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 18	99.99 2	100.00 1	99.98 10	99.78 17	100.00 1	99.92 22	100.00 1	99.87 29

mvs_tets		99.90 2	99.90 3	99.90 7	99.96 7	99.79 46	99.72 31	99.88 45	99.92 29	99.98 13	99.93 17	99.94 4	99.98 21	99.77 38	100.00 1	99.92 18

test_fmvsmconf0.01_n		99.89 3	99.88 6	99.91 2	99.98 3	99.76 61	99.12 201	100.00 1	100.00 1	99.99 7	99.91 24	99.98 1	100.00 1	99.97 4	100.00 1	99.99 1

test_vis3_rt		99.89 3	99.90 3	99.87 20	99.98 3	99.75 67	99.70 36	100.00 1	99.73 77	100.00 1	99.89 35	99.79 16	99.88 192	99.98 1	100.00 1	99.98 3

jajsoiax		99.89 3	99.89 5	99.89 10	99.96 7	99.78 49	99.70 36	99.86 50	99.89 39	99.98 13	99.90 30	99.94 4	99.98 21	99.75 39	100.00 1	99.90 20

ANet_high		99.88 6	99.87 10	99.91 2	99.99 1	99.91 4	99.65 60	100.00 1	99.90 33	100.00 1	99.97 11	99.61 32	99.97 35	99.75 39	100.00 1	99.84 35

LTVRE_ROB	99.19 1	99.88 6	99.87 10	99.88 16	99.91 30	99.90 7	99.96 1	99.92 30	99.90 33	99.97 19	99.87 48	99.81 14	99.95 66	99.54 60	99.99 16	99.80 46
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 8	99.86 12	99.91 2	99.97 6	99.74 73	99.01 231	99.99 10	99.99 2	99.98 13	99.88 43	99.97 2	99.99 8	99.96 9	100.00 1	99.98 3

fmvsm_s_conf0.1_n		99.86 9	99.85 16	99.89 10	99.93 24	99.78 49	99.07 218	99.98 11	99.99 2	99.98 13	99.90 30	99.88 8	99.92 119	99.93 20	99.99 16	99.98 3

pmmvs6		99.86 9	99.86 12	99.83 32	99.94 18	99.90 7	99.83 7	99.91 33	99.85 53	99.94 34	99.95 13	99.73 21	99.90 159	99.65 46	99.97 54	99.69 82

fmvsm_s_conf0.1_n_a		99.85 11	99.83 20	99.91 2	99.95 15	99.82 35	99.10 208	99.98 11	99.99 2	99.98 13	99.91 24	99.68 26	99.93 97	99.93 20	99.99 16	99.99 1

test_fmvsmconf_n		99.85 11	99.84 19	99.88 16	99.91 30	99.73 76	98.97 243	99.98 11	99.99 2	99.96 23	99.85 59	99.93 7	99.99 8	99.94 16	99.99 16	99.93 15

mvsany_test3		99.85 11	99.88 6	99.75 73	99.95 15	99.37 182	99.53 88	99.98 11	99.77 75	99.99 7	99.95 13	99.85 10	99.94 80	99.95 12	99.98 39	99.94 13

UniMVSNet_ETH3D		99.85 11	99.83 20	99.90 7	99.89 38	99.91 4	99.89 4	99.71 129	99.93 26	99.95 31	99.89 35	99.71 22	99.96 56	99.51 66	99.97 54	99.84 35

test_fmvsmvis_n_1920		99.84 15	99.86 12	99.81 39	99.88 43	99.55 141	99.17 181	99.98 11	99.99 2	99.96 23	99.84 65	99.96 3	99.99 8	99.96 9	99.99 16	99.88 24

test_fmvsm_n_1920		99.84 15	99.85 16	99.83 32	99.82 72	99.70 90	99.17 181	99.97 18	99.99 2	99.96 23	99.82 76	99.94 4	100.00 1	99.95 12	100.00 1	99.80 46

PS-MVSNAJss		99.84 15	99.82 22	99.89 10	99.96 7	99.77 54	99.68 47	99.85 55	99.95 21	99.98 13	99.92 21	99.28 66	99.98 21	99.75 39	100.00 1	99.94 13

test_djsdf		99.84 15	99.81 23	99.91 2	99.94 18	99.84 24	99.77 16	99.80 82	99.73 77	99.97 19	99.92 21	99.77 19	99.98 21	99.43 74	100.00 1	99.90 20

fmvsm_s_conf0.5_n		99.83 19	99.81 23	99.87 20	99.85 57	99.78 49	99.03 226	99.96 23	99.99 2	99.97 19	99.84 65	99.78 17	99.92 119	99.92 22	99.99 16	99.92 18

test_fmvs3		99.83 19	99.93 2	99.53 177	99.96 7	98.62 277	99.67 51	100.00 1	99.95 21	100.00 1	99.95 13	99.85 10	99.99 8	99.98 1	99.99 16	99.98 3

fmvsm_s_conf0.5_n_a		99.82 21	99.79 27	99.89 10	99.85 57	99.82 35	99.03 226	99.96 23	99.99 2	99.97 19	99.84 65	99.58 36	99.93 97	99.92 22	99.98 39	99.93 15

v7n		99.82 21	99.80 26	99.88 16	99.96 7	99.84 24	99.82 9	99.82 69	99.84 56	99.94 34	99.91 24	99.13 86	99.96 56	99.83 32	99.99 16	99.83 39

fmvsm_l_conf0.5_n_a		99.80 23	99.79 27	99.84 29	99.88 43	99.64 111	99.12 201	99.91 33	99.98 14	99.95 31	99.67 170	99.67 27	99.99 8	99.94 16	99.99 16	99.88 24

fmvsm_l_conf0.5_n		99.80 23	99.78 31	99.85 26	99.88 43	99.66 102	99.11 205	99.91 33	99.98 14	99.96 23	99.64 182	99.60 34	99.99 8	99.95 12	99.99 16	99.88 24

anonymousdsp		99.80 23	99.77 33	99.90 7	99.96 7	99.88 12	99.73 28	99.85 55	99.70 88	99.92 44	99.93 17	99.45 47	99.97 35	99.36 88	100.00 1	99.85 34

pm-mvs1		99.79 26	99.79 27	99.78 53	99.91 30	99.83 29	99.76 20	99.87 47	99.73 77	99.89 55	99.87 48	99.63 29	99.87 206	99.54 60	99.92 104	99.63 127

sd_testset		99.78 27	99.78 31	99.80 44	99.80 86	99.76 61	99.80 12	99.79 88	99.97 16	99.89 55	99.89 35	99.53 43	99.99 8	99.36 88	99.96 67	99.65 112

UA-Net		99.78 27	99.76 36	99.86 24	99.72 140	99.71 83	99.91 3	99.95 28	99.96 18	99.71 134	99.91 24	99.15 81	99.97 35	99.50 68	100.00 1	99.90 20

TransMVSNet (Re)		99.78 27	99.77 33	99.81 39	99.91 30	99.85 19	99.75 23	99.86 50	99.70 88	99.91 47	99.89 35	99.60 34	99.87 206	99.59 51	99.74 219	99.71 75

SDMVSNet		99.77 30	99.77 33	99.76 63	99.80 86	99.65 108	99.63 62	99.86 50	99.97 16	99.89 55	99.89 35	99.52 44	99.99 8	99.42 79	99.96 67	99.65 112

test_cas_vis1_n_1920		99.76 31	99.86 12	99.45 196	99.93 24	98.40 290	99.30 140	99.98 11	99.94 24	99.99 7	99.89 35	99.80 15	99.97 35	99.96 9	99.97 54	99.97 7

test_f		99.75 32	99.88 6	99.37 223	99.96 7	98.21 302	99.51 95	100.00 1	99.94 24	100.00 1	99.93 17	99.58 36	99.94 80	99.97 4	99.99 16	99.97 7

OurMVSNet-221017-0		99.75 32	99.71 39	99.84 29	99.96 7	99.83 29	99.83 7	99.85 55	99.80 67	99.93 38	99.93 17	98.54 165	99.93 97	99.59 51	99.98 39	99.76 65

Vis-MVSNet		99.75 32	99.74 37	99.79 50	99.88 43	99.66 102	99.69 43	99.92 30	99.67 97	99.77 107	99.75 119	99.61 32	99.98 21	99.35 91	99.98 39	99.72 72
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
mvsmamba		99.74 35	99.70 40	99.85 26	99.93 24	99.83 29	99.76 20	99.81 78	99.96 18	99.91 47	99.81 82	98.60 156	99.94 80	99.58 54	99.98 39	99.77 59

mamv4		99.73 36	99.74 37	99.70 103	99.66 171	99.87 14	99.69 43	99.93 29	99.93 26	99.93 38	99.86 54	99.07 94	100.00 1	99.66 44	99.92 104	99.24 273

test_vis1_n_1920		99.72 37	99.88 6	99.27 249	99.93 24	97.84 327	99.34 127	100.00 1	99.99 2	99.99 7	99.82 76	99.87 9	99.99 8	99.97 4	99.99 16	99.97 7

test_fmvs2		99.72 37	99.85 16	99.34 230	99.91 30	98.08 315	99.48 101	100.00 1	99.90 33	99.99 7	99.91 24	99.50 46	99.98 21	99.98 1	99.99 16	99.96 10

TDRefinement		99.72 37	99.70 40	99.77 56	99.90 36	99.85 19	99.86 6	99.92 30	99.69 91	99.78 102	99.92 21	99.37 56	99.88 192	98.93 151	99.95 81	99.60 152

XXY-MVS		99.71 40	99.67 48	99.81 39	99.89 38	99.72 81	99.59 77	99.82 69	99.39 149	99.82 82	99.84 65	99.38 54	99.91 141	99.38 84	99.93 99	99.80 46

nrg030		99.70 41	99.66 50	99.82 36	99.76 117	99.84 24	99.61 70	99.70 134	99.93 26	99.78 102	99.68 166	99.10 87	99.78 309	99.45 72	99.96 67	99.83 39

FC-MVSNet-test		99.70 41	99.65 52	99.86 24	99.88 43	99.86 18	99.72 31	99.78 94	99.90 33	99.82 82	99.83 69	98.45 180	99.87 206	99.51 66	99.97 54	99.86 31

bld_raw_dy_0_64		99.69 43	99.67 48	99.74 78	99.84 60	99.58 134	99.88 5	99.83 63	99.96 18	99.94 34	99.91 24	98.33 197	99.98 21	99.42 79	99.96 67	99.67 94

GeoE		99.69 43	99.66 50	99.78 53	99.76 117	99.76 61	99.60 76	99.82 69	99.46 136	99.75 116	99.56 236	99.63 29	99.95 66	99.43 74	99.88 135	99.62 138

v10		99.69 43	99.69 44	99.66 117	99.81 80	99.39 177	99.66 55	99.75 108	99.60 119	99.92 44	99.87 48	98.75 135	99.86 225	99.90 25	99.99 16	99.73 70

EC-MVSNet		99.69 43	99.69 44	99.68 107	99.71 143	99.91 4	99.76 20	99.96 23	99.86 48	99.51 213	99.39 283	99.57 38	99.93 97	99.64 48	99.86 154	99.20 286

test_vis1_n		99.68 47	99.79 27	99.36 227	99.94 18	98.18 305	99.52 89	100.00 1	99.86 48	100.00 1	99.88 43	98.99 105	99.96 56	99.97 4	99.96 67	99.95 11

test_fmvs1_n		99.68 47	99.81 23	99.28 246	99.95 15	97.93 324	99.49 100	100.00 1	99.82 61	99.99 7	99.89 35	99.21 75	99.98 21	99.97 4	99.98 39	99.93 15

CS-MVS-test		99.68 47	99.70 40	99.64 129	99.57 204	99.83 29	99.78 14	99.97 18	99.92 29	99.50 215	99.38 285	99.57 38	99.95 66	99.69 43	99.90 116	99.15 298

v8		99.68 47	99.69 44	99.65 122	99.80 86	99.40 174	99.66 55	99.76 103	99.64 105	99.93 38	99.85 59	98.66 148	99.84 257	99.88 29	99.99 16	99.71 75

DTE-MVSNet		99.68 47	99.61 62	99.88 16	99.80 86	99.87 14	99.67 51	99.71 129	99.72 81	99.84 77	99.78 104	98.67 146	99.97 35	99.30 100	99.95 81	99.80 46

casdiffmvs_mvg		99.68 47	99.68 47	99.69 105	99.81 80	99.59 130	99.29 147	99.90 38	99.71 83	99.79 98	99.73 126	99.54 41	99.84 257	99.36 88	99.96 67	99.65 112
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 53	99.70 40	99.58 158	99.53 224	99.84 24	99.79 13	99.96 23	99.90 33	99.61 176	99.41 275	99.51 45	99.95 66	99.66 44	99.89 126	98.96 339

VPA-MVSNet		99.66 54	99.62 58	99.79 50	99.68 163	99.75 67	99.62 65	99.69 141	99.85 53	99.80 93	99.81 82	98.81 123	99.91 141	99.47 70	99.88 135	99.70 78

PS-CasMVS		99.66 54	99.58 70	99.89 10	99.80 86	99.85 19	99.66 55	99.73 117	99.62 110	99.84 77	99.71 142	98.62 152	99.96 56	99.30 100	99.96 67	99.86 31

PEN-MVS		99.66 54	99.59 67	99.89 10	99.83 65	99.87 14	99.66 55	99.73 117	99.70 88	99.84 77	99.73 126	98.56 162	99.96 56	99.29 103	99.94 92	99.83 39

FMVSNet1		99.66 54	99.63 57	99.73 88	99.78 105	99.77 54	99.68 47	99.70 134	99.67 97	99.82 82	99.83 69	98.98 107	99.90 159	99.24 107	99.97 54	99.53 187

MIMVSNet1		99.66 54	99.62 58	99.80 44	99.94 18	99.87 14	99.69 43	99.77 97	99.78 70	99.93 38	99.89 35	97.94 232	99.92 119	99.65 46	99.98 39	99.62 138

FIs		99.65 59	99.58 70	99.84 29	99.84 60	99.85 19	99.66 55	99.75 108	99.86 48	99.74 124	99.79 96	98.27 205	99.85 242	99.37 87	99.93 99	99.83 39

iter_conf05		99.64 60	99.65 52	99.60 151	99.68 163	99.62 118	99.82 9	99.89 40	99.92 29	99.93 38	99.86 54	98.28 202	99.96 56	99.54 60	99.91 114	99.23 277

testf1		99.63 61	99.60 65	99.72 94	99.94 18	99.95 2	99.47 104	99.89 40	99.43 144	99.88 63	99.80 86	99.26 70	99.90 159	98.81 159	99.88 135	99.32 258

APD_test2		99.63 61	99.60 65	99.72 94	99.94 18	99.95 2	99.47 104	99.89 40	99.43 144	99.88 63	99.80 86	99.26 70	99.90 159	98.81 159	99.88 135	99.32 258

tt0805		99.63 61	99.57 74	99.81 39	99.87 50	99.88 12	99.58 79	98.70 350	99.72 81	99.91 47	99.60 217	99.43 48	99.81 296	99.81 36	99.53 288	99.73 70

KD-MVS_self_test		99.63 61	99.59 67	99.76 63	99.84 60	99.90 7	99.37 122	99.79 88	99.83 59	99.88 63	99.85 59	98.42 184	99.90 159	99.60 50	99.73 224	99.49 209

casdiffmvs		99.63 61	99.61 62	99.67 110	99.79 98	99.59 130	99.13 197	99.85 55	99.79 69	99.76 111	99.72 134	99.33 61	99.82 281	99.21 110	99.94 92	99.59 159
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 61	99.62 58	99.66 117	99.80 86	99.62 118	99.44 110	99.80 82	99.71 83	99.72 129	99.69 155	99.15 81	99.83 272	99.32 97	99.94 92	99.53 187

Anonymous20231211		99.62 67	99.57 74	99.76 63	99.61 183	99.60 128	99.81 11	99.73 117	99.82 61	99.90 51	99.90 30	97.97 231	99.86 225	99.42 79	99.96 67	99.80 46

DeepC-MVS	98.90 4	99.62 67	99.61 62	99.67 110	99.72 140	99.44 161	99.24 161	99.71 129	99.27 163	99.93 38	99.90 30	99.70 24	99.93 97	98.99 139	99.99 16	99.64 122
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 69	99.64 56	99.53 177	99.79 98	98.82 256	99.58 79	99.97 18	99.95 21	99.96 23	99.76 114	98.44 181	99.99 8	99.34 92	99.96 67	99.78 55

WR-MVS_H		99.61 69	99.53 84	99.87 20	99.80 86	99.83 29	99.67 51	99.75 108	99.58 122	99.85 74	99.69 155	98.18 216	99.94 80	99.28 105	99.95 81	99.83 39

ACMH	98.42 6	99.59 71	99.54 80	99.72 94	99.86 53	99.62 118	99.56 84	99.79 88	98.77 238	99.80 93	99.85 59	99.64 28	99.85 242	98.70 171	99.89 126	99.70 78
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		99.57 72	99.57 74	99.57 164	99.77 113	99.22 214	99.04 222	99.60 192	99.18 178	99.87 71	99.72 134	99.08 92	99.85 242	99.89 28	99.98 39	99.66 104

EG-PatchMatch MVS		99.57 72	99.56 79	99.62 145	99.77 113	99.33 192	99.26 154	99.76 103	99.32 157	99.80 93	99.78 104	99.29 64	99.87 206	99.15 122	99.91 114	99.66 104

Gipuma		99.57 72	99.59 67	99.49 184	99.98 3	99.71 83	99.72 31	99.84 61	99.81 64	99.94 34	99.78 104	98.91 115	99.71 335	98.41 185	99.95 81	99.05 326
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1921920		99.56 75	99.57 74	99.55 171	99.75 128	99.11 227	99.05 219	99.61 181	99.15 189	99.88 63	99.71 142	99.08 92	99.87 206	99.90 25	99.97 54	99.66 104

v1240		99.56 75	99.58 70	99.51 181	99.80 86	99.00 238	99.00 234	99.65 163	99.15 189	99.90 51	99.75 119	99.09 89	99.88 192	99.90 25	99.96 67	99.67 94

V42		99.56 75	99.54 80	99.63 136	99.79 98	99.46 154	99.39 116	99.59 198	99.24 169	99.86 72	99.70 149	98.55 163	99.82 281	99.79 37	99.95 81	99.60 152

MVSMamba_PlusPlus		99.55 78	99.58 70	99.47 190	99.68 163	99.40 174	99.52 89	99.70 134	99.92 29	99.77 107	99.86 54	98.28 202	99.96 56	99.54 60	99.90 116	99.05 326

v144192		99.55 78	99.54 80	99.58 158	99.78 105	99.20 219	99.11 205	99.62 174	99.18 178	99.89 55	99.72 134	98.66 148	99.87 206	99.88 29	99.97 54	99.66 104

test20.03		99.55 78	99.54 80	99.58 158	99.79 98	99.37 182	99.02 229	99.89 40	99.60 119	99.82 82	99.62 200	98.81 123	99.89 177	99.43 74	99.86 154	99.47 217

v1144		99.54 81	99.53 84	99.59 154	99.79 98	99.28 200	99.10 208	99.61 181	99.20 176	99.84 77	99.73 126	98.67 146	99.84 257	99.86 31	99.98 39	99.64 122

CP-MVSNet		99.54 81	99.43 100	99.87 20	99.76 117	99.82 35	99.57 82	99.61 181	99.54 123	99.80 93	99.64 182	97.79 243	99.95 66	99.21 110	99.94 92	99.84 35

TranMVSNet+NR-MVSNet		99.54 81	99.47 89	99.76 63	99.58 194	99.64 111	99.30 140	99.63 171	99.61 113	99.71 134	99.56 236	98.76 133	99.96 56	99.14 128	99.92 104	99.68 88

SSC-MVS		99.52 84	99.42 102	99.83 32	99.86 53	99.65 108	99.52 89	99.81 78	99.87 45	99.81 89	99.79 96	96.78 287	99.99 8	99.83 32	99.51 292	99.86 31

iter_conf05_11		99.51 85	99.49 87	99.57 164	99.42 271	99.67 99	99.52 89	99.77 97	99.78 70	99.77 107	99.73 126	98.10 220	99.89 177	99.42 79	99.93 99	99.16 295

patch_mono-2		99.51 85	99.46 93	99.64 129	99.70 151	99.11 227	99.04 222	99.87 47	99.71 83	99.47 220	99.79 96	98.24 207	99.98 21	99.38 84	99.96 67	99.83 39

v2v482		99.50 87	99.47 89	99.58 158	99.78 105	99.25 207	99.14 191	99.58 207	99.25 167	99.81 89	99.62 200	98.24 207	99.84 257	99.83 32	99.97 54	99.64 122

ACMH+	98.40 8	99.50 87	99.43 100	99.71 99	99.86 53	99.76 61	99.32 132	99.77 97	99.53 125	99.77 107	99.76 114	99.26 70	99.78 309	97.77 239	99.88 135	99.60 152

Baseline_NR-MVSNet		99.49 89	99.37 110	99.82 36	99.91 30	99.84 24	98.83 259	99.86 50	99.68 93	99.65 156	99.88 43	97.67 251	99.87 206	99.03 136	99.86 154	99.76 65

TAMVS		99.49 89	99.45 95	99.63 136	99.48 247	99.42 168	99.45 108	99.57 209	99.66 101	99.78 102	99.83 69	97.85 239	99.86 225	99.44 73	99.96 67	99.61 148

test_fmvs1		99.48 91	99.65 52	98.97 290	99.54 218	97.16 350	99.11 205	99.98 11	99.78 70	99.96 23	99.81 82	98.72 140	99.97 35	99.95 12	99.97 54	99.79 53

pmmvs-eth3d		99.48 91	99.47 89	99.51 181	99.77 113	99.41 173	98.81 264	99.66 153	99.42 148	99.75 116	99.66 175	99.20 76	99.76 319	98.98 141	99.99 16	99.36 248

EI-MVSNet-UG-set		99.48 91	99.50 86	99.42 205	99.57 204	98.65 274	99.24 161	99.46 259	99.68 93	99.80 93	99.66 175	98.99 105	99.89 177	99.19 114	99.90 116	99.72 72

APDe-MVS		99.48 91	99.36 113	99.85 26	99.55 216	99.81 40	99.50 96	99.69 141	98.99 204	99.75 116	99.71 142	98.79 128	99.93 97	98.46 183	99.85 158	99.80 46
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PMMVS2		99.48 91	99.45 95	99.57 164	99.76 117	98.99 239	98.09 336	99.90 38	98.95 210	99.78 102	99.58 225	99.57 38	99.93 97	99.48 69	99.95 81	99.79 53

DSMNet-mixed		99.48 91	99.65 52	98.95 293	99.71 143	97.27 347	99.50 96	99.82 69	99.59 121	99.41 238	99.85 59	99.62 31	100.00 1	99.53 64	99.89 126	99.59 159

DP-MVS		99.48 91	99.39 105	99.74 78	99.57 204	99.62 118	99.29 147	99.61 181	99.87 45	99.74 124	99.76 114	98.69 142	99.87 206	98.20 201	99.80 194	99.75 68

EI-MVSNet-Vis-set		99.47 98	99.49 87	99.42 205	99.57 204	98.66 271	99.24 161	99.46 259	99.67 97	99.79 98	99.65 180	98.97 109	99.89 177	99.15 122	99.89 126	99.71 75

VPNet		99.46 99	99.37 110	99.71 99	99.82 72	99.59 130	99.48 101	99.70 134	99.81 64	99.69 141	99.58 225	97.66 255	99.86 225	99.17 119	99.44 302	99.67 94

ACMM	98.09 11	99.46 99	99.38 107	99.72 94	99.80 86	99.69 94	99.13 197	99.65 163	98.99 204	99.64 157	99.72 134	99.39 50	99.86 225	98.23 198	99.81 189	99.60 152
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_vis1_rt		99.45 101	99.46 93	99.41 212	99.71 143	98.63 276	98.99 239	99.96 23	99.03 202	99.95 31	99.12 334	98.75 135	99.84 257	99.82 35	99.82 180	99.77 59

COLMAP_ROB	98.06 12	99.45 101	99.37 110	99.70 103	99.83 65	99.70 90	99.38 118	99.78 94	99.53 125	99.67 150	99.78 104	99.19 77	99.86 225	97.32 277	99.87 146	99.55 174
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WB-MVS		99.44 103	99.32 120	99.80 44	99.81 80	99.61 125	99.47 104	99.81 78	99.82 61	99.71 134	99.72 134	96.60 291	99.98 21	99.75 39	99.23 332	99.82 45

mvsany_test1		99.44 103	99.45 95	99.40 214	99.37 280	98.64 275	97.90 359	99.59 198	99.27 163	99.92 44	99.82 76	99.74 20	99.93 97	99.55 59	99.87 146	99.63 127

Anonymous20240521		99.44 103	99.42 102	99.49 184	99.89 38	98.96 244	99.62 65	99.76 103	99.85 53	99.82 82	99.88 43	96.39 301	99.97 35	99.59 51	99.98 39	99.55 174

tfpnnormal		99.43 106	99.38 107	99.60 151	99.87 50	99.75 67	99.59 77	99.78 94	99.71 83	99.90 51	99.69 155	98.85 121	99.90 159	97.25 288	99.78 204	99.15 298

HPM-MVS_fast		99.43 106	99.30 127	99.80 44	99.83 65	99.81 40	99.52 89	99.70 134	98.35 286	99.51 213	99.50 254	99.31 62	99.88 192	98.18 205	99.84 163	99.69 82

3Dnovator	99.15 2	99.43 106	99.36 113	99.65 122	99.39 275	99.42 168	99.70 36	99.56 214	99.23 171	99.35 248	99.80 86	99.17 79	99.95 66	98.21 200	99.84 163	99.59 159

Anonymous20240529		99.42 109	99.34 115	99.65 122	99.53 224	99.60 128	99.63 62	99.39 280	99.47 133	99.76 111	99.78 104	98.13 218	99.86 225	98.70 171	99.68 244	99.49 209

SixPastTwentyTwo		99.42 109	99.30 127	99.76 63	99.92 29	99.67 99	99.70 36	99.14 329	99.65 103	99.89 55	99.90 30	96.20 307	99.94 80	99.42 79	99.92 104	99.67 94

GBi-Net		99.42 109	99.31 122	99.73 88	99.49 242	99.77 54	99.68 47	99.70 134	99.44 139	99.62 170	99.83 69	97.21 272	99.90 159	98.96 145	99.90 116	99.53 187

test1		99.42 109	99.31 122	99.73 88	99.49 242	99.77 54	99.68 47	99.70 134	99.44 139	99.62 170	99.83 69	97.21 272	99.90 159	98.96 145	99.90 116	99.53 187

MVSFormer		99.41 113	99.44 98	99.31 240	99.57 204	98.40 290	99.77 16	99.80 82	99.73 77	99.63 161	99.30 304	98.02 226	99.98 21	99.43 74	99.69 239	99.55 174

IterMVS-LS		99.41 113	99.47 89	99.25 255	99.81 80	98.09 312	98.85 256	99.76 103	99.62 110	99.83 81	99.64 182	98.54 165	99.97 35	99.15 122	99.99 16	99.68 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SED-MVS		99.40 115	99.28 134	99.77 56	99.69 155	99.82 35	99.20 171	99.54 226	99.13 191	99.82 82	99.63 193	98.91 115	99.92 119	97.85 234	99.70 235	99.58 164

v148		99.40 115	99.41 104	99.39 217	99.76 117	98.94 245	99.09 212	99.59 198	99.17 183	99.81 89	99.61 209	98.41 185	99.69 344	99.32 97	99.94 92	99.53 187

NR-MVSNet		99.40 115	99.31 122	99.68 107	99.43 266	99.55 141	99.73 28	99.50 248	99.46 136	99.88 63	99.36 291	97.54 258	99.87 206	98.97 143	99.87 146	99.63 127

PVSNet_Blended_VisFu		99.40 115	99.38 107	99.44 199	99.90 36	98.66 271	98.94 248	99.91 33	97.97 312	99.79 98	99.73 126	99.05 99	99.97 35	99.15 122	99.99 16	99.68 88

EU-MVSNet		99.39 119	99.62 58	98.72 321	99.88 43	96.44 364	99.56 84	99.85 55	99.90 33	99.90 51	99.85 59	98.09 221	99.83 272	99.58 54	99.95 81	99.90 20

CHOSEN 1792x2688		99.39 119	99.30 127	99.65 122	99.88 43	99.25 207	98.78 271	99.88 45	98.66 249	99.96 23	99.79 96	97.45 261	99.93 97	99.34 92	99.99 16	99.78 55

DVP-MVS++		99.38 121	99.25 140	99.77 56	99.03 353	99.77 54	99.74 25	99.61 181	99.18 178	99.76 111	99.61 209	99.00 103	99.92 119	97.72 245	99.60 270	99.62 138

EI-MVSNet		99.38 121	99.44 98	99.21 259	99.58 194	98.09 312	99.26 154	99.46 259	99.62 110	99.75 116	99.67 170	98.54 165	99.85 242	99.15 122	99.92 104	99.68 88

UGNet		99.38 121	99.34 115	99.49 184	98.90 363	98.90 251	99.70 36	99.35 289	99.86 48	98.57 347	99.81 82	98.50 175	99.93 97	99.38 84	99.98 39	99.66 104
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
UniMVSNet_NR-MVSNet		99.37 124	99.25 140	99.72 94	99.47 253	99.56 138	98.97 243	99.61 181	99.43 144	99.67 150	99.28 308	97.85 239	99.95 66	99.17 119	99.81 189	99.65 112

UniMVSNet (Re)		99.37 124	99.26 138	99.68 107	99.51 231	99.58 134	98.98 242	99.60 192	99.43 144	99.70 138	99.36 291	97.70 247	99.88 192	99.20 113	99.87 146	99.59 159

CSCG		99.37 124	99.29 132	99.60 151	99.71 143	99.46 154	99.43 112	99.85 55	98.79 234	99.41 238	99.60 217	98.92 113	99.92 119	98.02 214	99.92 104	99.43 233

APD_test1		99.36 127	99.28 134	99.61 148	99.89 38	99.89 10	99.32 132	99.74 113	99.18 178	99.69 141	99.75 119	98.41 185	99.84 257	97.85 234	99.70 235	99.10 309

PM-MVS		99.36 127	99.29 132	99.58 158	99.83 65	99.66 102	98.95 246	99.86 50	98.85 225	99.81 89	99.73 126	98.40 189	99.92 119	98.36 188	99.83 171	99.17 293

new-patchmatchnet		99.35 129	99.57 74	98.71 323	99.82 72	96.62 362	98.55 295	99.75 108	99.50 127	99.88 63	99.87 48	99.31 62	99.88 192	99.43 74	100.00 1	99.62 138

Anonymous20231206		99.35 129	99.31 122	99.47 190	99.74 134	99.06 237	99.28 149	99.74 113	99.23 171	99.72 129	99.53 247	97.63 257	99.88 192	99.11 130	99.84 163	99.48 213

MTAPA		99.35 129	99.20 145	99.80 44	99.81 80	99.81 40	99.33 130	99.53 235	99.27 163	99.42 232	99.63 193	98.21 212	99.95 66	97.83 238	99.79 199	99.65 112

FMVSNet2		99.35 129	99.28 134	99.55 171	99.49 242	99.35 189	99.45 108	99.57 209	99.44 139	99.70 138	99.74 122	97.21 272	99.87 206	99.03 136	99.94 92	99.44 227

3Dnovator+	98.92 3	99.35 129	99.24 142	99.67 110	99.35 285	99.47 150	99.62 65	99.50 248	99.44 139	99.12 291	99.78 104	98.77 132	99.94 80	97.87 231	99.72 230	99.62 138

TSAR-MVS + MP.		99.34 134	99.24 142	99.63 136	99.82 72	99.37 182	99.26 154	99.35 289	98.77 238	99.57 187	99.70 149	99.27 69	99.88 192	97.71 247	99.75 212	99.65 112
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
diffmvs		99.34 134	99.32 120	99.39 217	99.67 170	98.77 262	98.57 293	99.81 78	99.61 113	99.48 218	99.41 275	98.47 176	99.86 225	98.97 143	99.90 116	99.53 187
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		99.34 134	99.30 127	99.48 188	99.51 231	99.36 186	98.12 332	99.53 235	99.36 153	99.41 238	99.61 209	99.22 74	99.87 206	99.21 110	99.68 244	99.20 286
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		99.33 137	99.21 144	99.71 99	99.43 266	99.56 138	98.83 259	99.53 235	99.38 150	99.67 150	99.36 291	97.67 251	99.95 66	99.17 119	99.81 189	99.63 127

ab-mvs		99.33 137	99.28 134	99.47 190	99.57 204	99.39 177	99.78 14	99.43 267	98.87 222	99.57 187	99.82 76	98.06 224	99.87 206	98.69 173	99.73 224	99.15 298

DVP-MVS		99.32 139	99.17 148	99.77 56	99.69 155	99.80 44	99.14 191	99.31 298	99.16 185	99.62 170	99.61 209	98.35 193	99.91 141	97.88 228	99.72 230	99.61 148
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		99.31 140	99.16 149	99.74 78	99.53 224	99.75 67	99.27 152	99.61 181	99.19 177	99.57 187	99.64 182	98.76 133	99.90 159	97.29 279	99.62 260	99.56 171

SteuartSystems-ACMMP		99.30 141	99.14 153	99.76 63	99.87 50	99.66 102	99.18 176	99.60 192	98.55 260	99.57 187	99.67 170	99.03 102	99.94 80	97.01 298	99.80 194	99.69 82
Skip Steuart: Steuart Systems R&D Blog.
testgi		99.29 142	99.26 138	99.37 223	99.75 128	98.81 257	98.84 257	99.89 40	98.38 279	99.75 116	99.04 344	99.36 59	99.86 225	99.08 133	99.25 328	99.45 222

ACMMP_NAP		99.28 143	99.11 162	99.79 50	99.75 128	99.81 40	98.95 246	99.53 235	98.27 295	99.53 206	99.73 126	98.75 135	99.87 206	97.70 250	99.83 171	99.68 88

LCM-MVSNet-Re		99.28 143	99.15 152	99.67 110	99.33 299	99.76 61	99.34 127	99.97 18	98.93 214	99.91 47	99.79 96	98.68 143	99.93 97	96.80 311	99.56 277	99.30 264

mvs_anonymous		99.28 143	99.39 105	98.94 294	99.19 327	97.81 329	99.02 229	99.55 220	99.78 70	99.85 74	99.80 86	98.24 207	99.86 225	99.57 56	99.50 295	99.15 298

MVS_Test		99.28 143	99.31 122	99.19 262	99.35 285	98.79 260	99.36 125	99.49 252	99.17 183	99.21 278	99.67 170	98.78 130	99.66 365	99.09 132	99.66 253	99.10 309

SR-MVS-dyc-post		99.27 147	99.11 162	99.73 88	99.54 218	99.74 73	99.26 154	99.62 174	99.16 185	99.52 208	99.64 182	98.41 185	99.91 141	97.27 282	99.61 267	99.54 182

XVS		99.27 147	99.11 162	99.75 73	99.71 143	99.71 83	99.37 122	99.61 181	99.29 159	98.76 330	99.47 265	98.47 176	99.88 192	97.62 258	99.73 224	99.67 94

OPM-MVS		99.26 149	99.13 155	99.63 136	99.70 151	99.61 125	98.58 289	99.48 253	98.50 267	99.52 208	99.63 193	99.14 84	99.76 319	97.89 227	99.77 208	99.51 199
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HFP-MVS		99.25 150	99.08 173	99.76 63	99.73 137	99.70 90	99.31 137	99.59 198	98.36 281	99.36 247	99.37 287	98.80 127	99.91 141	97.43 271	99.75 212	99.68 88

HPM-MVS		99.25 150	99.07 177	99.78 53	99.81 80	99.75 67	99.61 70	99.67 148	97.72 327	99.35 248	99.25 315	99.23 73	99.92 119	97.21 291	99.82 180	99.67 94
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		99.25 150	99.08 173	99.74 78	99.79 98	99.68 97	99.50 96	99.65 163	98.07 306	99.52 208	99.69 155	98.57 160	99.92 119	97.18 293	99.79 199	99.63 127
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		99.24 153	99.11 162	99.61 148	98.38 396	99.79 46	99.57 82	99.68 144	99.61 113	99.15 286	99.71 142	98.70 141	99.91 141	97.54 264	99.68 244	99.13 306

xiu_mvs_v1_base_debu		99.23 154	99.34 115	98.91 300	99.59 189	98.23 299	98.47 305	99.66 153	99.61 113	99.68 144	98.94 360	99.39 50	99.97 35	99.18 116	99.55 281	98.51 374

xiu_mvs_v1_base		99.23 154	99.34 115	98.91 300	99.59 189	98.23 299	98.47 305	99.66 153	99.61 113	99.68 144	98.94 360	99.39 50	99.97 35	99.18 116	99.55 281	98.51 374

xiu_mvs_v1_base_debi		99.23 154	99.34 115	98.91 300	99.59 189	98.23 299	98.47 305	99.66 153	99.61 113	99.68 144	98.94 360	99.39 50	99.97 35	99.18 116	99.55 281	98.51 374

region2R		99.23 154	99.05 183	99.77 56	99.76 117	99.70 90	99.31 137	99.59 198	98.41 275	99.32 256	99.36 291	98.73 139	99.93 97	97.29 279	99.74 219	99.67 94

ACMMPR		99.23 154	99.06 179	99.76 63	99.74 134	99.69 94	99.31 137	99.59 198	98.36 281	99.35 248	99.38 285	98.61 154	99.93 97	97.43 271	99.75 212	99.67 94

XVG-ACMP-BASELINE		99.23 154	99.10 170	99.63 136	99.82 72	99.58 134	98.83 259	99.72 126	98.36 281	99.60 179	99.71 142	98.92 113	99.91 141	97.08 296	99.84 163	99.40 238

CP-MVS		99.23 154	99.05 183	99.75 73	99.66 171	99.66 102	99.38 118	99.62 174	98.38 279	99.06 299	99.27 310	98.79 128	99.94 80	97.51 267	99.82 180	99.66 104

DeepC-MVS_fast	98.47 5	99.23 154	99.12 159	99.56 168	99.28 310	99.22 214	98.99 239	99.40 277	99.08 196	99.58 184	99.64 182	98.90 118	99.83 272	97.44 270	99.75 212	99.63 127
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		99.22 162	99.04 189	99.77 56	99.76 117	99.73 76	99.28 149	99.56 214	98.19 300	99.14 288	99.29 307	98.84 122	99.92 119	97.53 266	99.80 194	99.64 122

D2MVS		99.22 162	99.19 146	99.29 244	99.69 155	98.74 264	98.81 264	99.41 270	98.55 260	99.68 144	99.69 155	98.13 218	99.87 206	98.82 157	99.98 39	99.24 273

LPG-MVS_test		99.22 162	99.05 183	99.74 78	99.82 72	99.63 116	99.16 187	99.73 117	97.56 332	99.64 157	99.69 155	99.37 56	99.89 177	96.66 319	99.87 146	99.69 82

CDS-MVSNet		99.22 162	99.13 155	99.50 183	99.35 285	99.11 227	98.96 245	99.54 226	99.46 136	99.61 176	99.70 149	96.31 303	99.83 272	99.34 92	99.88 135	99.55 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_0402		99.22 162	99.14 153	99.45 196	99.79 98	99.43 165	99.28 149	99.68 144	99.54 123	99.40 243	99.56 236	99.07 94	99.82 281	96.01 350	99.96 67	99.11 307

AllTest		99.21 167	99.07 177	99.63 136	99.78 105	99.64 111	99.12 201	99.83 63	98.63 252	99.63 161	99.72 134	98.68 143	99.75 323	96.38 337	99.83 171	99.51 199

XVG-OURS		99.21 167	99.06 179	99.65 122	99.82 72	99.62 118	97.87 360	99.74 113	98.36 281	99.66 154	99.68 166	99.71 22	99.90 159	96.84 310	99.88 135	99.43 233

Fast-Effi-MVS+-dtu		99.20 169	99.12 159	99.43 203	99.25 315	99.69 94	99.05 219	99.82 69	99.50 127	98.97 303	99.05 342	98.98 107	99.98 21	98.20 201	99.24 330	98.62 365

VDD-MVS		99.20 169	99.11 162	99.44 199	99.43 266	98.98 240	99.50 96	98.32 372	99.80 67	99.56 194	99.69 155	96.99 282	99.85 242	98.99 139	99.73 224	99.50 204

PGM-MVS		99.20 169	99.01 196	99.77 56	99.75 128	99.71 83	99.16 187	99.72 126	97.99 310	99.42 232	99.60 217	98.81 123	99.93 97	96.91 304	99.74 219	99.66 104

SR-MVS		99.19 172	99.00 200	99.74 78	99.51 231	99.72 81	99.18 176	99.60 192	98.85 225	99.47 220	99.58 225	98.38 190	99.92 119	96.92 303	99.54 286	99.57 169

SMA-MVS		99.19 172	99.00 200	99.73 88	99.46 257	99.73 76	99.13 197	99.52 240	97.40 343	99.57 187	99.64 182	98.93 112	99.83 272	97.61 260	99.79 199	99.63 127
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		99.19 172	99.11 162	99.42 205	99.76 117	98.88 253	98.55 295	99.73 117	98.82 229	99.72 129	99.62 200	96.56 292	99.82 281	99.32 97	99.95 81	99.56 171

mPP-MVS		99.19 172	99.00 200	99.76 63	99.76 117	99.68 97	99.38 118	99.54 226	98.34 290	99.01 301	99.50 254	98.53 169	99.93 97	97.18 293	99.78 204	99.66 104

MM		99.18 176	99.05 183	99.55 171	99.35 285	98.81 257	99.05 219	97.79 383	99.99 2	99.48 218	99.59 222	96.29 305	99.95 66	99.94 16	99.98 39	99.88 24

ETV-MVS		99.18 176	99.18 147	99.16 265	99.34 294	99.28 200	99.12 201	99.79 88	99.48 129	98.93 307	98.55 381	99.40 49	99.93 97	98.51 181	99.52 291	98.28 384

VNet		99.18 176	99.06 179	99.56 168	99.24 317	99.36 186	99.33 130	99.31 298	99.67 97	99.47 220	99.57 232	96.48 295	99.84 257	99.15 122	99.30 321	99.47 217

RPSCF		99.18 176	99.02 193	99.64 129	99.83 65	99.85 19	99.44 110	99.82 69	98.33 291	99.50 215	99.78 104	97.90 234	99.65 371	96.78 312	99.83 171	99.44 227

DeepPCF-MVS	98.42 6	99.18 176	99.02 193	99.67 110	99.22 320	99.75 67	97.25 387	99.47 256	98.72 243	99.66 154	99.70 149	99.29 64	99.63 374	98.07 213	99.81 189	99.62 138

MVS_0304		99.17 181	99.03 191	99.59 154	99.44 262	98.90 251	99.04 222	95.32 400	99.99 2	99.68 144	99.57 232	98.30 200	99.97 35	99.94 16	99.98 39	99.88 24

EPP-MVSNet		99.17 181	99.00 200	99.66 117	99.80 86	99.43 165	99.70 36	99.24 314	99.48 129	99.56 194	99.77 111	94.89 319	99.93 97	98.72 170	99.89 126	99.63 127

GST-MVS		99.16 183	98.96 212	99.75 73	99.73 137	99.73 76	99.20 171	99.55 220	98.22 297	99.32 256	99.35 296	98.65 150	99.91 141	96.86 307	99.74 219	99.62 138

MVP-Stereo		99.16 183	99.08 173	99.43 203	99.48 247	99.07 235	99.08 215	99.55 220	98.63 252	99.31 260	99.68 166	98.19 214	99.78 309	98.18 205	99.58 275	99.45 222
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-OURS-SEG-HR		99.16 183	98.99 206	99.66 117	99.84 60	99.64 111	98.25 322	99.73 117	98.39 278	99.63 161	99.43 273	99.70 24	99.90 159	97.34 276	98.64 368	99.44 227

jason		99.16 183	99.11 162	99.32 237	99.75 128	98.44 287	98.26 321	99.39 280	98.70 246	99.74 124	99.30 304	98.54 165	99.97 35	98.48 182	99.82 180	99.55 174
jason: jason.
DPE-MVS		99.14 187	98.92 218	99.82 36	99.57 204	99.77 54	98.74 274	99.60 192	98.55 260	99.76 111	99.69 155	98.23 211	99.92 119	96.39 336	99.75 212	99.76 65
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		99.14 187	98.92 218	99.80 44	99.83 65	99.83 29	98.61 282	99.63 171	96.84 363	99.44 226	99.58 225	98.81 123	99.91 141	97.70 250	99.82 180	99.67 94
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs4		99.13 189	99.06 179	99.36 227	99.57 204	99.10 232	98.01 345	99.25 311	98.78 236	99.58 184	99.44 272	98.24 207	99.76 319	98.74 168	99.93 99	99.22 280

MVS_111021_LR		99.13 189	99.03 191	99.42 205	99.58 194	99.32 194	97.91 358	99.73 117	98.68 247	99.31 260	99.48 261	99.09 89	99.66 365	97.70 250	99.77 208	99.29 267

EIA-MVS		99.12 191	99.01 196	99.45 196	99.36 283	99.62 118	99.34 127	99.79 88	98.41 275	98.84 320	98.89 364	98.75 135	99.84 257	98.15 209	99.51 292	98.89 349

TSAR-MVS + GP.		99.12 191	99.04 189	99.38 220	99.34 294	99.16 222	98.15 328	99.29 302	98.18 301	99.63 161	99.62 200	99.18 78	99.68 356	98.20 201	99.74 219	99.30 264

MVS_111021_HR		99.12 191	99.02 193	99.40 214	99.50 237	99.11 227	97.92 356	99.71 129	98.76 241	99.08 295	99.47 265	99.17 79	99.54 387	97.85 234	99.76 210	99.54 182

CANet		99.11 194	99.05 183	99.28 246	98.83 370	98.56 280	98.71 278	99.41 270	99.25 167	99.23 273	99.22 322	97.66 255	99.94 80	99.19 114	99.97 54	99.33 255

WR-MVS		99.11 194	98.93 214	99.66 117	99.30 305	99.42 168	98.42 310	99.37 285	99.04 201	99.57 187	99.20 326	96.89 284	99.86 225	98.66 175	99.87 146	99.70 78

PHI-MVS		99.11 194	98.95 213	99.59 154	99.13 336	99.59 130	99.17 181	99.65 163	97.88 320	99.25 269	99.46 268	98.97 109	99.80 303	97.26 284	99.82 180	99.37 245

SF-MVS		99.10 197	98.93 214	99.62 145	99.58 194	99.51 146	99.13 197	99.65 163	97.97 312	99.42 232	99.61 209	98.86 120	99.87 206	96.45 334	99.68 244	99.49 209

MSDG		99.08 198	98.98 209	99.37 223	99.60 185	99.13 225	97.54 373	99.74 113	98.84 228	99.53 206	99.55 243	99.10 87	99.79 306	97.07 297	99.86 154	99.18 291

Effi-MVS+-dtu		99.07 199	98.92 218	99.52 179	98.89 366	99.78 49	99.15 189	99.66 153	99.34 154	98.92 310	99.24 320	97.69 249	99.98 21	98.11 211	99.28 324	98.81 356

Effi-MVS+		99.06 200	98.97 210	99.34 230	99.31 301	98.98 240	98.31 317	99.91 33	98.81 231	98.79 327	98.94 360	99.14 84	99.84 257	98.79 161	98.74 362	99.20 286

MP-MVS		99.06 200	98.83 230	99.76 63	99.76 117	99.71 83	99.32 132	99.50 248	98.35 286	98.97 303	99.48 261	98.37 191	99.92 119	95.95 356	99.75 212	99.63 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDA-MVSNet-bldmvs		99.06 200	99.05 183	99.07 281	99.80 86	97.83 328	98.89 251	99.72 126	99.29 159	99.63 161	99.70 149	96.47 296	99.89 177	98.17 207	99.82 180	99.50 204

MSLP-MVS++		99.05 203	99.09 171	98.91 300	99.21 322	98.36 295	98.82 263	99.47 256	98.85 225	98.90 313	99.56 236	98.78 130	99.09 402	98.57 178	99.68 244	99.26 270

1112_ss		99.05 203	98.84 228	99.67 110	99.66 171	99.29 198	98.52 301	99.82 69	97.65 330	99.43 230	99.16 328	96.42 298	99.91 141	99.07 134	99.84 163	99.80 46

ACMP	97.51 14	99.05 203	98.84 228	99.67 110	99.78 105	99.55 141	98.88 252	99.66 153	97.11 358	99.47 220	99.60 217	99.07 94	99.89 177	96.18 345	99.85 158	99.58 164
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSP-MVS		99.04 206	98.79 235	99.81 39	99.78 105	99.73 76	99.35 126	99.57 209	98.54 263	99.54 201	98.99 351	96.81 286	99.93 97	96.97 301	99.53 288	99.77 59
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		99.03 207	99.01 196	99.09 276	99.54 218	97.99 318	98.58 289	99.82 69	97.62 331	99.34 251	99.71 142	98.52 172	99.77 317	97.98 219	99.97 54	99.52 197

IS-MVSNet		99.03 207	98.85 226	99.55 171	99.80 86	99.25 207	99.73 28	99.15 328	99.37 151	99.61 176	99.71 142	94.73 322	99.81 296	97.70 250	99.88 135	99.58 164

MGCFI-Net		99.02 209	99.01 196	99.06 283	99.11 343	98.60 278	99.63 62	99.67 148	99.63 107	98.58 345	97.65 399	99.07 94	99.57 383	98.85 153	98.92 349	99.03 330

sasdasda		99.02 209	99.00 200	99.09 276	99.10 345	98.70 266	99.61 70	99.66 153	99.63 107	98.64 339	97.65 399	99.04 100	99.54 387	98.79 161	98.92 349	99.04 328

xiu_mvs_v2_base		99.02 209	99.11 162	98.77 318	99.37 280	98.09 312	98.13 331	99.51 244	99.47 133	99.42 232	98.54 382	99.38 54	99.97 35	98.83 155	99.33 317	98.24 386

Fast-Effi-MVS+		99.02 209	98.87 224	99.46 193	99.38 278	99.50 147	99.04 222	99.79 88	97.17 354	98.62 341	98.74 373	99.34 60	99.95 66	98.32 192	99.41 307	98.92 345

canonicalmvs		99.02 209	99.00 200	99.09 276	99.10 345	98.70 266	99.61 70	99.66 153	99.63 107	98.64 339	97.65 399	99.04 100	99.54 387	98.79 161	98.92 349	99.04 328

MCST-MVS		99.02 209	98.81 232	99.65 122	99.58 194	99.49 148	98.58 289	99.07 333	98.40 277	99.04 300	99.25 315	98.51 174	99.80 303	97.31 278	99.51 292	99.65 112

SD-MVS		99.01 215	99.30 127	98.15 346	99.50 237	99.40 174	98.94 248	99.61 181	99.22 175	99.75 116	99.82 76	99.54 41	95.51 411	97.48 268	99.87 146	99.54 182
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		99.01 215	98.92 218	99.27 249	99.71 143	99.28 200	98.59 287	99.77 97	98.32 292	99.39 244	99.41 275	98.62 152	99.84 257	96.62 324	99.84 163	98.69 363

IterMVS-SCA-FT		99.00 217	99.16 149	98.51 330	99.75 128	95.90 374	98.07 339	99.84 61	99.84 56	99.89 55	99.73 126	96.01 310	99.99 8	99.33 95	100.00 1	99.63 127

MS-PatchMatch		99.00 217	98.97 210	99.09 276	99.11 343	98.19 303	98.76 273	99.33 292	98.49 269	99.44 226	99.58 225	98.21 212	99.69 344	98.20 201	99.62 260	99.39 240

PS-MVSNAJ		99.00 217	99.08 173	98.76 319	99.37 280	98.10 311	98.00 347	99.51 244	99.47 133	99.41 238	98.50 384	99.28 66	99.97 35	98.83 155	99.34 316	98.20 390

CNVR-MVS		98.99 220	98.80 234	99.56 168	99.25 315	99.43 165	98.54 298	99.27 306	98.58 258	98.80 325	99.43 273	98.53 169	99.70 338	97.22 290	99.59 274	99.54 182

VDDNet		98.97 221	98.82 231	99.42 205	99.71 143	98.81 257	99.62 65	98.68 351	99.81 64	99.38 245	99.80 86	94.25 326	99.85 242	98.79 161	99.32 319	99.59 159

IterMVS		98.97 221	99.16 149	98.42 334	99.74 134	95.64 377	98.06 341	99.83 63	99.83 59	99.85 74	99.74 122	96.10 309	99.99 8	99.27 106	100.00 1	99.63 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap		98.97 221	98.93 214	99.07 281	99.46 257	98.19 303	97.75 364	99.75 108	98.79 234	99.54 201	99.70 149	98.97 109	99.62 375	96.63 323	99.83 171	99.41 237

HPM-MVS++		98.96 224	98.70 241	99.74 78	99.52 229	99.71 83	98.86 254	99.19 324	98.47 271	98.59 344	99.06 341	98.08 223	99.91 141	96.94 302	99.60 270	99.60 152

lupinMVS		98.96 224	98.87 224	99.24 257	99.57 204	98.40 290	98.12 332	99.18 325	98.28 294	99.63 161	99.13 330	98.02 226	99.97 35	98.22 199	99.69 239	99.35 251

USDC		98.96 224	98.93 214	99.05 284	99.54 218	97.99 318	97.07 393	99.80 82	98.21 298	99.75 116	99.77 111	98.43 182	99.64 373	97.90 226	99.88 135	99.51 199

YYNet1		98.95 227	98.99 206	98.84 311	99.64 176	97.14 352	98.22 324	99.32 294	98.92 216	99.59 182	99.66 175	97.40 263	99.83 272	98.27 195	99.90 116	99.55 174

MDA-MVSNet_test_wron		98.95 227	98.99 206	98.85 309	99.64 176	97.16 350	98.23 323	99.33 292	98.93 214	99.56 194	99.66 175	97.39 265	99.83 272	98.29 193	99.88 135	99.55 174

Test_1112_low_res		98.95 227	98.73 237	99.63 136	99.68 163	99.15 224	98.09 336	99.80 82	97.14 356	99.46 224	99.40 279	96.11 308	99.89 177	99.01 138	99.84 163	99.84 35

CANet_DTU		98.91 230	98.85 226	99.09 276	98.79 376	98.13 307	98.18 325	99.31 298	99.48 129	98.86 318	99.51 251	96.56 292	99.95 66	99.05 135	99.95 81	99.19 289

HyFIR lowres test		98.91 230	98.64 243	99.73 88	99.85 57	99.47 150	98.07 339	99.83 63	98.64 251	99.89 55	99.60 217	92.57 344	100.00 1	99.33 95	99.97 54	99.72 72

HQP_MVS		98.90 232	98.68 242	99.55 171	99.58 194	99.24 211	98.80 267	99.54 226	98.94 211	99.14 288	99.25 315	97.24 270	99.82 281	95.84 360	99.78 204	99.60 152

sss		98.90 232	98.77 236	99.27 249	99.48 247	98.44 287	98.72 276	99.32 294	97.94 316	99.37 246	99.35 296	96.31 303	99.91 141	98.85 153	99.63 259	99.47 217

OMC-MVS		98.90 232	98.72 238	99.44 199	99.39 275	99.42 168	98.58 289	99.64 169	97.31 348	99.44 226	99.62 200	98.59 157	99.69 344	96.17 346	99.79 199	99.22 280

ppachtmachnet_test		98.89 235	99.12 159	98.20 345	99.66 171	95.24 383	97.63 369	99.68 144	99.08 196	99.78 102	99.62 200	98.65 150	99.88 192	98.02 214	99.96 67	99.48 213

new_pmnet		98.88 236	98.89 222	98.84 311	99.70 151	97.62 336	98.15 328	99.50 248	97.98 311	99.62 170	99.54 245	98.15 217	99.94 80	97.55 263	99.84 163	98.95 341

K. test v3		98.87 237	98.60 246	99.69 105	99.93 24	99.46 154	99.74 25	94.97 401	99.78 70	99.88 63	99.88 43	93.66 334	99.97 35	99.61 49	99.95 81	99.64 122

APD-MVS		98.87 237	98.59 248	99.71 99	99.50 237	99.62 118	99.01 231	99.57 209	96.80 365	99.54 201	99.63 193	98.29 201	99.91 141	95.24 372	99.71 233	99.61 148
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
our_test_3		98.85 239	99.09 171	98.13 347	99.66 171	94.90 387	97.72 365	99.58 207	99.07 198	99.64 157	99.62 200	98.19 214	99.93 97	98.41 185	99.95 81	99.55 174

UnsupCasMVSNet_eth		98.83 240	98.57 252	99.59 154	99.68 163	99.45 159	98.99 239	99.67 148	99.48 129	99.55 199	99.36 291	94.92 318	99.86 225	98.95 149	96.57 402	99.45 222

NCCC		98.82 241	98.57 252	99.58 158	99.21 322	99.31 195	98.61 282	99.25 311	98.65 250	98.43 354	99.26 313	97.86 237	99.81 296	96.55 325	99.27 327	99.61 148

PMVS	92.94 21	98.82 241	98.81 232	98.85 309	99.84 60	97.99 318	99.20 171	99.47 256	99.71 83	99.42 232	99.82 76	98.09 221	99.47 395	93.88 391	99.85 158	99.07 324
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FMVSNet3		98.80 243	98.63 245	99.32 237	99.13 336	98.72 265	99.10 208	99.48 253	99.23 171	99.62 170	99.64 182	92.57 344	99.86 225	98.96 145	99.90 116	99.39 240

Patchmtry		98.78 244	98.54 256	99.49 184	98.89 366	99.19 220	99.32 132	99.67 148	99.65 103	99.72 129	99.79 96	91.87 352	99.95 66	98.00 218	99.97 54	99.33 255

Vis-MVSNet (Re-imp)		98.77 245	98.58 251	99.34 230	99.78 105	98.88 253	99.61 70	99.56 214	99.11 195	99.24 272	99.56 236	93.00 342	99.78 309	97.43 271	99.89 126	99.35 251

CLD-MVS		98.76 246	98.57 252	99.33 233	99.57 204	98.97 242	97.53 375	99.55 220	96.41 368	99.27 267	99.13 330	99.07 94	99.78 309	96.73 315	99.89 126	99.23 277
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous202405211		98.75 247	98.46 261	99.63 136	99.34 294	99.66 102	99.47 104	97.65 384	99.28 162	99.56 194	99.50 254	93.15 338	99.84 257	98.62 176	99.58 275	99.40 238

CPTT-MVS		98.74 248	98.44 263	99.64 129	99.61 183	99.38 179	99.18 176	99.55 220	96.49 367	99.27 267	99.37 287	97.11 278	99.92 119	95.74 363	99.67 250	99.62 138

F-COLMAP		98.74 248	98.45 262	99.62 145	99.57 204	99.47 150	98.84 257	99.65 163	96.31 371	98.93 307	99.19 327	97.68 250	99.87 206	96.52 327	99.37 312	99.53 187

N_pmnet		98.73 250	98.53 257	99.35 229	99.72 140	98.67 268	98.34 314	94.65 402	98.35 286	99.79 98	99.68 166	98.03 225	99.93 97	98.28 194	99.92 104	99.44 227

c3_l		98.72 251	98.71 239	98.72 321	99.12 338	97.22 349	97.68 368	99.56 214	98.90 218	99.54 201	99.48 261	96.37 302	99.73 329	97.88 228	99.88 135	99.21 282

CL-MVSNet_self_test		98.71 252	98.56 255	99.15 267	99.22 320	98.66 271	97.14 390	99.51 244	98.09 305	99.54 201	99.27 310	96.87 285	99.74 326	98.43 184	98.96 346	99.03 330

PVSNet_Blended		98.70 253	98.59 248	99.02 286	99.54 218	97.99 318	97.58 372	99.82 69	95.70 379	99.34 251	98.98 354	98.52 172	99.77 317	97.98 219	99.83 171	99.30 264

dmvs_re		98.69 254	98.48 259	99.31 240	99.55 216	99.42 168	99.54 87	98.38 369	99.32 157	98.72 333	98.71 374	96.76 288	99.21 400	96.01 350	99.35 315	99.31 262

eth_miper_zixun_eth		98.68 255	98.71 239	98.60 326	99.10 345	96.84 359	97.52 377	99.54 226	98.94 211	99.58 184	99.48 261	96.25 306	99.76 319	98.01 217	99.93 99	99.21 282

PatchMatch-RL		98.68 255	98.47 260	99.30 243	99.44 262	99.28 200	98.14 330	99.54 226	97.12 357	99.11 292	99.25 315	97.80 242	99.70 338	96.51 328	99.30 321	98.93 343

miper_lstm_enhance		98.65 257	98.60 246	98.82 316	99.20 325	97.33 346	97.78 363	99.66 153	99.01 203	99.59 182	99.50 254	94.62 323	99.85 242	98.12 210	99.90 116	99.26 270

h-mvs33		98.61 258	98.34 274	99.44 199	99.60 185	98.67 268	99.27 152	99.44 264	99.68 93	99.32 256	99.49 258	92.50 347	100.00 1	99.24 107	96.51 403	99.65 112

CVMVSNet		98.61 258	98.88 223	97.80 358	99.58 194	93.60 395	99.26 154	99.64 169	99.66 101	99.72 129	99.67 170	93.26 337	99.93 97	99.30 100	99.81 189	99.87 29

Patchmatch-RL test		98.60 260	98.36 271	99.33 233	99.77 113	99.07 235	98.27 319	99.87 47	98.91 217	99.74 124	99.72 134	90.57 369	99.79 306	98.55 179	99.85 158	99.11 307

RPMNet		98.60 260	98.53 257	98.83 313	99.05 351	98.12 308	99.30 140	99.62 174	99.86 48	99.16 284	99.74 122	92.53 346	99.92 119	98.75 167	98.77 358	98.44 379

AdaColmap		98.60 260	98.35 273	99.38 220	99.12 338	99.22 214	98.67 279	99.42 269	97.84 324	98.81 323	99.27 310	97.32 268	99.81 296	95.14 374	99.53 288	99.10 309

miper_ehance_all_eth		98.59 263	98.59 248	98.59 327	98.98 359	97.07 353	97.49 378	99.52 240	98.50 267	99.52 208	99.37 287	96.41 300	99.71 335	97.86 232	99.62 260	99.00 337

WTY-MVS		98.59 263	98.37 270	99.26 252	99.43 266	98.40 290	98.74 274	99.13 331	98.10 303	99.21 278	99.24 320	94.82 320	99.90 159	97.86 232	98.77 358	99.49 209

CNLPA		98.57 265	98.34 274	99.28 246	99.18 330	99.10 232	98.34 314	99.41 270	98.48 270	98.52 349	98.98 354	97.05 280	99.78 309	95.59 365	99.50 295	98.96 339

CDPH-MVS		98.56 266	98.20 284	99.61 148	99.50 237	99.46 154	98.32 316	99.41 270	95.22 384	99.21 278	99.10 338	98.34 195	99.82 281	95.09 376	99.66 253	99.56 171

UnsupCasMVSNet_bld		98.55 267	98.27 280	99.40 214	99.56 215	99.37 182	97.97 352	99.68 144	97.49 339	99.08 295	99.35 296	95.41 317	99.82 281	97.70 250	98.19 383	99.01 336

cl____		98.54 268	98.41 266	98.92 298	99.03 353	97.80 331	97.46 379	99.59 198	98.90 218	99.60 179	99.46 268	93.85 330	99.78 309	97.97 221	99.89 126	99.17 293

DIV-MVS_self_test		98.54 268	98.42 265	98.92 298	99.03 353	97.80 331	97.46 379	99.59 198	98.90 218	99.60 179	99.46 268	93.87 329	99.78 309	97.97 221	99.89 126	99.18 291

FA-MVS(test-final)		98.52 270	98.32 276	99.10 275	99.48 247	98.67 268	99.77 16	98.60 358	97.35 346	99.63 161	99.80 86	93.07 340	99.84 257	97.92 224	99.30 321	98.78 359

hse-mvs2		98.52 270	98.30 278	99.16 265	99.29 307	98.60 278	98.77 272	99.02 337	99.68 93	99.32 256	99.04 344	92.50 347	99.85 242	99.24 107	97.87 393	99.03 330

MG-MVS		98.52 270	98.39 268	98.94 294	99.15 333	97.39 345	98.18 325	99.21 321	98.89 221	99.23 273	99.63 193	97.37 266	99.74 326	94.22 385	99.61 267	99.69 82

DP-MVS Recon		98.50 273	98.23 281	99.31 240	99.49 242	99.46 154	98.56 294	99.63 171	94.86 390	98.85 319	99.37 287	97.81 241	99.59 381	96.08 347	99.44 302	98.88 350

CMPMVS	77.52 23	98.50 273	98.19 287	99.41 212	98.33 398	99.56 138	99.01 231	99.59 198	95.44 381	99.57 187	99.80 86	95.64 313	99.46 397	96.47 332	99.92 104	99.21 282
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t		98.49 275	98.11 292	99.64 129	99.73 137	99.58 134	99.24 161	99.76 103	89.94 402	99.42 232	99.56 236	97.76 246	99.86 225	97.74 244	99.82 180	99.47 217

PMMVS		98.49 275	98.29 279	99.11 273	98.96 360	98.42 289	97.54 373	99.32 294	97.53 336	98.47 352	98.15 391	97.88 236	99.82 281	97.46 269	99.24 330	99.09 313

MVSTER		98.47 277	98.22 282	99.24 257	99.06 350	98.35 296	99.08 215	99.46 259	99.27 163	99.75 116	99.66 175	88.61 380	99.85 242	99.14 128	99.92 104	99.52 197

LFMVS		98.46 278	98.19 287	99.26 252	99.24 317	98.52 283	99.62 65	96.94 392	99.87 45	99.31 260	99.58 225	91.04 360	99.81 296	98.68 174	99.42 306	99.45 222

PatchT		98.45 279	98.32 276	98.83 313	98.94 361	98.29 297	99.24 161	98.82 345	99.84 56	99.08 295	99.76 114	91.37 355	99.94 80	98.82 157	99.00 344	98.26 385

MIMVSNet		98.43 280	98.20 284	99.11 273	99.53 224	98.38 294	99.58 79	98.61 356	98.96 208	99.33 253	99.76 114	90.92 362	99.81 296	97.38 274	99.76 210	99.15 298

PVSNet	97.47 15	98.42 281	98.44 263	98.35 337	99.46 257	96.26 368	96.70 398	99.34 291	97.68 329	99.00 302	99.13 330	97.40 263	99.72 331	97.59 262	99.68 244	99.08 319

CHOSEN 280x420		98.41 282	98.41 266	98.40 335	99.34 294	95.89 375	96.94 395	99.44 264	98.80 233	99.25 269	99.52 249	93.51 336	99.98 21	98.94 150	99.98 39	99.32 258

BH-RMVSNet		98.41 282	98.14 290	99.21 259	99.21 322	98.47 284	98.60 284	98.26 373	98.35 286	98.93 307	99.31 302	97.20 275	99.66 365	94.32 383	99.10 337	99.51 199

QAPM		98.40 284	97.99 298	99.65 122	99.39 275	99.47 150	99.67 51	99.52 240	91.70 399	98.78 329	99.80 86	98.55 163	99.95 66	94.71 380	99.75 212	99.53 187

API-MVS		98.38 285	98.39 268	98.35 337	98.83 370	99.26 204	99.14 191	99.18 325	98.59 257	98.66 338	98.78 371	98.61 154	99.57 383	94.14 386	99.56 277	96.21 405

HQP-MVS		98.36 286	98.02 297	99.39 217	99.31 301	98.94 245	97.98 349	99.37 285	97.45 340	98.15 363	98.83 367	96.67 289	99.70 338	94.73 378	99.67 250	99.53 187

PAPM_NR		98.36 286	98.04 295	99.33 233	99.48 247	98.93 248	98.79 270	99.28 305	97.54 335	98.56 348	98.57 379	97.12 277	99.69 344	94.09 387	98.90 353	99.38 242

PLC	97.35 16	98.36 286	97.99 298	99.48 188	99.32 300	99.24 211	98.50 303	99.51 244	95.19 386	98.58 345	98.96 358	96.95 283	99.83 272	95.63 364	99.25 328	99.37 245
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		98.35 289	97.95 302	99.57 164	99.35 285	99.35 189	98.11 334	99.41 270	94.90 388	97.92 373	98.99 351	98.02 226	99.85 242	95.38 370	99.44 302	99.50 204

CR-MVSNet		98.35 289	98.20 284	98.83 313	99.05 351	98.12 308	99.30 140	99.67 148	97.39 344	99.16 284	99.79 96	91.87 352	99.91 141	98.78 165	98.77 358	98.44 379

WB-MVSnew		98.34 291	98.14 290	98.96 291	98.14 405	97.90 326	98.27 319	97.26 391	98.63 252	98.80 325	98.00 394	97.77 244	99.90 159	97.37 275	98.98 345	99.09 313

DPM-MVS		98.28 292	97.94 306	99.32 237	99.36 283	99.11 227	97.31 385	98.78 347	96.88 361	98.84 320	99.11 337	97.77 244	99.61 379	94.03 389	99.36 313	99.23 277

alignmvs		98.28 292	97.96 301	99.25 255	99.12 338	98.93 248	99.03 226	98.42 366	99.64 105	98.72 333	97.85 396	90.86 365	99.62 375	98.88 152	99.13 334	99.19 289

test_yl		98.25 294	97.95 302	99.13 271	99.17 331	98.47 284	99.00 234	98.67 353	98.97 206	99.22 276	99.02 349	91.31 356	99.69 344	97.26 284	98.93 347	99.24 273

DCV-MVSNet		98.25 294	97.95 302	99.13 271	99.17 331	98.47 284	99.00 234	98.67 353	98.97 206	99.22 276	99.02 349	91.31 356	99.69 344	97.26 284	98.93 347	99.24 273

MAR-MVS		98.24 296	97.92 308	99.19 262	98.78 378	99.65 108	99.17 181	99.14 329	95.36 382	98.04 370	98.81 370	97.47 260	99.72 331	95.47 368	99.06 338	98.21 388
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
OpenMVS	98.12 10	98.23 297	97.89 311	99.26 252	99.19 327	99.26 204	99.65 60	99.69 141	91.33 400	98.14 367	99.77 111	98.28 202	99.96 56	95.41 369	99.55 281	98.58 370

BH-untuned		98.22 298	98.09 293	98.58 329	99.38 278	97.24 348	98.55 295	98.98 340	97.81 325	99.20 283	98.76 372	97.01 281	99.65 371	94.83 377	98.33 376	98.86 352

HY-MVS	98.23 9	98.21 299	97.95 302	98.99 288	99.03 353	98.24 298	99.61 70	98.72 349	96.81 364	98.73 332	99.51 251	94.06 327	99.86 225	96.91 304	98.20 381	98.86 352

Syy-MVS		98.17 300	97.85 312	99.15 267	98.50 393	98.79 260	98.60 284	99.21 321	97.89 318	96.76 394	96.37 415	95.47 316	99.57 383	99.10 131	98.73 364	99.09 313

EPNet		98.13 301	97.77 316	99.18 264	94.57 414	97.99 318	99.24 161	97.96 378	99.74 76	97.29 387	99.62 200	93.13 339	99.97 35	98.59 177	99.83 171	99.58 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SCA		98.11 302	98.36 271	97.36 369	99.20 325	92.99 397	98.17 327	98.49 363	98.24 296	99.10 294	99.57 232	96.01 310	99.94 80	96.86 307	99.62 260	99.14 303

Patchmatch-test		98.10 303	97.98 300	98.48 332	99.27 312	96.48 363	99.40 114	99.07 333	98.81 231	99.23 273	99.57 232	90.11 373	99.87 206	96.69 316	99.64 257	99.09 313

pmmvs3		98.08 304	97.80 313	98.91 300	99.41 273	97.69 335	97.87 360	99.66 153	95.87 375	99.50 215	99.51 251	90.35 371	99.97 35	98.55 179	99.47 299	99.08 319

JIA-IIPM		98.06 305	97.92 308	98.50 331	98.59 389	97.02 354	98.80 267	98.51 361	99.88 44	97.89 375	99.87 48	91.89 351	99.90 159	98.16 208	97.68 395	98.59 368

miper_enhance_ethall		98.03 306	97.94 306	98.32 340	98.27 399	96.43 365	96.95 394	99.41 270	96.37 370	99.43 230	98.96 358	94.74 321	99.69 344	97.71 247	99.62 260	98.83 355

TAPA-MVS	97.92 13	98.03 306	97.55 322	99.46 193	99.47 253	99.44 161	98.50 303	99.62 174	86.79 403	99.07 298	99.26 313	98.26 206	99.62 375	97.28 281	99.73 224	99.31 262
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
1314		98.00 308	97.90 310	98.27 344	98.90 363	97.45 342	99.30 140	99.06 335	94.98 387	97.21 389	99.12 334	98.43 182	99.67 361	95.58 366	98.56 371	97.71 397

GA-MVS		97.99 309	97.68 319	98.93 297	99.52 229	98.04 316	97.19 389	99.05 336	98.32 292	98.81 323	98.97 356	89.89 376	99.41 398	98.33 191	99.05 340	99.34 254

MVS-HIRNet		97.86 310	98.22 282	96.76 378	99.28 310	91.53 405	98.38 312	92.60 408	99.13 191	99.31 260	99.96 12	97.18 276	99.68 356	98.34 190	99.83 171	99.07 324

FE-MVS		97.85 311	97.42 324	99.15 267	99.44 262	98.75 263	99.77 16	98.20 375	95.85 376	99.33 253	99.80 86	88.86 379	99.88 192	96.40 335	99.12 335	98.81 356

AUN-MVS		97.82 312	97.38 325	99.14 270	99.27 312	98.53 281	98.72 276	99.02 337	98.10 303	97.18 390	99.03 348	89.26 378	99.85 242	97.94 223	97.91 391	99.03 330

FMVSNet5		97.80 313	97.25 329	99.42 205	98.83 370	98.97 242	99.38 118	99.80 82	98.87 222	99.25 269	99.69 155	80.60 400	99.91 141	98.96 145	99.90 116	99.38 242

ADS-MVSNet2		97.78 314	97.66 321	98.12 348	99.14 334	95.36 380	99.22 168	98.75 348	96.97 359	98.25 359	99.64 182	90.90 363	99.94 80	96.51 328	99.56 277	99.08 319

test1111		97.74 315	98.16 289	96.49 383	99.60 185	89.86 413	99.71 35	91.21 409	99.89 39	99.88 63	99.87 48	93.73 333	99.90 159	99.56 57	99.99 16	99.70 78

ECVR-MVS		97.73 316	98.04 295	96.78 377	99.59 189	90.81 409	99.72 31	90.43 411	99.89 39	99.86 72	99.86 54	93.60 335	99.89 177	99.46 71	99.99 16	99.65 112

baseline1		97.73 316	97.33 326	98.96 291	99.30 305	97.73 333	99.40 114	98.42 366	99.33 156	99.46 224	99.21 324	91.18 358	99.82 281	98.35 189	91.26 409	99.32 258

tpmrst		97.73 316	98.07 294	96.73 380	98.71 385	92.00 401	99.10 208	98.86 342	98.52 265	98.92 310	99.54 245	91.90 350	99.82 281	98.02 214	99.03 342	98.37 381

ADS-MVSNet		97.72 319	97.67 320	97.86 356	99.14 334	94.65 388	99.22 168	98.86 342	96.97 359	98.25 359	99.64 182	90.90 363	99.84 257	96.51 328	99.56 277	99.08 319

PatchmatchNet		97.65 320	97.80 313	97.18 374	98.82 373	92.49 399	99.17 181	98.39 368	98.12 302	98.79 327	99.58 225	90.71 367	99.89 177	97.23 289	99.41 307	99.16 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tttt0517		97.62 321	97.20 330	98.90 306	99.76 117	97.40 344	99.48 101	94.36 403	99.06 200	99.70 138	99.49 258	84.55 395	99.94 80	98.73 169	99.65 255	99.36 248

EPNet_dtu		97.62 321	97.79 315	97.11 376	96.67 411	92.31 400	98.51 302	98.04 376	99.24 169	95.77 403	99.47 265	93.78 332	99.66 365	98.98 141	99.62 260	99.37 245
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		97.58 323	99.13 155	92.93 390	99.69 155	99.49 148	99.52 89	99.77 97	97.97 312	99.96 23	99.79 96	99.84 12	99.94 80	95.85 359	99.82 180	79.36 408

cl22		97.56 324	97.28 327	98.40 335	98.37 397	96.75 360	97.24 388	99.37 285	97.31 348	99.41 238	99.22 322	87.30 382	99.37 399	97.70 250	99.62 260	99.08 319

PAPR		97.56 324	97.07 332	99.04 285	98.80 374	98.11 310	97.63 369	99.25 311	94.56 393	98.02 371	98.25 389	97.43 262	99.68 356	90.90 398	98.74 362	99.33 255

thisisatest0530		97.45 326	96.95 336	98.94 294	99.68 163	97.73 333	99.09 212	94.19 405	98.61 256	99.56 194	99.30 304	84.30 396	99.93 97	98.27 195	99.54 286	99.16 295

TR-MVS		97.44 327	97.15 331	98.32 340	98.53 391	97.46 341	98.47 305	97.91 380	96.85 362	98.21 362	98.51 383	96.42 298	99.51 393	92.16 394	97.29 398	97.98 394

tpmvs		97.39 328	97.69 318	96.52 382	98.41 395	91.76 402	99.30 140	98.94 341	97.74 326	97.85 378	99.55 243	92.40 349	99.73 329	96.25 342	98.73 364	98.06 393

test0.0.03 1		97.37 329	96.91 339	98.74 320	97.72 407	97.57 337	97.60 371	97.36 390	98.00 308	99.21 278	98.02 392	90.04 374	99.79 306	98.37 187	95.89 406	98.86 352

OpenMVS_ROB	97.31 17	97.36 330	96.84 340	98.89 307	99.29 307	99.45 159	98.87 253	99.48 253	86.54 405	99.44 226	99.74 122	97.34 267	99.86 225	91.61 395	99.28 324	97.37 401

dmvs_testset		97.27 331	96.83 341	98.59 327	99.46 257	97.55 338	99.25 160	96.84 393	98.78 236	97.24 388	97.67 398	97.11 278	98.97 404	86.59 408	98.54 372	99.27 268

BH-w/o		97.20 332	97.01 334	97.76 359	99.08 349	95.69 376	98.03 344	98.52 360	95.76 378	97.96 372	98.02 392	95.62 314	99.47 395	92.82 393	97.25 399	98.12 392

test-LLR		97.15 333	96.95 336	97.74 361	98.18 402	95.02 385	97.38 381	96.10 394	98.00 308	97.81 380	98.58 377	90.04 374	99.91 141	97.69 256	98.78 356	98.31 382

tpm		97.15 333	96.95 336	97.75 360	98.91 362	94.24 390	99.32 132	97.96 378	97.71 328	98.29 357	99.32 300	86.72 390	99.92 119	98.10 212	96.24 405	99.09 313

E-PMN		97.14 335	97.43 323	96.27 385	98.79 376	91.62 404	95.54 403	99.01 339	99.44 139	98.88 314	99.12 334	92.78 343	99.68 356	94.30 384	99.03 342	97.50 398

cascas		96.99 336	96.82 342	97.48 365	97.57 410	95.64 377	96.43 400	99.56 214	91.75 398	97.13 392	97.61 402	95.58 315	98.63 406	96.68 317	99.11 336	98.18 391

thisisatest0515		96.98 337	96.42 344	98.66 324	99.42 271	97.47 340	97.27 386	94.30 404	97.24 350	99.15 286	98.86 366	85.01 393	99.87 206	97.10 295	99.39 309	98.63 364

EMVS		96.96 338	97.28 327	95.99 388	98.76 381	91.03 407	95.26 405	98.61 356	99.34 154	98.92 310	98.88 365	93.79 331	99.66 365	92.87 392	99.05 340	97.30 402

dp		96.86 339	97.07 332	96.24 386	98.68 387	90.30 412	99.19 175	98.38 369	97.35 346	98.23 361	99.59 222	87.23 383	99.82 281	96.27 341	98.73 364	98.59 368

baseline2		96.83 340	96.28 346	98.46 333	99.09 348	96.91 357	98.83 259	93.87 407	97.23 351	96.23 402	98.36 386	88.12 381	99.90 159	96.68 317	98.14 386	98.57 371

ET-MVSNet_ETH3D		96.78 341	96.07 350	98.91 300	99.26 314	97.92 325	97.70 367	96.05 397	97.96 315	92.37 409	98.43 385	87.06 384	99.90 159	98.27 195	97.56 396	98.91 346

tpm cat1		96.78 341	96.98 335	96.16 387	98.85 369	90.59 411	99.08 215	99.32 294	92.37 396	97.73 384	99.46 268	91.15 359	99.69 344	96.07 348	98.80 355	98.21 388

PCF-MVS	96.03 18	96.73 343	95.86 354	99.33 233	99.44 262	99.16 222	96.87 396	99.44 264	86.58 404	98.95 305	99.40 279	94.38 325	99.88 192	87.93 402	99.80 194	98.95 341
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		96.71 344	96.79 343	96.46 384	98.90 363	90.71 410	99.41 113	98.68 351	94.69 392	98.14 367	99.34 299	86.32 392	99.80 303	97.60 261	98.07 389	98.88 350

MVE	92.54 22	96.66 345	96.11 349	98.31 342	99.68 163	97.55 338	97.94 354	95.60 399	99.37 151	90.68 410	98.70 375	96.56 292	98.61 407	86.94 407	99.55 281	98.77 361
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres600view7		96.60 346	96.16 348	97.93 353	99.63 178	96.09 372	99.18 176	97.57 385	98.77 238	98.72 333	97.32 404	87.04 385	99.72 331	88.57 400	98.62 369	97.98 394

EPMVS		96.53 347	96.32 345	97.17 375	98.18 402	92.97 398	99.39 116	89.95 412	98.21 298	98.61 342	99.59 222	86.69 391	99.72 331	96.99 299	99.23 332	98.81 356

testing3		96.48 348	95.63 359	99.01 287	99.23 319	97.81 329	98.90 250	99.10 332	98.72 243	97.84 379	97.92 395	72.44 412	99.85 242	97.21 291	99.33 317	99.35 251

thres400		96.40 349	95.89 352	97.92 354	99.58 194	96.11 370	99.00 234	97.54 388	98.43 272	98.52 349	96.98 407	86.85 387	99.67 361	87.62 403	98.51 373	97.98 394

thres100view900		96.39 350	96.03 351	97.47 366	99.63 178	95.93 373	99.18 176	97.57 385	98.75 242	98.70 336	97.31 405	87.04 385	99.67 361	87.62 403	98.51 373	96.81 403

tpm2		96.35 351	96.22 347	96.73 380	98.88 368	91.75 403	99.21 170	98.51 361	93.27 395	97.89 375	99.21 324	84.83 394	99.70 338	96.04 349	98.18 384	98.75 362

FPMVS		96.32 352	95.50 360	98.79 317	99.60 185	98.17 306	98.46 309	98.80 346	97.16 355	96.28 399	99.63 193	82.19 397	99.09 402	88.45 401	98.89 354	99.10 309

tfpn200view9		96.30 353	95.89 352	97.53 363	99.58 194	96.11 370	99.00 234	97.54 388	98.43 272	98.52 349	96.98 407	86.85 387	99.67 361	87.62 403	98.51 373	96.81 403

TESTMET0.1,1		96.24 354	95.84 355	97.41 368	98.24 400	93.84 393	97.38 381	95.84 398	98.43 272	97.81 380	98.56 380	79.77 402	99.89 177	97.77 239	98.77 358	98.52 373

test-mter		96.23 355	95.73 357	97.74 361	98.18 402	95.02 385	97.38 381	96.10 394	97.90 317	97.81 380	98.58 377	79.12 405	99.91 141	97.69 256	98.78 356	98.31 382

UWE-MVS		96.21 356	95.78 356	97.49 364	98.53 391	93.83 394	98.04 342	93.94 406	98.96 208	98.46 353	98.17 390	79.86 401	99.87 206	96.99 299	99.06 338	98.78 359

ETVMVS		96.14 357	95.22 367	98.89 307	98.80 374	98.01 317	98.66 280	98.35 371	98.71 245	97.18 390	96.31 417	74.23 411	99.75 323	96.64 322	98.13 388	98.90 347

X-MVStestdata		96.09 358	94.87 370	99.75 73	99.71 143	99.71 83	99.37 122	99.61 181	99.29 159	98.76 330	61.30 418	98.47 176	99.88 192	97.62 258	99.73 224	99.67 94

thres200		96.09 358	95.68 358	97.33 371	99.48 247	96.22 369	98.53 300	97.57 385	98.06 307	98.37 356	96.73 411	86.84 389	99.61 379	86.99 406	98.57 370	96.16 406

testing11		96.05 360	95.41 362	97.97 351	98.78 378	95.27 382	98.59 287	98.23 374	98.86 224	96.56 397	96.91 409	75.20 408	99.69 344	97.26 284	98.29 378	98.93 343

testing91		96.00 361	95.32 365	98.02 349	98.76 381	95.39 379	98.38 312	98.65 355	98.82 229	96.84 393	96.71 412	75.06 409	99.71 335	96.46 333	98.23 380	98.98 338

KD-MVS_2432*1600		95.89 362	95.41 362	97.31 372	94.96 412	93.89 391	97.09 391	99.22 318	97.23 351	98.88 314	99.04 344	79.23 403	99.54 387	96.24 343	96.81 400	98.50 377

miper_refine_blended		95.89 362	95.41 362	97.31 372	94.96 412	93.89 391	97.09 391	99.22 318	97.23 351	98.88 314	99.04 344	79.23 403	99.54 387	96.24 343	96.81 400	98.50 377

gg-mvs-nofinetune		95.87 364	95.17 369	97.97 351	98.19 401	96.95 355	99.69 43	89.23 413	99.89 39	96.24 401	99.94 16	81.19 398	99.51 393	93.99 390	98.20 381	97.44 399

testing99		95.86 365	95.19 368	97.87 355	98.76 381	95.03 384	98.62 281	98.44 365	98.68 247	96.67 396	96.66 413	74.31 410	99.69 344	96.51 328	98.03 390	98.90 347

PVSNet_0	95.53 19	95.85 366	95.31 366	97.47 366	98.78 378	93.48 396	95.72 402	99.40 277	96.18 373	97.37 385	97.73 397	95.73 312	99.58 382	95.49 367	81.40 410	99.36 248

tmp_tt		95.75 367	95.42 361	96.76 378	89.90 416	94.42 389	98.86 254	97.87 382	78.01 407	99.30 265	99.69 155	97.70 247	95.89 409	99.29 103	98.14 386	99.95 11

MVS		95.72 368	94.63 373	98.99 288	98.56 390	97.98 323	99.30 140	98.86 342	72.71 409	97.30 386	99.08 339	98.34 195	99.74 326	89.21 399	98.33 376	99.26 270

myMVS_eth3d		95.63 369	94.73 371	98.34 339	98.50 393	96.36 366	98.60 284	99.21 321	97.89 318	96.76 394	96.37 415	72.10 413	99.57 383	94.38 382	98.73 364	99.09 313

PAPM		95.61 370	94.71 372	98.31 342	99.12 338	96.63 361	96.66 399	98.46 364	90.77 401	96.25 400	98.68 376	93.01 341	99.69 344	81.60 409	97.86 394	98.62 365

testing222		95.60 371	94.59 374	98.61 325	98.66 388	97.45 342	98.54 298	97.90 381	98.53 264	96.54 398	96.47 414	70.62 415	99.81 296	95.91 358	98.15 385	98.56 372

IB-MVS	95.41 20	95.30 372	94.46 376	97.84 357	98.76 381	95.33 381	97.33 384	96.07 396	96.02 374	95.37 406	97.41 403	76.17 407	99.96 56	97.54 264	95.44 408	98.22 387
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		94.73 373	94.59 374	95.15 389	99.59 189	85.90 415	99.75 23	74.01 417	99.89 39	99.71 134	99.86 54	79.00 406	99.90 159	99.52 65	99.99 16	99.65 112

test_method		91.72 374	92.32 377	89.91 392	93.49 415	70.18 418	90.28 406	99.56 214	61.71 410	95.39 405	99.52 249	93.90 328	99.94 80	98.76 166	98.27 379	99.62 138

dongtai		89.37 375	88.91 378	90.76 391	99.19 327	77.46 416	95.47 404	87.82 415	92.28 397	94.17 408	98.82 369	71.22 414	95.54 410	63.85 410	97.34 397	99.27 268

EGC-MVSNET		89.05 376	85.52 379	99.64 129	99.89 38	99.78 49	99.56 84	99.52 240	24.19 411	49.96 412	99.83 69	99.15 81	99.92 119	97.71 247	99.85 158	99.21 282

kuosan		85.65 377	84.57 380	88.90 393	97.91 406	77.11 417	96.37 401	87.62 416	85.24 406	85.45 411	96.83 410	69.94 416	90.98 412	45.90 411	95.83 407	98.62 365

test123		29.31 378	33.05 383	18.08 394	25.93 418	12.24 419	97.53 375	10.93 419	11.78 412	24.21 413	50.08 422	21.04 417	8.60 413	23.51 412	32.43 412	33.39 409

testmvs		28.94 379	33.33 381	15.79 395	26.03 417	9.81 420	96.77 397	15.67 418	11.55 413	23.87 414	50.74 421	19.03 418	8.53 414	23.21 413	33.07 411	29.03 410

cdsmvs_eth3d_5k		24.88 380	33.17 382	0.00 396	0.00 419	0.00 421	0.00 407	99.62 174	0.00 414	0.00 415	99.13 330	99.82 13	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		16.61 381	22.14 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	100.00 1	99.28 66	0.00 415	0.00 414	0.00 413	0.00 411

test_blank		8.33 382	11.11 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	100.00 1	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		8.33 382	11.11 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	100.00 1	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		8.33 382	11.11 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	100.00 1	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		8.33 382	11.11 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	100.00 1	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		8.33 382	11.11 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	100.00 1	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		8.33 382	11.11 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	100.00 1	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		8.33 382	11.11 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	100.00 1	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		8.33 382	11.11 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	100.00 1	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

ab-mvs-re		8.26 390	11.02 393	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	99.16 328	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						96.36 366								95.20 373

FOURS1					99.83 65	99.89 10	99.74 25	99.71 129	99.69 91	99.63 161

MSC_two_6792asdad				99.74 78	99.03 353	99.53 144		99.23 315					99.92 119	97.77 239	99.69 239	99.78 55

PC_three_1452									97.56 332	99.68 144	99.41 275	99.09 89	97.09 408	96.66 319	99.60 270	99.62 138

No_MVS				99.74 78	99.03 353	99.53 144		99.23 315					99.92 119	97.77 239	99.69 239	99.78 55

test_one_0601					99.63 178	99.76 61		99.55 220	99.23 171	99.31 260	99.61 209	98.59 157

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					99.43 266	99.61 125		99.43 267	96.38 369	99.11 292	99.07 340	97.86 237	99.92 119	94.04 388	99.49 297

RE-MVS-def			99.13 155		99.54 218	99.74 73	99.26 154	99.62 174	99.16 185	99.52 208	99.64 182	98.57 160		97.27 282	99.61 267	99.54 182

IU-MVS					99.69 155	99.77 54		99.22 318	97.50 338	99.69 141				97.75 243	99.70 235	99.77 59

OPU-MVS				99.29 244	99.12 338	99.44 161	99.20 171				99.40 279	99.00 103	98.84 405	96.54 326	99.60 270	99.58 164

test_241102_TWO								99.54 226	99.13 191	99.76 111	99.63 193	98.32 199	99.92 119	97.85 234	99.69 239	99.75 68

test_241102_ONE					99.69 155	99.82 35		99.54 226	99.12 194	99.82 82	99.49 258	98.91 115	99.52 392

9.14			98.64 243		99.45 261		98.81 264	99.60 192	97.52 337	99.28 266	99.56 236	98.53 169	99.83 272	95.36 371	99.64 257

save fliter					99.53 224	99.25 207	98.29 318	99.38 284	99.07 198

test_0728_THIRD									99.18 178	99.62 170	99.61 209	98.58 159	99.91 141	97.72 245	99.80 194	99.77 59

test_0728_SECOND				99.83 32	99.70 151	99.79 46	99.14 191	99.61 181					99.92 119	97.88 228	99.72 230	99.77 59

test0726					99.69 155	99.80 44	99.24 161	99.57 209	99.16 185	99.73 128	99.65 180	98.35 193

GSMVS																99.14 303

test_part2					99.62 182	99.67 99				99.55 199

sam_mvs1												90.81 366				99.14 303

sam_mvs												90.52 370

ambc				99.20 261	99.35 285	98.53 281	99.17 181	99.46 259		99.67 150	99.80 86	98.46 179	99.70 338	97.92 224	99.70 235	99.38 242

MTGPA								99.53 235

test_post1							99.14 191				51.63 420	89.54 377	99.82 281	96.86 307

test_post											52.41 419	90.25 372	99.86 225

patchmatchnet-post											99.62 200	90.58 368	99.94 80

GG-mvs-BLEND				97.36 369	97.59 408	96.87 358	99.70 36	88.49 414		94.64 407	97.26 406	80.66 399	99.12 401	91.50 396	96.50 404	96.08 407

MTMP							99.09 212	98.59 359

gm-plane-assit					97.59 408	89.02 414			93.47 394		98.30 387		99.84 257	96.38 337

test9_res														95.10 375	99.44 302	99.50 204

TEST9					99.35 285	99.35 189	98.11 334	99.41 270	94.83 391	97.92 373	98.99 351	98.02 226	99.85 242

test_8					99.34 294	99.31 195	98.08 338	99.40 277	94.90 388	97.87 377	98.97 356	98.02 226	99.84 257

agg_prior2														94.58 381	99.46 301	99.50 204

agg_prior					99.35 285	99.36 186		99.39 280		97.76 383			99.85 242

TestCases				99.63 136	99.78 105	99.64 111		99.83 63	98.63 252	99.63 161	99.72 134	98.68 143	99.75 323	96.38 337	99.83 171	99.51 199

test_prior4						99.19 220	98.00 347

test_prior2							97.95 353		97.87 321	98.05 369	99.05 342	97.90 234		95.99 353	99.49 297

test_prior				99.46 193	99.35 285	99.22 214		99.39 280					99.69 344			99.48 213

旧先验2							97.94 354		95.33 383	98.94 306			99.88 192	96.75 313

新几何2							98.04 342

新几何1				99.52 179	99.50 237	99.22 214		99.26 308	95.66 380	98.60 343	99.28 308	97.67 251	99.89 177	95.95 356	99.32 319	99.45 222

旧先验1					99.49 242	99.29 198		99.26 308			99.39 283	97.67 251			99.36 313	99.46 221

无先验							98.01 345	99.23 315	95.83 377				99.85 242	95.79 362		99.44 227

原ACMM2							97.92 356

原ACMM1				99.37 223	99.47 253	98.87 255		99.27 306	96.74 366	98.26 358	99.32 300	97.93 233	99.82 281	95.96 355	99.38 310	99.43 233

test222					99.51 231	99.08 234	97.83 362	99.29 302	95.21 385	98.68 337	99.31 302	97.28 269			99.38 310	99.43 233

testdata2													99.89 177	95.99 353

segment_acmp												98.37 191

testdata				99.42 205	99.51 231	98.93 248		99.30 301	96.20 372	98.87 317	99.40 279	98.33 197	99.89 177	96.29 340	99.28 324	99.44 227

testdata1							97.72 365		97.86 323

test12				99.54 176	99.29 307	99.33 192		99.16 327		98.43 354		97.54 258	99.82 281		99.47 299	99.48 213

plane_prior7					99.58 194	99.38 179

plane_prior6					99.47 253	99.26 204						97.24 270

plane_prior5								99.54 226					99.82 281	95.84 360	99.78 204	99.60 152

plane_prior4											99.25 315

plane_prior3						99.31 195			98.36 281	99.14 288

plane_prior2							98.80 267		98.94 211

plane_prior1					99.51 231

plane_prior						99.24 211	98.42 310		97.87 321						99.71 233

n2								0.00 420

nn								0.00 420

door-mid								99.83 63

lessismore_v0				99.64 129	99.86 53	99.38 179		90.66 410		99.89 55	99.83 69	94.56 324	99.97 35	99.56 57	99.92 104	99.57 169

LGP-MVS_train				99.74 78	99.82 72	99.63 116		99.73 117	97.56 332	99.64 157	99.69 155	99.37 56	99.89 177	96.66 319	99.87 146	99.69 82

test11								99.29 302

door								99.77 97

HQP5-MVS						98.94 245

HQP-NCC					99.31 301		97.98 349		97.45 340	98.15 363

ACMP_Plane					99.31 301		97.98 349		97.45 340	98.15 363

BP-MVS														94.73 378

HQP4-MVS										98.15 363			99.70 338			99.53 187

HQP3-MVS								99.37 285							99.67 250

HQP2-MVS												96.67 289

NP-MVS					99.40 274	99.13 225					98.83 367

MDTV_nov1_ep13_2view						91.44 406	99.14 191		97.37 345	99.21 278		91.78 354		96.75 313		99.03 330

MDTV_nov1_ep13			97.73 317		98.70 386	90.83 408	99.15 189	98.02 377	98.51 266	98.82 322	99.61 209	90.98 361	99.66 365	96.89 306	98.92 349

ACMMP++_ref															99.94 92

ACMMP++															99.79 199

Test By Simon												98.41 185

ITE_SJBPF				99.38 220	99.63 178	99.44 161		99.73 117	98.56 259	99.33 253	99.53 247	98.88 119	99.68 356	96.01 350	99.65 255	99.02 335

DeepMVS_CX				97.98 350	99.69 155	96.95 355		99.26 308	75.51 408	95.74 404	98.28 388	96.47 296	99.62 375	91.23 397	97.89 392	97.38 400

High-resolution multi-view benchmark