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

mvs_tets		99.90 2	99.90 4	99.90 8	99.96 7	99.79 54	99.72 33	99.88 65	99.92 44	99.98 14	99.93 22	99.94 4	99.98 27	99.77 53	100.00 1	99.92 24

test_fmvsmconf0.01_n		99.89 3	99.88 7	99.91 3	99.98 3	99.76 70	99.12 229	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	99.73 107	100.00 1	99.89 41	99.79 22	99.88 229	99.98 1	100.00 1	99.98 5

jajsoiax		99.89 3	99.89 6	99.89 11	99.96 7	99.78 57	99.70 38	99.86 73	99.89 54	99.98 14	99.90 36	99.94 4	99.98 27	99.75 54	100.00 1	99.90 28

mvs5depth		99.88 6	99.91 3	99.80 61	99.92 29	99.42 190	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

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

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 43	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
test_fmvsmconf0.1_n		99.87 9	99.86 13	99.91 3	99.97 6	99.74 85	99.01 267	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

fmvsm_s_conf0.1_n		99.86 10	99.85 17	99.89 11	99.93 24	99.78 57	99.07 249	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

pmmvs6		99.86 10	99.86 13	99.83 39	99.94 18	99.90 7	99.83 7	99.91 52	99.85 70	99.94 46	99.95 16	99.73 27	99.90 195	99.65 69	99.97 70	99.69 110

fmvsm_l_conf0.5_n_3		99.85 12	99.83 21	99.92 2	99.88 45	99.86 19	99.08 245	99.97 20	99.98 16	99.96 32	99.79 111	99.90 9	99.99 8	99.96 9	99.99 16	99.90 28

fmvsm_s_conf0.1_n_a		99.85 12	99.83 21	99.91 3	99.95 15	99.82 42	99.10 237	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_fmvsmconf_n		99.85 12	99.84 20	99.88 19	99.91 31	99.73 88	98.97 283	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

mvsany_test3		99.85 12	99.88 7	99.75 93	99.95 15	99.37 206	99.53 91	99.98 12	99.77 105	99.99 7	99.95 16	99.85 14	99.94 95	99.95 14	99.98 48	99.94 17

UniMVSNet_ETH3D		99.85 12	99.83 21	99.90 8	99.89 39	99.91 4	99.89 5	99.71 165	99.93 41	99.95 43	99.89 41	99.71 28	99.96 67	99.51 90	99.97 70	99.84 50

test_fmvsmvis_n_1920		99.84 17	99.86 13	99.81 52	99.88 45	99.55 159	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

test_fmvsm_n_1920		99.84 17	99.85 17	99.83 39	99.82 87	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

PS-MVSNAJss		99.84 17	99.82 25	99.89 11	99.96 7	99.77 63	99.68 49	99.85 79	99.95 30	99.98 14	99.92 27	99.28 86	99.98 27	99.75 54	100.00 1	99.94 17

test_djsdf		99.84 17	99.81 28	99.91 3	99.94 18	99.84 27	99.77 19	99.80 109	99.73 107	99.97 23	99.92 27	99.77 25	99.98 27	99.43 102	100.00 1	99.90 28

fmvsm_l_conf0.5_n_9		99.83 21	99.81 28	99.89 11	99.86 57	99.80 51	98.94 292	99.96 28	99.98 16	99.96 32	99.78 123	99.88 11	99.98 27	99.96 9	99.99 16	99.90 28

fmvsm_s_conf0.5_n		99.83 21	99.81 28	99.87 25	99.85 66	99.78 57	99.03 258	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

test_fmvs3		99.83 21	99.93 2	99.53 210	99.96 7	98.62 318	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

fmvsm_s_conf0.5_n_9		99.82 24	99.82 25	99.82 44	99.83 78	99.59 146	98.97 283	99.92 43	99.99 3	99.97 23	99.84 75	99.90 9	99.94 95	99.94 19	99.99 16	99.92 24

tt0320-xc		99.82 24	99.82 25	99.82 44	99.82 87	99.84 27	99.82 10	99.92 43	99.94 34	99.94 46	99.93 22	99.34 78	99.92 147	99.70 59	99.96 84	99.70 102

fmvsm_s_conf0.5_n_a		99.82 24	99.79 34	99.89 11	99.85 66	99.82 42	99.03 258	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

v7n		99.82 24	99.80 32	99.88 19	99.96 7	99.84 27	99.82 10	99.82 95	99.84 74	99.94 46	99.91 31	99.13 109	99.96 67	99.83 44	99.99 16	99.83 54

sc_t1		99.81 28	99.80 32	99.82 44	99.88 45	99.88 12	99.83 7	99.79 118	99.94 34	99.93 51	99.92 27	99.35 77	99.92 147	99.64 72	99.94 119	99.68 116

fmvsm_s_conf0.1_n_2		99.81 28	99.78 39	99.89 11	99.93 24	99.76 70	98.92 295	99.98 12	99.99 3	99.99 7	99.88 50	99.43 61	99.94 95	99.94 19	99.99 16	99.99 2

fmvsm_s_conf0.5_n_6		99.80 30	99.78 39	99.85 31	99.78 127	99.78 57	99.00 271	99.97 20	99.96 26	99.97 23	99.56 285	99.92 8	99.93 116	99.91 31	99.99 16	99.83 54

fmvsm_l_conf0.5_n_a		99.80 30	99.79 34	99.84 36	99.88 45	99.64 126	99.12 229	99.91 52	99.98 16	99.95 43	99.67 208	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	99.11 234	99.91 52	99.98 16	99.96 32	99.64 220	99.60 43	99.99 8	99.95 14	99.99 16	99.88 38

anonymousdsp		99.80 30	99.77 45	99.90 8	99.96 7	99.88 12	99.73 30	99.85 79	99.70 121	99.92 58	99.93 22	99.45 58	99.97 42	99.36 115	100.00 1	99.85 47

tt0320		99.79 34	99.79 34	99.81 52	99.82 87	99.84 27	99.82 10	99.90 58	99.94 34	99.94 46	99.94 19	99.07 121	99.92 147	99.68 64	99.97 70	99.67 125

fmvsm_s_conf0.5_n_3		99.79 34	99.77 45	99.85 31	99.81 99	99.71 98	98.97 283	99.92 43	99.98 16	99.97 23	99.86 63	99.53 53	99.95 78	99.88 39	99.99 16	99.89 35

pm-mvs1		99.79 34	99.79 34	99.78 72	99.91 31	99.83 34	99.76 23	99.87 67	99.73 107	99.89 71	99.87 56	99.63 37	99.87 244	99.54 85	99.92 134	99.63 163

fmvsm_s_conf0.5_n_5		99.78 37	99.76 49	99.85 31	99.79 119	99.72 93	98.84 305	99.96 28	99.96 26	99.96 32	99.72 164	99.71 28	99.99 8	99.93 24	99.98 48	99.85 47

fmvsm_s_conf0.5_n_4		99.78 37	99.78 39	99.79 68	99.75 155	99.56 155	98.98 281	99.94 38	99.92 44	99.97 23	99.72 164	99.84 16	99.92 147	99.91 31	99.98 48	99.89 35

fmvsm_s_conf0.5_n_2		99.78 37	99.75 51	99.88 19	99.82 87	99.76 70	98.88 298	99.92 43	99.98 16	99.98 14	99.85 68	99.42 63	99.94 95	99.93 24	99.98 48	99.94 17

mmtdpeth		99.78 37	99.83 21	99.66 139	99.85 66	99.05 272	99.79 15	99.97 20	100.00 1	99.43 278	99.94 19	99.64 35	99.94 95	99.83 44	99.99 16	99.98 5

sd_testset		99.78 37	99.78 39	99.80 61	99.80 106	99.76 70	99.80 14	99.79 118	99.97 23	99.89 71	99.89 41	99.53 53	99.99 8	99.36 115	99.96 84	99.65 145

UA-Net		99.78 37	99.76 49	99.86 29	99.72 172	99.71 98	99.91 4	99.95 36	99.96 26	99.71 172	99.91 31	99.15 104	99.97 42	99.50 92	100.00 1	99.90 28

TransMVSNet (Re)		99.78 37	99.77 45	99.81 52	99.91 31	99.85 22	99.75 25	99.86 73	99.70 121	99.91 61	99.89 41	99.60 43	99.87 244	99.59 77	99.74 262	99.71 99

SDMVSNet		99.77 44	99.77 45	99.76 82	99.80 106	99.65 123	99.63 64	99.86 73	99.97 23	99.89 71	99.89 41	99.52 55	99.99 8	99.42 107	99.96 84	99.65 145

fmvsm_s_conf0.5_n_8		99.76 45	99.72 54	99.88 19	99.82 87	99.75 78	99.02 262	99.87 67	99.98 16	99.98 14	99.81 95	99.07 121	99.97 42	99.91 31	99.99 16	99.92 24

test_cas_vis1_n_1920		99.76 45	99.86 13	99.45 234	99.93 24	98.40 337	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_f		99.75 47	99.88 7	99.37 264	99.96 7	98.21 349	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

OurMVSNet-221017-0		99.75 47	99.71 55	99.84 36	99.96 7	99.83 34	99.83 7	99.85 79	99.80 94	99.93 51	99.93 22	98.54 205	99.93 116	99.59 77	99.98 48	99.76 81

Vis-MVSNet		99.75 47	99.74 52	99.79 68	99.88 45	99.66 117	99.69 45	99.92 43	99.67 130	99.77 139	99.75 146	99.61 41	99.98 27	99.35 118	99.98 48	99.72 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_s_conf0.5_n_7		99.73 50	99.78 39	99.60 176	99.74 163	98.93 286	98.85 303	99.96 28	99.96 26	99.97 23	99.76 138	99.82 18	99.96 67	99.95 14	99.98 48	99.90 28

mamv4		99.73 50	99.74 52	99.70 124	99.66 212	99.87 15	99.69 45	99.93 39	99.93 41	99.93 51	99.86 63	99.07 121	100.00 1	99.66 67	99.92 134	99.24 326

test_vis1_n_1920		99.72 52	99.88 7	99.27 297	99.93 24	97.84 376	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

test_fmvs2		99.72 52	99.85 17	99.34 272	99.91 31	98.08 363	99.48 107	100.00 1	99.90 48	99.99 7	99.91 31	99.50 57	99.98 27	99.98 1	99.99 16	99.96 13

TDRefinement		99.72 52	99.70 56	99.77 75	99.90 37	99.85 22	99.86 6	99.92 43	99.69 124	99.78 127	99.92 27	99.37 71	99.88 229	98.93 184	99.95 104	99.60 189

XXY-MVS		99.71 55	99.67 63	99.81 52	99.89 39	99.72 93	99.59 80	99.82 95	99.39 200	99.82 106	99.84 75	99.38 69	99.91 176	99.38 111	99.93 130	99.80 62

nrg030		99.70 56	99.66 65	99.82 44	99.76 142	99.84 27	99.61 73	99.70 174	99.93 41	99.78 127	99.68 204	99.10 112	99.78 354	99.45 100	99.96 84	99.83 54

FC-MVSNet-test		99.70 56	99.65 67	99.86 29	99.88 45	99.86 19	99.72 33	99.78 127	99.90 48	99.82 106	99.83 81	98.45 221	99.87 244	99.51 90	99.97 70	99.86 44

Elysia		99.69 58	99.65 67	99.81 52	99.86 57	99.72 93	99.34 137	99.77 130	99.94 34	99.91 61	99.76 138	98.55 201	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	99.77 130	99.94 34	99.91 61	99.76 138	98.55 201	99.99 8	99.70 59	99.98 48	99.72 94

GeoE		99.69 58	99.66 65	99.78 72	99.76 142	99.76 70	99.60 79	99.82 95	99.46 179	99.75 148	99.56 285	99.63 37	99.95 78	99.43 102	99.88 170	99.62 174

v10		99.69 58	99.69 59	99.66 139	99.81 99	99.39 201	99.66 57	99.75 143	99.60 155	99.92 58	99.87 56	98.75 172	99.86 263	99.90 35	99.99 16	99.73 90

EC-MVSNet		99.69 58	99.69 59	99.68 128	99.71 176	99.91 4	99.76 23	99.96 28	99.86 64	99.51 260	99.39 336	99.57 47	99.93 116	99.64 72	99.86 190	99.20 339

test_vis1_n		99.68 63	99.79 34	99.36 269	99.94 18	98.18 352	99.52 92	100.00 1	99.86 64	100.00 1	99.88 50	98.99 135	99.96 67	99.97 4	99.96 84	99.95 14

test_fmvs1_n		99.68 63	99.81 28	99.28 292	99.95 15	97.93 372	99.49 105	100.00 1	99.82 84	99.99 7	99.89 41	99.21 95	99.98 27	99.97 4	99.98 48	99.93 20

SPE-MVS-test		99.68 63	99.70 56	99.64 152	99.57 247	99.83 34	99.78 17	99.97 20	99.92 44	99.50 263	99.38 338	99.57 47	99.95 78	99.69 62	99.90 147	99.15 351

v8		99.68 63	99.69 59	99.65 145	99.80 106	99.40 198	99.66 57	99.76 138	99.64 140	99.93 51	99.85 68	98.66 186	99.84 296	99.88 39	99.99 16	99.71 99

DTE-MVSNet		99.68 63	99.61 81	99.88 19	99.80 106	99.87 15	99.67 53	99.71 165	99.72 111	99.84 99	99.78 123	98.67 184	99.97 42	99.30 127	99.95 104	99.80 62

casdiffmvs_mvg		99.68 63	99.68 62	99.69 126	99.81 99	99.59 146	99.29 161	99.90 58	99.71 115	99.79 123	99.73 156	99.54 50	99.84 296	99.36 115	99.96 84	99.65 145
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS		99.67 69	99.70 56	99.58 182	99.53 270	99.84 27	99.79 15	99.96 28	99.90 48	99.61 220	99.41 328	99.51 56	99.95 78	99.66 67	99.89 161	98.96 393

KinetiMVS		99.66 70	99.63 75	99.76 82	99.89 39	99.57 154	99.37 129	99.82 95	99.95 30	99.90 66	99.63 235	98.57 197	99.97 42	99.65 69	99.94 119	99.74 86

VPA-MVSNet		99.66 70	99.62 77	99.79 68	99.68 204	99.75 78	99.62 67	99.69 182	99.85 70	99.80 117	99.81 95	98.81 160	99.91 176	99.47 97	99.88 170	99.70 102

PS-CasMVS		99.66 70	99.58 90	99.89 11	99.80 106	99.85 22	99.66 57	99.73 153	99.62 145	99.84 99	99.71 174	98.62 190	99.96 67	99.30 127	99.96 84	99.86 44

PEN-MVS		99.66 70	99.59 87	99.89 11	99.83 78	99.87 15	99.66 57	99.73 153	99.70 121	99.84 99	99.73 156	98.56 200	99.96 67	99.29 130	99.94 119	99.83 54

FMVSNet1		99.66 70	99.63 75	99.73 107	99.78 127	99.77 63	99.68 49	99.70 174	99.67 130	99.82 106	99.83 81	98.98 139	99.90 195	99.24 134	99.97 70	99.53 229

MIMVSNet1		99.66 70	99.62 77	99.80 61	99.94 18	99.87 15	99.69 45	99.77 130	99.78 101	99.93 51	99.89 41	97.94 271	99.92 147	99.65 69	99.98 48	99.62 174

FIs		99.65 76	99.58 90	99.84 36	99.84 71	99.85 22	99.66 57	99.75 143	99.86 64	99.74 158	99.79 111	98.27 243	99.85 281	99.37 114	99.93 130	99.83 54

SSC-MVS3.2		99.64 77	99.67 63	99.56 194	99.75 155	98.98 276	98.96 287	99.87 67	99.88 59	99.84 99	99.64 220	99.32 81	99.91 176	99.78 52	99.96 84	99.80 62

viewmacassd2359aftdt		99.63 78	99.61 81	99.68 128	99.84 71	99.61 140	99.14 217	99.87 67	99.71 115	99.75 148	99.77 133	99.54 50	99.72 379	98.91 185	99.96 84	99.70 102

testf1		99.63 78	99.60 85	99.72 114	99.94 18	99.95 2	99.47 110	99.89 61	99.43 191	99.88 81	99.80 101	99.26 90	99.90 195	98.81 194	99.88 170	99.32 311

APD_test2		99.63 78	99.60 85	99.72 114	99.94 18	99.95 2	99.47 110	99.89 61	99.43 191	99.88 81	99.80 101	99.26 90	99.90 195	98.81 194	99.88 170	99.32 311

tt0805		99.63 78	99.57 95	99.81 52	99.87 54	99.88 12	99.58 82	98.70 400	99.72 111	99.91 61	99.60 261	99.43 61	99.81 341	99.81 49	99.53 340	99.73 90

KD-MVS_self_test		99.63 78	99.59 87	99.76 82	99.84 71	99.90 7	99.37 129	99.79 118	99.83 80	99.88 81	99.85 68	98.42 225	99.90 195	99.60 76	99.73 268	99.49 251

casdiffmvs		99.63 78	99.61 81	99.67 132	99.79 119	99.59 146	99.13 224	99.85 79	99.79 98	99.76 143	99.72 164	99.33 80	99.82 325	99.21 140	99.94 119	99.59 196
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		99.63 78	99.62 77	99.66 139	99.80 106	99.62 134	99.44 116	99.80 109	99.71 115	99.72 167	99.69 193	99.15 104	99.83 312	99.32 124	99.94 119	99.53 229

viewdifsd2359ckpt11		99.62 85	99.64 72	99.56 194	99.86 57	99.19 248	99.02 262	99.93 39	99.83 80	99.88 81	99.81 95	98.99 135	99.83 312	99.48 94	99.96 84	99.65 145

viewmsd2359difaftdt		99.62 85	99.64 72	99.56 194	99.86 57	99.19 248	99.02 262	99.93 39	99.83 80	99.88 81	99.81 95	98.99 135	99.83 312	99.48 94	99.96 84	99.65 145

Anonymous20231211		99.62 85	99.57 95	99.76 82	99.61 225	99.60 144	99.81 13	99.73 153	99.82 84	99.90 66	99.90 36	97.97 270	99.86 263	99.42 107	99.96 84	99.80 62

DeepC-MVS	98.90 4	99.62 85	99.61 81	99.67 132	99.72 172	99.44 183	99.24 179	99.71 165	99.27 216	99.93 51	99.90 36	99.70 31	99.93 116	98.99 172	99.99 16	99.64 157
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
dcpmvs_2		99.61 89	99.64 72	99.53 210	99.79 119	98.82 295	99.58 82	99.97 20	99.95 30	99.96 32	99.76 138	98.44 222	99.99 8	99.34 119	99.96 84	99.78 72

WR-MVS_H		99.61 89	99.53 109	99.87 25	99.80 106	99.83 34	99.67 53	99.75 143	99.58 159	99.85 96	99.69 193	98.18 255	99.94 95	99.28 132	99.95 104	99.83 54

ACMH	98.42 6	99.59 91	99.54 105	99.72 114	99.86 57	99.62 134	99.56 87	99.79 118	98.77 295	99.80 117	99.85 68	99.64 35	99.85 281	98.70 213	99.89 161	99.70 102
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SSM_0404		99.57 92	99.58 90	99.54 206	99.76 142	99.28 224	99.19 196	99.84 85	99.80 94	99.78 127	99.70 184	99.44 59	99.93 116	98.74 203	99.95 104	99.41 287

v1192		99.57 92	99.57 95	99.57 190	99.77 138	99.22 242	99.04 255	99.60 236	99.18 232	99.87 91	99.72 164	99.08 118	99.85 281	99.89 38	99.98 48	99.66 136

EG-PatchMatch MVS		99.57 92	99.56 100	99.62 168	99.77 138	99.33 216	99.26 172	99.76 138	99.32 210	99.80 117	99.78 123	99.29 84	99.87 244	99.15 153	99.91 145	99.66 136

Gipuma		99.57 92	99.59 87	99.49 221	99.98 3	99.71 98	99.72 33	99.84 85	99.81 90	99.94 46	99.78 123	98.91 151	99.71 384	98.41 235	99.95 104	99.05 380
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
SSM_0407		99.56 96	99.56 100	99.54 206	99.71 176	99.24 236	99.15 214	99.84 85	99.80 94	99.78 127	99.70 184	99.44 59	99.93 116	98.74 203	99.90 147	99.45 264

lecture		99.56 96	99.48 115	99.81 52	99.78 127	99.86 19	99.50 100	99.70 174	99.59 157	99.75 148	99.71 174	98.94 144	99.92 147	98.59 222	99.76 251	99.66 136

v1921920		99.56 96	99.57 95	99.55 200	99.75 155	99.11 259	99.05 250	99.61 225	99.15 243	99.88 81	99.71 174	99.08 118	99.87 244	99.90 35	99.97 70	99.66 136

v1240		99.56 96	99.58 90	99.51 215	99.80 106	99.00 273	99.00 271	99.65 205	99.15 243	99.90 66	99.75 146	99.09 114	99.88 229	99.90 35	99.96 84	99.67 125

V42		99.56 96	99.54 105	99.63 159	99.79 119	99.46 175	99.39 122	99.59 242	99.24 222	99.86 93	99.70 184	98.55 201	99.82 325	99.79 51	99.95 104	99.60 189

SSM_04072		99.55 101	99.55 102	99.55 200	99.71 176	99.24 236	99.27 167	99.79 118	99.72 111	99.78 127	99.64 220	99.36 74	99.97 42	98.74 203	99.90 147	99.45 264

MVSMamba_PlusPlus		99.55 101	99.58 90	99.47 227	99.68 204	99.40 198	99.52 92	99.70 174	99.92 44	99.77 139	99.86 63	98.28 241	99.96 67	99.54 85	99.90 147	99.05 380

v144192		99.55 101	99.54 105	99.58 182	99.78 127	99.20 247	99.11 234	99.62 218	99.18 232	99.89 71	99.72 164	98.66 186	99.87 244	99.88 39	99.97 70	99.66 136

test20.03		99.55 101	99.54 105	99.58 182	99.79 119	99.37 206	99.02 262	99.89 61	99.60 155	99.82 106	99.62 244	98.81 160	99.89 214	99.43 102	99.86 190	99.47 259

mamba_0408		99.54 105	99.55 102	99.54 206	99.71 176	99.24 236	99.27 167	99.79 118	99.72 111	99.78 127	99.64 220	99.36 74	99.93 116	98.74 203	99.90 147	99.45 264

v1144		99.54 105	99.53 109	99.59 179	99.79 119	99.28 224	99.10 237	99.61 225	99.20 229	99.84 99	99.73 156	98.67 184	99.84 296	99.86 43	99.98 48	99.64 157

CP-MVSNet		99.54 105	99.43 130	99.87 25	99.76 142	99.82 42	99.57 85	99.61 225	99.54 160	99.80 117	99.64 220	97.79 282	99.95 78	99.21 140	99.94 119	99.84 50

TranMVSNet+NR-MVSNet		99.54 105	99.47 117	99.76 82	99.58 237	99.64 126	99.30 154	99.63 215	99.61 149	99.71 172	99.56 285	98.76 170	99.96 67	99.14 159	99.92 134	99.68 116

SSC-MVS		99.52 109	99.42 132	99.83 39	99.86 57	99.65 123	99.52 92	99.81 106	99.87 61	99.81 113	99.79 111	96.78 326	99.99 8	99.83 44	99.51 344	99.86 44

patch_mono-2		99.51 110	99.46 122	99.64 152	99.70 191	99.11 259	99.04 255	99.87 67	99.71 115	99.47 268	99.79 111	98.24 245	99.98 27	99.38 111	99.96 84	99.83 54

viewmanbaseed2359cas		99.50 111	99.47 117	99.61 172	99.73 167	99.52 164	99.03 258	99.83 89	99.49 168	99.65 198	99.64 220	99.18 98	99.71 384	98.73 208	99.92 134	99.58 201

reproduce_model		99.50 111	99.40 136	99.83 39	99.60 227	99.83 34	99.12 229	99.68 185	99.49 168	99.80 117	99.79 111	99.01 132	99.93 116	98.24 248	99.82 218	99.73 90

balanced_conf03		99.50 111	99.50 111	99.50 217	99.42 318	99.49 167	99.52 92	99.75 143	99.86 64	99.78 127	99.71 174	98.20 252	99.90 195	99.39 110	99.88 170	99.10 362

v2v482		99.50 111	99.47 117	99.58 182	99.78 127	99.25 232	99.14 217	99.58 251	99.25 220	99.81 113	99.62 244	98.24 245	99.84 296	99.83 44	99.97 70	99.64 157

ACMH+	98.40 8	99.50 111	99.43 130	99.71 119	99.86 57	99.76 70	99.32 146	99.77 130	99.53 162	99.77 139	99.76 138	99.26 90	99.78 354	97.77 292	99.88 170	99.60 189

Baseline_NR-MVSNet		99.49 116	99.37 143	99.82 44	99.91 31	99.84 27	98.83 308	99.86 73	99.68 126	99.65 198	99.88 50	97.67 290	99.87 244	99.03 169	99.86 190	99.76 81

TAMVS		99.49 116	99.45 124	99.63 159	99.48 295	99.42 190	99.45 114	99.57 253	99.66 134	99.78 127	99.83 81	97.85 278	99.86 263	99.44 101	99.96 84	99.61 185

viewcassd2359sk11		99.48 118	99.45 124	99.58 182	99.73 167	99.42 190	98.96 287	99.80 109	99.44 184	99.63 204	99.74 151	99.09 114	99.76 366	98.72 210	99.91 145	99.57 207

diffmvs_AUTHOR		99.48 118	99.48 115	99.47 227	99.80 106	98.89 291	98.71 328	99.82 95	99.79 98	99.66 195	99.63 235	98.87 156	99.88 229	99.13 161	99.95 104	99.62 174

ttmdpeth		99.48 118	99.55 102	99.29 289	99.76 142	98.16 354	99.33 143	99.95 36	99.79 98	99.36 297	99.89 41	99.13 109	99.77 363	99.09 164	99.64 305	99.93 20

test_fmvs1		99.48 118	99.65 67	98.97 339	99.54 263	97.16 399	99.11 234	99.98 12	99.78 101	99.96 32	99.81 95	98.72 177	99.97 42	99.95 14	99.97 70	99.79 70

pmmvs-eth3d		99.48 118	99.47 117	99.51 215	99.77 138	99.41 197	98.81 313	99.66 195	99.42 195	99.75 148	99.66 213	99.20 96	99.76 366	98.98 174	99.99 16	99.36 301

EI-MVSNet-UG-set		99.48 118	99.50 111	99.42 244	99.57 247	98.65 314	99.24 179	99.46 305	99.68 126	99.80 117	99.66 213	98.99 135	99.89 214	99.19 145	99.90 147	99.72 94

APDe-MVS		99.48 118	99.36 147	99.85 31	99.55 261	99.81 47	99.50 100	99.69 182	98.99 259	99.75 148	99.71 174	98.79 165	99.93 116	98.46 229	99.85 195	99.80 62
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PMMVS2		99.48 118	99.45 124	99.57 190	99.76 142	98.99 275	98.09 394	99.90 58	98.95 266	99.78 127	99.58 274	99.57 47	99.93 116	99.48 94	99.95 104	99.79 70

DSMNet-mixed		99.48 118	99.65 67	98.95 342	99.71 176	97.27 396	99.50 100	99.82 95	99.59 157	99.41 287	99.85 68	99.62 40	100.00 1	99.53 88	99.89 161	99.59 196

DP-MVS		99.48 118	99.39 137	99.74 98	99.57 247	99.62 134	99.29 161	99.61 225	99.87 61	99.74 158	99.76 138	98.69 180	99.87 244	98.20 252	99.80 235	99.75 84

viewmambaseed2359dif		99.47 128	99.50 111	99.37 264	99.70 191	98.80 299	98.67 330	99.92 43	99.49 168	99.77 139	99.71 174	99.08 118	99.78 354	99.20 143	99.94 119	99.54 223

EI-MVSNet-Vis-set		99.47 128	99.49 114	99.42 244	99.57 247	98.66 311	99.24 179	99.46 305	99.67 130	99.79 123	99.65 218	98.97 141	99.89 214	99.15 153	99.89 161	99.71 99

reproduce-ours		99.46 130	99.35 150	99.82 44	99.56 258	99.83 34	99.05 250	99.65 205	99.45 182	99.78 127	99.78 123	98.93 145	99.93 116	98.11 262	99.81 228	99.70 102

our_new_method		99.46 130	99.35 150	99.82 44	99.56 258	99.83 34	99.05 250	99.65 205	99.45 182	99.78 127	99.78 123	98.93 145	99.93 116	98.11 262	99.81 228	99.70 102

VPNet		99.46 130	99.37 143	99.71 119	99.82 87	99.59 146	99.48 107	99.70 174	99.81 90	99.69 179	99.58 274	97.66 294	99.86 263	99.17 150	99.44 354	99.67 125

ACMM	98.09 11	99.46 130	99.38 140	99.72 114	99.80 106	99.69 110	99.13 224	99.65 205	98.99 259	99.64 200	99.72 164	99.39 65	99.86 263	98.23 249	99.81 228	99.60 189
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FE-MVSNET		99.45 134	99.36 147	99.71 119	99.84 71	99.64 126	99.16 211	99.91 52	98.65 307	99.73 162	99.73 156	98.54 205	99.82 325	98.71 212	99.96 84	99.67 125

test_vis1_rt		99.45 134	99.46 122	99.41 252	99.71 176	98.63 317	98.99 278	99.96 28	99.03 256	99.95 43	99.12 389	98.75 172	99.84 296	99.82 48	99.82 218	99.77 76

COLMAP_ROB	98.06 12	99.45 134	99.37 143	99.70 124	99.83 78	99.70 106	99.38 125	99.78 127	99.53 162	99.67 189	99.78 123	99.19 97	99.86 263	97.32 331	99.87 182	99.55 213
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WB-MVS		99.44 137	99.32 157	99.80 61	99.81 99	99.61 140	99.47 110	99.81 106	99.82 84	99.71 172	99.72 164	96.60 330	99.98 27	99.75 54	99.23 385	99.82 61

mvsany_test1		99.44 137	99.45 124	99.40 255	99.37 327	98.64 316	97.90 417	99.59 242	99.27 216	99.92 58	99.82 88	99.74 26	99.93 116	99.55 84	99.87 182	99.63 163

Anonymous20240521		99.44 137	99.42 132	99.49 221	99.89 39	98.96 281	99.62 67	99.76 138	99.85 70	99.82 106	99.88 50	96.39 341	99.97 42	99.59 77	99.98 48	99.55 213

tfpnnormal		99.43 140	99.38 140	99.60 176	99.87 54	99.75 78	99.59 80	99.78 127	99.71 115	99.90 66	99.69 193	98.85 158	99.90 195	97.25 342	99.78 245	99.15 351

HPM-MVS_fast		99.43 140	99.30 164	99.80 61	99.83 78	99.81 47	99.52 92	99.70 174	98.35 344	99.51 260	99.50 306	99.31 82	99.88 229	98.18 256	99.84 200	99.69 110

3Dnovator	99.15 2	99.43 140	99.36 147	99.65 145	99.39 322	99.42 190	99.70 38	99.56 258	99.23 224	99.35 299	99.80 101	99.17 100	99.95 78	98.21 251	99.84 200	99.59 196

viewdifsd2359ckpt13		99.42 143	99.37 143	99.57 190	99.72 172	99.46 175	99.01 267	99.80 109	99.20 229	99.51 260	99.60 261	98.92 148	99.70 388	98.65 219	99.90 147	99.55 213

Anonymous20240529		99.42 143	99.34 152	99.65 145	99.53 270	99.60 144	99.63 64	99.39 326	99.47 176	99.76 143	99.78 123	98.13 257	99.86 263	98.70 213	99.68 292	99.49 251

SixPastTwentyTwo		99.42 143	99.30 164	99.76 82	99.92 29	99.67 115	99.70 38	99.14 377	99.65 137	99.89 71	99.90 36	96.20 348	99.94 95	99.42 107	99.92 134	99.67 125

GBi-Net		99.42 143	99.31 159	99.73 107	99.49 290	99.77 63	99.68 49	99.70 174	99.44 184	99.62 214	99.83 81	97.21 311	99.90 195	98.96 178	99.90 147	99.53 229

test1		99.42 143	99.31 159	99.73 107	99.49 290	99.77 63	99.68 49	99.70 174	99.44 184	99.62 214	99.83 81	97.21 311	99.90 195	98.96 178	99.90 147	99.53 229

MVSFormer		99.41 148	99.44 128	99.31 284	99.57 247	98.40 337	99.77 19	99.80 109	99.73 107	99.63 204	99.30 359	98.02 265	99.98 27	99.43 102	99.69 287	99.55 213

IterMVS-LS		99.41 148	99.47 117	99.25 303	99.81 99	98.09 360	98.85 303	99.76 138	99.62 145	99.83 105	99.64 220	98.54 205	99.97 42	99.15 153	99.99 16	99.68 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SED-MVS		99.40 150	99.28 172	99.77 75	99.69 196	99.82 42	99.20 190	99.54 270	99.13 245	99.82 106	99.63 235	98.91 151	99.92 147	97.85 287	99.70 279	99.58 201

v148		99.40 150	99.41 135	99.39 258	99.76 142	98.94 283	99.09 242	99.59 242	99.17 237	99.81 113	99.61 253	98.41 226	99.69 395	99.32 124	99.94 119	99.53 229

NR-MVSNet		99.40 150	99.31 159	99.68 128	99.43 313	99.55 159	99.73 30	99.50 294	99.46 179	99.88 81	99.36 345	97.54 297	99.87 244	98.97 176	99.87 182	99.63 163

PVSNet_Blended_VisFu		99.40 150	99.38 140	99.44 238	99.90 37	98.66 311	98.94 292	99.91 52	97.97 370	99.79 123	99.73 156	99.05 128	99.97 42	99.15 153	99.99 16	99.68 116

LuminaMVS		99.39 154	99.28 172	99.73 107	99.83 78	99.49 167	99.00 271	99.05 384	99.81 90	99.89 71	99.79 111	96.54 334	99.97 42	99.64 72	99.98 48	99.73 90

EU-MVSNet		99.39 154	99.62 77	98.72 371	99.88 45	96.44 415	99.56 87	99.85 79	99.90 48	99.90 66	99.85 68	98.09 260	99.83 312	99.58 80	99.95 104	99.90 28

CHOSEN 1792x2688		99.39 154	99.30 164	99.65 145	99.88 45	99.25 232	98.78 320	99.88 65	98.66 306	99.96 32	99.79 111	97.45 300	99.93 116	99.34 119	99.99 16	99.78 72

IMVS_0407		99.38 157	99.42 132	99.28 292	99.71 176	98.55 324	99.27 167	99.71 165	99.41 196	99.73 162	99.60 261	99.17 100	99.83 312	98.45 230	99.70 279	99.45 264

DVP-MVS++		99.38 157	99.25 179	99.77 75	99.03 405	99.77 63	99.74 27	99.61 225	99.18 232	99.76 143	99.61 253	99.00 133	99.92 147	97.72 298	99.60 320	99.62 174

EI-MVSNet		99.38 157	99.44 128	99.21 307	99.58 237	98.09 360	99.26 172	99.46 305	99.62 145	99.75 148	99.67 208	98.54 205	99.85 281	99.15 153	99.92 134	99.68 116

UGNet		99.38 157	99.34 152	99.49 221	98.90 416	98.90 290	99.70 38	99.35 335	99.86 64	98.57 400	99.81 95	98.50 216	99.93 116	99.38 111	99.98 48	99.66 136
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
IMVS_0403		99.37 161	99.39 137	99.28 292	99.71 176	98.55 324	99.19 196	99.71 165	99.41 196	99.67 189	99.60 261	99.12 111	99.84 296	98.45 230	99.70 279	99.45 264

UniMVSNet_NR-MVSNet		99.37 161	99.25 179	99.72 114	99.47 301	99.56 155	98.97 283	99.61 225	99.43 191	99.67 189	99.28 363	97.85 278	99.95 78	99.17 150	99.81 228	99.65 145

UniMVSNet (Re)		99.37 161	99.26 177	99.68 128	99.51 279	99.58 151	98.98 281	99.60 236	99.43 191	99.70 176	99.36 345	97.70 286	99.88 229	99.20 143	99.87 182	99.59 196

CSCG		99.37 161	99.29 169	99.60 176	99.71 176	99.46 175	99.43 118	99.85 79	98.79 291	99.41 287	99.60 261	98.92 148	99.92 147	98.02 267	99.92 134	99.43 282

APD_test1		99.36 165	99.28 172	99.61 172	99.89 39	99.89 10	99.32 146	99.74 149	99.18 232	99.69 179	99.75 146	98.41 226	99.84 296	97.85 287	99.70 279	99.10 362

PM-MVS		99.36 165	99.29 169	99.58 182	99.83 78	99.66 117	98.95 290	99.86 73	98.85 281	99.81 113	99.73 156	98.40 230	99.92 147	98.36 238	99.83 208	99.17 347

new-patchmatchnet		99.35 167	99.57 95	98.71 373	99.82 87	96.62 411	98.55 349	99.75 143	99.50 166	99.88 81	99.87 56	99.31 82	99.88 229	99.43 102	100.00 1	99.62 174

Anonymous20231206		99.35 167	99.31 159	99.47 227	99.74 163	99.06 271	99.28 163	99.74 149	99.23 224	99.72 167	99.53 297	97.63 296	99.88 229	99.11 162	99.84 200	99.48 255

MTAPA		99.35 167	99.20 185	99.80 61	99.81 99	99.81 47	99.33 143	99.53 280	99.27 216	99.42 281	99.63 235	98.21 250	99.95 78	97.83 291	99.79 240	99.65 145

FMVSNet2		99.35 167	99.28 172	99.55 200	99.49 290	99.35 213	99.45 114	99.57 253	99.44 184	99.70 176	99.74 151	97.21 311	99.87 244	99.03 169	99.94 119	99.44 276

3Dnovator+	98.92 3	99.35 167	99.24 181	99.67 132	99.35 334	99.47 171	99.62 67	99.50 294	99.44 184	99.12 344	99.78 123	98.77 169	99.94 95	97.87 284	99.72 274	99.62 174

TSAR-MVS + MP.		99.34 172	99.24 181	99.63 159	99.82 87	99.37 206	99.26 172	99.35 335	98.77 295	99.57 231	99.70 184	99.27 89	99.88 229	97.71 300	99.75 255	99.65 145
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
diffmvs		99.34 172	99.32 157	99.39 258	99.67 210	98.77 302	98.57 346	99.81 106	99.61 149	99.48 266	99.41 328	98.47 217	99.86 263	98.97 176	99.90 147	99.53 229
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DELS-MVS		99.34 172	99.30 164	99.48 225	99.51 279	99.36 210	98.12 390	99.53 280	99.36 205	99.41 287	99.61 253	99.22 94	99.87 244	99.21 140	99.68 292	99.20 339
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
DU-MVS		99.33 175	99.21 184	99.71 119	99.43 313	99.56 155	98.83 308	99.53 280	99.38 201	99.67 189	99.36 345	97.67 290	99.95 78	99.17 150	99.81 228	99.63 163

ab-mvs		99.33 175	99.28 172	99.47 227	99.57 247	99.39 201	99.78 17	99.43 313	98.87 278	99.57 231	99.82 88	98.06 263	99.87 244	98.69 215	99.73 268	99.15 351

DVP-MVS		99.32 177	99.17 189	99.77 75	99.69 196	99.80 51	99.14 217	99.31 344	99.16 239	99.62 214	99.61 253	98.35 234	99.91 176	97.88 281	99.72 274	99.61 185
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
APD-MVS_3200maxsize		99.31 178	99.16 190	99.74 98	99.53 270	99.75 78	99.27 167	99.61 225	99.19 231	99.57 231	99.64 220	98.76 170	99.90 195	97.29 333	99.62 310	99.56 210

icg_test_0407_2		99.30 179	99.29 169	99.31 284	99.71 176	98.55 324	98.17 384	99.71 165	99.41 196	99.73 162	99.60 261	99.17 100	99.92 147	98.45 230	99.70 279	99.45 264

SteuartSystems-ACMMP		99.30 179	99.14 194	99.76 82	99.87 54	99.66 117	99.18 200	99.60 236	98.55 318	99.57 231	99.67 208	99.03 131	99.94 95	97.01 353	99.80 235	99.69 110
Skip Steuart: Steuart Systems R&D Blog.
testgi		99.29 181	99.26 177	99.37 264	99.75 155	98.81 296	98.84 305	99.89 61	98.38 337	99.75 148	99.04 399	99.36 74	99.86 263	99.08 166	99.25 381	99.45 264

ACMMP_NAP		99.28 182	99.11 203	99.79 68	99.75 155	99.81 47	98.95 290	99.53 280	98.27 353	99.53 251	99.73 156	98.75 172	99.87 244	97.70 303	99.83 208	99.68 116

LCM-MVSNet-Re		99.28 182	99.15 193	99.67 132	99.33 348	99.76 70	99.34 137	99.97 20	98.93 270	99.91 61	99.79 111	98.68 181	99.93 116	96.80 367	99.56 329	99.30 317

mvs_anonymous		99.28 182	99.39 137	98.94 343	99.19 377	97.81 378	99.02 262	99.55 264	99.78 101	99.85 96	99.80 101	98.24 245	99.86 263	99.57 81	99.50 347	99.15 351

MVS_Test		99.28 182	99.31 159	99.19 310	99.35 334	98.79 300	99.36 133	99.49 298	99.17 237	99.21 331	99.67 208	98.78 167	99.66 417	99.09 164	99.66 301	99.10 362

SR-MVS-dyc-post		99.27 186	99.11 203	99.73 107	99.54 263	99.74 85	99.26 172	99.62 218	99.16 239	99.52 253	99.64 220	98.41 226	99.91 176	97.27 336	99.61 317	99.54 223

XVS		99.27 186	99.11 203	99.75 93	99.71 176	99.71 98	99.37 129	99.61 225	99.29 212	98.76 383	99.47 317	98.47 217	99.88 229	97.62 312	99.73 268	99.67 125

OPM-MVS		99.26 188	99.13 196	99.63 159	99.70 191	99.61 140	98.58 342	99.48 299	98.50 325	99.52 253	99.63 235	99.14 107	99.76 366	97.89 280	99.77 249	99.51 241
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HFP-MVS		99.25 189	99.08 214	99.76 82	99.73 167	99.70 106	99.31 151	99.59 242	98.36 339	99.36 297	99.37 341	98.80 164	99.91 176	97.43 325	99.75 255	99.68 116

HPM-MVS		99.25 189	99.07 218	99.78 72	99.81 99	99.75 78	99.61 73	99.67 190	97.72 385	99.35 299	99.25 370	99.23 93	99.92 147	97.21 345	99.82 218	99.67 125
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		99.25 189	99.08 214	99.74 98	99.79 119	99.68 113	99.50 100	99.65 205	98.07 364	99.52 253	99.69 193	98.57 197	99.92 147	97.18 347	99.79 240	99.63 163
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
LS3D		99.24 192	99.11 203	99.61 172	98.38 452	99.79 54	99.57 85	99.68 185	99.61 149	99.15 339	99.71 174	98.70 179	99.91 176	97.54 318	99.68 292	99.13 359

IMVS_0404		99.23 193	99.20 185	99.32 280	99.71 176	98.55 324	98.57 346	99.71 165	99.41 196	99.52 253	99.60 261	98.12 259	99.95 78	98.45 230	99.70 279	99.45 264

xiu_mvs_v1_base_debu		99.23 193	99.34 152	98.91 349	99.59 232	98.23 346	98.47 361	99.66 195	99.61 149	99.68 182	98.94 415	99.39 65	99.97 42	99.18 147	99.55 333	98.51 432

xiu_mvs_v1_base		99.23 193	99.34 152	98.91 349	99.59 232	98.23 346	98.47 361	99.66 195	99.61 149	99.68 182	98.94 415	99.39 65	99.97 42	99.18 147	99.55 333	98.51 432

xiu_mvs_v1_base_debi		99.23 193	99.34 152	98.91 349	99.59 232	98.23 346	98.47 361	99.66 195	99.61 149	99.68 182	98.94 415	99.39 65	99.97 42	99.18 147	99.55 333	98.51 432

region2R		99.23 193	99.05 225	99.77 75	99.76 142	99.70 106	99.31 151	99.59 242	98.41 333	99.32 308	99.36 345	98.73 176	99.93 116	97.29 333	99.74 262	99.67 125

ACMMPR		99.23 193	99.06 220	99.76 82	99.74 163	99.69 110	99.31 151	99.59 242	98.36 339	99.35 299	99.38 338	98.61 192	99.93 116	97.43 325	99.75 255	99.67 125

XVG-ACMP-BASELINE		99.23 193	99.10 211	99.63 159	99.82 87	99.58 151	98.83 308	99.72 162	98.36 339	99.60 223	99.71 174	98.92 148	99.91 176	97.08 351	99.84 200	99.40 290

CP-MVS		99.23 193	99.05 225	99.75 93	99.66 212	99.66 117	99.38 125	99.62 218	98.38 337	99.06 352	99.27 365	98.79 165	99.94 95	97.51 321	99.82 218	99.66 136

DeepC-MVS_fast	98.47 5	99.23 193	99.12 200	99.56 194	99.28 359	99.22 242	98.99 278	99.40 323	99.08 250	99.58 228	99.64 220	98.90 154	99.83 312	97.44 324	99.75 255	99.63 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		99.22 202	99.04 231	99.77 75	99.76 142	99.73 88	99.28 163	99.56 258	98.19 358	99.14 341	99.29 362	98.84 159	99.92 147	97.53 320	99.80 235	99.64 157

D2MVS		99.22 202	99.19 187	99.29 289	99.69 196	98.74 304	98.81 313	99.41 316	98.55 318	99.68 182	99.69 193	98.13 257	99.87 244	98.82 192	99.98 48	99.24 326

LPG-MVS_test		99.22 202	99.05 225	99.74 98	99.82 87	99.63 132	99.16 211	99.73 153	97.56 390	99.64 200	99.69 193	99.37 71	99.89 214	96.66 375	99.87 182	99.69 110

CDS-MVSNet		99.22 202	99.13 196	99.50 217	99.35 334	99.11 259	98.96 287	99.54 270	99.46 179	99.61 220	99.70 184	96.31 344	99.83 312	99.34 119	99.88 170	99.55 213
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_0402		99.22 202	99.14 194	99.45 234	99.79 119	99.43 187	99.28 163	99.68 185	99.54 160	99.40 292	99.56 285	99.07 121	99.82 325	96.01 406	99.96 84	99.11 360

AllTest		99.21 207	99.07 218	99.63 159	99.78 127	99.64 126	99.12 229	99.83 89	98.63 310	99.63 204	99.72 164	98.68 181	99.75 371	96.38 393	99.83 208	99.51 241

XVG-OURS		99.21 207	99.06 220	99.65 145	99.82 87	99.62 134	97.87 418	99.74 149	98.36 339	99.66 195	99.68 204	99.71 28	99.90 195	96.84 365	99.88 170	99.43 282

Fast-Effi-MVS+-dtu		99.20 209	99.12 200	99.43 242	99.25 365	99.69 110	99.05 250	99.82 95	99.50 166	98.97 356	99.05 397	98.98 139	99.98 27	98.20 252	99.24 383	98.62 422

VDD-MVS		99.20 209	99.11 203	99.44 238	99.43 313	98.98 276	99.50 100	98.32 424	99.80 94	99.56 239	99.69 193	96.99 321	99.85 281	98.99 172	99.73 268	99.50 246

PGM-MVS		99.20 209	99.01 238	99.77 75	99.75 155	99.71 98	99.16 211	99.72 162	97.99 368	99.42 281	99.60 261	98.81 160	99.93 116	96.91 359	99.74 262	99.66 136

SR-MVS		99.19 212	99.00 242	99.74 98	99.51 279	99.72 93	99.18 200	99.60 236	98.85 281	99.47 268	99.58 274	98.38 231	99.92 147	96.92 358	99.54 338	99.57 207

SMA-MVS		99.19 212	99.00 242	99.73 107	99.46 305	99.73 88	99.13 224	99.52 285	97.40 401	99.57 231	99.64 220	98.93 145	99.83 312	97.61 314	99.79 240	99.63 163
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
pmmvs5		99.19 212	99.11 203	99.42 244	99.76 142	98.88 292	98.55 349	99.73 153	98.82 286	99.72 167	99.62 244	96.56 331	99.82 325	99.32 124	99.95 104	99.56 210

mPP-MVS		99.19 212	99.00 242	99.76 82	99.76 142	99.68 113	99.38 125	99.54 270	98.34 348	99.01 354	99.50 306	98.53 210	99.93 116	97.18 347	99.78 245	99.66 136

MM		99.18 216	99.05 225	99.55 200	99.35 334	98.81 296	99.05 250	97.79 439	99.99 3	99.48 266	99.59 271	96.29 346	99.95 78	99.94 19	99.98 48	99.88 38

ETV-MVS		99.18 216	99.18 188	99.16 313	99.34 343	99.28 224	99.12 229	99.79 118	99.48 171	98.93 360	98.55 438	99.40 64	99.93 116	98.51 227	99.52 343	98.28 442

VNet		99.18 216	99.06 220	99.56 194	99.24 367	99.36 210	99.33 143	99.31 344	99.67 130	99.47 268	99.57 281	96.48 335	99.84 296	99.15 153	99.30 373	99.47 259

RPSCF		99.18 216	99.02 234	99.64 152	99.83 78	99.85 22	99.44 116	99.82 95	98.33 349	99.50 263	99.78 123	97.90 273	99.65 424	96.78 368	99.83 208	99.44 276

DeepPCF-MVS	98.42 6	99.18 216	99.02 234	99.67 132	99.22 370	99.75 78	97.25 445	99.47 302	98.72 300	99.66 195	99.70 184	99.29 84	99.63 428	98.07 266	99.81 228	99.62 174

EPP-MVSNet		99.17 221	99.00 242	99.66 139	99.80 106	99.43 187	99.70 38	99.24 360	99.48 171	99.56 239	99.77 133	94.89 365	99.93 116	98.72 210	99.89 161	99.63 163

GST-MVS		99.16 222	98.96 255	99.75 93	99.73 167	99.73 88	99.20 190	99.55 264	98.22 355	99.32 308	99.35 350	98.65 188	99.91 176	96.86 362	99.74 262	99.62 174

MVP-Stereo		99.16 222	99.08 214	99.43 242	99.48 295	99.07 269	99.08 245	99.55 264	98.63 310	99.31 313	99.68 204	98.19 253	99.78 354	98.18 256	99.58 326	99.45 264
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-OURS-SEG-HR		99.16 222	98.99 249	99.66 139	99.84 71	99.64 126	98.25 379	99.73 153	98.39 336	99.63 204	99.43 325	99.70 31	99.90 195	97.34 330	98.64 423	99.44 276

jason		99.16 222	99.11 203	99.32 280	99.75 155	98.44 334	98.26 378	99.39 326	98.70 303	99.74 158	99.30 359	98.54 205	99.97 42	98.48 228	99.82 218	99.55 213
jason: jason.
AstraMVS		99.15 226	99.06 220	99.42 244	99.85 66	98.59 321	99.13 224	97.26 447	99.84 74	99.87 91	99.77 133	96.11 349	99.93 116	99.71 58	99.96 84	99.74 86

DPE-MVS		99.14 227	98.92 262	99.82 44	99.57 247	99.77 63	98.74 324	99.60 236	98.55 318	99.76 143	99.69 193	98.23 249	99.92 147	96.39 392	99.75 255	99.76 81
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		99.14 227	98.92 262	99.80 61	99.83 78	99.83 34	98.61 335	99.63 215	96.84 421	99.44 274	99.58 274	98.81 160	99.91 176	97.70 303	99.82 218	99.67 125
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VortexMVS		99.13 229	99.24 181	98.79 366	99.67 210	96.60 413	99.24 179	99.80 109	99.85 70	99.93 51	99.84 75	95.06 363	99.89 214	99.80 50	99.98 48	99.89 35

pmmvs4		99.13 229	99.06 220	99.36 269	99.57 247	99.10 266	98.01 403	99.25 357	98.78 293	99.58 228	99.44 324	98.24 245	99.76 366	98.74 203	99.93 130	99.22 332

MVS_111021_LR		99.13 229	99.03 233	99.42 244	99.58 237	99.32 218	97.91 416	99.73 153	98.68 304	99.31 313	99.48 313	99.09 114	99.66 417	97.70 303	99.77 249	99.29 320

guyue		99.12 232	99.02 234	99.41 252	99.84 71	98.56 322	99.19 196	98.30 425	99.82 84	99.84 99	99.75 146	94.84 366	99.92 147	99.68 64	99.94 119	99.74 86

EIA-MVS		99.12 232	99.01 238	99.45 234	99.36 330	99.62 134	99.34 137	99.79 118	98.41 333	98.84 373	98.89 419	98.75 172	99.84 296	98.15 260	99.51 344	98.89 404

TSAR-MVS + GP.		99.12 232	99.04 231	99.38 261	99.34 343	99.16 253	98.15 386	99.29 348	98.18 359	99.63 204	99.62 244	99.18 98	99.68 407	98.20 252	99.74 262	99.30 317

MVS_111021_HR		99.12 232	99.02 234	99.40 255	99.50 285	99.11 259	97.92 414	99.71 165	98.76 298	99.08 348	99.47 317	99.17 100	99.54 442	97.85 287	99.76 251	99.54 223

CANet		99.11 236	99.05 225	99.28 292	98.83 426	98.56 322	98.71 328	99.41 316	99.25 220	99.23 326	99.22 377	97.66 294	99.94 95	99.19 145	99.97 70	99.33 308

WR-MVS		99.11 236	98.93 258	99.66 139	99.30 354	99.42 190	98.42 367	99.37 331	99.04 255	99.57 231	99.20 381	96.89 323	99.86 263	98.66 217	99.87 182	99.70 102

PHI-MVS		99.11 236	98.95 256	99.59 179	99.13 386	99.59 146	99.17 205	99.65 205	97.88 378	99.25 322	99.46 320	98.97 141	99.80 348	97.26 338	99.82 218	99.37 298

SF-MVS		99.10 239	98.93 258	99.62 168	99.58 237	99.51 165	99.13 224	99.65 205	97.97 370	99.42 281	99.61 253	98.86 157	99.87 244	96.45 390	99.68 292	99.49 251

NormalMVS		99.09 240	98.91 266	99.62 168	99.78 127	99.11 259	99.36 133	99.77 130	99.82 84	99.68 182	99.53 297	93.30 384	99.99 8	99.24 134	99.76 251	99.74 86

RRT-MVS		99.08 241	99.00 242	99.33 275	99.27 361	98.65 314	99.62 67	99.93 39	99.66 134	99.67 189	99.82 88	95.27 362	99.93 116	98.64 220	99.09 391	99.41 287

mvsmamba		99.08 241	98.95 256	99.45 234	99.36 330	99.18 252	99.39 122	98.81 395	99.37 202	99.35 299	99.70 184	96.36 343	99.94 95	98.66 217	99.59 324	99.22 332

MSDG		99.08 241	98.98 252	99.37 264	99.60 227	99.13 256	97.54 431	99.74 149	98.84 284	99.53 251	99.55 293	99.10 112	99.79 351	97.07 352	99.86 190	99.18 344

Effi-MVS+-dtu		99.07 244	98.92 262	99.52 212	98.89 419	99.78 57	99.15 214	99.66 195	99.34 206	98.92 363	99.24 375	97.69 288	99.98 27	98.11 262	99.28 376	98.81 411

Effi-MVS+		99.06 245	98.97 253	99.34 272	99.31 350	98.98 276	98.31 374	99.91 52	98.81 288	98.79 380	98.94 415	99.14 107	99.84 296	98.79 196	98.74 416	99.20 339

MP-MVS		99.06 245	98.83 275	99.76 82	99.76 142	99.71 98	99.32 146	99.50 294	98.35 344	98.97 356	99.48 313	98.37 232	99.92 147	95.95 412	99.75 255	99.63 163
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDA-MVSNet-bldmvs		99.06 245	99.05 225	99.07 329	99.80 106	97.83 377	98.89 297	99.72 162	99.29 212	99.63 204	99.70 184	96.47 336	99.89 214	98.17 258	99.82 218	99.50 246

MSLP-MVS++		99.05 248	99.09 212	98.91 349	99.21 372	98.36 342	98.82 312	99.47 302	98.85 281	98.90 366	99.56 285	98.78 167	99.09 458	98.57 224	99.68 292	99.26 323

1112_ss		99.05 248	98.84 273	99.67 132	99.66 212	99.29 222	98.52 355	99.82 95	97.65 388	99.43 278	99.16 383	96.42 338	99.91 176	99.07 167	99.84 200	99.80 62

ACMP	97.51 14	99.05 248	98.84 273	99.67 132	99.78 127	99.55 159	98.88 298	99.66 195	97.11 416	99.47 268	99.60 261	99.07 121	99.89 214	96.18 401	99.85 195	99.58 201
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSP-MVS		99.04 251	98.79 281	99.81 52	99.78 127	99.73 88	99.35 136	99.57 253	98.54 321	99.54 246	98.99 406	96.81 325	99.93 116	96.97 356	99.53 340	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
PVSNet_BlendedMVS		99.03 252	99.01 238	99.09 324	99.54 263	97.99 366	98.58 342	99.82 95	97.62 389	99.34 303	99.71 174	98.52 213	99.77 363	97.98 272	99.97 70	99.52 239

IS-MVSNet		99.03 252	98.85 271	99.55 200	99.80 106	99.25 232	99.73 30	99.15 376	99.37 202	99.61 220	99.71 174	94.73 369	99.81 341	97.70 303	99.88 170	99.58 201

MGCFI-Net		99.02 254	99.01 238	99.06 331	99.11 393	98.60 319	99.63 64	99.67 190	99.63 142	98.58 398	97.65 457	99.07 121	99.57 438	98.85 188	98.92 403	99.03 384

sasdasda		99.02 254	99.00 242	99.09 324	99.10 395	98.70 306	99.61 73	99.66 195	99.63 142	98.64 392	97.65 457	99.04 129	99.54 442	98.79 196	98.92 403	99.04 382

xiu_mvs_v2_base		99.02 254	99.11 203	98.77 368	99.37 327	98.09 360	98.13 389	99.51 290	99.47 176	99.42 281	98.54 439	99.38 69	99.97 42	98.83 190	99.33 369	98.24 444

Fast-Effi-MVS+		99.02 254	98.87 269	99.46 231	99.38 325	99.50 166	99.04 255	99.79 118	97.17 412	98.62 394	98.74 429	99.34 78	99.95 78	98.32 242	99.41 359	98.92 400

canonicalmvs		99.02 254	99.00 242	99.09 324	99.10 395	98.70 306	99.61 73	99.66 195	99.63 142	98.64 392	97.65 457	99.04 129	99.54 442	98.79 196	98.92 403	99.04 382

MCST-MVS		99.02 254	98.81 278	99.65 145	99.58 237	99.49 167	98.58 342	99.07 381	98.40 335	99.04 353	99.25 370	98.51 215	99.80 348	97.31 332	99.51 344	99.65 145

SymmetryMVS		99.01 260	98.82 276	99.58 182	99.65 217	99.11 259	99.36 133	99.20 370	99.82 84	99.68 182	99.53 297	93.30 384	99.99 8	99.24 134	99.63 308	99.64 157

SD-MVS		99.01 260	99.30 164	98.15 399	99.50 285	99.40 198	98.94 292	99.61 225	99.22 228	99.75 148	99.82 88	99.54 50	95.51 469	97.48 322	99.87 182	99.54 223
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
LF4IMVS		99.01 260	98.92 262	99.27 297	99.71 176	99.28 224	98.59 340	99.77 130	98.32 350	99.39 294	99.41 328	98.62 190	99.84 296	96.62 380	99.84 200	98.69 420

IterMVS-SCA-FT		99.00 263	99.16 190	98.51 381	99.75 155	95.90 427	98.07 397	99.84 85	99.84 74	99.89 71	99.73 156	96.01 352	99.99 8	99.33 122	100.00 1	99.63 163

MS-PatchMatch		99.00 263	98.97 253	99.09 324	99.11 393	98.19 350	98.76 322	99.33 338	98.49 327	99.44 274	99.58 274	98.21 250	99.69 395	98.20 252	99.62 310	99.39 293

PS-MVSNAJ		99.00 263	99.08 214	98.76 369	99.37 327	98.10 359	98.00 405	99.51 290	99.47 176	99.41 287	98.50 441	99.28 86	99.97 42	98.83 190	99.34 368	98.20 448

CNVR-MVS		98.99 266	98.80 280	99.56 194	99.25 365	99.43 187	98.54 352	99.27 352	98.58 316	98.80 378	99.43 325	98.53 210	99.70 388	97.22 344	99.59 324	99.54 223

VDDNet		98.97 267	98.82 276	99.42 244	99.71 176	98.81 296	99.62 67	98.68 401	99.81 90	99.38 295	99.80 101	94.25 373	99.85 281	98.79 196	99.32 371	99.59 196

IterMVS		98.97 267	99.16 190	98.42 386	99.74 163	95.64 431	98.06 399	99.83 89	99.83 80	99.85 96	99.74 151	96.10 351	99.99 8	99.27 133	100.00 1	99.63 163
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap		98.97 267	98.93 258	99.07 329	99.46 305	98.19 350	97.75 422	99.75 143	98.79 291	99.54 246	99.70 184	98.97 141	99.62 429	96.63 379	99.83 208	99.41 287

HPM-MVS++		98.96 270	98.70 287	99.74 98	99.52 277	99.71 98	98.86 301	99.19 371	98.47 329	98.59 397	99.06 396	98.08 262	99.91 176	96.94 357	99.60 320	99.60 189

lupinMVS		98.96 270	98.87 269	99.24 305	99.57 247	98.40 337	98.12 390	99.18 372	98.28 352	99.63 204	99.13 385	98.02 265	99.97 42	98.22 250	99.69 287	99.35 304

USDC		98.96 270	98.93 258	99.05 332	99.54 263	97.99 366	97.07 451	99.80 109	98.21 356	99.75 148	99.77 133	98.43 223	99.64 426	97.90 279	99.88 170	99.51 241

YYNet1		98.95 273	98.99 249	98.84 360	99.64 218	97.14 401	98.22 381	99.32 340	98.92 272	99.59 226	99.66 213	97.40 302	99.83 312	98.27 245	99.90 147	99.55 213

MDA-MVSNet_test_wron		98.95 273	98.99 249	98.85 358	99.64 218	97.16 399	98.23 380	99.33 338	98.93 270	99.56 239	99.66 213	97.39 304	99.83 312	98.29 243	99.88 170	99.55 213

Test_1112_low_res		98.95 273	98.73 283	99.63 159	99.68 204	99.15 255	98.09 394	99.80 109	97.14 414	99.46 272	99.40 332	96.11 349	99.89 214	99.01 171	99.84 200	99.84 50

CANet_DTU		98.91 276	98.85 271	99.09 324	98.79 432	98.13 355	98.18 382	99.31 344	99.48 171	98.86 371	99.51 303	96.56 331	99.95 78	99.05 168	99.95 104	99.19 342

HyFIR lowres test		98.91 276	98.64 289	99.73 107	99.85 66	99.47 171	98.07 397	99.83 89	98.64 309	99.89 71	99.60 261	92.57 393	100.00 1	99.33 122	99.97 70	99.72 94

HQP_MVS		98.90 278	98.68 288	99.55 200	99.58 237	99.24 236	98.80 316	99.54 270	98.94 267	99.14 341	99.25 370	97.24 309	99.82 325	95.84 416	99.78 245	99.60 189

sss		98.90 278	98.77 282	99.27 297	99.48 295	98.44 334	98.72 326	99.32 340	97.94 374	99.37 296	99.35 350	96.31 344	99.91 176	98.85 188	99.63 308	99.47 259

OMC-MVS		98.90 278	98.72 284	99.44 238	99.39 322	99.42 190	98.58 342	99.64 213	97.31 406	99.44 274	99.62 244	98.59 194	99.69 395	96.17 402	99.79 240	99.22 332

ppachtmachnet_test		98.89 281	99.12 200	98.20 398	99.66 212	95.24 438	97.63 427	99.68 185	99.08 250	99.78 127	99.62 244	98.65 188	99.88 229	98.02 267	99.96 84	99.48 255

new_pmnet		98.88 282	98.89 267	98.84 360	99.70 191	97.62 385	98.15 386	99.50 294	97.98 369	99.62 214	99.54 295	98.15 256	99.94 95	97.55 317	99.84 200	98.95 395

K. test v3		98.87 283	98.60 292	99.69 126	99.93 24	99.46 175	99.74 27	94.97 458	99.78 101	99.88 81	99.88 50	93.66 381	99.97 42	99.61 75	99.95 104	99.64 157

APD-MVS		98.87 283	98.59 294	99.71 119	99.50 285	99.62 134	99.01 267	99.57 253	96.80 423	99.54 246	99.63 235	98.29 240	99.91 176	95.24 428	99.71 277	99.61 185
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
our_test_3		98.85 285	99.09 212	98.13 400	99.66 212	94.90 442	97.72 423	99.58 251	99.07 252	99.64 200	99.62 244	98.19 253	99.93 116	98.41 235	99.95 104	99.55 213

UnsupCasMVSNet_eth		98.83 286	98.57 298	99.59 179	99.68 204	99.45 181	98.99 278	99.67 190	99.48 171	99.55 244	99.36 345	94.92 364	99.86 263	98.95 182	96.57 459	99.45 264

NCCC		98.82 287	98.57 298	99.58 182	99.21 372	99.31 219	98.61 335	99.25 357	98.65 307	98.43 408	99.26 368	97.86 276	99.81 341	96.55 381	99.27 379	99.61 185

PMVS	92.94 21	98.82 287	98.81 278	98.85 358	99.84 71	97.99 366	99.20 190	99.47 302	99.71 115	99.42 281	99.82 88	98.09 260	99.47 450	93.88 447	99.85 195	99.07 378
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GDP-MVS		98.81 289	98.57 298	99.50 217	99.53 270	99.12 258	99.28 163	99.86 73	99.53 162	99.57 231	99.32 354	90.88 414	99.98 27	99.46 98	99.74 262	99.42 286

FMVSNet3		98.80 290	98.63 291	99.32 280	99.13 386	98.72 305	99.10 237	99.48 299	99.23 224	99.62 214	99.64 220	92.57 393	99.86 263	98.96 178	99.90 147	99.39 293

Patchmtry		98.78 291	98.54 303	99.49 221	98.89 419	99.19 248	99.32 146	99.67 190	99.65 137	99.72 167	99.79 111	91.87 401	99.95 78	98.00 271	99.97 70	99.33 308

Vis-MVSNet (Re-imp)		98.77 292	98.58 297	99.34 272	99.78 127	98.88 292	99.61 73	99.56 258	99.11 249	99.24 325	99.56 285	93.00 391	99.78 354	97.43 325	99.89 161	99.35 304

CLD-MVS		98.76 293	98.57 298	99.33 275	99.57 247	98.97 279	97.53 433	99.55 264	96.41 426	99.27 320	99.13 385	99.07 121	99.78 354	96.73 371	99.89 161	99.23 330
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous202405211		98.75 294	98.46 308	99.63 159	99.34 343	99.66 117	99.47 110	97.65 440	99.28 215	99.56 239	99.50 306	93.15 387	99.84 296	98.62 221	99.58 326	99.40 290

CPTT-MVS		98.74 295	98.44 311	99.64 152	99.61 225	99.38 203	99.18 200	99.55 264	96.49 425	99.27 320	99.37 341	97.11 317	99.92 147	95.74 419	99.67 298	99.62 174

F-COLMAP		98.74 295	98.45 310	99.62 168	99.57 247	99.47 171	98.84 305	99.65 205	96.31 429	98.93 360	99.19 382	97.68 289	99.87 244	96.52 383	99.37 364	99.53 229

N_pmnet		98.73 297	98.53 304	99.35 271	99.72 172	98.67 308	98.34 371	94.65 459	98.35 344	99.79 123	99.68 204	98.03 264	99.93 116	98.28 244	99.92 134	99.44 276

BP-MVS1		98.72 298	98.46 308	99.50 217	99.53 270	99.00 273	99.34 137	98.53 410	99.65 137	99.73 162	99.38 338	90.62 418	99.96 67	99.50 92	99.86 190	99.55 213

c3_l		98.72 298	98.71 285	98.72 371	99.12 388	97.22 398	97.68 426	99.56 258	98.90 274	99.54 246	99.48 313	96.37 342	99.73 377	97.88 281	99.88 170	99.21 335

CL-MVSNet_self_test		98.71 300	98.56 302	99.15 315	99.22 370	98.66 311	97.14 448	99.51 290	98.09 363	99.54 246	99.27 365	96.87 324	99.74 374	98.43 234	98.96 400	99.03 384

PVSNet_Blended		98.70 301	98.59 294	99.02 334	99.54 263	97.99 366	97.58 430	99.82 95	95.70 437	99.34 303	98.98 409	98.52 213	99.77 363	97.98 272	99.83 208	99.30 317

dmvs_re		98.69 302	98.48 306	99.31 284	99.55 261	99.42 190	99.54 90	98.38 421	99.32 210	98.72 386	98.71 430	96.76 327	99.21 456	96.01 406	99.35 367	99.31 315

eth_miper_zixun_eth		98.68 303	98.71 285	98.60 377	99.10 395	96.84 408	97.52 435	99.54 270	98.94 267	99.58 228	99.48 313	96.25 347	99.76 366	98.01 270	99.93 130	99.21 335

PatchMatch-RL		98.68 303	98.47 307	99.30 288	99.44 310	99.28 224	98.14 388	99.54 270	97.12 415	99.11 345	99.25 370	97.80 281	99.70 388	96.51 384	99.30 373	98.93 398

miper_lstm_enhance		98.65 305	98.60 292	98.82 365	99.20 375	97.33 395	97.78 421	99.66 195	99.01 258	99.59 226	99.50 306	94.62 370	99.85 281	98.12 261	99.90 147	99.26 323

h-mvs33		98.61 306	98.34 322	99.44 238	99.60 227	98.67 308	99.27 167	99.44 310	99.68 126	99.32 308	99.49 310	92.50 396	100.00 1	99.24 134	96.51 460	99.65 145

MVS_0304		98.61 306	98.30 327	99.52 212	97.88 464	98.95 282	98.76 322	94.11 463	99.84 74	99.32 308	99.57 281	95.57 358	99.95 78	99.68 64	99.98 48	99.68 116

CVMVSNet		98.61 306	98.88 268	97.80 412	99.58 237	93.60 450	99.26 172	99.64 213	99.66 134	99.72 167	99.67 208	93.26 386	99.93 116	99.30 127	99.81 228	99.87 42

Patchmatch-RL test		98.60 309	98.36 319	99.33 275	99.77 138	99.07 269	98.27 376	99.87 67	98.91 273	99.74 158	99.72 164	90.57 420	99.79 351	98.55 225	99.85 195	99.11 360

RPMNet		98.60 309	98.53 304	98.83 362	99.05 401	98.12 356	99.30 154	99.62 218	99.86 64	99.16 337	99.74 151	92.53 395	99.92 147	98.75 202	98.77 412	98.44 437

AdaColmap		98.60 309	98.35 321	99.38 261	99.12 388	99.22 242	98.67 330	99.42 315	97.84 382	98.81 376	99.27 365	97.32 307	99.81 341	95.14 430	99.53 340	99.10 362

miper_ehance_all_eth		98.59 312	98.59 294	98.59 378	98.98 411	97.07 402	97.49 436	99.52 285	98.50 325	99.52 253	99.37 341	96.41 340	99.71 384	97.86 285	99.62 310	99.00 391

WTY-MVS		98.59 312	98.37 318	99.26 300	99.43 313	98.40 337	98.74 324	99.13 379	98.10 361	99.21 331	99.24 375	94.82 367	99.90 195	97.86 285	98.77 412	99.49 251

CNLPA		98.57 314	98.34 322	99.28 292	99.18 380	99.10 266	98.34 371	99.41 316	98.48 328	98.52 403	98.98 409	97.05 319	99.78 354	95.59 421	99.50 347	98.96 393

CDPH-MVS		98.56 315	98.20 334	99.61 172	99.50 285	99.46 175	98.32 373	99.41 316	95.22 442	99.21 331	99.10 393	98.34 236	99.82 325	95.09 432	99.66 301	99.56 210

UnsupCasMVSNet_bld		98.55 316	98.27 330	99.40 255	99.56 258	99.37 206	97.97 410	99.68 185	97.49 397	99.08 348	99.35 350	95.41 361	99.82 325	97.70 303	98.19 440	99.01 390

cl____		98.54 317	98.41 314	98.92 347	99.03 405	97.80 380	97.46 437	99.59 242	98.90 274	99.60 223	99.46 320	93.85 377	99.78 354	97.97 274	99.89 161	99.17 347

DIV-MVS_self_test		98.54 317	98.42 313	98.92 347	99.03 405	97.80 380	97.46 437	99.59 242	98.90 274	99.60 223	99.46 320	93.87 376	99.78 354	97.97 274	99.89 161	99.18 344

FA-MVS(test-final)		98.52 319	98.32 324	99.10 323	99.48 295	98.67 308	99.77 19	98.60 408	97.35 404	99.63 204	99.80 101	93.07 389	99.84 296	97.92 277	99.30 373	98.78 414

hse-mvs2		98.52 319	98.30 327	99.16 313	99.29 356	98.60 319	98.77 321	99.02 386	99.68 126	99.32 308	99.04 399	92.50 396	99.85 281	99.24 134	97.87 450	99.03 384

MG-MVS		98.52 319	98.39 316	98.94 343	99.15 383	97.39 394	98.18 382	99.21 367	98.89 277	99.23 326	99.63 235	97.37 305	99.74 374	94.22 441	99.61 317	99.69 110

DP-MVS Recon		98.50 322	98.23 331	99.31 284	99.49 290	99.46 175	98.56 348	99.63 215	94.86 448	98.85 372	99.37 341	97.81 280	99.59 436	96.08 403	99.44 354	98.88 405

CMPMVS	77.52 23	98.50 322	98.19 337	99.41 252	98.33 454	99.56 155	99.01 267	99.59 242	95.44 439	99.57 231	99.80 101	95.64 355	99.46 452	96.47 388	99.92 134	99.21 335
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t		98.49 324	98.11 342	99.64 152	99.73 167	99.58 151	99.24 179	99.76 138	89.94 460	99.42 281	99.56 285	97.76 285	99.86 263	97.74 297	99.82 218	99.47 259

PMMVS		98.49 324	98.29 329	99.11 321	98.96 413	98.42 336	97.54 431	99.32 340	97.53 394	98.47 406	98.15 449	97.88 275	99.82 325	97.46 323	99.24 383	99.09 367

MVSTER		98.47 326	98.22 332	99.24 305	99.06 400	98.35 343	99.08 245	99.46 305	99.27 216	99.75 148	99.66 213	88.61 431	99.85 281	99.14 159	99.92 134	99.52 239

LFMVS		98.46 327	98.19 337	99.26 300	99.24 367	98.52 330	99.62 67	96.94 449	99.87 61	99.31 313	99.58 274	91.04 409	99.81 341	98.68 216	99.42 358	99.45 264

PatchT		98.45 328	98.32 324	98.83 362	98.94 414	98.29 344	99.24 179	98.82 394	99.84 74	99.08 348	99.76 138	91.37 404	99.94 95	98.82 192	99.00 398	98.26 443

MIMVSNet		98.43 329	98.20 334	99.11 321	99.53 270	98.38 341	99.58 82	98.61 406	98.96 263	99.33 305	99.76 138	90.92 411	99.81 341	97.38 328	99.76 251	99.15 351

PVSNet	97.47 15	98.42 330	98.44 311	98.35 389	99.46 305	96.26 420	96.70 456	99.34 337	97.68 387	99.00 355	99.13 385	97.40 302	99.72 379	97.59 316	99.68 292	99.08 373

CHOSEN 280x420		98.41 331	98.41 314	98.40 387	99.34 343	95.89 428	96.94 453	99.44 310	98.80 290	99.25 322	99.52 301	93.51 383	99.98 27	98.94 183	99.98 48	99.32 311

BH-RMVSNet		98.41 331	98.14 340	99.21 307	99.21 372	98.47 331	98.60 337	98.26 426	98.35 344	98.93 360	99.31 357	97.20 314	99.66 417	94.32 439	99.10 390	99.51 241

QAPM		98.40 333	97.99 349	99.65 145	99.39 322	99.47 171	99.67 53	99.52 285	91.70 457	98.78 382	99.80 101	98.55 201	99.95 78	94.71 436	99.75 255	99.53 229

API-MVS		98.38 334	98.39 316	98.35 389	98.83 426	99.26 229	99.14 217	99.18 372	98.59 315	98.66 391	98.78 427	98.61 192	99.57 438	94.14 442	99.56 329	96.21 463

HQP-MVS		98.36 335	98.02 348	99.39 258	99.31 350	98.94 283	97.98 407	99.37 331	97.45 398	98.15 417	98.83 423	96.67 328	99.70 388	94.73 434	99.67 298	99.53 229

PAPM_NR		98.36 335	98.04 346	99.33 275	99.48 295	98.93 286	98.79 319	99.28 351	97.54 393	98.56 402	98.57 436	97.12 316	99.69 395	94.09 443	98.90 407	99.38 295

PLC	97.35 16	98.36 335	97.99 349	99.48 225	99.32 349	99.24 236	98.50 357	99.51 290	95.19 444	98.58 398	98.96 413	96.95 322	99.83 312	95.63 420	99.25 381	99.37 298
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		98.35 338	97.95 353	99.57 190	99.35 334	99.35 213	98.11 392	99.41 316	94.90 446	97.92 428	98.99 406	98.02 265	99.85 281	95.38 426	99.44 354	99.50 246

CR-MVSNet		98.35 338	98.20 334	98.83 362	99.05 401	98.12 356	99.30 154	99.67 190	97.39 402	99.16 337	99.79 111	91.87 401	99.91 176	98.78 200	98.77 412	98.44 437

WB-MVSnew		98.34 340	98.14 340	98.96 340	98.14 461	97.90 374	98.27 376	97.26 447	98.63 310	98.80 378	98.00 452	97.77 283	99.90 195	97.37 329	98.98 399	99.09 367

DPM-MVS		98.28 341	97.94 357	99.32 280	99.36 330	99.11 259	97.31 443	98.78 397	96.88 419	98.84 373	99.11 392	97.77 283	99.61 434	94.03 445	99.36 365	99.23 330

alignmvs		98.28 341	97.96 352	99.25 303	99.12 388	98.93 286	99.03 258	98.42 417	99.64 140	98.72 386	97.85 454	90.86 415	99.62 429	98.88 186	99.13 387	99.19 342

test_yl		98.25 343	97.95 353	99.13 319	99.17 381	98.47 331	99.00 271	98.67 403	98.97 261	99.22 329	99.02 404	91.31 405	99.69 395	97.26 338	98.93 401	99.24 326

DCV-MVSNet		98.25 343	97.95 353	99.13 319	99.17 381	98.47 331	99.00 271	98.67 403	98.97 261	99.22 329	99.02 404	91.31 405	99.69 395	97.26 338	98.93 401	99.24 326

MAR-MVS		98.24 345	97.92 359	99.19 310	98.78 434	99.65 123	99.17 205	99.14 377	95.36 440	98.04 424	98.81 426	97.47 299	99.72 379	95.47 424	99.06 392	98.21 446
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
MonoMVSNet		98.23 346	98.32 324	97.99 403	98.97 412	96.62 411	99.49 105	98.42 417	99.62 145	99.40 292	99.79 111	95.51 359	98.58 465	97.68 311	95.98 463	98.76 417

OpenMVS	98.12 10	98.23 346	97.89 362	99.26 300	99.19 377	99.26 229	99.65 62	99.69 182	91.33 458	98.14 421	99.77 133	98.28 241	99.96 67	95.41 425	99.55 333	98.58 427

MVStest1		98.22 348	98.09 343	98.62 375	99.04 404	96.23 421	99.20 190	99.92 43	99.44 184	99.98 14	99.87 56	85.87 444	99.67 412	99.91 31	99.57 328	99.95 14

BH-untuned		98.22 348	98.09 343	98.58 380	99.38 325	97.24 397	98.55 349	98.98 389	97.81 383	99.20 336	98.76 428	97.01 320	99.65 424	94.83 433	98.33 433	98.86 407

HY-MVS	98.23 9	98.21 350	97.95 353	98.99 336	99.03 405	98.24 345	99.61 73	98.72 399	96.81 422	98.73 385	99.51 303	94.06 374	99.86 263	96.91 359	98.20 438	98.86 407

Syy-MVS		98.17 351	97.85 363	99.15 315	98.50 449	98.79 300	98.60 337	99.21 367	97.89 376	96.76 452	96.37 475	95.47 360	99.57 438	99.10 163	98.73 419	99.09 367

EPNet		98.13 352	97.77 367	99.18 312	94.57 472	97.99 366	99.24 179	97.96 433	99.74 106	97.29 445	99.62 244	93.13 388	99.97 42	98.59 222	99.83 208	99.58 201
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SCA		98.11 353	98.36 319	97.36 424	99.20 375	92.99 452	98.17 384	98.49 414	98.24 354	99.10 347	99.57 281	96.01 352	99.94 95	96.86 362	99.62 310	99.14 356

Patchmatch-test		98.10 354	97.98 351	98.48 383	99.27 361	96.48 414	99.40 120	99.07 381	98.81 288	99.23 326	99.57 281	90.11 424	99.87 244	96.69 372	99.64 305	99.09 367

pmmvs3		98.08 355	97.80 364	98.91 349	99.41 320	97.69 384	97.87 418	99.66 195	95.87 433	99.50 263	99.51 303	90.35 422	99.97 42	98.55 225	99.47 351	99.08 373

JIA-IIPM		98.06 356	97.92 359	98.50 382	98.59 445	97.02 403	98.80 316	98.51 412	99.88 59	97.89 430	99.87 56	91.89 400	99.90 195	98.16 259	97.68 452	98.59 425

miper_enhance_ethall		98.03 357	97.94 357	98.32 392	98.27 455	96.43 416	96.95 452	99.41 316	96.37 428	99.43 278	98.96 413	94.74 368	99.69 395	97.71 300	99.62 310	98.83 410

TAPA-MVS	97.92 13	98.03 357	97.55 373	99.46 231	99.47 301	99.44 183	98.50 357	99.62 218	86.79 461	99.07 351	99.26 368	98.26 244	99.62 429	97.28 335	99.73 268	99.31 315
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
1314		98.00 359	97.90 361	98.27 397	98.90 416	97.45 391	99.30 154	99.06 383	94.98 445	97.21 447	99.12 389	98.43 223	99.67 412	95.58 422	98.56 426	97.71 455

GA-MVS		97.99 360	97.68 370	98.93 346	99.52 277	98.04 364	97.19 447	99.05 384	98.32 350	98.81 376	98.97 411	89.89 427	99.41 453	98.33 241	99.05 394	99.34 307

MVS-HIRNet		97.86 361	98.22 332	96.76 434	99.28 359	91.53 461	98.38 369	92.60 466	99.13 245	99.31 313	99.96 15	97.18 315	99.68 407	98.34 240	99.83 208	99.07 378

FE-MVS		97.85 362	97.42 376	99.15 315	99.44 310	98.75 303	99.77 19	98.20 428	95.85 434	99.33 305	99.80 101	88.86 430	99.88 229	96.40 391	99.12 388	98.81 411

AUN-MVS		97.82 363	97.38 377	99.14 318	99.27 361	98.53 328	98.72 326	99.02 386	98.10 361	97.18 448	99.03 403	89.26 429	99.85 281	97.94 276	97.91 448	99.03 384

FMVSNet5		97.80 364	97.25 381	99.42 244	98.83 426	98.97 279	99.38 125	99.80 109	98.87 278	99.25 322	99.69 193	80.60 454	99.91 176	98.96 178	99.90 147	99.38 295

ADS-MVSNet2		97.78 365	97.66 372	98.12 401	99.14 384	95.36 435	99.22 187	98.75 398	96.97 417	98.25 413	99.64 220	90.90 412	99.94 95	96.51 384	99.56 329	99.08 373

test1111		97.74 366	98.16 339	96.49 440	99.60 227	89.86 471	99.71 37	91.21 467	99.89 54	99.88 81	99.87 56	93.73 380	99.90 195	99.56 82	99.99 16	99.70 102

ECVR-MVS		97.73 367	98.04 346	96.78 433	99.59 232	90.81 466	99.72 33	90.43 469	99.89 54	99.86 93	99.86 63	93.60 382	99.89 214	99.46 98	99.99 16	99.65 145

baseline1		97.73 367	97.33 378	98.96 340	99.30 354	97.73 382	99.40 120	98.42 417	99.33 209	99.46 272	99.21 379	91.18 407	99.82 325	98.35 239	91.26 467	99.32 311

tpmrst		97.73 367	98.07 345	96.73 437	98.71 441	92.00 456	99.10 237	98.86 391	98.52 323	98.92 363	99.54 295	91.90 399	99.82 325	98.02 267	99.03 396	98.37 439

ADS-MVSNet		97.72 370	97.67 371	97.86 410	99.14 384	94.65 443	99.22 187	98.86 391	96.97 417	98.25 413	99.64 220	90.90 412	99.84 296	96.51 384	99.56 329	99.08 373

PatchmatchNet		97.65 371	97.80 364	97.18 430	98.82 429	92.49 454	99.17 205	98.39 420	98.12 360	98.79 380	99.58 274	90.71 417	99.89 214	97.23 343	99.41 359	99.16 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tttt0517		97.62 372	97.20 382	98.90 355	99.76 142	97.40 393	99.48 107	94.36 460	99.06 254	99.70 176	99.49 310	84.55 447	99.94 95	98.73 208	99.65 303	99.36 301

EPNet_dtu		97.62 372	97.79 366	97.11 432	96.67 469	92.31 455	98.51 356	98.04 431	99.24 222	95.77 461	99.47 317	93.78 379	99.66 417	98.98 174	99.62 310	99.37 298
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		97.58 374	99.13 196	92.93 448	99.69 196	99.49 167	99.52 92	99.77 130	97.97 370	99.96 32	99.79 111	99.84 16	99.94 95	95.85 415	99.82 218	79.36 466

cl22		97.56 375	97.28 379	98.40 387	98.37 453	96.75 409	97.24 446	99.37 331	97.31 406	99.41 287	99.22 377	87.30 433	99.37 454	97.70 303	99.62 310	99.08 373

PAPR		97.56 375	97.07 385	99.04 333	98.80 430	98.11 358	97.63 427	99.25 357	94.56 451	98.02 426	98.25 446	97.43 301	99.68 407	90.90 454	98.74 416	99.33 308

WBMVS		97.50 377	97.18 383	98.48 383	98.85 424	95.89 428	98.44 366	99.52 285	99.53 162	99.52 253	99.42 327	80.10 455	99.86 263	99.24 134	99.95 104	99.68 116

thisisatest0530		97.45 378	96.95 389	98.94 343	99.68 204	97.73 382	99.09 242	94.19 462	98.61 314	99.56 239	99.30 359	84.30 449	99.93 116	98.27 245	99.54 338	99.16 349

TR-MVS		97.44 379	97.15 384	98.32 392	98.53 447	97.46 390	98.47 361	97.91 435	96.85 420	98.21 416	98.51 440	96.42 338	99.51 448	92.16 450	97.29 455	97.98 452

SD_0403		97.42 380	96.90 393	98.98 338	99.54 263	97.90 374	99.52 92	99.54 270	99.34 206	97.87 432	98.85 422	98.72 177	99.64 426	78.93 467	99.83 208	99.40 290

reproduce_monomvs		97.40 381	97.46 374	97.20 429	99.05 401	91.91 457	99.20 190	99.18 372	99.84 74	99.86 93	99.75 146	80.67 452	99.83 312	99.69 62	99.95 104	99.85 47

tpmvs		97.39 382	97.69 369	96.52 439	98.41 451	91.76 458	99.30 154	98.94 390	97.74 384	97.85 434	99.55 293	92.40 398	99.73 377	96.25 398	98.73 419	98.06 451

test0.0.03 1		97.37 383	96.91 392	98.74 370	97.72 465	97.57 386	97.60 429	97.36 446	98.00 366	99.21 331	98.02 450	90.04 425	99.79 351	98.37 237	95.89 464	98.86 407

OpenMVS_ROB	97.31 17	97.36 384	96.84 394	98.89 356	99.29 356	99.45 181	98.87 300	99.48 299	86.54 463	99.44 274	99.74 151	97.34 306	99.86 263	91.61 451	99.28 376	97.37 459

dmvs_testset		97.27 385	96.83 395	98.59 378	99.46 305	97.55 387	99.25 178	96.84 450	98.78 293	97.24 446	97.67 456	97.11 317	98.97 460	86.59 465	98.54 427	99.27 321

BH-w/o		97.20 386	97.01 387	97.76 413	99.08 399	95.69 430	98.03 402	98.52 411	95.76 436	97.96 427	98.02 450	95.62 356	99.47 450	92.82 449	97.25 456	98.12 450

test-LLR		97.15 387	96.95 389	97.74 415	98.18 458	95.02 440	97.38 439	96.10 451	98.00 366	97.81 436	98.58 434	90.04 425	99.91 176	97.69 309	98.78 410	98.31 440

tpm		97.15 387	96.95 389	97.75 414	98.91 415	94.24 445	99.32 146	97.96 433	97.71 386	98.29 411	99.32 354	86.72 441	99.92 147	98.10 265	96.24 462	99.09 367

E-PMN		97.14 389	97.43 375	96.27 442	98.79 432	91.62 460	95.54 461	99.01 388	99.44 184	98.88 367	99.12 389	92.78 392	99.68 407	94.30 440	99.03 396	97.50 456

cascas		96.99 390	96.82 396	97.48 420	97.57 468	95.64 431	96.43 458	99.56 258	91.75 456	97.13 450	97.61 460	95.58 357	98.63 463	96.68 373	99.11 389	98.18 449

thisisatest0515		96.98 391	96.42 399	98.66 374	99.42 318	97.47 389	97.27 444	94.30 461	97.24 408	99.15 339	98.86 421	85.01 445	99.87 244	97.10 349	99.39 361	98.63 421

EMVS		96.96 392	97.28 379	95.99 446	98.76 437	91.03 464	95.26 463	98.61 406	99.34 206	98.92 363	98.88 420	93.79 378	99.66 417	92.87 448	99.05 394	97.30 460

dp		96.86 393	97.07 385	96.24 443	98.68 443	90.30 470	99.19 196	98.38 421	97.35 404	98.23 415	99.59 271	87.23 434	99.82 325	96.27 397	98.73 419	98.59 425

baseline2		96.83 394	96.28 401	98.46 385	99.09 398	96.91 406	98.83 308	93.87 465	97.23 409	96.23 460	98.36 443	88.12 432	99.90 195	96.68 373	98.14 443	98.57 429

ET-MVSNet_ETH3D		96.78 395	96.07 405	98.91 349	99.26 364	97.92 373	97.70 425	96.05 454	97.96 373	92.37 467	98.43 442	87.06 435	99.90 195	98.27 245	97.56 453	98.91 401

tpm cat1		96.78 395	96.98 388	96.16 444	98.85 424	90.59 468	99.08 245	99.32 340	92.37 454	97.73 440	99.46 320	91.15 408	99.69 395	96.07 404	98.80 409	98.21 446

PCF-MVS	96.03 18	96.73 397	95.86 410	99.33 275	99.44 310	99.16 253	96.87 454	99.44 310	86.58 462	98.95 358	99.40 332	94.38 372	99.88 229	87.93 459	99.80 235	98.95 395
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		96.71 398	96.79 397	96.46 441	98.90 416	90.71 467	99.41 119	98.68 401	94.69 450	98.14 421	99.34 353	86.32 443	99.80 348	97.60 315	98.07 446	98.88 405

MVE	92.54 22	96.66 399	96.11 404	98.31 394	99.68 204	97.55 387	97.94 412	95.60 457	99.37 202	90.68 468	98.70 432	96.56 331	98.61 464	86.94 464	99.55 333	98.77 416
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres600view7		96.60 400	96.16 403	97.93 407	99.63 220	96.09 425	99.18 200	97.57 441	98.77 295	98.72 386	97.32 462	87.04 436	99.72 379	88.57 457	98.62 424	97.98 452

UBG		96.53 401	95.95 407	98.29 396	98.87 422	96.31 419	98.48 360	98.07 430	98.83 285	97.32 443	96.54 473	79.81 457	99.62 429	96.84 365	98.74 416	98.95 395

EPMVS		96.53 401	96.32 400	97.17 431	98.18 458	92.97 453	99.39 122	89.95 470	98.21 356	98.61 395	99.59 271	86.69 442	99.72 379	96.99 354	99.23 385	98.81 411

testing3-2		96.51 403	96.43 398	96.74 436	99.36 330	91.38 463	99.10 237	97.87 437	99.48 171	98.57 400	98.71 430	76.65 464	99.66 417	98.87 187	99.26 380	99.18 344

testing3		96.48 404	95.63 416	99.01 335	99.23 369	97.81 378	98.90 296	99.10 380	98.72 300	97.84 435	97.92 453	72.44 470	99.85 281	97.21 345	99.33 369	99.35 304

thres400		96.40 405	95.89 408	97.92 408	99.58 237	96.11 423	99.00 271	97.54 444	98.43 330	98.52 403	96.98 466	86.85 438	99.67 412	87.62 460	98.51 428	97.98 452

thres100view900		96.39 406	96.03 406	97.47 421	99.63 220	95.93 426	99.18 200	97.57 441	98.75 299	98.70 389	97.31 463	87.04 436	99.67 412	87.62 460	98.51 428	96.81 461

tpm2		96.35 407	96.22 402	96.73 437	98.88 421	91.75 459	99.21 189	98.51 412	93.27 453	97.89 430	99.21 379	84.83 446	99.70 388	96.04 405	98.18 441	98.75 418

FPMVS		96.32 408	95.50 417	98.79 366	99.60 227	98.17 353	98.46 365	98.80 396	97.16 413	96.28 457	99.63 235	82.19 450	99.09 458	88.45 458	98.89 408	99.10 362

tfpn200view9		96.30 409	95.89 408	97.53 418	99.58 237	96.11 423	99.00 271	97.54 444	98.43 330	98.52 403	96.98 466	86.85 438	99.67 412	87.62 460	98.51 428	96.81 461

TESTMET0.1,1		96.24 410	95.84 411	97.41 423	98.24 456	93.84 448	97.38 439	95.84 455	98.43 330	97.81 436	98.56 437	79.77 458	99.89 214	97.77 292	98.77 412	98.52 431

myMVS_eth3d28		96.23 411	95.74 413	97.70 417	98.86 423	95.59 433	98.66 332	98.14 429	98.96 263	97.67 441	97.06 465	76.78 463	98.92 461	97.10 349	98.41 432	98.58 427

test-mter		96.23 411	95.73 414	97.74 415	98.18 458	95.02 440	97.38 439	96.10 451	97.90 375	97.81 436	98.58 434	79.12 461	99.91 176	97.69 309	98.78 410	98.31 440

UWE-MVS		96.21 413	95.78 412	97.49 419	98.53 447	93.83 449	98.04 400	93.94 464	98.96 263	98.46 407	98.17 448	79.86 456	99.87 244	96.99 354	99.06 392	98.78 414

ETVMVS		96.14 414	95.22 425	98.89 356	98.80 430	98.01 365	98.66 332	98.35 423	98.71 302	97.18 448	96.31 477	74.23 469	99.75 371	96.64 378	98.13 445	98.90 402

X-MVStestdata		96.09 415	94.87 428	99.75 93	99.71 176	99.71 98	99.37 129	99.61 225	99.29 212	98.76 383	61.30 478	98.47 217	99.88 229	97.62 312	99.73 268	99.67 125

thres200		96.09 415	95.68 415	97.33 426	99.48 295	96.22 422	98.53 354	97.57 441	98.06 365	98.37 410	96.73 470	86.84 440	99.61 434	86.99 463	98.57 425	96.16 464

testing11		96.05 417	95.41 420	97.97 405	98.78 434	95.27 437	98.59 340	98.23 427	98.86 280	96.56 455	96.91 468	75.20 466	99.69 395	97.26 338	98.29 435	98.93 398

testing91		96.00 418	95.32 423	98.02 402	98.76 437	95.39 434	98.38 369	98.65 405	98.82 286	96.84 451	96.71 471	75.06 467	99.71 384	96.46 389	98.23 437	98.98 392

KD-MVS_2432*1600		95.89 419	95.41 420	97.31 427	94.96 470	93.89 446	97.09 449	99.22 364	97.23 409	98.88 367	99.04 399	79.23 459	99.54 442	96.24 399	96.81 457	98.50 435

miper_refine_blended		95.89 419	95.41 420	97.31 427	94.96 470	93.89 446	97.09 449	99.22 364	97.23 409	98.88 367	99.04 399	79.23 459	99.54 442	96.24 399	96.81 457	98.50 435

gg-mvs-nofinetune		95.87 421	95.17 427	97.97 405	98.19 457	96.95 404	99.69 45	89.23 471	99.89 54	96.24 459	99.94 19	81.19 451	99.51 448	93.99 446	98.20 438	97.44 457

testing99		95.86 422	95.19 426	97.87 409	98.76 437	95.03 439	98.62 334	98.44 416	98.68 304	96.67 454	96.66 472	74.31 468	99.69 395	96.51 384	98.03 447	98.90 402

PVSNet_0	95.53 19	95.85 423	95.31 424	97.47 421	98.78 434	93.48 451	95.72 460	99.40 323	96.18 431	97.37 442	97.73 455	95.73 354	99.58 437	95.49 423	81.40 468	99.36 301

tmp_tt		95.75 424	95.42 419	96.76 434	89.90 474	94.42 444	98.86 301	97.87 437	78.01 465	99.30 318	99.69 193	97.70 286	95.89 467	99.29 130	98.14 443	99.95 14

MVS		95.72 425	94.63 431	98.99 336	98.56 446	97.98 371	99.30 154	98.86 391	72.71 467	97.30 444	99.08 394	98.34 236	99.74 374	89.21 455	98.33 433	99.26 323

UWE-MVS-28		95.64 426	95.47 418	96.14 445	97.98 462	90.39 469	98.49 359	95.81 456	99.02 257	98.03 425	98.19 447	84.49 448	99.28 455	88.75 456	98.47 431	98.75 418

myMVS_eth3d		95.63 427	94.73 429	98.34 391	98.50 449	96.36 417	98.60 337	99.21 367	97.89 376	96.76 452	96.37 475	72.10 471	99.57 438	94.38 438	98.73 419	99.09 367

PAPM		95.61 428	94.71 430	98.31 394	99.12 388	96.63 410	96.66 457	98.46 415	90.77 459	96.25 458	98.68 433	93.01 390	99.69 395	81.60 466	97.86 451	98.62 422

testing222		95.60 429	94.59 432	98.61 376	98.66 444	97.45 391	98.54 352	97.90 436	98.53 322	96.54 456	96.47 474	70.62 473	99.81 341	95.91 414	98.15 442	98.56 430

IB-MVS	95.41 20	95.30 430	94.46 434	97.84 411	98.76 437	95.33 436	97.33 442	96.07 453	96.02 432	95.37 464	97.41 461	76.17 465	99.96 67	97.54 318	95.44 466	98.22 445
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
test2506		94.73 431	94.59 432	95.15 447	99.59 232	85.90 473	99.75 25	74.01 475	99.89 54	99.71 172	99.86 63	79.00 462	99.90 195	99.52 89	99.99 16	99.65 145

test_method		91.72 432	92.32 435	89.91 450	93.49 473	70.18 476	90.28 464	99.56 258	61.71 468	95.39 463	99.52 301	93.90 375	99.94 95	98.76 201	98.27 436	99.62 174

dongtai		89.37 433	88.91 436	90.76 449	99.19 377	77.46 474	95.47 462	87.82 473	92.28 455	94.17 466	98.82 425	71.22 472	95.54 468	63.85 468	97.34 454	99.27 321

EGC-MVSNET		89.05 434	85.52 437	99.64 152	99.89 39	99.78 57	99.56 87	99.52 285	24.19 469	49.96 470	99.83 81	99.15 104	99.92 147	97.71 300	99.85 195	99.21 335

kuosan		85.65 435	84.57 438	88.90 451	97.91 463	77.11 475	96.37 459	87.62 474	85.24 464	85.45 469	96.83 469	69.94 474	90.98 470	45.90 469	95.83 465	98.62 422

test123		29.31 436	33.05 441	18.08 452	25.93 476	12.24 477	97.53 433	10.93 477	11.78 470	24.21 471	50.08 482	21.04 475	8.60 471	23.51 470	32.43 470	33.39 467

testmvs		28.94 437	33.33 439	15.79 453	26.03 475	9.81 478	96.77 455	15.67 476	11.55 471	23.87 472	50.74 481	19.03 476	8.53 472	23.21 471	33.07 469	29.03 468

cdsmvs_eth3d_5k		24.88 438	33.17 440	0.00 454	0.00 477	0.00 479	0.00 465	99.62 218	0.00 472	0.00 473	99.13 385	99.82 18	0.00 473	0.00 472	0.00 471	0.00 469

pcd_1.5k_mvsjas		16.61 439	22.14 442	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	99.28 86	0.00 473	0.00 472	0.00 471	0.00 469

mmdepth		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

monomultidepth		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

test_blank		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet_test		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

DCPMVS		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet-low-res		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

sosnet		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uncertanet		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

Regformer		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

uanet		8.33 440	11.11 443	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	100.00 1	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

ab-mvs-re		8.26 450	11.02 453	0.00 454	0.00 477	0.00 479	0.00 465	0.00 478	0.00 472	0.00 473	99.16 383	0.00 477	0.00 473	0.00 472	0.00 471	0.00 469

WAC-MVS						96.36 417								95.20 429

FOURS1					99.83 78	99.89 10	99.74 27	99.71 165	99.69 124	99.63 204

MSC_two_6792asdad				99.74 98	99.03 405	99.53 162		99.23 361					99.92 147	97.77 292	99.69 287	99.78 72

PC_three_1452									97.56 390	99.68 182	99.41 328	99.09 114	97.09 466	96.66 375	99.60 320	99.62 174

No_MVS				99.74 98	99.03 405	99.53 162		99.23 361					99.92 147	97.77 292	99.69 287	99.78 72

test_one_0601					99.63 220	99.76 70		99.55 264	99.23 224	99.31 313	99.61 253	98.59 194

eth-test2					0.00 477

eth-test					0.00 477

ZD-MVS					99.43 313	99.61 140		99.43 313	96.38 427	99.11 345	99.07 395	97.86 276	99.92 147	94.04 444	99.49 349

RE-MVS-def			99.13 196		99.54 263	99.74 85	99.26 172	99.62 218	99.16 239	99.52 253	99.64 220	98.57 197		97.27 336	99.61 317	99.54 223

IU-MVS					99.69 196	99.77 63		99.22 364	97.50 396	99.69 179				97.75 296	99.70 279	99.77 76

OPU-MVS				99.29 289	99.12 388	99.44 183	99.20 190				99.40 332	99.00 133	98.84 462	96.54 382	99.60 320	99.58 201

test_241102_TWO								99.54 270	99.13 245	99.76 143	99.63 235	98.32 239	99.92 147	97.85 287	99.69 287	99.75 84

test_241102_ONE					99.69 196	99.82 42		99.54 270	99.12 248	99.82 106	99.49 310	98.91 151	99.52 447

9.14			98.64 289		99.45 309		98.81 313	99.60 236	97.52 395	99.28 319	99.56 285	98.53 210	99.83 312	95.36 427	99.64 305

save fliter					99.53 270	99.25 232	98.29 375	99.38 330	99.07 252

test_0728_THIRD									99.18 232	99.62 214	99.61 253	98.58 196	99.91 176	97.72 298	99.80 235	99.77 76

test_0728_SECOND				99.83 39	99.70 191	99.79 54	99.14 217	99.61 225					99.92 147	97.88 281	99.72 274	99.77 76

test0726					99.69 196	99.80 51	99.24 179	99.57 253	99.16 239	99.73 162	99.65 218	98.35 234

GSMVS																99.14 356

test_part2					99.62 224	99.67 115				99.55 244

sam_mvs1												90.81 416				99.14 356

sam_mvs												90.52 421

ambc				99.20 309	99.35 334	98.53 328	99.17 205	99.46 305		99.67 189	99.80 101	98.46 220	99.70 388	97.92 277	99.70 279	99.38 295

MTGPA								99.53 280

test_post1							99.14 217				51.63 480	89.54 428	99.82 325	96.86 362

test_post											52.41 479	90.25 423	99.86 263

patchmatchnet-post											99.62 244	90.58 419	99.94 95

GG-mvs-BLEND				97.36 424	97.59 466	96.87 407	99.70 38	88.49 472		94.64 465	97.26 464	80.66 453	99.12 457	91.50 452	96.50 461	96.08 465

MTMP							99.09 242	98.59 409

gm-plane-assit					97.59 466	89.02 472			93.47 452		98.30 444		99.84 296	96.38 393

test9_res														95.10 431	99.44 354	99.50 246

TEST9					99.35 334	99.35 213	98.11 392	99.41 316	94.83 449	97.92 428	98.99 406	98.02 265	99.85 281

test_8					99.34 343	99.31 219	98.08 396	99.40 323	94.90 446	97.87 432	98.97 411	98.02 265	99.84 296

agg_prior2														94.58 437	99.46 353	99.50 246

agg_prior					99.35 334	99.36 210		99.39 326		97.76 439			99.85 281

TestCases				99.63 159	99.78 127	99.64 126		99.83 89	98.63 310	99.63 204	99.72 164	98.68 181	99.75 371	96.38 393	99.83 208	99.51 241

test_prior4						99.19 248	98.00 405

test_prior2							97.95 411		97.87 379	98.05 423	99.05 397	97.90 273		95.99 409	99.49 349

test_prior				99.46 231	99.35 334	99.22 242		99.39 326					99.69 395			99.48 255

旧先验2							97.94 412		95.33 441	98.94 359			99.88 229	96.75 369

新几何2							98.04 400

新几何1				99.52 212	99.50 285	99.22 242		99.26 354	95.66 438	98.60 396	99.28 363	97.67 290	99.89 214	95.95 412	99.32 371	99.45 264

旧先验1					99.49 290	99.29 222		99.26 354			99.39 336	97.67 290			99.36 365	99.46 263

无先验							98.01 403	99.23 361	95.83 435				99.85 281	95.79 418		99.44 276

原ACMM2							97.92 414

原ACMM1				99.37 264	99.47 301	98.87 294		99.27 352	96.74 424	98.26 412	99.32 354	97.93 272	99.82 325	95.96 411	99.38 362	99.43 282

test222					99.51 279	99.08 268	97.83 420	99.29 348	95.21 443	98.68 390	99.31 357	97.28 308			99.38 362	99.43 282

testdata2													99.89 214	95.99 409

segment_acmp												98.37 232

testdata				99.42 244	99.51 279	98.93 286		99.30 347	96.20 430	98.87 370	99.40 332	98.33 238	99.89 214	96.29 396	99.28 376	99.44 276

testdata1							97.72 423		97.86 381

test12				99.54 206	99.29 356	99.33 216		99.16 375		98.43 408		97.54 297	99.82 325		99.47 351	99.48 255

plane_prior7					99.58 237	99.38 203

plane_prior6					99.47 301	99.26 229						97.24 309

plane_prior5								99.54 270					99.82 325	95.84 416	99.78 245	99.60 189

plane_prior4											99.25 370

plane_prior3						99.31 219			98.36 339	99.14 341

plane_prior2							98.80 316		98.94 267

plane_prior1					99.51 279

plane_prior						99.24 236	98.42 367		97.87 379						99.71 277

n2								0.00 478

nn								0.00 478

door-mid								99.83 89

lessismore_v0				99.64 152	99.86 57	99.38 203		90.66 468		99.89 71	99.83 81	94.56 371	99.97 42	99.56 82	99.92 134	99.57 207

LGP-MVS_train				99.74 98	99.82 87	99.63 132		99.73 153	97.56 390	99.64 200	99.69 193	99.37 71	99.89 214	96.66 375	99.87 182	99.69 110

test11								99.29 348

door								99.77 130

HQP5-MVS						98.94 283

HQP-NCC					99.31 350		97.98 407		97.45 398	98.15 417

ACMP_Plane					99.31 350		97.98 407		97.45 398	98.15 417

BP-MVS														94.73 434

HQP4-MVS										98.15 417			99.70 388			99.53 229

HQP3-MVS								99.37 331							99.67 298

HQP2-MVS												96.67 328

NP-MVS					99.40 321	99.13 256					98.83 423

MDTV_nov1_ep13_2view						91.44 462	99.14 217		97.37 403	99.21 331		91.78 403		96.75 369		99.03 384

MDTV_nov1_ep13			97.73 368		98.70 442	90.83 465	99.15 214	98.02 432	98.51 324	98.82 375	99.61 253	90.98 410	99.66 417	96.89 361	98.92 403

ACMMP++_ref															99.94 119

ACMMP++															99.79 240

Test By Simon												98.41 226

ITE_SJBPF				99.38 261	99.63 220	99.44 183		99.73 153	98.56 317	99.33 305	99.53 297	98.88 155	99.68 407	96.01 406	99.65 303	99.02 389

DeepMVS_CX				97.98 404	99.69 196	96.95 404		99.26 354	75.51 466	95.74 462	98.28 445	96.47 336	99.62 429	91.23 453	97.89 449	97.38 458

High-resolution multi-view benchmark