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.

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

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

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 3	99.98 3	99.76 70	99.12 245	100.00 1	100.00 1	99.99 7	99.91 31	99.98 1	100.00 1	99.97 4	100.00 1	99.99 2

test_vis3_rt		99.89 3	99.90 4	99.87 26	99.98 3	99.75 79	99.70 38	100.00 1	99.73 113	100.00 1	99.89 41	99.79 22	99.88 242	99.98 1	100.00 1	99.98 5

Gipuma		99.57 102	99.59 96	99.49 244	99.98 3	99.71 101	99.72 33	99.84 105	99.81 92	99.94 48	99.78 134	98.91 167	99.71 444	98.41 283	99.95 116	99.05 428
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 87	99.01 286	99.99 12	99.99 3	99.98 14	99.88 50	99.97 2	99.99 7	99.96 9	100.00 1	99.98 5

test_fmvs3		99.83 21	99.93 2	99.53 232	99.96 7	98.62 376	99.67 53	100.00 1	99.95 32	100.00 1	99.95 16	99.85 14	99.99 7	99.98 1	99.99 19	99.98 5

test_f		99.75 49	99.88 7	99.37 295	99.96 7	98.21 410	99.51 101	100.00 1	99.94 36	100.00 1	99.93 22	99.58 50	99.94 98	99.97 4	99.99 19	99.97 10

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 95	99.70 130	99.92 59	99.93 22	99.45 63	99.97 44	99.36 119	100.00 1	99.85 50

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

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 63	99.68 48	99.85 95	99.95 32	99.98 14	99.92 27	99.28 93	99.98 26	99.75 56	100.00 1	99.94 18

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 57	99.70 38	99.86 89	99.89 56	99.98 14	99.90 36	99.94 4	99.98 26	99.75 56	100.00 1	99.90 30

mvs_tets		99.90 2	99.90 4	99.90 8	99.96 7	99.79 54	99.72 33	99.88 74	99.92 45	99.98 14	99.93 22	99.94 4	99.98 26	99.77 55	100.00 1	99.92 25

OurMVSNet-221017-0		99.75 49	99.71 56	99.84 38	99.96 7	99.83 33	99.83 7	99.85 95	99.80 96	99.93 53	99.93 22	98.54 225	99.93 120	99.59 78	99.98 54	99.76 86

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 41	99.10 254	99.98 13	99.99 3	99.98 14	99.91 31	99.68 33	99.93 120	99.93 25	99.99 19	99.99 2

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

mvsany_test3		99.85 12	99.88 7	99.75 98	99.95 15	99.37 231	99.53 92	99.98 13	99.77 108	99.99 7	99.95 16	99.85 14	99.94 98	99.95 14	99.98 54	99.94 18

test_vis1_n		99.68 64	99.79 34	99.36 301	99.94 18	98.18 413	99.52 94	100.00 1	99.86 66	100.00 1	99.88 50	98.99 151	99.96 69	99.97 4	99.96 91	99.95 15

testf1		99.63 86	99.60 93	99.72 122	99.94 18	99.95 2	99.47 112	99.89 68	99.43 217	99.88 82	99.80 109	99.26 97	99.90 205	98.81 229	99.88 203	99.32 355

APD_test2		99.63 86	99.60 93	99.72 122	99.94 18	99.95 2	99.47 112	99.89 68	99.43 217	99.88 82	99.80 109	99.26 97	99.90 205	98.81 229	99.88 203	99.32 355

pmmvs6		99.86 10	99.86 13	99.83 41	99.94 18	99.90 7	99.83 7	99.91 57	99.85 72	99.94 48	99.95 16	99.73 27	99.90 205	99.65 70	99.97 77	99.69 119

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

MIMVSNet1		99.66 77	99.62 85	99.80 64	99.94 18	99.87 15	99.69 45	99.77 170	99.78 103	99.93 53	99.89 41	97.94 302	99.92 154	99.65 70	99.98 54	99.62 188

fmvsm_s_conf0.1_n_2		99.81 28	99.78 39	99.89 11	99.93 24	99.76 70	98.92 318	99.98 13	99.99 3	99.99 7	99.88 50	99.43 67	99.94 98	99.94 20	99.99 19	99.99 2

fmvsm_s_conf0.1_n		99.86 10	99.85 17	99.89 11	99.93 24	99.78 57	99.07 267	99.98 13	99.99 3	99.98 14	99.90 36	99.88 11	99.92 154	99.93 25	99.99 19	99.98 5

test_cas_vis1_n_1920		99.76 46	99.86 13	99.45 259	99.93 24	98.40 398	99.30 167	99.98 13	99.94 36	99.99 7	99.89 41	99.80 21	99.97 44	99.96 9	99.97 77	99.97 10

test_vis1_n_1920		99.72 53	99.88 7	99.27 334	99.93 24	97.84 438	99.34 149	100.00 1	99.99 3	99.99 7	99.82 91	99.87 13	99.99 7	99.97 4	99.99 19	99.97 10

K. test v3		98.87 323	98.60 336	99.69 139	99.93 24	99.46 197	99.74 27	94.97 543	99.78 103	99.88 82	99.88 50	93.66 447	99.97 44	99.61 76	99.95 116	99.64 170

mvs5depth		99.88 6	99.91 3	99.80 64	99.92 29	99.42 212	99.94 3	100.00 1	99.97 25	99.89 72	99.99 12	99.63 37	99.97 44	99.87 44	99.99 19	100.00 1

SixPastTwentyTwo		99.42 163	99.30 188	99.76 87	99.92 29	99.67 120	99.70 38	99.14 442	99.65 156	99.89 72	99.90 36	96.20 398	99.94 98	99.42 111	99.92 158	99.67 135

test_fmvsmconf_n		99.85 12	99.84 20	99.88 19	99.91 31	99.73 90	98.97 305	99.98 13	99.99 3	99.96 34	99.85 68	99.93 7	99.99 7	99.94 20	99.99 19	99.93 21

test_fmvs2		99.72 53	99.85 17	99.34 309	99.91 31	98.08 425	99.48 109	100.00 1	99.90 49	99.99 7	99.91 31	99.50 62	99.98 26	99.98 1	99.99 19	99.96 13

pm-mvs1		99.79 34	99.79 34	99.78 76	99.91 31	99.83 33	99.76 23	99.87 80	99.73 113	99.89 72	99.87 56	99.63 37	99.87 259	99.54 87	99.92 158	99.63 176

TransMVSNet (Re)		99.78 37	99.77 45	99.81 54	99.91 31	99.85 21	99.75 25	99.86 89	99.70 130	99.91 62	99.89 41	99.60 44	99.87 259	99.59 78	99.74 311	99.71 104

Baseline_NR-MVSNet		99.49 132	99.37 164	99.82 46	99.91 31	99.84 26	98.83 335	99.86 89	99.68 136	99.65 227	99.88 50	97.67 323	99.87 259	99.03 191	99.86 225	99.76 86

LTVRE_ROB	99.19 1	99.88 6	99.87 11	99.88 19	99.91 31	99.90 7	99.96 1	99.92 47	99.90 49	99.97 24	99.87 56	99.81 20	99.95 81	99.54 87	99.99 19	99.80 67
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
dtuonlycased		99.24 220	99.47 132	98.56 436	99.90 37	96.17 496	97.62 483	99.85 95	99.66 151	99.86 96	99.50 346	99.39 71	99.93 120	99.55 85	99.85 232	99.59 215

PVSNet_Blended_VisFu		99.40 172	99.38 161	99.44 263	99.90 37	98.66 366	98.94 314	99.91 57	97.97 432	99.79 133	99.73 177	99.05 143	99.97 44	99.15 164	99.99 19	99.68 126

TDRefinement		99.72 53	99.70 57	99.77 80	99.90 37	99.85 21	99.86 6	99.92 47	99.69 133	99.78 139	99.92 27	99.37 78	99.88 242	98.93 213	99.95 116	99.60 208

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

APD_test1		99.36 189	99.28 197	99.61 191	99.89 40	99.89 10	99.32 158	99.74 189	99.18 263	99.69 201	99.75 164	98.41 249	99.84 315	97.85 338	99.70 333	99.10 408

EGC-MVSNET		89.05 515	85.52 518	99.64 167	99.89 40	99.78 57	99.56 87	99.52 337	24.19 555	49.96 558	99.83 83	99.15 115	99.92 154	97.71 354	99.85 232	99.21 379

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

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

XXY-MVS		99.71 56	99.67 65	99.81 54	99.89 40	99.72 95	99.59 80	99.82 122	99.39 227	99.82 112	99.84 76	99.38 76	99.91 186	99.38 115	99.93 149	99.80 67

sc_t1		99.81 28	99.80 32	99.82 46	99.88 46	99.88 12	99.83 7	99.79 152	99.94 36	99.93 53	99.92 27	99.35 84	99.92 154	99.64 73	99.94 135	99.68 126

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 46	99.86 18	99.08 262	99.97 21	99.98 18	99.96 34	99.79 121	99.90 9	99.99 7	99.96 9	99.99 19	99.90 30

fmvsm_l_conf0.5_n_a		99.80 30	99.79 34	99.84 38	99.88 46	99.64 136	99.12 245	99.91 57	99.98 18	99.95 45	99.67 235	99.67 34	99.99 7	99.94 20	99.99 19	99.88 41

fmvsm_l_conf0.5_n		99.80 30	99.78 39	99.85 32	99.88 46	99.66 123	99.11 250	99.91 57	99.98 18	99.96 34	99.64 250	99.60 44	99.99 7	99.95 14	99.99 19	99.88 41

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 54	99.88 46	99.55 173	99.17 220	99.98 13	99.99 3	99.96 34	99.84 76	99.96 3	99.99 7	99.96 9	99.99 19	99.88 41

FC-MVSNet-test		99.70 57	99.65 74	99.86 30	99.88 46	99.86 18	99.72 33	99.78 165	99.90 49	99.82 112	99.83 83	98.45 244	99.87 259	99.51 93	99.97 77	99.86 47

EU-MVSNet		99.39 176	99.62 85	98.72 422	99.88 46	96.44 488	99.56 87	99.85 95	99.90 49	99.90 67	99.85 68	98.09 290	99.83 338	99.58 81	99.95 116	99.90 30

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

Vis-MVSNet		99.75 49	99.74 53	99.79 72	99.88 46	99.66 123	99.69 45	99.92 47	99.67 144	99.77 151	99.75 164	99.61 41	99.98 26	99.35 122	99.98 54	99.72 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
E5new		99.68 64	99.67 65	99.70 133	99.87 55	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 396	99.13 174	99.96 91	99.70 107

E5		99.68 64	99.67 65	99.70 133	99.87 55	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 396	99.13 174	99.96 91	99.70 107

tt0805		99.63 86	99.57 105	99.81 54	99.87 55	99.88 12	99.58 82	98.70 469	99.72 117	99.91 62	99.60 296	99.43 67	99.81 378	99.81 51	99.53 397	99.73 95

tfpnnormal		99.43 159	99.38 161	99.60 195	99.87 55	99.75 79	99.59 80	99.78 165	99.71 123	99.90 67	99.69 216	98.85 175	99.90 205	97.25 406	99.78 287	99.15 396

SteuartSystems-ACMMP		99.30 204	99.14 224	99.76 87	99.87 55	99.66 123	99.18 215	99.60 285	98.55 367	99.57 266	99.67 235	99.03 146	99.94 98	97.01 421	99.80 273	99.69 119
Skip Steuart: Steuart Systems R&D Blog.
ELoFTR		99.25 216	99.26 202	99.21 346	99.86 60	98.66 366	99.00 292	99.93 43	98.56 365	99.83 110	99.83 83	97.34 343	99.92 154	99.03 191	100.00 1	99.04 431

casdiffseed414692147		99.68 64	99.68 63	99.67 145	99.86 60	99.65 129	99.32 158	99.87 80	99.75 111	99.77 151	99.80 109	99.61 41	99.68 467	99.21 146	99.95 116	99.67 135

usedtu_dtu_shiyan2		99.44 155	99.33 180	99.78 76	99.86 60	99.76 70	99.54 90	99.79 152	99.66 151	99.66 223	99.79 121	96.76 371	99.96 69	99.15 164	99.72 326	99.62 188

E6new		99.68 64	99.67 65	99.70 133	99.86 60	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 396	99.13 174	99.96 91	99.70 107

E6		99.68 64	99.67 65	99.70 133	99.86 60	99.62 144	99.41 122	99.84 105	99.68 136	99.77 151	99.81 98	99.59 46	99.78 396	99.13 174	99.96 91	99.70 107

FE-MVSNET2		99.68 64	99.67 65	99.72 122	99.86 60	99.68 117	99.46 116	99.88 74	99.62 165	99.87 92	99.85 68	99.06 141	99.85 298	99.44 104	99.98 54	99.63 176

viewdifsd2359ckpt11		99.62 94	99.64 79	99.56 214	99.86 60	99.19 280	99.02 281	99.93 43	99.83 82	99.88 82	99.81 98	98.99 151	99.83 338	99.48 97	99.96 91	99.65 158

viewmsd2359difaftdt		99.62 94	99.64 79	99.56 214	99.86 60	99.19 280	99.02 281	99.93 43	99.83 82	99.88 82	99.81 98	98.99 151	99.83 338	99.48 97	99.96 91	99.65 158

fmvsm_l_conf0.5_n_9		99.83 21	99.81 28	99.89 11	99.86 60	99.80 51	98.94 314	99.96 30	99.98 18	99.96 34	99.78 134	99.88 11	99.98 26	99.96 9	99.99 19	99.90 30

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

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

SSC-MVS		99.52 122	99.42 152	99.83 41	99.86 60	99.65 129	99.52 94	99.81 135	99.87 63	99.81 119	99.79 121	96.78 370	99.99 7	99.83 46	99.51 401	99.86 47

lessismore_v0				99.64 167	99.86 60	99.38 226		90.66 554		99.89 72	99.83 83	94.56 434	99.97 44	99.56 83	99.92 158	99.57 228

ACMH+	98.40 8	99.50 127	99.43 149	99.71 128	99.86 60	99.76 70	99.32 158	99.77 170	99.53 187	99.77 151	99.76 156	99.26 97	99.78 396	97.77 345	99.88 203	99.60 208

ACMH	98.42 6	99.59 101	99.54 116	99.72 122	99.86 60	99.62 144	99.56 87	99.79 152	98.77 340	99.80 126	99.85 68	99.64 35	99.85 298	98.70 252	99.89 192	99.70 107
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
hybridcas		99.65 83	99.63 82	99.70 133	99.85 75	99.67 120	99.30 167	99.87 80	99.67 144	99.81 119	99.77 146	99.21 105	99.81 378	99.24 139	99.94 135	99.61 203

AstraMVS		99.15 257	99.06 252	99.42 270	99.85 75	98.59 379	99.13 240	97.26 525	99.84 76	99.87 92	99.77 146	96.11 400	99.93 120	99.71 60	99.96 91	99.74 91

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

fmvsm_s_conf0.5_n_a		99.82 24	99.79 34	99.89 11	99.85 75	99.82 41	99.03 277	99.96 30	99.99 3	99.97 24	99.84 76	99.58 50	99.93 120	99.92 30	99.98 54	99.93 21

fmvsm_s_conf0.5_n		99.83 21	99.81 28	99.87 26	99.85 75	99.78 57	99.03 277	99.96 30	99.99 3	99.97 24	99.84 76	99.78 23	99.92 154	99.92 30	99.99 19	99.92 25

HyFIR lowres test		98.91 315	98.64 332	99.73 113	99.85 75	99.47 189	98.07 444	99.83 115	98.64 356	99.89 72	99.60 296	92.57 461	100.00 1	99.33 126	99.97 77	99.72 99

Casviewmamba		99.63 86	99.60 93	99.73 113	99.84 81	99.72 95	99.36 144	99.87 80	99.67 144	99.74 176	99.73 177	99.07 134	99.83 338	99.14 171	99.93 149	99.62 188

E4		99.61 98	99.59 96	99.66 153	99.84 81	99.53 176	99.08 262	99.84 105	99.65 156	99.74 176	99.80 109	99.45 63	99.77 409	98.93 213	99.95 116	99.69 119

viewmacassd2359aftdt		99.63 86	99.61 89	99.68 141	99.84 81	99.61 154	99.14 233	99.87 80	99.71 123	99.75 165	99.77 146	99.54 55	99.72 439	98.91 216	99.96 91	99.70 107

FE-MVSNET		99.45 151	99.36 169	99.71 128	99.84 81	99.64 136	99.16 226	99.91 57	98.65 354	99.73 182	99.73 177	98.54 225	99.82 361	98.71 250	99.96 91	99.67 135

guyue		99.12 265	99.02 268	99.41 280	99.84 81	98.56 382	99.19 211	98.30 497	99.82 86	99.84 104	99.75 164	94.84 428	99.92 154	99.68 66	99.94 135	99.74 91

KD-MVS_self_test		99.63 86	99.59 96	99.76 87	99.84 81	99.90 7	99.37 140	99.79 152	99.83 82	99.88 82	99.85 68	98.42 248	99.90 205	99.60 77	99.73 318	99.49 282

FIs		99.65 83	99.58 100	99.84 38	99.84 81	99.85 21	99.66 57	99.75 183	99.86 66	99.74 176	99.79 121	98.27 269	99.85 298	99.37 118	99.93 149	99.83 59

XVG-OURS-SEG-HR		99.16 253	98.99 285	99.66 153	99.84 81	99.64 136	98.25 422	99.73 194	98.39 387	99.63 238	99.43 367	99.70 31	99.90 205	97.34 391	98.64 492	99.44 312

PMVS	92.94 21	98.82 329	98.81 316	98.85 407	99.84 81	97.99 428	99.20 205	99.47 354	99.71 123	99.42 321	99.82 91	98.09 290	99.47 514	93.88 526	99.85 232	99.07 426
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
fmvsm_s_conf0.5_n_9		99.82 24	99.82 25	99.82 46	99.83 90	99.59 160	98.97 305	99.92 47	99.99 3	99.97 24	99.84 76	99.90 9	99.94 98	99.94 20	99.99 19	99.92 25

LuminaMVS		99.39 176	99.28 197	99.73 113	99.83 90	99.49 184	99.00 292	99.05 449	99.81 92	99.89 72	99.79 121	96.54 380	99.97 44	99.64 73	99.98 54	99.73 95

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

MP-MVS-pluss		99.14 259	98.92 300	99.80 64	99.83 90	99.83 33	98.61 371	99.63 262	96.84 496	99.44 314	99.58 309	98.81 177	99.91 186	97.70 357	99.82 256	99.67 135
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PM-MVS		99.36 189	99.29 194	99.58 202	99.83 90	99.66 123	98.95 312	99.86 89	98.85 322	99.81 119	99.73 177	98.40 253	99.92 154	98.36 286	99.83 246	99.17 392

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

HPM-MVS_fast		99.43 159	99.30 188	99.80 64	99.83 90	99.81 47	99.52 94	99.70 216	98.35 397	99.51 297	99.50 346	99.31 89	99.88 242	98.18 305	99.84 238	99.69 119

RPSCF		99.18 246	99.02 268	99.64 167	99.83 90	99.85 21	99.44 119	99.82 122	98.33 404	99.50 300	99.78 134	97.90 304	99.65 485	96.78 438	99.83 246	99.44 312

COLMAP_ROB	98.06 12	99.45 151	99.37 164	99.70 133	99.83 90	99.70 109	99.38 132	99.78 165	99.53 187	99.67 216	99.78 134	99.19 108	99.86 279	97.32 393	99.87 217	99.55 236
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DKM-HiRes		98.95 310	98.73 322	99.62 184	99.82 99	99.47 189	98.50 395	99.81 135	99.41 222	97.76 508	99.58 309	95.04 425	99.83 338	98.89 217	99.76 296	99.58 221

tt0320-xc		99.82 24	99.82 25	99.82 46	99.82 99	99.84 26	99.82 10	99.92 47	99.94 36	99.94 48	99.93 22	99.34 85	99.92 154	99.70 61	99.96 91	99.70 107

tt0320		99.79 34	99.79 34	99.81 54	99.82 99	99.84 26	99.82 10	99.90 64	99.94 36	99.94 48	99.94 19	99.07 134	99.92 154	99.68 66	99.97 77	99.67 135

fmvsm_s_conf0.5_n_8		99.76 46	99.72 55	99.88 19	99.82 99	99.75 79	99.02 281	99.87 80	99.98 18	99.98 14	99.81 98	99.07 134	99.97 44	99.91 33	99.99 19	99.92 25

fmvsm_s_conf0.5_n_2		99.78 37	99.75 51	99.88 19	99.82 99	99.76 70	98.88 323	99.92 47	99.98 18	99.98 14	99.85 68	99.42 69	99.94 98	99.93 25	99.98 54	99.94 18

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 41	99.82 99	99.70 109	99.17 220	99.97 21	99.99 3	99.96 34	99.82 91	99.94 4	100.00 1	99.95 14	100.00 1	99.80 67

TSAR-MVS + MP.		99.34 196	99.24 208	99.63 175	99.82 99	99.37 231	99.26 187	99.35 391	98.77 340	99.57 266	99.70 207	99.27 96	99.88 242	97.71 354	99.75 304	99.65 158
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
new-patchmatchnet		99.35 191	99.57 105	98.71 426	99.82 99	96.62 484	98.55 386	99.75 183	99.50 192	99.88 82	99.87 56	99.31 89	99.88 242	99.43 106	100.00 1	99.62 188

VPNet		99.46 147	99.37 164	99.71 128	99.82 99	99.59 160	99.48 109	99.70 216	99.81 92	99.69 201	99.58 309	97.66 327	99.86 279	99.17 159	99.44 413	99.67 135

XVG-OURS		99.21 237	99.06 252	99.65 160	99.82 99	99.62 144	97.87 466	99.74 189	98.36 391	99.66 223	99.68 229	99.71 28	99.90 205	96.84 435	99.88 203	99.43 319

XVG-ACMP-BASELINE		99.23 223	99.10 242	99.63 175	99.82 99	99.58 165	98.83 335	99.72 203	98.36 391	99.60 258	99.71 197	98.92 164	99.91 186	97.08 419	99.84 238	99.40 328

LPG-MVS_test		99.22 232	99.05 259	99.74 103	99.82 99	99.63 142	99.16 226	99.73 194	97.56 459	99.64 233	99.69 216	99.37 78	99.89 227	96.66 445	99.87 217	99.69 119

LGP-MVS_train				99.74 103	99.82 99	99.63 142		99.73 194	97.56 459	99.64 233	99.69 216	99.37 78	99.89 227	96.66 445	99.87 217	99.69 119

RoMa-HiRes		99.38 179	99.30 188	99.64 167	99.81 112	99.47 189	99.11 250	99.94 41	99.03 292	99.55 279	99.56 321	97.71 318	99.92 154	99.19 152	99.77 291	99.54 248

PMatch-Up-SfM		99.08 275	99.02 268	99.27 334	99.81 112	99.04 310	98.13 435	99.83 115	99.16 272	99.26 367	99.69 216	97.22 349	99.83 338	98.67 257	99.43 417	98.94 449

fmvsm_s_conf0.5_n_11		99.76 46	99.75 51	99.81 54	99.81 112	99.53 176	99.15 229	99.89 68	99.99 3	99.98 14	99.86 63	99.13 120	99.98 26	99.93 25	99.99 19	99.92 25

fmvsm_s_conf0.5_n_10		99.77 44	99.73 54	99.88 19	99.81 112	99.75 79	99.06 268	99.85 95	99.99 3	99.97 24	99.84 76	99.12 123	99.98 26	99.95 14	99.99 19	99.90 30

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 32	99.81 112	99.71 101	98.97 305	99.92 47	99.98 18	99.97 24	99.86 63	99.53 58	99.95 81	99.88 41	99.99 19	99.89 38

WB-MVS		99.44 155	99.32 181	99.80 64	99.81 112	99.61 154	99.47 112	99.81 135	99.82 86	99.71 193	99.72 187	96.60 376	99.98 26	99.75 56	99.23 446	99.82 66

MTAPA		99.35 191	99.20 214	99.80 64	99.81 112	99.81 47	99.33 155	99.53 332	99.27 246	99.42 321	99.63 266	98.21 277	99.95 81	97.83 344	99.79 279	99.65 158

v10		99.69 59	99.69 60	99.66 153	99.81 112	99.39 224	99.66 57	99.75 183	99.60 177	99.92 59	99.87 56	98.75 190	99.86 279	99.90 37	99.99 19	99.73 95

HPM-MVS		99.25 216	99.07 250	99.78 76	99.81 112	99.75 79	99.61 73	99.67 235	97.72 454	99.35 342	99.25 424	99.23 103	99.92 154	97.21 409	99.82 256	99.67 135
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
casdiffmvs_mvg		99.68 64	99.68 63	99.69 139	99.81 112	99.59 160	99.29 175	99.90 64	99.71 123	99.79 133	99.73 177	99.54 55	99.84 315	99.36 119	99.96 91	99.65 158
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IterMVS-LS		99.41 170	99.47 132	99.25 341	99.81 112	98.09 421	98.85 329	99.76 178	99.62 165	99.83 110	99.64 250	98.54 225	99.97 44	99.15 164	99.99 19	99.68 126
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewdifsd2359ckpt07		99.51 124	99.50 125	99.52 234	99.80 123	99.19 280	98.92 318	99.88 74	99.72 117	99.64 233	99.62 276	99.06 141	99.81 378	98.96 204	99.94 135	99.56 232

diffmvs_AUTHOR		99.48 135	99.48 130	99.47 252	99.80 123	98.89 337	98.71 360	99.82 122	99.79 100	99.66 223	99.63 266	98.87 173	99.88 242	99.13 174	99.95 116	99.62 188

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

sd_testset		99.78 37	99.78 39	99.80 64	99.80 123	99.76 70	99.80 14	99.79 152	99.97 25	99.89 72	99.89 41	99.53 58	99.99 7	99.36 119	99.96 91	99.65 158

v1240		99.56 106	99.58 100	99.51 238	99.80 123	99.00 313	99.00 292	99.65 250	99.15 277	99.90 67	99.75 164	99.09 127	99.88 242	99.90 37	99.96 91	99.67 135

v8		99.68 64	99.69 60	99.65 160	99.80 123	99.40 220	99.66 57	99.76 178	99.64 160	99.93 53	99.85 68	98.66 204	99.84 315	99.88 41	99.99 19	99.71 104

MDA-MVSNet-bldmvs		99.06 280	99.05 259	99.07 371	99.80 123	97.83 439	98.89 321	99.72 203	99.29 242	99.63 238	99.70 207	96.47 382	99.89 227	98.17 307	99.82 256	99.50 277

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

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

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

baseline		99.63 86	99.62 85	99.66 153	99.80 123	99.62 144	99.44 119	99.80 143	99.71 123	99.72 188	99.69 216	99.15 115	99.83 338	99.32 128	99.94 135	99.53 257

IS-MVSNet		99.03 288	98.85 309	99.55 221	99.80 123	99.25 259	99.73 30	99.15 440	99.37 229	99.61 255	99.71 197	94.73 431	99.81 378	97.70 357	99.88 203	99.58 221

EPP-MVSNet		99.17 251	99.00 278	99.66 153	99.80 123	99.43 209	99.70 38	99.24 423	99.48 197	99.56 274	99.77 146	94.89 427	99.93 120	98.72 248	99.89 192	99.63 176

ACMM	98.09 11	99.46 147	99.38 161	99.72 122	99.80 123	99.69 114	99.13 240	99.65 250	98.99 297	99.64 233	99.72 187	99.39 71	99.86 279	98.23 298	99.81 266	99.60 208
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMatch-SfM		98.91 315	98.81 316	99.22 345	99.79 137	98.89 337	98.18 426	99.61 273	99.18 263	99.03 408	99.61 286	96.13 399	99.80 388	98.71 250	99.04 461	98.99 442

fmvsm_s_conf0.5_n_5		99.78 37	99.76 49	99.85 32	99.79 137	99.72 95	98.84 332	99.96 30	99.96 28	99.96 34	99.72 187	99.71 28	99.99 7	99.93 25	99.98 54	99.85 50

dcpmvs_2		99.61 98	99.64 79	99.53 232	99.79 137	98.82 348	99.58 82	99.97 21	99.95 32	99.96 34	99.76 156	98.44 245	99.99 7	99.34 123	99.96 91	99.78 77

v1144		99.54 116	99.53 120	99.59 198	99.79 137	99.28 250	99.10 254	99.61 273	99.20 260	99.84 104	99.73 177	98.67 202	99.84 315	99.86 45	99.98 54	99.64 170

V42		99.56 106	99.54 116	99.63 175	99.79 137	99.46 197	99.39 129	99.59 291	99.24 253	99.86 96	99.70 207	98.55 220	99.82 361	99.79 53	99.95 116	99.60 208

test20.03		99.55 111	99.54 116	99.58 202	99.79 137	99.37 231	99.02 281	99.89 68	99.60 177	99.82 112	99.62 276	98.81 177	99.89 227	99.43 106	99.86 225	99.47 290

casdiffmvs		99.63 86	99.61 89	99.67 145	99.79 137	99.59 160	99.13 240	99.85 95	99.79 100	99.76 160	99.72 187	99.33 87	99.82 361	99.21 146	99.94 135	99.59 215
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_0402		99.22 232	99.14 224	99.45 259	99.79 137	99.43 209	99.28 177	99.68 230	99.54 185	99.40 332	99.56 321	99.07 134	99.82 361	96.01 480	99.96 91	99.11 405

ACMMP		99.25 216	99.08 246	99.74 103	99.79 137	99.68 117	99.50 102	99.65 250	98.07 425	99.52 290	99.69 216	98.57 216	99.92 154	97.18 414	99.79 279	99.63 176
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
E2		99.54 116	99.51 122	99.62 184	99.78 146	99.47 189	99.01 286	99.82 122	99.55 183	99.69 201	99.77 146	99.26 97	99.76 416	98.82 225	99.93 149	99.62 188

E3		99.54 116	99.51 122	99.62 184	99.78 146	99.47 189	99.01 286	99.82 122	99.55 183	99.69 201	99.77 146	99.25 101	99.76 416	98.82 225	99.93 149	99.62 188

NormalMVS		99.09 274	98.91 304	99.62 184	99.78 146	99.11 295	99.36 144	99.77 170	99.82 86	99.68 208	99.53 335	93.30 450	99.99 7	99.24 139	99.76 296	99.74 91

lecture		99.56 106	99.48 130	99.81 54	99.78 146	99.86 18	99.50 102	99.70 216	99.59 179	99.75 165	99.71 197	98.94 160	99.92 154	98.59 265	99.76 296	99.66 149

fmvsm_s_conf0.5_n_6		99.80 30	99.78 39	99.85 32	99.78 146	99.78 57	99.00 292	99.97 21	99.96 28	99.97 24	99.56 321	99.92 8	99.93 120	99.91 33	99.99 19	99.83 59

MSP-MVS		99.04 287	98.79 320	99.81 54	99.78 146	99.73 90	99.35 148	99.57 303	98.54 370	99.54 283	98.99 467	96.81 369	99.93 120	96.97 424	99.53 397	99.77 81
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
v144192		99.55 111	99.54 116	99.58 202	99.78 146	99.20 277	99.11 250	99.62 265	99.18 263	99.89 72	99.72 187	98.66 204	99.87 259	99.88 41	99.97 77	99.66 149

AllTest		99.21 237	99.07 250	99.63 175	99.78 146	99.64 136	99.12 245	99.83 115	98.63 357	99.63 238	99.72 187	98.68 199	99.75 427	96.38 465	99.83 246	99.51 271

TestCases				99.63 175	99.78 146	99.64 136		99.83 115	98.63 357	99.63 238	99.72 187	98.68 199	99.75 427	96.38 465	99.83 246	99.51 271

v2v482		99.50 127	99.47 132	99.58 202	99.78 146	99.25 259	99.14 233	99.58 300	99.25 251	99.81 119	99.62 276	98.24 271	99.84 315	99.83 46	99.97 77	99.64 170

FMVSNet1		99.66 77	99.63 82	99.73 113	99.78 146	99.77 63	99.68 48	99.70 216	99.67 144	99.82 112	99.83 83	98.98 155	99.90 205	99.24 139	99.97 77	99.53 257

Vis-MVSNet (Re-imp)		98.77 335	98.58 341	99.34 309	99.78 146	98.88 339	99.61 73	99.56 308	99.11 283	99.24 372	99.56 321	93.00 457	99.78 396	97.43 386	99.89 192	99.35 344

ACMP	97.51 14	99.05 284	98.84 311	99.67 145	99.78 146	99.55 173	98.88 323	99.66 240	97.11 487	99.47 307	99.60 296	99.07 134	99.89 227	96.18 475	99.85 232	99.58 221
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RoMa-SfM		99.32 201	99.23 211	99.59 198	99.77 159	99.53 176	98.89 321	99.88 74	98.78 337	99.65 227	99.52 339	97.78 314	99.90 205	98.96 204	99.86 225	99.35 344

pmmvs-eth3d		99.48 135	99.47 132	99.51 238	99.77 159	99.41 219	98.81 340	99.66 240	99.42 221	99.75 165	99.66 241	99.20 107	99.76 416	98.98 199	99.99 19	99.36 341

Patchmatch-RL test		98.60 354	98.36 373	99.33 312	99.77 159	99.07 305	98.27 419	99.87 80	98.91 314	99.74 176	99.72 187	90.57 495	99.79 392	98.55 270	99.85 232	99.11 405

v1192		99.57 102	99.57 105	99.57 210	99.77 159	99.22 270	99.04 274	99.60 285	99.18 263	99.87 92	99.72 187	99.08 131	99.85 298	99.89 40	99.98 54	99.66 149

EG-PatchMatch MVS		99.57 102	99.56 110	99.62 184	99.77 159	99.33 241	99.26 187	99.76 178	99.32 238	99.80 126	99.78 134	99.29 91	99.87 259	99.15 164	99.91 172	99.66 149

DenseAffine		99.17 251	99.06 252	99.49 244	99.76 164	99.33 241	98.43 407	99.97 21	99.11 283	99.17 386	99.61 286	97.05 359	99.76 416	98.56 269	99.88 203	99.38 334

dtuplus		99.52 122	99.55 112	99.43 267	99.76 164	98.90 334	98.71 360	99.89 68	99.67 144	99.79 133	99.77 146	99.25 101	99.81 378	99.18 155	99.96 91	99.57 228

aaatest				99.74 103	99.76 164	99.65 129	99.38 132	99.78 165	99.58 181	99.81 119	99.66 241		99.90 205	97.69 363	99.79 279	99.67 135

MED-MVS		99.51 124	99.42 152	99.80 64	99.76 164	99.65 129	99.38 132	99.78 165	99.77 108	99.81 119	99.78 134	99.02 147	99.90 205	97.69 363	99.76 296	99.85 50

TestfortrainingZip a		99.55 111	99.45 141	99.85 32	99.76 164	99.82 41	99.38 132	99.62 265	99.77 108	99.87 92	99.78 134	98.12 287	99.88 242	98.96 204	99.77 291	99.85 50

SSM_0404		99.57 102	99.58 100	99.54 227	99.76 164	99.28 250	99.19 211	99.84 105	99.80 96	99.78 139	99.70 207	99.44 65	99.93 120	98.74 241	99.95 116	99.41 325

ttmdpeth		99.48 135	99.55 112	99.29 326	99.76 164	98.16 415	99.33 155	99.95 38	99.79 100	99.36 338	99.89 41	99.13 120	99.77 409	99.09 182	99.64 360	99.93 21

GeoE		99.69 59	99.66 72	99.78 76	99.76 164	99.76 70	99.60 79	99.82 122	99.46 205	99.75 165	99.56 321	99.63 37	99.95 81	99.43 106	99.88 203	99.62 188

ZNCC-MVS		99.22 232	99.04 265	99.77 80	99.76 164	99.73 90	99.28 177	99.56 308	98.19 414	99.14 392	99.29 414	98.84 176	99.92 154	97.53 380	99.80 273	99.64 170

tttt0517		97.62 434	97.20 448	98.90 402	99.76 164	97.40 461	99.48 109	94.36 545	99.06 289	99.70 197	99.49 351	84.55 524	99.94 98	98.73 246	99.65 358	99.36 341

pmmvs5		99.19 242	99.11 233	99.42 270	99.76 164	98.88 339	98.55 386	99.73 194	98.82 329	99.72 188	99.62 276	96.56 377	99.82 361	99.32 128	99.95 116	99.56 232

nrg030		99.70 57	99.66 72	99.82 46	99.76 164	99.84 26	99.61 73	99.70 216	99.93 43	99.78 139	99.68 229	99.10 125	99.78 396	99.45 103	99.96 91	99.83 59

v148		99.40 172	99.41 156	99.39 286	99.76 164	98.94 326	99.09 259	99.59 291	99.17 270	99.81 119	99.61 286	98.41 249	99.69 455	99.32 128	99.94 135	99.53 257

region2R		99.23 223	99.05 259	99.77 80	99.76 164	99.70 109	99.31 164	99.59 291	98.41 384	99.32 351	99.36 394	98.73 194	99.93 120	97.29 397	99.74 311	99.67 135

MP-MVS		99.06 280	98.83 313	99.76 87	99.76 164	99.71 101	99.32 158	99.50 346	98.35 397	98.97 413	99.48 355	98.37 255	99.92 154	95.95 486	99.75 304	99.63 176
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMMVS2		99.48 135	99.45 141	99.57 210	99.76 164	98.99 315	98.09 441	99.90 64	98.95 304	99.78 139	99.58 309	99.57 52	99.93 120	99.48 97	99.95 116	99.79 75

CP-MVSNet		99.54 116	99.43 149	99.87 26	99.76 164	99.82 41	99.57 85	99.61 273	99.54 185	99.80 126	99.64 250	97.79 313	99.95 81	99.21 146	99.94 135	99.84 55

mPP-MVS		99.19 242	99.00 278	99.76 87	99.76 164	99.68 117	99.38 132	99.54 321	98.34 401	99.01 410	99.50 346	98.53 230	99.93 120	97.18 414	99.78 287	99.66 149

viewmamba		99.49 132	99.51 122	99.42 270	99.75 182	98.90 334	98.85 329	99.85 95	99.69 133	99.73 182	99.67 235	98.79 182	99.82 361	99.28 136	99.95 116	99.54 248

hybridnocas07		99.43 159	99.44 146	99.39 286	99.75 182	98.85 345	98.76 349	99.85 95	99.71 123	99.70 197	99.68 229	98.47 239	99.77 409	99.13 174	99.95 116	99.55 236

hybrid		99.42 163	99.43 149	99.37 295	99.75 182	98.77 355	98.72 357	99.84 105	99.61 170	99.65 227	99.68 229	98.53 230	99.79 392	99.16 163	99.94 135	99.54 248

fmvsm_s_conf0.5_n_4		99.78 37	99.78 39	99.79 72	99.75 182	99.56 169	98.98 303	99.94 41	99.92 45	99.97 24	99.72 187	99.84 16	99.92 154	99.91 33	99.98 54	99.89 38

SSC-MVS3.2		99.64 85	99.67 65	99.56 214	99.75 182	98.98 317	98.96 309	99.87 80	99.88 61	99.84 104	99.64 250	99.32 88	99.91 186	99.78 54	99.96 91	99.80 67

IterMVS-SCA-FT		99.00 300	99.16 219	98.51 437	99.75 182	95.90 502	98.07 444	99.84 105	99.84 76	99.89 72	99.73 177	96.01 403	99.99 7	99.33 126	100.00 1	99.63 176

ACMMP_NAP		99.28 208	99.11 233	99.79 72	99.75 182	99.81 47	98.95 312	99.53 332	98.27 409	99.53 288	99.73 177	98.75 190	99.87 259	97.70 357	99.83 246	99.68 126

v1921920		99.56 106	99.57 105	99.55 221	99.75 182	99.11 295	99.05 269	99.61 273	99.15 277	99.88 82	99.71 197	99.08 131	99.87 259	99.90 37	99.97 77	99.66 149

testgi		99.29 206	99.26 202	99.37 295	99.75 182	98.81 349	98.84 332	99.89 68	98.38 389	99.75 165	99.04 460	99.36 81	99.86 279	99.08 184	99.25 442	99.45 297

PGM-MVS		99.20 239	99.01 274	99.77 80	99.75 182	99.71 101	99.16 226	99.72 203	97.99 430	99.42 321	99.60 296	98.81 177	99.93 120	96.91 428	99.74 311	99.66 149

jason		99.16 253	99.11 233	99.32 317	99.75 182	98.44 395	98.26 421	99.39 380	98.70 348	99.74 176	99.30 410	98.54 225	99.97 44	98.48 275	99.82 256	99.55 236
jason: jason.
fmvsm_s_conf0.5_n_7		99.73 52	99.78 39	99.60 195	99.74 193	98.93 329	98.85 329	99.96 30	99.96 28	99.97 24	99.76 156	99.82 18	99.96 69	99.95 14	99.98 54	99.90 30

Anonymous20231206		99.35 191	99.31 183	99.47 252	99.74 193	99.06 307	99.28 177	99.74 189	99.23 255	99.72 188	99.53 335	97.63 332	99.88 242	99.11 180	99.84 238	99.48 286

ACMMPR		99.23 223	99.06 252	99.76 87	99.74 193	99.69 114	99.31 164	99.59 291	98.36 391	99.35 342	99.38 385	98.61 210	99.93 120	97.43 386	99.75 304	99.67 135

IterMVS		98.97 304	99.16 219	98.42 442	99.74 193	95.64 509	98.06 446	99.83 115	99.83 82	99.85 101	99.74 172	96.10 402	99.99 7	99.27 138	100.00 1	99.63 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewcassd2359sk11		99.48 135	99.45 141	99.58 202	99.73 197	99.42 212	98.96 309	99.80 143	99.44 210	99.63 238	99.74 172	99.09 127	99.76 416	98.72 248	99.91 172	99.57 228

viewmanbaseed2359cas		99.50 127	99.47 132	99.61 191	99.73 197	99.52 181	99.03 277	99.83 115	99.49 194	99.65 227	99.64 250	99.18 109	99.71 444	98.73 246	99.92 158	99.58 221

GST-MVS		99.16 253	98.96 292	99.75 98	99.73 197	99.73 90	99.20 205	99.55 315	98.22 411	99.32 351	99.35 399	98.65 206	99.91 186	96.86 431	99.74 311	99.62 188

HFP-MVS		99.25 216	99.08 246	99.76 87	99.73 197	99.70 109	99.31 164	99.59 291	98.36 391	99.36 338	99.37 389	98.80 181	99.91 186	97.43 386	99.75 304	99.68 126

114514_t		98.49 370	98.11 399	99.64 167	99.73 197	99.58 165	99.24 194	99.76 178	89.94 544	99.42 321	99.56 321	97.76 317	99.86 279	97.74 350	99.82 256	99.47 290

dtuonly		98.93 314	99.11 233	98.38 445	99.72 202	95.75 506	97.07 512	99.91 57	99.04 290	99.65 227	99.41 371	98.32 263	99.83 338	98.97 201	99.90 176	99.55 236

viewdifsd2359ckpt13		99.42 163	99.37 164	99.57 210	99.72 202	99.46 197	99.01 286	99.80 143	99.20 260	99.51 297	99.60 296	98.92 164	99.70 448	98.65 261	99.90 176	99.55 236

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

N_pmnet		98.73 340	98.53 348	99.35 305	99.72 202	98.67 363	98.34 412	94.65 544	98.35 397	99.79 133	99.68 229	98.03 294	99.93 120	98.28 292	99.92 158	99.44 312

DeepC-MVS	98.90 4	99.62 94	99.61 89	99.67 145	99.72 202	99.44 205	99.24 194	99.71 207	99.27 246	99.93 53	99.90 36	99.70 31	99.93 120	98.99 197	99.99 19	99.64 170
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DKM		99.12 265	98.98 288	99.54 227	99.71 207	99.48 188	98.53 391	99.88 74	99.18 263	98.99 412	99.64 250	96.25 395	99.75 427	98.66 258	99.93 149	99.40 328

mamba_0408		99.54 116	99.55 112	99.54 227	99.71 207	99.24 264	99.27 182	99.79 152	99.72 117	99.78 139	99.64 250	99.36 81	99.93 120	98.74 241	99.90 176	99.45 297

icg_test_0407_2		99.30 204	99.29 194	99.31 321	99.71 207	98.55 384	98.17 429	99.71 207	99.41 222	99.73 182	99.60 296	99.17 111	99.92 154	98.45 278	99.70 333	99.45 297

SSM_04072		99.55 111	99.55 112	99.55 221	99.71 207	99.24 264	99.27 182	99.79 152	99.72 117	99.78 139	99.64 250	99.36 81	99.97 44	98.74 241	99.90 176	99.45 297

SSM_0407		99.56 106	99.56 110	99.54 227	99.71 207	99.24 264	99.15 229	99.84 105	99.80 96	99.78 139	99.70 207	99.44 65	99.93 120	98.74 241	99.90 176	99.45 297

IMVS_0407		99.38 179	99.42 152	99.28 329	99.71 207	98.55 384	99.27 182	99.71 207	99.41 222	99.73 182	99.60 296	99.17 111	99.83 338	98.45 278	99.70 333	99.45 297

IMVS_0404		99.23 223	99.20 214	99.32 317	99.71 207	98.55 384	98.57 382	99.71 207	99.41 222	99.52 290	99.60 296	98.12 287	99.95 81	98.45 278	99.70 333	99.45 297

IMVS_0403		99.37 184	99.39 158	99.28 329	99.71 207	98.55 384	99.19 211	99.71 207	99.41 222	99.67 216	99.60 296	99.12 123	99.84 315	98.45 278	99.70 333	99.45 297

test_vis1_rt		99.45 151	99.46 138	99.41 280	99.71 207	98.63 375	98.99 300	99.96 30	99.03 292	99.95 45	99.12 449	98.75 190	99.84 315	99.82 50	99.82 256	99.77 81

XVS		99.27 212	99.11 233	99.75 98	99.71 207	99.71 101	99.37 140	99.61 273	99.29 242	98.76 440	99.47 359	98.47 239	99.88 242	97.62 371	99.73 318	99.67 135

X-MVStestdata		96.09 488	94.87 504	99.75 98	99.71 207	99.71 101	99.37 140	99.61 273	99.29 242	98.76 440	61.30 565	98.47 239	99.88 242	97.62 371	99.73 318	99.67 135

VDDNet		98.97 304	98.82 314	99.42 270	99.71 207	98.81 349	99.62 67	98.68 470	99.81 92	99.38 335	99.80 109	94.25 438	99.85 298	98.79 232	99.32 431	99.59 215

DSMNet-mixed		99.48 135	99.65 74	98.95 384	99.71 207	97.27 466	99.50 102	99.82 122	99.59 179	99.41 327	99.85 68	99.62 40	100.00 1	99.53 90	99.89 192	99.59 215

EC-MVSNet		99.69 59	99.69 60	99.68 141	99.71 207	99.91 4	99.76 23	99.96 30	99.86 66	99.51 297	99.39 382	99.57 52	99.93 120	99.64 73	99.86 225	99.20 384

CSCG		99.37 184	99.29 194	99.60 195	99.71 207	99.46 197	99.43 121	99.85 95	98.79 335	99.41 327	99.60 296	98.92 164	99.92 154	98.02 317	99.92 158	99.43 319

LF4IMVS		99.01 297	98.92 300	99.27 334	99.71 207	99.28 250	98.59 376	99.77 170	98.32 405	99.39 334	99.41 371	98.62 208	99.84 315	96.62 451	99.84 238	98.69 476

LoFTR		99.29 206	99.26 202	99.36 301	99.70 223	99.05 308	98.66 366	99.95 38	98.85 322	99.86 96	99.75 164	98.14 284	99.93 120	98.54 272	99.91 172	99.10 408

SIFT-PointCN		98.28 389	98.47 355	97.71 480	99.70 223	98.91 333	96.98 516	99.70 216	97.90 440	99.36 338	99.35 399	95.51 416	99.83 338	97.84 343	99.89 192	94.39 536

viewdifsd2359ckpt09		99.24 220	99.16 219	99.49 244	99.70 223	99.22 270	98.88 323	99.81 135	98.70 348	99.38 335	99.37 389	98.22 276	99.76 416	98.48 275	99.88 203	99.51 271

viewmambaseed2359dif		99.47 145	99.50 125	99.37 295	99.70 223	98.80 352	98.67 364	99.92 47	99.49 194	99.77 151	99.71 197	99.08 131	99.78 396	99.20 150	99.94 135	99.54 248

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

test_0728_SECOND				99.83 41	99.70 223	99.79 54	99.14 233	99.61 273					99.92 154	97.88 331	99.72 326	99.77 81

OPM-MVS		99.26 214	99.13 226	99.63 175	99.70 223	99.61 154	98.58 378	99.48 351	98.50 376	99.52 290	99.63 266	99.14 118	99.76 416	97.89 330	99.77 291	99.51 271
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
new_pmnet		98.88 322	98.89 305	98.84 409	99.70 223	97.62 448	98.15 432	99.50 346	97.98 431	99.62 248	99.54 332	98.15 283	99.94 98	97.55 377	99.84 238	98.95 446

onestephybrid01		99.45 151	99.46 138	99.42 270	99.69 231	98.88 339	98.76 349	99.81 135	99.78 103	99.67 216	99.73 177	98.61 210	99.84 315	99.17 159	99.93 149	99.52 268

SED-MVS		99.40 172	99.28 197	99.77 80	99.69 231	99.82 41	99.20 205	99.54 321	99.13 279	99.82 112	99.63 266	98.91 167	99.92 154	97.85 338	99.70 333	99.58 221

IU-MVS					99.69 231	99.77 63		99.22 427	97.50 465	99.69 201				97.75 349	99.70 333	99.77 81

test_241102_ONE					99.69 231	99.82 41		99.54 321	99.12 282	99.82 112	99.49 351	98.91 167	99.52 510

D2MVS		99.22 232	99.19 216	99.29 326	99.69 231	98.74 358	98.81 340	99.41 370	98.55 367	99.68 208	99.69 216	98.13 285	99.87 259	98.82 225	99.98 54	99.24 371

DVP-MVS		99.32 201	99.17 218	99.77 80	99.69 231	99.80 51	99.14 233	99.31 406	99.16 272	99.62 248	99.61 286	98.35 257	99.91 186	97.88 331	99.72 326	99.61 203
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					99.69 231	99.80 51	99.24 194	99.57 303	99.16 272	99.73 182	99.65 248	98.35 257

wuyk23d		97.58 436	99.13 226	92.93 531	99.69 231	99.49 184	99.52 94	99.77 170	97.97 432	99.96 34	99.79 121	99.84 16	99.94 98	95.85 490	99.82 256	79.36 552

DeepMVS_CX				97.98 464	99.69 231	96.95 474		99.26 416	75.51 550	95.74 539	98.28 513	96.47 382	99.62 490	91.23 534	97.89 522	97.38 525

E3new		99.42 163	99.37 164	99.56 214	99.68 240	99.38 226	98.93 317	99.79 152	99.30 241	99.55 279	99.69 216	98.88 171	99.76 416	98.63 263	99.89 192	99.53 257

MVSMamba_PlusPlus		99.55 111	99.58 100	99.47 252	99.68 240	99.40 220	99.52 94	99.70 216	99.92 45	99.77 151	99.86 63	98.28 267	99.96 69	99.54 87	99.90 176	99.05 428

thisisatest0530		97.45 444	96.95 458	98.94 386	99.68 240	97.73 444	99.09 259	94.19 547	98.61 362	99.56 274	99.30 410	84.30 526	99.93 120	98.27 294	99.54 395	99.16 394

VPA-MVSNet		99.66 77	99.62 85	99.79 72	99.68 240	99.75 79	99.62 67	99.69 225	99.85 72	99.80 126	99.81 98	98.81 177	99.91 186	99.47 100	99.88 203	99.70 107

UnsupCasMVSNet_eth		98.83 328	98.57 342	99.59 198	99.68 240	99.45 203	98.99 300	99.67 235	99.48 197	99.55 279	99.36 394	94.92 426	99.86 279	98.95 211	96.57 535	99.45 297

Test_1112_low_res		98.95 310	98.73 322	99.63 175	99.68 240	99.15 289	98.09 441	99.80 143	97.14 485	99.46 311	99.40 377	96.11 400	99.89 227	99.01 196	99.84 238	99.84 55

MVE	92.54 22	96.66 470	96.11 477	98.31 451	99.68 240	97.55 450	97.94 460	95.60 542	99.37 229	90.68 550	98.70 496	96.56 377	98.61 541	86.94 547	99.55 390	98.77 472
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NCMNet		98.18 401	98.46 357	97.36 495	99.67 247	99.19 280	96.33 534	98.99 455	98.83 327	99.62 248	99.63 266	95.41 420	99.33 521	97.64 369	100.00 1	93.54 548

VortexMVS		99.13 262	99.24 208	98.79 415	99.67 247	96.60 486	99.24 194	99.80 143	99.85 72	99.93 53	99.84 76	95.06 424	99.89 227	99.80 52	99.98 54	99.89 38

diffmvs		99.34 196	99.32 181	99.39 286	99.67 247	98.77 355	98.57 382	99.81 135	99.61 170	99.48 305	99.41 371	98.47 239	99.86 279	98.97 201	99.90 176	99.53 257
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
our_test_3		98.85 327	99.09 244	98.13 460	99.66 250	94.90 523	97.72 474	99.58 300	99.07 287	99.64 233	99.62 276	98.19 280	99.93 120	98.41 283	99.95 116	99.55 236

ppachtmachnet_test		98.89 321	99.12 230	98.20 458	99.66 250	95.24 518	97.63 481	99.68 230	99.08 285	99.78 139	99.62 276	98.65 206	99.88 242	98.02 317	99.96 91	99.48 286

CP-MVS		99.23 223	99.05 259	99.75 98	99.66 250	99.66 123	99.38 132	99.62 265	98.38 389	99.06 405	99.27 418	98.79 182	99.94 98	97.51 381	99.82 256	99.66 149

1112_ss		99.05 284	98.84 311	99.67 145	99.66 250	99.29 248	98.52 393	99.82 122	97.65 457	99.43 318	99.16 442	96.42 384	99.91 186	99.07 187	99.84 238	99.80 67

SIFT-PCN-Cal		98.24 394	98.51 350	97.43 490	99.65 254	98.64 373	97.09 509	99.35 391	98.16 416	99.69 201	99.52 339	95.59 411	99.83 338	97.57 376	100.00 1	93.81 544

SymmetryMVS		99.01 297	98.82 314	99.58 202	99.65 254	99.11 295	99.36 144	99.20 433	99.82 86	99.68 208	99.53 335	93.30 450	99.99 7	99.24 139	99.63 364	99.64 170

test-260524					99.64 256	99.70 109		99.58 300		99.69 201		97.64 330	99.87 259	98.68 255	99.76 296

SIFT-NN-NCMNet		97.22 453	97.27 445	97.07 508	99.64 256	99.20 277	96.53 530	95.91 534	96.91 493	97.38 516	98.95 476	96.01 403	98.29 544	94.87 510	99.21 448	93.73 546

PDCNetPlus		98.55 361	98.50 353	98.69 427	99.64 256	96.12 497	97.67 479	100.00 1	98.34 401	99.79 133	99.75 164	92.45 467	99.98 26	98.92 215	99.99 19	99.96 13

YYNet1		98.95 310	98.99 285	98.84 409	99.64 256	97.14 471	98.22 424	99.32 402	98.92 313	99.59 261	99.66 241	97.40 339	99.83 338	98.27 294	99.90 176	99.55 236

MDA-MVSNet_test_wron		98.95 310	98.99 285	98.85 407	99.64 256	97.16 469	98.23 423	99.33 400	98.93 310	99.56 274	99.66 241	97.39 341	99.83 338	98.29 291	99.88 203	99.55 236

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

thres100view900		96.39 478	96.03 479	97.47 487	99.63 261	95.93 501	99.18 215	97.57 518	98.75 344	98.70 446	97.31 537	87.04 513	99.67 473	87.62 543	98.51 497	96.81 529

thres600view7		96.60 472	96.16 476	97.93 468	99.63 261	96.09 500	99.18 215	97.57 518	98.77 340	98.72 443	97.32 536	87.04 513	99.72 439	88.57 539	98.62 493	97.98 515

ITE_SJBPF				99.38 290	99.63 261	99.44 205		99.73 194	98.56 365	99.33 348	99.53 335	98.88 171	99.68 467	96.01 480	99.65 358	99.02 439

ArgMatch-Sym		99.06 280	98.96 292	99.35 305	99.62 265	99.22 270	98.34 412	99.79 152	98.80 333	99.50 300	99.29 414	98.30 265	99.75 427	97.30 396	99.71 330	99.08 420

test_part2					99.62 265	99.67 120				99.55 279

SIFT-CM-Cal		97.96 419	98.15 396	97.39 492	99.61 267	99.15 289	96.75 525	98.41 491	98.04 427	99.03 408	99.54 332	95.24 423	99.41 517	96.97 424	99.80 273	93.61 547

Anonymous20231211		99.62 94	99.57 105	99.76 87	99.61 267	99.60 158	99.81 13	99.73 194	99.82 86	99.90 67	99.90 36	97.97 301	99.86 279	99.42 111	99.96 91	99.80 67

CPTT-MVS		98.74 338	98.44 362	99.64 167	99.61 267	99.38 226	99.18 215	99.55 315	96.49 501	99.27 363	99.37 389	97.11 357	99.92 154	95.74 496	99.67 353	99.62 188

aaEdge-Enhanced		99.26 214	99.10 242	99.73 113	99.60 270	99.65 129	98.75 353	99.45 362	99.31 240	99.65 227	99.66 241	98.00 300	99.86 279	97.69 363	99.79 279	99.67 135

reproduce_model		99.50 127	99.40 157	99.83 41	99.60 270	99.83 33	99.12 245	99.68 230	99.49 194	99.80 126	99.79 121	99.01 148	99.93 120	98.24 297	99.82 256	99.73 95

test1111		97.74 428	98.16 395	96.49 520	99.60 270	89.86 556	99.71 37	91.21 553	99.89 56	99.88 82	99.87 56	93.73 446	99.90 205	99.56 83	99.99 19	99.70 107

h-mvs33		98.61 351	98.34 376	99.44 263	99.60 270	98.67 363	99.27 182	99.44 363	99.68 136	99.32 351	99.49 351	92.50 465	100.00 1	99.24 139	96.51 540	99.65 158

MSDG		99.08 275	98.98 288	99.37 295	99.60 270	99.13 292	97.54 486	99.74 189	98.84 326	99.53 288	99.55 330	99.10 125	99.79 392	97.07 420	99.86 225	99.18 389

FPMVS		96.32 481	95.50 490	98.79 415	99.60 270	98.17 414	98.46 404	98.80 465	97.16 484	96.28 534	99.63 266	82.19 527	99.09 531	88.45 540	98.89 475	99.10 408

ArgMatch-SfM		99.14 259	99.06 252	99.36 301	99.59 276	99.14 291	98.45 405	99.81 135	98.67 352	99.50 300	99.42 369	98.55 220	99.84 315	97.85 338	99.73 318	99.11 405

SIFT-UM-Cal		98.18 401	98.45 360	97.37 494	99.59 276	98.95 324	96.76 524	99.39 380	98.39 387	99.46 311	99.31 407	96.23 397	99.24 525	97.21 409	99.70 333	93.90 543

SIFT-NN-PointCN		97.97 417	98.24 386	97.14 506	99.59 276	98.71 360	96.75 525	99.56 308	97.02 490	97.91 497	99.27 418	96.85 368	98.39 543	97.47 383	99.76 296	94.31 537

usedtu_dtu_shiyan1		98.87 323	98.71 325	99.35 305	99.59 276	98.88 339	97.17 505	99.64 258	98.94 305	99.27 363	99.22 433	95.57 413	99.83 338	99.08 184	99.92 158	99.35 344

FE-MVSNET3		98.87 323	98.71 325	99.35 305	99.59 276	98.88 339	97.17 505	99.64 258	98.94 305	99.27 363	99.22 433	95.57 413	99.83 338	99.08 184	99.92 158	99.35 344

test2506		94.73 508	94.59 508	95.15 528	99.59 276	85.90 558	99.75 25	74.01 561	99.89 56	99.71 193	99.86 63	79.00 540	99.90 205	99.52 91	99.99 19	99.65 158

ECVR-MVS		97.73 429	98.04 403	96.78 512	99.59 276	90.81 550	99.72 33	90.43 555	99.89 56	99.86 96	99.86 63	93.60 448	99.89 227	99.46 101	99.99 19	99.65 158

xiu_mvs_v1_base_debu		99.23 223	99.34 175	98.91 396	99.59 276	98.23 407	98.47 400	99.66 240	99.61 170	99.68 208	98.94 477	99.39 71	99.97 44	99.18 155	99.55 390	98.51 488

xiu_mvs_v1_base		99.23 223	99.34 175	98.91 396	99.59 276	98.23 407	98.47 400	99.66 240	99.61 170	99.68 208	98.94 477	99.39 71	99.97 44	99.18 155	99.55 390	98.51 488

xiu_mvs_v1_base_debi		99.23 223	99.34 175	98.91 396	99.59 276	98.23 407	98.47 400	99.66 240	99.61 170	99.68 208	98.94 477	99.39 71	99.97 44	99.18 155	99.55 390	98.51 488

SF-MVS		99.10 273	98.93 296	99.62 184	99.58 286	99.51 182	99.13 240	99.65 250	97.97 432	99.42 321	99.61 286	98.86 174	99.87 259	96.45 462	99.68 347	99.49 282

tfpn200view9		96.30 482	95.89 481	97.53 482	99.58 286	96.11 498	99.00 292	97.54 521	98.43 381	98.52 461	96.98 542	86.85 515	99.67 473	87.62 543	98.51 497	96.81 529

EI-MVSNet		99.38 179	99.44 146	99.21 346	99.58 286	98.09 421	99.26 187	99.46 357	99.62 165	99.75 165	99.67 235	98.54 225	99.85 298	99.15 164	99.92 158	99.68 126

CVMVSNet		98.61 351	98.88 306	97.80 474	99.58 286	93.60 533	99.26 187	99.64 258	99.66 151	99.72 188	99.67 235	93.26 452	99.93 120	99.30 131	99.81 266	99.87 45

thres400		96.40 477	95.89 481	97.92 469	99.58 286	96.11 498	99.00 292	97.54 521	98.43 381	98.52 461	96.98 542	86.85 515	99.67 473	87.62 543	98.51 497	97.98 515

MCST-MVS		99.02 291	98.81 316	99.65 160	99.58 286	99.49 184	98.58 378	99.07 446	98.40 386	99.04 407	99.25 424	98.51 236	99.80 388	97.31 394	99.51 401	99.65 158

HQP_MVS		98.90 318	98.68 330	99.55 221	99.58 286	99.24 264	98.80 343	99.54 321	98.94 305	99.14 392	99.25 424	97.24 347	99.82 361	95.84 491	99.78 287	99.60 208

plane_prior7					99.58 286	99.38 226

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

MVS_111021_LR		99.13 262	99.03 267	99.42 270	99.58 286	99.32 244	97.91 464	99.73 194	98.68 350	99.31 356	99.48 355	99.09 127	99.66 478	97.70 357	99.77 291	99.29 365

SIFT-NCM-Cal		98.18 401	98.41 366	97.48 485	99.57 296	99.28 250	97.26 501	98.08 503	98.30 407	99.23 373	99.39 382	97.13 355	99.04 534	96.86 431	99.86 225	94.12 540

SIFT-NN-UMatch		97.18 455	97.24 447	97.01 509	99.57 296	98.65 370	96.33 534	97.31 524	97.07 488	97.48 515	98.73 493	94.39 436	98.87 537	95.75 495	98.50 500	93.50 549

SIFT-UMatch		98.07 410	98.27 384	97.46 489	99.57 296	98.99 315	96.93 520	99.02 451	98.53 371	99.26 367	99.23 432	95.43 419	99.31 522	96.51 455	99.91 172	94.09 541

DPE-MVS		99.14 259	98.92 300	99.82 46	99.57 296	99.77 63	98.74 354	99.60 285	98.55 367	99.76 160	99.69 216	98.23 275	99.92 154	96.39 464	99.75 304	99.76 86
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SPE-MVS-test		99.68 64	99.70 57	99.64 167	99.57 296	99.83 33	99.78 17	99.97 21	99.92 45	99.50 300	99.38 385	99.57 52	99.95 81	99.69 64	99.90 176	99.15 396

EI-MVSNet-UG-set		99.48 135	99.50 125	99.42 270	99.57 296	98.65 370	99.24 194	99.46 357	99.68 136	99.80 126	99.66 241	98.99 151	99.89 227	99.19 152	99.90 176	99.72 99

EI-MVSNet-Vis-set		99.47 145	99.49 129	99.42 270	99.57 296	98.66 366	99.24 194	99.46 357	99.67 144	99.79 133	99.65 248	98.97 157	99.89 227	99.15 164	99.89 192	99.71 104

pmmvs4		99.13 262	99.06 252	99.36 301	99.57 296	99.10 302	98.01 450	99.25 419	98.78 337	99.58 263	99.44 366	98.24 271	99.76 416	98.74 241	99.93 149	99.22 376

MVSFormer		99.41 170	99.44 146	99.31 321	99.57 296	98.40 398	99.77 19	99.80 143	99.73 113	99.63 238	99.30 410	98.02 295	99.98 26	99.43 106	99.69 342	99.55 236

lupinMVS		98.96 307	98.87 307	99.24 343	99.57 296	98.40 398	98.12 437	99.18 436	98.28 408	99.63 238	99.13 445	98.02 295	99.97 44	98.22 299	99.69 342	99.35 344

ab-mvs		99.33 199	99.28 197	99.47 252	99.57 296	99.39 224	99.78 17	99.43 367	98.87 319	99.57 266	99.82 91	98.06 293	99.87 259	98.69 254	99.73 318	99.15 396

DP-MVS		99.48 135	99.39 158	99.74 103	99.57 296	99.62 144	99.29 175	99.61 273	99.87 63	99.74 176	99.76 156	98.69 198	99.87 259	98.20 301	99.80 273	99.75 89

F-COLMAP		98.74 338	98.45 360	99.62 184	99.57 296	99.47 189	98.84 332	99.65 250	96.31 505	98.93 417	99.19 441	97.68 322	99.87 259	96.52 454	99.37 424	99.53 257

CLD-MVS		98.76 336	98.57 342	99.33 312	99.57 296	98.97 320	97.53 488	99.55 315	96.41 502	99.27 363	99.13 445	99.07 134	99.78 396	96.73 441	99.89 192	99.23 374
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MatchFormer		99.03 288	99.02 268	99.08 370	99.56 310	98.47 391	98.57 382	99.90 64	98.13 418	99.80 126	99.75 164	98.34 259	99.84 315	97.18 414	99.90 176	98.92 452

reproduce-ours		99.46 147	99.35 173	99.82 46	99.56 310	99.83 33	99.05 269	99.65 250	99.45 208	99.78 139	99.78 134	98.93 161	99.93 120	98.11 311	99.81 266	99.70 107

our_new_method		99.46 147	99.35 173	99.82 46	99.56 310	99.83 33	99.05 269	99.65 250	99.45 208	99.78 139	99.78 134	98.93 161	99.93 120	98.11 311	99.81 266	99.70 107

UnsupCasMVSNet_bld		98.55 361	98.27 384	99.40 283	99.56 310	99.37 231	97.97 458	99.68 230	97.49 466	99.08 401	99.35 399	95.41 420	99.82 361	97.70 357	98.19 512	99.01 440

dmvs_re		98.69 345	98.48 354	99.31 321	99.55 314	99.42 212	99.54 90	98.38 493	99.32 238	98.72 443	98.71 494	96.76 371	99.21 526	96.01 480	99.35 427	99.31 360

APDe-MVS		99.48 135	99.36 169	99.85 32	99.55 314	99.81 47	99.50 102	99.69 225	98.99 297	99.75 165	99.71 197	98.79 182	99.93 120	98.46 277	99.85 232	99.80 67
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SIFT-NN-CMatch		97.30 451	97.34 441	97.18 502	99.54 316	98.85 345	96.02 536	95.77 541	97.05 489	97.55 514	98.70 496	96.35 390	98.75 539	95.82 493	99.26 440	93.95 542

SIFT-ConvMatch		98.16 405	98.37 371	97.52 483	99.54 316	99.20 277	96.97 517	98.47 485	98.09 422	99.14 392	99.40 377	95.93 406	99.05 533	97.87 334	99.92 158	94.31 537

SD_0403		97.42 446	96.90 462	98.98 380	99.54 316	97.90 436	99.52 94	99.54 321	99.34 234	97.87 500	98.85 484	98.72 195	99.64 487	78.93 553	99.83 246	99.40 328

test_fmvs1		99.48 135	99.65 74	98.97 381	99.54 316	97.16 469	99.11 250	99.98 13	99.78 103	99.96 34	99.81 98	98.72 195	99.97 44	99.95 14	99.97 77	99.79 75

SR-MVS-dyc-post		99.27 212	99.11 233	99.73 113	99.54 316	99.74 87	99.26 187	99.62 265	99.16 272	99.52 290	99.64 250	98.41 249	99.91 186	97.27 400	99.61 374	99.54 248

RE-MVS-def			99.13 226		99.54 316	99.74 87	99.26 187	99.62 265	99.16 272	99.52 290	99.64 250	98.57 216		97.27 400	99.61 374	99.54 248

PVSNet_BlendedMVS		99.03 288	99.01 274	99.09 365	99.54 316	97.99 428	98.58 378	99.82 122	97.62 458	99.34 346	99.71 197	98.52 234	99.77 409	97.98 322	99.97 77	99.52 268

PVSNet_Blended		98.70 344	98.59 338	99.02 376	99.54 316	97.99 428	97.58 485	99.82 122	95.70 514	99.34 346	98.98 470	98.52 234	99.77 409	97.98 322	99.83 246	99.30 362

USDC		98.96 307	98.93 296	99.05 374	99.54 316	97.99 428	97.07 512	99.80 143	98.21 412	99.75 165	99.77 146	98.43 246	99.64 487	97.90 329	99.88 203	99.51 271

GDP-MVS		98.81 331	98.57 342	99.50 240	99.53 325	99.12 294	99.28 177	99.86 89	99.53 187	99.57 266	99.32 404	90.88 488	99.98 26	99.46 101	99.74 311	99.42 324

BP-MVS1		98.72 341	98.46 357	99.50 240	99.53 325	99.00 313	99.34 149	98.53 480	99.65 156	99.73 182	99.38 385	90.62 493	99.96 69	99.50 95	99.86 225	99.55 236

save fliter					99.53 325	99.25 259	98.29 418	99.38 385	99.07 287

CS-MVS		99.67 76	99.70 57	99.58 202	99.53 325	99.84 26	99.79 15	99.96 30	99.90 49	99.61 255	99.41 371	99.51 61	99.95 81	99.66 69	99.89 192	98.96 444

Anonymous20240529		99.42 163	99.34 175	99.65 160	99.53 325	99.60 158	99.63 64	99.39 380	99.47 202	99.76 160	99.78 134	98.13 285	99.86 279	98.70 252	99.68 347	99.49 282

APD-MVS_3200maxsize		99.31 203	99.16 219	99.74 103	99.53 325	99.75 79	99.27 182	99.61 273	99.19 262	99.57 266	99.64 250	98.76 188	99.90 205	97.29 397	99.62 366	99.56 232

MIMVSNet		98.43 377	98.20 390	99.11 362	99.53 325	98.38 402	99.58 82	98.61 475	98.96 301	99.33 348	99.76 156	90.92 485	99.81 378	97.38 389	99.76 296	99.15 396

HPM-MVS++		98.96 307	98.70 329	99.74 103	99.52 332	99.71 101	98.86 327	99.19 434	98.47 380	98.59 455	99.06 457	98.08 292	99.91 186	96.94 426	99.60 377	99.60 208

GA-MVS		97.99 416	97.68 430	98.93 390	99.52 332	98.04 426	97.19 504	99.05 449	98.32 405	98.81 433	98.97 472	89.89 503	99.41 517	98.33 289	99.05 459	99.34 350

SR-MVS		99.19 242	99.00 278	99.74 103	99.51 334	99.72 95	99.18 215	99.60 285	98.85 322	99.47 307	99.58 309	98.38 254	99.92 154	96.92 427	99.54 395	99.57 228

test222					99.51 334	99.08 304	97.83 468	99.29 410	95.21 521	98.68 447	99.31 407	97.28 346			99.38 422	99.43 319

testdata				99.42 270	99.51 334	98.93 329		99.30 409	96.20 506	98.87 427	99.40 377	98.33 262	99.89 227	96.29 468	99.28 436	99.44 312

plane_prior1					99.51 334

UniMVSNet (Re)		99.37 184	99.26 202	99.68 141	99.51 334	99.58 165	98.98 303	99.60 285	99.43 217	99.70 197	99.36 394	97.70 319	99.88 242	99.20 150	99.87 217	99.59 215

DELS-MVS		99.34 196	99.30 188	99.48 250	99.51 334	99.36 235	98.12 437	99.53 332	99.36 233	99.41 327	99.61 286	99.22 104	99.87 259	99.21 146	99.68 347	99.20 384
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
新几何1				99.52 234	99.50 340	99.22 270		99.26 416	95.66 515	98.60 454	99.28 416	97.67 323	99.89 227	95.95 486	99.32 431	99.45 297

SD-MVS		99.01 297	99.30 188	98.15 459	99.50 340	99.40 220	98.94 314	99.61 273	99.22 259	99.75 165	99.82 91	99.54 55	95.51 552	97.48 382	99.87 217	99.54 248
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CDPH-MVS		98.56 360	98.20 390	99.61 191	99.50 340	99.46 197	98.32 416	99.41 370	95.22 520	99.21 379	99.10 453	98.34 259	99.82 361	95.09 509	99.66 356	99.56 232

APD-MVS		98.87 323	98.59 338	99.71 128	99.50 340	99.62 144	99.01 286	99.57 303	96.80 498	99.54 283	99.63 266	98.29 266	99.91 186	95.24 505	99.71 330	99.61 203
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		99.12 265	99.02 268	99.40 283	99.50 340	99.11 295	97.92 462	99.71 207	98.76 343	99.08 401	99.47 359	99.17 111	99.54 504	97.85 338	99.76 296	99.54 248

旧先验1					99.49 345	99.29 248		99.26 416			99.39 382	97.67 323			99.36 425	99.46 295

GBi-Net		99.42 163	99.31 183	99.73 113	99.49 345	99.77 63	99.68 48	99.70 216	99.44 210	99.62 248	99.83 83	97.21 350	99.90 205	98.96 204	99.90 176	99.53 257

test1		99.42 163	99.31 183	99.73 113	99.49 345	99.77 63	99.68 48	99.70 216	99.44 210	99.62 248	99.83 83	97.21 350	99.90 205	98.96 204	99.90 176	99.53 257

FMVSNet2		99.35 191	99.28 197	99.55 221	99.49 345	99.35 238	99.45 117	99.57 303	99.44 210	99.70 197	99.74 172	97.21 350	99.87 259	99.03 191	99.94 135	99.44 312

DP-MVS Recon		98.50 368	98.23 387	99.31 321	99.49 345	99.46 197	98.56 385	99.63 262	94.86 527	98.85 429	99.37 389	97.81 311	99.59 497	96.08 477	99.44 413	98.88 458

FA-MVS(test-final)		98.52 365	98.32 378	99.10 364	99.48 350	98.67 363	99.77 19	98.60 478	97.35 474	99.63 238	99.80 109	93.07 455	99.84 315	97.92 327	99.30 433	98.78 469

MVP-Stereo		99.16 253	99.08 246	99.43 267	99.48 350	99.07 305	99.08 262	99.55 315	98.63 357	99.31 356	99.68 229	98.19 280	99.78 396	98.18 305	99.58 383	99.45 297
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres200		96.09 488	95.68 488	97.33 498	99.48 350	96.22 495	98.53 391	97.57 518	98.06 426	98.37 469	96.73 550	86.84 517	99.61 495	86.99 546	98.57 494	96.16 533

sss		98.90 318	98.77 321	99.27 334	99.48 350	98.44 395	98.72 357	99.32 402	97.94 438	99.37 337	99.35 399	96.31 391	99.91 186	98.85 220	99.63 364	99.47 290

PAPM_NR		98.36 383	98.04 403	99.33 312	99.48 350	98.93 329	98.79 346	99.28 413	97.54 462	98.56 460	98.57 503	97.12 356	99.69 455	94.09 522	98.90 474	99.38 334

TAMVS		99.49 132	99.45 141	99.63 175	99.48 350	99.42 212	99.45 117	99.57 303	99.66 151	99.78 139	99.83 83	97.85 309	99.86 279	99.44 104	99.96 91	99.61 203

原ACMM1				99.37 295	99.47 356	98.87 344		99.27 414	96.74 500	98.26 473	99.32 404	97.93 303	99.82 361	95.96 485	99.38 422	99.43 319

plane_prior6					99.47 356	99.26 256						97.24 347

UniMVSNet_NR-MVSNet		99.37 184	99.25 206	99.72 122	99.47 356	99.56 169	98.97 305	99.61 273	99.43 217	99.67 216	99.28 416	97.85 309	99.95 81	99.17 159	99.81 266	99.65 158

TAPA-MVS	97.92 13	98.03 412	97.55 434	99.46 256	99.47 356	99.44 205	98.50 395	99.62 265	86.79 545	99.07 404	99.26 422	98.26 270	99.62 490	97.28 399	99.73 318	99.31 360
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_testset		97.27 452	96.83 464	98.59 432	99.46 360	97.55 450	99.25 193	96.84 529	98.78 337	97.24 521	97.67 527	97.11 357	98.97 535	86.59 548	98.54 496	99.27 366

SMA-MVS		99.19 242	99.00 278	99.73 113	99.46 360	99.73 90	99.13 240	99.52 337	97.40 471	99.57 266	99.64 250	98.93 161	99.83 338	97.61 373	99.79 279	99.63 176
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
PVSNet	97.47 15	98.42 378	98.44 362	98.35 446	99.46 360	96.26 493	96.70 528	99.34 395	97.68 456	99.00 411	99.13 445	97.40 339	99.72 439	97.59 375	99.68 347	99.08 420

TinyColmap		98.97 304	98.93 296	99.07 371	99.46 360	98.19 411	97.75 471	99.75 183	98.79 335	99.54 283	99.70 207	98.97 157	99.62 490	96.63 449	99.83 246	99.41 325

9.14			98.64 332		99.45 364		98.81 340	99.60 285	97.52 464	99.28 362	99.56 321	98.53 230	99.83 338	95.36 504	99.64 360

FE-MVS		97.85 422	97.42 439	99.15 355	99.44 365	98.75 357	99.77 19	98.20 500	95.85 510	99.33 348	99.80 109	88.86 506	99.88 242	96.40 463	99.12 452	98.81 466

PatchMatch-RL		98.68 346	98.47 355	99.30 325	99.44 365	99.28 250	98.14 434	99.54 321	97.12 486	99.11 397	99.25 424	97.80 312	99.70 448	96.51 455	99.30 433	98.93 450

PCF-MVS	96.03 18	96.73 467	95.86 483	99.33 312	99.44 365	99.16 287	96.87 522	99.44 363	86.58 546	98.95 415	99.40 377	94.38 437	99.88 242	87.93 542	99.80 273	98.95 446
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ZD-MVS					99.43 368	99.61 154		99.43 367	96.38 503	99.11 397	99.07 456	97.86 307	99.92 154	94.04 523	99.49 406

VDD-MVS		99.20 239	99.11 233	99.44 263	99.43 368	98.98 317	99.50 102	98.32 496	99.80 96	99.56 274	99.69 216	96.99 363	99.85 298	98.99 197	99.73 318	99.50 277

DU-MVS		99.33 199	99.21 213	99.71 128	99.43 368	99.56 169	98.83 335	99.53 332	99.38 228	99.67 216	99.36 394	97.67 323	99.95 81	99.17 159	99.81 266	99.63 176

NR-MVSNet		99.40 172	99.31 183	99.68 141	99.43 368	99.55 173	99.73 30	99.50 346	99.46 205	99.88 82	99.36 394	97.54 333	99.87 259	98.97 201	99.87 217	99.63 176

WTY-MVS		98.59 357	98.37 371	99.26 338	99.43 368	98.40 398	98.74 354	99.13 444	98.10 420	99.21 379	99.24 430	94.82 429	99.90 205	97.86 336	98.77 480	99.49 282

BridgeMVS		99.50 127	99.50 125	99.50 240	99.42 373	99.49 184	99.52 94	99.75 183	99.86 66	99.78 139	99.71 197	98.20 279	99.90 205	99.39 114	99.88 203	99.10 408

thisisatest0515		96.98 460	96.42 469	98.66 428	99.42 373	97.47 454	97.27 500	94.30 546	97.24 479	99.15 390	98.86 483	85.01 522	99.87 259	97.10 417	99.39 421	98.63 477

pmmvs3		98.08 409	97.80 422	98.91 396	99.41 375	97.69 446	97.87 466	99.66 240	95.87 509	99.50 300	99.51 343	90.35 497	99.97 44	98.55 270	99.47 409	99.08 420

NP-MVS					99.40 376	99.13 292					98.83 486

QAPM		98.40 381	97.99 406	99.65 160	99.39 377	99.47 189	99.67 53	99.52 337	91.70 541	98.78 439	99.80 109	98.55 220	99.95 81	94.71 514	99.75 304	99.53 257

OMC-MVS		98.90 318	98.72 324	99.44 263	99.39 377	99.42 212	98.58 378	99.64 258	97.31 476	99.44 314	99.62 276	98.59 213	99.69 455	96.17 476	99.79 279	99.22 376

3Dnovator	99.15 2	99.43 159	99.36 169	99.65 160	99.39 377	99.42 212	99.70 38	99.56 308	99.23 255	99.35 342	99.80 109	99.17 111	99.95 81	98.21 300	99.84 238	99.59 215

SIFT-MNN		97.55 439	97.74 427	96.98 510	99.38 380	98.85 345	96.92 521	98.61 475	98.36 391	98.63 451	99.10 453	92.51 464	97.85 546	96.63 449	99.48 408	94.25 539

Fast-Effi-MVS+		99.02 291	98.87 307	99.46 256	99.38 380	99.50 183	99.04 274	99.79 152	97.17 483	98.62 452	98.74 492	99.34 85	99.95 81	98.32 290	99.41 419	98.92 452

BH-untuned		98.22 398	98.09 400	98.58 435	99.38 380	97.24 467	98.55 386	98.98 456	97.81 450	99.20 384	98.76 491	97.01 361	99.65 485	94.83 511	98.33 505	98.86 460

mvsany_test1		99.44 155	99.45 141	99.40 283	99.37 383	98.64 373	97.90 465	99.59 291	99.27 246	99.92 59	99.82 91	99.74 26	99.93 120	99.55 85	99.87 217	99.63 176

xiu_mvs_v2_base		99.02 291	99.11 233	98.77 418	99.37 383	98.09 421	98.13 435	99.51 342	99.47 202	99.42 321	98.54 506	99.38 76	99.97 44	98.83 223	99.33 429	98.24 502

PS-MVSNAJ		99.00 300	99.08 246	98.76 419	99.37 383	98.10 420	98.00 453	99.51 342	99.47 202	99.41 327	98.50 508	99.28 93	99.97 44	98.83 223	99.34 428	98.20 506

testing3-2		96.51 475	96.43 468	96.74 516	99.36 386	91.38 547	99.10 254	97.87 513	99.48 197	98.57 458	98.71 494	76.65 545	99.66 478	98.87 219	99.26 440	99.18 389

EIA-MVS		99.12 265	99.01 274	99.45 259	99.36 386	99.62 144	99.34 149	99.79 152	98.41 384	98.84 430	98.89 481	98.75 190	99.84 315	98.15 309	99.51 401	98.89 457

DPM-MVS		98.28 389	97.94 414	99.32 317	99.36 386	99.11 295	97.31 499	98.78 466	96.88 494	98.84 430	99.11 452	97.77 315	99.61 495	94.03 524	99.36 425	99.23 374

mvsmamba		99.08 275	98.95 294	99.45 259	99.36 386	99.18 286	99.39 129	98.81 464	99.37 229	99.35 342	99.70 207	96.36 389	99.94 98	98.66 258	99.59 381	99.22 376

MM		99.18 246	99.05 259	99.55 221	99.35 390	98.81 349	99.05 269	97.79 515	99.99 3	99.48 305	99.59 306	96.29 394	99.95 81	99.94 20	99.98 54	99.88 41

ambc				99.20 349	99.35 390	98.53 388	99.17 220	99.46 357		99.67 216	99.80 109	98.46 243	99.70 448	97.92 327	99.70 333	99.38 334

TEST9					99.35 390	99.35 238	98.11 439	99.41 370	94.83 528	97.92 495	98.99 467	98.02 295	99.85 298

train_agg		98.35 386	97.95 410	99.57 210	99.35 390	99.35 238	98.11 439	99.41 370	94.90 525	97.92 495	98.99 467	98.02 295	99.85 298	95.38 503	99.44 413	99.50 277

agg_prior					99.35 390	99.36 235		99.39 380		97.76 508			99.85 298

test_prior				99.46 256	99.35 390	99.22 270		99.39 380					99.69 455			99.48 286

MVS_Test		99.28 208	99.31 183	99.19 350	99.35 390	98.79 353	99.36 144	99.49 350	99.17 270	99.21 379	99.67 235	98.78 185	99.66 478	99.09 182	99.66 356	99.10 408

CDS-MVSNet		99.22 232	99.13 226	99.50 240	99.35 390	99.11 295	98.96 309	99.54 321	99.46 205	99.61 255	99.70 207	96.31 391	99.83 338	99.34 123	99.88 203	99.55 236
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	98.92 3	99.35 191	99.24 208	99.67 145	99.35 390	99.47 189	99.62 67	99.50 346	99.44 210	99.12 396	99.78 134	98.77 187	99.94 98	97.87 334	99.72 326	99.62 188

ETV-MVS		99.18 246	99.18 217	99.16 353	99.34 399	99.28 250	99.12 245	99.79 152	99.48 197	98.93 417	98.55 505	99.40 70	99.93 120	98.51 274	99.52 400	98.28 498

Anonymous202405211		98.75 337	98.46 357	99.63 175	99.34 399	99.66 123	99.47 112	97.65 517	99.28 245	99.56 274	99.50 346	93.15 453	99.84 315	98.62 264	99.58 383	99.40 328

CHOSEN 280x420		98.41 379	98.41 366	98.40 443	99.34 399	95.89 503	96.94 519	99.44 363	98.80 333	99.25 369	99.52 339	93.51 449	99.98 26	98.94 212	99.98 54	99.32 355

test_8					99.34 399	99.31 245	98.08 443	99.40 377	94.90 525	97.87 500	98.97 472	98.02 295	99.84 315

TSAR-MVS + GP.		99.12 265	99.04 265	99.38 290	99.34 399	99.16 287	98.15 432	99.29 410	98.18 415	99.63 238	99.62 276	99.18 109	99.68 467	98.20 301	99.74 311	99.30 362

LCM-MVSNet-Re		99.28 208	99.15 223	99.67 145	99.33 404	99.76 70	99.34 149	99.97 21	98.93 310	99.91 62	99.79 121	98.68 199	99.93 120	96.80 437	99.56 386	99.30 362

MASt3R-SfM		98.45 375	98.51 350	98.26 456	99.32 405	97.43 460	97.43 494	99.69 225	94.97 524	99.75 165	99.41 371	98.49 238	99.75 427	97.73 351	99.79 279	97.61 522

PLC	97.35 16	98.36 383	97.99 406	99.48 250	99.32 405	99.24 264	98.50 395	99.51 342	95.19 522	98.58 456	98.96 474	96.95 364	99.83 338	95.63 497	99.25 442	99.37 338
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Effi-MVS+		99.06 280	98.97 290	99.34 309	99.31 407	98.98 317	98.31 417	99.91 57	98.81 331	98.79 437	98.94 477	99.14 118	99.84 315	98.79 232	98.74 485	99.20 384

HQP-NCC					99.31 407		97.98 455		97.45 467	98.15 483

ACMP_Plane					99.31 407		97.98 455		97.45 467	98.15 483

HQP-MVS		98.36 383	98.02 405	99.39 286	99.31 407	98.94 326	97.98 455	99.37 386	97.45 467	98.15 483	98.83 486	96.67 373	99.70 448	94.73 512	99.67 353	99.53 257

baseline1		97.73 429	97.33 442	98.96 382	99.30 411	97.73 444	99.40 127	98.42 488	99.33 237	99.46 311	99.21 437	91.18 481	99.82 361	98.35 287	91.26 548	99.32 355

WR-MVS		99.11 270	98.93 296	99.66 153	99.30 411	99.42 212	98.42 408	99.37 386	99.04 290	99.57 266	99.20 439	96.89 366	99.86 279	98.66 258	99.87 217	99.70 107

hse-mvs2		98.52 365	98.30 381	99.16 353	99.29 413	98.60 377	98.77 348	99.02 451	99.68 136	99.32 351	99.04 460	92.50 465	99.85 298	99.24 139	97.87 523	99.03 434

test12				99.54 227	99.29 413	99.33 241		99.16 439		98.43 466		97.54 333	99.82 361		99.47 409	99.48 286

OpenMVS_ROB	97.31 17	97.36 450	96.84 463	98.89 403	99.29 413	99.45 203	98.87 326	99.48 351	86.54 547	99.44 314	99.74 172	97.34 343	99.86 279	91.61 532	99.28 436	97.37 526

MVS-HIRNet		97.86 421	98.22 388	96.76 514	99.28 416	91.53 545	98.38 410	92.60 551	99.13 279	99.31 356	99.96 15	97.18 354	99.68 467	98.34 288	99.83 246	99.07 426

DeepC-MVS_fast	98.47 5	99.23 223	99.12 230	99.56 214	99.28 416	99.22 270	98.99 300	99.40 377	99.08 285	99.58 263	99.64 250	98.90 170	99.83 338	97.44 385	99.75 304	99.63 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AUN-MVS		97.82 423	97.38 440	99.14 358	99.27 418	98.53 388	98.72 357	99.02 451	98.10 420	97.18 523	99.03 464	89.26 505	99.85 298	97.94 326	97.91 521	99.03 434

Patchmatch-test		98.10 408	97.98 408	98.48 439	99.27 418	96.48 487	99.40 127	99.07 446	98.81 331	99.23 373	99.57 317	90.11 500	99.87 259	96.69 442	99.64 360	99.09 414

RRT-MVS		99.08 275	99.00 278	99.33 312	99.27 418	98.65 370	99.62 67	99.93 43	99.66 151	99.67 216	99.82 91	95.27 422	99.93 120	98.64 262	99.09 456	99.41 325

ET-MVSNet_ETH3D		96.78 464	96.07 478	98.91 396	99.26 421	97.92 435	97.70 477	96.05 533	97.96 435	92.37 549	98.43 509	87.06 512	99.90 205	98.27 294	97.56 526	98.91 454

Fast-Effi-MVS+-dtu		99.20 239	99.12 230	99.43 267	99.25 422	99.69 114	99.05 269	99.82 122	99.50 192	98.97 413	99.05 458	98.98 155	99.98 26	98.20 301	99.24 444	98.62 478

CNVR-MVS		98.99 303	98.80 319	99.56 214	99.25 422	99.43 209	98.54 389	99.27 414	98.58 364	98.80 435	99.43 367	98.53 230	99.70 448	97.22 408	99.59 381	99.54 248

LFMVS		98.46 374	98.19 393	99.26 338	99.24 424	98.52 390	99.62 67	96.94 528	99.87 63	99.31 356	99.58 309	91.04 483	99.81 378	98.68 255	99.42 418	99.45 297

VNet		99.18 246	99.06 252	99.56 214	99.24 424	99.36 235	99.33 155	99.31 406	99.67 144	99.47 307	99.57 317	96.48 381	99.84 315	99.15 164	99.30 433	99.47 290

testing3		96.48 476	95.63 489	99.01 377	99.23 426	97.81 440	98.90 320	99.10 445	98.72 345	97.84 503	97.92 522	72.44 553	99.85 298	97.21 409	99.33 429	99.35 344

CL-MVSNet_self_test		98.71 343	98.56 346	99.15 355	99.22 427	98.66 366	97.14 508	99.51 342	98.09 422	99.54 283	99.27 418	96.87 367	99.74 434	98.43 282	98.96 466	99.03 434

DeepPCF-MVS	98.42 6	99.18 246	99.02 268	99.67 145	99.22 427	99.75 79	97.25 502	99.47 354	98.72 345	99.66 223	99.70 207	99.29 91	99.63 489	98.07 316	99.81 266	99.62 188

MSLP-MVS++		99.05 284	99.09 244	98.91 396	99.21 429	98.36 403	98.82 339	99.47 354	98.85 322	98.90 423	99.56 321	98.78 185	99.09 531	98.57 268	99.68 347	99.26 368

NCCC		98.82 329	98.57 342	99.58 202	99.21 429	99.31 245	98.61 371	99.25 419	98.65 354	98.43 466	99.26 422	97.86 307	99.81 378	96.55 452	99.27 439	99.61 203

BH-RMVSNet		98.41 379	98.14 397	99.21 346	99.21 429	98.47 391	98.60 373	98.26 498	98.35 397	98.93 417	99.31 407	97.20 353	99.66 478	94.32 517	99.10 454	99.51 271

miper_lstm_enhance		98.65 349	98.60 336	98.82 414	99.20 432	97.33 464	97.78 470	99.66 240	99.01 295	99.59 261	99.50 346	94.62 433	99.85 298	98.12 310	99.90 176	99.26 368

SCA		98.11 407	98.36 373	97.36 495	99.20 432	92.99 535	98.17 429	98.49 484	98.24 410	99.10 400	99.57 317	96.01 403	99.94 98	96.86 431	99.62 366	99.14 401

dongtai		89.37 514	88.91 517	90.76 532	99.19 434	77.46 559	95.47 539	87.82 559	92.28 539	94.17 545	98.82 488	71.22 555	95.54 551	63.85 554	97.34 529	99.27 366

mvs_anonymous		99.28 208	99.39 158	98.94 386	99.19 434	97.81 440	99.02 281	99.55 315	99.78 103	99.85 101	99.80 109	98.24 271	99.86 279	99.57 82	99.50 404	99.15 396

OpenMVS	98.12 10	98.23 396	97.89 419	99.26 338	99.19 434	99.26 256	99.65 62	99.69 225	91.33 542	98.14 487	99.77 146	98.28 267	99.96 69	95.41 502	99.55 390	98.58 483

CNLPA		98.57 359	98.34 376	99.28 329	99.18 437	99.10 302	98.34 412	99.41 370	98.48 379	98.52 461	98.98 470	97.05 359	99.78 396	95.59 498	99.50 404	98.96 444

TestfortrainingZip				99.38 290	99.17 438	99.25 259	99.38 132	98.82 462	98.93 310	99.68 208	99.49 351	98.11 289	99.56 503		98.44 502	99.32 355

test_yl		98.25 392	97.95 410	99.13 360	99.17 438	98.47 391	99.00 292	98.67 472	98.97 299	99.22 377	99.02 465	91.31 479	99.69 455	97.26 402	98.93 468	99.24 371

DCV-MVSNet		98.25 392	97.95 410	99.13 360	99.17 438	98.47 391	99.00 292	98.67 472	98.97 299	99.22 377	99.02 465	91.31 479	99.69 455	97.26 402	98.93 468	99.24 371

MG-MVS		98.52 365	98.39 369	98.94 386	99.15 441	97.39 462	98.18 426	99.21 430	98.89 318	99.23 373	99.63 266	97.37 342	99.74 434	94.22 519	99.61 374	99.69 119

ADS-MVSNet2		97.78 427	97.66 432	98.12 461	99.14 442	95.36 514	99.22 202	98.75 467	96.97 491	98.25 474	99.64 250	90.90 486	99.94 98	96.51 455	99.56 386	99.08 420

ADS-MVSNet		97.72 432	97.67 431	97.86 472	99.14 442	94.65 524	99.22 202	98.86 459	96.97 491	98.25 474	99.64 250	90.90 486	99.84 315	96.51 455	99.56 386	99.08 420

FMVSNet3		98.80 332	98.63 334	99.32 317	99.13 444	98.72 359	99.10 254	99.48 351	99.23 255	99.62 248	99.64 250	92.57 461	99.86 279	98.96 204	99.90 176	99.39 332

PHI-MVS		99.11 270	98.95 294	99.59 198	99.13 444	99.59 160	99.17 220	99.65 250	97.88 444	99.25 369	99.46 362	98.97 157	99.80 388	97.26 402	99.82 256	99.37 338

OPU-MVS				99.29 326	99.12 446	99.44 205	99.20 205				99.40 377	99.00 149	98.84 538	96.54 453	99.60 377	99.58 221

c3_l		98.72 341	98.71 325	98.72 422	99.12 446	97.22 468	97.68 478	99.56 308	98.90 315	99.54 283	99.48 355	96.37 388	99.73 437	97.88 331	99.88 203	99.21 379

alignmvs		98.28 389	97.96 409	99.25 341	99.12 446	98.93 329	99.03 277	98.42 488	99.64 160	98.72 443	97.85 524	90.86 489	99.62 490	98.88 218	99.13 451	99.19 387

PAPM		95.61 502	94.71 506	98.31 451	99.12 446	96.63 483	96.66 529	98.46 486	90.77 543	96.25 535	98.68 498	93.01 456	99.69 455	81.60 550	97.86 524	98.62 478

AdaColmap		98.60 354	98.35 375	99.38 290	99.12 446	99.22 270	98.67 364	99.42 369	97.84 449	98.81 433	99.27 418	97.32 345	99.81 378	95.14 507	99.53 397	99.10 408

PRO-TEST		99.15 257	99.22 212	98.95 384	99.11 451	98.09 421	99.28 177	99.69 225	99.90 49	99.11 397	99.81 98	97.64 330	99.92 154	98.84 222	99.64 360	98.83 463

MGCFI-Net		99.02 291	99.01 274	99.06 373	99.11 451	98.60 377	99.63 64	99.67 235	99.63 162	98.58 456	97.65 528	99.07 134	99.57 499	98.85 220	98.92 470	99.03 434

MS-PatchMatch		99.00 300	98.97 290	99.09 365	99.11 451	98.19 411	98.76 349	99.33 400	98.49 378	99.44 314	99.58 309	98.21 277	99.69 455	98.20 301	99.62 366	99.39 332

sasdasda		99.02 291	99.00 278	99.09 365	99.10 454	98.70 361	99.61 73	99.66 240	99.63 162	98.64 449	97.65 528	99.04 144	99.54 504	98.79 232	98.92 470	99.04 431

eth_miper_zixun_eth		98.68 346	98.71 325	98.60 431	99.10 454	96.84 481	97.52 490	99.54 321	98.94 305	99.58 263	99.48 355	96.25 395	99.76 416	98.01 320	99.93 149	99.21 379

canonicalmvs		99.02 291	99.00 278	99.09 365	99.10 454	98.70 361	99.61 73	99.66 240	99.63 162	98.64 449	97.65 528	99.04 144	99.54 504	98.79 232	98.92 470	99.04 431

balanced_ft_v1		99.37 184	99.36 169	99.38 290	99.10 454	99.38 226	99.68 48	99.72 203	99.72 117	99.36 338	99.77 146	97.66 327	99.94 98	99.52 91	99.73 318	98.83 463

baseline2		96.83 463	96.28 471	98.46 441	99.09 458	96.91 477	98.83 335	93.87 550	97.23 480	96.23 537	98.36 511	88.12 509	99.90 205	96.68 443	98.14 515	98.57 485

BH-w/o		97.20 454	97.01 456	97.76 475	99.08 459	95.69 508	98.03 449	98.52 481	95.76 513	97.96 493	98.02 518	95.62 410	99.47 514	92.82 529	97.25 532	98.12 509

nomal-1		96.75 466	96.26 472	98.21 457	99.06 460	95.71 507	98.65 369	97.76 516	98.51 374	97.96 493	97.91 523	79.57 536	99.88 242	98.11 311	98.84 476	99.05 428

MVSTER		98.47 372	98.22 388	99.24 343	99.06 460	98.35 404	99.08 262	99.46 357	99.27 246	99.75 165	99.66 241	88.61 507	99.85 298	99.14 171	99.92 158	99.52 268

reproduce_monomvs		97.40 447	97.46 435	97.20 501	99.05 462	91.91 541	99.20 205	99.18 436	99.84 76	99.86 96	99.75 164	80.67 529	99.83 338	99.69 64	99.95 116	99.85 50

CR-MVSNet		98.35 386	98.20 390	98.83 411	99.05 462	98.12 417	99.30 167	99.67 235	97.39 472	99.16 387	99.79 121	91.87 474	99.91 186	98.78 238	98.77 480	98.44 493

RPMNet		98.60 354	98.53 348	98.83 411	99.05 462	98.12 417	99.30 167	99.62 265	99.86 66	99.16 387	99.74 172	92.53 463	99.92 154	98.75 240	98.77 480	98.44 493

MVStest1		98.22 398	98.09 400	98.62 429	99.04 465	96.23 494	99.20 205	99.92 47	99.44 210	99.98 14	99.87 56	85.87 521	99.67 473	99.91 33	99.57 385	99.95 15

DVP-MVS++		99.38 179	99.25 206	99.77 80	99.03 466	99.77 63	99.74 27	99.61 273	99.18 263	99.76 160	99.61 286	99.00 149	99.92 154	97.72 352	99.60 377	99.62 188

MSC_two_6792asdad				99.74 103	99.03 466	99.53 176		99.23 424					99.92 154	97.77 345	99.69 342	99.78 77

No_MVS				99.74 103	99.03 466	99.53 176		99.23 424					99.92 154	97.77 345	99.69 342	99.78 77

cl____		98.54 363	98.41 366	98.92 391	99.03 466	97.80 442	97.46 492	99.59 291	98.90 315	99.60 258	99.46 362	93.85 443	99.78 396	97.97 324	99.89 192	99.17 392

DIV-MVS_self_test		98.54 363	98.42 365	98.92 391	99.03 466	97.80 442	97.46 492	99.59 291	98.90 315	99.60 258	99.46 362	93.87 442	99.78 396	97.97 324	99.89 192	99.18 389

HY-MVS	98.23 9	98.21 400	97.95 410	98.99 378	99.03 466	98.24 406	99.61 73	98.72 468	96.81 497	98.73 442	99.51 343	94.06 440	99.86 279	96.91 428	98.20 510	98.86 460

ALIKED-LG		98.78 333	98.66 331	99.14 358	99.02 472	99.40 220	98.74 354	99.79 152	98.62 361	99.18 385	99.38 385	97.54 333	99.77 409	95.94 488	99.74 311	98.25 501

miper_ehance_all_eth		98.59 357	98.59 338	98.59 432	98.98 473	97.07 472	97.49 491	99.52 337	98.50 376	99.52 290	99.37 389	96.41 386	99.71 444	97.86 336	99.62 366	99.00 441

MonoMVSNet		98.23 396	98.32 378	97.99 463	98.97 474	96.62 484	99.49 107	98.42 488	99.62 165	99.40 332	99.79 121	95.51 416	98.58 542	97.68 368	95.98 544	98.76 473

PMMVS		98.49 370	98.29 383	99.11 362	98.96 475	98.42 397	97.54 486	99.32 402	97.53 463	98.47 464	98.15 517	97.88 306	99.82 361	97.46 384	99.24 444	99.09 414

PatchT		98.45 375	98.32 378	98.83 411	98.94 476	98.29 405	99.24 194	98.82 462	99.84 76	99.08 401	99.76 156	91.37 478	99.94 98	98.82 225	99.00 464	98.26 500

tpm		97.15 456	96.95 458	97.75 476	98.91 477	94.24 527	99.32 158	97.96 508	97.71 455	98.29 472	99.32 404	86.72 518	99.92 154	98.10 315	96.24 543	99.09 414

FBQ-MVS		96.06 490	95.42 492	97.98 464	98.90 478	95.77 505	98.71 360	98.20 500	98.34 401	97.83 504	97.34 534	74.90 550	99.39 519	96.20 474	98.40 504	98.78 469

1314		98.00 415	97.90 418	98.27 455	98.90 478	97.45 457	99.30 167	99.06 448	94.98 523	97.21 522	99.12 449	98.43 246	99.67 473	95.58 499	98.56 495	97.71 520

CostFormer		96.71 468	96.79 466	96.46 521	98.90 478	90.71 551	99.41 122	98.68 470	94.69 529	98.14 487	99.34 403	86.32 520	99.80 388	97.60 374	98.07 519	98.88 458

UGNet		99.38 179	99.34 175	99.49 244	98.90 478	98.90 334	99.70 38	99.35 391	99.86 66	98.57 458	99.81 98	98.50 237	99.93 120	99.38 115	99.98 54	99.66 149
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Effi-MVS+-dtu		99.07 279	98.92 300	99.52 234	98.89 482	99.78 57	99.15 229	99.66 240	99.34 234	98.92 420	99.24 430	97.69 321	99.98 26	98.11 311	99.28 436	98.81 466

Patchmtry		98.78 333	98.54 347	99.49 244	98.89 482	99.19 280	99.32 158	99.67 235	99.65 156	99.72 188	99.79 121	91.87 474	99.95 81	98.00 321	99.97 77	99.33 351

tpm2		96.35 480	96.22 475	96.73 517	98.88 484	91.75 543	99.21 204	98.51 482	93.27 534	97.89 498	99.21 437	84.83 523	99.70 448	96.04 479	98.18 513	98.75 474

UBG		96.53 473	95.95 480	98.29 454	98.87 485	96.31 492	98.48 399	98.07 504	98.83 327	97.32 518	96.54 553	79.81 534	99.62 490	96.84 435	98.74 485	98.95 446

myMVS_eth3d28		96.23 484	95.74 486	97.70 481	98.86 486	95.59 512	98.66 366	98.14 502	98.96 301	97.67 512	97.06 541	76.78 544	98.92 536	97.10 417	98.41 503	98.58 483

WBMVS		97.50 443	97.18 449	98.48 439	98.85 487	95.89 503	98.44 406	99.52 337	99.53 187	99.52 290	99.42 369	80.10 532	99.86 279	99.24 139	99.95 116	99.68 126

tpm cat1		96.78 464	96.98 457	96.16 524	98.85 487	90.59 552	99.08 262	99.32 402	92.37 537	97.73 510	99.46 362	91.15 482	99.69 455	96.07 478	98.80 477	98.21 504

ALIKED-MNN		98.03 412	97.78 425	98.78 417	98.84 489	98.97 320	98.16 431	99.74 189	97.31 476	96.60 531	98.85 484	96.61 375	99.48 513	94.16 520	99.77 291	97.91 519

CANet		99.11 270	99.05 259	99.28 329	98.83 490	98.56 382	98.71 360	99.41 370	99.25 251	99.23 373	99.22 433	97.66 327	99.94 98	99.19 152	99.97 77	99.33 351

FMVSNet5		97.80 426	97.25 446	99.42 270	98.83 490	98.97 320	99.38 132	99.80 143	98.87 319	99.25 369	99.69 216	80.60 531	99.91 186	98.96 204	99.90 176	99.38 334

API-MVS		98.38 382	98.39 369	98.35 446	98.83 490	99.26 256	99.14 233	99.18 436	98.59 363	98.66 448	98.78 490	98.61 210	99.57 499	94.14 521	99.56 386	96.21 531

PatchmatchNet		97.65 433	97.80 422	97.18 502	98.82 493	92.49 538	99.17 220	98.39 492	98.12 419	98.79 437	99.58 309	90.71 492	99.89 227	97.23 407	99.41 419	99.16 394
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ETVMVS		96.14 487	95.22 499	98.89 403	98.80 494	98.01 427	98.66 366	98.35 495	98.71 347	97.18 523	96.31 558	74.23 552	99.75 427	96.64 448	98.13 518	98.90 455

PAPR		97.56 437	97.07 453	99.04 375	98.80 494	98.11 419	97.63 481	99.25 419	94.56 531	98.02 492	98.25 514	97.43 338	99.68 467	90.90 535	98.74 485	99.33 351

CANet_DTU		98.91 315	98.85 309	99.09 365	98.79 496	98.13 416	98.18 426	99.31 406	99.48 197	98.86 428	99.51 343	96.56 377	99.95 81	99.05 188	99.95 116	99.19 387

E-PMN		97.14 458	97.43 438	96.27 522	98.79 496	91.62 544	95.54 538	99.01 454	99.44 210	98.88 424	99.12 449	92.78 458	99.68 467	94.30 518	99.03 462	97.50 523

testing11		96.05 491	95.41 494	97.97 466	98.78 498	95.27 517	98.59 376	98.23 499	98.86 321	96.56 532	96.91 545	75.20 548	99.69 455	97.26 402	98.29 507	98.93 450

PVSNet_0	95.53 19	95.85 497	95.31 498	97.47 487	98.78 498	93.48 534	95.72 537	99.40 377	96.18 507	97.37 517	97.73 526	95.73 408	99.58 498	95.49 500	81.40 554	99.36 341

MAR-MVS		98.24 394	97.92 416	99.19 350	98.78 498	99.65 129	99.17 220	99.14 442	95.36 518	98.04 490	98.81 489	97.47 336	99.72 439	95.47 501	99.06 457	98.21 504
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
testing91		96.00 492	95.32 497	98.02 462	98.76 501	95.39 513	98.38 410	98.65 474	98.82 329	96.84 527	96.71 551	75.06 549	99.71 444	96.46 461	98.23 509	98.98 443

testing99		95.86 496	95.19 500	97.87 471	98.76 501	95.03 520	98.62 370	98.44 487	98.68 350	96.67 530	96.66 552	74.31 551	99.69 455	96.51 455	98.03 520	98.90 455

EMVS		96.96 461	97.28 443	95.99 526	98.76 501	91.03 548	95.26 541	98.61 475	99.34 234	98.92 420	98.88 482	93.79 444	99.66 478	92.87 528	99.05 459	97.30 527

IB-MVS	95.41 20	95.30 504	94.46 510	97.84 473	98.76 501	95.33 515	97.33 498	96.07 532	96.02 508	95.37 541	97.41 532	76.17 546	99.96 69	97.54 378	95.44 547	98.22 503
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
tpmrst		97.73 429	98.07 402	96.73 517	98.71 505	92.00 540	99.10 254	98.86 459	98.52 373	98.92 420	99.54 332	91.90 472	99.82 361	98.02 317	99.03 462	98.37 495

MDTV_nov1_ep13			97.73 428		98.70 506	90.83 549	99.15 229	98.02 506	98.51 374	98.82 432	99.61 286	90.98 484	99.66 478	96.89 430	98.92 470

SP-LightGlue		98.62 350	98.51 350	98.94 386	98.69 507	99.01 312	98.34 412	99.54 321	99.27 246	97.72 511	99.15 444	95.88 407	99.54 504	98.53 273	99.47 409	98.27 499

dp		96.86 462	97.07 453	96.24 523	98.68 508	90.30 555	99.19 211	98.38 493	97.35 474	98.23 476	99.59 306	87.23 511	99.82 361	96.27 469	98.73 488	98.59 481

testing222		95.60 503	94.59 508	98.61 430	98.66 509	97.45 457	98.54 389	97.90 512	98.53 371	96.54 533	96.47 555	70.62 556	99.81 378	95.91 489	98.15 514	98.56 486

JIA-IIPM		98.06 411	97.92 416	98.50 438	98.59 510	97.02 473	98.80 343	98.51 482	99.88 61	97.89 498	99.87 56	91.89 473	99.90 205	98.16 308	97.68 525	98.59 481

MVS		95.72 499	94.63 507	98.99 378	98.56 511	97.98 433	99.30 167	98.86 459	72.71 551	97.30 519	99.08 455	98.34 259	99.74 434	89.21 536	98.33 505	99.26 368

UWE-MVS		96.21 486	95.78 485	97.49 484	98.53 512	93.83 531	98.04 447	93.94 549	98.96 301	98.46 465	98.17 516	79.86 533	99.87 259	96.99 422	99.06 457	98.78 469

TR-MVS		97.44 445	97.15 450	98.32 449	98.53 512	97.46 455	98.47 400	97.91 511	96.85 495	98.21 477	98.51 507	96.42 384	99.51 511	92.16 530	97.29 531	97.98 515

Syy-MVS		98.17 404	97.85 420	99.15 355	98.50 514	98.79 353	98.60 373	99.21 430	97.89 442	96.76 528	96.37 556	95.47 418	99.57 499	99.10 181	98.73 488	99.09 414

myMVS_eth3d		95.63 501	94.73 505	98.34 448	98.50 514	96.36 490	98.60 373	99.21 430	97.89 442	96.76 528	96.37 556	72.10 554	99.57 499	94.38 516	98.73 488	99.09 414

SP-SuperGlue		98.66 348	98.63 334	98.73 421	98.44 516	99.02 311	98.22 424	99.44 363	99.37 229	98.17 482	99.30 410	96.95 364	99.12 528	98.59 265	99.20 449	98.06 510

tpmvs		97.39 448	97.69 429	96.52 519	98.41 517	91.76 542	99.30 167	98.94 457	97.74 451	97.85 502	99.55 330	92.40 468	99.73 437	96.25 470	98.73 488	98.06 510

LS3D		99.24 220	99.11 233	99.61 191	98.38 518	99.79 54	99.57 85	99.68 230	99.61 170	99.15 390	99.71 197	98.70 197	99.91 186	97.54 378	99.68 347	99.13 404

cl22		97.56 437	97.28 443	98.40 443	98.37 519	96.75 482	97.24 503	99.37 386	97.31 476	99.41 327	99.22 433	87.30 510	99.37 520	97.70 357	99.62 366	99.08 420

CMPMVS	77.52 23	98.50 368	98.19 393	99.41 280	98.33 520	99.56 169	99.01 286	99.59 291	95.44 517	99.57 266	99.80 109	95.64 409	99.46 516	96.47 460	99.92 158	99.21 379
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SP-MNN		97.94 420	97.82 421	98.31 451	98.30 521	97.67 447	97.81 469	97.93 510	98.14 417	97.16 525	98.64 500	96.31 391	99.21 526	97.34 391	98.75 484	98.05 512

miper_enhance_ethall		98.03 412	97.94 414	98.32 449	98.27 522	96.43 489	96.95 518	99.41 370	96.37 504	99.43 318	98.96 474	94.74 430	99.69 455	97.71 354	99.62 366	98.83 463

TESTMET0.1,1		96.24 483	95.84 484	97.41 491	98.24 523	93.84 530	97.38 495	95.84 538	98.43 381	97.81 505	98.56 504	79.77 535	99.89 227	97.77 345	98.77 480	98.52 487

SIFT-NN		94.78 507	94.89 503	94.45 529	98.23 524	97.29 465	94.93 542	95.84 538	95.82 512	94.78 543	97.12 539	90.26 498	92.28 554	88.91 537	98.14 515	93.77 545

gg-mvs-nofinetune		95.87 495	95.17 501	97.97 466	98.19 525	96.95 474	99.69 45	89.23 557	99.89 56	96.24 536	99.94 19	81.19 528	99.51 511	93.99 525	98.20 510	97.44 524

test-LLR		97.15 456	96.95 458	97.74 477	98.18 526	95.02 521	97.38 495	96.10 530	98.00 428	97.81 505	98.58 501	90.04 501	99.91 186	97.69 363	98.78 478	98.31 496

test-mter		96.23 484	95.73 487	97.74 477	98.18 526	95.02 521	97.38 495	96.10 530	97.90 440	97.81 505	98.58 501	79.12 539	99.91 186	97.69 363	98.78 478	98.31 496

EPMVS		96.53 473	96.32 470	97.17 504	98.18 526	92.97 536	99.39 129	89.95 556	98.21 412	98.61 453	99.59 306	86.69 519	99.72 439	96.99 422	99.23 446	98.81 466

WB-MVSnew		98.34 388	98.14 397	98.96 382	98.14 529	97.90 436	98.27 419	97.26 525	98.63 357	98.80 435	98.00 520	97.77 315	99.90 205	97.37 390	98.98 465	99.09 414

blended_shiyan6		97.82 423	97.46 435	98.92 391	98.08 530	97.46 455	97.73 472	99.34 395	97.96 435	98.33 471	97.35 533	92.78 458	99.84 315	99.04 189	96.53 536	99.46 295

blended_shiyan8		97.82 423	97.45 437	98.92 391	98.06 531	97.45 457	97.73 472	99.35 391	97.96 435	98.35 470	97.34 534	92.76 460	99.84 315	99.04 189	96.49 542	99.47 290

UWE-MVS-28		95.64 500	95.47 491	96.14 525	97.98 532	90.39 553	98.49 398	95.81 540	99.02 294	98.03 491	98.19 515	84.49 525	99.28 523	88.75 538	98.47 501	98.75 474

blend_shiyan4		95.04 506	93.76 512	98.88 405	97.92 533	97.49 452	97.72 474	99.34 395	97.93 439	97.65 513	97.11 540	77.69 543	99.83 338	98.79 232	79.72 555	99.33 351

kuosan		85.65 516	84.57 519	88.90 534	97.91 534	77.11 560	96.37 533	87.62 560	85.24 548	85.45 554	96.83 546	69.94 557	90.98 555	45.90 556	95.83 546	98.62 478

MGCNet		98.61 351	98.30 381	99.52 234	97.88 535	98.95 324	98.76 349	94.11 548	99.84 76	99.32 351	99.57 317	95.57 413	99.95 81	99.68 66	99.98 54	99.68 126

test0.0.03 1		97.37 449	96.91 461	98.74 420	97.72 536	97.57 449	97.60 484	97.36 523	98.00 428	99.21 379	98.02 518	90.04 501	99.79 392	98.37 285	95.89 545	98.86 460

wanda-best-256-512		97.53 440	97.14 451	98.72 422	97.71 537	96.86 479	97.00 514	99.34 395	97.73 452	98.18 478	96.82 547	91.92 469	99.84 315	99.02 194	96.53 536	99.45 297

FE-blended-shiyan7		97.53 440	97.14 451	98.72 422	97.71 537	96.86 479	97.00 514	99.34 395	97.73 452	98.18 478	96.82 547	91.92 469	99.84 315	99.02 194	96.53 536	99.45 297

usedtu_blend_shiyan5		97.97 417	97.65 433	98.92 391	97.71 537	97.49 452	99.53 92	99.81 135	99.52 191	98.18 478	96.82 547	91.92 469	99.83 338	98.79 232	96.53 536	99.45 297

GG-mvs-BLEND				97.36 495	97.59 540	96.87 478	99.70 38	88.49 558		94.64 544	97.26 538	80.66 530	99.12 528	91.50 533	96.50 541	96.08 534

gm-plane-assit					97.59 540	89.02 557			93.47 533		98.30 512		99.84 315	96.38 465

cascas		96.99 459	96.82 465	97.48 485	97.57 542	95.64 509	96.43 532	99.56 308	91.75 540	97.13 526	97.61 531	95.58 412	98.63 540	96.68 443	99.11 453	98.18 507

ALIKED-NN		96.66 470	96.26 472	97.88 470	97.49 543	98.59 379	96.71 527	99.15 440	95.50 516	93.58 547	98.39 510	94.52 435	97.74 547	92.05 531	98.94 467	97.29 528

SP-DiffGlue		98.47 372	98.43 364	98.59 432	97.44 544	98.59 379	98.01 450	99.36 390	99.00 296	99.06 405	99.20 439	97.01 361	99.25 524	97.64 369	99.15 450	97.92 518

gbinet_0.2-2-1-0.02		97.52 442	97.07 453	98.88 405	97.35 545	97.35 463	97.17 505	99.25 419	97.86 447	98.41 468	96.54 553	90.74 491	99.85 298	98.80 231	97.51 527	99.43 319

SP-NN		96.37 479	96.23 474	96.77 513	96.83 546	96.95 474	96.47 531	97.07 527	96.75 499	93.41 548	97.75 525	94.13 439	95.69 550	96.25 470	97.43 528	97.68 521

XFeat-MNN		96.67 469	96.56 467	96.98 510	96.73 547	95.62 511	94.54 543	98.93 458	97.42 470	98.18 478	98.67 499	91.60 477	99.12 528	93.88 526	99.10 454	96.21 531

EPNet_dtu		97.62 434	97.79 424	97.11 507	96.67 548	92.31 539	98.51 394	98.04 505	99.24 253	95.77 538	99.47 359	93.78 445	99.66 478	98.98 199	99.62 366	99.37 338
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
0.4-1-1-0.1		93.18 510	91.66 514	97.73 479	95.83 549	95.29 516	95.30 540	95.90 536	93.59 532	90.58 551	94.40 559	77.87 541	99.77 409	97.31 394	84.20 550	98.15 508

XFeat-NN		93.89 509	93.91 511	93.83 530	95.49 550	92.69 537	90.85 546	97.98 507	94.69 529	95.08 542	96.98 542	88.36 508	94.23 553	88.42 541	97.34 529	94.57 535

KD-MVS_2432*1600		95.89 493	95.41 494	97.31 499	94.96 551	93.89 528	97.09 509	99.22 427	97.23 480	98.88 424	99.04 460	79.23 537	99.54 504	96.24 472	96.81 533	98.50 491

miper_refine_blended		95.89 493	95.41 494	97.31 499	94.96 551	93.89 528	97.09 509	99.22 427	97.23 480	98.88 424	99.04 460	79.23 537	99.54 504	96.24 472	96.81 533	98.50 491

0.3-1-1-0.015		92.36 512	90.68 516	97.39 492	94.94 553	94.41 526	94.21 544	95.89 537	92.87 535	88.87 553	93.49 562	75.30 547	99.76 416	97.19 412	83.41 552	98.02 513

GLUNet-SfM		95.26 505	95.06 502	95.87 527	94.84 554	90.39 553	90.24 548	99.92 47	92.30 538	99.16 387	99.25 424	94.69 432	98.01 545	85.55 549	99.62 366	99.21 379

0.4-1-1-0.2		92.59 511	91.07 515	97.15 505	94.73 555	93.68 532	93.50 545	95.91 534	92.68 536	90.48 552	93.52 561	77.77 542	99.75 427	97.19 412	83.88 551	98.01 514

EPNet		98.13 406	97.77 426	99.18 352	94.57 556	97.99 428	99.24 194	97.96 508	99.74 112	97.29 520	99.62 276	93.13 454	99.97 44	98.59 265	99.83 246	99.58 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_method		91.72 513	92.32 513	89.91 533	93.49 557	70.18 561	90.28 547	99.56 308	61.71 553	95.39 540	99.52 339	93.90 441	99.94 98	98.76 239	98.27 508	99.62 188

tmp_tt		95.75 498	95.42 492	96.76 514	89.90 558	94.42 525	98.86 327	97.87 513	78.01 549	99.30 361	99.69 216	97.70 319	95.89 549	99.29 134	98.14 515	99.95 15

MVS_clip		74.80 518	77.14 520	67.78 536	84.58 559	66.83 562	78.80 549	52.59 563	49.02 554	94.13 546	97.99 521	68.69 558	48.60 558	80.92 551	87.52 549	87.92 551

VLMVS_CLIP		76.68 517	76.70 521	76.61 535	60.81 560	61.63 563	78.48 550	91.77 552	64.66 552	83.93 555	93.59 560	55.35 559	75.94 556	79.82 552	81.86 553	92.28 550

VLMVS		62.60 519	63.55 522	59.72 537	60.35 561	58.44 564	68.37 551	54.75 562	23.35 556	80.04 556	90.18 564	54.59 560	52.33 557	63.04 555	77.30 556	68.41 553

MVS_baseline		39.37 520	46.36 523	18.41 538	48.75 562	10.55 566	42.43 552	13.32 565	4.65 559	75.25 557	91.61 563	29.41 561	0.06 561	38.83 557	72.99 557	44.63 554

testmvs		28.94 522	33.33 524	15.79 540	26.03 563	9.81 567	96.77 523	15.67 564	11.55 558	23.87 560	50.74 568	19.03 563	8.53 560	23.21 559	33.07 558	29.03 556

test123		29.31 521	33.05 526	18.08 539	25.93 564	12.24 565	97.53 488	10.93 566	11.78 557	24.21 559	50.08 569	21.04 562	8.60 559	23.51 558	32.43 559	33.39 555

PatchmatchNet2					0.00 565	95.19 519	97.64 480	99.19 434	98.09 422
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
mmdepth		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

monomultidepth		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

test_blank		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

eth-test2					0.00 565

eth-test					0.00 565

uanet_test		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

DCPMVS		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

cdsmvs_eth3d_5k		24.88 523	33.17 525	0.00 541	0.00 565	0.00 568	0.00 553	99.62 265	0.00 560	0.00 561	99.13 445	99.82 18	0.00 562	0.00 560	0.00 560	0.00 557

pcd_1.5k_mvsjas		16.61 524	22.14 527	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	99.28 93	0.00 562	0.00 560	0.00 560	0.00 557

sosnet-low-res		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

sosnet		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

uncertanet		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

Regformer		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

ab-mvs-re		8.26 535	11.02 538	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	99.16 442	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

uanet		8.33 525	11.11 528	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	100.00 1	0.00 564	0.00 562	0.00 560	0.00 560	0.00 557

PatchmatchNet1														98.28 292	99.92 158	99.44 312
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.93 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						96.36 490								95.20 506

PC_three_1452									97.56 459	99.68 208	99.41 371	99.09 127	97.09 548	96.66 445	99.60 377	99.62 188

test_241102_TWO								99.54 321	99.13 279	99.76 160	99.63 266	98.32 263	99.92 154	97.85 338	99.69 342	99.75 89

test_0728_THIRD									99.18 263	99.62 248	99.61 286	98.58 215	99.91 186	97.72 352	99.80 273	99.77 81

GSMVS																99.14 401

sam_mvs1												90.81 490				99.14 401

sam_mvs												90.52 496

MTGPA								99.53 332

test_post1							99.14 233				51.63 567	89.54 504	99.82 361	96.86 431

test_post											52.41 566	90.25 499	99.86 279

patchmatchnet-post											99.62 276	90.58 494	99.94 98

MTMP							99.09 259	98.59 479

test9_res														95.10 508	99.44 413	99.50 277

agg_prior2														94.58 515	99.46 412	99.50 277

test_prior4						99.19 280	98.00 453

test_prior2							97.95 459		97.87 445	98.05 489	99.05 458	97.90 304		95.99 483	99.49 406

旧先验2							97.94 460		95.33 519	98.94 416			99.88 242	96.75 439

新几何2							98.04 447

无先验							98.01 450	99.23 424	95.83 511				99.85 298	95.79 494		99.44 312

原ACMM2							97.92 462

testdata2													99.89 227	95.99 483

segment_acmp												98.37 255

testdata1							97.72 474		97.86 447

plane_prior5								99.54 321					99.82 361	95.84 491	99.78 287	99.60 208

plane_prior4											99.25 424

plane_prior3						99.31 245			98.36 391	99.14 392

plane_prior2							98.80 343		98.94 305

plane_prior						99.24 264	98.42 408		97.87 445						99.71 330

n2								0.00 567

nn								0.00 567

door-mid								99.83 115

test11								99.29 410

door								99.77 170

HQP5-MVS						98.94 326

BP-MVS														94.73 512

HQP4-MVS										98.15 483			99.70 448			99.53 257

HQP3-MVS								99.37 386							99.67 353

HQP2-MVS												96.67 373

MDTV_nov1_ep13_2view						91.44 546	99.14 233		97.37 473	99.21 379		91.78 476		96.75 439		99.03 434

ACMMP++_ref															99.94 135

ACMMP++															99.79 279

Test By Simon												98.41 249

High-resolution multi-view benchmark