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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

UniMVSNet_ETH3D		99.81 7	99.79 7	99.85 18	99.98 1	99.76 15	99.73 43	99.96 10	99.68 9	99.87 26	99.59 76	99.91 53	99.58 46	99.90 7	99.85 3	99.96 3	99.81 16

pmmvs6		99.88 4	99.87 1	99.89 9	99.97 2	99.76 15	99.89 5	99.96 10	99.82 2	99.90 13	99.92 16	99.95 23	99.68 29	99.93 3	99.88 2	99.95 7	99.86 9

UA-Net		99.64 31	99.62 21	99.66 65	99.97 2	99.82 6	99.14 148	99.96 10	98.95 93	99.52 131	99.38 106	99.86 72	99.55 50	99.72 33	99.66 19	99.80 56	99.94 1

v7n		99.89 2	99.86 3	99.93 1	99.97 2	99.83 2	99.93 1	99.96 10	99.77 3	99.89 15	99.99 1	99.86 72	99.84 5	99.89 8	99.81 8	99.97 1	99.88 6

SixPastTwentyTwo		99.89 2	99.85 5	99.93 1	99.97 2	99.88 1	99.92 2	99.97 1	99.66 12	99.94 3	99.94 11	99.74 98	99.81 7	99.97 1	99.89 1	99.96 3	99.89 4

LTVRE_ROB	99.39 1	99.90 1	99.87 1	99.93 1	99.97 2	99.82 6	99.91 3	99.92 31	99.75 4	99.93 4	99.89 30	100.00 1	99.87 2	99.93 3	99.82 7	99.96 3	99.90 2
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
FC-MVSNet-test		99.84 6	99.80 6	99.89 9	99.96 7	99.83 2	99.84 14	99.95 21	99.37 42	99.77 62	99.95 6	99.96 13	99.85 3	99.93 3	99.83 4	99.95 7	99.72 34

NR-MVSNet		99.52 53	99.29 71	99.80 31	99.96 7	99.38 87	99.55 76	99.81 90	98.86 106	99.87 26	99.51 92	98.81 156	99.72 26	99.86 14	99.04 88	99.89 24	99.54 82

gm-plane-assit		96.82 194	94.84 201	99.13 153	99.95 9	99.78 13	99.69 50	99.92 31	99.19 62	99.84 42	99.92 16	72.93 216	96.44 194	98.21 182	97.01 187	98.92 178	96.87 197

anonymousdsp		99.87 5	99.86 3	99.88 12	99.95 9	99.75 21	99.90 4	99.96 10	99.69 7	99.83 47	99.96 4	99.99 3	99.74 21	99.95 2	99.83 4	99.91 19	99.88 6

PS-CasMVS		99.73 18	99.59 27	99.90 8	99.95 9	99.80 11	99.85 13	99.97 1	98.95 93	99.86 32	99.73 51	99.36 134	99.81 7	99.83 17	99.67 18	99.95 7	99.83 12

PEN-MVS		99.77 11	99.65 16	99.91 5	99.95 9	99.80 11	99.86 10	99.97 1	99.08 76	99.89 15	99.69 61	99.68 108	99.84 5	99.81 21	99.64 21	99.95 7	99.81 16

TransMVSNet (Re)		99.72 21	99.59 27	99.88 12	99.95 9	99.76 15	99.88 7	99.94 22	99.58 23	99.92 8	99.90 27	98.55 160	99.65 35	99.89 8	99.76 12	99.95 7	99.70 39

DTE-MVSNet		99.75 15	99.61 22	99.92 4	99.95 9	99.81 9	99.86 10	99.96 10	99.18 64	99.92 8	99.66 64	99.45 127	99.85 3	99.80 22	99.56 27	99.96 3	99.79 21

CP-MVSNet		99.68 26	99.51 37	99.89 9	99.95 9	99.76 15	99.83 17	99.96 10	98.83 111	99.84 42	99.65 66	99.09 147	99.80 10	99.78 25	99.62 25	99.95 7	99.82 13

WR-MVS_H		99.73 18	99.61 22	99.88 12	99.95 9	99.82 6	99.83 17	99.96 10	99.01 85	99.84 42	99.71 58	99.41 133	99.74 21	99.77 27	99.70 17	99.95 7	99.82 13

WR-MVS		99.79 9	99.68 13	99.91 5	99.95 9	99.83 2	99.87 9	99.96 10	99.39 41	99.93 4	99.87 34	99.29 142	99.77 14	99.83 17	99.72 15	99.97 1	99.82 13

HyFIR lowres test		99.50 56	99.26 75	99.80 31	99.95 9	99.62 37	99.76 31	99.97 1	99.67 10	99.56 122	99.94 11	98.40 163	99.78 12	98.84 160	98.59 140	99.76 66	99.72 34

FC-MVSNet-train		99.70 23	99.67 14	99.74 50	99.94 19	99.71 26	99.82 21	99.91 36	99.14 72	99.53 125	99.70 59	99.88 64	99.33 82	99.88 11	99.61 26	99.94 14	99.77 23

pm-mvs1		99.77 11	99.69 12	99.86 15	99.94 19	99.68 30	99.84 14	99.93 24	99.59 21	99.87 26	99.92 16	99.21 145	99.65 35	99.88 11	99.77 11	99.93 16	99.78 22

EPP-MVSNet		99.34 91	99.10 109	99.62 77	99.94 19	99.74 23	99.66 54	99.80 97	99.07 78	98.93 183	99.61 71	96.13 180	99.49 65	99.67 39	99.63 23	99.92 17	99.86 9

thisisatest0515		99.73 18	99.67 14	99.81 26	99.93 22	99.74 23	99.68 51	99.91 36	99.59 21	99.88 20	99.73 51	99.81 85	99.55 50	99.59 49	99.53 33	99.89 24	99.70 39

tfpnnormal		99.74 16	99.63 19	99.86 15	99.93 22	99.75 21	99.80 25	99.89 44	99.31 48	99.88 20	99.43 98	99.66 111	99.77 14	99.80 22	99.71 16	99.92 17	99.76 26

CHOSEN 1792x2688		99.65 29	99.55 31	99.77 39	99.93 22	99.60 40	99.79 26	99.92 31	99.73 5	99.74 75	99.93 14	99.98 4	99.80 10	98.83 161	99.01 92	99.45 143	99.76 26

DCV-MVSNet		99.43 70	99.23 81	99.67 62	99.92 25	99.76 15	99.64 56	99.93 24	99.06 80	99.68 99	97.77 184	98.97 151	98.97 123	99.72 33	99.54 31	99.88 26	99.81 16

Vis-MVSNet		99.76 13	99.78 8	99.75 44	99.92 25	99.77 14	99.83 17	99.85 60	99.43 35	99.85 38	99.84 40	100.00 1	99.13 109	99.83 17	99.66 19	99.90 21	99.90 2
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS_MVSNet		99.15 122	99.12 106	99.19 148	99.92 25	99.73 25	99.55 76	99.86 53	98.45 144	96.91 210	98.74 152	98.33 165	99.02 119	99.54 54	99.47 41	99.88 26	99.61 58

Vis-MVSNet (Re-imp)		99.40 79	99.28 73	99.55 89	99.92 25	99.68 30	99.31 124	99.87 49	98.69 122	99.16 174	99.08 134	98.64 159	99.20 96	99.65 43	99.46 43	99.83 46	99.72 34

test20.03		99.68 26	99.60 25	99.76 40	99.91 29	99.70 29	99.68 51	99.87 49	99.05 82	99.88 20	99.92 16	99.88 64	99.50 61	99.77 27	99.42 47	99.75 69	99.49 93

MDA-MVSNet-bldmvs		99.11 125	99.11 108	99.12 155	99.91 29	99.38 87	99.77 28	98.72 199	99.31 48	99.85 38	99.43 98	98.26 167	99.48 68	99.85 15	98.47 145	96.99 196	99.08 150

PVSNet_Blended_VisFu		99.66 28	99.64 17	99.67 62	99.91 29	99.71 26	99.61 62	99.79 99	99.41 37	99.91 11	99.85 38	99.61 115	99.00 120	99.67 39	99.42 47	99.81 52	99.81 16

new-patchmatchnet		98.49 165	97.60 174	99.53 92	99.90 32	99.55 47	99.77 28	99.48 171	99.67 10	99.86 32	99.98 3	99.98 4	99.50 61	96.90 196	91.52 199	98.67 183	95.62 200

testgi		99.43 70	99.47 45	99.38 118	99.90 32	99.67 32	99.30 129	99.73 128	98.64 129	99.53 125	99.52 90	99.90 56	98.08 159	99.65 43	99.40 51	99.75 69	99.55 81

DeepC-MVS	99.05 5	99.74 16	99.64 17	99.84 19	99.90 32	99.39 84	99.79 26	99.81 90	99.69 7	99.90 13	99.87 34	99.98 4	99.81 7	99.62 48	99.32 54	99.83 46	99.65 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Anonymous20231211		99.47 63	99.39 57	99.57 84	99.89 35	99.60 40	99.50 92	99.69 133	98.91 100	99.62 108	99.17 125	99.35 137	98.86 132	99.63 45	99.46 43	99.84 42	99.62 55

v1192		99.60 38	99.41 53	99.82 22	99.89 35	99.43 76	99.81 23	99.84 69	99.63 17	99.85 38	99.95 6	99.35 137	99.72 26	99.01 129	98.90 107	99.82 49	99.58 70

v1240		99.58 43	99.38 60	99.82 22	99.89 35	99.49 66	99.82 21	99.83 75	99.63 17	99.86 32	99.96 4	98.92 154	99.75 18	99.15 118	98.96 101	99.76 66	99.56 76

casdiffmvs		99.61 33	99.55 31	99.68 60	99.89 35	99.53 53	99.64 56	99.68 139	99.51 30	99.62 108	99.90 27	99.96 13	99.37 76	99.28 92	99.25 57	99.88 26	99.44 104

MIMVSNet1		99.79 9	99.75 10	99.84 19	99.89 35	99.83 2	99.84 14	99.89 44	99.31 48	99.93 4	99.92 16	99.97 8	99.68 29	99.89 8	99.64 21	99.82 49	99.66 45

CSCG		99.61 33	99.52 36	99.71 54	99.89 35	99.62 37	99.52 85	99.76 120	99.61 19	99.69 92	99.73 51	99.96 13	99.57 48	99.27 95	98.62 137	99.81 52	99.85 11

zzz-MVS		99.51 54	99.36 61	99.68 60	99.88 41	99.38 87	99.53 81	99.84 69	99.11 75	99.59 116	98.93 142	99.95 23	99.58 46	99.44 68	99.21 60	99.65 94	99.52 90

v1921920		99.59 40	99.40 56	99.82 22	99.88 41	99.45 71	99.81 23	99.83 75	99.65 13	99.86 32	99.95 6	99.29 142	99.75 18	98.98 135	98.86 112	99.78 60	99.59 62

v1144		99.61 33	99.43 49	99.82 22	99.88 41	99.41 81	99.76 31	99.86 53	99.64 15	99.84 42	99.95 6	99.49 125	99.74 21	99.00 131	98.93 104	99.84 42	99.58 70

SteuartSystems-ACMMP		99.47 63	99.22 84	99.76 40	99.88 41	99.36 93	99.65 55	99.84 69	98.47 141	99.80 53	98.68 156	99.96 13	99.68 29	99.37 73	99.06 83	99.72 81	99.66 45
Skip Steuart: Steuart Systems R&D Blog.
UGNet		99.40 79	99.61 22	99.16 151	99.88 41	99.64 35	99.61 62	99.77 111	99.31 48	99.63 107	99.33 109	99.93 39	96.46 193	99.63 45	99.53 33	99.63 106	99.89 4
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
ACMH+	98.94 6	99.69 25	99.59 27	99.81 26	99.88 41	99.41 81	99.75 35	99.86 53	99.43 35	99.80 53	99.54 81	99.97 8	99.73 24	99.82 20	99.52 35	99.85 39	99.43 107

Anonymous202405211			99.14 102		99.87 47	99.55 47	99.50 92	99.70 131	98.55 138		98.61 160	98.46 161	98.76 137	99.66 41	99.50 36	99.85 39	99.63 52

IterMVS-SCA-FT		99.15 122	98.96 126	99.38 118	99.87 47	99.54 50	99.53 81	99.79 99	98.94 95	99.82 49	99.92 16	97.65 172	98.82 133	98.95 138	98.26 157	98.45 186	99.47 99

pmmvs-eth3d		99.61 33	99.48 42	99.75 44	99.87 47	99.30 107	99.75 35	99.89 44	99.23 56	99.85 38	99.88 33	99.97 8	99.49 65	99.46 61	99.01 92	99.68 88	99.52 90

v144192		99.58 43	99.39 57	99.80 31	99.87 47	99.44 73	99.77 28	99.84 69	99.64 15	99.86 32	99.93 14	99.35 137	99.72 26	98.92 141	98.82 116	99.74 74	99.66 45

v148		99.58 43	99.43 49	99.76 40	99.87 47	99.40 83	99.76 31	99.85 60	99.48 32	99.83 47	99.82 45	99.83 82	99.51 57	99.20 108	98.82 116	99.75 69	99.45 101

v2v482		99.56 49	99.35 63	99.81 26	99.87 47	99.35 97	99.75 35	99.85 60	99.56 25	99.87 26	99.95 6	99.44 129	99.66 33	98.91 144	98.76 122	99.86 36	99.45 101

ACMMPR		99.51 54	99.32 67	99.72 53	99.87 47	99.33 100	99.61 62	99.85 60	99.19 62	99.73 81	98.73 153	99.95 23	99.61 40	99.35 76	99.14 68	99.66 92	99.58 70

diffmvs		99.38 84	99.33 65	99.45 107	99.87 47	99.39 84	99.28 132	99.58 157	99.55 27	99.50 136	99.85 38	99.85 78	98.94 127	98.58 173	98.68 131	99.51 135	99.39 118

TranMVSNet+NR-MVSNet		99.59 40	99.42 52	99.80 31	99.87 47	99.55 47	99.64 56	99.86 53	99.05 82	99.88 20	99.72 55	99.33 140	99.64 37	99.47 60	99.14 68	99.91 19	99.67 44

LGP-MVS_train		99.46 67	99.18 94	99.78 36	99.87 47	99.25 117	99.71 49	99.87 49	98.02 171	99.79 57	98.90 143	99.96 13	99.66 33	99.49 56	99.17 64	99.79 59	99.49 93

IterMVS-LS		99.16 120	98.82 141	99.57 84	99.87 47	99.71 26	99.58 72	99.92 31	99.24 55	99.71 89	99.73 51	95.79 181	98.91 128	98.82 162	98.66 133	99.43 148	99.77 23
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		99.08 128	98.90 132	99.29 132	99.87 47	99.53 53	99.52 85	99.77 111	98.94 95	99.75 68	99.91 23	97.52 176	98.72 140	98.86 153	98.14 164	98.09 189	99.43 107
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	99.11 4	99.72 21	99.63 19	99.84 19	99.87 47	99.59 42	99.83 17	99.88 48	99.46 34	99.87 26	99.66 64	99.95 23	99.76 16	99.73 32	99.47 41	99.84 42	99.52 90
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Gipuma		99.55 51	99.23 81	99.91 5	99.87 47	99.52 60	99.86 10	99.93 24	99.87 1	99.96 2	96.72 197	99.55 121	99.97 1	99.77 27	99.46 43	99.87 32	99.74 29
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CS-MVS		99.38 84	99.19 91	99.59 78	99.86 61	99.65 33	99.28 132	99.77 111	97.97 174	99.75 68	98.42 172	99.70 106	99.03 118	99.57 51	99.42 47	99.87 32	99.61 58

Fast-Effi-MVS+		99.39 81	99.18 94	99.63 73	99.86 61	99.28 112	99.45 104	99.91 36	98.47 141	99.61 111	99.50 94	99.57 119	99.17 98	99.24 98	98.66 133	99.78 60	99.59 62

XVS					99.86 61	99.30 107	99.72 47			99.69 92		99.93 39				99.60 116

X-MVStestdata					99.86 61	99.30 107	99.72 47			99.69 92		99.93 39				99.60 116

PGM-MVS		99.32 95	98.99 122	99.71 54	99.86 61	99.31 105	99.59 68	99.86 53	97.51 186	99.75 68	98.23 176	99.94 33	99.53 53	99.29 87	99.08 81	99.65 94	99.54 82

UniMVSNet_NR-MVSNet		99.41 75	99.12 106	99.76 40	99.86 61	99.48 67	99.50 92	99.81 90	98.84 109	99.89 15	99.45 97	98.32 166	99.59 43	99.22 102	98.89 108	99.90 21	99.63 52

UniMVSNet (Re)		99.50 56	99.29 71	99.75 44	99.86 61	99.47 69	99.51 88	99.82 82	98.90 101	99.89 15	99.64 67	99.00 149	99.55 50	99.32 81	99.08 81	99.90 21	99.59 62

ACMMP		99.36 86	99.06 113	99.71 54	99.86 61	99.36 93	99.63 60	99.85 60	98.33 154	99.72 84	97.73 186	99.94 33	99.53 53	99.37 73	99.13 75	99.65 94	99.56 76
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
ACMM	98.37 12	99.47 63	99.23 81	99.74 50	99.86 61	99.19 129	99.68 51	99.86 53	99.16 69	99.71 89	98.52 165	99.95 23	99.62 39	99.35 76	99.02 90	99.74 74	99.42 110
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_0304		99.36 86	99.35 63	99.37 124	99.85 70	99.36 93	99.39 111	99.56 158	99.36 44	99.75 68	99.23 119	99.90 56	97.97 166	99.00 131	98.83 115	99.69 87	99.77 23

ACMMP_NAP		99.47 63	99.33 65	99.63 73	99.85 70	99.28 112	99.56 75	99.83 75	98.75 117	99.48 140	99.03 139	99.95 23	99.47 71	99.48 57	99.19 61	99.57 124	99.59 62

X-MVS		99.30 101	98.99 122	99.66 65	99.85 70	99.30 107	99.49 99	99.82 82	98.32 155	99.69 92	97.31 194	99.93 39	99.50 61	99.37 73	99.16 65	99.60 116	99.53 85

APDe-MVS		99.60 38	99.48 42	99.73 52	99.85 70	99.51 64	99.75 35	99.85 60	99.17 65	99.81 52	99.56 79	99.94 33	99.44 72	99.42 70	99.22 58	99.67 90	99.54 82

DU-MVS		99.48 60	99.26 75	99.75 44	99.85 70	99.38 87	99.50 92	99.81 90	98.86 106	99.89 15	99.51 92	98.98 150	99.59 43	99.46 61	98.97 99	99.87 32	99.63 52

Baseline_NR-MVSNet		99.62 32	99.48 42	99.78 36	99.85 70	99.76 15	99.59 68	99.82 82	98.84 109	99.88 20	99.91 23	99.04 148	99.61 40	99.46 61	99.78 10	99.94 14	99.60 60

pmmvs5		99.58 43	99.47 45	99.70 57	99.84 76	99.50 65	99.58 72	99.80 97	98.98 90	99.73 81	99.92 16	99.81 85	99.49 65	99.28 92	99.05 86	99.77 64	99.73 31

V42		99.57 48	99.41 53	99.75 44	99.84 76	99.37 91	99.73 43	99.83 75	99.41 37	99.75 68	99.89 30	99.42 131	99.60 42	99.15 118	98.96 101	99.76 66	99.65 48

thres600view7		97.86 178	96.53 190	99.41 112	99.84 76	99.52 60	99.36 116	99.76 120	97.32 189	98.38 205	93.24 205	87.25 209	99.23 95	99.11 122	99.75 13	99.88 26	99.48 96

MP-MVS		99.35 89	99.09 111	99.65 67	99.84 76	99.22 125	99.59 68	99.78 105	98.13 163	99.67 100	98.44 169	99.93 39	99.43 74	99.31 83	99.09 80	99.60 116	99.49 93
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS					99.84 76							99.92 47

COLMAP_ROB	99.18 2	99.70 23	99.60 25	99.81 26	99.84 76	99.37 91	99.76 31	99.84 69	99.54 29	99.82 49	99.64 67	99.95 23	99.75 18	99.79 24	99.56 27	99.83 46	99.37 121
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EIA-MVS		99.23 109	99.03 117	99.47 103	99.83 82	99.64 35	99.16 145	99.81 90	97.11 193	99.65 104	98.44 169	99.78 96	98.61 145	99.46 61	99.22 58	99.75 69	99.59 62

gg-mvs-nofinetune		98.40 169	98.26 162	98.57 184	99.83 82	98.86 160	98.77 188	99.97 1	99.57 24	99.99 1	99.99 1	93.81 186	93.50 206	98.91 144	98.20 161	99.33 160	98.52 173

v10		99.65 29	99.51 37	99.81 26	99.83 82	99.61 39	99.75 35	99.94 22	99.56 25	99.76 65	99.94 11	99.60 117	99.73 24	99.11 122	99.01 92	99.85 39	99.74 29

TDRefinement		99.81 7	99.76 9	99.86 15	99.83 82	99.53 53	99.89 5	99.91 36	99.73 5	99.88 20	99.83 43	99.96 13	99.76 16	99.91 6	99.81 8	99.86 36	99.59 62

ACMP	98.32 13	99.44 69	99.18 94	99.75 44	99.83 82	99.18 130	99.64 56	99.83 75	98.81 113	99.79 57	98.42 172	99.96 13	99.64 37	99.46 61	98.98 98	99.74 74	99.44 104
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS		99.58 43	99.50 40	99.67 62	99.82 87	99.57 45	99.60 66	99.79 99	98.87 104	99.80 53	99.53 87	99.62 114	99.18 97	99.74 30	99.54 31	99.87 32	99.62 55

DVP-MVS		99.53 52	99.51 37	99.55 89	99.82 87	99.58 44	99.54 80	99.78 105	99.28 54	99.21 172	99.70 59	99.97 8	99.32 85	99.32 81	99.14 68	99.64 103	99.58 70

SMA-MVS		99.43 70	99.41 53	99.45 107	99.82 87	99.31 105	99.02 163	99.59 154	99.06 80	99.34 165	99.53 87	99.96 13	99.38 75	99.29 87	99.13 75	99.53 132	99.59 62

HFP-MVS		99.46 67	99.30 69	99.65 67	99.82 87	99.25 117	99.50 92	99.82 82	99.23 56	99.58 120	98.86 144	99.94 33	99.56 49	99.14 120	99.12 78	99.63 106	99.56 76

EU-MVSNet		99.76 13	99.74 11	99.78 36	99.82 87	99.81 9	99.88 7	99.87 49	99.31 48	99.75 68	99.91 23	99.76 97	99.78 12	99.84 16	99.74 14	99.56 127	99.81 16

EG-PatchMatch MVS		99.59 40	99.49 41	99.70 57	99.82 87	99.26 114	99.39 111	99.83 75	98.99 87	99.93 4	99.54 81	99.92 47	99.51 57	99.78 25	99.50 36	99.73 78	99.41 111

thres400		97.82 180	96.47 191	99.40 113	99.81 93	99.44 73	99.29 131	99.69 133	97.15 191	98.57 199	92.82 209	87.96 208	99.16 101	98.96 136	99.55 30	99.86 36	99.41 111

CANet		99.36 86	99.39 57	99.34 129	99.80 94	99.35 97	99.41 110	99.47 172	99.20 59	99.74 75	99.54 81	99.68 108	98.05 161	99.23 100	98.97 99	99.57 124	99.73 31

v8		99.61 33	99.45 47	99.79 35	99.80 94	99.59 42	99.73 43	99.93 24	99.48 32	99.77 62	99.90 27	99.48 126	99.67 32	99.11 122	98.89 108	99.84 42	99.73 31

PMMVS2		99.23 109	99.22 84	99.24 139	99.80 94	99.14 135	99.50 92	99.82 82	99.12 74	98.41 204	99.91 23	99.98 4	98.51 147	99.48 57	98.76 122	99.38 154	98.14 184

CP-MVS		99.41 75	99.20 89	99.65 67	99.80 94	99.23 124	99.44 105	99.75 125	98.60 134	99.74 75	98.66 157	99.93 39	99.48 68	99.33 80	99.16 65	99.73 78	99.48 96

tttt0517		98.77 154	98.25 164	99.38 118	99.79 98	99.46 70	99.07 155	99.64 147	98.40 152	99.38 157	99.21 122	92.54 196	98.63 143	99.34 79	99.14 68	99.80 56	99.62 55

Effi-MVS+		99.20 114	98.93 128	99.50 100	99.79 98	99.26 114	98.82 185	99.96 10	98.37 153	99.60 114	99.12 130	98.36 164	99.05 116	98.93 139	98.82 116	99.78 60	99.68 41

Anonymous20231206		99.48 60	99.31 68	99.69 59	99.79 98	99.57 45	99.63 60	99.79 99	98.88 103	99.91 11	99.72 55	99.93 39	99.59 43	99.24 98	98.63 136	99.43 148	99.18 139

DI_MVS_plusplus_trai		98.74 155	98.08 172	99.51 98	99.79 98	99.29 111	99.61 62	99.60 151	99.20 59	99.46 144	99.09 133	92.93 189	98.97 123	98.27 181	98.35 153	99.65 94	99.45 101

thisisatest0530		98.78 153	98.26 162	99.39 114	99.78 102	99.43 76	99.07 155	99.64 147	98.44 145	99.42 151	99.22 120	92.68 195	98.63 143	99.30 84	99.14 68	99.80 56	99.60 60

baseline1		98.39 170	97.59 175	99.31 130	99.78 102	99.45 71	99.13 149	99.53 166	98.06 169	98.87 187	98.63 159	90.04 201	98.76 137	98.85 155	98.84 114	99.81 52	99.28 131

baseline		99.24 108	99.30 69	99.17 150	99.78 102	99.14 135	99.10 153	99.69 133	98.97 91	99.49 138	99.84 40	99.88 64	97.99 165	98.85 155	98.73 126	98.98 177	99.72 34

APD-MVS		99.17 117	98.92 129	99.46 105	99.78 102	99.24 122	99.34 120	99.78 105	97.79 180	99.48 140	98.25 175	99.88 64	98.77 136	99.18 114	98.92 105	99.63 106	99.18 139
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
N_pmnet		98.64 160	98.23 167	99.11 158	99.78 102	99.25 117	99.75 35	99.39 183	99.65 13	99.70 91	99.78 48	99.89 59	98.81 135	97.60 189	94.28 194	97.24 195	97.15 196

QAPM		99.41 75	99.21 88	99.64 72	99.78 102	99.16 132	99.51 88	99.85 60	99.20 59	99.72 84	99.43 98	99.81 85	99.25 93	98.87 150	98.71 128	99.71 84	99.30 129

3Dnovator	99.16 3	99.42 73	99.22 84	99.65 67	99.78 102	99.13 139	99.50 92	99.85 60	99.40 39	99.80 53	98.59 161	99.79 93	99.30 89	99.20 108	99.06 83	99.71 84	99.35 124

CHOSEN 280x420		98.99 138	98.91 131	99.07 161	99.77 109	99.26 114	99.55 76	99.92 31	98.62 130	98.67 197	99.62 70	97.20 178	98.44 149	99.50 55	99.18 62	98.08 190	98.99 161

pmmvs4		99.34 91	99.15 101	99.57 84	99.77 109	98.90 155	99.51 88	99.77 111	99.07 78	99.73 81	99.72 55	99.84 80	99.07 113	98.85 155	98.39 151	99.55 130	99.27 132

3Dnovator+	98.92 7	99.31 99	99.03 117	99.63 73	99.77 109	98.90 155	99.52 85	99.81 90	99.37 42	99.72 84	98.03 181	99.73 101	99.32 85	98.99 134	98.81 119	99.67 90	99.36 122

LS3D		99.39 81	99.28 73	99.52 96	99.77 109	99.39 84	99.55 76	99.82 82	98.93 97	99.64 105	98.52 165	99.67 110	98.58 146	99.74 30	99.63 23	99.75 69	99.06 153

MSP-MVS		99.32 95	99.26 75	99.38 118	99.76 113	99.54 50	99.42 107	99.72 129	98.92 99	98.84 190	98.96 141	99.96 13	98.91 128	98.72 168	99.14 68	99.63 106	99.58 70

OPM-MVS		99.39 81	99.22 84	99.59 78	99.76 113	98.82 161	99.51 88	99.79 99	99.17 65	99.53 125	99.31 113	99.95 23	99.35 78	99.22 102	98.79 121	99.60 116	99.27 132

thres200		97.87 176	96.56 187	99.39 114	99.76 113	99.52 60	99.13 149	99.76 120	96.88 203	98.66 198	92.87 208	88.77 207	99.16 101	99.11 122	99.42 47	99.88 26	99.33 125

PM-MVS		99.49 59	99.43 49	99.57 84	99.76 113	99.34 99	99.53 81	99.77 111	98.93 97	99.75 68	99.46 96	99.83 82	99.11 111	99.72 33	99.29 56	99.49 138	99.46 100

our_test_3					99.75 117	99.11 143	99.74 42

HPM-MVS++		99.23 109	98.98 124	99.53 92	99.75 117	99.02 148	99.44 105	99.77 111	98.65 125	99.52 131	98.72 154	99.92 47	99.33 82	98.77 166	98.40 150	99.40 152	99.36 122

MCST-MVS		99.17 117	98.82 141	99.57 84	99.75 117	98.70 172	99.25 138	99.69 133	98.62 130	99.59 116	98.54 163	99.79 93	99.53 53	98.48 177	98.15 163	99.64 103	99.43 107

CDPH-MVS		99.05 132	98.63 148	99.54 91	99.75 117	98.78 164	99.59 68	99.68 139	97.79 180	99.37 159	98.20 177	99.86 72	99.14 107	98.58 173	98.01 171	99.68 88	99.16 145

MDTV_nov1_ep13_2view		98.73 157	98.31 161	99.22 143	99.75 117	99.24 122	99.75 35	99.93 24	99.31 48	99.84 42	99.86 37	99.81 85	99.31 88	97.40 194	94.77 193	96.73 198	97.81 189

OpenMVS	98.82 8	99.17 117	98.85 137	99.53 92	99.75 117	99.06 146	99.36 116	99.82 82	98.28 157	99.76 65	98.47 167	99.61 115	98.91 128	98.80 163	98.70 129	99.60 116	99.04 157

MSDG		99.32 95	99.09 111	99.58 81	99.75 117	98.74 168	99.36 116	99.54 161	99.14 72	99.72 84	99.24 117	99.89 59	99.51 57	99.30 84	98.76 122	99.62 112	98.54 172

CLD-MVS		99.30 101	99.01 121	99.63 73	99.75 117	98.89 158	99.35 119	99.60 151	98.53 139	99.86 32	99.57 78	99.94 33	99.52 56	98.96 136	98.10 167	99.70 86	99.08 150
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thres100view900		97.69 184	96.37 192	99.23 140	99.74 125	99.21 128	98.81 186	99.43 178	96.10 210	98.70 195	92.99 206	89.10 205	98.88 131	98.58 173	99.31 55	99.82 49	99.27 132

tfpn200view9		97.85 179	96.54 188	99.38 118	99.74 125	99.52 60	99.17 144	99.76 120	96.10 210	98.70 195	92.99 206	89.10 205	99.00 120	99.11 122	99.56 27	99.88 26	99.41 111

SR-MVS					99.73 127			99.74 127				99.88 64

RPSCF		99.48 60	99.45 47	99.52 96	99.73 127	99.33 100	99.13 149	99.77 111	99.33 46	99.47 143	99.39 105	99.92 47	99.36 77	99.63 45	99.13 75	99.63 106	99.41 111

ambc			98.83 138		99.72 129	98.52 181	98.84 182		98.96 92	99.92 8	99.34 108	99.74 98	99.04 117	98.68 169	97.57 181	99.46 141	98.99 161

CPTT-MVS		99.21 112	98.89 133	99.58 81	99.72 129	99.12 142	99.30 129	99.76 120	98.62 130	99.66 102	97.51 189	99.89 59	99.48 68	99.01 129	98.64 135	99.58 123	99.40 116

USDC		99.29 105	98.98 124	99.65 67	99.72 129	98.87 159	99.47 101	99.66 145	99.35 45	99.87 26	99.58 77	99.87 71	99.51 57	98.85 155	97.93 173	99.65 94	98.38 176

TSAR-MVS + GP.		99.33 93	99.17 98	99.51 98	99.71 132	99.00 150	98.84 182	99.71 130	98.23 160	99.74 75	99.53 87	99.90 56	99.35 78	99.38 72	98.85 113	99.72 81	99.31 127

PCF-MVS	97.86 15	98.95 141	98.53 153	99.44 109	99.70 133	98.80 163	98.96 168	99.69 133	98.65 125	99.59 116	99.33 109	99.94 33	99.12 110	98.01 187	97.11 184	99.59 122	97.83 188
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TSAR-MVS + MP.		99.56 49	99.54 34	99.58 81	99.69 134	99.14 135	99.73 43	99.45 174	99.50 31	99.35 162	99.60 74	99.93 39	99.50 61	99.56 52	99.37 52	99.77 64	99.64 51
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
train_agg		98.89 146	98.48 158	99.38 118	99.69 134	98.76 167	99.31 124	99.60 151	97.71 182	98.98 180	97.89 182	99.89 59	99.29 90	98.32 178	97.59 180	99.42 151	99.16 145

PVSNet_BlendedMVS		99.20 114	99.17 98	99.23 140	99.69 134	99.33 100	99.04 158	99.13 192	98.41 149	99.79 57	99.33 109	99.36 134	98.10 157	99.29 87	98.87 110	99.65 94	99.56 76

PVSNet_Blended		99.20 114	99.17 98	99.23 140	99.69 134	99.33 100	99.04 158	99.13 192	98.41 149	99.79 57	99.33 109	99.36 134	98.10 157	99.29 87	98.87 110	99.65 94	99.56 76

TAMVS		99.05 132	99.02 120	99.08 160	99.69 134	99.22 125	99.33 121	99.32 188	99.16 69	98.97 181	99.87 34	97.36 177	97.76 169	99.21 105	99.00 96	99.44 145	99.33 125

PHI-MVS		99.33 93	99.19 91	99.49 101	99.69 134	99.25 117	99.27 134	99.59 154	98.44 145	99.78 61	99.15 126	99.92 47	98.95 126	99.39 71	99.04 88	99.64 103	99.18 139

canonicalmvs		99.00 136	98.68 147	99.37 124	99.68 140	99.42 80	98.94 172	99.89 44	99.00 86	98.99 179	98.43 171	95.69 182	98.96 125	99.18 114	99.18 62	99.74 74	99.88 6

FMVSNet1		99.50 56	99.57 30	99.42 110	99.67 141	99.65 33	99.60 66	99.91 36	99.40 39	99.39 156	99.83 43	99.27 144	98.14 156	99.68 36	99.50 36	99.81 52	99.68 41

DPE-MVS		99.41 75	99.36 61	99.47 103	99.66 142	99.48 67	99.46 103	99.75 125	98.65 125	99.41 153	99.67 62	99.95 23	98.82 133	99.21 105	99.14 68	99.72 81	99.40 116

SD-MVS		99.35 89	99.26 75	99.46 105	99.66 142	99.15 134	98.92 173	99.67 142	99.55 27	99.35 162	98.83 146	99.91 53	99.35 78	99.19 111	98.53 142	99.78 60	99.68 41
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + ACMM		99.31 99	99.26 75	99.37 124	99.66 142	98.97 153	99.20 141	99.56 158	99.33 46	99.19 173	99.54 81	99.91 53	99.32 85	99.12 121	98.34 154	99.29 162	99.65 48

pmmvs3		98.85 149	98.60 149	99.13 153	99.66 142	98.72 170	99.37 115	99.06 195	98.44 145	99.76 65	99.74 49	99.55 121	99.15 105	99.04 127	96.00 192	97.80 191	98.72 170

PatchT		98.11 171	97.12 182	99.26 138	99.65 146	98.34 190	99.57 74	99.97 1	97.48 187	99.43 148	99.04 138	90.84 199	98.15 154	98.04 184	97.78 174	98.82 180	98.30 179

DeepC-MVS_fast	98.69 9	99.32 95	99.13 104	99.53 92	99.63 147	98.78 164	99.53 81	99.33 187	99.08 76	99.77 62	99.18 124	99.89 59	99.29 90	99.00 131	98.70 129	99.65 94	99.30 129
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test-LLR		97.74 182	97.46 177	98.08 195	99.62 148	98.37 188	98.26 202	99.41 179	97.03 196	97.38 208	99.54 81	92.89 190	95.12 201	98.78 164	97.68 178	98.65 184	97.90 186

test0.0.03 1		98.41 168	98.41 160	98.40 190	99.62 148	99.16 132	98.87 179	99.41 179	97.15 191	96.60 212	99.31 113	97.00 179	96.55 192	98.91 144	98.51 144	99.37 155	98.82 166

NCCC		98.88 147	98.42 159	99.42 110	99.62 148	98.81 162	99.10 153	99.54 161	98.76 115	99.53 125	95.97 200	99.80 91	99.16 101	98.49 176	98.06 170	99.55 130	99.05 155

IB-MVS	98.10 14	97.76 181	97.40 179	98.18 192	99.62 148	99.11 143	98.24 204	98.35 204	96.56 206	99.44 146	91.28 210	98.96 153	93.84 204	98.09 183	98.62 137	99.56 127	99.18 139
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
MVS_Test		99.09 127	98.92 129	99.29 132	99.61 152	99.07 145	99.04 158	99.81 90	98.58 136	99.37 159	99.74 49	98.87 155	98.41 150	98.61 172	98.01 171	99.50 137	99.57 75

CNVR-MVS		99.08 128	98.83 138	99.37 124	99.61 152	98.74 168	99.15 146	99.54 161	98.59 135	99.37 159	98.15 178	99.88 64	99.08 112	98.91 144	98.46 146	99.48 139	99.06 153

HQP-MVS		98.70 159	98.19 168	99.28 136	99.61 152	98.52 181	98.71 190	99.35 184	97.97 174	99.53 125	97.38 192	99.85 78	99.14 107	97.53 190	96.85 188	99.36 156	99.26 135

TinyColmap		99.21 112	98.89 133	99.59 78	99.61 152	98.61 177	99.47 101	99.67 142	99.02 84	99.82 49	99.15 126	99.74 98	99.35 78	99.17 116	98.33 155	99.63 106	98.22 182

Effi-MVS+-dtu		99.01 135	99.05 114	98.98 164	99.60 156	99.13 139	99.03 162	99.61 149	98.52 140	99.01 178	98.53 164	99.83 82	96.95 186	99.48 57	98.59 140	99.66 92	99.25 136

EPNet		98.06 174	98.11 171	98.00 198	99.60 156	98.99 152	98.38 200	99.68 139	98.18 162	98.85 189	97.89 182	95.60 183	92.72 208	98.30 179	98.10 167	98.76 181	99.72 34
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 175	96.80 185	99.22 143	99.60 156	98.23 193	98.91 174	99.97 1	96.89 201	99.43 148	99.10 132	89.24 204	98.15 154	98.04 184	97.78 174	99.26 165	98.30 179

PMVS	94.32 17	99.27 106	99.55 31	98.94 167	99.60 156	99.43 76	99.39 111	99.54 161	98.99 87	99.69 92	99.60 74	99.81 85	95.68 198	99.88 11	99.83 4	99.73 78	99.31 127
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GA-MVS		98.59 163	98.15 169	99.09 159	99.59 160	99.13 139	98.84 182	99.52 168	98.61 133	99.35 162	99.67 62	93.03 188	97.73 171	98.90 148	98.26 157	99.51 135	99.48 96

ADS-MVSNet		97.29 192	96.17 195	98.59 183	99.59 160	98.70 172	99.32 122	99.86 53	98.47 141	99.56 122	99.08 134	98.16 168	97.34 177	92.92 202	91.17 200	95.91 201	94.72 204

CDS-MVSNet		99.15 122	99.10 109	99.21 145	99.59 160	99.22 125	99.48 100	99.47 172	98.89 102	99.41 153	99.84 40	98.11 169	97.76 169	99.26 97	99.01 92	99.57 124	99.38 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		99.03 134	99.18 94	98.87 172	99.58 163	99.03 147	99.18 143	99.41 179	98.65 125	99.74 75	99.55 80	99.71 103	96.13 196	99.19 111	98.92 105	99.17 171	99.18 139

EPNet_dtu		98.09 173	98.25 164	97.91 199	99.58 163	98.02 201	98.19 206	99.67 142	97.94 176	99.74 75	99.07 136	98.71 158	93.40 207	97.50 191	97.09 185	96.89 197	99.44 104
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_HR		99.30 101	99.14 102	99.48 102	99.58 163	99.25 117	99.27 134	99.61 149	98.74 118	99.66 102	99.02 140	99.84 80	99.33 82	99.20 108	98.76 122	99.44 145	99.18 139

RPMNet		97.70 183	96.54 188	99.06 162	99.57 166	98.23 193	98.95 171	99.97 1	96.89 201	99.49 138	99.13 128	89.63 202	97.09 181	96.68 197	97.02 186	99.26 165	98.19 183

DPM-MVS		98.10 172	97.32 180	99.01 163	99.52 167	97.92 203	98.47 199	99.45 174	98.25 158	98.91 184	93.99 204	99.69 107	98.73 139	96.29 198	96.32 191	99.00 176	98.77 167

MS-PatchMatch		98.94 142	98.71 146	99.21 145	99.52 167	98.22 196	98.97 167	99.53 166	98.76 115	99.50 136	98.59 161	99.56 120	98.68 141	98.63 171	98.45 148	99.05 174	98.73 168

CVMVSNet		99.06 131	98.88 136	99.28 136	99.52 167	99.53 53	99.42 107	99.69 133	98.74 118	98.27 206	99.89 30	95.48 184	99.44 72	99.46 61	99.33 53	99.32 161	99.75 28

tpmrst		96.18 200	94.47 203	98.18 192	99.52 167	97.89 205	98.96 168	99.79 99	98.07 168	99.16 174	99.30 116	92.69 194	96.69 190	90.76 206	88.85 207	94.96 206	93.69 207

DELS-MVS		99.42 73	99.53 35	99.29 132	99.52 167	99.43 76	99.42 107	99.28 189	99.16 69	99.72 84	99.82 45	99.97 8	98.17 153	99.56 52	99.16 65	99.65 94	99.59 62
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
AdaColmap		98.93 143	98.53 153	99.39 114	99.52 167	98.65 175	99.11 152	99.59 154	98.08 167	99.44 146	97.46 191	99.45 127	99.24 94	98.92 141	98.44 149	99.44 145	98.73 168

Fast-Effi-MVS+-dtu		98.82 150	98.80 143	98.84 175	99.51 173	98.90 155	98.96 168	99.91 36	98.29 156	99.11 177	98.47 167	99.63 113	96.03 197	99.21 105	98.12 165	99.52 133	99.01 158

new_pmnet		98.91 145	98.89 133	98.94 167	99.51 173	98.27 192	99.15 146	98.66 200	99.17 65	99.48 140	99.79 47	99.80 91	98.49 148	99.23 100	98.20 161	98.34 187	97.74 192

MVS_111021_LR		99.25 107	99.13 104	99.39 114	99.50 175	99.14 135	99.23 139	99.50 169	98.67 123	99.61 111	99.12 130	99.81 85	99.16 101	99.28 92	98.67 132	99.35 158	99.21 138

abl_6				99.21 145	99.49 176	98.62 176	98.90 177	99.44 177	97.08 194	99.61 111	97.19 195	99.73 101	98.35 151			99.45 143	98.84 163

CostFormer		95.61 201	93.35 206	98.24 191	99.48 177	98.03 200	98.65 191	99.83 75	96.93 199	99.42 151	98.83 146	83.65 213	97.08 182	90.39 207	89.54 206	94.94 207	96.11 199

MDTV_nov1_ep13		97.41 191	96.26 194	98.76 177	99.47 178	98.43 186	99.26 137	99.82 82	98.06 169	99.23 170	99.22 120	92.86 192	98.05 161	95.33 200	93.66 196	96.73 198	96.26 198

MVSTER		97.55 189	96.75 186	98.48 187	99.46 179	99.54 50	98.24 204	99.77 111	97.56 185	99.41 153	99.31 113	84.86 211	94.66 203	98.86 153	97.75 176	99.34 159	99.38 119

E-PMN		96.72 197	95.78 197	97.81 201	99.45 180	95.46 211	98.14 208	98.33 206	97.99 173	98.73 194	98.09 179	98.97 151	97.54 174	97.45 192	91.09 201	94.70 209	91.40 209

PLC	97.83 16	98.88 147	98.52 155	99.30 131	99.45 180	98.60 178	98.65 191	99.49 170	98.66 124	99.59 116	96.33 198	99.59 118	99.17 98	98.87 150	98.53 142	99.46 141	99.05 155
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSLP-MVS++		98.92 144	98.73 145	99.14 152	99.44 182	99.00 150	98.36 201	99.35 184	98.82 112	99.38 157	96.06 199	99.79 93	99.07 113	98.88 149	99.05 86	99.27 164	99.53 85

TAPA-MVS	98.54 10	99.30 101	99.24 80	99.36 128	99.44 182	98.77 166	99.00 165	99.41 179	99.23 56	99.60 114	99.50 94	99.86 72	99.15 105	99.29 87	98.95 103	99.56 127	99.08 150
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchmatchNet		96.81 195	95.41 198	98.43 189	99.43 184	98.30 191	99.23 139	99.93 24	98.19 161	99.64 105	98.81 149	93.50 187	97.43 176	92.89 203	90.78 202	94.94 207	95.41 201
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EMVS		96.47 199	95.38 199	97.74 204	99.42 185	95.37 212	98.07 210	98.27 207	97.85 178	98.90 185	97.48 190	98.73 157	97.20 178	97.21 195	90.39 203	94.59 211	90.65 210

EPMVS		96.76 196	95.30 200	98.46 188	99.42 185	98.47 184	99.32 122	99.91 36	98.42 148	99.51 134	99.07 136	92.81 193	97.12 180	92.39 204	91.71 198	95.51 203	94.20 206

PatchMatch-RL		98.80 152	98.52 155	99.12 155	99.38 187	98.70 172	98.56 194	99.55 160	97.81 179	99.34 165	97.57 187	99.31 141	98.67 142	99.27 95	98.62 137	99.22 169	98.35 178

DWT-MVSNet_training		94.92 204	92.14 207	98.15 194	99.37 188	98.43 186	98.99 166	98.51 201	96.76 205	99.52 131	97.35 193	77.20 215	97.08 182	89.76 208	90.38 204	95.43 204	95.13 203

OMC-MVS		99.11 125	98.95 127	99.29 132	99.37 188	98.57 179	99.19 142	99.20 191	98.87 104	99.58 120	99.13 128	99.88 64	99.00 120	99.19 111	98.46 146	99.43 148	98.57 171

MAR-MVS		98.54 164	98.15 169	98.98 164	99.37 188	98.09 199	98.56 194	99.65 146	96.11 209	99.27 167	97.16 196	99.50 124	98.03 163	98.87 150	98.23 159	99.01 175	99.13 148
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
SCA		97.25 193	96.05 196	98.64 182	99.36 191	99.02 148	99.27 134	99.96 10	98.25 158	99.69 92	98.71 155	94.66 185	97.95 167	93.95 201	92.35 197	95.64 202	95.40 202

baseline2		97.87 176	97.18 181	98.67 181	99.34 192	99.17 131	98.48 198	98.82 198	97.08 194	98.83 191	98.75 151	89.47 203	97.03 185	98.67 170	98.27 156	99.52 133	98.83 165

dps		95.59 202	93.46 205	98.08 195	99.33 193	98.22 196	98.87 179	99.70 131	96.17 208	98.87 187	97.75 185	86.85 210	96.60 191	91.24 205	89.62 205	95.10 205	94.34 205

CMPMVS	76.62 19	98.64 160	98.60 149	98.68 180	99.33 193	97.07 208	98.11 209	98.50 202	97.69 183	99.26 168	98.35 174	99.66 111	97.62 172	99.43 69	99.02 90	99.24 167	99.01 158
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		99.00 136	99.03 117	98.97 166	99.32 195	99.32 104	99.39 111	99.91 36	98.41 149	98.76 193	99.24 117	99.17 146	97.13 179	99.30 84	98.80 120	99.29 162	99.01 158

TSAR-MVS + COLMAP		98.74 155	98.58 151	98.93 169	99.29 196	98.23 193	99.04 158	99.24 190	98.79 114	98.80 192	99.37 107	99.71 103	98.06 160	98.02 186	97.46 182	99.16 172	98.48 174

tpm cat1		95.52 203	93.49 204	97.88 200	99.28 197	97.87 206	98.65 191	99.77 111	97.27 190	99.46 144	98.04 180	90.99 198	95.46 199	88.57 209	88.14 208	94.64 210	93.54 208

PMMVS		98.71 158	98.55 152	98.90 171	99.28 197	98.45 185	98.53 197	99.45 174	97.67 184	99.15 176	98.76 150	99.54 123	97.79 168	98.77 166	98.23 159	99.16 172	98.46 175

CNLPA		98.82 150	98.52 155	99.18 149	99.21 199	98.50 183	98.73 189	99.34 186	98.73 120	99.56 122	97.55 188	99.42 131	99.06 115	98.93 139	98.10 167	99.21 170	98.38 176

DeepPCF-MVS	98.38 11	99.16 120	99.20 89	99.12 155	99.20 200	98.71 171	98.85 181	99.06 195	99.17 65	98.96 182	99.61 71	99.86 72	99.29 90	99.17 116	98.72 127	99.36 156	99.15 147

tpm		96.56 198	94.68 202	98.74 178	99.12 201	97.90 204	98.79 187	99.93 24	96.79 204	99.69 92	99.19 123	81.48 214	97.56 173	95.46 199	93.97 195	97.37 194	97.99 185

MVS-HIRNet		98.45 167	98.25 164	98.69 179	99.12 201	97.81 207	98.55 196	99.85 60	98.58 136	99.67 100	99.61 71	99.86 72	97.46 175	97.95 188	96.37 190	97.49 193	97.56 193

FPMVS		98.48 166	98.83 138	98.07 197	99.09 203	97.98 202	99.07 155	98.04 208	98.99 87	99.22 171	98.85 145	99.43 130	93.79 205	99.66 41	99.11 79	99.24 167	97.76 190

TESTMET0.1,1		97.62 188	97.46 177	97.81 201	99.07 204	98.37 188	98.26 202	98.35 204	97.03 196	97.38 208	99.54 81	92.89 190	95.12 201	98.78 164	97.68 178	98.65 184	97.90 186

GBi-Net		98.96 139	99.05 114	98.85 173	99.02 205	99.53 53	99.31 124	99.78 105	98.13 163	98.48 200	99.43 98	97.58 173	96.92 187	99.68 36	99.50 36	99.61 113	99.53 85

test1		98.96 139	99.05 114	98.85 173	99.02 205	99.53 53	99.31 124	99.78 105	98.13 163	98.48 200	99.43 98	97.58 173	96.92 187	99.68 36	99.50 36	99.61 113	99.53 85

FMVSNet2		99.07 130	99.19 91	98.93 169	99.02 205	99.53 53	99.31 124	99.84 69	98.86 106	98.88 186	99.64 67	98.44 162	96.92 187	99.35 76	99.00 96	99.61 113	99.53 85

test-mter		97.65 187	97.57 176	97.75 203	98.90 208	98.56 180	98.15 207	98.45 203	96.92 200	96.84 211	99.52 90	92.53 197	95.24 200	99.04 127	98.12 165	98.90 179	98.29 181

FMVSNet5		97.69 184	96.98 183	98.53 185	98.53 209	99.36 93	98.90 177	99.54 161	96.38 207	98.44 203	95.38 202	90.08 200	97.05 184	99.46 61	99.06 83	98.73 182	99.12 149

MVE	91.08 18	97.68 186	97.65 173	97.71 205	98.46 210	91.62 214	97.92 211	98.86 197	98.73 120	97.99 207	98.64 158	99.96 13	99.17 98	99.59 49	97.75 176	93.87 212	97.27 194
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
FMVSNet3		98.63 162	98.75 144	98.49 186	98.10 211	99.44 73	99.02 163	99.78 105	98.13 163	98.48 200	99.43 98	97.58 173	96.16 195	98.85 155	98.39 151	99.40 152	99.41 111

ET-MVSNet_ETH3D		97.44 190	96.29 193	98.78 176	97.93 212	98.95 154	98.91 174	99.09 194	98.00 172	99.24 169	98.83 146	84.62 212	98.02 164	97.43 193	97.38 183	99.48 139	98.84 163

tmp_tt				88.14 206	96.68 213	91.91 213	93.70 213	61.38 210	99.61 19	90.51 213	99.40 104	99.71 103	90.32 209	99.22 102	99.44 46	96.25 200

testmvs		22.33 206	29.66 208	13.79 208	8.97 214	10.35 215	15.53 217	8.09 212	32.51 212	19.87 215	45.18 211	30.56 218	17.05 211	29.96 210	24.74 209	13.21 213	34.30 211

test123		21.52 207	28.47 209	13.42 209	7.29 215	10.12 216	15.70 216	8.31 211	31.54 213	19.34 216	36.33 212	37.40 217	17.14 210	27.45 211	23.17 210	12.73 214	33.30 212

GG-mvs-BLEND		70.44 205	96.91 184	39.57 207	3.32 216	96.51 209	91.01 214	4.05 213	97.03 196	33.20 214	94.67 203	97.75 171	7.59 212	98.28 180	96.85 188	98.24 188	97.26 195

sosnet-low-res		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

sosnet		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

test_part1																	99.23 137

MTAPA										99.62 108		99.95 23

MTMP										99.53 125		99.92 47

Patchmatch-RL test							65.75 215

NP-MVS									97.37 188

Patchmtry						98.19 198	98.91 174	99.97 1		99.43 148

DeepMVS_CX						96.39 210	97.15 212	88.89 209	97.94 176	99.51 134	95.71 201	97.88 170	98.19 152	98.92 141		97.73 192	97.75 191

High-resolution multi-view benchmark