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	99.87 154	99.54 44	99.92 88	99.63 98

UA-Net		99.78 15	99.76 18	99.86 18	99.72 128	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	99.87 154	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 61	99.86 35	99.55 94	99.77 13	99.86 22	99.79 33	99.96 8	99.91 20	98.90 83	99.87 154	99.91 5	100.00 1	99.78 31

v12		99.75 19	99.77 14	99.72 66	99.85 39	99.53 97	99.75 17	99.86 22	99.78 34	99.96 8	99.90 23	98.88 86	99.86 174	99.91 5	100.00 1	99.77 34

v11		99.75 19	99.76 18	99.71 70	99.85 39	99.49 100	99.73 21	99.84 37	99.75 39	99.95 16	99.90 23	98.93 79	99.86 174	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 69	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 70	99.84 42	99.50 98	99.74 19	99.86 22	99.76 38	99.96 8	99.90 23	98.83 89	99.85 190	99.91 5	100.00 1	99.77 34

V14		99.73 24	99.74 21	99.69 77	99.83 46	99.48 103	99.72 25	99.85 29	99.74 40	99.96 8	99.89 31	98.79 97	99.85 190	99.91 5	100.00 1	99.76 37

v15		99.72 25	99.73 24	99.68 80	99.82 53	99.44 115	99.70 29	99.85 29	99.72 45	99.95 16	99.88 34	98.76 104	99.84 206	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	99.88 137	98.93 121	99.95 65	99.60 122

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	99.78 264	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	99.87 154	99.51 47	99.97 47	99.86 12

v17		99.70 28	99.71 25	99.67 83	99.81 61	99.44 115	99.70 29	99.83 40	99.69 53	99.94 20	99.87 37	98.70 112	99.84 206	99.88 14	99.99 20	99.73 43

v16		99.70 28	99.71 25	99.67 83	99.81 61	99.43 121	99.70 29	99.83 40	99.70 49	99.94 20	99.87 37	98.69 114	99.84 206	99.88 14	99.99 20	99.73 43

v10		99.69 32	99.69 29	99.66 91	99.81 61	99.39 133	99.66 49	99.75 84	99.60 80	99.92 31	99.87 37	98.75 107	99.86 174	99.90 9	99.99 20	99.73 43

v18		99.68 33	99.69 29	99.65 95	99.79 82	99.40 130	99.68 41	99.83 40	99.66 62	99.93 26	99.85 45	98.65 123	99.84 206	99.87 18	99.99 20	99.71 49

v8		99.68 33	99.69 29	99.65 95	99.80 69	99.40 130	99.66 49	99.76 79	99.64 67	99.93 26	99.85 45	98.66 121	99.84 206	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	99.68 147	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 61	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	99.53 155

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	97.94 188	99.92 83	99.65 35	99.98 36	99.62 111

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	98.27 166	99.85 190	99.37 60	99.93 85	99.83 18

wuykxyi23d		99.65 41	99.64 36	99.69 77	99.92 19	99.20 184	98.89 209	99.99 2	98.73 194	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 83	99.72 128	99.44 115	99.24 138	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 94
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	98.18 175	99.94 55	99.28 76	99.95 65	99.83 18

ACMH	98.42 6	99.59 45	99.54 53	99.72 66	99.86 35	99.62 81	99.56 70	99.79 68	98.77 187	99.80 74	99.85 45	99.64 14	99.85 190	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	99.62 114	99.81 61	99.44 115	99.14 169	99.43 224	99.69 53	99.82 65	99.79 70	99.14 54	99.79 256	99.31 70	99.95 65	99.63 98

v1192		99.57 47	99.57 48	99.57 135	99.77 98	99.22 178	99.04 187	99.60 161	99.18 140	99.87 51	99.72 104	99.08 64	99.85 190	99.89 13	99.98 36	99.66 79

EG-PatchMatch MVS		99.57 47	99.56 51	99.62 114	99.77 98	99.33 152	99.26 132	99.76 79	99.32 119	99.80 74	99.78 79	99.29 37	99.87 154	99.15 92	99.91 98	99.66 79

Gipuma		99.57 47	99.59 43	99.49 158	99.98 3	99.71 51	99.72 25	99.84 37	99.81 28	99.94 20	99.78 79	98.91 82	99.71 289	98.41 151	99.95 65	99.05 271
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1921920		99.56 50	99.57 48	99.55 144	99.75 111	99.11 193	99.05 185	99.61 147	99.15 147	99.88 46	99.71 111	99.08 64	99.87 154	99.90 9	99.97 47	99.66 79

v1240		99.56 50	99.58 45	99.51 154	99.80 69	99.00 204	99.00 194	99.65 132	99.15 147	99.90 35	99.75 92	99.09 61	99.88 137	99.90 9	99.96 59	99.67 69

v7		99.56 50	99.54 53	99.61 117	99.80 69	99.39 133	99.30 119	99.59 165	99.14 149	99.82 65	99.72 104	98.75 107	99.84 206	99.83 20	99.94 77	99.61 116

V42		99.56 50	99.54 53	99.63 106	99.79 82	99.46 108	99.39 86	99.59 165	99.24 132	99.86 56	99.70 118	98.55 137	99.82 229	99.79 26	99.95 65	99.60 122

v144192		99.55 54	99.54 53	99.58 129	99.78 88	99.20 184	99.11 176	99.62 143	99.18 140	99.89 38	99.72 104	98.66 121	99.87 154	99.88 14	99.97 47	99.66 79

v1neww		99.55 54	99.54 53	99.61 117	99.80 69	99.39 133	99.32 109	99.61 147	99.18 140	99.87 51	99.69 124	98.64 125	99.82 229	99.79 26	99.94 77	99.60 122

v7new		99.55 54	99.54 53	99.61 117	99.80 69	99.39 133	99.32 109	99.61 147	99.18 140	99.87 51	99.69 124	98.64 125	99.82 229	99.79 26	99.94 77	99.60 122

v6		99.55 54	99.54 53	99.61 117	99.80 69	99.39 133	99.32 109	99.60 161	99.18 140	99.87 51	99.68 136	98.65 123	99.82 229	99.79 26	99.95 65	99.61 116

test20.03		99.55 54	99.54 53	99.58 129	99.79 82	99.37 142	99.02 190	99.89 15	99.60 80	99.82 65	99.62 166	98.81 90	99.89 122	99.43 53	99.86 126	99.47 183

v1144		99.54 59	99.53 61	99.59 125	99.79 82	99.28 161	99.10 177	99.61 147	99.20 138	99.84 60	99.73 98	98.67 119	99.84 206	99.86 19	99.98 36	99.64 94

v1141		99.54 59	99.52 63	99.57 135	99.78 88	99.27 165	99.15 164	99.61 147	99.26 127	99.89 38	99.69 124	98.56 133	99.82 229	99.82 23	99.97 47	99.63 98

divwei89l23v2f112		99.54 59	99.52 63	99.57 135	99.78 88	99.27 165	99.15 164	99.61 147	99.26 127	99.89 38	99.69 124	98.56 133	99.82 229	99.82 23	99.96 59	99.63 98

v1		99.54 59	99.52 63	99.58 129	99.77 98	99.28 161	99.15 164	99.61 147	99.26 127	99.88 46	99.68 136	98.56 133	99.82 229	99.82 23	99.97 47	99.63 98

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	97.79 199	99.95 41	99.21 79	99.94 77	99.84 15

TranMVSNet+NR-MVSNet		99.54 59	99.47 69	99.76 42	99.58 170	99.64 75	99.30 119	99.63 140	99.61 75	99.71 106	99.56 194	98.76 104	99.96 33	99.14 98	99.92 88	99.68 62

testmv		99.53 65	99.51 66	99.59 125	99.73 120	99.31 155	98.48 254	99.92 7	99.57 84	99.87 51	99.79 70	99.12 58	99.91 92	99.16 91	99.99 20	99.55 145

v2v482		99.50 66	99.47 69	99.58 129	99.78 88	99.25 171	99.14 169	99.58 173	99.25 130	99.81 71	99.62 166	98.24 168	99.84 206	99.83 20	99.97 47	99.64 94

ACMH+	98.40 8	99.50 66	99.43 78	99.71 70	99.86 35	99.76 41	99.32 109	99.77 73	99.53 87	99.77 86	99.76 88	99.26 45	99.78 264	97.77 194	99.88 112	99.60 122

Baseline_NR-MVSNet		99.49 68	99.37 87	99.82 25	99.91 21	99.84 18	98.83 220	99.86 22	99.68 56	99.65 125	99.88 34	97.67 208	99.87 154	99.03 105	99.86 126	99.76 37

TAMVS		99.49 68	99.45 73	99.63 106	99.48 218	99.42 125	99.45 79	99.57 174	99.66 62	99.78 82	99.83 51	97.85 195	99.86 174	99.44 52	99.96 59	99.61 116

pmmvs-eth3d		99.48 70	99.47 69	99.51 154	99.77 98	99.41 129	98.81 224	99.66 123	99.42 108	99.75 90	99.66 146	99.20 48	99.76 272	98.98 110	99.99 20	99.36 217

EI-MVSNet-UG-set		99.48 70	99.50 67	99.42 177	99.57 179	98.65 236	99.24 138	99.46 216	99.68 56	99.80 74	99.66 146	98.99 72	99.89 122	99.19 83	99.90 100	99.72 46

APDe-MVS		99.48 70	99.36 90	99.85 20	99.55 192	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	99.57 135	99.76 103	98.99 205	98.09 289	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	98.95 247	99.71 131	97.27 291	99.50 74	99.82 48	99.59 82	99.41 187	99.85 45	99.62 16	100.00 1	99.53 46	99.89 106	99.59 133

DP-MVS		99.48 70	99.39 82	99.74 55	99.57 179	99.62 81	99.29 127	99.61 147	99.87 13	99.74 98	99.76 88	98.69 114	99.87 154	98.20 167	99.80 165	99.75 40

EI-MVSNet-Vis-set		99.47 76	99.49 68	99.42 177	99.57 179	98.66 234	99.24 138	99.46 216	99.67 58	99.79 79	99.65 151	98.97 75	99.89 122	99.15 92	99.89 106	99.71 49

VPNet		99.46 77	99.37 87	99.71 70	99.82 53	99.59 86	99.48 78	99.70 107	99.81 28	99.69 110	99.58 184	97.66 212	99.86 174	99.17 88	99.44 248	99.67 69

ACMM	98.09 11	99.46 77	99.38 84	99.72 66	99.80 69	99.69 62	99.13 174	99.65 132	98.99 162	99.64 127	99.72 104	99.39 24	99.86 174	98.23 164	99.81 160	99.60 122
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-4		99.45 79	99.44 75	99.50 156	99.52 198	98.94 211	99.17 156	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 76	99.83 46	99.70 58	99.38 92	99.78 70	99.53 87	99.67 115	99.78 79	99.19 49	99.86 174	97.32 221	99.87 119	99.55 145
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	99.60 123	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	99.15 248

HPM-MVS_fast		99.43 81	99.30 101	99.80 29	99.83 46	99.81 28	99.52 72	99.70 107	98.35 228	99.51 167	99.50 212	99.31 35	99.88 137	98.18 171	99.84 133	99.69 56

3Dnovator	99.15 2	99.43 81	99.36 90	99.65 95	99.39 241	99.42 125	99.70 29	99.56 177	99.23 134	99.35 201	99.80 63	99.17 51	99.95 41	98.21 166	99.84 133	99.59 133

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	96.20 260	99.94 55	99.42 57	99.92 88	99.67 69

GBi-Net		99.42 84	99.31 96	99.73 61	99.49 212	99.77 37	99.68 41	99.70 107	99.44 101	99.62 137	99.83 51	97.21 232	99.90 109	98.96 115	99.90 100	99.53 155

test1		99.42 84	99.31 96	99.73 61	99.49 212	99.77 37	99.68 41	99.70 107	99.44 101	99.62 137	99.83 51	97.21 232	99.90 109	98.96 115	99.90 100	99.53 155

Regformer-3		99.41 87	99.41 80	99.40 185	99.52 198	98.70 231	99.17 156	99.44 221	99.62 71	99.75 90	99.60 176	98.90 83	99.85 190	98.89 123	99.84 133	99.65 88

MVSFormer		99.41 87	99.44 75	99.31 207	99.57 179	98.40 245	99.77 13	99.80 60	99.73 42	99.63 130	99.30 251	98.02 183	99.98 7	99.43 53	99.69 204	99.55 145

IterMVS-LS		99.41 87	99.47 69	99.25 220	99.81 61	98.09 268	98.85 217	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	99.39 188	99.76 103	98.94 211	99.09 181	99.59 165	99.17 145	99.81 71	99.61 173	98.41 155	99.69 296	99.32 68	99.94 77	99.53 155

NR-MVSNet		99.40 90	99.31 96	99.68 80	99.43 233	99.55 94	99.73 21	99.50 204	99.46 99	99.88 46	99.36 238	97.54 215	99.87 154	98.97 114	99.87 119	99.63 98

PVSNet_Blended_VisFu		99.40 90	99.38 84	99.44 172	99.90 25	98.66 234	98.94 207	99.91 11	97.97 250	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	98.72 272	99.88 28	96.44 302	99.56 70	99.85 29	99.90 6	99.90 35	99.85 45	98.09 177	99.83 222	99.58 41	99.95 65	99.90 5

CHOSEN 1792x2688		99.39 93	99.30 101	99.65 95	99.88 28	99.25 171	98.78 229	99.88 18	98.66 198	99.96 8	99.79 70	97.45 219	99.93 66	99.34 63	99.99 20	99.78 31

EI-MVSNet		99.38 95	99.44 75	99.21 225	99.58 170	98.09 268	99.26 132	99.46 216	99.62 71	99.75 90	99.67 142	98.54 139	99.85 190	99.15 92	99.92 88	99.68 62

UGNet		99.38 95	99.34 92	99.49 158	98.90 307	98.90 218	99.70 29	99.35 245	99.86 16	98.57 291	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 66	99.47 223	99.56 91	98.97 202	99.61 147	99.43 106	99.67 115	99.28 255	97.85 195	99.95 41	99.17 88	99.81 160	99.65 88

UniMVSNet (Re)		99.37 97	99.26 112	99.68 80	99.51 202	99.58 88	98.98 201	99.60 161	99.43 106	99.70 108	99.36 238	97.70 203	99.88 137	99.20 82	99.87 119	99.59 133

CSCG		99.37 97	99.29 106	99.60 123	99.71 131	99.46 108	99.43 82	99.85 29	98.79 184	99.41 187	99.60 176	98.92 80	99.92 83	98.02 180	99.92 88	99.43 200

PM-MVS		99.36 100	99.29 106	99.58 129	99.83 46	99.66 69	98.95 204	99.86 22	98.85 176	99.81 71	99.73 98	98.40 157	99.92 83	98.36 154	99.83 143	99.17 246

abl_6		99.36 100	99.23 117	99.75 51	99.71 131	99.74 48	99.33 106	99.76 79	99.07 158	99.65 125	99.63 159	99.09 61	99.92 83	97.13 235	99.76 179	99.58 137

new-patchmatchnet		99.35 102	99.57 48	98.71 273	99.82 53	96.62 300	98.55 245	99.75 84	99.50 90	99.88 46	99.87 37	99.31 35	99.88 137	99.43 53	100.00 1	99.62 111

Anonymous20231206		99.35 102	99.31 96	99.47 163	99.74 117	99.06 203	99.28 128	99.74 89	99.23 134	99.72 102	99.53 203	97.63 214	99.88 137	99.11 100	99.84 133	99.48 179

MTAPA		99.35 102	99.20 120	99.80 29	99.81 61	99.81 28	99.33 106	99.53 189	99.27 123	99.42 181	99.63 159	98.21 171	99.95 41	97.83 191	99.79 168	99.65 88

FMVSNet2		99.35 102	99.28 108	99.55 144	99.49 212	99.35 149	99.45 79	99.57 174	99.44 101	99.70 108	99.74 94	97.21 232	99.87 154	99.03 105	99.94 77	99.44 194

3Dnovator+	98.92 3	99.35 102	99.24 115	99.67 83	99.35 249	99.47 104	99.62 56	99.50 204	99.44 101	99.12 238	99.78 79	98.77 103	99.94 55	97.87 188	99.72 200	99.62 111

TSAR-MVS + MP.		99.34 107	99.24 115	99.63 106	99.82 53	99.37 142	99.26 132	99.35 245	98.77 187	99.57 148	99.70 118	99.27 42	99.88 137	97.71 197	99.75 182	99.65 88

Regformer-2		99.34 107	99.27 110	99.53 149	99.41 237	99.10 196	98.99 197	99.53 189	99.47 96	99.66 119	99.52 205	98.80 94	99.89 122	98.31 159	99.74 189	99.60 122

DELS-MVS		99.34 107	99.30 101	99.48 161	99.51 202	99.36 145	98.12 285	99.53 189	99.36 115	99.41 187	99.61 173	99.22 47	99.87 154	99.21 79	99.68 206	99.20 239
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 70	99.43 233	99.56 91	98.83 220	99.53 189	99.38 112	99.67 115	99.36 238	97.67 208	99.95 41	99.17 88	99.81 160	99.63 98

ab-mvs		99.33 110	99.28 108	99.47 163	99.57 179	99.39 133	99.78 12	99.43 224	98.87 174	99.57 148	99.82 58	98.06 180	99.87 154	98.69 138	99.73 195	99.15 248

Regformer-1		99.32 112	99.27 110	99.47 163	99.41 237	98.95 210	98.99 197	99.48 209	99.48 92	99.66 119	99.52 205	98.78 100	99.87 154	98.36 154	99.74 189	99.60 122

APD-MVS_3200maxsize		99.31 113	99.16 121	99.74 55	99.53 196	99.75 43	99.27 131	99.61 147	99.19 139	99.57 148	99.64 152	98.76 104	99.90 109	97.29 223	99.62 219	99.56 142

MPTG		99.30 114	99.14 124	99.80 29	99.81 61	99.81 28	98.73 232	99.53 189	99.27 123	99.42 181	99.63 159	98.21 171	99.95 41	97.83 191	99.79 168	99.65 88

SteuartSystems-ACMMP		99.30 114	99.14 124	99.76 42	99.87 32	99.66 69	99.18 150	99.60 161	98.55 207	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	99.37 194	99.75 111	98.81 227	98.84 218	99.89 15	98.38 221	99.75 90	99.04 293	99.36 33	99.86 174	99.08 102	99.25 275	99.45 189

ACMMP_Plus		99.28 117	99.11 132	99.79 34	99.75 111	99.81 28	98.95 204	99.53 189	98.27 237	99.53 163	99.73 98	98.75 107	99.87 154	97.70 198	99.83 143	99.68 62

LCM-MVSNet-Re		99.28 117	99.15 123	99.67 83	99.33 263	99.76 41	99.34 104	99.97 3	98.93 168	99.91 33	99.79 70	98.68 116	99.93 66	96.80 249	99.56 227	99.30 228

mvs_anonymous		99.28 117	99.39 82	98.94 248	99.19 284	97.81 279	99.02 190	99.55 180	99.78 34	99.85 57	99.80 63	98.24 168	99.86 174	99.57 42	99.50 241	99.15 248

MVS_Test		99.28 117	99.31 96	99.19 228	99.35 249	98.79 229	99.36 98	99.49 208	99.17 145	99.21 227	99.67 142	98.78 100	99.66 314	99.09 101	99.66 214	99.10 259

no-one		99.28 117	99.23 117	99.45 170	99.87 32	99.08 199	98.95 204	99.52 198	98.88 173	99.77 86	99.83 51	97.78 200	99.90 109	98.46 149	99.99 20	99.38 210

XVS		99.27 122	99.11 132	99.75 51	99.71 131	99.71 51	99.37 96	99.61 147	99.29 120	98.76 276	99.47 217	98.47 149	99.88 137	97.62 205	99.73 195	99.67 69

OPM-MVS		99.26 123	99.13 127	99.63 106	99.70 138	99.61 85	98.58 240	99.48 209	98.50 211	99.52 165	99.63 159	99.14 54	99.76 272	97.89 187	99.77 177	99.51 166

HFP-MVS		99.25 124	99.08 142	99.76 42	99.73 120	99.70 58	99.31 116	99.59 165	98.36 223	99.36 199	99.37 233	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	97.72 263	99.35 201	99.25 261	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	98.07 244	99.52 165	99.69 124	98.57 132	99.92 83	97.18 233	99.79 168	99.63 98
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	99.61 117	98.38 337	99.79 33	99.57 68	99.68 116	99.61 75	99.15 235	99.71 111	98.70 112	99.91 92	97.54 210	99.68 206	99.13 253

xiu_mvs_v1_base_debu		99.23 128	99.34 92	98.91 251	99.59 167	98.23 257	98.47 255	99.66 123	99.61 75	99.68 112	98.94 305	99.39 24	99.97 16	99.18 85	99.55 233	98.51 300

xiu_mvs_v1_base		99.23 128	99.34 92	98.91 251	99.59 167	98.23 257	98.47 255	99.66 123	99.61 75	99.68 112	98.94 305	99.39 24	99.97 16	99.18 85	99.55 233	98.51 300

xiu_mvs_v1_base_debi		99.23 128	99.34 92	98.91 251	99.59 167	98.23 257	98.47 255	99.66 123	99.61 75	99.68 112	98.94 305	99.39 24	99.97 16	99.18 85	99.55 233	98.51 300

region2R		99.23 128	99.05 152	99.77 39	99.76 103	99.70 58	99.31 116	99.59 165	98.41 218	99.32 209	99.36 238	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	99.31 116	99.59 165	98.36 223	99.35 201	99.38 232	98.61 129	99.93 66	97.43 216	99.75 182	99.67 69

XVG-ACMP-BASELINE		99.23 128	99.10 139	99.63 106	99.82 53	99.58 88	98.83 220	99.72 101	98.36 223	99.60 144	99.71 111	98.92 80	99.91 92	97.08 236	99.84 133	99.40 205

CP-MVS		99.23 128	99.05 152	99.75 51	99.66 152	99.66 69	99.38 92	99.62 143	98.38 221	99.06 245	99.27 257	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	99.56 141	99.28 272	99.22 178	98.99 197	99.40 233	99.08 157	99.58 146	99.64 152	98.90 83	99.83 222	97.44 215	99.75 182	99.63 98
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 79	99.16 162	99.73 92	97.56 270	99.64 127	99.69 124	99.37 30	99.89 122	96.66 257	99.87 119	99.69 56

CDS-MVSNet		99.22 136	99.13 127	99.50 156	99.35 249	99.11 193	98.96 203	99.54 184	99.46 99	99.61 142	99.70 118	96.31 257	99.83 222	99.34 63	99.88 112	99.55 145
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	99.45 170	99.79 82	99.43 121	99.28 128	99.68 116	99.54 85	99.40 191	99.56 194	99.07 66	99.82 229	96.01 282	99.96 59	99.11 255

AllTest		99.21 139	99.07 146	99.63 106	99.78 88	99.64 75	99.12 175	99.83 40	98.63 201	99.63 130	99.72 104	98.68 116	99.75 278	96.38 268	99.83 143	99.51 166

XVG-OURS		99.21 139	99.06 148	99.65 95	99.82 53	99.62 81	97.87 313	99.74 89	98.36 223	99.66 119	99.68 136	99.71 11	99.90 109	96.84 247	99.88 112	99.43 200

Fast-Effi-MVS+-dtu		99.20 141	99.12 130	99.43 175	99.25 275	99.69 62	99.05 185	99.82 48	99.50 90	98.97 251	99.05 290	98.98 73	99.98 7	98.20 167	99.24 277	98.62 293

VDD-MVS		99.20 141	99.11 132	99.44 172	99.43 233	98.98 206	99.50 74	98.32 314	99.80 31	99.56 155	99.69 124	96.99 242	99.85 190	98.99 108	99.73 195	99.50 172

PGM-MVS		99.20 141	99.01 162	99.77 39	99.75 111	99.71 51	99.16 162	99.72 101	97.99 248	99.42 181	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	99.42 177	99.76 103	98.88 221	98.55 245	99.73 92	98.82 180	99.72 102	99.62 166	96.56 249	99.82 229	99.32 68	99.95 65	99.56 142

mPP-MVS		99.19 144	99.00 164	99.76 42	99.76 103	99.68 65	99.38 92	99.54 184	98.34 232	99.01 248	99.50 212	98.53 143	99.93 66	97.18 233	99.78 173	99.66 79

VNet		99.18 146	99.06 148	99.56 141	99.24 277	99.36 145	99.33 106	99.31 254	99.67 58	99.47 172	99.57 191	96.48 252	99.84 206	99.15 92	99.30 269	99.47 183

RPSCF		99.18 146	99.02 159	99.64 102	99.83 46	99.85 13	99.44 81	99.82 48	98.33 233	99.50 169	99.78 79	97.90 190	99.65 321	96.78 250	99.83 143	99.44 194

DeepPCF-MVS	98.42 6	99.18 146	99.02 159	99.67 83	99.22 279	99.75 43	97.25 331	99.47 213	98.72 195	99.66 119	99.70 118	99.29 37	99.63 325	98.07 179	99.81 160	99.62 111

MVS_0304		99.17 149	99.10 139	99.38 190	99.08 298	98.86 224	98.46 259	99.73 92	99.53 87	99.35 201	99.30 251	97.11 238	99.96 33	99.33 65	99.99 20	99.33 222

EPP-MVSNet		99.17 149	99.00 164	99.66 91	99.80 69	99.43 121	99.70 29	99.24 270	99.48 92	99.56 155	99.77 85	94.89 273	99.93 66	98.72 136	99.89 106	99.63 98

MVP-Stereo		99.16 151	99.08 142	99.43 175	99.48 218	99.07 201	99.08 182	99.55 180	98.63 201	99.31 211	99.68 136	98.19 174	99.78 264	98.18 171	99.58 226	99.45 189
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 91	99.84 42	99.64 75	98.25 274	99.73 92	98.39 220	99.63 130	99.43 223	99.70 12	99.90 109	97.34 220	98.64 310	99.44 194

jason		99.16 151	99.11 132	99.32 205	99.75 111	98.44 242	98.26 273	99.39 236	98.70 196	99.74 98	99.30 251	98.54 139	99.97 16	98.48 148	99.82 152	99.55 145
jason: jason.
MP-MVS-pluss		99.14 154	98.92 177	99.80 29	99.83 46	99.83 22	98.61 236	99.63 140	96.84 293	99.44 175	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	99.36 197	99.57 179	99.10 196	98.01 297	99.25 267	98.78 186	99.58 146	99.44 222	98.24 168	99.76 272	98.74 134	99.93 85	99.22 235

MVS_111021_LR		99.13 155	99.03 158	99.42 177	99.58 170	99.32 154	97.91 312	99.73 92	98.68 197	99.31 211	99.48 214	99.09 61	99.66 314	97.70 198	99.77 177	99.29 231

#test#		99.12 157	98.90 180	99.76 42	99.73 120	99.70 58	99.10 177	99.59 165	97.60 268	99.36 199	99.37 233	98.80 94	99.91 92	96.84 247	99.75 182	99.68 62

TSAR-MVS + GP.		99.12 157	99.04 157	99.38 190	99.34 259	99.16 188	98.15 281	99.29 258	98.18 241	99.63 130	99.62 166	99.18 50	99.68 304	98.20 167	99.74 189	99.30 228

MVS_111021_HR		99.12 157	99.02 159	99.40 185	99.50 207	99.11 193	97.92 310	99.71 104	98.76 190	99.08 241	99.47 217	99.17 51	99.54 334	97.85 190	99.76 179	99.54 152

CANet		99.11 160	99.05 152	99.28 210	98.83 317	98.56 237	98.71 234	99.41 227	99.25 130	99.23 223	99.22 270	97.66 212	99.94 55	99.19 83	99.97 47	99.33 222

WR-MVS		99.11 160	98.93 174	99.66 91	99.30 269	99.42 125	98.42 263	99.37 242	99.04 159	99.57 148	99.20 272	96.89 244	99.86 174	98.66 141	99.87 119	99.70 53

PHI-MVS		99.11 160	98.95 173	99.59 125	99.13 290	99.59 86	99.17 156	99.65 132	97.88 254	99.25 219	99.46 220	98.97 75	99.80 253	97.26 226	99.82 152	99.37 214

MSDG		99.08 163	98.98 170	99.37 194	99.60 164	99.13 191	97.54 321	99.74 89	98.84 179	99.53 163	99.55 199	99.10 59	99.79 256	97.07 237	99.86 126	99.18 244

Effi-MVS+-dtu		99.07 164	98.92 177	99.52 151	98.89 311	99.78 35	99.15 164	99.66 123	99.34 116	98.92 261	99.24 266	97.69 205	99.98 7	98.11 176	99.28 271	98.81 288

Effi-MVS+		99.06 165	98.97 171	99.34 199	99.31 265	98.98 206	98.31 271	99.91 11	98.81 181	98.79 273	98.94 305	99.14 54	99.84 206	98.79 129	98.74 305	99.20 239

MP-MVS		99.06 165	98.83 191	99.76 42	99.76 103	99.71 51	99.32 109	99.50 204	98.35 228	98.97 251	99.48 214	98.37 159	99.92 83	95.95 288	99.75 182	99.63 98

MDA-MVSNet-bldmvs		99.06 165	99.05 152	99.07 239	99.80 69	97.83 278	98.89 209	99.72 101	99.29 120	99.63 130	99.70 118	96.47 253	99.89 122	98.17 173	99.82 152	99.50 172

MSLP-MVS++		99.05 168	99.09 141	98.91 251	99.21 280	98.36 249	98.82 223	99.47 213	98.85 176	98.90 264	99.56 194	98.78 100	99.09 344	98.57 143	99.68 206	99.26 232

1112_ss		99.05 168	98.84 188	99.67 83	99.66 152	99.29 159	98.52 250	99.82 48	97.65 266	99.43 179	99.16 274	96.42 255	99.91 92	99.07 103	99.84 133	99.80 25

ACMP	97.51 14	99.05 168	98.84 188	99.67 83	99.78 88	99.55 94	98.88 211	99.66 123	97.11 289	99.47 172	99.60 176	99.07 66	99.89 122	96.18 274	99.85 129	99.58 137
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		99.03 171	99.01 162	99.09 235	99.54 193	97.99 272	98.58 240	99.82 48	97.62 267	99.34 205	99.71 111	98.52 145	99.77 270	97.98 183	99.97 47	99.52 163

IS-MVSNet		99.03 171	98.85 186	99.55 144	99.80 69	99.25 171	99.73 21	99.15 278	99.37 113	99.61 142	99.71 111	94.73 275	99.81 248	97.70 198	99.88 112	99.58 137

xiu_mvs_v2_base		99.02 173	99.11 132	98.77 265	99.37 246	98.09 268	98.13 284	99.51 200	99.47 96	99.42 181	98.54 325	99.38 28	99.97 16	98.83 126	99.33 266	98.24 312

Fast-Effi-MVS+		99.02 173	98.87 183	99.46 166	99.38 244	99.50 98	99.04 187	99.79 68	97.17 284	98.62 286	98.74 318	99.34 34	99.95 41	98.32 158	99.41 257	98.92 280

canonicalmvs		99.02 173	99.00 164	99.09 235	99.10 297	98.70 231	99.61 60	99.66 123	99.63 70	98.64 285	97.65 342	99.04 70	99.54 334	98.79 129	98.92 290	99.04 272

MCST-MVS		99.02 173	98.81 193	99.65 95	99.58 170	99.49 100	98.58 240	99.07 282	98.40 219	99.04 246	99.25 261	98.51 147	99.80 253	97.31 222	99.51 240	99.65 88

HSP-MVS		99.01 177	98.76 197	99.76 42	99.78 88	99.73 49	99.35 99	99.31 254	98.54 208	99.54 160	98.99 294	96.81 245	99.93 66	96.97 241	99.53 238	99.61 116

SD-MVS		99.01 177	99.30 101	98.15 291	99.50 207	99.40 130	98.94 207	99.61 147	99.22 137	99.75 90	99.82 58	99.54 22	95.51 350	97.48 213	99.87 119	99.54 152

LF4IMVS		99.01 177	98.92 177	99.27 212	99.71 131	99.28 161	98.59 239	99.77 73	98.32 234	99.39 192	99.41 227	98.62 127	99.84 206	96.62 260	99.84 133	98.69 292

MS-PatchMatch		99.00 180	98.97 171	99.09 235	99.11 295	98.19 260	98.76 230	99.33 248	98.49 212	99.44 175	99.58 184	98.21 171	99.69 296	98.20 167	99.62 219	99.39 207

PS-MVSNAJ		99.00 180	99.08 142	98.76 266	99.37 246	98.10 267	98.00 299	99.51 200	99.47 96	99.41 187	98.50 327	99.28 39	99.97 16	98.83 126	99.34 264	98.20 316

CNVR-MVS		98.99 182	98.80 195	99.56 141	99.25 275	99.43 121	98.54 248	99.27 262	98.58 205	98.80 272	99.43 223	98.53 143	99.70 290	97.22 229	99.59 225	99.54 152

VDDNet		98.97 183	98.82 192	99.42 177	99.71 131	98.81 227	99.62 56	98.68 300	99.81 28	99.38 197	99.80 63	94.25 279	99.85 190	98.79 129	99.32 267	99.59 133

IterMVS		98.97 183	99.16 121	98.42 280	99.74 117	95.64 319	98.06 294	99.83 40	99.83 26	99.85 57	99.74 94	96.10 263	99.99 4	99.27 77	100.00 1	99.63 98
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap		98.97 183	98.93 174	99.07 239	99.46 227	98.19 260	97.75 316	99.75 84	98.79 184	99.54 160	99.70 118	98.97 75	99.62 326	96.63 259	99.83 143	99.41 204

HPM-MVS++		98.96 186	98.70 200	99.74 55	99.52 198	99.71 51	98.86 214	99.19 274	98.47 214	98.59 289	99.06 289	98.08 179	99.91 92	96.94 242	99.60 224	99.60 122

lupinMVS		98.96 186	98.87 183	99.24 222	99.57 179	98.40 245	98.12 285	99.18 275	98.28 236	99.63 130	99.13 276	98.02 183	99.97 16	98.22 165	99.69 204	99.35 219

USDC		98.96 186	98.93 174	99.05 241	99.54 193	97.99 272	97.07 333	99.80 60	98.21 239	99.75 90	99.77 85	98.43 153	99.64 323	97.90 186	99.88 112	99.51 166

YYNet1		98.95 189	98.99 167	98.84 259	99.64 156	97.14 294	98.22 276	99.32 250	98.92 170	99.59 145	99.66 146	97.40 221	99.83 222	98.27 163	99.90 100	99.55 145

MDA-MVSNet_test_wron		98.95 189	98.99 167	98.85 257	99.64 156	97.16 293	98.23 275	99.33 248	98.93 168	99.56 155	99.66 146	97.39 223	99.83 222	98.29 161	99.88 112	99.55 145

Test_1112_low_res		98.95 189	98.73 198	99.63 106	99.68 147	99.15 190	98.09 289	99.80 60	97.14 286	99.46 174	99.40 228	96.11 262	99.89 122	99.01 107	99.84 133	99.84 15

diffmvs		98.94 192	98.87 183	99.13 232	99.37 246	98.90 218	99.25 136	99.64 137	97.55 272	99.04 246	99.58 184	97.23 231	99.64 323	98.73 135	99.44 248	98.86 284

test1235678		98.93 193	98.84 188	99.19 228	99.46 227	98.55 238	97.53 323	99.77 73	98.76 190	99.69 110	99.48 214	96.69 246	99.90 109	98.30 160	99.91 98	99.11 255

CANet_DTU		98.91 194	98.85 186	99.09 235	98.79 322	98.13 263	98.18 278	99.31 254	99.48 92	98.86 267	99.51 209	96.56 249	99.95 41	99.05 104	99.95 65	99.19 241

HyFIR lowres test		98.91 194	98.64 205	99.73 61	99.85 39	99.47 104	98.07 293	99.83 40	98.64 200	99.89 38	99.60 176	92.57 292	100.00 1	99.33 65	99.97 47	99.72 46

HQP_MVS		98.90 196	98.68 202	99.55 144	99.58 170	99.24 174	98.80 225	99.54 184	98.94 166	99.14 236	99.25 261	97.24 229	99.82 229	95.84 291	99.78 173	99.60 122

sss		98.90 196	98.77 196	99.27 212	99.48 218	98.44 242	98.72 233	99.32 250	97.94 252	99.37 198	99.35 243	96.31 257	99.91 92	98.85 125	99.63 218	99.47 183

OMC-MVS		98.90 196	98.72 199	99.44 172	99.39 241	99.42 125	98.58 240	99.64 137	97.31 282	99.44 175	99.62 166	98.59 131	99.69 296	96.17 275	99.79 168	99.22 235

new_pmnet		98.88 199	98.89 181	98.84 259	99.70 138	97.62 285	98.15 281	99.50 204	97.98 249	99.62 137	99.54 201	98.15 176	99.94 55	97.55 209	99.84 133	98.95 277

K. test v3		98.87 200	98.60 207	99.69 77	99.93 18	99.46 108	99.74 19	94.97 349	99.78 34	99.88 46	99.88 34	93.66 283	99.97 16	99.61 38	99.95 65	99.64 94

APD-MVS		98.87 200	98.59 208	99.71 70	99.50 207	99.62 81	99.01 192	99.57 174	96.80 295	99.54 160	99.63 159	98.29 164	99.91 92	95.24 310	99.71 201	99.61 116
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs-test1		98.83 202	98.70 200	99.22 224	98.89 311	99.65 73	98.88 211	99.66 123	99.34 116	98.29 302	98.94 305	97.69 205	99.96 33	98.11 176	98.54 318	98.04 320

UnsupCasMVSNet_eth		98.83 202	98.57 211	99.59 125	99.68 147	99.45 113	98.99 197	99.67 119	99.48 92	99.55 158	99.36 238	94.92 272	99.86 174	98.95 119	96.57 343	99.45 189

test_normal		98.82 204	98.67 203	99.27 212	99.56 190	98.83 226	98.22 276	98.01 318	99.03 160	99.49 171	99.24 266	96.21 259	99.76 272	98.69 138	99.56 227	99.22 235

NCCC		98.82 204	98.57 211	99.58 129	99.21 280	99.31 155	98.61 236	99.25 267	98.65 199	98.43 299	99.26 259	97.86 194	99.81 248	96.55 262	99.27 274	99.61 116

PMVS	92.94 21	98.82 204	98.81 193	98.85 257	99.84 42	97.99 272	99.20 148	99.47 213	99.71 47	99.42 181	99.82 58	98.09 177	99.47 338	93.88 325	99.85 129	99.07 269
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DI_MVS_plusplus_test		98.80 207	98.65 204	99.27 212	99.57 179	98.90 218	98.44 261	97.95 321	99.02 161	99.51 167	99.23 269	96.18 261	99.76 272	98.52 147	99.42 255	99.14 252

FMVSNet3		98.80 207	98.63 206	99.32 205	99.13 290	98.72 230	99.10 177	99.48 209	99.23 134	99.62 137	99.64 152	92.57 292	99.86 174	98.96 115	99.90 100	99.39 207

Patchmtry		98.78 209	98.54 214	99.49 158	98.89 311	99.19 186	99.32 109	99.67 119	99.65 65	99.72 102	99.79 70	91.87 298	99.95 41	98.00 182	99.97 47	99.33 222

Vis-MVSNet (Re-imp)		98.77 210	98.58 210	99.34 199	99.78 88	98.88 221	99.61 60	99.56 177	99.11 152	99.24 222	99.56 194	93.00 290	99.78 264	97.43 216	99.89 106	99.35 219

CLD-MVS		98.76 211	98.57 211	99.33 201	99.57 179	98.97 208	97.53 323	99.55 180	96.41 302	99.27 216	99.13 276	99.07 66	99.78 264	96.73 254	99.89 106	99.23 234
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CPTT-MVS		98.74 212	98.44 218	99.64 102	99.61 163	99.38 139	99.18 150	99.55 180	96.49 301	99.27 216	99.37 233	97.11 238	99.92 83	95.74 295	99.67 211	99.62 111

F-COLMAP		98.74 212	98.45 217	99.62 114	99.57 179	99.47 104	98.84 218	99.65 132	96.31 303	98.93 259	99.19 273	97.68 207	99.87 154	96.52 263	99.37 262	99.53 155

N_pmnet		98.73 214	98.53 215	99.35 198	99.72 128	98.67 233	98.34 268	94.65 350	98.35 228	99.79 79	99.68 136	98.03 181	99.93 66	98.28 162	99.92 88	99.44 194

PVSNet_Blended		98.70 215	98.59 208	99.02 244	99.54 193	97.99 272	97.58 320	99.82 48	95.70 314	99.34 205	98.98 297	98.52 145	99.77 270	97.98 183	99.83 143	99.30 228

PatchMatch-RL		98.68 216	98.47 216	99.30 209	99.44 231	99.28 161	98.14 283	99.54 184	97.12 288	99.11 239	99.25 261	97.80 198	99.70 290	96.51 264	99.30 269	98.93 279

Test4		98.65 217	98.44 218	99.27 212	99.57 179	98.86 224	98.43 262	99.41 227	98.85 176	99.57 148	98.95 304	93.05 288	99.75 278	98.57 143	99.56 227	99.19 241

test_prior3		98.62 218	98.34 231	99.46 166	99.35 249	99.22 178	97.95 306	99.39 236	97.87 255	98.05 315	99.05 290	97.90 190	99.69 296	95.99 284	99.49 243	99.48 179

CVMVSNet		98.61 219	98.88 182	97.80 304	99.58 170	93.60 331	99.26 132	99.64 137	99.66 62	99.72 102	99.67 142	93.26 286	99.93 66	99.30 71	99.81 160	99.87 10

Patchmatch-RL test		98.60 220	98.36 229	99.33 201	99.77 98	99.07 201	98.27 272	99.87 20	98.91 171	99.74 98	99.72 104	90.57 312	99.79 256	98.55 145	99.85 129	99.11 255

AdaColmap		98.60 220	98.35 230	99.38 190	99.12 292	99.22 178	98.67 235	99.42 226	97.84 259	98.81 270	99.27 257	97.32 227	99.81 248	95.14 311	99.53 238	99.10 259

WTY-MVS		98.59 222	98.37 228	99.26 217	99.43 233	98.40 245	98.74 231	99.13 281	98.10 243	99.21 227	99.24 266	94.82 274	99.90 109	97.86 189	98.77 301	99.49 178

CNLPA		98.57 223	98.34 231	99.28 210	99.18 286	99.10 196	98.34 268	99.41 227	98.48 213	98.52 293	98.98 297	97.05 240	99.78 264	95.59 301	99.50 241	98.96 276

1121		98.56 224	98.24 236	99.52 151	99.49 212	99.24 174	99.30 119	99.22 272	95.77 312	98.52 293	99.29 254	97.39 223	99.85 190	95.79 293	99.34 264	99.46 187

CDPH-MVS		98.56 224	98.20 240	99.61 117	99.50 207	99.46 108	98.32 270	99.41 227	95.22 320	99.21 227	99.10 282	98.34 161	99.82 229	95.09 313	99.66 214	99.56 142

UnsupCasMVSNet_bld		98.55 226	98.27 235	99.40 185	99.56 190	99.37 142	97.97 305	99.68 116	97.49 275	99.08 241	99.35 243	95.41 271	99.82 229	97.70 198	98.19 329	99.01 275

RPMNet		98.53 227	98.44 218	98.83 261	99.05 301	98.12 264	99.30 119	98.78 295	99.86 16	99.16 233	99.74 94	92.53 294	99.91 92	98.75 133	98.77 301	98.44 303

MG-MVS		98.52 228	98.39 225	98.94 248	99.15 287	97.39 290	98.18 278	99.21 273	98.89 172	99.23 223	99.63 159	97.37 225	99.74 282	94.22 321	99.61 223	99.69 56

DP-MVS Recon		98.50 229	98.23 237	99.31 207	99.49 212	99.46 108	98.56 244	99.63 140	94.86 326	98.85 268	99.37 233	97.81 197	99.59 331	96.08 277	99.44 248	98.88 282

CMPMVS	77.52 23	98.50 229	98.19 243	99.41 184	98.33 338	99.56 91	99.01 192	99.59 165	95.44 317	99.57 148	99.80 63	95.64 267	99.46 341	96.47 267	99.92 88	99.21 238
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t		98.49 231	98.11 246	99.64 102	99.73 120	99.58 88	99.24 138	99.76 79	89.94 341	99.42 181	99.56 194	97.76 201	99.86 174	97.74 196	99.82 152	99.47 183

PMMVS		98.49 231	98.29 234	99.11 233	98.96 304	98.42 244	97.54 321	99.32 250	97.53 274	98.47 298	98.15 332	97.88 193	99.82 229	97.46 214	99.24 277	99.09 262

MVSTER		98.47 233	98.22 238	99.24 222	99.06 300	98.35 250	99.08 182	99.46 216	99.27 123	99.75 90	99.66 146	88.61 322	99.85 190	99.14 98	99.92 88	99.52 163

LFMVS		98.46 234	98.19 243	99.26 217	99.24 277	98.52 240	99.62 56	96.94 336	99.87 13	99.31 211	99.58 184	91.04 303	99.81 248	98.68 140	99.42 255	99.45 189

PatchT		98.45 235	98.32 233	98.83 261	98.94 305	98.29 255	99.24 138	98.82 293	99.84 23	99.08 241	99.76 88	91.37 301	99.94 55	98.82 128	99.00 289	98.26 310

test12356		98.43 236	98.39 225	98.55 276	99.46 227	96.36 303	97.32 330	99.81 56	97.60 268	99.62 137	99.37 233	94.57 276	99.89 122	97.80 193	99.92 88	99.40 205

MIMVSNet		98.43 236	98.20 240	99.11 233	99.53 196	98.38 248	99.58 67	98.61 302	98.96 164	99.33 207	99.76 88	90.92 305	99.81 248	97.38 219	99.76 179	99.15 248

PVSNet	97.47 15	98.42 238	98.44 218	98.35 284	99.46 227	96.26 304	96.70 338	99.34 247	97.68 265	99.00 249	99.13 276	97.40 221	99.72 285	97.59 208	99.68 206	99.08 265

CHOSEN 280x420		98.41 239	98.41 223	98.40 282	99.34 259	95.89 313	96.94 334	99.44 221	98.80 183	99.25 219	99.52 205	93.51 284	99.98 7	98.94 120	99.98 36	99.32 226

BH-RMVSNet		98.41 239	98.14 245	99.21 225	99.21 280	98.47 241	98.60 238	98.26 315	98.35 228	98.93 259	99.31 248	97.20 235	99.66 314	94.32 319	99.10 283	99.51 166

QAPM		98.40 241	97.99 252	99.65 95	99.39 241	99.47 104	99.67 46	99.52 198	91.70 338	98.78 275	99.80 63	98.55 137	99.95 41	94.71 317	99.75 182	99.53 155

API-MVS		98.38 242	98.39 225	98.35 284	98.83 317	99.26 167	99.14 169	99.18 275	98.59 204	98.66 284	98.78 315	98.61 129	99.57 333	94.14 322	99.56 227	96.21 342

HQP-MVS		98.36 243	98.02 251	99.39 188	99.31 265	98.94 211	97.98 302	99.37 242	97.45 276	98.15 309	98.83 311	96.67 247	99.70 290	94.73 315	99.67 211	99.53 155

PAPM_NR		98.36 243	98.04 250	99.33 201	99.48 218	98.93 215	98.79 228	99.28 261	97.54 273	98.56 292	98.57 323	97.12 237	99.69 296	94.09 323	98.90 292	99.38 210

PLC	97.35 16	98.36 243	97.99 252	99.48 161	99.32 264	99.24 174	98.50 252	99.51 200	95.19 322	98.58 290	98.96 302	96.95 243	99.83 222	95.63 300	99.25 275	99.37 214
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		98.35 246	97.95 256	99.57 135	99.35 249	99.35 149	98.11 287	99.41 227	94.90 324	97.92 320	98.99 294	98.02 183	99.85 190	95.38 308	99.44 248	99.50 172

CR-MVSNet		98.35 246	98.20 240	98.83 261	99.05 301	98.12 264	99.30 119	99.67 119	97.39 279	99.16 233	99.79 70	91.87 298	99.91 92	98.78 132	98.77 301	98.44 303

LP		98.34 248	98.44 218	98.05 293	98.88 314	95.31 324	99.28 128	98.74 297	99.12 151	98.98 250	99.79 70	93.40 285	99.93 66	98.38 152	99.41 257	98.90 281

agg_prior1		98.33 249	97.92 258	99.57 135	99.35 249	99.36 145	97.99 301	99.39 236	94.85 327	97.76 330	98.98 297	98.03 181	99.85 190	95.49 303	99.44 248	99.51 166

alignmvs		98.28 250	97.96 255	99.25 220	99.12 292	98.93 215	99.03 189	98.42 311	99.64 67	98.72 279	97.85 335	90.86 308	99.62 326	98.88 124	99.13 281	99.19 241

agg_prior3		98.24 251	97.81 264	99.53 149	99.34 259	99.26 167	98.09 289	99.39 236	94.21 332	97.77 329	98.96 302	97.74 202	99.84 206	95.38 308	99.44 248	99.50 172

MAR-MVS		98.24 251	97.92 258	99.19 228	98.78 324	99.65 73	99.17 156	99.14 279	95.36 318	98.04 317	98.81 313	97.47 218	99.72 285	95.47 305	99.06 284	98.21 314
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	98.23 253	97.89 262	99.26 217	99.19 284	99.26 167	99.65 54	99.69 113	91.33 339	98.14 313	99.77 85	98.28 165	99.96 33	95.41 307	99.55 233	98.58 297

BH-untuned		98.22 254	98.09 247	98.58 275	99.38 244	97.24 292	98.55 245	98.98 288	97.81 261	99.20 232	98.76 316	97.01 241	99.65 321	94.83 314	98.33 324	98.86 284

HY-MVS	98.23 9	98.21 255	97.95 256	98.99 245	99.03 303	98.24 256	99.61 60	98.72 298	96.81 294	98.73 278	99.51 209	94.06 280	99.86 174	96.91 243	98.20 327	98.86 284

testus		98.15 256	98.06 249	98.40 282	99.11 295	95.95 309	96.77 336	99.89 15	95.83 310	99.23 223	98.47 328	97.50 217	99.84 206	96.58 261	99.20 280	99.39 207

Patchmatch-test1		98.13 257	98.40 224	97.31 317	99.20 283	92.99 333	98.17 280	98.49 308	98.24 238	99.10 240	99.52 205	96.01 264	99.83 222	97.22 229	99.62 219	99.12 254

EPNet		98.13 257	97.77 268	99.18 231	94.57 351	97.99 272	99.24 138	97.96 319	99.74 40	97.29 336	99.62 166	93.13 287	99.97 16	98.59 142	99.83 143	99.58 137
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test		98.10 259	97.98 254	98.48 279	99.27 274	96.48 301	99.40 85	99.07 282	98.81 181	99.23 223	99.57 191	90.11 316	99.87 154	96.69 255	99.64 217	99.09 262

pmmvs3		98.08 260	97.80 265	98.91 251	99.41 237	97.69 283	97.87 313	99.66 123	95.87 309	99.50 169	99.51 209	90.35 314	99.97 16	98.55 145	99.47 245	99.08 265

JIA-IIPM		98.06 261	97.92 258	98.50 278	98.59 332	97.02 295	98.80 225	98.51 306	99.88 12	97.89 322	99.87 37	91.89 297	99.90 109	98.16 174	97.68 339	98.59 295

TAPA-MVS	97.92 13	98.03 262	97.55 274	99.46 166	99.47 223	99.44 115	98.50 252	99.62 143	86.79 342	99.07 244	99.26 259	98.26 167	99.62 326	97.28 225	99.73 195	99.31 227
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
1314		98.00 263	97.90 261	98.27 289	98.90 307	97.45 289	99.30 119	99.06 284	94.98 323	97.21 337	99.12 280	98.43 153	99.67 309	95.58 302	98.56 317	97.71 332

GA-MVS		97.99 264	97.68 271	98.93 250	99.52 198	98.04 271	97.19 332	99.05 285	98.32 234	98.81 270	98.97 300	89.89 319	99.41 342	98.33 157	99.05 285	99.34 221

MVS-HIRNet		97.86 265	98.22 238	96.76 321	99.28 272	91.53 343	98.38 265	92.60 351	99.13 150	99.31 211	99.96 11	97.18 236	99.68 304	98.34 156	99.83 143	99.07 269

FMVSNet5		97.80 266	97.25 277	99.42 177	98.83 317	98.97 208	99.38 92	99.80 60	98.87 174	99.25 219	99.69 124	80.60 351	99.91 92	98.96 115	99.90 100	99.38 210

ADS-MVSNet2		97.78 267	97.66 273	98.12 292	99.14 288	95.36 322	99.22 144	98.75 296	96.97 290	98.25 305	99.64 152	90.90 306	99.94 55	96.51 264	99.56 227	99.08 265

tpmrst		97.73 268	98.07 248	96.73 323	98.71 329	92.00 337	99.10 177	98.86 290	98.52 209	98.92 261	99.54 201	91.90 296	99.82 229	98.02 180	99.03 287	98.37 305

ADS-MVSNet		97.72 269	97.67 272	97.86 302	99.14 288	94.65 327	99.22 144	98.86 290	96.97 290	98.25 305	99.64 152	90.90 306	99.84 206	96.51 264	99.56 227	99.08 265

PatchmatchNet		97.65 270	97.80 265	97.18 318	98.82 320	92.49 335	99.17 156	98.39 312	98.12 242	98.79 273	99.58 184	90.71 310	99.89 122	97.23 228	99.41 257	99.16 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		97.62 271	97.79 267	97.11 320	96.67 350	92.31 336	98.51 251	98.04 316	99.24 132	95.77 345	99.47 217	93.78 282	99.66 314	98.98 110	99.62 219	99.37 214
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		97.58 272	99.13 127	92.93 335	99.69 140	99.49 100	99.52 72	99.77 73	97.97 250	99.96 8	99.79 70	99.84 4	99.94 55	95.85 290	99.82 152	79.36 346

PAPR		97.56 273	97.07 279	99.04 242	98.80 321	98.11 266	97.63 318	99.25 267	94.56 330	98.02 318	98.25 331	97.43 220	99.68 304	90.90 332	98.74 305	99.33 222

TR-MVS		97.44 274	97.15 278	98.32 286	98.53 334	97.46 288	98.47 255	97.91 322	96.85 292	98.21 308	98.51 326	96.42 255	99.51 336	92.16 328	97.29 340	97.98 325

tpmvs		97.39 275	97.69 270	96.52 327	98.41 336	91.76 340	99.30 119	98.94 289	97.74 262	97.85 325	99.55 199	92.40 295	99.73 284	96.25 273	98.73 307	98.06 319

test0.0.03 1		97.37 276	96.91 286	98.74 271	97.72 344	97.57 286	97.60 319	97.36 335	98.00 246	99.21 227	98.02 333	90.04 317	99.79 256	98.37 153	95.89 346	98.86 284

OpenMVS_ROB	97.31 17	97.36 277	96.84 287	98.89 256	99.29 270	99.45 113	98.87 213	99.48 209	86.54 344	99.44 175	99.74 94	97.34 226	99.86 174	91.61 329	99.28 271	97.37 337

1111		97.29 278	96.71 295	99.04 242	99.65 154	97.72 280	98.35 266	99.80 60	99.40 109	99.66 119	99.43 223	75.10 355	99.87 154	98.98 110	99.98 36	99.52 163

tfpn1000		97.28 279	96.83 288	98.64 274	99.67 151	97.68 284	99.41 83	95.47 347	97.14 286	99.43 179	99.07 288	85.87 341	99.88 137	96.78 250	98.67 309	98.34 307

thresconf0.02		97.25 280	96.74 291	98.75 267	99.73 120	98.35 250	99.35 99	95.78 342	96.54 297	99.39 192	99.08 283	86.57 336	99.88 137	95.69 296	98.57 312	98.02 321

tfpn_n400		97.25 280	96.74 291	98.75 267	99.73 120	98.35 250	99.35 99	95.78 342	96.54 297	99.39 192	99.08 283	86.57 336	99.88 137	95.69 296	98.57 312	98.02 321

tfpnconf		97.25 280	96.74 291	98.75 267	99.73 120	98.35 250	99.35 99	95.78 342	96.54 297	99.39 192	99.08 283	86.57 336	99.88 137	95.69 296	98.57 312	98.02 321

tfpnview11		97.25 280	96.74 291	98.75 267	99.73 120	98.35 250	99.35 99	95.78 342	96.54 297	99.39 192	99.08 283	86.57 336	99.88 137	95.69 296	98.57 312	98.02 321

BH-w/o		97.20 284	97.01 282	97.76 305	99.08 298	95.69 318	98.03 296	98.52 305	95.76 313	97.96 319	98.02 333	95.62 268	99.47 338	92.82 327	97.25 341	98.12 318

test-LLR		97.15 285	96.95 284	97.74 307	98.18 341	95.02 325	97.38 326	96.10 338	98.00 246	97.81 326	98.58 321	90.04 317	99.91 92	97.69 203	98.78 299	98.31 308

tpm		97.15 285	96.95 284	97.75 306	98.91 306	94.24 329	99.32 109	97.96 319	97.71 264	98.29 302	99.32 246	86.72 334	99.92 83	98.10 178	96.24 345	99.09 262

E-PMN		97.14 287	97.43 275	96.27 329	98.79 322	91.62 342	95.54 342	99.01 287	99.44 101	98.88 265	99.12 280	92.78 291	99.68 304	94.30 320	99.03 287	97.50 334

PNet_i23d		97.02 288	97.87 263	94.49 334	99.69 140	84.81 353	95.18 345	99.85 29	97.83 260	99.32 209	99.57 191	95.53 270	99.47 338	96.09 276	97.74 338	99.18 244

cascas		96.99 289	96.82 289	97.48 311	97.57 347	95.64 319	96.43 340	99.56 177	91.75 337	97.13 338	97.61 343	95.58 269	98.63 347	96.68 256	99.11 282	98.18 317

EMVS		96.96 290	97.28 276	95.99 333	98.76 326	91.03 345	95.26 344	98.61 302	99.34 116	98.92 261	98.88 310	93.79 281	99.66 314	92.87 326	99.05 285	97.30 338

PatchFormer-LS_test		96.95 291	97.07 279	96.62 326	98.76 326	91.85 339	99.18 150	98.45 310	97.29 283	97.73 332	97.22 350	88.77 321	99.76 272	98.13 175	98.04 333	98.25 311

tfpn_ndepth		96.93 292	96.43 300	98.42 280	99.60 164	97.72 280	99.22 144	95.16 348	95.91 308	99.26 218	98.79 314	85.56 342	99.87 154	96.03 281	98.35 323	97.68 333

view600		96.86 293	96.52 296	97.88 298	99.69 140	95.87 314	99.39 86	97.68 325	99.11 152	98.96 253	97.82 337	87.40 323	99.79 256	89.78 333	98.83 294	97.98 325

view800		96.86 293	96.52 296	97.88 298	99.69 140	95.87 314	99.39 86	97.68 325	99.11 152	98.96 253	97.82 337	87.40 323	99.79 256	89.78 333	98.83 294	97.98 325

conf0.05thres1000		96.86 293	96.52 296	97.88 298	99.69 140	95.87 314	99.39 86	97.68 325	99.11 152	98.96 253	97.82 337	87.40 323	99.79 256	89.78 333	98.83 294	97.98 325

tfpn		96.86 293	96.52 296	97.88 298	99.69 140	95.87 314	99.39 86	97.68 325	99.11 152	98.96 253	97.82 337	87.40 323	99.79 256	89.78 333	98.83 294	97.98 325

dp		96.86 293	97.07 279	96.24 331	98.68 331	90.30 350	99.19 149	98.38 313	97.35 281	98.23 307	99.59 182	87.23 327	99.82 229	96.27 272	98.73 307	98.59 295

tpm cat1		96.78 298	96.98 283	96.16 332	98.85 316	90.59 349	99.08 182	99.32 250	92.37 336	97.73 332	99.46 220	91.15 302	99.69 296	96.07 278	98.80 298	98.21 314

PCF-MVS	96.03 18	96.73 299	95.86 310	99.33 201	99.44 231	99.16 188	96.87 335	99.44 221	86.58 343	98.95 257	99.40 228	94.38 278	99.88 137	87.93 340	99.80 165	98.95 277
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		96.71 300	96.79 290	96.46 328	98.90 307	90.71 347	99.41 83	98.68 300	94.69 329	98.14 313	99.34 245	86.32 340	99.80 253	97.60 207	98.07 332	98.88 282

MVE	92.54 22	96.66 301	96.11 304	98.31 287	99.68 147	97.55 287	97.94 308	95.60 346	99.37 113	90.68 349	98.70 319	96.56 249	98.61 348	86.94 346	99.55 233	98.77 290
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres600view7		96.60 302	96.16 303	97.93 296	99.63 158	96.09 308	99.18 150	97.57 329	98.77 187	98.72 279	97.32 346	87.04 328	99.72 285	88.57 338	98.62 311	97.98 325

EPMVS		96.53 303	96.32 301	97.17 319	98.18 341	92.97 334	99.39 86	89.95 353	98.21 239	98.61 287	99.59 182	86.69 335	99.72 285	96.99 240	99.23 279	98.81 288

conf200view11		96.43 304	96.03 306	97.63 309	99.63 158	95.93 310	99.18 150	97.57 329	98.75 192	98.70 281	97.31 347	87.04 328	99.67 309	87.62 341	98.51 319	97.30 338

thres400		96.40 305	95.89 308	97.92 297	99.58 170	96.11 306	99.00 194	97.54 333	98.43 215	98.52 293	96.98 351	86.85 331	99.67 309	87.62 341	98.51 319	97.98 325

thres100view900		96.39 306	96.03 306	97.47 312	99.63 158	95.93 310	99.18 150	97.57 329	98.75 192	98.70 281	97.31 347	87.04 328	99.67 309	87.62 341	98.51 319	96.81 340

tpm2		96.35 307	96.22 302	96.73 323	98.88 314	91.75 341	99.21 147	98.51 306	93.27 335	97.89 322	99.21 271	84.83 343	99.70 290	96.04 280	98.18 330	98.75 291

FPMVS		96.32 308	95.50 315	98.79 264	99.60 164	98.17 262	98.46 259	98.80 294	97.16 285	96.28 341	99.63 159	82.19 346	99.09 344	88.45 339	98.89 293	99.10 259

tfpn200view9		96.30 309	95.89 308	97.53 310	99.58 170	96.11 306	99.00 194	97.54 333	98.43 215	98.52 293	96.98 351	86.85 331	99.67 309	87.62 341	98.51 319	96.81 340

TESTMET0.1,1		96.24 310	95.84 311	97.41 314	98.24 339	93.84 330	97.38 326	95.84 341	98.43 215	97.81 326	98.56 324	79.77 352	99.89 122	97.77 194	98.77 301	98.52 299

test-mter		96.23 311	95.73 313	97.74 307	98.18 341	95.02 325	97.38 326	96.10 338	97.90 253	97.81 326	98.58 321	79.12 353	99.91 92	97.69 203	98.78 299	98.31 308

tpmp4_e23		96.11 312	96.06 305	96.27 329	98.90 307	90.70 348	99.34 104	99.03 286	93.72 333	96.56 340	99.31 248	83.63 344	99.75 278	96.06 279	98.02 334	98.35 306

X-MVStestdata		96.09 313	94.87 321	99.75 51	99.71 131	99.71 51	99.37 96	99.61 147	99.29 120	98.76 276	61.30 354	98.47 149	99.88 137	97.62 205	99.73 195	99.67 69

thres200		96.09 313	95.68 314	97.33 316	99.48 218	96.22 305	98.53 249	97.57 329	98.06 245	98.37 301	96.73 353	86.84 333	99.61 330	86.99 345	98.57 312	96.16 343

DWT-MVSNet_test		96.03 315	95.80 312	96.71 325	98.50 335	91.93 338	99.25 136	97.87 323	95.99 307	96.81 339	97.61 343	81.02 348	99.66 314	97.20 232	97.98 335	98.54 298

test2356		95.99 316	95.26 319	98.18 290	96.93 349	95.53 321	95.31 343	98.71 299	95.67 315	98.48 297	97.83 336	80.72 349	99.88 137	95.47 305	98.21 326	99.11 255

gg-mvs-nofinetune		95.87 317	95.17 320	97.97 295	98.19 340	96.95 296	99.69 38	89.23 354	99.89 10	96.24 343	99.94 13	81.19 347	99.51 336	93.99 324	98.20 327	97.44 335

PVSNet_0	95.53 19	95.85 318	95.31 317	97.47 312	98.78 324	93.48 332	95.72 341	99.40 233	96.18 305	97.37 334	97.73 341	95.73 266	99.58 332	95.49 303	81.40 348	99.36 217

tmp_tt		95.75 319	95.42 316	96.76 321	89.90 352	94.42 328	98.86 214	97.87 323	78.01 345	99.30 215	99.69 124	97.70 203	95.89 349	99.29 74	98.14 331	99.95 1

MVS		95.72 320	94.63 323	98.99 245	98.56 333	97.98 277	99.30 119	98.86 290	72.71 347	97.30 335	99.08 283	98.34 161	99.74 282	89.21 337	98.33 324	99.26 232

PAPM		95.61 321	94.71 322	98.31 287	99.12 292	96.63 299	96.66 339	98.46 309	90.77 340	96.25 342	98.68 320	93.01 289	99.69 296	81.60 347	97.86 337	98.62 293

IB-MVS	95.41 20	95.30 322	94.46 324	97.84 303	98.76 326	95.33 323	97.33 329	96.07 340	96.02 306	95.37 347	97.41 345	76.17 354	99.96 33	97.54 210	95.44 347	98.22 313
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		94.48 323	95.31 317	91.99 336	97.22 348	89.64 351	98.86 214	96.52 337	94.36 331	96.09 344	98.76 316	82.21 345	98.73 346	97.05 238	96.74 342	87.60 345

.test1245		85.84 324	89.27 325	75.54 337	99.65 154	97.72 280	98.35 266	99.80 60	99.40 109	99.66 119	99.43 223	75.10 355	99.87 154	98.98 110	33.07 349	29.03 348

pcd1.5k->3k		49.97 325	55.52 326	33.31 338	99.95 13	0.00 356	0.00 346	99.81 56	0.00 350	0.00 352	100.00 1	99.96 1	0.00 353	0.00 350	100.00 1	99.92 3

test123		29.31 326	33.05 329	18.08 339	25.93 354	12.24 354	97.53 323	10.93 357	11.78 348	24.21 350	50.08 358	21.04 357	8.60 351	23.51 348	32.43 351	33.39 347

testmvs		28.94 327	33.33 327	15.79 340	26.03 353	9.81 355	96.77 336	15.67 356	11.55 349	23.87 351	50.74 357	19.03 358	8.53 352	23.21 349	33.07 349	29.03 348

cdsmvs_eth3d_5k		24.88 328	33.17 328	0.00 341	0.00 355	0.00 356	0.00 346	99.62 143	0.00 350	0.00 352	99.13 276	99.82 6	0.00 353	0.00 350	0.00 352	0.00 350

pcd_1.5k_mvsjas		16.61 329	22.14 330	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	100.00 1	99.28 39	0.00 353	0.00 350	0.00 352	0.00 350

sosnet-low-res		8.33 330	11.11 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	100.00 1	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

sosnet		8.33 330	11.11 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	100.00 1	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uncertanet		8.33 330	11.11 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	100.00 1	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

Regformer		8.33 330	11.11 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	100.00 1	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uanet		8.33 330	11.11 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	100.00 1	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

ab-mvs-re		8.26 335	11.02 336	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	99.16 274	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

ESAPD_part2					99.62 162	99.67 67				99.55 158

ESAPD_part1								99.53 189				98.40 157			99.68 206	99.66 79

ESAPD								99.51 200

sam_mvs1												90.81 309

sam_mvs												90.52 313

semantic-postprocess				98.51 277	99.75 111	95.90 312		99.84 37	99.84 23	99.89 38	99.73 98	95.96 265	99.99 4	99.33 65	100.00 1	99.63 98

ambc				99.20 227	99.35 249	98.53 239	99.17 156	99.46 216		99.67 115	99.80 63	98.46 151	99.70 290	97.92 185	99.70 203	99.38 210

MTGPA								99.53 189

test_post1							99.14 169				51.63 356	89.54 320	99.82 229	96.86 246

test_post											52.41 355	90.25 315	99.86 174

patchmatchnet-post											99.62 166	90.58 311	99.94 55

GG-mvs-BLEND				97.36 315	97.59 345	96.87 298	99.70 29	88.49 355		94.64 348	97.26 349	80.66 350	99.12 343	91.50 330	96.50 344	96.08 344

MTMP								98.59 304

gm-plane-assit					97.59 345	89.02 352			93.47 334		98.30 329		99.84 206	96.38 268

test9_res														95.10 312	99.44 248	99.50 172

TEST9					99.35 249	99.35 149	98.11 287	99.41 227	94.83 328	97.92 320	98.99 294	98.02 183	99.85 190

test_8					99.34 259	99.31 155	98.08 292	99.40 233	94.90 324	97.87 324	98.97 300	98.02 183	99.84 206

agg_prior2														94.58 318	99.46 247	99.50 172

agg_prior					99.35 249	99.36 145		99.39 236		97.76 330			99.85 190

TestCases				99.63 106	99.78 88	99.64 75		99.83 40	98.63 201	99.63 130	99.72 104	98.68 116	99.75 278	96.38 268	99.83 143	99.51 166

test_prior4						99.19 186	98.00 299

test_prior2							97.95 306		97.87 255	98.05 315	99.05 290	97.90 190		95.99 284	99.49 243

test_prior				99.46 166	99.35 249	99.22 178		99.39 236					99.69 296			99.48 179

旧先验2							97.94 308		95.33 319	98.94 258			99.88 137	96.75 252

新几何2							98.04 295

新几何1				99.52 151	99.50 207	99.22 178		99.26 264	95.66 316	98.60 288	99.28 255	97.67 208	99.89 122	95.95 288	99.32 267	99.45 189

旧先验1					99.49 212	99.29 159		99.26 264			99.39 231	97.67 208			99.36 263	99.46 187

无先验							98.01 297	99.23 271	95.83 310				99.85 190	95.79 293		99.44 194

原ACMM2							97.92 310

原ACMM1				99.37 194	99.47 223	98.87 223		99.27 262	96.74 296	98.26 304	99.32 246	97.93 189	99.82 229	95.96 287	99.38 260	99.43 200

test222					99.51 202	99.08 199	97.83 315	99.29 258	95.21 321	98.68 283	99.31 248	97.28 228			99.38 260	99.43 200

testdata2													99.89 122	95.99 284

segment_acmp												98.37 159

testdata				99.42 177	99.51 202	98.93 215		99.30 257	96.20 304	98.87 266	99.40 228	98.33 163	99.89 122	96.29 271	99.28 271	99.44 194

testdata1							97.72 317		97.86 258

test12				99.54 148	99.29 270	99.33 152		99.16 277		98.43 299		97.54 215	99.82 229		99.47 245	99.48 179

plane_prior7					99.58 170	99.38 139

plane_prior6					99.47 223	99.26 167						97.24 229

plane_prior5								99.54 184					99.82 229	95.84 291	99.78 173	99.60 122

plane_prior4											99.25 261

plane_prior3						99.31 155			98.36 223	99.14 236

plane_prior2							98.80 225		98.94 166

plane_prior1					99.51 202

plane_prior						99.24 174	98.42 263		97.87 255						99.71 201

n2								0.00 358

nn								0.00 358

door-mid								99.83 40

lessismore_v0				99.64 102	99.86 35	99.38 139		90.66 352		99.89 38	99.83 51	94.56 277	99.97 16	99.56 43	99.92 88	99.57 141

LGP-MVS_train				99.74 55	99.82 53	99.63 79		99.73 92	97.56 270	99.64 127	99.69 124	99.37 30	99.89 122	96.66 257	99.87 119	99.69 56

test11								99.29 258

door								99.77 73

HQP5-MVS						98.94 211

HQP-NCC					99.31 265		97.98 302		97.45 276	98.15 309

ACMP_Plane					99.31 265		97.98 302		97.45 276	98.15 309

BP-MVS														94.73 315

HQP4-MVS										98.15 309			99.70 290			99.53 155

HQP3-MVS								99.37 242							99.67 211

HQP2-MVS												96.67 247

NP-MVS					99.40 240	99.13 191					98.83 311

MDTV_nov1_ep13_2view						91.44 344	99.14 169		97.37 280	99.21 227		91.78 300		96.75 252		99.03 273

MDTV_nov1_ep13			97.73 269		98.70 330	90.83 346	99.15 164	98.02 317	98.51 210	98.82 269	99.61 173	90.98 304	99.66 314	96.89 245	98.92 290

ACMMP++_ref															99.94 77

ACMMP++															99.79 168

Test By Simon												98.41 155

ITE_SJBPF				99.38 190	99.63 158	99.44 115		99.73 92	98.56 206	99.33 207	99.53 203	98.88 86	99.68 304	96.01 282	99.65 216	99.02 274

DeepMVS_CX				97.98 294	99.69 140	96.95 296		99.26 264	75.51 346	95.74 346	98.28 330	96.47 253	99.62 326	91.23 331	97.89 336	97.38 336

High-resolution multi-view benchmark