553	48.06 554	77.66 550	81.72 548

kuosan		69.30 516	68.95 519	70.34 534	87.68 555	65.00 560	91.11 537	59.90 560	69.02 548	74.46 554	88.89 547	48.58 558	68.03 555	28.61 555	72.33 554	77.99 549

VLMVS		32.15 519	34.06 522	26.43 537	35.38 561	29.60 564	32.69 551	19.27 563	3.29 558	44.01 557	60.07 553	35.02 560	20.44 558	22.64 556	54.15 557	29.25 553

MVS_baseline		25.61 520	31.27 524	8.63 538	32.09 562	3.00 567	22.13 552	5.43 565	1.36 559	58.03 556	69.99 552	18.40 561	0.00 561	18.79 557	55.18 556	22.88 554

test123		17.04 523	20.11 526	7.82 539	10.25 564	4.91 565	94.80 480	4.47 566	4.93 556	10.00 560	24.28 556	9.69 562	3.64 559	10.14 558	12.43 559	14.92 555

testmvs		17.12 522	20.53 525	6.87 540	12.05 563	4.20 566	93.62 520	6.73 564	4.62 557	10.41 559	24.33 555	8.28 563	3.56 560	9.69 559	15.07 558	12.86 556

mmdepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

monomultidepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

test_blank		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet_test		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

DCPMVS		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

cdsmvs_eth3d_5k		24.66 521	32.88 523	0.00 541	0.00 565	0.00 568	0.00 553	99.10 316	0.00 560	0.00 561	97.58 439	99.21 18	0.00 561	0.00 560	0.00 560	0.00 557

pcd_1.5k_mvsjas		8.17 524	10.90 527	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	98.07 128	0.00 561	0.00 560	0.00 560	0.00 557

sosnet-low-res		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

sosnet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uncertanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

Regformer		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

ab-mvs-re		8.12 525	10.83 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	97.48 448	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

PatchmatchNet2					0.00 565	90.12 514	94.29 501	98.12 431	94.40 460
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.85 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TestfortrainingZip				98.97 162	98.30 425	98.43 120	98.68 109	98.26 422	97.76 252	98.86 250	98.16 394	95.15 326	99.47 446		97.55 489	99.02 353

FOURS1					99.73 38	99.67 2	99.43 15	99.54 132	99.43 54	99.26 157

test_one_0601					99.39 185	99.20 38		99.31 247	98.49 180	98.66 286	99.02 213	97.64 168

eth-test2					0.00 565

eth-test					0.00 565

test_241102_ONE					99.49 150	99.17 43		99.31 247	97.98 230	99.66 60	98.90 257	98.36 90	99.48 443

save fliter					99.11 273	97.97 178	96.53 392	99.02 334	98.24 200

test0726					99.50 141	99.21 32	98.17 182	99.35 228	97.97 231	99.26 157	99.06 200	97.61 172

GSMVS																98.81 394

test_part2					99.36 194	99.10 65				99.05 201

sam_mvs1												84.74 483				98.81 394

sam_mvs												84.29 489

MTGPA								99.20 290

test_post											21.25 557	83.86 492	99.70 321

patchmatchnet-post											98.77 291	84.37 486	99.85 158

MTMP							97.93 230	91.91 541

TEST9					98.71 365	98.08 162	95.96 435	99.03 331	91.40 509	95.85 485	97.53 442	96.52 258	99.76 273

test_8					98.67 379	98.01 171	95.91 441	99.02 334	91.64 504	95.79 488	97.50 446	96.47 260	99.76 273

agg_prior					98.68 378	97.99 174		99.01 337		95.59 489			99.77 267

test_prior4						97.97 178	95.86 442

test_prior				98.95 166	98.69 374	97.95 182		99.03 331					99.59 398			99.30 282

新几何2							95.93 438

旧先验1					98.82 345	97.45 239		98.76 382			98.34 371	95.50 315			99.01 409	99.23 304

原ACMM2							95.53 455

test222					98.92 323	96.93 290	95.54 454	98.78 379	85.72 539	96.86 448	98.11 398	94.43 350			99.10 398	99.23 304

segment_acmp												97.02 221

testdata1							95.44 460		96.32 375

test12				98.93 170	98.58 396	97.83 197		98.66 394		96.53 465		95.51 314	99.69 331		99.13 393	99.27 291

plane_prior7					99.19 249	97.87 193

plane_prior6					98.99 310	97.70 217						94.90 332

plane_prior4											97.98 410

plane_prior3						97.78 207			97.41 293	97.79 384

plane_prior2							97.77 255		98.20 209

plane_prior1					99.05 290

plane_prior						97.65 221	97.07 349		96.72 355						99.36 347

n2								0.00 567

nn								0.00 567

door-mid								99.57 111

test11								98.87 360

door								99.41 205

HQP5-MVS						96.79 299

HQP-NCC					98.67 379		96.29 410		96.05 389	95.55 492

ACMP_Plane					98.67 379		96.29 410		96.05 389	95.55 492

HQP4-MVS										95.56 491			99.54 421			99.32 273

HQP3-MVS								99.04 329							99.26 371

HQP2-MVS												93.84 370

NP-MVS					98.84 340	97.39 244					96.84 469

ACMMP++_ref															99.77 172

ACMMP++															99.68 240

Test By Simon												96.52 258

High-resolution multi-view benchmark