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 2	99.99 1	100.00 1	99.98 10	99.78 6	100.00 1	99.92 1	100.00 1	99.87 10

mvs_tets		99.90 2	99.90 2	99.90 5	99.96 4	99.79 43	99.72 29	99.88 19	99.92 10	99.98 3	99.93 15	99.94 1	99.98 9	99.77 12	100.00 1	99.92 3

jajsoiax		99.89 3	99.89 3	99.89 9	99.96 4	99.78 46	99.70 34	99.86 24	99.89 17	99.98 3	99.90 23	99.94 1	99.98 9	99.75 13	100.00 1	99.90 4

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 57	100.00 1	99.90 12	100.00 1	99.97 11	99.61 17	99.97 19	99.75 13	100.00 1	99.84 15

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 13	99.91 20	99.90 5	99.96 1	99.92 9	99.90 12	99.97 6	99.87 34	99.81 5	99.95 47	99.54 34	99.99 12	99.80 26
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
pmmvs6		99.86 6	99.86 6	99.83 26	99.94 11	99.90 5	99.83 6	99.91 12	99.85 32	99.94 12	99.95 13	99.73 8	99.90 142	99.65 19	99.97 38	99.69 60

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 5	99.89 26	99.91 2	99.89 4	99.71 98	99.93 8	99.95 11	99.89 27	99.71 9	99.96 37	99.51 39	99.97 38	99.84 15

PS-MVSNAJss		99.84 8	99.82 8	99.89 9	99.96 4	99.77 49	99.68 43	99.85 28	99.95 5	99.98 3	99.92 18	99.28 43	99.98 9	99.75 13	100.00 1	99.94 2

test_djsdf		99.84 8	99.81 9	99.91 2	99.94 11	99.84 22	99.77 14	99.80 52	99.73 53	99.97 6	99.92 18	99.77 7	99.98 9	99.43 47	100.00 1	99.90 4

v7n		99.82 10	99.80 10	99.88 13	99.96 4	99.84 22	99.82 8	99.82 41	99.84 35	99.94 12	99.91 21	99.13 61	99.96 37	99.83 9	99.99 12	99.83 19

pm-mvs1		99.79 13	99.79 11	99.78 42	99.91 20	99.83 27	99.76 17	99.87 21	99.73 53	99.89 32	99.87 34	99.63 14	99.87 185	99.54 34	99.92 84	99.63 105

bld_raw_conf005		99.81 11	99.79 11	99.86 18	99.94 11	99.85 15	99.77 14	99.90 15	99.97 2	99.92 19	99.86 41	99.21 50	99.94 62	99.59 24	99.98 26	99.78 34

anonymousdsp		99.80 12	99.77 13	99.90 5	99.96 4	99.88 9	99.73 26	99.85 28	99.70 61	99.92 19	99.93 15	99.45 25	99.97 19	99.36 59	100.00 1	99.85 14

TransMVSNet (Re)		99.78 14	99.77 13	99.81 31	99.91 20	99.85 15	99.75 20	99.86 24	99.70 61	99.91 22	99.89 27	99.60 19	99.87 185	99.59 24	99.74 197	99.71 53

UA-Net		99.78 14	99.76 15	99.86 18	99.72 116	99.71 76	99.91 3	99.95 8	99.96 3	99.71 108	99.91 21	99.15 56	99.97 19	99.50 41	100.00 1	99.90 4

Vis-MVSNet		99.75 16	99.74 16	99.79 39	99.88 30	99.66 94	99.69 40	99.92 9	99.67 70	99.77 79	99.75 93	99.61 17	99.98 9	99.35 60	99.98 26	99.72 50
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OurMVSNet-221017-0		99.75 16	99.71 17	99.84 24	99.96 4	99.83 27	99.83 6	99.85 28	99.80 44	99.93 15	99.93 15	98.54 142	99.93 78	99.59 24	99.98 26	99.76 44

test_low_dy_conf_001		99.75 16	99.70 18	99.90 5	99.94 11	99.85 15	99.74 22	99.54 199	99.88 22	99.90 27	99.89 27	98.84 97	99.95 47	99.59 24	99.98 26	99.90 4

CS-MVS		99.67 31	99.70 18	99.58 143	99.53 195	99.84 22	99.79 10	99.96 6	99.90 12	99.61 149	99.41 253	99.51 24	99.95 47	99.66 18	99.89 103	98.96 314

CS-MVS-test		99.68 28	99.70 18	99.64 116	99.57 177	99.83 27	99.78 11	99.97 2	99.92 10	99.50 188	99.38 263	99.57 20	99.95 47	99.69 16	99.90 94	99.15 281

mvsmamba		99.74 19	99.70 18	99.85 21	99.93 17	99.83 27	99.76 17	99.81 50	99.96 3	99.91 22	99.81 61	98.60 133	99.94 62	99.58 29	99.98 26	99.77 39

TDRefinement		99.72 20	99.70 18	99.77 45	99.90 24	99.85 15	99.86 5	99.92 9	99.69 64	99.78 74	99.92 18	99.37 33	99.88 173	98.93 122	99.95 61	99.60 130

v8		99.68 28	99.69 23	99.65 109	99.80 63	99.40 160	99.66 51	99.76 71	99.64 78	99.93 15	99.85 45	98.66 125	99.84 240	99.88 6	99.99 12	99.71 53

v10		99.69 25	99.69 23	99.66 104	99.81 58	99.39 162	99.66 51	99.75 78	99.60 92	99.92 19	99.87 34	98.75 114	99.86 205	99.90 2	99.99 12	99.73 49

DROMVSNet		99.69 25	99.69 23	99.68 94	99.71 119	99.91 2	99.76 17	99.96 6	99.86 27	99.51 186	99.39 261	99.57 20	99.93 78	99.64 21	99.86 128	99.20 270

XXY-MVS		99.71 21	99.67 26	99.81 31	99.89 26	99.72 74	99.59 71	99.82 41	99.39 122	99.82 56	99.84 50	99.38 31	99.91 122	99.38 54	99.93 80	99.80 26

GeoE		99.69 25	99.66 27	99.78 42	99.76 92	99.76 57	99.60 70	99.82 41	99.46 111	99.75 89	99.56 210	99.63 14	99.95 47	99.43 47	99.88 112	99.62 116

nrg030		99.70 22	99.66 27	99.82 28	99.76 92	99.84 22	99.61 65	99.70 104	99.93 8	99.78 74	99.68 136	99.10 62	99.78 288	99.45 45	99.96 52	99.83 19

bld_raw_dy_0_64		99.70 22	99.65 29	99.85 21	99.95 10	99.77 49	99.66 51	99.71 98	99.95 5	99.91 22	99.77 84	98.35 170	100.00 1	99.54 34	99.99 12	99.79 32

FC-MVSNet-test		99.70 22	99.65 29	99.86 18	99.88 30	99.86 14	99.72 29	99.78 63	99.90 12	99.82 56	99.83 51	98.45 157	99.87 185	99.51 39	99.97 38	99.86 12

DSMNet-mixed		99.48 63	99.65 29	98.95 271	99.71 119	97.27 323	99.50 82	99.82 41	99.59 94	99.41 213	99.85 45	99.62 16	100.00 1	99.53 37	99.89 103	99.59 139

dcpmvs_2		99.61 44	99.64 32	99.53 162	99.79 73	98.82 249	99.58 73	99.97 2	99.95 5	99.96 8	99.76 88	98.44 158	99.99 6	99.34 61	99.96 52	99.78 34

FMVSNet1		99.66 33	99.63 33	99.73 78	99.78 80	99.77 49	99.68 43	99.70 104	99.67 70	99.82 56	99.83 51	98.98 79	99.90 142	99.24 78	99.97 38	99.53 170

EU-MVSNet		99.39 89	99.62 34	98.72 298	99.88 30	96.44 339	99.56 77	99.85 28	99.90 12	99.90 27	99.85 45	98.09 195	99.83 251	99.58 29	99.95 61	99.90 4

VPA-MVSNet		99.66 33	99.62 34	99.79 39	99.68 139	99.75 61	99.62 60	99.69 110	99.85 32	99.80 66	99.81 61	98.81 99	99.91 122	99.47 43	99.88 112	99.70 56

baseline		99.63 39	99.62 34	99.66 104	99.80 63	99.62 107	99.44 92	99.80 52	99.71 57	99.72 103	99.69 125	99.15 56	99.83 251	99.32 67	99.94 72	99.53 170

MIMVSNet1		99.66 33	99.62 34	99.80 34	99.94 11	99.87 11	99.69 40	99.77 66	99.78 49	99.93 15	99.89 27	97.94 208	99.92 98	99.65 19	99.98 26	99.62 116

casdiffmvs		99.63 39	99.61 38	99.67 97	99.79 73	99.59 118	99.13 178	99.85 28	99.79 47	99.76 81	99.72 105	99.33 38	99.82 261	99.21 81	99.94 72	99.59 139

DTE-MVSNet		99.68 28	99.61 38	99.88 13	99.80 63	99.87 11	99.67 47	99.71 98	99.72 56	99.84 51	99.78 77	98.67 123	99.97 19	99.30 71	99.95 61	99.80 26

DeepC-MVS	98.90 4	99.62 42	99.61 38	99.67 97	99.72 116	99.44 148	99.24 141	99.71 98	99.27 136	99.93 15	99.90 23	99.70 11	99.93 78	98.99 110	99.99 12	99.64 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
KD-MVS_self_test		99.63 39	99.59 41	99.76 52	99.84 40	99.90 5	99.37 104	99.79 58	99.83 38	99.88 38	99.85 45	98.42 161	99.90 142	99.60 23	99.73 204	99.49 194

RRT_MVS		99.67 31	99.59 41	99.91 2	99.94 11	99.88 9	99.78 11	99.27 291	99.87 24	99.91 22	99.87 34	98.04 199	99.96 37	99.68 17	99.99 12	99.90 4

PEN-MVS		99.66 33	99.59 41	99.89 9	99.83 44	99.87 11	99.66 51	99.73 86	99.70 61	99.84 51	99.73 99	98.56 139	99.96 37	99.29 74	99.94 72	99.83 19

Gipuma		99.57 47	99.59 41	99.49 173	99.98 3	99.71 76	99.72 29	99.84 34	99.81 41	99.94 12	99.78 77	98.91 88	99.71 313	98.41 154	99.95 61	99.05 305
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FIs		99.65 38	99.58 45	99.84 24	99.84 40	99.85 15	99.66 51	99.75 78	99.86 27	99.74 98	99.79 70	98.27 179	99.85 223	99.37 57	99.93 80	99.83 19

v1240		99.56 50	99.58 45	99.51 167	99.80 63	99.00 229	99.00 206	99.65 132	99.15 161	99.90 27	99.75 93	99.09 64	99.88 173	99.90 2	99.96 52	99.67 73

PS-CasMVS		99.66 33	99.58 45	99.89 9	99.80 63	99.85 15	99.66 51	99.73 86	99.62 82	99.84 51	99.71 112	98.62 129	99.96 37	99.30 71	99.96 52	99.86 12

new-patchmatchnet		99.35 99	99.57 48	98.71 300	99.82 51	96.62 337	98.55 265	99.75 78	99.50 100	99.88 38	99.87 34	99.31 39	99.88 173	99.43 47	100.00 1	99.62 116

Anonymous20231211		99.62 42	99.57 48	99.76 52	99.61 156	99.60 115	99.81 9	99.73 86	99.82 40	99.90 27	99.90 23	97.97 207	99.86 205	99.42 52	99.96 52	99.80 26

v1921920		99.56 50	99.57 48	99.55 156	99.75 103	99.11 217	99.05 196	99.61 150	99.15 161	99.88 38	99.71 112	99.08 68	99.87 185	99.90 2	99.97 38	99.66 83

v1192		99.57 47	99.57 48	99.57 149	99.77 88	99.22 203	99.04 198	99.60 163	99.18 151	99.87 45	99.72 105	99.08 68	99.85 223	99.89 5	99.98 26	99.66 83

EG-PatchMatch MVS		99.57 47	99.56 52	99.62 132	99.77 88	99.33 178	99.26 134	99.76 71	99.32 131	99.80 66	99.78 77	99.29 41	99.87 185	99.15 94	99.91 93	99.66 83

v144192		99.55 53	99.54 53	99.58 143	99.78 80	99.20 209	99.11 184	99.62 143	99.18 151	99.89 32	99.72 105	98.66 125	99.87 185	99.88 6	99.97 38	99.66 83

V42		99.56 50	99.54 53	99.63 123	99.79 73	99.46 141	99.39 98	99.59 170	99.24 142	99.86 46	99.70 119	98.55 140	99.82 261	99.79 11	99.95 61	99.60 130

test20.03		99.55 53	99.54 53	99.58 143	99.79 73	99.37 168	99.02 202	99.89 17	99.60 92	99.82 56	99.62 171	98.81 99	99.89 158	99.43 47	99.86 128	99.47 204

ACMH	98.42 6	99.59 46	99.54 53	99.72 84	99.86 36	99.62 107	99.56 77	99.79 58	98.77 208	99.80 66	99.85 45	99.64 13	99.85 223	98.70 140	99.89 103	99.70 56
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1144		99.54 55	99.53 57	99.59 139	99.79 73	99.28 186	99.10 185	99.61 150	99.20 149	99.84 51	99.73 99	98.67 123	99.84 240	99.86 8	99.98 26	99.64 100

WR-MVS_H		99.61 44	99.53 57	99.87 16	99.80 63	99.83 27	99.67 47	99.75 78	99.58 95	99.85 48	99.69 125	98.18 191	99.94 62	99.28 76	99.95 61	99.83 19

EI-MVSNet-UG-set		99.48 63	99.50 59	99.42 194	99.57 177	98.65 264	99.24 141	99.46 240	99.68 66	99.80 66	99.66 145	98.99 78	99.89 158	99.19 85	99.90 94	99.72 50

EI-MVSNet-Vis-set		99.47 69	99.49 60	99.42 194	99.57 177	98.66 261	99.24 141	99.46 240	99.67 70	99.79 71	99.65 150	98.97 81	99.89 158	99.15 94	99.89 103	99.71 53

pmmvs-eth3d		99.48 63	99.47 61	99.51 167	99.77 88	99.41 159	98.81 237	99.66 122	99.42 121	99.75 89	99.66 145	99.20 51	99.76 298	98.98 112	99.99 12	99.36 238

v2v482		99.50 59	99.47 61	99.58 143	99.78 80	99.25 194	99.14 172	99.58 180	99.25 140	99.81 63	99.62 171	98.24 181	99.84 240	99.83 9	99.97 38	99.64 100

TranMVSNet+NR-MVSNet		99.54 55	99.47 61	99.76 52	99.58 167	99.64 101	99.30 121	99.63 140	99.61 86	99.71 108	99.56 210	98.76 112	99.96 37	99.14 100	99.92 84	99.68 66

IterMVS-LS		99.41 82	99.47 61	99.25 239	99.81 58	98.09 295	98.85 229	99.76 71	99.62 82	99.83 55	99.64 152	98.54 142	99.97 19	99.15 94	99.99 12	99.68 66
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
patch_mono-2		99.51 58	99.46 65	99.64 116	99.70 127	99.11 217	99.04 198	99.87 21	99.71 57	99.47 193	99.79 70	98.24 181	99.98 9	99.38 54	99.96 52	99.83 19

PMMVS2		99.48 63	99.45 66	99.57 149	99.76 92	98.99 230	98.09 306	99.90 15	98.95 183	99.78 74	99.58 197	99.57 20	99.93 78	99.48 42	99.95 61	99.79 32

TAMVS		99.49 61	99.45 66	99.63 123	99.48 223	99.42 155	99.45 89	99.57 182	99.66 74	99.78 74	99.83 51	97.85 217	99.86 205	99.44 46	99.96 52	99.61 126

Regformer-4		99.45 72	99.44 68	99.50 170	99.52 201	98.94 237	99.17 162	99.53 209	99.64 78	99.76 81	99.60 189	98.96 84	99.90 142	98.91 123	99.84 137	99.67 73

EI-MVSNet		99.38 91	99.44 68	99.21 244	99.58 167	98.09 295	99.26 134	99.46 240	99.62 82	99.75 89	99.67 141	98.54 142	99.85 223	99.15 94	99.92 84	99.68 66

MVSFormer		99.41 82	99.44 68	99.31 227	99.57 177	98.40 276	99.77 14	99.80 52	99.73 53	99.63 135	99.30 284	98.02 202	99.98 9	99.43 47	99.69 218	99.55 156

CP-MVSNet		99.54 55	99.43 71	99.87 16	99.76 92	99.82 33	99.57 75	99.61 150	99.54 96	99.80 66	99.64 152	97.79 221	99.95 47	99.21 81	99.94 72	99.84 15

ACMH+	98.40 8	99.50 59	99.43 71	99.71 88	99.86 36	99.76 57	99.32 114	99.77 66	99.53 98	99.77 79	99.76 88	99.26 47	99.78 288	97.77 210	99.88 112	99.60 130

Anonymous20240521		99.44 74	99.42 73	99.49 173	99.89 26	98.96 235	99.62 60	99.76 71	99.85 32	99.82 56	99.88 31	96.39 278	99.97 19	99.59 24	99.98 26	99.55 156

v148		99.40 85	99.41 74	99.39 206	99.76 92	98.94 237	99.09 190	99.59 170	99.17 155	99.81 63	99.61 180	98.41 162	99.69 321	99.32 67	99.94 72	99.53 170

Regformer-3		99.41 82	99.41 74	99.40 202	99.52 201	98.70 257	99.17 162	99.44 245	99.62 82	99.75 89	99.60 189	98.90 91	99.85 223	98.89 124	99.84 137	99.65 91

mvs_anonymous		99.28 116	99.39 76	98.94 272	99.19 304	97.81 308	99.02 202	99.55 193	99.78 49	99.85 48	99.80 64	98.24 181	99.86 205	99.57 31	99.50 276	99.15 281

DP-MVS		99.48 63	99.39 76	99.74 68	99.57 177	99.62 107	99.29 128	99.61 150	99.87 24	99.74 98	99.76 88	98.69 119	99.87 185	98.20 172	99.80 169	99.75 47

tfpnnormal		99.43 75	99.38 78	99.60 137	99.87 34	99.75 61	99.59 71	99.78 63	99.71 57	99.90 27	99.69 125	98.85 96	99.90 142	97.25 257	99.78 180	99.15 281

PVSNet_Blended_VisFu		99.40 85	99.38 78	99.44 188	99.90 24	98.66 261	98.94 220	99.91 12	97.97 278	99.79 71	99.73 99	99.05 73	99.97 19	99.15 94	99.99 12	99.68 66

ACMM	98.09 11	99.46 70	99.38 78	99.72 84	99.80 63	99.69 87	99.13 178	99.65 132	98.99 177	99.64 131	99.72 105	99.39 27	99.86 205	98.23 169	99.81 164	99.60 130
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPNet		99.46 70	99.37 81	99.71 88	99.82 51	99.59 118	99.48 86	99.70 104	99.81 41	99.69 114	99.58 197	97.66 233	99.86 205	99.17 90	99.44 284	99.67 73

Baseline_NR-MVSNet		99.49 61	99.37 81	99.82 28	99.91 20	99.84 22	98.83 232	99.86 24	99.68 66	99.65 129	99.88 31	97.67 229	99.87 185	99.03 107	99.86 128	99.76 44

COLMAP_ROB	98.06 12	99.45 72	99.37 81	99.70 92	99.83 44	99.70 83	99.38 100	99.78 63	99.53 98	99.67 121	99.78 77	99.19 52	99.86 205	97.32 247	99.87 121	99.55 156
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
APDe-MVS		99.48 63	99.36 84	99.85 21	99.55 189	99.81 36	99.50 82	99.69 110	98.99 177	99.75 89	99.71 112	98.79 106	99.93 78	98.46 152	99.85 132	99.80 26

3Dnovator	99.15 2	99.43 75	99.36 84	99.65 109	99.39 251	99.42 155	99.70 34	99.56 187	99.23 144	99.35 224	99.80 64	99.17 54	99.95 47	98.21 171	99.84 137	99.59 139

Anonymous20240529		99.42 78	99.34 86	99.65 109	99.53 195	99.60 115	99.63 59	99.39 262	99.47 107	99.76 81	99.78 77	98.13 193	99.86 205	98.70 140	99.68 223	99.49 194

xiu_mvs_v1_base_debu		99.23 127	99.34 86	98.91 278	99.59 162	98.23 284	98.47 274	99.66 122	99.61 86	99.68 116	98.94 343	99.39 27	99.97 19	99.18 87	99.55 263	98.51 341

xiu_mvs_v1_base		99.23 127	99.34 86	98.91 278	99.59 162	98.23 284	98.47 274	99.66 122	99.61 86	99.68 116	98.94 343	99.39 27	99.97 19	99.18 87	99.55 263	98.51 341

xiu_mvs_v1_base_debi		99.23 127	99.34 86	98.91 278	99.59 162	98.23 284	98.47 274	99.66 122	99.61 86	99.68 116	98.94 343	99.39 27	99.97 19	99.18 87	99.55 263	98.51 341

UGNet		99.38 91	99.34 86	99.49 173	98.90 339	98.90 245	99.70 34	99.35 273	99.86 27	98.57 321	99.81 61	98.50 152	99.93 78	99.38 54	99.98 26	99.66 83
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
diffmvs		99.34 104	99.32 91	99.39 206	99.67 144	98.77 253	98.57 263	99.81 50	99.61 86	99.48 191	99.41 253	98.47 153	99.86 205	98.97 114	99.90 94	99.53 170

Anonymous20231206		99.35 99	99.31 92	99.47 179	99.74 109	99.06 228	99.28 129	99.74 83	99.23 144	99.72 103	99.53 221	97.63 235	99.88 173	99.11 102	99.84 137	99.48 199

MVS_Test		99.28 116	99.31 92	99.19 247	99.35 261	98.79 252	99.36 107	99.49 230	99.17 155	99.21 254	99.67 141	98.78 108	99.66 341	99.09 103	99.66 234	99.10 292

NR-MVSNet		99.40 85	99.31 92	99.68 94	99.43 240	99.55 127	99.73 26	99.50 225	99.46 111	99.88 38	99.36 270	97.54 237	99.87 185	98.97 114	99.87 121	99.63 105

GBi-Net		99.42 78	99.31 92	99.73 78	99.49 217	99.77 49	99.68 43	99.70 104	99.44 114	99.62 143	99.83 51	97.21 252	99.90 142	98.96 116	99.90 94	99.53 170

test1		99.42 78	99.31 92	99.73 78	99.49 217	99.77 49	99.68 43	99.70 104	99.44 114	99.62 143	99.83 51	97.21 252	99.90 142	98.96 116	99.90 94	99.53 170

SD-MVS		99.01 187	99.30 97	98.15 320	99.50 212	99.40 160	98.94 220	99.61 150	99.22 148	99.75 89	99.82 58	99.54 23	95.51 380	97.48 239	99.87 121	99.54 164
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
HPM-MVS_fast		99.43 75	99.30 97	99.80 34	99.83 44	99.81 36	99.52 80	99.70 104	98.35 252	99.51 186	99.50 230	99.31 39	99.88 173	98.18 176	99.84 137	99.69 60

SixPastTwentyTwo		99.42 78	99.30 97	99.76 52	99.92 19	99.67 92	99.70 34	99.14 314	99.65 76	99.89 32	99.90 23	96.20 283	99.94 62	99.42 52	99.92 84	99.67 73

CHOSEN 1792x2688		99.39 89	99.30 97	99.65 109	99.88 30	99.25 194	98.78 244	99.88 19	98.66 216	99.96 8	99.79 70	97.45 240	99.93 78	99.34 61	99.99 12	99.78 34

DELS-MVS		99.34 104	99.30 97	99.48 177	99.51 206	99.36 171	98.12 302	99.53 209	99.36 126	99.41 213	99.61 180	99.22 49	99.87 185	99.21 81	99.68 223	99.20 270
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
PM-MVS		99.36 97	99.29 102	99.58 143	99.83 44	99.66 94	98.95 218	99.86 24	98.85 197	99.81 63	99.73 99	98.40 166	99.92 98	98.36 157	99.83 147	99.17 277

CSCG		99.37 94	99.29 102	99.60 137	99.71 119	99.46 141	99.43 94	99.85 28	98.79 205	99.41 213	99.60 189	98.92 86	99.92 98	98.02 186	99.92 84	99.43 221

SED-MVS		99.40 85	99.28 104	99.77 45	99.69 131	99.82 33	99.20 151	99.54 199	99.13 163	99.82 56	99.63 162	98.91 88	99.92 98	97.85 205	99.70 215	99.58 144

FMVSNet2		99.35 99	99.28 104	99.55 156	99.49 217	99.35 175	99.45 89	99.57 182	99.44 114	99.70 111	99.74 95	97.21 252	99.87 185	99.03 107	99.94 72	99.44 215

ab-mvs		99.33 108	99.28 104	99.47 179	99.57 177	99.39 162	99.78 11	99.43 249	98.87 195	99.57 160	99.82 58	98.06 198	99.87 185	98.69 142	99.73 204	99.15 281

Regformer-1		99.32 110	99.27 107	99.47 179	99.41 246	98.95 236	98.99 211	99.48 232	99.48 102	99.66 125	99.52 223	98.78 108	99.87 185	98.36 157	99.74 197	99.60 130

Regformer-2		99.34 104	99.27 107	99.53 162	99.41 246	99.10 222	98.99 211	99.53 209	99.47 107	99.66 125	99.52 223	98.80 103	99.89 158	98.31 163	99.74 197	99.60 130

testgi		99.29 115	99.26 109	99.37 213	99.75 103	98.81 250	98.84 230	99.89 17	98.38 245	99.75 89	99.04 326	99.36 36	99.86 205	99.08 104	99.25 311	99.45 210

UniMVSNet (Re)		99.37 94	99.26 109	99.68 94	99.51 206	99.58 121	98.98 215	99.60 163	99.43 119	99.70 111	99.36 270	97.70 224	99.88 173	99.20 84	99.87 121	99.59 139

DVP-MVS++		99.38 91	99.25 111	99.77 45	99.03 329	99.77 49	99.74 22	99.61 150	99.18 151	99.76 81	99.61 180	99.00 76	99.92 98	97.72 216	99.60 252	99.62 116

UniMVSNet_NR-MVSNet		99.37 94	99.25 111	99.72 84	99.47 228	99.56 124	98.97 216	99.61 150	99.43 119	99.67 121	99.28 289	97.85 217	99.95 47	99.17 90	99.81 164	99.65 91

TSAR-MVS + MP.		99.34 104	99.24 113	99.63 123	99.82 51	99.37 168	99.26 134	99.35 273	98.77 208	99.57 160	99.70 119	99.27 46	99.88 173	97.71 218	99.75 189	99.65 91
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
3Dnovator+	98.92 3	99.35 99	99.24 113	99.67 97	99.35 261	99.47 137	99.62 60	99.50 225	99.44 114	99.12 268	99.78 77	98.77 111	99.94 62	97.87 202	99.72 210	99.62 116

abl_6		99.36 97	99.23 115	99.75 62	99.71 119	99.74 67	99.33 111	99.76 71	99.07 170	99.65 129	99.63 162	99.09 64	99.92 98	97.13 264	99.76 186	99.58 144

DU-MVS		99.33 108	99.21 116	99.71 88	99.43 240	99.56 124	98.83 232	99.53 209	99.38 123	99.67 121	99.36 270	97.67 229	99.95 47	99.17 90	99.81 164	99.63 105

MTAPA		99.35 99	99.20 117	99.80 34	99.81 58	99.81 36	99.33 111	99.53 209	99.27 136	99.42 205	99.63 162	98.21 186	99.95 47	97.83 208	99.79 174	99.65 91

D2MVS		99.22 136	99.19 118	99.29 230	99.69 131	98.74 255	98.81 237	99.41 252	98.55 227	99.68 116	99.69 125	98.13 193	99.87 185	98.82 129	99.98 26	99.24 259

ETV-MVS		99.18 150	99.18 119	99.16 250	99.34 271	99.28 186	99.12 182	99.79 58	99.48 102	98.93 284	98.55 363	99.40 26	99.93 78	98.51 150	99.52 273	98.28 351

DVP-MVS		99.32 110	99.17 120	99.77 45	99.69 131	99.80 41	99.14 172	99.31 282	99.16 157	99.62 143	99.61 180	98.35 170	99.91 122	97.88 199	99.72 210	99.61 126
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
IterMVS-SCA-FT		99.00 189	99.16 121	98.51 305	99.75 103	95.90 347	98.07 309	99.84 34	99.84 35	99.89 32	99.73 99	96.01 287	99.99 6	99.33 65	100.00 1	99.63 105

APD-MVS_3200maxsize		99.31 112	99.16 121	99.74 68	99.53 195	99.75 61	99.27 132	99.61 150	99.19 150	99.57 160	99.64 152	98.76 112	99.90 142	97.29 249	99.62 242	99.56 153

IterMVS		98.97 193	99.16 121	98.42 309	99.74 109	95.64 350	98.06 311	99.83 36	99.83 38	99.85 48	99.74 95	96.10 286	99.99 6	99.27 77	100.00 1	99.63 105
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LCM-MVSNet-Re		99.28 116	99.15 124	99.67 97	99.33 276	99.76 57	99.34 109	99.97 2	98.93 187	99.91 22	99.79 70	98.68 120	99.93 78	96.80 281	99.56 259	99.30 250

zzz-MVS		99.30 113	99.14 125	99.80 34	99.81 58	99.81 36	98.73 250	99.53 209	99.27 136	99.42 205	99.63 162	98.21 186	99.95 47	97.83 208	99.79 174	99.65 91

SteuartSystems-ACMMP		99.30 113	99.14 125	99.76 52	99.87 34	99.66 94	99.18 157	99.60 163	98.55 227	99.57 160	99.67 141	99.03 75	99.94 62	97.01 268	99.80 169	99.69 60
Skip Steuart: Steuart Systems R&D Blog.
test_0402		99.22 136	99.14 125	99.45 186	99.79 73	99.43 152	99.28 129	99.68 113	99.54 96	99.40 218	99.56 210	99.07 70	99.82 261	96.01 317	99.96 52	99.11 290

RE-MVS-def			99.13 128		99.54 190	99.74 67	99.26 134	99.62 143	99.16 157	99.52 181	99.64 152	98.57 137		97.27 252	99.61 249	99.54 164

OPM-MVS		99.26 122	99.13 128	99.63 123	99.70 127	99.61 113	98.58 259	99.48 232	98.50 233	99.52 181	99.63 162	99.14 59	99.76 298	97.89 198	99.77 184	99.51 183
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CDS-MVSNet		99.22 136	99.13 128	99.50 170	99.35 261	99.11 217	98.96 217	99.54 199	99.46 111	99.61 149	99.70 119	96.31 280	99.83 251	99.34 61	99.88 112	99.55 156
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
wuyk23d		97.58 302	99.13 128	92.93 360	99.69 131	99.49 134	99.52 80	99.77 66	97.97 278	99.96 8	99.79 70	99.84 3	99.94 62	95.85 325	99.82 156	79.36 376

ppachtmachnet_test		98.89 207	99.12 132	98.20 319	99.66 145	95.24 354	97.63 339	99.68 113	99.08 168	99.78 74	99.62 171	98.65 127	99.88 173	98.02 186	99.96 52	99.48 199

Fast-Effi-MVS+-dtu		99.20 143	99.12 132	99.43 192	99.25 293	99.69 87	99.05 196	99.82 41	99.50 100	98.97 280	99.05 323	98.98 79	99.98 9	98.20 172	99.24 313	98.62 334

DeepC-MVS_fast	98.47 5	99.23 127	99.12 132	99.56 153	99.28 288	99.22 203	98.99 211	99.40 259	99.08 168	99.58 157	99.64 152	98.90 91	99.83 251	97.44 241	99.75 189	99.63 105
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		99.27 120	99.11 135	99.73 78	99.54 190	99.74 67	99.26 134	99.62 143	99.16 157	99.52 181	99.64 152	98.41 162	99.91 122	97.27 252	99.61 249	99.54 164

ACMMP_NAP		99.28 116	99.11 135	99.79 39	99.75 103	99.81 36	98.95 218	99.53 209	98.27 261	99.53 179	99.73 99	98.75 114	99.87 185	97.70 221	99.83 147	99.68 66

xiu_mvs_v2_base		99.02 183	99.11 135	98.77 295	99.37 257	98.09 295	98.13 301	99.51 221	99.47 107	99.42 205	98.54 364	99.38 31	99.97 19	98.83 127	99.33 302	98.24 353

pmmvs5		99.19 146	99.11 135	99.42 194	99.76 92	98.88 246	98.55 265	99.73 86	98.82 201	99.72 103	99.62 171	96.56 269	99.82 261	99.32 67	99.95 61	99.56 153

XVS		99.27 120	99.11 135	99.75 62	99.71 119	99.71 76	99.37 104	99.61 150	99.29 132	98.76 307	99.47 243	98.47 153	99.88 173	97.62 229	99.73 204	99.67 73

VDD-MVS		99.20 143	99.11 135	99.44 188	99.43 240	98.98 231	99.50 82	98.32 350	99.80 44	99.56 167	99.69 125	96.99 262	99.85 223	98.99 110	99.73 204	99.50 189

jason		99.16 155	99.11 135	99.32 224	99.75 103	98.44 273	98.26 291	99.39 262	98.70 214	99.74 98	99.30 284	98.54 142	99.97 19	98.48 151	99.82 156	99.55 156
jason: jason.
LS3D		99.24 126	99.11 135	99.61 135	98.38 366	99.79 43	99.57 75	99.68 113	99.61 86	99.15 263	99.71 112	98.70 118	99.91 122	97.54 235	99.68 223	99.13 289

XVG-ACMP-BASELINE		99.23 127	99.10 143	99.63 123	99.82 51	99.58 121	98.83 232	99.72 95	98.36 247	99.60 152	99.71 112	98.92 86	99.91 122	97.08 266	99.84 137	99.40 227

our_test_3		98.85 212	99.09 144	98.13 321	99.66 145	94.90 357	97.72 335	99.58 180	99.07 170	99.64 131	99.62 171	98.19 189	99.93 78	98.41 154	99.95 61	99.55 156

MSLP-MVS++		99.05 177	99.09 144	98.91 278	99.21 299	98.36 280	98.82 236	99.47 236	98.85 197	98.90 290	99.56 210	98.78 108	99.09 373	98.57 147	99.68 223	99.26 256

MVP-Stereo		99.16 155	99.08 146	99.43 192	99.48 223	99.07 226	99.08 193	99.55 193	98.63 219	99.31 235	99.68 136	98.19 189	99.78 288	98.18 176	99.58 257	99.45 210
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HFP-MVS		99.25 123	99.08 146	99.76 52	99.73 112	99.70 83	99.31 118	99.59 170	98.36 247	99.36 222	99.37 265	98.80 103	99.91 122	97.43 242	99.75 189	99.68 66

PS-MVSNAJ		99.00 189	99.08 146	98.76 296	99.37 257	98.10 294	98.00 316	99.51 221	99.47 107	99.41 213	98.50 366	99.28 43	99.97 19	98.83 127	99.34 300	98.20 357

ACMMP		99.25 123	99.08 146	99.74 68	99.79 73	99.68 90	99.50 82	99.65 132	98.07 272	99.52 181	99.69 125	98.57 137	99.92 98	97.18 261	99.79 174	99.63 105
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
AllTest		99.21 141	99.07 150	99.63 123	99.78 80	99.64 101	99.12 182	99.83 36	98.63 219	99.63 135	99.72 105	98.68 120	99.75 302	96.38 304	99.83 147	99.51 183

HPM-MVS		99.25 123	99.07 150	99.78 42	99.81 58	99.75 61	99.61 65	99.67 118	97.72 292	99.35 224	99.25 296	99.23 48	99.92 98	97.21 260	99.82 156	99.67 73
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs4		99.13 161	99.06 152	99.36 216	99.57 177	99.10 222	98.01 314	99.25 297	98.78 207	99.58 157	99.44 250	98.24 181	99.76 298	98.74 137	99.93 80	99.22 264

VNet		99.18 150	99.06 152	99.56 153	99.24 295	99.36 171	99.33 111	99.31 282	99.67 70	99.47 193	99.57 207	96.48 272	99.84 240	99.15 94	99.30 305	99.47 204

ACMMPR		99.23 127	99.06 152	99.76 52	99.74 109	99.69 87	99.31 118	99.59 170	98.36 247	99.35 224	99.38 263	98.61 131	99.93 78	97.43 242	99.75 189	99.67 73

XVG-OURS		99.21 141	99.06 152	99.65 109	99.82 51	99.62 107	97.87 330	99.74 83	98.36 247	99.66 125	99.68 136	99.71 9	99.90 142	96.84 279	99.88 112	99.43 221

test1172		99.23 127	99.05 156	99.74 68	99.52 201	99.75 61	99.20 151	99.61 150	98.97 179	99.48 191	99.58 197	98.41 162	99.91 122	97.15 263	99.55 263	99.57 150

CANet		99.11 167	99.05 156	99.28 232	98.83 348	98.56 266	98.71 253	99.41 252	99.25 140	99.23 248	99.22 303	97.66 233	99.94 62	99.19 85	99.97 38	99.33 244

region2R		99.23 127	99.05 156	99.77 45	99.76 92	99.70 83	99.31 118	99.59 170	98.41 241	99.32 231	99.36 270	98.73 117	99.93 78	97.29 249	99.74 197	99.67 73

MDA-MVSNet-bldmvs		99.06 174	99.05 156	99.07 263	99.80 63	97.83 307	98.89 222	99.72 95	99.29 132	99.63 135	99.70 119	96.47 273	99.89 158	98.17 178	99.82 156	99.50 189

LPG-MVS_test		99.22 136	99.05 156	99.74 68	99.82 51	99.63 105	99.16 168	99.73 86	97.56 298	99.64 131	99.69 125	99.37 33	99.89 158	96.66 289	99.87 121	99.69 60

CP-MVS		99.23 127	99.05 156	99.75 62	99.66 145	99.66 94	99.38 100	99.62 143	98.38 245	99.06 276	99.27 291	98.79 106	99.94 62	97.51 238	99.82 156	99.66 83

ZNCC-MVS		99.22 136	99.04 162	99.77 45	99.76 92	99.73 70	99.28 129	99.56 187	98.19 266	99.14 265	99.29 287	98.84 97	99.92 98	97.53 237	99.80 169	99.64 100

TSAR-MVS + GP.		99.12 163	99.04 162	99.38 210	99.34 271	99.16 212	98.15 298	99.29 287	98.18 267	99.63 135	99.62 171	99.18 53	99.68 332	98.20 172	99.74 197	99.30 250

MVS_111021_LR		99.13 161	99.03 164	99.42 194	99.58 167	99.32 180	97.91 329	99.73 86	98.68 215	99.31 235	99.48 238	99.09 64	99.66 341	97.70 221	99.77 184	99.29 253

RPSCF		99.18 150	99.02 165	99.64 116	99.83 44	99.85 15	99.44 92	99.82 41	98.33 257	99.50 188	99.78 77	97.90 211	99.65 347	96.78 282	99.83 147	99.44 215

MVS_111021_HR		99.12 163	99.02 165	99.40 202	99.50 212	99.11 217	97.92 327	99.71 98	98.76 211	99.08 272	99.47 243	99.17 54	99.54 360	97.85 205	99.76 186	99.54 164

DeepPCF-MVS	98.42 6	99.18 150	99.02 165	99.67 97	99.22 297	99.75 61	97.25 357	99.47 236	98.72 213	99.66 125	99.70 119	99.29 41	99.63 350	98.07 185	99.81 164	99.62 116

EIA-MVS		99.12 163	99.01 168	99.45 186	99.36 259	99.62 107	99.34 109	99.79 58	98.41 241	98.84 297	98.89 348	98.75 114	99.84 240	98.15 180	99.51 274	98.89 321

PGM-MVS		99.20 143	99.01 168	99.77 45	99.75 103	99.71 76	99.16 168	99.72 95	97.99 276	99.42 205	99.60 189	98.81 99	99.93 78	96.91 273	99.74 197	99.66 83

PVSNet_BlendedMVS		99.03 181	99.01 168	99.09 259	99.54 190	97.99 299	98.58 259	99.82 41	97.62 296	99.34 227	99.71 112	98.52 149	99.77 296	97.98 191	99.97 38	99.52 181

SR-MVS		99.19 146	99.00 171	99.74 68	99.51 206	99.72 74	99.18 157	99.60 163	98.85 197	99.47 193	99.58 197	98.38 167	99.92 98	96.92 272	99.54 269	99.57 150

SMA-MVS		99.19 146	99.00 171	99.73 78	99.46 233	99.73 70	99.13 178	99.52 217	97.40 309	99.57 160	99.64 152	98.93 85	99.83 251	97.61 231	99.79 174	99.63 105
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
canonicalmvs		99.02 183	99.00 171	99.09 259	99.10 320	98.70 257	99.61 65	99.66 122	99.63 81	98.64 315	97.65 377	99.04 74	99.54 360	98.79 131	98.92 328	99.04 306

mPP-MVS		99.19 146	99.00 171	99.76 52	99.76 92	99.68 90	99.38 100	99.54 199	98.34 256	99.01 278	99.50 230	98.53 146	99.93 78	97.18 261	99.78 180	99.66 83

EPP-MVSNet		99.17 154	99.00 171	99.66 104	99.80 63	99.43 152	99.70 34	99.24 300	99.48 102	99.56 167	99.77 84	94.89 296	99.93 78	98.72 139	99.89 103	99.63 105

YYNet1		98.95 199	98.99 176	98.84 288	99.64 149	97.14 327	98.22 294	99.32 278	98.92 189	99.59 155	99.66 145	97.40 242	99.83 251	98.27 166	99.90 94	99.55 156

MDA-MVSNet_test_wron		98.95 199	98.99 176	98.85 286	99.64 149	97.16 326	98.23 293	99.33 276	98.93 187	99.56 167	99.66 145	97.39 244	99.83 251	98.29 164	99.88 112	99.55 156

XVG-OURS-SEG-HR		99.16 155	98.99 176	99.66 104	99.84 40	99.64 101	98.25 292	99.73 86	98.39 244	99.63 135	99.43 251	99.70 11	99.90 142	97.34 246	98.64 343	99.44 215

MSDG		99.08 172	98.98 179	99.37 213	99.60 158	99.13 215	97.54 343	99.74 83	98.84 200	99.53 179	99.55 217	99.10 62	99.79 285	97.07 267	99.86 128	99.18 275

Effi-MVS+		99.06 174	98.97 180	99.34 218	99.31 279	98.98 231	98.31 287	99.91 12	98.81 202	98.79 303	98.94 343	99.14 59	99.84 240	98.79 131	98.74 339	99.20 270

MS-PatchMatch		99.00 189	98.97 180	99.09 259	99.11 319	98.19 287	98.76 247	99.33 276	98.49 235	99.44 199	99.58 197	98.21 186	99.69 321	98.20 172	99.62 242	99.39 230

xxxxxxxxxxxxxcwj		99.11 167	98.96 182	99.54 160	99.53 195	99.25 194	98.29 288	99.76 71	99.07 170	99.42 205	99.61 180	98.86 94	99.87 185	96.45 301	99.68 223	99.49 194

GST-MVS		99.16 155	98.96 182	99.75 62	99.73 112	99.73 70	99.20 151	99.55 193	98.22 263	99.32 231	99.35 275	98.65 127	99.91 122	96.86 276	99.74 197	99.62 116

PHI-MVS		99.11 167	98.95 184	99.59 139	99.13 312	99.59 118	99.17 162	99.65 132	97.88 284	99.25 244	99.46 246	98.97 81	99.80 282	97.26 254	99.82 156	99.37 235

SF-MVS		99.10 171	98.93 185	99.62 132	99.58 167	99.51 132	99.13 178	99.65 132	97.97 278	99.42 205	99.61 180	98.86 94	99.87 185	96.45 301	99.68 223	99.49 194

WR-MVS		99.11 167	98.93 185	99.66 104	99.30 283	99.42 155	98.42 280	99.37 269	99.04 175	99.57 160	99.20 307	96.89 264	99.86 205	98.66 144	99.87 121	99.70 56

USDC		98.96 196	98.93 185	99.05 265	99.54 190	97.99 299	97.07 363	99.80 52	98.21 264	99.75 89	99.77 84	98.43 159	99.64 349	97.90 197	99.88 112	99.51 183

TinyColmap		98.97 193	98.93 185	99.07 263	99.46 233	98.19 287	97.75 334	99.75 78	98.79 205	99.54 174	99.70 119	98.97 81	99.62 351	96.63 292	99.83 147	99.41 225

DPE-MVS		99.14 159	98.92 189	99.82 28	99.57 177	99.77 49	98.74 248	99.60 163	98.55 227	99.76 81	99.69 125	98.23 185	99.92 98	96.39 303	99.75 189	99.76 44
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
Effi-MVS+-dtu		99.07 173	98.92 189	99.52 164	98.89 342	99.78 46	99.15 170	99.66 122	99.34 127	98.92 287	99.24 301	97.69 226	99.98 9	98.11 182	99.28 307	98.81 328

MP-MVS-pluss		99.14 159	98.92 189	99.80 34	99.83 44	99.83 27	98.61 255	99.63 140	96.84 330	99.44 199	99.58 197	98.81 99	99.91 122	97.70 221	99.82 156	99.67 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
LF4IMVS		99.01 187	98.92 189	99.27 234	99.71 119	99.28 186	98.59 258	99.77 66	98.32 258	99.39 219	99.41 253	98.62 129	99.84 240	96.62 293	99.84 137	98.69 332

#test#		99.12 163	98.90 193	99.76 52	99.73 112	99.70 83	99.10 185	99.59 170	97.60 297	99.36 222	99.37 265	98.80 103	99.91 122	96.84 279	99.75 189	99.68 66

new_pmnet		98.88 208	98.89 194	98.84 288	99.70 127	97.62 314	98.15 298	99.50 225	97.98 277	99.62 143	99.54 219	98.15 192	99.94 62	97.55 234	99.84 137	98.95 316

CVMVSNet		98.61 235	98.88 195	97.80 329	99.58 167	93.60 364	99.26 134	99.64 138	99.66 74	99.72 103	99.67 141	93.26 314	99.93 78	99.30 71	99.81 164	99.87 10

Fast-Effi-MVS+		99.02 183	98.87 196	99.46 182	99.38 254	99.50 133	99.04 198	99.79 58	97.17 320	98.62 316	98.74 356	99.34 37	99.95 47	98.32 162	99.41 290	98.92 319

lupinMVS		98.96 196	98.87 196	99.24 241	99.57 177	98.40 276	98.12 302	99.18 310	98.28 260	99.63 135	99.13 312	98.02 202	99.97 19	98.22 170	99.69 218	99.35 241

CANet_DTU		98.91 202	98.85 198	99.09 259	98.79 353	98.13 290	98.18 295	99.31 282	99.48 102	98.86 295	99.51 227	96.56 269	99.95 47	99.05 106	99.95 61	99.19 273

IS-MVSNet		99.03 181	98.85 198	99.55 156	99.80 63	99.25 194	99.73 26	99.15 313	99.37 124	99.61 149	99.71 112	94.73 299	99.81 277	97.70 221	99.88 112	99.58 144

1112_ss		99.05 177	98.84 200	99.67 97	99.66 145	99.29 184	98.52 270	99.82 41	97.65 295	99.43 203	99.16 310	96.42 275	99.91 122	99.07 105	99.84 137	99.80 26

ACMP	97.51 14	99.05 177	98.84 200	99.67 97	99.78 80	99.55 127	98.88 223	99.66 122	97.11 324	99.47 193	99.60 189	99.07 70	99.89 158	96.18 312	99.85 132	99.58 144
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MP-MVS		99.06 174	98.83 202	99.76 52	99.76 92	99.71 76	99.32 114	99.50 225	98.35 252	98.97 280	99.48 238	98.37 168	99.92 98	95.95 323	99.75 189	99.63 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
VDDNet		98.97 193	98.82 203	99.42 194	99.71 119	98.81 250	99.62 60	98.68 334	99.81 41	99.38 220	99.80 64	94.25 303	99.85 223	98.79 131	99.32 303	99.59 139

MCST-MVS		99.02 183	98.81 204	99.65 109	99.58 167	99.49 134	98.58 259	99.07 317	98.40 243	99.04 277	99.25 296	98.51 151	99.80 282	97.31 248	99.51 274	99.65 91

PMVS	92.94 21	98.82 215	98.81 204	98.85 286	99.84 40	97.99 299	99.20 151	99.47 236	99.71 57	99.42 205	99.82 58	98.09 195	99.47 367	93.88 359	99.85 132	99.07 303
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CNVR-MVS		98.99 192	98.80 206	99.56 153	99.25 293	99.43 152	98.54 268	99.27 291	98.58 224	98.80 302	99.43 251	98.53 146	99.70 315	97.22 259	99.59 256	99.54 164

MSP-MVS		99.04 180	98.79 207	99.81 31	99.78 80	99.73 70	99.35 108	99.57 182	98.54 230	99.54 174	98.99 333	96.81 266	99.93 78	96.97 270	99.53 271	99.77 39
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
sss		98.90 204	98.77 208	99.27 234	99.48 223	98.44 273	98.72 251	99.32 278	97.94 282	99.37 221	99.35 275	96.31 280	99.91 122	98.85 126	99.63 241	99.47 204

Test_1112_low_res		98.95 199	98.73 209	99.63 123	99.68 139	99.15 214	98.09 306	99.80 52	97.14 322	99.46 197	99.40 257	96.11 285	99.89 158	99.01 109	99.84 137	99.84 15

OMC-MVS		98.90 204	98.72 210	99.44 188	99.39 251	99.42 155	98.58 259	99.64 138	97.31 314	99.44 199	99.62 171	98.59 134	99.69 321	96.17 313	99.79 174	99.22 264

eth_miper_zixun_eth		98.68 230	98.71 211	98.60 302	99.10 320	96.84 334	97.52 347	99.54 199	98.94 184	99.58 157	99.48 238	96.25 282	99.76 298	98.01 189	99.93 80	99.21 266

c3_l		98.72 227	98.71 211	98.72 298	99.12 314	97.22 325	97.68 338	99.56 187	98.90 191	99.54 174	99.48 238	96.37 279	99.73 307	97.88 199	99.88 112	99.21 266

MVS_0304		98.88 208	98.71 211	99.39 206	98.85 346	98.91 244	99.45 89	99.30 285	98.56 225	97.26 365	99.68 136	96.18 284	99.96 37	99.17 90	99.94 72	99.29 253

mvs-test1		98.83 213	98.70 214	99.22 243	98.89 342	99.65 99	98.88 223	99.66 122	99.34 127	98.29 332	98.94 343	97.69 226	99.96 37	98.11 182	98.54 347	98.04 361

HPM-MVS++		98.96 196	98.70 214	99.74 68	99.52 201	99.71 76	98.86 227	99.19 309	98.47 237	98.59 319	99.06 322	98.08 197	99.91 122	96.94 271	99.60 252	99.60 130

HQP_MVS		98.90 204	98.68 216	99.55 156	99.58 167	99.24 199	98.80 240	99.54 199	98.94 184	99.14 265	99.25 296	97.24 250	99.82 261	95.84 326	99.78 180	99.60 130

9.14			98.64 217		99.45 236		98.81 237	99.60 163	97.52 303	99.28 241	99.56 210	98.53 146	99.83 251	95.36 339	99.64 239

HyFIR lowres test		98.91 202	98.64 217	99.73 78	99.85 39	99.47 137	98.07 309	99.83 36	98.64 218	99.89 32	99.60 189	92.57 320	100.00 1	99.33 65	99.97 38	99.72 50

FMVSNet3		98.80 217	98.63 219	99.32 224	99.13 312	98.72 256	99.10 185	99.48 232	99.23 144	99.62 143	99.64 152	92.57 320	99.86 205	98.96 116	99.90 94	99.39 230

miper_lstm_enhance		98.65 232	98.60 220	98.82 293	99.20 302	97.33 322	97.78 333	99.66 122	99.01 176	99.59 155	99.50 230	94.62 300	99.85 223	98.12 181	99.90 94	99.26 256

K. test v3		98.87 210	98.60 220	99.69 93	99.93 17	99.46 141	99.74 22	94.97 372	99.78 49	99.88 38	99.88 31	93.66 311	99.97 19	99.61 22	99.95 61	99.64 100

miper_ehance_all_eth		98.59 240	98.59 222	98.59 303	98.98 335	97.07 328	97.49 348	99.52 217	98.50 233	99.52 181	99.37 265	96.41 277	99.71 313	97.86 203	99.62 242	99.00 313

APD-MVS		98.87 210	98.59 222	99.71 88	99.50 212	99.62 107	99.01 204	99.57 182	96.80 332	99.54 174	99.63 162	98.29 177	99.91 122	95.24 340	99.71 213	99.61 126
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PVSNet_Blended		98.70 229	98.59 222	99.02 267	99.54 190	97.99 299	97.58 342	99.82 41	95.70 347	99.34 227	98.98 336	98.52 149	99.77 296	97.98 191	99.83 147	99.30 250

Vis-MVSNet (Re-imp)		98.77 219	98.58 225	99.34 218	99.78 80	98.88 246	99.61 65	99.56 187	99.11 167	99.24 247	99.56 210	93.00 318	99.78 288	97.43 242	99.89 103	99.35 241

NCCC		98.82 215	98.57 226	99.58 143	99.21 299	99.31 181	98.61 255	99.25 297	98.65 217	98.43 329	99.26 294	97.86 215	99.81 277	96.55 294	99.27 310	99.61 126

UnsupCasMVSNet_eth		98.83 213	98.57 226	99.59 139	99.68 139	99.45 146	98.99 211	99.67 118	99.48 102	99.55 172	99.36 270	94.92 295	99.86 205	98.95 120	96.57 371	99.45 210

CLD-MVS		98.76 221	98.57 226	99.33 220	99.57 177	98.97 233	97.53 345	99.55 193	96.41 336	99.27 242	99.13 312	99.07 70	99.78 288	96.73 285	99.89 103	99.23 262
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CL-MVSNet_self_test		98.71 228	98.56 229	99.15 252	99.22 297	98.66 261	97.14 360	99.51 221	98.09 271	99.54 174	99.27 291	96.87 265	99.74 304	98.43 153	98.96 325	99.03 307

iter_conf_final		98.75 222	98.54 230	99.40 202	99.33 276	98.75 254	99.26 134	99.59 170	99.80 44	99.76 81	99.58 197	90.17 349	99.92 98	99.37 57	99.97 38	99.54 164

Patchmtry		98.78 218	98.54 230	99.49 173	98.89 342	99.19 210	99.32 114	99.67 118	99.65 76	99.72 103	99.79 70	91.87 328	99.95 47	98.00 190	99.97 38	99.33 244

RPMNet		98.60 237	98.53 232	98.83 290	99.05 325	98.12 291	99.30 121	99.62 143	99.86 27	99.16 261	99.74 95	92.53 322	99.92 98	98.75 136	98.77 335	98.44 346

N_pmnet		98.73 226	98.53 232	99.35 217	99.72 116	98.67 259	98.34 283	94.65 373	98.35 252	99.79 71	99.68 136	98.03 200	99.93 78	98.28 165	99.92 84	99.44 215

ETH3D-3000-0.1		98.77 219	98.50 234	99.59 139	99.47 228	99.53 129	98.77 245	99.60 163	97.33 313	99.23 248	99.50 230	97.91 210	99.83 251	95.02 344	99.67 230	99.41 225

PatchMatch-RL		98.68 230	98.47 235	99.30 229	99.44 238	99.28 186	98.14 300	99.54 199	97.12 323	99.11 269	99.25 296	97.80 220	99.70 315	96.51 297	99.30 305	98.93 318

Anonymous202405211		98.75 222	98.46 236	99.63 123	99.34 271	99.66 94	99.47 88	97.65 358	99.28 135	99.56 167	99.50 230	93.15 315	99.84 240	98.62 145	99.58 257	99.40 227

F-COLMAP		98.74 224	98.45 237	99.62 132	99.57 177	99.47 137	98.84 230	99.65 132	96.31 339	98.93 284	99.19 309	97.68 228	99.87 185	96.52 296	99.37 297	99.53 170

CPTT-MVS		98.74 224	98.44 238	99.64 116	99.61 156	99.38 165	99.18 157	99.55 193	96.49 335	99.27 242	99.37 265	97.11 258	99.92 98	95.74 330	99.67 230	99.62 116

PVSNet	97.47 15	98.42 261	98.44 238	98.35 312	99.46 233	96.26 341	96.70 368	99.34 275	97.68 294	99.00 279	99.13 312	97.40 242	99.72 309	97.59 233	99.68 223	99.08 298

DIV-MVS_self_test		98.54 247	98.42 240	98.92 276	99.03 329	97.80 309	97.46 349	99.59 170	98.90 191	99.60 152	99.46 246	93.87 306	99.78 288	97.97 193	99.89 103	99.18 275

cl____		98.54 247	98.41 241	98.92 276	99.03 329	97.80 309	97.46 349	99.59 170	98.90 191	99.60 152	99.46 246	93.85 307	99.78 288	97.97 193	99.89 103	99.17 277

CHOSEN 280x420		98.41 262	98.41 241	98.40 310	99.34 271	95.89 348	96.94 365	99.44 245	98.80 204	99.25 244	99.52 223	93.51 313	99.98 9	98.94 121	99.98 26	99.32 247

API-MVS		98.38 265	98.39 243	98.35 312	98.83 348	99.26 190	99.14 172	99.18 310	98.59 223	98.66 314	98.78 354	98.61 131	99.57 359	94.14 354	99.56 259	96.21 373

MG-MVS		98.52 249	98.39 243	98.94 272	99.15 309	97.39 321	98.18 295	99.21 308	98.89 194	99.23 248	99.63 162	97.37 246	99.74 304	94.22 353	99.61 249	99.69 60

WTY-MVS		98.59 240	98.37 245	99.26 236	99.43 240	98.40 276	98.74 248	99.13 316	98.10 269	99.21 254	99.24 301	94.82 297	99.90 142	97.86 203	98.77 335	99.49 194

SCA		98.11 281	98.36 246	97.36 339	99.20 302	92.99 366	98.17 297	98.49 344	98.24 262	99.10 271	99.57 207	96.01 287	99.94 62	96.86 276	99.62 242	99.14 286

Patchmatch-RL test		98.60 237	98.36 246	99.33 220	99.77 88	99.07 226	98.27 290	99.87 21	98.91 190	99.74 98	99.72 105	90.57 345	99.79 285	98.55 148	99.85 132	99.11 290

AdaColmap		98.60 237	98.35 248	99.38 210	99.12 314	99.22 203	98.67 254	99.42 251	97.84 289	98.81 300	99.27 291	97.32 248	99.81 277	95.14 341	99.53 271	99.10 292

h-mvs33		98.61 235	98.34 249	99.44 188	99.60 158	98.67 259	99.27 132	99.44 245	99.68 66	99.32 231	99.49 235	92.50 323	100.00 1	99.24 78	96.51 372	99.65 91

test_prior3		98.62 234	98.34 249	99.46 182	99.35 261	99.22 203	97.95 323	99.39 262	97.87 285	98.05 345	99.05 323	97.90 211	99.69 321	95.99 319	99.49 278	99.48 199

CNLPA		98.57 242	98.34 249	99.28 232	99.18 306	99.10 222	98.34 283	99.41 252	98.48 236	98.52 324	98.98 336	97.05 260	99.78 288	95.59 332	99.50 276	98.96 314

PatchT		98.45 259	98.32 252	98.83 290	98.94 337	98.29 282	99.24 141	98.82 329	99.84 35	99.08 272	99.76 88	91.37 331	99.94 62	98.82 129	99.00 324	98.26 352

hse-mvs2		98.52 249	98.30 253	99.16 250	99.29 285	98.60 265	98.77 245	99.02 321	99.68 66	99.32 231	99.04 326	92.50 323	99.85 223	99.24 78	97.87 363	99.03 307

PMMVS		98.49 254	98.29 254	99.11 257	98.96 336	98.42 275	97.54 343	99.32 278	97.53 302	98.47 328	98.15 372	97.88 214	99.82 261	97.46 240	99.24 313	99.09 295

UnsupCasMVSNet_bld		98.55 246	98.27 255	99.40 202	99.56 188	99.37 168	97.97 322	99.68 113	97.49 305	99.08 272	99.35 275	95.41 294	99.82 261	97.70 221	98.19 356	99.01 312

test_part1		98.63 233	98.26 256	99.75 62	99.40 249	99.49 134	99.67 47	99.68 113	99.86 27	99.88 38	99.86 41	86.73 367	99.93 78	99.34 61	99.97 38	99.81 25

1121		98.56 243	98.24 257	99.52 164	99.49 217	99.24 199	99.30 121	99.22 304	95.77 345	98.52 324	99.29 287	97.39 244	99.85 223	95.79 328	99.34 300	99.46 208

iter_conf05		98.46 257	98.23 258	99.15 252	99.04 327	97.99 299	99.10 185	99.61 150	99.79 47	99.76 81	99.58 197	87.88 358	99.92 98	99.31 70	99.97 38	99.53 170

DP-MVS Recon		98.50 251	98.23 258	99.31 227	99.49 217	99.46 141	98.56 264	99.63 140	94.86 358	98.85 296	99.37 265	97.81 219	99.59 357	96.08 314	99.44 284	98.88 322

MVSTER		98.47 256	98.22 260	99.24 241	99.06 324	98.35 281	99.08 193	99.46 240	99.27 136	99.75 89	99.66 145	88.61 356	99.85 223	99.14 100	99.92 84	99.52 181

MVS-HIRNet		97.86 289	98.22 260	96.76 348	99.28 288	91.53 374	98.38 282	92.60 378	99.13 163	99.31 235	99.96 12	97.18 256	99.68 332	98.34 160	99.83 147	99.07 303

CDPH-MVS		98.56 243	98.20 262	99.61 135	99.50 212	99.46 141	98.32 286	99.41 252	95.22 352	99.21 254	99.10 319	98.34 173	99.82 261	95.09 343	99.66 234	99.56 153

CR-MVSNet		98.35 269	98.20 262	98.83 290	99.05 325	98.12 291	99.30 121	99.67 118	97.39 310	99.16 261	99.79 70	91.87 328	99.91 122	98.78 134	98.77 335	98.44 346

MIMVSNet		98.43 260	98.20 262	99.11 257	99.53 195	98.38 279	99.58 73	98.61 338	98.96 182	99.33 229	99.76 88	90.92 338	99.81 277	97.38 245	99.76 186	99.15 281

LFMVS		98.46 257	98.19 265	99.26 236	99.24 295	98.52 269	99.62 60	96.94 365	99.87 24	99.31 235	99.58 197	91.04 336	99.81 277	98.68 143	99.42 289	99.45 210

CMPMVS	77.52 23	98.50 251	98.19 265	99.41 201	98.33 368	99.56 124	99.01 204	99.59 170	95.44 349	99.57 160	99.80 64	95.64 291	99.46 369	96.47 300	99.92 84	99.21 266
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testtj		98.56 243	98.17 267	99.72 84	99.45 236	99.60 115	98.88 223	99.50 225	96.88 327	99.18 260	99.48 238	97.08 259	99.92 98	93.69 360	99.38 293	99.63 105

test1111		97.74 294	98.16 268	96.49 353	99.60 158	89.86 382	99.71 33	91.21 379	99.89 17	99.88 38	99.87 34	93.73 310	99.90 142	99.56 32	99.99 12	99.70 56

ETH3D cwj APD-0.16		98.50 251	98.16 268	99.51 167	99.04 327	99.39 162	98.47 274	99.47 236	96.70 334	98.78 305	99.33 279	97.62 236	99.86 205	94.69 349	99.38 293	99.28 255

BH-RMVSNet		98.41 262	98.14 270	99.21 244	99.21 299	98.47 270	98.60 257	98.26 351	98.35 252	98.93 284	99.31 282	97.20 255	99.66 341	94.32 351	99.10 318	99.51 183

114514_t		98.49 254	98.11 271	99.64 116	99.73 112	99.58 121	99.24 141	99.76 71	89.94 371	99.42 205	99.56 210	97.76 223	99.86 205	97.74 215	99.82 156	99.47 204

BH-untuned		98.22 278	98.09 272	98.58 304	99.38 254	97.24 324	98.55 265	98.98 324	97.81 290	99.20 259	98.76 355	97.01 261	99.65 347	94.83 345	98.33 351	98.86 324

tpmrst		97.73 295	98.07 273	96.73 350	98.71 359	92.00 370	99.10 185	98.86 326	98.52 231	98.92 287	99.54 219	91.90 326	99.82 261	98.02 186	99.03 322	98.37 348

ECVR-MVS		97.73 295	98.04 274	96.78 347	99.59 162	90.81 378	99.72 29	90.43 381	99.89 17	99.86 46	99.86 41	93.60 312	99.89 158	99.46 44	99.99 12	99.65 91

PAPM_NR		98.36 266	98.04 274	99.33 220	99.48 223	98.93 241	98.79 243	99.28 290	97.54 301	98.56 322	98.57 361	97.12 257	99.69 321	94.09 355	98.90 330	99.38 232

HQP-MVS		98.36 266	98.02 276	99.39 206	99.31 279	98.94 237	97.98 319	99.37 269	97.45 306	98.15 339	98.83 351	96.67 267	99.70 315	94.73 346	99.67 230	99.53 170

QAPM		98.40 264	97.99 277	99.65 109	99.39 251	99.47 137	99.67 47	99.52 217	91.70 368	98.78 305	99.80 64	98.55 140	99.95 47	94.71 348	99.75 189	99.53 170

PLC	97.35 16	98.36 266	97.99 277	99.48 177	99.32 278	99.24 199	98.50 272	99.51 221	95.19 354	98.58 320	98.96 341	96.95 263	99.83 251	95.63 331	99.25 311	99.37 235
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Patchmatch-test		98.10 282	97.98 279	98.48 307	99.27 290	96.48 338	99.40 96	99.07 317	98.81 202	99.23 248	99.57 207	90.11 350	99.87 185	96.69 286	99.64 239	99.09 295

alignmvs		98.28 272	97.96 280	99.25 239	99.12 314	98.93 241	99.03 201	98.42 346	99.64 78	98.72 310	97.85 375	90.86 341	99.62 351	98.88 125	99.13 316	99.19 273

test_yl		98.25 274	97.95 281	99.13 255	99.17 307	98.47 270	99.00 206	98.67 336	98.97 179	99.22 252	99.02 331	91.31 332	99.69 321	97.26 254	98.93 326	99.24 259

DCV-MVSNet		98.25 274	97.95 281	99.13 255	99.17 307	98.47 270	99.00 206	98.67 336	98.97 179	99.22 252	99.02 331	91.31 332	99.69 321	97.26 254	98.93 326	99.24 259

train_agg		98.35 269	97.95 281	99.57 149	99.35 261	99.35 175	98.11 304	99.41 252	94.90 356	97.92 350	98.99 333	98.02 202	99.85 223	95.38 338	99.44 284	99.50 189

HY-MVS	98.23 9	98.21 279	97.95 281	98.99 268	99.03 329	98.24 283	99.61 65	98.72 333	96.81 331	98.73 309	99.51 227	94.06 304	99.86 205	96.91 273	98.20 354	98.86 324

miper_enhance_ethall		98.03 285	97.94 285	98.32 314	98.27 369	96.43 340	96.95 364	99.41 252	96.37 338	99.43 203	98.96 341	94.74 298	99.69 321	97.71 218	99.62 242	98.83 327

DPM-MVS		98.28 272	97.94 285	99.32 224	99.36 259	99.11 217	97.31 355	98.78 331	96.88 327	98.84 297	99.11 318	97.77 222	99.61 355	94.03 357	99.36 298	99.23 262

agg_prior1		98.33 271	97.92 287	99.57 149	99.35 261	99.36 171	97.99 318	99.39 262	94.85 359	97.76 359	98.98 336	98.03 200	99.85 223	95.49 334	99.44 284	99.51 183

JIA-IIPM		98.06 284	97.92 287	98.50 306	98.59 362	97.02 329	98.80 240	98.51 342	99.88 22	97.89 352	99.87 34	91.89 327	99.90 142	98.16 179	97.68 365	98.59 336

MAR-MVS		98.24 276	97.92 287	99.19 247	98.78 355	99.65 99	99.17 162	99.14 314	95.36 350	98.04 347	98.81 353	97.47 239	99.72 309	95.47 336	99.06 319	98.21 355
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
1314		98.00 287	97.90 290	98.27 318	98.90 339	97.45 319	99.30 121	99.06 319	94.98 355	97.21 366	99.12 316	98.43 159	99.67 337	95.58 333	98.56 346	97.71 365

OpenMVS	98.12 10	98.23 277	97.89 291	99.26 236	99.19 304	99.26 190	99.65 57	99.69 110	91.33 369	98.14 343	99.77 84	98.28 178	99.96 37	95.41 337	99.55 263	98.58 338

pmmvs3		98.08 283	97.80 292	98.91 278	99.41 246	97.69 313	97.87 330	99.66 122	95.87 343	99.50 188	99.51 227	90.35 347	99.97 19	98.55 148	99.47 281	99.08 298

PatchmatchNet		97.65 299	97.80 292	97.18 344	98.82 351	92.49 368	99.17 162	98.39 348	98.12 268	98.79 303	99.58 197	90.71 343	99.89 158	97.23 258	99.41 290	99.16 279
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		97.62 300	97.79 294	97.11 346	96.67 379	92.31 369	98.51 271	98.04 352	99.24 142	95.77 373	99.47 243	93.78 309	99.66 341	98.98 112	99.62 242	99.37 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.13 280	97.77 295	99.18 249	94.57 382	97.99 299	99.24 141	97.96 354	99.74 52	97.29 364	99.62 171	93.13 316	99.97 19	98.59 146	99.83 147	99.58 144
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDTV_nov1_ep13			97.73 296		98.70 360	90.83 377	99.15 170	98.02 353	98.51 232	98.82 299	99.61 180	90.98 337	99.66 341	96.89 275	98.92 328

tpmvs		97.39 307	97.69 297	96.52 352	98.41 365	91.76 371	99.30 121	98.94 325	97.74 291	97.85 355	99.55 217	92.40 325	99.73 307	96.25 309	98.73 341	98.06 360

GA-MVS		97.99 288	97.68 298	98.93 275	99.52 201	98.04 298	97.19 359	99.05 320	98.32 258	98.81 300	98.97 339	89.89 353	99.41 370	98.33 161	99.05 320	99.34 243

ADS-MVSNet		97.72 298	97.67 299	97.86 327	99.14 310	94.65 358	99.22 148	98.86 326	96.97 325	98.25 335	99.64 152	90.90 339	99.84 240	96.51 297	99.56 259	99.08 298

ADS-MVSNet2		97.78 292	97.66 300	98.12 322	99.14 310	95.36 352	99.22 148	98.75 332	96.97 325	98.25 335	99.64 152	90.90 339	99.94 62	96.51 297	99.56 259	99.08 298

TAPA-MVS	97.92 13	98.03 285	97.55 301	99.46 182	99.47 228	99.44 148	98.50 272	99.62 143	86.79 372	99.07 275	99.26 294	98.26 180	99.62 351	97.28 251	99.73 204	99.31 249
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
E-PMN		97.14 313	97.43 302	96.27 355	98.79 353	91.62 373	95.54 372	99.01 323	99.44 114	98.88 291	99.12 316	92.78 319	99.68 332	94.30 352	99.03 322	97.50 366

AUN-MVS		97.82 290	97.38 303	99.14 254	99.27 290	98.53 267	98.72 251	99.02 321	98.10 269	97.18 367	99.03 330	89.26 355	99.85 223	97.94 195	97.91 361	99.03 307

baseline1		97.73 295	97.33 304	98.96 270	99.30 283	97.73 311	99.40 96	98.42 346	99.33 130	99.46 197	99.21 305	91.18 334	99.82 261	98.35 159	91.26 377	99.32 247

cl22		97.56 303	97.28 305	98.40 310	98.37 367	96.75 335	97.24 358	99.37 269	97.31 314	99.41 213	99.22 303	87.30 359	99.37 371	97.70 221	99.62 242	99.08 298

EMVS		96.96 316	97.28 305	95.99 358	98.76 357	91.03 376	95.26 373	98.61 338	99.34 127	98.92 287	98.88 349	93.79 308	99.66 341	92.87 361	99.05 320	97.30 370

FMVSNet5		97.80 291	97.25 307	99.42 194	98.83 348	98.97 233	99.38 100	99.80 52	98.87 195	99.25 244	99.69 125	80.60 378	99.91 122	98.96 116	99.90 94	99.38 232

tttt0517		97.62 300	97.20 308	98.90 284	99.76 92	97.40 320	99.48 86	94.36 374	99.06 174	99.70 111	99.49 235	84.55 373	99.94 62	98.73 138	99.65 237	99.36 238

ETH3 D test6400		97.76 293	97.19 309	99.50 170	99.38 254	99.26 190	98.34 283	99.49 230	92.99 365	98.54 323	99.20 307	95.92 289	99.82 261	91.14 367	99.66 234	99.40 227

TR-MVS		97.44 306	97.15 310	98.32 314	98.53 364	97.46 318	98.47 274	97.91 356	96.85 329	98.21 338	98.51 365	96.42 275	99.51 365	92.16 363	97.29 367	97.98 362

dp		96.86 317	97.07 311	96.24 356	98.68 361	90.30 381	99.19 156	98.38 349	97.35 312	98.23 337	99.59 195	87.23 360	99.82 261	96.27 308	98.73 341	98.59 336

PAPR		97.56 303	97.07 311	99.04 266	98.80 352	98.11 293	97.63 339	99.25 297	94.56 362	98.02 348	98.25 371	97.43 241	99.68 332	90.90 368	98.74 339	99.33 244

BH-w/o		97.20 310	97.01 313	97.76 330	99.08 323	95.69 349	98.03 313	98.52 341	95.76 346	97.96 349	98.02 373	95.62 292	99.47 367	92.82 362	97.25 368	98.12 359

tpm cat1		96.78 319	96.98 314	96.16 357	98.85 346	90.59 380	99.08 193	99.32 278	92.37 366	97.73 361	99.46 246	91.15 335	99.69 321	96.07 315	98.80 332	98.21 355

thisisatest0530		97.45 305	96.95 315	98.94 272	99.68 139	97.73 311	99.09 190	94.19 376	98.61 222	99.56 167	99.30 284	84.30 374	99.93 78	98.27 166	99.54 269	99.16 279

test-LLR		97.15 311	96.95 315	97.74 332	98.18 372	95.02 355	97.38 351	96.10 366	98.00 274	97.81 356	98.58 359	90.04 351	99.91 122	97.69 227	98.78 333	98.31 349

tpm		97.15 311	96.95 315	97.75 331	98.91 338	94.24 360	99.32 114	97.96 354	97.71 293	98.29 332	99.32 280	86.72 368	99.92 98	98.10 184	96.24 374	99.09 295

test0.0.03 1		97.37 308	96.91 318	98.74 297	97.72 375	97.57 315	97.60 341	97.36 364	98.00 274	99.21 254	98.02 373	90.04 351	99.79 285	98.37 156	95.89 375	98.86 324

OpenMVS_ROB	97.31 17	97.36 309	96.84 319	98.89 285	99.29 285	99.45 146	98.87 226	99.48 232	86.54 374	99.44 199	99.74 95	97.34 247	99.86 205	91.61 364	99.28 307	97.37 369

cascas		96.99 314	96.82 320	97.48 335	97.57 378	95.64 350	96.43 370	99.56 187	91.75 367	97.13 368	97.61 378	95.58 293	98.63 376	96.68 287	99.11 317	98.18 358

CostFormer		96.71 322	96.79 321	96.46 354	98.90 339	90.71 379	99.41 95	98.68 334	94.69 361	98.14 343	99.34 278	86.32 370	99.80 282	97.60 232	98.07 360	98.88 322

thisisatest0515		96.98 315	96.42 322	98.66 301	99.42 245	97.47 317	97.27 356	94.30 375	97.24 316	99.15 263	98.86 350	85.01 371	99.87 185	97.10 265	99.39 292	98.63 333

EPMVS		96.53 325	96.32 323	97.17 345	98.18 372	92.97 367	99.39 98	89.95 382	98.21 264	98.61 317	99.59 195	86.69 369	99.72 309	96.99 269	99.23 315	98.81 328

baseline2		96.83 318	96.28 324	98.46 308	99.09 322	96.91 332	98.83 232	93.87 377	97.23 317	96.23 372	98.36 368	88.12 357	99.90 142	96.68 287	98.14 358	98.57 339

tpm2		96.35 328	96.22 325	96.73 350	98.88 345	91.75 372	99.21 150	98.51 342	93.27 364	97.89 352	99.21 305	84.83 372	99.70 315	96.04 316	98.18 357	98.75 331

thres600view7		96.60 324	96.16 326	97.93 325	99.63 151	96.09 345	99.18 157	97.57 359	98.77 208	98.72 310	97.32 380	87.04 362	99.72 309	88.57 370	98.62 344	97.98 362

MVE	92.54 22	96.66 323	96.11 327	98.31 316	99.68 139	97.55 316	97.94 325	95.60 371	99.37 124	90.68 379	98.70 357	96.56 269	98.61 377	86.94 377	99.55 263	98.77 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ET-MVSNet_ETH3D		96.78 319	96.07 328	98.91 278	99.26 292	97.92 306	97.70 337	96.05 369	97.96 281	92.37 378	98.43 367	87.06 361	99.90 142	98.27 166	97.56 366	98.91 320

thres100view900		96.39 327	96.03 329	97.47 336	99.63 151	95.93 346	99.18 157	97.57 359	98.75 212	98.70 312	97.31 381	87.04 362	99.67 337	87.62 373	98.51 348	96.81 371

tfpn200view9		96.30 330	95.89 330	97.53 334	99.58 167	96.11 343	99.00 206	97.54 362	98.43 238	98.52 324	96.98 383	86.85 364	99.67 337	87.62 373	98.51 348	96.81 371

thres400		96.40 326	95.89 330	97.92 326	99.58 167	96.11 343	99.00 206	97.54 362	98.43 238	98.52 324	96.98 383	86.85 364	99.67 337	87.62 373	98.51 348	97.98 362

PCF-MVS	96.03 18	96.73 321	95.86 332	99.33 220	99.44 238	99.16 212	96.87 366	99.44 245	86.58 373	98.95 282	99.40 257	94.38 302	99.88 173	87.93 372	99.80 169	98.95 316
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TESTMET0.1,1		96.24 331	95.84 333	97.41 338	98.24 370	93.84 363	97.38 351	95.84 370	98.43 238	97.81 356	98.56 362	79.77 379	99.89 158	97.77 210	98.77 335	98.52 340

test-mter		96.23 332	95.73 334	97.74 332	98.18 372	95.02 355	97.38 351	96.10 366	97.90 283	97.81 356	98.58 359	79.12 382	99.91 122	97.69 227	98.78 333	98.31 349

thres200		96.09 333	95.68 335	97.33 341	99.48 223	96.22 342	98.53 269	97.57 359	98.06 273	98.37 331	96.73 385	86.84 366	99.61 355	86.99 376	98.57 345	96.16 374

FPMVS		96.32 329	95.50 336	98.79 294	99.60 158	98.17 289	98.46 279	98.80 330	97.16 321	96.28 369	99.63 162	82.19 375	99.09 373	88.45 371	98.89 331	99.10 292

tmp_tt		95.75 339	95.42 337	96.76 348	89.90 384	94.42 359	98.86 227	97.87 357	78.01 375	99.30 240	99.69 125	97.70 224	95.89 379	99.29 74	98.14 358	99.95 1

KD-MVS_2432*1600		95.89 335	95.41 338	97.31 342	94.96 380	93.89 361	97.09 361	99.22 304	97.23 317	98.88 291	99.04 326	79.23 380	99.54 360	96.24 310	96.81 369	98.50 344

miper_refine_blended		95.89 335	95.41 338	97.31 342	94.96 380	93.89 361	97.09 361	99.22 304	97.23 317	98.88 291	99.04 326	79.23 380	99.54 360	96.24 310	96.81 369	98.50 344

PVSNet_0	95.53 19	95.85 338	95.31 340	97.47 336	98.78 355	93.48 365	95.72 371	99.40 259	96.18 341	97.37 362	97.73 376	95.73 290	99.58 358	95.49 334	81.40 378	99.36 238

gg-mvs-nofinetune		95.87 337	95.17 341	97.97 324	98.19 371	96.95 330	99.69 40	89.23 383	99.89 17	96.24 371	99.94 14	81.19 376	99.51 365	93.99 358	98.20 354	97.44 367

X-MVStestdata		96.09 333	94.87 342	99.75 62	99.71 119	99.71 76	99.37 104	99.61 150	99.29 132	98.76 307	61.30 386	98.47 153	99.88 173	97.62 229	99.73 204	99.67 73

PAPM		95.61 341	94.71 343	98.31 316	99.12 314	96.63 336	96.66 369	98.46 345	90.77 370	96.25 370	98.68 358	93.01 317	99.69 321	81.60 378	97.86 364	98.62 334

MVS		95.72 340	94.63 344	98.99 268	98.56 363	97.98 305	99.30 121	98.86 326	72.71 377	97.30 363	99.08 320	98.34 173	99.74 304	89.21 369	98.33 351	99.26 256

test2506		94.73 343	94.59 345	95.15 359	99.59 162	85.90 384	99.75 20	74.01 385	99.89 17	99.71 108	99.86 41	79.00 383	99.90 142	99.52 38	99.99 12	99.65 91

IB-MVS	95.41 20	95.30 342	94.46 346	97.84 328	98.76 357	95.33 353	97.33 354	96.07 368	96.02 342	95.37 376	97.41 379	76.17 384	99.96 37	97.54 235	95.44 376	98.22 354
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
test_method		91.72 344	92.32 347	89.91 361	93.49 383	70.18 385	90.28 374	99.56 187	61.71 378	95.39 375	99.52 223	93.90 305	99.94 62	98.76 135	98.27 353	99.62 116

EGC-MVSNET		89.05 345	85.52 348	99.64 116	99.89 26	99.78 46	99.56 77	99.52 217	24.19 379	49.96 380	99.83 51	99.15 56	99.92 98	97.71 218	99.85 132	99.21 266

testmvs		28.94 347	33.33 349	15.79 363	26.03 385	9.81 387	96.77 367	15.67 386	11.55 381	23.87 382	50.74 389	19.03 386	8.53 382	23.21 380	33.07 379	29.03 378

cdsmvs_eth3d_5k		24.88 348	33.17 350	0.00 364	0.00 387	0.00 388	0.00 375	99.62 143	0.00 382	0.00 383	99.13 312	99.82 4	0.00 383	0.00 381	0.00 381	0.00 379

test123		29.31 346	33.05 351	18.08 362	25.93 386	12.24 386	97.53 345	10.93 387	11.78 380	24.21 381	50.08 390	21.04 385	8.60 381	23.51 379	32.43 380	33.39 377

pcd_1.5k_mvsjas		16.61 349	22.14 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	99.28 43	0.00 383	0.00 381	0.00 381	0.00 379

test_blank		8.33 350	11.11 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		8.33 350	11.11 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		8.33 350	11.11 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		8.33 350	11.11 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		8.33 350	11.11 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		8.33 350	11.11 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		8.33 350	11.11 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		8.33 350	11.11 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		8.26 358	11.02 361	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	99.16 310	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					99.83 44	99.89 8	99.74 22	99.71 98	99.69 64	99.63 135

MSC_two_6792asdad				99.74 68	99.03 329	99.53 129		99.23 301					99.92 98	97.77 210	99.69 218	99.78 34

PC_three_1452									97.56 298	99.68 116	99.41 253	99.09 64	97.09 378	96.66 289	99.60 252	99.62 116

No_MVS				99.74 68	99.03 329	99.53 129		99.23 301					99.92 98	97.77 210	99.69 218	99.78 34

test_one_0601					99.63 151	99.76 57		99.55 193	99.23 144	99.31 235	99.61 180	98.59 134

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					99.43 240	99.61 113		99.43 249	96.38 337	99.11 269	99.07 321	97.86 215	99.92 98	94.04 356	99.49 278

IU-MVS					99.69 131	99.77 49		99.22 304	97.50 304	99.69 114				97.75 214	99.70 215	99.77 39

OPU-MVS				99.29 230	99.12 314	99.44 148	99.20 151				99.40 257	99.00 76	98.84 375	96.54 295	99.60 252	99.58 144

test_241102_TWO								99.54 199	99.13 163	99.76 81	99.63 162	98.32 176	99.92 98	97.85 205	99.69 218	99.75 47

test_241102_ONE					99.69 131	99.82 33		99.54 199	99.12 166	99.82 56	99.49 235	98.91 88	99.52 364

save fliter					99.53 195	99.25 194	98.29 288	99.38 268	99.07 170

test_0728_THIRD									99.18 151	99.62 143	99.61 180	98.58 136	99.91 122	97.72 216	99.80 169	99.77 39

test_0728_SECOND				99.83 26	99.70 127	99.79 43	99.14 172	99.61 150					99.92 98	97.88 199	99.72 210	99.77 39

test0726					99.69 131	99.80 41	99.24 141	99.57 182	99.16 157	99.73 102	99.65 150	98.35 170

GSMVS																99.14 286

test_part2					99.62 155	99.67 92				99.55 172

sam_mvs1												90.81 342				99.14 286

sam_mvs												90.52 346

ambc				99.20 246	99.35 261	98.53 267	99.17 162	99.46 240		99.67 121	99.80 64	98.46 156	99.70 315	97.92 196	99.70 215	99.38 232

MTGPA								99.53 209

test_post1							99.14 172				51.63 388	89.54 354	99.82 261	96.86 276

test_post											52.41 387	90.25 348	99.86 205

patchmatchnet-post											99.62 171	90.58 344	99.94 62

GG-mvs-BLEND				97.36 339	97.59 376	96.87 333	99.70 34	88.49 384		94.64 377	97.26 382	80.66 377	99.12 372	91.50 365	96.50 373	96.08 375

MTMP							99.09 190	98.59 340

gm-plane-assit					97.59 376	89.02 383			93.47 363		98.30 369		99.84 240	96.38 304

test9_res														95.10 342	99.44 284	99.50 189

TEST9					99.35 261	99.35 175	98.11 304	99.41 252	94.83 360	97.92 350	98.99 333	98.02 202	99.85 223

test_8					99.34 271	99.31 181	98.08 308	99.40 259	94.90 356	97.87 354	98.97 339	98.02 202	99.84 240

agg_prior2														94.58 350	99.46 283	99.50 189

agg_prior					99.35 261	99.36 171		99.39 262		97.76 359			99.85 223

TestCases				99.63 123	99.78 80	99.64 101		99.83 36	98.63 219	99.63 135	99.72 105	98.68 120	99.75 302	96.38 304	99.83 147	99.51 183

test_prior4						99.19 210	98.00 316

test_prior2							97.95 323		97.87 285	98.05 345	99.05 323	97.90 211		95.99 319	99.49 278

test_prior				99.46 182	99.35 261	99.22 203		99.39 262					99.69 321			99.48 199

旧先验2							97.94 325		95.33 351	98.94 283			99.88 173	96.75 283

新几何2							98.04 312

新几何1				99.52 164	99.50 212	99.22 203		99.26 294	95.66 348	98.60 318	99.28 289	97.67 229	99.89 158	95.95 323	99.32 303	99.45 210

旧先验1					99.49 217	99.29 184		99.26 294			99.39 261	97.67 229			99.36 298	99.46 208

无先验							98.01 314	99.23 301	95.83 344				99.85 223	95.79 328		99.44 215

原ACMM2							97.92 327

原ACMM1				99.37 213	99.47 228	98.87 248		99.27 291	96.74 333	98.26 334	99.32 280	97.93 209	99.82 261	95.96 322	99.38 293	99.43 221

test222					99.51 206	99.08 225	97.83 332	99.29 287	95.21 353	98.68 313	99.31 282	97.28 249			99.38 293	99.43 221

testdata2													99.89 158	95.99 319

segment_acmp												98.37 168

testdata				99.42 194	99.51 206	98.93 241		99.30 285	96.20 340	98.87 294	99.40 257	98.33 175	99.89 158	96.29 307	99.28 307	99.44 215

testdata1							97.72 335		97.86 288

test12				99.54 160	99.29 285	99.33 178		99.16 312		98.43 329		97.54 237	99.82 261		99.47 281	99.48 199

plane_prior7					99.58 167	99.38 165

plane_prior6					99.47 228	99.26 190						97.24 250

plane_prior5								99.54 199					99.82 261	95.84 326	99.78 180	99.60 130

plane_prior4											99.25 296

plane_prior3						99.31 181			98.36 247	99.14 265

plane_prior2							98.80 240		98.94 184

plane_prior1					99.51 206

plane_prior						99.24 199	98.42 280		97.87 285						99.71 213

n2								0.00 388

nn								0.00 388

door-mid								99.83 36

lessismore_v0				99.64 116	99.86 36	99.38 165		90.66 380		99.89 32	99.83 51	94.56 301	99.97 19	99.56 32	99.92 84	99.57 150

LGP-MVS_train				99.74 68	99.82 51	99.63 105		99.73 86	97.56 298	99.64 131	99.69 125	99.37 33	99.89 158	96.66 289	99.87 121	99.69 60

test11								99.29 287

door								99.77 66

HQP5-MVS						98.94 237

HQP-NCC					99.31 279		97.98 319		97.45 306	98.15 339

ACMP_Plane					99.31 279		97.98 319		97.45 306	98.15 339

BP-MVS														94.73 346

HQP4-MVS										98.15 339			99.70 315			99.53 170

HQP3-MVS								99.37 269							99.67 230

HQP2-MVS												96.67 267

NP-MVS					99.40 249	99.13 215					98.83 351

MDTV_nov1_ep13_2view						91.44 375	99.14 172		97.37 311	99.21 254		91.78 330		96.75 283		99.03 307

ACMMP++_ref															99.94 72

ACMMP++															99.79 174

Test By Simon												98.41 162

ITE_SJBPF				99.38 210	99.63 151	99.44 148		99.73 86	98.56 225	99.33 229	99.53 221	98.88 93	99.68 332	96.01 317	99.65 237	99.02 311

DeepMVS_CX				97.98 323	99.69 131	96.95 330		99.26 294	75.51 376	95.74 374	98.28 370	96.47 273	99.62 351	91.23 366	97.89 362	97.38 368

High-resolution multi-view benchmark