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		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 14	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 17	100.00 1	99.85 31

Gipuma		99.03 89	99.16 63	98.64 237	99.94 2	98.51 114	99.32 26	99.75 44	99.58 39	98.60 301	99.62 41	98.22 114	99.51 435	97.70 209	99.73 200	97.89 473
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
OurMVSNet-221017-0		99.37 28	99.31 42	99.53 38	99.91 3	98.98 72	99.63 7	99.58 104	99.44 53	99.78 40	99.76 16	96.39 266	99.92 66	99.44 55	99.92 72	99.68 74

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 26	99.53 9	99.76 40	99.64 26	99.84 31	99.83 4	99.50 9	99.87 136	99.36 58	99.92 72	99.64 87

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 149	99.20 49	99.65 78	99.48 45	99.92 8	99.71 23	98.07 129	99.96 13	99.53 49	100.00 1	99.93 12

testf1		99.25 41	99.16 63	99.51 49	99.89 6	99.63 3	98.71 106	99.69 58	98.90 137	99.43 109	99.35 112	98.86 35	99.67 349	97.81 192	99.81 141	99.24 303

APD_test2		99.25 41	99.16 63	99.51 49	99.89 6	99.63 3	98.71 106	99.69 58	98.90 137	99.43 109	99.35 112	98.86 35	99.67 349	97.81 192	99.81 141	99.24 303

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 158	99.14 58	99.93 6	99.82 8	99.93 6	99.81 8	99.17 20	99.94 42	99.31 62	100.00 1	99.82 37

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 58	98.93 133	99.65 64	99.72 22	98.93 33	99.95 26	99.11 78	100.00 1	99.82 37

v7n		99.53 12	99.57 13	99.41 69	99.88 9	98.54 112	99.45 14	99.61 93	99.66 23	99.68 58	99.66 33	98.44 85	99.95 26	99.73 29	99.96 29	99.75 63

mvs_tets		99.63 6	99.67 6	99.49 55	99.88 9	98.61 104	99.34 23	99.71 49	99.27 75	99.90 14	99.74 19	99.68 4	99.97 6	99.55 44	99.99 5	99.88 21

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 74	99.87 12	98.13 152	98.08 196	99.95 2	99.45 51	99.98 2	99.75 17	99.80 1	99.97 6	99.82 13	99.99 5	99.99 2

jajsoiax		99.58 9	99.61 11	99.48 57	99.87 12	98.61 104	99.28 40	99.66 72	99.09 111	99.89 18	99.68 26	99.53 7	99.97 6	99.50 51	99.99 5	99.87 23

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 92	99.39 20	99.56 122	99.11 101	99.70 52	99.73 21	99.00 27	99.97 6	99.26 66	99.98 12	99.89 17

MIMVSNet1		99.38 27	99.32 40	99.55 28	99.86 14	99.19 41	99.41 17	99.59 101	99.59 37	99.71 50	99.57 50	97.12 215	99.90 82	99.21 71	99.87 101	99.54 144

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 26	99.85 16	99.11 64	99.90 1	99.78 37	99.63 29	99.78 40	99.67 31	99.48 10	99.81 227	99.30 63	99.97 21	99.77 54
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
UniMVSNet_ETH3D		99.69 2	99.69 4	99.69 3	99.84 17	99.34 19	99.69 5	99.58 104	99.90 3	99.86 24	99.78 14	99.58 6	99.95 26	99.00 88	99.95 40	99.78 51

SixPastTwentyTwo		98.75 139	98.62 155	99.16 119	99.83 18	97.96 181	99.28 40	98.20 427	99.37 61	99.70 52	99.65 37	92.65 401	99.93 54	99.04 85	99.84 115	99.60 103

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 83	99.88 4	99.86 24	99.80 12	99.03 24	99.89 98	99.48 53	99.93 58	99.60 103

Baseline_NR-MVSNet		98.98 99	98.86 116	99.36 74	99.82 19	98.55 109	97.47 308	99.57 112	99.37 61	99.21 175	99.61 44	96.76 243	99.83 198	98.06 165	99.83 127	99.71 66

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 101	99.29 36	99.63 83	99.30 72	99.65 64	99.60 46	99.16 22	99.82 210	99.07 81	99.83 127	99.56 131

TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 107	99.27 42	99.57 112	99.39 59	99.75 45	99.62 41	99.17 20	99.83 198	99.06 83	99.62 268	99.66 81

K. test v3		98.00 269	97.66 299	99.03 149	99.79 23	97.56 229	99.19 53	92.47 534	99.62 33	99.52 88	99.66 33	89.61 442	99.96 13	99.25 68	99.81 141	99.56 131

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 154	97.77 255	99.90 12	99.33 67	99.97 3	99.66 33	99.71 3	99.96 13	99.79 20	99.99 5	99.96 9

APD_test1		98.83 123	98.66 147	99.34 83	99.78 24	99.47 8	98.42 151	99.45 181	98.28 200	98.98 215	99.19 160	97.76 159	99.58 406	96.57 324	99.55 299	98.97 365

test_vis3_rt		99.14 63	99.17 61	99.07 139	99.78 24	98.38 124	98.92 83	99.94 3	97.80 248	99.91 12	99.67 31	97.15 213	98.91 505	99.76 24	99.56 294	99.92 13

EGC-MVSNET		85.24 513	80.54 516	99.34 83	99.77 27	99.20 38	99.08 62	99.29 264	12.08 556	20.84 559	99.42 90	97.55 179	99.85 159	97.08 266	99.72 209	98.96 368

Anonymous20240521		98.69 152	98.87 112	98.16 319	99.77 27	95.11 390	99.08 62	99.44 189	99.34 66	99.33 139	99.55 57	94.10 368	99.94 42	99.25 68	99.96 29	99.42 220

FC-MVSNet-test		99.27 38	99.25 53	99.34 83	99.77 27	98.37 126	99.30 35	99.57 112	99.61 35	99.40 118	99.50 69	97.12 215	99.85 159	99.02 87	99.94 52	99.80 46

test_vis1_n		98.31 228	98.50 176	97.73 367	99.76 30	94.17 424	98.68 109	99.91 10	96.31 377	99.79 39	99.57 50	92.85 397	99.42 460	99.79 20	99.84 115	99.60 103

test_fmvs3		99.12 70	99.41 26	98.25 306	99.76 30	95.07 391	99.05 68	99.94 3	97.78 252	99.82 35	99.84 3	98.56 74	99.71 313	99.96 1	99.96 29	99.97 4

XXY-MVS		99.14 63	99.15 68	99.10 131	99.76 30	97.74 213	98.85 93	99.62 90	98.48 182	99.37 126	99.49 75	98.75 48	99.86 145	98.20 153	99.80 153	99.71 66

TDRefinement		99.42 23	99.38 28	99.55 28	99.76 30	99.33 20	99.68 6	99.71 49	99.38 60	99.53 84	99.61 44	98.64 62	99.80 236	98.24 148	99.84 115	99.52 162

dtuonlycased		97.70 304	98.19 237	96.24 459	99.75 34	89.51 521	94.69 487	99.64 80	98.23 202	99.46 102	98.57 341	98.25 108	99.85 159	95.65 384	99.44 336	99.36 253

fmvsm_s_conf0.1_n_a		99.17 53	99.30 45	98.80 198	99.75 34	96.59 309	97.97 228	99.86 17	98.22 204	99.88 21	99.71 23	98.59 68	99.84 180	99.73 29	99.98 12	99.98 3

tt0805		98.69 152	98.62 155	98.90 179	99.75 34	99.30 21	99.15 57	96.97 471	98.86 143	98.87 250	97.62 439	98.63 64	98.96 501	99.41 57	98.29 462	98.45 437

test_vis1_n_1920		98.40 208	98.92 103	96.81 437	99.74 37	90.76 510	98.15 184	99.91 10	98.33 191	99.89 18	99.55 57	95.07 330	99.88 116	99.76 24	99.93 58	99.79 48

usedtu_dtu_shiyan2		98.99 95	98.86 116	99.39 72	99.73 38	98.71 98	99.05 68	99.47 172	99.16 95	99.49 95	99.12 187	96.34 272	99.93 54	98.05 167	99.36 348	99.54 144

FOURS1					99.73 38	99.67 2	99.43 15	99.54 133	99.43 55	99.26 158

PEN-MVS		99.41 24	99.34 36	99.62 9	99.73 38	99.14 57	99.29 36	99.54 133	99.62 33	99.56 75	99.42 90	98.16 123	99.96 13	98.78 103	99.93 58	99.77 54

lessismore_v0				98.97 163	99.73 38	97.53 232		86.71 552		99.37 126	99.52 68	89.93 437	99.92 66	98.99 89	99.72 209	99.44 211

SteuartSystems-ACMMP		98.79 132	98.54 168	99.54 31	99.73 38	99.16 48	98.23 173	99.31 248	97.92 238	98.90 238	98.90 258	98.00 135	99.88 116	96.15 359	99.72 209	99.58 118
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended_VisFu		98.17 252	98.15 244	98.22 312	99.73 38	95.15 387	97.36 322	99.68 65	94.45 459	98.99 214	99.27 132	96.87 232	99.94 42	97.13 263	99.91 81	99.57 125

Vis-MVSNet		99.34 30	99.36 33	99.27 99	99.73 38	98.26 138	99.17 54	99.78 37	99.11 101	99.27 154	99.48 76	98.82 39	99.95 26	98.94 92	99.93 58	99.59 110
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_l_mol_unc0.5_1		99.35 29	99.38 28	99.25 104	99.72 45	97.83 197	96.88 364	99.84 23	99.64 26	99.86 24	99.81 8	98.84 37	99.96 13	99.86 4	99.97 21	99.97 4

tt0320-xc		99.64 5	99.68 5	99.50 54	99.72 45	98.98 72	99.51 10	99.85 19	99.86 6	99.88 21	99.82 5	99.02 26	99.90 82	99.54 45	99.95 40	99.61 101

SSC-MVS		98.71 143	98.74 129	98.62 243	99.72 45	96.08 337	98.74 99	98.64 398	99.74 12	99.67 60	99.24 145	94.57 347	99.95 26	99.11 78	99.24 374	99.82 37

test_f		98.67 162	98.87 112	98.05 334	99.72 45	95.59 356	98.51 135	99.81 33	96.30 379	99.78 40	99.82 5	96.14 281	98.63 513	99.82 13	99.93 58	99.95 10

ACMH	96.65 7	99.25 41	99.24 54	99.26 101	99.72 45	98.38 124	99.07 65	99.55 127	98.30 195	99.65 64	99.45 85	99.22 17	99.76 274	98.44 132	99.77 173	99.64 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0320		99.61 8	99.65 9	99.48 57	99.71 50	98.94 79	99.54 8	99.83 27	99.87 5	99.89 18	99.82 5	98.75 48	99.90 82	99.54 45	99.95 40	99.59 110

fmvsm_s_conf0.1_n		99.16 57	99.33 38	98.64 237	99.71 50	96.10 332	97.87 241	99.85 19	98.56 178	99.90 14	99.68 26	98.69 58	99.85 159	99.72 31	99.98 12	99.97 4

PS-CasMVS		99.40 25	99.33 38	99.62 9	99.71 50	99.10 65	99.29 36	99.53 137	99.53 42	99.46 102	99.41 95	98.23 111	99.95 26	98.89 97	99.95 40	99.81 42

DTE-MVSNet		99.43 22	99.35 34	99.66 7	99.71 50	99.30 21	99.31 30	99.51 145	99.64 26	99.56 75	99.46 81	98.23 111	99.97 6	98.78 103	99.93 58	99.72 65

WR-MVS_H		99.33 31	99.22 55	99.65 8	99.71 50	99.24 29	99.32 26	99.55 127	99.46 50	99.50 94	99.34 116	97.30 202	99.93 54	98.90 95	99.93 58	99.77 54

HPM-MVS_fast		99.01 91	98.82 122	99.57 21	99.71 50	99.35 16	99.00 73	99.50 150	97.33 302	98.94 233	98.86 269	98.75 48	99.82 210	97.53 225	99.71 218	99.56 131

ACMH+	96.62 9	99.08 80	99.00 95	99.33 89	99.71 50	98.83 87	98.60 121	99.58 104	99.11 101	99.53 84	99.18 164	98.81 40	99.67 349	96.71 307	99.77 173	99.50 170

PMVS	91.26 20	97.86 286	97.94 269	97.65 376	99.71 50	97.94 184	98.52 130	98.68 393	98.99 125	97.52 405	99.35 112	97.41 195	98.18 520	91.59 496	99.67 247	96.82 510
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FE-MVSNET2		99.15 58	99.22 55	98.94 168	99.70 58	97.49 233	98.62 118	99.67 71	98.85 146	99.34 136	99.54 63	98.47 78	99.81 227	98.93 93	99.91 81	99.51 166

KinetiMVS		99.03 89	99.02 91	99.03 149	99.70 58	97.48 236	98.43 148	99.29 264	99.70 15	99.60 72	99.07 200	96.13 283	99.94 42	99.42 56	99.87 101	99.68 74

FIs		99.14 63	99.09 83	99.29 95	99.70 58	98.28 136	99.13 59	99.52 143	99.48 45	99.24 168	99.41 95	96.79 240	99.82 210	98.69 113	99.88 96	99.76 59

VPNet		98.87 113	98.83 121	99.01 154	99.70 58	97.62 226	98.43 148	99.35 229	99.47 48	99.28 152	99.05 208	96.72 247	99.82 210	98.09 162	99.36 348	99.59 110

fmvsm_s_conf0.1_n_2		99.20 51	99.38 28	98.65 235	99.69 62	96.08 337	97.49 303	99.90 12	99.53 42	99.88 21	99.64 38	98.51 77	99.90 82	99.83 11	99.98 12	99.97 4

test_cas_vis1_n_1920		98.33 223	98.68 142	97.27 409	99.69 62	92.29 481	98.03 207	99.85 19	97.62 264	99.96 4	99.62 41	93.98 369	99.74 293	99.52 50	99.86 108	99.79 48

MP-MVS-pluss		98.57 179	98.23 231	99.60 16	99.69 62	99.35 16	97.16 345	99.38 215	94.87 445	98.97 219	98.99 232	98.01 134	99.88 116	97.29 246	99.70 229	99.58 118
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SDMVSNet		99.23 46	99.32 40	98.96 165	99.68 65	97.35 246	98.84 95	99.48 160	99.69 17	99.63 67	99.68 26	99.03 24	99.96 13	97.97 178	99.92 72	99.57 125

sd_testset		99.28 37	99.31 42	99.19 113	99.68 65	98.06 168	99.41 17	99.30 256	99.69 17	99.63 67	99.68 26	99.25 16	99.96 13	97.25 249	99.92 72	99.57 125

test_fmvs1_n		98.09 260	98.28 220	97.52 394	99.68 65	93.47 457	98.63 116	99.93 6	95.41 430	99.68 58	99.64 38	91.88 416	99.48 444	99.82 13	99.87 101	99.62 93

CHOSEN 1792x2688		97.49 320	97.14 337	98.54 266	99.68 65	96.09 335	96.50 395	99.62 90	91.58 507	98.84 255	98.97 239	92.36 404	99.88 116	96.76 299	99.95 40	99.67 79

tfpnnormal		98.90 109	98.90 106	98.91 176	99.67 69	97.82 203	99.00 73	99.44 189	99.45 51	99.51 93	99.24 145	98.20 118	99.86 145	95.92 369	99.69 235	99.04 351

MTAPA		98.88 112	98.64 151	99.61 13	99.67 69	99.36 15	98.43 148	99.20 291	98.83 150	98.89 241	98.90 258	96.98 226	99.92 66	97.16 256	99.70 229	99.56 131

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 266	99.66 71	96.97 286	98.00 215	99.85 19	99.24 78	99.92 8	99.50 69	99.39 12	99.95 26	99.89 3	99.98 12	98.71 410

mvs5depth		99.30 34	99.59 12	98.44 282	99.65 72	95.35 375	99.82 3	99.94 3	99.83 7	99.42 113	99.94 2	98.13 126	99.96 13	99.63 37	99.96 29	100.00 1

fmvsm_l_conf0.5_n_a		99.19 52	99.27 48	98.94 168	99.65 72	97.05 281	97.80 250	99.76 40	98.70 160	99.78 40	99.11 189	98.79 44	99.95 26	99.85 7	99.96 29	99.83 34

WB-MVS		98.52 194	98.55 166	98.43 283	99.65 72	95.59 356	98.52 130	98.77 381	99.65 25	99.52 88	99.00 230	94.34 357	99.93 54	98.65 115	98.83 425	99.76 59

CP-MVSNet		99.21 48	99.09 83	99.56 26	99.65 72	98.96 78	99.13 59	99.34 235	99.42 56	99.33 139	99.26 138	97.01 224	99.94 42	98.74 108	99.93 58	99.79 48

HPM-MVS		98.79 132	98.53 170	99.59 20	99.65 72	99.29 23	99.16 55	99.43 195	96.74 355	98.61 298	98.38 367	98.62 65	99.87 136	96.47 336	99.67 247	99.59 110
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RPSCF		98.62 171	98.36 205	99.42 67	99.65 72	99.42 10	98.55 126	99.57 112	97.72 257	98.90 238	99.26 138	96.12 285	99.52 429	95.72 380	99.71 218	99.32 274

NormalMVS		98.26 236	97.97 265	99.15 124	99.64 78	97.83 197	98.28 167	99.43 195	99.24 78	98.80 264	98.85 272	89.76 440	99.94 42	98.04 168	99.67 247	99.68 74

lecture		99.25 41	99.12 72	99.62 9	99.64 78	99.40 11	98.89 88	99.51 145	99.19 90	99.37 126	99.25 143	98.36 91	99.88 116	98.23 150	99.67 247	99.59 110

fmvsm_l_conf0.5_n		99.21 48	99.28 47	99.02 152	99.64 78	97.28 258	97.82 246	99.76 40	98.73 152	99.82 35	99.09 198	98.81 40	99.95 26	99.86 4	99.96 29	99.83 34

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 78	98.10 157	97.68 270	99.84 23	99.29 73	99.92 8	99.57 50	99.60 5	99.96 13	99.74 28	99.98 12	99.89 17

TSAR-MVS + MP.		98.63 168	98.49 181	99.06 145	99.64 78	97.90 190	98.51 135	98.94 345	96.96 335	99.24 168	98.89 264	97.83 152	99.81 227	96.88 289	99.49 324	99.48 189
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PM-MVS		98.82 126	98.72 133	99.12 127	99.64 78	98.54 112	97.98 224	99.68 65	97.62 264	99.34 136	99.18 164	97.54 181	99.77 268	97.79 194	99.74 196	99.04 351

Elysia		99.15 58	99.14 69	99.18 114	99.63 84	97.92 186	98.50 137	99.43 195	99.67 20	99.70 52	99.13 183	96.66 250	99.98 4	99.54 45	99.96 29	99.64 87

StellarMVS		99.15 58	99.14 69	99.18 114	99.63 84	97.92 186	98.50 137	99.43 195	99.67 20	99.70 52	99.13 183	96.66 250	99.98 4	99.54 45	99.96 29	99.64 87

KD-MVS_self_test		99.25 41	99.18 60	99.44 65	99.63 84	99.06 70	98.69 108	99.54 133	99.31 70	99.62 70	99.53 65	97.36 199	99.86 145	99.24 70	99.71 218	99.39 233

EU-MVSNet		97.66 308	98.50 176	95.13 500	99.63 84	85.84 536	98.35 161	98.21 426	98.23 202	99.54 80	99.46 81	95.02 331	99.68 344	98.24 148	99.87 101	99.87 23

HyFIR lowres test		97.19 350	96.60 381	98.96 165	99.62 88	97.28 258	95.17 471	99.50 150	94.21 465	99.01 209	98.32 377	86.61 463	99.99 2	97.10 265	99.84 115	99.60 103

E5new		99.05 84	99.11 75	98.85 183	99.60 89	97.30 252	98.42 151	99.63 83	98.73 152	99.26 158	99.39 101	98.71 52	99.70 322	98.43 134	99.84 115	99.54 144

E6new		99.05 84	99.11 75	98.85 183	99.60 89	97.30 252	98.42 151	99.63 83	98.73 152	99.26 158	99.39 101	98.71 52	99.70 322	98.43 134	99.84 115	99.54 144

E6		99.05 84	99.11 75	98.85 183	99.60 89	97.30 252	98.42 151	99.63 83	98.73 152	99.26 158	99.39 101	98.71 52	99.70 322	98.43 134	99.84 115	99.54 144

E5		99.05 84	99.11 75	98.85 183	99.60 89	97.30 252	98.42 151	99.63 83	98.73 152	99.26 158	99.39 101	98.71 52	99.70 322	98.43 134	99.84 115	99.54 144

fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 161	99.59 93	97.18 272	97.44 312	99.83 27	99.56 40	99.91 12	99.34 116	99.36 13	99.93 54	99.83 11	99.98 12	99.85 31

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 93	98.21 146	97.82 246	99.84 23	99.41 58	99.92 8	99.41 95	99.51 8	99.95 26	99.84 10	99.97 21	99.87 23

aaatest				99.45 64	99.58 95	98.93 80	98.68 109	99.60 95	96.46 371	99.53 84	98.77 292		99.83 198	96.67 313	99.64 259	99.58 118

MED-MVS		99.01 91	98.84 120	99.52 44	99.58 95	98.93 80	98.68 109	99.60 95	98.85 146	99.53 84	99.16 171	97.87 150	99.83 198	96.67 313	99.62 268	99.81 42

TestfortrainingZip a		99.09 74	98.92 103	99.61 13	99.58 95	99.17 43	98.68 109	99.27 271	98.85 146	99.61 71	99.16 171	97.14 214	99.86 145	98.39 139	99.57 290	99.81 42

FE-MVSNET		98.59 176	98.50 176	98.87 180	99.58 95	97.30 252	98.08 196	99.74 45	96.94 337	98.97 219	99.10 192	96.94 228	99.74 293	97.33 242	99.86 108	99.55 138

mmtdpeth		99.30 34	99.42 25	98.92 174	99.58 95	96.89 294	99.48 13	99.92 8	99.92 2	98.26 343	99.80 12	98.33 97	99.91 75	99.56 42	99.95 40	99.97 4

ACMMP_NAP		98.75 139	98.48 182	99.57 21	99.58 95	99.29 23	97.82 246	99.25 279	96.94 337	98.78 266	99.12 187	98.02 133	99.84 180	97.13 263	99.67 247	99.59 110

nrg030		99.40 25	99.35 34	99.54 31	99.58 95	99.13 60	98.98 76	99.48 160	99.68 19	99.46 102	99.26 138	98.62 65	99.73 300	99.17 75	99.92 72	99.76 59

VDDNet		98.21 244	97.95 266	99.01 154	99.58 95	97.74 213	99.01 71	97.29 459	99.67 20	98.97 219	99.50 69	90.45 434	99.80 236	97.88 185	99.20 383	99.48 189

COLMAP_ROB	96.50 10	98.99 95	98.85 119	99.41 69	99.58 95	99.10 65	98.74 99	99.56 122	99.09 111	99.33 139	99.19 160	98.40 87	99.72 311	95.98 367	99.76 189	99.42 220
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_fmvsm_n_1920		99.33 31	99.45 23	98.99 157	99.57 104	97.73 215	97.93 230	99.83 27	99.22 81	99.93 6	99.30 126	99.42 11	99.96 13	99.85 7	99.99 5	99.29 285

ZNCC-MVS		98.68 158	98.40 194	99.54 31	99.57 104	99.21 32	98.46 145	99.29 264	97.28 309	98.11 355	98.39 365	98.00 135	99.87 136	96.86 292	99.64 259	99.55 138

MSP-MVS		98.40 208	98.00 260	99.61 13	99.57 104	99.25 28	98.57 124	99.35 229	97.55 275	99.31 148	97.71 432	94.61 346	99.88 116	96.14 360	99.19 386	99.70 71
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
testgi		98.32 224	98.39 197	98.13 321	99.57 104	95.54 359	97.78 252	99.49 158	97.37 299	99.19 177	97.65 436	98.96 30	99.49 440	96.50 335	98.99 413	99.34 263

MP-MVS		98.46 200	98.09 249	99.54 31	99.57 104	99.22 31	98.50 137	99.19 295	97.61 267	97.58 399	98.66 322	97.40 196	99.88 116	94.72 411	99.60 277	99.54 144
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LPG-MVS_test		98.71 143	98.46 186	99.47 61	99.57 104	98.97 74	98.23 173	99.48 160	96.60 362	99.10 190	99.06 201	98.71 52	99.83 198	95.58 389	99.78 165	99.62 93

LGP-MVS_train				99.47 61	99.57 104	98.97 74		99.48 160	96.60 362	99.10 190	99.06 201	98.71 52	99.83 198	95.58 389	99.78 165	99.62 93

IS-MVSNet		98.19 247	97.90 275	99.08 137	99.57 104	97.97 178	99.31 30	98.32 420	99.01 124	98.98 215	99.03 213	91.59 418	99.79 250	95.49 392	99.80 153	99.48 189

viewdifsd2359ckpt11		98.84 120	99.04 88	98.24 308	99.56 112	95.51 361	97.38 317	99.70 55	99.16 95	99.57 73	99.40 98	98.26 106	99.71 313	98.55 126	99.82 134	99.50 170

viewmsd2359difaftdt		98.84 120	99.04 88	98.24 308	99.56 112	95.51 361	97.38 317	99.70 55	99.16 95	99.57 73	99.40 98	98.26 106	99.71 313	98.55 126	99.82 134	99.50 170

dcpmvs_2		98.78 134	99.11 75	97.78 357	99.56 112	93.67 452	99.06 66	99.86 17	99.50 44	99.66 61	99.26 138	97.21 210	99.99 2	98.00 173	99.91 81	99.68 74

test_0402		98.76 138	98.71 136	98.93 171	99.56 112	98.14 151	98.45 147	99.34 235	99.28 74	98.95 225	98.91 255	98.34 96	99.79 250	95.63 385	99.91 81	98.86 386

EPP-MVSNet		98.30 229	98.04 256	99.07 139	99.56 112	97.83 197	99.29 36	98.07 434	99.03 122	98.59 303	99.13 183	92.16 409	99.90 82	96.87 290	99.68 241	99.49 178

ACMMP		98.75 139	98.50 176	99.52 44	99.56 112	99.16 48	98.87 89	99.37 219	97.16 325	98.82 260	99.01 226	97.71 162	99.87 136	96.29 351	99.69 235	99.54 144
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
casdiffseed414692147		99.09 74	99.12 72	99.01 154	99.55 118	97.91 188	98.30 165	99.68 65	99.04 120	99.19 177	99.37 105	98.98 28	99.61 390	98.13 157	99.83 127	99.50 170

fmvsm_s_conf0.5_n_a		99.10 73	99.20 59	98.78 205	99.55 118	96.59 309	97.79 251	99.82 32	98.21 206	99.81 37	99.53 65	98.46 83	99.84 180	99.70 34	99.97 21	99.90 16

fmvsm_s_conf0.5_n		99.09 74	99.26 51	98.61 247	99.55 118	96.09 335	97.74 263	99.81 33	98.55 179	99.85 28	99.55 57	98.60 67	99.84 180	99.69 36	99.98 12	99.89 17

FMVSNet1		99.17 53	99.17 61	99.17 116	99.55 118	98.24 140	99.20 49	99.44 189	99.21 83	99.43 109	99.55 57	97.82 155	99.86 145	98.42 138	99.89 95	99.41 223

Vis-MVSNet (Re-imp)		97.46 322	97.16 334	98.34 296	99.55 118	96.10 332	98.94 81	98.44 413	98.32 193	98.16 349	98.62 334	88.76 447	99.73 300	93.88 438	99.79 160	99.18 325

ACMM	96.08 12	98.91 107	98.73 131	99.48 57	99.55 118	99.14 57	98.07 200	99.37 219	97.62 264	99.04 204	98.96 243	98.84 37	99.79 250	97.43 236	99.65 257	99.49 178
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
hybridcas		99.08 80	99.13 71	98.92 174	99.54 124	97.61 227	98.22 177	99.66 72	99.27 75	99.40 118	99.24 145	98.47 78	99.70 322	98.59 119	99.80 153	99.46 201

test_fmvs2		98.70 148	98.97 99	97.89 347	99.54 124	94.05 429	98.55 126	99.92 8	96.78 353	99.72 48	99.78 14	96.60 255	99.67 349	99.91 2	99.90 89	99.94 11

mPP-MVS		98.64 166	98.34 209	99.54 31	99.54 124	99.17 43	98.63 116	99.24 285	97.47 284	98.09 357	98.68 316	97.62 171	99.89 98	96.22 354	99.62 268	99.57 125

XVG-ACMP-BASELINE		98.56 181	98.34 209	99.22 110	99.54 124	98.59 106	97.71 266	99.46 177	97.25 313	98.98 215	98.99 232	97.54 181	99.84 180	95.88 370	99.74 196	99.23 305

Casviewmamba		99.12 70	99.12 72	99.09 135	99.53 128	98.08 162	98.34 163	99.66 72	99.35 65	99.35 131	99.23 151	98.39 89	99.72 311	98.46 130	99.81 141	99.47 198

viewmacassd2359aftdt		98.86 117	98.87 112	98.83 191	99.53 128	97.32 251	97.70 268	99.64 80	98.22 204	99.25 166	99.27 132	98.40 87	99.61 390	97.98 177	99.87 101	99.55 138

region2R		98.69 152	98.40 194	99.54 31	99.53 128	99.17 43	98.52 130	99.31 248	97.46 289	98.44 325	98.51 349	97.83 152	99.88 116	96.46 337	99.58 286	99.58 118

PGM-MVS		98.66 163	98.37 203	99.55 28	99.53 128	99.18 42	98.23 173	99.49 158	97.01 334	98.69 281	98.88 266	98.00 135	99.89 98	95.87 373	99.59 281	99.58 118

E4		98.87 113	98.88 109	98.81 195	99.52 132	97.23 262	97.62 281	99.61 93	98.58 173	99.18 182	99.33 119	98.29 100	99.69 332	97.99 176	99.83 127	99.52 162

Patchmatch-RL test		97.26 342	97.02 344	97.99 340	99.52 132	95.53 360	96.13 424	99.71 49	97.47 284	99.27 154	99.16 171	84.30 489	99.62 382	97.89 182	99.77 173	98.81 395

ACMMPR		98.70 148	98.42 192	99.54 31	99.52 132	99.14 57	98.52 130	99.31 248	97.47 284	98.56 309	98.54 344	97.75 160	99.88 116	96.57 324	99.59 281	99.58 118

DKM-HiRes		98.14 255	97.80 283	99.16 119	99.51 135	98.40 121	96.70 376	99.63 83	97.55 275	97.45 413	98.74 299	93.27 383	99.54 422	97.78 195	99.55 299	99.53 158

fmvsm_s_conf0.5_n_9		99.17 53	99.38 28	98.53 268	99.51 135	95.82 350	97.62 281	99.78 37	99.72 14	99.90 14	99.48 76	98.66 60	99.89 98	99.85 7	99.93 58	99.89 17

AstraMVS		98.16 254	98.07 254	98.41 286	99.51 135	95.86 347	98.00 215	95.14 512	98.97 128	99.43 109	99.24 145	93.25 384	99.84 180	99.21 71	99.87 101	99.54 144

fmvsm_s_conf0.5_n_8		99.13 67	99.26 51	98.74 218	99.51 135	96.44 322	97.65 276	99.65 78	99.66 23	99.78 40	99.48 76	97.92 143	99.93 54	99.72 31	99.95 40	99.87 23

GST-MVS		98.61 172	98.30 217	99.52 44	99.51 135	99.20 38	98.26 171	99.25 279	97.44 292	98.67 285	98.39 365	97.68 163	99.85 159	96.00 365	99.51 312	99.52 162

Anonymous20231206		98.21 244	98.21 232	98.20 313	99.51 135	95.43 371	98.13 186	99.32 243	96.16 386	98.93 234	98.82 282	96.00 290	99.83 198	97.32 244	99.73 200	99.36 253

ACMP	95.32 15	98.41 205	98.09 249	99.36 74	99.51 135	98.79 90	97.68 270	99.38 215	95.76 411	98.81 262	98.82 282	98.36 91	99.82 210	94.75 408	99.77 173	99.48 189
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RoMa-HiRes		98.68 158	98.52 171	99.16 119	99.50 142	98.35 130	98.01 213	99.71 49	96.94 337	99.35 131	98.66 322	96.38 268	99.63 377	98.39 139	99.71 218	99.48 189

LuminaMVS		98.39 215	98.20 233	98.98 161	99.50 142	97.49 233	97.78 252	97.69 443	98.75 151	99.49 95	99.25 143	92.30 407	99.94 42	99.14 76	99.88 96	99.50 170

DVP-MVS		98.77 137	98.52 171	99.52 44	99.50 142	99.21 32	98.02 210	98.84 370	97.97 232	99.08 192	99.02 214	97.61 173	99.88 116	96.99 274	99.63 264	99.48 189
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.60 16	99.50 142	99.23 30	98.02 210	99.32 243					99.88 116	96.99 274	99.63 264	99.68 74

test0726					99.50 142	99.21 32	98.17 182	99.35 229	97.97 232	99.26 158	99.06 201	97.61 173

AllTest		98.44 203	98.20 233	99.16 119	99.50 142	98.55 109	98.25 172	99.58 104	96.80 351	98.88 245	99.06 201	97.65 166	99.57 408	94.45 418	99.61 275	99.37 245

TestCases				99.16 119	99.50 142	98.55 109		99.58 104	96.80 351	98.88 245	99.06 201	97.65 166	99.57 408	94.45 418	99.61 275	99.37 245

XVG-OURS		98.53 190	98.34 209	99.11 129	99.50 142	98.82 89	95.97 434	99.50 150	97.30 307	99.05 202	98.98 237	99.35 14	99.32 475	95.72 380	99.68 241	99.18 325

EG-PatchMatch MVS		98.99 95	99.01 93	98.94 168	99.50 142	97.47 237	98.04 205	99.59 101	98.15 222	99.40 118	99.36 111	98.58 73	99.76 274	98.78 103	99.68 241	99.59 110

fmvsm_s_conf0.5_n_2		99.14 63	99.31 42	98.63 241	99.49 151	96.08 337	97.38 317	99.81 33	99.48 45	99.84 31	99.57 50	98.46 83	99.89 98	99.82 13	99.97 21	99.91 14

SED-MVS		98.91 107	98.72 133	99.49 55	99.49 151	99.17 43	98.10 193	99.31 248	98.03 228	99.66 61	99.02 214	98.36 91	99.88 116	96.91 282	99.62 268	99.41 223

IU-MVS					99.49 151	99.15 52		98.87 361	92.97 490	99.41 115				96.76 299	99.62 268	99.66 81

test_241102_ONE					99.49 151	99.17 43		99.31 248	97.98 231	99.66 61	98.90 258	98.36 91	99.48 444

UA-Net		99.47 16	99.40 27	99.70 2	99.49 151	99.29 23	99.80 4	99.72 47	99.82 8	99.04 204	99.81 8	98.05 132	99.96 13	98.85 99	99.99 5	99.86 29

HFP-MVS		98.71 143	98.44 189	99.51 49	99.49 151	99.16 48	98.52 130	99.31 248	97.47 284	98.58 305	98.50 353	97.97 139	99.85 159	96.57 324	99.59 281	99.53 158

VPA-MVSNet		99.30 34	99.30 45	99.28 96	99.49 151	98.36 129	99.00 73	99.45 181	99.63 29	99.52 88	99.44 86	98.25 108	99.88 116	99.09 80	99.84 115	99.62 93

XVG-OURS-SEG-HR		98.49 197	98.28 220	99.14 125	99.49 151	98.83 87	96.54 391	99.48 160	97.32 304	99.11 187	98.61 336	99.33 15	99.30 478	96.23 353	98.38 456	99.28 288

fmvsm_s_conf0.5_n_11		99.21 48	99.34 36	98.80 198	99.48 159	96.56 314	97.97 228	99.69 58	99.63 29	99.84 31	99.54 63	98.21 116	99.94 42	99.76 24	99.95 40	99.88 21

114514_t		96.50 388	95.77 408	98.69 228	99.48 159	97.43 243	97.84 245	99.55 127	81.42 546	96.51 470	98.58 340	95.53 313	99.67 349	93.41 455	99.58 286	98.98 361

IterMVS-LS		98.55 185	98.70 139	98.09 326	99.48 159	94.73 407	97.22 339	99.39 213	98.97 128	99.38 122	99.31 125	96.00 290	99.93 54	98.58 120	99.97 21	99.60 103
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_s_conf0.5_n_10		99.15 58	99.27 48	98.78 205	99.47 162	96.56 314	97.75 261	99.71 49	99.60 36	99.74 47	99.44 86	97.96 140	99.95 26	99.86 4	99.94 52	99.82 37

fmvsm_s_conf0.5_n_5		99.07 83	99.10 81	98.99 157	99.47 162	97.22 265	97.40 314	99.83 27	97.61 267	99.85 28	99.30 126	98.80 42	99.95 26	99.71 33	99.90 89	99.78 51

v8		99.01 91	99.16 63	98.57 254	99.47 162	96.31 327	98.90 84	99.47 172	99.03 122	99.52 88	99.57 50	96.93 229	99.81 227	99.60 38	99.98 12	99.60 103

SSC-MVS3.2		98.53 190	98.79 125	97.74 364	99.46 165	93.62 455	96.45 398	99.34 235	99.33 67	98.93 234	98.70 312	97.90 144	99.90 82	99.12 77	99.92 72	99.69 73

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 205	99.46 165	96.58 312	97.65 276	99.72 47	99.47 48	99.86 24	99.50 69	98.94 31	99.89 98	99.75 27	99.97 21	99.86 29

XVS		98.72 142	98.45 187	99.53 38	99.46 165	99.21 32	98.65 114	99.34 235	98.62 167	97.54 403	98.63 331	97.50 187	99.83 198	96.79 295	99.53 306	99.56 131

X-MVStestdata		94.32 460	92.59 483	99.53 38	99.46 165	99.21 32	98.65 114	99.34 235	98.62 167	97.54 403	45.85 555	97.50 187	99.83 198	96.79 295	99.53 306	99.56 131

test20.03		98.78 134	98.77 128	98.78 205	99.46 165	97.20 268	97.78 252	99.24 285	99.04 120	99.41 115	98.90 258	97.65 166	99.76 274	97.70 209	99.79 160	99.39 233

guyue		98.01 268	97.93 271	98.26 304	99.45 170	95.48 366	98.08 196	96.24 490	98.89 139	99.34 136	99.14 181	91.32 425	99.82 210	99.07 81	99.83 127	99.48 189

CSCG		98.68 158	98.50 176	99.20 111	99.45 170	98.63 101	98.56 125	99.57 112	97.87 242	98.85 252	98.04 406	97.66 165	99.84 180	96.72 305	99.81 141	99.13 340

GeoE		99.05 84	98.99 97	99.25 104	99.44 172	98.35 130	98.73 103	99.56 122	98.42 185	98.91 237	98.81 285	98.94 31	99.91 75	98.35 142	99.73 200	99.49 178

v148		98.45 202	98.60 160	98.00 338	99.44 172	94.98 393	97.44 312	99.06 323	98.30 195	99.32 145	98.97 239	96.65 252	99.62 382	98.37 141	99.85 110	99.39 233

v10		98.97 100	99.11 75	98.55 261	99.44 172	96.21 331	98.90 84	99.55 127	98.73 152	99.48 97	99.60 46	96.63 254	99.83 198	99.70 34	99.99 5	99.61 101

V42		98.78 134	98.78 127	98.76 212	99.44 172	97.04 282	98.27 170	99.19 295	97.87 242	99.25 166	99.16 171	96.84 233	99.78 262	99.21 71	99.84 115	99.46 201

MDA-MVSNet-bldmvs		97.94 276	97.91 274	98.06 332	99.44 172	94.96 394	96.63 385	99.15 311	98.35 188	98.83 257	99.11 189	94.31 359	99.85 159	96.60 321	98.72 434	99.37 245

viewdifsd2359ckpt07		98.71 143	98.86 116	98.26 304	99.43 177	95.65 355	97.20 340	99.66 72	99.20 85	99.29 150	99.01 226	98.29 100	99.73 300	97.92 181	99.75 193	99.39 233

casdiffmvs_mvg		99.12 70	99.16 63	98.99 157	99.43 177	97.73 215	98.00 215	99.62 90	99.22 81	99.55 78	99.22 153	98.93 33	99.75 286	98.66 114	99.81 141	99.50 170
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SSM_0404		98.90 109	99.01 93	98.57 254	99.42 179	96.59 309	98.13 186	99.66 72	99.09 111	99.30 149	99.02 214	98.79 44	99.89 98	97.87 187	99.80 153	99.23 305

test1111		96.49 389	96.82 360	95.52 491	99.42 179	87.08 533	99.22 46	87.14 551	99.11 101	99.46 102	99.58 48	88.69 448	99.86 145	98.80 101	99.95 40	99.62 93

v2v482		98.56 181	98.62 155	98.37 293	99.42 179	95.81 351	97.58 290	99.16 306	97.90 240	99.28 152	99.01 226	95.98 295	99.79 250	99.33 60	99.90 89	99.51 166

OPM-MVS		98.56 181	98.32 215	99.25 104	99.41 182	98.73 95	97.13 347	99.18 299	97.10 328	98.75 272	98.92 252	98.18 119	99.65 369	96.68 311	99.56 294	99.37 245
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PMMVS2		98.07 262	98.08 252	98.04 335	99.41 182	94.59 413	94.59 492	99.40 211	97.50 281	98.82 260	98.83 279	96.83 235	99.84 180	97.50 228	99.81 141	99.71 66

E2		98.70 148	98.68 142	98.73 220	99.40 184	97.10 279	97.48 304	99.57 112	98.09 225	99.00 210	99.20 157	97.90 144	99.67 349	97.73 206	99.77 173	99.43 215

E3		98.69 152	98.68 142	98.73 220	99.40 184	97.10 279	97.48 304	99.57 112	98.09 225	99.00 210	99.20 157	97.90 144	99.67 349	97.73 206	99.77 173	99.43 215

ELoFTR		97.81 297	97.74 288	98.04 335	99.39 186	95.79 352	97.28 333	99.58 104	94.13 468	99.38 122	99.37 105	93.31 382	99.60 394	97.23 250	99.96 29	98.74 408

test_one_0601					99.39 186	99.20 38		99.31 248	98.49 181	98.66 287	99.02 214	97.64 169

mvsany_test3		98.87 113	98.92 103	98.74 218	99.38 188	96.94 290	98.58 123	99.10 317	96.49 368	99.96 4	99.81 8	98.18 119	99.45 454	98.97 90	99.79 160	99.83 34

patch_mono-2		98.51 195	98.63 153	98.17 316	99.38 188	94.78 404	97.36 322	99.69 58	98.16 217	98.49 318	99.29 129	97.06 218	99.97 6	98.29 146	99.91 81	99.76 59

test2506		92.39 496	91.89 498	93.89 516	99.38 188	82.28 552	99.32 26	66.03 560	99.08 115	98.77 269	99.57 50	66.26 540	99.84 180	98.71 111	99.95 40	99.54 144

ECVR-MVS		96.42 395	96.61 379	95.85 480	99.38 188	88.18 528	99.22 46	86.00 553	99.08 115	99.36 129	99.57 50	88.47 453	99.82 210	98.52 128	99.95 40	99.54 144

casdiffmvs		98.95 103	99.00 95	98.81 195	99.38 188	97.33 248	97.82 246	99.57 112	99.17 94	99.35 131	99.17 169	98.35 95	99.69 332	98.46 130	99.73 200	99.41 223
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		98.96 102	99.02 91	98.76 212	99.38 188	97.26 260	98.49 140	99.50 150	98.86 143	99.19 177	99.06 201	98.23 111	99.69 332	98.71 111	99.76 189	99.33 269

TranMVSNet+NR-MVSNet		99.17 53	99.07 86	99.46 63	99.37 194	98.87 85	98.39 157	99.42 202	99.42 56	99.36 129	99.06 201	98.38 90	99.95 26	98.34 143	99.90 89	99.57 125

fmvsm_s_conf0.5_n_6		99.08 80	99.21 58	98.69 228	99.36 195	96.51 316	97.62 281	99.68 65	98.43 184	99.85 28	99.10 192	99.12 23	99.88 116	99.77 23	99.92 72	99.67 79

tttt0517		95.64 431	94.98 444	97.64 379	99.36 195	93.81 447	98.72 104	90.47 545	98.08 227	98.67 285	98.34 372	73.88 525	99.92 66	97.77 198	99.51 312	99.20 315

test_part2					99.36 195	99.10 65				99.05 202

v1144		98.60 174	98.66 147	98.41 286	99.36 195	95.90 344	97.58 290	99.34 235	97.51 280	99.27 154	99.15 177	96.34 272	99.80 236	99.47 54	99.93 58	99.51 166

CP-MVS		98.70 148	98.42 192	99.52 44	99.36 195	99.12 62	98.72 104	99.36 223	97.54 278	98.30 337	98.40 364	97.86 151	99.89 98	96.53 333	99.72 209	99.56 131

RoMa-SfM		98.46 200	98.27 223	99.02 152	99.35 200	98.32 133	97.56 292	99.70 55	95.88 401	99.38 122	98.65 325	96.41 264	99.46 451	97.78 195	99.71 218	99.28 288

DKM		98.18 249	97.95 266	98.85 183	99.35 200	98.31 134	96.68 378	99.69 58	96.90 343	98.61 298	98.77 292	94.41 352	98.93 503	97.32 244	99.84 115	99.32 274

diffmvs_AUTHOR		98.50 196	98.59 162	98.23 311	99.35 200	95.48 366	96.61 387	99.60 95	98.37 186	98.90 238	99.00 230	97.37 198	99.76 274	98.22 151	99.85 110	99.46 201

Test_1112_low_res		96.99 367	96.55 383	98.31 299	99.35 200	95.47 369	95.84 446	99.53 137	91.51 509	96.80 452	98.48 356	91.36 424	99.83 198	96.58 322	99.53 306	99.62 93

DeepC-MVS	97.60 4	98.97 100	98.93 102	99.10 131	99.35 200	97.98 177	98.01 213	99.46 177	97.56 273	99.54 80	99.50 69	98.97 29	99.84 180	98.06 165	99.92 72	99.49 178
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
1112_ss		97.29 341	96.86 356	98.58 251	99.34 205	96.32 326	96.75 372	99.58 104	93.14 486	96.89 446	97.48 449	92.11 412	99.86 145	96.91 282	99.54 302	99.57 125

test-260524					99.33 206	99.02 71		99.25 279		99.23 170		96.59 256	99.85 159	98.10 161	99.62 268

reproduce_model		99.15 58	98.97 99	99.67 4	99.33 206	99.44 9	98.15 184	99.47 172	99.12 100	99.52 88	99.32 124	98.31 98	99.90 82	97.78 195	99.73 200	99.66 81

DenseAffine		98.10 257	97.86 279	98.84 189	99.32 208	97.93 185	96.62 386	99.76 40	96.68 360	98.65 288	98.72 303	94.46 350	99.33 473	96.76 299	99.75 193	99.25 299

MVSMamba_PlusPlus		98.83 123	98.98 98	98.36 294	99.32 208	96.58 312	98.90 84	99.41 206	99.75 10	98.72 276	99.50 69	96.17 280	99.94 42	99.27 65	99.78 165	98.57 430

fmvsm_s_conf0.5_n_4		99.01 91	99.22 55	98.38 290	99.31 210	95.48 366	97.56 292	99.73 46	98.87 141	99.75 45	99.27 132	98.80 42	99.86 145	99.80 18	99.90 89	99.81 42

SF-MVS		98.53 190	98.27 223	99.32 91	99.31 210	98.75 91	98.19 178	99.41 206	96.77 354	98.83 257	98.90 258	97.80 156	99.82 210	95.68 383	99.52 309	99.38 242

CPTT-MVS		97.84 293	97.36 321	99.27 99	99.31 210	98.46 117	98.29 166	99.27 271	94.90 444	97.83 381	98.37 368	94.90 333	99.84 180	93.85 440	99.54 302	99.51 166

UnsupCasMVSNet_eth		97.89 280	97.60 305	98.75 214	99.31 210	97.17 274	97.62 281	99.35 229	98.72 158	98.76 271	98.68 316	92.57 402	99.74 293	97.76 202	95.60 533	99.34 263

fmvsm_s_conf0.5_n_7		98.83 123	99.04 88	98.20 313	99.30 214	94.83 402	97.23 335	99.36 223	98.64 162	99.84 31	99.43 89	98.10 128	99.91 75	99.56 42	99.96 29	99.87 23

pmmvs-eth3d		98.47 199	98.34 209	98.86 182	99.30 214	97.76 211	97.16 345	99.28 268	95.54 421	99.42 113	99.19 160	97.27 205	99.63 377	97.89 182	99.97 21	99.20 315

viewcassd2359sk11		98.55 185	98.51 173	98.67 231	99.29 216	96.99 285	97.39 315	99.54 133	97.73 255	98.81 262	99.08 199	97.55 179	99.66 362	97.52 227	99.67 247	99.36 253

SymmetryMVS		98.05 264	97.71 294	99.09 135	99.29 216	97.83 197	98.28 167	97.64 448	99.24 78	98.80 264	98.85 272	89.76 440	99.94 42	98.04 168	99.50 320	99.49 178

Anonymous20231211		99.27 38	99.27 48	99.26 101	99.29 216	98.18 147	99.49 12	99.51 145	99.70 15	99.80 38	99.68 26	96.84 233	99.83 198	99.21 71	99.91 81	99.77 54

viewmanbaseed2359cas		98.58 178	98.54 168	98.70 226	99.28 219	97.13 278	97.47 308	99.55 127	97.55 275	98.96 224	98.92 252	97.77 158	99.59 399	97.59 219	99.77 173	99.39 233

UnsupCasMVSNet_bld		97.30 339	96.92 351	98.45 280	99.28 219	96.78 303	96.20 418	99.27 271	95.42 427	98.28 341	98.30 379	93.16 387	99.71 313	94.99 402	97.37 500	98.87 385

EC-MVSNet		99.09 74	99.05 87	99.20 111	99.28 219	98.93 80	99.24 44	99.84 23	99.08 115	98.12 354	98.37 368	98.72 51	99.90 82	99.05 84	99.77 173	98.77 403

mamba_0408		98.80 130	98.88 109	98.55 261	99.27 222	96.50 317	98.00 215	99.60 95	98.93 133	99.22 172	98.84 277	98.59 68	99.89 98	97.74 204	99.72 209	99.27 292

SSM_04072		98.80 130	98.88 109	98.56 259	99.27 222	96.50 317	98.00 215	99.60 95	98.93 133	99.22 172	98.84 277	98.59 68	99.90 82	97.74 204	99.72 209	99.27 292

SSM_0407		98.86 117	98.96 101	98.55 261	99.27 222	96.50 317	98.04 205	99.66 72	99.09 111	99.22 172	99.02 214	98.79 44	99.87 136	97.87 187	99.72 209	99.27 292

reproduce-ours		99.09 74	98.90 106	99.67 4	99.27 222	99.49 5	98.00 215	99.42 202	99.05 118	99.48 97	99.27 132	98.29 100	99.89 98	97.61 216	99.71 218	99.62 93

our_new_method		99.09 74	98.90 106	99.67 4	99.27 222	99.49 5	98.00 215	99.42 202	99.05 118	99.48 97	99.27 132	98.29 100	99.89 98	97.61 216	99.71 218	99.62 93

DPE-MVS		98.59 176	98.26 226	99.57 21	99.27 222	99.15 52	97.01 351	99.39 213	97.67 260	99.44 108	98.99 232	97.53 183	99.89 98	95.40 394	99.68 241	99.66 81
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
IterMVS-SCA-FT		97.85 292	98.18 239	96.87 433	99.27 222	91.16 502	95.53 456	99.25 279	99.10 108	99.41 115	99.35 112	93.10 390	99.96 13	98.65 115	99.94 52	99.49 178

v1192		98.60 174	98.66 147	98.41 286	99.27 222	95.88 346	97.52 298	99.36 223	97.41 294	99.33 139	99.20 157	96.37 270	99.82 210	99.57 40	99.92 72	99.55 138

N_pmnet		97.63 310	97.17 333	98.99 157	99.27 222	97.86 194	95.98 433	93.41 531	95.25 434	99.47 101	98.90 258	95.63 309	99.85 159	96.91 282	99.73 200	99.27 292

PMatch-SfM		97.89 280	97.64 301	98.66 233	99.26 231	97.44 242	96.08 428	99.51 145	96.72 356	98.47 321	99.13 183	93.62 379	99.70 322	97.14 260	98.80 428	98.83 388

viewdifsd2359ckpt13		98.39 215	98.29 219	98.70 226	99.26 231	97.19 269	97.51 300	99.48 160	96.94 337	98.58 305	98.82 282	97.47 193	99.55 416	97.21 252	99.33 355	99.34 263

FPMVS		93.44 479	92.23 488	97.08 419	99.25 233	97.86 194	95.61 453	97.16 465	92.90 493	93.76 528	98.65 325	75.94 522	95.66 546	79.30 546	97.49 493	97.73 485

aaEdge-Enhanced		98.61 172	98.33 214	99.44 65	99.24 234	98.93 80	97.45 310	99.06 323	98.14 223	99.06 194	98.77 292	96.97 227	99.82 210	96.67 313	99.64 259	99.58 118

new-patchmatchnet		98.35 218	98.74 129	97.18 413	99.24 234	92.23 483	96.42 402	99.48 160	98.30 195	99.69 56	99.53 65	97.44 194	99.82 210	98.84 100	99.77 173	99.49 178

MCST-MVS		98.00 269	97.63 303	99.10 131	99.24 234	98.17 148	96.89 363	98.73 390	95.66 413	97.92 371	97.70 434	97.17 212	99.66 362	96.18 358	99.23 377	99.47 198

UniMVSNet (Re)		98.87 113	98.71 136	99.35 80	99.24 234	98.73 95	97.73 265	99.38 215	98.93 133	99.12 186	98.73 301	96.77 241	99.86 145	98.63 117	99.80 153	99.46 201

jason		97.45 324	97.35 322	97.76 361	99.24 234	93.93 441	95.86 443	98.42 416	94.24 464	98.50 317	98.13 396	94.82 337	99.91 75	97.22 251	99.73 200	99.43 215
jason: jason.
IterMVS		97.73 301	98.11 248	96.57 446	99.24 234	90.28 513	95.52 458	99.21 289	98.86 143	99.33 139	99.33 119	93.11 389	99.94 42	98.49 129	99.94 52	99.48 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		98.55 185	98.62 155	98.32 297	99.22 240	95.58 358	97.51 300	99.45 181	97.16 325	99.45 107	99.24 145	96.12 285	99.85 159	99.60 38	99.88 96	99.55 138

ITE_SJBPF				98.87 180	99.22 240	98.48 116		99.35 229	97.50 281	98.28 341	98.60 338	97.64 169	99.35 470	93.86 439	99.27 368	98.79 401

h-mvs33		97.77 299	97.33 324	99.10 131	99.21 242	97.84 196	98.35 161	98.57 404	99.11 101	98.58 305	99.02 214	88.65 451	99.96 13	98.11 159	96.34 519	99.49 178

v144192		98.54 188	98.57 164	98.45 280	99.21 242	95.98 340	97.63 280	99.36 223	97.15 327	99.32 145	99.18 164	95.84 302	99.84 180	99.50 51	99.91 81	99.54 144

APDe-MVS		98.99 95	98.79 125	99.60 16	99.21 242	99.15 52	98.87 89	99.48 160	97.57 271	99.35 131	99.24 145	97.83 152	99.89 98	97.88 185	99.70 229	99.75 63
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DP-MVS		98.93 105	98.81 124	99.28 96	99.21 242	98.45 118	98.46 145	99.33 241	99.63 29	99.48 97	99.15 177	97.23 208	99.75 286	97.17 255	99.66 255	99.63 92

viewmamba		98.57 179	98.66 147	98.31 299	99.20 246	95.89 345	96.92 361	99.57 112	98.71 159	99.02 208	99.04 210	97.48 191	99.71 313	98.28 147	99.70 229	99.35 259

SR-MVS-dyc-post		98.81 128	98.55 166	99.57 21	99.20 246	99.38 12	98.48 143	99.30 256	98.64 162	98.95 225	98.96 243	97.49 190	99.86 145	96.56 328	99.39 344	99.45 207

RE-MVS-def			98.58 163		99.20 246	99.38 12	98.48 143	99.30 256	98.64 162	98.95 225	98.96 243	97.75 160		96.56 328	99.39 344	99.45 207

v1921920		98.54 188	98.60 160	98.38 290	99.20 246	95.76 354	97.56 292	99.36 223	97.23 319	99.38 122	99.17 169	96.02 288	99.84 180	99.57 40	99.90 89	99.54 144

E3new		98.41 205	98.34 209	98.62 243	99.19 250	96.90 293	97.32 325	99.50 150	97.40 296	98.63 292	98.92 252	97.21 210	99.65 369	97.34 240	99.52 309	99.31 279

thisisatest0530		95.27 444	94.45 456	97.74 364	99.19 250	94.37 417	97.86 242	90.20 546	97.17 324	98.22 344	97.65 436	73.53 526	99.90 82	96.90 287	99.35 351	98.95 369

Anonymous20240529		98.93 105	98.87 112	99.12 127	99.19 250	98.22 145	99.01 71	98.99 341	99.25 77	99.54 80	99.37 105	97.04 219	99.80 236	97.89 182	99.52 309	99.35 259

APD-MVS_3200maxsize		98.84 120	98.61 159	99.53 38	99.19 250	99.27 26	98.49 140	99.33 241	98.64 162	99.03 207	98.98 237	97.89 148	99.85 159	96.54 332	99.42 340	99.46 201

HQP_MVS		97.99 272	97.67 296	98.93 171	99.19 250	97.65 222	97.77 255	99.27 271	98.20 210	97.79 385	97.98 411	94.90 333	99.70 322	94.42 421	99.51 312	99.45 207

plane_prior7					99.19 250	97.87 193

ab-mvs		98.41 205	98.36 205	98.59 250	99.19 250	97.23 262	99.32 26	98.81 375	97.66 261	98.62 296	99.40 98	96.82 236	99.80 236	95.88 370	99.51 312	98.75 406

F-COLMAP		97.30 339	96.68 370	99.14 125	99.19 250	98.39 123	97.27 334	99.30 256	92.93 491	96.62 461	98.00 409	95.73 305	99.68 344	92.62 477	98.46 454	99.35 259

viewdifsd2359ckpt09		98.13 256	97.92 272	98.77 210	99.18 258	97.35 246	97.29 329	99.53 137	95.81 408	98.09 357	98.47 357	96.34 272	99.66 362	97.02 270	99.51 312	99.29 285

SR-MVS		98.71 143	98.43 190	99.57 21	99.18 258	99.35 16	98.36 160	99.29 264	98.29 198	98.88 245	98.85 272	97.53 183	99.87 136	96.14 360	99.31 360	99.48 189

UniMVSNet_NR-MVSNet		98.86 117	98.68 142	99.40 71	99.17 260	98.74 92	97.68 270	99.40 211	99.14 99	99.06 194	98.59 339	96.71 248	99.93 54	98.57 122	99.77 173	99.53 158

LF4IMVS		97.90 278	97.69 295	98.52 270	99.17 260	97.66 220	97.19 344	99.47 172	96.31 377	97.85 380	98.20 391	96.71 248	99.52 429	94.62 412	99.72 209	98.38 447

SMA-MVS		98.40 208	98.03 257	99.51 49	99.16 262	99.21 32	98.05 203	99.22 288	94.16 467	98.98 215	99.10 192	97.52 185	99.79 250	96.45 338	99.64 259	99.53 158
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
DU-MVS		98.82 126	98.63 153	99.39 72	99.16 262	98.74 92	97.54 296	99.25 279	98.84 149	99.06 194	98.76 297	96.76 243	99.93 54	98.57 122	99.77 173	99.50 170

NR-MVSNet		98.95 103	98.82 122	99.36 74	99.16 262	98.72 97	99.22 46	99.20 291	99.10 108	99.72 48	98.76 297	96.38 268	99.86 145	98.00 173	99.82 134	99.50 170

MVS_111021_LR		98.30 229	98.12 247	98.83 191	99.16 262	98.03 170	96.09 427	99.30 256	97.58 270	98.10 356	98.24 387	98.25 108	99.34 471	96.69 310	99.65 257	99.12 341

dtuplus		98.32 224	98.39 197	98.10 324	99.15 266	95.29 379	96.68 378	99.51 145	97.32 304	99.18 182	99.15 177	97.61 173	99.62 382	97.19 253	99.74 196	99.38 242

DSMNet-mixed		97.42 327	97.60 305	96.87 433	99.15 266	91.46 491	98.54 128	99.12 314	92.87 494	97.58 399	99.63 40	96.21 279	99.90 82	95.74 379	99.54 302	99.27 292

hybridnocas07		98.32 224	98.37 203	98.17 316	99.14 268	95.51 361	96.67 380	99.56 122	97.85 244	98.75 272	98.95 247	96.65 252	99.63 377	98.00 173	99.78 165	99.37 245

D2MVS		97.84 293	97.84 281	97.83 352	99.14 268	94.74 406	96.94 357	98.88 359	95.84 403	98.89 241	98.96 243	94.40 354	99.69 332	97.55 222	99.95 40	99.05 347

pmmvs5		97.64 309	97.49 312	98.08 329	99.14 268	95.12 389	96.70 376	99.05 327	93.77 477	98.62 296	98.83 279	93.23 385	99.75 286	98.33 145	99.76 189	99.36 253

hybrid		98.22 241	98.27 223	98.08 329	99.13 271	95.24 381	96.61 387	99.53 137	97.43 293	98.46 322	98.97 239	96.75 246	99.65 369	97.84 190	99.69 235	99.35 259

SPE-MVS-test		99.13 67	99.09 83	99.26 101	99.13 271	98.97 74	99.31 30	99.88 15	99.44 53	98.16 349	98.51 349	98.64 62	99.93 54	98.91 94	99.85 110	98.88 384

VDD-MVS		98.56 181	98.39 197	99.07 139	99.13 271	98.07 165	98.59 122	97.01 468	99.59 37	99.11 187	99.27 132	94.82 337	99.79 250	98.34 143	99.63 264	99.34 263

save fliter					99.11 274	97.97 178	96.53 393	99.02 335	98.24 201

APD-MVS		98.10 257	97.67 296	99.42 67	99.11 274	98.93 80	97.76 258	99.28 268	94.97 442	98.72 276	98.77 292	97.04 219	99.85 159	93.79 441	99.54 302	99.49 178
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EI-MVSNet-UG-set		98.69 152	98.71 136	98.62 243	99.10 276	96.37 324	97.23 335	98.87 361	99.20 85	99.19 177	98.99 232	97.30 202	99.85 159	98.77 106	99.79 160	99.65 86

EI-MVSNet		98.40 208	98.51 173	98.04 335	99.10 276	94.73 407	97.20 340	98.87 361	98.97 128	99.06 194	99.02 214	96.00 290	99.80 236	98.58 120	99.82 134	99.60 103

CVMVSNet		96.25 405	97.21 332	93.38 524	99.10 276	80.56 556	97.20 340	98.19 429	96.94 337	99.00 210	99.02 214	89.50 444	99.80 236	96.36 345	99.59 281	99.78 51

EI-MVSNet-Vis-set		98.68 158	98.70 139	98.63 241	99.09 279	96.40 323	97.23 335	98.86 366	99.20 85	99.18 182	98.97 239	97.29 204	99.85 159	98.72 110	99.78 165	99.64 87

HPM-MVS++		98.10 257	97.64 301	99.48 57	99.09 279	99.13 60	97.52 298	98.75 387	97.46 289	96.90 445	97.83 424	96.01 289	99.84 180	95.82 377	99.35 351	99.46 201

DP-MVS Recon		97.33 336	96.92 351	98.57 254	99.09 279	97.99 174	96.79 367	99.35 229	93.18 485	97.71 389	98.07 404	95.00 332	99.31 476	93.97 434	99.13 394	98.42 444

MVS_111021_HR		98.25 239	98.08 252	98.75 214	99.09 279	97.46 239	95.97 434	99.27 271	97.60 269	97.99 367	98.25 385	98.15 125	99.38 466	96.87 290	99.57 290	99.42 220

BP-MVS1		97.40 329	96.97 347	98.71 224	99.07 283	96.81 299	98.34 163	97.18 463	98.58 173	98.17 346	98.61 336	84.01 491	99.94 42	98.97 90	99.78 165	99.37 245

9.14			97.78 285		99.07 283		97.53 297	99.32 243	95.53 422	98.54 313	98.70 312	97.58 176	99.76 274	94.32 426	99.46 327

PAPM_NR		96.82 375	96.32 394	98.30 301	99.07 283	96.69 307	97.48 304	98.76 383	95.81 408	96.61 462	96.47 480	94.12 367	99.17 490	90.82 513	97.78 485	99.06 346

TAMVS		98.24 240	98.05 255	98.80 198	99.07 283	97.18 272	97.88 238	98.81 375	96.66 361	99.17 185	99.21 155	94.81 339	99.77 268	96.96 279	99.88 96	99.44 211

CLD-MVS		97.49 320	97.16 334	98.48 277	99.07 283	97.03 283	94.71 483	99.21 289	94.46 456	98.06 360	97.16 464	97.57 177	99.48 444	94.46 417	99.78 165	98.95 369
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CS-MVS		99.13 67	99.10 81	99.24 107	99.06 288	99.15 52	99.36 22	99.88 15	99.36 64	98.21 345	98.46 358	98.68 59	99.93 54	99.03 86	99.85 110	98.64 422

thres100view900		94.19 464	93.67 468	95.75 483	99.06 288	91.35 495	98.03 207	94.24 524	98.33 191	97.40 417	94.98 513	79.84 507	99.62 382	83.05 539	98.08 474	96.29 518

thres600view7		94.45 458	93.83 465	96.29 456	99.06 288	91.53 490	97.99 222	94.24 524	98.34 189	97.44 415	95.01 511	79.84 507	99.67 349	84.33 536	98.23 463	97.66 488

onestephybrid01		98.40 208	98.39 197	98.42 284	99.05 291	96.23 329	96.73 374	99.41 206	98.18 213	98.65 288	99.02 214	97.02 222	99.69 332	97.73 206	99.70 229	99.33 269

plane_prior1					99.05 291

YYNet1		97.60 311	97.67 296	97.39 405	99.04 293	93.04 464	95.27 467	98.38 419	97.25 313	98.92 236	98.95 247	95.48 317	99.73 300	96.99 274	98.74 432	99.41 223

MDA-MVSNet_test_wron		97.60 311	97.66 299	97.41 404	99.04 293	93.09 460	95.27 467	98.42 416	97.26 312	98.88 245	98.95 247	95.43 319	99.73 300	97.02 270	98.72 434	99.41 223

MIMVSNet		96.62 382	96.25 399	97.71 368	99.04 293	94.66 410	99.16 55	96.92 476	97.23 319	97.87 376	99.10 192	86.11 469	99.65 369	91.65 494	99.21 381	98.82 390

PMatch-Up-SfM		97.79 298	97.48 315	98.72 222	99.03 296	97.78 208	96.05 430	99.48 160	96.90 343	98.72 276	99.18 164	92.00 414	99.71 313	97.15 259	98.77 429	98.69 414

usedtu_dtu_shiyan1		97.37 331	97.13 338	98.11 322	99.03 296	95.40 372	94.47 495	98.99 341	96.87 346	97.97 368	97.81 425	92.12 410	99.75 286	97.49 233	99.43 338	99.16 335

FE-MVSNET3		97.37 331	97.13 338	98.11 322	99.03 296	95.40 372	94.47 495	98.99 341	96.87 346	97.97 368	97.81 425	92.12 410	99.75 286	97.49 233	99.43 338	99.16 335

icg_test_0407_2		98.20 246	98.38 201	97.65 376	99.03 296	94.03 432	95.78 448	99.45 181	98.16 217	99.06 194	98.71 305	98.27 104	99.68 344	97.50 228	99.45 329	99.22 310

IMVS_0407		98.39 215	98.64 151	97.66 374	99.03 296	94.03 432	98.10 193	99.45 181	98.16 217	99.06 194	98.71 305	98.27 104	99.71 313	97.50 228	99.45 329	99.22 310

IMVS_0404		98.07 262	98.20 233	97.69 369	99.03 296	94.03 432	96.67 380	99.45 181	98.16 217	98.03 364	98.71 305	96.80 239	99.82 210	97.50 228	99.45 329	99.22 310

IMVS_0403		98.34 219	98.56 165	97.66 374	99.03 296	94.03 432	97.98 224	99.45 181	98.16 217	98.89 241	98.71 305	97.90 144	99.74 293	97.50 228	99.45 329	99.22 310

PatchMatch-RL		97.24 345	96.78 363	98.61 247	99.03 296	97.83 197	96.36 406	99.06 323	93.49 482	97.36 421	97.78 427	95.75 304	99.49 440	93.44 454	98.77 429	98.52 432

ArgMatch-SfM		97.96 275	97.72 292	98.66 233	99.02 304	97.33 248	96.49 396	99.52 143	95.46 425	98.71 280	98.29 382	96.14 281	99.69 332	96.30 349	99.56 294	98.97 365

viewmambaseed2359dif		98.19 247	98.26 226	97.99 340	99.02 304	95.03 392	96.59 390	99.53 137	96.21 381	99.00 210	98.99 232	97.62 171	99.61 390	97.62 215	99.72 209	99.33 269

GDP-MVS		97.50 317	97.11 340	98.67 231	99.02 304	96.85 297	98.16 183	99.71 49	98.32 193	98.52 316	98.54 344	83.39 495	99.95 26	98.79 102	99.56 294	99.19 321

ZD-MVS					99.01 307	98.84 86		99.07 322	94.10 470	98.05 362	98.12 398	96.36 271	99.86 145	92.70 476	99.19 386

CDPH-MVS		97.26 342	96.66 374	99.07 139	99.00 308	98.15 149	96.03 431	99.01 338	91.21 513	97.79 385	97.85 422	96.89 231	99.69 332	92.75 474	99.38 347	99.39 233

diffmvs		98.22 241	98.24 230	98.17 316	99.00 308	95.44 370	96.38 404	99.58 104	97.79 251	98.53 314	98.50 353	96.76 243	99.74 293	97.95 180	99.64 259	99.34 263
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
WR-MVS		98.40 208	98.19 237	99.03 149	99.00 308	97.65 222	96.85 365	98.94 345	98.57 175	98.89 241	98.50 353	95.60 311	99.85 159	97.54 224	99.85 110	99.59 110

plane_prior6					98.99 311	97.70 218						94.90 333

ArgMatch-Sym		97.83 295	97.54 307	98.71 224	98.98 312	97.65 222	96.25 416	99.43 195	95.60 416	98.85 252	97.98 411	95.72 306	99.56 411	95.54 391	99.50 320	98.92 375

xiu_mvs_v1_base_debu		97.86 286	98.17 240	96.92 430	98.98 312	93.91 442	96.45 398	99.17 303	97.85 244	98.41 328	97.14 466	98.47 78	99.92 66	98.02 170	99.05 401	96.92 506

xiu_mvs_v1_base		97.86 286	98.17 240	96.92 430	98.98 312	93.91 442	96.45 398	99.17 303	97.85 244	98.41 328	97.14 466	98.47 78	99.92 66	98.02 170	99.05 401	96.92 506

xiu_mvs_v1_base_debi		97.86 286	98.17 240	96.92 430	98.98 312	93.91 442	96.45 398	99.17 303	97.85 244	98.41 328	97.14 466	98.47 78	99.92 66	98.02 170	99.05 401	96.92 506

MVP-Stereo		98.08 261	97.92 272	98.57 254	98.96 316	96.79 300	97.90 236	99.18 299	96.41 373	98.46 322	98.95 247	95.93 299	99.60 394	96.51 334	98.98 416	99.31 279
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 208	98.68 142	97.54 392	98.96 316	97.99 174	97.88 238	99.36 223	98.20 210	99.63 67	99.04 210	98.76 47	95.33 548	96.56 328	99.74 196	99.31 279
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
新几何1				98.91 176	98.94 318	97.76 211		98.76 383	87.58 537	96.75 454	98.10 400	94.80 340	99.78 262	92.73 475	99.00 411	99.20 315

USDC		97.41 328	97.40 317	97.44 402	98.94 318	93.67 452	95.17 471	99.53 137	94.03 473	98.97 219	99.10 192	95.29 322	99.34 471	95.84 376	99.73 200	99.30 283

tfpn200view9		94.03 468	93.44 470	95.78 482	98.93 320	91.44 493	97.60 287	94.29 521	97.94 236	97.10 429	94.31 522	79.67 509	99.62 382	83.05 539	98.08 474	96.29 518

testdata				98.09 326	98.93 320	95.40 372		98.80 377	90.08 522	97.45 413	98.37 368	95.26 323	99.70 322	93.58 448	98.95 419	99.17 329

thres400		94.14 466	93.44 470	96.24 459	98.93 320	91.44 493	97.60 287	94.29 521	97.94 236	97.10 429	94.31 522	79.67 509	99.62 382	83.05 539	98.08 474	97.66 488

TAPA-MVS	96.21 11	96.63 381	95.95 404	98.65 235	98.93 320	98.09 158	96.93 359	99.28 268	83.58 543	98.13 353	97.78 427	96.13 283	99.40 462	93.52 450	99.29 366	98.45 437
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.92 324	96.93 291	95.54 455	98.78 380	85.72 540	96.86 449	98.11 399	94.43 351			99.10 399	99.23 305

PVSNet_BlendedMVS		97.55 316	97.53 309	97.60 383	98.92 324	93.77 449	96.64 384	99.43 195	94.49 454	97.62 395	99.18 164	96.82 236	99.67 349	94.73 409	99.93 58	99.36 253

PVSNet_Blended		96.88 370	96.68 370	97.47 400	98.92 324	93.77 449	94.71 483	99.43 195	90.98 516	97.62 395	97.36 458	96.82 236	99.67 349	94.73 409	99.56 294	98.98 361

MSDG		97.71 303	97.52 310	98.28 303	98.91 327	96.82 298	94.42 497	99.37 219	97.65 262	98.37 334	98.29 382	97.40 196	99.33 473	94.09 432	99.22 378	98.68 419

Anonymous202405211		97.90 278	97.50 311	99.08 137	98.90 328	98.25 139	98.53 129	96.16 491	98.87 141	99.11 187	98.86 269	90.40 435	99.78 262	97.36 239	99.31 360	99.19 321

原ACMM1				98.35 295	98.90 328	96.25 328		98.83 374	92.48 498	96.07 481	98.10 400	95.39 320	99.71 313	92.61 478	98.99 413	99.08 343

GBi-Net		98.65 164	98.47 184	99.17 116	98.90 328	98.24 140	99.20 49	99.44 189	98.59 170	98.95 225	99.55 57	94.14 364	99.86 145	97.77 198	99.69 235	99.41 223

test1		98.65 164	98.47 184	99.17 116	98.90 328	98.24 140	99.20 49	99.44 189	98.59 170	98.95 225	99.55 57	94.14 364	99.86 145	97.77 198	99.69 235	99.41 223

FMVSNet2		98.49 197	98.40 194	98.75 214	98.90 328	97.14 277	98.61 120	99.13 313	98.59 170	99.19 177	99.28 130	94.14 364	99.82 210	97.97 178	99.80 153	99.29 285

OMC-MVS		97.88 283	97.49 312	99.04 148	98.89 333	98.63 101	96.94 357	99.25 279	95.02 440	98.53 314	98.51 349	97.27 205	99.47 447	93.50 452	99.51 312	99.01 356

VortexMVS		97.98 273	98.31 216	97.02 423	98.88 334	91.45 492	98.03 207	99.47 172	98.65 161	99.55 78	99.47 79	91.49 422	99.81 227	99.32 61	99.91 81	99.80 46

MVSFormer		98.26 236	98.43 190	97.77 358	98.88 334	93.89 445	99.39 20	99.56 122	99.11 101	98.16 349	98.13 396	93.81 373	99.97 6	99.26 66	99.57 290	99.43 215

lupinMVS		97.06 360	96.86 356	97.65 376	98.88 334	93.89 445	95.48 459	97.97 436	93.53 480	98.16 349	97.58 440	93.81 373	99.91 75	96.77 298	99.57 290	99.17 329

dmvs_re		95.98 417	95.39 428	97.74 364	98.86 337	97.45 240	98.37 159	95.69 506	97.95 234	96.56 464	95.95 490	90.70 432	97.68 528	88.32 524	96.13 523	98.11 461

DELS-MVS		98.27 234	98.20 233	98.48 277	98.86 337	96.70 306	95.60 454	99.20 291	97.73 255	98.45 324	98.71 305	97.50 187	99.82 210	98.21 152	99.59 281	98.93 374
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
TinyColmap		97.89 280	97.98 262	97.60 383	98.86 337	94.35 418	96.21 417	99.44 189	97.45 291	99.06 194	98.88 266	97.99 138	99.28 482	94.38 425	99.58 286	99.18 325

LCM-MVSNet-Re		98.64 166	98.48 182	99.11 129	98.85 340	98.51 114	98.49 140	99.83 27	98.37 186	99.69 56	99.46 81	98.21 116	99.92 66	94.13 431	99.30 364	98.91 379

pmmvs4		97.58 314	97.28 325	98.51 271	98.84 341	96.93 291	95.40 463	98.52 410	93.60 479	98.61 298	98.65 325	95.10 329	99.60 394	96.97 278	99.79 160	98.99 360

NP-MVS					98.84 341	97.39 245					96.84 470

sss		97.21 348	96.93 349	98.06 332	98.83 343	95.22 385	96.75 372	98.48 412	94.49 454	97.27 423	97.90 418	92.77 398	99.80 236	96.57 324	99.32 358	99.16 335

PVSNet	93.40 17	95.67 429	95.70 411	95.57 489	98.83 343	88.57 524	92.50 533	97.72 441	92.69 496	96.49 473	96.44 481	93.72 376	99.43 458	93.61 445	99.28 367	98.71 410

MVE	83.40 22	92.50 495	91.92 496	94.25 509	98.83 343	91.64 488	92.71 531	83.52 555	95.92 399	86.46 549	95.46 504	95.20 324	95.40 547	80.51 544	98.64 443	95.73 526
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testing3-2		93.78 473	93.91 463	93.39 523	98.82 346	81.72 554	97.76 258	95.28 510	98.60 169	96.54 465	96.66 475	65.85 543	99.62 382	96.65 317	98.99 413	98.82 390

ambc				98.24 308	98.82 346	95.97 342	98.62 118	99.00 340		99.27 154	99.21 155	96.99 225	99.50 436	96.55 331	99.50 320	99.26 298

旧先验1					98.82 346	97.45 240		98.76 383			98.34 372	95.50 316			99.01 410	99.23 305

test_vis1_rt		97.75 300	97.72 292	97.83 352	98.81 349	96.35 325	97.30 328	99.69 58	94.61 452	97.87 376	98.05 405	96.26 277	98.32 517	98.74 108	98.18 466	98.82 390

WTY-MVS		96.67 379	96.27 398	97.87 350	98.81 349	94.61 412	96.77 370	97.92 438	94.94 443	97.12 428	97.74 431	91.11 427	99.82 210	93.89 437	98.15 470	99.18 325

3Dnovator+	97.89 3	98.69 152	98.51 173	99.24 107	98.81 349	98.40 121	99.02 70	99.19 295	98.99 125	98.07 359	99.28 130	97.11 217	99.84 180	96.84 293	99.32 358	99.47 198

dtuonly		96.49 389	97.28 325	94.10 512	98.80 352	83.27 548	93.66 520	99.48 160	95.10 438	97.87 376	98.30 379	95.61 310	99.68 344	96.98 277	99.75 193	99.33 269

LoFTR		97.97 274	97.79 284	98.53 268	98.80 352	97.47 237	97.01 351	99.55 127	95.55 419	99.46 102	99.22 153	94.22 362	99.44 456	96.45 338	99.82 134	98.68 419

QAPM		97.31 337	96.81 362	98.82 193	98.80 352	97.49 233	99.06 66	99.19 295	90.22 520	97.69 391	99.16 171	96.91 230	99.90 82	90.89 511	99.41 341	99.07 345

VNet		98.42 204	98.30 217	98.79 202	98.79 355	97.29 257	98.23 173	98.66 395	99.31 70	98.85 252	98.80 286	94.80 340	99.78 262	98.13 157	99.13 394	99.31 279

DPM-MVS		96.32 399	95.59 418	98.51 271	98.76 356	97.21 267	94.54 494	98.26 423	91.94 504	96.37 474	97.25 462	93.06 392	99.43 458	91.42 499	98.74 432	98.89 381

3Dnovator	98.27 2	98.81 128	98.73 131	99.05 146	98.76 356	97.81 206	99.25 43	99.30 256	98.57 175	98.55 311	99.33 119	97.95 141	99.90 82	97.16 256	99.67 247	99.44 211

PLC	94.65 16	96.51 386	95.73 410	98.85 183	98.75 358	97.91 188	96.42 402	99.06 323	90.94 517	95.59 490	97.38 456	94.41 352	99.59 399	90.93 509	98.04 479	99.05 347
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		96.83 373	96.75 366	97.08 419	98.74 359	93.33 458	96.71 375	98.26 423	96.72 356	98.44 325	97.37 457	95.20 324	99.47 447	91.89 489	97.43 497	98.44 440

hse-mvs2		97.46 322	97.07 341	98.64 237	98.73 360	97.33 248	97.45 310	97.64 448	99.11 101	98.58 305	97.98 411	88.65 451	99.79 250	98.11 159	97.39 499	98.81 395

CDS-MVSNet		97.69 305	97.35 322	98.69 228	98.73 360	97.02 284	96.92 361	98.75 387	95.89 400	98.59 303	98.67 318	92.08 413	99.74 293	96.72 305	99.81 141	99.32 274
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SD_0403		96.28 402	95.83 406	97.64 379	98.72 362	94.30 419	98.87 89	98.77 381	97.80 248	96.53 466	98.02 408	97.34 200	99.47 447	76.93 548	99.48 325	99.16 335

EIA-MVS		98.00 269	97.74 288	98.80 198	98.72 362	98.09 158	98.05 203	99.60 95	97.39 297	96.63 460	95.55 499	97.68 163	99.80 236	96.73 304	99.27 368	98.52 432

LFMVS		97.20 349	96.72 367	98.64 237	98.72 362	96.95 289	98.93 82	94.14 526	99.74 12	98.78 266	99.01 226	84.45 486	99.73 300	97.44 235	99.27 368	99.25 299

new_pmnet		96.99 367	96.76 364	97.67 372	98.72 362	94.89 399	95.95 438	98.20 427	92.62 497	98.55 311	98.54 344	94.88 336	99.52 429	93.96 435	99.44 336	98.59 429

Fast-Effi-MVS+		97.67 307	97.38 319	98.57 254	98.71 366	97.43 243	97.23 335	99.45 181	94.82 447	96.13 478	96.51 477	98.52 76	99.91 75	96.19 356	98.83 425	98.37 449

TEST9					98.71 366	98.08 162	95.96 436	99.03 332	91.40 510	95.85 486	97.53 443	96.52 259	99.76 274

train_agg		97.10 355	96.45 390	99.07 139	98.71 366	98.08 162	95.96 436	99.03 332	91.64 505	95.85 486	97.53 443	96.47 261	99.76 274	93.67 444	99.16 389	99.36 253

TSAR-MVS + GP.		98.18 249	97.98 262	98.77 210	98.71 366	97.88 192	96.32 409	98.66 395	96.33 375	99.23 170	98.51 349	97.48 191	99.40 462	97.16 256	99.46 327	99.02 354

FA-MVS(test-final)		96.99 367	96.82 360	97.50 396	98.70 370	94.78 404	99.34 23	96.99 469	95.07 439	98.48 320	99.33 119	88.41 454	99.65 369	96.13 362	98.92 422	98.07 464

AUN-MVS		96.24 407	95.45 424	98.60 249	98.70 370	97.22 265	97.38 317	97.65 446	95.95 398	95.53 497	97.96 416	82.11 503	99.79 250	96.31 347	97.44 496	98.80 400

our_test_3		97.39 330	97.73 291	96.34 454	98.70 370	89.78 519	94.61 491	98.97 344	96.50 367	99.04 204	98.85 272	95.98 295	99.84 180	97.26 248	99.67 247	99.41 223

ppachtmachnet_test		97.50 317	97.74 288	96.78 440	98.70 370	91.23 501	94.55 493	99.05 327	96.36 374	99.21 175	98.79 288	96.39 266	99.78 262	96.74 302	99.82 134	99.34 263

PCF-MVS	92.86 18	94.36 459	93.00 479	98.42 284	98.70 370	97.56 229	93.16 530	99.11 316	79.59 547	97.55 402	97.43 453	92.19 408	99.73 300	79.85 545	99.45 329	97.97 470
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ttmdpeth		97.91 277	98.02 258	97.58 385	98.69 375	94.10 428	98.13 186	98.90 355	97.95 234	97.32 422	99.58 48	95.95 298	98.75 510	96.41 341	99.22 378	99.87 23

ETV-MVS		98.03 265	97.86 279	98.56 259	98.69 375	98.07 165	97.51 300	99.50 150	98.10 224	97.50 407	95.51 500	98.41 86	99.88 116	96.27 352	99.24 374	97.71 487

test_prior				98.95 167	98.69 375	97.95 182		99.03 332					99.59 399			99.30 283

mvsmamba		97.57 315	97.26 327	98.51 271	98.69 375	96.73 305	98.74 99	97.25 460	97.03 333	97.88 375	99.23 151	90.95 428	99.87 136	96.61 320	99.00 411	98.91 379

agg_prior					98.68 379	97.99 174		99.01 338		95.59 490			99.77 268

test_8					98.67 380	98.01 171	95.91 442	99.02 335	91.64 505	95.79 489	97.50 447	96.47 261	99.76 274

HQP-NCC					98.67 380		96.29 411		96.05 390	95.55 493

ACMP_Plane					98.67 380		96.29 411		96.05 390	95.55 493

CNVR-MVS		98.17 252	97.87 278	99.07 139	98.67 380	98.24 140	97.01 351	98.93 348	97.25 313	97.62 395	98.34 372	97.27 205	99.57 408	96.42 340	99.33 355	99.39 233

HQP-MVS		97.00 366	96.49 386	98.55 261	98.67 380	96.79 300	96.29 411	99.04 330	96.05 390	95.55 493	96.84 470	93.84 371	99.54 422	92.82 470	99.26 372	99.32 274

MM		98.22 241	97.99 261	98.91 176	98.66 385	96.97 286	97.89 237	94.44 518	99.54 41	98.95 225	99.14 181	93.50 380	99.92 66	99.80 18	99.96 29	99.85 31

test_fmvs1		97.72 302	97.94 269	97.07 421	98.66 385	92.39 478	97.68 270	99.81 33	95.20 437	99.54 80	99.44 86	91.56 420	99.41 461	99.78 22	99.77 173	99.40 232

BridgeMVS		98.63 168	98.72 133	98.38 290	98.66 385	96.68 308	98.90 84	99.42 202	98.99 125	98.97 219	99.19 160	95.81 303	99.85 159	98.77 106	99.77 173	98.60 426

thres200		93.72 475	93.14 476	95.46 494	98.66 385	91.29 497	96.61 387	94.63 516	97.39 297	96.83 450	93.71 525	79.88 506	99.56 411	82.40 542	98.13 471	95.54 527

wuyk23d		96.06 410	97.62 304	91.38 528	98.65 389	98.57 108	98.85 93	96.95 473	96.86 349	99.90 14	99.16 171	99.18 19	98.40 516	89.23 522	99.77 173	77.18 551

NCCC		97.86 286	97.47 316	99.05 146	98.61 390	98.07 165	96.98 354	98.90 355	97.63 263	97.04 435	97.93 417	95.99 294	99.66 362	95.31 395	98.82 427	99.43 215

DeepC-MVS_fast	96.85 6	98.30 229	98.15 244	98.75 214	98.61 390	97.23 262	97.76 258	99.09 319	97.31 306	98.75 272	98.66 322	97.56 178	99.64 374	96.10 364	99.55 299	99.39 233
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testing3		93.51 477	92.09 490	97.75 362	98.60 392	94.40 416	97.32 325	95.26 511	97.56 273	96.79 453	95.50 501	53.57 556	99.77 268	95.26 397	98.97 417	99.08 343

thisisatest0515		94.12 467	93.16 475	96.97 427	98.60 392	92.90 467	93.77 518	90.61 544	94.10 470	96.91 442	95.87 493	74.99 523	99.80 236	94.52 415	99.12 397	98.20 456

GA-MVS		95.86 423	95.32 433	97.49 397	98.60 392	94.15 425	93.83 517	97.93 437	95.49 423	96.68 458	97.42 454	83.21 496	99.30 478	96.22 354	98.55 451	99.01 356

dmvs_testset		92.94 490	92.21 489	95.13 500	98.59 395	90.99 505	97.65 276	92.09 537	96.95 336	94.00 524	93.55 526	92.34 406	96.97 538	72.20 549	92.52 543	97.43 497

OPU-MVS				98.82 193	98.59 395	98.30 135	98.10 193				98.52 348	98.18 119	98.75 510	94.62 412	99.48 325	99.41 223

MSLP-MVS++		98.02 266	98.14 246	97.64 379	98.58 397	95.19 386	97.48 304	99.23 287	97.47 284	97.90 373	98.62 334	97.04 219	98.81 508	97.55 222	99.41 341	98.94 373

test12				98.93 171	98.58 397	97.83 197		98.66 395		96.53 466		95.51 315	99.69 332		99.13 394	99.27 292

CL-MVSNet_self_test		97.44 325	97.22 331	98.08 329	98.57 399	95.78 353	94.30 501	98.79 378	96.58 364	98.60 301	98.19 392	94.74 343	99.64 374	96.41 341	98.84 424	98.82 390

PS-MVSNAJ		97.08 358	97.39 318	96.16 467	98.56 400	92.46 476	95.24 469	98.85 369	97.25 313	97.49 408	95.99 489	98.07 129	99.90 82	96.37 343	98.67 442	96.12 523

CNLPA		97.17 352	96.71 368	98.55 261	98.56 400	98.05 169	96.33 408	98.93 348	96.91 342	97.06 433	97.39 455	94.38 355	99.45 454	91.66 493	99.18 388	98.14 460

xiu_mvs_v2_base		97.16 353	97.49 312	96.17 465	98.54 402	92.46 476	95.45 460	98.84 370	97.25 313	97.48 409	96.49 478	98.31 98	99.90 82	96.34 346	98.68 441	96.15 522

alignmvs		97.35 334	96.88 355	98.78 205	98.54 402	98.09 158	97.71 266	97.69 443	99.20 85	97.59 398	95.90 492	88.12 457	99.55 416	98.18 154	98.96 418	98.70 413

FE-MVS		95.66 430	94.95 446	97.77 358	98.53 404	95.28 380	99.40 19	96.09 495	93.11 487	97.96 370	99.26 138	79.10 513	99.77 268	92.40 483	98.71 436	98.27 454

Effi-MVS+		98.02 266	97.82 282	98.62 243	98.53 404	97.19 269	97.33 324	99.68 65	97.30 307	96.68 458	97.46 452	98.56 74	99.80 236	96.63 318	98.20 465	98.86 386

baseline1		95.96 420	95.44 425	97.52 394	98.51 406	93.99 439	98.39 157	96.09 495	98.21 206	98.40 333	97.76 429	86.88 461	99.63 377	95.42 393	89.27 546	98.95 369

MVS_Test		98.18 249	98.36 205	97.67 372	98.48 407	94.73 407	98.18 179	99.02 335	97.69 258	98.04 363	99.11 189	97.22 209	99.56 411	98.57 122	98.90 423	98.71 410

MGCFI-Net		98.34 219	98.28 220	98.51 271	98.47 408	97.59 228	98.96 78	99.48 160	99.18 93	97.40 417	95.50 501	98.66 60	99.50 436	98.18 154	98.71 436	98.44 440

BH-RMVSNet		96.83 373	96.58 382	97.58 385	98.47 408	94.05 429	96.67 380	97.36 453	96.70 359	97.87 376	97.98 411	95.14 328	99.44 456	90.47 515	98.58 449	99.25 299

sasdasda		98.34 219	98.26 226	98.58 251	98.46 410	97.82 203	98.96 78	99.46 177	99.19 90	97.46 410	95.46 504	98.59 68	99.46 451	98.08 163	98.71 436	98.46 434

canonicalmvs		98.34 219	98.26 226	98.58 251	98.46 410	97.82 203	98.96 78	99.46 177	99.19 90	97.46 410	95.46 504	98.59 68	99.46 451	98.08 163	98.71 436	98.46 434

MVS-HIRNet		94.32 460	95.62 414	90.42 531	98.46 410	75.36 557	96.29 411	89.13 548	95.25 434	95.38 499	99.75 17	92.88 395	99.19 488	94.07 433	99.39 344	96.72 513

MatchFormer		97.07 359	96.92 351	97.49 397	98.44 413	95.92 343	96.79 367	99.14 312	93.08 488	99.32 145	99.10 192	93.89 370	99.03 496	92.78 473	99.78 165	97.52 493

PHI-MVS		98.29 232	97.95 266	99.34 83	98.44 413	99.16 48	98.12 190	99.38 215	96.01 394	98.06 360	98.43 361	97.80 156	99.67 349	95.69 382	99.58 286	99.20 315

DVP-MVS++		98.90 109	98.70 139	99.51 49	98.43 415	99.15 52	99.43 15	99.32 243	98.17 214	99.26 158	99.02 214	98.18 119	99.88 116	97.07 267	99.45 329	99.49 178

MSC_two_6792asdad				99.32 91	98.43 415	98.37 126		98.86 366					99.89 98	97.14 260	99.60 277	99.71 66

No_MVS				99.32 91	98.43 415	98.37 126		98.86 366					99.89 98	97.14 260	99.60 277	99.71 66

Fast-Effi-MVS+-dtu		98.27 234	98.09 249	98.81 195	98.43 415	98.11 154	97.61 286	99.50 150	98.64 162	97.39 419	97.52 446	98.12 127	99.95 26	96.90 287	98.71 436	98.38 447

OpenMVS_ROB	95.38 14	95.84 425	95.18 441	97.81 354	98.41 419	97.15 276	97.37 321	98.62 399	83.86 542	98.65 288	98.37 368	94.29 360	99.68 344	88.41 523	98.62 447	96.60 514

SIFT-CM-Cal		96.28 402	96.31 395	96.16 467	98.39 420	98.11 154	93.46 525	96.47 487	94.81 448	98.49 318	98.43 361	94.48 349	97.34 534	92.60 479	99.70 229	93.02 541

DeepPCF-MVS	96.93 5	98.32 224	98.01 259	99.23 109	98.39 420	98.97 74	95.03 475	99.18 299	96.88 345	99.33 139	98.78 290	98.16 123	99.28 482	96.74 302	99.62 268	99.44 211

Patchmatch-test		96.55 385	96.34 393	97.17 415	98.35 422	93.06 461	98.40 156	97.79 439	97.33 302	98.41 328	98.67 318	83.68 494	99.69 332	95.16 400	99.31 360	98.77 403

AdaColmap		97.14 354	96.71 368	98.46 279	98.34 423	97.80 207	96.95 356	98.93 348	95.58 418	96.92 440	97.66 435	95.87 301	99.53 425	90.97 507	99.14 392	98.04 465

OpenMVS	96.65 7	97.09 357	96.68 370	98.32 297	98.32 424	97.16 275	98.86 92	99.37 219	89.48 525	96.29 476	99.15 177	96.56 257	99.90 82	92.90 467	99.20 383	97.89 473

MG-MVS		96.77 376	96.61 379	97.26 410	98.31 425	93.06 461	95.93 439	98.12 432	96.45 372	97.92 371	98.73 301	93.77 375	99.39 464	91.19 504	99.04 404	99.33 269

TestfortrainingZip				98.97 163	98.30 426	98.43 120	98.68 109	98.26 423	97.76 253	98.86 251	98.16 395	95.15 327	99.47 447		97.55 490	99.02 354

SIFT-NN-NCMNet		95.39 441	95.22 438	95.92 476	98.29 427	98.34 132	93.58 522	94.60 517	94.07 472	94.84 509	97.53 443	94.37 356	96.62 541	91.01 506	98.64 443	92.80 544

SIFT-UMatch		96.33 398	96.47 387	95.89 478	98.29 427	97.95 182	93.84 516	97.24 461	95.78 410	98.72 276	98.04 406	93.45 381	96.81 539	93.14 462	99.73 200	92.91 543

test_yl		96.69 377	96.29 396	97.90 345	98.28 429	95.24 381	97.29 329	97.36 453	98.21 206	98.17 346	97.86 420	86.27 465	99.55 416	94.87 406	98.32 458	98.89 381

DCV-MVSNet		96.69 377	96.29 396	97.90 345	98.28 429	95.24 381	97.29 329	97.36 453	98.21 206	98.17 346	97.86 420	86.27 465	99.55 416	94.87 406	98.32 458	98.89 381

SIFT-NCM-Cal		96.56 384	96.68 370	96.20 463	98.27 431	98.44 119	94.40 498	96.67 481	95.29 432	97.63 394	98.17 393	96.40 265	96.59 543	93.61 445	99.66 255	93.57 534

SIFT-NN-UMatch		95.38 442	95.26 435	95.75 483	98.25 432	97.78 208	93.24 529	95.66 508	94.01 474	95.10 504	97.47 451	93.12 388	96.78 540	92.42 482	98.04 479	92.69 546

CHOSEN 280x420		95.51 436	95.47 422	95.65 488	98.25 432	88.27 527	93.25 528	98.88 359	93.53 480	94.65 513	97.15 465	86.17 467	99.93 54	97.41 237	99.93 58	98.73 409

SIFT-NN-CMatch		95.63 432	95.48 421	96.08 471	98.24 434	98.00 172	92.71 531	94.29 521	94.20 466	95.85 486	97.26 461	95.72 306	97.01 536	91.99 487	99.02 408	93.23 538

SCA		96.41 396	96.66 374	95.67 486	98.24 434	88.35 526	95.85 445	96.88 477	96.11 388	97.67 392	98.67 318	93.10 390	99.85 159	94.16 427	99.22 378	98.81 395

DeepMVS_CX				93.44 522	98.24 434	94.21 422		94.34 520	64.28 551	91.34 540	94.87 517	89.45 445	92.77 551	77.54 547	93.14 542	93.35 537

MS-PatchMatch		97.68 306	97.75 287	97.45 401	98.23 437	93.78 448	97.29 329	98.84 370	96.10 389	98.64 291	98.65 325	96.04 287	99.36 467	96.84 293	99.14 392	99.20 315

SIFT-ConvMatch		96.57 383	96.62 377	96.43 450	98.20 438	98.27 137	93.88 515	96.88 477	95.29 432	98.88 245	98.25 385	95.18 326	97.43 532	93.22 460	99.83 127	93.59 533

BH-w/o		95.13 448	94.89 448	95.86 479	98.20 438	91.31 496	95.65 452	97.37 452	93.64 478	96.52 469	95.70 497	93.04 393	99.02 498	88.10 525	95.82 531	97.24 503

SIFT-MNN		95.92 421	95.97 403	95.74 485	98.18 440	98.00 172	94.17 506	96.99 469	95.74 412	97.16 427	97.90 418	90.71 431	95.79 545	93.71 443	99.21 381	93.44 535

mvs_anonymous		97.83 295	98.16 243	96.87 433	98.18 440	91.89 485	97.31 327	98.90 355	97.37 299	98.83 257	99.46 81	96.28 275	99.79 250	98.90 95	98.16 469	98.95 369

SIFT-PCN-Cal		96.34 397	96.46 389	96.01 474	98.17 442	96.89 294	93.48 524	97.35 456	94.84 446	99.35 131	98.30 379	94.70 344	97.92 524	92.03 486	99.88 96	93.21 540

miper_lstm_enhance		97.18 351	97.16 334	97.25 411	98.16 443	92.85 469	95.15 473	99.31 248	97.25 313	98.74 275	98.78 290	90.07 436	99.78 262	97.19 253	99.80 153	99.11 342

RRT-MVS		97.88 283	97.98 262	97.61 382	98.15 444	93.77 449	98.97 77	99.64 80	99.16 95	98.69 281	99.42 90	91.60 417	99.89 98	97.63 214	98.52 453	99.16 335

ET-MVSNet_ETH3D		94.30 462	93.21 474	97.58 385	98.14 445	94.47 415	94.78 482	93.24 533	94.72 449	89.56 544	95.87 493	78.57 517	99.81 227	96.91 282	97.11 509	98.46 434

ADS-MVSNet2		95.43 440	94.98 444	96.76 441	98.14 445	91.74 486	97.92 233	97.76 440	90.23 518	96.51 470	98.91 255	85.61 475	99.85 159	92.88 468	96.90 511	98.69 414

ADS-MVSNet		95.24 445	94.93 447	96.18 464	98.14 445	90.10 516	97.92 233	97.32 458	90.23 518	96.51 470	98.91 255	85.61 475	99.74 293	92.88 468	96.90 511	98.69 414

SIFT-UM-Cal		96.49 389	96.62 377	96.12 470	98.13 448	97.89 191	93.35 526	98.44 413	95.48 424	98.63 292	98.34 372	95.45 318	97.45 531	92.22 485	99.50 320	93.02 541

SIFT-PointCN		96.45 394	96.47 387	96.39 452	98.13 448	97.54 231	93.31 527	97.23 462	94.67 451	98.68 284	98.32 377	94.64 345	97.81 526	93.50 452	99.77 173	93.83 531

c3_l		97.36 333	97.37 320	97.31 406	98.09 450	93.25 459	95.01 476	99.16 306	97.05 330	98.77 269	98.72 303	92.88 395	99.64 374	96.93 281	99.76 189	99.05 347

balanced_ft_v1		98.28 233	98.35 208	98.10 324	98.08 451	96.23 329	99.23 45	99.26 277	98.34 189	97.46 410	99.42 90	95.38 321	99.88 116	98.60 118	99.34 353	98.17 458

FMVSNet3		97.50 317	97.24 329	98.29 302	98.08 451	95.83 349	97.86 242	98.91 354	97.89 241	98.95 225	98.95 247	87.06 460	99.81 227	97.77 198	99.69 235	99.23 305

PAPM		91.88 505	90.34 507	96.51 447	98.06 453	92.56 474	92.44 534	97.17 464	86.35 538	90.38 543	96.01 488	86.61 463	99.21 487	70.65 551	95.43 534	97.75 483

Effi-MVS+-dtu		98.26 236	97.90 275	99.35 80	98.02 454	99.49 5	98.02 210	99.16 306	98.29 198	97.64 393	97.99 410	96.44 263	99.95 26	96.66 316	98.93 421	98.60 426

PRO-TEST		97.86 286	97.88 277	97.81 354	98.01 455	94.96 394	97.99 222	99.48 160	97.80 248	97.83 381	97.76 429	96.27 276	99.80 236	96.68 311	99.07 400	98.69 414

eth_miper_zixun_eth		97.23 346	97.25 328	97.17 415	98.00 456	92.77 471	94.71 483	99.18 299	97.27 311	98.56 309	98.74 299	91.89 415	99.69 332	97.06 269	99.81 141	99.05 347

ALIKED-LG		97.10 355	96.63 376	98.50 275	97.96 457	98.68 100	97.75 261	99.68 65	95.86 402	98.36 336	98.33 376	91.58 419	99.04 495	90.87 512	99.31 360	97.77 482

nomal-1		94.03 468	93.02 478	97.07 421	97.95 458	92.86 468	96.66 383	95.37 509	96.16 386	94.89 508	94.68 519	69.16 532	99.73 300	94.43 420	97.86 484	98.62 425

HY-MVS	95.94 13	95.90 422	95.35 430	97.55 391	97.95 458	94.79 403	98.81 98	96.94 474	92.28 501	95.17 502	98.57 341	89.90 438	99.75 286	91.20 503	97.33 504	98.10 462

UGNet		98.53 190	98.45 187	98.79 202	97.94 460	96.96 288	99.08 62	98.54 407	99.10 108	96.82 451	99.47 79	96.55 258	99.84 180	98.56 125	99.94 52	99.55 138
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
MAR-MVS		96.47 392	95.70 411	98.79 202	97.92 461	99.12 62	98.28 167	98.60 400	92.16 502	95.54 496	96.17 486	94.77 342	99.52 429	89.62 519	98.23 463	97.72 486
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
MVSTER		96.86 372	96.55 383	97.79 356	97.91 462	94.21 422	97.56 292	98.87 361	97.49 283	99.06 194	99.05 208	80.72 504	99.80 236	98.44 132	99.82 134	99.37 245

SIFT-NN-PointCN		96.06 410	96.11 401	95.91 477	97.88 463	97.73 215	93.49 523	97.51 450	93.22 484	96.57 463	98.26 384	96.23 278	96.60 542	92.54 480	99.27 368	93.40 536

API-MVS		97.04 362	96.91 354	97.42 403	97.88 463	98.23 144	98.18 179	98.50 411	97.57 271	97.39 419	96.75 473	96.77 241	99.15 492	90.16 516	99.02 408	94.88 529

PDCNetPlus		95.22 446	94.73 453	96.70 443	97.85 465	91.14 503	93.94 514	99.97 1	93.06 489	98.95 225	98.89 264	74.32 524	99.14 493	95.63 385	99.93 58	99.82 37

myMVS_eth3d28		92.92 491	92.31 486	94.77 503	97.84 466	87.59 531	96.19 419	96.11 493	97.08 329	94.27 516	93.49 528	66.07 542	98.78 509	91.78 491	97.93 483	97.92 472

miper_ehance_all_eth		97.06 360	97.03 343	97.16 417	97.83 467	93.06 461	94.66 488	99.09 319	95.99 396	98.69 281	98.45 359	92.73 400	99.61 390	96.79 295	99.03 405	98.82 390

cl____		97.02 363	96.83 359	97.58 385	97.82 468	94.04 431	94.66 488	99.16 306	97.04 331	98.63 292	98.71 305	88.68 450	99.69 332	97.00 272	99.81 141	99.00 359

DIV-MVS_self_test		97.02 363	96.84 358	97.58 385	97.82 468	94.03 432	94.66 488	99.16 306	97.04 331	98.63 292	98.71 305	88.69 448	99.69 332	97.00 272	99.81 141	99.01 356

FBQ-MVS		93.12 485	91.90 497	96.81 437	97.80 470	92.96 465	97.12 348	95.93 499	95.83 406	94.07 521	93.03 533	65.21 545	99.18 489	90.94 508	97.13 507	98.28 452

SIFT-NCMNet		96.30 400	96.40 391	96.03 473	97.80 470	97.68 219	92.34 535	96.94 474	95.55 419	98.84 255	98.63 331	94.17 363	97.63 529	93.57 449	99.71 218	92.77 545

CANet		97.87 285	97.76 286	98.19 315	97.75 472	95.51 361	96.76 371	99.05 327	97.74 254	96.93 439	98.21 390	95.59 312	99.89 98	97.86 189	99.93 58	99.19 321

UBG		93.25 483	92.32 485	96.04 472	97.72 473	90.16 514	95.92 441	95.91 500	96.03 393	93.95 526	93.04 532	69.60 531	99.52 429	90.72 514	97.98 481	98.45 437

mvsany_test1		97.60 311	97.54 307	97.77 358	97.72 473	95.35 375	95.36 464	97.13 466	94.13 468	99.71 50	99.33 119	97.93 142	99.30 478	97.60 218	98.94 420	98.67 421

PVSNet_0	89.98 21	91.15 506	90.30 508	93.70 518	97.72 473	84.34 545	90.24 540	97.42 451	90.20 521	93.79 527	93.09 531	90.90 430	98.89 507	86.57 532	72.76 554	97.87 475

CR-MVSNet		96.28 402	95.95 404	97.28 408	97.71 476	94.22 420	98.11 191	98.92 352	92.31 500	96.91 442	99.37 105	85.44 478	99.81 227	97.39 238	97.36 502	97.81 478

RPMNet		97.02 363	96.93 349	97.30 407	97.71 476	94.22 420	98.11 191	99.30 256	99.37 61	96.91 442	99.34 116	86.72 462	99.87 136	97.53 225	97.36 502	97.81 478

ETVMVS		92.60 494	91.08 503	97.18 413	97.70 478	93.65 454	96.54 391	95.70 504	96.51 365	94.68 512	92.39 538	61.80 552	99.50 436	86.97 528	97.41 498	98.40 445

pmmvs3		95.03 450	94.40 458	96.93 429	97.70 478	92.53 475	95.08 474	97.71 442	88.57 532	97.71 389	98.08 403	79.39 511	99.82 210	96.19 356	99.11 398	98.43 442

baseline2		93.73 474	92.83 481	96.42 451	97.70 478	91.28 498	96.84 366	89.77 547	93.96 476	92.44 536	95.93 491	79.14 512	99.77 268	92.94 465	96.76 515	98.21 455

WBMVS		95.18 447	94.78 449	96.37 453	97.68 481	89.74 520	95.80 447	98.73 390	97.54 278	98.30 337	98.44 360	70.06 529	99.82 210	96.62 319	99.87 101	99.54 144

tpm		94.67 455	94.34 460	95.66 487	97.68 481	88.42 525	97.88 238	94.90 513	94.46 456	96.03 485	98.56 343	78.66 515	99.79 250	95.88 370	95.01 536	98.78 402

ALIKED-MNN		95.97 419	95.30 434	98.00 338	97.66 483	98.12 153	96.98 354	99.41 206	91.11 515	94.04 523	97.30 460	91.56 420	98.61 514	89.99 517	99.63 264	97.28 502

CANet_DTU		97.26 342	97.06 342	97.84 351	97.57 484	94.65 411	96.19 419	98.79 378	97.23 319	95.14 503	98.24 387	93.22 386	99.84 180	97.34 240	99.84 115	99.04 351

testing11		93.08 487	92.02 492	96.26 458	97.56 485	90.83 508	96.32 409	95.70 504	96.47 370	92.66 534	93.73 524	64.36 547	99.59 399	93.77 442	97.57 489	98.37 449

tpm2		93.09 486	92.58 484	94.62 506	97.56 485	86.53 534	97.66 274	95.79 503	86.15 539	94.07 521	98.23 389	75.95 521	99.53 425	90.91 510	96.86 514	97.81 478

testing91		93.32 481	92.27 487	96.47 449	97.54 487	91.25 499	96.17 423	96.76 480	97.18 323	93.65 529	93.50 527	65.11 546	99.63 377	93.04 463	97.45 495	98.53 431

TR-MVS		95.55 434	95.12 442	96.86 436	97.54 487	93.94 440	96.49 396	96.53 486	94.36 463	97.03 437	96.61 476	94.26 361	99.16 491	86.91 530	96.31 520	97.47 495

testing99		93.04 488	91.98 495	96.23 461	97.53 489	90.70 511	96.35 407	95.94 498	96.87 346	93.41 530	93.43 529	63.84 548	99.59 399	93.24 459	97.19 505	98.40 445

1314		95.74 427	95.60 416	96.17 465	97.53 489	92.75 472	98.07 200	98.31 421	91.22 512	94.25 517	96.68 474	95.53 313	99.03 496	91.64 495	97.18 506	96.74 512

CostFormer		93.97 470	93.78 466	94.51 507	97.53 489	85.83 537	97.98 224	95.96 497	89.29 527	94.99 506	98.63 331	78.63 516	99.62 382	94.54 414	96.50 517	98.09 463

FMVSNet5		96.01 414	95.20 440	98.41 286	97.53 489	96.10 332	98.74 99	99.50 150	97.22 322	98.03 364	99.04 210	69.80 530	99.88 116	97.27 247	99.71 218	99.25 299

PMMVS		96.51 386	95.98 402	98.09 326	97.53 489	95.84 348	94.92 478	98.84 370	91.58 507	96.05 483	95.58 498	95.68 308	99.66 362	95.59 388	98.09 473	98.76 405

reproduce_monomvs		95.00 452	95.25 436	94.22 510	97.51 494	83.34 547	97.86 242	98.44 413	98.51 180	99.29 150	99.30 126	67.68 536	99.56 411	98.89 97	99.81 141	99.77 54

PAPR		95.29 443	94.47 455	97.75 362	97.50 495	95.14 388	94.89 480	98.71 392	91.39 511	95.35 500	95.48 503	94.57 347	99.14 493	84.95 535	97.37 500	98.97 365

testing222		91.96 503	90.37 506	96.72 442	97.47 496	92.59 473	96.11 426	94.76 514	96.83 350	92.90 532	92.87 534	57.92 554	99.55 416	86.93 529	97.52 491	98.00 469

PatchT		96.65 380	96.35 392	97.54 392	97.40 497	95.32 378	97.98 224	96.64 483	99.33 67	96.89 446	99.42 90	84.32 488	99.81 227	97.69 211	97.49 493	97.48 494

tpm cat1		93.29 482	93.13 477	93.75 517	97.39 498	84.74 540	97.39 315	97.65 446	83.39 544	94.16 518	98.41 363	82.86 499	99.39 464	91.56 497	95.35 535	97.14 504

PatchmatchNet		95.58 433	95.67 413	95.30 499	97.34 499	87.32 532	97.65 276	96.65 482	95.30 431	97.07 432	98.69 314	84.77 483	99.75 286	94.97 404	98.64 443	98.83 388
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SP-LightGlue		97.22 347	97.01 345	97.88 348	97.33 500	97.19 269	96.38 404	99.08 321	97.28 309	96.53 466	97.50 447	92.36 404	98.70 512	97.84 190	98.76 431	97.74 484

Patchmtry		97.35 334	96.97 347	98.50 275	97.31 501	96.47 320	98.18 179	98.92 352	98.95 132	98.78 266	99.37 105	85.44 478	99.85 159	95.96 368	99.83 127	99.17 329

LS3D		98.63 168	98.38 201	99.36 74	97.25 502	99.38 12	99.12 61	99.32 243	99.21 83	98.44 325	98.88 266	97.31 201	99.80 236	96.58 322	99.34 353	98.92 375

IB-MVS	91.63 19	92.24 500	90.90 504	96.27 457	97.22 503	91.24 500	94.36 500	93.33 532	92.37 499	92.24 538	94.58 521	66.20 541	99.89 98	93.16 461	94.63 538	97.66 488
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
UWE-MVS		92.38 497	91.76 500	94.21 511	97.16 504	84.65 541	95.42 462	88.45 549	95.96 397	96.17 477	95.84 495	66.36 539	99.71 313	91.87 490	98.64 443	98.28 452

tpmrst		95.07 449	95.46 423	93.91 515	97.11 505	84.36 544	97.62 281	96.96 472	94.98 441	96.35 475	98.80 286	85.46 477	99.59 399	95.60 387	96.23 521	97.79 481

Syy-MVS		96.04 412	95.56 420	97.49 397	97.10 506	94.48 414	96.18 421	96.58 484	95.65 414	94.77 510	92.29 540	91.27 426	99.36 467	98.17 156	98.05 477	98.63 423

myMVS_eth3d		91.92 504	90.45 505	96.30 455	97.10 506	90.90 506	96.18 421	96.58 484	95.65 414	94.77 510	92.29 540	53.88 555	99.36 467	89.59 521	98.05 477	98.63 423

blended_shiyan6		95.99 416	95.33 431	97.95 342	97.06 508	94.89 399	95.34 465	98.58 402	96.17 382	97.06 433	92.41 537	87.64 458	99.76 274	97.64 213	96.09 524	99.19 321

MDTV_nov1_ep13			95.22 438		97.06 508	83.20 549	97.74 263	96.16 491	94.37 462	96.99 438	98.83 279	83.95 492	99.53 425	93.90 436	97.95 482

blended_shiyan8		95.98 417	95.33 431	97.94 343	97.05 510	94.87 401	95.34 465	98.59 401	96.17 382	97.09 431	92.39 538	87.62 459	99.76 274	97.65 212	96.05 530	99.20 315

MASt3R-SfM		96.02 413	95.82 407	96.60 445	97.03 511	94.90 398	94.26 504	98.53 408	88.40 534	98.41 328	98.67 318	92.39 403	97.62 530	95.31 395	99.41 341	97.29 501

SP-SuperGlue		97.31 337	97.23 330	97.57 390	96.96 512	97.24 261	96.26 415	98.76 383	97.68 259	96.88 448	97.85 422	94.32 358	98.01 522	97.76 202	98.57 450	97.45 496

SIFT-NN		92.96 489	92.79 482	93.46 520	96.92 513	96.45 321	91.89 537	94.39 519	92.91 492	92.54 535	95.46 504	88.26 455	90.71 553	85.22 534	97.52 491	93.22 539

MVS		93.19 484	92.09 490	96.50 448	96.91 514	94.03 432	98.07 200	98.06 435	68.01 550	94.56 515	96.48 479	95.96 297	99.30 478	83.84 537	96.89 513	96.17 520

E-PMN		94.17 465	94.37 459	93.58 519	96.86 515	85.71 538	90.11 542	97.07 467	98.17 214	97.82 384	97.19 463	84.62 485	98.94 502	89.77 518	97.68 488	96.09 524

JIA-IIPM		95.52 435	95.03 443	97.00 424	96.85 516	94.03 432	96.93 359	95.82 501	99.20 85	94.63 514	99.71 23	83.09 497	99.60 394	94.42 421	94.64 537	97.36 499

EMVS		93.83 472	94.02 462	93.23 525	96.83 517	84.96 539	89.77 543	96.32 489	97.92 238	97.43 416	96.36 484	86.17 467	98.93 503	87.68 526	97.73 487	95.81 525

blend_shiyan4		92.09 502	90.16 509	97.88 348	96.78 518	94.93 396	95.24 469	98.58 402	96.22 380	96.07 481	91.42 542	63.46 551	99.73 300	96.70 308	76.98 552	98.98 361

cl22		95.79 426	95.39 428	96.98 426	96.77 519	92.79 470	94.40 498	98.53 408	94.59 453	97.89 374	98.17 393	82.82 500	99.24 484	96.37 343	99.03 405	98.92 375

SP-MNN		96.46 393	96.24 400	97.10 418	96.71 520	95.98 340	96.00 432	97.33 457	95.82 407	94.93 507	97.10 469	93.70 377	98.01 522	96.30 349	98.30 461	97.30 500

WB-MVSnew		95.73 428	95.57 419	96.23 461	96.70 521	90.70 511	96.07 429	93.86 528	95.60 416	97.04 435	95.45 508	96.00 290	99.55 416	91.04 505	98.31 460	98.43 442

dp		93.47 478	93.59 469	93.13 526	96.64 522	81.62 555	97.66 274	96.42 488	92.80 495	96.11 479	98.64 329	78.55 518	99.59 399	93.31 456	92.18 545	98.16 459

MonoMVSNet		96.25 405	96.53 385	95.39 495	96.57 523	91.01 504	98.82 97	97.68 445	98.57 175	98.03 364	99.37 105	90.92 429	97.78 527	94.99 402	93.88 541	97.38 498

wanda-best-256-512		95.48 437	94.74 451	97.68 370	96.53 524	94.12 426	94.17 506	98.57 404	95.84 403	96.71 455	91.16 543	86.05 470	99.76 274	97.57 220	96.09 524	99.17 329

FE-blended-shiyan7		95.48 437	94.74 451	97.68 370	96.53 524	94.12 426	94.17 506	98.57 404	95.84 403	96.71 455	91.16 543	86.05 470	99.76 274	97.57 220	96.09 524	99.17 329

usedtu_blend_shiyan5		96.20 408	95.62 414	97.94 343	96.53 524	94.93 396	98.83 96	99.59 101	98.89 139	96.71 455	91.16 543	86.05 470	99.73 300	96.70 308	96.09 524	99.17 329

test-LLR		93.90 471	93.85 464	94.04 513	96.53 524	84.62 542	94.05 511	92.39 535	96.17 382	94.12 519	95.07 509	82.30 501	99.67 349	95.87 373	98.18 466	97.82 476

test-mter		92.33 499	91.76 500	94.04 513	96.53 524	84.62 542	94.05 511	92.39 535	94.00 475	94.12 519	95.07 509	65.63 544	99.67 349	95.87 373	98.18 466	97.82 476

TESTMET0.1,1		92.19 501	91.77 499	93.46 520	96.48 529	82.80 551	94.05 511	91.52 543	94.45 459	94.00 524	94.88 515	66.65 538	99.56 411	95.78 378	98.11 472	98.02 466

MGCNet		97.44 325	97.01 345	98.72 222	96.42 530	96.74 304	97.20 340	91.97 541	98.46 183	98.30 337	98.79 288	92.74 399	99.91 75	99.30 63	99.94 52	99.52 162

miper_enhance_ethall		96.01 414	95.74 409	96.81 437	96.41 531	92.27 482	93.69 519	98.89 358	91.14 514	98.30 337	97.35 459	90.58 433	99.58 406	96.31 347	99.03 405	98.60 426

tpmvs		95.02 451	95.25 436	94.33 508	96.39 532	85.87 535	98.08 196	96.83 479	95.46 425	95.51 498	98.69 314	85.91 473	99.53 425	94.16 427	96.23 521	97.58 491

CMPMVS	75.91 23	96.29 401	95.44 425	98.84 189	96.25 533	98.69 99	97.02 350	99.12 314	88.90 529	97.83 381	98.86 269	89.51 443	98.90 506	91.92 488	99.51 312	98.92 375
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ALIKED-NN		94.29 463	93.41 472	96.94 428	96.18 534	97.66 220	94.90 479	98.68 393	88.85 530	90.43 542	96.81 472	89.82 439	96.59 543	86.67 531	98.33 457	96.58 515

test0.0.03 1		94.51 457	93.69 467	96.99 425	96.05 535	93.61 456	94.97 477	93.49 530	96.17 382	97.57 401	94.88 515	82.30 501	99.01 500	93.60 447	94.17 540	98.37 449

EPMVS		93.72 475	93.27 473	95.09 502	96.04 536	87.76 529	98.13 186	85.01 554	94.69 450	96.92 440	98.64 329	78.47 519	99.31 476	95.04 401	96.46 518	98.20 456

cascas		94.79 454	94.33 461	96.15 469	96.02 537	92.36 480	92.34 535	99.26 277	85.34 541	95.08 505	94.96 514	92.96 394	98.53 515	94.41 424	98.59 448	97.56 492

gbinet_0.2-2-1-0.02		95.44 439	94.55 454	98.14 320	95.99 538	95.34 377	94.71 483	98.29 422	96.00 395	96.05 483	90.50 547	84.99 480	99.79 250	97.33 242	97.07 510	99.28 288

MVStest1		95.86 423	95.60 416	96.63 444	95.87 539	91.70 487	97.93 230	98.94 345	98.03 228	99.56 75	99.66 33	71.83 527	98.26 518	99.35 59	99.24 374	99.91 14

gg-mvs-nofinetune		92.37 498	91.20 502	95.85 480	95.80 540	92.38 479	99.31 30	81.84 556	99.75 10	91.83 539	99.74 19	68.29 533	99.02 498	87.15 527	97.12 508	96.16 521

SP-DiffGlue		96.87 371	96.76 364	97.21 412	95.17 541	96.88 296	96.12 425	98.93 348	96.51 365	98.37 334	97.55 442	93.65 378	97.83 525	96.11 363	98.45 455	96.92 506

SP-NN		94.67 455	94.44 457	95.36 497	95.12 542	95.23 384	94.27 503	96.10 494	94.46 456	90.91 541	95.76 496	91.47 423	93.87 550	95.23 398	96.62 516	97.00 505

gm-plane-assit					94.83 543	81.97 553			88.07 536		94.99 512		99.60 394	91.76 492

GG-mvs-BLEND				94.76 504	94.54 544	92.13 484	99.31 30	80.47 557		88.73 547	91.01 546	67.59 537	98.16 521	82.30 543	94.53 539	93.98 530

UWE-MVS-28		90.22 507	89.28 510	93.02 527	94.50 545	82.87 550	96.52 394	87.51 550	95.21 436	92.36 537	96.04 487	71.57 528	98.25 519	72.04 550	97.77 486	97.94 471

EPNet_dtu		94.93 453	94.78 449	95.38 496	93.58 546	87.68 530	96.78 369	95.69 506	97.35 301	89.14 546	98.09 402	88.15 456	99.49 440	94.95 405	99.30 364	98.98 361
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
0.4-1-1-0.1		88.42 509	85.91 512	95.94 475	93.08 547	91.54 489	90.99 539	92.04 539	89.96 524	84.83 551	83.25 549	63.75 549	99.52 429	93.25 458	82.07 547	96.75 511

XFeat-MNN		93.41 480	92.98 480	94.68 505	92.63 548	92.92 466	89.72 544	95.81 502	92.10 503	97.23 426	96.29 485	84.95 481	97.31 535	89.60 520	98.54 452	93.81 532

dongtai		76.24 516	75.95 519	77.12 534	92.39 549	67.91 560	90.16 541	59.44 562	82.04 545	89.42 545	94.67 520	49.68 557	81.74 554	48.06 555	77.66 551	81.72 549

0.3-1-1-0.015		87.27 511	84.50 515	95.57 489	91.70 550	90.77 509	89.41 545	92.04 539	88.98 528	82.46 553	81.35 550	60.36 553	99.50 436	92.96 464	81.23 549	96.45 516

KD-MVS_2432*1600		92.87 492	91.99 493	95.51 492	91.37 551	89.27 522	94.07 509	98.14 430	95.42 427	97.25 424	96.44 481	67.86 534	99.24 484	91.28 501	96.08 528	98.02 466

miper_refine_blended		92.87 492	91.99 493	95.51 492	91.37 551	89.27 522	94.07 509	98.14 430	95.42 427	97.25 424	96.44 481	67.86 534	99.24 484	91.28 501	96.08 528	98.02 466

0.4-1-1-0.2		87.49 510	84.89 513	95.31 498	91.33 553	90.08 517	88.47 546	92.07 538	88.70 531	84.06 552	81.08 551	63.62 550	99.49 440	92.93 466	81.71 548	96.37 517

XFeat-NN		89.63 508	89.13 511	91.14 529	90.93 554	90.02 518	84.90 547	94.05 527	88.10 535	92.89 533	93.33 530	78.74 514	90.89 552	83.46 538	95.72 532	92.52 547

EPNet		96.14 409	95.44 425	98.25 306	90.76 555	95.50 365	97.92 233	94.65 515	98.97 128	92.98 531	98.85 272	89.12 446	99.87 136	95.99 366	99.68 241	99.39 233
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
kuosan		69.30 517	68.95 520	70.34 535	87.68 556	65.00 561	91.11 538	59.90 561	69.02 549	74.46 555	88.89 548	48.58 559	68.03 556	28.61 556	72.33 555	77.99 550

GLUNet-SfM		86.26 512	84.68 514	91.01 530	80.58 557	83.56 546	78.04 548	93.59 529	76.70 548	95.29 501	94.72 518	77.51 520	94.26 549	66.39 552	99.33 355	95.20 528

test_method		79.78 514	79.50 517	80.62 532	80.21 558	45.76 563	70.82 549	98.41 418	31.08 554	80.89 554	97.71 432	84.85 482	97.37 533	91.51 498	80.03 550	98.75 406

MVS_clip		56.94 519	60.93 521	44.97 537	71.47 559	51.70 562	61.73 550	21.77 563	28.88 555	86.09 550	92.75 536	48.89 558	27.00 558	61.70 553	75.08 553	56.23 552

tmp_tt		78.77 515	78.73 518	78.90 533	58.45 560	74.76 559	94.20 505	78.26 558	39.16 553	86.71 548	92.82 535	80.50 505	75.19 555	86.16 533	92.29 544	86.74 548

VLMVS_CLIP		57.57 518	58.80 522	53.85 536	47.22 561	42.89 564	60.06 551	76.87 559	39.44 552	65.76 556	80.47 552	36.24 560	64.75 557	58.06 554	65.11 556	53.91 553

VLMVS		32.15 520	34.06 523	26.43 538	35.38 562	29.60 565	32.69 552	19.27 564	3.29 559	44.01 558	60.07 554	35.02 561	20.44 559	22.64 557	54.15 558	29.25 554

MVS_baseline		25.61 521	31.27 525	8.63 539	32.09 563	3.00 568	22.13 553	5.43 566	1.36 560	58.03 557	69.99 553	18.40 562	0.00 562	18.79 558	55.18 557	22.88 555

testmvs		17.12 523	20.53 526	6.87 541	12.05 564	4.20 567	93.62 521	6.73 565	4.62 558	10.41 560	24.33 556	8.28 564	3.56 561	9.69 560	15.07 559	12.86 557

test123		17.04 524	20.11 527	7.82 540	10.25 565	4.91 566	94.80 481	4.47 567	4.93 557	10.00 561	24.28 557	9.69 563	3.64 560	10.14 559	12.43 560	14.92 556

PatchmatchNet2					0.00 566	90.12 515	94.29 502	98.12 432	94.40 461
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
mmdepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

monomultidepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

test_blank		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

eth-test2					0.00 566

eth-test					0.00 566

uanet_test		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

DCPMVS		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

cdsmvs_eth3d_5k		24.66 522	32.88 524	0.00 542	0.00 566	0.00 569	0.00 554	99.10 317	0.00 561	0.00 562	97.58 440	99.21 18	0.00 562	0.00 561	0.00 561	0.00 558

pcd_1.5k_mvsjas		8.17 525	10.90 528	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	98.07 129	0.00 562	0.00 561	0.00 561	0.00 558

sosnet-low-res		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

sosnet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uncertanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

Regformer		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

ab-mvs-re		8.12 526	10.83 529	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	97.48 449	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

PatchmatchNet1														96.95 280	99.71 218	99.28 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													99.85 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						90.90 506								91.37 500

PC_three_1452									93.27 483	99.40 118	98.54 344	98.22 114	97.00 537	95.17 399	99.45 329	99.49 178

test_241102_TWO								99.30 256	98.03 228	99.26 158	99.02 214	97.51 186	99.88 116	96.91 282	99.60 277	99.66 81

test_0728_THIRD									98.17 214	99.08 192	99.02 214	97.89 148	99.88 116	97.07 267	99.71 218	99.70 71

GSMVS																98.81 395

sam_mvs1												84.74 484				98.81 395

sam_mvs												84.29 490

MTGPA								99.20 291

test_post1							97.59 289				20.48 559	83.07 498	99.66 362	94.16 427

test_post											21.25 558	83.86 493	99.70 322

patchmatchnet-post											98.77 292	84.37 487	99.85 159

MTMP							97.93 230	91.91 542

test9_res														93.28 457	99.15 391	99.38 242

agg_prior2														92.50 481	99.16 389	99.37 245

test_prior4						97.97 178	95.86 443

test_prior2							95.74 450		96.48 369	96.11 479	97.63 438	95.92 300		94.16 427	99.20 383

旧先验2							95.76 449		88.56 533	97.52 405			99.66 362	94.48 416

新几何2							95.93 439

无先验							95.74 450	98.74 389	89.38 526				99.73 300	92.38 484		99.22 310

原ACMM2							95.53 456

testdata2													99.79 250	92.80 472

segment_acmp												97.02 222

testdata1							95.44 461		96.32 376

plane_prior5								99.27 271					99.70 322	94.42 421	99.51 312	99.45 207

plane_prior4											97.98 411

plane_prior3						97.78 208			97.41 294	97.79 385

plane_prior2							97.77 255		98.20 210

plane_prior						97.65 222	97.07 349		96.72 356						99.36 348

n2								0.00 568

nn								0.00 568

door-mid								99.57 112

test11								98.87 361

door								99.41 206

HQP5-MVS						96.79 300

BP-MVS														92.82 470

HQP4-MVS										95.56 492			99.54 422			99.32 274

HQP3-MVS								99.04 330							99.26 372

HQP2-MVS												93.84 371

MDTV_nov1_ep13_2view						74.92 558	97.69 269		90.06 523	97.75 388		85.78 474		93.52 450		98.69 414

ACMMP++_ref															99.77 173

ACMMP++															99.68 241

Test By Simon												96.52 259

High-resolution multi-view benchmark