High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 20	99.99 3	100.00 1	99.98 13	99.78 23	100.00 1	99.92 28	100.00 1	99.87 42

ANet_high		99.88 6	99.87 11	99.91 3	99.99 1	99.91 4	99.65 62	100.00 1	99.90 48	100.00 1	99.97 14	99.61 41	99.97 42	99.75 54	100.00 1	99.84 50

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 3	99.98 3	99.76 70	99.12 227	100.00 1	100.00 1	99.99 7	99.91 31	99.98 1	100.00 1	99.97 4	100.00 1	99.99 2

test_vis3_rt		99.89 3	99.90 4	99.87 25	99.98 3	99.75 78	99.70 38	100.00 1	99.73 105	100.00 1	99.89 41	99.79 22	99.88 229	99.98 1	100.00 1	99.98 5

Gipuma		99.57 89	99.59 84	99.49 215	99.98 3	99.71 98	99.72 33	99.84 81	99.81 88	99.94 46	99.78 121	98.91 146	99.71 379	98.41 229	99.95 100	99.05 374
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 85	99.01 263	99.99 11	99.99 3	99.98 14	99.88 50	99.97 2	99.99 8	99.96 9	100.00 1	99.98 5

test_fmvs3		99.83 21	99.93 2	99.53 204	99.96 7	98.62 312	99.67 53	100.00 1	99.95 30	100.00 1	99.95 16	99.85 14	99.99 8	99.98 1	99.99 16	99.98 5

test_f		99.75 47	99.88 7	99.37 258	99.96 7	98.21 343	99.51 99	100.00 1	99.94 34	100.00 1	99.93 22	99.58 45	99.94 95	99.97 4	99.99 16	99.97 10

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 75	99.70 118	99.92 58	99.93 22	99.45 57	99.97 42	99.36 113	100.00 1	99.85 47

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 27	99.82 10	99.82 91	99.84 74	99.94 46	99.91 31	99.13 108	99.96 67	99.83 44	99.99 16	99.83 54

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 63	99.68 49	99.85 75	99.95 30	99.98 14	99.92 27	99.28 85	99.98 27	99.75 54	100.00 1	99.94 17

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 57	99.70 38	99.86 69	99.89 54	99.98 14	99.90 36	99.94 4	99.98 27	99.75 54	100.00 1	99.90 28

mvs_tets		99.90 2	99.90 4	99.90 8	99.96 7	99.79 54	99.72 33	99.88 62	99.92 44	99.98 14	99.93 22	99.94 4	99.98 27	99.77 53	100.00 1	99.92 24

OurMVSNet-221017-0		99.75 47	99.71 55	99.84 36	99.96 7	99.83 34	99.83 7	99.85 75	99.80 92	99.93 51	99.93 22	98.54 200	99.93 116	99.59 77	99.98 48	99.76 81

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 42	99.10 235	99.98 12	99.99 3	99.98 14	99.91 31	99.68 33	99.93 116	99.93 24	99.99 16	99.99 2

test_fmvs1_n		99.68 63	99.81 28	99.28 286	99.95 15	97.93 366	99.49 105	100.00 1	99.82 82	99.99 7	99.89 41	99.21 94	99.98 27	99.97 4	99.98 48	99.93 20

mvsany_test3		99.85 12	99.88 7	99.75 93	99.95 15	99.37 202	99.53 91	99.98 12	99.77 103	99.99 7	99.95 16	99.85 14	99.94 95	99.95 14	99.98 48	99.94 17

test_vis1_n		99.68 63	99.79 34	99.36 263	99.94 18	98.18 346	99.52 92	100.00 1	99.86 64	100.00 1	99.88 50	98.99 133	99.96 67	99.97 4	99.96 84	99.95 14

testf1		99.63 78	99.60 82	99.72 114	99.94 18	99.95 2	99.47 110	99.89 58	99.43 187	99.88 81	99.80 99	99.26 89	99.90 195	98.81 191	99.88 164	99.32 305

APD_test2		99.63 78	99.60 82	99.72 114	99.94 18	99.95 2	99.47 110	99.89 58	99.43 187	99.88 81	99.80 99	99.26 89	99.90 195	98.81 191	99.88 164	99.32 305

pmmvs6		99.86 10	99.86 13	99.83 39	99.94 18	99.90 7	99.83 7	99.91 50	99.85 70	99.94 46	99.95 16	99.73 27	99.90 195	99.65 69	99.97 70	99.69 109

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 27	99.77 19	99.80 105	99.73 105	99.97 23	99.92 27	99.77 25	99.98 27	99.43 100	100.00 1	99.90 28

MIMVSNet1		99.66 70	99.62 75	99.80 61	99.94 18	99.87 15	99.69 45	99.77 124	99.78 99	99.93 51	99.89 41	97.94 265	99.92 147	99.65 69	99.98 48	99.62 170

fmvsm_s_conf0.1_n_2		99.81 28	99.78 39	99.89 11	99.93 24	99.76 70	98.92 289	99.98 12	99.99 3	99.99 7	99.88 50	99.43 60	99.94 95	99.94 19	99.99 16	99.99 2

fmvsm_s_conf0.1_n		99.86 10	99.85 17	99.89 11	99.93 24	99.78 57	99.07 247	99.98 12	99.99 3	99.98 14	99.90 36	99.88 11	99.92 147	99.93 24	99.99 16	99.98 5

test_cas_vis1_n_1920		99.76 45	99.86 13	99.45 228	99.93 24	98.40 331	99.30 154	99.98 12	99.94 34	99.99 7	99.89 41	99.80 21	99.97 42	99.96 9	99.97 70	99.97 10

test_vis1_n_1920		99.72 52	99.88 7	99.27 291	99.93 24	97.84 370	99.34 137	100.00 1	99.99 3	99.99 7	99.82 88	99.87 13	99.99 8	99.97 4	99.99 16	99.97 10

K. test v3		98.87 277	98.60 286	99.69 125	99.93 24	99.46 173	99.74 27	94.97 452	99.78 99	99.88 81	99.88 50	93.66 375	99.97 42	99.61 75	99.95 100	99.64 153

mvs5depth		99.88 6	99.91 3	99.80 61	99.92 29	99.42 187	99.94 3	100.00 1	99.97 23	99.89 71	99.99 12	99.63 37	99.97 42	99.87 42	99.99 16	100.00 1

SixPastTwentyTwo		99.42 138	99.30 158	99.76 82	99.92 29	99.67 115	99.70 38	99.14 371	99.65 134	99.89 71	99.90 36	96.20 342	99.94 95	99.42 105	99.92 130	99.67 124

test_fmvsmconf_n		99.85 12	99.84 20	99.88 19	99.91 31	99.73 88	98.97 278	99.98 12	99.99 3	99.96 32	99.85 68	99.93 7	99.99 8	99.94 19	99.99 16	99.93 20

test_fmvs2		99.72 52	99.85 17	99.34 266	99.91 31	98.08 357	99.48 107	100.00 1	99.90 48	99.99 7	99.91 31	99.50 56	99.98 27	99.98 1	99.99 16	99.96 13

pm-mvs1		99.79 34	99.79 34	99.78 72	99.91 31	99.83 34	99.76 23	99.87 64	99.73 105	99.89 71	99.87 56	99.63 37	99.87 244	99.54 85	99.92 130	99.63 159

TransMVSNet (Re)		99.78 37	99.77 45	99.81 52	99.91 31	99.85 22	99.75 25	99.86 69	99.70 118	99.91 61	99.89 41	99.60 43	99.87 244	99.59 77	99.74 256	99.71 99

Baseline_NR-MVSNet		99.49 113	99.37 139	99.82 44	99.91 31	99.84 27	98.83 302	99.86 69	99.68 123	99.65 194	99.88 50	97.67 284	99.87 244	99.03 167	99.86 184	99.76 81

LTVRE_ROB	99.19 1	99.88 6	99.87 11	99.88 19	99.91 31	99.90 7	99.96 1	99.92 41	99.90 48	99.97 23	99.87 56	99.81 20	99.95 78	99.54 85	99.99 16	99.80 62
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
PVSNet_Blended_VisFu		99.40 144	99.38 136	99.44 232	99.90 37	98.66 305	98.94 286	99.91 50	97.97 364	99.79 121	99.73 152	99.05 126	99.97 42	99.15 151	99.99 16	99.68 115

TDRefinement		99.72 52	99.70 56	99.77 75	99.90 37	99.85 22	99.86 6	99.92 41	99.69 121	99.78 125	99.92 27	99.37 70	99.88 229	98.93 182	99.95 100	99.60 185

KinetiMVS		99.66 70	99.63 73	99.76 82	99.89 39	99.57 152	99.37 129	99.82 91	99.95 30	99.90 66	99.63 230	98.57 192	99.97 42	99.65 69	99.94 115	99.74 86

APD_test1		99.36 159	99.28 166	99.61 170	99.89 39	99.89 10	99.32 146	99.74 143	99.18 227	99.69 175	99.75 143	98.41 220	99.84 296	97.85 281	99.70 273	99.10 356

EGC-MVSNET		89.05 428	85.52 431	99.64 150	99.89 39	99.78 57	99.56 87	99.52 279	24.19 463	49.96 464	99.83 81	99.15 103	99.92 147	97.71 294	99.85 189	99.21 329

Anonymous20240521		99.44 132	99.42 128	99.49 215	99.89 39	98.96 275	99.62 67	99.76 132	99.85 70	99.82 104	99.88 50	96.39 335	99.97 42	99.59 77	99.98 48	99.55 208

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 159	99.93 41	99.95 43	99.89 41	99.71 28	99.96 67	99.51 90	99.97 70	99.84 50

XXY-MVS		99.71 55	99.67 63	99.81 52	99.89 39	99.72 93	99.59 80	99.82 91	99.39 196	99.82 104	99.84 75	99.38 68	99.91 176	99.38 109	99.93 126	99.80 62

sc_t1		99.81 28	99.80 32	99.82 44	99.88 45	99.88 12	99.83 7	99.79 112	99.94 34	99.93 51	99.92 27	99.35 76	99.92 147	99.64 72	99.94 115	99.68 115

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 45	99.86 19	99.08 243	99.97 20	99.98 16	99.96 32	99.79 109	99.90 9	99.99 8	99.96 9	99.99 16	99.90 28

fmvsm_l_conf0.5_n_a		99.80 30	99.79 34	99.84 36	99.88 45	99.64 126	99.12 227	99.91 50	99.98 16	99.95 43	99.67 203	99.67 34	99.99 8	99.94 19	99.99 16	99.88 38

fmvsm_l_conf0.5_n		99.80 30	99.78 39	99.85 31	99.88 45	99.66 117	99.11 232	99.91 50	99.98 16	99.96 32	99.64 215	99.60 43	99.99 8	99.95 14	99.99 16	99.88 38

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 52	99.88 45	99.55 157	99.17 205	99.98 12	99.99 3	99.96 32	99.84 75	99.96 3	99.99 8	99.96 9	99.99 16	99.88 38

FC-MVSNet-test		99.70 56	99.65 67	99.86 29	99.88 45	99.86 19	99.72 33	99.78 121	99.90 48	99.82 104	99.83 81	98.45 215	99.87 244	99.51 90	99.97 70	99.86 44

EU-MVSNet		99.39 148	99.62 75	98.72 365	99.88 45	96.44 409	99.56 87	99.85 75	99.90 48	99.90 66	99.85 68	98.09 254	99.83 312	99.58 80	99.95 100	99.90 28

CHOSEN 1792x2688		99.39 148	99.30 158	99.65 143	99.88 45	99.25 228	98.78 314	99.88 62	98.66 301	99.96 32	99.79 109	97.45 294	99.93 116	99.34 117	99.99 16	99.78 72

Vis-MVSNet		99.75 47	99.74 52	99.79 68	99.88 45	99.66 117	99.69 45	99.92 41	99.67 127	99.77 137	99.75 143	99.61 41	99.98 27	99.35 116	99.98 48	99.72 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tt0805		99.63 78	99.57 92	99.81 52	99.87 54	99.88 12	99.58 82	98.70 394	99.72 109	99.91 61	99.60 256	99.43 60	99.81 338	99.81 49	99.53 334	99.73 90

tfpnnormal		99.43 135	99.38 136	99.60 174	99.87 54	99.75 78	99.59 80	99.78 121	99.71 113	99.90 66	99.69 188	98.85 153	99.90 195	97.25 336	99.78 239	99.15 345

SteuartSystems-ACMMP		99.30 173	99.14 188	99.76 82	99.87 54	99.66 117	99.18 200	99.60 230	98.55 312	99.57 226	99.67 203	99.03 129	99.94 95	97.01 347	99.80 229	99.69 109
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_l_conf0.5_n_9		99.83 21	99.81 28	99.89 11	99.86 57	99.80 51	98.94 286	99.96 28	99.98 16	99.96 32	99.78 121	99.88 11	99.98 27	99.96 9	99.99 16	99.90 28

Elysia		99.69 58	99.65 67	99.81 52	99.86 57	99.72 93	99.34 137	99.77 124	99.94 34	99.91 61	99.76 135	98.55 196	99.99 8	99.70 59	99.98 48	99.72 94

StellarMVS		99.69 58	99.65 67	99.81 52	99.86 57	99.72 93	99.34 137	99.77 124	99.94 34	99.91 61	99.76 135	98.55 196	99.99 8	99.70 59	99.98 48	99.72 94

SSC-MVS		99.52 106	99.42 128	99.83 39	99.86 57	99.65 123	99.52 92	99.81 102	99.87 61	99.81 111	99.79 109	96.78 320	99.99 8	99.83 44	99.51 338	99.86 44

lessismore_v0				99.64 150	99.86 57	99.38 199		90.66 462		99.89 71	99.83 81	94.56 365	99.97 42	99.56 82	99.92 130	99.57 203

ACMH+	98.40 8	99.50 108	99.43 126	99.71 119	99.86 57	99.76 70	99.32 146	99.77 124	99.53 159	99.77 137	99.76 135	99.26 89	99.78 351	97.77 286	99.88 164	99.60 185

ACMH	98.42 6	99.59 88	99.54 102	99.72 114	99.86 57	99.62 133	99.56 87	99.79 112	98.77 290	99.80 115	99.85 68	99.64 35	99.85 281	98.70 208	99.89 155	99.70 102
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		99.15 220	99.06 214	99.42 238	99.85 64	98.59 315	99.13 222	97.26 441	99.84 74	99.87 89	99.77 131	96.11 343	99.93 116	99.71 58	99.96 84	99.74 86

mmtdpeth		99.78 37	99.83 21	99.66 137	99.85 64	99.05 266	99.79 15	99.97 20	100.00 1	99.43 272	99.94 19	99.64 35	99.94 95	99.83 44	99.99 16	99.98 5

fmvsm_s_conf0.5_n_a		99.82 24	99.79 34	99.89 11	99.85 64	99.82 42	99.03 256	99.96 28	99.99 3	99.97 23	99.84 75	99.58 45	99.93 116	99.92 28	99.98 48	99.93 20

fmvsm_s_conf0.5_n		99.83 21	99.81 28	99.87 25	99.85 64	99.78 57	99.03 256	99.96 28	99.99 3	99.97 23	99.84 75	99.78 23	99.92 147	99.92 28	99.99 16	99.92 24

HyFIR lowres test		98.91 270	98.64 283	99.73 107	99.85 64	99.47 169	98.07 391	99.83 85	98.64 303	99.89 71	99.60 256	92.57 387	100.00 1	99.33 120	99.97 70	99.72 94

guyue		99.12 226	99.02 228	99.41 246	99.84 69	98.56 316	99.19 196	98.30 419	99.82 82	99.84 97	99.75 143	94.84 360	99.92 147	99.68 64	99.94 115	99.74 86

KD-MVS_self_test		99.63 78	99.59 84	99.76 82	99.84 69	99.90 7	99.37 129	99.79 112	99.83 80	99.88 81	99.85 68	98.42 219	99.90 195	99.60 76	99.73 262	99.49 245

FIs		99.65 76	99.58 87	99.84 36	99.84 69	99.85 22	99.66 57	99.75 137	99.86 64	99.74 155	99.79 109	98.27 237	99.85 281	99.37 112	99.93 126	99.83 54

XVG-OURS-SEG-HR		99.16 216	98.99 243	99.66 137	99.84 69	99.64 126	98.25 373	99.73 147	98.39 330	99.63 200	99.43 319	99.70 31	99.90 195	97.34 324	98.64 417	99.44 270

PMVS	92.94 21	98.82 281	98.81 272	98.85 352	99.84 69	97.99 360	99.20 190	99.47 296	99.71 113	99.42 275	99.82 88	98.09 254	99.47 444	93.88 441	99.85 189	99.07 372
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
fmvsm_s_conf0.5_n_9		99.82 24	99.82 25	99.82 44	99.83 74	99.59 144	98.97 278	99.92 41	99.99 3	99.97 23	99.84 75	99.90 9	99.94 95	99.94 19	99.99 16	99.92 24

LuminaMVS		99.39 148	99.28 166	99.73 107	99.83 74	99.49 165	99.00 266	99.05 378	99.81 88	99.89 71	99.79 109	96.54 328	99.97 42	99.64 72	99.98 48	99.73 90

FOURS1					99.83 74	99.89 10	99.74 27	99.71 159	99.69 121	99.63 200

MP-MVS-pluss		99.14 221	98.92 256	99.80 61	99.83 74	99.83 34	98.61 329	99.63 209	96.84 415	99.44 268	99.58 268	98.81 155	99.91 176	97.70 297	99.82 212	99.67 124
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PM-MVS		99.36 159	99.29 163	99.58 180	99.83 74	99.66 117	98.95 284	99.86 69	98.85 276	99.81 111	99.73 152	98.40 224	99.92 147	98.36 232	99.83 202	99.17 341

PEN-MVS		99.66 70	99.59 84	99.89 11	99.83 74	99.87 15	99.66 57	99.73 147	99.70 118	99.84 97	99.73 152	98.56 195	99.96 67	99.29 128	99.94 115	99.83 54

HPM-MVS_fast		99.43 135	99.30 158	99.80 61	99.83 74	99.81 47	99.52 92	99.70 168	98.35 338	99.51 255	99.50 300	99.31 81	99.88 229	98.18 250	99.84 194	99.69 109

RPSCF		99.18 210	99.02 228	99.64 150	99.83 74	99.85 22	99.44 116	99.82 91	98.33 343	99.50 257	99.78 121	97.90 267	99.65 418	96.78 362	99.83 202	99.44 270

COLMAP_ROB	98.06 12	99.45 130	99.37 139	99.70 123	99.83 74	99.70 106	99.38 125	99.78 121	99.53 159	99.67 185	99.78 121	99.19 96	99.86 263	97.32 325	99.87 176	99.55 208
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tt0320-xc		99.82 24	99.82 25	99.82 44	99.82 83	99.84 27	99.82 10	99.92 41	99.94 34	99.94 46	99.93 22	99.34 77	99.92 147	99.70 59	99.96 84	99.70 102

tt0320		99.79 34	99.79 34	99.81 52	99.82 83	99.84 27	99.82 10	99.90 55	99.94 34	99.94 46	99.94 19	99.07 119	99.92 147	99.68 64	99.97 70	99.67 124

fmvsm_s_conf0.5_n_8		99.76 45	99.72 54	99.88 19	99.82 83	99.75 78	99.02 260	99.87 64	99.98 16	99.98 14	99.81 95	99.07 119	99.97 42	99.91 31	99.99 16	99.92 24

fmvsm_s_conf0.5_n_2		99.78 37	99.75 51	99.88 19	99.82 83	99.76 70	98.88 292	99.92 41	99.98 16	99.98 14	99.85 68	99.42 62	99.94 95	99.93 24	99.98 48	99.94 17

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 39	99.82 83	99.70 106	99.17 205	99.97 20	99.99 3	99.96 32	99.82 88	99.94 4	100.00 1	99.95 14	100.00 1	99.80 62

TSAR-MVS + MP.		99.34 166	99.24 175	99.63 157	99.82 83	99.37 202	99.26 172	99.35 329	98.77 290	99.57 226	99.70 179	99.27 88	99.88 229	97.71 294	99.75 249	99.65 143
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
new-patchmatchnet		99.35 161	99.57 92	98.71 367	99.82 83	96.62 405	98.55 343	99.75 137	99.50 163	99.88 81	99.87 56	99.31 81	99.88 229	99.43 100	100.00 1	99.62 170

VPNet		99.46 126	99.37 139	99.71 119	99.82 83	99.59 144	99.48 107	99.70 168	99.81 88	99.69 175	99.58 268	97.66 288	99.86 263	99.17 148	99.44 348	99.67 124

XVG-OURS		99.21 201	99.06 214	99.65 143	99.82 83	99.62 133	97.87 412	99.74 143	98.36 333	99.66 191	99.68 199	99.71 28	99.90 195	96.84 359	99.88 164	99.43 276

XVG-ACMP-BASELINE		99.23 187	99.10 205	99.63 157	99.82 83	99.58 149	98.83 302	99.72 156	98.36 333	99.60 218	99.71 169	98.92 144	99.91 176	97.08 345	99.84 194	99.40 284

LPG-MVS_test		99.22 196	99.05 219	99.74 98	99.82 83	99.63 131	99.16 211	99.73 147	97.56 384	99.64 196	99.69 188	99.37 70	99.89 214	96.66 369	99.87 176	99.69 109

LGP-MVS_train				99.74 98	99.82 83	99.63 131		99.73 147	97.56 384	99.64 196	99.69 188	99.37 70	99.89 214	96.66 369	99.87 176	99.69 109

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 31	99.81 95	99.71 98	98.97 278	99.92 41	99.98 16	99.97 23	99.86 63	99.53 52	99.95 78	99.88 39	99.99 16	99.89 35

WB-MVS		99.44 132	99.32 151	99.80 61	99.81 95	99.61 139	99.47 110	99.81 102	99.82 82	99.71 168	99.72 159	96.60 324	99.98 27	99.75 54	99.23 379	99.82 61

MTAPA		99.35 161	99.20 179	99.80 61	99.81 95	99.81 47	99.33 143	99.53 274	99.27 212	99.42 275	99.63 230	98.21 244	99.95 78	97.83 285	99.79 234	99.65 143

v10		99.69 58	99.69 59	99.66 137	99.81 95	99.39 197	99.66 57	99.75 137	99.60 152	99.92 58	99.87 56	98.75 167	99.86 263	99.90 35	99.99 16	99.73 90

HPM-MVS		99.25 183	99.07 212	99.78 72	99.81 95	99.75 78	99.61 73	99.67 184	97.72 379	99.35 293	99.25 364	99.23 92	99.92 147	97.21 339	99.82 212	99.67 124
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
casdiffmvs_mvg		99.68 63	99.68 62	99.69 125	99.81 95	99.59 144	99.29 161	99.90 55	99.71 113	99.79 121	99.73 152	99.54 50	99.84 296	99.36 113	99.96 84	99.65 143
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IterMVS-LS		99.41 142	99.47 114	99.25 297	99.81 95	98.09 354	98.85 297	99.76 132	99.62 142	99.83 103	99.64 215	98.54 200	99.97 42	99.15 151	99.99 16	99.68 115
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs_AUTHOR		99.48 115	99.48 112	99.47 221	99.80 102	98.89 285	98.71 322	99.82 91	99.79 96	99.66 191	99.63 230	98.87 151	99.88 229	99.13 159	99.95 100	99.62 170

SDMVSNet		99.77 44	99.77 45	99.76 82	99.80 102	99.65 123	99.63 64	99.86 69	99.97 23	99.89 71	99.89 41	99.52 54	99.99 8	99.42 105	99.96 84	99.65 143

sd_testset		99.78 37	99.78 39	99.80 61	99.80 102	99.76 70	99.80 14	99.79 112	99.97 23	99.89 71	99.89 41	99.53 52	99.99 8	99.36 113	99.96 84	99.65 143

v1240		99.56 93	99.58 87	99.51 209	99.80 102	99.00 267	99.00 266	99.65 199	99.15 238	99.90 66	99.75 143	99.09 113	99.88 229	99.90 35	99.96 84	99.67 124

v8		99.68 63	99.69 59	99.65 143	99.80 102	99.40 194	99.66 57	99.76 132	99.64 137	99.93 51	99.85 68	98.66 181	99.84 296	99.88 39	99.99 16	99.71 99

MDA-MVSNet-bldmvs		99.06 239	99.05 219	99.07 323	99.80 102	97.83 371	98.89 291	99.72 156	99.29 208	99.63 200	99.70 179	96.47 330	99.89 214	98.17 252	99.82 212	99.50 240

PS-CasMVS		99.66 70	99.58 87	99.89 11	99.80 102	99.85 22	99.66 57	99.73 147	99.62 142	99.84 97	99.71 169	98.62 185	99.96 67	99.30 125	99.96 84	99.86 44

DTE-MVSNet		99.68 63	99.61 79	99.88 19	99.80 102	99.87 15	99.67 53	99.71 159	99.72 109	99.84 97	99.78 121	98.67 179	99.97 42	99.30 125	99.95 100	99.80 62

WR-MVS_H		99.61 86	99.53 106	99.87 25	99.80 102	99.83 34	99.67 53	99.75 137	99.58 156	99.85 94	99.69 188	98.18 249	99.94 95	99.28 130	99.95 100	99.83 54

baseline		99.63 78	99.62 75	99.66 137	99.80 102	99.62 133	99.44 116	99.80 105	99.71 113	99.72 163	99.69 188	99.15 103	99.83 312	99.32 122	99.94 115	99.53 223

IS-MVSNet		99.03 246	98.85 265	99.55 194	99.80 102	99.25 228	99.73 30	99.15 370	99.37 198	99.61 215	99.71 169	94.73 363	99.81 338	97.70 297	99.88 164	99.58 197

EPP-MVSNet		99.17 215	99.00 236	99.66 137	99.80 102	99.43 184	99.70 38	99.24 354	99.48 168	99.56 234	99.77 131	94.89 359	99.93 116	98.72 207	99.89 155	99.63 159

ACMM	98.09 11	99.46 126	99.38 136	99.72 114	99.80 102	99.69 110	99.13 222	99.65 199	98.99 254	99.64 196	99.72 159	99.39 64	99.86 263	98.23 243	99.81 222	99.60 185
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_5		99.78 37	99.76 49	99.85 31	99.79 115	99.72 93	98.84 299	99.96 28	99.96 26	99.96 32	99.72 159	99.71 28	99.99 8	99.93 24	99.98 48	99.85 47

dcpmvs_2		99.61 86	99.64 72	99.53 204	99.79 115	98.82 289	99.58 82	99.97 20	99.95 30	99.96 32	99.76 135	98.44 216	99.99 8	99.34 117	99.96 84	99.78 72

v1144		99.54 102	99.53 106	99.59 177	99.79 115	99.28 220	99.10 235	99.61 219	99.20 225	99.84 97	99.73 152	98.67 179	99.84 296	99.86 43	99.98 48	99.64 153

V42		99.56 93	99.54 102	99.63 157	99.79 115	99.46 173	99.39 122	99.59 236	99.24 218	99.86 91	99.70 179	98.55 196	99.82 323	99.79 51	99.95 100	99.60 185

test20.03		99.55 98	99.54 102	99.58 180	99.79 115	99.37 202	99.02 260	99.89 58	99.60 152	99.82 104	99.62 239	98.81 155	99.89 214	99.43 100	99.86 184	99.47 253

casdiffmvs		99.63 78	99.61 79	99.67 130	99.79 115	99.59 144	99.13 222	99.85 75	99.79 96	99.76 141	99.72 159	99.33 79	99.82 323	99.21 138	99.94 115	99.59 192
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_0402		99.22 196	99.14 188	99.45 228	99.79 115	99.43 184	99.28 163	99.68 179	99.54 157	99.40 286	99.56 279	99.07 119	99.82 323	96.01 400	99.96 84	99.11 354

ACMMP		99.25 183	99.08 208	99.74 98	99.79 115	99.68 113	99.50 100	99.65 199	98.07 358	99.52 248	99.69 188	98.57 192	99.92 147	97.18 341	99.79 234	99.63 159
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
NormalMVS		99.09 234	98.91 260	99.62 166	99.78 123	99.11 253	99.36 133	99.77 124	99.82 82	99.68 178	99.53 291	93.30 378	99.99 8	99.24 132	99.76 245	99.74 86

lecture		99.56 93	99.48 112	99.81 52	99.78 123	99.86 19	99.50 100	99.70 168	99.59 154	99.75 146	99.71 169	98.94 140	99.92 147	98.59 216	99.76 245	99.66 134

fmvsm_s_conf0.5_n_6		99.80 30	99.78 39	99.85 31	99.78 123	99.78 57	99.00 266	99.97 20	99.96 26	99.97 23	99.56 279	99.92 8	99.93 116	99.91 31	99.99 16	99.83 54

MSP-MVS		99.04 245	98.79 275	99.81 52	99.78 123	99.73 88	99.35 136	99.57 247	98.54 315	99.54 241	98.99 400	96.81 319	99.93 116	96.97 350	99.53 334	99.77 76
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
v144192		99.55 98	99.54 102	99.58 180	99.78 123	99.20 243	99.11 232	99.62 212	99.18 227	99.89 71	99.72 159	98.66 181	99.87 244	99.88 39	99.97 70	99.66 134

AllTest		99.21 201	99.07 212	99.63 157	99.78 123	99.64 126	99.12 227	99.83 85	98.63 304	99.63 200	99.72 159	98.68 176	99.75 367	96.38 387	99.83 202	99.51 235

TestCases				99.63 157	99.78 123	99.64 126		99.83 85	98.63 304	99.63 200	99.72 159	98.68 176	99.75 367	96.38 387	99.83 202	99.51 235

v2v482		99.50 108	99.47 114	99.58 180	99.78 123	99.25 228	99.14 216	99.58 245	99.25 216	99.81 111	99.62 239	98.24 239	99.84 296	99.83 44	99.97 70	99.64 153

FMVSNet1		99.66 70	99.63 73	99.73 107	99.78 123	99.77 63	99.68 49	99.70 168	99.67 127	99.82 104	99.83 81	98.98 135	99.90 195	99.24 132	99.97 70	99.53 223

Vis-MVSNet (Re-imp)		98.77 286	98.58 291	99.34 266	99.78 123	98.88 286	99.61 73	99.56 252	99.11 244	99.24 319	99.56 279	93.00 385	99.78 351	97.43 319	99.89 155	99.35 298

ACMP	97.51 14	99.05 242	98.84 267	99.67 130	99.78 123	99.55 157	98.88 292	99.66 189	97.11 410	99.47 262	99.60 256	99.07 119	99.89 214	96.18 395	99.85 189	99.58 197
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs-eth3d		99.48 115	99.47 114	99.51 209	99.77 134	99.41 193	98.81 307	99.66 189	99.42 191	99.75 146	99.66 208	99.20 95	99.76 363	98.98 172	99.99 16	99.36 295

Patchmatch-RL test		98.60 303	98.36 313	99.33 269	99.77 134	99.07 263	98.27 370	99.87 64	98.91 268	99.74 155	99.72 159	90.57 414	99.79 348	98.55 219	99.85 189	99.11 354

v1192		99.57 89	99.57 92	99.57 187	99.77 134	99.22 238	99.04 253	99.60 230	99.18 227	99.87 89	99.72 159	99.08 116	99.85 281	99.89 38	99.98 48	99.66 134

EG-PatchMatch MVS		99.57 89	99.56 97	99.62 166	99.77 134	99.33 212	99.26 172	99.76 132	99.32 206	99.80 115	99.78 121	99.29 83	99.87 244	99.15 151	99.91 141	99.66 134

SSM_0404		99.57 89	99.58 87	99.54 200	99.76 138	99.28 220	99.19 196	99.84 81	99.80 92	99.78 125	99.70 179	99.44 58	99.93 116	98.74 200	99.95 100	99.41 281

ttmdpeth		99.48 115	99.55 99	99.29 283	99.76 138	98.16 348	99.33 143	99.95 36	99.79 96	99.36 291	99.89 41	99.13 108	99.77 360	99.09 162	99.64 299	99.93 20

GeoE		99.69 58	99.66 65	99.78 72	99.76 138	99.76 70	99.60 79	99.82 91	99.46 176	99.75 146	99.56 279	99.63 37	99.95 78	99.43 100	99.88 164	99.62 170

ZNCC-MVS		99.22 196	99.04 225	99.77 75	99.76 138	99.73 88	99.28 163	99.56 252	98.19 352	99.14 335	99.29 356	98.84 154	99.92 147	97.53 314	99.80 229	99.64 153

tttt0517		97.62 366	97.20 376	98.90 349	99.76 138	97.40 387	99.48 107	94.36 454	99.06 249	99.70 172	99.49 304	84.55 441	99.94 95	98.73 205	99.65 297	99.36 295

pmmvs5		99.19 206	99.11 197	99.42 238	99.76 138	98.88 286	98.55 343	99.73 147	98.82 281	99.72 163	99.62 239	96.56 325	99.82 323	99.32 122	99.95 100	99.56 205

nrg030		99.70 56	99.66 65	99.82 44	99.76 138	99.84 27	99.61 73	99.70 168	99.93 41	99.78 125	99.68 199	99.10 111	99.78 351	99.45 98	99.96 84	99.83 54

v148		99.40 144	99.41 131	99.39 252	99.76 138	98.94 277	99.09 240	99.59 236	99.17 232	99.81 111	99.61 248	98.41 220	99.69 389	99.32 122	99.94 115	99.53 223

region2R		99.23 187	99.05 219	99.77 75	99.76 138	99.70 106	99.31 151	99.59 236	98.41 327	99.32 302	99.36 339	98.73 171	99.93 116	97.29 327	99.74 256	99.67 124

MP-MVS		99.06 239	98.83 269	99.76 82	99.76 138	99.71 98	99.32 146	99.50 288	98.35 338	98.97 350	99.48 307	98.37 226	99.92 147	95.95 406	99.75 249	99.63 159
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMMVS2		99.48 115	99.45 121	99.57 187	99.76 138	98.99 269	98.09 388	99.90 55	98.95 261	99.78 125	99.58 268	99.57 47	99.93 116	99.48 94	99.95 100	99.79 70

CP-MVSNet		99.54 102	99.43 126	99.87 25	99.76 138	99.82 42	99.57 85	99.61 219	99.54 157	99.80 115	99.64 215	97.79 276	99.95 78	99.21 138	99.94 115	99.84 50

mPP-MVS		99.19 206	99.00 236	99.76 82	99.76 138	99.68 113	99.38 125	99.54 264	98.34 342	99.01 348	99.50 300	98.53 204	99.93 116	97.18 341	99.78 239	99.66 134

fmvsm_s_conf0.5_n_4		99.78 37	99.78 39	99.79 68	99.75 151	99.56 153	98.98 276	99.94 38	99.92 44	99.97 23	99.72 159	99.84 16	99.92 147	99.91 31	99.98 48	99.89 35

SSC-MVS3.2		99.64 77	99.67 63	99.56 190	99.75 151	98.98 270	98.96 282	99.87 64	99.88 59	99.84 97	99.64 215	99.32 80	99.91 176	99.78 52	99.96 84	99.80 62

IterMVS-SCA-FT		99.00 257	99.16 184	98.51 375	99.75 151	95.90 421	98.07 391	99.84 81	99.84 74	99.89 71	99.73 152	96.01 346	99.99 8	99.33 120	100.00 1	99.63 159

ACMMP_NAP		99.28 176	99.11 197	99.79 68	99.75 151	99.81 47	98.95 284	99.53 274	98.27 347	99.53 246	99.73 152	98.75 167	99.87 244	97.70 297	99.83 202	99.68 115

v1921920		99.56 93	99.57 92	99.55 194	99.75 151	99.11 253	99.05 248	99.61 219	99.15 238	99.88 81	99.71 169	99.08 116	99.87 244	99.90 35	99.97 70	99.66 134

testgi		99.29 175	99.26 171	99.37 258	99.75 151	98.81 290	98.84 299	99.89 58	98.38 331	99.75 146	99.04 393	99.36 73	99.86 263	99.08 164	99.25 375	99.45 258

PGM-MVS		99.20 203	99.01 232	99.77 75	99.75 151	99.71 98	99.16 211	99.72 156	97.99 362	99.42 275	99.60 256	98.81 155	99.93 116	96.91 353	99.74 256	99.66 134

jason		99.16 216	99.11 197	99.32 274	99.75 151	98.44 328	98.26 372	99.39 320	98.70 298	99.74 155	99.30 353	98.54 200	99.97 42	98.48 222	99.82 212	99.55 208
jason: jason.
fmvsm_s_conf0.5_n_7		99.73 50	99.78 39	99.60 174	99.74 159	98.93 280	98.85 297	99.96 28	99.96 26	99.97 23	99.76 135	99.82 18	99.96 67	99.95 14	99.98 48	99.90 28

Anonymous20231206		99.35 161	99.31 153	99.47 221	99.74 159	99.06 265	99.28 163	99.74 143	99.23 220	99.72 163	99.53 291	97.63 290	99.88 229	99.11 160	99.84 194	99.48 249

ACMMPR		99.23 187	99.06 214	99.76 82	99.74 159	99.69 110	99.31 151	99.59 236	98.36 333	99.35 293	99.38 332	98.61 187	99.93 116	97.43 319	99.75 249	99.67 124

IterMVS		98.97 261	99.16 184	98.42 380	99.74 159	95.64 425	98.06 393	99.83 85	99.83 80	99.85 94	99.74 148	96.10 345	99.99 8	99.27 131	100.00 1	99.63 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewmanbaseed2359cas		99.50 108	99.47 114	99.61 170	99.73 163	99.52 162	99.03 256	99.83 85	99.49 165	99.65 194	99.64 215	99.18 97	99.71 379	98.73 205	99.92 130	99.58 197

GST-MVS		99.16 216	98.96 249	99.75 93	99.73 163	99.73 88	99.20 190	99.55 258	98.22 349	99.32 302	99.35 344	98.65 183	99.91 176	96.86 356	99.74 256	99.62 170

HFP-MVS		99.25 183	99.08 208	99.76 82	99.73 163	99.70 106	99.31 151	99.59 236	98.36 333	99.36 291	99.37 335	98.80 159	99.91 176	97.43 319	99.75 249	99.68 115

114514_t		98.49 318	98.11 336	99.64 150	99.73 163	99.58 149	99.24 179	99.76 132	89.94 454	99.42 275	99.56 279	97.76 279	99.86 263	97.74 291	99.82 212	99.47 253

UA-Net		99.78 37	99.76 49	99.86 29	99.72 167	99.71 98	99.91 4	99.95 36	99.96 26	99.71 168	99.91 31	99.15 103	99.97 42	99.50 92	100.00 1	99.90 28

N_pmnet		98.73 291	98.53 298	99.35 265	99.72 167	98.67 302	98.34 365	94.65 453	98.35 338	99.79 121	99.68 199	98.03 258	99.93 116	98.28 238	99.92 130	99.44 270

DeepC-MVS	98.90 4	99.62 84	99.61 79	99.67 130	99.72 167	99.44 180	99.24 179	99.71 159	99.27 212	99.93 51	99.90 36	99.70 31	99.93 116	98.99 170	99.99 16	99.64 153
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mamba_0408		99.54 102	99.55 99	99.54 200	99.71 170	99.24 232	99.27 167	99.79 112	99.72 109	99.78 125	99.64 215	99.36 73	99.93 116	98.74 200	99.90 142	99.45 258

icg_test_0407_2		99.30 173	99.29 163	99.31 278	99.71 170	98.55 318	98.17 378	99.71 159	99.41 192	99.73 159	99.60 256	99.17 99	99.92 147	98.45 224	99.70 273	99.45 258

SSM_04072		99.55 98	99.55 99	99.55 194	99.71 170	99.24 232	99.27 167	99.79 112	99.72 109	99.78 125	99.64 215	99.36 73	99.97 42	98.74 200	99.90 142	99.45 258

SSM_0407		99.56 93	99.56 97	99.54 200	99.71 170	99.24 232	99.15 213	99.84 81	99.80 92	99.78 125	99.70 179	99.44 58	99.93 116	98.74 200	99.90 142	99.45 258

IMVS_0407		99.38 151	99.42 128	99.28 286	99.71 170	98.55 318	99.27 167	99.71 159	99.41 192	99.73 159	99.60 256	99.17 99	99.83 312	98.45 224	99.70 273	99.45 258

IMVS_0404		99.23 187	99.20 179	99.32 274	99.71 170	98.55 318	98.57 340	99.71 159	99.41 192	99.52 248	99.60 256	98.12 253	99.95 78	98.45 224	99.70 273	99.45 258

IMVS_0403		99.37 155	99.39 133	99.28 286	99.71 170	98.55 318	99.19 196	99.71 159	99.41 192	99.67 185	99.60 256	99.12 110	99.84 296	98.45 224	99.70 273	99.45 258

test_vis1_rt		99.45 130	99.46 119	99.41 246	99.71 170	98.63 311	98.99 273	99.96 28	99.03 251	99.95 43	99.12 383	98.75 167	99.84 296	99.82 48	99.82 212	99.77 76

XVS		99.27 180	99.11 197	99.75 93	99.71 170	99.71 98	99.37 129	99.61 219	99.29 208	98.76 377	99.47 311	98.47 211	99.88 229	97.62 306	99.73 262	99.67 124

X-MVStestdata		96.09 409	94.87 422	99.75 93	99.71 170	99.71 98	99.37 129	99.61 219	99.29 208	98.76 377	61.30 472	98.47 211	99.88 229	97.62 306	99.73 262	99.67 124

VDDNet		98.97 261	98.82 270	99.42 238	99.71 170	98.81 290	99.62 67	98.68 395	99.81 88	99.38 289	99.80 99	94.25 367	99.85 281	98.79 193	99.32 365	99.59 192

DSMNet-mixed		99.48 115	99.65 67	98.95 336	99.71 170	97.27 390	99.50 100	99.82 91	99.59 154	99.41 281	99.85 68	99.62 40	100.00 1	99.53 88	99.89 155	99.59 192

EC-MVSNet		99.69 58	99.69 59	99.68 127	99.71 170	99.91 4	99.76 23	99.96 28	99.86 64	99.51 255	99.39 330	99.57 47	99.93 116	99.64 72	99.86 184	99.20 333

CSCG		99.37 155	99.29 163	99.60 174	99.71 170	99.46 173	99.43 118	99.85 75	98.79 286	99.41 281	99.60 256	98.92 144	99.92 147	98.02 261	99.92 130	99.43 276

LF4IMVS		99.01 254	98.92 256	99.27 291	99.71 170	99.28 220	98.59 334	99.77 124	98.32 344	99.39 288	99.41 322	98.62 185	99.84 296	96.62 374	99.84 194	98.69 414

viewmambaseed2359dif		99.47 124	99.50 108	99.37 258	99.70 185	98.80 293	98.67 324	99.92 41	99.49 165	99.77 137	99.71 169	99.08 116	99.78 351	99.20 141	99.94 115	99.54 217

patch_mono-2		99.51 107	99.46 119	99.64 150	99.70 185	99.11 253	99.04 253	99.87 64	99.71 113	99.47 262	99.79 109	98.24 239	99.98 27	99.38 109	99.96 84	99.83 54

test_0728_SECOND				99.83 39	99.70 185	99.79 54	99.14 216	99.61 219					99.92 147	97.88 275	99.72 268	99.77 76

OPM-MVS		99.26 182	99.13 190	99.63 157	99.70 185	99.61 139	98.58 336	99.48 293	98.50 319	99.52 248	99.63 230	99.14 106	99.76 363	97.89 274	99.77 243	99.51 235
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
new_pmnet		98.88 276	98.89 261	98.84 354	99.70 185	97.62 379	98.15 380	99.50 288	97.98 363	99.62 209	99.54 289	98.15 250	99.94 95	97.55 311	99.84 194	98.95 389

SED-MVS		99.40 144	99.28 166	99.77 75	99.69 190	99.82 42	99.20 190	99.54 264	99.13 240	99.82 104	99.63 230	98.91 146	99.92 147	97.85 281	99.70 273	99.58 197

IU-MVS					99.69 190	99.77 63		99.22 358	97.50 390	99.69 175				97.75 290	99.70 273	99.77 76

test_241102_ONE					99.69 190	99.82 42		99.54 264	99.12 243	99.82 104	99.49 304	98.91 146	99.52 441

D2MVS		99.22 196	99.19 181	99.29 283	99.69 190	98.74 298	98.81 307	99.41 310	98.55 312	99.68 178	99.69 188	98.13 251	99.87 244	98.82 189	99.98 48	99.24 320

DVP-MVS		99.32 171	99.17 183	99.77 75	99.69 190	99.80 51	99.14 216	99.31 338	99.16 234	99.62 209	99.61 248	98.35 228	99.91 176	97.88 275	99.72 268	99.61 181
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					99.69 190	99.80 51	99.24 179	99.57 247	99.16 234	99.73 159	99.65 213	98.35 228

wuyk23d		97.58 368	99.13 190	92.93 442	99.69 190	99.49 165	99.52 92	99.77 124	97.97 364	99.96 32	99.79 109	99.84 16	99.94 95	95.85 409	99.82 212	79.36 460

DeepMVS_CX				97.98 398	99.69 190	96.95 398		99.26 348	75.51 460	95.74 456	98.28 439	96.47 330	99.62 423	91.23 447	97.89 443	97.38 452

MVSMamba_PlusPlus		99.55 98	99.58 87	99.47 221	99.68 198	99.40 194	99.52 92	99.70 168	99.92 44	99.77 137	99.86 63	98.28 235	99.96 67	99.54 85	99.90 142	99.05 374

thisisatest0530		97.45 372	96.95 383	98.94 337	99.68 198	97.73 376	99.09 240	94.19 456	98.61 308	99.56 234	99.30 353	84.30 443	99.93 116	98.27 239	99.54 332	99.16 343

VPA-MVSNet		99.66 70	99.62 75	99.79 68	99.68 198	99.75 78	99.62 67	99.69 176	99.85 70	99.80 115	99.81 95	98.81 155	99.91 176	99.47 95	99.88 164	99.70 102

UnsupCasMVSNet_eth		98.83 280	98.57 292	99.59 177	99.68 198	99.45 178	98.99 273	99.67 184	99.48 168	99.55 239	99.36 339	94.92 358	99.86 263	98.95 180	96.57 453	99.45 258

Test_1112_low_res		98.95 267	98.73 277	99.63 157	99.68 198	99.15 249	98.09 388	99.80 105	97.14 408	99.46 266	99.40 326	96.11 343	99.89 214	99.01 169	99.84 194	99.84 50

MVE	92.54 22	96.66 393	96.11 398	98.31 388	99.68 198	97.55 381	97.94 406	95.60 451	99.37 198	90.68 462	98.70 426	96.56 325	98.61 458	86.94 458	99.55 327	98.77 410
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
VortexMVS		99.13 223	99.24 175	98.79 360	99.67 204	96.60 407	99.24 179	99.80 105	99.85 70	99.93 51	99.84 75	95.06 357	99.89 214	99.80 50	99.98 48	99.89 35

diffmvs		99.34 166	99.32 151	99.39 252	99.67 204	98.77 296	98.57 340	99.81 102	99.61 146	99.48 260	99.41 322	98.47 211	99.86 263	98.97 174	99.90 142	99.53 223
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
our_test_3		98.85 279	99.09 206	98.13 394	99.66 206	94.90 436	97.72 417	99.58 245	99.07 247	99.64 196	99.62 239	98.19 247	99.93 116	98.41 229	99.95 100	99.55 208

ppachtmachnet_test		98.89 275	99.12 194	98.20 392	99.66 206	95.24 432	97.63 421	99.68 179	99.08 245	99.78 125	99.62 239	98.65 183	99.88 229	98.02 261	99.96 84	99.48 249

mamv4		99.73 50	99.74 52	99.70 123	99.66 206	99.87 15	99.69 45	99.93 39	99.93 41	99.93 51	99.86 63	99.07 119	100.00 1	99.66 67	99.92 130	99.24 320

CP-MVS		99.23 187	99.05 219	99.75 93	99.66 206	99.66 117	99.38 125	99.62 212	98.38 331	99.06 346	99.27 359	98.79 160	99.94 95	97.51 315	99.82 212	99.66 134

1112_ss		99.05 242	98.84 267	99.67 130	99.66 206	99.29 218	98.52 349	99.82 91	97.65 382	99.43 272	99.16 377	96.42 332	99.91 176	99.07 165	99.84 194	99.80 62

SymmetryMVS		99.01 254	98.82 270	99.58 180	99.65 211	99.11 253	99.36 133	99.20 364	99.82 82	99.68 178	99.53 291	93.30 378	99.99 8	99.24 132	99.63 302	99.64 153

YYNet1		98.95 267	98.99 243	98.84 354	99.64 212	97.14 395	98.22 375	99.32 334	98.92 267	99.59 221	99.66 208	97.40 296	99.83 312	98.27 239	99.90 142	99.55 208

MDA-MVSNet_test_wron		98.95 267	98.99 243	98.85 352	99.64 212	97.16 393	98.23 374	99.33 332	98.93 265	99.56 234	99.66 208	97.39 298	99.83 312	98.29 237	99.88 164	99.55 208

test_one_0601					99.63 214	99.76 70		99.55 258	99.23 220	99.31 307	99.61 248	98.59 189

thres100view900		96.39 400	96.03 400	97.47 415	99.63 214	95.93 420	99.18 200	97.57 435	98.75 294	98.70 383	97.31 457	87.04 430	99.67 406	87.62 454	98.51 422	96.81 455

thres600view7		96.60 394	96.16 397	97.93 401	99.63 214	96.09 419	99.18 200	97.57 435	98.77 290	98.72 380	97.32 456	87.04 430	99.72 375	88.57 451	98.62 418	97.98 446

ITE_SJBPF				99.38 255	99.63 214	99.44 180		99.73 147	98.56 311	99.33 299	99.53 291	98.88 150	99.68 401	96.01 400	99.65 297	99.02 383

test_part2					99.62 218	99.67 115				99.55 239

Anonymous20231211		99.62 84	99.57 92	99.76 82	99.61 219	99.60 142	99.81 13	99.73 147	99.82 82	99.90 66	99.90 36	97.97 264	99.86 263	99.42 105	99.96 84	99.80 62

CPTT-MVS		98.74 289	98.44 305	99.64 150	99.61 219	99.38 199	99.18 200	99.55 258	96.49 419	99.27 314	99.37 335	97.11 311	99.92 147	95.74 413	99.67 292	99.62 170

reproduce_model		99.50 108	99.40 132	99.83 39	99.60 221	99.83 34	99.12 227	99.68 179	99.49 165	99.80 115	99.79 109	99.01 130	99.93 116	98.24 242	99.82 212	99.73 90

test1111		97.74 360	98.16 333	96.49 434	99.60 221	89.86 465	99.71 37	91.21 461	99.89 54	99.88 81	99.87 56	93.73 374	99.90 195	99.56 82	99.99 16	99.70 102

h-mvs33		98.61 300	98.34 316	99.44 232	99.60 221	98.67 302	99.27 167	99.44 304	99.68 123	99.32 302	99.49 304	92.50 390	100.00 1	99.24 132	96.51 454	99.65 143

MSDG		99.08 235	98.98 246	99.37 258	99.60 221	99.13 250	97.54 425	99.74 143	98.84 279	99.53 246	99.55 287	99.10 111	99.79 348	97.07 346	99.86 184	99.18 338

FPMVS		96.32 402	95.50 411	98.79 360	99.60 221	98.17 347	98.46 359	98.80 390	97.16 407	96.28 451	99.63 230	82.19 444	99.09 452	88.45 452	98.89 402	99.10 356

test2506		94.73 425	94.59 426	95.15 441	99.59 226	85.90 467	99.75 25	74.01 469	99.89 54	99.71 168	99.86 63	79.00 456	99.90 195	99.52 89	99.99 16	99.65 143

ECVR-MVS		97.73 361	98.04 340	96.78 427	99.59 226	90.81 460	99.72 33	90.43 463	99.89 54	99.86 91	99.86 63	93.60 376	99.89 214	99.46 96	99.99 16	99.65 143

xiu_mvs_v1_base_debu		99.23 187	99.34 146	98.91 343	99.59 226	98.23 340	98.47 355	99.66 189	99.61 146	99.68 178	98.94 409	99.39 64	99.97 42	99.18 145	99.55 327	98.51 426

xiu_mvs_v1_base		99.23 187	99.34 146	98.91 343	99.59 226	98.23 340	98.47 355	99.66 189	99.61 146	99.68 178	98.94 409	99.39 64	99.97 42	99.18 145	99.55 327	98.51 426

xiu_mvs_v1_base_debi		99.23 187	99.34 146	98.91 343	99.59 226	98.23 340	98.47 355	99.66 189	99.61 146	99.68 178	98.94 409	99.39 64	99.97 42	99.18 145	99.55 327	98.51 426

SF-MVS		99.10 233	98.93 252	99.62 166	99.58 231	99.51 163	99.13 222	99.65 199	97.97 364	99.42 275	99.61 248	98.86 152	99.87 244	96.45 384	99.68 286	99.49 245

tfpn200view9		96.30 403	95.89 402	97.53 412	99.58 231	96.11 417	99.00 266	97.54 438	98.43 324	98.52 397	96.98 460	86.85 432	99.67 406	87.62 454	98.51 422	96.81 455

EI-MVSNet		99.38 151	99.44 124	99.21 301	99.58 231	98.09 354	99.26 172	99.46 299	99.62 142	99.75 146	99.67 203	98.54 200	99.85 281	99.15 151	99.92 130	99.68 115

CVMVSNet		98.61 300	98.88 262	97.80 406	99.58 231	93.60 444	99.26 172	99.64 207	99.66 131	99.72 163	99.67 203	93.26 380	99.93 116	99.30 125	99.81 222	99.87 42

thres400		96.40 399	95.89 402	97.92 402	99.58 231	96.11 417	99.00 266	97.54 438	98.43 324	98.52 397	96.98 460	86.85 432	99.67 406	87.62 454	98.51 422	97.98 446

MCST-MVS		99.02 248	98.81 272	99.65 143	99.58 231	99.49 165	98.58 336	99.07 375	98.40 329	99.04 347	99.25 364	98.51 209	99.80 345	97.31 326	99.51 338	99.65 143

HQP_MVS		98.90 272	98.68 282	99.55 194	99.58 231	99.24 232	98.80 310	99.54 264	98.94 262	99.14 335	99.25 364	97.24 303	99.82 323	95.84 410	99.78 239	99.60 185

plane_prior7					99.58 231	99.38 199

TranMVSNet+NR-MVSNet		99.54 102	99.47 114	99.76 82	99.58 231	99.64 126	99.30 154	99.63 209	99.61 146	99.71 168	99.56 279	98.76 165	99.96 67	99.14 157	99.92 130	99.68 115

MVS_111021_LR		99.13 223	99.03 227	99.42 238	99.58 231	99.32 214	97.91 410	99.73 147	98.68 299	99.31 307	99.48 307	99.09 113	99.66 411	97.70 297	99.77 243	99.29 314

DPE-MVS		99.14 221	98.92 256	99.82 44	99.57 241	99.77 63	98.74 318	99.60 230	98.55 312	99.76 141	99.69 188	98.23 243	99.92 147	96.39 386	99.75 249	99.76 81
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SPE-MVS-test		99.68 63	99.70 56	99.64 150	99.57 241	99.83 34	99.78 17	99.97 20	99.92 44	99.50 257	99.38 332	99.57 47	99.95 78	99.69 62	99.90 142	99.15 345

EI-MVSNet-UG-set		99.48 115	99.50 108	99.42 238	99.57 241	98.65 308	99.24 179	99.46 299	99.68 123	99.80 115	99.66 208	98.99 133	99.89 214	99.19 143	99.90 142	99.72 94

EI-MVSNet-Vis-set		99.47 124	99.49 111	99.42 238	99.57 241	98.66 305	99.24 179	99.46 299	99.67 127	99.79 121	99.65 213	98.97 137	99.89 214	99.15 151	99.89 155	99.71 99

pmmvs4		99.13 223	99.06 214	99.36 263	99.57 241	99.10 260	98.01 397	99.25 351	98.78 288	99.58 223	99.44 318	98.24 239	99.76 363	98.74 200	99.93 126	99.22 326

MVSFormer		99.41 142	99.44 124	99.31 278	99.57 241	98.40 331	99.77 19	99.80 105	99.73 105	99.63 200	99.30 353	98.02 259	99.98 27	99.43 100	99.69 281	99.55 208

lupinMVS		98.96 264	98.87 263	99.24 299	99.57 241	98.40 331	98.12 384	99.18 366	98.28 346	99.63 200	99.13 379	98.02 259	99.97 42	98.22 244	99.69 281	99.35 298

ab-mvs		99.33 169	99.28 166	99.47 221	99.57 241	99.39 197	99.78 17	99.43 307	98.87 273	99.57 226	99.82 88	98.06 257	99.87 244	98.69 210	99.73 262	99.15 345

DP-MVS		99.48 115	99.39 133	99.74 98	99.57 241	99.62 133	99.29 161	99.61 219	99.87 61	99.74 155	99.76 135	98.69 175	99.87 244	98.20 246	99.80 229	99.75 84

F-COLMAP		98.74 289	98.45 304	99.62 166	99.57 241	99.47 169	98.84 299	99.65 199	96.31 423	98.93 354	99.19 376	97.68 283	99.87 244	96.52 377	99.37 358	99.53 223

CLD-MVS		98.76 287	98.57 292	99.33 269	99.57 241	98.97 273	97.53 427	99.55 258	96.41 420	99.27 314	99.13 379	99.07 119	99.78 351	96.73 365	99.89 155	99.23 324
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
reproduce-ours		99.46 126	99.35 144	99.82 44	99.56 252	99.83 34	99.05 248	99.65 199	99.45 179	99.78 125	99.78 121	98.93 141	99.93 116	98.11 256	99.81 222	99.70 102

our_new_method		99.46 126	99.35 144	99.82 44	99.56 252	99.83 34	99.05 248	99.65 199	99.45 179	99.78 125	99.78 121	98.93 141	99.93 116	98.11 256	99.81 222	99.70 102

UnsupCasMVSNet_bld		98.55 310	98.27 324	99.40 249	99.56 252	99.37 202	97.97 404	99.68 179	97.49 391	99.08 342	99.35 344	95.41 355	99.82 323	97.70 297	98.19 434	99.01 384

dmvs_re		98.69 296	98.48 300	99.31 278	99.55 255	99.42 187	99.54 90	98.38 415	99.32 206	98.72 380	98.71 424	96.76 321	99.21 450	96.01 400	99.35 361	99.31 309

APDe-MVS		99.48 115	99.36 142	99.85 31	99.55 255	99.81 47	99.50 100	99.69 176	98.99 254	99.75 146	99.71 169	98.79 160	99.93 116	98.46 223	99.85 189	99.80 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SD_0403		97.42 374	96.90 387	98.98 332	99.54 257	97.90 368	99.52 92	99.54 264	99.34 202	97.87 426	98.85 416	98.72 172	99.64 420	78.93 461	99.83 202	99.40 284

test_fmvs1		99.48 115	99.65 67	98.97 333	99.54 257	97.16 393	99.11 232	99.98 12	99.78 99	99.96 32	99.81 95	98.72 172	99.97 42	99.95 14	99.97 70	99.79 70

SR-MVS-dyc-post		99.27 180	99.11 197	99.73 107	99.54 257	99.74 85	99.26 172	99.62 212	99.16 234	99.52 248	99.64 215	98.41 220	99.91 176	97.27 330	99.61 311	99.54 217

RE-MVS-def			99.13 190		99.54 257	99.74 85	99.26 172	99.62 212	99.16 234	99.52 248	99.64 215	98.57 192		97.27 330	99.61 311	99.54 217

PVSNet_BlendedMVS		99.03 246	99.01 232	99.09 318	99.54 257	97.99 360	98.58 336	99.82 91	97.62 383	99.34 297	99.71 169	98.52 207	99.77 360	97.98 266	99.97 70	99.52 233

PVSNet_Blended		98.70 295	98.59 288	99.02 328	99.54 257	97.99 360	97.58 424	99.82 91	95.70 431	99.34 297	98.98 403	98.52 207	99.77 360	97.98 266	99.83 202	99.30 311

USDC		98.96 264	98.93 252	99.05 326	99.54 257	97.99 360	97.07 445	99.80 105	98.21 350	99.75 146	99.77 131	98.43 217	99.64 420	97.90 273	99.88 164	99.51 235

GDP-MVS		98.81 283	98.57 292	99.50 211	99.53 264	99.12 252	99.28 163	99.86 69	99.53 159	99.57 226	99.32 348	90.88 408	99.98 27	99.46 96	99.74 256	99.42 280

BP-MVS1		98.72 292	98.46 302	99.50 211	99.53 264	99.00 267	99.34 137	98.53 404	99.65 134	99.73 159	99.38 332	90.62 412	99.96 67	99.50 92	99.86 184	99.55 208

save fliter					99.53 264	99.25 228	98.29 369	99.38 324	99.07 247

CS-MVS		99.67 69	99.70 56	99.58 180	99.53 264	99.84 27	99.79 15	99.96 28	99.90 48	99.61 215	99.41 322	99.51 55	99.95 78	99.66 67	99.89 155	98.96 387

Anonymous20240529		99.42 138	99.34 146	99.65 143	99.53 264	99.60 142	99.63 64	99.39 320	99.47 173	99.76 141	99.78 121	98.13 251	99.86 263	98.70 208	99.68 286	99.49 245

APD-MVS_3200maxsize		99.31 172	99.16 184	99.74 98	99.53 264	99.75 78	99.27 167	99.61 219	99.19 226	99.57 226	99.64 215	98.76 165	99.90 195	97.29 327	99.62 304	99.56 205

MIMVSNet		98.43 323	98.20 328	99.11 315	99.53 264	98.38 335	99.58 82	98.61 400	98.96 258	99.33 299	99.76 135	90.92 405	99.81 338	97.38 322	99.76 245	99.15 345

HPM-MVS++		98.96 264	98.70 281	99.74 98	99.52 271	99.71 98	98.86 295	99.19 365	98.47 323	98.59 391	99.06 390	98.08 256	99.91 176	96.94 351	99.60 314	99.60 185

GA-MVS		97.99 354	97.68 364	98.93 340	99.52 271	98.04 358	97.19 441	99.05 378	98.32 344	98.81 370	98.97 405	89.89 421	99.41 447	98.33 235	99.05 388	99.34 301

SR-MVS		99.19 206	99.00 236	99.74 98	99.51 273	99.72 93	99.18 200	99.60 230	98.85 276	99.47 262	99.58 268	98.38 225	99.92 147	96.92 352	99.54 332	99.57 203

test222					99.51 273	99.08 262	97.83 414	99.29 342	95.21 437	98.68 384	99.31 351	97.28 302			99.38 356	99.43 276

testdata				99.42 238	99.51 273	98.93 280		99.30 341	96.20 424	98.87 364	99.40 326	98.33 232	99.89 214	96.29 390	99.28 370	99.44 270

plane_prior1					99.51 273

UniMVSNet (Re)		99.37 155	99.26 171	99.68 127	99.51 273	99.58 149	98.98 276	99.60 230	99.43 187	99.70 172	99.36 339	97.70 280	99.88 229	99.20 141	99.87 176	99.59 192

DELS-MVS		99.34 166	99.30 158	99.48 219	99.51 273	99.36 206	98.12 384	99.53 274	99.36 201	99.41 281	99.61 248	99.22 93	99.87 244	99.21 138	99.68 286	99.20 333
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
新几何1				99.52 206	99.50 279	99.22 238		99.26 348	95.66 432	98.60 390	99.28 357	97.67 284	99.89 214	95.95 406	99.32 365	99.45 258

SD-MVS		99.01 254	99.30 158	98.15 393	99.50 279	99.40 194	98.94 286	99.61 219	99.22 224	99.75 146	99.82 88	99.54 50	95.51 463	97.48 316	99.87 176	99.54 217
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CDPH-MVS		98.56 309	98.20 328	99.61 170	99.50 279	99.46 173	98.32 367	99.41 310	95.22 436	99.21 325	99.10 387	98.34 230	99.82 323	95.09 426	99.66 295	99.56 205

APD-MVS		98.87 277	98.59 288	99.71 119	99.50 279	99.62 133	99.01 263	99.57 247	96.80 417	99.54 241	99.63 230	98.29 234	99.91 176	95.24 422	99.71 271	99.61 181
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		99.12 226	99.02 228	99.40 249	99.50 279	99.11 253	97.92 408	99.71 159	98.76 293	99.08 342	99.47 311	99.17 99	99.54 436	97.85 281	99.76 245	99.54 217

旧先验1					99.49 284	99.29 218		99.26 348			99.39 330	97.67 284			99.36 359	99.46 257

GBi-Net		99.42 138	99.31 153	99.73 107	99.49 284	99.77 63	99.68 49	99.70 168	99.44 181	99.62 209	99.83 81	97.21 305	99.90 195	98.96 176	99.90 142	99.53 223

test1		99.42 138	99.31 153	99.73 107	99.49 284	99.77 63	99.68 49	99.70 168	99.44 181	99.62 209	99.83 81	97.21 305	99.90 195	98.96 176	99.90 142	99.53 223

FMVSNet2		99.35 161	99.28 166	99.55 194	99.49 284	99.35 209	99.45 114	99.57 247	99.44 181	99.70 172	99.74 148	97.21 305	99.87 244	99.03 167	99.94 115	99.44 270

DP-MVS Recon		98.50 316	98.23 325	99.31 278	99.49 284	99.46 173	98.56 342	99.63 209	94.86 442	98.85 366	99.37 335	97.81 274	99.59 430	96.08 397	99.44 348	98.88 399

FA-MVS(test-final)		98.52 313	98.32 318	99.10 317	99.48 289	98.67 302	99.77 19	98.60 402	97.35 398	99.63 200	99.80 99	93.07 383	99.84 296	97.92 271	99.30 367	98.78 408

MVP-Stereo		99.16 216	99.08 208	99.43 236	99.48 289	99.07 263	99.08 243	99.55 258	98.63 304	99.31 307	99.68 199	98.19 247	99.78 351	98.18 250	99.58 320	99.45 258
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
thres200		96.09 409	95.68 409	97.33 420	99.48 289	96.22 416	98.53 348	97.57 435	98.06 359	98.37 404	96.73 464	86.84 434	99.61 428	86.99 457	98.57 419	96.16 458

sss		98.90 272	98.77 276	99.27 291	99.48 289	98.44 328	98.72 320	99.32 334	97.94 368	99.37 290	99.35 344	96.31 338	99.91 176	98.85 185	99.63 302	99.47 253

PAPM_NR		98.36 329	98.04 340	99.33 269	99.48 289	98.93 280	98.79 313	99.28 345	97.54 387	98.56 396	98.57 430	97.12 310	99.69 389	94.09 437	98.90 401	99.38 289

TAMVS		99.49 113	99.45 121	99.63 157	99.48 289	99.42 187	99.45 114	99.57 247	99.66 131	99.78 125	99.83 81	97.85 272	99.86 263	99.44 99	99.96 84	99.61 181

原ACMM1				99.37 258	99.47 295	98.87 288		99.27 346	96.74 418	98.26 406	99.32 348	97.93 266	99.82 323	95.96 405	99.38 356	99.43 276

plane_prior6					99.47 295	99.26 225						97.24 303

UniMVSNet_NR-MVSNet		99.37 155	99.25 173	99.72 114	99.47 295	99.56 153	98.97 278	99.61 219	99.43 187	99.67 185	99.28 357	97.85 272	99.95 78	99.17 148	99.81 222	99.65 143

TAPA-MVS	97.92 13	98.03 351	97.55 367	99.46 225	99.47 295	99.44 180	98.50 351	99.62 212	86.79 455	99.07 345	99.26 362	98.26 238	99.62 423	97.28 329	99.73 262	99.31 309
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_testset		97.27 379	96.83 389	98.59 372	99.46 299	97.55 381	99.25 178	96.84 444	98.78 288	97.24 440	97.67 450	97.11 311	98.97 454	86.59 459	98.54 421	99.27 315

SMA-MVS		99.19 206	99.00 236	99.73 107	99.46 299	99.73 88	99.13 222	99.52 279	97.40 395	99.57 226	99.64 215	98.93 141	99.83 312	97.61 308	99.79 234	99.63 159
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
PVSNet	97.47 15	98.42 324	98.44 305	98.35 383	99.46 299	96.26 414	96.70 450	99.34 331	97.68 381	99.00 349	99.13 379	97.40 296	99.72 375	97.59 310	99.68 286	99.08 367

TinyColmap		98.97 261	98.93 252	99.07 323	99.46 299	98.19 344	97.75 416	99.75 137	98.79 286	99.54 241	99.70 179	98.97 137	99.62 423	96.63 373	99.83 202	99.41 281

9.14			98.64 283		99.45 303		98.81 307	99.60 230	97.52 389	99.28 313	99.56 279	98.53 204	99.83 312	95.36 421	99.64 299

FE-MVS		97.85 356	97.42 370	99.15 309	99.44 304	98.75 297	99.77 19	98.20 422	95.85 428	99.33 299	99.80 99	88.86 424	99.88 229	96.40 385	99.12 382	98.81 405

PatchMatch-RL		98.68 297	98.47 301	99.30 282	99.44 304	99.28 220	98.14 382	99.54 264	97.12 409	99.11 339	99.25 364	97.80 275	99.70 383	96.51 378	99.30 367	98.93 392

PCF-MVS	96.03 18	96.73 391	95.86 404	99.33 269	99.44 304	99.16 247	96.87 448	99.44 304	86.58 456	98.95 352	99.40 326	94.38 366	99.88 229	87.93 453	99.80 229	98.95 389
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ZD-MVS					99.43 307	99.61 139		99.43 307	96.38 421	99.11 339	99.07 389	97.86 270	99.92 147	94.04 438	99.49 343

VDD-MVS		99.20 203	99.11 197	99.44 232	99.43 307	98.98 270	99.50 100	98.32 418	99.80 92	99.56 234	99.69 188	96.99 315	99.85 281	98.99 170	99.73 262	99.50 240

DU-MVS		99.33 169	99.21 178	99.71 119	99.43 307	99.56 153	98.83 302	99.53 274	99.38 197	99.67 185	99.36 339	97.67 284	99.95 78	99.17 148	99.81 222	99.63 159

NR-MVSNet		99.40 144	99.31 153	99.68 127	99.43 307	99.55 157	99.73 30	99.50 288	99.46 176	99.88 81	99.36 339	97.54 291	99.87 244	98.97 174	99.87 176	99.63 159

WTY-MVS		98.59 306	98.37 312	99.26 294	99.43 307	98.40 331	98.74 318	99.13 373	98.10 355	99.21 325	99.24 369	94.82 361	99.90 195	97.86 279	98.77 406	99.49 245

balanced_conf03		99.50 108	99.50 108	99.50 211	99.42 312	99.49 165	99.52 92	99.75 137	99.86 64	99.78 125	99.71 169	98.20 246	99.90 195	99.39 108	99.88 164	99.10 356

thisisatest0515		96.98 385	96.42 393	98.66 368	99.42 312	97.47 383	97.27 438	94.30 455	97.24 402	99.15 333	98.86 415	85.01 439	99.87 244	97.10 343	99.39 355	98.63 415

pmmvs3		98.08 349	97.80 358	98.91 343	99.41 314	97.69 378	97.87 412	99.66 189	95.87 427	99.50 257	99.51 297	90.35 416	99.97 42	98.55 219	99.47 345	99.08 367

NP-MVS					99.40 315	99.13 250					98.83 417

QAPM		98.40 327	97.99 343	99.65 143	99.39 316	99.47 169	99.67 53	99.52 279	91.70 451	98.78 376	99.80 99	98.55 196	99.95 78	94.71 430	99.75 249	99.53 223

OMC-MVS		98.90 272	98.72 278	99.44 232	99.39 316	99.42 187	98.58 336	99.64 207	97.31 400	99.44 268	99.62 239	98.59 189	99.69 389	96.17 396	99.79 234	99.22 326

3Dnovator	99.15 2	99.43 135	99.36 142	99.65 143	99.39 316	99.42 187	99.70 38	99.56 252	99.23 220	99.35 293	99.80 99	99.17 99	99.95 78	98.21 245	99.84 194	99.59 192

Fast-Effi-MVS+		99.02 248	98.87 263	99.46 225	99.38 319	99.50 164	99.04 253	99.79 112	97.17 406	98.62 388	98.74 423	99.34 77	99.95 78	98.32 236	99.41 353	98.92 394

BH-untuned		98.22 342	98.09 337	98.58 374	99.38 319	97.24 391	98.55 343	98.98 383	97.81 377	99.20 330	98.76 422	97.01 314	99.65 418	94.83 427	98.33 427	98.86 401

mvsany_test1		99.44 132	99.45 121	99.40 249	99.37 321	98.64 310	97.90 411	99.59 236	99.27 212	99.92 58	99.82 88	99.74 26	99.93 116	99.55 84	99.87 176	99.63 159

xiu_mvs_v2_base		99.02 248	99.11 197	98.77 362	99.37 321	98.09 354	98.13 383	99.51 284	99.47 173	99.42 275	98.54 433	99.38 68	99.97 42	98.83 187	99.33 363	98.24 438

PS-MVSNAJ		99.00 257	99.08 208	98.76 363	99.37 321	98.10 353	98.00 399	99.51 284	99.47 173	99.41 281	98.50 435	99.28 85	99.97 42	98.83 187	99.34 362	98.20 442

testing3-2		96.51 397	96.43 392	96.74 430	99.36 324	91.38 457	99.10 235	97.87 431	99.48 168	98.57 394	98.71 424	76.65 458	99.66 411	98.87 184	99.26 374	99.18 338

EIA-MVS		99.12 226	99.01 232	99.45 228	99.36 324	99.62 133	99.34 137	99.79 112	98.41 327	98.84 367	98.89 413	98.75 167	99.84 296	98.15 254	99.51 338	98.89 398

DPM-MVS		98.28 335	97.94 351	99.32 274	99.36 324	99.11 253	97.31 437	98.78 391	96.88 413	98.84 367	99.11 386	97.77 277	99.61 428	94.03 439	99.36 359	99.23 324

mvsmamba		99.08 235	98.95 250	99.45 228	99.36 324	99.18 246	99.39 122	98.81 389	99.37 198	99.35 293	99.70 179	96.36 337	99.94 95	98.66 212	99.59 318	99.22 326

MM		99.18 210	99.05 219	99.55 194	99.35 328	98.81 290	99.05 248	97.79 433	99.99 3	99.48 260	99.59 265	96.29 340	99.95 78	99.94 19	99.98 48	99.88 38

ambc				99.20 303	99.35 328	98.53 322	99.17 205	99.46 299		99.67 185	99.80 99	98.46 214	99.70 383	97.92 271	99.70 273	99.38 289

TEST9					99.35 328	99.35 209	98.11 386	99.41 310	94.83 443	97.92 422	98.99 400	98.02 259	99.85 281

train_agg		98.35 332	97.95 347	99.57 187	99.35 328	99.35 209	98.11 386	99.41 310	94.90 440	97.92 422	98.99 400	98.02 259	99.85 281	95.38 420	99.44 348	99.50 240

agg_prior					99.35 328	99.36 206		99.39 320		97.76 433			99.85 281

test_prior				99.46 225	99.35 328	99.22 238		99.39 320					99.69 389			99.48 249

MVS_Test		99.28 176	99.31 153	99.19 304	99.35 328	98.79 294	99.36 133	99.49 292	99.17 232	99.21 325	99.67 203	98.78 162	99.66 411	99.09 162	99.66 295	99.10 356

CDS-MVSNet		99.22 196	99.13 190	99.50 211	99.35 328	99.11 253	98.96 282	99.54 264	99.46 176	99.61 215	99.70 179	96.31 338	99.83 312	99.34 117	99.88 164	99.55 208
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	98.92 3	99.35 161	99.24 175	99.67 130	99.35 328	99.47 169	99.62 67	99.50 288	99.44 181	99.12 338	99.78 121	98.77 164	99.94 95	97.87 278	99.72 268	99.62 170

ETV-MVS		99.18 210	99.18 182	99.16 307	99.34 337	99.28 220	99.12 227	99.79 112	99.48 168	98.93 354	98.55 432	99.40 63	99.93 116	98.51 221	99.52 337	98.28 436

Anonymous202405211		98.75 288	98.46 302	99.63 157	99.34 337	99.66 117	99.47 110	97.65 434	99.28 211	99.56 234	99.50 300	93.15 381	99.84 296	98.62 215	99.58 320	99.40 284

CHOSEN 280x420		98.41 325	98.41 308	98.40 381	99.34 337	95.89 422	96.94 447	99.44 304	98.80 285	99.25 316	99.52 295	93.51 377	99.98 27	98.94 181	99.98 48	99.32 305

test_8					99.34 337	99.31 215	98.08 390	99.40 317	94.90 440	97.87 426	98.97 405	98.02 259	99.84 296

TSAR-MVS + GP.		99.12 226	99.04 225	99.38 255	99.34 337	99.16 247	98.15 380	99.29 342	98.18 353	99.63 200	99.62 239	99.18 97	99.68 401	98.20 246	99.74 256	99.30 311

LCM-MVSNet-Re		99.28 176	99.15 187	99.67 130	99.33 342	99.76 70	99.34 137	99.97 20	98.93 265	99.91 61	99.79 109	98.68 176	99.93 116	96.80 361	99.56 323	99.30 311

PLC	97.35 16	98.36 329	97.99 343	99.48 219	99.32 343	99.24 232	98.50 351	99.51 284	95.19 438	98.58 392	98.96 407	96.95 316	99.83 312	95.63 414	99.25 375	99.37 292
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Effi-MVS+		99.06 239	98.97 247	99.34 266	99.31 344	98.98 270	98.31 368	99.91 50	98.81 283	98.79 374	98.94 409	99.14 106	99.84 296	98.79 193	98.74 410	99.20 333

HQP-NCC					99.31 344		97.98 401		97.45 392	98.15 411

ACMP_Plane					99.31 344		97.98 401		97.45 392	98.15 411

HQP-MVS		98.36 329	98.02 342	99.39 252	99.31 344	98.94 277	97.98 401	99.37 325	97.45 392	98.15 411	98.83 417	96.67 322	99.70 383	94.73 428	99.67 292	99.53 223

baseline1		97.73 361	97.33 372	98.96 334	99.30 348	97.73 376	99.40 120	98.42 411	99.33 205	99.46 266	99.21 373	91.18 401	99.82 323	98.35 233	91.26 461	99.32 305

WR-MVS		99.11 230	98.93 252	99.66 137	99.30 348	99.42 187	98.42 361	99.37 325	99.04 250	99.57 226	99.20 375	96.89 317	99.86 263	98.66 212	99.87 176	99.70 102

hse-mvs2		98.52 313	98.30 321	99.16 307	99.29 350	98.60 313	98.77 315	99.02 380	99.68 123	99.32 302	99.04 393	92.50 390	99.85 281	99.24 132	97.87 444	99.03 378

test12				99.54 200	99.29 350	99.33 212		99.16 369		98.43 402		97.54 291	99.82 323		99.47 345	99.48 249

OpenMVS_ROB	97.31 17	97.36 378	96.84 388	98.89 350	99.29 350	99.45 178	98.87 294	99.48 293	86.54 457	99.44 268	99.74 148	97.34 300	99.86 263	91.61 445	99.28 370	97.37 453

MVS-HIRNet		97.86 355	98.22 326	96.76 428	99.28 353	91.53 455	98.38 363	92.60 460	99.13 240	99.31 307	99.96 15	97.18 309	99.68 401	98.34 234	99.83 202	99.07 372

DeepC-MVS_fast	98.47 5	99.23 187	99.12 194	99.56 190	99.28 353	99.22 238	98.99 273	99.40 317	99.08 245	99.58 223	99.64 215	98.90 149	99.83 312	97.44 318	99.75 249	99.63 159
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AUN-MVS		97.82 357	97.38 371	99.14 312	99.27 355	98.53 322	98.72 320	99.02 380	98.10 355	97.18 442	99.03 397	89.26 423	99.85 281	97.94 270	97.91 442	99.03 378

Patchmatch-test		98.10 348	97.98 345	98.48 377	99.27 355	96.48 408	99.40 120	99.07 375	98.81 283	99.23 320	99.57 275	90.11 418	99.87 244	96.69 366	99.64 299	99.09 361

RRT-MVS		99.08 235	99.00 236	99.33 269	99.27 355	98.65 308	99.62 67	99.93 39	99.66 131	99.67 185	99.82 88	95.27 356	99.93 116	98.64 214	99.09 385	99.41 281

ET-MVSNet_ETH3D		96.78 389	96.07 399	98.91 343	99.26 358	97.92 367	97.70 419	96.05 448	97.96 367	92.37 461	98.43 436	87.06 429	99.90 195	98.27 239	97.56 447	98.91 395

Fast-Effi-MVS+-dtu		99.20 203	99.12 194	99.43 236	99.25 359	99.69 110	99.05 248	99.82 91	99.50 163	98.97 350	99.05 391	98.98 135	99.98 27	98.20 246	99.24 377	98.62 416

CNVR-MVS		98.99 260	98.80 274	99.56 190	99.25 359	99.43 184	98.54 346	99.27 346	98.58 310	98.80 372	99.43 319	98.53 204	99.70 383	97.22 338	99.59 318	99.54 217

LFMVS		98.46 321	98.19 331	99.26 294	99.24 361	98.52 324	99.62 67	96.94 443	99.87 61	99.31 307	99.58 268	91.04 403	99.81 338	98.68 211	99.42 352	99.45 258

VNet		99.18 210	99.06 214	99.56 190	99.24 361	99.36 206	99.33 143	99.31 338	99.67 127	99.47 262	99.57 275	96.48 329	99.84 296	99.15 151	99.30 367	99.47 253

testing3		96.48 398	95.63 410	99.01 329	99.23 363	97.81 372	98.90 290	99.10 374	98.72 295	97.84 429	97.92 447	72.44 464	99.85 281	97.21 339	99.33 363	99.35 298

CL-MVSNet_self_test		98.71 294	98.56 296	99.15 309	99.22 364	98.66 305	97.14 442	99.51 284	98.09 357	99.54 241	99.27 359	96.87 318	99.74 370	98.43 228	98.96 394	99.03 378

DeepPCF-MVS	98.42 6	99.18 210	99.02 228	99.67 130	99.22 364	99.75 78	97.25 439	99.47 296	98.72 295	99.66 191	99.70 179	99.29 83	99.63 422	98.07 260	99.81 222	99.62 170

MSLP-MVS++		99.05 242	99.09 206	98.91 343	99.21 366	98.36 336	98.82 306	99.47 296	98.85 276	98.90 360	99.56 279	98.78 162	99.09 452	98.57 218	99.68 286	99.26 317

NCCC		98.82 281	98.57 292	99.58 180	99.21 366	99.31 215	98.61 329	99.25 351	98.65 302	98.43 402	99.26 362	97.86 270	99.81 338	96.55 375	99.27 373	99.61 181

BH-RMVSNet		98.41 325	98.14 334	99.21 301	99.21 366	98.47 325	98.60 331	98.26 420	98.35 338	98.93 354	99.31 351	97.20 308	99.66 411	94.32 433	99.10 384	99.51 235

miper_lstm_enhance		98.65 299	98.60 286	98.82 359	99.20 369	97.33 389	97.78 415	99.66 189	99.01 253	99.59 221	99.50 300	94.62 364	99.85 281	98.12 255	99.90 142	99.26 317

SCA		98.11 347	98.36 313	97.36 418	99.20 369	92.99 446	98.17 378	98.49 408	98.24 348	99.10 341	99.57 275	96.01 346	99.94 95	96.86 356	99.62 304	99.14 350

dongtai		89.37 427	88.91 430	90.76 443	99.19 371	77.46 468	95.47 456	87.82 467	92.28 449	94.17 460	98.82 419	71.22 466	95.54 462	63.85 462	97.34 448	99.27 315

mvs_anonymous		99.28 176	99.39 133	98.94 337	99.19 371	97.81 372	99.02 260	99.55 258	99.78 99	99.85 94	99.80 99	98.24 239	99.86 263	99.57 81	99.50 341	99.15 345

OpenMVS	98.12 10	98.23 340	97.89 356	99.26 294	99.19 371	99.26 225	99.65 62	99.69 176	91.33 452	98.14 415	99.77 131	98.28 235	99.96 67	95.41 419	99.55 327	98.58 421

CNLPA		98.57 308	98.34 316	99.28 286	99.18 374	99.10 260	98.34 365	99.41 310	98.48 322	98.52 397	98.98 403	97.05 313	99.78 351	95.59 415	99.50 341	98.96 387

test_yl		98.25 337	97.95 347	99.13 313	99.17 375	98.47 325	99.00 266	98.67 397	98.97 256	99.22 323	99.02 398	91.31 399	99.69 389	97.26 332	98.93 395	99.24 320

DCV-MVSNet		98.25 337	97.95 347	99.13 313	99.17 375	98.47 325	99.00 266	98.67 397	98.97 256	99.22 323	99.02 398	91.31 399	99.69 389	97.26 332	98.93 395	99.24 320

MG-MVS		98.52 313	98.39 310	98.94 337	99.15 377	97.39 388	98.18 376	99.21 361	98.89 272	99.23 320	99.63 230	97.37 299	99.74 370	94.22 435	99.61 311	99.69 109

ADS-MVSNet2		97.78 359	97.66 366	98.12 395	99.14 378	95.36 429	99.22 187	98.75 392	96.97 411	98.25 407	99.64 215	90.90 406	99.94 95	96.51 378	99.56 323	99.08 367

ADS-MVSNet		97.72 364	97.67 365	97.86 404	99.14 378	94.65 437	99.22 187	98.86 385	96.97 411	98.25 407	99.64 215	90.90 406	99.84 296	96.51 378	99.56 323	99.08 367

FMVSNet3		98.80 284	98.63 285	99.32 274	99.13 380	98.72 299	99.10 235	99.48 293	99.23 220	99.62 209	99.64 215	92.57 387	99.86 263	98.96 176	99.90 142	99.39 287

PHI-MVS		99.11 230	98.95 250	99.59 177	99.13 380	99.59 144	99.17 205	99.65 199	97.88 372	99.25 316	99.46 314	98.97 137	99.80 345	97.26 332	99.82 212	99.37 292

OPU-MVS				99.29 283	99.12 382	99.44 180	99.20 190				99.40 326	99.00 131	98.84 456	96.54 376	99.60 314	99.58 197

c3_l		98.72 292	98.71 279	98.72 365	99.12 382	97.22 392	97.68 420	99.56 252	98.90 269	99.54 241	99.48 307	96.37 336	99.73 373	97.88 275	99.88 164	99.21 329

alignmvs		98.28 335	97.96 346	99.25 297	99.12 382	98.93 280	99.03 256	98.42 411	99.64 137	98.72 380	97.85 448	90.86 409	99.62 423	98.88 183	99.13 381	99.19 336

PAPM		95.61 422	94.71 424	98.31 388	99.12 382	96.63 404	96.66 451	98.46 409	90.77 453	96.25 452	98.68 427	93.01 384	99.69 389	81.60 460	97.86 445	98.62 416

AdaColmap		98.60 303	98.35 315	99.38 255	99.12 382	99.22 238	98.67 324	99.42 309	97.84 376	98.81 370	99.27 359	97.32 301	99.81 338	95.14 424	99.53 334	99.10 356

MGCFI-Net		99.02 248	99.01 232	99.06 325	99.11 387	98.60 313	99.63 64	99.67 184	99.63 139	98.58 392	97.65 451	99.07 119	99.57 432	98.85 185	98.92 397	99.03 378

MS-PatchMatch		99.00 257	98.97 247	99.09 318	99.11 387	98.19 344	98.76 316	99.33 332	98.49 321	99.44 268	99.58 268	98.21 244	99.69 389	98.20 246	99.62 304	99.39 287

sasdasda		99.02 248	99.00 236	99.09 318	99.10 389	98.70 300	99.61 73	99.66 189	99.63 139	98.64 386	97.65 451	99.04 127	99.54 436	98.79 193	98.92 397	99.04 376

eth_miper_zixun_eth		98.68 297	98.71 279	98.60 371	99.10 389	96.84 402	97.52 429	99.54 264	98.94 262	99.58 223	99.48 307	96.25 341	99.76 363	98.01 264	99.93 126	99.21 329

canonicalmvs		99.02 248	99.00 236	99.09 318	99.10 389	98.70 300	99.61 73	99.66 189	99.63 139	98.64 386	97.65 451	99.04 127	99.54 436	98.79 193	98.92 397	99.04 376

baseline2		96.83 388	96.28 395	98.46 379	99.09 392	96.91 400	98.83 302	93.87 459	97.23 403	96.23 454	98.36 437	88.12 426	99.90 195	96.68 367	98.14 437	98.57 423

BH-w/o		97.20 380	97.01 381	97.76 407	99.08 393	95.69 424	98.03 396	98.52 405	95.76 430	97.96 421	98.02 444	95.62 350	99.47 444	92.82 443	97.25 450	98.12 444

MVSTER		98.47 320	98.22 326	99.24 299	99.06 394	98.35 337	99.08 243	99.46 299	99.27 212	99.75 146	99.66 208	88.61 425	99.85 281	99.14 157	99.92 130	99.52 233

reproduce_monomvs		97.40 375	97.46 368	97.20 423	99.05 395	91.91 451	99.20 190	99.18 366	99.84 74	99.86 91	99.75 143	80.67 446	99.83 312	99.69 62	99.95 100	99.85 47

CR-MVSNet		98.35 332	98.20 328	98.83 356	99.05 395	98.12 350	99.30 154	99.67 184	97.39 396	99.16 331	99.79 109	91.87 395	99.91 176	98.78 197	98.77 406	98.44 431

RPMNet		98.60 303	98.53 298	98.83 356	99.05 395	98.12 350	99.30 154	99.62 212	99.86 64	99.16 331	99.74 148	92.53 389	99.92 147	98.75 199	98.77 406	98.44 431

MVStest1		98.22 342	98.09 337	98.62 369	99.04 398	96.23 415	99.20 190	99.92 41	99.44 181	99.98 14	99.87 56	85.87 438	99.67 406	99.91 31	99.57 322	99.95 14

DVP-MVS++		99.38 151	99.25 173	99.77 75	99.03 399	99.77 63	99.74 27	99.61 219	99.18 227	99.76 141	99.61 248	99.00 131	99.92 147	97.72 292	99.60 314	99.62 170

MSC_two_6792asdad				99.74 98	99.03 399	99.53 160		99.23 355					99.92 147	97.77 286	99.69 281	99.78 72

No_MVS				99.74 98	99.03 399	99.53 160		99.23 355					99.92 147	97.77 286	99.69 281	99.78 72

cl____		98.54 311	98.41 308	98.92 341	99.03 399	97.80 374	97.46 431	99.59 236	98.90 269	99.60 218	99.46 314	93.85 371	99.78 351	97.97 268	99.89 155	99.17 341

DIV-MVS_self_test		98.54 311	98.42 307	98.92 341	99.03 399	97.80 374	97.46 431	99.59 236	98.90 269	99.60 218	99.46 314	93.87 370	99.78 351	97.97 268	99.89 155	99.18 338

HY-MVS	98.23 9	98.21 344	97.95 347	98.99 330	99.03 399	98.24 339	99.61 73	98.72 393	96.81 416	98.73 379	99.51 297	94.06 368	99.86 263	96.91 353	98.20 432	98.86 401

miper_ehance_all_eth		98.59 306	98.59 288	98.59 372	98.98 405	97.07 396	97.49 430	99.52 279	98.50 319	99.52 248	99.37 335	96.41 334	99.71 379	97.86 279	99.62 304	99.00 385

MonoMVSNet		98.23 340	98.32 318	97.99 397	98.97 406	96.62 405	99.49 105	98.42 411	99.62 142	99.40 286	99.79 109	95.51 353	98.58 459	97.68 305	95.98 457	98.76 411

PMMVS		98.49 318	98.29 323	99.11 315	98.96 407	98.42 330	97.54 425	99.32 334	97.53 388	98.47 400	98.15 443	97.88 269	99.82 323	97.46 317	99.24 377	99.09 361

PatchT		98.45 322	98.32 318	98.83 356	98.94 408	98.29 338	99.24 179	98.82 388	99.84 74	99.08 342	99.76 135	91.37 398	99.94 95	98.82 189	99.00 392	98.26 437

tpm		97.15 381	96.95 383	97.75 408	98.91 409	94.24 439	99.32 146	97.96 427	97.71 380	98.29 405	99.32 348	86.72 435	99.92 147	98.10 259	96.24 456	99.09 361

1314		98.00 353	97.90 355	98.27 391	98.90 410	97.45 385	99.30 154	99.06 377	94.98 439	97.21 441	99.12 383	98.43 217	99.67 406	95.58 416	98.56 420	97.71 449

CostFormer		96.71 392	96.79 391	96.46 435	98.90 410	90.71 461	99.41 119	98.68 395	94.69 444	98.14 415	99.34 347	86.32 437	99.80 345	97.60 309	98.07 440	98.88 399

UGNet		99.38 151	99.34 146	99.49 215	98.90 410	98.90 284	99.70 38	99.35 329	99.86 64	98.57 394	99.81 95	98.50 210	99.93 116	99.38 109	99.98 48	99.66 134
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Effi-MVS+-dtu		99.07 238	98.92 256	99.52 206	98.89 413	99.78 57	99.15 213	99.66 189	99.34 202	98.92 357	99.24 369	97.69 282	99.98 27	98.11 256	99.28 370	98.81 405

Patchmtry		98.78 285	98.54 297	99.49 215	98.89 413	99.19 244	99.32 146	99.67 184	99.65 134	99.72 163	99.79 109	91.87 395	99.95 78	98.00 265	99.97 70	99.33 302

tpm2		96.35 401	96.22 396	96.73 431	98.88 415	91.75 453	99.21 189	98.51 406	93.27 447	97.89 424	99.21 373	84.83 440	99.70 383	96.04 399	98.18 435	98.75 412

UBG		96.53 395	95.95 401	98.29 390	98.87 416	96.31 413	98.48 354	98.07 424	98.83 280	97.32 437	96.54 467	79.81 451	99.62 423	96.84 359	98.74 410	98.95 389

myMVS_eth3d28		96.23 405	95.74 407	97.70 411	98.86 417	95.59 427	98.66 326	98.14 423	98.96 258	97.67 435	97.06 459	76.78 457	98.92 455	97.10 343	98.41 426	98.58 421

WBMVS		97.50 371	97.18 377	98.48 377	98.85 418	95.89 422	98.44 360	99.52 279	99.53 159	99.52 248	99.42 321	80.10 449	99.86 263	99.24 132	99.95 100	99.68 115

tpm cat1		96.78 389	96.98 382	96.16 438	98.85 418	90.59 462	99.08 243	99.32 334	92.37 448	97.73 434	99.46 314	91.15 402	99.69 389	96.07 398	98.80 403	98.21 440

CANet		99.11 230	99.05 219	99.28 286	98.83 420	98.56 316	98.71 322	99.41 310	99.25 216	99.23 320	99.22 371	97.66 288	99.94 95	99.19 143	99.97 70	99.33 302

FMVSNet5		97.80 358	97.25 375	99.42 238	98.83 420	98.97 273	99.38 125	99.80 105	98.87 273	99.25 316	99.69 188	80.60 448	99.91 176	98.96 176	99.90 142	99.38 289

API-MVS		98.38 328	98.39 310	98.35 383	98.83 420	99.26 225	99.14 216	99.18 366	98.59 309	98.66 385	98.78 421	98.61 187	99.57 432	94.14 436	99.56 323	96.21 457

PatchmatchNet		97.65 365	97.80 358	97.18 424	98.82 423	92.49 448	99.17 205	98.39 414	98.12 354	98.79 374	99.58 268	90.71 411	99.89 214	97.23 337	99.41 353	99.16 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ETVMVS		96.14 408	95.22 419	98.89 350	98.80 424	98.01 359	98.66 326	98.35 417	98.71 297	97.18 442	96.31 471	74.23 463	99.75 367	96.64 372	98.13 439	98.90 396

PAPR		97.56 369	97.07 379	99.04 327	98.80 424	98.11 352	97.63 421	99.25 351	94.56 445	98.02 420	98.25 440	97.43 295	99.68 401	90.90 448	98.74 410	99.33 302

CANet_DTU		98.91 270	98.85 265	99.09 318	98.79 426	98.13 349	98.18 376	99.31 338	99.48 168	98.86 365	99.51 297	96.56 325	99.95 78	99.05 166	99.95 100	99.19 336

E-PMN		97.14 383	97.43 369	96.27 436	98.79 426	91.62 454	95.54 455	99.01 382	99.44 181	98.88 361	99.12 383	92.78 386	99.68 401	94.30 434	99.03 390	97.50 450

testing11		96.05 411	95.41 414	97.97 399	98.78 428	95.27 431	98.59 334	98.23 421	98.86 275	96.56 449	96.91 462	75.20 460	99.69 389	97.26 332	98.29 429	98.93 392

PVSNet_0	95.53 19	95.85 417	95.31 418	97.47 415	98.78 428	93.48 445	95.72 454	99.40 317	96.18 425	97.37 436	97.73 449	95.73 348	99.58 431	95.49 417	81.40 462	99.36 295

MAR-MVS		98.24 339	97.92 353	99.19 304	98.78 428	99.65 123	99.17 205	99.14 371	95.36 434	98.04 418	98.81 420	97.47 293	99.72 375	95.47 418	99.06 386	98.21 440
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
testing91		96.00 412	95.32 417	98.02 396	98.76 431	95.39 428	98.38 363	98.65 399	98.82 281	96.84 445	96.71 465	75.06 461	99.71 379	96.46 383	98.23 431	98.98 386

testing99		95.86 416	95.19 420	97.87 403	98.76 431	95.03 433	98.62 328	98.44 410	98.68 299	96.67 448	96.66 466	74.31 462	99.69 389	96.51 378	98.03 441	98.90 396

EMVS		96.96 386	97.28 373	95.99 440	98.76 431	91.03 458	95.26 457	98.61 400	99.34 202	98.92 357	98.88 414	93.79 372	99.66 411	92.87 442	99.05 388	97.30 454

IB-MVS	95.41 20	95.30 424	94.46 428	97.84 405	98.76 431	95.33 430	97.33 436	96.07 447	96.02 426	95.37 458	97.41 455	76.17 459	99.96 67	97.54 312	95.44 460	98.22 439
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
tpmrst		97.73 361	98.07 339	96.73 431	98.71 435	92.00 450	99.10 235	98.86 385	98.52 317	98.92 357	99.54 289	91.90 393	99.82 323	98.02 261	99.03 390	98.37 433

MDTV_nov1_ep13			97.73 362		98.70 436	90.83 459	99.15 213	98.02 426	98.51 318	98.82 369	99.61 248	90.98 404	99.66 411	96.89 355	98.92 397

dp		96.86 387	97.07 379	96.24 437	98.68 437	90.30 464	99.19 196	98.38 415	97.35 398	98.23 409	99.59 265	87.23 428	99.82 323	96.27 391	98.73 413	98.59 419

testing222		95.60 423	94.59 426	98.61 370	98.66 438	97.45 385	98.54 346	97.90 430	98.53 316	96.54 450	96.47 468	70.62 467	99.81 338	95.91 408	98.15 436	98.56 424

JIA-IIPM		98.06 350	97.92 353	98.50 376	98.59 439	97.02 397	98.80 310	98.51 406	99.88 59	97.89 424	99.87 56	91.89 394	99.90 195	98.16 253	97.68 446	98.59 419

MVS		95.72 419	94.63 425	98.99 330	98.56 440	97.98 365	99.30 154	98.86 385	72.71 461	97.30 438	99.08 388	98.34 230	99.74 370	89.21 449	98.33 427	99.26 317

UWE-MVS		96.21 407	95.78 406	97.49 413	98.53 441	93.83 443	98.04 394	93.94 458	98.96 258	98.46 401	98.17 442	79.86 450	99.87 244	96.99 348	99.06 386	98.78 408

TR-MVS		97.44 373	97.15 378	98.32 386	98.53 441	97.46 384	98.47 355	97.91 429	96.85 414	98.21 410	98.51 434	96.42 332	99.51 442	92.16 444	97.29 449	97.98 446

Syy-MVS		98.17 345	97.85 357	99.15 309	98.50 443	98.79 294	98.60 331	99.21 361	97.89 370	96.76 446	96.37 469	95.47 354	99.57 432	99.10 161	98.73 413	99.09 361

myMVS_eth3d		95.63 421	94.73 423	98.34 385	98.50 443	96.36 411	98.60 331	99.21 361	97.89 370	96.76 446	96.37 469	72.10 465	99.57 432	94.38 432	98.73 413	99.09 361

tpmvs		97.39 376	97.69 363	96.52 433	98.41 445	91.76 452	99.30 154	98.94 384	97.74 378	97.85 428	99.55 287	92.40 392	99.73 373	96.25 392	98.73 413	98.06 445

LS3D		99.24 186	99.11 197	99.61 170	98.38 446	99.79 54	99.57 85	99.68 179	99.61 146	99.15 333	99.71 169	98.70 174	99.91 176	97.54 312	99.68 286	99.13 353

cl22		97.56 369	97.28 373	98.40 381	98.37 447	96.75 403	97.24 440	99.37 325	97.31 400	99.41 281	99.22 371	87.30 427	99.37 448	97.70 297	99.62 304	99.08 367

CMPMVS	77.52 23	98.50 316	98.19 331	99.41 246	98.33 448	99.56 153	99.01 263	99.59 236	95.44 433	99.57 226	99.80 99	95.64 349	99.46 446	96.47 382	99.92 130	99.21 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
miper_enhance_ethall		98.03 351	97.94 351	98.32 386	98.27 449	96.43 410	96.95 446	99.41 310	96.37 422	99.43 272	98.96 407	94.74 362	99.69 389	97.71 294	99.62 304	98.83 404

TESTMET0.1,1		96.24 404	95.84 405	97.41 417	98.24 450	93.84 442	97.38 433	95.84 449	98.43 324	97.81 430	98.56 431	79.77 452	99.89 214	97.77 286	98.77 406	98.52 425

gg-mvs-nofinetune		95.87 415	95.17 421	97.97 399	98.19 451	96.95 398	99.69 45	89.23 465	99.89 54	96.24 453	99.94 19	81.19 445	99.51 442	93.99 440	98.20 432	97.44 451

test-LLR		97.15 381	96.95 383	97.74 409	98.18 452	95.02 434	97.38 433	96.10 445	98.00 360	97.81 430	98.58 428	90.04 419	99.91 176	97.69 303	98.78 404	98.31 434

test-mter		96.23 405	95.73 408	97.74 409	98.18 452	95.02 434	97.38 433	96.10 445	97.90 369	97.81 430	98.58 428	79.12 455	99.91 176	97.69 303	98.78 404	98.31 434

EPMVS		96.53 395	96.32 394	97.17 425	98.18 452	92.97 447	99.39 122	89.95 464	98.21 350	98.61 389	99.59 265	86.69 436	99.72 375	96.99 348	99.23 379	98.81 405

WB-MVSnew		98.34 334	98.14 334	98.96 334	98.14 455	97.90 368	98.27 370	97.26 441	98.63 304	98.80 372	98.00 446	97.77 277	99.90 195	97.37 323	98.98 393	99.09 361

UWE-MVS-28		95.64 420	95.47 412	96.14 439	97.98 456	90.39 463	98.49 353	95.81 450	99.02 252	98.03 419	98.19 441	84.49 442	99.28 449	88.75 450	98.47 425	98.75 412

kuosan		85.65 429	84.57 432	88.90 445	97.91 457	77.11 469	96.37 453	87.62 468	85.24 458	85.45 463	96.83 463	69.94 468	90.98 464	45.90 463	95.83 459	98.62 416

MVS_0304		98.61 300	98.30 321	99.52 206	97.88 458	98.95 276	98.76 316	94.11 457	99.84 74	99.32 302	99.57 275	95.57 352	99.95 78	99.68 64	99.98 48	99.68 115

test0.0.03 1		97.37 377	96.91 386	98.74 364	97.72 459	97.57 380	97.60 423	97.36 440	98.00 360	99.21 325	98.02 444	90.04 419	99.79 348	98.37 231	95.89 458	98.86 401

GG-mvs-BLEND				97.36 418	97.59 460	96.87 401	99.70 38	88.49 466		94.64 459	97.26 458	80.66 447	99.12 451	91.50 446	96.50 455	96.08 459

gm-plane-assit					97.59 460	89.02 466			93.47 446		98.30 438		99.84 296	96.38 387

cascas		96.99 384	96.82 390	97.48 414	97.57 462	95.64 425	96.43 452	99.56 252	91.75 450	97.13 444	97.61 454	95.58 351	98.63 457	96.68 367	99.11 383	98.18 443

EPNet_dtu		97.62 366	97.79 360	97.11 426	96.67 463	92.31 449	98.51 350	98.04 425	99.24 218	95.77 455	99.47 311	93.78 373	99.66 411	98.98 172	99.62 304	99.37 292
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
KD-MVS_2432*1600		95.89 413	95.41 414	97.31 421	94.96 464	93.89 440	97.09 443	99.22 358	97.23 403	98.88 361	99.04 393	79.23 453	99.54 436	96.24 393	96.81 451	98.50 429

miper_refine_blended		95.89 413	95.41 414	97.31 421	94.96 464	93.89 440	97.09 443	99.22 358	97.23 403	98.88 361	99.04 393	79.23 453	99.54 436	96.24 393	96.81 451	98.50 429

EPNet		98.13 346	97.77 361	99.18 306	94.57 466	97.99 360	99.24 179	97.96 427	99.74 104	97.29 439	99.62 239	93.13 382	99.97 42	98.59 216	99.83 202	99.58 197
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_method		91.72 426	92.32 429	89.91 444	93.49 467	70.18 470	90.28 458	99.56 252	61.71 462	95.39 457	99.52 295	93.90 369	99.94 95	98.76 198	98.27 430	99.62 170

tmp_tt		95.75 418	95.42 413	96.76 428	89.90 468	94.42 438	98.86 295	97.87 431	78.01 459	99.30 312	99.69 188	97.70 280	95.89 461	99.29 128	98.14 437	99.95 14

testmvs		28.94 431	33.33 433	15.79 447	26.03 469	9.81 472	96.77 449	15.67 470	11.55 465	23.87 466	50.74 475	19.03 470	8.53 466	23.21 465	33.07 463	29.03 462

test123		29.31 430	33.05 435	18.08 446	25.93 470	12.24 471	97.53 427	10.93 471	11.78 464	24.21 465	50.08 476	21.04 469	8.60 465	23.51 464	32.43 464	33.39 461

mmdepth		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

monomultidepth		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

test_blank		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

eth-test2					0.00 471

eth-test					0.00 471

uanet_test		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

DCPMVS		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

cdsmvs_eth3d_5k		24.88 432	33.17 434	0.00 448	0.00 471	0.00 473	0.00 459	99.62 212	0.00 466	0.00 467	99.13 379	99.82 18	0.00 467	0.00 466	0.00 465	0.00 463

pcd_1.5k_mvsjas		16.61 433	22.14 436	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	99.28 85	0.00 467	0.00 466	0.00 465	0.00 463

sosnet-low-res		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

sosnet		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

uncertanet		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

Regformer		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

ab-mvs-re		8.26 444	11.02 447	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	99.16 377	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

uanet		8.33 434	11.11 437	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	100.00 1	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

WAC-MVS						96.36 411								95.20 423

PC_three_1452									97.56 384	99.68 178	99.41 322	99.09 113	97.09 460	96.66 369	99.60 314	99.62 170

test_241102_TWO								99.54 264	99.13 240	99.76 141	99.63 230	98.32 233	99.92 147	97.85 281	99.69 281	99.75 84

test_0728_THIRD									99.18 227	99.62 209	99.61 248	98.58 191	99.91 176	97.72 292	99.80 229	99.77 76

GSMVS																99.14 350

sam_mvs1												90.81 410				99.14 350

sam_mvs												90.52 415

MTGPA								99.53 274

test_post1							99.14 216				51.63 474	89.54 422	99.82 323	96.86 356

test_post											52.41 473	90.25 417	99.86 263

patchmatchnet-post											99.62 239	90.58 413	99.94 95

MTMP							99.09 240	98.59 403

test9_res														95.10 425	99.44 348	99.50 240

agg_prior2														94.58 431	99.46 347	99.50 240

test_prior4						99.19 244	98.00 399

test_prior2							97.95 405		97.87 373	98.05 417	99.05 391	97.90 267		95.99 403	99.49 343

旧先验2							97.94 406		95.33 435	98.94 353			99.88 229	96.75 363

新几何2							98.04 394

无先验							98.01 397	99.23 355	95.83 429				99.85 281	95.79 412		99.44 270

原ACMM2							97.92 408

testdata2													99.89 214	95.99 403

segment_acmp												98.37 226

testdata1							97.72 417		97.86 375

plane_prior5								99.54 264					99.82 323	95.84 410	99.78 239	99.60 185

plane_prior4											99.25 364

plane_prior3						99.31 215			98.36 333	99.14 335

plane_prior2							98.80 310		98.94 262

plane_prior						99.24 232	98.42 361		97.87 373						99.71 271

n2								0.00 472

nn								0.00 472

door-mid								99.83 85

test11								99.29 342

door								99.77 124

HQP5-MVS						98.94 277

BP-MVS														94.73 428

HQP4-MVS										98.15 411			99.70 383			99.53 223

HQP3-MVS								99.37 325							99.67 292

HQP2-MVS												96.67 322

MDTV_nov1_ep13_2view						91.44 456	99.14 216		97.37 397	99.21 325		91.78 397		96.75 363		99.03 378

ACMMP++_ref															99.94 115

ACMMP++															99.79 234

Test By Simon												98.41 220

High-resolution multi-view benchmark