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 13	99.98 1	99.99 1	99.96 1	99.77 2	100.00 1	99.81 15	100.00 1	99.85 29

Gipuma		99.03 78	99.16 60	98.64 208	99.94 2	98.51 108	99.32 26	99.75 42	99.58 37	98.60 257	99.62 40	98.22 102	99.51 385	97.70 182	99.73 174	97.89 418
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
OurMVSNet-221017-0		99.37 29	99.31 41	99.53 38	99.91 3	98.98 71	99.63 7	99.58 81	99.44 51	99.78 39	99.76 15	96.39 239	99.92 63	99.44 53	99.92 67	99.68 68

pmmvs6		99.67 3	99.70 3	99.60 15	99.90 4	99.27 27	99.53 9	99.76 39	99.64 27	99.84 30	99.83 4	99.50 9	99.87 132	99.36 56	99.92 67	99.64 81

PS-MVSNAJss		99.46 17	99.49 16	99.35 76	99.90 4	98.15 135	99.20 48	99.65 65	99.48 43	99.92 8	99.71 22	98.07 116	99.96 14	99.53 46	100.00 1	99.93 11

testf1		99.25 41	99.16 60	99.51 48	99.89 6	99.63 4	98.71 104	99.69 53	98.90 130	99.43 101	99.35 102	98.86 34	99.67 312	97.81 171	99.81 126	99.24 266

APD_test2		99.25 41	99.16 60	99.51 48	99.89 6	99.63 4	98.71 104	99.69 53	98.90 130	99.43 101	99.35 102	98.86 34	99.67 312	97.81 171	99.81 126	99.24 266

ANet_high		99.57 10	99.67 6	99.28 92	99.89 6	98.09 142	99.14 57	99.93 5	99.82 8	99.93 6	99.81 8	99.17 20	99.94 41	99.31 60	100.00 1	99.82 35

anonymousdsp		99.51 14	99.47 21	99.62 9	99.88 9	99.08 69	99.34 23	99.69 53	98.93 126	99.65 62	99.72 21	98.93 32	99.95 26	99.11 76	100.00 1	99.82 35

v7n		99.53 12	99.57 13	99.41 66	99.88 9	98.54 106	99.45 14	99.61 74	99.66 24	99.68 56	99.66 32	98.44 77	99.95 26	99.73 26	99.96 28	99.75 57

mvs_tets		99.63 6	99.67 6	99.49 54	99.88 9	98.61 98	99.34 23	99.71 47	99.27 72	99.90 14	99.74 18	99.68 4	99.97 7	99.55 41	99.99 5	99.88 20

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 70	99.87 12	98.13 138	98.08 182	99.95 1	99.45 49	99.98 2	99.75 16	99.80 1	99.97 7	99.82 11	99.99 5	99.99 2

jajsoiax		99.58 9	99.61 11	99.48 56	99.87 12	98.61 98	99.28 40	99.66 62	99.09 105	99.89 18	99.68 25	99.53 7	99.97 7	99.50 49	99.99 5	99.87 21

test_djsdf		99.52 13	99.51 15	99.53 38	99.86 14	98.74 88	99.39 20	99.56 95	99.11 95	99.70 50	99.73 20	99.00 27	99.97 7	99.26 64	99.98 12	99.89 16

MIMVSNet1		99.38 28	99.32 39	99.55 28	99.86 14	99.19 42	99.41 17	99.59 79	99.59 35	99.71 48	99.57 49	97.12 195	99.90 79	99.21 69	99.87 95	99.54 136

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 26	99.85 16	99.11 64	99.90 1	99.78 36	99.63 29	99.78 39	99.67 30	99.48 10	99.81 216	99.30 61	99.97 21	99.77 48
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 20	99.69 5	99.58 81	99.90 3	99.86 24	99.78 13	99.58 6	99.95 26	99.00 86	99.95 38	99.78 45

SixPastTwentyTwo		98.75 125	98.62 137	99.16 114	99.83 18	97.96 162	99.28 40	98.20 365	99.37 59	99.70 50	99.65 36	92.65 344	99.93 52	99.04 83	99.84 109	99.60 97

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 69	99.88 4	99.86 24	99.80 11	99.03 24	99.89 95	99.48 51	99.93 54	99.60 97

Baseline_NR-MVSNet		98.98 86	98.86 104	99.36 70	99.82 19	98.55 103	97.47 285	99.57 88	99.37 59	99.21 153	99.61 43	96.76 221	99.83 190	98.06 150	99.83 116	99.71 60

pm-mvs1		99.44 19	99.48 18	99.33 85	99.80 21	98.63 95	99.29 36	99.63 69	99.30 69	99.65 62	99.60 45	99.16 22	99.82 200	99.07 79	99.83 116	99.56 123

TransMVSNet (Re)		99.44 19	99.47 21	99.36 70	99.80 21	98.58 101	99.27 42	99.57 88	99.39 57	99.75 44	99.62 40	99.17 20	99.83 190	99.06 81	99.62 229	99.66 75

K. test v3		98.00 238	97.66 263	99.03 141	99.79 23	97.56 198	99.19 52	92.47 451	99.62 32	99.52 83	99.66 32	89.61 375	99.96 14	99.25 66	99.81 126	99.56 123

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 79	99.78 24	98.11 139	97.77 238	99.90 11	99.33 64	99.97 3	99.66 32	99.71 3	99.96 14	99.79 18	99.99 5	99.96 8

APD_test1		98.83 109	98.66 130	99.34 79	99.78 24	99.47 9	98.42 144	99.45 142	98.28 182	98.98 186	99.19 144	97.76 143	99.58 359	96.57 274	99.55 256	98.97 320

test_vis3_rt		99.14 60	99.17 58	99.07 131	99.78 24	98.38 115	98.92 82	99.94 2	97.80 224	99.91 12	99.67 30	97.15 194	98.91 444	99.76 22	99.56 252	99.92 12

EGC-MVSNET		85.24 431	80.54 434	99.34 79	99.77 27	99.20 39	99.08 61	99.29 222	12.08 469	20.84 470	99.42 87	97.55 162	99.85 154	97.08 226	99.72 182	98.96 322

Anonymous20240521		98.69 136	98.87 100	98.16 283	99.77 27	95.11 324	99.08 61	99.44 150	99.34 63	99.33 124	99.55 57	94.10 319	99.94 41	99.25 66	99.96 28	99.42 198

FC-MVSNet-test		99.27 38	99.25 51	99.34 79	99.77 27	98.37 117	99.30 35	99.57 88	99.61 34	99.40 110	99.50 67	97.12 195	99.85 154	99.02 85	99.94 49	99.80 40

test_vis1_n		98.31 203	98.50 157	97.73 317	99.76 30	94.17 352	98.68 107	99.91 9	96.31 336	99.79 38	99.57 49	92.85 340	99.42 405	99.79 18	99.84 109	99.60 97

test_fmvs3		99.12 67	99.41 26	98.25 271	99.76 30	95.07 325	99.05 67	99.94 2	97.78 227	99.82 33	99.84 3	98.56 68	99.71 288	99.96 1	99.96 28	99.97 4

XXY-MVS		99.14 60	99.15 65	99.10 124	99.76 30	97.74 187	98.85 92	99.62 71	98.48 165	99.37 115	99.49 73	98.75 46	99.86 141	98.20 140	99.80 137	99.71 60

TDRefinement		99.42 24	99.38 29	99.55 28	99.76 30	99.33 21	99.68 6	99.71 47	99.38 58	99.53 81	99.61 43	98.64 56	99.80 224	98.24 135	99.84 109	99.52 148

fmvsm_s_conf0.1_n_a		99.17 52	99.30 44	98.80 178	99.75 34	96.59 260	97.97 212	99.86 16	98.22 185	99.88 21	99.71 22	98.59 62	99.84 172	99.73 26	99.98 12	99.98 3

tt0805		98.69 136	98.62 137	98.90 166	99.75 34	99.30 22	99.15 56	96.97 402	98.86 135	98.87 219	97.62 380	98.63 58	98.96 441	99.41 55	98.29 393	98.45 384

test_vis1_n_1920		98.40 186	98.92 93	96.81 379	99.74 36	90.76 430	98.15 170	99.91 9	98.33 173	99.89 18	99.55 57	95.07 290	99.88 113	99.76 22	99.93 54	99.79 42

FOURS1					99.73 37	99.67 3	99.43 15	99.54 104	99.43 53	99.26 141

PEN-MVS		99.41 25	99.34 36	99.62 9	99.73 37	99.14 57	99.29 36	99.54 104	99.62 32	99.56 72	99.42 87	98.16 110	99.96 14	98.78 100	99.93 54	99.77 48

lessismore_v0				98.97 153	99.73 37	97.53 200		86.71 466		99.37 115	99.52 66	89.93 371	99.92 63	98.99 87	99.72 182	99.44 191

SteuartSystems-ACMMP		98.79 118	98.54 150	99.54 31	99.73 37	99.16 48	98.23 160	99.31 206	97.92 215	98.90 208	98.90 229	98.00 122	99.88 113	96.15 306	99.72 182	99.58 112
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended_VisFu		98.17 224	98.15 216	98.22 277	99.73 37	95.15 321	97.36 298	99.68 58	94.45 392	98.99 185	99.27 121	96.87 210	99.94 41	97.13 223	99.91 76	99.57 117

Vis-MVSNet		99.34 30	99.36 33	99.27 95	99.73 37	98.26 124	99.17 53	99.78 36	99.11 95	99.27 137	99.48 74	98.82 37	99.95 26	98.94 90	99.93 54	99.59 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tt0320-xc		99.64 5	99.68 5	99.50 53	99.72 43	98.98 71	99.51 10	99.85 18	99.86 6	99.88 21	99.82 5	99.02 26	99.90 79	99.54 42	99.95 38	99.61 95

SSC-MVS		98.71 129	98.74 114	98.62 214	99.72 43	96.08 283	98.74 97	98.64 345	99.74 13	99.67 58	99.24 134	94.57 305	99.95 26	99.11 76	99.24 317	99.82 35

test_f		98.67 144	98.87 100	98.05 292	99.72 43	95.59 297	98.51 128	99.81 31	96.30 338	99.78 39	99.82 5	96.14 249	98.63 451	99.82 11	99.93 54	99.95 9

ACMH	96.65 7	99.25 41	99.24 52	99.26 97	99.72 43	98.38 115	99.07 64	99.55 99	98.30 177	99.65 62	99.45 83	99.22 17	99.76 260	98.44 126	99.77 154	99.64 81
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0320		99.61 8	99.65 9	99.48 56	99.71 47	98.94 78	99.54 8	99.83 25	99.87 5	99.89 18	99.82 5	98.75 46	99.90 79	99.54 42	99.95 38	99.59 104

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 208	99.71 47	96.10 278	97.87 224	99.85 18	98.56 161	99.90 14	99.68 25	98.69 52	99.85 154	99.72 28	99.98 12	99.97 4

PS-CasMVS		99.40 26	99.33 37	99.62 9	99.71 47	99.10 65	99.29 36	99.53 108	99.53 40	99.46 96	99.41 91	98.23 99	99.95 26	98.89 94	99.95 38	99.81 38

DTE-MVSNet		99.43 23	99.35 34	99.66 7	99.71 47	99.30 22	99.31 30	99.51 113	99.64 27	99.56 72	99.46 79	98.23 99	99.97 7	98.78 100	99.93 54	99.72 59

WR-MVS_H		99.33 31	99.22 53	99.65 8	99.71 47	99.24 30	99.32 26	99.55 99	99.46 48	99.50 89	99.34 106	97.30 184	99.93 52	98.90 92	99.93 54	99.77 48

HPM-MVS_fast		99.01 80	98.82 107	99.57 21	99.71 47	99.35 17	99.00 72	99.50 116	97.33 272	98.94 203	98.86 239	98.75 46	99.82 200	97.53 194	99.71 191	99.56 123

ACMH+	96.62 9	99.08 74	99.00 85	99.33 85	99.71 47	98.83 83	98.60 114	99.58 81	99.11 95	99.53 81	99.18 148	98.81 38	99.67 312	96.71 263	99.77 154	99.50 154

PMVS	91.26 20	97.86 252	97.94 240	97.65 324	99.71 47	97.94 164	98.52 123	98.68 341	98.99 118	97.52 349	99.35 102	97.41 177	98.18 457	91.59 420	99.67 212	96.82 446
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
KinetiMVS		99.03 78	99.02 81	99.03 141	99.70 55	97.48 203	98.43 141	99.29 222	99.70 16	99.60 69	99.07 177	96.13 250	99.94 41	99.42 54	99.87 95	99.68 68

FIs		99.14 60	99.09 73	99.29 91	99.70 55	98.28 123	99.13 58	99.52 112	99.48 43	99.24 147	99.41 91	96.79 218	99.82 200	98.69 110	99.88 91	99.76 53

VPNet		98.87 100	98.83 106	99.01 145	99.70 55	97.62 196	98.43 141	99.35 187	99.47 46	99.28 135	99.05 185	96.72 224	99.82 200	98.09 147	99.36 296	99.59 104

fmvsm_s_conf0.1_n_2		99.20 50	99.38 29	98.65 206	99.69 58	96.08 283	97.49 282	99.90 11	99.53 40	99.88 21	99.64 37	98.51 71	99.90 79	99.83 9	99.98 12	99.97 4

test_cas_vis1_n_1920		98.33 200	98.68 127	97.27 355	99.69 58	92.29 404	98.03 193	99.85 18	97.62 237	99.96 4	99.62 40	93.98 320	99.74 273	99.52 48	99.86 102	99.79 42

MP-MVS-pluss		98.57 160	98.23 204	99.60 15	99.69 58	99.35 17	97.16 317	99.38 173	94.87 382	98.97 190	98.99 206	98.01 121	99.88 113	97.29 210	99.70 198	99.58 112
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SDMVSNet		99.23 46	99.32 39	98.96 154	99.68 61	97.35 211	98.84 94	99.48 125	99.69 18	99.63 65	99.68 25	99.03 24	99.96 14	97.97 160	99.92 67	99.57 117

sd_testset		99.28 37	99.31 41	99.19 108	99.68 61	98.06 151	99.41 17	99.30 214	99.69 18	99.63 65	99.68 25	99.25 16	99.96 14	97.25 213	99.92 67	99.57 117

test_fmvs1_n		98.09 229	98.28 195	97.52 341	99.68 61	93.47 383	98.63 110	99.93 5	95.41 370	99.68 56	99.64 37	91.88 354	99.48 392	99.82 11	99.87 95	99.62 87

CHOSEN 1792x2688		97.49 281	97.14 296	98.54 236	99.68 61	96.09 281	96.50 353	99.62 71	91.58 430	98.84 222	98.97 213	92.36 346	99.88 113	96.76 256	99.95 38	99.67 73

tfpnnormal		98.90 96	98.90 95	98.91 163	99.67 65	97.82 179	99.00 72	99.44 150	99.45 49	99.51 88	99.24 134	98.20 105	99.86 141	95.92 315	99.69 201	99.04 307

MTAPA		98.88 99	98.64 133	99.61 13	99.67 65	99.36 16	98.43 141	99.20 246	98.83 139	98.89 211	98.90 229	96.98 205	99.92 63	97.16 218	99.70 198	99.56 123

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 236	99.66 67	96.97 240	98.00 200	99.85 18	99.24 74	99.92 8	99.50 67	99.39 12	99.95 26	99.89 3	99.98 12	98.71 361

mvs5depth		99.30 34	99.59 12	98.44 250	99.65 68	95.35 313	99.82 3	99.94 2	99.83 7	99.42 105	99.94 2	98.13 113	99.96 14	99.63 34	99.96 28	100.00 1

fmvsm_l_conf0.5_n_a		99.19 51	99.27 47	98.94 157	99.65 68	97.05 235	97.80 233	99.76 39	98.70 144	99.78 39	99.11 167	98.79 42	99.95 26	99.85 5	99.96 28	99.83 32

WB-MVS		98.52 174	98.55 148	98.43 251	99.65 68	95.59 297	98.52 123	98.77 330	99.65 26	99.52 83	99.00 204	94.34 311	99.93 52	98.65 112	98.83 365	99.76 53

CP-MVSNet		99.21 48	99.09 73	99.56 26	99.65 68	98.96 77	99.13 58	99.34 193	99.42 54	99.33 124	99.26 127	97.01 203	99.94 41	98.74 105	99.93 54	99.79 42

HPM-MVS		98.79 118	98.53 152	99.59 19	99.65 68	99.29 24	99.16 54	99.43 156	96.74 318	98.61 255	98.38 325	98.62 59	99.87 132	96.47 286	99.67 212	99.59 104
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RPSCF		98.62 153	98.36 183	99.42 64	99.65 68	99.42 11	98.55 119	99.57 88	97.72 231	98.90 208	99.26 127	96.12 252	99.52 380	95.72 326	99.71 191	99.32 244

NormalMVS		98.26 210	97.97 237	99.15 117	99.64 74	97.83 174	98.28 154	99.43 156	99.24 74	98.80 230	98.85 242	89.76 373	99.94 41	98.04 152	99.67 212	99.68 68

lecture		99.25 41	99.12 68	99.62 9	99.64 74	99.40 12	98.89 87	99.51 113	99.19 85	99.37 115	99.25 132	98.36 82	99.88 113	98.23 137	99.67 212	99.59 104

fmvsm_l_conf0.5_n		99.21 48	99.28 46	99.02 144	99.64 74	97.28 217	97.82 229	99.76 39	98.73 141	99.82 33	99.09 175	98.81 38	99.95 26	99.86 4	99.96 28	99.83 32

test_fmvsmconf_n		99.44 19	99.48 18	99.31 90	99.64 74	98.10 141	97.68 251	99.84 22	99.29 70	99.92 8	99.57 49	99.60 5	99.96 14	99.74 25	99.98 12	99.89 16

TSAR-MVS + MP.		98.63 150	98.49 162	99.06 137	99.64 74	97.90 168	98.51 128	98.94 295	96.96 303	99.24 147	98.89 235	97.83 135	99.81 216	96.88 246	99.49 276	99.48 172
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PM-MVS		98.82 112	98.72 118	99.12 120	99.64 74	98.54 106	97.98 208	99.68 58	97.62 237	99.34 122	99.18 148	97.54 164	99.77 254	97.79 173	99.74 171	99.04 307

Elysia		99.15 57	99.14 66	99.18 109	99.63 80	97.92 165	98.50 130	99.43 156	99.67 21	99.70 50	99.13 163	96.66 227	99.98 4	99.54 42	99.96 28	99.64 81

StellarMVS		99.15 57	99.14 66	99.18 109	99.63 80	97.92 165	98.50 130	99.43 156	99.67 21	99.70 50	99.13 163	96.66 227	99.98 4	99.54 42	99.96 28	99.64 81

KD-MVS_self_test		99.25 41	99.18 57	99.44 63	99.63 80	99.06 70	98.69 106	99.54 104	99.31 67	99.62 68	99.53 63	97.36 181	99.86 141	99.24 68	99.71 191	99.39 211

EU-MVSNet		97.66 269	98.50 157	95.13 421	99.63 80	85.84 452	98.35 150	98.21 364	98.23 184	99.54 77	99.46 79	95.02 291	99.68 308	98.24 135	99.87 95	99.87 21

HyFIR lowres test		97.19 307	96.60 331	98.96 154	99.62 84	97.28 217	95.17 418	99.50 116	94.21 397	99.01 182	98.32 333	86.61 393	99.99 2	97.10 225	99.84 109	99.60 97

fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 151	99.59 85	97.18 228	97.44 288	99.83 25	99.56 38	99.91 12	99.34 106	99.36 13	99.93 52	99.83 9	99.98 12	99.85 29

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 79	99.59 85	98.21 132	97.82 229	99.84 22	99.41 56	99.92 8	99.41 91	99.51 8	99.95 26	99.84 8	99.97 21	99.87 21

FE-MVSNET		98.59 157	98.50 157	98.87 167	99.58 87	97.30 215	98.08 182	99.74 43	96.94 305	98.97 190	99.10 170	96.94 206	99.74 273	97.33 208	99.86 102	99.55 130

mmtdpeth		99.30 34	99.42 25	98.92 162	99.58 87	96.89 247	99.48 13	99.92 7	99.92 2	98.26 293	99.80 11	98.33 88	99.91 72	99.56 39	99.95 38	99.97 4

ACMMP_NAP		98.75 125	98.48 163	99.57 21	99.58 87	99.29 24	97.82 229	99.25 235	96.94 305	98.78 232	99.12 166	98.02 120	99.84 172	97.13 223	99.67 212	99.59 104

nrg030		99.40 26	99.35 34	99.54 31	99.58 87	99.13 60	98.98 75	99.48 125	99.68 20	99.46 96	99.26 127	98.62 59	99.73 280	99.17 73	99.92 67	99.76 53

VDDNet		98.21 217	97.95 238	99.01 145	99.58 87	97.74 187	99.01 70	97.29 393	99.67 21	98.97 190	99.50 67	90.45 368	99.80 224	97.88 166	99.20 325	99.48 172

COLMAP_ROB	96.50 10	98.99 83	98.85 105	99.41 66	99.58 87	99.10 65	98.74 97	99.56 95	99.09 105	99.33 124	99.19 144	98.40 79	99.72 287	95.98 313	99.76 167	99.42 198
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	98.99 147	99.57 93	97.73 189	97.93 213	99.83 25	99.22 77	99.93 6	99.30 115	99.42 11	99.96 14	99.85 5	99.99 5	99.29 253

ZNCC-MVS		98.68 141	98.40 175	99.54 31	99.57 93	99.21 33	98.46 138	99.29 222	97.28 278	98.11 305	98.39 323	98.00 122	99.87 132	96.86 249	99.64 223	99.55 130

MSP-MVS		98.40 186	98.00 232	99.61 13	99.57 93	99.25 29	98.57 117	99.35 187	97.55 248	99.31 132	97.71 373	94.61 304	99.88 113	96.14 307	99.19 328	99.70 65
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		98.32 201	98.39 178	98.13 284	99.57 93	95.54 300	97.78 235	99.49 123	97.37 269	99.19 155	97.65 377	98.96 29	99.49 389	96.50 285	98.99 353	99.34 235

MP-MVS		98.46 180	98.09 221	99.54 31	99.57 93	99.22 32	98.50 130	99.19 250	97.61 240	97.58 343	98.66 286	97.40 178	99.88 113	94.72 352	99.60 236	99.54 136
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LPG-MVS_test		98.71 129	98.46 167	99.47 60	99.57 93	98.97 73	98.23 160	99.48 125	96.60 323	99.10 165	99.06 178	98.71 50	99.83 190	95.58 333	99.78 148	99.62 87

LGP-MVS_train				99.47 60	99.57 93	98.97 73		99.48 125	96.60 323	99.10 165	99.06 178	98.71 50	99.83 190	95.58 333	99.78 148	99.62 87

IS-MVSNet		98.19 220	97.90 245	99.08 129	99.57 93	97.97 159	99.31 30	98.32 360	99.01 117	98.98 186	99.03 189	91.59 356	99.79 237	95.49 335	99.80 137	99.48 172

viewdifsd2359ckpt11		98.84 106	99.04 78	98.24 273	99.56 101	95.51 302	97.38 293	99.70 51	99.16 90	99.57 70	99.40 94	98.26 95	99.71 288	98.55 121	99.82 120	99.50 154

viewmsd2359difaftdt		98.84 106	99.04 78	98.24 273	99.56 101	95.51 302	97.38 293	99.70 51	99.16 90	99.57 70	99.40 94	98.26 95	99.71 288	98.55 121	99.82 120	99.50 154

dcpmvs_2		98.78 120	99.11 69	97.78 307	99.56 101	93.67 378	99.06 65	99.86 16	99.50 42	99.66 59	99.26 127	97.21 192	99.99 2	98.00 157	99.91 76	99.68 68

test_0402		98.76 124	98.71 121	98.93 159	99.56 101	98.14 137	98.45 140	99.34 193	99.28 71	98.95 196	98.91 226	98.34 87	99.79 237	95.63 330	99.91 76	98.86 339

EPP-MVSNet		98.30 204	98.04 228	99.07 131	99.56 101	97.83 174	99.29 36	98.07 371	99.03 115	98.59 259	99.13 163	92.16 350	99.90 79	96.87 247	99.68 206	99.49 161

ACMMP		98.75 125	98.50 157	99.52 44	99.56 101	99.16 48	98.87 88	99.37 177	97.16 293	98.82 226	99.01 201	97.71 146	99.87 132	96.29 298	99.69 201	99.54 136
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
fmvsm_s_conf0.5_n_a		99.10 69	99.20 56	98.78 184	99.55 107	96.59 260	97.79 234	99.82 30	98.21 187	99.81 36	99.53 63	98.46 75	99.84 172	99.70 31	99.97 21	99.90 15

fmvsm_s_conf0.5_n		99.09 70	99.26 49	98.61 217	99.55 107	96.09 281	97.74 244	99.81 31	98.55 162	99.85 27	99.55 57	98.60 61	99.84 172	99.69 33	99.98 12	99.89 16

FMVSNet1		99.17 52	99.17 58	99.17 111	99.55 107	98.24 126	99.20 48	99.44 150	99.21 79	99.43 101	99.55 57	97.82 138	99.86 141	98.42 128	99.89 89	99.41 201

Vis-MVSNet (Re-imp)		97.46 283	97.16 293	98.34 263	99.55 107	96.10 278	98.94 80	98.44 354	98.32 175	98.16 299	98.62 295	88.76 380	99.73 280	93.88 378	99.79 143	99.18 286

ACMM	96.08 12	98.91 94	98.73 116	99.48 56	99.55 107	99.14 57	98.07 186	99.37 177	97.62 237	99.04 178	98.96 216	98.84 36	99.79 237	97.43 203	99.65 221	99.49 161
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs2		98.70 133	98.97 89	97.89 300	99.54 112	94.05 355	98.55 119	99.92 7	96.78 316	99.72 46	99.78 13	96.60 231	99.67 312	99.91 2	99.90 83	99.94 10

mPP-MVS		98.64 148	98.34 186	99.54 31	99.54 112	99.17 44	98.63 110	99.24 240	97.47 256	98.09 307	98.68 281	97.62 155	99.89 95	96.22 301	99.62 229	99.57 117

XVG-ACMP-BASELINE		98.56 161	98.34 186	99.22 105	99.54 112	98.59 100	97.71 247	99.46 138	97.25 281	98.98 186	98.99 206	97.54 164	99.84 172	95.88 316	99.74 171	99.23 268

viewmacassd2359aftdt		98.86 103	98.87 100	98.83 172	99.53 115	97.32 214	97.70 249	99.64 67	98.22 185	99.25 145	99.27 121	98.40 79	99.61 345	97.98 159	99.87 95	99.55 130

region2R		98.69 136	98.40 175	99.54 31	99.53 115	99.17 44	98.52 123	99.31 206	97.46 261	98.44 278	98.51 309	97.83 135	99.88 113	96.46 287	99.58 245	99.58 112

PGM-MVS		98.66 145	98.37 182	99.55 28	99.53 115	99.18 43	98.23 160	99.49 123	97.01 302	98.69 243	98.88 236	98.00 122	99.89 95	95.87 319	99.59 240	99.58 112

Patchmatch-RL test		97.26 300	97.02 301	97.99 296	99.52 118	95.53 301	96.13 378	99.71 47	97.47 256	99.27 137	99.16 154	84.30 414	99.62 338	97.89 163	99.77 154	98.81 347

ACMMPR		98.70 133	98.42 173	99.54 31	99.52 118	99.14 57	98.52 123	99.31 206	97.47 256	98.56 265	98.54 304	97.75 144	99.88 113	96.57 274	99.59 240	99.58 112

fmvsm_s_conf0.5_n_9		99.17 52	99.38 29	98.53 238	99.51 120	95.82 293	97.62 262	99.78 36	99.72 15	99.90 14	99.48 74	98.66 54	99.89 95	99.85 5	99.93 54	99.89 16

AstraMVS		98.16 226	98.07 226	98.41 253	99.51 120	95.86 290	98.00 200	95.14 434	98.97 121	99.43 101	99.24 134	93.25 328	99.84 172	99.21 69	99.87 95	99.54 136

fmvsm_s_conf0.5_n_8		99.13 64	99.26 49	98.74 195	99.51 120	96.44 270	97.65 257	99.65 65	99.66 24	99.78 39	99.48 74	97.92 129	99.93 52	99.72 28	99.95 38	99.87 21

GST-MVS		98.61 154	98.30 192	99.52 44	99.51 120	99.20 39	98.26 158	99.25 235	97.44 264	98.67 246	98.39 323	97.68 147	99.85 154	96.00 311	99.51 267	99.52 148

Anonymous20231206		98.21 217	98.21 205	98.20 278	99.51 120	95.43 311	98.13 172	99.32 201	96.16 342	98.93 204	98.82 252	96.00 257	99.83 190	97.32 209	99.73 174	99.36 228

ACMP	95.32 15	98.41 184	98.09 221	99.36 70	99.51 120	98.79 86	97.68 251	99.38 173	95.76 357	98.81 228	98.82 252	98.36 82	99.82 200	94.75 349	99.77 154	99.48 172
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LuminaMVS		98.39 192	98.20 206	98.98 151	99.50 126	97.49 201	97.78 235	97.69 380	98.75 140	99.49 90	99.25 132	92.30 348	99.94 41	99.14 74	99.88 91	99.50 154

DVP-MVS		98.77 123	98.52 153	99.52 44	99.50 126	99.21 33	98.02 196	98.84 319	97.97 209	99.08 167	99.02 190	97.61 157	99.88 113	96.99 233	99.63 226	99.48 172
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 15	99.50 126	99.23 31	98.02 196	99.32 201					99.88 113	96.99 233	99.63 226	99.68 68

test0726					99.50 126	99.21 33	98.17 168	99.35 187	97.97 209	99.26 141	99.06 178	97.61 157

AllTest		98.44 182	98.20 206	99.16 114	99.50 126	98.55 103	98.25 159	99.58 81	96.80 314	98.88 215	99.06 178	97.65 150	99.57 361	94.45 359	99.61 234	99.37 221

TestCases				99.16 114	99.50 126	98.55 103		99.58 81	96.80 314	98.88 215	99.06 178	97.65 150	99.57 361	94.45 359	99.61 234	99.37 221

XVG-OURS		98.53 170	98.34 186	99.11 122	99.50 126	98.82 85	95.97 384	99.50 116	97.30 276	99.05 176	98.98 211	99.35 14	99.32 419	95.72 326	99.68 206	99.18 286

EG-PatchMatch MVS		98.99 83	99.01 83	98.94 157	99.50 126	97.47 204	98.04 191	99.59 79	98.15 202	99.40 110	99.36 101	98.58 67	99.76 260	98.78 100	99.68 206	99.59 104

fmvsm_s_conf0.5_n_2		99.14 60	99.31 41	98.63 212	99.49 134	96.08 283	97.38 293	99.81 31	99.48 43	99.84 30	99.57 49	98.46 75	99.89 95	99.82 11	99.97 21	99.91 13

SED-MVS		98.91 94	98.72 118	99.49 54	99.49 134	99.17 44	98.10 179	99.31 206	98.03 205	99.66 59	99.02 190	98.36 82	99.88 113	96.91 239	99.62 229	99.41 201

IU-MVS					99.49 134	99.15 52		98.87 310	92.97 415	99.41 107				96.76 256	99.62 229	99.66 75

test_241102_ONE					99.49 134	99.17 44		99.31 206	97.98 208	99.66 59	98.90 229	98.36 82	99.48 392

UA-Net		99.47 16	99.40 27	99.70 2	99.49 134	99.29 24	99.80 4	99.72 45	99.82 8	99.04 178	99.81 8	98.05 119	99.96 14	98.85 96	99.99 5	99.86 27

HFP-MVS		98.71 129	98.44 170	99.51 48	99.49 134	99.16 48	98.52 123	99.31 206	97.47 256	98.58 261	98.50 313	97.97 126	99.85 154	96.57 274	99.59 240	99.53 145

VPA-MVSNet		99.30 34	99.30 44	99.28 92	99.49 134	98.36 120	99.00 72	99.45 142	99.63 29	99.52 83	99.44 84	98.25 97	99.88 113	99.09 78	99.84 109	99.62 87

XVG-OURS-SEG-HR		98.49 177	98.28 195	99.14 118	99.49 134	98.83 83	96.54 349	99.48 125	97.32 274	99.11 162	98.61 297	99.33 15	99.30 422	96.23 300	98.38 389	99.28 255

114514_t		96.50 340	95.77 349	98.69 201	99.48 142	97.43 208	97.84 228	99.55 99	81.42 462	96.51 402	98.58 301	95.53 277	99.67 312	93.41 391	99.58 245	98.98 317

IterMVS-LS		98.55 165	98.70 124	98.09 285	99.48 142	94.73 335	97.22 312	99.39 171	98.97 121	99.38 113	99.31 114	96.00 257	99.93 52	98.58 115	99.97 21	99.60 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_s_conf0.5_n_5		99.07 76	99.10 71	98.99 147	99.47 144	97.22 222	97.40 290	99.83 25	97.61 240	99.85 27	99.30 115	98.80 40	99.95 26	99.71 30	99.90 83	99.78 45

v8		99.01 80	99.16 60	98.57 224	99.47 144	96.31 275	98.90 83	99.47 134	99.03 115	99.52 83	99.57 49	96.93 207	99.81 216	99.60 35	99.98 12	99.60 97

SSC-MVS3.2		98.53 170	98.79 110	97.74 314	99.46 146	93.62 381	96.45 355	99.34 193	99.33 64	98.93 204	98.70 277	97.90 130	99.90 79	99.12 75	99.92 67	99.69 67

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 184	99.46 146	96.58 263	97.65 257	99.72 45	99.47 46	99.86 24	99.50 67	98.94 30	99.89 95	99.75 24	99.97 21	99.86 27

XVS		98.72 128	98.45 168	99.53 38	99.46 146	99.21 33	98.65 108	99.34 193	98.62 151	97.54 347	98.63 293	97.50 170	99.83 190	96.79 252	99.53 262	99.56 123

X-MVStestdata		94.32 389	92.59 408	99.53 38	99.46 146	99.21 33	98.65 108	99.34 193	98.62 151	97.54 347	45.85 467	97.50 170	99.83 190	96.79 252	99.53 262	99.56 123

test20.03		98.78 120	98.77 113	98.78 184	99.46 146	97.20 225	97.78 235	99.24 240	99.04 114	99.41 107	98.90 229	97.65 150	99.76 260	97.70 182	99.79 143	99.39 211

guyue		98.01 237	97.93 242	98.26 270	99.45 151	95.48 306	98.08 182	96.24 417	98.89 132	99.34 122	99.14 161	91.32 360	99.82 200	99.07 79	99.83 116	99.48 172

CSCG		98.68 141	98.50 157	99.20 106	99.45 151	98.63 95	98.56 118	99.57 88	97.87 219	98.85 220	98.04 354	97.66 149	99.84 172	96.72 261	99.81 126	99.13 296

GeoE		99.05 77	98.99 87	99.25 100	99.44 153	98.35 121	98.73 101	99.56 95	98.42 168	98.91 207	98.81 255	98.94 30	99.91 72	98.35 130	99.73 174	99.49 161

v148		98.45 181	98.60 142	98.00 295	99.44 153	94.98 327	97.44 288	99.06 276	98.30 177	99.32 130	98.97 213	96.65 229	99.62 338	98.37 129	99.85 104	99.39 211

v10		98.97 87	99.11 69	98.55 231	99.44 153	96.21 277	98.90 83	99.55 99	98.73 141	99.48 91	99.60 45	96.63 230	99.83 190	99.70 31	99.99 5	99.61 95

V42		98.78 120	98.78 112	98.76 189	99.44 153	97.04 236	98.27 157	99.19 250	97.87 219	99.25 145	99.16 154	96.84 211	99.78 248	99.21 69	99.84 109	99.46 182

MDA-MVSNet-bldmvs		97.94 243	97.91 244	98.06 290	99.44 153	94.96 328	96.63 345	99.15 266	98.35 171	98.83 223	99.11 167	94.31 312	99.85 154	96.60 271	98.72 371	99.37 221

casdiffmvs_mvg		99.12 67	99.16 60	98.99 147	99.43 158	97.73 189	98.00 200	99.62 71	99.22 77	99.55 75	99.22 140	98.93 32	99.75 268	98.66 111	99.81 126	99.50 154
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 96	99.01 83	98.57 224	99.42 159	96.59 260	98.13 172	99.66 62	99.09 105	99.30 133	99.02 190	98.79 42	99.89 95	97.87 168	99.80 137	99.23 268

test1111		96.49 341	96.82 315	95.52 414	99.42 159	87.08 449	99.22 45	87.14 465	99.11 95	99.46 96	99.58 47	88.69 381	99.86 141	98.80 98	99.95 38	99.62 87

v2v482		98.56 161	98.62 137	98.37 260	99.42 159	95.81 294	97.58 270	99.16 261	97.90 217	99.28 135	99.01 201	95.98 262	99.79 237	99.33 58	99.90 83	99.51 151

OPM-MVS		98.56 161	98.32 190	99.25 100	99.41 162	98.73 91	97.13 319	99.18 254	97.10 296	98.75 238	98.92 224	98.18 106	99.65 329	96.68 265	99.56 252	99.37 221
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PMMVS2		98.07 231	98.08 224	98.04 293	99.41 162	94.59 341	94.59 436	99.40 169	97.50 253	98.82 226	98.83 249	96.83 213	99.84 172	97.50 197	99.81 126	99.71 60

test_one_0601					99.39 164	99.20 39		99.31 206	98.49 164	98.66 248	99.02 190	97.64 153

mvsany_test3		98.87 100	98.92 93	98.74 195	99.38 165	96.94 244	98.58 116	99.10 271	96.49 328	99.96 4	99.81 8	98.18 106	99.45 400	98.97 88	99.79 143	99.83 32

patch_mono-2		98.51 175	98.63 135	98.17 281	99.38 165	94.78 332	97.36 298	99.69 53	98.16 197	98.49 274	99.29 118	97.06 198	99.97 7	98.29 134	99.91 76	99.76 53

test2506		92.39 420	91.89 422	93.89 435	99.38 165	82.28 466	99.32 26	66.03 473	99.08 109	98.77 235	99.57 49	66.26 461	99.84 172	98.71 108	99.95 38	99.54 136

ECVR-MVS		96.42 343	96.61 329	95.85 406	99.38 165	88.18 444	99.22 45	86.00 467	99.08 109	99.36 118	99.57 49	88.47 386	99.82 200	98.52 123	99.95 38	99.54 136

casdiffmvs		98.95 90	99.00 85	98.81 176	99.38 165	97.33 212	97.82 229	99.57 88	99.17 89	99.35 120	99.17 152	98.35 86	99.69 299	98.46 125	99.73 174	99.41 201
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 89	99.02 81	98.76 189	99.38 165	97.26 219	98.49 133	99.50 116	98.86 135	99.19 155	99.06 178	98.23 99	99.69 299	98.71 108	99.76 167	99.33 241

TranMVSNet+NR-MVSNet		99.17 52	99.07 76	99.46 62	99.37 171	98.87 81	98.39 146	99.42 162	99.42 54	99.36 118	99.06 178	98.38 81	99.95 26	98.34 131	99.90 83	99.57 117

fmvsm_s_conf0.5_n_6		99.08 74	99.21 55	98.69 201	99.36 172	96.51 265	97.62 262	99.68 58	98.43 167	99.85 27	99.10 170	99.12 23	99.88 113	99.77 21	99.92 67	99.67 73

tttt0517		95.64 368	94.98 378	97.64 327	99.36 172	93.81 373	98.72 102	90.47 459	98.08 204	98.67 246	98.34 330	73.88 447	99.92 63	97.77 175	99.51 267	99.20 278

test_part2					99.36 172	99.10 65				99.05 176

v1144		98.60 155	98.66 130	98.41 253	99.36 172	95.90 288	97.58 270	99.34 193	97.51 252	99.27 137	99.15 158	96.34 244	99.80 224	99.47 52	99.93 54	99.51 151

CP-MVS		98.70 133	98.42 173	99.52 44	99.36 172	99.12 62	98.72 102	99.36 181	97.54 250	98.30 287	98.40 322	97.86 134	99.89 95	96.53 283	99.72 182	99.56 123

diffmvs_AUTHOR		98.50 176	98.59 144	98.23 276	99.35 177	95.48 306	96.61 346	99.60 75	98.37 169	98.90 208	99.00 204	97.37 180	99.76 260	98.22 138	99.85 104	99.46 182

Test_1112_low_res		96.99 322	96.55 333	98.31 266	99.35 177	95.47 309	95.84 396	99.53 108	91.51 432	96.80 389	98.48 316	91.36 359	99.83 190	96.58 272	99.53 262	99.62 87

DeepC-MVS	97.60 4	98.97 87	98.93 92	99.10 124	99.35 177	97.98 158	98.01 199	99.46 138	97.56 246	99.54 77	99.50 67	98.97 28	99.84 172	98.06 150	99.92 67	99.49 161
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
1112_ss		97.29 299	96.86 311	98.58 221	99.34 180	96.32 274	96.75 338	99.58 81	93.14 413	96.89 384	97.48 387	92.11 351	99.86 141	96.91 239	99.54 258	99.57 117

reproduce_model		99.15 57	98.97 89	99.67 4	99.33 181	99.44 10	98.15 170	99.47 134	99.12 94	99.52 83	99.32 113	98.31 89	99.90 79	97.78 174	99.73 174	99.66 75

MVSMamba_PlusPlus		98.83 109	98.98 88	98.36 261	99.32 182	96.58 263	98.90 83	99.41 166	99.75 11	98.72 241	99.50 67	96.17 248	99.94 41	99.27 63	99.78 148	98.57 377

fmvsm_s_conf0.5_n_4		99.01 80	99.22 53	98.38 257	99.31 183	95.48 306	97.56 272	99.73 44	98.87 133	99.75 44	99.27 121	98.80 40	99.86 141	99.80 16	99.90 83	99.81 38

SF-MVS		98.53 170	98.27 198	99.32 87	99.31 183	98.75 87	98.19 164	99.41 166	96.77 317	98.83 223	98.90 229	97.80 140	99.82 200	95.68 329	99.52 265	99.38 219

CPTT-MVS		97.84 258	97.36 282	99.27 95	99.31 183	98.46 111	98.29 153	99.27 229	94.90 381	97.83 327	98.37 326	94.90 293	99.84 172	93.85 380	99.54 258	99.51 151

UnsupCasMVSNet_eth		97.89 247	97.60 268	98.75 191	99.31 183	97.17 230	97.62 262	99.35 187	98.72 143	98.76 237	98.68 281	92.57 345	99.74 273	97.76 179	95.60 451	99.34 235

fmvsm_s_conf0.5_n_7		98.83 109	99.04 78	98.20 278	99.30 187	94.83 330	97.23 308	99.36 181	98.64 146	99.84 30	99.43 86	98.10 115	99.91 72	99.56 39	99.96 28	99.87 21

pmmvs-eth3d		98.47 179	98.34 186	98.86 169	99.30 187	97.76 185	97.16 317	99.28 226	95.54 363	99.42 105	99.19 144	97.27 187	99.63 335	97.89 163	99.97 21	99.20 278

mamv4		99.44 19	99.39 28	99.58 20	99.30 187	99.74 2	99.04 68	99.81 31	99.77 10	99.82 33	99.57 49	97.82 138	99.98 4	99.53 46	99.89 89	99.01 311

viewcassd2359sk11		98.55 165	98.51 154	98.67 204	99.29 190	96.99 239	97.39 291	99.54 104	97.73 229	98.81 228	99.08 176	97.55 162	99.66 323	97.52 196	99.67 212	99.36 228

SymmetryMVS		98.05 233	97.71 258	99.09 128	99.29 190	97.83 174	98.28 154	97.64 385	99.24 74	98.80 230	98.85 242	89.76 373	99.94 41	98.04 152	99.50 274	99.49 161

Anonymous20231211		99.27 38	99.27 47	99.26 97	99.29 190	98.18 133	99.49 12	99.51 113	99.70 16	99.80 37	99.68 25	96.84 211	99.83 190	99.21 69	99.91 76	99.77 48

viewmanbaseed2359cas		98.58 159	98.54 150	98.70 199	99.28 193	97.13 234	97.47 285	99.55 99	97.55 248	98.96 195	98.92 224	97.77 142	99.59 352	97.59 190	99.77 154	99.39 211

UnsupCasMVSNet_bld		97.30 297	96.92 307	98.45 248	99.28 193	96.78 254	96.20 372	99.27 229	95.42 367	98.28 291	98.30 334	93.16 331	99.71 288	94.99 343	97.37 427	98.87 338

EC-MVSNet		99.09 70	99.05 77	99.20 106	99.28 193	98.93 79	99.24 44	99.84 22	99.08 109	98.12 304	98.37 326	98.72 49	99.90 79	99.05 82	99.77 154	98.77 355

mamba_0408		98.80 116	98.88 98	98.55 231	99.27 196	96.50 266	98.00 200	99.60 75	98.93 126	99.22 150	98.84 247	98.59 62	99.89 95	97.74 180	99.72 182	99.27 256

SSM_04072		98.80 116	98.88 98	98.56 229	99.27 196	96.50 266	98.00 200	99.60 75	98.93 126	99.22 150	98.84 247	98.59 62	99.90 79	97.74 180	99.72 182	99.27 256

SSM_0407		98.86 103	98.96 91	98.55 231	99.27 196	96.50 266	98.04 191	99.66 62	99.09 105	99.22 150	99.02 190	98.79 42	99.87 132	97.87 168	99.72 182	99.27 256

reproduce-ours		99.09 70	98.90 95	99.67 4	99.27 196	99.49 6	98.00 200	99.42 162	99.05 112	99.48 91	99.27 121	98.29 91	99.89 95	97.61 187	99.71 191	99.62 87

our_new_method		99.09 70	98.90 95	99.67 4	99.27 196	99.49 6	98.00 200	99.42 162	99.05 112	99.48 91	99.27 121	98.29 91	99.89 95	97.61 187	99.71 191	99.62 87

DPE-MVS		98.59 157	98.26 199	99.57 21	99.27 196	99.15 52	97.01 322	99.39 171	97.67 233	99.44 100	98.99 206	97.53 166	99.89 95	95.40 337	99.68 206	99.66 75
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
IterMVS-SCA-FT		97.85 257	98.18 211	96.87 375	99.27 196	91.16 424	95.53 406	99.25 235	99.10 102	99.41 107	99.35 102	93.10 333	99.96 14	98.65 112	99.94 49	99.49 161

v1192		98.60 155	98.66 130	98.41 253	99.27 196	95.88 289	97.52 277	99.36 181	97.41 265	99.33 124	99.20 143	96.37 242	99.82 200	99.57 37	99.92 67	99.55 130

N_pmnet		97.63 271	97.17 292	98.99 147	99.27 196	97.86 171	95.98 383	93.41 448	95.25 372	99.47 95	98.90 229	95.63 274	99.85 154	96.91 239	99.73 174	99.27 256

viewdifsd2359ckpt13		98.39 192	98.29 194	98.70 199	99.26 205	97.19 226	97.51 279	99.48 125	96.94 305	98.58 261	98.82 252	97.47 175	99.55 368	97.21 215	99.33 301	99.34 235

FPMVS		93.44 406	92.23 413	97.08 363	99.25 206	97.86 171	95.61 403	97.16 397	92.90 417	93.76 450	98.65 288	75.94 445	95.66 464	79.30 462	97.49 420	97.73 428

new-patchmatchnet		98.35 195	98.74 114	97.18 358	99.24 207	92.23 406	96.42 359	99.48 125	98.30 177	99.69 54	99.53 63	97.44 176	99.82 200	98.84 97	99.77 154	99.49 161

MCST-MVS		98.00 238	97.63 266	99.10 124	99.24 207	98.17 134	96.89 331	98.73 338	95.66 358	97.92 318	97.70 375	97.17 193	99.66 323	96.18 305	99.23 320	99.47 180

UniMVSNet (Re)		98.87 100	98.71 121	99.35 76	99.24 207	98.73 91	97.73 246	99.38 173	98.93 126	99.12 161	98.73 267	96.77 219	99.86 141	98.63 114	99.80 137	99.46 182

jason		97.45 285	97.35 283	97.76 311	99.24 207	93.93 367	95.86 393	98.42 356	94.24 396	98.50 273	98.13 344	94.82 297	99.91 72	97.22 214	99.73 174	99.43 195
jason: jason.
IterMVS		97.73 263	98.11 220	96.57 385	99.24 207	90.28 433	95.52 408	99.21 244	98.86 135	99.33 124	99.33 109	93.11 332	99.94 41	98.49 124	99.94 49	99.48 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		98.55 165	98.62 137	98.32 264	99.22 212	95.58 299	97.51 279	99.45 142	97.16 293	99.45 99	99.24 134	96.12 252	99.85 154	99.60 35	99.88 91	99.55 130

ITE_SJBPF				98.87 167	99.22 212	98.48 110		99.35 187	97.50 253	98.28 291	98.60 299	97.64 153	99.35 415	93.86 379	99.27 312	98.79 353

h-mvs33		97.77 261	97.33 285	99.10 124	99.21 214	97.84 173	98.35 150	98.57 348	99.11 95	98.58 261	99.02 190	88.65 384	99.96 14	98.11 145	96.34 443	99.49 161

v144192		98.54 168	98.57 146	98.45 248	99.21 214	95.98 286	97.63 261	99.36 181	97.15 295	99.32 130	99.18 148	95.84 269	99.84 172	99.50 49	99.91 76	99.54 136

APDe-MVS		98.99 83	98.79 110	99.60 15	99.21 214	99.15 52	98.87 88	99.48 125	97.57 244	99.35 120	99.24 134	97.83 135	99.89 95	97.88 166	99.70 198	99.75 57
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DP-MVS		98.93 92	98.81 109	99.28 92	99.21 214	98.45 112	98.46 138	99.33 199	99.63 29	99.48 91	99.15 158	97.23 190	99.75 268	97.17 217	99.66 220	99.63 86

SR-MVS-dyc-post		98.81 114	98.55 148	99.57 21	99.20 218	99.38 13	98.48 136	99.30 214	98.64 146	98.95 196	98.96 216	97.49 173	99.86 141	96.56 278	99.39 292	99.45 187

RE-MVS-def			98.58 145		99.20 218	99.38 13	98.48 136	99.30 214	98.64 146	98.95 196	98.96 216	97.75 144		96.56 278	99.39 292	99.45 187

v1921920		98.54 168	98.60 142	98.38 257	99.20 218	95.76 296	97.56 272	99.36 181	97.23 287	99.38 113	99.17 152	96.02 255	99.84 172	99.57 37	99.90 83	99.54 136

thisisatest0530		95.27 375	94.45 386	97.74 314	99.19 221	94.37 345	97.86 225	90.20 460	97.17 292	98.22 294	97.65 377	73.53 448	99.90 79	96.90 244	99.35 298	98.95 323

Anonymous20240529		98.93 92	98.87 100	99.12 120	99.19 221	98.22 131	99.01 70	98.99 293	99.25 73	99.54 77	99.37 97	97.04 199	99.80 224	97.89 163	99.52 265	99.35 233

APD-MVS_3200maxsize		98.84 106	98.61 141	99.53 38	99.19 221	99.27 27	98.49 133	99.33 199	98.64 146	99.03 181	98.98 211	97.89 132	99.85 154	96.54 282	99.42 289	99.46 182

HQP_MVS		97.99 241	97.67 260	98.93 159	99.19 221	97.65 193	97.77 238	99.27 229	98.20 191	97.79 330	97.98 358	94.90 293	99.70 295	94.42 361	99.51 267	99.45 187

plane_prior7					99.19 221	97.87 170

ab-mvs		98.41 184	98.36 183	98.59 220	99.19 221	97.23 220	99.32 26	98.81 324	97.66 234	98.62 253	99.40 94	96.82 214	99.80 224	95.88 316	99.51 267	98.75 358

F-COLMAP		97.30 297	96.68 324	99.14 118	99.19 221	98.39 114	97.27 307	99.30 214	92.93 416	96.62 395	98.00 356	95.73 272	99.68 308	92.62 407	98.46 388	99.35 233

SR-MVS		98.71 129	98.43 171	99.57 21	99.18 228	99.35 17	98.36 149	99.29 222	98.29 180	98.88 215	98.85 242	97.53 166	99.87 132	96.14 307	99.31 305	99.48 172

UniMVSNet_NR-MVSNet		98.86 103	98.68 127	99.40 68	99.17 229	98.74 88	97.68 251	99.40 169	99.14 93	99.06 169	98.59 300	96.71 225	99.93 52	98.57 117	99.77 154	99.53 145

LF4IMVS		97.90 245	97.69 259	98.52 239	99.17 229	97.66 192	97.19 316	99.47 134	96.31 336	97.85 326	98.20 341	96.71 225	99.52 380	94.62 353	99.72 182	98.38 394

SMA-MVS		98.40 186	98.03 229	99.51 48	99.16 231	99.21 33	98.05 189	99.22 243	94.16 398	98.98 186	99.10 170	97.52 168	99.79 237	96.45 288	99.64 223	99.53 145
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		98.82 112	98.63 135	99.39 69	99.16 231	98.74 88	97.54 275	99.25 235	98.84 138	99.06 169	98.76 264	96.76 221	99.93 52	98.57 117	99.77 154	99.50 154

NR-MVSNet		98.95 90	98.82 107	99.36 70	99.16 231	98.72 93	99.22 45	99.20 246	99.10 102	99.72 46	98.76 264	96.38 241	99.86 141	98.00 157	99.82 120	99.50 154

MVS_111021_LR		98.30 204	98.12 219	98.83 172	99.16 231	98.03 153	96.09 380	99.30 214	97.58 243	98.10 306	98.24 337	98.25 97	99.34 416	96.69 264	99.65 221	99.12 297

DSMNet-mixed		97.42 288	97.60 268	96.87 375	99.15 235	91.46 413	98.54 121	99.12 268	92.87 418	97.58 343	99.63 39	96.21 247	99.90 79	95.74 325	99.54 258	99.27 256

D2MVS		97.84 258	97.84 249	97.83 303	99.14 236	94.74 334	96.94 326	98.88 308	95.84 355	98.89 211	98.96 216	94.40 309	99.69 299	97.55 191	99.95 38	99.05 303

pmmvs5		97.64 270	97.49 274	98.08 288	99.14 236	95.12 323	96.70 341	99.05 279	93.77 405	98.62 253	98.83 249	93.23 329	99.75 268	98.33 133	99.76 167	99.36 228

SPE-MVS-test		99.13 64	99.09 73	99.26 97	99.13 238	98.97 73	99.31 30	99.88 14	99.44 51	98.16 299	98.51 309	98.64 56	99.93 52	98.91 91	99.85 104	98.88 337

VDD-MVS		98.56 161	98.39 178	99.07 131	99.13 238	98.07 148	98.59 115	97.01 400	99.59 35	99.11 162	99.27 121	94.82 297	99.79 237	98.34 131	99.63 226	99.34 235

save fliter					99.11 240	97.97 159	96.53 351	99.02 287	98.24 183

APD-MVS		98.10 227	97.67 260	99.42 64	99.11 240	98.93 79	97.76 241	99.28 226	94.97 379	98.72 241	98.77 262	97.04 199	99.85 154	93.79 381	99.54 258	99.49 161
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EI-MVSNet-UG-set		98.69 136	98.71 121	98.62 214	99.10 242	96.37 272	97.23 308	98.87 310	99.20 81	99.19 155	98.99 206	97.30 184	99.85 154	98.77 103	99.79 143	99.65 80

EI-MVSNet		98.40 186	98.51 154	98.04 293	99.10 242	94.73 335	97.20 313	98.87 310	98.97 121	99.06 169	99.02 190	96.00 257	99.80 224	98.58 115	99.82 120	99.60 97

CVMVSNet		96.25 349	97.21 291	93.38 442	99.10 242	80.56 470	97.20 313	98.19 367	96.94 305	99.00 183	99.02 190	89.50 377	99.80 224	96.36 294	99.59 240	99.78 45

EI-MVSNet-Vis-set		98.68 141	98.70 124	98.63 212	99.09 245	96.40 271	97.23 308	98.86 315	99.20 81	99.18 159	98.97 213	97.29 186	99.85 154	98.72 107	99.78 148	99.64 81

HPM-MVS++		98.10 227	97.64 265	99.48 56	99.09 245	99.13 60	97.52 277	98.75 335	97.46 261	96.90 383	97.83 368	96.01 256	99.84 172	95.82 323	99.35 298	99.46 182

DP-MVS Recon		97.33 295	96.92 307	98.57 224	99.09 245	97.99 155	96.79 334	99.35 187	93.18 412	97.71 334	98.07 352	95.00 292	99.31 420	93.97 374	99.13 336	98.42 391

MVS_111021_HR		98.25 213	98.08 224	98.75 191	99.09 245	97.46 205	95.97 384	99.27 229	97.60 242	97.99 316	98.25 336	98.15 112	99.38 411	96.87 247	99.57 249	99.42 198

BP-MVS1		97.40 290	96.97 303	98.71 198	99.07 249	96.81 250	98.34 152	97.18 395	98.58 157	98.17 296	98.61 297	84.01 416	99.94 41	98.97 88	99.78 148	99.37 221

9.14			97.78 251		99.07 249		97.53 276	99.32 201	95.53 364	98.54 269	98.70 277	97.58 159	99.76 260	94.32 366	99.46 279

PAPM_NR		96.82 329	96.32 340	98.30 267	99.07 249	96.69 258	97.48 283	98.76 332	95.81 356	96.61 396	96.47 413	94.12 318	99.17 433	90.82 434	97.78 414	99.06 302

TAMVS		98.24 214	98.05 227	98.80 178	99.07 249	97.18 228	97.88 221	98.81 324	96.66 322	99.17 160	99.21 141	94.81 299	99.77 254	96.96 237	99.88 91	99.44 191

CLD-MVS		97.49 281	97.16 293	98.48 245	99.07 249	97.03 237	94.71 429	99.21 244	94.46 390	98.06 309	97.16 399	97.57 160	99.48 392	94.46 358	99.78 148	98.95 323
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 64	99.10 71	99.24 102	99.06 254	99.15 52	99.36 22	99.88 14	99.36 62	98.21 295	98.46 317	98.68 53	99.93 52	99.03 84	99.85 104	98.64 370

thres100view900		94.19 392	93.67 397	95.75 409	99.06 254	91.35 417	98.03 193	94.24 443	98.33 173	97.40 359	94.98 443	79.84 432	99.62 338	83.05 455	98.08 405	96.29 450

thres600view7		94.45 387	93.83 394	96.29 393	99.06 254	91.53 412	97.99 207	94.24 443	98.34 172	97.44 357	95.01 441	79.84 432	99.67 312	84.33 453	98.23 394	97.66 431

plane_prior1					99.05 257

YYNet1		97.60 272	97.67 260	97.39 351	99.04 258	93.04 390	95.27 415	98.38 359	97.25 281	98.92 206	98.95 220	95.48 281	99.73 280	96.99 233	98.74 369	99.41 201

MDA-MVSNet_test_wron		97.60 272	97.66 263	97.41 350	99.04 258	93.09 386	95.27 415	98.42 356	97.26 280	98.88 215	98.95 220	95.43 282	99.73 280	97.02 230	98.72 371	99.41 201

MIMVSNet		96.62 336	96.25 344	97.71 318	99.04 258	94.66 338	99.16 54	96.92 406	97.23 287	97.87 323	99.10 170	86.11 399	99.65 329	91.65 418	99.21 324	98.82 342

icg_test_0407_2		98.20 219	98.38 180	97.65 324	99.03 261	94.03 358	95.78 398	99.45 142	98.16 197	99.06 169	98.71 270	98.27 93	99.68 308	97.50 197	99.45 281	99.22 273

IMVS_0407		98.39 192	98.64 133	97.66 322	99.03 261	94.03 358	98.10 179	99.45 142	98.16 197	99.06 169	98.71 270	98.27 93	99.71 288	97.50 197	99.45 281	99.22 273

IMVS_0404		98.07 231	98.20 206	97.69 319	99.03 261	94.03 358	96.67 342	99.45 142	98.16 197	98.03 313	98.71 270	96.80 217	99.82 200	97.50 197	99.45 281	99.22 273

IMVS_0403		98.34 196	98.56 147	97.66 322	99.03 261	94.03 358	97.98 208	99.45 142	98.16 197	98.89 211	98.71 270	97.90 130	99.74 273	97.50 197	99.45 281	99.22 273

PatchMatch-RL		97.24 303	96.78 318	98.61 217	99.03 261	97.83 174	96.36 362	99.06 276	93.49 410	97.36 363	97.78 369	95.75 271	99.49 389	93.44 390	98.77 368	98.52 379

viewmambaseed2359dif		98.19 220	98.26 199	97.99 296	99.02 266	95.03 326	96.59 348	99.53 108	96.21 339	99.00 183	98.99 206	97.62 155	99.61 345	97.62 186	99.72 182	99.33 241

GDP-MVS		97.50 278	97.11 297	98.67 204	99.02 266	96.85 248	98.16 169	99.71 47	98.32 175	98.52 272	98.54 304	83.39 420	99.95 26	98.79 99	99.56 252	99.19 283

ZD-MVS					99.01 268	98.84 82		99.07 275	94.10 400	98.05 311	98.12 346	96.36 243	99.86 141	92.70 406	99.19 328

CDPH-MVS		97.26 300	96.66 327	99.07 131	99.00 269	98.15 135	96.03 382	99.01 290	91.21 436	97.79 330	97.85 367	96.89 209	99.69 299	92.75 404	99.38 295	99.39 211

diffmvs		98.22 215	98.24 203	98.17 281	99.00 269	95.44 310	96.38 361	99.58 81	97.79 226	98.53 270	98.50 313	96.76 221	99.74 273	97.95 162	99.64 223	99.34 235
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		98.40 186	98.19 210	99.03 141	99.00 269	97.65 193	96.85 332	98.94 295	98.57 158	98.89 211	98.50 313	95.60 275	99.85 154	97.54 193	99.85 104	99.59 104

plane_prior6					98.99 272	97.70 191						94.90 293

xiu_mvs_v1_base_debu		97.86 252	98.17 212	96.92 372	98.98 273	93.91 368	96.45 355	99.17 258	97.85 221	98.41 281	97.14 401	98.47 72	99.92 63	98.02 154	99.05 342	96.92 443

xiu_mvs_v1_base		97.86 252	98.17 212	96.92 372	98.98 273	93.91 368	96.45 355	99.17 258	97.85 221	98.41 281	97.14 401	98.47 72	99.92 63	98.02 154	99.05 342	96.92 443

xiu_mvs_v1_base_debi		97.86 252	98.17 212	96.92 372	98.98 273	93.91 368	96.45 355	99.17 258	97.85 221	98.41 281	97.14 401	98.47 72	99.92 63	98.02 154	99.05 342	96.92 443

MVP-Stereo		98.08 230	97.92 243	98.57 224	98.96 276	96.79 251	97.90 219	99.18 254	96.41 332	98.46 276	98.95 220	95.93 266	99.60 348	96.51 284	98.98 356	99.31 248
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 186	98.68 127	97.54 339	98.96 276	97.99 155	97.88 221	99.36 181	98.20 191	99.63 65	99.04 187	98.76 45	95.33 466	96.56 278	99.74 171	99.31 248
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
新几何1				98.91 163	98.94 278	97.76 185		98.76 332	87.58 453	96.75 391	98.10 348	94.80 300	99.78 248	92.73 405	99.00 351	99.20 278

USDC		97.41 289	97.40 278	97.44 348	98.94 278	93.67 378	95.17 418	99.53 108	94.03 402	98.97 190	99.10 170	95.29 284	99.34 416	95.84 322	99.73 174	99.30 251

tfpn200view9		94.03 396	93.44 399	95.78 408	98.93 280	91.44 415	97.60 267	94.29 441	97.94 213	97.10 369	94.31 450	79.67 434	99.62 338	83.05 455	98.08 405	96.29 450

testdata				98.09 285	98.93 280	95.40 312		98.80 326	90.08 444	97.45 356	98.37 326	95.26 285	99.70 295	93.58 386	98.95 359	99.17 290

thres400		94.14 394	93.44 399	96.24 396	98.93 280	91.44 415	97.60 267	94.29 441	97.94 213	97.10 369	94.31 450	79.67 434	99.62 338	83.05 455	98.08 405	97.66 431

TAPA-MVS	96.21 11	96.63 335	95.95 346	98.65 206	98.93 280	98.09 142	96.93 328	99.28 226	83.58 459	98.13 303	97.78 369	96.13 250	99.40 407	93.52 387	99.29 310	98.45 384
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.92 284	96.93 245	95.54 405	98.78 329	85.72 456	96.86 386	98.11 347	94.43 307			99.10 341	99.23 268

PVSNet_BlendedMVS		97.55 277	97.53 271	97.60 331	98.92 284	93.77 375	96.64 344	99.43 156	94.49 388	97.62 339	99.18 148	96.82 214	99.67 312	94.73 350	99.93 54	99.36 228

PVSNet_Blended		96.88 325	96.68 324	97.47 346	98.92 284	93.77 375	94.71 429	99.43 156	90.98 438	97.62 339	97.36 395	96.82 214	99.67 312	94.73 350	99.56 252	98.98 317

MSDG		97.71 265	97.52 272	98.28 269	98.91 287	96.82 249	94.42 439	99.37 177	97.65 235	98.37 286	98.29 335	97.40 178	99.33 418	94.09 372	99.22 321	98.68 368

Anonymous202405211		97.90 245	97.50 273	99.08 129	98.90 288	98.25 125	98.53 122	96.16 418	98.87 133	99.11 162	98.86 239	90.40 369	99.78 248	97.36 206	99.31 305	99.19 283

原ACMM1				98.35 262	98.90 288	96.25 276		98.83 323	92.48 422	96.07 413	98.10 348	95.39 283	99.71 288	92.61 408	98.99 353	99.08 299

GBi-Net		98.65 146	98.47 165	99.17 111	98.90 288	98.24 126	99.20 48	99.44 150	98.59 154	98.95 196	99.55 57	94.14 315	99.86 141	97.77 175	99.69 201	99.41 201

test1		98.65 146	98.47 165	99.17 111	98.90 288	98.24 126	99.20 48	99.44 150	98.59 154	98.95 196	99.55 57	94.14 315	99.86 141	97.77 175	99.69 201	99.41 201

FMVSNet2		98.49 177	98.40 175	98.75 191	98.90 288	97.14 233	98.61 113	99.13 267	98.59 154	99.19 155	99.28 119	94.14 315	99.82 200	97.97 160	99.80 137	99.29 253

OMC-MVS		97.88 249	97.49 274	99.04 140	98.89 293	98.63 95	96.94 326	99.25 235	95.02 377	98.53 270	98.51 309	97.27 187	99.47 395	93.50 389	99.51 267	99.01 311

VortexMVS		97.98 242	98.31 191	97.02 366	98.88 294	91.45 414	98.03 193	99.47 134	98.65 145	99.55 75	99.47 77	91.49 358	99.81 216	99.32 59	99.91 76	99.80 40

MVSFormer		98.26 210	98.43 171	97.77 308	98.88 294	93.89 371	99.39 20	99.56 95	99.11 95	98.16 299	98.13 344	93.81 323	99.97 7	99.26 64	99.57 249	99.43 195

lupinMVS		97.06 315	96.86 311	97.65 324	98.88 294	93.89 371	95.48 409	97.97 373	93.53 408	98.16 299	97.58 381	93.81 323	99.91 72	96.77 255	99.57 249	99.17 290

dmvs_re		95.98 357	95.39 367	97.74 314	98.86 297	97.45 206	98.37 148	95.69 430	97.95 211	96.56 397	95.95 422	90.70 366	97.68 460	88.32 443	96.13 447	98.11 406

DELS-MVS		98.27 208	98.20 206	98.48 245	98.86 297	96.70 257	95.60 404	99.20 246	97.73 229	98.45 277	98.71 270	97.50 170	99.82 200	98.21 139	99.59 240	98.93 328
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		97.89 247	97.98 234	97.60 331	98.86 297	94.35 346	96.21 371	99.44 150	97.45 263	99.06 169	98.88 236	97.99 125	99.28 426	94.38 365	99.58 245	99.18 286

LCM-MVSNet-Re		98.64 148	98.48 163	99.11 122	98.85 300	98.51 108	98.49 133	99.83 25	98.37 169	99.69 54	99.46 79	98.21 104	99.92 63	94.13 371	99.30 308	98.91 332

pmmvs4		97.58 275	97.28 286	98.51 240	98.84 301	96.93 245	95.40 413	98.52 351	93.60 407	98.61 255	98.65 288	95.10 289	99.60 348	96.97 236	99.79 143	98.99 316

NP-MVS					98.84 301	97.39 210					96.84 404

sss		97.21 305	96.93 305	98.06 290	98.83 303	95.22 319	96.75 338	98.48 353	94.49 388	97.27 365	97.90 364	92.77 341	99.80 224	96.57 274	99.32 303	99.16 293

PVSNet	93.40 17	95.67 366	95.70 352	95.57 413	98.83 303	88.57 440	92.50 456	97.72 378	92.69 420	96.49 405	96.44 414	93.72 326	99.43 403	93.61 384	99.28 311	98.71 361

MVE	83.40 22	92.50 419	91.92 421	94.25 429	98.83 303	91.64 411	92.71 455	83.52 469	95.92 353	86.46 467	95.46 435	95.20 286	95.40 465	80.51 460	98.64 380	95.73 458
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testing3-2		93.78 400	93.91 392	93.39 441	98.82 306	81.72 468	97.76 241	95.28 432	98.60 153	96.54 398	96.66 408	65.85 464	99.62 338	96.65 267	98.99 353	98.82 342

ambc				98.24 273	98.82 306	95.97 287	98.62 112	99.00 292		99.27 137	99.21 141	96.99 204	99.50 386	96.55 281	99.50 274	99.26 262

旧先验1					98.82 306	97.45 206		98.76 332			98.34 330	95.50 280			99.01 350	99.23 268

test_vis1_rt		97.75 262	97.72 257	97.83 303	98.81 309	96.35 273	97.30 303	99.69 53	94.61 386	97.87 323	98.05 353	96.26 246	98.32 454	98.74 105	98.18 397	98.82 342

WTY-MVS		96.67 333	96.27 343	97.87 301	98.81 309	94.61 340	96.77 336	97.92 375	94.94 380	97.12 368	97.74 372	91.11 362	99.82 200	93.89 377	98.15 401	99.18 286

3Dnovator+	97.89 3	98.69 136	98.51 154	99.24 102	98.81 309	98.40 113	99.02 69	99.19 250	98.99 118	98.07 308	99.28 119	97.11 197	99.84 172	96.84 250	99.32 303	99.47 180

QAPM		97.31 296	96.81 317	98.82 174	98.80 312	97.49 201	99.06 65	99.19 250	90.22 442	97.69 336	99.16 154	96.91 208	99.90 79	90.89 433	99.41 290	99.07 301

VNet		98.42 183	98.30 192	98.79 181	98.79 313	97.29 216	98.23 160	98.66 342	99.31 67	98.85 220	98.80 256	94.80 300	99.78 248	98.13 144	99.13 336	99.31 248

DPM-MVS		96.32 345	95.59 358	98.51 240	98.76 314	97.21 224	94.54 438	98.26 362	91.94 427	96.37 406	97.25 397	93.06 335	99.43 403	91.42 423	98.74 369	98.89 334

3Dnovator	98.27 2	98.81 114	98.73 116	99.05 138	98.76 314	97.81 182	99.25 43	99.30 214	98.57 158	98.55 267	99.33 109	97.95 127	99.90 79	97.16 218	99.67 212	99.44 191

PLC	94.65 16	96.51 338	95.73 351	98.85 170	98.75 316	97.91 167	96.42 359	99.06 276	90.94 439	95.59 419	97.38 393	94.41 308	99.59 352	90.93 431	98.04 410	99.05 303
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		96.83 327	96.75 320	97.08 363	98.74 317	93.33 384	96.71 340	98.26 362	96.72 319	98.44 278	97.37 394	95.20 286	99.47 395	91.89 413	97.43 424	98.44 387

hse-mvs2		97.46 283	97.07 298	98.64 208	98.73 318	97.33 212	97.45 287	97.64 385	99.11 95	98.58 261	97.98 358	88.65 384	99.79 237	98.11 145	97.39 426	98.81 347

CDS-MVSNet		97.69 266	97.35 283	98.69 201	98.73 318	97.02 238	96.92 330	98.75 335	95.89 354	98.59 259	98.67 283	92.08 352	99.74 273	96.72 261	99.81 126	99.32 244
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SD_0403		96.28 347	95.83 348	97.64 327	98.72 320	94.30 347	98.87 88	98.77 330	97.80 224	96.53 399	98.02 355	97.34 182	99.47 395	76.93 464	99.48 277	99.16 293

EIA-MVS		98.00 238	97.74 254	98.80 178	98.72 320	98.09 142	98.05 189	99.60 75	97.39 267	96.63 394	95.55 430	97.68 147	99.80 224	96.73 260	99.27 312	98.52 379

LFMVS		97.20 306	96.72 321	98.64 208	98.72 320	96.95 243	98.93 81	94.14 445	99.74 13	98.78 232	99.01 201	84.45 411	99.73 280	97.44 202	99.27 312	99.25 263

new_pmnet		96.99 322	96.76 319	97.67 320	98.72 320	94.89 329	95.95 388	98.20 365	92.62 421	98.55 267	98.54 304	94.88 296	99.52 380	93.96 375	99.44 288	98.59 376

Fast-Effi-MVS+		97.67 268	97.38 280	98.57 224	98.71 324	97.43 208	97.23 308	99.45 142	94.82 383	96.13 410	96.51 410	98.52 70	99.91 72	96.19 303	98.83 365	98.37 396

TEST9					98.71 324	98.08 146	95.96 386	99.03 284	91.40 433	95.85 416	97.53 383	96.52 234	99.76 260

train_agg		97.10 312	96.45 337	99.07 131	98.71 324	98.08 146	95.96 386	99.03 284	91.64 428	95.85 416	97.53 383	96.47 236	99.76 260	93.67 383	99.16 331	99.36 228

TSAR-MVS + GP.		98.18 222	97.98 234	98.77 188	98.71 324	97.88 169	96.32 365	98.66 342	96.33 334	99.23 149	98.51 309	97.48 174	99.40 407	97.16 218	99.46 279	99.02 310

FA-MVS(test-final)		96.99 322	96.82 315	97.50 343	98.70 328	94.78 332	99.34 23	96.99 401	95.07 376	98.48 275	99.33 109	88.41 387	99.65 329	96.13 309	98.92 362	98.07 409

AUN-MVS		96.24 351	95.45 363	98.60 219	98.70 328	97.22 222	97.38 293	97.65 383	95.95 352	95.53 426	97.96 362	82.11 428	99.79 237	96.31 296	97.44 423	98.80 352

our_test_3		97.39 291	97.73 256	96.34 391	98.70 328	89.78 436	94.61 435	98.97 294	96.50 327	99.04 178	98.85 242	95.98 262	99.84 172	97.26 212	99.67 212	99.41 201

ppachtmachnet_test		97.50 278	97.74 254	96.78 381	98.70 328	91.23 423	94.55 437	99.05 279	96.36 333	99.21 153	98.79 258	96.39 239	99.78 248	96.74 258	99.82 120	99.34 235

PCF-MVS	92.86 18	94.36 388	93.00 406	98.42 252	98.70 328	97.56 198	93.16 454	99.11 270	79.59 463	97.55 346	97.43 390	92.19 349	99.73 280	79.85 461	99.45 281	97.97 415
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ttmdpeth		97.91 244	98.02 230	97.58 333	98.69 333	94.10 354	98.13 172	98.90 304	97.95 211	97.32 364	99.58 47	95.95 265	98.75 449	96.41 290	99.22 321	99.87 21

ETV-MVS		98.03 234	97.86 248	98.56 229	98.69 333	98.07 148	97.51 279	99.50 116	98.10 203	97.50 351	95.51 431	98.41 78	99.88 113	96.27 299	99.24 317	97.71 430

test_prior				98.95 156	98.69 333	97.95 163		99.03 284					99.59 352			99.30 251

mvsmamba		97.57 276	97.26 287	98.51 240	98.69 333	96.73 256	98.74 97	97.25 394	97.03 301	97.88 322	99.23 139	90.95 363	99.87 132	96.61 270	99.00 351	98.91 332

agg_prior					98.68 337	97.99 155		99.01 290		95.59 419			99.77 254

test_8					98.67 338	98.01 154	95.91 392	99.02 287	91.64 428	95.79 418	97.50 386	96.47 236	99.76 260

HQP-NCC					98.67 338		96.29 367		96.05 345	95.55 422

ACMP_Plane					98.67 338		96.29 367		96.05 345	95.55 422

CNVR-MVS		98.17 224	97.87 247	99.07 131	98.67 338	98.24 126	97.01 322	98.93 298	97.25 281	97.62 339	98.34 330	97.27 187	99.57 361	96.42 289	99.33 301	99.39 211

HQP-MVS		97.00 321	96.49 336	98.55 231	98.67 338	96.79 251	96.29 367	99.04 282	96.05 345	95.55 422	96.84 404	93.84 321	99.54 374	92.82 401	99.26 315	99.32 244

MM		98.22 215	97.99 233	98.91 163	98.66 343	96.97 240	97.89 220	94.44 439	99.54 39	98.95 196	99.14 161	93.50 327	99.92 63	99.80 16	99.96 28	99.85 29

test_fmvs1		97.72 264	97.94 240	97.07 365	98.66 343	92.39 401	97.68 251	99.81 31	95.20 375	99.54 77	99.44 84	91.56 357	99.41 406	99.78 20	99.77 154	99.40 210

balanced_conf03		98.63 150	98.72 118	98.38 257	98.66 343	96.68 259	98.90 83	99.42 162	98.99 118	98.97 190	99.19 144	95.81 270	99.85 154	98.77 103	99.77 154	98.60 373

thres200		93.72 402	93.14 404	95.46 417	98.66 343	91.29 419	96.61 346	94.63 438	97.39 267	96.83 387	93.71 453	79.88 431	99.56 364	82.40 458	98.13 402	95.54 459

wuyk23d		96.06 353	97.62 267	91.38 446	98.65 347	98.57 102	98.85 92	96.95 404	96.86 312	99.90 14	99.16 154	99.18 19	98.40 453	89.23 441	99.77 154	77.18 466

NCCC		97.86 252	97.47 277	99.05 138	98.61 348	98.07 148	96.98 324	98.90 304	97.63 236	97.04 373	97.93 363	95.99 261	99.66 323	95.31 338	98.82 367	99.43 195

DeepC-MVS_fast	96.85 6	98.30 204	98.15 216	98.75 191	98.61 348	97.23 220	97.76 241	99.09 273	97.31 275	98.75 238	98.66 286	97.56 161	99.64 332	96.10 310	99.55 256	99.39 211
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testing3		93.51 404	92.09 415	97.75 312	98.60 350	94.40 344	97.32 301	95.26 433	97.56 246	96.79 390	95.50 432	53.57 472	99.77 254	95.26 339	98.97 357	99.08 299

thisisatest0515		94.12 395	93.16 403	96.97 370	98.60 350	92.90 391	93.77 450	90.61 458	94.10 400	96.91 380	95.87 425	74.99 446	99.80 224	94.52 356	99.12 339	98.20 402

GA-MVS		95.86 360	95.32 370	97.49 344	98.60 350	94.15 353	93.83 449	97.93 374	95.49 365	96.68 392	97.42 391	83.21 421	99.30 422	96.22 301	98.55 386	99.01 311

dmvs_testset		92.94 414	92.21 414	95.13 421	98.59 353	90.99 426	97.65 257	92.09 454	96.95 304	94.00 446	93.55 454	92.34 347	96.97 463	72.20 465	92.52 461	97.43 438

OPU-MVS				98.82 174	98.59 353	98.30 122	98.10 179				98.52 308	98.18 106	98.75 449	94.62 353	99.48 277	99.41 201

MSLP-MVS++		98.02 235	98.14 218	97.64 327	98.58 355	95.19 320	97.48 283	99.23 242	97.47 256	97.90 320	98.62 295	97.04 199	98.81 447	97.55 191	99.41 290	98.94 327

test12				98.93 159	98.58 355	97.83 174		98.66 342		96.53 399		95.51 279	99.69 299		99.13 336	99.27 256

CL-MVSNet_self_test		97.44 286	97.22 290	98.08 288	98.57 357	95.78 295	94.30 442	98.79 327	96.58 325	98.60 257	98.19 342	94.74 303	99.64 332	96.41 290	98.84 364	98.82 342

PS-MVSNAJ		97.08 314	97.39 279	96.16 402	98.56 358	92.46 399	95.24 417	98.85 318	97.25 281	97.49 352	95.99 421	98.07 116	99.90 79	96.37 292	98.67 379	96.12 455

CNLPA		97.17 309	96.71 322	98.55 231	98.56 358	98.05 152	96.33 364	98.93 298	96.91 309	97.06 372	97.39 392	94.38 310	99.45 400	91.66 417	99.18 330	98.14 405

xiu_mvs_v2_base		97.16 310	97.49 274	96.17 400	98.54 360	92.46 399	95.45 410	98.84 319	97.25 281	97.48 353	96.49 411	98.31 89	99.90 79	96.34 295	98.68 378	96.15 454

alignmvs		97.35 293	96.88 310	98.78 184	98.54 360	98.09 142	97.71 247	97.69 380	99.20 81	97.59 342	95.90 424	88.12 389	99.55 368	98.18 141	98.96 358	98.70 364

FE-MVS		95.66 367	94.95 380	97.77 308	98.53 362	95.28 316	99.40 19	96.09 421	93.11 414	97.96 317	99.26 127	79.10 438	99.77 254	92.40 410	98.71 373	98.27 400

Effi-MVS+		98.02 235	97.82 250	98.62 214	98.53 362	97.19 226	97.33 300	99.68 58	97.30 276	96.68 392	97.46 389	98.56 68	99.80 224	96.63 268	98.20 396	98.86 339

baseline1		95.96 358	95.44 364	97.52 341	98.51 364	93.99 365	98.39 146	96.09 421	98.21 187	98.40 285	97.76 371	86.88 391	99.63 335	95.42 336	89.27 464	98.95 323

MVS_Test		98.18 222	98.36 183	97.67 320	98.48 365	94.73 335	98.18 165	99.02 287	97.69 232	98.04 312	99.11 167	97.22 191	99.56 364	98.57 117	98.90 363	98.71 361

MGCFI-Net		98.34 196	98.28 195	98.51 240	98.47 366	97.59 197	98.96 77	99.48 125	99.18 88	97.40 359	95.50 432	98.66 54	99.50 386	98.18 141	98.71 373	98.44 387

BH-RMVSNet		96.83 327	96.58 332	97.58 333	98.47 366	94.05 355	96.67 342	97.36 389	96.70 321	97.87 323	97.98 358	95.14 288	99.44 402	90.47 436	98.58 385	99.25 263

sasdasda		98.34 196	98.26 199	98.58 221	98.46 368	97.82 179	98.96 77	99.46 138	99.19 85	97.46 354	95.46 435	98.59 62	99.46 398	98.08 148	98.71 373	98.46 381

canonicalmvs		98.34 196	98.26 199	98.58 221	98.46 368	97.82 179	98.96 77	99.46 138	99.19 85	97.46 354	95.46 435	98.59 62	99.46 398	98.08 148	98.71 373	98.46 381

MVS-HIRNet		94.32 389	95.62 355	90.42 447	98.46 368	75.36 471	96.29 367	89.13 462	95.25 372	95.38 428	99.75 16	92.88 338	99.19 432	94.07 373	99.39 292	96.72 448

PHI-MVS		98.29 207	97.95 238	99.34 79	98.44 371	99.16 48	98.12 176	99.38 173	96.01 349	98.06 309	98.43 320	97.80 140	99.67 312	95.69 328	99.58 245	99.20 278

DVP-MVS++		98.90 96	98.70 124	99.51 48	98.43 372	99.15 52	99.43 15	99.32 201	98.17 194	99.26 141	99.02 190	98.18 106	99.88 113	97.07 227	99.45 281	99.49 161

MSC_two_6792asdad				99.32 87	98.43 372	98.37 117		98.86 315					99.89 95	97.14 221	99.60 236	99.71 60

No_MVS				99.32 87	98.43 372	98.37 117		98.86 315					99.89 95	97.14 221	99.60 236	99.71 60

Fast-Effi-MVS+-dtu		98.27 208	98.09 221	98.81 176	98.43 372	98.11 139	97.61 266	99.50 116	98.64 146	97.39 361	97.52 385	98.12 114	99.95 26	96.90 244	98.71 373	98.38 394

OpenMVS_ROB	95.38 14	95.84 362	95.18 375	97.81 305	98.41 376	97.15 232	97.37 297	98.62 346	83.86 458	98.65 249	98.37 326	94.29 313	99.68 308	88.41 442	98.62 383	96.60 449

DeepPCF-MVS	96.93 5	98.32 201	98.01 231	99.23 104	98.39 377	98.97 73	95.03 422	99.18 254	96.88 310	99.33 124	98.78 260	98.16 110	99.28 426	96.74 258	99.62 229	99.44 191

Patchmatch-test		96.55 337	96.34 339	97.17 360	98.35 378	93.06 387	98.40 145	97.79 376	97.33 272	98.41 281	98.67 283	83.68 419	99.69 299	95.16 341	99.31 305	98.77 355

AdaColmap		97.14 311	96.71 322	98.46 247	98.34 379	97.80 183	96.95 325	98.93 298	95.58 362	96.92 378	97.66 376	95.87 268	99.53 376	90.97 430	99.14 334	98.04 410

OpenMVS	96.65 7	97.09 313	96.68 324	98.32 264	98.32 380	97.16 231	98.86 91	99.37 177	89.48 446	96.29 408	99.15 158	96.56 232	99.90 79	92.90 398	99.20 325	97.89 418

MG-MVS		96.77 330	96.61 329	97.26 356	98.31 381	93.06 387	95.93 389	98.12 370	96.45 331	97.92 318	98.73 267	93.77 325	99.39 409	91.19 428	99.04 345	99.33 241

test_yl		96.69 331	96.29 341	97.90 298	98.28 382	95.24 317	97.29 304	97.36 389	98.21 187	98.17 296	97.86 365	86.27 395	99.55 368	94.87 347	98.32 390	98.89 334

DCV-MVSNet		96.69 331	96.29 341	97.90 298	98.28 382	95.24 317	97.29 304	97.36 389	98.21 187	98.17 296	97.86 365	86.27 395	99.55 368	94.87 347	98.32 390	98.89 334

CHOSEN 280x420		95.51 372	95.47 361	95.65 412	98.25 384	88.27 443	93.25 453	98.88 308	93.53 408	94.65 437	97.15 400	86.17 397	99.93 52	97.41 204	99.93 54	98.73 360

SCA		96.41 344	96.66 327	95.67 410	98.24 385	88.35 442	95.85 395	96.88 407	96.11 343	97.67 337	98.67 283	93.10 333	99.85 154	94.16 367	99.22 321	98.81 347

DeepMVS_CX				93.44 440	98.24 385	94.21 350		94.34 440	64.28 466	91.34 460	94.87 447	89.45 378	92.77 467	77.54 463	93.14 460	93.35 462

MS-PatchMatch		97.68 267	97.75 253	97.45 347	98.23 387	93.78 374	97.29 304	98.84 319	96.10 344	98.64 250	98.65 288	96.04 254	99.36 412	96.84 250	99.14 334	99.20 278

BH-w/o		95.13 378	94.89 382	95.86 405	98.20 388	91.31 418	95.65 402	97.37 388	93.64 406	96.52 401	95.70 428	93.04 336	99.02 438	88.10 444	95.82 450	97.24 441

mvs_anonymous		97.83 260	98.16 215	96.87 375	98.18 389	91.89 408	97.31 302	98.90 304	97.37 269	98.83 223	99.46 79	96.28 245	99.79 237	98.90 92	98.16 400	98.95 323

miper_lstm_enhance		97.18 308	97.16 293	97.25 357	98.16 390	92.85 392	95.15 420	99.31 206	97.25 281	98.74 240	98.78 260	90.07 370	99.78 248	97.19 216	99.80 137	99.11 298

RRT-MVS		97.88 249	97.98 234	97.61 330	98.15 391	93.77 375	98.97 76	99.64 67	99.16 90	98.69 243	99.42 87	91.60 355	99.89 95	97.63 185	98.52 387	99.16 293

ET-MVSNet_ETH3D		94.30 391	93.21 402	97.58 333	98.14 392	94.47 343	94.78 428	93.24 450	94.72 384	89.56 462	95.87 425	78.57 441	99.81 216	96.91 239	97.11 435	98.46 381

ADS-MVSNet2		95.43 373	94.98 378	96.76 382	98.14 392	91.74 409	97.92 216	97.76 377	90.23 440	96.51 402	98.91 226	85.61 402	99.85 154	92.88 399	96.90 436	98.69 365

ADS-MVSNet		95.24 376	94.93 381	96.18 399	98.14 392	90.10 435	97.92 216	97.32 392	90.23 440	96.51 402	98.91 226	85.61 402	99.74 273	92.88 399	96.90 436	98.69 365

c3_l		97.36 292	97.37 281	97.31 352	98.09 395	93.25 385	95.01 423	99.16 261	97.05 298	98.77 235	98.72 269	92.88 338	99.64 332	96.93 238	99.76 167	99.05 303

FMVSNet3		97.50 278	97.24 289	98.29 268	98.08 396	95.83 292	97.86 225	98.91 303	97.89 218	98.95 196	98.95 220	87.06 390	99.81 216	97.77 175	99.69 201	99.23 268

PAPM		91.88 428	90.34 431	96.51 386	98.06 397	92.56 397	92.44 457	97.17 396	86.35 454	90.38 461	96.01 420	86.61 393	99.21 431	70.65 467	95.43 452	97.75 427

Effi-MVS+-dtu		98.26 210	97.90 245	99.35 76	98.02 398	99.49 6	98.02 196	99.16 261	98.29 180	97.64 338	97.99 357	96.44 238	99.95 26	96.66 266	98.93 361	98.60 373

eth_miper_zixun_eth		97.23 304	97.25 288	97.17 360	98.00 399	92.77 394	94.71 429	99.18 254	97.27 279	98.56 265	98.74 266	91.89 353	99.69 299	97.06 229	99.81 126	99.05 303

HY-MVS	95.94 13	95.90 359	95.35 369	97.55 338	97.95 400	94.79 331	98.81 96	96.94 405	92.28 425	95.17 430	98.57 302	89.90 372	99.75 268	91.20 427	97.33 431	98.10 407

UGNet		98.53 170	98.45 168	98.79 181	97.94 401	96.96 242	99.08 61	98.54 349	99.10 102	96.82 388	99.47 77	96.55 233	99.84 172	98.56 120	99.94 49	99.55 130
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		96.47 342	95.70 352	98.79 181	97.92 402	99.12 62	98.28 154	98.60 347	92.16 426	95.54 425	96.17 418	94.77 302	99.52 380	89.62 439	98.23 394	97.72 429
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		96.86 326	96.55 333	97.79 306	97.91 403	94.21 350	97.56 272	98.87 310	97.49 255	99.06 169	99.05 185	80.72 429	99.80 224	98.44 126	99.82 120	99.37 221

API-MVS		97.04 317	96.91 309	97.42 349	97.88 404	98.23 130	98.18 165	98.50 352	97.57 244	97.39 361	96.75 406	96.77 219	99.15 435	90.16 437	99.02 349	94.88 460

myMVS_eth3d28		92.92 415	92.31 411	94.77 424	97.84 405	87.59 447	96.19 373	96.11 420	97.08 297	94.27 440	93.49 456	66.07 463	98.78 448	91.78 415	97.93 413	97.92 417

miper_ehance_all_eth		97.06 315	97.03 300	97.16 362	97.83 406	93.06 387	94.66 432	99.09 273	95.99 350	98.69 243	98.45 318	92.73 343	99.61 345	96.79 252	99.03 346	98.82 342

cl____		97.02 318	96.83 314	97.58 333	97.82 407	94.04 357	94.66 432	99.16 261	97.04 299	98.63 251	98.71 270	88.68 383	99.69 299	97.00 231	99.81 126	99.00 315

DIV-MVS_self_test		97.02 318	96.84 313	97.58 333	97.82 407	94.03 358	94.66 432	99.16 261	97.04 299	98.63 251	98.71 270	88.69 381	99.69 299	97.00 231	99.81 126	99.01 311

CANet		97.87 251	97.76 252	98.19 280	97.75 409	95.51 302	96.76 337	99.05 279	97.74 228	96.93 377	98.21 340	95.59 276	99.89 95	97.86 170	99.93 54	99.19 283

UBG		93.25 409	92.32 410	96.04 404	97.72 410	90.16 434	95.92 391	95.91 425	96.03 348	93.95 448	93.04 459	69.60 453	99.52 380	90.72 435	97.98 411	98.45 384

mvsany_test1		97.60 272	97.54 270	97.77 308	97.72 410	95.35 313	95.36 414	97.13 398	94.13 399	99.71 48	99.33 109	97.93 128	99.30 422	97.60 189	98.94 360	98.67 369

PVSNet_0	89.98 21	91.15 429	90.30 432	93.70 437	97.72 410	84.34 461	90.24 460	97.42 387	90.20 443	93.79 449	93.09 458	90.90 365	98.89 446	86.57 450	72.76 467	97.87 420

CR-MVSNet		96.28 347	95.95 346	97.28 354	97.71 413	94.22 348	98.11 177	98.92 301	92.31 424	96.91 380	99.37 97	85.44 405	99.81 216	97.39 205	97.36 429	97.81 423

RPMNet		97.02 318	96.93 305	97.30 353	97.71 413	94.22 348	98.11 177	99.30 214	99.37 59	96.91 380	99.34 106	86.72 392	99.87 132	97.53 194	97.36 429	97.81 423

ETVMVS		92.60 418	91.08 427	97.18 358	97.70 415	93.65 380	96.54 349	95.70 428	96.51 326	94.68 436	92.39 462	61.80 469	99.50 386	86.97 447	97.41 425	98.40 392

pmmvs3		95.03 380	94.40 387	96.93 371	97.70 415	92.53 398	95.08 421	97.71 379	88.57 450	97.71 334	98.08 351	79.39 436	99.82 200	96.19 303	99.11 340	98.43 389

baseline2		93.73 401	92.83 407	96.42 389	97.70 415	91.28 420	96.84 333	89.77 461	93.96 404	92.44 456	95.93 423	79.14 437	99.77 254	92.94 397	96.76 440	98.21 401

WBMVS		95.18 377	94.78 383	96.37 390	97.68 418	89.74 437	95.80 397	98.73 338	97.54 250	98.30 287	98.44 319	70.06 451	99.82 200	96.62 269	99.87 95	99.54 136

tpm		94.67 385	94.34 389	95.66 411	97.68 418	88.42 441	97.88 221	94.90 435	94.46 390	96.03 415	98.56 303	78.66 439	99.79 237	95.88 316	95.01 454	98.78 354

CANet_DTU		97.26 300	97.06 299	97.84 302	97.57 420	94.65 339	96.19 373	98.79 327	97.23 287	95.14 431	98.24 337	93.22 330	99.84 172	97.34 207	99.84 109	99.04 307

testing11		93.08 412	92.02 417	96.26 395	97.56 421	90.83 429	96.32 365	95.70 428	96.47 330	92.66 455	93.73 452	64.36 467	99.59 352	93.77 382	97.57 418	98.37 396

tpm2		93.09 411	92.58 409	94.62 426	97.56 421	86.53 450	97.66 255	95.79 427	86.15 455	94.07 445	98.23 339	75.95 444	99.53 376	90.91 432	96.86 439	97.81 423

testing91		93.32 407	92.27 412	96.47 388	97.54 423	91.25 421	96.17 377	96.76 409	97.18 291	93.65 451	93.50 455	65.11 466	99.63 335	93.04 396	97.45 422	98.53 378

TR-MVS		95.55 370	95.12 376	96.86 378	97.54 423	93.94 366	96.49 354	96.53 414	94.36 395	97.03 375	96.61 409	94.26 314	99.16 434	86.91 449	96.31 444	97.47 437

testing99		93.04 413	91.98 420	96.23 397	97.53 425	90.70 431	96.35 363	95.94 424	96.87 311	93.41 452	93.43 457	63.84 468	99.59 352	93.24 394	97.19 432	98.40 392

1314		95.74 364	95.60 356	96.17 400	97.53 425	92.75 395	98.07 186	98.31 361	91.22 435	94.25 441	96.68 407	95.53 277	99.03 437	91.64 419	97.18 433	96.74 447

CostFormer		93.97 397	93.78 395	94.51 427	97.53 425	85.83 453	97.98 208	95.96 423	89.29 448	94.99 433	98.63 293	78.63 440	99.62 338	94.54 355	96.50 441	98.09 408

FMVSNet5		96.01 355	95.20 374	98.41 253	97.53 425	96.10 278	98.74 97	99.50 116	97.22 290	98.03 313	99.04 187	69.80 452	99.88 113	97.27 211	99.71 191	99.25 263

PMMVS		96.51 338	95.98 345	98.09 285	97.53 425	95.84 291	94.92 425	98.84 319	91.58 430	96.05 414	95.58 429	95.68 273	99.66 323	95.59 332	98.09 404	98.76 357

reproduce_monomvs		95.00 382	95.25 371	94.22 430	97.51 430	83.34 462	97.86 225	98.44 354	98.51 163	99.29 134	99.30 115	67.68 457	99.56 364	98.89 94	99.81 126	99.77 48

PAPR		95.29 374	94.47 385	97.75 312	97.50 431	95.14 322	94.89 426	98.71 340	91.39 434	95.35 429	95.48 434	94.57 305	99.14 436	84.95 452	97.37 427	98.97 320

testing222		91.96 426	90.37 430	96.72 383	97.47 432	92.59 396	96.11 379	94.76 436	96.83 313	92.90 454	92.87 460	57.92 470	99.55 368	86.93 448	97.52 419	98.00 414

PatchT		96.65 334	96.35 338	97.54 339	97.40 433	95.32 315	97.98 208	96.64 411	99.33 64	96.89 384	99.42 87	84.32 413	99.81 216	97.69 184	97.49 420	97.48 436

tpm cat1		93.29 408	93.13 405	93.75 436	97.39 434	84.74 456	97.39 291	97.65 383	83.39 460	94.16 442	98.41 321	82.86 424	99.39 409	91.56 421	95.35 453	97.14 442

PatchmatchNet		95.58 369	95.67 354	95.30 420	97.34 435	87.32 448	97.65 257	96.65 410	95.30 371	97.07 371	98.69 279	84.77 408	99.75 268	94.97 345	98.64 380	98.83 341
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmtry		97.35 293	96.97 303	98.50 244	97.31 436	96.47 269	98.18 165	98.92 301	98.95 125	98.78 232	99.37 97	85.44 405	99.85 154	95.96 314	99.83 116	99.17 290

LS3D		98.63 150	98.38 180	99.36 70	97.25 437	99.38 13	99.12 60	99.32 201	99.21 79	98.44 278	98.88 236	97.31 183	99.80 224	96.58 272	99.34 300	98.92 329

IB-MVS	91.63 19	92.24 424	90.90 428	96.27 394	97.22 438	91.24 422	94.36 441	93.33 449	92.37 423	92.24 458	94.58 449	66.20 462	99.89 95	93.16 395	94.63 456	97.66 431
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		92.38 421	91.76 424	94.21 431	97.16 439	84.65 457	95.42 412	88.45 463	95.96 351	96.17 409	95.84 427	66.36 460	99.71 288	91.87 414	98.64 380	98.28 399

tpmrst		95.07 379	95.46 362	93.91 434	97.11 440	84.36 460	97.62 262	96.96 403	94.98 378	96.35 407	98.80 256	85.46 404	99.59 352	95.60 331	96.23 445	97.79 426

Syy-MVS		96.04 354	95.56 360	97.49 344	97.10 441	94.48 342	96.18 375	96.58 412	95.65 359	94.77 434	92.29 463	91.27 361	99.36 412	98.17 143	98.05 408	98.63 371

myMVS_eth3d		91.92 427	90.45 429	96.30 392	97.10 441	90.90 427	96.18 375	96.58 412	95.65 359	94.77 434	92.29 463	53.88 471	99.36 412	89.59 440	98.05 408	98.63 371

MDTV_nov1_ep13			95.22 373		97.06 443	83.20 463	97.74 244	96.16 418	94.37 394	96.99 376	98.83 249	83.95 417	99.53 376	93.90 376	97.95 412

MVS		93.19 410	92.09 415	96.50 387	96.91 444	94.03 358	98.07 186	98.06 372	68.01 465	94.56 439	96.48 412	95.96 264	99.30 422	83.84 454	96.89 438	96.17 452

E-PMN		94.17 393	94.37 388	93.58 438	96.86 445	85.71 454	90.11 462	97.07 399	98.17 194	97.82 329	97.19 398	84.62 410	98.94 442	89.77 438	97.68 417	96.09 456

JIA-IIPM		95.52 371	95.03 377	97.00 367	96.85 446	94.03 358	96.93 328	95.82 426	99.20 81	94.63 438	99.71 22	83.09 422	99.60 348	94.42 361	94.64 455	97.36 440

EMVS		93.83 399	94.02 391	93.23 443	96.83 447	84.96 455	89.77 463	96.32 416	97.92 215	97.43 358	96.36 417	86.17 397	98.93 443	87.68 445	97.73 416	95.81 457

cl22		95.79 363	95.39 367	96.98 369	96.77 448	92.79 393	94.40 440	98.53 350	94.59 387	97.89 321	98.17 343	82.82 425	99.24 428	96.37 292	99.03 346	98.92 329

WB-MVSnew		95.73 365	95.57 359	96.23 397	96.70 449	90.70 431	96.07 381	93.86 446	95.60 361	97.04 373	95.45 438	96.00 257	99.55 368	91.04 429	98.31 392	98.43 389

dp		93.47 405	93.59 398	93.13 444	96.64 450	81.62 469	97.66 255	96.42 415	92.80 419	96.11 411	98.64 291	78.55 442	99.59 352	93.31 392	92.18 463	98.16 404

MonoMVSNet		96.25 349	96.53 335	95.39 418	96.57 451	91.01 425	98.82 95	97.68 382	98.57 158	98.03 313	99.37 97	90.92 364	97.78 459	94.99 343	93.88 459	97.38 439

test-LLR		93.90 398	93.85 393	94.04 432	96.53 452	84.62 458	94.05 446	92.39 452	96.17 340	94.12 443	95.07 439	82.30 426	99.67 312	95.87 319	98.18 397	97.82 421

test-mter		92.33 423	91.76 424	94.04 432	96.53 452	84.62 458	94.05 446	92.39 452	94.00 403	94.12 443	95.07 439	65.63 465	99.67 312	95.87 319	98.18 397	97.82 421

TESTMET0.1,1		92.19 425	91.77 423	93.46 439	96.48 454	82.80 465	94.05 446	91.52 457	94.45 392	94.00 446	94.88 445	66.65 459	99.56 364	95.78 324	98.11 403	98.02 411

MVS_0304		97.44 286	97.01 302	98.72 197	96.42 455	96.74 255	97.20 313	91.97 455	98.46 166	98.30 287	98.79 258	92.74 342	99.91 72	99.30 61	99.94 49	99.52 148

miper_enhance_ethall		96.01 355	95.74 350	96.81 379	96.41 456	92.27 405	93.69 451	98.89 307	91.14 437	98.30 287	97.35 396	90.58 367	99.58 359	96.31 296	99.03 346	98.60 373

tpmvs		95.02 381	95.25 371	94.33 428	96.39 457	85.87 451	98.08 182	96.83 408	95.46 366	95.51 427	98.69 279	85.91 400	99.53 376	94.16 367	96.23 445	97.58 434

CMPMVS	75.91 23	96.29 346	95.44 364	98.84 171	96.25 458	98.69 94	97.02 321	99.12 268	88.90 449	97.83 327	98.86 239	89.51 376	98.90 445	91.92 412	99.51 267	98.92 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 1		94.51 386	93.69 396	96.99 368	96.05 459	93.61 382	94.97 424	93.49 447	96.17 340	97.57 345	94.88 445	82.30 426	99.01 440	93.60 385	94.17 458	98.37 396

EPMVS		93.72 402	93.27 401	95.09 423	96.04 460	87.76 445	98.13 172	85.01 468	94.69 385	96.92 378	98.64 291	78.47 443	99.31 420	95.04 342	96.46 442	98.20 402

cascas		94.79 384	94.33 390	96.15 403	96.02 461	92.36 403	92.34 458	99.26 234	85.34 457	95.08 432	94.96 444	92.96 337	98.53 452	94.41 364	98.59 384	97.56 435

MVStest1		95.86 360	95.60 356	96.63 384	95.87 462	91.70 410	97.93 213	98.94 295	98.03 205	99.56 72	99.66 32	71.83 449	98.26 455	99.35 57	99.24 317	99.91 13

gg-mvs-nofinetune		92.37 422	91.20 426	95.85 406	95.80 463	92.38 402	99.31 30	81.84 470	99.75 11	91.83 459	99.74 18	68.29 454	99.02 438	87.15 446	97.12 434	96.16 453

gm-plane-assit					94.83 464	81.97 467			88.07 452		94.99 442		99.60 348	91.76 416

GG-mvs-BLEND				94.76 425	94.54 465	92.13 407	99.31 30	80.47 471		88.73 465	91.01 465	67.59 458	98.16 458	82.30 459	94.53 457	93.98 461

UWE-MVS-28		90.22 430	89.28 433	93.02 445	94.50 466	82.87 464	96.52 352	87.51 464	95.21 374	92.36 457	96.04 419	71.57 450	98.25 456	72.04 466	97.77 415	97.94 416

EPNet_dtu		94.93 383	94.78 383	95.38 419	93.58 467	87.68 446	96.78 335	95.69 430	97.35 271	89.14 464	98.09 350	88.15 388	99.49 389	94.95 346	99.30 308	98.98 317
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dongtai		76.24 434	75.95 437	77.12 450	92.39 468	67.91 474	90.16 461	59.44 475	82.04 461	89.42 463	94.67 448	49.68 473	81.74 468	48.06 468	77.66 466	81.72 464

KD-MVS_2432*1600		92.87 416	91.99 418	95.51 415	91.37 469	89.27 438	94.07 444	98.14 368	95.42 367	97.25 366	96.44 414	67.86 455	99.24 428	91.28 425	96.08 448	98.02 411

miper_refine_blended		92.87 416	91.99 418	95.51 415	91.37 469	89.27 438	94.07 444	98.14 368	95.42 367	97.25 366	96.44 414	67.86 455	99.24 428	91.28 425	96.08 448	98.02 411

EPNet		96.14 352	95.44 364	98.25 271	90.76 471	95.50 305	97.92 216	94.65 437	98.97 121	92.98 453	98.85 242	89.12 379	99.87 132	95.99 312	99.68 206	99.39 211
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
kuosan		69.30 435	68.95 438	70.34 451	87.68 472	65.00 475	91.11 459	59.90 474	69.02 464	74.46 469	88.89 466	48.58 474	68.03 470	28.61 469	72.33 468	77.99 465

test_method		79.78 432	79.50 435	80.62 448	80.21 473	45.76 476	70.82 464	98.41 358	31.08 468	80.89 468	97.71 373	84.85 407	97.37 461	91.51 422	80.03 465	98.75 358

tmp_tt		78.77 433	78.73 436	78.90 449	58.45 474	74.76 473	94.20 443	78.26 472	39.16 467	86.71 466	92.82 461	80.50 430	75.19 469	86.16 451	92.29 462	86.74 463

testmvs		17.12 437	20.53 440	6.87 453	12.05 475	4.20 478	93.62 452	6.73 476	4.62 471	10.41 471	24.33 468	8.28 476	3.56 472	9.69 471	15.07 469	12.86 468

test123		17.04 438	20.11 441	7.82 452	10.25 476	4.91 477	94.80 427	4.47 477	4.93 470	10.00 472	24.28 469	9.69 475	3.64 471	10.14 470	12.43 470	14.92 467

mmdepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

monomultidepth		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

test_blank		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

eth-test2					0.00 477

eth-test					0.00 477

uanet_test		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

DCPMVS		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

cdsmvs_eth3d_5k		24.66 436	32.88 439	0.00 454	0.00 477	0.00 479	0.00 465	99.10 271	0.00 472	0.00 473	97.58 381	99.21 18	0.00 473	0.00 472	0.00 471	0.00 469

pcd_1.5k_mvsjas		8.17 439	10.90 442	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	98.07 116	0.00 473	0.00 472	0.00 471	0.00 469

sosnet-low-res		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uncertanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

Regformer		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

ab-mvs-re		8.12 440	10.83 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	97.48 387	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet		0.00 441	0.00 444	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	0.00 472	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

WAC-MVS						90.90 427								91.37 424

PC_three_1452									93.27 411	99.40 110	98.54 304	98.22 102	97.00 462	95.17 340	99.45 281	99.49 161

test_241102_TWO								99.30 214	98.03 205	99.26 141	99.02 190	97.51 169	99.88 113	96.91 239	99.60 236	99.66 75

test_0728_THIRD									98.17 194	99.08 167	99.02 190	97.89 132	99.88 113	97.07 227	99.71 191	99.70 65

GSMVS																98.81 347

sam_mvs1												84.74 409				98.81 347

sam_mvs												84.29 415

MTGPA								99.20 246

test_post1							97.59 269				20.48 471	83.07 423	99.66 323	94.16 367

test_post											21.25 470	83.86 418	99.70 295

patchmatchnet-post											98.77 262	84.37 412	99.85 154

MTMP							97.93 213	91.91 456

test9_res														93.28 393	99.15 333	99.38 219

agg_prior2														92.50 409	99.16 331	99.37 221

test_prior4						97.97 159	95.86 393

test_prior2							95.74 400		96.48 329	96.11 411	97.63 379	95.92 267		94.16 367	99.20 325

旧先验2							95.76 399		88.56 451	97.52 349			99.66 323	94.48 357

新几何2							95.93 389

无先验							95.74 400	98.74 337	89.38 447				99.73 280	92.38 411		99.22 273

原ACMM2							95.53 406

testdata2													99.79 237	92.80 403

segment_acmp												97.02 202

testdata1							95.44 411		96.32 335

plane_prior5								99.27 229					99.70 295	94.42 361	99.51 267	99.45 187

plane_prior4											97.98 358

plane_prior3						97.78 184			97.41 265	97.79 330

plane_prior2							97.77 238		98.20 191

plane_prior						97.65 193	97.07 320		96.72 319						99.36 296

n2								0.00 478

nn								0.00 478

door-mid								99.57 88

test11								98.87 310

door								99.41 166

HQP5-MVS						96.79 251

BP-MVS														92.82 401

HQP4-MVS										95.56 421			99.54 374			99.32 244

HQP3-MVS								99.04 282							99.26 315

HQP2-MVS												93.84 321

MDTV_nov1_ep13_2view						74.92 472	97.69 250		90.06 445	97.75 333		85.78 401		93.52 387		98.69 365

ACMMP++_ref															99.77 154

ACMMP++															99.68 206

Test By Simon												96.52 234

High-resolution multi-view benchmark