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 3	99.99 1	100.00 1	99.98 8	99.78 8	100.00 1	99.92 3	100.00 1	99.87 10

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 4	99.79 33	99.72 26	99.88 17	99.92 5	99.98 3	99.93 13	99.94 2	99.98 7	99.77 30	100.00 1	99.92 3

jajsoiax		99.89 3	99.89 3	99.89 6	99.96 4	99.78 35	99.70 30	99.86 21	99.89 10	99.98 3	99.90 22	99.94 2	99.98 7	99.75 31	100.00 1	99.90 5

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 2	99.65 55	100.00 1	99.90 6	100.00 1	99.97 9	99.61 17	99.97 16	99.75 31	100.00 1	99.84 15

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 11	99.91 20	99.90 3	99.96 1	99.92 6	99.90 6	99.97 6	99.87 37	99.81 7	99.95 41	99.54 46	99.99 20	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 14	99.90 3	99.83 7	99.91 10	99.85 18	99.94 20	99.95 11	99.73 10	99.90 111	99.65 35	99.97 46	99.69 58

v52		99.85 7	99.84 7	99.89 6	99.96 4	99.89 5	99.87 4	99.81 55	99.85 18	99.96 8	99.90 22	99.27 41	99.95 41	99.93 1	99.99 20	99.82 23

V4		99.85 7	99.84 7	99.88 11	99.96 4	99.89 5	99.87 4	99.81 55	99.85 18	99.96 8	99.90 22	99.27 41	99.95 41	99.93 1	100.00 1	99.82 23

PS-MVSNAJss		99.84 9	99.82 9	99.89 6	99.96 4	99.77 37	99.68 42	99.85 28	99.95 3	99.98 3	99.92 16	99.28 38	99.98 7	99.75 31	100.00 1	99.94 2

test_djsdf		99.84 9	99.81 10	99.91 2	99.94 14	99.84 18	99.77 14	99.80 59	99.73 43	99.97 6	99.92 16	99.77 9	99.98 7	99.43 55	100.00 1	99.90 5

v7n		99.82 11	99.80 11	99.88 11	99.96 4	99.84 18	99.82 9	99.82 47	99.84 22	99.94 20	99.91 19	99.13 56	99.96 33	99.83 20	99.99 20	99.83 18

anonymousdsp		99.80 12	99.77 13	99.90 4	99.96 4	99.88 7	99.73 22	99.85 28	99.70 51	99.92 30	99.93 13	99.45 22	99.97 16	99.36 65	100.00 1	99.85 14

pm-mvs1		99.79 13	99.79 12	99.78 38	99.91 20	99.83 22	99.76 17	99.87 19	99.73 43	99.89 39	99.87 37	99.63 15	99.87 161	99.54 46	99.92 91	99.63 99

UA-Net		99.78 14	99.76 17	99.86 17	99.72 130	99.71 53	99.91 3	99.95 5	99.96 2	99.71 110	99.91 19	99.15 52	99.97 16	99.50 50	100.00 1	99.90 5

TransMVSNet (Re)		99.78 14	99.77 13	99.81 28	99.91 20	99.85 12	99.75 18	99.86 21	99.70 51	99.91 33	99.89 31	99.60 19	99.87 161	99.59 39	99.74 194	99.71 51

v748		99.76 16	99.74 20	99.84 20	99.95 12	99.83 22	99.82 9	99.80 59	99.82 26	99.95 16	99.87 37	98.72 112	99.93 65	99.72 34	99.98 36	99.75 42

v13		99.76 16	99.77 13	99.73 63	99.86 35	99.55 99	99.77 14	99.86 21	99.79 33	99.96 8	99.91 19	98.90 83	99.87 161	99.91 5	100.00 1	99.78 32

v12		99.75 18	99.77 13	99.72 69	99.85 39	99.53 102	99.75 18	99.86 21	99.78 34	99.96 8	99.90 22	98.88 86	99.86 181	99.91 5	100.00 1	99.77 34

v11		99.75 18	99.76 17	99.71 73	99.85 39	99.49 105	99.73 22	99.84 36	99.75 39	99.95 16	99.90 22	98.93 78	99.86 181	99.92 3	100.00 1	99.77 34

OurMVSNet-221017-0		99.75 18	99.71 24	99.84 20	99.96 4	99.83 22	99.83 7	99.85 28	99.80 31	99.93 25	99.93 13	98.54 141	99.93 65	99.59 39	99.98 36	99.76 38

Vis-MVSNet		99.75 18	99.74 20	99.79 35	99.88 28	99.66 71	99.69 39	99.92 6	99.67 60	99.77 88	99.75 95	99.61 17	99.98 7	99.35 66	99.98 36	99.72 48
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V9		99.74 22	99.75 19	99.71 73	99.84 42	99.50 103	99.74 20	99.86 21	99.76 38	99.96 8	99.90 22	98.83 89	99.85 199	99.91 5	100.00 1	99.77 34

V14		99.73 23	99.74 20	99.69 80	99.83 46	99.48 108	99.72 26	99.85 28	99.74 40	99.96 8	99.89 31	98.79 97	99.85 199	99.91 5	100.00 1	99.76 38

v15		99.72 24	99.73 23	99.68 83	99.82 53	99.44 120	99.70 30	99.85 28	99.72 46	99.95 16	99.88 34	98.76 105	99.84 215	99.90 9	100.00 1	99.75 42

TDRefinement		99.72 24	99.70 27	99.77 40	99.90 25	99.85 12	99.86 6	99.92 6	99.69 55	99.78 83	99.92 16	99.37 29	99.88 141	98.93 126	99.95 67	99.60 124

XXY-MVS		99.71 26	99.67 31	99.81 28	99.89 27	99.72 52	99.59 67	99.82 47	99.39 117	99.82 66	99.84 50	99.38 27	99.91 93	99.38 62	99.93 88	99.80 25

nrg030		99.70 27	99.66 32	99.82 25	99.76 103	99.84 18	99.61 62	99.70 109	99.93 4	99.78 83	99.68 140	99.10 58	99.78 275	99.45 53	99.96 59	99.83 18

FC-MVSNet-test		99.70 27	99.65 33	99.86 17	99.88 28	99.86 11	99.72 26	99.78 69	99.90 6	99.82 66	99.83 51	98.45 155	99.87 161	99.51 49	99.97 46	99.86 12

v17		99.70 27	99.71 24	99.67 86	99.81 61	99.44 120	99.70 30	99.83 39	99.69 55	99.94 20	99.87 37	98.70 113	99.84 215	99.88 14	99.99 20	99.73 45

v16		99.70 27	99.71 24	99.67 86	99.81 61	99.43 126	99.70 30	99.83 39	99.70 51	99.94 20	99.87 37	98.69 115	99.84 215	99.88 14	99.99 20	99.73 45

v10		99.69 31	99.69 28	99.66 94	99.81 61	99.39 138	99.66 50	99.75 85	99.60 84	99.92 30	99.87 37	98.75 108	99.86 181	99.90 9	99.99 20	99.73 45

v18		99.68 32	99.69 28	99.65 98	99.79 82	99.40 135	99.68 42	99.83 39	99.66 65	99.93 25	99.85 45	98.65 124	99.84 215	99.87 18	99.99 20	99.71 51

v8		99.68 32	99.69 28	99.65 98	99.80 69	99.40 135	99.66 50	99.76 79	99.64 70	99.93 25	99.85 45	98.66 122	99.84 215	99.88 14	99.99 20	99.71 51

DTE-MVSNet		99.68 32	99.61 41	99.88 11	99.80 69	99.87 8	99.67 47	99.71 106	99.72 46	99.84 61	99.78 80	98.67 120	99.97 16	99.30 75	99.95 67	99.80 25

Anonymous20240521		99.67 35	99.62 37	99.84 20	99.91 20	99.85 12	99.81 11	99.76 79	99.72 46	99.92 30	99.83 51	98.10 182	99.90 111	99.58 42	99.97 46	99.77 34

VPA-MVSNet		99.66 36	99.62 37	99.79 35	99.68 149	99.75 44	99.62 58	99.69 115	99.85 18	99.80 75	99.81 62	98.81 90	99.91 93	99.47 52	99.88 115	99.70 55

PS-CasMVS		99.66 36	99.58 45	99.89 6	99.80 69	99.85 12	99.66 50	99.73 93	99.62 74	99.84 61	99.71 114	98.62 129	99.96 33	99.30 75	99.96 59	99.86 12

PEN-MVS		99.66 36	99.59 43	99.89 6	99.83 46	99.87 8	99.66 50	99.73 93	99.70 51	99.84 61	99.73 101	98.56 135	99.96 33	99.29 79	99.94 80	99.83 18

FMVSNet1		99.66 36	99.63 36	99.73 63	99.78 88	99.77 37	99.68 42	99.70 109	99.67 60	99.82 66	99.83 51	98.98 72	99.90 111	99.24 83	99.97 46	99.53 158

MIMVSNet1		99.66 36	99.62 37	99.80 30	99.94 14	99.87 8	99.69 39	99.77 72	99.78 34	99.93 25	99.89 31	97.94 196	99.92 83	99.65 35	99.98 36	99.62 113

FIs		99.65 41	99.58 45	99.84 20	99.84 42	99.85 12	99.66 50	99.75 85	99.86 15	99.74 102	99.79 71	98.27 168	99.85 199	99.37 64	99.93 88	99.83 18

wuykxyi23d		99.65 41	99.64 35	99.69 80	99.92 18	99.20 190	98.89 221	99.99 2	98.73 205	99.95 16	99.80 64	99.84 4	99.99 4	99.64 37	99.98 36	99.89 9

Anonymous20231211		99.62 43	99.57 48	99.76 43	99.61 169	99.60 89	99.81 11	99.73 93	99.82 26	99.90 35	99.90 22	97.97 195	99.86 181	99.42 59	99.96 59	99.80 25

DeepC-MVS	98.90 4	99.62 43	99.61 41	99.67 86	99.72 130	99.44 120	99.24 144	99.71 106	99.27 130	99.93 25	99.90 22	99.70 12	99.93 65	98.99 113	99.99 20	99.64 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		99.61 45	99.53 62	99.87 15	99.80 69	99.83 22	99.67 47	99.75 85	99.58 87	99.85 58	99.69 127	98.18 179	99.94 54	99.28 81	99.95 67	99.83 18

ACMH	98.42 6	99.59 46	99.54 54	99.72 69	99.86 35	99.62 84	99.56 72	99.79 67	98.77 197	99.80 75	99.85 45	99.64 14	99.85 199	98.70 141	99.89 109	99.70 55
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testing_2		99.58 47	99.56 52	99.62 119	99.81 61	99.44 120	99.14 178	99.43 232	99.69 55	99.82 66	99.79 71	99.14 53	99.79 267	99.31 74	99.95 67	99.63 99

v1192		99.57 48	99.57 48	99.57 140	99.77 98	99.22 184	99.04 198	99.60 165	99.18 147	99.87 52	99.72 107	99.08 63	99.85 199	99.89 13	99.98 36	99.66 81

EG-PatchMatch MVS		99.57 48	99.56 52	99.62 119	99.77 98	99.33 157	99.26 139	99.76 79	99.32 125	99.80 75	99.78 80	99.29 36	99.87 161	99.15 96	99.91 101	99.66 81

Gipuma		99.57 48	99.59 43	99.49 163	99.98 3	99.71 53	99.72 26	99.84 36	99.81 28	99.94 20	99.78 80	98.91 82	99.71 303	98.41 157	99.95 67	99.05 283
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1921920		99.56 51	99.57 48	99.55 149	99.75 111	99.11 199	99.05 196	99.61 151	99.15 154	99.88 47	99.71 114	99.08 63	99.87 161	99.90 9	99.97 46	99.66 81

v1240		99.56 51	99.58 45	99.51 159	99.80 69	99.00 210	99.00 205	99.65 136	99.15 154	99.90 35	99.75 95	99.09 60	99.88 141	99.90 9	99.96 59	99.67 71

v7		99.56 51	99.54 54	99.61 122	99.80 69	99.39 138	99.30 126	99.59 170	99.14 156	99.82 66	99.72 107	98.75 108	99.84 215	99.83 20	99.94 80	99.61 118

V42		99.56 51	99.54 54	99.63 110	99.79 82	99.46 113	99.39 91	99.59 170	99.24 139	99.86 57	99.70 121	98.55 139	99.82 240	99.79 26	99.95 67	99.60 124

v144192		99.55 55	99.54 54	99.58 134	99.78 88	99.20 190	99.11 186	99.62 147	99.18 147	99.89 39	99.72 107	98.66 122	99.87 161	99.88 14	99.97 46	99.66 81

v1neww		99.55 55	99.54 54	99.61 122	99.80 69	99.39 138	99.32 116	99.61 151	99.18 147	99.87 52	99.69 127	98.64 127	99.82 240	99.79 26	99.94 80	99.60 124

v7new		99.55 55	99.54 54	99.61 122	99.80 69	99.39 138	99.32 116	99.61 151	99.18 147	99.87 52	99.69 127	98.64 127	99.82 240	99.79 26	99.94 80	99.60 124

v6		99.55 55	99.54 54	99.61 122	99.80 69	99.39 138	99.32 116	99.60 165	99.18 147	99.87 52	99.68 140	98.65 124	99.82 240	99.79 26	99.95 67	99.61 118

test20.03		99.55 55	99.54 54	99.58 134	99.79 82	99.37 147	99.02 201	99.89 14	99.60 84	99.82 66	99.62 174	98.81 90	99.89 126	99.43 55	99.86 129	99.47 188

v1144		99.54 60	99.53 62	99.59 130	99.79 82	99.28 167	99.10 187	99.61 151	99.20 145	99.84 61	99.73 101	98.67 120	99.84 215	99.86 19	99.98 36	99.64 95

v1141		99.54 60	99.52 64	99.57 140	99.78 88	99.27 171	99.15 173	99.61 151	99.26 134	99.89 39	99.69 127	98.56 135	99.82 240	99.82 23	99.97 46	99.63 99

divwei89l23v2f112		99.54 60	99.52 64	99.57 140	99.78 88	99.27 171	99.15 173	99.61 151	99.26 134	99.89 39	99.69 127	98.56 135	99.82 240	99.82 23	99.96 59	99.63 99

v1		99.54 60	99.52 64	99.58 134	99.77 98	99.28 167	99.15 173	99.61 151	99.26 134	99.88 47	99.68 140	98.56 135	99.82 240	99.82 23	99.97 46	99.63 99

CP-MVSNet		99.54 60	99.43 79	99.87 15	99.76 103	99.82 27	99.57 70	99.61 151	99.54 89	99.80 75	99.64 159	97.79 208	99.95 41	99.21 84	99.94 80	99.84 15

TranMVSNet+NR-MVSNet		99.54 60	99.47 70	99.76 43	99.58 177	99.64 78	99.30 126	99.63 144	99.61 78	99.71 110	99.56 204	98.76 105	99.96 33	99.14 102	99.92 91	99.68 64

testmv		99.53 66	99.51 67	99.59 130	99.73 120	99.31 160	98.48 267	99.92 6	99.57 88	99.87 52	99.79 71	99.12 57	99.91 93	99.16 95	99.99 20	99.55 147

v2v482		99.50 67	99.47 70	99.58 134	99.78 88	99.25 177	99.14 178	99.58 178	99.25 137	99.81 72	99.62 174	98.24 170	99.84 215	99.83 20	99.97 46	99.64 95

ACMH+	98.40 8	99.50 67	99.43 79	99.71 73	99.86 35	99.76 42	99.32 116	99.77 72	99.53 91	99.77 88	99.76 91	99.26 44	99.78 275	97.77 202	99.88 115	99.60 124

Baseline_NR-MVSNet		99.49 69	99.37 89	99.82 25	99.91 20	99.84 18	98.83 232	99.86 21	99.68 58	99.65 129	99.88 34	97.67 217	99.87 161	99.03 110	99.86 129	99.76 38

TAMVS		99.49 69	99.45 74	99.63 110	99.48 228	99.42 130	99.45 84	99.57 181	99.66 65	99.78 83	99.83 51	97.85 203	99.86 181	99.44 54	99.96 59	99.61 118

pmmvs-eth3d		99.48 71	99.47 70	99.51 159	99.77 98	99.41 134	98.81 236	99.66 127	99.42 114	99.75 94	99.66 153	99.20 47	99.76 283	98.98 115	99.99 20	99.36 223

EI-MVSNet-UG-set		99.48 71	99.50 68	99.42 183	99.57 186	98.65 243	99.24 144	99.46 224	99.68 58	99.80 75	99.66 153	98.99 71	99.89 126	99.19 87	99.90 103	99.72 48

APDe-MVS		99.48 71	99.36 92	99.85 19	99.55 200	99.81 28	99.50 77	99.69 115	98.99 171	99.75 94	99.71 114	98.79 97	99.93 65	98.46 155	99.85 132	99.80 25

PMMVS2		99.48 71	99.45 74	99.57 140	99.76 103	98.99 211	98.09 302	99.90 13	98.95 175	99.78 83	99.58 194	99.57 20	99.93 65	99.48 51	99.95 67	99.79 31

DSMNet-mixed		99.48 71	99.65 33	98.95 256	99.71 133	97.27 302	99.50 77	99.82 47	99.59 86	99.41 195	99.85 45	99.62 16	100.00 1	99.53 48	99.89 109	99.59 135

DP-MVS		99.48 71	99.39 83	99.74 57	99.57 186	99.62 84	99.29 134	99.61 151	99.87 13	99.74 102	99.76 91	98.69 115	99.87 161	98.20 174	99.80 168	99.75 42

EI-MVSNet-Vis-set		99.47 77	99.49 69	99.42 183	99.57 186	98.66 241	99.24 144	99.46 224	99.67 60	99.79 80	99.65 158	98.97 74	99.89 126	99.15 96	99.89 109	99.71 51

VPNet		99.46 78	99.37 89	99.71 73	99.82 53	99.59 91	99.48 81	99.70 109	99.81 28	99.69 114	99.58 194	97.66 221	99.86 181	99.17 92	99.44 256	99.67 71

ACMM	98.09 11	99.46 78	99.38 85	99.72 69	99.80 69	99.69 64	99.13 183	99.65 136	98.99 171	99.64 131	99.72 107	99.39 23	99.86 181	98.23 171	99.81 163	99.60 124
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-4		99.45 80	99.44 76	99.50 161	99.52 207	98.94 217	99.17 165	99.53 196	99.64 70	99.76 91	99.60 186	98.96 77	99.90 111	98.91 127	99.84 136	99.67 71

COLMAP_ROB	98.06 12	99.45 80	99.37 89	99.70 79	99.83 46	99.70 60	99.38 97	99.78 69	99.53 91	99.67 119	99.78 80	99.19 48	99.86 181	97.32 230	99.87 122	99.55 147
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpnnormal		99.43 82	99.38 85	99.60 128	99.87 32	99.75 44	99.59 67	99.78 69	99.71 49	99.90 35	99.69 127	98.85 88	99.90 111	97.25 237	99.78 177	99.15 257

HPM-MVS_fast		99.43 82	99.30 105	99.80 30	99.83 46	99.81 28	99.52 75	99.70 109	98.35 240	99.51 175	99.50 222	99.31 34	99.88 141	98.18 178	99.84 136	99.69 58

3Dnovator	99.15 2	99.43 82	99.36 92	99.65 98	99.39 254	99.42 130	99.70 30	99.56 184	99.23 141	99.35 211	99.80 64	99.17 50	99.95 41	98.21 173	99.84 136	99.59 135

Anonymous20240529		99.42 85	99.34 95	99.65 98	99.53 204	99.60 89	99.63 57	99.39 244	99.47 100	99.76 91	99.78 80	98.13 181	99.86 181	98.70 141	99.68 211	99.49 181

SixPastTwentyTwo		99.42 85	99.30 105	99.76 43	99.92 18	99.67 69	99.70 30	99.14 288	99.65 68	99.89 39	99.90 22	96.20 268	99.94 54	99.42 59	99.92 91	99.67 71

GBi-Net		99.42 85	99.31 100	99.73 63	99.49 222	99.77 37	99.68 42	99.70 109	99.44 107	99.62 142	99.83 51	97.21 240	99.90 111	98.96 120	99.90 103	99.53 158

test1		99.42 85	99.31 100	99.73 63	99.49 222	99.77 37	99.68 42	99.70 109	99.44 107	99.62 142	99.83 51	97.21 240	99.90 111	98.96 120	99.90 103	99.53 158

Regformer-3		99.41 89	99.41 81	99.40 191	99.52 207	98.70 238	99.17 165	99.44 229	99.62 74	99.75 94	99.60 186	98.90 83	99.85 199	98.89 128	99.84 136	99.65 89

MVSFormer		99.41 89	99.44 76	99.31 214	99.57 186	98.40 254	99.77 14	99.80 59	99.73 43	99.63 135	99.30 262	98.02 190	99.98 7	99.43 55	99.69 209	99.55 147

IterMVS-LS		99.41 89	99.47 70	99.25 228	99.81 61	98.09 279	98.85 229	99.76 79	99.62 74	99.83 65	99.64 159	98.54 141	99.97 16	99.15 96	99.99 20	99.68 64
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		99.40 92	99.41 81	99.39 194	99.76 103	98.94 217	99.09 191	99.59 170	99.17 152	99.81 72	99.61 183	98.41 158	99.69 310	99.32 72	99.94 80	99.53 158

casdiffmvs1		99.40 92	99.38 85	99.46 171	99.51 211	99.31 160	99.53 74	99.64 141	99.74 40	99.08 252	99.77 87	98.10 182	99.73 295	99.59 39	99.47 252	99.33 228

NR-MVSNet		99.40 92	99.31 100	99.68 83	99.43 244	99.55 99	99.73 22	99.50 212	99.46 104	99.88 47	99.36 249	97.54 224	99.87 161	98.97 119	99.87 122	99.63 99

PVSNet_Blended_VisFu		99.40 92	99.38 85	99.44 178	99.90 25	98.66 241	98.94 219	99.91 10	97.97 262	99.79 80	99.73 101	99.05 68	99.97 16	99.15 96	99.99 20	99.68 64

EU-MVSNet		99.39 96	99.62 37	98.72 281	99.88 28	96.44 313	99.56 72	99.85 28	99.90 6	99.90 35	99.85 45	98.09 184	99.83 232	99.58 42	99.95 67	99.90 5

CHOSEN 1792x2688		99.39 96	99.30 105	99.65 98	99.88 28	99.25 177	98.78 241	99.88 17	98.66 209	99.96 8	99.79 71	97.45 228	99.93 65	99.34 67	99.99 20	99.78 32

EI-MVSNet		99.38 98	99.44 76	99.21 233	99.58 177	98.09 279	99.26 139	99.46 224	99.62 74	99.75 94	99.67 147	98.54 141	99.85 199	99.15 96	99.92 91	99.68 64

UGNet		99.38 98	99.34 95	99.49 163	98.90 322	98.90 225	99.70 30	99.35 254	99.86 15	98.57 305	99.81 62	98.50 150	99.93 65	99.38 62	99.98 36	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		99.37 100	99.25 118	99.72 69	99.47 233	99.56 96	98.97 214	99.61 151	99.43 112	99.67 119	99.28 266	97.85 203	99.95 41	99.17 92	99.81 163	99.65 89

UniMVSNet (Re)		99.37 100	99.26 116	99.68 83	99.51 211	99.58 93	98.98 213	99.60 165	99.43 112	99.70 112	99.36 249	97.70 212	99.88 141	99.20 86	99.87 122	99.59 135

CSCG		99.37 100	99.29 110	99.60 128	99.71 133	99.46 113	99.43 87	99.85 28	98.79 194	99.41 195	99.60 186	98.92 80	99.92 83	98.02 187	99.92 91	99.43 205

PM-MVS		99.36 103	99.29 110	99.58 134	99.83 46	99.66 71	98.95 216	99.86 21	98.85 186	99.81 72	99.73 101	98.40 160	99.92 83	98.36 161	99.83 146	99.17 255

abl_6		99.36 103	99.23 121	99.75 53	99.71 133	99.74 49	99.33 113	99.76 79	99.07 166	99.65 129	99.63 167	99.09 60	99.92 83	97.13 245	99.76 183	99.58 139

new-patchmatchnet		99.35 105	99.57 48	98.71 282	99.82 53	96.62 311	98.55 258	99.75 85	99.50 94	99.88 47	99.87 37	99.31 34	99.88 141	99.43 55	100.00 1	99.62 113

Anonymous20231206		99.35 105	99.31 100	99.47 168	99.74 117	99.06 209	99.28 135	99.74 90	99.23 141	99.72 106	99.53 213	97.63 223	99.88 141	99.11 104	99.84 136	99.48 183

MTAPA		99.35 105	99.20 125	99.80 30	99.81 61	99.81 28	99.33 113	99.53 196	99.27 130	99.42 189	99.63 167	98.21 174	99.95 41	97.83 199	99.79 171	99.65 89

FMVSNet2		99.35 105	99.28 112	99.55 149	99.49 222	99.35 154	99.45 84	99.57 181	99.44 107	99.70 112	99.74 97	97.21 240	99.87 161	99.03 110	99.94 80	99.44 199

3Dnovator+	98.92 3	99.35 105	99.24 119	99.67 86	99.35 262	99.47 109	99.62 58	99.50 212	99.44 107	99.12 249	99.78 80	98.77 103	99.94 54	97.87 196	99.72 205	99.62 113

TSAR-MVS + MP.		99.34 110	99.24 119	99.63 110	99.82 53	99.37 147	99.26 139	99.35 254	98.77 197	99.57 153	99.70 121	99.27 41	99.88 141	97.71 205	99.75 186	99.65 89

Regformer-2		99.34 110	99.27 114	99.53 154	99.41 250	99.10 202	98.99 209	99.53 196	99.47 100	99.66 123	99.52 215	98.80 94	99.89 126	98.31 166	99.74 194	99.60 124

diffmvs1		99.34 110	99.35 94	99.32 211	99.42 248	98.94 217	99.22 151	99.77 72	99.61 78	98.78 286	99.67 147	98.77 103	99.90 111	99.30 75	99.59 230	99.13 264

DELS-MVS		99.34 110	99.30 105	99.48 166	99.51 211	99.36 150	98.12 298	99.53 196	99.36 121	99.41 195	99.61 183	99.22 46	99.87 161	99.21 84	99.68 211	99.20 247
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		99.33 114	99.21 124	99.71 73	99.43 244	99.56 96	98.83 232	99.53 196	99.38 118	99.67 119	99.36 249	97.67 217	99.95 41	99.17 92	99.81 163	99.63 99

ab-mvs		99.33 114	99.28 112	99.47 168	99.57 186	99.39 138	99.78 13	99.43 232	98.87 184	99.57 153	99.82 59	98.06 187	99.87 161	98.69 143	99.73 200	99.15 257

Regformer-1		99.32 116	99.27 114	99.47 168	99.41 250	98.95 216	98.99 209	99.48 217	99.48 96	99.66 123	99.52 215	98.78 100	99.87 161	98.36 161	99.74 194	99.60 124

APD-MVS_3200maxsize		99.31 117	99.16 127	99.74 57	99.53 204	99.75 44	99.27 138	99.61 151	99.19 146	99.57 153	99.64 159	98.76 105	99.90 111	97.29 232	99.62 224	99.56 144

zzz-MVS		99.30 118	99.14 130	99.80 30	99.81 61	99.81 28	98.73 245	99.53 196	99.27 130	99.42 189	99.63 167	98.21 174	99.95 41	97.83 199	99.79 171	99.65 89

SteuartSystems-ACMMP		99.30 118	99.14 130	99.76 43	99.87 32	99.66 71	99.18 158	99.60 165	98.55 218	99.57 153	99.67 147	99.03 70	99.94 54	97.01 249	99.80 168	99.69 58
Skip Steuart: Steuart Systems R&D Blog.
testgi		99.29 120	99.26 116	99.37 200	99.75 111	98.81 234	98.84 230	99.89 14	98.38 233	99.75 94	99.04 306	99.36 32	99.86 181	99.08 107	99.25 284	99.45 194

ACMMP_Plus		99.28 121	99.11 139	99.79 35	99.75 111	99.81 28	98.95 216	99.53 196	98.27 249	99.53 171	99.73 101	98.75 108	99.87 161	97.70 206	99.83 146	99.68 64

LCM-MVSNet-Re		99.28 121	99.15 129	99.67 86	99.33 277	99.76 42	99.34 111	99.97 3	98.93 178	99.91 33	99.79 71	98.68 117	99.93 65	96.80 259	99.56 234	99.30 235

mvs_anonymous		99.28 121	99.39 83	98.94 257	99.19 298	97.81 290	99.02 201	99.55 187	99.78 34	99.85 58	99.80 64	98.24 170	99.86 181	99.57 44	99.50 248	99.15 257

MVS_Test		99.28 121	99.31 100	99.19 236	99.35 262	98.79 236	99.36 103	99.49 216	99.17 152	99.21 238	99.67 147	98.78 100	99.66 329	99.09 106	99.66 219	99.10 271

no-one		99.28 121	99.23 121	99.45 176	99.87 32	99.08 205	98.95 216	99.52 206	98.88 183	99.77 88	99.83 51	97.78 209	99.90 111	98.46 155	99.99 20	99.38 216

XVS		99.27 126	99.11 139	99.75 53	99.71 133	99.71 53	99.37 101	99.61 151	99.29 126	98.76 288	99.47 228	98.47 152	99.88 141	97.62 213	99.73 200	99.67 71

OPM-MVS		99.26 127	99.13 133	99.63 110	99.70 140	99.61 88	98.58 253	99.48 217	98.50 223	99.52 173	99.63 167	99.14 53	99.76 283	97.89 195	99.77 181	99.51 169

HFP-MVS		99.25 128	99.08 150	99.76 43	99.73 120	99.70 60	99.31 123	99.59 170	98.36 235	99.36 209	99.37 244	98.80 94	99.91 93	97.43 225	99.75 186	99.68 64

HPM-MVS		99.25 128	99.07 154	99.78 38	99.81 61	99.75 44	99.61 62	99.67 123	97.72 275	99.35 211	99.25 272	99.23 45	99.92 83	97.21 241	99.82 155	99.67 71
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		99.25 128	99.08 150	99.74 57	99.79 82	99.68 67	99.50 77	99.65 136	98.07 256	99.52 173	99.69 127	98.57 134	99.92 83	97.18 243	99.79 171	99.63 99
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
casdiffmvs		99.24 131	99.23 121	99.26 224	99.42 248	98.85 232	99.48 81	99.58 178	99.67 60	98.70 293	99.67 147	97.85 203	99.72 297	99.41 61	99.28 279	99.20 247

LS3D		99.24 131	99.11 139	99.61 122	98.38 352	99.79 33	99.57 70	99.68 118	99.61 78	99.15 246	99.71 114	98.70 113	99.91 93	97.54 219	99.68 211	99.13 264

xiu_mvs_v1_base_debu		99.23 133	99.34 95	98.91 260	99.59 174	98.23 268	98.47 268	99.66 127	99.61 78	99.68 116	98.94 319	99.39 23	99.97 16	99.18 89	99.55 240	98.51 312

xiu_mvs_v1_base		99.23 133	99.34 95	98.91 260	99.59 174	98.23 268	98.47 268	99.66 127	99.61 78	99.68 116	98.94 319	99.39 23	99.97 16	99.18 89	99.55 240	98.51 312

xiu_mvs_v1_base_debi		99.23 133	99.34 95	98.91 260	99.59 174	98.23 268	98.47 268	99.66 127	99.61 78	99.68 116	98.94 319	99.39 23	99.97 16	99.18 89	99.55 240	98.51 312

region2R		99.23 133	99.05 160	99.77 40	99.76 103	99.70 60	99.31 123	99.59 170	98.41 230	99.32 219	99.36 249	98.73 111	99.93 65	97.29 232	99.74 194	99.67 71

ACMMPR		99.23 133	99.06 156	99.76 43	99.74 117	99.69 64	99.31 123	99.59 170	98.36 235	99.35 211	99.38 243	98.61 131	99.93 65	97.43 225	99.75 186	99.67 71

XVG-ACMP-BASELINE		99.23 133	99.10 146	99.63 110	99.82 53	99.58 93	98.83 232	99.72 103	98.36 235	99.60 149	99.71 114	98.92 80	99.91 93	97.08 246	99.84 136	99.40 210

CP-MVS		99.23 133	99.05 160	99.75 53	99.66 154	99.66 71	99.38 97	99.62 147	98.38 233	99.06 257	99.27 268	98.79 97	99.94 54	97.51 221	99.82 155	99.66 81

DeepC-MVS_fast	98.47 5	99.23 133	99.12 136	99.56 146	99.28 286	99.22 184	98.99 209	99.40 241	99.08 164	99.58 151	99.64 159	98.90 83	99.83 232	97.44 224	99.75 186	99.63 99
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LPG-MVS_test		99.22 141	99.05 160	99.74 57	99.82 53	99.63 82	99.16 171	99.73 93	97.56 283	99.64 131	99.69 127	99.37 29	99.89 126	96.66 267	99.87 122	99.69 58

CDS-MVSNet		99.22 141	99.13 133	99.50 161	99.35 262	99.11 199	98.96 215	99.54 191	99.46 104	99.61 147	99.70 121	96.31 265	99.83 232	99.34 67	99.88 115	99.55 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_0402		99.22 141	99.14 130	99.45 176	99.79 82	99.43 126	99.28 135	99.68 118	99.54 89	99.40 199	99.56 204	99.07 65	99.82 240	96.01 293	99.96 59	99.11 267

AllTest		99.21 144	99.07 154	99.63 110	99.78 88	99.64 78	99.12 185	99.83 39	98.63 212	99.63 135	99.72 107	98.68 117	99.75 289	96.38 279	99.83 146	99.51 169

XVG-OURS		99.21 144	99.06 156	99.65 98	99.82 53	99.62 84	97.87 326	99.74 90	98.36 235	99.66 123	99.68 140	99.71 11	99.90 111	96.84 257	99.88 115	99.43 205

Fast-Effi-MVS+-dtu		99.20 146	99.12 136	99.43 181	99.25 289	99.69 64	99.05 196	99.82 47	99.50 94	98.97 262	99.05 303	98.98 72	99.98 7	98.20 174	99.24 286	98.62 305

VDD-MVS		99.20 146	99.11 139	99.44 178	99.43 244	98.98 212	99.50 77	98.32 324	99.80 31	99.56 161	99.69 127	96.99 250	99.85 199	98.99 113	99.73 200	99.50 175

PGM-MVS		99.20 146	99.01 170	99.77 40	99.75 111	99.71 53	99.16 171	99.72 103	97.99 260	99.42 189	99.60 186	98.81 90	99.93 65	96.91 253	99.74 194	99.66 81

SMA-MVS		99.19 149	99.00 172	99.73 63	99.46 237	99.73 50	99.13 183	99.52 206	97.40 291	99.57 153	99.64 159	98.93 78	99.83 232	97.61 215	99.79 171	99.63 99

pmmvs5		99.19 149	99.11 139	99.42 183	99.76 103	98.88 227	98.55 258	99.73 93	98.82 190	99.72 106	99.62 174	96.56 257	99.82 240	99.32 72	99.95 67	99.56 144

mPP-MVS		99.19 149	99.00 172	99.76 43	99.76 103	99.68 67	99.38 97	99.54 191	98.34 244	99.01 259	99.50 222	98.53 145	99.93 65	97.18 243	99.78 177	99.66 81

VNet		99.18 152	99.06 156	99.56 146	99.24 291	99.36 150	99.33 113	99.31 263	99.67 60	99.47 180	99.57 200	96.48 260	99.84 215	99.15 96	99.30 277	99.47 188

RPSCF		99.18 152	99.02 167	99.64 106	99.83 46	99.85 12	99.44 86	99.82 47	98.33 245	99.50 177	99.78 80	97.90 198	99.65 336	96.78 260	99.83 146	99.44 199

DeepPCF-MVS	98.42 6	99.18 152	99.02 167	99.67 86	99.22 293	99.75 44	97.25 346	99.47 221	98.72 206	99.66 123	99.70 121	99.29 36	99.63 339	98.07 186	99.81 163	99.62 113

MVS_0304		99.17 155	99.10 146	99.38 196	99.08 313	98.86 230	98.46 272	99.73 93	99.53 91	99.35 211	99.30 262	97.11 246	99.96 33	99.33 69	99.99 20	99.33 228

diffmvs		99.17 155	99.19 126	99.10 243	99.36 261	98.41 253	99.24 144	99.68 118	99.46 104	98.30 316	99.68 140	98.49 151	99.91 93	99.10 105	99.43 262	98.98 288

EPP-MVSNet		99.17 155	99.00 172	99.66 94	99.80 69	99.43 126	99.70 30	99.24 279	99.48 96	99.56 161	99.77 87	94.89 281	99.93 65	98.72 140	99.89 109	99.63 99

MVP-Stereo		99.16 158	99.08 150	99.43 181	99.48 228	99.07 207	99.08 193	99.55 187	98.63 212	99.31 221	99.68 140	98.19 177	99.78 275	98.18 178	99.58 232	99.45 194
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-OURS-SEG-HR		99.16 158	98.99 176	99.66 94	99.84 42	99.64 78	98.25 287	99.73 93	98.39 232	99.63 135	99.43 234	99.70 12	99.90 111	97.34 229	98.64 320	99.44 199

jason		99.16 158	99.11 139	99.32 211	99.75 111	98.44 250	98.26 286	99.39 244	98.70 207	99.74 102	99.30 262	98.54 141	99.97 16	98.48 154	99.82 155	99.55 147
jason: jason.
ESAPD		99.14 161	98.92 186	99.82 25	99.57 186	99.77 37	98.74 243	99.60 165	98.55 218	99.76 91	99.69 127	98.23 173	99.92 83	96.39 278	99.75 186	99.76 38

MP-MVS-pluss		99.14 161	98.92 186	99.80 30	99.83 46	99.83 22	98.61 249	99.63 144	96.84 306	99.44 183	99.58 194	98.81 90	99.91 93	97.70 206	99.82 155	99.67 71
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs4		99.13 163	99.06 156	99.36 203	99.57 186	99.10 202	98.01 310	99.25 276	98.78 196	99.58 151	99.44 233	98.24 170	99.76 283	98.74 139	99.93 88	99.22 243

MVS_111021_LR		99.13 163	99.03 166	99.42 183	99.58 177	99.32 159	97.91 325	99.73 93	98.68 208	99.31 221	99.48 225	99.09 60	99.66 329	97.70 206	99.77 181	99.29 238

#test#		99.12 165	98.90 190	99.76 43	99.73 120	99.70 60	99.10 187	99.59 170	97.60 281	99.36 209	99.37 244	98.80 94	99.91 93	96.84 257	99.75 186	99.68 64

TSAR-MVS + GP.		99.12 165	99.04 165	99.38 196	99.34 272	99.16 194	98.15 294	99.29 267	98.18 253	99.63 135	99.62 174	99.18 49	99.68 319	98.20 174	99.74 194	99.30 235

MVS_111021_HR		99.12 165	99.02 167	99.40 191	99.50 217	99.11 199	97.92 323	99.71 106	98.76 200	99.08 252	99.47 228	99.17 50	99.54 348	97.85 198	99.76 183	99.54 155

CANet		99.11 168	99.05 160	99.28 217	98.83 332	98.56 244	98.71 247	99.41 235	99.25 137	99.23 233	99.22 281	97.66 221	99.94 54	99.19 87	99.97 46	99.33 228

WR-MVS		99.11 168	98.93 183	99.66 94	99.30 283	99.42 130	98.42 276	99.37 251	99.04 168	99.57 153	99.20 283	96.89 252	99.86 181	98.66 146	99.87 122	99.70 55

PHI-MVS		99.11 168	98.95 182	99.59 130	99.13 305	99.59 91	99.17 165	99.65 136	97.88 266	99.25 229	99.46 231	98.97 74	99.80 264	97.26 235	99.82 155	99.37 220

MSDG		99.08 171	98.98 179	99.37 200	99.60 171	99.13 197	97.54 336	99.74 90	98.84 189	99.53 171	99.55 209	99.10 58	99.79 267	97.07 247	99.86 129	99.18 253

Effi-MVS+-dtu		99.07 172	98.92 186	99.52 156	98.89 326	99.78 35	99.15 173	99.66 127	99.34 122	98.92 272	99.24 277	97.69 214	99.98 7	98.11 183	99.28 279	98.81 300

Effi-MVS+		99.06 173	98.97 180	99.34 205	99.31 279	98.98 212	98.31 284	99.91 10	98.81 191	98.79 284	98.94 319	99.14 53	99.84 215	98.79 134	98.74 315	99.20 247

MP-MVS		99.06 173	98.83 200	99.76 43	99.76 103	99.71 53	99.32 116	99.50 212	98.35 240	98.97 262	99.48 225	98.37 161	99.92 83	95.95 299	99.75 186	99.63 99
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDA-MVSNet-bldmvs		99.06 173	99.05 160	99.07 248	99.80 69	97.83 289	98.89 221	99.72 103	99.29 126	99.63 135	99.70 121	96.47 261	99.89 126	98.17 180	99.82 155	99.50 175

MSLP-MVS++		99.05 176	99.09 148	98.91 260	99.21 294	98.36 258	98.82 235	99.47 221	98.85 186	98.90 275	99.56 204	98.78 100	99.09 358	98.57 149	99.68 211	99.26 239

1112_ss		99.05 176	98.84 197	99.67 86	99.66 154	99.29 165	98.52 263	99.82 47	97.65 279	99.43 187	99.16 285	96.42 263	99.91 93	99.07 108	99.84 136	99.80 25

ACMP	97.51 14	99.05 176	98.84 197	99.67 86	99.78 88	99.55 99	98.88 223	99.66 127	97.11 302	99.47 180	99.60 186	99.07 65	99.89 126	96.18 285	99.85 132	99.58 139
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		99.03 179	99.01 170	99.09 244	99.54 201	97.99 283	98.58 253	99.82 47	97.62 280	99.34 215	99.71 114	98.52 147	99.77 281	97.98 191	99.97 46	99.52 166

IS-MVSNet		99.03 179	98.85 195	99.55 149	99.80 69	99.25 177	99.73 22	99.15 287	99.37 119	99.61 147	99.71 114	94.73 283	99.81 259	97.70 206	99.88 115	99.58 139

xiu_mvs_v2_base		99.02 181	99.11 139	98.77 274	99.37 259	98.09 279	98.13 297	99.51 209	99.47 100	99.42 189	98.54 339	99.38 27	99.97 16	98.83 131	99.33 274	98.24 324

Fast-Effi-MVS+		99.02 181	98.87 193	99.46 171	99.38 257	99.50 103	99.04 198	99.79 67	97.17 297	98.62 300	98.74 332	99.34 33	99.95 41	98.32 165	99.41 265	98.92 293

canonicalmvs		99.02 181	99.00 172	99.09 244	99.10 312	98.70 238	99.61 62	99.66 127	99.63 73	98.64 299	97.65 356	99.04 69	99.54 348	98.79 134	98.92 300	99.04 284

MCST-MVS		99.02 181	98.81 202	99.65 98	99.58 177	99.49 105	98.58 253	99.07 291	98.40 231	99.04 258	99.25 272	98.51 149	99.80 264	97.31 231	99.51 247	99.65 89

HSP-MVS		99.01 185	98.76 206	99.76 43	99.78 88	99.73 50	99.35 104	99.31 263	98.54 220	99.54 168	98.99 308	96.81 253	99.93 65	96.97 251	99.53 245	99.61 118

SD-MVS		99.01 185	99.30 105	98.15 303	99.50 217	99.40 135	98.94 219	99.61 151	99.22 144	99.75 94	99.82 59	99.54 21	95.51 364	97.48 222	99.87 122	99.54 155

LF4IMVS		99.01 185	98.92 186	99.27 219	99.71 133	99.28 167	98.59 252	99.77 72	98.32 246	99.39 200	99.41 238	98.62 129	99.84 215	96.62 270	99.84 136	98.69 304

MS-PatchMatch		99.00 188	98.97 180	99.09 244	99.11 310	98.19 271	98.76 242	99.33 257	98.49 224	99.44 183	99.58 194	98.21 174	99.69 310	98.20 174	99.62 224	99.39 213

PS-MVSNAJ		99.00 188	99.08 150	98.76 275	99.37 259	98.10 278	98.00 312	99.51 209	99.47 100	99.41 195	98.50 341	99.28 38	99.97 16	98.83 131	99.34 272	98.20 328

CNVR-MVS		98.99 190	98.80 204	99.56 146	99.25 289	99.43 126	98.54 261	99.27 271	98.58 216	98.80 283	99.43 234	98.53 145	99.70 304	97.22 239	99.59 230	99.54 155

VDDNet		98.97 191	98.82 201	99.42 183	99.71 133	98.81 234	99.62 58	98.68 309	99.81 28	99.38 207	99.80 64	94.25 287	99.85 199	98.79 134	99.32 275	99.59 135

IterMVS		98.97 191	99.16 127	98.42 291	99.74 117	95.64 331	98.06 307	99.83 39	99.83 25	99.85 58	99.74 97	96.10 271	99.99 4	99.27 82	100.00 1	99.63 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap		98.97 191	98.93 183	99.07 248	99.46 237	98.19 271	97.75 329	99.75 85	98.79 194	99.54 168	99.70 121	98.97 74	99.62 340	96.63 269	99.83 146	99.41 209

HPM-MVS++		98.96 194	98.70 209	99.74 57	99.52 207	99.71 53	98.86 226	99.19 283	98.47 226	98.59 303	99.06 302	98.08 186	99.91 93	96.94 252	99.60 229	99.60 124

lupinMVS		98.96 194	98.87 193	99.24 230	99.57 186	98.40 254	98.12 298	99.18 284	98.28 248	99.63 135	99.13 287	98.02 190	99.97 16	98.22 172	99.69 209	99.35 225

USDC		98.96 194	98.93 183	99.05 250	99.54 201	97.99 283	97.07 348	99.80 59	98.21 251	99.75 94	99.77 87	98.43 156	99.64 338	97.90 194	99.88 115	99.51 169

YYNet1		98.95 197	98.99 176	98.84 268	99.64 160	97.14 305	98.22 289	99.32 259	98.92 180	99.59 150	99.66 153	97.40 230	99.83 232	98.27 170	99.90 103	99.55 147

MDA-MVSNet_test_wron		98.95 197	98.99 176	98.85 266	99.64 160	97.16 304	98.23 288	99.33 257	98.93 178	99.56 161	99.66 153	97.39 232	99.83 232	98.29 168	99.88 115	99.55 147

Test_1112_low_res		98.95 197	98.73 207	99.63 110	99.68 149	99.15 196	98.09 302	99.80 59	97.14 299	99.46 182	99.40 239	96.11 270	99.89 126	99.01 112	99.84 136	99.84 15

test1235678		98.93 200	98.84 197	99.19 236	99.46 237	98.55 245	97.53 338	99.77 72	98.76 200	99.69 114	99.48 225	96.69 254	99.90 111	98.30 167	99.91 101	99.11 267

CANet_DTU		98.91 201	98.85 195	99.09 244	98.79 337	98.13 274	98.18 291	99.31 263	99.48 96	98.86 278	99.51 219	96.56 257	99.95 41	99.05 109	99.95 67	99.19 250

HyFIR lowres test		98.91 201	98.64 214	99.73 63	99.85 39	99.47 109	98.07 306	99.83 39	98.64 211	99.89 39	99.60 186	92.57 301	100.00 1	99.33 69	99.97 46	99.72 48

HQP_MVS		98.90 203	98.68 211	99.55 149	99.58 177	99.24 180	98.80 237	99.54 191	98.94 176	99.14 247	99.25 272	97.24 238	99.82 240	95.84 302	99.78 177	99.60 124

sss		98.90 203	98.77 205	99.27 219	99.48 228	98.44 250	98.72 246	99.32 259	97.94 264	99.37 208	99.35 254	96.31 265	99.91 93	98.85 130	99.63 223	99.47 188

OMC-MVS		98.90 203	98.72 208	99.44 178	99.39 254	99.42 130	98.58 253	99.64 141	97.31 295	99.44 183	99.62 174	98.59 133	99.69 310	96.17 286	99.79 171	99.22 243

ppachtmachnet_test		98.89 206	99.12 136	98.20 301	99.66 154	95.24 337	97.63 332	99.68 118	99.08 164	99.78 83	99.62 174	98.65 124	99.88 141	98.02 187	99.96 59	99.48 183

new_pmnet		98.88 207	98.89 191	98.84 268	99.70 140	97.62 296	98.15 294	99.50 212	97.98 261	99.62 142	99.54 211	98.15 180	99.94 54	97.55 218	99.84 136	98.95 290

K. test v3		98.87 208	98.60 216	99.69 80	99.93 17	99.46 113	99.74 20	94.97 363	99.78 34	99.88 47	99.88 34	93.66 291	99.97 16	99.61 38	99.95 67	99.64 95

APD-MVS		98.87 208	98.59 217	99.71 73	99.50 217	99.62 84	99.01 203	99.57 181	96.80 308	99.54 168	99.63 167	98.29 166	99.91 93	95.24 323	99.71 206	99.61 118
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
our_test_3		98.85 210	99.09 148	98.13 304	99.66 154	94.90 340	97.72 330	99.58 178	99.07 166	99.64 131	99.62 174	98.19 177	99.93 65	98.41 157	99.95 67	99.55 147

mvs-test1		98.83 211	98.70 209	99.22 232	98.89 326	99.65 76	98.88 223	99.66 127	99.34 122	98.29 317	98.94 319	97.69 214	99.96 33	98.11 183	98.54 331	98.04 332

UnsupCasMVSNet_eth		98.83 211	98.57 220	99.59 130	99.68 149	99.45 118	98.99 209	99.67 123	99.48 96	99.55 165	99.36 249	94.92 280	99.86 181	98.95 124	96.57 356	99.45 194

test_normal		98.82 213	98.67 212	99.27 219	99.56 198	98.83 233	98.22 289	98.01 328	99.03 169	99.49 179	99.24 277	96.21 267	99.76 283	98.69 143	99.56 234	99.22 243

NCCC		98.82 213	98.57 220	99.58 134	99.21 294	99.31 160	98.61 249	99.25 276	98.65 210	98.43 313	99.26 270	97.86 202	99.81 259	96.55 272	99.27 283	99.61 118

PMVS	92.94 21	98.82 213	98.81 202	98.85 266	99.84 42	97.99 283	99.20 156	99.47 221	99.71 49	99.42 189	99.82 59	98.09 184	99.47 352	93.88 338	99.85 132	99.07 281
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DI_MVS_plusplus_test		98.80 216	98.65 213	99.27 219	99.57 186	98.90 225	98.44 274	97.95 331	99.02 170	99.51 175	99.23 280	96.18 269	99.76 283	98.52 153	99.42 263	99.14 261

FMVSNet3		98.80 216	98.63 215	99.32 211	99.13 305	98.72 237	99.10 187	99.48 217	99.23 141	99.62 142	99.64 159	92.57 301	99.86 181	98.96 120	99.90 103	99.39 213

Patchmtry		98.78 218	98.54 223	99.49 163	98.89 326	99.19 192	99.32 116	99.67 123	99.65 68	99.72 106	99.79 71	91.87 307	99.95 41	98.00 190	99.97 46	99.33 228

Vis-MVSNet (Re-imp)		98.77 219	98.58 219	99.34 205	99.78 88	98.88 227	99.61 62	99.56 184	99.11 159	99.24 232	99.56 204	93.00 299	99.78 275	97.43 225	99.89 109	99.35 225

CLD-MVS		98.76 220	98.57 220	99.33 207	99.57 186	98.97 214	97.53 338	99.55 187	96.41 317	99.27 226	99.13 287	99.07 65	99.78 275	96.73 264	99.89 109	99.23 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Anonymous202405211		98.75 221	98.46 226	99.63 110	99.34 272	99.66 71	99.47 83	97.65 339	99.28 129	99.56 161	99.50 222	93.15 295	99.84 215	98.62 147	99.58 232	99.40 210

CPTT-MVS		98.74 222	98.44 228	99.64 106	99.61 169	99.38 144	99.18 158	99.55 187	96.49 316	99.27 226	99.37 244	97.11 246	99.92 83	95.74 306	99.67 216	99.62 113

F-COLMAP		98.74 222	98.45 227	99.62 119	99.57 186	99.47 109	98.84 230	99.65 136	96.31 318	98.93 270	99.19 284	97.68 216	99.87 161	96.52 273	99.37 270	99.53 158

N_pmnet		98.73 224	98.53 224	99.35 204	99.72 130	98.67 240	98.34 281	94.65 364	98.35 240	99.79 80	99.68 140	98.03 188	99.93 65	98.28 169	99.92 91	99.44 199

PVSNet_Blended		98.70 225	98.59 217	99.02 253	99.54 201	97.99 283	97.58 335	99.82 47	95.70 329	99.34 215	98.98 311	98.52 147	99.77 281	97.98 191	99.83 146	99.30 235

PatchMatch-RL		98.68 226	98.47 225	99.30 216	99.44 242	99.28 167	98.14 296	99.54 191	97.12 301	99.11 250	99.25 272	97.80 207	99.70 304	96.51 274	99.30 277	98.93 292

Test4		98.65 227	98.44 228	99.27 219	99.57 186	98.86 230	98.43 275	99.41 235	98.85 186	99.57 153	98.95 318	93.05 297	99.75 289	98.57 149	99.56 234	99.19 250

test_prior3		98.62 228	98.34 241	99.46 171	99.35 262	99.22 184	97.95 319	99.39 244	97.87 267	98.05 330	99.05 303	97.90 198	99.69 310	95.99 295	99.49 250	99.48 183

CVMVSNet		98.61 229	98.88 192	97.80 317	99.58 177	93.60 345	99.26 139	99.64 141	99.66 65	99.72 106	99.67 147	93.26 294	99.93 65	99.30 75	99.81 163	99.87 10

Patchmatch-RL test		98.60 230	98.36 239	99.33 207	99.77 98	99.07 207	98.27 285	99.87 19	98.91 181	99.74 102	99.72 107	90.57 322	99.79 267	98.55 151	99.85 132	99.11 267

AdaColmap		98.60 230	98.35 240	99.38 196	99.12 307	99.22 184	98.67 248	99.42 234	97.84 271	98.81 281	99.27 268	97.32 236	99.81 259	95.14 324	99.53 245	99.10 271

WTY-MVS		98.59 232	98.37 238	99.26 224	99.43 244	98.40 254	98.74 243	99.13 290	98.10 255	99.21 238	99.24 277	94.82 282	99.90 111	97.86 197	98.77 311	99.49 181

CNLPA		98.57 233	98.34 241	99.28 217	99.18 300	99.10 202	98.34 281	99.41 235	98.48 225	98.52 307	98.98 311	97.05 248	99.78 275	95.59 314	99.50 248	98.96 289

1121		98.56 234	98.24 246	99.52 156	99.49 222	99.24 180	99.30 126	99.22 281	95.77 327	98.52 307	99.29 265	97.39 232	99.85 199	95.79 304	99.34 272	99.46 192

CDPH-MVS		98.56 234	98.20 250	99.61 122	99.50 217	99.46 113	98.32 283	99.41 235	95.22 335	99.21 238	99.10 293	98.34 163	99.82 240	95.09 326	99.66 219	99.56 144

UnsupCasMVSNet_bld		98.55 236	98.27 245	99.40 191	99.56 198	99.37 147	97.97 318	99.68 118	97.49 287	99.08 252	99.35 254	95.41 279	99.82 240	97.70 206	98.19 342	99.01 287

RPMNet		98.53 237	98.44 228	98.83 270	99.05 316	98.12 275	99.30 126	98.78 304	99.86 15	99.16 244	99.74 97	92.53 303	99.91 93	98.75 138	98.77 311	98.44 315

MG-MVS		98.52 238	98.39 235	98.94 257	99.15 302	97.39 301	98.18 291	99.21 282	98.89 182	99.23 233	99.63 167	97.37 234	99.74 293	94.22 334	99.61 228	99.69 58

DP-MVS Recon		98.50 239	98.23 247	99.31 214	99.49 222	99.46 113	98.56 257	99.63 144	94.86 341	98.85 279	99.37 244	97.81 206	99.59 345	96.08 288	99.44 256	98.88 295

CMPMVS	77.52 23	98.50 239	98.19 253	99.41 190	98.33 353	99.56 96	99.01 203	99.59 170	95.44 332	99.57 153	99.80 64	95.64 275	99.46 355	96.47 277	99.92 91	99.21 246
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t		98.49 241	98.11 256	99.64 106	99.73 120	99.58 93	99.24 144	99.76 79	89.94 356	99.42 189	99.56 204	97.76 210	99.86 181	97.74 204	99.82 155	99.47 188

PMMVS		98.49 241	98.29 244	99.11 241	98.96 319	98.42 252	97.54 336	99.32 259	97.53 286	98.47 312	98.15 346	97.88 201	99.82 240	97.46 223	99.24 286	99.09 274

MVSTER		98.47 243	98.22 248	99.24 230	99.06 315	98.35 259	99.08 193	99.46 224	99.27 130	99.75 94	99.66 153	88.61 332	99.85 199	99.14 102	99.92 91	99.52 166

LFMVS		98.46 244	98.19 253	99.26 224	99.24 291	98.52 247	99.62 58	96.94 348	99.87 13	99.31 221	99.58 194	91.04 313	99.81 259	98.68 145	99.42 263	99.45 194

PatchT		98.45 245	98.32 243	98.83 270	98.94 320	98.29 266	99.24 144	98.82 302	99.84 22	99.08 252	99.76 91	91.37 310	99.94 54	98.82 133	99.00 298	98.26 322

test12356		98.43 246	98.39 235	98.55 285	99.46 237	96.36 314	97.32 345	99.81 55	97.60 281	99.62 142	99.37 244	94.57 284	99.89 126	97.80 201	99.92 91	99.40 210

MIMVSNet		98.43 246	98.20 250	99.11 241	99.53 204	98.38 257	99.58 69	98.61 312	98.96 174	99.33 217	99.76 91	90.92 315	99.81 259	97.38 228	99.76 183	99.15 257

PVSNet	97.47 15	98.42 248	98.44 228	98.35 295	99.46 237	96.26 315	96.70 353	99.34 256	97.68 278	99.00 260	99.13 287	97.40 230	99.72 297	97.59 217	99.68 211	99.08 277

CHOSEN 280x420		98.41 249	98.41 233	98.40 293	99.34 272	95.89 325	96.94 349	99.44 229	98.80 193	99.25 229	99.52 215	93.51 292	99.98 7	98.94 125	99.98 36	99.32 233

BH-RMVSNet		98.41 249	98.14 255	99.21 233	99.21 294	98.47 248	98.60 251	98.26 325	98.35 240	98.93 270	99.31 259	97.20 243	99.66 329	94.32 332	99.10 292	99.51 169

QAPM		98.40 251	97.99 262	99.65 98	99.39 254	99.47 109	99.67 47	99.52 206	91.70 353	98.78 286	99.80 64	98.55 139	99.95 41	94.71 330	99.75 186	99.53 158

API-MVS		98.38 252	98.39 235	98.35 295	98.83 332	99.26 173	99.14 178	99.18 284	98.59 215	98.66 298	98.78 329	98.61 131	99.57 347	94.14 335	99.56 234	96.21 357

HQP-MVS		98.36 253	98.02 261	99.39 194	99.31 279	98.94 217	97.98 315	99.37 251	97.45 288	98.15 324	98.83 325	96.67 255	99.70 304	94.73 328	99.67 216	99.53 158

PAPM_NR		98.36 253	98.04 260	99.33 207	99.48 228	98.93 222	98.79 240	99.28 270	97.54 285	98.56 306	98.57 337	97.12 245	99.69 310	94.09 336	98.90 302	99.38 216

PLC	97.35 16	98.36 253	97.99 262	99.48 166	99.32 278	99.24 180	98.50 265	99.51 209	95.19 337	98.58 304	98.96 316	96.95 251	99.83 232	95.63 313	99.25 284	99.37 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		98.35 256	97.95 266	99.57 140	99.35 262	99.35 154	98.11 300	99.41 235	94.90 339	97.92 335	98.99 308	98.02 190	99.85 199	95.38 321	99.44 256	99.50 175

CR-MVSNet		98.35 256	98.20 250	98.83 270	99.05 316	98.12 275	99.30 126	99.67 123	97.39 292	99.16 244	99.79 71	91.87 307	99.91 93	98.78 137	98.77 311	98.44 315

LP		98.34 258	98.44 228	98.05 306	98.88 329	95.31 336	99.28 135	98.74 306	99.12 158	98.98 261	99.79 71	93.40 293	99.93 65	98.38 159	99.41 265	98.90 294

agg_prior1		98.33 259	97.92 269	99.57 140	99.35 262	99.36 150	97.99 314	99.39 244	94.85 342	97.76 345	98.98 311	98.03 188	99.85 199	95.49 316	99.44 256	99.51 169

alignmvs		98.28 260	97.96 265	99.25 228	99.12 307	98.93 222	99.03 200	98.42 321	99.64 70	98.72 291	97.85 349	90.86 318	99.62 340	98.88 129	99.13 290	99.19 250

0601test		98.25 261	97.95 266	99.13 240	99.17 301	98.47 248	99.00 205	98.67 311	98.97 173	99.22 237	99.02 307	91.31 311	99.69 310	97.26 235	98.93 299	99.24 241

agg_prior3		98.24 262	97.81 275	99.53 154	99.34 272	99.26 173	98.09 302	99.39 244	94.21 347	97.77 344	98.96 316	97.74 211	99.84 215	95.38 321	99.44 256	99.50 175

MAR-MVS		98.24 262	97.92 269	99.19 236	98.78 339	99.65 76	99.17 165	99.14 288	95.36 333	98.04 332	98.81 327	97.47 227	99.72 297	95.47 318	99.06 293	98.21 326
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	98.23 264	97.89 273	99.26 224	99.19 298	99.26 173	99.65 55	99.69 115	91.33 354	98.14 328	99.77 87	98.28 167	99.96 33	95.41 320	99.55 240	98.58 309

BH-untuned		98.22 265	98.09 257	98.58 284	99.38 257	97.24 303	98.55 258	98.98 297	97.81 273	99.20 243	98.76 330	97.01 249	99.65 336	94.83 327	98.33 337	98.86 297

HY-MVS	98.23 9	98.21 266	97.95 266	98.99 254	99.03 318	98.24 267	99.61 62	98.72 307	96.81 307	98.73 290	99.51 219	94.06 288	99.86 181	96.91 253	98.20 340	98.86 297

testus		98.15 267	98.06 259	98.40 293	99.11 310	95.95 320	96.77 351	99.89 14	95.83 325	99.23 233	98.47 342	97.50 226	99.84 215	96.58 271	99.20 289	99.39 213

Patchmatch-test1		98.13 268	98.40 234	97.31 331	99.20 297	92.99 347	98.17 293	98.49 318	98.24 250	99.10 251	99.52 215	96.01 272	99.83 232	97.22 239	99.62 224	99.12 266

EPNet		98.13 268	97.77 279	99.18 239	94.57 366	97.99 283	99.24 144	97.96 329	99.74 40	97.29 351	99.62 174	93.13 296	99.97 16	98.59 148	99.83 146	99.58 139
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test		98.10 270	97.98 264	98.48 290	99.27 288	96.48 312	99.40 90	99.07 291	98.81 191	99.23 233	99.57 200	90.11 326	99.87 161	96.69 265	99.64 222	99.09 274

pmmvs3		98.08 271	97.80 276	98.91 260	99.41 250	97.69 294	97.87 326	99.66 127	95.87 324	99.50 177	99.51 219	90.35 324	99.97 16	98.55 151	99.47 252	99.08 277

JIA-IIPM		98.06 272	97.92 269	98.50 289	98.59 347	97.02 306	98.80 237	98.51 316	99.88 12	97.89 337	99.87 37	91.89 306	99.90 111	98.16 181	97.68 352	98.59 307

TAPA-MVS	97.92 13	98.03 273	97.55 285	99.46 171	99.47 233	99.44 120	98.50 265	99.62 147	86.79 357	99.07 256	99.26 270	98.26 169	99.62 340	97.28 234	99.73 200	99.31 234
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
1314		98.00 274	97.90 272	98.27 300	98.90 322	97.45 300	99.30 126	99.06 293	94.98 338	97.21 352	99.12 291	98.43 156	99.67 324	95.58 315	98.56 330	97.71 344

GA-MVS		97.99 275	97.68 282	98.93 259	99.52 207	98.04 282	97.19 347	99.05 294	98.32 246	98.81 281	98.97 314	89.89 329	99.41 356	98.33 164	99.05 294	99.34 227

MVS-HIRNet		97.86 276	98.22 248	96.76 335	99.28 286	91.53 357	98.38 278	92.60 365	99.13 157	99.31 221	99.96 10	97.18 244	99.68 319	98.34 163	99.83 146	99.07 281

FMVSNet5		97.80 277	97.25 288	99.42 183	98.83 332	98.97 214	99.38 97	99.80 59	98.87 184	99.25 229	99.69 127	80.60 364	99.91 93	98.96 120	99.90 103	99.38 216

ADS-MVSNet2		97.78 278	97.66 284	98.12 305	99.14 303	95.36 334	99.22 151	98.75 305	96.97 303	98.25 320	99.64 159	90.90 316	99.94 54	96.51 274	99.56 234	99.08 277

tpmrst		97.73 279	98.07 258	96.73 337	98.71 344	92.00 351	99.10 187	98.86 299	98.52 221	98.92 272	99.54 211	91.90 305	99.82 240	98.02 187	99.03 296	98.37 317

ADS-MVSNet		97.72 280	97.67 283	97.86 315	99.14 303	94.65 341	99.22 151	98.86 299	96.97 303	98.25 320	99.64 159	90.90 316	99.84 215	96.51 274	99.56 234	99.08 277

PatchmatchNet		97.65 281	97.80 276	97.18 332	98.82 335	92.49 349	99.17 165	98.39 322	98.12 254	98.79 284	99.58 194	90.71 320	99.89 126	97.23 238	99.41 265	99.16 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		97.62 282	97.79 278	97.11 334	96.67 365	92.31 350	98.51 264	98.04 326	99.24 139	95.77 360	99.47 228	93.78 290	99.66 329	98.98 115	99.62 224	99.37 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		97.58 283	99.13 133	92.93 349	99.69 142	99.49 105	99.52 75	99.77 72	97.97 262	99.96 8	99.79 71	99.84 4	99.94 54	95.85 301	99.82 155	79.36 361

PAPR		97.56 284	97.07 290	99.04 251	98.80 336	98.11 277	97.63 332	99.25 276	94.56 345	98.02 333	98.25 345	97.43 229	99.68 319	90.90 345	98.74 315	99.33 228

TR-MVS		97.44 285	97.15 289	98.32 297	98.53 349	97.46 299	98.47 268	97.91 332	96.85 305	98.21 323	98.51 340	96.42 263	99.51 350	92.16 341	97.29 353	97.98 337

tpmvs		97.39 286	97.69 281	96.52 341	98.41 351	91.76 354	99.30 126	98.94 298	97.74 274	97.85 340	99.55 209	92.40 304	99.73 295	96.25 284	98.73 317	98.06 331

test0.0.03 1		97.37 287	96.91 297	98.74 280	97.72 359	97.57 297	97.60 334	97.36 347	98.00 258	99.21 238	98.02 347	90.04 327	99.79 267	98.37 160	95.89 359	98.86 297

OpenMVS_ROB	97.31 17	97.36 288	96.84 298	98.89 265	99.29 284	99.45 118	98.87 225	99.48 217	86.54 359	99.44 183	99.74 97	97.34 235	99.86 181	91.61 342	99.28 279	97.37 349

1111		97.29 289	96.71 308	99.04 251	99.65 158	97.72 291	98.35 279	99.80 59	99.40 115	99.66 123	99.43 234	75.10 368	99.87 161	98.98 115	99.98 36	99.52 166

tfpn1000		97.28 290	96.83 299	98.64 283	99.67 153	97.68 295	99.41 88	95.47 361	97.14 299	99.43 187	99.07 301	85.87 354	99.88 141	96.78 260	98.67 319	98.34 319

thresconf0.02		97.25 291	96.74 302	98.75 276	99.73 120	98.35 259	99.35 104	95.78 354	96.54 310	99.39 200	99.08 294	86.57 347	99.88 141	95.69 307	98.57 323	98.02 333

tfpn_n400		97.25 291	96.74 302	98.75 276	99.73 120	98.35 259	99.35 104	95.78 354	96.54 310	99.39 200	99.08 294	86.57 347	99.88 141	95.69 307	98.57 323	98.02 333

tfpnconf		97.25 291	96.74 302	98.75 276	99.73 120	98.35 259	99.35 104	95.78 354	96.54 310	99.39 200	99.08 294	86.57 347	99.88 141	95.69 307	98.57 323	98.02 333

tfpnview11		97.25 291	96.74 302	98.75 276	99.73 120	98.35 259	99.35 104	95.78 354	96.54 310	99.39 200	99.08 294	86.57 347	99.88 141	95.69 307	98.57 323	98.02 333

BH-w/o		97.20 295	97.01 293	97.76 318	99.08 313	95.69 330	98.03 309	98.52 315	95.76 328	97.96 334	98.02 347	95.62 276	99.47 352	92.82 340	97.25 354	98.12 330

conf0.01		97.19 296	96.74 302	98.51 286	99.73 120	98.35 259	99.35 104	95.78 354	96.54 310	99.39 200	99.08 294	86.57 347	99.88 141	95.69 307	98.57 323	97.30 350

conf0.002		97.19 296	96.74 302	98.51 286	99.73 120	98.35 259	99.35 104	95.78 354	96.54 310	99.39 200	99.08 294	86.57 347	99.88 141	95.69 307	98.57 323	97.30 350

test-LLR		97.15 298	96.95 295	97.74 320	98.18 356	95.02 338	97.38 341	96.10 350	98.00 258	97.81 341	98.58 335	90.04 327	99.91 93	97.69 211	98.78 309	98.31 320

tpm		97.15 298	96.95 295	97.75 319	98.91 321	94.24 343	99.32 116	97.96 329	97.71 276	98.29 317	99.32 257	86.72 345	99.92 83	98.10 185	96.24 358	99.09 274

E-PMN		97.14 300	97.43 286	96.27 343	98.79 337	91.62 356	95.54 357	99.01 296	99.44 107	98.88 276	99.12 291	92.78 300	99.68 319	94.30 333	99.03 296	97.50 346

PNet_i23d		97.02 301	97.87 274	94.49 348	99.69 142	84.81 367	95.18 360	99.85 28	97.83 272	99.32 219	99.57 200	95.53 278	99.47 352	96.09 287	97.74 351	99.18 253

cascas		96.99 302	96.82 300	97.48 325	97.57 362	95.64 331	96.43 355	99.56 184	91.75 352	97.13 353	97.61 357	95.58 277	98.63 361	96.68 266	99.11 291	98.18 329

EMVS		96.96 303	97.28 287	95.99 347	98.76 341	91.03 359	95.26 359	98.61 312	99.34 122	98.92 272	98.88 324	93.79 289	99.66 329	92.87 339	99.05 294	97.30 350

PatchFormer-LS_test		96.95 304	97.07 290	96.62 340	98.76 341	91.85 353	99.18 158	98.45 320	97.29 296	97.73 347	97.22 365	88.77 331	99.76 283	98.13 182	98.04 346	98.25 323

tfpn_ndepth		96.93 305	96.43 313	98.42 291	99.60 171	97.72 291	99.22 151	95.16 362	95.91 323	99.26 228	98.79 328	85.56 355	99.87 161	96.03 292	98.35 336	97.68 345

view600		96.86 306	96.52 309	97.88 311	99.69 142	95.87 326	99.39 91	97.68 335	99.11 159	98.96 264	97.82 351	87.40 333	99.79 267	89.78 346	98.83 304	97.98 337

view800		96.86 306	96.52 309	97.88 311	99.69 142	95.87 326	99.39 91	97.68 335	99.11 159	98.96 264	97.82 351	87.40 333	99.79 267	89.78 346	98.83 304	97.98 337

conf0.05thres1000		96.86 306	96.52 309	97.88 311	99.69 142	95.87 326	99.39 91	97.68 335	99.11 159	98.96 264	97.82 351	87.40 333	99.79 267	89.78 346	98.83 304	97.98 337

tfpn		96.86 306	96.52 309	97.88 311	99.69 142	95.87 326	99.39 91	97.68 335	99.11 159	98.96 264	97.82 351	87.40 333	99.79 267	89.78 346	98.83 304	97.98 337

dp		96.86 306	97.07 290	96.24 345	98.68 346	90.30 364	99.19 157	98.38 323	97.35 294	98.23 322	99.59 192	87.23 337	99.82 240	96.27 283	98.73 317	98.59 307

tpm cat1		96.78 311	96.98 294	96.16 346	98.85 331	90.59 363	99.08 193	99.32 259	92.37 351	97.73 347	99.46 231	91.15 312	99.69 310	96.07 289	98.80 308	98.21 326

PCF-MVS	96.03 18	96.73 312	95.86 324	99.33 207	99.44 242	99.16 194	96.87 350	99.44 229	86.58 358	98.95 268	99.40 239	94.38 286	99.88 141	87.93 354	99.80 168	98.95 290
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		96.71 313	96.79 301	96.46 342	98.90 322	90.71 361	99.41 88	98.68 309	94.69 344	98.14 328	99.34 256	86.32 353	99.80 264	97.60 216	98.07 345	98.88 295

MVE	92.54 22	96.66 314	96.11 318	98.31 298	99.68 149	97.55 298	97.94 321	95.60 360	99.37 119	90.68 364	98.70 333	96.56 257	98.61 362	86.94 360	99.55 240	98.77 302
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres600view7		96.60 315	96.16 316	97.93 309	99.63 162	96.09 319	99.18 158	97.57 340	98.77 197	98.72 291	97.32 360	87.04 338	99.72 297	88.57 351	98.62 321	97.98 337

EPMVS		96.53 316	96.32 314	97.17 333	98.18 356	92.97 348	99.39 91	89.95 367	98.21 251	98.61 301	99.59 192	86.69 346	99.72 297	96.99 250	99.23 288	98.81 300

tfpn111		96.50 317	96.12 317	97.65 322	99.63 162	95.93 321	99.18 158	97.57 340	98.75 202	98.70 293	97.31 361	87.04 338	99.72 297	88.27 353	98.61 322	97.30 350

conf200view11		96.43 318	96.03 320	97.63 323	99.63 162	95.93 321	99.18 158	97.57 340	98.75 202	98.70 293	97.31 361	87.04 338	99.67 324	87.62 355	98.51 332	97.30 350

thres400		96.40 319	95.89 322	97.92 310	99.58 177	96.11 317	99.00 205	97.54 345	98.43 227	98.52 307	96.98 366	86.85 342	99.67 324	87.62 355	98.51 332	97.98 337

thres100view900		96.39 320	96.03 320	97.47 326	99.63 162	95.93 321	99.18 158	97.57 340	98.75 202	98.70 293	97.31 361	87.04 338	99.67 324	87.62 355	98.51 332	96.81 355

tpm2		96.35 321	96.22 315	96.73 337	98.88 329	91.75 355	99.21 155	98.51 316	93.27 350	97.89 337	99.21 282	84.83 356	99.70 304	96.04 291	98.18 343	98.75 303

FPMVS		96.32 322	95.50 329	98.79 273	99.60 171	98.17 273	98.46 272	98.80 303	97.16 298	96.28 356	99.63 167	82.19 359	99.09 358	88.45 352	98.89 303	99.10 271

tfpn200view9		96.30 323	95.89 322	97.53 324	99.58 177	96.11 317	99.00 205	97.54 345	98.43 227	98.52 307	96.98 366	86.85 342	99.67 324	87.62 355	98.51 332	96.81 355

TESTMET0.1,1		96.24 324	95.84 325	97.41 328	98.24 354	93.84 344	97.38 341	95.84 353	98.43 227	97.81 341	98.56 338	79.77 365	99.89 126	97.77 202	98.77 311	98.52 311

test-mter		96.23 325	95.73 327	97.74 320	98.18 356	95.02 338	97.38 341	96.10 350	97.90 265	97.81 341	98.58 335	79.12 366	99.91 93	97.69 211	98.78 309	98.31 320

tpmp4_e23		96.11 326	96.06 319	96.27 343	98.90 322	90.70 362	99.34 111	99.03 295	93.72 348	96.56 355	99.31 259	83.63 357	99.75 289	96.06 290	98.02 347	98.35 318

X-MVStestdata		96.09 327	94.87 335	99.75 53	99.71 133	99.71 53	99.37 101	99.61 151	99.29 126	98.76 288	61.30 369	98.47 152	99.88 141	97.62 213	99.73 200	99.67 71

thres200		96.09 327	95.68 328	97.33 330	99.48 228	96.22 316	98.53 262	97.57 340	98.06 257	98.37 315	96.73 368	86.84 344	99.61 344	86.99 359	98.57 323	96.16 358

DWT-MVSNet_test		96.03 329	95.80 326	96.71 339	98.50 350	91.93 352	99.25 143	97.87 333	95.99 322	96.81 354	97.61 357	81.02 361	99.66 329	97.20 242	97.98 348	98.54 310

test2356		95.99 330	95.26 333	98.18 302	96.93 364	95.53 333	95.31 358	98.71 308	95.67 330	98.48 311	97.83 350	80.72 362	99.88 141	95.47 318	98.21 339	99.11 267

gg-mvs-nofinetune		95.87 331	95.17 334	97.97 308	98.19 355	96.95 307	99.69 39	89.23 368	99.89 10	96.24 358	99.94 12	81.19 360	99.51 350	93.99 337	98.20 340	97.44 347

PVSNet_0	95.53 19	95.85 332	95.31 331	97.47 326	98.78 339	93.48 346	95.72 356	99.40 241	96.18 320	97.37 349	97.73 355	95.73 274	99.58 346	95.49 316	81.40 361	99.36 223

tmp_tt		95.75 333	95.42 330	96.76 335	89.90 367	94.42 342	98.86 226	97.87 333	78.01 360	99.30 225	99.69 127	97.70 212	95.89 363	99.29 79	98.14 344	99.95 1

MVS		95.72 334	94.63 337	98.99 254	98.56 348	97.98 288	99.30 126	98.86 299	72.71 362	97.30 350	99.08 294	98.34 163	99.74 293	89.21 350	98.33 337	99.26 239

PAPM		95.61 335	94.71 336	98.31 298	99.12 307	96.63 310	96.66 354	98.46 319	90.77 355	96.25 357	98.68 334	93.01 298	99.69 310	81.60 361	97.86 350	98.62 305

IB-MVS	95.41 20	95.30 336	94.46 338	97.84 316	98.76 341	95.33 335	97.33 344	96.07 352	96.02 321	95.37 362	97.41 359	76.17 367	99.96 33	97.54 219	95.44 360	98.22 325
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
testpf		94.48 337	95.31 331	91.99 350	97.22 363	89.64 365	98.86 226	96.52 349	94.36 346	96.09 359	98.76 330	82.21 358	98.73 360	97.05 248	96.74 355	87.60 360

.test1245		85.84 338	89.27 339	75.54 351	99.65 158	97.72 291	98.35 279	99.80 59	99.40 115	99.66 123	99.43 234	75.10 368	99.87 161	98.98 115	33.07 362	29.03 363

pcd1.5k->3k		49.97 339	55.52 340	33.31 352	99.95 12	0.00 370	0.00 361	99.81 55	0.00 365	0.00 367	100.00 1	99.96 1	0.00 367	0.00 364	100.00 1	99.92 3

v1.0		41.33 340	55.11 341	0.00 355	99.62 167	0.00 370	0.00 361	99.53 196	97.71 276	99.55 165	99.57 200	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

test123		29.31 341	33.05 344	18.08 353	25.93 369	12.24 368	97.53 338	10.93 371	11.78 363	24.21 365	50.08 373	21.04 370	8.60 365	23.51 362	32.43 364	33.39 362

testmvs		28.94 342	33.33 342	15.79 354	26.03 368	9.81 369	96.77 351	15.67 370	11.55 364	23.87 366	50.74 372	19.03 371	8.53 366	23.21 363	33.07 362	29.03 363

cdsmvs_eth3d_5k		24.88 343	33.17 343	0.00 355	0.00 370	0.00 370	0.00 361	99.62 147	0.00 365	0.00 367	99.13 287	99.82 6	0.00 367	0.00 364	0.00 365	0.00 365

pcd_1.5k_mvsjas		16.61 344	22.14 345	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	100.00 1	99.28 38	0.00 367	0.00 364	0.00 365	0.00 365

sosnet-low-res		8.33 345	11.11 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	100.00 1	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sosnet		8.33 345	11.11 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	100.00 1	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uncertanet		8.33 345	11.11 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	100.00 1	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

Regformer		8.33 345	11.11 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	100.00 1	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

uanet		8.33 345	11.11 346	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	100.00 1	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

ab-mvs-re		8.26 350	11.02 351	0.00 355	0.00 370	0.00 370	0.00 361	0.00 372	0.00 365	0.00 367	99.16 285	0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

GSMVS																99.14 261

test_part2					99.62 167	99.67 69				99.55 165

test_part1				0.00 355		0.00 370	0.00 361	99.53 196				0.00 372	0.00 367	0.00 364	0.00 365	0.00 365

sam_mvs1												90.81 319				99.14 261

sam_mvs												90.52 323

semantic-postprocess				98.51 286	99.75 111	95.90 324		99.84 36	99.84 22	99.89 39	99.73 101	95.96 273	99.99 4	99.33 69	100.00 1	99.63 99

ambc				99.20 235	99.35 262	98.53 246	99.17 165	99.46 224		99.67 119	99.80 64	98.46 154	99.70 304	97.92 193	99.70 208	99.38 216

MTGPA								99.53 196

test_post1							99.14 178				51.63 371	89.54 330	99.82 240	96.86 256

test_post											52.41 370	90.25 325	99.86 181

patchmatchnet-post											99.62 174	90.58 321	99.94 54

GG-mvs-BLEND				97.36 329	97.59 360	96.87 309	99.70 30	88.49 369		94.64 363	97.26 364	80.66 363	99.12 357	91.50 343	96.50 357	96.08 359

MTMP							99.09 191	98.59 314

gm-plane-assit					97.59 360	89.02 366			93.47 349		98.30 343		99.84 215	96.38 279

test9_res														95.10 325	99.44 256	99.50 175

TEST9					99.35 262	99.35 154	98.11 300	99.41 235	94.83 343	97.92 335	98.99 308	98.02 190	99.85 199

test_8					99.34 272	99.31 160	98.08 305	99.40 241	94.90 339	97.87 339	98.97 314	98.02 190	99.84 215

agg_prior2														94.58 331	99.46 255	99.50 175

agg_prior					99.35 262	99.36 150		99.39 244		97.76 345			99.85 199

TestCases				99.63 110	99.78 88	99.64 78		99.83 39	98.63 212	99.63 135	99.72 107	98.68 117	99.75 289	96.38 279	99.83 146	99.51 169

test_prior4						99.19 192	98.00 312

test_prior2							97.95 319		97.87 267	98.05 330	99.05 303	97.90 198		95.99 295	99.49 250

test_prior				99.46 171	99.35 262	99.22 184		99.39 244					99.69 310			99.48 183

旧先验2							97.94 321		95.33 334	98.94 269			99.88 141	96.75 262

新几何2							98.04 308

新几何1				99.52 156	99.50 217	99.22 184		99.26 273	95.66 331	98.60 302	99.28 266	97.67 217	99.89 126	95.95 299	99.32 275	99.45 194

旧先验1					99.49 222	99.29 165		99.26 273			99.39 242	97.67 217			99.36 271	99.46 192

无先验							98.01 310	99.23 280	95.83 325				99.85 199	95.79 304		99.44 199

原ACMM2							97.92 323

原ACMM1				99.37 200	99.47 233	98.87 229		99.27 271	96.74 309	98.26 319	99.32 257	97.93 197	99.82 240	95.96 298	99.38 268	99.43 205

test222					99.51 211	99.08 205	97.83 328	99.29 267	95.21 336	98.68 297	99.31 259	97.28 237			99.38 268	99.43 205

testdata2													99.89 126	95.99 295

segment_acmp												98.37 161

testdata				99.42 183	99.51 211	98.93 222		99.30 266	96.20 319	98.87 277	99.40 239	98.33 165	99.89 126	96.29 282	99.28 279	99.44 199

testdata1							97.72 330		97.86 270

test12				99.54 153	99.29 284	99.33 157		99.16 286		98.43 313		97.54 224	99.82 240		99.47 252	99.48 183

plane_prior7					99.58 177	99.38 144

plane_prior6					99.47 233	99.26 173						97.24 238

plane_prior5								99.54 191					99.82 240	95.84 302	99.78 177	99.60 124

plane_prior4											99.25 272

plane_prior3						99.31 160			98.36 235	99.14 247

plane_prior2							98.80 237		98.94 176

plane_prior1					99.51 211

plane_prior						99.24 180	98.42 276		97.87 267						99.71 206

n2								0.00 372

nn								0.00 372

door-mid								99.83 39

lessismore_v0				99.64 106	99.86 35	99.38 144		90.66 366		99.89 39	99.83 51	94.56 285	99.97 16	99.56 45	99.92 91	99.57 143

LGP-MVS_train				99.74 57	99.82 53	99.63 82		99.73 93	97.56 283	99.64 131	99.69 127	99.37 29	99.89 126	96.66 267	99.87 122	99.69 58

test11								99.29 267

door								99.77 72

HQP5-MVS						98.94 217

HQP-NCC					99.31 279		97.98 315		97.45 288	98.15 324

ACMP_Plane					99.31 279		97.98 315		97.45 288	98.15 324

BP-MVS														94.73 328

HQP4-MVS										98.15 324			99.70 304			99.53 158

HQP3-MVS								99.37 251							99.67 216

HQP2-MVS												96.67 255

NP-MVS					99.40 253	99.13 197					98.83 325

MDTV_nov1_ep13_2view						91.44 358	99.14 178		97.37 293	99.21 238		91.78 309		96.75 262		99.03 285

MDTV_nov1_ep13			97.73 280		98.70 345	90.83 360	99.15 173	98.02 327	98.51 222	98.82 280	99.61 183	90.98 314	99.66 329	96.89 255	98.92 300

ACMMP++_ref															99.94 80

ACMMP++															99.79 171

Test By Simon												98.41 158

ITE_SJBPF				99.38 196	99.63 162	99.44 120		99.73 93	98.56 217	99.33 217	99.53 213	98.88 86	99.68 319	96.01 293	99.65 221	99.02 286

DeepMVS_CX				97.98 307	99.69 142	96.95 307		99.26 273	75.51 361	95.74 361	98.28 344	96.47 261	99.62 340	91.23 344	97.89 349	97.38 348

High-resolution multi-view benchmark