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 9

mvs_tets		99.90 2	99.90 2	99.90 5	99.96 4	99.79 41	99.72 29	99.88 18	99.92 9	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 8	99.96 4	99.78 44	99.70 34	99.86 23	99.89 16	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 11	100.00 1	99.97 11	99.61 17	99.97 19	99.75 13	100.00 1	99.84 14

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 12	99.91 18	99.90 5	99.96 1	99.92 9	99.90 11	99.97 6	99.87 33	99.81 5	99.95 47	99.54 32	99.99 12	99.80 25
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 24	99.94 11	99.90 5	99.83 6	99.91 12	99.85 30	99.94 12	99.95 13	99.73 8	99.90 140	99.65 19	99.97 36	99.69 58

UniMVSNet_ETH3D		99.85 7	99.83 7	99.90 5	99.89 24	99.91 2	99.89 4	99.71 97	99.93 7	99.95 11	99.89 27	99.71 9	99.96 37	99.51 37	99.97 36	99.84 14

PS-MVSNAJss		99.84 8	99.82 8	99.89 8	99.96 4	99.77 47	99.68 43	99.85 27	99.95 4	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 20	99.77 14	99.80 51	99.73 51	99.97 6	99.92 18	99.77 7	99.98 9	99.43 45	100.00 1	99.90 4

v7n		99.82 10	99.80 10	99.88 12	99.96 4	99.84 20	99.82 8	99.82 40	99.84 33	99.94 12	99.91 21	99.13 60	99.96 37	99.83 9	99.99 12	99.83 18

anonymousdsp		99.80 11	99.77 12	99.90 5	99.96 4	99.88 9	99.73 26	99.85 27	99.70 59	99.92 19	99.93 15	99.45 25	99.97 19	99.36 57	100.00 1	99.85 13

pm-mvs1		99.79 12	99.79 11	99.78 40	99.91 18	99.83 25	99.76 18	99.87 20	99.73 51	99.89 30	99.87 33	99.63 14	99.87 184	99.54 32	99.92 82	99.63 103

UA-Net		99.78 13	99.76 14	99.86 17	99.72 114	99.71 74	99.91 3	99.95 8	99.96 2	99.71 106	99.91 21	99.15 55	99.97 19	99.50 39	100.00 1	99.90 4

TransMVSNet (Re)		99.78 13	99.77 12	99.81 29	99.91 18	99.85 15	99.75 21	99.86 23	99.70 59	99.91 21	99.89 27	99.60 19	99.87 184	99.59 24	99.74 195	99.71 51

OurMVSNet-221017-0		99.75 15	99.71 16	99.84 22	99.96 4	99.83 25	99.83 6	99.85 27	99.80 42	99.93 15	99.93 15	98.54 140	99.93 76	99.59 24	99.98 26	99.76 42

Vis-MVSNet		99.75 15	99.74 15	99.79 37	99.88 28	99.66 92	99.69 40	99.92 9	99.67 68	99.77 77	99.75 93	99.61 17	99.98 9	99.35 58	99.98 26	99.72 48
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
mvsmamba		99.74 17	99.70 17	99.85 19	99.93 15	99.83 25	99.76 18	99.81 49	99.96 2	99.91 21	99.81 59	98.60 131	99.94 61	99.58 27	99.98 26	99.77 37

TDRefinement		99.72 18	99.70 17	99.77 43	99.90 22	99.85 15	99.86 5	99.92 9	99.69 62	99.78 72	99.92 18	99.37 33	99.88 171	98.93 120	99.95 59	99.60 128

XXY-MVS		99.71 19	99.67 24	99.81 29	99.89 24	99.72 72	99.59 71	99.82 40	99.39 120	99.82 54	99.84 48	99.38 31	99.91 120	99.38 52	99.93 78	99.80 25

bld_raw_dy_0_64		99.70 20	99.65 27	99.85 19	99.95 10	99.77 47	99.66 51	99.71 97	99.95 4	99.91 21	99.77 84	98.35 168	100.00 1	99.54 32	99.99 12	99.79 31

nrg030		99.70 20	99.66 25	99.82 26	99.76 90	99.84 20	99.61 65	99.70 103	99.93 7	99.78 72	99.68 136	99.10 61	99.78 288	99.45 43	99.96 50	99.83 18

FC-MVSNet-test		99.70 20	99.65 27	99.86 17	99.88 28	99.86 14	99.72 29	99.78 62	99.90 11	99.82 54	99.83 49	98.45 155	99.87 184	99.51 37	99.97 36	99.86 11

GeoE		99.69 23	99.66 25	99.78 40	99.76 90	99.76 55	99.60 70	99.82 40	99.46 109	99.75 87	99.56 210	99.63 14	99.95 47	99.43 45	99.88 110	99.62 114

v10		99.69 23	99.69 21	99.66 102	99.81 56	99.39 160	99.66 51	99.75 77	99.60 90	99.92 19	99.87 33	98.75 112	99.86 204	99.90 2	99.99 12	99.73 47

DROMVSNet		99.69 23	99.69 21	99.68 92	99.71 117	99.91 2	99.76 18	99.96 6	99.86 25	99.51 185	99.39 261	99.57 20	99.93 76	99.64 21	99.86 126	99.20 268

CS-MVS-test		99.68 26	99.70 17	99.64 114	99.57 175	99.83 25	99.78 11	99.97 2	99.92 9	99.50 187	99.38 263	99.57 20	99.95 47	99.69 16	99.90 92	99.15 279

v8		99.68 26	99.69 21	99.65 107	99.80 61	99.40 158	99.66 51	99.76 70	99.64 76	99.93 15	99.85 43	98.66 123	99.84 239	99.88 6	99.99 12	99.71 51

DTE-MVSNet		99.68 26	99.61 36	99.88 12	99.80 61	99.87 11	99.67 47	99.71 97	99.72 54	99.84 49	99.78 77	98.67 121	99.97 19	99.30 69	99.95 59	99.80 25

CS-MVS		99.67 29	99.70 17	99.58 141	99.53 193	99.84 20	99.79 10	99.96 6	99.90 11	99.61 148	99.41 253	99.51 24	99.95 47	99.66 18	99.89 101	98.96 312

RRT_MVS		99.67 29	99.59 39	99.91 2	99.94 11	99.88 9	99.78 11	99.27 289	99.87 22	99.91 21	99.87 33	98.04 197	99.96 37	99.68 17	99.99 12	99.90 4

VPA-MVSNet		99.66 31	99.62 32	99.79 37	99.68 137	99.75 59	99.62 60	99.69 109	99.85 30	99.80 64	99.81 59	98.81 97	99.91 120	99.47 41	99.88 110	99.70 54

PS-CasMVS		99.66 31	99.58 43	99.89 8	99.80 61	99.85 15	99.66 51	99.73 85	99.62 80	99.84 49	99.71 112	98.62 127	99.96 37	99.30 69	99.96 50	99.86 11

PEN-MVS		99.66 31	99.59 39	99.89 8	99.83 42	99.87 11	99.66 51	99.73 85	99.70 59	99.84 49	99.73 99	98.56 137	99.96 37	99.29 72	99.94 70	99.83 18

FMVSNet1		99.66 31	99.63 31	99.73 76	99.78 78	99.77 47	99.68 43	99.70 103	99.67 68	99.82 54	99.83 49	98.98 78	99.90 140	99.24 76	99.97 36	99.53 168

MIMVSNet1		99.66 31	99.62 32	99.80 32	99.94 11	99.87 11	99.69 40	99.77 65	99.78 47	99.93 15	99.89 27	97.94 206	99.92 96	99.65 19	99.98 26	99.62 114

FIs		99.65 36	99.58 43	99.84 22	99.84 38	99.85 15	99.66 51	99.75 77	99.86 25	99.74 96	99.79 70	98.27 177	99.85 222	99.37 55	99.93 78	99.83 18

KD-MVS_self_test		99.63 37	99.59 39	99.76 50	99.84 38	99.90 5	99.37 104	99.79 57	99.83 36	99.88 36	99.85 43	98.42 159	99.90 140	99.60 23	99.73 202	99.49 192

casdiffmvs		99.63 37	99.61 36	99.67 95	99.79 71	99.59 116	99.13 178	99.85 27	99.79 45	99.76 79	99.72 105	99.33 38	99.82 261	99.21 79	99.94 70	99.59 137

baseline		99.63 37	99.62 32	99.66 102	99.80 61	99.62 105	99.44 92	99.80 51	99.71 55	99.72 101	99.69 125	99.15 55	99.83 251	99.32 65	99.94 70	99.53 168

Anonymous20231211		99.62 40	99.57 46	99.76 50	99.61 154	99.60 113	99.81 9	99.73 85	99.82 38	99.90 26	99.90 23	97.97 205	99.86 204	99.42 50	99.96 50	99.80 25

DeepC-MVS	98.90 4	99.62 40	99.61 36	99.67 95	99.72 114	99.44 146	99.24 141	99.71 97	99.27 134	99.93 15	99.90 23	99.70 11	99.93 76	98.99 108	99.99 12	99.64 98
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 42	99.64 30	99.53 160	99.79 71	98.82 247	99.58 73	99.97 2	99.95 4	99.96 8	99.76 88	98.44 156	99.99 6	99.34 59	99.96 50	99.78 33

WR-MVS_H		99.61 42	99.53 55	99.87 15	99.80 61	99.83 25	99.67 47	99.75 77	99.58 93	99.85 46	99.69 125	98.18 189	99.94 61	99.28 74	99.95 59	99.83 18

ACMH	98.42 6	99.59 44	99.54 51	99.72 82	99.86 34	99.62 105	99.56 77	99.79 57	98.77 206	99.80 64	99.85 43	99.64 13	99.85 222	98.70 138	99.89 101	99.70 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1192		99.57 45	99.57 46	99.57 147	99.77 86	99.22 201	99.04 198	99.60 162	99.18 149	99.87 43	99.72 105	99.08 67	99.85 222	99.89 5	99.98 26	99.66 81

EG-PatchMatch MVS		99.57 45	99.56 50	99.62 130	99.77 86	99.33 176	99.26 134	99.76 70	99.32 129	99.80 64	99.78 77	99.29 41	99.87 184	99.15 92	99.91 91	99.66 81

Gipuma		99.57 45	99.59 39	99.49 171	99.98 3	99.71 74	99.72 29	99.84 33	99.81 39	99.94 12	99.78 77	98.91 87	99.71 313	98.41 152	99.95 59	99.05 303
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1921920		99.56 48	99.57 46	99.55 154	99.75 101	99.11 215	99.05 196	99.61 149	99.15 159	99.88 36	99.71 112	99.08 67	99.87 184	99.90 2	99.97 36	99.66 81

v1240		99.56 48	99.58 43	99.51 165	99.80 61	99.00 227	99.00 206	99.65 131	99.15 159	99.90 26	99.75 93	99.09 63	99.88 171	99.90 2	99.96 50	99.67 71

V42		99.56 48	99.54 51	99.63 121	99.79 71	99.46 139	99.39 98	99.59 169	99.24 140	99.86 44	99.70 119	98.55 138	99.82 261	99.79 11	99.95 59	99.60 128

v144192		99.55 51	99.54 51	99.58 141	99.78 78	99.20 207	99.11 184	99.62 142	99.18 149	99.89 30	99.72 105	98.66 123	99.87 184	99.88 6	99.97 36	99.66 81

test20.03		99.55 51	99.54 51	99.58 141	99.79 71	99.37 166	99.02 202	99.89 16	99.60 90	99.82 54	99.62 171	98.81 97	99.89 156	99.43 45	99.86 126	99.47 202

v1144		99.54 53	99.53 55	99.59 137	99.79 71	99.28 184	99.10 185	99.61 149	99.20 147	99.84 49	99.73 99	98.67 121	99.84 239	99.86 8	99.98 26	99.64 98

CP-MVSNet		99.54 53	99.43 69	99.87 15	99.76 90	99.82 31	99.57 75	99.61 149	99.54 94	99.80 64	99.64 152	97.79 219	99.95 47	99.21 79	99.94 70	99.84 14

TranMVSNet+NR-MVSNet		99.54 53	99.47 59	99.76 50	99.58 165	99.64 99	99.30 121	99.63 139	99.61 84	99.71 106	99.56 210	98.76 110	99.96 37	99.14 98	99.92 82	99.68 64

patch_mono-2		99.51 56	99.46 63	99.64 114	99.70 125	99.11 215	99.04 198	99.87 20	99.71 55	99.47 192	99.79 70	98.24 179	99.98 9	99.38 52	99.96 50	99.83 18

v2v482		99.50 57	99.47 59	99.58 141	99.78 78	99.25 192	99.14 172	99.58 179	99.25 138	99.81 61	99.62 171	98.24 179	99.84 239	99.83 9	99.97 36	99.64 98

ACMH+	98.40 8	99.50 57	99.43 69	99.71 86	99.86 34	99.76 55	99.32 114	99.77 65	99.53 96	99.77 77	99.76 88	99.26 47	99.78 288	97.77 209	99.88 110	99.60 128

Baseline_NR-MVSNet		99.49 59	99.37 79	99.82 26	99.91 18	99.84 20	98.83 232	99.86 23	99.68 64	99.65 127	99.88 30	97.67 227	99.87 184	99.03 105	99.86 126	99.76 42

TAMVS		99.49 59	99.45 64	99.63 121	99.48 221	99.42 153	99.45 89	99.57 181	99.66 72	99.78 72	99.83 49	97.85 215	99.86 204	99.44 44	99.96 50	99.61 124

pmmvs-eth3d		99.48 61	99.47 59	99.51 165	99.77 86	99.41 157	98.81 237	99.66 121	99.42 119	99.75 87	99.66 145	99.20 50	99.76 298	98.98 110	99.99 12	99.36 236

EI-MVSNet-UG-set		99.48 61	99.50 57	99.42 192	99.57 175	98.65 264	99.24 141	99.46 238	99.68 64	99.80 64	99.66 145	98.99 77	99.89 156	99.19 83	99.90 92	99.72 48

APDe-MVS		99.48 61	99.36 82	99.85 19	99.55 187	99.81 34	99.50 82	99.69 109	98.99 175	99.75 87	99.71 112	98.79 104	99.93 76	98.46 150	99.85 130	99.80 25

PMMVS2		99.48 61	99.45 64	99.57 147	99.76 90	98.99 228	98.09 306	99.90 15	98.95 181	99.78 72	99.58 197	99.57 20	99.93 76	99.48 40	99.95 59	99.79 31

DSMNet-mixed		99.48 61	99.65 27	98.95 271	99.71 117	97.27 323	99.50 82	99.82 40	99.59 92	99.41 212	99.85 43	99.62 16	100.00 1	99.53 35	99.89 101	99.59 137

DP-MVS		99.48 61	99.39 74	99.74 66	99.57 175	99.62 105	99.29 128	99.61 149	99.87 22	99.74 96	99.76 88	98.69 117	99.87 184	98.20 170	99.80 167	99.75 45

EI-MVSNet-Vis-set		99.47 67	99.49 58	99.42 192	99.57 175	98.66 261	99.24 141	99.46 238	99.67 68	99.79 69	99.65 150	98.97 80	99.89 156	99.15 92	99.89 101	99.71 51

VPNet		99.46 68	99.37 79	99.71 86	99.82 49	99.59 116	99.48 86	99.70 103	99.81 39	99.69 112	99.58 197	97.66 231	99.86 204	99.17 88	99.44 282	99.67 71

ACMM	98.09 11	99.46 68	99.38 76	99.72 82	99.80 61	99.69 85	99.13 178	99.65 131	98.99 175	99.64 129	99.72 105	99.39 27	99.86 204	98.23 167	99.81 162	99.60 128
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-4		99.45 70	99.44 66	99.50 168	99.52 199	98.94 235	99.17 162	99.53 207	99.64 76	99.76 79	99.60 189	98.96 83	99.90 140	98.91 121	99.84 135	99.67 71

COLMAP_ROB	98.06 12	99.45 70	99.37 79	99.70 90	99.83 42	99.70 81	99.38 100	99.78 62	99.53 96	99.67 119	99.78 77	99.19 51	99.86 204	97.32 246	99.87 119	99.55 154
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		99.44 72	99.42 71	99.49 171	99.89 24	98.96 233	99.62 60	99.76 70	99.85 30	99.82 54	99.88 30	96.39 276	99.97 19	99.59 24	99.98 26	99.55 154

tfpnnormal		99.43 73	99.38 76	99.60 135	99.87 32	99.75 59	99.59 71	99.78 62	99.71 55	99.90 26	99.69 125	98.85 95	99.90 140	97.25 256	99.78 178	99.15 279

HPM-MVS_fast		99.43 73	99.30 95	99.80 32	99.83 42	99.81 34	99.52 80	99.70 103	98.35 250	99.51 185	99.50 230	99.31 39	99.88 171	98.18 174	99.84 135	99.69 58

3Dnovator	99.15 2	99.43 73	99.36 82	99.65 107	99.39 251	99.42 153	99.70 34	99.56 186	99.23 142	99.35 223	99.80 62	99.17 53	99.95 47	98.21 169	99.84 135	99.59 137

Anonymous20240529		99.42 76	99.34 84	99.65 107	99.53 193	99.60 113	99.63 59	99.39 260	99.47 105	99.76 79	99.78 77	98.13 191	99.86 204	98.70 138	99.68 221	99.49 192

SixPastTwentyTwo		99.42 76	99.30 95	99.76 50	99.92 17	99.67 90	99.70 34	99.14 312	99.65 74	99.89 30	99.90 23	96.20 281	99.94 61	99.42 50	99.92 82	99.67 71

GBi-Net		99.42 76	99.31 90	99.73 76	99.49 215	99.77 47	99.68 43	99.70 103	99.44 112	99.62 142	99.83 49	97.21 250	99.90 140	98.96 114	99.90 92	99.53 168

test1		99.42 76	99.31 90	99.73 76	99.49 215	99.77 47	99.68 43	99.70 103	99.44 112	99.62 142	99.83 49	97.21 250	99.90 140	98.96 114	99.90 92	99.53 168

Regformer-3		99.41 80	99.41 72	99.40 200	99.52 199	98.70 256	99.17 162	99.44 243	99.62 80	99.75 87	99.60 189	98.90 90	99.85 222	98.89 122	99.84 135	99.65 89

MVSFormer		99.41 80	99.44 66	99.31 225	99.57 175	98.40 276	99.77 14	99.80 51	99.73 51	99.63 133	99.30 284	98.02 200	99.98 9	99.43 45	99.69 216	99.55 154

IterMVS-LS		99.41 80	99.47 59	99.25 237	99.81 56	98.09 295	98.85 229	99.76 70	99.62 80	99.83 53	99.64 152	98.54 140	99.97 19	99.15 92	99.99 12	99.68 64
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SED-MVS		99.40 83	99.28 102	99.77 43	99.69 129	99.82 31	99.20 151	99.54 198	99.13 161	99.82 54	99.63 162	98.91 87	99.92 96	97.85 204	99.70 213	99.58 142

v148		99.40 83	99.41 72	99.39 204	99.76 90	98.94 235	99.09 190	99.59 169	99.17 153	99.81 61	99.61 180	98.41 160	99.69 321	99.32 65	99.94 70	99.53 168

NR-MVSNet		99.40 83	99.31 90	99.68 92	99.43 240	99.55 125	99.73 26	99.50 223	99.46 109	99.88 36	99.36 270	97.54 235	99.87 184	98.97 112	99.87 119	99.63 103

PVSNet_Blended_VisFu		99.40 83	99.38 76	99.44 186	99.90 22	98.66 261	98.94 220	99.91 12	97.97 276	99.79 69	99.73 99	99.05 72	99.97 19	99.15 92	99.99 12	99.68 64

EU-MVSNet		99.39 87	99.62 32	98.72 298	99.88 28	96.44 339	99.56 77	99.85 27	99.90 11	99.90 26	99.85 43	98.09 193	99.83 251	99.58 27	99.95 59	99.90 4

CHOSEN 1792x2688		99.39 87	99.30 95	99.65 107	99.88 28	99.25 192	98.78 244	99.88 18	98.66 214	99.96 8	99.79 70	97.45 238	99.93 76	99.34 59	99.99 12	99.78 33

DVP-MVS++		99.38 89	99.25 109	99.77 43	99.03 329	99.77 47	99.74 23	99.61 149	99.18 149	99.76 79	99.61 180	99.00 75	99.92 96	97.72 215	99.60 250	99.62 114

EI-MVSNet		99.38 89	99.44 66	99.21 242	99.58 165	98.09 295	99.26 134	99.46 238	99.62 80	99.75 87	99.67 141	98.54 140	99.85 222	99.15 92	99.92 82	99.68 64

UGNet		99.38 89	99.34 84	99.49 171	98.90 339	98.90 243	99.70 34	99.35 271	99.86 25	98.57 321	99.81 59	98.50 150	99.93 76	99.38 52	99.98 26	99.66 81
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 92	99.25 109	99.72 82	99.47 227	99.56 122	98.97 216	99.61 149	99.43 117	99.67 119	99.28 289	97.85 215	99.95 47	99.17 88	99.81 162	99.65 89

UniMVSNet (Re)		99.37 92	99.26 107	99.68 92	99.51 204	99.58 119	98.98 215	99.60 162	99.43 117	99.70 109	99.36 270	97.70 222	99.88 171	99.20 82	99.87 119	99.59 137

CSCG		99.37 92	99.29 100	99.60 135	99.71 117	99.46 139	99.43 94	99.85 27	98.79 203	99.41 212	99.60 189	98.92 85	99.92 96	98.02 184	99.92 82	99.43 219

PM-MVS		99.36 95	99.29 100	99.58 141	99.83 42	99.66 92	98.95 218	99.86 23	98.85 195	99.81 61	99.73 99	98.40 164	99.92 96	98.36 155	99.83 145	99.17 275

abl_6		99.36 95	99.23 113	99.75 60	99.71 117	99.74 65	99.33 111	99.76 70	99.07 168	99.65 127	99.63 162	99.09 63	99.92 96	97.13 263	99.76 184	99.58 142

new-patchmatchnet		99.35 97	99.57 46	98.71 300	99.82 49	96.62 337	98.55 265	99.75 77	99.50 98	99.88 36	99.87 33	99.31 39	99.88 171	99.43 45	100.00 1	99.62 114

Anonymous20231206		99.35 97	99.31 90	99.47 177	99.74 107	99.06 226	99.28 129	99.74 82	99.23 142	99.72 101	99.53 221	97.63 233	99.88 171	99.11 100	99.84 135	99.48 197

MTAPA		99.35 97	99.20 115	99.80 32	99.81 56	99.81 34	99.33 111	99.53 207	99.27 134	99.42 204	99.63 162	98.21 184	99.95 47	97.83 207	99.79 172	99.65 89

FMVSNet2		99.35 97	99.28 102	99.55 154	99.49 215	99.35 173	99.45 89	99.57 181	99.44 112	99.70 109	99.74 95	97.21 250	99.87 184	99.03 105	99.94 70	99.44 213

3Dnovator+	98.92 3	99.35 97	99.24 111	99.67 95	99.35 261	99.47 135	99.62 60	99.50 223	99.44 112	99.12 268	99.78 77	98.77 109	99.94 61	97.87 201	99.72 208	99.62 114

TSAR-MVS + MP.		99.34 102	99.24 111	99.63 121	99.82 49	99.37 166	99.26 134	99.35 271	98.77 206	99.57 159	99.70 119	99.27 46	99.88 171	97.71 217	99.75 187	99.65 89
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Regformer-2		99.34 102	99.27 105	99.53 160	99.41 246	99.10 220	98.99 211	99.53 207	99.47 105	99.66 123	99.52 223	98.80 101	99.89 156	98.31 161	99.74 195	99.60 128

diffmvs		99.34 102	99.32 89	99.39 204	99.67 142	98.77 251	98.57 263	99.81 49	99.61 84	99.48 190	99.41 253	98.47 151	99.86 204	98.97 112	99.90 92	99.53 168

DELS-MVS		99.34 102	99.30 95	99.48 175	99.51 204	99.36 169	98.12 302	99.53 207	99.36 124	99.41 212	99.61 180	99.22 49	99.87 184	99.21 79	99.68 221	99.20 268
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		99.33 106	99.21 114	99.71 86	99.43 240	99.56 122	98.83 232	99.53 207	99.38 121	99.67 119	99.36 270	97.67 227	99.95 47	99.17 88	99.81 162	99.63 103

ab-mvs		99.33 106	99.28 102	99.47 177	99.57 175	99.39 160	99.78 11	99.43 247	98.87 193	99.57 159	99.82 56	98.06 196	99.87 184	98.69 140	99.73 202	99.15 279

DVP-MVS		99.32 108	99.17 118	99.77 43	99.69 129	99.80 39	99.14 172	99.31 280	99.16 155	99.62 142	99.61 180	98.35 168	99.91 120	97.88 198	99.72 208	99.61 124
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
Regformer-1		99.32 108	99.27 105	99.47 177	99.41 246	98.95 234	98.99 211	99.48 230	99.48 100	99.66 123	99.52 223	98.78 106	99.87 184	98.36 155	99.74 195	99.60 128

APD-MVS_3200maxsize		99.31 110	99.16 119	99.74 66	99.53 193	99.75 59	99.27 132	99.61 149	99.19 148	99.57 159	99.64 152	98.76 110	99.90 140	97.29 248	99.62 240	99.56 151

zzz-MVS		99.30 111	99.14 123	99.80 32	99.81 56	99.81 34	98.73 250	99.53 207	99.27 134	99.42 204	99.63 162	98.21 184	99.95 47	97.83 207	99.79 172	99.65 89

SteuartSystems-ACMMP		99.30 111	99.14 123	99.76 50	99.87 32	99.66 92	99.18 157	99.60 162	98.55 225	99.57 159	99.67 141	99.03 74	99.94 61	97.01 267	99.80 167	99.69 58
Skip Steuart: Steuart Systems R&D Blog.
testgi		99.29 113	99.26 107	99.37 211	99.75 101	98.81 248	98.84 230	99.89 16	98.38 243	99.75 87	99.04 326	99.36 36	99.86 204	99.08 102	99.25 310	99.45 208

ACMMP_NAP		99.28 114	99.11 133	99.79 37	99.75 101	99.81 34	98.95 218	99.53 207	98.27 259	99.53 178	99.73 99	98.75 112	99.87 184	97.70 220	99.83 145	99.68 64

LCM-MVSNet-Re		99.28 114	99.15 122	99.67 95	99.33 276	99.76 55	99.34 109	99.97 2	98.93 185	99.91 21	99.79 70	98.68 118	99.93 76	96.80 280	99.56 257	99.30 248

mvs_anonymous		99.28 114	99.39 74	98.94 272	99.19 304	97.81 308	99.02 202	99.55 192	99.78 47	99.85 46	99.80 62	98.24 179	99.86 204	99.57 29	99.50 274	99.15 279

MVS_Test		99.28 114	99.31 90	99.19 245	99.35 261	98.79 250	99.36 107	99.49 228	99.17 153	99.21 254	99.67 141	98.78 106	99.66 341	99.09 101	99.66 232	99.10 290

SR-MVS-dyc-post		99.27 118	99.11 133	99.73 76	99.54 188	99.74 65	99.26 134	99.62 142	99.16 155	99.52 180	99.64 152	98.41 160	99.91 120	97.27 251	99.61 247	99.54 162

XVS		99.27 118	99.11 133	99.75 60	99.71 117	99.71 74	99.37 104	99.61 149	99.29 130	98.76 307	99.47 243	98.47 151	99.88 171	97.62 228	99.73 202	99.67 71

OPM-MVS		99.26 120	99.13 126	99.63 121	99.70 125	99.61 111	98.58 259	99.48 230	98.50 231	99.52 180	99.63 162	99.14 58	99.76 298	97.89 197	99.77 182	99.51 181
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HFP-MVS		99.25 121	99.08 144	99.76 50	99.73 110	99.70 81	99.31 118	99.59 169	98.36 245	99.36 221	99.37 265	98.80 101	99.91 120	97.43 241	99.75 187	99.68 64

HPM-MVS		99.25 121	99.07 148	99.78 40	99.81 56	99.75 59	99.61 65	99.67 117	97.72 290	99.35 223	99.25 296	99.23 48	99.92 96	97.21 259	99.82 154	99.67 71
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		99.25 121	99.08 144	99.74 66	99.79 71	99.68 88	99.50 82	99.65 131	98.07 270	99.52 180	99.69 125	98.57 135	99.92 96	97.18 260	99.79 172	99.63 103
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		99.24 124	99.11 133	99.61 133	98.38 366	99.79 41	99.57 75	99.68 112	99.61 84	99.15 263	99.71 112	98.70 116	99.91 120	97.54 234	99.68 221	99.13 287

test1172		99.23 125	99.05 154	99.74 66	99.52 199	99.75 59	99.20 151	99.61 149	98.97 177	99.48 190	99.58 197	98.41 160	99.91 120	97.15 262	99.55 261	99.57 148

xiu_mvs_v1_base_debu		99.23 125	99.34 84	98.91 278	99.59 160	98.23 284	98.47 274	99.66 121	99.61 84	99.68 114	98.94 343	99.39 27	99.97 19	99.18 85	99.55 261	98.51 341

xiu_mvs_v1_base		99.23 125	99.34 84	98.91 278	99.59 160	98.23 284	98.47 274	99.66 121	99.61 84	99.68 114	98.94 343	99.39 27	99.97 19	99.18 85	99.55 261	98.51 341

xiu_mvs_v1_base_debi		99.23 125	99.34 84	98.91 278	99.59 160	98.23 284	98.47 274	99.66 121	99.61 84	99.68 114	98.94 343	99.39 27	99.97 19	99.18 85	99.55 261	98.51 341

region2R		99.23 125	99.05 154	99.77 43	99.76 90	99.70 81	99.31 118	99.59 169	98.41 239	99.32 231	99.36 270	98.73 115	99.93 76	97.29 248	99.74 195	99.67 71

ACMMPR		99.23 125	99.06 150	99.76 50	99.74 107	99.69 85	99.31 118	99.59 169	98.36 245	99.35 223	99.38 263	98.61 129	99.93 76	97.43 241	99.75 187	99.67 71

XVG-ACMP-BASELINE		99.23 125	99.10 141	99.63 121	99.82 49	99.58 119	98.83 232	99.72 94	98.36 245	99.60 151	99.71 112	98.92 85	99.91 120	97.08 265	99.84 135	99.40 225

CP-MVS		99.23 125	99.05 154	99.75 60	99.66 143	99.66 92	99.38 100	99.62 142	98.38 243	99.06 276	99.27 291	98.79 104	99.94 61	97.51 237	99.82 154	99.66 81

DeepC-MVS_fast	98.47 5	99.23 125	99.12 130	99.56 151	99.28 288	99.22 201	98.99 211	99.40 257	99.08 166	99.58 156	99.64 152	98.90 90	99.83 251	97.44 240	99.75 187	99.63 103
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		99.22 134	99.04 160	99.77 43	99.76 90	99.73 68	99.28 129	99.56 186	98.19 264	99.14 265	99.29 287	98.84 96	99.92 96	97.53 236	99.80 167	99.64 98

D2MVS		99.22 134	99.19 116	99.29 228	99.69 129	98.74 254	98.81 237	99.41 250	98.55 225	99.68 114	99.69 125	98.13 191	99.87 184	98.82 127	99.98 26	99.24 257

LPG-MVS_test		99.22 134	99.05 154	99.74 66	99.82 49	99.63 103	99.16 168	99.73 85	97.56 296	99.64 129	99.69 125	99.37 33	99.89 156	96.66 288	99.87 119	99.69 58

CDS-MVSNet		99.22 134	99.13 126	99.50 168	99.35 261	99.11 215	98.96 217	99.54 198	99.46 109	99.61 148	99.70 119	96.31 278	99.83 251	99.34 59	99.88 110	99.55 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_0402		99.22 134	99.14 123	99.45 184	99.79 71	99.43 150	99.28 129	99.68 112	99.54 94	99.40 217	99.56 210	99.07 69	99.82 261	96.01 317	99.96 50	99.11 288

AllTest		99.21 139	99.07 148	99.63 121	99.78 78	99.64 99	99.12 182	99.83 35	98.63 217	99.63 133	99.72 105	98.68 118	99.75 302	96.38 304	99.83 145	99.51 181

XVG-OURS		99.21 139	99.06 150	99.65 107	99.82 49	99.62 105	97.87 330	99.74 82	98.36 245	99.66 123	99.68 136	99.71 9	99.90 140	96.84 278	99.88 110	99.43 219

Fast-Effi-MVS+-dtu		99.20 141	99.12 130	99.43 190	99.25 293	99.69 85	99.05 196	99.82 40	99.50 98	98.97 280	99.05 323	98.98 78	99.98 9	98.20 170	99.24 312	98.62 334

VDD-MVS		99.20 141	99.11 133	99.44 186	99.43 240	98.98 229	99.50 82	98.32 349	99.80 42	99.56 166	99.69 125	96.99 260	99.85 222	98.99 108	99.73 202	99.50 187

PGM-MVS		99.20 141	99.01 166	99.77 43	99.75 101	99.71 74	99.16 168	99.72 94	97.99 274	99.42 204	99.60 189	98.81 97	99.93 76	96.91 272	99.74 195	99.66 81

SR-MVS		99.19 144	99.00 169	99.74 66	99.51 204	99.72 72	99.18 157	99.60 162	98.85 195	99.47 192	99.58 197	98.38 165	99.92 96	96.92 271	99.54 267	99.57 148

SMA-MVS		99.19 144	99.00 169	99.73 76	99.46 232	99.73 68	99.13 178	99.52 215	97.40 307	99.57 159	99.64 152	98.93 84	99.83 251	97.61 230	99.79 172	99.63 103
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		99.19 144	99.11 133	99.42 192	99.76 90	98.88 244	98.55 265	99.73 85	98.82 199	99.72 101	99.62 171	96.56 267	99.82 261	99.32 65	99.95 59	99.56 151

mPP-MVS		99.19 144	99.00 169	99.76 50	99.76 90	99.68 88	99.38 100	99.54 198	98.34 254	99.01 278	99.50 230	98.53 144	99.93 76	97.18 260	99.78 178	99.66 81

ETV-MVS		99.18 148	99.18 117	99.16 248	99.34 271	99.28 184	99.12 182	99.79 57	99.48 100	98.93 284	98.55 363	99.40 26	99.93 76	98.51 148	99.52 271	98.28 351

VNet		99.18 148	99.06 150	99.56 151	99.24 295	99.36 169	99.33 111	99.31 280	99.67 68	99.47 192	99.57 207	96.48 270	99.84 239	99.15 92	99.30 303	99.47 202

RPSCF		99.18 148	99.02 163	99.64 114	99.83 42	99.85 15	99.44 92	99.82 40	98.33 255	99.50 187	99.78 77	97.90 209	99.65 347	96.78 281	99.83 145	99.44 213

DeepPCF-MVS	98.42 6	99.18 148	99.02 163	99.67 95	99.22 297	99.75 59	97.25 357	99.47 234	98.72 211	99.66 123	99.70 119	99.29 41	99.63 350	98.07 183	99.81 162	99.62 114

EPP-MVSNet		99.17 152	99.00 169	99.66 102	99.80 61	99.43 150	99.70 34	99.24 298	99.48 100	99.56 166	99.77 84	94.89 294	99.93 76	98.72 137	99.89 101	99.63 103

GST-MVS		99.16 153	98.96 180	99.75 60	99.73 110	99.73 68	99.20 151	99.55 192	98.22 261	99.32 231	99.35 275	98.65 125	99.91 120	96.86 275	99.74 195	99.62 114

MVP-Stereo		99.16 153	99.08 144	99.43 190	99.48 221	99.07 224	99.08 193	99.55 192	98.63 217	99.31 235	99.68 136	98.19 187	99.78 288	98.18 174	99.58 255	99.45 208
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-OURS-SEG-HR		99.16 153	98.99 174	99.66 102	99.84 38	99.64 99	98.25 292	99.73 85	98.39 242	99.63 133	99.43 251	99.70 11	99.90 140	97.34 245	98.64 343	99.44 213

jason		99.16 153	99.11 133	99.32 222	99.75 101	98.44 273	98.26 291	99.39 260	98.70 212	99.74 96	99.30 284	98.54 140	99.97 19	98.48 149	99.82 154	99.55 154
jason: jason.
DPE-MVS		99.14 157	98.92 187	99.82 26	99.57 175	99.77 47	98.74 248	99.60 162	98.55 225	99.76 79	99.69 125	98.23 183	99.92 96	96.39 303	99.75 187	99.76 42
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		99.14 157	98.92 187	99.80 32	99.83 42	99.83 25	98.61 255	99.63 139	96.84 329	99.44 198	99.58 197	98.81 97	99.91 120	97.70 220	99.82 154	99.67 71
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs4		99.13 159	99.06 150	99.36 214	99.57 175	99.10 220	98.01 314	99.25 295	98.78 205	99.58 156	99.44 250	98.24 179	99.76 298	98.74 135	99.93 78	99.22 262

MVS_111021_LR		99.13 159	99.03 162	99.42 192	99.58 165	99.32 178	97.91 329	99.73 85	98.68 213	99.31 235	99.48 238	99.09 63	99.66 341	97.70 220	99.77 182	99.29 251

EIA-MVS		99.12 161	99.01 166	99.45 184	99.36 259	99.62 105	99.34 109	99.79 57	98.41 239	98.84 297	98.89 348	98.75 112	99.84 239	98.15 178	99.51 272	98.89 319

#test#		99.12 161	98.90 191	99.76 50	99.73 110	99.70 81	99.10 185	99.59 169	97.60 295	99.36 221	99.37 265	98.80 101	99.91 120	96.84 278	99.75 187	99.68 64

TSAR-MVS + GP.		99.12 161	99.04 160	99.38 208	99.34 271	99.16 210	98.15 298	99.29 285	98.18 265	99.63 133	99.62 171	99.18 52	99.68 332	98.20 170	99.74 195	99.30 248

MVS_111021_HR		99.12 161	99.02 163	99.40 200	99.50 210	99.11 215	97.92 327	99.71 97	98.76 209	99.08 272	99.47 243	99.17 53	99.54 360	97.85 204	99.76 184	99.54 162

xxxxxxxxxxxxxcwj		99.11 165	98.96 180	99.54 158	99.53 193	99.25 192	98.29 288	99.76 70	99.07 168	99.42 204	99.61 180	98.86 93	99.87 184	96.45 300	99.68 221	99.49 192

CANet		99.11 165	99.05 154	99.28 230	98.83 348	98.56 266	98.71 253	99.41 250	99.25 138	99.23 248	99.22 303	97.66 231	99.94 61	99.19 83	99.97 36	99.33 242

WR-MVS		99.11 165	98.93 183	99.66 102	99.30 283	99.42 153	98.42 280	99.37 267	99.04 173	99.57 159	99.20 307	96.89 262	99.86 204	98.66 142	99.87 119	99.70 54

PHI-MVS		99.11 165	98.95 182	99.59 137	99.13 312	99.59 116	99.17 162	99.65 131	97.88 282	99.25 244	99.46 246	98.97 80	99.80 282	97.26 253	99.82 154	99.37 233

SF-MVS		99.10 169	98.93 183	99.62 130	99.58 165	99.51 130	99.13 178	99.65 131	97.97 276	99.42 204	99.61 180	98.86 93	99.87 184	96.45 300	99.68 221	99.49 192

MSDG		99.08 170	98.98 177	99.37 211	99.60 156	99.13 213	97.54 343	99.74 82	98.84 198	99.53 178	99.55 217	99.10 61	99.79 285	97.07 266	99.86 126	99.18 273

Effi-MVS+-dtu		99.07 171	98.92 187	99.52 162	98.89 342	99.78 44	99.15 170	99.66 121	99.34 125	98.92 287	99.24 301	97.69 224	99.98 9	98.11 180	99.28 306	98.81 326

Effi-MVS+		99.06 172	98.97 178	99.34 216	99.31 279	98.98 229	98.31 287	99.91 12	98.81 200	98.79 303	98.94 343	99.14 58	99.84 239	98.79 129	98.74 339	99.20 268

MP-MVS		99.06 172	98.83 200	99.76 50	99.76 90	99.71 74	99.32 114	99.50 223	98.35 250	98.97 280	99.48 238	98.37 166	99.92 96	95.95 323	99.75 187	99.63 103
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDA-MVSNet-bldmvs		99.06 172	99.05 154	99.07 263	99.80 61	97.83 307	98.89 222	99.72 94	99.29 130	99.63 133	99.70 119	96.47 271	99.89 156	98.17 176	99.82 154	99.50 187

MSLP-MVS++		99.05 175	99.09 142	98.91 278	99.21 299	98.36 280	98.82 236	99.47 234	98.85 195	98.90 290	99.56 210	98.78 106	99.09 373	98.57 145	99.68 221	99.26 254

1112_ss		99.05 175	98.84 198	99.67 95	99.66 143	99.29 182	98.52 270	99.82 40	97.65 293	99.43 202	99.16 310	96.42 273	99.91 120	99.07 103	99.84 135	99.80 25

ACMP	97.51 14	99.05 175	98.84 198	99.67 95	99.78 78	99.55 125	98.88 223	99.66 121	97.11 323	99.47 192	99.60 189	99.07 69	99.89 156	96.18 312	99.85 130	99.58 142
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSP-MVS		99.04 178	98.79 205	99.81 29	99.78 78	99.73 68	99.35 108	99.57 181	98.54 228	99.54 173	98.99 333	96.81 264	99.93 76	96.97 269	99.53 269	99.77 37
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		99.03 179	99.01 166	99.09 259	99.54 188	97.99 299	98.58 259	99.82 40	97.62 294	99.34 226	99.71 112	98.52 147	99.77 296	97.98 189	99.97 36	99.52 179

IS-MVSNet		99.03 179	98.85 196	99.55 154	99.80 61	99.25 192	99.73 26	99.15 311	99.37 122	99.61 148	99.71 112	94.73 297	99.81 277	97.70 220	99.88 110	99.58 142

xiu_mvs_v2_base		99.02 181	99.11 133	98.77 295	99.37 257	98.09 295	98.13 301	99.51 219	99.47 105	99.42 204	98.54 364	99.38 31	99.97 19	98.83 125	99.33 300	98.24 353

Fast-Effi-MVS+		99.02 181	98.87 194	99.46 180	99.38 254	99.50 131	99.04 198	99.79 57	97.17 319	98.62 316	98.74 356	99.34 37	99.95 47	98.32 160	99.41 288	98.92 317

canonicalmvs		99.02 181	99.00 169	99.09 259	99.10 320	98.70 256	99.61 65	99.66 121	99.63 79	98.64 315	97.65 377	99.04 73	99.54 360	98.79 129	98.92 328	99.04 304

MCST-MVS		99.02 181	98.81 202	99.65 107	99.58 165	99.49 132	98.58 259	99.07 315	98.40 241	99.04 277	99.25 296	98.51 149	99.80 282	97.31 247	99.51 272	99.65 89

SD-MVS		99.01 185	99.30 95	98.15 320	99.50 210	99.40 158	98.94 220	99.61 149	99.22 146	99.75 87	99.82 56	99.54 23	95.51 380	97.48 238	99.87 119	99.54 162
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		99.01 185	98.92 187	99.27 232	99.71 117	99.28 184	98.59 258	99.77 65	98.32 256	99.39 218	99.41 253	98.62 127	99.84 239	96.62 292	99.84 135	98.69 332

IterMVS-SCA-FT		99.00 187	99.16 119	98.51 305	99.75 101	95.90 347	98.07 309	99.84 33	99.84 33	99.89 30	99.73 99	96.01 285	99.99 6	99.33 63	100.00 1	99.63 103

MS-PatchMatch		99.00 187	98.97 178	99.09 259	99.11 319	98.19 287	98.76 247	99.33 274	98.49 233	99.44 198	99.58 197	98.21 184	99.69 321	98.20 170	99.62 240	99.39 228

PS-MVSNAJ		99.00 187	99.08 144	98.76 296	99.37 257	98.10 294	98.00 316	99.51 219	99.47 105	99.41 212	98.50 366	99.28 43	99.97 19	98.83 125	99.34 298	98.20 357

CNVR-MVS		98.99 190	98.80 204	99.56 151	99.25 293	99.43 150	98.54 268	99.27 289	98.58 222	98.80 302	99.43 251	98.53 144	99.70 315	97.22 258	99.59 254	99.54 162

VDDNet		98.97 191	98.82 201	99.42 192	99.71 117	98.81 248	99.62 60	98.68 332	99.81 39	99.38 219	99.80 62	94.25 301	99.85 222	98.79 129	99.32 301	99.59 137

IterMVS		98.97 191	99.16 119	98.42 309	99.74 107	95.64 350	98.06 311	99.83 35	99.83 36	99.85 46	99.74 95	96.10 284	99.99 6	99.27 75	100.00 1	99.63 103
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap		98.97 191	98.93 183	99.07 263	99.46 232	98.19 287	97.75 334	99.75 77	98.79 203	99.54 173	99.70 119	98.97 80	99.62 351	96.63 291	99.83 145	99.41 223

HPM-MVS++		98.96 194	98.70 212	99.74 66	99.52 199	99.71 74	98.86 227	99.19 307	98.47 235	98.59 319	99.06 322	98.08 195	99.91 120	96.94 270	99.60 250	99.60 128

lupinMVS		98.96 194	98.87 194	99.24 239	99.57 175	98.40 276	98.12 302	99.18 308	98.28 258	99.63 133	99.13 312	98.02 200	99.97 19	98.22 168	99.69 216	99.35 239

USDC		98.96 194	98.93 183	99.05 265	99.54 188	97.99 299	97.07 363	99.80 51	98.21 262	99.75 87	99.77 84	98.43 157	99.64 349	97.90 196	99.88 110	99.51 181

YYNet1		98.95 197	98.99 174	98.84 288	99.64 147	97.14 327	98.22 294	99.32 276	98.92 187	99.59 154	99.66 145	97.40 240	99.83 251	98.27 164	99.90 92	99.55 154

MDA-MVSNet_test_wron		98.95 197	98.99 174	98.85 286	99.64 147	97.16 326	98.23 293	99.33 274	98.93 185	99.56 166	99.66 145	97.39 242	99.83 251	98.29 162	99.88 110	99.55 154

Test_1112_low_res		98.95 197	98.73 207	99.63 121	99.68 137	99.15 212	98.09 306	99.80 51	97.14 321	99.46 196	99.40 257	96.11 283	99.89 156	99.01 107	99.84 135	99.84 14

CANet_DTU		98.91 200	98.85 196	99.09 259	98.79 353	98.13 290	98.18 295	99.31 280	99.48 100	98.86 295	99.51 227	96.56 267	99.95 47	99.05 104	99.95 59	99.19 271

HyFIR lowres test		98.91 200	98.64 215	99.73 76	99.85 37	99.47 135	98.07 309	99.83 35	98.64 216	99.89 30	99.60 189	92.57 319	100.00 1	99.33 63	99.97 36	99.72 48

HQP_MVS		98.90 202	98.68 214	99.55 154	99.58 165	99.24 197	98.80 240	99.54 198	98.94 182	99.14 265	99.25 296	97.24 248	99.82 261	95.84 326	99.78 178	99.60 128

sss		98.90 202	98.77 206	99.27 232	99.48 221	98.44 273	98.72 251	99.32 276	97.94 280	99.37 220	99.35 275	96.31 278	99.91 120	98.85 124	99.63 239	99.47 202

OMC-MVS		98.90 202	98.72 208	99.44 186	99.39 251	99.42 153	98.58 259	99.64 137	97.31 313	99.44 198	99.62 171	98.59 132	99.69 321	96.17 313	99.79 172	99.22 262

ppachtmachnet_test		98.89 205	99.12 130	98.20 319	99.66 143	95.24 354	97.63 339	99.68 112	99.08 166	99.78 72	99.62 171	98.65 125	99.88 171	98.02 184	99.96 50	99.48 197

MVS_0304		98.88 206	98.71 209	99.39 204	98.85 346	98.91 242	99.45 89	99.30 283	98.56 223	97.26 365	99.68 136	96.18 282	99.96 37	99.17 88	99.94 70	99.29 251

new_pmnet		98.88 206	98.89 192	98.84 288	99.70 125	97.62 314	98.15 298	99.50 223	97.98 275	99.62 142	99.54 219	98.15 190	99.94 61	97.55 233	99.84 135	98.95 314

K. test v3		98.87 208	98.60 218	99.69 91	99.93 15	99.46 139	99.74 23	94.97 372	99.78 47	99.88 36	99.88 30	93.66 309	99.97 19	99.61 22	99.95 59	99.64 98

APD-MVS		98.87 208	98.59 220	99.71 86	99.50 210	99.62 105	99.01 204	99.57 181	96.80 331	99.54 173	99.63 162	98.29 175	99.91 120	95.24 340	99.71 211	99.61 124
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
our_test_3		98.85 210	99.09 142	98.13 321	99.66 143	94.90 357	97.72 335	99.58 179	99.07 168	99.64 129	99.62 171	98.19 187	99.93 76	98.41 152	99.95 59	99.55 154

mvs-test1		98.83 211	98.70 212	99.22 241	98.89 342	99.65 97	98.88 223	99.66 121	99.34 125	98.29 332	98.94 343	97.69 224	99.96 37	98.11 180	98.54 347	98.04 361

UnsupCasMVSNet_eth		98.83 211	98.57 224	99.59 137	99.68 137	99.45 144	98.99 211	99.67 117	99.48 100	99.55 171	99.36 270	94.92 293	99.86 204	98.95 118	96.57 371	99.45 208

NCCC		98.82 213	98.57 224	99.58 141	99.21 299	99.31 179	98.61 255	99.25 295	98.65 215	98.43 329	99.26 294	97.86 213	99.81 277	96.55 293	99.27 309	99.61 124

PMVS	92.94 21	98.82 213	98.81 202	98.85 286	99.84 38	97.99 299	99.20 151	99.47 234	99.71 55	99.42 204	99.82 56	98.09 193	99.47 367	93.88 359	99.85 130	99.07 301
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FMVSNet3		98.80 215	98.63 217	99.32 222	99.13 312	98.72 255	99.10 185	99.48 230	99.23 142	99.62 142	99.64 152	92.57 319	99.86 204	98.96 114	99.90 92	99.39 228

Patchmtry		98.78 216	98.54 228	99.49 171	98.89 342	99.19 208	99.32 114	99.67 117	99.65 74	99.72 101	99.79 70	91.87 327	99.95 47	98.00 188	99.97 36	99.33 242

ETH3D-3000-0.1		98.77 217	98.50 232	99.59 137	99.47 227	99.53 127	98.77 245	99.60 162	97.33 312	99.23 248	99.50 230	97.91 208	99.83 251	95.02 344	99.67 228	99.41 223

Vis-MVSNet (Re-imp)		98.77 217	98.58 223	99.34 216	99.78 78	98.88 244	99.61 65	99.56 186	99.11 165	99.24 247	99.56 210	93.00 317	99.78 288	97.43 241	99.89 101	99.35 239

CLD-MVS		98.76 219	98.57 224	99.33 218	99.57 175	98.97 231	97.53 345	99.55 192	96.41 335	99.27 242	99.13 312	99.07 69	99.78 288	96.73 284	99.89 101	99.23 260
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
iter_conf_final		98.75 220	98.54 228	99.40 200	99.33 276	98.75 252	99.26 134	99.59 169	99.80 42	99.76 79	99.58 197	90.17 348	99.92 96	99.37 55	99.97 36	99.54 162

Anonymous202405211		98.75 220	98.46 234	99.63 121	99.34 271	99.66 92	99.47 88	97.65 358	99.28 133	99.56 166	99.50 230	93.15 313	99.84 239	98.62 143	99.58 255	99.40 225

CPTT-MVS		98.74 222	98.44 236	99.64 114	99.61 154	99.38 163	99.18 157	99.55 192	96.49 334	99.27 242	99.37 265	97.11 256	99.92 96	95.74 330	99.67 228	99.62 114

F-COLMAP		98.74 222	98.45 235	99.62 130	99.57 175	99.47 135	98.84 230	99.65 131	96.31 338	98.93 284	99.19 309	97.68 226	99.87 184	96.52 295	99.37 295	99.53 168

N_pmnet		98.73 224	98.53 230	99.35 215	99.72 114	98.67 258	98.34 283	94.65 373	98.35 250	99.79 69	99.68 136	98.03 198	99.93 76	98.28 163	99.92 82	99.44 213

c3_l		98.72 225	98.71 209	98.72 298	99.12 314	97.22 325	97.68 338	99.56 186	98.90 189	99.54 173	99.48 238	96.37 277	99.73 307	97.88 198	99.88 110	99.21 264

CL-MVSNet_self_test		98.71 226	98.56 227	99.15 250	99.22 297	98.66 261	97.14 360	99.51 219	98.09 269	99.54 173	99.27 291	96.87 263	99.74 304	98.43 151	98.96 325	99.03 305

PVSNet_Blended		98.70 227	98.59 220	99.02 267	99.54 188	97.99 299	97.58 342	99.82 40	95.70 347	99.34 226	98.98 336	98.52 147	99.77 296	97.98 189	99.83 145	99.30 248

eth_miper_zixun_eth		98.68 228	98.71 209	98.60 302	99.10 320	96.84 334	97.52 347	99.54 198	98.94 182	99.58 156	99.48 238	96.25 280	99.76 298	98.01 187	99.93 78	99.21 264

PatchMatch-RL		98.68 228	98.47 233	99.30 227	99.44 237	99.28 184	98.14 300	99.54 198	97.12 322	99.11 269	99.25 296	97.80 218	99.70 315	96.51 296	99.30 303	98.93 316

miper_lstm_enhance		98.65 230	98.60 218	98.82 293	99.20 302	97.33 322	97.78 333	99.66 121	99.01 174	99.59 154	99.50 230	94.62 298	99.85 222	98.12 179	99.90 92	99.26 254

test_part1		98.63 231	98.26 255	99.75 60	99.40 249	99.49 132	99.67 47	99.68 112	99.86 25	99.88 36	99.86 40	86.73 367	99.93 76	99.34 59	99.97 36	99.81 24

test_prior3		98.62 232	98.34 247	99.46 180	99.35 261	99.22 201	97.95 323	99.39 260	97.87 283	98.05 345	99.05 323	97.90 209	99.69 321	95.99 319	99.49 276	99.48 197

h-mvs33		98.61 233	98.34 247	99.44 186	99.60 156	98.67 258	99.27 132	99.44 243	99.68 64	99.32 231	99.49 235	92.50 322	100.00 1	99.24 76	96.51 372	99.65 89

CVMVSNet		98.61 233	98.88 193	97.80 329	99.58 165	93.60 364	99.26 134	99.64 137	99.66 72	99.72 101	99.67 141	93.26 312	99.93 76	99.30 69	99.81 162	99.87 9

Patchmatch-RL test		98.60 235	98.36 244	99.33 218	99.77 86	99.07 224	98.27 290	99.87 20	98.91 188	99.74 96	99.72 105	90.57 344	99.79 285	98.55 146	99.85 130	99.11 288

RPMNet		98.60 235	98.53 230	98.83 290	99.05 325	98.12 291	99.30 121	99.62 142	99.86 25	99.16 261	99.74 95	92.53 321	99.92 96	98.75 134	98.77 335	98.44 346

AdaColmap		98.60 235	98.35 246	99.38 208	99.12 314	99.22 201	98.67 254	99.42 249	97.84 287	98.81 300	99.27 291	97.32 246	99.81 277	95.14 341	99.53 269	99.10 290

miper_ehance_all_eth		98.59 238	98.59 220	98.59 303	98.98 335	97.07 328	97.49 348	99.52 215	98.50 231	99.52 180	99.37 265	96.41 275	99.71 313	97.86 202	99.62 240	99.00 311

WTY-MVS		98.59 238	98.37 243	99.26 234	99.43 240	98.40 276	98.74 248	99.13 314	98.10 267	99.21 254	99.24 301	94.82 295	99.90 140	97.86 202	98.77 335	99.49 192

CNLPA		98.57 240	98.34 247	99.28 230	99.18 306	99.10 220	98.34 283	99.41 250	98.48 234	98.52 324	98.98 336	97.05 258	99.78 288	95.59 332	99.50 274	98.96 312

testtj		98.56 241	98.17 266	99.72 82	99.45 235	99.60 113	98.88 223	99.50 223	96.88 326	99.18 260	99.48 238	97.08 257	99.92 96	93.69 360	99.38 291	99.63 103

1121		98.56 241	98.24 256	99.52 162	99.49 215	99.24 197	99.30 121	99.22 302	95.77 345	98.52 324	99.29 287	97.39 242	99.85 222	95.79 328	99.34 298	99.46 206

CDPH-MVS		98.56 241	98.20 261	99.61 133	99.50 210	99.46 139	98.32 286	99.41 250	95.22 352	99.21 254	99.10 319	98.34 171	99.82 261	95.09 343	99.66 232	99.56 151

UnsupCasMVSNet_bld		98.55 244	98.27 254	99.40 200	99.56 186	99.37 166	97.97 322	99.68 112	97.49 303	99.08 272	99.35 275	95.41 292	99.82 261	97.70 220	98.19 356	99.01 310

cl____		98.54 245	98.41 239	98.92 276	99.03 329	97.80 309	97.46 349	99.59 169	98.90 189	99.60 151	99.46 246	93.85 305	99.78 288	97.97 191	99.89 101	99.17 275

DIV-MVS_self_test		98.54 245	98.42 238	98.92 276	99.03 329	97.80 309	97.46 349	99.59 169	98.90 189	99.60 151	99.46 246	93.87 304	99.78 288	97.97 191	99.89 101	99.18 273

FA-MVS(test-final)		98.52 247	98.32 250	99.10 258	99.48 221	98.67 258	99.77 14	98.60 338	97.35 310	99.63 133	99.80 62	93.07 315	99.84 239	97.92 194	99.30 303	98.78 329

hse-mvs2		98.52 247	98.30 252	99.16 248	99.29 285	98.60 265	98.77 245	99.02 319	99.68 64	99.32 231	99.04 326	92.50 322	99.85 222	99.24 76	97.87 363	99.03 305

MG-MVS		98.52 247	98.39 241	98.94 272	99.15 309	97.39 321	98.18 295	99.21 306	98.89 192	99.23 248	99.63 162	97.37 244	99.74 304	94.22 353	99.61 247	99.69 58

ETH3D cwj APD-0.16		98.50 250	98.16 267	99.51 165	99.04 327	99.39 160	98.47 274	99.47 234	96.70 333	98.78 305	99.33 279	97.62 234	99.86 204	94.69 349	99.38 291	99.28 253

DP-MVS Recon		98.50 250	98.23 257	99.31 225	99.49 215	99.46 139	98.56 264	99.63 139	94.86 358	98.85 296	99.37 265	97.81 217	99.59 357	96.08 314	99.44 282	98.88 320

CMPMVS	77.52 23	98.50 250	98.19 264	99.41 199	98.33 368	99.56 122	99.01 204	99.59 169	95.44 349	99.57 159	99.80 62	95.64 289	99.46 369	96.47 299	99.92 82	99.21 264
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t		98.49 253	98.11 270	99.64 114	99.73 110	99.58 119	99.24 141	99.76 70	89.94 371	99.42 204	99.56 210	97.76 221	99.86 204	97.74 214	99.82 154	99.47 202

PMMVS		98.49 253	98.29 253	99.11 256	98.96 336	98.42 275	97.54 343	99.32 276	97.53 300	98.47 328	98.15 372	97.88 212	99.82 261	97.46 239	99.24 312	99.09 293

MVSTER		98.47 255	98.22 259	99.24 239	99.06 324	98.35 281	99.08 193	99.46 238	99.27 134	99.75 87	99.66 145	88.61 356	99.85 222	99.14 98	99.92 82	99.52 179

iter_conf05		98.46 256	98.23 257	99.15 250	99.04 327	97.99 299	99.10 185	99.61 149	99.79 45	99.76 79	99.58 197	87.88 358	99.92 96	99.31 68	99.97 36	99.53 168

LFMVS		98.46 256	98.19 264	99.26 234	99.24 295	98.52 269	99.62 60	96.94 365	99.87 22	99.31 235	99.58 197	91.04 335	99.81 277	98.68 141	99.42 287	99.45 208

PatchT		98.45 258	98.32 250	98.83 290	98.94 337	98.29 282	99.24 141	98.82 327	99.84 33	99.08 272	99.76 88	91.37 330	99.94 61	98.82 127	99.00 324	98.26 352

MIMVSNet		98.43 259	98.20 261	99.11 256	99.53 193	98.38 279	99.58 73	98.61 336	98.96 180	99.33 228	99.76 88	90.92 337	99.81 277	97.38 244	99.76 184	99.15 279

PVSNet	97.47 15	98.42 260	98.44 236	98.35 312	99.46 232	96.26 341	96.70 368	99.34 273	97.68 292	99.00 279	99.13 312	97.40 240	99.72 309	97.59 232	99.68 221	99.08 296

CHOSEN 280x420		98.41 261	98.41 239	98.40 310	99.34 271	95.89 348	96.94 365	99.44 243	98.80 202	99.25 244	99.52 223	93.51 311	99.98 9	98.94 119	99.98 26	99.32 245

BH-RMVSNet		98.41 261	98.14 269	99.21 242	99.21 299	98.47 270	98.60 257	98.26 350	98.35 250	98.93 284	99.31 282	97.20 253	99.66 341	94.32 351	99.10 318	99.51 181

QAPM		98.40 263	97.99 276	99.65 107	99.39 251	99.47 135	99.67 47	99.52 215	91.70 368	98.78 305	99.80 62	98.55 138	99.95 47	94.71 348	99.75 187	99.53 168

API-MVS		98.38 264	98.39 241	98.35 312	98.83 348	99.26 188	99.14 172	99.18 308	98.59 221	98.66 314	98.78 354	98.61 129	99.57 359	94.14 354	99.56 257	96.21 373

HQP-MVS		98.36 265	98.02 275	99.39 204	99.31 279	98.94 235	97.98 319	99.37 267	97.45 304	98.15 339	98.83 351	96.67 265	99.70 315	94.73 346	99.67 228	99.53 168

PAPM_NR		98.36 265	98.04 273	99.33 218	99.48 221	98.93 239	98.79 243	99.28 288	97.54 299	98.56 322	98.57 361	97.12 255	99.69 321	94.09 355	98.90 330	99.38 230

PLC	97.35 16	98.36 265	97.99 276	99.48 175	99.32 278	99.24 197	98.50 272	99.51 219	95.19 354	98.58 320	98.96 341	96.95 261	99.83 251	95.63 331	99.25 310	99.37 233
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		98.35 268	97.95 280	99.57 147	99.35 261	99.35 173	98.11 304	99.41 250	94.90 356	97.92 350	98.99 333	98.02 200	99.85 222	95.38 338	99.44 282	99.50 187

CR-MVSNet		98.35 268	98.20 261	98.83 290	99.05 325	98.12 291	99.30 121	99.67 117	97.39 308	99.16 261	99.79 70	91.87 327	99.91 120	98.78 132	98.77 335	98.44 346

agg_prior1		98.33 270	97.92 286	99.57 147	99.35 261	99.36 169	97.99 318	99.39 260	94.85 359	97.76 359	98.98 336	98.03 198	99.85 222	95.49 334	99.44 282	99.51 181

DPM-MVS		98.28 271	97.94 284	99.32 222	99.36 259	99.11 215	97.31 355	98.78 329	96.88 326	98.84 297	99.11 318	97.77 220	99.61 355	94.03 357	99.36 296	99.23 260

alignmvs		98.28 271	97.96 279	99.25 237	99.12 314	98.93 239	99.03 201	98.42 345	99.64 76	98.72 310	97.85 375	90.86 340	99.62 351	98.88 123	99.13 315	99.19 271

test_yl		98.25 273	97.95 280	99.13 254	99.17 307	98.47 270	99.00 206	98.67 334	98.97 177	99.22 252	99.02 331	91.31 331	99.69 321	97.26 253	98.93 326	99.24 257

DCV-MVSNet		98.25 273	97.95 280	99.13 254	99.17 307	98.47 270	99.00 206	98.67 334	98.97 177	99.22 252	99.02 331	91.31 331	99.69 321	97.26 253	98.93 326	99.24 257

MAR-MVS		98.24 275	97.92 286	99.19 245	98.78 355	99.65 97	99.17 162	99.14 312	95.36 350	98.04 347	98.81 353	97.47 237	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
OpenMVS	98.12 10	98.23 276	97.89 290	99.26 234	99.19 304	99.26 188	99.65 57	99.69 109	91.33 369	98.14 343	99.77 84	98.28 176	99.96 37	95.41 337	99.55 261	98.58 338

BH-untuned		98.22 277	98.09 271	98.58 304	99.38 254	97.24 324	98.55 265	98.98 322	97.81 288	99.20 259	98.76 355	97.01 259	99.65 347	94.83 345	98.33 351	98.86 322

HY-MVS	98.23 9	98.21 278	97.95 280	98.99 268	99.03 329	98.24 283	99.61 65	98.72 331	96.81 330	98.73 309	99.51 227	94.06 302	99.86 204	96.91 272	98.20 354	98.86 322

EPNet		98.13 279	97.77 294	99.18 247	94.57 382	97.99 299	99.24 141	97.96 354	99.74 50	97.29 364	99.62 171	93.13 314	99.97 19	98.59 144	99.83 145	99.58 142
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SCA		98.11 280	98.36 244	97.36 339	99.20 302	92.99 366	98.17 297	98.49 343	98.24 260	99.10 271	99.57 207	96.01 285	99.94 61	96.86 275	99.62 240	99.14 284

Patchmatch-test		98.10 281	97.98 278	98.48 307	99.27 290	96.48 338	99.40 96	99.07 315	98.81 200	99.23 248	99.57 207	90.11 349	99.87 184	96.69 285	99.64 237	99.09 293

pmmvs3		98.08 282	97.80 291	98.91 278	99.41 246	97.69 313	97.87 330	99.66 121	95.87 342	99.50 187	99.51 227	90.35 346	99.97 19	98.55 146	99.47 279	99.08 296

JIA-IIPM		98.06 283	97.92 286	98.50 306	98.59 362	97.02 329	98.80 240	98.51 341	99.88 21	97.89 352	99.87 33	91.89 326	99.90 140	98.16 177	97.68 365	98.59 336

miper_enhance_ethall		98.03 284	97.94 284	98.32 314	98.27 369	96.43 340	96.95 364	99.41 250	96.37 337	99.43 202	98.96 341	94.74 296	99.69 321	97.71 217	99.62 240	98.83 325

TAPA-MVS	97.92 13	98.03 284	97.55 300	99.46 180	99.47 227	99.44 146	98.50 272	99.62 142	86.79 372	99.07 275	99.26 294	98.26 178	99.62 351	97.28 250	99.73 202	99.31 247
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
1314		98.00 286	97.90 289	98.27 318	98.90 339	97.45 319	99.30 121	99.06 317	94.98 355	97.21 366	99.12 316	98.43 157	99.67 337	95.58 333	98.56 346	97.71 365

GA-MVS		97.99 287	97.68 297	98.93 275	99.52 199	98.04 298	97.19 359	99.05 318	98.32 256	98.81 300	98.97 339	89.89 352	99.41 370	98.33 159	99.05 320	99.34 241

MVS-HIRNet		97.86 288	98.22 259	96.76 348	99.28 288	91.53 374	98.38 282	92.60 378	99.13 161	99.31 235	99.96 12	97.18 254	99.68 332	98.34 158	99.83 145	99.07 301

FE-MVS		97.85 289	97.42 302	99.15 250	99.44 237	98.75 252	99.77 14	98.20 351	95.85 343	99.33 228	99.80 62	88.86 355	99.88 171	96.40 302	99.12 316	98.81 326

AUN-MVS		97.82 290	97.38 303	99.14 253	99.27 290	98.53 267	98.72 251	99.02 319	98.10 267	97.18 367	99.03 330	89.26 354	99.85 222	97.94 193	97.91 361	99.03 305

FMVSNet5		97.80 291	97.25 307	99.42 192	98.83 348	98.97 231	99.38 100	99.80 51	98.87 193	99.25 244	99.69 125	80.60 378	99.91 120	98.96 114	99.90 92	99.38 230

ADS-MVSNet2		97.78 292	97.66 299	98.12 322	99.14 310	95.36 352	99.22 148	98.75 330	96.97 324	98.25 335	99.64 152	90.90 338	99.94 61	96.51 296	99.56 257	99.08 296

ETH3 D test6400		97.76 293	97.19 309	99.50 168	99.38 254	99.26 188	98.34 283	99.49 228	92.99 365	98.54 323	99.20 307	95.92 287	99.82 261	91.14 367	99.66 232	99.40 225

test1111		97.74 294	98.16 267	96.49 353	99.60 156	89.86 382	99.71 33	91.21 379	99.89 16	99.88 36	99.87 33	93.73 308	99.90 140	99.56 30	99.99 12	99.70 54

ECVR-MVS		97.73 295	98.04 273	96.78 347	99.59 160	90.81 378	99.72 29	90.43 381	99.89 16	99.86 44	99.86 40	93.60 310	99.89 156	99.46 42	99.99 12	99.65 89

baseline1		97.73 295	97.33 304	98.96 270	99.30 283	97.73 311	99.40 96	98.42 345	99.33 128	99.46 196	99.21 305	91.18 333	99.82 261	98.35 157	91.26 377	99.32 245

tpmrst		97.73 295	98.07 272	96.73 350	98.71 359	92.00 370	99.10 185	98.86 324	98.52 229	98.92 287	99.54 219	91.90 325	99.82 261	98.02 184	99.03 322	98.37 348

ADS-MVSNet		97.72 298	97.67 298	97.86 327	99.14 310	94.65 358	99.22 148	98.86 324	96.97 324	98.25 335	99.64 152	90.90 338	99.84 239	96.51 296	99.56 257	99.08 296

PatchmatchNet		97.65 299	97.80 291	97.18 344	98.82 351	92.49 368	99.17 162	98.39 347	98.12 266	98.79 303	99.58 197	90.71 342	99.89 156	97.23 257	99.41 288	99.16 277
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tttt0517		97.62 300	97.20 308	98.90 284	99.76 90	97.40 320	99.48 86	94.36 374	99.06 172	99.70 109	99.49 235	84.55 373	99.94 61	98.73 136	99.65 235	99.36 236

EPNet_dtu		97.62 300	97.79 293	97.11 346	96.67 379	92.31 369	98.51 271	98.04 352	99.24 140	95.77 373	99.47 243	93.78 307	99.66 341	98.98 110	99.62 240	99.37 233
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		97.58 302	99.13 126	92.93 360	99.69 129	99.49 132	99.52 80	99.77 65	97.97 276	99.96 8	99.79 70	99.84 3	99.94 61	95.85 325	99.82 154	79.36 376

cl22		97.56 303	97.28 305	98.40 310	98.37 367	96.75 335	97.24 358	99.37 267	97.31 313	99.41 212	99.22 303	87.30 359	99.37 371	97.70 220	99.62 240	99.08 296

PAPR		97.56 303	97.07 311	99.04 266	98.80 352	98.11 293	97.63 339	99.25 295	94.56 362	98.02 348	98.25 371	97.43 239	99.68 332	90.90 368	98.74 339	99.33 242

thisisatest0530		97.45 305	96.95 315	98.94 272	99.68 137	97.73 311	99.09 190	94.19 376	98.61 220	99.56 166	99.30 284	84.30 374	99.93 76	98.27 164	99.54 267	99.16 277

TR-MVS		97.44 306	97.15 310	98.32 314	98.53 364	97.46 318	98.47 274	97.91 356	96.85 328	98.21 338	98.51 365	96.42 273	99.51 365	92.16 363	97.29 367	97.98 362

tpmvs		97.39 307	97.69 296	96.52 352	98.41 365	91.76 371	99.30 121	98.94 323	97.74 289	97.85 355	99.55 217	92.40 324	99.73 307	96.25 309	98.73 341	98.06 360

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	98.00 272	99.21 254	98.02 373	90.04 350	99.79 285	98.37 154	95.89 375	98.86 322

OpenMVS_ROB	97.31 17	97.36 309	96.84 319	98.89 285	99.29 285	99.45 144	98.87 226	99.48 230	86.54 374	99.44 198	99.74 95	97.34 245	99.86 204	91.61 364	99.28 306	97.37 369

BH-w/o		97.20 310	97.01 313	97.76 330	99.08 323	95.69 349	98.03 313	98.52 340	95.76 346	97.96 349	98.02 373	95.62 290	99.47 367	92.82 362	97.25 368	98.12 359

test-LLR		97.15 311	96.95 315	97.74 332	98.18 372	95.02 355	97.38 351	96.10 366	98.00 272	97.81 356	98.58 359	90.04 350	99.91 120	97.69 226	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	97.71 291	98.29 332	99.32 280	86.72 368	99.92 96	98.10 182	96.24 374	99.09 293

E-PMN		97.14 313	97.43 301	96.27 355	98.79 353	91.62 373	95.54 372	99.01 321	99.44 112	98.88 291	99.12 316	92.78 318	99.68 332	94.30 352	99.03 322	97.50 366

cascas		96.99 314	96.82 320	97.48 335	97.57 378	95.64 350	96.43 370	99.56 186	91.75 367	97.13 368	97.61 378	95.58 291	98.63 376	96.68 286	99.11 317	98.18 358

thisisatest0515		96.98 315	96.42 322	98.66 301	99.42 245	97.47 317	97.27 356	94.30 375	97.24 315	99.15 263	98.86 350	85.01 371	99.87 184	97.10 264	99.39 290	98.63 333

EMVS		96.96 316	97.28 305	95.99 358	98.76 357	91.03 376	95.26 373	98.61 336	99.34 125	98.92 287	98.88 349	93.79 306	99.66 341	92.87 361	99.05 320	97.30 370

dp		96.86 317	97.07 311	96.24 356	98.68 361	90.30 381	99.19 156	98.38 348	97.35 310	98.23 337	99.59 195	87.23 360	99.82 261	96.27 308	98.73 341	98.59 336

baseline2		96.83 318	96.28 324	98.46 308	99.09 322	96.91 332	98.83 232	93.87 377	97.23 316	96.23 372	98.36 368	88.12 357	99.90 140	96.68 286	98.14 358	98.57 339

ET-MVSNet_ETH3D		96.78 319	96.07 328	98.91 278	99.26 292	97.92 306	97.70 337	96.05 369	97.96 279	92.37 378	98.43 367	87.06 361	99.90 140	98.27 164	97.56 366	98.91 318

tpm cat1		96.78 319	96.98 314	96.16 357	98.85 346	90.59 380	99.08 193	99.32 276	92.37 366	97.73 361	99.46 246	91.15 334	99.69 321	96.07 315	98.80 332	98.21 355

PCF-MVS	96.03 18	96.73 321	95.86 332	99.33 218	99.44 237	99.16 210	96.87 366	99.44 243	86.58 373	98.95 282	99.40 257	94.38 300	99.88 171	87.93 372	99.80 167	98.95 314
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		96.71 322	96.79 321	96.46 354	98.90 339	90.71 379	99.41 95	98.68 332	94.69 361	98.14 343	99.34 278	86.32 370	99.80 282	97.60 231	98.07 360	98.88 320

MVE	92.54 22	96.66 323	96.11 327	98.31 316	99.68 137	97.55 316	97.94 325	95.60 371	99.37 122	90.68 379	98.70 357	96.56 267	98.61 377	86.94 377	99.55 261	98.77 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres600view7		96.60 324	96.16 326	97.93 325	99.63 149	96.09 345	99.18 157	97.57 359	98.77 206	98.72 310	97.32 380	87.04 362	99.72 309	88.57 370	98.62 344	97.98 362

EPMVS		96.53 325	96.32 323	97.17 345	98.18 372	92.97 367	99.39 98	89.95 382	98.21 262	98.61 317	99.59 195	86.69 369	99.72 309	96.99 268	99.23 314	98.81 326

thres400		96.40 326	95.89 330	97.92 326	99.58 165	96.11 343	99.00 206	97.54 362	98.43 236	98.52 324	96.98 383	86.85 364	99.67 337	87.62 373	98.51 348	97.98 362

thres100view900		96.39 327	96.03 329	97.47 336	99.63 149	95.93 346	99.18 157	97.57 359	98.75 210	98.70 312	97.31 381	87.04 362	99.67 337	87.62 373	98.51 348	96.81 371

tpm2		96.35 328	96.22 325	96.73 350	98.88 345	91.75 372	99.21 150	98.51 341	93.27 364	97.89 352	99.21 305	84.83 372	99.70 315	96.04 316	98.18 357	98.75 331

FPMVS		96.32 329	95.50 336	98.79 294	99.60 156	98.17 289	98.46 279	98.80 328	97.16 320	96.28 369	99.63 162	82.19 375	99.09 373	88.45 371	98.89 331	99.10 290

tfpn200view9		96.30 330	95.89 330	97.53 334	99.58 165	96.11 343	99.00 206	97.54 362	98.43 236	98.52 324	96.98 383	86.85 364	99.67 337	87.62 373	98.51 348	96.81 371

TESTMET0.1,1		96.24 331	95.84 333	97.41 338	98.24 370	93.84 363	97.38 351	95.84 370	98.43 236	97.81 356	98.56 362	79.77 379	99.89 156	97.77 209	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	97.90 281	97.81 356	98.58 359	79.12 382	99.91 120	97.69 226	98.78 333	98.31 349

X-MVStestdata		96.09 333	94.87 342	99.75 60	99.71 117	99.71 74	99.37 104	99.61 149	99.29 130	98.76 307	61.30 386	98.47 151	99.88 171	97.62 228	99.73 202	99.67 71

thres200		96.09 333	95.68 335	97.33 341	99.48 221	96.22 342	98.53 269	97.57 359	98.06 271	98.37 331	96.73 385	86.84 366	99.61 355	86.99 376	98.57 345	96.16 374

KD-MVS_2432*1600		95.89 335	95.41 338	97.31 342	94.96 380	93.89 361	97.09 361	99.22 302	97.23 316	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	99.22 302	97.23 316	98.88 291	99.04 326	79.23 380	99.54 360	96.24 310	96.81 369	98.50 344

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 16	96.24 371	99.94 14	81.19 376	99.51 365	93.99 358	98.20 354	97.44 367

PVSNet_0	95.53 19	95.85 338	95.31 340	97.47 336	98.78 355	93.48 365	95.72 371	99.40 257	96.18 340	97.37 362	97.73 376	95.73 288	99.58 358	95.49 334	81.40 378	99.36 236

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	97.70 222	95.89 379	99.29 72	98.14 358	99.95 1

MVS		95.72 340	94.63 344	98.99 268	98.56 363	97.98 305	99.30 121	98.86 324	72.71 377	97.30 363	99.08 320	98.34 171	99.74 304	89.21 369	98.33 351	99.26 254

PAPM		95.61 341	94.71 343	98.31 316	99.12 314	96.63 336	96.66 369	98.46 344	90.77 370	96.25 370	98.68 358	93.01 316	99.69 321	81.60 378	97.86 364	98.62 334

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	96.02 341	95.37 376	97.41 379	76.17 384	99.96 37	97.54 234	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
test2506		94.73 343	94.59 345	95.15 359	99.59 160	85.90 384	99.75 21	74.01 385	99.89 16	99.71 106	99.86 40	79.00 383	99.90 140	99.52 36	99.99 12	99.65 89

test_method		91.72 344	92.32 347	89.91 361	93.49 383	70.18 385	90.28 374	99.56 186	61.71 378	95.39 375	99.52 223	93.90 303	99.94 61	98.76 133	98.27 353	99.62 114

EGC-MVSNET		89.05 345	85.52 348	99.64 114	99.89 24	99.78 44	99.56 77	99.52 215	24.19 379	49.96 380	99.83 49	99.15 55	99.92 96	97.71 217	99.85 130	99.21 264

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

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	99.62 142	0.00 382	0.00 383	99.13 312	99.82 4	0.00 383	0.00 381	0.00 381	0.00 379

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 42	99.89 8	99.74 23	99.71 97	99.69 62	99.63 133

MSC_two_6792asdad				99.74 66	99.03 329	99.53 127		99.23 299					99.92 96	97.77 209	99.69 216	99.78 33

PC_three_1452									97.56 296	99.68 114	99.41 253	99.09 63	97.09 378	96.66 288	99.60 250	99.62 114

No_MVS				99.74 66	99.03 329	99.53 127		99.23 299					99.92 96	97.77 209	99.69 216	99.78 33

test_one_0601					99.63 149	99.76 55		99.55 192	99.23 142	99.31 235	99.61 180	98.59 132

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					99.43 240	99.61 111		99.43 247	96.38 336	99.11 269	99.07 321	97.86 213	99.92 96	94.04 356	99.49 276

RE-MVS-def			99.13 126		99.54 188	99.74 65	99.26 134	99.62 142	99.16 155	99.52 180	99.64 152	98.57 135		97.27 251	99.61 247	99.54 162

IU-MVS					99.69 129	99.77 47		99.22 302	97.50 302	99.69 112				97.75 213	99.70 213	99.77 37

OPU-MVS				99.29 228	99.12 314	99.44 146	99.20 151				99.40 257	99.00 75	98.84 375	96.54 294	99.60 250	99.58 142

test_241102_TWO								99.54 198	99.13 161	99.76 79	99.63 162	98.32 174	99.92 96	97.85 204	99.69 216	99.75 45

test_241102_ONE					99.69 129	99.82 31		99.54 198	99.12 164	99.82 54	99.49 235	98.91 87	99.52 364

9.14			98.64 215		99.45 235		98.81 237	99.60 162	97.52 301	99.28 241	99.56 210	98.53 144	99.83 251	95.36 339	99.64 237

save fliter					99.53 193	99.25 192	98.29 288	99.38 266	99.07 168

test_0728_THIRD									99.18 149	99.62 142	99.61 180	98.58 134	99.91 120	97.72 215	99.80 167	99.77 37

test_0728_SECOND				99.83 24	99.70 125	99.79 41	99.14 172	99.61 149					99.92 96	97.88 198	99.72 208	99.77 37

test0726					99.69 129	99.80 39	99.24 141	99.57 181	99.16 155	99.73 100	99.65 150	98.35 168

GSMVS																99.14 284

test_part2					99.62 153	99.67 90				99.55 171

sam_mvs1												90.81 341				99.14 284

sam_mvs												90.52 345

ambc				99.20 244	99.35 261	98.53 267	99.17 162	99.46 238		99.67 119	99.80 62	98.46 154	99.70 315	97.92 194	99.70 213	99.38 230

MTGPA								99.53 207

test_post1							99.14 172				51.63 388	89.54 353	99.82 261	96.86 275

test_post											52.41 387	90.25 347	99.86 204

patchmatchnet-post											99.62 171	90.58 343	99.94 61

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	98.59 339

gm-plane-assit					97.59 376	89.02 383			93.47 363		98.30 369		99.84 239	96.38 304

test9_res														95.10 342	99.44 282	99.50 187

TEST9					99.35 261	99.35 173	98.11 304	99.41 250	94.83 360	97.92 350	98.99 333	98.02 200	99.85 222

test_8					99.34 271	99.31 179	98.08 308	99.40 257	94.90 356	97.87 354	98.97 339	98.02 200	99.84 239

agg_prior2														94.58 350	99.46 281	99.50 187

agg_prior					99.35 261	99.36 169		99.39 260		97.76 359			99.85 222

TestCases				99.63 121	99.78 78	99.64 99		99.83 35	98.63 217	99.63 133	99.72 105	98.68 118	99.75 302	96.38 304	99.83 145	99.51 181

test_prior4						99.19 208	98.00 316

test_prior2							97.95 323		97.87 283	98.05 345	99.05 323	97.90 209		95.99 319	99.49 276

test_prior				99.46 180	99.35 261	99.22 201		99.39 260					99.69 321			99.48 197

旧先验2							97.94 325		95.33 351	98.94 283			99.88 171	96.75 282

新几何2							98.04 312

新几何1				99.52 162	99.50 210	99.22 201		99.26 292	95.66 348	98.60 318	99.28 289	97.67 227	99.89 156	95.95 323	99.32 301	99.45 208

旧先验1					99.49 215	99.29 182		99.26 292			99.39 261	97.67 227			99.36 296	99.46 206

无先验							98.01 314	99.23 299	95.83 344				99.85 222	95.79 328		99.44 213

原ACMM2							97.92 327

原ACMM1				99.37 211	99.47 227	98.87 246		99.27 289	96.74 332	98.26 334	99.32 280	97.93 207	99.82 261	95.96 322	99.38 291	99.43 219

test222					99.51 204	99.08 223	97.83 332	99.29 285	95.21 353	98.68 313	99.31 282	97.28 247			99.38 291	99.43 219

testdata2													99.89 156	95.99 319

segment_acmp												98.37 166

testdata				99.42 192	99.51 204	98.93 239		99.30 283	96.20 339	98.87 294	99.40 257	98.33 173	99.89 156	96.29 307	99.28 306	99.44 213

testdata1							97.72 335		97.86 286

test12				99.54 158	99.29 285	99.33 176		99.16 310		98.43 329		97.54 235	99.82 261		99.47 279	99.48 197

plane_prior7					99.58 165	99.38 163

plane_prior6					99.47 227	99.26 188						97.24 248

plane_prior5								99.54 198					99.82 261	95.84 326	99.78 178	99.60 128

plane_prior4											99.25 296

plane_prior3						99.31 179			98.36 245	99.14 265

plane_prior2							98.80 240		98.94 182

plane_prior1					99.51 204

plane_prior						99.24 197	98.42 280		97.87 283						99.71 211

n2								0.00 388

nn								0.00 388

door-mid								99.83 35

lessismore_v0				99.64 114	99.86 34	99.38 163		90.66 380		99.89 30	99.83 49	94.56 299	99.97 19	99.56 30	99.92 82	99.57 148

LGP-MVS_train				99.74 66	99.82 49	99.63 103		99.73 85	97.56 296	99.64 129	99.69 125	99.37 33	99.89 156	96.66 288	99.87 119	99.69 58

test11								99.29 285

door								99.77 65

HQP5-MVS						98.94 235

HQP-NCC					99.31 279		97.98 319		97.45 304	98.15 339

ACMP_Plane					99.31 279		97.98 319		97.45 304	98.15 339

BP-MVS														94.73 346

HQP4-MVS										98.15 339			99.70 315			99.53 168

HQP3-MVS								99.37 267							99.67 228

HQP2-MVS												96.67 265

NP-MVS					99.40 249	99.13 213					98.83 351

MDTV_nov1_ep13_2view						91.44 375	99.14 172		97.37 309	99.21 254		91.78 329		96.75 282		99.03 305

MDTV_nov1_ep13			97.73 295		98.70 360	90.83 377	99.15 170	98.02 353	98.51 230	98.82 299	99.61 180	90.98 336	99.66 341	96.89 274	98.92 328

ACMMP++_ref															99.94 70

ACMMP++															99.79 172

Test By Simon												98.41 160

ITE_SJBPF				99.38 208	99.63 149	99.44 146		99.73 85	98.56 223	99.33 228	99.53 221	98.88 92	99.68 332	96.01 317	99.65 235	99.02 309

DeepMVS_CX				97.98 323	99.69 129	96.95 330		99.26 292	75.51 376	95.74 374	98.28 370	96.47 271	99.62 351	91.23 366	97.89 362	97.38 368

High-resolution multi-view benchmark