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 30

Gipuma		99.03 84	99.16 63	98.64 222	99.94 2	98.51 112	99.32 26	99.75 42	99.58 39	98.60 272	99.62 40	98.22 108	99.51 405	97.70 193	99.73 184	97.89 438
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
OurMVSNet-221017-0		99.37 29	99.31 42	99.53 39	99.91 3	98.98 72	99.63 7	99.58 93	99.44 53	99.78 40	99.76 15	96.39 253	99.92 65	99.44 55	99.92 69	99.68 71

pmmvs6		99.67 3	99.70 3	99.60 16	99.90 4	99.27 28	99.53 9	99.76 39	99.64 27	99.84 30	99.83 4	99.50 9	99.87 134	99.36 58	99.92 69	99.64 84

PS-MVSNAJss		99.46 17	99.49 16	99.35 80	99.90 4	98.15 139	99.20 48	99.65 69	99.48 45	99.92 8	99.71 22	98.07 123	99.96 14	99.53 48	100.00 1	99.93 11

testf1		99.25 41	99.16 63	99.51 49	99.89 6	99.63 4	98.71 105	99.69 54	98.90 133	99.43 106	99.35 108	98.86 34	99.67 328	97.81 180	99.81 133	99.24 282

APD_test2		99.25 41	99.16 63	99.51 49	99.89 6	99.63 4	98.71 105	99.69 54	98.90 133	99.43 106	99.35 108	98.86 34	99.67 328	97.81 180	99.81 133	99.24 282

ANet_high		99.57 10	99.67 6	99.28 96	99.89 6	98.09 146	99.14 57	99.93 5	99.82 8	99.93 6	99.81 8	99.17 20	99.94 42	99.31 62	100.00 1	99.82 36

anonymousdsp		99.51 14	99.47 21	99.62 10	99.88 9	99.08 70	99.34 23	99.69 54	98.93 129	99.65 64	99.72 21	98.93 32	99.95 26	99.11 78	100.00 1	99.82 36

v7n		99.53 12	99.57 13	99.41 70	99.88 9	98.54 110	99.45 14	99.61 81	99.66 24	99.68 58	99.66 32	98.44 81	99.95 26	99.73 28	99.96 28	99.75 60

mvs_tets		99.63 6	99.67 6	99.49 55	99.88 9	98.61 102	99.34 23	99.71 47	99.27 74	99.90 14	99.74 18	99.68 4	99.97 7	99.55 43	99.99 5	99.88 20

test_fmvsmconf0.01_n		99.57 10	99.63 10	99.36 74	99.87 12	98.13 142	98.08 191	99.95 1	99.45 51	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 57	99.87 12	98.61 102	99.28 40	99.66 65	99.09 108	99.89 18	99.68 25	99.53 7	99.97 7	99.50 51	99.99 5	99.87 22

test_djsdf		99.52 13	99.51 15	99.53 39	99.86 14	98.74 92	99.39 20	99.56 109	99.11 98	99.70 52	99.73 20	99.00 27	99.97 7	99.26 66	99.98 12	99.89 16

MIMVSNet1		99.38 28	99.32 40	99.55 29	99.86 14	99.19 43	99.41 17	99.59 90	99.59 37	99.71 50	99.57 49	97.12 208	99.90 81	99.21 71	99.87 98	99.54 142

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 27	99.85 16	99.11 65	99.90 1	99.78 36	99.63 29	99.78 40	99.67 30	99.48 10	99.81 222	99.30 63	99.97 21	99.77 50
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 93	99.90 3	99.86 24	99.78 13	99.58 6	99.95 26	99.00 88	99.95 38	99.78 47

SixPastTwentyTwo		98.75 134	98.62 149	99.16 118	99.83 18	97.96 166	99.28 40	98.20 385	99.37 61	99.70 52	99.65 36	92.65 359	99.93 54	99.04 85	99.84 112	99.60 100

sc_t1		99.62 7	99.66 8	99.53 39	99.82 19	99.09 69	99.50 11	99.63 73	99.88 4	99.86 24	99.80 11	99.03 24	99.89 97	99.48 53	99.93 56	99.60 100

Baseline_NR-MVSNet		98.98 92	98.86 111	99.36 74	99.82 19	98.55 107	97.47 299	99.57 100	99.37 61	99.21 163	99.61 43	96.76 235	99.83 192	98.06 157	99.83 122	99.71 63

pm-mvs1		99.44 19	99.48 18	99.33 89	99.80 21	98.63 99	99.29 36	99.63 73	99.30 71	99.65 64	99.60 45	99.16 22	99.82 205	99.07 81	99.83 122	99.56 129

TransMVSNet (Re)		99.44 19	99.47 21	99.36 74	99.80 21	98.58 105	99.27 42	99.57 100	99.39 59	99.75 45	99.62 40	99.17 20	99.83 192	99.06 83	99.62 243	99.66 78

K. test v3		98.00 253	97.66 278	99.03 145	99.79 23	97.56 202	99.19 52	92.47 471	99.62 33	99.52 88	99.66 32	89.61 391	99.96 14	99.25 68	99.81 133	99.56 129

test_fmvsmconf0.1_n		99.49 15	99.54 14	99.34 83	99.78 24	98.11 143	97.77 248	99.90 11	99.33 66	99.97 3	99.66 32	99.71 3	99.96 14	99.79 19	99.99 5	99.96 8

APD_test1		98.83 118	98.66 142	99.34 83	99.78 24	99.47 9	98.42 150	99.45 158	98.28 191	98.98 200	99.19 153	97.76 155	99.58 379	96.57 294	99.55 270	98.97 340

test_vis3_rt		99.14 63	99.17 61	99.07 135	99.78 24	98.38 119	98.92 82	99.94 2	97.80 237	99.91 12	99.67 30	97.15 207	98.91 464	99.76 23	99.56 266	99.92 12

EGC-MVSNET		85.24 450	80.54 453	99.34 83	99.77 27	99.20 40	99.08 61	99.29 238	12.08 489	20.84 490	99.42 90	97.55 174	99.85 156	97.08 240	99.72 192	98.96 342

Anonymous20240521		98.69 147	98.87 107	98.16 299	99.77 27	95.11 341	99.08 61	99.44 166	99.34 65	99.33 130	99.55 57	94.10 334	99.94 42	99.25 68	99.96 28	99.42 211

FC-MVSNet-test		99.27 38	99.25 53	99.34 83	99.77 27	98.37 121	99.30 35	99.57 100	99.61 35	99.40 115	99.50 69	97.12 208	99.85 156	99.02 87	99.94 50	99.80 42

test_vis1_n		98.31 217	98.50 169	97.73 337	99.76 30	94.17 372	98.68 108	99.91 9	96.31 353	99.79 39	99.57 49	92.85 355	99.42 425	99.79 19	99.84 112	99.60 100

test_fmvs3		99.12 70	99.41 26	98.25 287	99.76 30	95.07 342	99.05 67	99.94 2	97.78 240	99.82 34	99.84 3	98.56 71	99.71 300	99.96 1	99.96 28	99.97 4

XXY-MVS		99.14 63	99.15 68	99.10 128	99.76 30	97.74 191	98.85 92	99.62 78	98.48 174	99.37 120	99.49 75	98.75 46	99.86 143	98.20 147	99.80 144	99.71 63

TDRefinement		99.42 24	99.38 29	99.55 29	99.76 30	99.33 21	99.68 6	99.71 47	99.38 60	99.53 83	99.61 43	98.64 59	99.80 231	98.24 142	99.84 112	99.52 157

fmvsm_s_conf0.1_n_a		99.17 53	99.30 45	98.80 187	99.75 34	96.59 273	97.97 221	99.86 16	98.22 194	99.88 21	99.71 22	98.59 65	99.84 174	99.73 28	99.98 12	99.98 3

tt0805		98.69 147	98.62 149	98.90 171	99.75 34	99.30 23	99.15 56	96.97 422	98.86 139	98.87 233	97.62 397	98.63 61	98.96 461	99.41 57	98.29 410	98.45 404

test_vis1_n_1920		98.40 200	98.92 99	96.81 399	99.74 36	90.76 450	98.15 179	99.91 9	98.33 182	99.89 18	99.55 57	95.07 305	99.88 115	99.76 23	99.93 56	99.79 44

FOURS1					99.73 37	99.67 3	99.43 15	99.54 118	99.43 55	99.26 148

PEN-MVS		99.41 25	99.34 36	99.62 10	99.73 37	99.14 58	99.29 36	99.54 118	99.62 33	99.56 74	99.42 90	98.16 117	99.96 14	98.78 103	99.93 56	99.77 50

lessismore_v0				98.97 157	99.73 37	97.53 204		86.71 486		99.37 120	99.52 68	89.93 387	99.92 65	98.99 89	99.72 192	99.44 202

SteuartSystems-ACMMP		98.79 127	98.54 162	99.54 32	99.73 37	99.16 49	98.23 169	99.31 222	97.92 228	98.90 222	98.90 240	98.00 129	99.88 115	96.15 326	99.72 192	99.58 115
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended_VisFu		98.17 238	98.15 230	98.22 293	99.73 37	95.15 338	97.36 313	99.68 60	94.45 412	98.99 199	99.27 128	96.87 224	99.94 42	97.13 237	99.91 78	99.57 123

Vis-MVSNet		99.34 30	99.36 33	99.27 99	99.73 37	98.26 128	99.17 53	99.78 36	99.11 98	99.27 144	99.48 76	98.82 37	99.95 26	98.94 92	99.93 56	99.59 107
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 54	99.72 43	98.98 72	99.51 10	99.85 18	99.86 6	99.88 21	99.82 5	99.02 26	99.90 81	99.54 44	99.95 38	99.61 98

SSC-MVS		98.71 138	98.74 122	98.62 228	99.72 43	96.08 298	98.74 98	98.64 363	99.74 13	99.67 60	99.24 141	94.57 320	99.95 26	99.11 78	99.24 334	99.82 36

test_f		98.67 156	98.87 107	98.05 309	99.72 43	95.59 313	98.51 134	99.81 31	96.30 355	99.78 40	99.82 5	96.14 264	98.63 471	99.82 12	99.93 56	99.95 9

ACMH	96.65 7	99.25 41	99.24 54	99.26 101	99.72 43	98.38 119	99.07 64	99.55 113	98.30 186	99.65 64	99.45 85	99.22 17	99.76 267	98.44 129	99.77 161	99.64 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0320		99.61 8	99.65 9	99.48 57	99.71 47	98.94 79	99.54 8	99.83 25	99.87 5	99.89 18	99.82 5	98.75 46	99.90 81	99.54 44	99.95 38	99.59 107

fmvsm_s_conf0.1_n		99.16 57	99.33 38	98.64 222	99.71 47	96.10 293	97.87 234	99.85 18	98.56 170	99.90 14	99.68 25	98.69 55	99.85 156	99.72 30	99.98 12	99.97 4

PS-CasMVS		99.40 26	99.33 38	99.62 10	99.71 47	99.10 66	99.29 36	99.53 122	99.53 42	99.46 101	99.41 94	98.23 105	99.95 26	98.89 97	99.95 38	99.81 40

DTE-MVSNet		99.43 23	99.35 34	99.66 7	99.71 47	99.30 23	99.31 30	99.51 128	99.64 27	99.56 74	99.46 81	98.23 105	99.97 7	98.78 103	99.93 56	99.72 62

WR-MVS_H		99.33 31	99.22 55	99.65 8	99.71 47	99.24 31	99.32 26	99.55 113	99.46 50	99.50 94	99.34 112	97.30 196	99.93 54	98.90 95	99.93 56	99.77 50

HPM-MVS_fast		99.01 86	98.82 115	99.57 22	99.71 47	99.35 17	99.00 72	99.50 131	97.33 286	98.94 217	98.86 250	98.75 46	99.82 205	97.53 206	99.71 201	99.56 129

ACMH+	96.62 9	99.08 77	99.00 91	99.33 89	99.71 47	98.83 87	98.60 120	99.58 93	99.11 98	99.53 83	99.18 157	98.81 38	99.67 328	96.71 278	99.77 161	99.50 165

PMVS	91.26 20	97.86 267	97.94 254	97.65 344	99.71 47	97.94 168	98.52 129	98.68 359	98.99 121	97.52 366	99.35 108	97.41 189	98.18 477	91.59 440	99.67 222	96.82 466
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FE-MVSNET2		99.15 58	99.22 55	98.94 161	99.70 55	97.49 205	98.62 117	99.67 64	98.85 142	99.34 127	99.54 63	98.47 75	99.81 222	98.93 93	99.91 78	99.51 161

KinetiMVS		99.03 84	99.02 87	99.03 145	99.70 55	97.48 208	98.43 147	99.29 238	99.70 16	99.60 71	99.07 186	96.13 265	99.94 42	99.42 56	99.87 98	99.68 71

FIs		99.14 63	99.09 79	99.29 95	99.70 55	98.28 127	99.13 58	99.52 127	99.48 45	99.24 157	99.41 94	96.79 232	99.82 205	98.69 113	99.88 94	99.76 56

VPNet		98.87 108	98.83 114	99.01 149	99.70 55	97.62 200	98.43 147	99.35 203	99.47 48	99.28 142	99.05 194	96.72 238	99.82 205	98.09 154	99.36 313	99.59 107

fmvsm_s_conf0.1_n_2		99.20 51	99.38 29	98.65 220	99.69 59	96.08 298	97.49 294	99.90 11	99.53 42	99.88 21	99.64 37	98.51 74	99.90 81	99.83 10	99.98 12	99.97 4

test_cas_vis1_n_1920		98.33 214	98.68 137	97.27 375	99.69 59	92.29 424	98.03 202	99.85 18	97.62 250	99.96 4	99.62 40	93.98 335	99.74 282	99.52 50	99.86 105	99.79 44

MP-MVS-pluss		98.57 173	98.23 218	99.60 16	99.69 59	99.35 17	97.16 335	99.38 189	94.87 402	98.97 204	98.99 216	98.01 128	99.88 115	97.29 224	99.70 208	99.58 115
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SDMVSNet		99.23 46	99.32 40	98.96 158	99.68 62	97.35 216	98.84 94	99.48 141	99.69 18	99.63 67	99.68 25	99.03 24	99.96 14	97.97 168	99.92 69	99.57 123

sd_testset		99.28 37	99.31 42	99.19 112	99.68 62	98.06 155	99.41 17	99.30 230	99.69 18	99.63 67	99.68 25	99.25 16	99.96 14	97.25 227	99.92 69	99.57 123

test_fmvs1_n		98.09 244	98.28 209	97.52 361	99.68 62	93.47 403	98.63 115	99.93 5	95.41 390	99.68 58	99.64 37	91.88 370	99.48 412	99.82 12	99.87 98	99.62 90

CHOSEN 1792x2688		97.49 296	97.14 311	98.54 251	99.68 62	96.09 296	96.50 371	99.62 78	91.58 450	98.84 236	98.97 223	92.36 361	99.88 115	96.76 271	99.95 38	99.67 76

tfpnnormal		98.90 103	98.90 101	98.91 168	99.67 66	97.82 183	99.00 72	99.44 166	99.45 51	99.51 93	99.24 141	98.20 112	99.86 143	95.92 335	99.69 211	99.04 326

MTAPA		98.88 107	98.64 145	99.61 14	99.67 66	99.36 16	98.43 147	99.20 262	98.83 144	98.89 225	98.90 240	96.98 218	99.92 65	97.16 232	99.70 208	99.56 129

test_fmvsmvis_n_1920		99.26 40	99.49 16	98.54 251	99.66 68	96.97 252	98.00 209	99.85 18	99.24 76	99.92 8	99.50 69	99.39 12	99.95 26	99.89 3	99.98 12	98.71 381

mvs5depth		99.30 34	99.59 12	98.44 265	99.65 69	95.35 330	99.82 3	99.94 2	99.83 7	99.42 110	99.94 2	98.13 120	99.96 14	99.63 36	99.96 28	100.00 1

fmvsm_l_conf0.5_n_a		99.19 52	99.27 48	98.94 161	99.65 69	97.05 247	97.80 243	99.76 39	98.70 152	99.78 40	99.11 176	98.79 42	99.95 26	99.85 6	99.96 28	99.83 33

WB-MVS		98.52 187	98.55 160	98.43 266	99.65 69	95.59 313	98.52 129	98.77 348	99.65 26	99.52 88	99.00 214	94.34 326	99.93 54	98.65 115	98.83 382	99.76 56

CP-MVSNet		99.21 48	99.09 79	99.56 27	99.65 69	98.96 78	99.13 58	99.34 209	99.42 56	99.33 130	99.26 134	97.01 216	99.94 42	98.74 108	99.93 56	99.79 44

HPM-MVS		98.79 127	98.53 164	99.59 20	99.65 69	99.29 25	99.16 54	99.43 172	96.74 333	98.61 270	98.38 341	98.62 62	99.87 134	96.47 306	99.67 222	99.59 107
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
RPSCF		98.62 165	98.36 195	99.42 68	99.65 69	99.42 11	98.55 125	99.57 100	97.72 244	98.90 222	99.26 134	96.12 267	99.52 400	95.72 346	99.71 201	99.32 258

NormalMVS		98.26 224	97.97 251	99.15 121	99.64 75	97.83 178	98.28 163	99.43 172	99.24 76	98.80 244	98.85 253	89.76 389	99.94 42	98.04 159	99.67 222	99.68 71

lecture		99.25 41	99.12 71	99.62 10	99.64 75	99.40 12	98.89 87	99.51 128	99.19 88	99.37 120	99.25 139	98.36 86	99.88 115	98.23 144	99.67 222	99.59 107

fmvsm_l_conf0.5_n		99.21 48	99.28 47	99.02 148	99.64 75	97.28 226	97.82 239	99.76 39	98.73 146	99.82 34	99.09 184	98.81 38	99.95 26	99.86 4	99.96 28	99.83 33

test_fmvsmconf_n		99.44 19	99.48 18	99.31 94	99.64 75	98.10 145	97.68 262	99.84 22	99.29 72	99.92 8	99.57 49	99.60 5	99.96 14	99.74 27	99.98 12	99.89 16

TSAR-MVS + MP.		98.63 162	98.49 174	99.06 141	99.64 75	97.90 172	98.51 134	98.94 313	96.96 317	99.24 157	98.89 246	97.83 147	99.81 222	96.88 261	99.49 292	99.48 183
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PM-MVS		98.82 121	98.72 126	99.12 124	99.64 75	98.54 110	97.98 217	99.68 60	97.62 250	99.34 127	99.18 157	97.54 176	99.77 261	97.79 182	99.74 181	99.04 326

Elysia		99.15 58	99.14 69	99.18 113	99.63 81	97.92 169	98.50 136	99.43 172	99.67 21	99.70 52	99.13 172	96.66 241	99.98 4	99.54 44	99.96 28	99.64 84

StellarMVS		99.15 58	99.14 69	99.18 113	99.63 81	97.92 169	98.50 136	99.43 172	99.67 21	99.70 52	99.13 172	96.66 241	99.98 4	99.54 44	99.96 28	99.64 84

KD-MVS_self_test		99.25 41	99.18 60	99.44 66	99.63 81	99.06 71	98.69 107	99.54 118	99.31 69	99.62 70	99.53 65	97.36 193	99.86 143	99.24 70	99.71 201	99.39 224

EU-MVSNet		97.66 284	98.50 169	95.13 441	99.63 81	85.84 472	98.35 159	98.21 384	98.23 193	99.54 79	99.46 81	95.02 306	99.68 324	98.24 142	99.87 98	99.87 22

HyFIR lowres test		97.19 323	96.60 347	98.96 158	99.62 85	97.28 226	95.17 438	99.50 131	94.21 417	99.01 194	98.32 349	86.61 410	99.99 2	97.10 239	99.84 112	99.60 100

E6new		99.05 80	99.11 72	98.85 175	99.60 86	97.30 221	98.42 150	99.63 73	98.73 146	99.26 148	99.39 100	98.71 50	99.70 307	98.43 131	99.84 112	99.54 142

E6		99.05 80	99.11 72	98.85 175	99.60 86	97.30 221	98.42 150	99.63 73	98.73 146	99.26 148	99.39 100	98.71 50	99.70 307	98.43 131	99.84 112	99.54 142

E5		99.05 80	99.11 72	98.85 175	99.60 86	97.30 221	98.42 150	99.63 73	98.73 146	99.26 148	99.39 100	98.71 50	99.70 307	98.43 131	99.84 112	99.54 142

fmvsm_l_conf0.5_n_9		99.32 33	99.43 24	98.98 155	99.59 89	97.18 238	97.44 303	99.83 25	99.56 40	99.91 12	99.34 112	99.36 13	99.93 54	99.83 10	99.98 12	99.85 30

fmvsm_l_conf0.5_n_3		99.45 18	99.48 18	99.34 83	99.59 89	98.21 136	97.82 239	99.84 22	99.41 58	99.92 8	99.41 94	99.51 8	99.95 26	99.84 9	99.97 21	99.87 22

MED-MVS test				99.45 64	99.58 91	98.93 80	98.68 108	99.60 83	96.46 346	99.53 83	98.77 273		99.83 192	96.67 283	99.64 233	99.58 115

MED-MVS		98.90 103	98.72 126	99.45 64	99.58 91	98.93 80	98.68 108	99.60 83	98.14 212	99.53 83	98.77 273	97.87 144	99.83 192	96.67 283	99.64 233	99.58 115

TestfortrainingZip a		98.95 96	98.72 126	99.64 9	99.58 91	99.32 22	98.68 108	99.60 83	96.46 346	99.53 83	98.77 273	97.87 144	99.83 192	98.39 135	99.64 233	99.77 50

FE-MVSNET		98.59 170	98.50 169	98.87 172	99.58 91	97.30 221	98.08 191	99.74 43	96.94 319	98.97 204	99.10 179	96.94 220	99.74 282	97.33 222	99.86 105	99.55 136

mmtdpeth		99.30 34	99.42 25	98.92 167	99.58 91	96.89 260	99.48 13	99.92 7	99.92 2	98.26 308	99.80 11	98.33 92	99.91 74	99.56 41	99.95 38	99.97 4

ACMMP_NAP		98.75 134	98.48 175	99.57 22	99.58 91	99.29 25	97.82 239	99.25 251	96.94 319	98.78 246	99.12 175	98.02 127	99.84 174	97.13 237	99.67 222	99.59 107

nrg030		99.40 26	99.35 34	99.54 32	99.58 91	99.13 61	98.98 75	99.48 141	99.68 20	99.46 101	99.26 134	98.62 62	99.73 289	99.17 75	99.92 69	99.76 56

VDDNet		98.21 231	97.95 252	99.01 149	99.58 91	97.74 191	99.01 70	97.29 413	99.67 21	98.97 204	99.50 69	90.45 384	99.80 231	97.88 175	99.20 342	99.48 183

COLMAP_ROB	96.50 10	98.99 89	98.85 113	99.41 70	99.58 91	99.10 66	98.74 98	99.56 109	99.09 108	99.33 130	99.19 153	98.40 83	99.72 299	95.98 333	99.76 176	99.42 211
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	98.99 151	99.57 100	97.73 193	97.93 223	99.83 25	99.22 79	99.93 6	99.30 122	99.42 11	99.96 14	99.85 6	99.99 5	99.29 268

ZNCC-MVS		98.68 153	98.40 187	99.54 32	99.57 100	99.21 34	98.46 144	99.29 238	97.28 292	98.11 320	98.39 339	98.00 129	99.87 134	96.86 264	99.64 233	99.55 136

MSP-MVS		98.40 200	98.00 246	99.61 14	99.57 100	99.25 30	98.57 123	99.35 203	97.55 261	99.31 138	97.71 390	94.61 319	99.88 115	96.14 327	99.19 345	99.70 68
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		98.32 215	98.39 190	98.13 300	99.57 100	95.54 316	97.78 245	99.49 139	97.37 283	99.19 165	97.65 394	98.96 29	99.49 409	96.50 305	98.99 370	99.34 249

MP-MVS		98.46 193	98.09 235	99.54 32	99.57 100	99.22 33	98.50 136	99.19 266	97.61 253	97.58 360	98.66 301	97.40 190	99.88 115	94.72 372	99.60 250	99.54 142
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LPG-MVS_test		98.71 138	98.46 179	99.47 61	99.57 100	98.97 74	98.23 169	99.48 141	96.60 338	99.10 176	99.06 187	98.71 50	99.83 192	95.58 353	99.78 155	99.62 90

LGP-MVS_train				99.47 61	99.57 100	98.97 74		99.48 141	96.60 338	99.10 176	99.06 187	98.71 50	99.83 192	95.58 353	99.78 155	99.62 90

IS-MVSNet		98.19 234	97.90 260	99.08 133	99.57 100	97.97 163	99.31 30	98.32 380	99.01 120	98.98 200	99.03 198	91.59 372	99.79 244	95.49 355	99.80 144	99.48 183

viewdifsd2359ckpt11		98.84 115	99.04 84	98.24 289	99.56 108	95.51 318	97.38 308	99.70 52	99.16 93	99.57 72	99.40 97	98.26 101	99.71 300	98.55 124	99.82 127	99.50 165

viewmsd2359difaftdt		98.84 115	99.04 84	98.24 289	99.56 108	95.51 318	97.38 308	99.70 52	99.16 93	99.57 72	99.40 97	98.26 101	99.71 300	98.55 124	99.82 127	99.50 165

dcpmvs_2		98.78 129	99.11 72	97.78 327	99.56 108	93.67 398	99.06 65	99.86 16	99.50 44	99.66 61	99.26 134	97.21 204	99.99 2	98.00 164	99.91 78	99.68 71

test_0402		98.76 133	98.71 131	98.93 164	99.56 108	98.14 141	98.45 146	99.34 209	99.28 73	98.95 210	98.91 237	98.34 91	99.79 244	95.63 350	99.91 78	98.86 359

EPP-MVSNet		98.30 218	98.04 242	99.07 135	99.56 108	97.83 178	99.29 36	98.07 391	99.03 118	98.59 274	99.13 172	92.16 365	99.90 81	96.87 262	99.68 216	99.49 172

ACMMP		98.75 134	98.50 169	99.52 45	99.56 108	99.16 49	98.87 88	99.37 193	97.16 307	98.82 240	99.01 210	97.71 158	99.87 134	96.29 318	99.69 211	99.54 142
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 72	99.20 59	98.78 194	99.55 114	96.59 273	97.79 244	99.82 30	98.21 196	99.81 37	99.53 65	98.46 79	99.84 174	99.70 33	99.97 21	99.90 15

fmvsm_s_conf0.5_n		99.09 73	99.26 51	98.61 232	99.55 114	96.09 296	97.74 255	99.81 31	98.55 171	99.85 27	99.55 57	98.60 64	99.84 174	99.69 35	99.98 12	99.89 16

FMVSNet1		99.17 53	99.17 61	99.17 115	99.55 114	98.24 130	99.20 48	99.44 166	99.21 81	99.43 106	99.55 57	97.82 150	99.86 143	98.42 134	99.89 92	99.41 214

Vis-MVSNet (Re-imp)		97.46 298	97.16 308	98.34 278	99.55 114	96.10 293	98.94 80	98.44 374	98.32 184	98.16 314	98.62 310	88.76 396	99.73 289	93.88 398	99.79 150	99.18 303

ACMM	96.08 12	98.91 101	98.73 124	99.48 57	99.55 114	99.14 58	98.07 195	99.37 193	97.62 250	99.04 190	98.96 226	98.84 36	99.79 244	97.43 216	99.65 231	99.49 172
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvs2		98.70 143	98.97 95	97.89 319	99.54 119	94.05 375	98.55 125	99.92 7	96.78 331	99.72 48	99.78 13	96.60 245	99.67 328	99.91 2	99.90 86	99.94 10

mPP-MVS		98.64 160	98.34 198	99.54 32	99.54 119	99.17 45	98.63 115	99.24 256	97.47 269	98.09 322	98.68 296	97.62 167	99.89 97	96.22 321	99.62 243	99.57 123

XVG-ACMP-BASELINE		98.56 174	98.34 198	99.22 109	99.54 119	98.59 104	97.71 258	99.46 154	97.25 295	98.98 200	98.99 216	97.54 176	99.84 174	95.88 336	99.74 181	99.23 284

viewmacassd2359aftdt		98.86 112	98.87 107	98.83 180	99.53 122	97.32 220	97.70 260	99.64 71	98.22 194	99.25 155	99.27 128	98.40 83	99.61 365	97.98 167	99.87 98	99.55 136

region2R		98.69 147	98.40 187	99.54 32	99.53 122	99.17 45	98.52 129	99.31 222	97.46 274	98.44 293	98.51 324	97.83 147	99.88 115	96.46 307	99.58 259	99.58 115

PGM-MVS		98.66 157	98.37 194	99.55 29	99.53 122	99.18 44	98.23 169	99.49 139	97.01 316	98.69 257	98.88 247	98.00 129	99.89 97	95.87 339	99.59 254	99.58 115

E4		98.87 108	98.88 104	98.81 184	99.52 125	97.23 229	97.62 273	99.61 81	98.58 165	99.18 169	99.33 115	98.29 95	99.69 314	97.99 166	99.83 122	99.52 157

Patchmatch-RL test		97.26 316	97.02 317	97.99 313	99.52 125	95.53 317	96.13 396	99.71 47	97.47 269	99.27 144	99.16 163	84.30 432	99.62 358	97.89 172	99.77 161	98.81 367

ACMMPR		98.70 143	98.42 185	99.54 32	99.52 125	99.14 58	98.52 129	99.31 222	97.47 269	98.56 280	98.54 319	97.75 156	99.88 115	96.57 294	99.59 254	99.58 115

fmvsm_s_conf0.5_n_9		99.17 53	99.38 29	98.53 253	99.51 128	95.82 308	97.62 273	99.78 36	99.72 15	99.90 14	99.48 76	98.66 57	99.89 97	99.85 6	99.93 56	99.89 16

AstraMVS		98.16 240	98.07 240	98.41 268	99.51 128	95.86 305	98.00 209	95.14 454	98.97 124	99.43 106	99.24 141	93.25 343	99.84 174	99.21 71	99.87 98	99.54 142

fmvsm_s_conf0.5_n_8		99.13 67	99.26 51	98.74 207	99.51 128	96.44 285	97.65 268	99.65 69	99.66 24	99.78 40	99.48 76	97.92 137	99.93 54	99.72 30	99.95 38	99.87 22

GST-MVS		98.61 166	98.30 206	99.52 45	99.51 128	99.20 40	98.26 167	99.25 251	97.44 277	98.67 260	98.39 339	97.68 159	99.85 156	96.00 331	99.51 282	99.52 157

Anonymous20231206		98.21 231	98.21 219	98.20 294	99.51 128	95.43 327	98.13 181	99.32 217	96.16 361	98.93 218	98.82 263	96.00 272	99.83 192	97.32 223	99.73 184	99.36 242

ACMP	95.32 15	98.41 197	98.09 235	99.36 74	99.51 128	98.79 90	97.68 262	99.38 189	95.76 377	98.81 242	98.82 263	98.36 86	99.82 205	94.75 369	99.77 161	99.48 183
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LuminaMVS		98.39 206	98.20 220	98.98 155	99.50 134	97.49 205	97.78 245	97.69 400	98.75 145	99.49 95	99.25 139	92.30 363	99.94 42	99.14 76	99.88 94	99.50 165

DVP-MVS		98.77 132	98.52 165	99.52 45	99.50 134	99.21 34	98.02 205	98.84 337	97.97 222	99.08 178	99.02 199	97.61 169	99.88 115	96.99 248	99.63 240	99.48 183
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.60 16	99.50 134	99.23 32	98.02 205	99.32 217					99.88 115	96.99 248	99.63 240	99.68 71

test0726					99.50 134	99.21 34	98.17 177	99.35 203	97.97 222	99.26 148	99.06 187	97.61 169

AllTest		98.44 195	98.20 220	99.16 118	99.50 134	98.55 107	98.25 168	99.58 93	96.80 329	98.88 229	99.06 187	97.65 162	99.57 381	94.45 379	99.61 248	99.37 235

TestCases				99.16 118	99.50 134	98.55 107		99.58 93	96.80 329	98.88 229	99.06 187	97.65 162	99.57 381	94.45 379	99.61 248	99.37 235

XVG-OURS		98.53 183	98.34 198	99.11 126	99.50 134	98.82 89	95.97 402	99.50 131	97.30 290	99.05 188	98.98 221	99.35 14	99.32 439	95.72 346	99.68 216	99.18 303

EG-PatchMatch MVS		98.99 89	99.01 89	98.94 161	99.50 134	97.47 209	98.04 200	99.59 90	98.15 211	99.40 115	99.36 107	98.58 70	99.76 267	98.78 103	99.68 216	99.59 107

fmvsm_s_conf0.5_n_2		99.14 63	99.31 42	98.63 226	99.49 142	96.08 298	97.38 308	99.81 31	99.48 45	99.84 30	99.57 49	98.46 79	99.89 97	99.82 12	99.97 21	99.91 13

SED-MVS		98.91 101	98.72 126	99.49 55	99.49 142	99.17 45	98.10 188	99.31 222	98.03 218	99.66 61	99.02 199	98.36 86	99.88 115	96.91 254	99.62 243	99.41 214

IU-MVS					99.49 142	99.15 53		98.87 328	92.97 435	99.41 112				96.76 271	99.62 243	99.66 78

test_241102_ONE					99.49 142	99.17 45		99.31 222	97.98 221	99.66 61	98.90 240	98.36 86	99.48 412

UA-Net		99.47 16	99.40 27	99.70 2	99.49 142	99.29 25	99.80 4	99.72 45	99.82 8	99.04 190	99.81 8	98.05 126	99.96 14	98.85 99	99.99 5	99.86 28

HFP-MVS		98.71 138	98.44 182	99.51 49	99.49 142	99.16 49	98.52 129	99.31 222	97.47 269	98.58 276	98.50 328	97.97 133	99.85 156	96.57 294	99.59 254	99.53 154

VPA-MVSNet		99.30 34	99.30 45	99.28 96	99.49 142	98.36 124	99.00 72	99.45 158	99.63 29	99.52 88	99.44 86	98.25 103	99.88 115	99.09 80	99.84 112	99.62 90

XVG-OURS-SEG-HR		98.49 190	98.28 209	99.14 122	99.49 142	98.83 87	96.54 367	99.48 141	97.32 288	99.11 173	98.61 312	99.33 15	99.30 442	96.23 320	98.38 406	99.28 271

fmvsm_s_conf0.5_n_11		99.21 48	99.34 36	98.80 187	99.48 150	96.56 278	97.97 221	99.69 54	99.63 29	99.84 30	99.54 63	98.21 110	99.94 42	99.76 23	99.95 38	99.88 20

114514_t		96.50 356	95.77 365	98.69 215	99.48 150	97.43 213	97.84 238	99.55 113	81.42 482	96.51 421	98.58 316	95.53 292	99.67 328	93.41 411	99.58 259	98.98 336

IterMVS-LS		98.55 178	98.70 134	98.09 302	99.48 150	94.73 355	97.22 329	99.39 187	98.97 124	99.38 118	99.31 121	96.00 272	99.93 54	98.58 118	99.97 21	99.60 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_s_conf0.5_n_10		99.15 58	99.27 48	98.78 194	99.47 153	96.56 278	97.75 254	99.71 47	99.60 36	99.74 47	99.44 86	97.96 134	99.95 26	99.86 4	99.94 50	99.82 36

fmvsm_s_conf0.5_n_5		99.07 79	99.10 77	98.99 151	99.47 153	97.22 232	97.40 305	99.83 25	97.61 253	99.85 27	99.30 122	98.80 40	99.95 26	99.71 32	99.90 86	99.78 47

v8		99.01 86	99.16 63	98.57 239	99.47 153	96.31 290	98.90 83	99.47 150	99.03 118	99.52 88	99.57 49	96.93 221	99.81 222	99.60 37	99.98 12	99.60 100

SSC-MVS3.2		98.53 183	98.79 118	97.74 334	99.46 156	93.62 401	96.45 373	99.34 209	99.33 66	98.93 218	98.70 292	97.90 138	99.90 81	99.12 77	99.92 69	99.69 70

fmvsm_s_conf0.5_n_3		99.22 47	99.37 32	98.78 194	99.46 156	96.58 276	97.65 268	99.72 45	99.47 48	99.86 24	99.50 69	98.94 30	99.89 97	99.75 26	99.97 21	99.86 28

XVS		98.72 137	98.45 180	99.53 39	99.46 156	99.21 34	98.65 113	99.34 209	98.62 159	97.54 364	98.63 308	97.50 182	99.83 192	96.79 267	99.53 276	99.56 129

X-MVStestdata		94.32 407	92.59 426	99.53 39	99.46 156	99.21 34	98.65 113	99.34 209	98.62 159	97.54 364	45.85 487	97.50 182	99.83 192	96.79 267	99.53 276	99.56 129

test20.03		98.78 129	98.77 121	98.78 194	99.46 156	97.20 235	97.78 245	99.24 256	99.04 117	99.41 112	98.90 240	97.65 162	99.76 267	97.70 193	99.79 150	99.39 224

guyue		98.01 252	97.93 256	98.26 285	99.45 161	95.48 322	98.08 191	96.24 437	98.89 135	99.34 127	99.14 170	91.32 376	99.82 205	99.07 81	99.83 122	99.48 183

CSCG		98.68 153	98.50 169	99.20 110	99.45 161	98.63 99	98.56 124	99.57 100	97.87 232	98.85 234	98.04 370	97.66 161	99.84 174	96.72 276	99.81 133	99.13 315

GeoE		99.05 80	98.99 93	99.25 104	99.44 163	98.35 125	98.73 102	99.56 109	98.42 177	98.91 221	98.81 266	98.94 30	99.91 74	98.35 137	99.73 184	99.49 172

v148		98.45 194	98.60 154	98.00 312	99.44 163	94.98 344	97.44 303	99.06 292	98.30 186	99.32 136	98.97 223	96.65 243	99.62 358	98.37 136	99.85 107	99.39 224

v10		98.97 93	99.11 72	98.55 246	99.44 163	96.21 292	98.90 83	99.55 113	98.73 146	99.48 96	99.60 45	96.63 244	99.83 192	99.70 33	99.99 5	99.61 98

V42		98.78 129	98.78 120	98.76 201	99.44 163	97.04 248	98.27 166	99.19 266	97.87 232	99.25 155	99.16 163	96.84 225	99.78 255	99.21 71	99.84 112	99.46 193

MDA-MVSNet-bldmvs		97.94 258	97.91 259	98.06 307	99.44 163	94.96 345	96.63 363	99.15 282	98.35 180	98.83 237	99.11 176	94.31 327	99.85 156	96.60 291	98.72 388	99.37 235

viewdifsd2359ckpt07		98.71 138	98.86 111	98.26 285	99.43 168	95.65 312	97.20 330	99.66 65	99.20 83	99.29 140	99.01 210	98.29 95	99.73 289	97.92 171	99.75 180	99.39 224

casdiffmvs_mvg		99.12 70	99.16 63	98.99 151	99.43 168	97.73 193	98.00 209	99.62 78	99.22 79	99.55 77	99.22 147	98.93 32	99.75 276	98.66 114	99.81 133	99.50 165
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		98.90 103	99.01 89	98.57 239	99.42 170	96.59 273	98.13 181	99.66 65	99.09 108	99.30 139	99.02 199	98.79 42	99.89 97	97.87 177	99.80 144	99.23 284

test1111		96.49 357	96.82 331	95.52 434	99.42 170	87.08 469	99.22 45	87.14 485	99.11 98	99.46 101	99.58 47	88.69 397	99.86 143	98.80 101	99.95 38	99.62 90

v2v482		98.56 174	98.62 149	98.37 275	99.42 170	95.81 309	97.58 282	99.16 277	97.90 230	99.28 142	99.01 210	95.98 277	99.79 244	99.33 60	99.90 86	99.51 161

OPM-MVS		98.56 174	98.32 204	99.25 104	99.41 173	98.73 95	97.13 337	99.18 270	97.10 310	98.75 252	98.92 234	98.18 113	99.65 348	96.68 282	99.56 266	99.37 235
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PMMVS2		98.07 246	98.08 238	98.04 310	99.41 173	94.59 361	94.59 456	99.40 185	97.50 266	98.82 240	98.83 260	96.83 227	99.84 174	97.50 209	99.81 133	99.71 63

E2		98.70 143	98.68 137	98.73 209	99.40 175	97.10 245	97.48 295	99.57 100	98.09 215	99.00 195	99.20 150	97.90 138	99.67 328	97.73 191	99.77 161	99.43 206

E3		98.69 147	98.68 137	98.73 209	99.40 175	97.10 245	97.48 295	99.57 100	98.09 215	99.00 195	99.20 150	97.90 138	99.67 328	97.73 191	99.77 161	99.43 206

test_one_0601					99.39 177	99.20 40		99.31 222	98.49 173	98.66 262	99.02 199	97.64 165

mvsany_test3		98.87 108	98.92 99	98.74 207	99.38 178	96.94 256	98.58 122	99.10 287	96.49 343	99.96 4	99.81 8	98.18 113	99.45 420	98.97 90	99.79 150	99.83 33

patch_mono-2		98.51 188	98.63 147	98.17 297	99.38 178	94.78 352	97.36 313	99.69 54	98.16 206	98.49 289	99.29 125	97.06 211	99.97 7	98.29 141	99.91 78	99.76 56

test2506		92.39 438	91.89 440	93.89 455	99.38 178	82.28 486	99.32 26	66.03 493	99.08 112	98.77 249	99.57 49	66.26 479	99.84 174	98.71 111	99.95 38	99.54 142

ECVR-MVS		96.42 359	96.61 345	95.85 426	99.38 178	88.18 464	99.22 45	86.00 487	99.08 112	99.36 123	99.57 49	88.47 402	99.82 205	98.52 126	99.95 38	99.54 142

casdiffmvs		98.95 96	99.00 91	98.81 184	99.38 178	97.33 218	97.82 239	99.57 100	99.17 92	99.35 125	99.17 161	98.35 90	99.69 314	98.46 128	99.73 184	99.41 214
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 95	99.02 87	98.76 201	99.38 178	97.26 228	98.49 139	99.50 131	98.86 139	99.19 165	99.06 187	98.23 105	99.69 314	98.71 111	99.76 176	99.33 255

TranMVSNet+NR-MVSNet		99.17 53	99.07 82	99.46 63	99.37 184	98.87 85	98.39 155	99.42 178	99.42 56	99.36 123	99.06 187	98.38 85	99.95 26	98.34 138	99.90 86	99.57 123

fmvsm_s_conf0.5_n_6		99.08 77	99.21 58	98.69 215	99.36 185	96.51 280	97.62 273	99.68 60	98.43 176	99.85 27	99.10 179	99.12 23	99.88 115	99.77 22	99.92 69	99.67 76

tttt0517		95.64 386	94.98 396	97.64 347	99.36 185	93.81 393	98.72 103	90.47 479	98.08 217	98.67 260	98.34 346	73.88 465	99.92 65	97.77 184	99.51 282	99.20 294

test_part2					99.36 185	99.10 66				99.05 188

v1144		98.60 168	98.66 142	98.41 268	99.36 185	95.90 303	97.58 282	99.34 209	97.51 265	99.27 144	99.15 167	96.34 258	99.80 231	99.47 54	99.93 56	99.51 161

CP-MVS		98.70 143	98.42 185	99.52 45	99.36 185	99.12 63	98.72 103	99.36 197	97.54 263	98.30 302	98.40 338	97.86 146	99.89 97	96.53 303	99.72 192	99.56 129

diffmvs_AUTHOR		98.50 189	98.59 156	98.23 292	99.35 190	95.48 322	96.61 364	99.60 83	98.37 178	98.90 222	99.00 214	97.37 192	99.76 267	98.22 145	99.85 107	99.46 193

Test_1112_low_res		96.99 338	96.55 349	98.31 281	99.35 190	95.47 325	95.84 414	99.53 122	91.51 452	96.80 407	98.48 331	91.36 375	99.83 192	96.58 292	99.53 276	99.62 90

DeepC-MVS	97.60 4	98.97 93	98.93 98	99.10 128	99.35 190	97.98 162	98.01 208	99.46 154	97.56 259	99.54 79	99.50 69	98.97 28	99.84 174	98.06 157	99.92 69	99.49 172
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
1112_ss		97.29 315	96.86 327	98.58 236	99.34 193	96.32 289	96.75 356	99.58 93	93.14 433	96.89 402	97.48 404	92.11 367	99.86 143	96.91 254	99.54 272	99.57 123

reproduce_model		99.15 58	98.97 95	99.67 4	99.33 194	99.44 10	98.15 179	99.47 150	99.12 97	99.52 88	99.32 120	98.31 93	99.90 81	97.78 183	99.73 184	99.66 78

MVSMamba_PlusPlus		98.83 118	98.98 94	98.36 276	99.32 195	96.58 276	98.90 83	99.41 182	99.75 11	98.72 255	99.50 69	96.17 263	99.94 42	99.27 65	99.78 155	98.57 397

fmvsm_s_conf0.5_n_4		99.01 86	99.22 55	98.38 272	99.31 196	95.48 322	97.56 284	99.73 44	98.87 137	99.75 45	99.27 128	98.80 40	99.86 143	99.80 17	99.90 86	99.81 40

SF-MVS		98.53 183	98.27 212	99.32 91	99.31 196	98.75 91	98.19 173	99.41 182	96.77 332	98.83 237	98.90 240	97.80 152	99.82 205	95.68 349	99.52 279	99.38 233

CPTT-MVS		97.84 273	97.36 297	99.27 99	99.31 196	98.46 115	98.29 162	99.27 245	94.90 401	97.83 344	98.37 342	94.90 308	99.84 174	93.85 400	99.54 272	99.51 161

UnsupCasMVSNet_eth		97.89 262	97.60 283	98.75 203	99.31 196	97.17 240	97.62 273	99.35 203	98.72 151	98.76 251	98.68 296	92.57 360	99.74 282	97.76 188	95.60 470	99.34 249

fmvsm_s_conf0.5_n_7		98.83 118	99.04 84	98.20 294	99.30 200	94.83 350	97.23 325	99.36 197	98.64 154	99.84 30	99.43 89	98.10 122	99.91 74	99.56 41	99.96 28	99.87 22

pmmvs-eth3d		98.47 192	98.34 198	98.86 174	99.30 200	97.76 189	97.16 335	99.28 242	95.54 383	99.42 110	99.19 153	97.27 199	99.63 355	97.89 172	99.97 21	99.20 294

mamv4		99.44 19	99.39 28	99.58 21	99.30 200	99.74 2	99.04 68	99.81 31	99.77 10	99.82 34	99.57 49	97.82 150	99.98 4	99.53 48	99.89 92	99.01 330

viewcassd2359sk11		98.55 178	98.51 166	98.67 218	99.29 203	96.99 251	97.39 306	99.54 118	97.73 242	98.81 242	99.08 185	97.55 174	99.66 341	97.52 208	99.67 222	99.36 242

SymmetryMVS		98.05 248	97.71 273	99.09 132	99.29 203	97.83 178	98.28 163	97.64 405	99.24 76	98.80 244	98.85 253	89.76 389	99.94 42	98.04 159	99.50 290	99.49 172

Anonymous20231211		99.27 38	99.27 48	99.26 101	99.29 203	98.18 137	99.49 12	99.51 128	99.70 16	99.80 38	99.68 25	96.84 225	99.83 192	99.21 71	99.91 78	99.77 50

viewmanbaseed2359cas		98.58 172	98.54 162	98.70 213	99.28 206	97.13 244	97.47 299	99.55 113	97.55 261	98.96 209	98.92 234	97.77 154	99.59 372	97.59 202	99.77 161	99.39 224

UnsupCasMVSNet_bld		97.30 313	96.92 323	98.45 263	99.28 206	96.78 267	96.20 390	99.27 245	95.42 387	98.28 306	98.30 350	93.16 346	99.71 300	94.99 363	97.37 444	98.87 358

EC-MVSNet		99.09 73	99.05 83	99.20 110	99.28 206	98.93 80	99.24 44	99.84 22	99.08 112	98.12 319	98.37 342	98.72 49	99.90 81	99.05 84	99.77 161	98.77 375

mamba_0408		98.80 125	98.88 104	98.55 246	99.27 209	96.50 281	98.00 209	99.60 83	98.93 129	99.22 160	98.84 258	98.59 65	99.89 97	97.74 189	99.72 192	99.27 272

SSM_04072		98.80 125	98.88 104	98.56 244	99.27 209	96.50 281	98.00 209	99.60 83	98.93 129	99.22 160	98.84 258	98.59 65	99.90 81	97.74 189	99.72 192	99.27 272

SSM_0407		98.86 112	98.96 97	98.55 246	99.27 209	96.50 281	98.04 200	99.66 65	99.09 108	99.22 160	99.02 199	98.79 42	99.87 134	97.87 177	99.72 192	99.27 272

reproduce-ours		99.09 73	98.90 101	99.67 4	99.27 209	99.49 6	98.00 209	99.42 178	99.05 115	99.48 96	99.27 128	98.29 95	99.89 97	97.61 199	99.71 201	99.62 90

our_new_method		99.09 73	98.90 101	99.67 4	99.27 209	99.49 6	98.00 209	99.42 178	99.05 115	99.48 96	99.27 128	98.29 95	99.89 97	97.61 199	99.71 201	99.62 90

DPE-MVS		98.59 170	98.26 213	99.57 22	99.27 209	99.15 53	97.01 340	99.39 187	97.67 246	99.44 105	98.99 216	97.53 178	99.89 97	95.40 357	99.68 216	99.66 78
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
IterMVS-SCA-FT		97.85 272	98.18 225	96.87 395	99.27 209	91.16 444	95.53 424	99.25 251	99.10 105	99.41 112	99.35 108	93.10 348	99.96 14	98.65 115	99.94 50	99.49 172

v1192		98.60 168	98.66 142	98.41 268	99.27 209	95.88 304	97.52 289	99.36 197	97.41 278	99.33 130	99.20 150	96.37 256	99.82 205	99.57 39	99.92 69	99.55 136

N_pmnet		97.63 286	97.17 307	98.99 151	99.27 209	97.86 175	95.98 401	93.41 468	95.25 392	99.47 100	98.90 240	95.63 289	99.85 156	96.91 254	99.73 184	99.27 272

viewdifsd2359ckpt13		98.39 206	98.29 208	98.70 213	99.26 218	97.19 236	97.51 291	99.48 141	96.94 319	98.58 276	98.82 263	97.47 187	99.55 388	97.21 229	99.33 318	99.34 249

FPMVS		93.44 424	92.23 431	97.08 383	99.25 219	97.86 175	95.61 421	97.16 417	92.90 437	93.76 470	98.65 303	75.94 463	95.66 484	79.30 482	97.49 437	97.73 448

ME-MVS		98.61 166	98.33 203	99.44 66	99.24 220	98.93 80	97.45 301	99.06 292	98.14 212	99.06 180	98.77 273	96.97 219	99.82 205	96.67 283	99.64 233	99.58 115

new-patchmatchnet		98.35 209	98.74 122	97.18 378	99.24 220	92.23 426	96.42 377	99.48 141	98.30 186	99.69 56	99.53 65	97.44 188	99.82 205	98.84 100	99.77 161	99.49 172

MCST-MVS		98.00 253	97.63 281	99.10 128	99.24 220	98.17 138	96.89 349	98.73 356	95.66 378	97.92 335	97.70 392	97.17 206	99.66 341	96.18 325	99.23 337	99.47 191

UniMVSNet (Re)		98.87 108	98.71 131	99.35 80	99.24 220	98.73 95	97.73 257	99.38 189	98.93 129	99.12 172	98.73 282	96.77 233	99.86 143	98.63 117	99.80 144	99.46 193

jason		97.45 300	97.35 298	97.76 331	99.24 220	93.93 387	95.86 411	98.42 376	94.24 416	98.50 288	98.13 360	94.82 312	99.91 74	97.22 228	99.73 184	99.43 206
jason: jason.
IterMVS		97.73 278	98.11 234	96.57 405	99.24 220	90.28 453	95.52 426	99.21 260	98.86 139	99.33 130	99.33 115	93.11 347	99.94 42	98.49 127	99.94 50	99.48 183
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		98.55 178	98.62 149	98.32 279	99.22 226	95.58 315	97.51 291	99.45 158	97.16 307	99.45 104	99.24 141	96.12 267	99.85 156	99.60 37	99.88 94	99.55 136

ITE_SJBPF				98.87 172	99.22 226	98.48 114		99.35 203	97.50 266	98.28 306	98.60 314	97.64 165	99.35 435	93.86 399	99.27 329	98.79 373

h-mvs33		97.77 276	97.33 300	99.10 128	99.21 228	97.84 177	98.35 159	98.57 368	99.11 98	98.58 276	99.02 199	88.65 400	99.96 14	98.11 152	96.34 460	99.49 172

v144192		98.54 181	98.57 158	98.45 263	99.21 228	95.98 301	97.63 272	99.36 197	97.15 309	99.32 136	99.18 157	95.84 284	99.84 174	99.50 51	99.91 78	99.54 142

APDe-MVS		98.99 89	98.79 118	99.60 16	99.21 228	99.15 53	98.87 88	99.48 141	97.57 257	99.35 125	99.24 141	97.83 147	99.89 97	97.88 175	99.70 208	99.75 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DP-MVS		98.93 99	98.81 117	99.28 96	99.21 228	98.45 116	98.46 144	99.33 215	99.63 29	99.48 96	99.15 167	97.23 202	99.75 276	97.17 231	99.66 230	99.63 89

SR-MVS-dyc-post		98.81 123	98.55 160	99.57 22	99.20 232	99.38 13	98.48 142	99.30 230	98.64 154	98.95 210	98.96 226	97.49 185	99.86 143	96.56 298	99.39 309	99.45 198

RE-MVS-def			98.58 157		99.20 232	99.38 13	98.48 142	99.30 230	98.64 154	98.95 210	98.96 226	97.75 156		96.56 298	99.39 309	99.45 198

v1921920		98.54 181	98.60 154	98.38 272	99.20 232	95.76 311	97.56 284	99.36 197	97.23 301	99.38 118	99.17 161	96.02 270	99.84 174	99.57 39	99.90 86	99.54 142

E3new		98.41 197	98.34 198	98.62 228	99.19 235	96.90 259	97.32 316	99.50 131	97.40 280	98.63 265	98.92 234	97.21 204	99.65 348	97.34 220	99.52 279	99.31 262

thisisatest0530		95.27 393	94.45 404	97.74 334	99.19 235	94.37 365	97.86 235	90.20 480	97.17 306	98.22 309	97.65 394	73.53 466	99.90 81	96.90 259	99.35 315	98.95 343

Anonymous20240529		98.93 99	98.87 107	99.12 124	99.19 235	98.22 135	99.01 70	98.99 310	99.25 75	99.54 79	99.37 103	97.04 212	99.80 231	97.89 172	99.52 279	99.35 247

APD-MVS_3200maxsize		98.84 115	98.61 153	99.53 39	99.19 235	99.27 28	98.49 139	99.33 215	98.64 154	99.03 193	98.98 221	97.89 142	99.85 156	96.54 302	99.42 306	99.46 193

HQP_MVS		97.99 256	97.67 275	98.93 164	99.19 235	97.65 197	97.77 248	99.27 245	98.20 200	97.79 347	97.98 374	94.90 308	99.70 307	94.42 381	99.51 282	99.45 198

plane_prior7					99.19 235	97.87 174

ab-mvs		98.41 197	98.36 195	98.59 235	99.19 235	97.23 229	99.32 26	98.81 342	97.66 247	98.62 268	99.40 97	96.82 228	99.80 231	95.88 336	99.51 282	98.75 378

F-COLMAP		97.30 313	96.68 340	99.14 122	99.19 235	98.39 118	97.27 324	99.30 230	92.93 436	96.62 414	98.00 372	95.73 287	99.68 324	92.62 427	98.46 405	99.35 247

viewdifsd2359ckpt09		98.13 241	97.92 257	98.77 199	99.18 243	97.35 216	97.29 320	99.53 122	95.81 375	98.09 322	98.47 332	96.34 258	99.66 341	97.02 244	99.51 282	99.29 268

SR-MVS		98.71 138	98.43 183	99.57 22	99.18 243	99.35 17	98.36 158	99.29 238	98.29 189	98.88 229	98.85 253	97.53 178	99.87 134	96.14 327	99.31 322	99.48 183

UniMVSNet_NR-MVSNet		98.86 112	98.68 137	99.40 72	99.17 245	98.74 92	97.68 262	99.40 185	99.14 96	99.06 180	98.59 315	96.71 239	99.93 54	98.57 120	99.77 161	99.53 154

LF4IMVS		97.90 260	97.69 274	98.52 254	99.17 245	97.66 196	97.19 334	99.47 150	96.31 353	97.85 343	98.20 357	96.71 239	99.52 400	94.62 373	99.72 192	98.38 414

SMA-MVS		98.40 200	98.03 243	99.51 49	99.16 247	99.21 34	98.05 198	99.22 259	94.16 418	98.98 200	99.10 179	97.52 180	99.79 244	96.45 308	99.64 233	99.53 154
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		98.82 121	98.63 147	99.39 73	99.16 247	98.74 92	97.54 287	99.25 251	98.84 143	99.06 180	98.76 279	96.76 235	99.93 54	98.57 120	99.77 161	99.50 165

NR-MVSNet		98.95 96	98.82 115	99.36 74	99.16 247	98.72 97	99.22 45	99.20 262	99.10 105	99.72 48	98.76 279	96.38 255	99.86 143	98.00 164	99.82 127	99.50 165

MVS_111021_LR		98.30 218	98.12 233	98.83 180	99.16 247	98.03 157	96.09 398	99.30 230	97.58 256	98.10 321	98.24 353	98.25 103	99.34 436	96.69 281	99.65 231	99.12 316

DSMNet-mixed		97.42 303	97.60 283	96.87 395	99.15 251	91.46 433	98.54 127	99.12 284	92.87 438	97.58 360	99.63 39	96.21 262	99.90 81	95.74 345	99.54 272	99.27 272

D2MVS		97.84 273	97.84 264	97.83 323	99.14 252	94.74 354	96.94 344	98.88 326	95.84 374	98.89 225	98.96 226	94.40 324	99.69 314	97.55 203	99.95 38	99.05 322

pmmvs5		97.64 285	97.49 289	98.08 305	99.14 252	95.12 340	96.70 359	99.05 296	93.77 425	98.62 268	98.83 260	93.23 344	99.75 276	98.33 140	99.76 176	99.36 242

SPE-MVS-test		99.13 67	99.09 79	99.26 101	99.13 254	98.97 74	99.31 30	99.88 14	99.44 53	98.16 314	98.51 324	98.64 59	99.93 54	98.91 94	99.85 107	98.88 357

VDD-MVS		98.56 174	98.39 190	99.07 135	99.13 254	98.07 152	98.59 121	97.01 420	99.59 37	99.11 173	99.27 128	94.82 312	99.79 244	98.34 138	99.63 240	99.34 249

save fliter					99.11 256	97.97 163	96.53 369	99.02 304	98.24 192

APD-MVS		98.10 242	97.67 275	99.42 68	99.11 256	98.93 80	97.76 251	99.28 242	94.97 399	98.72 255	98.77 273	97.04 212	99.85 156	93.79 401	99.54 272	99.49 172
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EI-MVSNet-UG-set		98.69 147	98.71 131	98.62 228	99.10 258	96.37 287	97.23 325	98.87 328	99.20 83	99.19 165	98.99 216	97.30 196	99.85 156	98.77 106	99.79 150	99.65 83

EI-MVSNet		98.40 200	98.51 166	98.04 310	99.10 258	94.73 355	97.20 330	98.87 328	98.97 124	99.06 180	99.02 199	96.00 272	99.80 231	98.58 118	99.82 127	99.60 100

CVMVSNet		96.25 365	97.21 306	93.38 462	99.10 258	80.56 490	97.20 330	98.19 387	96.94 319	99.00 195	99.02 199	89.50 393	99.80 231	96.36 314	99.59 254	99.78 47

EI-MVSNet-Vis-set		98.68 153	98.70 134	98.63 226	99.09 261	96.40 286	97.23 325	98.86 333	99.20 83	99.18 169	98.97 223	97.29 198	99.85 156	98.72 110	99.78 155	99.64 84

HPM-MVS++		98.10 242	97.64 280	99.48 57	99.09 261	99.13 61	97.52 289	98.75 353	97.46 274	96.90 401	97.83 384	96.01 271	99.84 174	95.82 343	99.35 315	99.46 193

DP-MVS Recon		97.33 311	96.92 323	98.57 239	99.09 261	97.99 159	96.79 352	99.35 203	93.18 432	97.71 351	98.07 368	95.00 307	99.31 440	93.97 394	99.13 353	98.42 411

MVS_111021_HR		98.25 227	98.08 238	98.75 203	99.09 261	97.46 210	95.97 402	99.27 245	97.60 255	97.99 332	98.25 352	98.15 119	99.38 431	96.87 262	99.57 263	99.42 211

BP-MVS1		97.40 305	96.97 319	98.71 212	99.07 265	96.81 263	98.34 161	97.18 415	98.58 165	98.17 311	98.61 312	84.01 434	99.94 42	98.97 90	99.78 155	99.37 235

9.14			97.78 266		99.07 265		97.53 288	99.32 217	95.53 384	98.54 284	98.70 292	97.58 171	99.76 267	94.32 386	99.46 295

PAPM_NR		96.82 345	96.32 356	98.30 282	99.07 265	96.69 271	97.48 295	98.76 350	95.81 375	96.61 415	96.47 430	94.12 333	99.17 453	90.82 454	97.78 431	99.06 321

TAMVS		98.24 228	98.05 241	98.80 187	99.07 265	97.18 238	97.88 231	98.81 342	96.66 337	99.17 171	99.21 148	94.81 314	99.77 261	96.96 252	99.88 94	99.44 202

CLD-MVS		97.49 296	97.16 308	98.48 260	99.07 265	97.03 249	94.71 449	99.21 260	94.46 410	98.06 325	97.16 416	97.57 172	99.48 412	94.46 378	99.78 155	98.95 343
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CS-MVS		99.13 67	99.10 77	99.24 106	99.06 270	99.15 53	99.36 22	99.88 14	99.36 64	98.21 310	98.46 333	98.68 56	99.93 54	99.03 86	99.85 107	98.64 390

thres100view900		94.19 410	93.67 415	95.75 429	99.06 270	91.35 437	98.03 202	94.24 463	98.33 182	97.40 376	94.98 460	79.84 450	99.62 358	83.05 475	98.08 422	96.29 470

thres600view7		94.45 405	93.83 412	96.29 413	99.06 270	91.53 432	97.99 216	94.24 463	98.34 181	97.44 374	95.01 458	79.84 450	99.67 328	84.33 473	98.23 411	97.66 451

plane_prior1					99.05 273

YYNet1		97.60 287	97.67 275	97.39 371	99.04 274	93.04 410	95.27 434	98.38 379	97.25 295	98.92 220	98.95 230	95.48 296	99.73 289	96.99 248	98.74 386	99.41 214

MDA-MVSNet_test_wron		97.60 287	97.66 278	97.41 370	99.04 274	93.09 406	95.27 434	98.42 376	97.26 294	98.88 229	98.95 230	95.43 297	99.73 289	97.02 244	98.72 388	99.41 214

MIMVSNet		96.62 352	96.25 360	97.71 338	99.04 274	94.66 358	99.16 54	96.92 426	97.23 301	97.87 340	99.10 179	86.11 416	99.65 348	91.65 438	99.21 341	98.82 362

FE-MVSNET3		97.37 307	97.13 312	98.11 301	99.03 277	95.40 328	94.47 459	98.99 310	96.87 325	97.97 333	97.81 385	92.12 366	99.75 276	97.49 214	99.43 305	99.16 311

icg_test_0407_2		98.20 233	98.38 192	97.65 344	99.03 277	94.03 378	95.78 416	99.45 158	98.16 206	99.06 180	98.71 285	98.27 99	99.68 324	97.50 209	99.45 297	99.22 289

IMVS_0407		98.39 206	98.64 145	97.66 342	99.03 277	94.03 378	98.10 188	99.45 158	98.16 206	99.06 180	98.71 285	98.27 99	99.71 300	97.50 209	99.45 297	99.22 289

IMVS_0404		98.07 246	98.20 220	97.69 339	99.03 277	94.03 378	96.67 360	99.45 158	98.16 206	98.03 329	98.71 285	96.80 231	99.82 205	97.50 209	99.45 297	99.22 289

IMVS_0403		98.34 210	98.56 159	97.66 342	99.03 277	94.03 378	97.98 217	99.45 158	98.16 206	98.89 225	98.71 285	97.90 138	99.74 282	97.50 209	99.45 297	99.22 289

PatchMatch-RL		97.24 319	96.78 334	98.61 232	99.03 277	97.83 178	96.36 380	99.06 292	93.49 430	97.36 380	97.78 386	95.75 286	99.49 409	93.44 410	98.77 385	98.52 399

viewmambaseed2359dif		98.19 234	98.26 213	97.99 313	99.02 283	95.03 343	96.59 366	99.53 122	96.21 357	99.00 195	98.99 216	97.62 167	99.61 365	97.62 198	99.72 192	99.33 255

GDP-MVS		97.50 293	97.11 313	98.67 218	99.02 283	96.85 261	98.16 178	99.71 47	98.32 184	98.52 287	98.54 319	83.39 438	99.95 26	98.79 102	99.56 266	99.19 299

ZD-MVS					99.01 285	98.84 86		99.07 291	94.10 420	98.05 327	98.12 362	96.36 257	99.86 143	92.70 426	99.19 345

CDPH-MVS		97.26 316	96.66 343	99.07 135	99.00 286	98.15 139	96.03 400	99.01 307	91.21 456	97.79 347	97.85 383	96.89 223	99.69 314	92.75 424	99.38 312	99.39 224

diffmvs		98.22 229	98.24 217	98.17 297	99.00 286	95.44 326	96.38 379	99.58 93	97.79 239	98.53 285	98.50 328	96.76 235	99.74 282	97.95 170	99.64 233	99.34 249
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		98.40 200	98.19 224	99.03 145	99.00 286	97.65 197	96.85 350	98.94 313	98.57 167	98.89 225	98.50 328	95.60 290	99.85 156	97.54 205	99.85 107	99.59 107

plane_prior6					98.99 289	97.70 195						94.90 308

xiu_mvs_v1_base_debu		97.86 267	98.17 226	96.92 392	98.98 290	93.91 388	96.45 373	99.17 274	97.85 234	98.41 296	97.14 418	98.47 75	99.92 65	98.02 161	99.05 359	96.92 463

xiu_mvs_v1_base		97.86 267	98.17 226	96.92 392	98.98 290	93.91 388	96.45 373	99.17 274	97.85 234	98.41 296	97.14 418	98.47 75	99.92 65	98.02 161	99.05 359	96.92 463

xiu_mvs_v1_base_debi		97.86 267	98.17 226	96.92 392	98.98 290	93.91 388	96.45 373	99.17 274	97.85 234	98.41 296	97.14 418	98.47 75	99.92 65	98.02 161	99.05 359	96.92 463

MVP-Stereo		98.08 245	97.92 257	98.57 239	98.96 293	96.79 264	97.90 229	99.18 270	96.41 349	98.46 291	98.95 230	95.93 281	99.60 368	96.51 304	98.98 373	99.31 262
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
SD-MVS		98.40 200	98.68 137	97.54 359	98.96 293	97.99 159	97.88 231	99.36 197	98.20 200	99.63 67	99.04 196	98.76 45	95.33 486	96.56 298	99.74 181	99.31 262
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				98.91 168	98.94 295	97.76 189		98.76 350	87.58 473	96.75 409	98.10 364	94.80 315	99.78 255	92.73 425	99.00 368	99.20 294

USDC		97.41 304	97.40 293	97.44 368	98.94 295	93.67 398	95.17 438	99.53 122	94.03 422	98.97 204	99.10 179	95.29 299	99.34 436	95.84 342	99.73 184	99.30 266

tfpn200view9		94.03 414	93.44 417	95.78 428	98.93 297	91.44 435	97.60 279	94.29 461	97.94 226	97.10 386	94.31 467	79.67 452	99.62 358	83.05 475	98.08 422	96.29 470

testdata				98.09 302	98.93 297	95.40 328		98.80 344	90.08 464	97.45 373	98.37 342	95.26 300	99.70 307	93.58 406	98.95 376	99.17 307

thres400		94.14 412	93.44 417	96.24 416	98.93 297	91.44 435	97.60 279	94.29 461	97.94 226	97.10 386	94.31 467	79.67 452	99.62 358	83.05 475	98.08 422	97.66 451

TAPA-MVS	96.21 11	96.63 351	95.95 362	98.65 220	98.93 297	98.09 146	96.93 346	99.28 242	83.58 479	98.13 318	97.78 386	96.13 265	99.40 427	93.52 407	99.29 327	98.45 404
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test222					98.92 301	96.93 257	95.54 423	98.78 347	85.72 476	96.86 404	98.11 363	94.43 322			99.10 358	99.23 284

PVSNet_BlendedMVS		97.55 292	97.53 286	97.60 351	98.92 301	93.77 395	96.64 362	99.43 172	94.49 408	97.62 356	99.18 157	96.82 228	99.67 328	94.73 370	99.93 56	99.36 242

PVSNet_Blended		96.88 341	96.68 340	97.47 366	98.92 301	93.77 395	94.71 449	99.43 172	90.98 458	97.62 356	97.36 412	96.82 228	99.67 328	94.73 370	99.56 266	98.98 336

MSDG		97.71 280	97.52 287	98.28 284	98.91 304	96.82 262	94.42 460	99.37 193	97.65 248	98.37 301	98.29 351	97.40 190	99.33 438	94.09 392	99.22 338	98.68 388

Anonymous202405211		97.90 260	97.50 288	99.08 133	98.90 305	98.25 129	98.53 128	96.16 438	98.87 137	99.11 173	98.86 250	90.40 385	99.78 255	97.36 219	99.31 322	99.19 299

原ACMM1				98.35 277	98.90 305	96.25 291		98.83 341	92.48 442	96.07 432	98.10 364	95.39 298	99.71 300	92.61 428	98.99 370	99.08 318

GBi-Net		98.65 158	98.47 177	99.17 115	98.90 305	98.24 130	99.20 48	99.44 166	98.59 162	98.95 210	99.55 57	94.14 330	99.86 143	97.77 184	99.69 211	99.41 214

test1		98.65 158	98.47 177	99.17 115	98.90 305	98.24 130	99.20 48	99.44 166	98.59 162	98.95 210	99.55 57	94.14 330	99.86 143	97.77 184	99.69 211	99.41 214

FMVSNet2		98.49 190	98.40 187	98.75 203	98.90 305	97.14 243	98.61 119	99.13 283	98.59 162	99.19 165	99.28 126	94.14 330	99.82 205	97.97 168	99.80 144	99.29 268

OMC-MVS		97.88 264	97.49 289	99.04 144	98.89 310	98.63 99	96.94 344	99.25 251	95.02 397	98.53 285	98.51 324	97.27 199	99.47 415	93.50 409	99.51 282	99.01 330

VortexMVS		97.98 257	98.31 205	97.02 386	98.88 311	91.45 434	98.03 202	99.47 150	98.65 153	99.55 77	99.47 79	91.49 374	99.81 222	99.32 61	99.91 78	99.80 42

MVSFormer		98.26 224	98.43 183	97.77 328	98.88 311	93.89 391	99.39 20	99.56 109	99.11 98	98.16 314	98.13 360	93.81 338	99.97 7	99.26 66	99.57 263	99.43 206

lupinMVS		97.06 331	96.86 327	97.65 344	98.88 311	93.89 391	95.48 427	97.97 393	93.53 428	98.16 314	97.58 398	93.81 338	99.91 74	96.77 270	99.57 263	99.17 307

dmvs_re		95.98 375	95.39 384	97.74 334	98.86 314	97.45 211	98.37 157	95.69 450	97.95 224	96.56 416	95.95 439	90.70 382	97.68 480	88.32 463	96.13 464	98.11 426

DELS-MVS		98.27 222	98.20 220	98.48 260	98.86 314	96.70 270	95.60 422	99.20 262	97.73 242	98.45 292	98.71 285	97.50 182	99.82 205	98.21 146	99.59 254	98.93 348
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		97.89 262	97.98 248	97.60 351	98.86 314	94.35 366	96.21 389	99.44 166	97.45 276	99.06 180	98.88 247	97.99 132	99.28 446	94.38 385	99.58 259	99.18 303

LCM-MVSNet-Re		98.64 160	98.48 175	99.11 126	98.85 317	98.51 112	98.49 139	99.83 25	98.37 178	99.69 56	99.46 81	98.21 110	99.92 65	94.13 391	99.30 325	98.91 352

pmmvs4		97.58 290	97.28 301	98.51 255	98.84 318	96.93 257	95.40 431	98.52 371	93.60 427	98.61 270	98.65 303	95.10 304	99.60 368	96.97 251	99.79 150	98.99 335

NP-MVS					98.84 318	97.39 215					96.84 421

sss		97.21 321	96.93 321	98.06 307	98.83 320	95.22 336	96.75 356	98.48 373	94.49 408	97.27 382	97.90 380	92.77 356	99.80 231	96.57 294	99.32 320	99.16 311

PVSNet	93.40 17	95.67 384	95.70 368	95.57 433	98.83 320	88.57 460	92.50 477	97.72 398	92.69 440	96.49 424	96.44 431	93.72 341	99.43 423	93.61 404	99.28 328	98.71 381

MVE	83.40 22	92.50 437	91.92 439	94.25 449	98.83 320	91.64 431	92.71 476	83.52 489	95.92 372	86.46 487	95.46 452	95.20 301	95.40 485	80.51 480	98.64 397	95.73 478
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testing3-2		93.78 418	93.91 410	93.39 461	98.82 323	81.72 488	97.76 251	95.28 452	98.60 161	96.54 417	96.66 425	65.85 482	99.62 358	96.65 287	98.99 370	98.82 362

ambc				98.24 289	98.82 323	95.97 302	98.62 117	99.00 309		99.27 144	99.21 148	96.99 217	99.50 406	96.55 301	99.50 290	99.26 278

旧先验1					98.82 323	97.45 211		98.76 350			98.34 346	95.50 295			99.01 367	99.23 284

test_vis1_rt		97.75 277	97.72 272	97.83 323	98.81 326	96.35 288	97.30 319	99.69 54	94.61 406	97.87 340	98.05 369	96.26 261	98.32 474	98.74 108	98.18 414	98.82 362

WTY-MVS		96.67 349	96.27 359	97.87 321	98.81 326	94.61 360	96.77 354	97.92 395	94.94 400	97.12 385	97.74 389	91.11 378	99.82 205	93.89 397	98.15 418	99.18 303

3Dnovator+	97.89 3	98.69 147	98.51 166	99.24 106	98.81 326	98.40 117	99.02 69	99.19 266	98.99 121	98.07 324	99.28 126	97.11 210	99.84 174	96.84 265	99.32 320	99.47 191

QAPM		97.31 312	96.81 333	98.82 182	98.80 329	97.49 205	99.06 65	99.19 266	90.22 462	97.69 353	99.16 163	96.91 222	99.90 81	90.89 453	99.41 307	99.07 320

VNet		98.42 196	98.30 206	98.79 191	98.79 330	97.29 225	98.23 169	98.66 360	99.31 69	98.85 234	98.80 267	94.80 315	99.78 255	98.13 151	99.13 353	99.31 262

DPM-MVS		96.32 361	95.59 375	98.51 255	98.76 331	97.21 234	94.54 458	98.26 382	91.94 447	96.37 425	97.25 414	93.06 350	99.43 423	91.42 443	98.74 386	98.89 354

3Dnovator	98.27 2	98.81 123	98.73 124	99.05 142	98.76 331	97.81 186	99.25 43	99.30 230	98.57 167	98.55 282	99.33 115	97.95 135	99.90 81	97.16 232	99.67 222	99.44 202

PLC	94.65 16	96.51 354	95.73 367	98.85 175	98.75 333	97.91 171	96.42 377	99.06 292	90.94 459	95.59 439	97.38 410	94.41 323	99.59 372	90.93 451	98.04 427	99.05 322
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
BH-untuned		96.83 343	96.75 336	97.08 383	98.74 334	93.33 404	96.71 358	98.26 382	96.72 334	98.44 293	97.37 411	95.20 301	99.47 415	91.89 433	97.43 441	98.44 407

hse-mvs2		97.46 298	97.07 314	98.64 222	98.73 335	97.33 218	97.45 301	97.64 405	99.11 98	98.58 276	97.98 374	88.65 400	99.79 244	98.11 152	97.39 443	98.81 367

CDS-MVSNet		97.69 281	97.35 298	98.69 215	98.73 335	97.02 250	96.92 348	98.75 353	95.89 373	98.59 274	98.67 298	92.08 368	99.74 282	96.72 276	99.81 133	99.32 258
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SD_0403		96.28 363	95.83 364	97.64 347	98.72 337	94.30 367	98.87 88	98.77 348	97.80 237	96.53 418	98.02 371	97.34 194	99.47 415	76.93 484	99.48 293	99.16 311

EIA-MVS		98.00 253	97.74 269	98.80 187	98.72 337	98.09 146	98.05 198	99.60 83	97.39 281	96.63 413	95.55 447	97.68 159	99.80 231	96.73 275	99.27 329	98.52 399

LFMVS		97.20 322	96.72 337	98.64 222	98.72 337	96.95 255	98.93 81	94.14 465	99.74 13	98.78 246	99.01 210	84.45 429	99.73 289	97.44 215	99.27 329	99.25 279

new_pmnet		96.99 338	96.76 335	97.67 340	98.72 337	94.89 348	95.95 406	98.20 385	92.62 441	98.55 282	98.54 319	94.88 311	99.52 400	93.96 395	99.44 304	98.59 396

Fast-Effi-MVS+		97.67 283	97.38 295	98.57 239	98.71 341	97.43 213	97.23 325	99.45 158	94.82 403	96.13 429	96.51 427	98.52 73	99.91 74	96.19 323	98.83 382	98.37 416

TEST9					98.71 341	98.08 150	95.96 404	99.03 301	91.40 453	95.85 436	97.53 400	96.52 248	99.76 267

train_agg		97.10 328	96.45 353	99.07 135	98.71 341	98.08 150	95.96 404	99.03 301	91.64 448	95.85 436	97.53 400	96.47 250	99.76 267	93.67 403	99.16 348	99.36 242

TSAR-MVS + GP.		98.18 236	97.98 248	98.77 199	98.71 341	97.88 173	96.32 383	98.66 360	96.33 351	99.23 159	98.51 324	97.48 186	99.40 427	97.16 232	99.46 295	99.02 329

FA-MVS(test-final)		96.99 338	96.82 331	97.50 363	98.70 345	94.78 352	99.34 23	96.99 421	95.07 396	98.48 290	99.33 115	88.41 403	99.65 348	96.13 329	98.92 379	98.07 429

AUN-MVS		96.24 367	95.45 380	98.60 234	98.70 345	97.22 232	97.38 308	97.65 403	95.95 371	95.53 446	97.96 378	82.11 446	99.79 244	96.31 316	97.44 440	98.80 372

our_test_3		97.39 306	97.73 271	96.34 411	98.70 345	89.78 456	94.61 455	98.97 312	96.50 342	99.04 190	98.85 253	95.98 277	99.84 174	97.26 226	99.67 222	99.41 214

ppachtmachnet_test		97.50 293	97.74 269	96.78 401	98.70 345	91.23 443	94.55 457	99.05 296	96.36 350	99.21 163	98.79 269	96.39 253	99.78 255	96.74 273	99.82 127	99.34 249

PCF-MVS	92.86 18	94.36 406	93.00 424	98.42 267	98.70 345	97.56 202	93.16 475	99.11 286	79.59 483	97.55 363	97.43 407	92.19 364	99.73 289	79.85 481	99.45 297	97.97 435
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ttmdpeth		97.91 259	98.02 244	97.58 353	98.69 350	94.10 374	98.13 181	98.90 322	97.95 224	97.32 381	99.58 47	95.95 280	98.75 469	96.41 310	99.22 338	99.87 22

ETV-MVS		98.03 249	97.86 263	98.56 244	98.69 350	98.07 152	97.51 291	99.50 131	98.10 214	97.50 368	95.51 448	98.41 82	99.88 115	96.27 319	99.24 334	97.71 450

test_prior				98.95 160	98.69 350	97.95 167		99.03 301					99.59 372			99.30 266

mvsmamba		97.57 291	97.26 302	98.51 255	98.69 350	96.73 269	98.74 98	97.25 414	97.03 315	97.88 339	99.23 146	90.95 379	99.87 134	96.61 290	99.00 368	98.91 352

agg_prior					98.68 354	97.99 159		99.01 307		95.59 439			99.77 261

test_8					98.67 355	98.01 158	95.91 410	99.02 304	91.64 448	95.79 438	97.50 403	96.47 250	99.76 267

HQP-NCC					98.67 355		96.29 385		96.05 364	95.55 442

ACMP_Plane					98.67 355		96.29 385		96.05 364	95.55 442

CNVR-MVS		98.17 238	97.87 262	99.07 135	98.67 355	98.24 130	97.01 340	98.93 316	97.25 295	97.62 356	98.34 346	97.27 199	99.57 381	96.42 309	99.33 318	99.39 224

HQP-MVS		97.00 337	96.49 352	98.55 246	98.67 355	96.79 264	96.29 385	99.04 299	96.05 364	95.55 442	96.84 421	93.84 336	99.54 394	92.82 421	99.26 332	99.32 258

MM		98.22 229	97.99 247	98.91 168	98.66 360	96.97 252	97.89 230	94.44 459	99.54 41	98.95 210	99.14 170	93.50 342	99.92 65	99.80 17	99.96 28	99.85 30

test_fmvs1		97.72 279	97.94 254	97.07 385	98.66 360	92.39 421	97.68 262	99.81 31	95.20 395	99.54 79	99.44 86	91.56 373	99.41 426	99.78 21	99.77 161	99.40 223

balanced_conf03		98.63 162	98.72 126	98.38 272	98.66 360	96.68 272	98.90 83	99.42 178	98.99 121	98.97 204	99.19 153	95.81 285	99.85 156	98.77 106	99.77 161	98.60 393

thres200		93.72 420	93.14 422	95.46 437	98.66 360	91.29 439	96.61 364	94.63 458	97.39 281	96.83 405	93.71 470	79.88 449	99.56 384	82.40 478	98.13 419	95.54 479

wuyk23d		96.06 370	97.62 282	91.38 466	98.65 364	98.57 106	98.85 92	96.95 424	96.86 327	99.90 14	99.16 163	99.18 19	98.40 473	89.23 461	99.77 161	77.18 486

NCCC		97.86 267	97.47 292	99.05 142	98.61 365	98.07 152	96.98 342	98.90 322	97.63 249	97.04 391	97.93 379	95.99 276	99.66 341	95.31 358	98.82 384	99.43 206

DeepC-MVS_fast	96.85 6	98.30 218	98.15 230	98.75 203	98.61 365	97.23 229	97.76 251	99.09 289	97.31 289	98.75 252	98.66 301	97.56 173	99.64 352	96.10 330	99.55 270	99.39 224
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testing3		93.51 422	92.09 433	97.75 332	98.60 367	94.40 364	97.32 316	95.26 453	97.56 259	96.79 408	95.50 449	53.57 491	99.77 261	95.26 359	98.97 374	99.08 318

thisisatest0515		94.12 413	93.16 421	96.97 390	98.60 367	92.90 411	93.77 471	90.61 478	94.10 420	96.91 398	95.87 442	74.99 464	99.80 231	94.52 376	99.12 356	98.20 422

GA-MVS		95.86 378	95.32 388	97.49 364	98.60 367	94.15 373	93.83 470	97.93 394	95.49 385	96.68 411	97.42 408	83.21 439	99.30 442	96.22 321	98.55 403	99.01 330

dmvs_testset		92.94 432	92.21 432	95.13 441	98.59 370	90.99 446	97.65 268	92.09 474	96.95 318	94.00 466	93.55 471	92.34 362	96.97 483	72.20 485	92.52 480	97.43 458

OPU-MVS				98.82 182	98.59 370	98.30 126	98.10 188				98.52 323	98.18 113	98.75 469	94.62 373	99.48 293	99.41 214

MSLP-MVS++		98.02 250	98.14 232	97.64 347	98.58 372	95.19 337	97.48 295	99.23 258	97.47 269	97.90 337	98.62 310	97.04 212	98.81 467	97.55 203	99.41 307	98.94 347

test12				98.93 164	98.58 372	97.83 178		98.66 360		96.53 418		95.51 294	99.69 314		99.13 353	99.27 272

CL-MVSNet_self_test		97.44 301	97.22 305	98.08 305	98.57 374	95.78 310	94.30 463	98.79 345	96.58 340	98.60 272	98.19 358	94.74 318	99.64 352	96.41 310	98.84 381	98.82 362

PS-MVSNAJ		97.08 330	97.39 294	96.16 422	98.56 375	92.46 419	95.24 436	98.85 336	97.25 295	97.49 369	95.99 438	98.07 123	99.90 81	96.37 312	98.67 396	96.12 475

CNLPA		97.17 325	96.71 338	98.55 246	98.56 375	98.05 156	96.33 382	98.93 316	96.91 323	97.06 389	97.39 409	94.38 325	99.45 420	91.66 437	99.18 347	98.14 425

xiu_mvs_v2_base		97.16 326	97.49 289	96.17 420	98.54 377	92.46 419	95.45 428	98.84 337	97.25 295	97.48 370	96.49 428	98.31 93	99.90 81	96.34 315	98.68 395	96.15 474

alignmvs		97.35 309	96.88 326	98.78 194	98.54 377	98.09 146	97.71 258	97.69 400	99.20 83	97.59 359	95.90 441	88.12 405	99.55 388	98.18 148	98.96 375	98.70 384

FE-MVS		95.66 385	94.95 398	97.77 328	98.53 379	95.28 333	99.40 19	96.09 441	93.11 434	97.96 334	99.26 134	79.10 456	99.77 261	92.40 430	98.71 390	98.27 420

Effi-MVS+		98.02 250	97.82 265	98.62 228	98.53 379	97.19 236	97.33 315	99.68 60	97.30 290	96.68 411	97.46 406	98.56 71	99.80 231	96.63 288	98.20 413	98.86 359

baseline1		95.96 376	95.44 381	97.52 361	98.51 381	93.99 385	98.39 155	96.09 441	98.21 196	98.40 300	97.76 388	86.88 408	99.63 355	95.42 356	89.27 483	98.95 343

MVS_Test		98.18 236	98.36 195	97.67 340	98.48 382	94.73 355	98.18 174	99.02 304	97.69 245	98.04 328	99.11 176	97.22 203	99.56 384	98.57 120	98.90 380	98.71 381

MGCFI-Net		98.34 210	98.28 209	98.51 255	98.47 383	97.59 201	98.96 77	99.48 141	99.18 91	97.40 376	95.50 449	98.66 57	99.50 406	98.18 148	98.71 390	98.44 407

BH-RMVSNet		96.83 343	96.58 348	97.58 353	98.47 383	94.05 375	96.67 360	97.36 409	96.70 336	97.87 340	97.98 374	95.14 303	99.44 422	90.47 456	98.58 402	99.25 279

sasdasda		98.34 210	98.26 213	98.58 236	98.46 385	97.82 183	98.96 77	99.46 154	99.19 88	97.46 371	95.46 452	98.59 65	99.46 418	98.08 155	98.71 390	98.46 401

canonicalmvs		98.34 210	98.26 213	98.58 236	98.46 385	97.82 183	98.96 77	99.46 154	99.19 88	97.46 371	95.46 452	98.59 65	99.46 418	98.08 155	98.71 390	98.46 401

MVS-HIRNet		94.32 407	95.62 371	90.42 467	98.46 385	75.36 491	96.29 385	89.13 482	95.25 392	95.38 448	99.75 16	92.88 353	99.19 452	94.07 393	99.39 309	96.72 468

PHI-MVS		98.29 221	97.95 252	99.34 83	98.44 388	99.16 49	98.12 185	99.38 189	96.01 368	98.06 325	98.43 336	97.80 152	99.67 328	95.69 348	99.58 259	99.20 294

DVP-MVS++		98.90 103	98.70 134	99.51 49	98.43 389	99.15 53	99.43 15	99.32 217	98.17 203	99.26 148	99.02 199	98.18 113	99.88 115	97.07 241	99.45 297	99.49 172

MSC_two_6792asdad				99.32 91	98.43 389	98.37 121		98.86 333					99.89 97	97.14 235	99.60 250	99.71 63

No_MVS				99.32 91	98.43 389	98.37 121		98.86 333					99.89 97	97.14 235	99.60 250	99.71 63

Fast-Effi-MVS+-dtu		98.27 222	98.09 235	98.81 184	98.43 389	98.11 143	97.61 278	99.50 131	98.64 154	97.39 378	97.52 402	98.12 121	99.95 26	96.90 259	98.71 390	98.38 414

OpenMVS_ROB	95.38 14	95.84 380	95.18 393	97.81 325	98.41 393	97.15 242	97.37 312	98.62 364	83.86 478	98.65 263	98.37 342	94.29 328	99.68 324	88.41 462	98.62 400	96.60 469

DeepPCF-MVS	96.93 5	98.32 215	98.01 245	99.23 108	98.39 394	98.97 74	95.03 442	99.18 270	96.88 324	99.33 130	98.78 271	98.16 117	99.28 446	96.74 273	99.62 243	99.44 202

Patchmatch-test		96.55 353	96.34 355	97.17 380	98.35 395	93.06 407	98.40 154	97.79 396	97.33 286	98.41 296	98.67 298	83.68 437	99.69 314	95.16 361	99.31 322	98.77 375

AdaColmap		97.14 327	96.71 338	98.46 262	98.34 396	97.80 187	96.95 343	98.93 316	95.58 382	96.92 396	97.66 393	95.87 283	99.53 396	90.97 450	99.14 351	98.04 430

OpenMVS	96.65 7	97.09 329	96.68 340	98.32 279	98.32 397	97.16 241	98.86 91	99.37 193	89.48 466	96.29 427	99.15 167	96.56 246	99.90 81	92.90 418	99.20 342	97.89 438

MG-MVS		96.77 346	96.61 345	97.26 376	98.31 398	93.06 407	95.93 407	98.12 390	96.45 348	97.92 335	98.73 282	93.77 340	99.39 429	91.19 448	99.04 362	99.33 255

test_yl		96.69 347	96.29 357	97.90 317	98.28 399	95.24 334	97.29 320	97.36 409	98.21 196	98.17 311	97.86 381	86.27 412	99.55 388	94.87 367	98.32 407	98.89 354

DCV-MVSNet		96.69 347	96.29 357	97.90 317	98.28 399	95.24 334	97.29 320	97.36 409	98.21 196	98.17 311	97.86 381	86.27 412	99.55 388	94.87 367	98.32 407	98.89 354

CHOSEN 280x420		95.51 390	95.47 378	95.65 432	98.25 401	88.27 463	93.25 474	98.88 326	93.53 428	94.65 457	97.15 417	86.17 414	99.93 54	97.41 217	99.93 56	98.73 380

SCA		96.41 360	96.66 343	95.67 430	98.24 402	88.35 462	95.85 413	96.88 427	96.11 362	97.67 354	98.67 298	93.10 348	99.85 156	94.16 387	99.22 338	98.81 367

DeepMVS_CX				93.44 460	98.24 402	94.21 370		94.34 460	64.28 486	91.34 480	94.87 464	89.45 394	92.77 487	77.54 483	93.14 479	93.35 482

MS-PatchMatch		97.68 282	97.75 268	97.45 367	98.23 404	93.78 394	97.29 320	98.84 337	96.10 363	98.64 264	98.65 303	96.04 269	99.36 432	96.84 265	99.14 351	99.20 294

BH-w/o		95.13 396	94.89 400	95.86 425	98.20 405	91.31 438	95.65 420	97.37 408	93.64 426	96.52 420	95.70 445	93.04 351	99.02 458	88.10 464	95.82 469	97.24 461

mvs_anonymous		97.83 275	98.16 229	96.87 395	98.18 406	91.89 428	97.31 318	98.90 322	97.37 283	98.83 237	99.46 81	96.28 260	99.79 244	98.90 95	98.16 417	98.95 343

miper_lstm_enhance		97.18 324	97.16 308	97.25 377	98.16 407	92.85 412	95.15 440	99.31 222	97.25 295	98.74 254	98.78 271	90.07 386	99.78 255	97.19 230	99.80 144	99.11 317

RRT-MVS		97.88 264	97.98 248	97.61 350	98.15 408	93.77 395	98.97 76	99.64 71	99.16 93	98.69 257	99.42 90	91.60 371	99.89 97	97.63 197	98.52 404	99.16 311

ET-MVSNet_ETH3D		94.30 409	93.21 420	97.58 353	98.14 409	94.47 363	94.78 448	93.24 470	94.72 404	89.56 482	95.87 442	78.57 459	99.81 222	96.91 254	97.11 452	98.46 401

ADS-MVSNet2		95.43 391	94.98 396	96.76 402	98.14 409	91.74 429	97.92 226	97.76 397	90.23 460	96.51 421	98.91 237	85.61 420	99.85 156	92.88 419	96.90 453	98.69 385

ADS-MVSNet		95.24 394	94.93 399	96.18 419	98.14 409	90.10 455	97.92 226	97.32 412	90.23 460	96.51 421	98.91 237	85.61 420	99.74 282	92.88 419	96.90 453	98.69 385

c3_l		97.36 308	97.37 296	97.31 372	98.09 412	93.25 405	95.01 443	99.16 277	97.05 312	98.77 249	98.72 284	92.88 353	99.64 352	96.93 253	99.76 176	99.05 322

FMVSNet3		97.50 293	97.24 304	98.29 283	98.08 413	95.83 307	97.86 235	98.91 321	97.89 231	98.95 210	98.95 230	87.06 407	99.81 222	97.77 184	99.69 211	99.23 284

PAPM		91.88 447	90.34 449	96.51 406	98.06 414	92.56 417	92.44 478	97.17 416	86.35 474	90.38 481	96.01 437	86.61 410	99.21 451	70.65 487	95.43 471	97.75 447

Effi-MVS+-dtu		98.26 224	97.90 260	99.35 80	98.02 415	99.49 6	98.02 205	99.16 277	98.29 189	97.64 355	97.99 373	96.44 252	99.95 26	96.66 286	98.93 378	98.60 393

eth_miper_zixun_eth		97.23 320	97.25 303	97.17 380	98.00 416	92.77 414	94.71 449	99.18 270	97.27 293	98.56 280	98.74 281	91.89 369	99.69 314	97.06 243	99.81 133	99.05 322

HY-MVS	95.94 13	95.90 377	95.35 386	97.55 358	97.95 417	94.79 351	98.81 97	96.94 425	92.28 445	95.17 450	98.57 317	89.90 388	99.75 276	91.20 447	97.33 448	98.10 427

UGNet		98.53 183	98.45 180	98.79 191	97.94 418	96.96 254	99.08 61	98.54 369	99.10 105	96.82 406	99.47 79	96.55 247	99.84 174	98.56 123	99.94 50	99.55 136
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		96.47 358	95.70 368	98.79 191	97.92 419	99.12 63	98.28 163	98.60 365	92.16 446	95.54 445	96.17 435	94.77 317	99.52 400	89.62 459	98.23 411	97.72 449
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		96.86 342	96.55 349	97.79 326	97.91 420	94.21 370	97.56 284	98.87 328	97.49 268	99.06 180	99.05 194	80.72 447	99.80 231	98.44 129	99.82 127	99.37 235

API-MVS		97.04 333	96.91 325	97.42 369	97.88 421	98.23 134	98.18 174	98.50 372	97.57 257	97.39 378	96.75 423	96.77 233	99.15 455	90.16 457	99.02 366	94.88 480

myMVS_eth3d28		92.92 433	92.31 429	94.77 444	97.84 422	87.59 467	96.19 391	96.11 440	97.08 311	94.27 460	93.49 473	66.07 481	98.78 468	91.78 435	97.93 430	97.92 437

miper_ehance_all_eth		97.06 331	97.03 316	97.16 382	97.83 423	93.06 407	94.66 452	99.09 289	95.99 369	98.69 257	98.45 334	92.73 358	99.61 365	96.79 267	99.03 363	98.82 362

cl____		97.02 334	96.83 330	97.58 353	97.82 424	94.04 377	94.66 452	99.16 277	97.04 313	98.63 265	98.71 285	88.68 399	99.69 314	97.00 246	99.81 133	99.00 334

DIV-MVS_self_test		97.02 334	96.84 329	97.58 353	97.82 424	94.03 378	94.66 452	99.16 277	97.04 313	98.63 265	98.71 285	88.69 397	99.69 314	97.00 246	99.81 133	99.01 330

CANet		97.87 266	97.76 267	98.19 296	97.75 426	95.51 318	96.76 355	99.05 296	97.74 241	96.93 395	98.21 356	95.59 291	99.89 97	97.86 179	99.93 56	99.19 299

UBG		93.25 427	92.32 428	96.04 424	97.72 427	90.16 454	95.92 409	95.91 445	96.03 367	93.95 468	93.04 476	69.60 471	99.52 400	90.72 455	97.98 428	98.45 404

mvsany_test1		97.60 287	97.54 285	97.77 328	97.72 427	95.35 330	95.36 432	97.13 418	94.13 419	99.71 50	99.33 115	97.93 136	99.30 442	97.60 201	98.94 377	98.67 389

PVSNet_0	89.98 21	91.15 448	90.30 450	93.70 457	97.72 427	84.34 481	90.24 481	97.42 407	90.20 463	93.79 469	93.09 475	90.90 381	98.89 466	86.57 470	72.76 487	97.87 440

CR-MVSNet		96.28 363	95.95 362	97.28 374	97.71 430	94.22 368	98.11 186	98.92 319	92.31 444	96.91 398	99.37 103	85.44 423	99.81 222	97.39 218	97.36 446	97.81 443

RPMNet		97.02 334	96.93 321	97.30 373	97.71 430	94.22 368	98.11 186	99.30 230	99.37 61	96.91 398	99.34 112	86.72 409	99.87 134	97.53 206	97.36 446	97.81 443

ETVMVS		92.60 436	91.08 445	97.18 378	97.70 432	93.65 400	96.54 367	95.70 448	96.51 341	94.68 456	92.39 480	61.80 488	99.50 406	86.97 467	97.41 442	98.40 412

pmmvs3		95.03 398	94.40 405	96.93 391	97.70 432	92.53 418	95.08 441	97.71 399	88.57 470	97.71 351	98.08 367	79.39 454	99.82 205	96.19 323	99.11 357	98.43 409

baseline2		93.73 419	92.83 425	96.42 409	97.70 432	91.28 440	96.84 351	89.77 481	93.96 424	92.44 476	95.93 440	79.14 455	99.77 261	92.94 417	96.76 457	98.21 421

WBMVS		95.18 395	94.78 401	96.37 410	97.68 435	89.74 457	95.80 415	98.73 356	97.54 263	98.30 302	98.44 335	70.06 469	99.82 205	96.62 289	99.87 98	99.54 142

tpm		94.67 403	94.34 407	95.66 431	97.68 435	88.42 461	97.88 231	94.90 455	94.46 410	96.03 435	98.56 318	78.66 457	99.79 244	95.88 336	95.01 473	98.78 374

CANet_DTU		97.26 316	97.06 315	97.84 322	97.57 437	94.65 359	96.19 391	98.79 345	97.23 301	95.14 451	98.24 353	93.22 345	99.84 174	97.34 220	99.84 112	99.04 326

testing11		93.08 430	92.02 435	96.26 415	97.56 438	90.83 449	96.32 383	95.70 448	96.47 345	92.66 475	93.73 469	64.36 485	99.59 372	93.77 402	97.57 435	98.37 416

tpm2		93.09 429	92.58 427	94.62 446	97.56 438	86.53 470	97.66 266	95.79 447	86.15 475	94.07 465	98.23 355	75.95 462	99.53 396	90.91 452	96.86 456	97.81 443

testing91		93.32 425	92.27 430	96.47 408	97.54 440	91.25 441	96.17 395	96.76 429	97.18 305	93.65 471	93.50 472	65.11 484	99.63 355	93.04 416	97.45 439	98.53 398

TR-MVS		95.55 388	95.12 394	96.86 398	97.54 440	93.94 386	96.49 372	96.53 434	94.36 415	97.03 393	96.61 426	94.26 329	99.16 454	86.91 469	96.31 461	97.47 457

testing99		93.04 431	91.98 438	96.23 417	97.53 442	90.70 451	96.35 381	95.94 444	96.87 325	93.41 472	93.43 474	63.84 486	99.59 372	93.24 414	97.19 449	98.40 412

1314		95.74 382	95.60 373	96.17 420	97.53 442	92.75 415	98.07 195	98.31 381	91.22 455	94.25 461	96.68 424	95.53 292	99.03 457	91.64 439	97.18 450	96.74 467

CostFormer		93.97 415	93.78 413	94.51 447	97.53 442	85.83 473	97.98 217	95.96 443	89.29 468	94.99 453	98.63 308	78.63 458	99.62 358	94.54 375	96.50 458	98.09 428

FMVSNet5		96.01 372	95.20 392	98.41 268	97.53 442	96.10 293	98.74 98	99.50 131	97.22 304	98.03 329	99.04 196	69.80 470	99.88 115	97.27 225	99.71 201	99.25 279

PMMVS		96.51 354	95.98 361	98.09 302	97.53 442	95.84 306	94.92 445	98.84 337	91.58 450	96.05 434	95.58 446	95.68 288	99.66 341	95.59 352	98.09 421	98.76 377

reproduce_monomvs		95.00 400	95.25 389	94.22 450	97.51 447	83.34 482	97.86 235	98.44 374	98.51 172	99.29 140	99.30 122	67.68 475	99.56 384	98.89 97	99.81 133	99.77 50

PAPR		95.29 392	94.47 403	97.75 332	97.50 448	95.14 339	94.89 446	98.71 358	91.39 454	95.35 449	95.48 451	94.57 320	99.14 456	84.95 472	97.37 444	98.97 340

testing222		91.96 445	90.37 448	96.72 403	97.47 449	92.59 416	96.11 397	94.76 456	96.83 328	92.90 474	92.87 477	57.92 489	99.55 388	86.93 468	97.52 436	98.00 434

PatchT		96.65 350	96.35 354	97.54 359	97.40 450	95.32 332	97.98 217	96.64 431	99.33 66	96.89 402	99.42 90	84.32 431	99.81 222	97.69 195	97.49 437	97.48 456

tpm cat1		93.29 426	93.13 423	93.75 456	97.39 451	84.74 476	97.39 306	97.65 403	83.39 480	94.16 462	98.41 337	82.86 442	99.39 429	91.56 441	95.35 472	97.14 462

PatchmatchNet		95.58 387	95.67 370	95.30 440	97.34 452	87.32 468	97.65 268	96.65 430	95.30 391	97.07 388	98.69 294	84.77 426	99.75 276	94.97 365	98.64 397	98.83 361
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmtry		97.35 309	96.97 319	98.50 259	97.31 453	96.47 284	98.18 174	98.92 319	98.95 128	98.78 246	99.37 103	85.44 423	99.85 156	95.96 334	99.83 122	99.17 307

LS3D		98.63 162	98.38 192	99.36 74	97.25 454	99.38 13	99.12 60	99.32 217	99.21 81	98.44 293	98.88 247	97.31 195	99.80 231	96.58 292	99.34 317	98.92 349

IB-MVS	91.63 19	92.24 442	90.90 446	96.27 414	97.22 455	91.24 442	94.36 462	93.33 469	92.37 443	92.24 478	94.58 466	66.20 480	99.89 97	93.16 415	94.63 475	97.66 451
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		92.38 439	91.76 442	94.21 451	97.16 456	84.65 477	95.42 430	88.45 483	95.96 370	96.17 428	95.84 444	66.36 478	99.71 300	91.87 434	98.64 397	98.28 419

tpmrst		95.07 397	95.46 379	93.91 454	97.11 457	84.36 480	97.62 273	96.96 423	94.98 398	96.35 426	98.80 267	85.46 422	99.59 372	95.60 351	96.23 462	97.79 446

Syy-MVS		96.04 371	95.56 377	97.49 364	97.10 458	94.48 362	96.18 393	96.58 432	95.65 379	94.77 454	92.29 481	91.27 377	99.36 432	98.17 150	98.05 425	98.63 391

myMVS_eth3d		91.92 446	90.45 447	96.30 412	97.10 458	90.90 447	96.18 393	96.58 432	95.65 379	94.77 454	92.29 481	53.88 490	99.36 432	89.59 460	98.05 425	98.63 391

blended_shiyan6		95.99 374	95.33 387	97.95 315	97.06 460	94.89 348	95.34 433	98.58 366	96.17 358	97.06 389	92.41 479	87.64 406	99.76 267	97.64 196	96.09 465	99.19 299

MDTV_nov1_ep13			95.22 391		97.06 460	83.20 483	97.74 255	96.16 438	94.37 414	96.99 394	98.83 260	83.95 435	99.53 396	93.90 396	97.95 429

MVS		93.19 428	92.09 433	96.50 407	96.91 462	94.03 378	98.07 195	98.06 392	68.01 485	94.56 459	96.48 429	95.96 279	99.30 442	83.84 474	96.89 455	96.17 472

E-PMN		94.17 411	94.37 406	93.58 458	96.86 463	85.71 474	90.11 483	97.07 419	98.17 203	97.82 346	97.19 415	84.62 428	98.94 462	89.77 458	97.68 434	96.09 476

JIA-IIPM		95.52 389	95.03 395	97.00 387	96.85 464	94.03 378	96.93 346	95.82 446	99.20 83	94.63 458	99.71 22	83.09 440	99.60 368	94.42 381	94.64 474	97.36 460

EMVS		93.83 417	94.02 409	93.23 463	96.83 465	84.96 475	89.77 484	96.32 436	97.92 228	97.43 375	96.36 434	86.17 414	98.93 463	87.68 465	97.73 433	95.81 477

blend_shiyan4		92.09 444	90.16 451	97.88 320	96.78 466	94.93 346	95.24 436	98.58 366	96.22 356	96.07 432	91.42 483	63.46 487	99.73 289	96.70 279	76.98 486	98.98 336

cl22		95.79 381	95.39 384	96.98 389	96.77 467	92.79 413	94.40 461	98.53 370	94.59 407	97.89 338	98.17 359	82.82 443	99.24 448	96.37 312	99.03 363	98.92 349

WB-MVSnew		95.73 383	95.57 376	96.23 417	96.70 468	90.70 451	96.07 399	93.86 466	95.60 381	97.04 391	95.45 455	96.00 272	99.55 388	91.04 449	98.31 409	98.43 409

dp		93.47 423	93.59 416	93.13 464	96.64 469	81.62 489	97.66 266	96.42 435	92.80 439	96.11 430	98.64 306	78.55 460	99.59 372	93.31 412	92.18 482	98.16 424

MonoMVSNet		96.25 365	96.53 351	95.39 438	96.57 470	91.01 445	98.82 96	97.68 402	98.57 167	98.03 329	99.37 103	90.92 380	97.78 479	94.99 363	93.88 478	97.38 459

usedtu_blend_shiyan5		96.20 368	95.62 371	97.94 316	96.53 471	94.93 346	98.83 95	99.59 90	98.89 135	96.71 410	91.16 484	86.05 417	99.73 289	96.70 279	96.09 465	99.17 307

test-LLR		93.90 416	93.85 411	94.04 452	96.53 471	84.62 478	94.05 467	92.39 472	96.17 358	94.12 463	95.07 456	82.30 444	99.67 328	95.87 339	98.18 414	97.82 441

test-mter		92.33 441	91.76 442	94.04 452	96.53 471	84.62 478	94.05 467	92.39 472	94.00 423	94.12 463	95.07 456	65.63 483	99.67 328	95.87 339	98.18 414	97.82 441

TESTMET0.1,1		92.19 443	91.77 441	93.46 459	96.48 474	82.80 485	94.05 467	91.52 477	94.45 412	94.00 466	94.88 462	66.65 477	99.56 384	95.78 344	98.11 420	98.02 431

MGCNet		97.44 301	97.01 318	98.72 211	96.42 475	96.74 268	97.20 330	91.97 475	98.46 175	98.30 302	98.79 269	92.74 357	99.91 74	99.30 63	99.94 50	99.52 157

miper_enhance_ethall		96.01 372	95.74 366	96.81 399	96.41 476	92.27 425	93.69 472	98.89 325	91.14 457	98.30 302	97.35 413	90.58 383	99.58 379	96.31 316	99.03 363	98.60 393

tpmvs		95.02 399	95.25 389	94.33 448	96.39 477	85.87 471	98.08 191	96.83 428	95.46 386	95.51 447	98.69 294	85.91 418	99.53 396	94.16 387	96.23 462	97.58 454

CMPMVS	75.91 23	96.29 362	95.44 381	98.84 179	96.25 478	98.69 98	97.02 339	99.12 284	88.90 469	97.83 344	98.86 250	89.51 392	98.90 465	91.92 432	99.51 282	98.92 349
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 1		94.51 404	93.69 414	96.99 388	96.05 479	93.61 402	94.97 444	93.49 467	96.17 358	97.57 362	94.88 462	82.30 444	99.01 460	93.60 405	94.17 477	98.37 416

EPMVS		93.72 420	93.27 419	95.09 443	96.04 480	87.76 465	98.13 181	85.01 488	94.69 405	96.92 396	98.64 306	78.47 461	99.31 440	95.04 362	96.46 459	98.20 422

cascas		94.79 402	94.33 408	96.15 423	96.02 481	92.36 423	92.34 479	99.26 250	85.34 477	95.08 452	94.96 461	92.96 352	98.53 472	94.41 384	98.59 401	97.56 455

MVStest1		95.86 378	95.60 373	96.63 404	95.87 482	91.70 430	97.93 223	98.94 313	98.03 218	99.56 74	99.66 32	71.83 467	98.26 475	99.35 59	99.24 334	99.91 13

gg-mvs-nofinetune		92.37 440	91.20 444	95.85 426	95.80 483	92.38 422	99.31 30	81.84 490	99.75 11	91.83 479	99.74 18	68.29 472	99.02 458	87.15 466	97.12 451	96.16 473

gm-plane-assit					94.83 484	81.97 487			88.07 472		94.99 459		99.60 368	91.76 436

GG-mvs-BLEND				94.76 445	94.54 485	92.13 427	99.31 30	80.47 491		88.73 485	91.01 485	67.59 476	98.16 478	82.30 479	94.53 476	93.98 481

UWE-MVS-28		90.22 449	89.28 452	93.02 465	94.50 486	82.87 484	96.52 370	87.51 484	95.21 394	92.36 477	96.04 436	71.57 468	98.25 476	72.04 486	97.77 432	97.94 436

EPNet_dtu		94.93 401	94.78 401	95.38 439	93.58 487	87.68 466	96.78 353	95.69 450	97.35 285	89.14 484	98.09 366	88.15 404	99.49 409	94.95 366	99.30 325	98.98 336
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dongtai		76.24 453	75.95 456	77.12 470	92.39 488	67.91 494	90.16 482	59.44 495	82.04 481	89.42 483	94.67 465	49.68 492	81.74 488	48.06 488	77.66 485	81.72 484

KD-MVS_2432*1600		92.87 434	91.99 436	95.51 435	91.37 489	89.27 458	94.07 465	98.14 388	95.42 387	97.25 383	96.44 431	67.86 473	99.24 448	91.28 445	96.08 467	98.02 431

miper_refine_blended		92.87 434	91.99 436	95.51 435	91.37 489	89.27 458	94.07 465	98.14 388	95.42 387	97.25 383	96.44 431	67.86 473	99.24 448	91.28 445	96.08 467	98.02 431

EPNet		96.14 369	95.44 381	98.25 287	90.76 491	95.50 321	97.92 226	94.65 457	98.97 124	92.98 473	98.85 253	89.12 395	99.87 134	95.99 332	99.68 216	99.39 224
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
kuosan		69.30 454	68.95 457	70.34 471	87.68 492	65.00 495	91.11 480	59.90 494	69.02 484	74.46 489	88.89 486	48.58 493	68.03 490	28.61 489	72.33 488	77.99 485

test_method		79.78 451	79.50 454	80.62 468	80.21 493	45.76 496	70.82 485	98.41 378	31.08 488	80.89 488	97.71 390	84.85 425	97.37 481	91.51 442	80.03 484	98.75 378

tmp_tt		78.77 452	78.73 455	78.90 469	58.45 494	74.76 493	94.20 464	78.26 492	39.16 487	86.71 486	92.82 478	80.50 448	75.19 489	86.16 471	92.29 481	86.74 483

testmvs		17.12 456	20.53 459	6.87 473	12.05 495	4.20 498	93.62 473	6.73 496	4.62 491	10.41 491	24.33 488	8.28 495	3.56 492	9.69 491	15.07 489	12.86 488

test123		17.04 457	20.11 460	7.82 472	10.25 496	4.91 497	94.80 447	4.47 497	4.93 490	10.00 492	24.28 489	9.69 494	3.64 491	10.14 490	12.43 490	14.92 487

mmdepth		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

monomultidepth		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

test_blank		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

eth-test2					0.00 497

eth-test					0.00 497

uanet_test		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

DCPMVS		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

cdsmvs_eth3d_5k		24.66 455	32.88 458	0.00 474	0.00 497	0.00 499	0.00 486	99.10 287	0.00 492	0.00 493	97.58 398	99.21 18	0.00 493	0.00 492	0.00 491	0.00 489

pcd_1.5k_mvsjas		8.17 458	10.90 461	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	98.07 123	0.00 493	0.00 492	0.00 491	0.00 489

sosnet-low-res		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

sosnet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uncertanet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

Regformer		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

ab-mvs-re		8.12 459	10.83 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	97.48 404	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uanet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

TestfortrainingZip							98.68 108

WAC-MVS						90.90 447								91.37 444

PC_three_1452									93.27 431	99.40 115	98.54 319	98.22 108	97.00 482	95.17 360	99.45 297	99.49 172

test_241102_TWO								99.30 230	98.03 218	99.26 148	99.02 199	97.51 181	99.88 115	96.91 254	99.60 250	99.66 78

test_0728_THIRD									98.17 203	99.08 178	99.02 199	97.89 142	99.88 115	97.07 241	99.71 201	99.70 68

GSMVS																98.81 367

sam_mvs1												84.74 427				98.81 367

sam_mvs												84.29 433

MTGPA								99.20 262

test_post1							97.59 281				20.48 491	83.07 441	99.66 341	94.16 387

test_post											21.25 490	83.86 436	99.70 307

patchmatchnet-post											98.77 273	84.37 430	99.85 156

MTMP							97.93 223	91.91 476

test9_res														93.28 413	99.15 350	99.38 233

agg_prior2														92.50 429	99.16 348	99.37 235

test_prior4						97.97 163	95.86 411

test_prior2							95.74 418		96.48 344	96.11 430	97.63 396	95.92 282		94.16 387	99.20 342

旧先验2							95.76 417		88.56 471	97.52 366			99.66 341	94.48 377

新几何2							95.93 407

无先验							95.74 418	98.74 355	89.38 467				99.73 289	92.38 431		99.22 289

原ACMM2							95.53 424

testdata2													99.79 244	92.80 423

segment_acmp												97.02 215

testdata1							95.44 429		96.32 352

plane_prior5								99.27 245					99.70 307	94.42 381	99.51 282	99.45 198

plane_prior4											97.98 374

plane_prior3						97.78 188			97.41 278	97.79 347

plane_prior2							97.77 248		98.20 200

plane_prior						97.65 197	97.07 338		96.72 334						99.36 313

n2								0.00 498

nn								0.00 498

door-mid								99.57 100

test11								98.87 328

door								99.41 182

HQP5-MVS						96.79 264

BP-MVS														92.82 421

HQP4-MVS										95.56 441			99.54 394			99.32 258

HQP3-MVS								99.04 299							99.26 332

HQP2-MVS												93.84 336

MDTV_nov1_ep13_2view						74.92 492	97.69 261		90.06 465	97.75 350		85.78 419		93.52 407		98.69 385

ACMMP++_ref															99.77 161

ACMMP++															99.68 216

Test By Simon												96.52 248

High-resolution multi-view benchmark