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 16	100.00 1	99.85 29

Gipuma		99.03 79	99.16 61	98.64 209	99.94 2	98.51 108	99.32 26	99.75 42	99.58 38	98.60 259	99.62 40	98.22 103	99.51 387	97.70 184	99.73 176	97.89 420
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 83	99.44 52	99.78 39	99.76 15	96.39 241	99.92 64	99.44 54	99.92 68	99.68 69

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	99.87 133	99.36 57	99.92 68	99.64 82

PS-MVSNAJss		99.46 17	99.49 16	99.35 76	99.90 4	98.15 135	99.20 48	99.65 67	99.48 44	99.92 8	99.71 22	98.07 117	99.96 14	99.53 47	100.00 1	99.93 11

testf1		99.25 41	99.16 61	99.51 48	99.89 6	99.63 4	98.71 104	99.69 54	98.90 132	99.43 102	99.35 103	98.86 34	99.67 314	97.81 173	99.81 127	99.24 268

APD_test2		99.25 41	99.16 61	99.51 48	99.89 6	99.63 4	98.71 104	99.69 54	98.90 132	99.43 102	99.35 103	98.86 34	99.67 314	97.81 173	99.81 127	99.24 268

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 42	99.31 61	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 54	98.93 128	99.65 63	99.72 21	98.93 32	99.95 26	99.11 77	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 76	99.66 24	99.68 57	99.66 32	98.44 77	99.95 26	99.73 27	99.96 28	99.75 58

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 73	99.90 14	99.74 18	99.68 4	99.97 7	99.55 42	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 50	99.98 2	99.75 16	99.80 1	99.97 7	99.82 12	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 63	99.09 107	99.89 18	99.68 25	99.53 7	99.97 7	99.50 50	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 97	99.11 97	99.70 51	99.73 20	99.00 27	99.97 7	99.26 65	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 81	99.59 36	99.71 49	99.57 49	97.12 197	99.90 80	99.21 70	99.87 96	99.54 137

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	99.81 217	99.30 62	99.97 21	99.77 49
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 83	99.90 3	99.86 24	99.78 13	99.58 6	99.95 26	99.00 87	99.95 38	99.78 46

SixPastTwentyTwo		98.75 126	98.62 139	99.16 114	99.83 18	97.96 162	99.28 40	98.20 367	99.37 60	99.70 51	99.65 36	92.65 346	99.93 53	99.04 84	99.84 110	99.60 98

sc_t1		99.62 7	99.66 8	99.53 38	99.82 19	99.09 68	99.50 11	99.63 71	99.88 4	99.86 24	99.80 11	99.03 24	99.89 96	99.48 52	99.93 55	99.60 98

Baseline_NR-MVSNet		98.98 87	98.86 105	99.36 70	99.82 19	98.55 103	97.47 286	99.57 90	99.37 60	99.21 155	99.61 43	96.76 223	99.83 191	98.06 151	99.83 117	99.71 61

pm-mvs1		99.44 19	99.48 18	99.33 85	99.80 21	98.63 95	99.29 36	99.63 71	99.30 70	99.65 63	99.60 45	99.16 22	99.82 201	99.07 80	99.83 117	99.56 124

TransMVSNet (Re)		99.44 19	99.47 21	99.36 70	99.80 21	98.58 101	99.27 42	99.57 90	99.39 58	99.75 44	99.62 40	99.17 20	99.83 191	99.06 82	99.62 231	99.66 76

K. test v3		98.00 240	97.66 265	99.03 141	99.79 23	97.56 198	99.19 52	92.47 453	99.62 32	99.52 84	99.66 32	89.61 377	99.96 14	99.25 67	99.81 127	99.56 124

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 65	99.97 3	99.66 32	99.71 3	99.96 14	99.79 19	99.99 5	99.96 8

APD_test1		98.83 110	98.66 132	99.34 79	99.78 24	99.47 9	98.42 144	99.45 144	98.28 184	98.98 188	99.19 145	97.76 145	99.58 361	96.57 276	99.55 258	98.97 322

test_vis3_rt		99.14 61	99.17 59	99.07 131	99.78 24	98.38 115	98.92 82	99.94 2	97.80 226	99.91 12	99.67 30	97.15 196	98.91 446	99.76 23	99.56 254	99.92 12

EGC-MVSNET		85.24 433	80.54 436	99.34 79	99.77 27	99.20 39	99.08 61	99.29 224	12.08 471	20.84 472	99.42 88	97.55 164	99.85 155	97.08 228	99.72 184	98.96 324

Anonymous20240521		98.69 138	98.87 101	98.16 285	99.77 27	95.11 326	99.08 61	99.44 152	99.34 64	99.33 125	99.55 57	94.10 321	99.94 42	99.25 67	99.96 28	99.42 199

FC-MVSNet-test		99.27 38	99.25 52	99.34 79	99.77 27	98.37 117	99.30 35	99.57 90	99.61 34	99.40 111	99.50 67	97.12 197	99.85 155	99.02 86	99.94 49	99.80 41

test_vis1_n		98.31 205	98.50 159	97.73 319	99.76 30	94.17 354	98.68 107	99.91 9	96.31 338	99.79 38	99.57 49	92.85 342	99.42 407	99.79 19	99.84 110	99.60 98

test_fmvs3		99.12 68	99.41 26	98.25 273	99.76 30	95.07 327	99.05 67	99.94 2	97.78 229	99.82 33	99.84 3	98.56 68	99.71 290	99.96 1	99.96 28	99.97 4

XXY-MVS		99.14 61	99.15 66	99.10 124	99.76 30	97.74 187	98.85 92	99.62 73	98.48 167	99.37 116	99.49 73	98.75 46	99.86 142	98.20 141	99.80 138	99.71 61

TDRefinement		99.42 24	99.38 29	99.55 28	99.76 30	99.33 21	99.68 6	99.71 47	99.38 59	99.53 82	99.61 43	98.64 56	99.80 225	98.24 136	99.84 110	99.52 149

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	98.22 187	99.88 21	99.71 22	98.59 62	99.84 173	99.73 27	99.98 12	99.98 3

tt0805		98.69 138	98.62 139	98.90 166	99.75 34	99.30 22	99.15 56	96.97 404	98.86 137	98.87 221	97.62 382	98.63 58	98.96 443	99.41 56	98.29 395	98.45 386

test_vis1_n_1920		98.40 188	98.92 94	96.81 381	99.74 36	90.76 432	98.15 170	99.91 9	98.33 175	99.89 18	99.55 57	95.07 292	99.88 114	99.76 23	99.93 55	99.79 43

FOURS1					99.73 37	99.67 3	99.43 15	99.54 106	99.43 54	99.26 143

PEN-MVS		99.41 25	99.34 36	99.62 9	99.73 37	99.14 57	99.29 36	99.54 106	99.62 32	99.56 73	99.42 88	98.16 111	99.96 14	98.78 101	99.93 55	99.77 49

lessismore_v0				98.97 153	99.73 37	97.53 200		86.71 468		99.37 116	99.52 66	89.93 373	99.92 64	98.99 88	99.72 184	99.44 192

SteuartSystems-ACMMP		98.79 119	98.54 152	99.54 31	99.73 37	99.16 48	98.23 160	99.31 208	97.92 217	98.90 210	98.90 231	98.00 123	99.88 114	96.15 308	99.72 184	99.58 113
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended_VisFu		98.17 226	98.15 218	98.22 279	99.73 37	95.15 323	97.36 299	99.68 59	94.45 394	98.99 187	99.27 122	96.87 212	99.94 42	97.13 225	99.91 77	99.57 118

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 97	99.27 139	99.48 74	98.82 37	99.95 26	98.94 91	99.93 55	99.59 105
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 80	99.54 43	99.95 38	99.61 96

SSC-MVS		98.71 130	98.74 116	98.62 215	99.72 43	96.08 284	98.74 97	98.64 347	99.74 13	99.67 59	99.24 135	94.57 307	99.95 26	99.11 77	99.24 319	99.82 35

test_f		98.67 146	98.87 101	98.05 294	99.72 43	95.59 299	98.51 128	99.81 31	96.30 340	99.78 39	99.82 5	96.14 251	98.63 453	99.82 12	99.93 55	99.95 9

ACMH	96.65 7	99.25 41	99.24 53	99.26 97	99.72 43	98.38 115	99.07 64	99.55 101	98.30 179	99.65 63	99.45 83	99.22 17	99.76 261	98.44 127	99.77 155	99.64 82
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 80	99.54 43	99.95 38	99.59 105

fmvsm_s_conf0.1_n		99.16 56	99.33 37	98.64 209	99.71 47	96.10 279	97.87 224	99.85 18	98.56 163	99.90 14	99.68 25	98.69 52	99.85 155	99.72 29	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	99.53 110	99.53 41	99.46 97	99.41 92	98.23 100	99.95 26	98.89 95	99.95 38	99.81 39

DTE-MVSNet		99.43 23	99.35 34	99.66 7	99.71 47	99.30 22	99.31 30	99.51 115	99.64 27	99.56 73	99.46 79	98.23 100	99.97 7	98.78 101	99.93 55	99.72 60

WR-MVS_H		99.33 31	99.22 54	99.65 8	99.71 47	99.24 30	99.32 26	99.55 101	99.46 49	99.50 90	99.34 107	97.30 186	99.93 53	98.90 93	99.93 55	99.77 49

HPM-MVS_fast		99.01 81	98.82 109	99.57 21	99.71 47	99.35 17	99.00 72	99.50 118	97.33 274	98.94 205	98.86 241	98.75 46	99.82 201	97.53 196	99.71 193	99.56 124

ACMH+	96.62 9	99.08 75	99.00 86	99.33 85	99.71 47	98.83 83	98.60 114	99.58 83	99.11 97	99.53 82	99.18 149	98.81 38	99.67 314	96.71 265	99.77 155	99.50 155

PMVS	91.26 20	97.86 254	97.94 242	97.65 326	99.71 47	97.94 164	98.52 123	98.68 343	98.99 120	97.52 351	99.35 103	97.41 179	98.18 459	91.59 422	99.67 214	96.82 448
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
KinetiMVS		99.03 79	99.02 82	99.03 141	99.70 55	97.48 203	98.43 141	99.29 224	99.70 16	99.60 70	99.07 178	96.13 252	99.94 42	99.42 55	99.87 96	99.68 69

FIs		99.14 61	99.09 74	99.29 91	99.70 55	98.28 123	99.13 58	99.52 114	99.48 44	99.24 149	99.41 92	96.79 220	99.82 201	98.69 111	99.88 92	99.76 54

VPNet		98.87 101	98.83 108	99.01 145	99.70 55	97.62 196	98.43 141	99.35 189	99.47 47	99.28 137	99.05 186	96.72 226	99.82 201	98.09 148	99.36 298	99.59 105

fmvsm_s_conf0.1_n_2		99.20 50	99.38 29	98.65 207	99.69 58	96.08 284	97.49 283	99.90 11	99.53 41	99.88 21	99.64 37	98.51 71	99.90 80	99.83 10	99.98 12	99.97 4

test_cas_vis1_n_1920		98.33 202	98.68 129	97.27 357	99.69 58	92.29 406	98.03 193	99.85 18	97.62 239	99.96 4	99.62 40	93.98 322	99.74 274	99.52 49	99.86 103	99.79 43

MP-MVS-pluss		98.57 162	98.23 206	99.60 15	99.69 58	99.35 17	97.16 319	99.38 175	94.87 384	98.97 192	98.99 208	98.01 122	99.88 114	97.29 212	99.70 200	99.58 113
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	99.48 127	99.69 18	99.63 66	99.68 25	99.03 24	99.96 14	97.97 161	99.92 68	99.57 118

sd_testset		99.28 37	99.31 41	99.19 108	99.68 61	98.06 151	99.41 17	99.30 216	99.69 18	99.63 66	99.68 25	99.25 16	99.96 14	97.25 215	99.92 68	99.57 118

test_fmvs1_n		98.09 231	98.28 197	97.52 343	99.68 61	93.47 385	98.63 110	99.93 5	95.41 372	99.68 57	99.64 37	91.88 356	99.48 394	99.82 12	99.87 96	99.62 88

CHOSEN 1792x2688		97.49 283	97.14 298	98.54 237	99.68 61	96.09 282	96.50 355	99.62 73	91.58 432	98.84 224	98.97 215	92.36 348	99.88 114	96.76 258	99.95 38	99.67 74

tfpnnormal		98.90 97	98.90 96	98.91 163	99.67 65	97.82 179	99.00 72	99.44 152	99.45 50	99.51 89	99.24 135	98.20 106	99.86 142	95.92 317	99.69 203	99.04 309

MTAPA		98.88 100	98.64 135	99.61 13	99.67 65	99.36 16	98.43 141	99.20 248	98.83 141	98.89 213	98.90 231	96.98 207	99.92 64	97.16 220	99.70 200	99.56 124

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 237	99.66 67	96.97 240	98.00 200	99.85 18	99.24 75	99.92 8	99.50 67	99.39 12	99.95 26	99.89 3	99.98 12	98.71 363

mvs5depth		99.30 34	99.59 12	98.44 251	99.65 68	95.35 315	99.82 3	99.94 2	99.83 7	99.42 106	99.94 2	98.13 114	99.96 14	99.63 35	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	98.70 146	99.78 39	99.11 168	98.79 42	99.95 26	99.85 6	99.96 28	99.83 32

WB-MVS		98.52 176	98.55 150	98.43 252	99.65 68	95.59 299	98.52 123	98.77 332	99.65 26	99.52 84	99.00 206	94.34 313	99.93 53	98.65 113	98.83 367	99.76 54

CP-MVSNet		99.21 48	99.09 74	99.56 26	99.65 68	98.96 77	99.13 58	99.34 195	99.42 55	99.33 125	99.26 128	97.01 205	99.94 42	98.74 106	99.93 55	99.79 43

HPM-MVS		98.79 119	98.53 154	99.59 19	99.65 68	99.29 24	99.16 54	99.43 158	96.74 320	98.61 257	98.38 327	98.62 59	99.87 133	96.47 288	99.67 214	99.59 105
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RPSCF		98.62 155	98.36 185	99.42 64	99.65 68	99.42 11	98.55 119	99.57 90	97.72 233	98.90 210	99.26 128	96.12 254	99.52 382	95.72 328	99.71 193	99.32 246

NormalMVS		98.26 212	97.97 239	99.15 117	99.64 74	97.83 174	98.28 154	99.43 158	99.24 75	98.80 232	98.85 244	89.76 375	99.94 42	98.04 153	99.67 214	99.68 69

lecture		99.25 41	99.12 69	99.62 9	99.64 74	99.40 12	98.89 87	99.51 115	99.19 87	99.37 116	99.25 133	98.36 82	99.88 114	98.23 138	99.67 214	99.59 105

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	98.73 143	99.82 33	99.09 176	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	97.68 252	99.84 22	99.29 71	99.92 8	99.57 49	99.60 5	99.96 14	99.74 26	99.98 12	99.89 16

TSAR-MVS + MP.		98.63 152	98.49 164	99.06 137	99.64 74	97.90 168	98.51 128	98.94 297	96.96 305	99.24 149	98.89 237	97.83 137	99.81 217	96.88 248	99.49 278	99.48 173
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PM-MVS		98.82 113	98.72 120	99.12 120	99.64 74	98.54 106	97.98 208	99.68 59	97.62 239	99.34 123	99.18 149	97.54 166	99.77 255	97.79 175	99.74 173	99.04 309

Elysia		99.15 57	99.14 67	99.18 109	99.63 80	97.92 165	98.50 130	99.43 158	99.67 21	99.70 51	99.13 164	96.66 229	99.98 4	99.54 43	99.96 28	99.64 82

StellarMVS		99.15 57	99.14 67	99.18 109	99.63 80	97.92 165	98.50 130	99.43 158	99.67 21	99.70 51	99.13 164	96.66 229	99.98 4	99.54 43	99.96 28	99.64 82

KD-MVS_self_test		99.25 41	99.18 58	99.44 63	99.63 80	99.06 70	98.69 106	99.54 106	99.31 68	99.62 69	99.53 63	97.36 183	99.86 142	99.24 69	99.71 193	99.39 212

EU-MVSNet		97.66 271	98.50 159	95.13 423	99.63 80	85.84 454	98.35 150	98.21 366	98.23 186	99.54 78	99.46 79	95.02 293	99.68 310	98.24 136	99.87 96	99.87 21

HyFIR lowres test		97.19 309	96.60 333	98.96 154	99.62 84	97.28 217	95.17 420	99.50 118	94.21 399	99.01 184	98.32 335	86.61 395	99.99 2	97.10 227	99.84 110	99.60 98

fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 151	99.59 85	97.18 228	97.44 289	99.83 25	99.56 39	99.91 12	99.34 107	99.36 13	99.93 53	99.83 10	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 57	99.92 8	99.41 92	99.51 8	99.95 26	99.84 9	99.97 21	99.87 21

FE-MVSNET		98.59 159	98.50 159	98.87 167	99.58 87	97.30 215	98.08 182	99.74 43	96.94 307	98.97 192	99.10 171	96.94 208	99.74 274	97.33 210	99.86 103	99.55 131

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	98.26 295	99.80 11	98.33 88	99.91 73	99.56 40	99.95 38	99.97 4

ACMMP_NAP		98.75 126	98.48 165	99.57 21	99.58 87	99.29 24	97.82 229	99.25 237	96.94 307	98.78 234	99.12 167	98.02 121	99.84 173	97.13 225	99.67 214	99.59 105

nrg030		99.40 26	99.35 34	99.54 31	99.58 87	99.13 60	98.98 75	99.48 127	99.68 20	99.46 97	99.26 128	98.62 59	99.73 281	99.17 74	99.92 68	99.76 54

VDDNet		98.21 219	97.95 240	99.01 145	99.58 87	97.74 187	99.01 70	97.29 395	99.67 21	98.97 192	99.50 67	90.45 370	99.80 225	97.88 168	99.20 327	99.48 173

COLMAP_ROB	96.50 10	98.99 84	98.85 107	99.41 66	99.58 87	99.10 65	98.74 97	99.56 97	99.09 107	99.33 125	99.19 145	98.40 79	99.72 289	95.98 315	99.76 168	99.42 199
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 78	99.93 6	99.30 116	99.42 11	99.96 14	99.85 6	99.99 5	99.29 255

ZNCC-MVS		98.68 143	98.40 177	99.54 31	99.57 93	99.21 33	98.46 138	99.29 224	97.28 280	98.11 307	98.39 325	98.00 123	99.87 133	96.86 251	99.64 225	99.55 131

MSP-MVS		98.40 188	98.00 234	99.61 13	99.57 93	99.25 29	98.57 117	99.35 189	97.55 250	99.31 133	97.71 375	94.61 306	99.88 114	96.14 309	99.19 330	99.70 66
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		98.32 203	98.39 180	98.13 286	99.57 93	95.54 302	97.78 235	99.49 125	97.37 271	99.19 157	97.65 379	98.96 29	99.49 391	96.50 287	98.99 355	99.34 237

MP-MVS		98.46 182	98.09 223	99.54 31	99.57 93	99.22 32	98.50 130	99.19 252	97.61 242	97.58 345	98.66 288	97.40 180	99.88 114	94.72 354	99.60 238	99.54 137
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LPG-MVS_test		98.71 130	98.46 169	99.47 60	99.57 93	98.97 73	98.23 160	99.48 127	96.60 325	99.10 167	99.06 179	98.71 50	99.83 191	95.58 335	99.78 149	99.62 88

LGP-MVS_train				99.47 60	99.57 93	98.97 73		99.48 127	96.60 325	99.10 167	99.06 179	98.71 50	99.83 191	95.58 335	99.78 149	99.62 88

IS-MVSNet		98.19 222	97.90 247	99.08 129	99.57 93	97.97 159	99.31 30	98.32 362	99.01 119	98.98 188	99.03 190	91.59 358	99.79 238	95.49 337	99.80 138	99.48 173

viewdifsd2359ckpt11		98.84 107	99.04 79	98.24 275	99.56 101	95.51 304	97.38 294	99.70 52	99.16 92	99.57 71	99.40 95	98.26 96	99.71 290	98.55 122	99.82 121	99.50 155

viewmsd2359difaftdt		98.84 107	99.04 79	98.24 275	99.56 101	95.51 304	97.38 294	99.70 52	99.16 92	99.57 71	99.40 95	98.26 96	99.71 290	98.55 122	99.82 121	99.50 155

dcpmvs_2		98.78 121	99.11 70	97.78 309	99.56 101	93.67 380	99.06 65	99.86 16	99.50 43	99.66 60	99.26 128	97.21 194	99.99 2	98.00 158	99.91 77	99.68 69

test_0402		98.76 125	98.71 123	98.93 159	99.56 101	98.14 137	98.45 140	99.34 195	99.28 72	98.95 198	98.91 228	98.34 87	99.79 238	95.63 332	99.91 77	98.86 341

EPP-MVSNet		98.30 206	98.04 230	99.07 131	99.56 101	97.83 174	99.29 36	98.07 373	99.03 117	98.59 261	99.13 164	92.16 352	99.90 80	96.87 249	99.68 208	99.49 162

ACMMP		98.75 126	98.50 159	99.52 44	99.56 101	99.16 48	98.87 88	99.37 179	97.16 295	98.82 228	99.01 202	97.71 148	99.87 133	96.29 300	99.69 203	99.54 137
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 70	99.20 57	98.78 184	99.55 107	96.59 260	97.79 234	99.82 30	98.21 189	99.81 36	99.53 63	98.46 75	99.84 173	99.70 32	99.97 21	99.90 15

fmvsm_s_conf0.5_n		99.09 71	99.26 50	98.61 218	99.55 107	96.09 282	97.74 245	99.81 31	98.55 164	99.85 27	99.55 57	98.60 61	99.84 173	99.69 34	99.98 12	99.89 16

FMVSNet1		99.17 52	99.17 59	99.17 111	99.55 107	98.24 126	99.20 48	99.44 152	99.21 80	99.43 102	99.55 57	97.82 140	99.86 142	98.42 129	99.89 90	99.41 202

Vis-MVSNet (Re-imp)		97.46 285	97.16 295	98.34 264	99.55 107	96.10 279	98.94 80	98.44 356	98.32 177	98.16 301	98.62 297	88.76 382	99.73 281	93.88 380	99.79 144	99.18 288

ACMM	96.08 12	98.91 95	98.73 118	99.48 56	99.55 107	99.14 57	98.07 186	99.37 179	97.62 239	99.04 180	98.96 218	98.84 36	99.79 238	97.43 205	99.65 223	99.49 162
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs2		98.70 135	98.97 90	97.89 302	99.54 112	94.05 357	98.55 119	99.92 7	96.78 318	99.72 47	99.78 13	96.60 233	99.67 314	99.91 2	99.90 84	99.94 10

mPP-MVS		98.64 150	98.34 188	99.54 31	99.54 112	99.17 44	98.63 110	99.24 242	97.47 258	98.09 309	98.68 283	97.62 157	99.89 96	96.22 303	99.62 231	99.57 118

XVG-ACMP-BASELINE		98.56 163	98.34 188	99.22 105	99.54 112	98.59 100	97.71 248	99.46 140	97.25 283	98.98 188	98.99 208	97.54 166	99.84 173	95.88 318	99.74 173	99.23 270

viewmacassd2359aftdt		98.86 104	98.87 101	98.83 172	99.53 115	97.32 214	97.70 250	99.64 69	98.22 187	99.25 147	99.27 122	98.40 79	99.61 347	97.98 160	99.87 96	99.55 131

region2R		98.69 138	98.40 177	99.54 31	99.53 115	99.17 44	98.52 123	99.31 208	97.46 263	98.44 280	98.51 311	97.83 137	99.88 114	96.46 289	99.58 247	99.58 113

PGM-MVS		98.66 147	98.37 184	99.55 28	99.53 115	99.18 43	98.23 160	99.49 125	97.01 304	98.69 245	98.88 238	98.00 123	99.89 96	95.87 321	99.59 242	99.58 113

Patchmatch-RL test		97.26 302	97.02 303	97.99 298	99.52 118	95.53 303	96.13 380	99.71 47	97.47 258	99.27 139	99.16 155	84.30 416	99.62 340	97.89 165	99.77 155	98.81 349

ACMMPR		98.70 135	98.42 175	99.54 31	99.52 118	99.14 57	98.52 123	99.31 208	97.47 258	98.56 267	98.54 306	97.75 146	99.88 114	96.57 276	99.59 242	99.58 113

fmvsm_s_conf0.5_n_9		99.17 52	99.38 29	98.53 239	99.51 120	95.82 294	97.62 263	99.78 36	99.72 15	99.90 14	99.48 74	98.66 54	99.89 96	99.85 6	99.93 55	99.89 16

AstraMVS		98.16 228	98.07 228	98.41 254	99.51 120	95.86 291	98.00 200	95.14 436	98.97 123	99.43 102	99.24 135	93.25 330	99.84 173	99.21 70	99.87 96	99.54 137

fmvsm_s_conf0.5_n_8		99.13 65	99.26 50	98.74 196	99.51 120	96.44 271	97.65 258	99.65 67	99.66 24	99.78 39	99.48 74	97.92 131	99.93 53	99.72 29	99.95 38	99.87 21

GST-MVS		98.61 156	98.30 194	99.52 44	99.51 120	99.20 39	98.26 158	99.25 237	97.44 266	98.67 248	98.39 325	97.68 149	99.85 155	96.00 313	99.51 269	99.52 149

Anonymous20231206		98.21 219	98.21 207	98.20 280	99.51 120	95.43 313	98.13 172	99.32 203	96.16 344	98.93 206	98.82 254	96.00 259	99.83 191	97.32 211	99.73 176	99.36 230

ACMP	95.32 15	98.41 186	98.09 223	99.36 70	99.51 120	98.79 86	97.68 252	99.38 175	95.76 359	98.81 230	98.82 254	98.36 82	99.82 201	94.75 351	99.77 155	99.48 173
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LuminaMVS		98.39 194	98.20 208	98.98 151	99.50 126	97.49 201	97.78 235	97.69 382	98.75 142	99.49 91	99.25 133	92.30 350	99.94 42	99.14 75	99.88 92	99.50 155

DVP-MVS		98.77 124	98.52 155	99.52 44	99.50 126	99.21 33	98.02 196	98.84 321	97.97 211	99.08 169	99.02 191	97.61 159	99.88 114	96.99 235	99.63 228	99.48 173
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	99.32 203					99.88 114	96.99 235	99.63 228	99.68 69

test0726					99.50 126	99.21 33	98.17 168	99.35 189	97.97 211	99.26 143	99.06 179	97.61 159

AllTest		98.44 184	98.20 208	99.16 114	99.50 126	98.55 103	98.25 159	99.58 83	96.80 316	98.88 217	99.06 179	97.65 152	99.57 363	94.45 361	99.61 236	99.37 223

TestCases				99.16 114	99.50 126	98.55 103		99.58 83	96.80 316	98.88 217	99.06 179	97.65 152	99.57 363	94.45 361	99.61 236	99.37 223

XVG-OURS		98.53 172	98.34 188	99.11 122	99.50 126	98.82 85	95.97 386	99.50 118	97.30 278	99.05 178	98.98 213	99.35 14	99.32 421	95.72 328	99.68 208	99.18 288

EG-PatchMatch MVS		98.99 84	99.01 84	98.94 157	99.50 126	97.47 204	98.04 191	99.59 81	98.15 204	99.40 111	99.36 102	98.58 67	99.76 261	98.78 101	99.68 208	99.59 105

fmvsm_s_conf0.5_n_2		99.14 61	99.31 41	98.63 213	99.49 134	96.08 284	97.38 294	99.81 31	99.48 44	99.84 30	99.57 49	98.46 75	99.89 96	99.82 12	99.97 21	99.91 13

SED-MVS		98.91 95	98.72 120	99.49 54	99.49 134	99.17 44	98.10 179	99.31 208	98.03 207	99.66 60	99.02 191	98.36 82	99.88 114	96.91 241	99.62 231	99.41 202

IU-MVS					99.49 134	99.15 52		98.87 312	92.97 417	99.41 108				96.76 258	99.62 231	99.66 76

test_241102_ONE					99.49 134	99.17 44		99.31 208	97.98 210	99.66 60	98.90 231	98.36 82	99.48 394

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	99.04 180	99.81 8	98.05 120	99.96 14	98.85 97	99.99 5	99.86 27

HFP-MVS		98.71 130	98.44 172	99.51 48	99.49 134	99.16 48	98.52 123	99.31 208	97.47 258	98.58 263	98.50 315	97.97 127	99.85 155	96.57 276	99.59 242	99.53 146

VPA-MVSNet		99.30 34	99.30 44	99.28 92	99.49 134	98.36 120	99.00 72	99.45 144	99.63 29	99.52 84	99.44 84	98.25 98	99.88 114	99.09 79	99.84 110	99.62 88

XVG-OURS-SEG-HR		98.49 179	98.28 197	99.14 118	99.49 134	98.83 83	96.54 351	99.48 127	97.32 276	99.11 164	98.61 299	99.33 15	99.30 424	96.23 302	98.38 391	99.28 257

114514_t		96.50 342	95.77 351	98.69 202	99.48 142	97.43 208	97.84 228	99.55 101	81.42 464	96.51 404	98.58 303	95.53 279	99.67 314	93.41 393	99.58 247	98.98 319

IterMVS-LS		98.55 167	98.70 126	98.09 287	99.48 142	94.73 337	97.22 313	99.39 173	98.97 123	99.38 114	99.31 115	96.00 259	99.93 53	98.58 116	99.97 21	99.60 98
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 57	99.27 47	98.78 184	99.47 144	96.56 265	97.75 244	99.71 47	99.60 35	99.74 46	99.44 84	97.96 128	99.95 26	99.86 4	99.94 49	99.82 35

fmvsm_s_conf0.5_n_5		99.07 77	99.10 72	98.99 147	99.47 144	97.22 222	97.40 291	99.83 25	97.61 242	99.85 27	99.30 116	98.80 40	99.95 26	99.71 31	99.90 84	99.78 46

v8		99.01 81	99.16 61	98.57 225	99.47 144	96.31 276	98.90 83	99.47 136	99.03 117	99.52 84	99.57 49	96.93 209	99.81 217	99.60 36	99.98 12	99.60 98

SSC-MVS3.2		98.53 172	98.79 112	97.74 316	99.46 147	93.62 383	96.45 357	99.34 195	99.33 65	98.93 206	98.70 279	97.90 132	99.90 80	99.12 76	99.92 68	99.69 68

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 184	99.46 147	96.58 263	97.65 258	99.72 45	99.47 47	99.86 24	99.50 67	98.94 30	99.89 96	99.75 25	99.97 21	99.86 27

XVS		98.72 129	98.45 170	99.53 38	99.46 147	99.21 33	98.65 108	99.34 195	98.62 153	97.54 349	98.63 295	97.50 172	99.83 191	96.79 254	99.53 264	99.56 124

X-MVStestdata		94.32 391	92.59 410	99.53 38	99.46 147	99.21 33	98.65 108	99.34 195	98.62 153	97.54 349	45.85 469	97.50 172	99.83 191	96.79 254	99.53 264	99.56 124

test20.03		98.78 121	98.77 115	98.78 184	99.46 147	97.20 225	97.78 235	99.24 242	99.04 116	99.41 108	98.90 231	97.65 152	99.76 261	97.70 184	99.79 144	99.39 212

guyue		98.01 239	97.93 244	98.26 271	99.45 152	95.48 308	98.08 182	96.24 419	98.89 134	99.34 123	99.14 162	91.32 362	99.82 201	99.07 80	99.83 117	99.48 173

CSCG		98.68 143	98.50 159	99.20 106	99.45 152	98.63 95	98.56 118	99.57 90	97.87 221	98.85 222	98.04 356	97.66 151	99.84 173	96.72 263	99.81 127	99.13 298

GeoE		99.05 78	98.99 88	99.25 100	99.44 154	98.35 121	98.73 101	99.56 97	98.42 170	98.91 209	98.81 257	98.94 30	99.91 73	98.35 131	99.73 176	99.49 162

v148		98.45 183	98.60 144	98.00 297	99.44 154	94.98 329	97.44 289	99.06 278	98.30 179	99.32 131	98.97 215	96.65 231	99.62 340	98.37 130	99.85 105	99.39 212

v10		98.97 88	99.11 70	98.55 232	99.44 154	96.21 278	98.90 83	99.55 101	98.73 143	99.48 92	99.60 45	96.63 232	99.83 191	99.70 32	99.99 5	99.61 96

V42		98.78 121	98.78 114	98.76 190	99.44 154	97.04 236	98.27 157	99.19 252	97.87 221	99.25 147	99.16 155	96.84 213	99.78 249	99.21 70	99.84 110	99.46 183

MDA-MVSNet-bldmvs		97.94 245	97.91 246	98.06 292	99.44 154	94.96 330	96.63 347	99.15 268	98.35 173	98.83 225	99.11 168	94.31 314	99.85 155	96.60 273	98.72 373	99.37 223

viewdifsd2359ckpt07		98.71 130	98.86 105	98.26 271	99.43 159	95.65 298	97.20 314	99.66 63	99.20 82	99.29 135	99.01 202	98.29 91	99.73 281	97.92 164	99.75 172	99.39 212

casdiffmvs_mvg		99.12 68	99.16 61	98.99 147	99.43 159	97.73 189	98.00 200	99.62 73	99.22 78	99.55 76	99.22 141	98.93 32	99.75 269	98.66 112	99.81 127	99.50 155
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 97	99.01 84	98.57 225	99.42 161	96.59 260	98.13 172	99.66 63	99.09 107	99.30 134	99.02 191	98.79 42	99.89 96	97.87 170	99.80 138	99.23 270

test1111		96.49 343	96.82 317	95.52 416	99.42 161	87.08 451	99.22 45	87.14 467	99.11 97	99.46 97	99.58 47	88.69 383	99.86 142	98.80 99	99.95 38	99.62 88

v2v482		98.56 163	98.62 139	98.37 261	99.42 161	95.81 295	97.58 271	99.16 263	97.90 219	99.28 137	99.01 202	95.98 264	99.79 238	99.33 59	99.90 84	99.51 152

OPM-MVS		98.56 163	98.32 192	99.25 100	99.41 164	98.73 91	97.13 321	99.18 256	97.10 298	98.75 240	98.92 226	98.18 107	99.65 331	96.68 267	99.56 254	99.37 223
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PMMVS2		98.07 233	98.08 226	98.04 295	99.41 164	94.59 343	94.59 438	99.40 171	97.50 255	98.82 228	98.83 251	96.83 215	99.84 173	97.50 199	99.81 127	99.71 61

test_one_0601					99.39 166	99.20 39		99.31 208	98.49 166	98.66 250	99.02 191	97.64 155

mvsany_test3		98.87 101	98.92 94	98.74 196	99.38 167	96.94 244	98.58 116	99.10 273	96.49 330	99.96 4	99.81 8	98.18 107	99.45 402	98.97 89	99.79 144	99.83 32

patch_mono-2		98.51 177	98.63 137	98.17 283	99.38 167	94.78 334	97.36 299	99.69 54	98.16 199	98.49 276	99.29 119	97.06 200	99.97 7	98.29 135	99.91 77	99.76 54

test2506		92.39 422	91.89 424	93.89 437	99.38 167	82.28 468	99.32 26	66.03 475	99.08 111	98.77 237	99.57 49	66.26 463	99.84 173	98.71 109	99.95 38	99.54 137

ECVR-MVS		96.42 345	96.61 331	95.85 408	99.38 167	88.18 446	99.22 45	86.00 469	99.08 111	99.36 119	99.57 49	88.47 388	99.82 201	98.52 124	99.95 38	99.54 137

casdiffmvs		98.95 91	99.00 86	98.81 176	99.38 167	97.33 212	97.82 229	99.57 90	99.17 91	99.35 121	99.17 153	98.35 86	99.69 301	98.46 126	99.73 176	99.41 202
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 90	99.02 82	98.76 190	99.38 167	97.26 219	98.49 133	99.50 118	98.86 137	99.19 157	99.06 179	98.23 100	99.69 301	98.71 109	99.76 168	99.33 243

TranMVSNet+NR-MVSNet		99.17 52	99.07 77	99.46 62	99.37 173	98.87 81	98.39 146	99.42 164	99.42 55	99.36 119	99.06 179	98.38 81	99.95 26	98.34 132	99.90 84	99.57 118

fmvsm_s_conf0.5_n_6		99.08 75	99.21 56	98.69 202	99.36 174	96.51 266	97.62 263	99.68 59	98.43 169	99.85 27	99.10 171	99.12 23	99.88 114	99.77 22	99.92 68	99.67 74

tttt0517		95.64 370	94.98 380	97.64 329	99.36 174	93.81 375	98.72 102	90.47 461	98.08 206	98.67 248	98.34 332	73.88 449	99.92 64	97.77 177	99.51 269	99.20 280

test_part2					99.36 174	99.10 65				99.05 178

v1144		98.60 157	98.66 132	98.41 254	99.36 174	95.90 289	97.58 271	99.34 195	97.51 254	99.27 139	99.15 159	96.34 246	99.80 225	99.47 53	99.93 55	99.51 152

CP-MVS		98.70 135	98.42 175	99.52 44	99.36 174	99.12 62	98.72 102	99.36 183	97.54 252	98.30 289	98.40 324	97.86 136	99.89 96	96.53 285	99.72 184	99.56 124

diffmvs_AUTHOR		98.50 178	98.59 146	98.23 278	99.35 179	95.48 308	96.61 348	99.60 77	98.37 171	98.90 210	99.00 206	97.37 182	99.76 261	98.22 139	99.85 105	99.46 183

Test_1112_low_res		96.99 324	96.55 335	98.31 267	99.35 179	95.47 311	95.84 398	99.53 110	91.51 434	96.80 391	98.48 318	91.36 361	99.83 191	96.58 274	99.53 264	99.62 88

DeepC-MVS	97.60 4	98.97 88	98.93 93	99.10 124	99.35 179	97.98 158	98.01 199	99.46 140	97.56 248	99.54 78	99.50 67	98.97 28	99.84 173	98.06 151	99.92 68	99.49 162
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
1112_ss		97.29 301	96.86 313	98.58 222	99.34 182	96.32 275	96.75 340	99.58 83	93.14 415	96.89 386	97.48 389	92.11 353	99.86 142	96.91 241	99.54 260	99.57 118

reproduce_model		99.15 57	98.97 90	99.67 4	99.33 183	99.44 10	98.15 170	99.47 136	99.12 96	99.52 84	99.32 114	98.31 89	99.90 80	97.78 176	99.73 176	99.66 76

MVSMamba_PlusPlus		98.83 110	98.98 89	98.36 262	99.32 184	96.58 263	98.90 83	99.41 168	99.75 11	98.72 243	99.50 67	96.17 250	99.94 42	99.27 64	99.78 149	98.57 379

fmvsm_s_conf0.5_n_4		99.01 81	99.22 54	98.38 258	99.31 185	95.48 308	97.56 273	99.73 44	98.87 135	99.75 44	99.27 122	98.80 40	99.86 142	99.80 17	99.90 84	99.81 39

SF-MVS		98.53 172	98.27 200	99.32 87	99.31 185	98.75 87	98.19 164	99.41 168	96.77 319	98.83 225	98.90 231	97.80 142	99.82 201	95.68 331	99.52 267	99.38 221

CPTT-MVS		97.84 260	97.36 284	99.27 95	99.31 185	98.46 111	98.29 153	99.27 231	94.90 383	97.83 329	98.37 328	94.90 295	99.84 173	93.85 382	99.54 260	99.51 152

UnsupCasMVSNet_eth		97.89 249	97.60 270	98.75 192	99.31 185	97.17 230	97.62 263	99.35 189	98.72 145	98.76 239	98.68 283	92.57 347	99.74 274	97.76 181	95.60 453	99.34 237

fmvsm_s_conf0.5_n_7		98.83 110	99.04 79	98.20 280	99.30 189	94.83 332	97.23 309	99.36 183	98.64 148	99.84 30	99.43 87	98.10 116	99.91 73	99.56 40	99.96 28	99.87 21

pmmvs-eth3d		98.47 181	98.34 188	98.86 169	99.30 189	97.76 185	97.16 319	99.28 228	95.54 365	99.42 106	99.19 145	97.27 189	99.63 337	97.89 165	99.97 21	99.20 280

mamv4		99.44 19	99.39 28	99.58 20	99.30 189	99.74 2	99.04 68	99.81 31	99.77 10	99.82 33	99.57 49	97.82 140	99.98 4	99.53 47	99.89 90	99.01 313

viewcassd2359sk11		98.55 167	98.51 156	98.67 205	99.29 192	96.99 239	97.39 292	99.54 106	97.73 231	98.81 230	99.08 177	97.55 164	99.66 325	97.52 198	99.67 214	99.36 230

SymmetryMVS		98.05 235	97.71 260	99.09 128	99.29 192	97.83 174	98.28 154	97.64 387	99.24 75	98.80 232	98.85 244	89.76 375	99.94 42	98.04 153	99.50 276	99.49 162

Anonymous20231211		99.27 38	99.27 47	99.26 97	99.29 192	98.18 133	99.49 12	99.51 115	99.70 16	99.80 37	99.68 25	96.84 213	99.83 191	99.21 70	99.91 77	99.77 49

viewmanbaseed2359cas		98.58 161	98.54 152	98.70 200	99.28 195	97.13 234	97.47 286	99.55 101	97.55 250	98.96 197	98.92 226	97.77 144	99.59 354	97.59 192	99.77 155	99.39 212

UnsupCasMVSNet_bld		97.30 299	96.92 309	98.45 249	99.28 195	96.78 254	96.20 374	99.27 231	95.42 369	98.28 293	98.30 336	93.16 333	99.71 290	94.99 345	97.37 429	98.87 340

EC-MVSNet		99.09 71	99.05 78	99.20 106	99.28 195	98.93 79	99.24 44	99.84 22	99.08 111	98.12 306	98.37 328	98.72 49	99.90 80	99.05 83	99.77 155	98.77 357

mamba_0408		98.80 117	98.88 99	98.55 232	99.27 198	96.50 267	98.00 200	99.60 77	98.93 128	99.22 152	98.84 249	98.59 62	99.89 96	97.74 182	99.72 184	99.27 258

SSM_04072		98.80 117	98.88 99	98.56 230	99.27 198	96.50 267	98.00 200	99.60 77	98.93 128	99.22 152	98.84 249	98.59 62	99.90 80	97.74 182	99.72 184	99.27 258

SSM_0407		98.86 104	98.96 92	98.55 232	99.27 198	96.50 267	98.04 191	99.66 63	99.09 107	99.22 152	99.02 191	98.79 42	99.87 133	97.87 170	99.72 184	99.27 258

reproduce-ours		99.09 71	98.90 96	99.67 4	99.27 198	99.49 6	98.00 200	99.42 164	99.05 114	99.48 92	99.27 122	98.29 91	99.89 96	97.61 189	99.71 193	99.62 88

our_new_method		99.09 71	98.90 96	99.67 4	99.27 198	99.49 6	98.00 200	99.42 164	99.05 114	99.48 92	99.27 122	98.29 91	99.89 96	97.61 189	99.71 193	99.62 88

DPE-MVS		98.59 159	98.26 201	99.57 21	99.27 198	99.15 52	97.01 324	99.39 173	97.67 235	99.44 101	98.99 208	97.53 168	99.89 96	95.40 339	99.68 208	99.66 76
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
IterMVS-SCA-FT		97.85 259	98.18 213	96.87 377	99.27 198	91.16 426	95.53 408	99.25 237	99.10 104	99.41 108	99.35 103	93.10 335	99.96 14	98.65 113	99.94 49	99.49 162

v1192		98.60 157	98.66 132	98.41 254	99.27 198	95.88 290	97.52 278	99.36 183	97.41 267	99.33 125	99.20 144	96.37 244	99.82 201	99.57 38	99.92 68	99.55 131

N_pmnet		97.63 273	97.17 294	98.99 147	99.27 198	97.86 171	95.98 385	93.41 450	95.25 374	99.47 96	98.90 231	95.63 276	99.85 155	96.91 241	99.73 176	99.27 258

viewdifsd2359ckpt13		98.39 194	98.29 196	98.70 200	99.26 207	97.19 226	97.51 280	99.48 127	96.94 307	98.58 263	98.82 254	97.47 177	99.55 370	97.21 217	99.33 303	99.34 237

FPMVS		93.44 408	92.23 415	97.08 365	99.25 208	97.86 171	95.61 405	97.16 399	92.90 419	93.76 452	98.65 290	75.94 447	95.66 466	79.30 464	97.49 422	97.73 430

new-patchmatchnet		98.35 197	98.74 116	97.18 360	99.24 209	92.23 408	96.42 361	99.48 127	98.30 179	99.69 55	99.53 63	97.44 178	99.82 201	98.84 98	99.77 155	99.49 162

MCST-MVS		98.00 240	97.63 268	99.10 124	99.24 209	98.17 134	96.89 333	98.73 340	95.66 360	97.92 320	97.70 377	97.17 195	99.66 325	96.18 307	99.23 322	99.47 181

UniMVSNet (Re)		98.87 101	98.71 123	99.35 76	99.24 209	98.73 91	97.73 247	99.38 175	98.93 128	99.12 163	98.73 269	96.77 221	99.86 142	98.63 115	99.80 138	99.46 183

jason		97.45 287	97.35 285	97.76 313	99.24 209	93.93 369	95.86 395	98.42 358	94.24 398	98.50 275	98.13 346	94.82 299	99.91 73	97.22 216	99.73 176	99.43 196
jason: jason.
IterMVS		97.73 265	98.11 222	96.57 387	99.24 209	90.28 435	95.52 410	99.21 246	98.86 137	99.33 125	99.33 110	93.11 334	99.94 42	98.49 125	99.94 49	99.48 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		98.55 167	98.62 139	98.32 265	99.22 214	95.58 301	97.51 280	99.45 144	97.16 295	99.45 100	99.24 135	96.12 254	99.85 155	99.60 36	99.88 92	99.55 131

ITE_SJBPF				98.87 167	99.22 214	98.48 110		99.35 189	97.50 255	98.28 293	98.60 301	97.64 155	99.35 417	93.86 381	99.27 314	98.79 355

h-mvs33		97.77 263	97.33 287	99.10 124	99.21 216	97.84 173	98.35 150	98.57 350	99.11 97	98.58 263	99.02 191	88.65 386	99.96 14	98.11 146	96.34 445	99.49 162

v144192		98.54 170	98.57 148	98.45 249	99.21 216	95.98 287	97.63 262	99.36 183	97.15 297	99.32 131	99.18 149	95.84 271	99.84 173	99.50 50	99.91 77	99.54 137

APDe-MVS		98.99 84	98.79 112	99.60 15	99.21 216	99.15 52	98.87 88	99.48 127	97.57 246	99.35 121	99.24 135	97.83 137	99.89 96	97.88 168	99.70 200	99.75 58
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DP-MVS		98.93 93	98.81 111	99.28 92	99.21 216	98.45 112	98.46 138	99.33 201	99.63 29	99.48 92	99.15 159	97.23 192	99.75 269	97.17 219	99.66 222	99.63 87

SR-MVS-dyc-post		98.81 115	98.55 150	99.57 21	99.20 220	99.38 13	98.48 136	99.30 216	98.64 148	98.95 198	98.96 218	97.49 175	99.86 142	96.56 280	99.39 294	99.45 188

RE-MVS-def			98.58 147		99.20 220	99.38 13	98.48 136	99.30 216	98.64 148	98.95 198	98.96 218	97.75 146		96.56 280	99.39 294	99.45 188

v1921920		98.54 170	98.60 144	98.38 258	99.20 220	95.76 297	97.56 273	99.36 183	97.23 289	99.38 114	99.17 153	96.02 257	99.84 173	99.57 38	99.90 84	99.54 137

thisisatest0530		95.27 377	94.45 388	97.74 316	99.19 223	94.37 347	97.86 225	90.20 462	97.17 294	98.22 296	97.65 379	73.53 450	99.90 80	96.90 246	99.35 300	98.95 325

Anonymous20240529		98.93 93	98.87 101	99.12 120	99.19 223	98.22 131	99.01 70	98.99 295	99.25 74	99.54 78	99.37 98	97.04 201	99.80 225	97.89 165	99.52 267	99.35 235

APD-MVS_3200maxsize		98.84 107	98.61 143	99.53 38	99.19 223	99.27 27	98.49 133	99.33 201	98.64 148	99.03 183	98.98 213	97.89 134	99.85 155	96.54 284	99.42 291	99.46 183

HQP_MVS		97.99 243	97.67 262	98.93 159	99.19 223	97.65 193	97.77 238	99.27 231	98.20 193	97.79 332	97.98 360	94.90 295	99.70 297	94.42 363	99.51 269	99.45 188

plane_prior7					99.19 223	97.87 170

ab-mvs		98.41 186	98.36 185	98.59 221	99.19 223	97.23 220	99.32 26	98.81 326	97.66 236	98.62 255	99.40 95	96.82 216	99.80 225	95.88 318	99.51 269	98.75 360

F-COLMAP		97.30 299	96.68 326	99.14 118	99.19 223	98.39 114	97.27 308	99.30 216	92.93 418	96.62 397	98.00 358	95.73 274	99.68 310	92.62 409	98.46 390	99.35 235

SR-MVS		98.71 130	98.43 173	99.57 21	99.18 230	99.35 17	98.36 149	99.29 224	98.29 182	98.88 217	98.85 244	97.53 168	99.87 133	96.14 309	99.31 307	99.48 173

UniMVSNet_NR-MVSNet		98.86 104	98.68 129	99.40 68	99.17 231	98.74 88	97.68 252	99.40 171	99.14 95	99.06 171	98.59 302	96.71 227	99.93 53	98.57 118	99.77 155	99.53 146

LF4IMVS		97.90 247	97.69 261	98.52 240	99.17 231	97.66 192	97.19 318	99.47 136	96.31 338	97.85 328	98.20 343	96.71 227	99.52 382	94.62 355	99.72 184	98.38 396

SMA-MVS		98.40 188	98.03 231	99.51 48	99.16 233	99.21 33	98.05 189	99.22 245	94.16 400	98.98 188	99.10 171	97.52 170	99.79 238	96.45 290	99.64 225	99.53 146
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		98.82 113	98.63 137	99.39 69	99.16 233	98.74 88	97.54 276	99.25 237	98.84 140	99.06 171	98.76 266	96.76 223	99.93 53	98.57 118	99.77 155	99.50 155

NR-MVSNet		98.95 91	98.82 109	99.36 70	99.16 233	98.72 93	99.22 45	99.20 248	99.10 104	99.72 47	98.76 266	96.38 243	99.86 142	98.00 158	99.82 121	99.50 155

MVS_111021_LR		98.30 206	98.12 221	98.83 172	99.16 233	98.03 153	96.09 382	99.30 216	97.58 245	98.10 308	98.24 339	98.25 98	99.34 418	96.69 266	99.65 223	99.12 299

DSMNet-mixed		97.42 290	97.60 270	96.87 377	99.15 237	91.46 415	98.54 121	99.12 270	92.87 420	97.58 345	99.63 39	96.21 249	99.90 80	95.74 327	99.54 260	99.27 258

D2MVS		97.84 260	97.84 251	97.83 305	99.14 238	94.74 336	96.94 328	98.88 310	95.84 357	98.89 213	98.96 218	94.40 311	99.69 301	97.55 193	99.95 38	99.05 305

pmmvs5		97.64 272	97.49 276	98.08 290	99.14 238	95.12 325	96.70 343	99.05 281	93.77 407	98.62 255	98.83 251	93.23 331	99.75 269	98.33 134	99.76 168	99.36 230

SPE-MVS-test		99.13 65	99.09 74	99.26 97	99.13 240	98.97 73	99.31 30	99.88 14	99.44 52	98.16 301	98.51 311	98.64 56	99.93 53	98.91 92	99.85 105	98.88 339

VDD-MVS		98.56 163	98.39 180	99.07 131	99.13 240	98.07 148	98.59 115	97.01 402	99.59 36	99.11 164	99.27 122	94.82 299	99.79 238	98.34 132	99.63 228	99.34 237

save fliter					99.11 242	97.97 159	96.53 353	99.02 289	98.24 185

APD-MVS		98.10 229	97.67 262	99.42 64	99.11 242	98.93 79	97.76 241	99.28 228	94.97 381	98.72 243	98.77 264	97.04 201	99.85 155	93.79 383	99.54 260	99.49 162
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EI-MVSNet-UG-set		98.69 138	98.71 123	98.62 215	99.10 244	96.37 273	97.23 309	98.87 312	99.20 82	99.19 157	98.99 208	97.30 186	99.85 155	98.77 104	99.79 144	99.65 81

EI-MVSNet		98.40 188	98.51 156	98.04 295	99.10 244	94.73 337	97.20 314	98.87 312	98.97 123	99.06 171	99.02 191	96.00 259	99.80 225	98.58 116	99.82 121	99.60 98

CVMVSNet		96.25 351	97.21 293	93.38 444	99.10 244	80.56 472	97.20 314	98.19 369	96.94 307	99.00 185	99.02 191	89.50 379	99.80 225	96.36 296	99.59 242	99.78 46

EI-MVSNet-Vis-set		98.68 143	98.70 126	98.63 213	99.09 247	96.40 272	97.23 309	98.86 317	99.20 82	99.18 161	98.97 215	97.29 188	99.85 155	98.72 108	99.78 149	99.64 82

HPM-MVS++		98.10 229	97.64 267	99.48 56	99.09 247	99.13 60	97.52 278	98.75 337	97.46 263	96.90 385	97.83 370	96.01 258	99.84 173	95.82 325	99.35 300	99.46 183

DP-MVS Recon		97.33 297	96.92 309	98.57 225	99.09 247	97.99 155	96.79 336	99.35 189	93.18 414	97.71 336	98.07 354	95.00 294	99.31 422	93.97 376	99.13 338	98.42 393

MVS_111021_HR		98.25 215	98.08 226	98.75 192	99.09 247	97.46 205	95.97 386	99.27 231	97.60 244	97.99 318	98.25 338	98.15 113	99.38 413	96.87 249	99.57 251	99.42 199

BP-MVS1		97.40 292	96.97 305	98.71 199	99.07 251	96.81 250	98.34 152	97.18 397	98.58 159	98.17 298	98.61 299	84.01 418	99.94 42	98.97 89	99.78 149	99.37 223

9.14			97.78 253		99.07 251		97.53 277	99.32 203	95.53 366	98.54 271	98.70 279	97.58 161	99.76 261	94.32 368	99.46 281

PAPM_NR		96.82 331	96.32 342	98.30 268	99.07 251	96.69 258	97.48 284	98.76 334	95.81 358	96.61 398	96.47 415	94.12 320	99.17 435	90.82 436	97.78 416	99.06 304

TAMVS		98.24 216	98.05 229	98.80 178	99.07 251	97.18 228	97.88 221	98.81 326	96.66 324	99.17 162	99.21 142	94.81 301	99.77 255	96.96 239	99.88 92	99.44 192

CLD-MVS		97.49 283	97.16 295	98.48 246	99.07 251	97.03 237	94.71 431	99.21 246	94.46 392	98.06 311	97.16 401	97.57 162	99.48 394	94.46 360	99.78 149	98.95 325
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 65	99.10 72	99.24 102	99.06 256	99.15 52	99.36 22	99.88 14	99.36 63	98.21 297	98.46 319	98.68 53	99.93 53	99.03 85	99.85 105	98.64 372

thres100view900		94.19 394	93.67 399	95.75 411	99.06 256	91.35 419	98.03 193	94.24 445	98.33 175	97.40 361	94.98 445	79.84 434	99.62 340	83.05 457	98.08 407	96.29 452

thres600view7		94.45 389	93.83 396	96.29 395	99.06 256	91.53 414	97.99 207	94.24 445	98.34 174	97.44 359	95.01 443	79.84 434	99.67 314	84.33 455	98.23 396	97.66 433

plane_prior1					99.05 259

YYNet1		97.60 274	97.67 262	97.39 353	99.04 260	93.04 392	95.27 417	98.38 361	97.25 283	98.92 208	98.95 222	95.48 283	99.73 281	96.99 235	98.74 371	99.41 202

MDA-MVSNet_test_wron		97.60 274	97.66 265	97.41 352	99.04 260	93.09 388	95.27 417	98.42 358	97.26 282	98.88 217	98.95 222	95.43 284	99.73 281	97.02 232	98.72 373	99.41 202

MIMVSNet		96.62 338	96.25 346	97.71 320	99.04 260	94.66 340	99.16 54	96.92 408	97.23 289	97.87 325	99.10 171	86.11 401	99.65 331	91.65 420	99.21 326	98.82 344

icg_test_0407_2		98.20 221	98.38 182	97.65 326	99.03 263	94.03 360	95.78 400	99.45 144	98.16 199	99.06 171	98.71 272	98.27 94	99.68 310	97.50 199	99.45 283	99.22 275

IMVS_0407		98.39 194	98.64 135	97.66 324	99.03 263	94.03 360	98.10 179	99.45 144	98.16 199	99.06 171	98.71 272	98.27 94	99.71 290	97.50 199	99.45 283	99.22 275

IMVS_0404		98.07 233	98.20 208	97.69 321	99.03 263	94.03 360	96.67 344	99.45 144	98.16 199	98.03 315	98.71 272	96.80 219	99.82 201	97.50 199	99.45 283	99.22 275

IMVS_0403		98.34 198	98.56 149	97.66 324	99.03 263	94.03 360	97.98 208	99.45 144	98.16 199	98.89 213	98.71 272	97.90 132	99.74 274	97.50 199	99.45 283	99.22 275

PatchMatch-RL		97.24 305	96.78 320	98.61 218	99.03 263	97.83 174	96.36 364	99.06 278	93.49 412	97.36 365	97.78 371	95.75 273	99.49 391	93.44 392	98.77 370	98.52 381

viewmambaseed2359dif		98.19 222	98.26 201	97.99 298	99.02 268	95.03 328	96.59 350	99.53 110	96.21 341	99.00 185	98.99 208	97.62 157	99.61 347	97.62 188	99.72 184	99.33 243

GDP-MVS		97.50 280	97.11 299	98.67 205	99.02 268	96.85 248	98.16 169	99.71 47	98.32 177	98.52 274	98.54 306	83.39 422	99.95 26	98.79 100	99.56 254	99.19 285

ZD-MVS					99.01 270	98.84 82		99.07 277	94.10 402	98.05 313	98.12 348	96.36 245	99.86 142	92.70 408	99.19 330

CDPH-MVS		97.26 302	96.66 329	99.07 131	99.00 271	98.15 135	96.03 384	99.01 292	91.21 438	97.79 332	97.85 369	96.89 211	99.69 301	92.75 406	99.38 297	99.39 212

diffmvs		98.22 217	98.24 205	98.17 283	99.00 271	95.44 312	96.38 363	99.58 83	97.79 228	98.53 272	98.50 315	96.76 223	99.74 274	97.95 163	99.64 225	99.34 237
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		98.40 188	98.19 212	99.03 141	99.00 271	97.65 193	96.85 334	98.94 297	98.57 160	98.89 213	98.50 315	95.60 277	99.85 155	97.54 195	99.85 105	99.59 105

plane_prior6					98.99 274	97.70 191						94.90 295

xiu_mvs_v1_base_debu		97.86 254	98.17 214	96.92 374	98.98 275	93.91 370	96.45 357	99.17 260	97.85 223	98.41 283	97.14 403	98.47 72	99.92 64	98.02 155	99.05 344	96.92 445

xiu_mvs_v1_base		97.86 254	98.17 214	96.92 374	98.98 275	93.91 370	96.45 357	99.17 260	97.85 223	98.41 283	97.14 403	98.47 72	99.92 64	98.02 155	99.05 344	96.92 445

xiu_mvs_v1_base_debi		97.86 254	98.17 214	96.92 374	98.98 275	93.91 370	96.45 357	99.17 260	97.85 223	98.41 283	97.14 403	98.47 72	99.92 64	98.02 155	99.05 344	96.92 445

MVP-Stereo		98.08 232	97.92 245	98.57 225	98.96 278	96.79 251	97.90 219	99.18 256	96.41 334	98.46 278	98.95 222	95.93 268	99.60 350	96.51 286	98.98 358	99.31 250
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 188	98.68 129	97.54 341	98.96 278	97.99 155	97.88 221	99.36 183	98.20 193	99.63 66	99.04 188	98.76 45	95.33 468	96.56 280	99.74 173	99.31 250
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	98.94 280	97.76 185		98.76 334	87.58 455	96.75 393	98.10 350	94.80 302	99.78 249	92.73 407	99.00 353	99.20 280

USDC		97.41 291	97.40 280	97.44 350	98.94 280	93.67 380	95.17 420	99.53 110	94.03 404	98.97 192	99.10 171	95.29 286	99.34 418	95.84 324	99.73 176	99.30 253

tfpn200view9		94.03 398	93.44 401	95.78 410	98.93 282	91.44 417	97.60 268	94.29 443	97.94 215	97.10 371	94.31 452	79.67 436	99.62 340	83.05 457	98.08 407	96.29 452

testdata				98.09 287	98.93 282	95.40 314		98.80 328	90.08 446	97.45 358	98.37 328	95.26 287	99.70 297	93.58 388	98.95 361	99.17 292

thres400		94.14 396	93.44 401	96.24 398	98.93 282	91.44 417	97.60 268	94.29 443	97.94 215	97.10 371	94.31 452	79.67 436	99.62 340	83.05 457	98.08 407	97.66 433

TAPA-MVS	96.21 11	96.63 337	95.95 348	98.65 207	98.93 282	98.09 142	96.93 330	99.28 228	83.58 461	98.13 305	97.78 371	96.13 252	99.40 409	93.52 389	99.29 312	98.45 386
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.92 286	96.93 245	95.54 407	98.78 331	85.72 458	96.86 388	98.11 349	94.43 309			99.10 343	99.23 270

PVSNet_BlendedMVS		97.55 279	97.53 273	97.60 333	98.92 286	93.77 377	96.64 346	99.43 158	94.49 390	97.62 341	99.18 149	96.82 216	99.67 314	94.73 352	99.93 55	99.36 230

PVSNet_Blended		96.88 327	96.68 326	97.47 348	98.92 286	93.77 377	94.71 431	99.43 158	90.98 440	97.62 341	97.36 397	96.82 216	99.67 314	94.73 352	99.56 254	98.98 319

MSDG		97.71 267	97.52 274	98.28 270	98.91 289	96.82 249	94.42 441	99.37 179	97.65 237	98.37 288	98.29 337	97.40 180	99.33 420	94.09 374	99.22 323	98.68 370

Anonymous202405211		97.90 247	97.50 275	99.08 129	98.90 290	98.25 125	98.53 122	96.16 420	98.87 135	99.11 164	98.86 241	90.40 371	99.78 249	97.36 208	99.31 307	99.19 285

原ACMM1				98.35 263	98.90 290	96.25 277		98.83 325	92.48 424	96.07 415	98.10 350	95.39 285	99.71 290	92.61 410	98.99 355	99.08 301

GBi-Net		98.65 148	98.47 167	99.17 111	98.90 290	98.24 126	99.20 48	99.44 152	98.59 156	98.95 198	99.55 57	94.14 317	99.86 142	97.77 177	99.69 203	99.41 202

test1		98.65 148	98.47 167	99.17 111	98.90 290	98.24 126	99.20 48	99.44 152	98.59 156	98.95 198	99.55 57	94.14 317	99.86 142	97.77 177	99.69 203	99.41 202

FMVSNet2		98.49 179	98.40 177	98.75 192	98.90 290	97.14 233	98.61 113	99.13 269	98.59 156	99.19 157	99.28 120	94.14 317	99.82 201	97.97 161	99.80 138	99.29 255

OMC-MVS		97.88 251	97.49 276	99.04 140	98.89 295	98.63 95	96.94 328	99.25 237	95.02 379	98.53 272	98.51 311	97.27 189	99.47 397	93.50 391	99.51 269	99.01 313

VortexMVS		97.98 244	98.31 193	97.02 368	98.88 296	91.45 416	98.03 193	99.47 136	98.65 147	99.55 76	99.47 77	91.49 360	99.81 217	99.32 60	99.91 77	99.80 41

MVSFormer		98.26 212	98.43 173	97.77 310	98.88 296	93.89 373	99.39 20	99.56 97	99.11 97	98.16 301	98.13 346	93.81 325	99.97 7	99.26 65	99.57 251	99.43 196

lupinMVS		97.06 317	96.86 313	97.65 326	98.88 296	93.89 373	95.48 411	97.97 375	93.53 410	98.16 301	97.58 383	93.81 325	99.91 73	96.77 257	99.57 251	99.17 292

dmvs_re		95.98 359	95.39 369	97.74 316	98.86 299	97.45 206	98.37 148	95.69 432	97.95 213	96.56 399	95.95 424	90.70 368	97.68 462	88.32 445	96.13 449	98.11 408

DELS-MVS		98.27 210	98.20 208	98.48 246	98.86 299	96.70 257	95.60 406	99.20 248	97.73 231	98.45 279	98.71 272	97.50 172	99.82 201	98.21 140	99.59 242	98.93 330
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		97.89 249	97.98 236	97.60 333	98.86 299	94.35 348	96.21 373	99.44 152	97.45 265	99.06 171	98.88 238	97.99 126	99.28 428	94.38 367	99.58 247	99.18 288

LCM-MVSNet-Re		98.64 150	98.48 165	99.11 122	98.85 302	98.51 108	98.49 133	99.83 25	98.37 171	99.69 55	99.46 79	98.21 105	99.92 64	94.13 373	99.30 310	98.91 334

pmmvs4		97.58 277	97.28 288	98.51 241	98.84 303	96.93 245	95.40 415	98.52 353	93.60 409	98.61 257	98.65 290	95.10 291	99.60 350	96.97 238	99.79 144	98.99 318

NP-MVS					98.84 303	97.39 210					96.84 406

sss		97.21 307	96.93 307	98.06 292	98.83 305	95.22 321	96.75 340	98.48 355	94.49 390	97.27 367	97.90 366	92.77 343	99.80 225	96.57 276	99.32 305	99.16 295

PVSNet	93.40 17	95.67 368	95.70 354	95.57 415	98.83 305	88.57 442	92.50 458	97.72 380	92.69 422	96.49 407	96.44 416	93.72 328	99.43 405	93.61 386	99.28 313	98.71 363

MVE	83.40 22	92.50 421	91.92 423	94.25 431	98.83 305	91.64 413	92.71 457	83.52 471	95.92 355	86.46 469	95.46 437	95.20 288	95.40 467	80.51 462	98.64 382	95.73 460
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testing3-2		93.78 402	93.91 394	93.39 443	98.82 308	81.72 470	97.76 241	95.28 434	98.60 155	96.54 400	96.66 410	65.85 466	99.62 340	96.65 269	98.99 355	98.82 344

ambc				98.24 275	98.82 308	95.97 288	98.62 112	99.00 294		99.27 139	99.21 142	96.99 206	99.50 388	96.55 283	99.50 276	99.26 264

旧先验1					98.82 308	97.45 206		98.76 334			98.34 332	95.50 282			99.01 352	99.23 270

test_vis1_rt		97.75 264	97.72 259	97.83 305	98.81 311	96.35 274	97.30 304	99.69 54	94.61 388	97.87 325	98.05 355	96.26 248	98.32 456	98.74 106	98.18 399	98.82 344

WTY-MVS		96.67 335	96.27 345	97.87 303	98.81 311	94.61 342	96.77 338	97.92 377	94.94 382	97.12 370	97.74 374	91.11 364	99.82 201	93.89 379	98.15 403	99.18 288

3Dnovator+	97.89 3	98.69 138	98.51 156	99.24 102	98.81 311	98.40 113	99.02 69	99.19 252	98.99 120	98.07 310	99.28 120	97.11 199	99.84 173	96.84 252	99.32 305	99.47 181

QAPM		97.31 298	96.81 319	98.82 174	98.80 314	97.49 201	99.06 65	99.19 252	90.22 444	97.69 338	99.16 155	96.91 210	99.90 80	90.89 435	99.41 292	99.07 303

VNet		98.42 185	98.30 194	98.79 181	98.79 315	97.29 216	98.23 160	98.66 344	99.31 68	98.85 222	98.80 258	94.80 302	99.78 249	98.13 145	99.13 338	99.31 250

DPM-MVS		96.32 347	95.59 360	98.51 241	98.76 316	97.21 224	94.54 440	98.26 364	91.94 429	96.37 408	97.25 399	93.06 337	99.43 405	91.42 425	98.74 371	98.89 336

3Dnovator	98.27 2	98.81 115	98.73 118	99.05 138	98.76 316	97.81 182	99.25 43	99.30 216	98.57 160	98.55 269	99.33 110	97.95 129	99.90 80	97.16 220	99.67 214	99.44 192

PLC	94.65 16	96.51 340	95.73 353	98.85 170	98.75 318	97.91 167	96.42 361	99.06 278	90.94 441	95.59 421	97.38 395	94.41 310	99.59 354	90.93 433	98.04 412	99.05 305
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		96.83 329	96.75 322	97.08 365	98.74 319	93.33 386	96.71 342	98.26 364	96.72 321	98.44 280	97.37 396	95.20 288	99.47 397	91.89 415	97.43 426	98.44 389

hse-mvs2		97.46 285	97.07 300	98.64 209	98.73 320	97.33 212	97.45 288	97.64 387	99.11 97	98.58 263	97.98 360	88.65 386	99.79 238	98.11 146	97.39 428	98.81 349

CDS-MVSNet		97.69 268	97.35 285	98.69 202	98.73 320	97.02 238	96.92 332	98.75 337	95.89 356	98.59 261	98.67 285	92.08 354	99.74 274	96.72 263	99.81 127	99.32 246
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SD_0403		96.28 349	95.83 350	97.64 329	98.72 322	94.30 349	98.87 88	98.77 332	97.80 226	96.53 401	98.02 357	97.34 184	99.47 397	76.93 466	99.48 279	99.16 295

EIA-MVS		98.00 240	97.74 256	98.80 178	98.72 322	98.09 142	98.05 189	99.60 77	97.39 269	96.63 396	95.55 432	97.68 149	99.80 225	96.73 262	99.27 314	98.52 381

LFMVS		97.20 308	96.72 323	98.64 209	98.72 322	96.95 243	98.93 81	94.14 447	99.74 13	98.78 234	99.01 202	84.45 413	99.73 281	97.44 204	99.27 314	99.25 265

new_pmnet		96.99 324	96.76 321	97.67 322	98.72 322	94.89 331	95.95 390	98.20 367	92.62 423	98.55 269	98.54 306	94.88 298	99.52 382	93.96 377	99.44 290	98.59 378

Fast-Effi-MVS+		97.67 270	97.38 282	98.57 225	98.71 326	97.43 208	97.23 309	99.45 144	94.82 385	96.13 412	96.51 412	98.52 70	99.91 73	96.19 305	98.83 367	98.37 398

TEST9					98.71 326	98.08 146	95.96 388	99.03 286	91.40 435	95.85 418	97.53 385	96.52 236	99.76 261

train_agg		97.10 314	96.45 339	99.07 131	98.71 326	98.08 146	95.96 388	99.03 286	91.64 430	95.85 418	97.53 385	96.47 238	99.76 261	93.67 385	99.16 333	99.36 230

TSAR-MVS + GP.		98.18 224	97.98 236	98.77 189	98.71 326	97.88 169	96.32 367	98.66 344	96.33 336	99.23 151	98.51 311	97.48 176	99.40 409	97.16 220	99.46 281	99.02 312

FA-MVS(test-final)		96.99 324	96.82 317	97.50 345	98.70 330	94.78 334	99.34 23	96.99 403	95.07 378	98.48 277	99.33 110	88.41 389	99.65 331	96.13 311	98.92 364	98.07 411

AUN-MVS		96.24 353	95.45 365	98.60 220	98.70 330	97.22 222	97.38 294	97.65 385	95.95 354	95.53 428	97.96 364	82.11 430	99.79 238	96.31 298	97.44 425	98.80 354

our_test_3		97.39 293	97.73 258	96.34 393	98.70 330	89.78 438	94.61 437	98.97 296	96.50 329	99.04 180	98.85 244	95.98 264	99.84 173	97.26 214	99.67 214	99.41 202

ppachtmachnet_test		97.50 280	97.74 256	96.78 383	98.70 330	91.23 425	94.55 439	99.05 281	96.36 335	99.21 155	98.79 260	96.39 241	99.78 249	96.74 260	99.82 121	99.34 237

PCF-MVS	92.86 18	94.36 390	93.00 408	98.42 253	98.70 330	97.56 198	93.16 456	99.11 272	79.59 465	97.55 348	97.43 392	92.19 351	99.73 281	79.85 463	99.45 283	97.97 417
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ttmdpeth		97.91 246	98.02 232	97.58 335	98.69 335	94.10 356	98.13 172	98.90 306	97.95 213	97.32 366	99.58 47	95.95 267	98.75 451	96.41 292	99.22 323	99.87 21

ETV-MVS		98.03 236	97.86 250	98.56 230	98.69 335	98.07 148	97.51 280	99.50 118	98.10 205	97.50 353	95.51 433	98.41 78	99.88 114	96.27 301	99.24 319	97.71 432

test_prior				98.95 156	98.69 335	97.95 163		99.03 286					99.59 354			99.30 253

mvsmamba		97.57 278	97.26 289	98.51 241	98.69 335	96.73 256	98.74 97	97.25 396	97.03 303	97.88 324	99.23 140	90.95 365	99.87 133	96.61 272	99.00 353	98.91 334

agg_prior					98.68 339	97.99 155		99.01 292		95.59 421			99.77 255

test_8					98.67 340	98.01 154	95.91 394	99.02 289	91.64 430	95.79 420	97.50 388	96.47 238	99.76 261

HQP-NCC					98.67 340		96.29 369		96.05 347	95.55 424

ACMP_Plane					98.67 340		96.29 369		96.05 347	95.55 424

CNVR-MVS		98.17 226	97.87 249	99.07 131	98.67 340	98.24 126	97.01 324	98.93 300	97.25 283	97.62 341	98.34 332	97.27 189	99.57 363	96.42 291	99.33 303	99.39 212

HQP-MVS		97.00 323	96.49 338	98.55 232	98.67 340	96.79 251	96.29 369	99.04 284	96.05 347	95.55 424	96.84 406	93.84 323	99.54 376	92.82 403	99.26 317	99.32 246

MM		98.22 217	97.99 235	98.91 163	98.66 345	96.97 240	97.89 220	94.44 441	99.54 40	98.95 198	99.14 162	93.50 329	99.92 64	99.80 17	99.96 28	99.85 29

test_fmvs1		97.72 266	97.94 242	97.07 367	98.66 345	92.39 403	97.68 252	99.81 31	95.20 377	99.54 78	99.44 84	91.56 359	99.41 408	99.78 21	99.77 155	99.40 211

balanced_conf03		98.63 152	98.72 120	98.38 258	98.66 345	96.68 259	98.90 83	99.42 164	98.99 120	98.97 192	99.19 145	95.81 272	99.85 155	98.77 104	99.77 155	98.60 375

thres200		93.72 404	93.14 406	95.46 419	98.66 345	91.29 421	96.61 348	94.63 440	97.39 269	96.83 389	93.71 455	79.88 433	99.56 366	82.40 460	98.13 404	95.54 461

wuyk23d		96.06 355	97.62 269	91.38 448	98.65 349	98.57 102	98.85 92	96.95 406	96.86 314	99.90 14	99.16 155	99.18 19	98.40 455	89.23 443	99.77 155	77.18 468

NCCC		97.86 254	97.47 279	99.05 138	98.61 350	98.07 148	96.98 326	98.90 306	97.63 238	97.04 375	97.93 365	95.99 263	99.66 325	95.31 340	98.82 369	99.43 196

DeepC-MVS_fast	96.85 6	98.30 206	98.15 218	98.75 192	98.61 350	97.23 220	97.76 241	99.09 275	97.31 277	98.75 240	98.66 288	97.56 163	99.64 334	96.10 312	99.55 258	99.39 212
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testing3		93.51 406	92.09 417	97.75 314	98.60 352	94.40 346	97.32 302	95.26 435	97.56 248	96.79 392	95.50 434	53.57 474	99.77 255	95.26 341	98.97 359	99.08 301

thisisatest0515		94.12 397	93.16 405	96.97 372	98.60 352	92.90 393	93.77 452	90.61 460	94.10 402	96.91 382	95.87 427	74.99 448	99.80 225	94.52 358	99.12 341	98.20 404

GA-MVS		95.86 362	95.32 372	97.49 346	98.60 352	94.15 355	93.83 451	97.93 376	95.49 367	96.68 394	97.42 393	83.21 423	99.30 424	96.22 303	98.55 388	99.01 313

dmvs_testset		92.94 416	92.21 416	95.13 423	98.59 355	90.99 428	97.65 258	92.09 456	96.95 306	94.00 448	93.55 456	92.34 349	96.97 465	72.20 467	92.52 463	97.43 440

OPU-MVS				98.82 174	98.59 355	98.30 122	98.10 179				98.52 310	98.18 107	98.75 451	94.62 355	99.48 279	99.41 202

MSLP-MVS++		98.02 237	98.14 220	97.64 329	98.58 357	95.19 322	97.48 284	99.23 244	97.47 258	97.90 322	98.62 297	97.04 201	98.81 449	97.55 193	99.41 292	98.94 329

test12				98.93 159	98.58 357	97.83 174		98.66 344		96.53 401		95.51 281	99.69 301		99.13 338	99.27 258

CL-MVSNet_self_test		97.44 288	97.22 292	98.08 290	98.57 359	95.78 296	94.30 444	98.79 329	96.58 327	98.60 259	98.19 344	94.74 305	99.64 334	96.41 292	98.84 366	98.82 344

PS-MVSNAJ		97.08 316	97.39 281	96.16 404	98.56 360	92.46 401	95.24 419	98.85 320	97.25 283	97.49 354	95.99 423	98.07 117	99.90 80	96.37 294	98.67 381	96.12 457

CNLPA		97.17 311	96.71 324	98.55 232	98.56 360	98.05 152	96.33 366	98.93 300	96.91 311	97.06 374	97.39 394	94.38 312	99.45 402	91.66 419	99.18 332	98.14 407

xiu_mvs_v2_base		97.16 312	97.49 276	96.17 402	98.54 362	92.46 401	95.45 412	98.84 321	97.25 283	97.48 355	96.49 413	98.31 89	99.90 80	96.34 297	98.68 380	96.15 456

alignmvs		97.35 295	96.88 312	98.78 184	98.54 362	98.09 142	97.71 248	97.69 382	99.20 82	97.59 344	95.90 426	88.12 391	99.55 370	98.18 142	98.96 360	98.70 366

FE-MVS		95.66 369	94.95 382	97.77 310	98.53 364	95.28 318	99.40 19	96.09 423	93.11 416	97.96 319	99.26 128	79.10 440	99.77 255	92.40 412	98.71 375	98.27 402

Effi-MVS+		98.02 237	97.82 252	98.62 215	98.53 364	97.19 226	97.33 301	99.68 59	97.30 278	96.68 394	97.46 391	98.56 68	99.80 225	96.63 270	98.20 398	98.86 341

baseline1		95.96 360	95.44 366	97.52 343	98.51 366	93.99 367	98.39 146	96.09 423	98.21 189	98.40 287	97.76 373	86.88 393	99.63 337	95.42 338	89.27 466	98.95 325

MVS_Test		98.18 224	98.36 185	97.67 322	98.48 367	94.73 337	98.18 165	99.02 289	97.69 234	98.04 314	99.11 168	97.22 193	99.56 366	98.57 118	98.90 365	98.71 363

MGCFI-Net		98.34 198	98.28 197	98.51 241	98.47 368	97.59 197	98.96 77	99.48 127	99.18 90	97.40 361	95.50 434	98.66 54	99.50 388	98.18 142	98.71 375	98.44 389

BH-RMVSNet		96.83 329	96.58 334	97.58 335	98.47 368	94.05 357	96.67 344	97.36 391	96.70 323	97.87 325	97.98 360	95.14 290	99.44 404	90.47 438	98.58 387	99.25 265

sasdasda		98.34 198	98.26 201	98.58 222	98.46 370	97.82 179	98.96 77	99.46 140	99.19 87	97.46 356	95.46 437	98.59 62	99.46 400	98.08 149	98.71 375	98.46 383

canonicalmvs		98.34 198	98.26 201	98.58 222	98.46 370	97.82 179	98.96 77	99.46 140	99.19 87	97.46 356	95.46 437	98.59 62	99.46 400	98.08 149	98.71 375	98.46 383

MVS-HIRNet		94.32 391	95.62 357	90.42 449	98.46 370	75.36 473	96.29 369	89.13 464	95.25 374	95.38 430	99.75 16	92.88 340	99.19 434	94.07 375	99.39 294	96.72 450

PHI-MVS		98.29 209	97.95 240	99.34 79	98.44 373	99.16 48	98.12 176	99.38 175	96.01 351	98.06 311	98.43 322	97.80 142	99.67 314	95.69 330	99.58 247	99.20 280

DVP-MVS++		98.90 97	98.70 126	99.51 48	98.43 374	99.15 52	99.43 15	99.32 203	98.17 196	99.26 143	99.02 191	98.18 107	99.88 114	97.07 229	99.45 283	99.49 162

MSC_two_6792asdad				99.32 87	98.43 374	98.37 117		98.86 317					99.89 96	97.14 223	99.60 238	99.71 61

No_MVS				99.32 87	98.43 374	98.37 117		98.86 317					99.89 96	97.14 223	99.60 238	99.71 61

Fast-Effi-MVS+-dtu		98.27 210	98.09 223	98.81 176	98.43 374	98.11 139	97.61 267	99.50 118	98.64 148	97.39 363	97.52 387	98.12 115	99.95 26	96.90 246	98.71 375	98.38 396

OpenMVS_ROB	95.38 14	95.84 364	95.18 377	97.81 307	98.41 378	97.15 232	97.37 298	98.62 348	83.86 460	98.65 251	98.37 328	94.29 315	99.68 310	88.41 444	98.62 385	96.60 451

DeepPCF-MVS	96.93 5	98.32 203	98.01 233	99.23 104	98.39 379	98.97 73	95.03 424	99.18 256	96.88 312	99.33 125	98.78 262	98.16 111	99.28 428	96.74 260	99.62 231	99.44 192

Patchmatch-test		96.55 339	96.34 341	97.17 362	98.35 380	93.06 389	98.40 145	97.79 378	97.33 274	98.41 283	98.67 285	83.68 421	99.69 301	95.16 343	99.31 307	98.77 357

AdaColmap		97.14 313	96.71 324	98.46 248	98.34 381	97.80 183	96.95 327	98.93 300	95.58 364	96.92 380	97.66 378	95.87 270	99.53 378	90.97 432	99.14 336	98.04 412

OpenMVS	96.65 7	97.09 315	96.68 326	98.32 265	98.32 382	97.16 231	98.86 91	99.37 179	89.48 448	96.29 410	99.15 159	96.56 234	99.90 80	92.90 400	99.20 327	97.89 420

MG-MVS		96.77 332	96.61 331	97.26 358	98.31 383	93.06 389	95.93 391	98.12 372	96.45 333	97.92 320	98.73 269	93.77 327	99.39 411	91.19 430	99.04 347	99.33 243

test_yl		96.69 333	96.29 343	97.90 300	98.28 384	95.24 319	97.29 305	97.36 391	98.21 189	98.17 298	97.86 367	86.27 397	99.55 370	94.87 349	98.32 392	98.89 336

DCV-MVSNet		96.69 333	96.29 343	97.90 300	98.28 384	95.24 319	97.29 305	97.36 391	98.21 189	98.17 298	97.86 367	86.27 397	99.55 370	94.87 349	98.32 392	98.89 336

CHOSEN 280x420		95.51 374	95.47 363	95.65 414	98.25 386	88.27 445	93.25 455	98.88 310	93.53 410	94.65 439	97.15 402	86.17 399	99.93 53	97.41 206	99.93 55	98.73 362

SCA		96.41 346	96.66 329	95.67 412	98.24 387	88.35 444	95.85 397	96.88 409	96.11 345	97.67 339	98.67 285	93.10 335	99.85 155	94.16 369	99.22 323	98.81 349

DeepMVS_CX				93.44 442	98.24 387	94.21 352		94.34 442	64.28 468	91.34 462	94.87 449	89.45 380	92.77 469	77.54 465	93.14 462	93.35 464

MS-PatchMatch		97.68 269	97.75 255	97.45 349	98.23 389	93.78 376	97.29 305	98.84 321	96.10 346	98.64 252	98.65 290	96.04 256	99.36 414	96.84 252	99.14 336	99.20 280

BH-w/o		95.13 380	94.89 384	95.86 407	98.20 390	91.31 420	95.65 404	97.37 390	93.64 408	96.52 403	95.70 430	93.04 338	99.02 440	88.10 446	95.82 452	97.24 443

mvs_anonymous		97.83 262	98.16 217	96.87 377	98.18 391	91.89 410	97.31 303	98.90 306	97.37 271	98.83 225	99.46 79	96.28 247	99.79 238	98.90 93	98.16 402	98.95 325

miper_lstm_enhance		97.18 310	97.16 295	97.25 359	98.16 392	92.85 394	95.15 422	99.31 208	97.25 283	98.74 242	98.78 262	90.07 372	99.78 249	97.19 218	99.80 138	99.11 300

RRT-MVS		97.88 251	97.98 236	97.61 332	98.15 393	93.77 377	98.97 76	99.64 69	99.16 92	98.69 245	99.42 88	91.60 357	99.89 96	97.63 187	98.52 389	99.16 295

ET-MVSNet_ETH3D		94.30 393	93.21 404	97.58 335	98.14 394	94.47 345	94.78 430	93.24 452	94.72 386	89.56 464	95.87 427	78.57 443	99.81 217	96.91 241	97.11 437	98.46 383

ADS-MVSNet2		95.43 375	94.98 380	96.76 384	98.14 394	91.74 411	97.92 216	97.76 379	90.23 442	96.51 404	98.91 228	85.61 404	99.85 155	92.88 401	96.90 438	98.69 367

ADS-MVSNet		95.24 378	94.93 383	96.18 401	98.14 394	90.10 437	97.92 216	97.32 394	90.23 442	96.51 404	98.91 228	85.61 404	99.74 274	92.88 401	96.90 438	98.69 367

c3_l		97.36 294	97.37 283	97.31 354	98.09 397	93.25 387	95.01 425	99.16 263	97.05 300	98.77 237	98.72 271	92.88 340	99.64 334	96.93 240	99.76 168	99.05 305

FMVSNet3		97.50 280	97.24 291	98.29 269	98.08 398	95.83 293	97.86 225	98.91 305	97.89 220	98.95 198	98.95 222	87.06 392	99.81 217	97.77 177	99.69 203	99.23 270

PAPM		91.88 430	90.34 433	96.51 388	98.06 399	92.56 399	92.44 459	97.17 398	86.35 456	90.38 463	96.01 422	86.61 395	99.21 433	70.65 469	95.43 454	97.75 429

Effi-MVS+-dtu		98.26 212	97.90 247	99.35 76	98.02 400	99.49 6	98.02 196	99.16 263	98.29 182	97.64 340	97.99 359	96.44 240	99.95 26	96.66 268	98.93 363	98.60 375

eth_miper_zixun_eth		97.23 306	97.25 290	97.17 362	98.00 401	92.77 396	94.71 431	99.18 256	97.27 281	98.56 267	98.74 268	91.89 355	99.69 301	97.06 231	99.81 127	99.05 305

HY-MVS	95.94 13	95.90 361	95.35 371	97.55 340	97.95 402	94.79 333	98.81 96	96.94 407	92.28 427	95.17 432	98.57 304	89.90 374	99.75 269	91.20 429	97.33 433	98.10 409

UGNet		98.53 172	98.45 170	98.79 181	97.94 403	96.96 242	99.08 61	98.54 351	99.10 104	96.82 390	99.47 77	96.55 235	99.84 173	98.56 121	99.94 49	99.55 131
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		96.47 344	95.70 354	98.79 181	97.92 404	99.12 62	98.28 154	98.60 349	92.16 428	95.54 427	96.17 420	94.77 304	99.52 382	89.62 441	98.23 396	97.72 431
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		96.86 328	96.55 335	97.79 308	97.91 405	94.21 352	97.56 273	98.87 312	97.49 257	99.06 171	99.05 186	80.72 431	99.80 225	98.44 127	99.82 121	99.37 223

API-MVS		97.04 319	96.91 311	97.42 351	97.88 406	98.23 130	98.18 165	98.50 354	97.57 246	97.39 363	96.75 408	96.77 221	99.15 437	90.16 439	99.02 351	94.88 462

myMVS_eth3d28		92.92 417	92.31 413	94.77 426	97.84 407	87.59 449	96.19 375	96.11 422	97.08 299	94.27 442	93.49 458	66.07 465	98.78 450	91.78 417	97.93 415	97.92 419

miper_ehance_all_eth		97.06 317	97.03 302	97.16 364	97.83 408	93.06 389	94.66 434	99.09 275	95.99 352	98.69 245	98.45 320	92.73 345	99.61 347	96.79 254	99.03 348	98.82 344

cl____		97.02 320	96.83 316	97.58 335	97.82 409	94.04 359	94.66 434	99.16 263	97.04 301	98.63 253	98.71 272	88.68 385	99.69 301	97.00 233	99.81 127	99.00 317

DIV-MVS_self_test		97.02 320	96.84 315	97.58 335	97.82 409	94.03 360	94.66 434	99.16 263	97.04 301	98.63 253	98.71 272	88.69 383	99.69 301	97.00 233	99.81 127	99.01 313

CANet		97.87 253	97.76 254	98.19 282	97.75 411	95.51 304	96.76 339	99.05 281	97.74 230	96.93 379	98.21 342	95.59 278	99.89 96	97.86 172	99.93 55	99.19 285

UBG		93.25 411	92.32 412	96.04 406	97.72 412	90.16 436	95.92 393	95.91 427	96.03 350	93.95 450	93.04 461	69.60 455	99.52 382	90.72 437	97.98 413	98.45 386

mvsany_test1		97.60 274	97.54 272	97.77 310	97.72 412	95.35 315	95.36 416	97.13 400	94.13 401	99.71 49	99.33 110	97.93 130	99.30 424	97.60 191	98.94 362	98.67 371

PVSNet_0	89.98 21	91.15 431	90.30 434	93.70 439	97.72 412	84.34 463	90.24 462	97.42 389	90.20 445	93.79 451	93.09 460	90.90 367	98.89 448	86.57 452	72.76 469	97.87 422

CR-MVSNet		96.28 349	95.95 348	97.28 356	97.71 415	94.22 350	98.11 177	98.92 303	92.31 426	96.91 382	99.37 98	85.44 407	99.81 217	97.39 207	97.36 431	97.81 425

RPMNet		97.02 320	96.93 307	97.30 355	97.71 415	94.22 350	98.11 177	99.30 216	99.37 60	96.91 382	99.34 107	86.72 394	99.87 133	97.53 196	97.36 431	97.81 425

ETVMVS		92.60 420	91.08 429	97.18 360	97.70 417	93.65 382	96.54 351	95.70 430	96.51 328	94.68 438	92.39 464	61.80 471	99.50 388	86.97 449	97.41 427	98.40 394

pmmvs3		95.03 382	94.40 389	96.93 373	97.70 417	92.53 400	95.08 423	97.71 381	88.57 452	97.71 336	98.08 353	79.39 438	99.82 201	96.19 305	99.11 342	98.43 391

baseline2		93.73 403	92.83 409	96.42 391	97.70 417	91.28 422	96.84 335	89.77 463	93.96 406	92.44 458	95.93 425	79.14 439	99.77 255	92.94 399	96.76 442	98.21 403

WBMVS		95.18 379	94.78 385	96.37 392	97.68 420	89.74 439	95.80 399	98.73 340	97.54 252	98.30 289	98.44 321	70.06 453	99.82 201	96.62 271	99.87 96	99.54 137

tpm		94.67 387	94.34 391	95.66 413	97.68 420	88.42 443	97.88 221	94.90 437	94.46 392	96.03 417	98.56 305	78.66 441	99.79 238	95.88 318	95.01 456	98.78 356

CANet_DTU		97.26 302	97.06 301	97.84 304	97.57 422	94.65 341	96.19 375	98.79 329	97.23 289	95.14 433	98.24 339	93.22 332	99.84 173	97.34 209	99.84 110	99.04 309

testing11		93.08 414	92.02 419	96.26 397	97.56 423	90.83 431	96.32 367	95.70 430	96.47 332	92.66 457	93.73 454	64.36 469	99.59 354	93.77 384	97.57 420	98.37 398

tpm2		93.09 413	92.58 411	94.62 428	97.56 423	86.53 452	97.66 256	95.79 429	86.15 457	94.07 447	98.23 341	75.95 446	99.53 378	90.91 434	96.86 441	97.81 425

testing91		93.32 409	92.27 414	96.47 390	97.54 425	91.25 423	96.17 379	96.76 411	97.18 293	93.65 453	93.50 457	65.11 468	99.63 337	93.04 398	97.45 424	98.53 380

TR-MVS		95.55 372	95.12 378	96.86 380	97.54 425	93.94 368	96.49 356	96.53 416	94.36 397	97.03 377	96.61 411	94.26 316	99.16 436	86.91 451	96.31 446	97.47 439

testing99		93.04 415	91.98 422	96.23 399	97.53 427	90.70 433	96.35 365	95.94 426	96.87 313	93.41 454	93.43 459	63.84 470	99.59 354	93.24 396	97.19 434	98.40 394

1314		95.74 366	95.60 358	96.17 402	97.53 427	92.75 397	98.07 186	98.31 363	91.22 437	94.25 443	96.68 409	95.53 279	99.03 439	91.64 421	97.18 435	96.74 449

CostFormer		93.97 399	93.78 397	94.51 429	97.53 427	85.83 455	97.98 208	95.96 425	89.29 450	94.99 435	98.63 295	78.63 442	99.62 340	94.54 357	96.50 443	98.09 410

FMVSNet5		96.01 357	95.20 376	98.41 254	97.53 427	96.10 279	98.74 97	99.50 118	97.22 292	98.03 315	99.04 188	69.80 454	99.88 114	97.27 213	99.71 193	99.25 265

PMMVS		96.51 340	95.98 347	98.09 287	97.53 427	95.84 292	94.92 427	98.84 321	91.58 432	96.05 416	95.58 431	95.68 275	99.66 325	95.59 334	98.09 406	98.76 359

reproduce_monomvs		95.00 384	95.25 373	94.22 432	97.51 432	83.34 464	97.86 225	98.44 356	98.51 165	99.29 135	99.30 116	67.68 459	99.56 366	98.89 95	99.81 127	99.77 49

PAPR		95.29 376	94.47 387	97.75 314	97.50 433	95.14 324	94.89 428	98.71 342	91.39 436	95.35 431	95.48 436	94.57 307	99.14 438	84.95 454	97.37 429	98.97 322

testing222		91.96 428	90.37 432	96.72 385	97.47 434	92.59 398	96.11 381	94.76 438	96.83 315	92.90 456	92.87 462	57.92 472	99.55 370	86.93 450	97.52 421	98.00 416

PatchT		96.65 336	96.35 340	97.54 341	97.40 435	95.32 317	97.98 208	96.64 413	99.33 65	96.89 386	99.42 88	84.32 415	99.81 217	97.69 186	97.49 422	97.48 438

tpm cat1		93.29 410	93.13 407	93.75 438	97.39 436	84.74 458	97.39 292	97.65 385	83.39 462	94.16 444	98.41 323	82.86 426	99.39 411	91.56 423	95.35 455	97.14 444

PatchmatchNet		95.58 371	95.67 356	95.30 422	97.34 437	87.32 450	97.65 258	96.65 412	95.30 373	97.07 373	98.69 281	84.77 410	99.75 269	94.97 347	98.64 382	98.83 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmtry		97.35 295	96.97 305	98.50 245	97.31 438	96.47 270	98.18 165	98.92 303	98.95 127	98.78 234	99.37 98	85.44 407	99.85 155	95.96 316	99.83 117	99.17 292

LS3D		98.63 152	98.38 182	99.36 70	97.25 439	99.38 13	99.12 60	99.32 203	99.21 80	98.44 280	98.88 238	97.31 185	99.80 225	96.58 274	99.34 302	98.92 331

IB-MVS	91.63 19	92.24 426	90.90 430	96.27 396	97.22 440	91.24 424	94.36 443	93.33 451	92.37 425	92.24 460	94.58 451	66.20 464	99.89 96	93.16 397	94.63 458	97.66 433
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		92.38 423	91.76 426	94.21 433	97.16 441	84.65 459	95.42 414	88.45 465	95.96 353	96.17 411	95.84 429	66.36 462	99.71 290	91.87 416	98.64 382	98.28 401

tpmrst		95.07 381	95.46 364	93.91 436	97.11 442	84.36 462	97.62 263	96.96 405	94.98 380	96.35 409	98.80 258	85.46 406	99.59 354	95.60 333	96.23 447	97.79 428

Syy-MVS		96.04 356	95.56 362	97.49 346	97.10 443	94.48 344	96.18 377	96.58 414	95.65 361	94.77 436	92.29 465	91.27 363	99.36 414	98.17 144	98.05 410	98.63 373

myMVS_eth3d		91.92 429	90.45 431	96.30 394	97.10 443	90.90 429	96.18 377	96.58 414	95.65 361	94.77 436	92.29 465	53.88 473	99.36 414	89.59 442	98.05 410	98.63 373

MDTV_nov1_ep13			95.22 375		97.06 445	83.20 465	97.74 245	96.16 420	94.37 396	96.99 378	98.83 251	83.95 419	99.53 378	93.90 378	97.95 414

MVS		93.19 412	92.09 417	96.50 389	96.91 446	94.03 360	98.07 186	98.06 374	68.01 467	94.56 441	96.48 414	95.96 266	99.30 424	83.84 456	96.89 440	96.17 454

E-PMN		94.17 395	94.37 390	93.58 440	96.86 447	85.71 456	90.11 464	97.07 401	98.17 196	97.82 331	97.19 400	84.62 412	98.94 444	89.77 440	97.68 419	96.09 458

JIA-IIPM		95.52 373	95.03 379	97.00 369	96.85 448	94.03 360	96.93 330	95.82 428	99.20 82	94.63 440	99.71 22	83.09 424	99.60 350	94.42 363	94.64 457	97.36 442

EMVS		93.83 401	94.02 393	93.23 445	96.83 449	84.96 457	89.77 465	96.32 418	97.92 217	97.43 360	96.36 419	86.17 399	98.93 445	87.68 447	97.73 418	95.81 459

cl22		95.79 365	95.39 369	96.98 371	96.77 450	92.79 395	94.40 442	98.53 352	94.59 389	97.89 323	98.17 345	82.82 427	99.24 430	96.37 294	99.03 348	98.92 331

WB-MVSnew		95.73 367	95.57 361	96.23 399	96.70 451	90.70 433	96.07 383	93.86 448	95.60 363	97.04 375	95.45 440	96.00 259	99.55 370	91.04 431	98.31 394	98.43 391

dp		93.47 407	93.59 400	93.13 446	96.64 452	81.62 471	97.66 256	96.42 417	92.80 421	96.11 413	98.64 293	78.55 444	99.59 354	93.31 394	92.18 465	98.16 406

MonoMVSNet		96.25 351	96.53 337	95.39 420	96.57 453	91.01 427	98.82 95	97.68 384	98.57 160	98.03 315	99.37 98	90.92 366	97.78 461	94.99 345	93.88 461	97.38 441

test-LLR		93.90 400	93.85 395	94.04 434	96.53 454	84.62 460	94.05 448	92.39 454	96.17 342	94.12 445	95.07 441	82.30 428	99.67 314	95.87 321	98.18 399	97.82 423

test-mter		92.33 425	91.76 426	94.04 434	96.53 454	84.62 460	94.05 448	92.39 454	94.00 405	94.12 445	95.07 441	65.63 467	99.67 314	95.87 321	98.18 399	97.82 423

TESTMET0.1,1		92.19 427	91.77 425	93.46 441	96.48 456	82.80 467	94.05 448	91.52 459	94.45 394	94.00 448	94.88 447	66.65 461	99.56 366	95.78 326	98.11 405	98.02 413

MGCNet		97.44 288	97.01 304	98.72 198	96.42 457	96.74 255	97.20 314	91.97 457	98.46 168	98.30 289	98.79 260	92.74 344	99.91 73	99.30 62	99.94 49	99.52 149

miper_enhance_ethall		96.01 357	95.74 352	96.81 381	96.41 458	92.27 407	93.69 453	98.89 309	91.14 439	98.30 289	97.35 398	90.58 369	99.58 361	96.31 298	99.03 348	98.60 375

tpmvs		95.02 383	95.25 373	94.33 430	96.39 459	85.87 453	98.08 182	96.83 410	95.46 368	95.51 429	98.69 281	85.91 402	99.53 378	94.16 369	96.23 447	97.58 436

CMPMVS	75.91 23	96.29 348	95.44 366	98.84 171	96.25 460	98.69 94	97.02 323	99.12 270	88.90 451	97.83 329	98.86 241	89.51 378	98.90 447	91.92 414	99.51 269	98.92 331
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 1		94.51 388	93.69 398	96.99 370	96.05 461	93.61 384	94.97 426	93.49 449	96.17 342	97.57 347	94.88 447	82.30 428	99.01 442	93.60 387	94.17 460	98.37 398

EPMVS		93.72 404	93.27 403	95.09 425	96.04 462	87.76 447	98.13 172	85.01 470	94.69 387	96.92 380	98.64 293	78.47 445	99.31 422	95.04 344	96.46 444	98.20 404

cascas		94.79 386	94.33 392	96.15 405	96.02 463	92.36 405	92.34 460	99.26 236	85.34 459	95.08 434	94.96 446	92.96 339	98.53 454	94.41 366	98.59 386	97.56 437

MVStest1		95.86 362	95.60 358	96.63 386	95.87 464	91.70 412	97.93 213	98.94 297	98.03 207	99.56 73	99.66 32	71.83 451	98.26 457	99.35 58	99.24 319	99.91 13

gg-mvs-nofinetune		92.37 424	91.20 428	95.85 408	95.80 465	92.38 404	99.31 30	81.84 472	99.75 11	91.83 461	99.74 18	68.29 456	99.02 440	87.15 448	97.12 436	96.16 455

gm-plane-assit					94.83 466	81.97 469			88.07 454		94.99 444		99.60 350	91.76 418

GG-mvs-BLEND				94.76 427	94.54 467	92.13 409	99.31 30	80.47 473		88.73 467	91.01 467	67.59 460	98.16 460	82.30 461	94.53 459	93.98 463

UWE-MVS-28		90.22 432	89.28 435	93.02 447	94.50 468	82.87 466	96.52 354	87.51 466	95.21 376	92.36 459	96.04 421	71.57 452	98.25 458	72.04 468	97.77 417	97.94 418

EPNet_dtu		94.93 385	94.78 385	95.38 421	93.58 469	87.68 448	96.78 337	95.69 432	97.35 273	89.14 466	98.09 352	88.15 390	99.49 391	94.95 348	99.30 310	98.98 319
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dongtai		76.24 436	75.95 439	77.12 452	92.39 470	67.91 476	90.16 463	59.44 477	82.04 463	89.42 465	94.67 450	49.68 475	81.74 470	48.06 470	77.66 468	81.72 466

KD-MVS_2432*1600		92.87 418	91.99 420	95.51 417	91.37 471	89.27 440	94.07 446	98.14 370	95.42 369	97.25 368	96.44 416	67.86 457	99.24 430	91.28 427	96.08 450	98.02 413

miper_refine_blended		92.87 418	91.99 420	95.51 417	91.37 471	89.27 440	94.07 446	98.14 370	95.42 369	97.25 368	96.44 416	67.86 457	99.24 430	91.28 427	96.08 450	98.02 413

EPNet		96.14 354	95.44 366	98.25 273	90.76 473	95.50 307	97.92 216	94.65 439	98.97 123	92.98 455	98.85 244	89.12 381	99.87 133	95.99 314	99.68 208	99.39 212
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
kuosan		69.30 437	68.95 440	70.34 453	87.68 474	65.00 477	91.11 461	59.90 476	69.02 466	74.46 471	88.89 468	48.58 476	68.03 472	28.61 471	72.33 470	77.99 467

test_method		79.78 434	79.50 437	80.62 450	80.21 475	45.76 478	70.82 466	98.41 360	31.08 470	80.89 470	97.71 375	84.85 409	97.37 463	91.51 424	80.03 467	98.75 360

tmp_tt		78.77 435	78.73 438	78.90 451	58.45 476	74.76 475	94.20 445	78.26 474	39.16 469	86.71 468	92.82 463	80.50 432	75.19 471	86.16 453	92.29 464	86.74 465

testmvs		17.12 439	20.53 442	6.87 455	12.05 477	4.20 480	93.62 454	6.73 478	4.62 473	10.41 473	24.33 470	8.28 478	3.56 474	9.69 473	15.07 471	12.86 470

test123		17.04 440	20.11 443	7.82 454	10.25 478	4.91 479	94.80 429	4.47 479	4.93 472	10.00 474	24.28 471	9.69 477	3.64 473	10.14 472	12.43 472	14.92 469

mmdepth		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

monomultidepth		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

test_blank		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

eth-test2					0.00 479

eth-test					0.00 479

uanet_test		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

DCPMVS		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

cdsmvs_eth3d_5k		24.66 438	32.88 441	0.00 456	0.00 479	0.00 481	0.00 467	99.10 273	0.00 474	0.00 475	97.58 383	99.21 18	0.00 475	0.00 474	0.00 473	0.00 471

pcd_1.5k_mvsjas		8.17 441	10.90 444	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	98.07 117	0.00 475	0.00 474	0.00 473	0.00 471

sosnet-low-res		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

sosnet		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

uncertanet		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

Regformer		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

ab-mvs-re		8.12 442	10.83 445	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	97.48 389	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

uanet		0.00 443	0.00 446	0.00 456	0.00 479	0.00 481	0.00 467	0.00 480	0.00 474	0.00 475	0.00 474	0.00 479	0.00 475	0.00 474	0.00 473	0.00 471

WAC-MVS						90.90 429								91.37 426

PC_three_1452									93.27 413	99.40 111	98.54 306	98.22 103	97.00 464	95.17 342	99.45 283	99.49 162

test_241102_TWO								99.30 216	98.03 207	99.26 143	99.02 191	97.51 171	99.88 114	96.91 241	99.60 238	99.66 76

test_0728_THIRD									98.17 196	99.08 169	99.02 191	97.89 134	99.88 114	97.07 229	99.71 193	99.70 66

GSMVS																98.81 349

sam_mvs1												84.74 411				98.81 349

sam_mvs												84.29 417

MTGPA								99.20 248

test_post1							97.59 270				20.48 473	83.07 425	99.66 325	94.16 369

test_post											21.25 472	83.86 420	99.70 297

patchmatchnet-post											98.77 264	84.37 414	99.85 155

MTMP							97.93 213	91.91 458

test9_res														93.28 395	99.15 335	99.38 221

agg_prior2														92.50 411	99.16 333	99.37 223

test_prior4						97.97 159	95.86 395

test_prior2							95.74 402		96.48 331	96.11 413	97.63 381	95.92 269		94.16 369	99.20 327

旧先验2							95.76 401		88.56 453	97.52 351			99.66 325	94.48 359

新几何2							95.93 391

无先验							95.74 402	98.74 339	89.38 449				99.73 281	92.38 413		99.22 275

原ACMM2							95.53 408

testdata2													99.79 238	92.80 405

segment_acmp												97.02 204

testdata1							95.44 413		96.32 337

plane_prior5								99.27 231					99.70 297	94.42 363	99.51 269	99.45 188

plane_prior4											97.98 360

plane_prior3						97.78 184			97.41 267	97.79 332

plane_prior2							97.77 238		98.20 193

plane_prior						97.65 193	97.07 322		96.72 321						99.36 298

n2								0.00 480

nn								0.00 480

door-mid								99.57 90

test11								98.87 312

door								99.41 168

HQP5-MVS						96.79 251

BP-MVS														92.82 403

HQP4-MVS										95.56 423			99.54 376			99.32 246

HQP3-MVS								99.04 284							99.26 317

HQP2-MVS												93.84 323

MDTV_nov1_ep13_2view						74.92 474	97.69 251		90.06 447	97.75 335		85.78 403		93.52 389		98.69 367

ACMMP++_ref															99.77 155

ACMMP++															99.68 208

Test By Simon												96.52 236

High-resolution multi-view benchmark