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 5	99.79 33	99.72 25	99.88 18	99.92 5	99.98 3	99.93 14	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 5	99.78 35	99.70 29	99.86 22	99.89 10	99.98 3	99.90 23	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 3	99.65 54	100.00 1	99.90 6	100.00 1	99.97 10	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 12	99.91 21	99.90 4	99.96 1	99.92 7	99.90 6	99.97 6	99.87 37	99.81 7	99.95 41	99.54 44	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 15	99.90 4	99.83 8	99.91 11	99.85 19	99.94 20	99.95 12	99.73 10	99.90 109	99.65 35	99.97 47	99.69 56

v52		99.85 7	99.84 7	99.89 6	99.96 5	99.89 6	99.87 5	99.81 56	99.85 19	99.96 8	99.90 23	99.27 42	99.95 41	99.93 1	99.99 20	99.82 23

V4		99.85 7	99.84 7	99.88 12	99.96 5	99.89 6	99.87 5	99.81 56	99.85 19	99.96 8	99.90 23	99.27 42	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 5	99.77 37	99.68 41	99.85 29	99.95 3	99.98 3	99.92 17	99.28 39	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 15	99.84 18	99.77 13	99.80 60	99.73 42	99.97 6	99.92 17	99.77 9	99.98 7	99.43 53	100.00 1	99.90 5

Anonymous20231211		99.83 11	99.81 10	99.89 6	99.97 4	99.95 2	99.88 4	99.93 6	99.87 13	99.94 20	99.98 8	99.55 21	99.95 41	99.21 79	99.98 36	99.78 31

v7n		99.82 12	99.80 12	99.88 12	99.96 5	99.84 18	99.82 10	99.82 48	99.84 23	99.94 20	99.91 20	99.13 57	99.96 33	99.83 20	99.99 20	99.83 18

anonymousdsp		99.80 13	99.77 14	99.90 4	99.96 5	99.88 8	99.73 21	99.85 29	99.70 49	99.92 31	99.93 14	99.45 23	99.97 16	99.36 61	100.00 1	99.85 14

pm-mvs1		99.79 14	99.79 13	99.78 37	99.91 21	99.83 22	99.76 16	99.87 20	99.73 42	99.89 38	99.87 37	99.63 15	99.87 158	99.54 44	99.92 88	99.63 99

UA-Net		99.78 15	99.76 18	99.86 18	99.72 130	99.71 51	99.91 3	99.95 5	99.96 2	99.71 106	99.91 20	99.15 53	99.97 16	99.50 48	100.00 1	99.90 5

TransMVSNet (Re)		99.78 15	99.77 14	99.81 27	99.91 21	99.85 13	99.75 17	99.86 22	99.70 49	99.91 33	99.89 31	99.60 19	99.87 158	99.59 39	99.74 189	99.71 49

v748		99.76 17	99.74 21	99.84 21	99.95 13	99.83 22	99.82 10	99.80 60	99.82 27	99.95 16	99.87 37	98.72 111	99.93 66	99.72 34	99.98 36	99.75 40

v13		99.76 17	99.77 14	99.73 62	99.86 35	99.55 95	99.77 13	99.86 22	99.79 33	99.96 8	99.91 20	98.90 83	99.87 158	99.91 5	100.00 1	99.78 31

v12		99.75 19	99.77 14	99.72 67	99.85 39	99.53 98	99.75 17	99.86 22	99.78 34	99.96 8	99.90 23	98.88 86	99.86 178	99.91 5	100.00 1	99.77 34

v11		99.75 19	99.76 18	99.71 71	99.85 39	99.49 101	99.73 21	99.84 37	99.75 39	99.95 16	99.90 23	98.93 79	99.86 178	99.92 3	100.00 1	99.77 34

OurMVSNet-221017-0		99.75 19	99.71 25	99.84 21	99.96 5	99.83 22	99.83 8	99.85 29	99.80 31	99.93 26	99.93 14	98.54 139	99.93 66	99.59 39	99.98 36	99.76 37

Vis-MVSNet		99.75 19	99.74 21	99.79 34	99.88 28	99.66 70	99.69 38	99.92 7	99.67 58	99.77 86	99.75 92	99.61 17	99.98 7	99.35 62	99.98 36	99.72 46
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V9		99.74 23	99.75 20	99.71 71	99.84 42	99.50 99	99.74 19	99.86 22	99.76 38	99.96 8	99.90 23	98.83 89	99.85 194	99.91 5	100.00 1	99.77 34

V14		99.73 24	99.74 21	99.69 78	99.83 46	99.48 104	99.72 25	99.85 29	99.74 40	99.96 8	99.89 31	98.79 97	99.85 194	99.91 5	100.00 1	99.76 37

v15		99.72 25	99.73 24	99.68 81	99.82 53	99.44 116	99.70 29	99.85 29	99.72 45	99.95 16	99.88 34	98.76 104	99.84 210	99.90 9	100.00 1	99.75 40

TDRefinement		99.72 25	99.70 28	99.77 39	99.90 25	99.85 13	99.86 7	99.92 7	99.69 53	99.78 82	99.92 17	99.37 30	99.88 139	98.93 121	99.95 65	99.60 123

XXY-MVS		99.71 27	99.67 32	99.81 27	99.89 27	99.72 50	99.59 65	99.82 48	99.39 111	99.82 65	99.84 50	99.38 28	99.91 92	99.38 58	99.93 85	99.80 25

nrg030		99.70 28	99.66 33	99.82 25	99.76 103	99.84 18	99.61 60	99.70 107	99.93 4	99.78 82	99.68 136	99.10 59	99.78 268	99.45 51	99.96 59	99.83 18

FC-MVSNet-test		99.70 28	99.65 34	99.86 18	99.88 28	99.86 12	99.72 25	99.78 70	99.90 6	99.82 65	99.83 51	98.45 152	99.87 158	99.51 47	99.97 47	99.86 12

v17		99.70 28	99.71 25	99.67 84	99.81 61	99.44 116	99.70 29	99.83 40	99.69 53	99.94 20	99.87 37	98.70 112	99.84 210	99.88 14	99.99 20	99.73 43

v16		99.70 28	99.71 25	99.67 84	99.81 61	99.43 122	99.70 29	99.83 40	99.70 49	99.94 20	99.87 37	98.69 114	99.84 210	99.88 14	99.99 20	99.73 43

v10		99.69 32	99.69 29	99.66 92	99.81 61	99.39 134	99.66 49	99.75 84	99.60 80	99.92 31	99.87 37	98.75 107	99.86 178	99.90 9	99.99 20	99.73 43

v18		99.68 33	99.69 29	99.65 96	99.79 82	99.40 131	99.68 41	99.83 40	99.66 62	99.93 26	99.85 45	98.65 123	99.84 210	99.87 18	99.99 20	99.71 49

v8		99.68 33	99.69 29	99.65 96	99.80 69	99.40 131	99.66 49	99.76 79	99.64 67	99.93 26	99.85 45	98.66 121	99.84 210	99.88 14	99.99 20	99.71 49

DTE-MVSNet		99.68 33	99.61 41	99.88 12	99.80 69	99.87 9	99.67 46	99.71 104	99.72 45	99.84 60	99.78 79	98.67 119	99.97 16	99.30 71	99.95 65	99.80 25

VPA-MVSNet		99.66 36	99.62 38	99.79 34	99.68 149	99.75 43	99.62 56	99.69 113	99.85 19	99.80 74	99.81 61	98.81 90	99.91 92	99.47 50	99.88 112	99.70 53

PS-CasMVS		99.66 36	99.58 45	99.89 6	99.80 69	99.85 13	99.66 49	99.73 92	99.62 71	99.84 60	99.71 111	98.62 127	99.96 33	99.30 71	99.96 59	99.86 12

PEN-MVS		99.66 36	99.59 43	99.89 6	99.83 46	99.87 9	99.66 49	99.73 92	99.70 49	99.84 60	99.73 98	98.56 133	99.96 33	99.29 74	99.94 77	99.83 18

FMVSNet1		99.66 36	99.63 37	99.73 62	99.78 88	99.77 37	99.68 41	99.70 107	99.67 58	99.82 65	99.83 51	98.98 73	99.90 109	99.24 78	99.97 47	99.53 156

MIMVSNet1		99.66 36	99.62 38	99.80 29	99.94 15	99.87 9	99.69 38	99.77 73	99.78 34	99.93 26	99.89 31	97.94 189	99.92 83	99.65 35	99.98 36	99.62 112

FIs		99.65 41	99.58 45	99.84 21	99.84 42	99.85 13	99.66 49	99.75 84	99.86 16	99.74 98	99.79 70	98.27 167	99.85 194	99.37 60	99.93 85	99.83 18

wuykxyi23d		99.65 41	99.64 36	99.69 78	99.92 19	99.20 185	98.89 212	99.99 2	98.73 195	99.95 16	99.80 63	99.84 4	99.99 4	99.64 37	99.98 36	99.89 9

DeepC-MVS	98.90 4	99.62 43	99.61 41	99.67 84	99.72 130	99.44 116	99.24 140	99.71 104	99.27 123	99.93 26	99.90 23	99.70 12	99.93 66	98.99 108	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 44	99.53 61	99.87 16	99.80 69	99.83 22	99.67 46	99.75 84	99.58 83	99.85 57	99.69 124	98.18 176	99.94 55	99.28 76	99.95 65	99.83 18

ACMH	98.42 6	99.59 45	99.54 53	99.72 67	99.86 35	99.62 82	99.56 70	99.79 68	98.77 187	99.80 74	99.85 45	99.64 14	99.85 194	98.70 137	99.89 106	99.70 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testing_2		99.58 46	99.56 51	99.62 115	99.81 61	99.44 116	99.14 172	99.43 224	99.69 53	99.82 65	99.79 70	99.14 54	99.79 260	99.31 70	99.95 65	99.63 99

v1192		99.57 47	99.57 48	99.57 136	99.77 98	99.22 179	99.04 190	99.60 161	99.18 140	99.87 51	99.72 104	99.08 64	99.85 194	99.89 13	99.98 36	99.66 79

EG-PatchMatch MVS		99.57 47	99.56 51	99.62 115	99.77 98	99.33 153	99.26 134	99.76 79	99.32 119	99.80 74	99.78 79	99.29 37	99.87 158	99.15 92	99.91 98	99.66 79

Gipuma		99.57 47	99.59 43	99.49 159	99.98 3	99.71 51	99.72 25	99.84 37	99.81 28	99.94 20	99.78 79	98.91 82	99.71 294	98.41 151	99.95 65	99.05 274
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1921920		99.56 50	99.57 48	99.55 145	99.75 111	99.11 194	99.05 188	99.61 147	99.15 147	99.88 46	99.71 111	99.08 64	99.87 158	99.90 9	99.97 47	99.66 79

v1240		99.56 50	99.58 45	99.51 155	99.80 69	99.00 205	99.00 197	99.65 132	99.15 147	99.90 35	99.75 92	99.09 61	99.88 139	99.90 9	99.96 59	99.67 69

v7		99.56 50	99.54 53	99.61 118	99.80 69	99.39 134	99.30 121	99.59 165	99.14 149	99.82 65	99.72 104	98.75 107	99.84 210	99.83 20	99.94 77	99.61 117

V42		99.56 50	99.54 53	99.63 107	99.79 82	99.46 109	99.39 86	99.59 165	99.24 132	99.86 56	99.70 118	98.55 137	99.82 233	99.79 26	99.95 65	99.60 123

v144192		99.55 54	99.54 53	99.58 130	99.78 88	99.20 185	99.11 179	99.62 143	99.18 140	99.89 38	99.72 104	98.66 121	99.87 158	99.88 14	99.97 47	99.66 79

v1neww		99.55 54	99.54 53	99.61 118	99.80 69	99.39 134	99.32 111	99.61 147	99.18 140	99.87 51	99.69 124	98.64 125	99.82 233	99.79 26	99.94 77	99.60 123

v7new		99.55 54	99.54 53	99.61 118	99.80 69	99.39 134	99.32 111	99.61 147	99.18 140	99.87 51	99.69 124	98.64 125	99.82 233	99.79 26	99.94 77	99.60 123

v6		99.55 54	99.54 53	99.61 118	99.80 69	99.39 134	99.32 111	99.60 161	99.18 140	99.87 51	99.68 136	98.65 123	99.82 233	99.79 26	99.95 65	99.61 117

test20.03		99.55 54	99.54 53	99.58 130	99.79 82	99.37 143	99.02 193	99.89 15	99.60 80	99.82 65	99.62 166	98.81 90	99.89 124	99.43 53	99.86 126	99.47 184

v1144		99.54 59	99.53 61	99.59 126	99.79 82	99.28 162	99.10 180	99.61 147	99.20 138	99.84 60	99.73 98	98.67 119	99.84 210	99.86 19	99.98 36	99.64 95

v1141		99.54 59	99.52 63	99.57 136	99.78 88	99.27 166	99.15 167	99.61 147	99.26 127	99.89 38	99.69 124	98.56 133	99.82 233	99.82 23	99.97 47	99.63 99

divwei89l23v2f112		99.54 59	99.52 63	99.57 136	99.78 88	99.27 166	99.15 167	99.61 147	99.26 127	99.89 38	99.69 124	98.56 133	99.82 233	99.82 23	99.96 59	99.63 99

v1		99.54 59	99.52 63	99.58 130	99.77 98	99.28 162	99.15 167	99.61 147	99.26 127	99.88 46	99.68 136	98.56 133	99.82 233	99.82 23	99.97 47	99.63 99

CP-MVSNet		99.54 59	99.43 78	99.87 16	99.76 103	99.82 27	99.57 68	99.61 147	99.54 85	99.80 74	99.64 152	97.79 200	99.95 41	99.21 79	99.94 77	99.84 15

TranMVSNet+NR-MVSNet		99.54 59	99.47 69	99.76 42	99.58 174	99.64 76	99.30 121	99.63 140	99.61 75	99.71 106	99.56 196	98.76 104	99.96 33	99.14 98	99.92 88	99.68 62

testmv		99.53 65	99.51 66	99.59 126	99.73 120	99.31 156	98.48 259	99.92 7	99.57 84	99.87 51	99.79 70	99.12 58	99.91 92	99.16 91	99.99 20	99.55 146

v2v482		99.50 66	99.47 69	99.58 130	99.78 88	99.25 172	99.14 172	99.58 173	99.25 130	99.81 71	99.62 166	98.24 169	99.84 210	99.83 20	99.97 47	99.64 95

ACMH+	98.40 8	99.50 66	99.43 78	99.71 71	99.86 35	99.76 41	99.32 111	99.77 73	99.53 87	99.77 86	99.76 88	99.26 45	99.78 268	97.77 194	99.88 112	99.60 123

Baseline_NR-MVSNet		99.49 68	99.37 87	99.82 25	99.91 21	99.84 18	98.83 223	99.86 22	99.68 56	99.65 125	99.88 34	97.67 209	99.87 158	99.03 105	99.86 126	99.76 37

TAMVS		99.49 68	99.45 73	99.63 107	99.48 222	99.42 126	99.45 79	99.57 174	99.66 62	99.78 82	99.83 51	97.85 196	99.86 178	99.44 52	99.96 59	99.61 117

pmmvs-eth3d		99.48 70	99.47 69	99.51 155	99.77 98	99.41 130	98.81 227	99.66 123	99.42 108	99.75 90	99.66 146	99.20 48	99.76 276	98.98 110	99.99 20	99.36 218

EI-MVSNet-UG-set		99.48 70	99.50 67	99.42 178	99.57 183	98.65 237	99.24 140	99.46 216	99.68 56	99.80 74	99.66 146	98.99 72	99.89 124	99.19 83	99.90 100	99.72 46

APDe-MVS		99.48 70	99.36 90	99.85 20	99.55 196	99.81 28	99.50 74	99.69 113	98.99 162	99.75 90	99.71 111	98.79 97	99.93 66	98.46 149	99.85 129	99.80 25

PMMVS2		99.48 70	99.45 73	99.57 136	99.76 103	98.99 206	98.09 294	99.90 14	98.95 165	99.78 82	99.58 184	99.57 20	99.93 66	99.48 49	99.95 65	99.79 30

DSMNet-mixed		99.48 70	99.65 34	98.95 248	99.71 133	97.27 294	99.50 74	99.82 48	99.59 82	99.41 188	99.85 45	99.62 16	100.00 1	99.53 46	99.89 106	99.59 134

DP-MVS		99.48 70	99.39 82	99.74 55	99.57 183	99.62 82	99.29 129	99.61 147	99.87 13	99.74 98	99.76 88	98.69 114	99.87 158	98.20 167	99.80 165	99.75 40

EI-MVSNet-Vis-set		99.47 76	99.49 68	99.42 178	99.57 183	98.66 235	99.24 140	99.46 216	99.67 58	99.79 79	99.65 151	98.97 75	99.89 124	99.15 92	99.89 106	99.71 49

VPNet		99.46 77	99.37 87	99.71 71	99.82 53	99.59 87	99.48 78	99.70 107	99.81 28	99.69 110	99.58 184	97.66 213	99.86 178	99.17 88	99.44 249	99.67 69

ACMM	98.09 11	99.46 77	99.38 84	99.72 67	99.80 69	99.69 62	99.13 177	99.65 132	98.99 162	99.64 127	99.72 104	99.39 24	99.86 178	98.23 164	99.81 160	99.60 123
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-4		99.45 79	99.44 75	99.50 157	99.52 202	98.94 212	99.17 159	99.53 189	99.64 67	99.76 89	99.60 176	98.96 78	99.90 109	98.91 122	99.84 133	99.67 69

COLMAP_ROB	98.06 12	99.45 79	99.37 87	99.70 77	99.83 46	99.70 58	99.38 92	99.78 70	99.53 87	99.67 115	99.78 79	99.19 49	99.86 178	97.32 221	99.87 119	99.55 146
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpnnormal		99.43 81	99.38 84	99.60 124	99.87 32	99.75 43	99.59 65	99.78 70	99.71 47	99.90 35	99.69 124	98.85 88	99.90 109	97.25 227	99.78 173	99.15 249

HPM-MVS_fast		99.43 81	99.30 101	99.80 29	99.83 46	99.81 28	99.52 72	99.70 107	98.35 229	99.51 168	99.50 214	99.31 35	99.88 139	98.18 171	99.84 133	99.69 56

3Dnovator	99.15 2	99.43 81	99.36 90	99.65 96	99.39 245	99.42 126	99.70 29	99.56 177	99.23 134	99.35 204	99.80 63	99.17 51	99.95 41	98.21 166	99.84 133	99.59 134

SixPastTwentyTwo		99.42 84	99.30 101	99.76 42	99.92 19	99.67 67	99.70 29	99.14 279	99.65 65	99.89 38	99.90 23	96.20 261	99.94 55	99.42 57	99.92 88	99.67 69

GBi-Net		99.42 84	99.31 96	99.73 62	99.49 216	99.77 37	99.68 41	99.70 107	99.44 101	99.62 137	99.83 51	97.21 233	99.90 109	98.96 115	99.90 100	99.53 156

test1		99.42 84	99.31 96	99.73 62	99.49 216	99.77 37	99.68 41	99.70 107	99.44 101	99.62 137	99.83 51	97.21 233	99.90 109	98.96 115	99.90 100	99.53 156

Regformer-3		99.41 87	99.41 80	99.40 186	99.52 202	98.70 232	99.17 159	99.44 221	99.62 71	99.75 90	99.60 176	98.90 83	99.85 194	98.89 123	99.84 133	99.65 89

MVSFormer		99.41 87	99.44 75	99.31 208	99.57 183	98.40 246	99.77 13	99.80 60	99.73 42	99.63 130	99.30 253	98.02 184	99.98 7	99.43 53	99.69 204	99.55 146

IterMVS-LS		99.41 87	99.47 69	99.25 221	99.81 61	98.09 271	98.85 220	99.76 79	99.62 71	99.83 64	99.64 152	98.54 139	99.97 16	99.15 92	99.99 20	99.68 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		99.40 90	99.41 80	99.39 189	99.76 103	98.94 212	99.09 184	99.59 165	99.17 145	99.81 71	99.61 173	98.41 155	99.69 301	99.32 68	99.94 77	99.53 156

NR-MVSNet		99.40 90	99.31 96	99.68 81	99.43 237	99.55 95	99.73 21	99.50 204	99.46 99	99.88 46	99.36 240	97.54 216	99.87 158	98.97 114	99.87 119	99.63 99

PVSNet_Blended_VisFu		99.40 90	99.38 84	99.44 173	99.90 25	98.66 235	98.94 210	99.91 11	97.97 251	99.79 79	99.73 98	99.05 69	99.97 16	99.15 92	99.99 20	99.68 62

EU-MVSNet		99.39 93	99.62 38	98.72 273	99.88 28	96.44 305	99.56 70	99.85 29	99.90 6	99.90 35	99.85 45	98.09 178	99.83 226	99.58 41	99.95 65	99.90 5

CHOSEN 1792x2688		99.39 93	99.30 101	99.65 96	99.88 28	99.25 172	98.78 232	99.88 18	98.66 199	99.96 8	99.79 70	97.45 220	99.93 66	99.34 63	99.99 20	99.78 31

EI-MVSNet		99.38 95	99.44 75	99.21 226	99.58 174	98.09 271	99.26 134	99.46 216	99.62 71	99.75 90	99.67 142	98.54 139	99.85 194	99.15 92	99.92 88	99.68 62

UGNet		99.38 95	99.34 92	99.49 159	98.90 311	98.90 219	99.70 29	99.35 245	99.86 16	98.57 295	99.81 61	98.50 148	99.93 66	99.38 58	99.98 36	99.66 79
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 97	99.25 114	99.72 67	99.47 227	99.56 92	98.97 205	99.61 147	99.43 106	99.67 115	99.28 257	97.85 196	99.95 41	99.17 88	99.81 160	99.65 89

UniMVSNet (Re)		99.37 97	99.26 112	99.68 81	99.51 206	99.58 89	98.98 204	99.60 161	99.43 106	99.70 108	99.36 240	97.70 204	99.88 139	99.20 82	99.87 119	99.59 134

CSCG		99.37 97	99.29 106	99.60 124	99.71 133	99.46 109	99.43 82	99.85 29	98.79 184	99.41 188	99.60 176	98.92 80	99.92 83	98.02 180	99.92 88	99.43 201

PM-MVS		99.36 100	99.29 106	99.58 130	99.83 46	99.66 70	98.95 207	99.86 22	98.85 176	99.81 71	99.73 98	98.40 157	99.92 83	98.36 154	99.83 143	99.17 247

abl_6		99.36 100	99.23 117	99.75 51	99.71 133	99.74 48	99.33 108	99.76 79	99.07 158	99.65 125	99.63 159	99.09 61	99.92 83	97.13 235	99.76 179	99.58 138

new-patchmatchnet		99.35 102	99.57 48	98.71 274	99.82 53	96.62 303	98.55 250	99.75 84	99.50 90	99.88 46	99.87 37	99.31 35	99.88 139	99.43 53	100.00 1	99.62 112

Anonymous20231206		99.35 102	99.31 96	99.47 164	99.74 117	99.06 204	99.28 130	99.74 89	99.23 134	99.72 102	99.53 205	97.63 215	99.88 139	99.11 100	99.84 133	99.48 180

MTAPA		99.35 102	99.20 120	99.80 29	99.81 61	99.81 28	99.33 108	99.53 189	99.27 123	99.42 182	99.63 159	98.21 172	99.95 41	97.83 191	99.79 168	99.65 89

FMVSNet2		99.35 102	99.28 108	99.55 145	99.49 216	99.35 150	99.45 79	99.57 174	99.44 101	99.70 108	99.74 94	97.21 233	99.87 158	99.03 105	99.94 77	99.44 195

3Dnovator+	98.92 3	99.35 102	99.24 115	99.67 84	99.35 253	99.47 105	99.62 56	99.50 204	99.44 101	99.12 241	99.78 79	98.77 103	99.94 55	97.87 188	99.72 200	99.62 112

TSAR-MVS + MP.		99.34 107	99.24 115	99.63 107	99.82 53	99.37 143	99.26 134	99.35 245	98.77 187	99.57 148	99.70 118	99.27 42	99.88 139	97.71 197	99.75 182	99.65 89

Regformer-2		99.34 107	99.27 110	99.53 150	99.41 241	99.10 197	98.99 200	99.53 189	99.47 96	99.66 119	99.52 207	98.80 94	99.89 124	98.31 159	99.74 189	99.60 123

DELS-MVS		99.34 107	99.30 101	99.48 162	99.51 206	99.36 146	98.12 290	99.53 189	99.36 115	99.41 188	99.61 173	99.22 47	99.87 158	99.21 79	99.68 206	99.20 240
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		99.33 110	99.21 119	99.71 71	99.43 237	99.56 92	98.83 223	99.53 189	99.38 112	99.67 115	99.36 240	97.67 209	99.95 41	99.17 88	99.81 160	99.63 99

ab-mvs		99.33 110	99.28 108	99.47 164	99.57 183	99.39 134	99.78 12	99.43 224	98.87 174	99.57 148	99.82 58	98.06 181	99.87 158	98.69 138	99.73 195	99.15 249

Regformer-1		99.32 112	99.27 110	99.47 164	99.41 241	98.95 211	98.99 200	99.48 209	99.48 92	99.66 119	99.52 207	98.78 100	99.87 158	98.36 154	99.74 189	99.60 123

APD-MVS_3200maxsize		99.31 113	99.16 121	99.74 55	99.53 200	99.75 43	99.27 133	99.61 147	99.19 139	99.57 148	99.64 152	98.76 104	99.90 109	97.29 223	99.62 220	99.56 143

MPTG		99.30 114	99.14 124	99.80 29	99.81 61	99.81 28	98.73 237	99.53 189	99.27 123	99.42 182	99.63 159	98.21 172	99.95 41	97.83 191	99.79 168	99.65 89

SteuartSystems-ACMMP		99.30 114	99.14 124	99.76 42	99.87 32	99.66 70	99.18 152	99.60 161	98.55 208	99.57 148	99.67 142	99.03 71	99.94 55	97.01 239	99.80 165	99.69 56
Skip Steuart: Steuart Systems R&D Blog.
testgi		99.29 116	99.26 112	99.37 195	99.75 111	98.81 228	98.84 221	99.89 15	98.38 222	99.75 90	99.04 297	99.36 33	99.86 178	99.08 102	99.25 276	99.45 190

ACMMP_Plus		99.28 117	99.11 132	99.79 34	99.75 111	99.81 28	98.95 207	99.53 189	98.27 238	99.53 164	99.73 98	98.75 107	99.87 158	97.70 198	99.83 143	99.68 62

LCM-MVSNet-Re		99.28 117	99.15 123	99.67 84	99.33 267	99.76 41	99.34 106	99.97 3	98.93 168	99.91 33	99.79 70	98.68 116	99.93 66	96.80 249	99.56 228	99.30 229

mvs_anonymous		99.28 117	99.39 82	98.94 249	99.19 288	97.81 282	99.02 193	99.55 180	99.78 34	99.85 57	99.80 63	98.24 169	99.86 178	99.57 42	99.50 242	99.15 249

MVS_Test		99.28 117	99.31 96	99.19 229	99.35 253	98.79 230	99.36 98	99.49 208	99.17 145	99.21 230	99.67 142	98.78 100	99.66 319	99.09 101	99.66 215	99.10 262

no-one		99.28 117	99.23 117	99.45 171	99.87 32	99.08 200	98.95 207	99.52 199	98.88 173	99.77 86	99.83 51	97.78 201	99.90 109	98.46 149	99.99 20	99.38 211

XVS		99.27 122	99.11 132	99.75 51	99.71 133	99.71 51	99.37 96	99.61 147	99.29 120	98.76 279	99.47 219	98.47 149	99.88 139	97.62 205	99.73 195	99.67 69

OPM-MVS		99.26 123	99.13 127	99.63 107	99.70 140	99.61 86	98.58 245	99.48 209	98.50 212	99.52 166	99.63 159	99.14 54	99.76 276	97.89 187	99.77 177	99.51 167

HFP-MVS		99.25 124	99.08 142	99.76 42	99.73 120	99.70 58	99.31 118	99.59 165	98.36 224	99.36 202	99.37 235	98.80 94	99.91 92	97.43 216	99.75 182	99.68 62

HPM-MVS		99.25 124	99.07 146	99.78 37	99.81 61	99.75 43	99.61 60	99.67 119	97.72 264	99.35 204	99.25 263	99.23 46	99.92 83	97.21 231	99.82 152	99.67 69

ACMMP		99.25 124	99.08 142	99.74 55	99.79 82	99.68 65	99.50 74	99.65 132	98.07 245	99.52 166	99.69 124	98.57 132	99.92 83	97.18 233	99.79 168	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
LS3D		99.24 127	99.11 132	99.61 118	98.38 341	99.79 33	99.57 68	99.68 116	99.61 75	99.15 238	99.71 111	98.70 112	99.91 92	97.54 210	99.68 206	99.13 256

xiu_mvs_v1_base_debu		99.23 128	99.34 92	98.91 252	99.59 171	98.23 260	98.47 260	99.66 123	99.61 75	99.68 112	98.94 309	99.39 24	99.97 16	99.18 85	99.55 234	98.51 303

xiu_mvs_v1_base		99.23 128	99.34 92	98.91 252	99.59 171	98.23 260	98.47 260	99.66 123	99.61 75	99.68 112	98.94 309	99.39 24	99.97 16	99.18 85	99.55 234	98.51 303

xiu_mvs_v1_base_debi		99.23 128	99.34 92	98.91 252	99.59 171	98.23 260	98.47 260	99.66 123	99.61 75	99.68 112	98.94 309	99.39 24	99.97 16	99.18 85	99.55 234	98.51 303

region2R		99.23 128	99.05 152	99.77 39	99.76 103	99.70 58	99.31 118	99.59 165	98.41 219	99.32 212	99.36 240	98.73 110	99.93 66	97.29 223	99.74 189	99.67 69

ACMMPR		99.23 128	99.06 148	99.76 42	99.74 117	99.69 62	99.31 118	99.59 165	98.36 224	99.35 204	99.38 234	98.61 129	99.93 66	97.43 216	99.75 182	99.67 69

XVG-ACMP-BASELINE		99.23 128	99.10 139	99.63 107	99.82 53	99.58 89	98.83 223	99.72 101	98.36 224	99.60 144	99.71 111	98.92 80	99.91 92	97.08 236	99.84 133	99.40 206

CP-MVS		99.23 128	99.05 152	99.75 51	99.66 154	99.66 70	99.38 92	99.62 143	98.38 222	99.06 248	99.27 259	98.79 97	99.94 55	97.51 212	99.82 152	99.66 79

DeepC-MVS_fast	98.47 5	99.23 128	99.12 130	99.56 142	99.28 276	99.22 179	98.99 200	99.40 233	99.08 157	99.58 146	99.64 152	98.90 83	99.83 226	97.44 215	99.75 182	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		99.22 136	99.05 152	99.74 55	99.82 53	99.63 80	99.16 165	99.73 92	97.56 273	99.64 127	99.69 124	99.37 30	99.89 124	96.66 257	99.87 119	99.69 56

CDS-MVSNet		99.22 136	99.13 127	99.50 157	99.35 253	99.11 194	98.96 206	99.54 184	99.46 99	99.61 142	99.70 118	96.31 258	99.83 226	99.34 63	99.88 112	99.55 146
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_0402		99.22 136	99.14 124	99.45 171	99.79 82	99.43 122	99.28 130	99.68 116	99.54 85	99.40 192	99.56 196	99.07 66	99.82 233	96.01 282	99.96 59	99.11 258

AllTest		99.21 139	99.07 146	99.63 107	99.78 88	99.64 76	99.12 178	99.83 40	98.63 202	99.63 130	99.72 104	98.68 116	99.75 282	96.38 268	99.83 143	99.51 167

XVG-OURS		99.21 139	99.06 148	99.65 96	99.82 53	99.62 82	97.87 318	99.74 89	98.36 224	99.66 119	99.68 136	99.71 11	99.90 109	96.84 247	99.88 112	99.43 201

Fast-Effi-MVS+-dtu		99.20 141	99.12 130	99.43 176	99.25 279	99.69 62	99.05 188	99.82 48	99.50 90	98.97 254	99.05 294	98.98 73	99.98 7	98.20 167	99.24 278	98.62 296

VDD-MVS		99.20 141	99.11 132	99.44 173	99.43 237	98.98 207	99.50 74	98.32 314	99.80 31	99.56 155	99.69 124	96.99 243	99.85 194	98.99 108	99.73 195	99.50 173

PGM-MVS		99.20 141	99.01 162	99.77 39	99.75 111	99.71 51	99.16 165	99.72 101	97.99 249	99.42 182	99.60 176	98.81 90	99.93 66	96.91 243	99.74 189	99.66 79

pmmvs5		99.19 144	99.11 132	99.42 178	99.76 103	98.88 222	98.55 250	99.73 92	98.82 180	99.72 102	99.62 166	96.56 250	99.82 233	99.32 68	99.95 65	99.56 143

mPP-MVS		99.19 144	99.00 164	99.76 42	99.76 103	99.68 65	99.38 92	99.54 184	98.34 233	99.01 251	99.50 214	98.53 143	99.93 66	97.18 233	99.78 173	99.66 79

VNet		99.18 146	99.06 148	99.56 142	99.24 281	99.36 146	99.33 108	99.31 254	99.67 58	99.47 173	99.57 191	96.48 253	99.84 210	99.15 92	99.30 270	99.47 184

RPSCF		99.18 146	99.02 159	99.64 103	99.83 46	99.85 13	99.44 81	99.82 48	98.33 234	99.50 170	99.78 79	97.90 191	99.65 326	96.78 250	99.83 143	99.44 195

DeepPCF-MVS	98.42 6	99.18 146	99.02 159	99.67 84	99.22 283	99.75 43	97.25 336	99.47 213	98.72 196	99.66 119	99.70 118	99.29 37	99.63 330	98.07 179	99.81 160	99.62 112

MVS_0304		99.17 149	99.10 139	99.38 191	99.08 302	98.86 225	98.46 264	99.73 92	99.53 87	99.35 204	99.30 253	97.11 239	99.96 33	99.33 65	99.99 20	99.33 223

EPP-MVSNet		99.17 149	99.00 164	99.66 92	99.80 69	99.43 122	99.70 29	99.24 270	99.48 92	99.56 155	99.77 85	94.89 274	99.93 66	98.72 136	99.89 106	99.63 99

MVP-Stereo		99.16 151	99.08 142	99.43 176	99.48 222	99.07 202	99.08 185	99.55 180	98.63 202	99.31 214	99.68 136	98.19 175	99.78 268	98.18 171	99.58 227	99.45 190
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-OURS-SEG-HR		99.16 151	98.99 167	99.66 92	99.84 42	99.64 76	98.25 279	99.73 92	98.39 221	99.63 130	99.43 225	99.70 12	99.90 109	97.34 220	98.64 311	99.44 195

jason		99.16 151	99.11 132	99.32 206	99.75 111	98.44 243	98.26 278	99.39 236	98.70 197	99.74 98	99.30 253	98.54 139	99.97 16	98.48 148	99.82 152	99.55 146
jason: jason.
MP-MVS-pluss		99.14 154	98.92 177	99.80 29	99.83 46	99.83 22	98.61 241	99.63 140	96.84 296	99.44 176	99.58 184	98.81 90	99.91 92	97.70 198	99.82 152	99.67 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs4		99.13 155	99.06 148	99.36 198	99.57 183	99.10 197	98.01 302	99.25 267	98.78 186	99.58 146	99.44 224	98.24 169	99.76 276	98.74 134	99.93 85	99.22 236

MVS_111021_LR		99.13 155	99.03 158	99.42 178	99.58 174	99.32 155	97.91 317	99.73 92	98.68 198	99.31 214	99.48 216	99.09 61	99.66 319	97.70 198	99.77 177	99.29 232

#test#		99.12 157	98.90 180	99.76 42	99.73 120	99.70 58	99.10 180	99.59 165	97.60 271	99.36 202	99.37 235	98.80 94	99.91 92	96.84 247	99.75 182	99.68 62

TSAR-MVS + GP.		99.12 157	99.04 157	99.38 191	99.34 263	99.16 189	98.15 286	99.29 258	98.18 242	99.63 130	99.62 166	99.18 50	99.68 309	98.20 167	99.74 189	99.30 229

MVS_111021_HR		99.12 157	99.02 159	99.40 186	99.50 211	99.11 194	97.92 315	99.71 104	98.76 190	99.08 244	99.47 219	99.17 51	99.54 339	97.85 190	99.76 179	99.54 153

CANet		99.11 160	99.05 152	99.28 211	98.83 321	98.56 238	98.71 239	99.41 227	99.25 130	99.23 226	99.22 272	97.66 213	99.94 55	99.19 83	99.97 47	99.33 223

WR-MVS		99.11 160	98.93 174	99.66 92	99.30 273	99.42 126	98.42 268	99.37 242	99.04 159	99.57 148	99.20 274	96.89 245	99.86 178	98.66 141	99.87 119	99.70 53

PHI-MVS		99.11 160	98.95 173	99.59 126	99.13 294	99.59 87	99.17 159	99.65 132	97.88 255	99.25 222	99.46 222	98.97 75	99.80 257	97.26 226	99.82 152	99.37 215

MSDG		99.08 163	98.98 170	99.37 195	99.60 168	99.13 192	97.54 326	99.74 89	98.84 179	99.53 164	99.55 201	99.10 59	99.79 260	97.07 237	99.86 126	99.18 245

Effi-MVS+-dtu		99.07 164	98.92 177	99.52 152	98.89 315	99.78 35	99.15 167	99.66 123	99.34 116	98.92 264	99.24 268	97.69 206	99.98 7	98.11 176	99.28 272	98.81 291

Effi-MVS+		99.06 165	98.97 171	99.34 200	99.31 269	98.98 207	98.31 276	99.91 11	98.81 181	98.79 276	98.94 309	99.14 54	99.84 210	98.79 129	98.74 306	99.20 240

MP-MVS		99.06 165	98.83 191	99.76 42	99.76 103	99.71 51	99.32 111	99.50 204	98.35 229	98.97 254	99.48 216	98.37 160	99.92 83	95.95 288	99.75 182	99.63 99

MDA-MVSNet-bldmvs		99.06 165	99.05 152	99.07 240	99.80 69	97.83 281	98.89 212	99.72 101	99.29 120	99.63 130	99.70 118	96.47 254	99.89 124	98.17 173	99.82 152	99.50 173

MSLP-MVS++		99.05 168	99.09 141	98.91 252	99.21 284	98.36 250	98.82 226	99.47 213	98.85 176	98.90 267	99.56 196	98.78 100	99.09 349	98.57 143	99.68 206	99.26 233

1112_ss		99.05 168	98.84 188	99.67 84	99.66 154	99.29 160	98.52 255	99.82 48	97.65 269	99.43 180	99.16 276	96.42 256	99.91 92	99.07 103	99.84 133	99.80 25

ACMP	97.51 14	99.05 168	98.84 188	99.67 84	99.78 88	99.55 95	98.88 214	99.66 123	97.11 292	99.47 173	99.60 176	99.07 66	99.89 124	96.18 274	99.85 129	99.58 138
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		99.03 171	99.01 162	99.09 236	99.54 197	97.99 275	98.58 245	99.82 48	97.62 270	99.34 208	99.71 111	98.52 145	99.77 274	97.98 183	99.97 47	99.52 164

IS-MVSNet		99.03 171	98.85 186	99.55 145	99.80 69	99.25 172	99.73 21	99.15 278	99.37 113	99.61 142	99.71 111	94.73 276	99.81 252	97.70 198	99.88 112	99.58 138

xiu_mvs_v2_base		99.02 173	99.11 132	98.77 266	99.37 250	98.09 271	98.13 289	99.51 201	99.47 96	99.42 182	98.54 329	99.38 28	99.97 16	98.83 126	99.33 267	98.24 315

Fast-Effi-MVS+		99.02 173	98.87 183	99.46 167	99.38 248	99.50 99	99.04 190	99.79 68	97.17 287	98.62 290	98.74 322	99.34 34	99.95 41	98.32 158	99.41 258	98.92 283

canonicalmvs		99.02 173	99.00 164	99.09 236	99.10 301	98.70 232	99.61 60	99.66 123	99.63 70	98.64 289	97.65 346	99.04 70	99.54 339	98.79 129	98.92 291	99.04 275

MCST-MVS		99.02 173	98.81 193	99.65 96	99.58 174	99.49 101	98.58 245	99.07 282	98.40 220	99.04 249	99.25 263	98.51 147	99.80 257	97.31 222	99.51 241	99.65 89

HSP-MVS		99.01 177	98.76 197	99.76 42	99.78 88	99.73 49	99.35 99	99.31 254	98.54 209	99.54 161	98.99 298	96.81 246	99.93 66	96.97 241	99.53 239	99.61 117

SD-MVS		99.01 177	99.30 101	98.15 294	99.50 211	99.40 131	98.94 210	99.61 147	99.22 137	99.75 90	99.82 58	99.54 22	95.51 355	97.48 213	99.87 119	99.54 153

LF4IMVS		99.01 177	98.92 177	99.27 213	99.71 133	99.28 162	98.59 244	99.77 73	98.32 235	99.39 193	99.41 229	98.62 127	99.84 210	96.62 260	99.84 133	98.69 295

MS-PatchMatch		99.00 180	98.97 171	99.09 236	99.11 299	98.19 263	98.76 233	99.33 248	98.49 213	99.44 176	99.58 184	98.21 172	99.69 301	98.20 167	99.62 220	99.39 208

PS-MVSNAJ		99.00 180	99.08 142	98.76 267	99.37 250	98.10 270	98.00 304	99.51 201	99.47 96	99.41 188	98.50 331	99.28 39	99.97 16	98.83 126	99.34 265	98.20 319

CNVR-MVS		98.99 182	98.80 195	99.56 142	99.25 279	99.43 122	98.54 253	99.27 262	98.58 206	98.80 275	99.43 225	98.53 143	99.70 295	97.22 229	99.59 226	99.54 153

VDDNet		98.97 183	98.82 192	99.42 178	99.71 133	98.81 228	99.62 56	98.68 300	99.81 28	99.38 200	99.80 63	94.25 280	99.85 194	98.79 129	99.32 268	99.59 134

IterMVS		98.97 183	99.16 121	98.42 283	99.74 117	95.64 323	98.06 299	99.83 40	99.83 26	99.85 57	99.74 94	96.10 264	99.99 4	99.27 77	100.00 1	99.63 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap		98.97 183	98.93 174	99.07 240	99.46 231	98.19 263	97.75 321	99.75 84	98.79 184	99.54 161	99.70 118	98.97 75	99.62 331	96.63 259	99.83 143	99.41 205

HPM-MVS++		98.96 186	98.70 200	99.74 55	99.52 202	99.71 51	98.86 217	99.19 274	98.47 215	98.59 293	99.06 293	98.08 180	99.91 92	96.94 242	99.60 225	99.60 123

lupinMVS		98.96 186	98.87 183	99.24 223	99.57 183	98.40 246	98.12 290	99.18 275	98.28 237	99.63 130	99.13 278	98.02 184	99.97 16	98.22 165	99.69 204	99.35 220

USDC		98.96 186	98.93 174	99.05 242	99.54 197	97.99 275	97.07 338	99.80 60	98.21 240	99.75 90	99.77 85	98.43 153	99.64 328	97.90 186	99.88 112	99.51 167

YYNet1		98.95 189	98.99 167	98.84 260	99.64 158	97.14 297	98.22 281	99.32 250	98.92 170	99.59 145	99.66 146	97.40 222	99.83 226	98.27 163	99.90 100	99.55 146

MDA-MVSNet_test_wron		98.95 189	98.99 167	98.85 258	99.64 158	97.16 296	98.23 280	99.33 248	98.93 168	99.56 155	99.66 146	97.39 224	99.83 226	98.29 161	99.88 112	99.55 146

Test_1112_low_res		98.95 189	98.73 198	99.63 107	99.68 149	99.15 191	98.09 294	99.80 60	97.14 289	99.46 175	99.40 230	96.11 263	99.89 124	99.01 107	99.84 133	99.84 15

diffmvs		98.94 192	98.87 183	99.13 233	99.37 250	98.90 219	99.25 138	99.64 137	97.55 275	99.04 249	99.58 184	97.23 232	99.64 328	98.73 135	99.44 249	98.86 287

test1235678		98.93 193	98.84 188	99.19 229	99.46 231	98.55 239	97.53 328	99.77 73	98.76 190	99.69 110	99.48 216	96.69 247	99.90 109	98.30 160	99.91 98	99.11 258

CANet_DTU		98.91 194	98.85 186	99.09 236	98.79 326	98.13 266	98.18 283	99.31 254	99.48 92	98.86 270	99.51 211	96.56 250	99.95 41	99.05 104	99.95 65	99.19 242

HyFIR lowres test		98.91 194	98.64 205	99.73 62	99.85 39	99.47 105	98.07 298	99.83 40	98.64 201	99.89 38	99.60 176	92.57 293	100.00 1	99.33 65	99.97 47	99.72 46

HQP_MVS		98.90 196	98.68 202	99.55 145	99.58 174	99.24 175	98.80 228	99.54 184	98.94 166	99.14 239	99.25 263	97.24 230	99.82 233	95.84 291	99.78 173	99.60 123

sss		98.90 196	98.77 196	99.27 213	99.48 222	98.44 243	98.72 238	99.32 250	97.94 253	99.37 201	99.35 245	96.31 258	99.91 92	98.85 125	99.63 219	99.47 184

OMC-MVS		98.90 196	98.72 199	99.44 173	99.39 245	99.42 126	98.58 245	99.64 137	97.31 285	99.44 176	99.62 166	98.59 131	99.69 301	96.17 275	99.79 168	99.22 236

new_pmnet		98.88 199	98.89 181	98.84 260	99.70 140	97.62 288	98.15 286	99.50 204	97.98 250	99.62 137	99.54 203	98.15 177	99.94 55	97.55 209	99.84 133	98.95 280

ESAPD		98.87 200	98.58 210	99.74 55	99.62 165	99.67 67	98.74 234	99.53 189	97.71 265	99.55 158	99.57 191	98.40 157	99.90 109	94.47 321	99.68 206	99.66 79

K. test v3		98.87 200	98.60 207	99.69 78	99.93 18	99.46 109	99.74 19	94.97 352	99.78 34	99.88 46	99.88 34	93.66 284	99.97 16	99.61 38	99.95 65	99.64 95

APD-MVS		98.87 200	98.59 208	99.71 71	99.50 211	99.62 82	99.01 195	99.57 174	96.80 298	99.54 161	99.63 159	98.29 165	99.91 92	95.24 312	99.71 201	99.61 117
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs-test1		98.83 203	98.70 200	99.22 225	98.89 315	99.65 74	98.88 214	99.66 123	99.34 116	98.29 306	98.94 309	97.69 206	99.96 33	98.11 176	98.54 322	98.04 323

UnsupCasMVSNet_eth		98.83 203	98.57 212	99.59 126	99.68 149	99.45 114	98.99 200	99.67 119	99.48 92	99.55 158	99.36 240	94.92 273	99.86 178	98.95 119	96.57 347	99.45 190

test_normal		98.82 205	98.67 203	99.27 213	99.56 194	98.83 227	98.22 281	98.01 318	99.03 160	99.49 172	99.24 268	96.21 260	99.76 276	98.69 138	99.56 228	99.22 236

NCCC		98.82 205	98.57 212	99.58 130	99.21 284	99.31 156	98.61 241	99.25 267	98.65 200	98.43 303	99.26 261	97.86 195	99.81 252	96.55 262	99.27 275	99.61 117

PMVS	92.94 21	98.82 205	98.81 193	98.85 258	99.84 42	97.99 275	99.20 150	99.47 213	99.71 47	99.42 182	99.82 58	98.09 178	99.47 343	93.88 329	99.85 129	99.07 272
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DI_MVS_plusplus_test		98.80 208	98.65 204	99.27 213	99.57 183	98.90 219	98.44 266	97.95 321	99.02 161	99.51 168	99.23 271	96.18 262	99.76 276	98.52 147	99.42 256	99.14 253

FMVSNet3		98.80 208	98.63 206	99.32 206	99.13 294	98.72 231	99.10 180	99.48 209	99.23 134	99.62 137	99.64 152	92.57 293	99.86 178	98.96 115	99.90 100	99.39 208

Patchmtry		98.78 210	98.54 215	99.49 159	98.89 315	99.19 187	99.32 111	99.67 119	99.65 65	99.72 102	99.79 70	91.87 299	99.95 41	98.00 182	99.97 47	99.33 223

Vis-MVSNet (Re-imp)		98.77 211	98.58 210	99.34 200	99.78 88	98.88 222	99.61 60	99.56 177	99.11 152	99.24 225	99.56 196	93.00 291	99.78 268	97.43 216	99.89 106	99.35 220

CLD-MVS		98.76 212	98.57 212	99.33 202	99.57 183	98.97 209	97.53 328	99.55 180	96.41 307	99.27 219	99.13 278	99.07 66	99.78 268	96.73 254	99.89 106	99.23 235
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CPTT-MVS		98.74 213	98.44 219	99.64 103	99.61 167	99.38 140	99.18 152	99.55 180	96.49 306	99.27 219	99.37 235	97.11 239	99.92 83	95.74 295	99.67 212	99.62 112

F-COLMAP		98.74 213	98.45 218	99.62 115	99.57 183	99.47 105	98.84 221	99.65 132	96.31 308	98.93 262	99.19 275	97.68 208	99.87 158	96.52 263	99.37 263	99.53 156

N_pmnet		98.73 215	98.53 216	99.35 199	99.72 130	98.67 234	98.34 273	94.65 353	98.35 229	99.79 79	99.68 136	98.03 182	99.93 66	98.28 162	99.92 88	99.44 195

PVSNet_Blended		98.70 216	98.59 208	99.02 245	99.54 197	97.99 275	97.58 325	99.82 48	95.70 319	99.34 208	98.98 301	98.52 145	99.77 274	97.98 183	99.83 143	99.30 229

PatchMatch-RL		98.68 217	98.47 217	99.30 210	99.44 235	99.28 162	98.14 288	99.54 184	97.12 291	99.11 242	99.25 263	97.80 199	99.70 295	96.51 264	99.30 270	98.93 282

Test4		98.65 218	98.44 219	99.27 213	99.57 183	98.86 225	98.43 267	99.41 227	98.85 176	99.57 148	98.95 308	93.05 289	99.75 282	98.57 143	99.56 228	99.19 242

test_prior3		98.62 219	98.34 232	99.46 167	99.35 253	99.22 179	97.95 311	99.39 236	97.87 256	98.05 319	99.05 294	97.90 191	99.69 301	95.99 284	99.49 244	99.48 180

CVMVSNet		98.61 220	98.88 182	97.80 307	99.58 174	93.60 335	99.26 134	99.64 137	99.66 62	99.72 102	99.67 142	93.26 287	99.93 66	99.30 71	99.81 160	99.87 10

Patchmatch-RL test		98.60 221	98.36 230	99.33 202	99.77 98	99.07 202	98.27 277	99.87 20	98.91 171	99.74 98	99.72 104	90.57 313	99.79 260	98.55 145	99.85 129	99.11 258

AdaColmap		98.60 221	98.35 231	99.38 191	99.12 296	99.22 179	98.67 240	99.42 226	97.84 260	98.81 273	99.27 259	97.32 228	99.81 252	95.14 313	99.53 239	99.10 262

WTY-MVS		98.59 223	98.37 229	99.26 218	99.43 237	98.40 246	98.74 234	99.13 281	98.10 244	99.21 230	99.24 268	94.82 275	99.90 109	97.86 189	98.77 302	99.49 179

CNLPA		98.57 224	98.34 232	99.28 211	99.18 290	99.10 197	98.34 273	99.41 227	98.48 214	98.52 297	98.98 301	97.05 241	99.78 268	95.59 303	99.50 242	98.96 279

1121		98.56 225	98.24 237	99.52 152	99.49 216	99.24 175	99.30 121	99.22 272	95.77 317	98.52 297	99.29 256	97.39 224	99.85 194	95.79 293	99.34 265	99.46 188

CDPH-MVS		98.56 225	98.20 241	99.61 118	99.50 211	99.46 109	98.32 275	99.41 227	95.22 325	99.21 230	99.10 284	98.34 162	99.82 233	95.09 315	99.66 215	99.56 143

UnsupCasMVSNet_bld		98.55 227	98.27 236	99.40 186	99.56 194	99.37 143	97.97 310	99.68 116	97.49 278	99.08 244	99.35 245	95.41 272	99.82 233	97.70 198	98.19 333	99.01 278

RPMNet		98.53 228	98.44 219	98.83 262	99.05 305	98.12 267	99.30 121	98.78 295	99.86 16	99.16 236	99.74 94	92.53 295	99.91 92	98.75 133	98.77 302	98.44 306

MG-MVS		98.52 229	98.39 226	98.94 249	99.15 291	97.39 293	98.18 283	99.21 273	98.89 172	99.23 226	99.63 159	97.37 226	99.74 286	94.22 325	99.61 224	99.69 56

DP-MVS Recon		98.50 230	98.23 238	99.31 208	99.49 216	99.46 109	98.56 249	99.63 140	94.86 331	98.85 271	99.37 235	97.81 198	99.59 336	96.08 277	99.44 249	98.88 285

CMPMVS	77.52 23	98.50 230	98.19 244	99.41 185	98.33 342	99.56 92	99.01 195	99.59 165	95.44 322	99.57 148	99.80 63	95.64 268	99.46 346	96.47 267	99.92 88	99.21 239
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t		98.49 232	98.11 247	99.64 103	99.73 120	99.58 89	99.24 140	99.76 79	89.94 346	99.42 182	99.56 196	97.76 202	99.86 178	97.74 196	99.82 152	99.47 184

PMMVS		98.49 232	98.29 235	99.11 234	98.96 308	98.42 245	97.54 326	99.32 250	97.53 277	98.47 302	98.15 336	97.88 194	99.82 233	97.46 214	99.24 278	99.09 265

MVSTER		98.47 234	98.22 239	99.24 223	99.06 304	98.35 251	99.08 185	99.46 216	99.27 123	99.75 90	99.66 146	88.61 323	99.85 194	99.14 98	99.92 88	99.52 164

LFMVS		98.46 235	98.19 244	99.26 218	99.24 281	98.52 241	99.62 56	96.94 337	99.87 13	99.31 214	99.58 184	91.04 304	99.81 252	98.68 140	99.42 256	99.45 190

PatchT		98.45 236	98.32 234	98.83 262	98.94 309	98.29 258	99.24 140	98.82 293	99.84 23	99.08 244	99.76 88	91.37 302	99.94 55	98.82 128	99.00 290	98.26 313

test12356		98.43 237	98.39 226	98.55 277	99.46 231	96.36 306	97.32 335	99.81 56	97.60 271	99.62 137	99.37 235	94.57 277	99.89 124	97.80 193	99.92 88	99.40 206

MIMVSNet		98.43 237	98.20 241	99.11 234	99.53 200	98.38 249	99.58 67	98.61 302	98.96 164	99.33 210	99.76 88	90.92 306	99.81 252	97.38 219	99.76 179	99.15 249

PVSNet	97.47 15	98.42 239	98.44 219	98.35 287	99.46 231	96.26 307	96.70 343	99.34 247	97.68 268	99.00 252	99.13 278	97.40 222	99.72 289	97.59 208	99.68 206	99.08 268

CHOSEN 280x420		98.41 240	98.41 224	98.40 285	99.34 263	95.89 317	96.94 339	99.44 221	98.80 183	99.25 222	99.52 207	93.51 285	99.98 7	98.94 120	99.98 36	99.32 227

BH-RMVSNet		98.41 240	98.14 246	99.21 226	99.21 284	98.47 242	98.60 243	98.26 315	98.35 229	98.93 262	99.31 250	97.20 236	99.66 319	94.32 323	99.10 284	99.51 167

QAPM		98.40 242	97.99 253	99.65 96	99.39 245	99.47 105	99.67 46	99.52 199	91.70 343	98.78 278	99.80 63	98.55 137	99.95 41	94.71 319	99.75 182	99.53 156

API-MVS		98.38 243	98.39 226	98.35 287	98.83 321	99.26 168	99.14 172	99.18 275	98.59 205	98.66 288	98.78 319	98.61 129	99.57 338	94.14 326	99.56 228	96.21 348

HQP-MVS		98.36 244	98.02 252	99.39 189	99.31 269	98.94 212	97.98 307	99.37 242	97.45 279	98.15 313	98.83 315	96.67 248	99.70 295	94.73 317	99.67 212	99.53 156

PAPM_NR		98.36 244	98.04 251	99.33 202	99.48 222	98.93 216	98.79 231	99.28 261	97.54 276	98.56 296	98.57 327	97.12 238	99.69 301	94.09 327	98.90 293	99.38 211

PLC	97.35 16	98.36 244	97.99 253	99.48 162	99.32 268	99.24 175	98.50 257	99.51 201	95.19 327	98.58 294	98.96 306	96.95 244	99.83 226	95.63 302	99.25 276	99.37 215
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		98.35 247	97.95 257	99.57 136	99.35 253	99.35 150	98.11 292	99.41 227	94.90 329	97.92 324	98.99 298	98.02 184	99.85 194	95.38 310	99.44 249	99.50 173

CR-MVSNet		98.35 247	98.20 241	98.83 262	99.05 305	98.12 267	99.30 121	99.67 119	97.39 282	99.16 236	99.79 70	91.87 299	99.91 92	98.78 132	98.77 302	98.44 306

LP		98.34 249	98.44 219	98.05 296	98.88 318	95.31 328	99.28 130	98.74 297	99.12 151	98.98 253	99.79 70	93.40 286	99.93 66	98.38 152	99.41 258	98.90 284

agg_prior1		98.33 250	97.92 259	99.57 136	99.35 253	99.36 146	97.99 306	99.39 236	94.85 332	97.76 334	98.98 301	98.03 182	99.85 194	95.49 305	99.44 249	99.51 167

alignmvs		98.28 251	97.96 256	99.25 221	99.12 296	98.93 216	99.03 192	98.42 311	99.64 67	98.72 282	97.85 339	90.86 309	99.62 331	98.88 124	99.13 282	99.19 242

agg_prior3		98.24 252	97.81 265	99.53 150	99.34 263	99.26 168	98.09 294	99.39 236	94.21 337	97.77 333	98.96 306	97.74 203	99.84 210	95.38 310	99.44 249	99.50 173

MAR-MVS		98.24 252	97.92 259	99.19 229	98.78 328	99.65 74	99.17 159	99.14 279	95.36 323	98.04 321	98.81 317	97.47 219	99.72 289	95.47 307	99.06 285	98.21 317
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	98.23 254	97.89 263	99.26 218	99.19 288	99.26 168	99.65 54	99.69 113	91.33 344	98.14 317	99.77 85	98.28 166	99.96 33	95.41 309	99.55 234	98.58 300

BH-untuned		98.22 255	98.09 248	98.58 276	99.38 248	97.24 295	98.55 250	98.98 288	97.81 262	99.20 235	98.76 320	97.01 242	99.65 326	94.83 316	98.33 328	98.86 287

HY-MVS	98.23 9	98.21 256	97.95 257	98.99 246	99.03 307	98.24 259	99.61 60	98.72 298	96.81 297	98.73 281	99.51 211	94.06 281	99.86 178	96.91 243	98.20 331	98.86 287

testus		98.15 257	98.06 250	98.40 285	99.11 299	95.95 312	96.77 341	99.89 15	95.83 315	99.23 226	98.47 332	97.50 218	99.84 210	96.58 261	99.20 281	99.39 208

Patchmatch-test1		98.13 258	98.40 225	97.31 321	99.20 287	92.99 337	98.17 285	98.49 308	98.24 239	99.10 243	99.52 207	96.01 265	99.83 226	97.22 229	99.62 220	99.12 257

EPNet		98.13 258	97.77 269	99.18 232	94.57 355	97.99 275	99.24 140	97.96 319	99.74 40	97.29 340	99.62 166	93.13 288	99.97 16	98.59 142	99.83 143	99.58 138
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test		98.10 260	97.98 255	98.48 282	99.27 278	96.48 304	99.40 85	99.07 282	98.81 181	99.23 226	99.57 191	90.11 317	99.87 158	96.69 255	99.64 218	99.09 265

pmmvs3		98.08 261	97.80 266	98.91 252	99.41 241	97.69 286	97.87 318	99.66 123	95.87 314	99.50 170	99.51 211	90.35 315	99.97 16	98.55 145	99.47 246	99.08 268

JIA-IIPM		98.06 262	97.92 259	98.50 281	98.59 336	97.02 298	98.80 228	98.51 306	99.88 12	97.89 326	99.87 37	91.89 298	99.90 109	98.16 174	97.68 343	98.59 298

TAPA-MVS	97.92 13	98.03 263	97.55 275	99.46 167	99.47 227	99.44 116	98.50 257	99.62 143	86.79 347	99.07 247	99.26 261	98.26 168	99.62 331	97.28 225	99.73 195	99.31 228
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
1314		98.00 264	97.90 262	98.27 292	98.90 311	97.45 292	99.30 121	99.06 284	94.98 328	97.21 341	99.12 282	98.43 153	99.67 314	95.58 304	98.56 321	97.71 335

GA-MVS		97.99 265	97.68 272	98.93 251	99.52 202	98.04 274	97.19 337	99.05 285	98.32 235	98.81 273	98.97 304	89.89 320	99.41 347	98.33 157	99.05 286	99.34 222

MVS-HIRNet		97.86 266	98.22 239	96.76 325	99.28 276	91.53 347	98.38 270	92.60 354	99.13 150	99.31 214	99.96 11	97.18 237	99.68 309	98.34 156	99.83 143	99.07 272

FMVSNet5		97.80 267	97.25 278	99.42 178	98.83 321	98.97 209	99.38 92	99.80 60	98.87 174	99.25 222	99.69 124	80.60 355	99.91 92	98.96 115	99.90 100	99.38 211

ADS-MVSNet2		97.78 268	97.66 274	98.12 295	99.14 292	95.36 326	99.22 146	98.75 296	96.97 293	98.25 309	99.64 152	90.90 307	99.94 55	96.51 264	99.56 228	99.08 268

tpmrst		97.73 269	98.07 249	96.73 327	98.71 333	92.00 341	99.10 180	98.86 290	98.52 210	98.92 264	99.54 203	91.90 297	99.82 233	98.02 180	99.03 288	98.37 308

ADS-MVSNet		97.72 270	97.67 273	97.86 305	99.14 292	94.65 331	99.22 146	98.86 290	96.97 293	98.25 309	99.64 152	90.90 307	99.84 210	96.51 264	99.56 228	99.08 268

PatchmatchNet		97.65 271	97.80 266	97.18 322	98.82 324	92.49 339	99.17 159	98.39 312	98.12 243	98.79 276	99.58 184	90.71 311	99.89 124	97.23 228	99.41 258	99.16 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		97.62 272	97.79 268	97.11 324	96.67 354	92.31 340	98.51 256	98.04 316	99.24 132	95.77 349	99.47 219	93.78 283	99.66 319	98.98 110	99.62 220	99.37 215
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		97.58 273	99.13 127	92.93 339	99.69 142	99.49 101	99.52 72	99.77 73	97.97 251	99.96 8	99.79 70	99.84 4	99.94 55	95.85 290	99.82 152	79.36 352

PAPR		97.56 274	97.07 280	99.04 243	98.80 325	98.11 269	97.63 323	99.25 267	94.56 335	98.02 322	98.25 335	97.43 221	99.68 309	90.90 336	98.74 306	99.33 223

TR-MVS		97.44 275	97.15 279	98.32 289	98.53 338	97.46 291	98.47 260	97.91 322	96.85 295	98.21 312	98.51 330	96.42 256	99.51 341	92.16 332	97.29 344	97.98 328

tpmvs		97.39 276	97.69 271	96.52 331	98.41 340	91.76 344	99.30 121	98.94 289	97.74 263	97.85 329	99.55 201	92.40 296	99.73 288	96.25 273	98.73 308	98.06 322

test0.0.03 1		97.37 277	96.91 287	98.74 272	97.72 348	97.57 289	97.60 324	97.36 336	98.00 247	99.21 230	98.02 337	90.04 318	99.79 260	98.37 153	95.89 350	98.86 287

OpenMVS_ROB	97.31 17	97.36 278	96.84 288	98.89 257	99.29 274	99.45 114	98.87 216	99.48 209	86.54 349	99.44 176	99.74 94	97.34 227	99.86 178	91.61 333	99.28 272	97.37 340

1111		97.29 279	96.71 298	99.04 243	99.65 156	97.72 283	98.35 271	99.80 60	99.40 109	99.66 119	99.43 225	75.10 359	99.87 158	98.98 110	99.98 36	99.52 164

tfpn1000		97.28 280	96.83 289	98.64 275	99.67 153	97.68 287	99.41 83	95.47 350	97.14 289	99.43 180	99.07 292	85.87 345	99.88 139	96.78 250	98.67 310	98.34 310

thresconf0.02		97.25 281	96.74 292	98.75 268	99.73 120	98.35 251	99.35 99	95.78 343	96.54 300	99.39 193	99.08 285	86.57 338	99.88 139	95.69 296	98.57 314	98.02 324

tfpn_n400		97.25 281	96.74 292	98.75 268	99.73 120	98.35 251	99.35 99	95.78 343	96.54 300	99.39 193	99.08 285	86.57 338	99.88 139	95.69 296	98.57 314	98.02 324

tfpnconf		97.25 281	96.74 292	98.75 268	99.73 120	98.35 251	99.35 99	95.78 343	96.54 300	99.39 193	99.08 285	86.57 338	99.88 139	95.69 296	98.57 314	98.02 324

tfpnview11		97.25 281	96.74 292	98.75 268	99.73 120	98.35 251	99.35 99	95.78 343	96.54 300	99.39 193	99.08 285	86.57 338	99.88 139	95.69 296	98.57 314	98.02 324

BH-w/o		97.20 285	97.01 283	97.76 308	99.08 302	95.69 322	98.03 301	98.52 305	95.76 318	97.96 323	98.02 337	95.62 269	99.47 343	92.82 331	97.25 345	98.12 321

conf0.01		97.19 286	96.74 292	98.51 278	99.73 120	98.35 251	99.35 99	95.78 343	96.54 300	99.39 193	99.08 285	86.57 338	99.88 139	95.69 296	98.57 314	97.30 341

conf0.002		97.19 286	96.74 292	98.51 278	99.73 120	98.35 251	99.35 99	95.78 343	96.54 300	99.39 193	99.08 285	86.57 338	99.88 139	95.69 296	98.57 314	97.30 341

test-LLR		97.15 288	96.95 285	97.74 310	98.18 345	95.02 329	97.38 331	96.10 339	98.00 247	97.81 330	98.58 325	90.04 318	99.91 92	97.69 203	98.78 300	98.31 311

tpm		97.15 288	96.95 285	97.75 309	98.91 310	94.24 333	99.32 111	97.96 319	97.71 265	98.29 306	99.32 248	86.72 336	99.92 83	98.10 178	96.24 349	99.09 265

E-PMN		97.14 290	97.43 276	96.27 333	98.79 326	91.62 346	95.54 347	99.01 287	99.44 101	98.88 268	99.12 282	92.78 292	99.68 309	94.30 324	99.03 288	97.50 337

PNet_i23d		97.02 291	97.87 264	94.49 338	99.69 142	84.81 357	95.18 350	99.85 29	97.83 261	99.32 212	99.57 191	95.53 271	99.47 343	96.09 276	97.74 342	99.18 245

cascas		96.99 292	96.82 290	97.48 315	97.57 351	95.64 323	96.43 345	99.56 177	91.75 342	97.13 342	97.61 347	95.58 270	98.63 352	96.68 256	99.11 283	98.18 320

EMVS		96.96 293	97.28 277	95.99 337	98.76 330	91.03 349	95.26 349	98.61 302	99.34 116	98.92 264	98.88 314	93.79 282	99.66 319	92.87 330	99.05 286	97.30 341

PatchFormer-LS_test		96.95 294	97.07 280	96.62 330	98.76 330	91.85 343	99.18 152	98.45 310	97.29 286	97.73 336	97.22 355	88.77 322	99.76 276	98.13 175	98.04 337	98.25 314

tfpn_ndepth		96.93 295	96.43 303	98.42 283	99.60 168	97.72 283	99.22 146	95.16 351	95.91 313	99.26 221	98.79 318	85.56 346	99.87 158	96.03 281	98.35 327	97.68 336

view600		96.86 296	96.52 299	97.88 301	99.69 142	95.87 318	99.39 86	97.68 325	99.11 152	98.96 256	97.82 341	87.40 324	99.79 260	89.78 337	98.83 295	97.98 328

view800		96.86 296	96.52 299	97.88 301	99.69 142	95.87 318	99.39 86	97.68 325	99.11 152	98.96 256	97.82 341	87.40 324	99.79 260	89.78 337	98.83 295	97.98 328

conf0.05thres1000		96.86 296	96.52 299	97.88 301	99.69 142	95.87 318	99.39 86	97.68 325	99.11 152	98.96 256	97.82 341	87.40 324	99.79 260	89.78 337	98.83 295	97.98 328

tfpn		96.86 296	96.52 299	97.88 301	99.69 142	95.87 318	99.39 86	97.68 325	99.11 152	98.96 256	97.82 341	87.40 324	99.79 260	89.78 337	98.83 295	97.98 328

dp		96.86 296	97.07 280	96.24 335	98.68 335	90.30 354	99.19 151	98.38 313	97.35 284	98.23 311	99.59 182	87.23 328	99.82 233	96.27 272	98.73 308	98.59 298

tpm cat1		96.78 301	96.98 284	96.16 336	98.85 320	90.59 353	99.08 185	99.32 250	92.37 341	97.73 336	99.46 222	91.15 303	99.69 301	96.07 278	98.80 299	98.21 317

PCF-MVS	96.03 18	96.73 302	95.86 314	99.33 202	99.44 235	99.16 189	96.87 340	99.44 221	86.58 348	98.95 260	99.40 230	94.38 279	99.88 139	87.93 345	99.80 165	98.95 280
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		96.71 303	96.79 291	96.46 332	98.90 311	90.71 351	99.41 83	98.68 300	94.69 334	98.14 317	99.34 247	86.32 344	99.80 257	97.60 207	98.07 336	98.88 285

MVE	92.54 22	96.66 304	96.11 308	98.31 290	99.68 149	97.55 290	97.94 313	95.60 349	99.37 113	90.68 353	98.70 323	96.56 250	98.61 353	86.94 351	99.55 234	98.77 293
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres600view7		96.60 305	96.16 306	97.93 299	99.63 160	96.09 311	99.18 152	97.57 329	98.77 187	98.72 282	97.32 350	87.04 329	99.72 289	88.57 342	98.62 312	97.98 328

EPMVS		96.53 306	96.32 304	97.17 323	98.18 345	92.97 338	99.39 86	89.95 356	98.21 240	98.61 291	99.59 182	86.69 337	99.72 289	96.99 240	99.23 280	98.81 291

tfpn111		96.50 307	96.12 307	97.65 312	99.63 160	95.93 313	99.18 152	97.57 329	98.75 192	98.70 284	97.31 351	87.04 329	99.72 289	88.27 344	98.61 313	97.30 341

conf200view11		96.43 308	96.03 310	97.63 313	99.63 160	95.93 313	99.18 152	97.57 329	98.75 192	98.70 284	97.31 351	87.04 329	99.67 314	87.62 346	98.51 323	97.30 341

thres400		96.40 309	95.89 312	97.92 300	99.58 174	96.11 309	99.00 197	97.54 334	98.43 216	98.52 297	96.98 356	86.85 333	99.67 314	87.62 346	98.51 323	97.98 328

thres100view900		96.39 310	96.03 310	97.47 316	99.63 160	95.93 313	99.18 152	97.57 329	98.75 192	98.70 284	97.31 351	87.04 329	99.67 314	87.62 346	98.51 323	96.81 346

tpm2		96.35 311	96.22 305	96.73 327	98.88 318	91.75 345	99.21 149	98.51 306	93.27 340	97.89 326	99.21 273	84.83 347	99.70 295	96.04 280	98.18 334	98.75 294

FPMVS		96.32 312	95.50 319	98.79 265	99.60 168	98.17 265	98.46 264	98.80 294	97.16 288	96.28 345	99.63 159	82.19 350	99.09 349	88.45 343	98.89 294	99.10 262

tfpn200view9		96.30 313	95.89 312	97.53 314	99.58 174	96.11 309	99.00 197	97.54 334	98.43 216	98.52 297	96.98 356	86.85 333	99.67 314	87.62 346	98.51 323	96.81 346

TESTMET0.1,1		96.24 314	95.84 315	97.41 318	98.24 343	93.84 334	97.38 331	95.84 342	98.43 216	97.81 330	98.56 328	79.77 356	99.89 124	97.77 194	98.77 302	98.52 302

test-mter		96.23 315	95.73 317	97.74 310	98.18 345	95.02 329	97.38 331	96.10 339	97.90 254	97.81 330	98.58 325	79.12 357	99.91 92	97.69 203	98.78 300	98.31 311

tpmp4_e23		96.11 316	96.06 309	96.27 333	98.90 311	90.70 352	99.34 106	99.03 286	93.72 338	96.56 344	99.31 250	83.63 348	99.75 282	96.06 279	98.02 338	98.35 309

X-MVStestdata		96.09 317	94.87 325	99.75 51	99.71 133	99.71 51	99.37 96	99.61 147	99.29 120	98.76 279	61.30 359	98.47 149	99.88 139	97.62 205	99.73 195	99.67 69

thres200		96.09 317	95.68 318	97.33 320	99.48 222	96.22 308	98.53 254	97.57 329	98.06 246	98.37 305	96.73 358	86.84 335	99.61 335	86.99 350	98.57 314	96.16 349

DWT-MVSNet_test		96.03 319	95.80 316	96.71 329	98.50 339	91.93 342	99.25 138	97.87 323	95.99 312	96.81 343	97.61 347	81.02 352	99.66 319	97.20 232	97.98 339	98.54 301

test2356		95.99 320	95.26 323	98.18 293	96.93 353	95.53 325	95.31 348	98.71 299	95.67 320	98.48 301	97.83 340	80.72 353	99.88 139	95.47 307	98.21 330	99.11 258

gg-mvs-nofinetune		95.87 321	95.17 324	97.97 298	98.19 344	96.95 299	99.69 38	89.23 357	99.89 10	96.24 347	99.94 13	81.19 351	99.51 341	93.99 328	98.20 331	97.44 338

PVSNet_0	95.53 19	95.85 322	95.31 321	97.47 316	98.78 328	93.48 336	95.72 346	99.40 233	96.18 310	97.37 338	97.73 345	95.73 267	99.58 337	95.49 305	81.40 352	99.36 218

tmp_tt		95.75 323	95.42 320	96.76 325	89.90 356	94.42 332	98.86 217	97.87 323	78.01 350	99.30 218	99.69 124	97.70 204	95.89 354	99.29 74	98.14 335	99.95 1

MVS		95.72 324	94.63 327	98.99 246	98.56 337	97.98 280	99.30 121	98.86 290	72.71 352	97.30 339	99.08 285	98.34 162	99.74 286	89.21 341	98.33 328	99.26 233

PAPM		95.61 325	94.71 326	98.31 290	99.12 296	96.63 302	96.66 344	98.46 309	90.77 345	96.25 346	98.68 324	93.01 290	99.69 301	81.60 352	97.86 341	98.62 296

IB-MVS	95.41 20	95.30 326	94.46 328	97.84 306	98.76 330	95.33 327	97.33 334	96.07 341	96.02 311	95.37 351	97.41 349	76.17 358	99.96 33	97.54 210	95.44 351	98.22 316
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		94.48 327	95.31 321	91.99 340	97.22 352	89.64 355	98.86 217	96.52 338	94.36 336	96.09 348	98.76 320	82.21 349	98.73 351	97.05 238	96.74 346	87.60 351

.test1245		85.84 328	89.27 329	75.54 341	99.65 156	97.72 283	98.35 271	99.80 60	99.40 109	99.66 119	99.43 225	75.10 359	99.87 158	98.98 110	33.07 353	29.03 354

pcd1.5k->3k		49.97 329	55.52 330	33.31 342	99.95 13	0.00 360	0.00 351	99.81 56	0.00 355	0.00 356	100.00 1	99.96 1	0.00 358	0.00 355	100.00 1	99.92 3

test123		29.31 330	33.05 333	18.08 343	25.93 358	12.24 358	97.53 328	10.93 360	11.78 353	24.21 354	50.08 363	21.04 361	8.60 356	23.51 353	32.43 355	33.39 353

testmvs		28.94 331	33.33 331	15.79 344	26.03 357	9.81 359	96.77 341	15.67 359	11.55 354	23.87 355	50.74 362	19.03 362	8.53 357	23.21 354	33.07 353	29.03 354

cdsmvs_eth3d_5k		24.88 332	33.17 332	0.00 345	0.00 359	0.00 360	0.00 351	99.62 143	0.00 355	0.00 356	99.13 278	99.82 6	0.00 358	0.00 355	0.00 356	0.00 356

pcd_1.5k_mvsjas		16.61 333	22.14 334	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	100.00 1	99.28 39	0.00 358	0.00 355	0.00 356	0.00 356

sosnet-low-res		8.33 334	11.11 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	100.00 1	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		8.33 334	11.11 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	100.00 1	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		8.33 334	11.11 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	100.00 1	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		8.33 334	11.11 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	100.00 1	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		8.33 334	11.11 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	100.00 1	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		8.26 339	11.02 340	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	99.16 276	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

GSMVS																99.14 253

test_part3							98.74 234		97.71 265		99.57 191		99.90 109	94.47 321

test_part2					99.62 165	99.67 67				99.55 158

test_part1								99.53 189				98.40 157			99.68 206	99.66 79

sam_mvs1												90.81 310				99.14 253

sam_mvs												90.52 314

semantic-postprocess				98.51 278	99.75 111	95.90 316		99.84 37	99.84 23	99.89 38	99.73 98	95.96 266	99.99 4	99.33 65	100.00 1	99.63 99

ambc				99.20 228	99.35 253	98.53 240	99.17 159	99.46 216		99.67 115	99.80 63	98.46 151	99.70 295	97.92 185	99.70 203	99.38 211

MTGPA								99.53 189

test_post1							99.14 172				51.63 361	89.54 321	99.82 233	96.86 246

test_post											52.41 360	90.25 316	99.86 178

patchmatchnet-post											99.62 166	90.58 312	99.94 55

GG-mvs-BLEND				97.36 319	97.59 349	96.87 301	99.70 29	88.49 358		94.64 352	97.26 354	80.66 354	99.12 348	91.50 334	96.50 348	96.08 350

MTMP								98.59 304

gm-plane-assit					97.59 349	89.02 356			93.47 339		98.30 333		99.84 210	96.38 268

test9_res														95.10 314	99.44 249	99.50 173

TEST9					99.35 253	99.35 150	98.11 292	99.41 227	94.83 333	97.92 324	98.99 298	98.02 184	99.85 194

test_8					99.34 263	99.31 156	98.08 297	99.40 233	94.90 329	97.87 328	98.97 304	98.02 184	99.84 210

agg_prior2														94.58 320	99.46 248	99.50 173

agg_prior					99.35 253	99.36 146		99.39 236		97.76 334			99.85 194

TestCases				99.63 107	99.78 88	99.64 76		99.83 40	98.63 202	99.63 130	99.72 104	98.68 116	99.75 282	96.38 268	99.83 143	99.51 167

test_prior4						99.19 187	98.00 304

test_prior2							97.95 311		97.87 256	98.05 319	99.05 294	97.90 191		95.99 284	99.49 244

test_prior				99.46 167	99.35 253	99.22 179		99.39 236					99.69 301			99.48 180

旧先验2							97.94 313		95.33 324	98.94 261			99.88 139	96.75 252

新几何2							98.04 300

新几何1				99.52 152	99.50 211	99.22 179		99.26 264	95.66 321	98.60 292	99.28 257	97.67 209	99.89 124	95.95 288	99.32 268	99.45 190

旧先验1					99.49 216	99.29 160		99.26 264			99.39 233	97.67 209			99.36 264	99.46 188

无先验							98.01 302	99.23 271	95.83 315				99.85 194	95.79 293		99.44 195

原ACMM2							97.92 315

原ACMM1				99.37 195	99.47 227	98.87 224		99.27 262	96.74 299	98.26 308	99.32 248	97.93 190	99.82 233	95.96 287	99.38 261	99.43 201

test222					99.51 206	99.08 200	97.83 320	99.29 258	95.21 326	98.68 287	99.31 250	97.28 229			99.38 261	99.43 201

testdata2													99.89 124	95.99 284

segment_acmp												98.37 160

testdata				99.42 178	99.51 206	98.93 216		99.30 257	96.20 309	98.87 269	99.40 230	98.33 164	99.89 124	96.29 271	99.28 272	99.44 195

testdata1							97.72 322		97.86 259

test12				99.54 149	99.29 274	99.33 153		99.16 277		98.43 303		97.54 216	99.82 233		99.47 246	99.48 180

plane_prior7					99.58 174	99.38 140

plane_prior6					99.47 227	99.26 168						97.24 230

plane_prior5								99.54 184					99.82 233	95.84 291	99.78 173	99.60 123

plane_prior4											99.25 263

plane_prior3						99.31 156			98.36 224	99.14 239

plane_prior2							98.80 228		98.94 166

plane_prior1					99.51 206

plane_prior						99.24 175	98.42 268		97.87 256						99.71 201

n2								0.00 361

nn								0.00 361

door-mid								99.83 40

lessismore_v0				99.64 103	99.86 35	99.38 140		90.66 355		99.89 38	99.83 51	94.56 278	99.97 16	99.56 43	99.92 88	99.57 142

LGP-MVS_train				99.74 55	99.82 53	99.63 80		99.73 92	97.56 273	99.64 127	99.69 124	99.37 30	99.89 124	96.66 257	99.87 119	99.69 56

test11								99.29 258

door								99.77 73

HQP5-MVS						98.94 212

HQP-NCC					99.31 269		97.98 307		97.45 279	98.15 313

ACMP_Plane					99.31 269		97.98 307		97.45 279	98.15 313

BP-MVS														94.73 317

HQP4-MVS										98.15 313			99.70 295			99.53 156

HQP3-MVS								99.37 242							99.67 212

HQP2-MVS												96.67 248

NP-MVS					99.40 244	99.13 192					98.83 315

MDTV_nov1_ep13_2view						91.44 348	99.14 172		97.37 283	99.21 230		91.78 301		96.75 252		99.03 276

MDTV_nov1_ep13			97.73 270		98.70 334	90.83 350	99.15 167	98.02 317	98.51 211	98.82 272	99.61 173	90.98 305	99.66 319	96.89 245	98.92 291

ACMMP++_ref															99.94 77

ACMMP++															99.79 168

Test By Simon												98.41 155

ITE_SJBPF				99.38 191	99.63 160	99.44 116		99.73 92	98.56 207	99.33 210	99.53 205	98.88 86	99.68 309	96.01 282	99.65 217	99.02 277

DeepMVS_CX				97.98 297	99.69 142	96.95 299		99.26 264	75.51 351	95.74 350	98.28 334	96.47 254	99.62 331	91.23 335	97.89 340	97.38 339

High-resolution multi-view benchmark