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.

Anonymous20231211		99.90 1	99.87 1	99.92 4	99.98 1	99.91 1	99.92 2	99.97 1	99.86 2	99.98 2	99.82 64	100.00 1	99.70 40	99.86 18	99.79 12	99.96 3	99.87 12

pmmvs6		99.88 8	99.87 1	99.89 14	99.97 2	99.76 20	99.89 9	99.96 15	99.82 3	99.90 15	99.92 26	99.95 23	99.68 41	99.93 4	99.88 2	99.95 10	99.86 13

UA-Net		99.64 41	99.62 26	99.66 85	99.97 2	99.82 7	99.14 176	99.96 15	98.95 111	99.52 148	99.38 125	99.86 70	99.55 73	99.72 38	99.66 27	99.80 82	99.94 1

v7n		99.89 3	99.86 5	99.93 1	99.97 2	99.83 3	99.93 1	99.96 15	99.77 6	99.89 19	99.99 1	99.86 70	99.84 5	99.89 9	99.81 10	99.97 1	99.88 9

v52		99.89 3	99.85 7	99.92 4	99.97 2	99.80 13	99.92 2	99.97 1	99.78 4	99.90 15	99.96 5	99.85 76	99.82 7	99.88 13	99.82 6	99.96 3	99.89 5

V4		99.89 3	99.85 7	99.92 4	99.97 2	99.80 13	99.92 2	99.97 1	99.78 4	99.90 15	99.96 5	99.84 78	99.82 7	99.88 13	99.82 6	99.96 3	99.89 5

SixPastTwentyTwo		99.89 3	99.85 7	99.93 1	99.97 2	99.88 2	99.92 2	99.97 1	99.66 15	99.94 4	99.94 15	99.74 96	99.81 9	99.97 2	99.89 1	99.96 3	99.89 5

LTVRE_ROB	99.39 1	99.90 1	99.87 1	99.93 1	99.97 2	99.82 7	99.91 6	99.92 44	99.75 8	99.93 5	99.89 42	100.00 1	99.87 2	99.93 4	99.82 6	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 10	99.80 10	99.89 14	99.96 9	99.83 3	99.84 19	99.95 29	99.37 59	99.77 77	99.95 10	99.96 14	99.85 3	99.93 4	99.83 3	99.95 10	99.72 47

v748		99.89 3	99.87 1	99.92 4	99.96 9	99.80 13	99.91 6	99.95 29	99.77 6	99.92 9	99.96 5	99.93 39	99.81 9	99.92 7	99.82 6	99.96 3	99.90 2

NR-MVSNet		99.52 70	99.29 84	99.80 40	99.96 9	99.38 105	99.55 100	99.81 119	98.86 121	99.87 30	99.51 111	98.81 168	99.72 35	99.86 18	99.04 99	99.89 34	99.54 92

Anonymous20240521		99.82 11	99.75 13	99.90 12	99.95 12	99.81 10	99.87 13	99.95 29	99.40 55	99.88 24	99.75 71	99.78 94	99.79 15	99.89 9	99.78 13	99.95 10	99.86 13

gm-plane-assit		96.82 215	94.84 221	99.13 179	99.95 12	99.78 18	99.69 72	99.92 44	99.19 80	99.84 46	99.92 26	72.93 238	96.44 217	98.21 207	97.01 211	98.92 202	96.87 220

anonymousdsp		99.87 9	99.86 5	99.88 17	99.95 12	99.75 24	99.90 8	99.96 15	99.69 11	99.83 55	99.96 5	99.99 4	99.74 27	99.95 3	99.83 3	99.91 26	99.88 9

PS-CasMVS		99.73 22	99.59 34	99.90 12	99.95 12	99.80 13	99.85 18	99.97 1	98.95 111	99.86 35	99.73 74	99.36 147	99.81 9	99.83 22	99.67 26	99.95 10	99.83 17

PEN-MVS		99.77 15	99.65 20	99.91 9	99.95 12	99.80 13	99.86 15	99.97 1	99.08 96	99.89 19	99.69 82	99.68 104	99.84 5	99.81 26	99.64 29	99.95 10	99.81 21

TransMVSNet (Re)		99.72 26	99.59 34	99.88 17	99.95 12	99.76 20	99.88 11	99.94 33	99.58 31	99.92 9	99.90 39	98.55 172	99.65 56	99.89 9	99.76 16	99.95 10	99.70 51

DTE-MVSNet		99.75 19	99.61 28	99.92 4	99.95 12	99.81 10	99.86 15	99.96 15	99.18 82	99.92 9	99.66 84	99.45 138	99.85 3	99.80 27	99.56 35	99.96 3	99.79 25

CP-MVSNet		99.68 35	99.51 46	99.89 14	99.95 12	99.76 20	99.83 22	99.96 15	98.83 127	99.84 46	99.65 87	99.09 160	99.80 13	99.78 30	99.62 33	99.95 10	99.82 18

WR-MVS_H		99.73 22	99.61 28	99.88 17	99.95 12	99.82 7	99.83 22	99.96 15	99.01 104	99.84 46	99.71 80	99.41 144	99.74 27	99.77 32	99.70 24	99.95 10	99.82 18

WR-MVS		99.79 13	99.68 18	99.91 9	99.95 12	99.83 3	99.87 13	99.96 15	99.39 58	99.93 5	99.87 50	99.29 154	99.77 18	99.83 22	99.72 22	99.97 1	99.82 18

HyFIR lowres test		99.50 73	99.26 88	99.80 40	99.95 12	99.62 44	99.76 41	99.97 1	99.67 13	99.56 139	99.94 15	98.40 175	99.78 16	98.84 182	98.59 163	99.76 90	99.72 47

FC-MVSNet-train		99.70 31	99.67 19	99.74 70	99.94 23	99.71 29	99.82 26	99.91 49	99.14 91	99.53 142	99.70 81	99.88 64	99.33 109	99.88 13	99.61 34	99.94 18	99.77 30

pm-mvs1		99.77 15	99.69 17	99.86 20	99.94 23	99.68 37	99.84 19	99.93 37	99.59 29	99.87 30	99.92 26	99.21 157	99.65 56	99.88 13	99.77 15	99.93 20	99.78 28

EPP-MVSNet		99.34 105	99.10 121	99.62 98	99.94 23	99.74 26	99.66 75	99.80 125	99.07 99	98.93 203	99.61 92	96.13 191	99.49 90	99.67 43	99.63 31	99.92 24	99.86 13

tfpnnormal		99.74 20	99.63 23	99.86 20	99.93 26	99.75 24	99.80 31	99.89 57	99.31 65	99.88 24	99.43 117	99.66 107	99.77 18	99.80 27	99.71 23	99.92 24	99.76 34

CHOSEN 1792x2688		99.65 39	99.55 40	99.77 54	99.93 26	99.60 49	99.79 33	99.92 44	99.73 9	99.74 93	99.93 19	99.98 5	99.80 13	98.83 183	99.01 103	99.45 164	99.76 34

conf0.05thres1000		98.36 188	97.28 197	99.63 93	99.92 28	99.74 26	99.66 75	99.88 61	98.68 140	98.92 204	97.30 215	86.02 230	99.49 90	99.77 32	99.73 20	99.93 20	99.69 52

Vis-MVSNet		99.76 17	99.78 11	99.75 64	99.92 28	99.77 19	99.83 22	99.85 79	99.43 50	99.85 42	99.84 60	100.00 1	99.13 138	99.83 22	99.66 27	99.90 29	99.90 2
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IS_MVSNet		99.15 135	99.12 118	99.19 174	99.92 28	99.73 28	99.55 100	99.86 67	98.45 166	96.91 238	98.74 171	98.33 177	99.02 147	99.54 54	99.47 50	99.88 38	99.61 69

Vis-MVSNet (Re-imp)		99.40 93	99.28 86	99.55 111	99.92 28	99.68 37	99.31 150	99.87 63	98.69 139	99.16 191	99.08 152	98.64 171	99.20 125	99.65 46	99.46 52	99.83 71	99.72 47

test20.03		99.68 35	99.60 32	99.76 58	99.91 32	99.70 34	99.68 73	99.87 63	99.05 101	99.88 24	99.92 26	99.88 64	99.50 86	99.77 32	99.42 56	99.75 93	99.49 108

MDA-MVSNet-bldmvs		99.11 138	99.11 120	99.12 181	99.91 32	99.38 105	99.77 38	98.72 225	99.31 65	99.85 42	99.43 117	98.26 179	99.48 94	99.85 20	98.47 168	96.99 220	99.08 173

PVSNet_Blended_VisFu		99.66 38	99.64 21	99.67 84	99.91 32	99.71 29	99.61 86	99.79 128	99.41 53	99.91 13	99.85 57	99.61 111	99.00 148	99.67 43	99.42 56	99.81 79	99.81 21

new-patchmatchnet		98.49 183	97.60 191	99.53 113	99.90 35	99.55 63	99.77 38	99.48 200	99.67 13	99.86 35	99.98 3	99.98 5	99.50 86	96.90 222	91.52 223	98.67 208	95.62 224

testgi		99.43 87	99.47 53	99.38 141	99.90 35	99.67 40	99.30 155	99.73 157	98.64 147	99.53 142	99.52 109	99.90 56	98.08 183	99.65 46	99.40 59	99.75 93	99.55 91

1111		96.83 214	95.02 220	98.95 193	99.90 35	99.57 56	99.62 84	99.97 1	98.58 154	98.06 232	99.87 50	69.04 241	96.43 218	99.36 79	99.14 82	99.73 101	99.54 92

.test1245		79.44 232	75.07 234	84.53 234	99.90 35	99.57 56	99.62 84	99.97 1	98.58 154	98.06 232	99.87 50	69.04 241	96.43 218	99.36 79	24.74 235	13.21 239	34.30 236

DeepC-MVS	99.05 5	99.74 20	99.64 21	99.84 26	99.90 35	99.39 100	99.79 33	99.81 119	99.69 11	99.90 15	99.87 50	99.98 5	99.81 9	99.62 50	99.32 63	99.83 71	99.65 62
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v1192		99.60 50	99.41 65	99.82 32	99.89 40	99.43 93	99.81 28	99.84 91	99.63 20	99.85 42	99.95 10	99.35 150	99.72 35	99.01 149	98.90 121	99.82 75	99.58 81

v1240		99.58 56	99.38 76	99.82 32	99.89 40	99.49 83	99.82 26	99.83 99	99.63 20	99.86 35	99.96 5	98.92 166	99.75 22	99.15 129	98.96 113	99.76 90	99.56 86

MIMVSNet1		99.79 13	99.75 13	99.84 26	99.89 40	99.83 3	99.84 19	99.89 57	99.31 65	99.93 5	99.92 26	99.97 10	99.68 41	99.89 9	99.64 29	99.82 75	99.66 58

CSCG		99.61 44	99.52 45	99.71 74	99.89 40	99.62 44	99.52 107	99.76 148	99.61 24	99.69 112	99.73 74	99.96 14	99.57 71	99.27 101	98.62 160	99.81 79	99.85 16

tfpn		96.77 217	94.47 223	99.45 129	99.88 44	99.62 44	99.46 125	99.83 99	97.61 203	98.27 230	94.22 226	71.45 240	99.34 108	99.32 87	99.46 52	99.90 29	99.58 81

zzz-MVS		99.51 71	99.36 77	99.68 82	99.88 44	99.38 105	99.53 104	99.84 91	99.11 94	99.59 133	98.93 163	99.95 23	99.58 70	99.44 68	99.21 72	99.65 117	99.52 102

v1921920		99.59 52	99.40 67	99.82 32	99.88 44	99.45 87	99.81 28	99.83 99	99.65 16	99.86 35	99.95 10	99.29 154	99.75 22	98.98 155	98.86 130	99.78 84	99.59 72

v1144		99.61 44	99.43 61	99.82 32	99.88 44	99.41 97	99.76 41	99.86 67	99.64 18	99.84 46	99.95 10	99.49 134	99.74 27	99.00 151	98.93 118	99.84 61	99.58 81

SteuartSystems-ACMMP		99.47 80	99.22 95	99.76 58	99.88 44	99.36 114	99.65 77	99.84 91	98.47 161	99.80 67	98.68 175	99.96 14	99.68 41	99.37 76	99.06 93	99.72 105	99.66 58
Skip Steuart: Steuart Systems R&D Blog.
UGNet		99.40 93	99.61 28	99.16 177	99.88 44	99.64 43	99.61 86	99.77 140	99.31 65	99.63 125	99.33 128	99.93 39	96.46 216	99.63 48	99.53 44	99.63 128	99.89 5
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 33	99.59 34	99.81 36	99.88 44	99.41 97	99.75 47	99.86 67	99.43 50	99.80 67	99.54 102	99.97 10	99.73 31	99.82 25	99.52 45	99.85 58	99.43 123

pmmvs-eth3d		99.61 44	99.48 49	99.75 64	99.87 51	99.30 131	99.75 47	99.89 57	99.23 72	99.85 42	99.88 48	99.97 10	99.49 90	99.46 62	99.01 103	99.68 111	99.52 102

v144192		99.58 56	99.39 71	99.80 40	99.87 51	99.44 89	99.77 38	99.84 91	99.64 18	99.86 35	99.93 19	99.35 150	99.72 35	98.92 163	98.82 136	99.74 97	99.66 58

v148		99.58 56	99.43 61	99.76 58	99.87 51	99.40 99	99.76 41	99.85 79	99.48 45	99.83 55	99.82 64	99.83 81	99.51 82	99.20 116	98.82 136	99.75 93	99.45 116

v1141		99.58 56	99.39 71	99.80 40	99.87 51	99.39 100	99.74 55	99.85 79	99.58 31	99.84 46	99.92 26	99.49 134	99.68 41	98.98 155	98.83 133	99.84 61	99.52 102

divwei89l23v2f112		99.58 56	99.39 71	99.80 40	99.87 51	99.39 100	99.74 55	99.85 79	99.57 34	99.84 46	99.92 26	99.48 136	99.67 45	98.98 155	98.83 133	99.84 61	99.52 102

v13		99.73 22	99.63 23	99.85 23	99.87 51	99.71 29	99.80 31	99.96 15	99.62 23	99.83 55	99.93 19	99.66 107	99.75 22	99.41 71	99.26 68	99.89 34	99.80 24

v11		99.72 26	99.62 26	99.85 23	99.87 51	99.71 29	99.81 28	99.96 15	99.63 20	99.83 55	99.97 4	99.58 118	99.75 22	99.33 85	99.33 61	99.87 48	99.79 25

v2v482		99.56 67	99.35 78	99.81 36	99.87 51	99.35 118	99.75 47	99.85 79	99.56 36	99.87 30	99.95 10	99.44 140	99.66 53	98.91 166	98.76 145	99.86 54	99.45 116

v1		99.58 56	99.39 71	99.80 40	99.87 51	99.39 100	99.74 55	99.85 79	99.58 31	99.84 46	99.92 26	99.51 129	99.67 45	98.98 155	98.82 136	99.84 61	99.52 102

ACMMPR		99.51 71	99.32 81	99.72 73	99.87 51	99.33 125	99.61 86	99.85 79	99.19 80	99.73 99	98.73 172	99.95 23	99.61 64	99.35 81	99.14 82	99.66 115	99.58 81

TranMVSNet+NR-MVSNet		99.59 52	99.42 64	99.80 40	99.87 51	99.55 63	99.64 78	99.86 67	99.05 101	99.88 24	99.72 77	99.33 152	99.64 59	99.47 60	99.14 82	99.91 26	99.67 56

LGP-MVS_train		99.46 84	99.18 106	99.78 50	99.87 51	99.25 142	99.71 70	99.87 63	98.02 190	99.79 70	98.90 164	99.96 14	99.66 53	99.49 56	99.17 76	99.79 83	99.49 108

IterMVS-LS		99.16 133	98.82 153	99.57 107	99.87 51	99.71 29	99.58 95	99.92 44	99.24 71	99.71 108	99.73 74	95.79 192	98.91 158	98.82 184	98.66 156	99.43 169	99.77 30
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS		99.08 141	98.90 144	99.29 155	99.87 51	99.53 67	99.52 107	99.77 140	98.94 113	99.75 87	99.91 35	97.52 187	98.72 166	98.86 176	98.14 187	98.09 213	99.43 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	99.11 4	99.72 26	99.63 23	99.84 26	99.87 51	99.59 52	99.83 22	99.88 61	99.46 47	99.87 30	99.66 84	99.95 23	99.76 20	99.73 37	99.47 50	99.84 61	99.52 102
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Gipuma		99.55 69	99.23 93	99.91 9	99.87 51	99.52 73	99.86 15	99.93 37	99.87 1	99.96 3	96.72 218	99.55 122	99.97 1	99.77 32	99.46 52	99.87 48	99.74 40
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
view800		97.89 195	96.56 204	99.45 129	99.86 67	99.57 56	99.42 129	99.80 125	97.50 206	98.40 228	93.78 227	86.63 229	99.31 114	99.24 104	99.68 25	99.89 34	99.54 92

Fast-Effi-MVS+		99.39 95	99.18 106	99.63 93	99.86 67	99.28 136	99.45 126	99.91 49	98.47 161	99.61 127	99.50 113	99.57 120	99.17 126	99.24 104	98.66 156	99.78 84	99.59 72

XVS					99.86 67	99.30 131	99.72 64			99.69 112		99.93 39				99.60 138

X-MVStestdata					99.86 67	99.30 131	99.72 64			99.69 112		99.93 39				99.60 138

v15		99.67 37	99.54 42	99.83 31	99.86 67	99.67 40	99.76 41	99.95 29	99.59 29	99.83 55	99.93 19	99.55 122	99.71 39	99.23 107	99.05 96	99.87 48	99.75 37

v12		99.72 26	99.61 28	99.85 23	99.86 67	99.70 34	99.79 33	99.96 15	99.61 24	99.83 55	99.93 19	99.61 111	99.74 27	99.38 74	99.22 70	99.89 34	99.79 25

V14		99.69 33	99.56 39	99.84 26	99.86 67	99.68 37	99.78 36	99.96 15	99.60 28	99.83 55	99.93 19	99.58 118	99.72 35	99.28 98	99.11 88	99.88 38	99.77 30

V9		99.71 30	99.59 34	99.84 26	99.86 67	99.69 36	99.78 36	99.96 15	99.61 24	99.84 46	99.93 19	99.61 111	99.73 31	99.34 84	99.17 76	99.88 38	99.78 28

PGM-MVS		99.32 109	98.99 135	99.71 74	99.86 67	99.31 130	99.59 91	99.86 67	97.51 205	99.75 87	98.23 192	99.94 33	99.53 78	99.29 94	99.08 91	99.65 117	99.54 92

UniMVSNet_NR-MVSNet		99.41 90	99.12 118	99.76 58	99.86 67	99.48 84	99.50 114	99.81 119	98.84 125	99.89 19	99.45 116	98.32 178	99.59 67	99.22 110	98.89 122	99.90 29	99.63 66

UniMVSNet (Re)		99.50 73	99.29 84	99.75 64	99.86 67	99.47 85	99.51 110	99.82 107	98.90 117	99.89 19	99.64 88	99.00 162	99.55 73	99.32 87	99.08 91	99.90 29	99.59 72

ACMMP		99.36 100	99.06 126	99.71 74	99.86 67	99.36 114	99.63 80	99.85 79	98.33 174	99.72 103	97.73 205	99.94 33	99.53 78	99.37 76	99.13 85	99.65 117	99.56 86
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 80	99.23 93	99.74 70	99.86 67	99.19 154	99.68 73	99.86 67	99.16 87	99.71 108	98.52 182	99.95 23	99.62 63	99.35 81	99.02 101	99.74 97	99.42 127
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpn_n400		99.08 141	98.56 164	99.70 77	99.85 80	99.56 61	99.63 80	99.86 67	99.21 75	99.37 174	98.95 160	94.24 198	99.55 73	99.20 116	99.29 65	99.93 20	99.44 119

tfpnconf		99.08 141	98.56 164	99.70 77	99.85 80	99.56 61	99.63 80	99.86 67	99.21 75	99.37 174	98.95 160	94.24 198	99.55 73	99.20 116	99.29 65	99.93 20	99.44 119

MVS_0304		99.36 100	99.35 78	99.37 144	99.85 80	99.36 114	99.39 134	99.56 187	99.36 61	99.75 87	99.23 138	99.90 56	97.97 190	99.00 151	98.83 133	99.69 110	99.77 30

ACMMP_Plus		99.47 80	99.33 80	99.63 93	99.85 80	99.28 136	99.56 98	99.83 99	98.75 133	99.48 157	99.03 157	99.95 23	99.47 97	99.48 57	99.19 73	99.57 148	99.59 72

X-MVS		99.30 114	98.99 135	99.66 85	99.85 80	99.30 131	99.49 119	99.82 107	98.32 175	99.69 112	97.31 214	99.93 39	99.50 86	99.37 76	99.16 78	99.60 138	99.53 97

APDe-MVS		99.60 50	99.48 49	99.73 72	99.85 80	99.51 80	99.75 47	99.85 79	99.17 83	99.81 65	99.56 100	99.94 33	99.44 99	99.42 70	99.22 70	99.67 113	99.54 92

DU-MVS		99.48 77	99.26 88	99.75 64	99.85 80	99.38 105	99.50 114	99.81 119	98.86 121	99.89 19	99.51 111	98.98 163	99.59 67	99.46 62	98.97 111	99.87 48	99.63 66

Baseline_NR-MVSNet		99.62 42	99.48 49	99.78 50	99.85 80	99.76 20	99.59 91	99.82 107	98.84 125	99.88 24	99.91 35	99.04 161	99.61 64	99.46 62	99.78 13	99.94 18	99.60 71

tfpnview11		99.04 149	98.49 173	99.68 82	99.84 88	99.58 54	99.56 98	99.86 67	98.86 121	99.37 174	98.95 160	94.24 198	99.54 77	98.87 172	99.54 43	99.91 26	99.39 136

tfpn1000		98.73 173	98.07 188	99.50 121	99.84 88	99.61 47	99.48 121	99.84 91	98.71 138	98.74 211	98.71 174	91.70 212	99.17 126	98.81 185	99.55 41	99.90 29	99.43 123

view600		97.88 196	96.55 206	99.44 131	99.84 88	99.52 73	99.38 138	99.76 148	97.36 209	98.50 222	93.29 228	87.31 225	99.26 120	99.13 133	99.76 16	99.88 38	99.48 111

pmmvs5		99.58 56	99.47 53	99.70 77	99.84 88	99.50 81	99.58 95	99.80 125	98.98 109	99.73 99	99.92 26	99.81 84	99.49 90	99.28 98	99.05 96	99.77 88	99.73 43

V42		99.57 63	99.41 65	99.75 64	99.84 88	99.37 111	99.73 59	99.83 99	99.41 53	99.75 87	99.89 42	99.42 142	99.60 66	99.15 129	98.96 113	99.76 90	99.65 62

thres600view7		97.86 198	96.53 210	99.41 136	99.84 88	99.52 73	99.36 142	99.76 148	97.32 210	98.38 229	93.24 229	87.25 226	99.23 123	99.11 135	99.75 18	99.88 38	99.48 111

MP-MVS		99.35 103	99.09 123	99.65 87	99.84 88	99.22 150	99.59 91	99.78 134	98.13 183	99.67 119	98.44 186	99.93 39	99.43 101	99.31 89	99.09 90	99.60 138	99.49 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmv		99.39 95	99.19 103	99.62 98	99.84 88	99.38 105	99.37 140	99.86 67	98.47 161	99.79 70	99.82 64	99.39 146	99.63 61	99.30 90	98.70 152	99.21 191	99.28 152

test1235678		99.39 95	99.20 100	99.62 98	99.84 88	99.38 105	99.38 138	99.86 67	98.47 161	99.79 70	99.82 64	99.41 144	99.63 61	99.30 90	98.71 150	99.21 191	99.28 152

mPP-MVS					99.84 88							99.92 48

COLMAP_ROB	99.18 2	99.70 31	99.60 32	99.81 36	99.84 88	99.37 111	99.76 41	99.84 91	99.54 40	99.82 62	99.64 88	99.95 23	99.75 22	99.79 29	99.56 35	99.83 71	99.37 141
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SMA-MVS		99.47 80	99.45 57	99.50 121	99.83 99	99.34 120	99.14 176	99.60 180	99.09 95	99.36 180	99.60 95	99.96 14	99.46 98	99.41 71	99.16 78	99.59 144	99.61 69

gg-mvs-nofinetune		98.40 187	98.26 179	98.57 209	99.83 99	98.86 185	98.77 213	99.97 1	99.57 34	99.99 1	99.99 1	93.81 201	93.50 232	98.91 166	98.20 183	99.33 181	98.52 196

v7		99.61 44	99.46 56	99.79 47	99.83 99	99.37 111	99.75 47	99.84 91	99.56 36	99.76 80	99.94 15	99.60 115	99.73 31	99.11 135	99.01 103	99.85 58	99.63 66

v10		99.65 39	99.51 46	99.81 36	99.83 99	99.61 47	99.75 47	99.94 33	99.56 36	99.76 80	99.94 15	99.60 115	99.73 31	99.11 135	99.01 103	99.85 58	99.74 40

TDRefinement		99.81 12	99.76 12	99.86 20	99.83 99	99.53 67	99.89 9	99.91 49	99.73 9	99.88 24	99.83 62	99.96 14	99.76 20	99.91 8	99.81 10	99.86 54	99.59 72

ACMP	98.32 13	99.44 86	99.18 106	99.75 64	99.83 99	99.18 155	99.64 78	99.83 99	98.81 129	99.79 70	98.42 188	99.96 14	99.64 59	99.46 62	98.98 110	99.74 97	99.44 119
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HFP-MVS		99.46 84	99.30 83	99.65 87	99.82 105	99.25 142	99.50 114	99.82 107	99.23 72	99.58 137	98.86 165	99.94 33	99.56 72	99.14 132	99.12 87	99.63 128	99.56 86

EU-MVSNet		99.76 17	99.74 15	99.78 50	99.82 105	99.81 10	99.88 11	99.87 63	99.31 65	99.75 87	99.91 35	99.76 95	99.78 16	99.84 21	99.74 19	99.56 151	99.81 21

EG-PatchMatch MVS		99.59 52	99.49 48	99.70 77	99.82 105	99.26 139	99.39 134	99.83 99	98.99 106	99.93 5	99.54 102	99.92 48	99.51 82	99.78 30	99.50 46	99.73 101	99.41 128

thres400		97.82 201	96.47 211	99.40 137	99.81 108	99.44 89	99.29 157	99.69 166	97.15 213	98.57 219	92.82 234	87.96 223	99.16 130	98.96 159	99.55 41	99.86 54	99.41 128

CANet		99.36 100	99.39 71	99.34 151	99.80 109	99.35 118	99.41 132	99.47 201	99.20 77	99.74 93	99.54 102	99.68 104	98.05 187	99.23 107	98.97 111	99.57 148	99.73 43

v1neww		99.57 63	99.40 67	99.77 54	99.80 109	99.34 120	99.72 64	99.82 107	99.49 42	99.76 80	99.89 42	99.50 131	99.67 45	99.10 143	98.89 122	99.84 61	99.59 72

v7new		99.57 63	99.40 67	99.77 54	99.80 109	99.34 120	99.72 64	99.82 107	99.49 42	99.76 80	99.89 42	99.50 131	99.67 45	99.10 143	98.89 122	99.84 61	99.59 72

v17		99.62 42	99.48 49	99.79 47	99.80 109	99.60 49	99.73 59	99.94 33	99.46 47	99.73 99	99.88 48	99.52 127	99.67 45	99.16 128	98.96 113	99.84 61	99.75 37

v8		99.61 44	99.45 57	99.79 47	99.80 109	99.59 52	99.73 59	99.93 37	99.48 45	99.77 77	99.90 39	99.48 136	99.67 45	99.11 135	98.89 122	99.84 61	99.73 43

v6		99.57 63	99.40 67	99.77 54	99.80 109	99.34 120	99.72 64	99.82 107	99.49 42	99.76 80	99.89 42	99.52 127	99.67 45	99.10 143	98.89 122	99.84 61	99.59 72

PMMVS2		99.23 122	99.22 95	99.24 163	99.80 109	99.14 159	99.50 114	99.82 107	99.12 93	98.41 227	99.91 35	99.98 5	98.51 170	99.48 57	98.76 145	99.38 175	98.14 207

CP-MVS		99.41 90	99.20 100	99.65 87	99.80 109	99.23 149	99.44 127	99.75 156	98.60 152	99.74 93	98.66 176	99.93 39	99.48 94	99.33 85	99.16 78	99.73 101	99.48 111

ESAPD		99.21 124	99.14 114	99.29 155	99.79 117	99.44 89	99.02 189	99.79 128	97.96 193	99.12 195	99.22 139	99.95 23	98.50 171	99.21 113	98.84 132	99.56 151	99.34 145

Effi-MVS+		99.20 127	98.93 140	99.50 121	99.79 117	99.26 139	98.82 210	99.96 15	98.37 173	99.60 131	99.12 147	98.36 176	99.05 145	98.93 161	98.82 136	99.78 84	99.68 53

Anonymous20231206		99.48 77	99.31 82	99.69 81	99.79 117	99.57 56	99.63 80	99.79 128	98.88 119	99.91 13	99.72 77	99.93 39	99.59 67	99.24 104	98.63 159	99.43 169	99.18 161

DI_MVS_plusplus_trai		98.74 170	98.08 187	99.51 119	99.79 117	99.29 135	99.61 86	99.60 180	99.20 77	99.46 161	99.09 151	92.93 205	98.97 155	98.27 206	98.35 176	99.65 117	99.45 116

v16		99.61 44	99.47 53	99.78 50	99.79 117	99.60 49	99.72 64	99.94 33	99.45 49	99.70 110	99.85 57	99.54 125	99.67 45	99.15 129	98.96 113	99.83 71	99.76 34

v18		99.59 52	99.44 60	99.76 58	99.78 122	99.57 56	99.70 71	99.93 37	99.43 50	99.69 112	99.85 57	99.51 129	99.65 56	99.08 146	98.87 127	99.82 75	99.74 40

test12356		99.12 137	99.03 130	99.23 164	99.78 122	98.95 178	99.10 181	99.72 159	98.26 178	99.81 65	99.87 50	99.20 158	98.06 185	99.47 60	98.80 142	98.91 203	98.67 192

APD-MVS		99.17 130	98.92 141	99.46 127	99.78 122	99.24 147	99.34 146	99.78 134	97.79 198	99.48 157	98.25 191	99.88 64	98.77 164	99.18 124	98.92 119	99.63 128	99.18 161
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
N_pmnet		98.64 178	98.23 182	99.11 184	99.78 122	99.25 142	99.75 47	99.39 211	99.65 16	99.70 110	99.78 70	99.89 59	98.81 163	97.60 215	94.28 218	97.24 219	97.15 219

QAPM		99.41 90	99.21 99	99.64 92	99.78 122	99.16 156	99.51 110	99.85 79	99.20 77	99.72 103	99.43 117	99.81 84	99.25 121	98.87 172	98.71 150	99.71 107	99.30 150

3Dnovator	99.16 3	99.42 88	99.22 95	99.65 87	99.78 122	99.13 162	99.50 114	99.85 79	99.40 55	99.80 67	98.59 178	99.79 91	99.30 116	99.20 116	99.06 93	99.71 107	99.35 144

CHOSEN 280x420		98.99 154	98.91 143	99.07 187	99.77 128	99.26 139	99.55 100	99.92 44	98.62 148	98.67 215	99.62 91	97.20 189	98.44 173	99.50 55	99.18 74	98.08 214	98.99 185

pmmvs4		99.34 105	99.15 113	99.57 107	99.77 128	98.90 180	99.51 110	99.77 140	99.07 99	99.73 99	99.72 77	99.84 78	99.07 142	98.85 178	98.39 174	99.55 155	99.27 154

no-one		99.73 22	99.70 16	99.76 58	99.77 128	99.58 54	99.76 41	99.90 56	99.08 96	99.86 35	99.90 39	99.98 5	99.66 53	99.98 1	99.73 20	99.59 144	99.67 56

3Dnovator+	98.92 7	99.31 112	99.03 130	99.63 93	99.77 128	98.90 180	99.52 107	99.81 119	99.37 59	99.72 103	98.03 200	99.73 99	99.32 112	98.99 154	98.81 141	99.67 113	99.36 142

LS3D		99.39 95	99.28 86	99.52 117	99.77 128	99.39 100	99.55 100	99.82 107	98.93 115	99.64 123	98.52 182	99.67 106	98.58 169	99.74 36	99.63 31	99.75 93	99.06 176

HSP-MVS		99.27 119	99.07 125	99.50 121	99.76 133	99.54 65	99.73 59	99.72 159	98.94 113	99.23 187	98.96 159	99.96 14	98.91 158	98.72 192	97.71 202	99.63 128	99.66 58

OPM-MVS		99.39 95	99.22 95	99.59 101	99.76 133	98.82 186	99.51 110	99.79 128	99.17 83	99.53 142	99.31 132	99.95 23	99.35 104	99.22 110	98.79 144	99.60 138	99.27 154

thres200		97.87 197	96.56 204	99.39 138	99.76 133	99.52 73	99.13 178	99.76 148	96.88 224	98.66 216	92.87 233	88.77 222	99.16 130	99.11 135	99.42 56	99.88 38	99.33 146

PM-MVS		99.49 76	99.43 61	99.57 107	99.76 133	99.34 120	99.53 104	99.77 140	98.93 115	99.75 87	99.46 115	99.83 81	99.11 140	99.72 38	99.29 65	99.49 160	99.46 115

our_test_3					99.75 137	99.11 166	99.74 55

HPM-MVS++		99.23 122	98.98 137	99.53 113	99.75 137	99.02 172	99.44 127	99.77 140	98.65 143	99.52 148	98.72 173	99.92 48	99.33 109	98.77 190	98.40 173	99.40 173	99.36 142

MCST-MVS		99.17 130	98.82 153	99.57 107	99.75 137	98.70 197	99.25 162	99.69 166	98.62 148	99.59 133	98.54 180	99.79 91	99.53 78	98.48 200	98.15 186	99.64 126	99.43 123

CDPH-MVS		99.05 147	98.63 160	99.54 112	99.75 137	98.78 189	99.59 91	99.68 170	97.79 198	99.37 174	98.20 195	99.86 70	99.14 136	98.58 196	98.01 195	99.68 111	99.16 167

MDTV_nov1_ep13_2view		98.73 173	98.31 178	99.22 169	99.75 137	99.24 147	99.75 47	99.93 37	99.31 65	99.84 46	99.86 56	99.81 84	99.31 114	97.40 219	94.77 217	96.73 222	97.81 212

OpenMVS	98.82 8	99.17 130	98.85 149	99.53 113	99.75 137	99.06 169	99.36 142	99.82 107	98.28 177	99.76 80	98.47 184	99.61 111	98.91 158	98.80 186	98.70 152	99.60 138	99.04 181

MSDG		99.32 109	99.09 123	99.58 103	99.75 137	98.74 193	99.36 142	99.54 190	99.14 91	99.72 103	99.24 136	99.89 59	99.51 82	99.30 90	98.76 145	99.62 134	98.54 195

CLD-MVS		99.30 114	99.01 134	99.63 93	99.75 137	98.89 183	99.35 145	99.60 180	98.53 158	99.86 35	99.57 99	99.94 33	99.52 81	98.96 159	98.10 190	99.70 109	99.08 173
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tfpn111		98.25 189	97.29 196	99.37 144	99.74 145	99.52 73	99.17 169	99.76 148	96.10 232	98.65 217	98.23 192	89.10 218	99.00 148	99.11 135	99.56 35	99.88 38	99.41 128

conf200view11		97.85 199	96.54 207	99.37 144	99.74 145	99.52 73	99.17 169	99.76 148	96.10 232	98.65 217	92.99 230	89.10 218	99.00 148	99.11 135	99.56 35	99.88 38	99.41 128

thres100view900		97.69 205	96.37 212	99.23 164	99.74 145	99.21 153	98.81 211	99.43 206	96.10 232	98.70 213	92.99 230	89.10 218	98.88 161	98.58 196	99.31 64	99.82 75	99.27 154

tfpn200view9		97.85 199	96.54 207	99.38 141	99.74 145	99.52 73	99.17 169	99.76 148	96.10 232	98.70 213	92.99 230	89.10 218	99.00 148	99.11 135	99.56 35	99.88 38	99.41 128

RPSCF		99.48 77	99.45 57	99.52 117	99.73 149	99.33 125	99.13 178	99.77 140	99.33 63	99.47 160	99.39 124	99.92 48	99.36 103	99.63 48	99.13 85	99.63 128	99.41 128

ambc			98.83 150		99.72 150	98.52 206	98.84 207		98.96 110	99.92 9	99.34 127	99.74 96	99.04 146	98.68 193	97.57 206	99.46 162	98.99 185

CPTT-MVS		99.21 124	98.89 145	99.58 103	99.72 150	99.12 165	99.30 155	99.76 148	98.62 148	99.66 121	97.51 208	99.89 59	99.48 94	99.01 149	98.64 158	99.58 147	99.40 135

USDC		99.29 118	98.98 137	99.65 87	99.72 150	98.87 184	99.47 123	99.66 176	99.35 62	99.87 30	99.58 98	99.87 69	99.51 82	98.85 178	97.93 197	99.65 117	98.38 199

conf0.01		96.70 220	94.44 225	99.34 151	99.71 153	99.46 86	99.17 169	99.73 157	96.10 232	98.53 220	91.96 235	75.75 236	99.00 148	98.85 178	99.56 35	99.87 48	99.38 137

thresconf0.02		98.10 191	96.83 201	99.58 103	99.71 153	99.28 136	99.40 133	99.72 159	98.65 143	99.39 171	98.23 192	86.73 228	99.43 101	97.73 214	98.17 185	99.86 54	99.05 178

testus		98.74 170	98.33 177	99.23 164	99.71 153	99.03 170	98.17 231	99.60 180	97.18 212	99.52 148	98.07 198	98.45 173	99.21 124	98.30 203	98.06 193	99.14 197	99.21 159

TSAR-MVS + GP.		99.33 107	99.17 110	99.51 119	99.71 153	99.00 173	98.84 207	99.71 162	98.23 179	99.74 93	99.53 108	99.90 56	99.35 104	99.38 74	98.85 131	99.72 105	99.31 148

conf0.002		96.39 223	93.87 227	99.33 153	99.70 157	99.45 87	99.17 169	99.71 162	96.10 232	98.51 221	91.88 236	72.65 239	99.00 148	98.80 186	98.82 136	99.87 48	99.38 137

PCF-MVS	97.86 15	98.95 157	98.53 167	99.44 131	99.70 157	98.80 188	98.96 194	99.69 166	98.65 143	99.59 133	99.33 128	99.94 33	99.12 139	98.01 212	97.11 208	99.59 144	97.83 211
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TSAR-MVS + MP.		99.56 67	99.54 42	99.58 103	99.69 159	99.14 159	99.73 59	99.45 203	99.50 41	99.35 181	99.60 95	99.93 39	99.50 86	99.56 52	99.37 60	99.77 88	99.64 65

train_agg		98.89 162	98.48 174	99.38 141	99.69 159	98.76 192	99.31 150	99.60 180	97.71 200	98.98 200	97.89 202	99.89 59	99.29 117	98.32 202	97.59 205	99.42 172	99.16 167

LP		97.43 211	96.28 213	98.77 203	99.69 159	98.92 179	99.49 119	99.70 164	98.53 158	99.82 62	99.12 147	95.67 194	97.30 201	94.65 226	91.76 221	96.65 224	95.34 226

PVSNet_BlendedMVS		99.20 127	99.17 110	99.23 164	99.69 159	99.33 125	99.04 184	99.13 220	98.41 170	99.79 70	99.33 128	99.36 147	98.10 181	99.29 94	98.87 127	99.65 117	99.56 86

PVSNet_Blended		99.20 127	99.17 110	99.23 164	99.69 159	99.33 125	99.04 184	99.13 220	98.41 170	99.79 70	99.33 128	99.36 147	98.10 181	99.29 94	98.87 127	99.65 117	99.56 86

TAMVS		99.05 147	99.02 133	99.08 186	99.69 159	99.22 150	99.33 147	99.32 216	99.16 87	98.97 201	99.87 50	97.36 188	97.76 192	99.21 113	99.00 108	99.44 166	99.33 146

PHI-MVS		99.33 107	99.19 103	99.49 125	99.69 159	99.25 142	99.27 159	99.59 185	98.44 167	99.78 76	99.15 143	99.92 48	98.95 157	99.39 73	99.04 99	99.64 126	99.18 161

canonicalmvs		99.00 152	98.68 159	99.37 144	99.68 166	99.42 96	98.94 198	99.89 57	99.00 105	98.99 199	98.43 187	95.69 193	98.96 156	99.18 124	99.18 74	99.74 97	99.88 9

FMVSNet1		99.50 73	99.57 38	99.42 133	99.67 167	99.65 42	99.60 90	99.91 49	99.40 55	99.39 171	99.83 62	99.27 156	98.14 180	99.68 40	99.50 46	99.81 79	99.68 53

SD-MVS		99.35 103	99.26 88	99.46 127	99.66 168	99.15 158	98.92 199	99.67 172	99.55 39	99.35 181	98.83 167	99.91 54	99.35 104	99.19 121	98.53 165	99.78 84	99.68 53

TSAR-MVS + ACMM		99.31 112	99.26 88	99.37 144	99.66 168	98.97 177	99.20 165	99.56 187	99.33 63	99.19 190	99.54 102	99.91 54	99.32 112	99.12 134	98.34 177	99.29 183	99.65 62

pmmvs3		98.85 165	98.60 161	99.13 179	99.66 168	98.72 195	99.37 140	99.06 222	98.44 167	99.76 80	99.74 72	99.55 122	99.15 134	99.04 147	96.00 216	97.80 215	98.72 191

tfpn_ndepth		98.67 177	98.03 189	99.42 133	99.65 171	99.50 81	99.29 157	99.78 134	98.17 182	99.04 197	98.36 189	93.29 203	98.88 161	98.46 201	99.26 68	99.88 38	99.14 170

PatchT		98.11 190	97.12 198	99.26 162	99.65 171	98.34 215	99.57 97	99.97 1	97.48 207	99.43 165	99.04 156	90.84 214	98.15 178	98.04 209	97.78 198	98.82 205	98.30 202

DeepC-MVS_fast	98.69 9	99.32 109	99.13 116	99.53 113	99.63 173	98.78 189	99.53 104	99.33 215	99.08 96	99.77 77	99.18 142	99.89 59	99.29 117	99.00 151	98.70 152	99.65 117	99.30 150
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test-LLR		97.74 203	97.46 193	98.08 221	99.62 174	98.37 213	98.26 226	99.41 207	97.03 217	97.38 236	99.54 102	92.89 206	95.12 227	98.78 188	97.68 203	98.65 209	97.90 209

test0.0.03 1		98.41 186	98.41 176	98.40 215	99.62 174	99.16 156	98.87 204	99.41 207	97.15 213	96.60 240	99.31 132	97.00 190	96.55 215	98.91 166	98.51 167	99.37 176	98.82 188

diffmvs		98.83 166	98.51 172	99.19 174	99.62 174	98.98 176	99.18 167	99.82 107	99.15 90	99.51 152	99.66 84	95.37 197	98.07 184	98.49 198	98.22 182	98.96 201	99.73 43

NCCC		98.88 163	98.42 175	99.42 133	99.62 174	98.81 187	99.10 181	99.54 190	98.76 131	99.53 142	95.97 221	99.80 89	99.16 130	98.49 198	98.06 193	99.55 155	99.05 178

IB-MVS	98.10 14	97.76 202	97.40 195	98.18 218	99.62 174	99.11 166	98.24 228	98.35 230	96.56 227	99.44 163	91.28 237	98.96 165	93.84 230	98.09 208	98.62 160	99.56 151	99.18 161
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		99.09 140	98.92 141	99.29 155	99.61 179	99.07 168	99.04 184	99.81 119	98.58 154	99.37 174	99.74 72	98.87 167	98.41 174	98.61 195	98.01 195	99.50 159	99.57 85

CNVR-MVS		99.08 141	98.83 150	99.37 144	99.61 179	98.74 193	99.15 174	99.54 190	98.59 153	99.37 174	98.15 196	99.88 64	99.08 141	98.91 166	98.46 169	99.48 161	99.06 176

HQP-MVS		98.70 176	98.19 183	99.28 160	99.61 179	98.52 206	98.71 216	99.35 212	97.97 192	99.53 142	97.38 212	99.85 76	99.14 136	97.53 216	96.85 213	99.36 177	99.26 157

TinyColmap		99.21 124	98.89 145	99.59 101	99.61 179	98.61 202	99.47 123	99.67 172	99.02 103	99.82 62	99.15 143	99.74 96	99.35 104	99.17 126	98.33 178	99.63 128	98.22 205

Effi-MVS+-dtu		99.01 151	99.05 127	98.98 190	99.60 183	99.13 162	99.03 188	99.61 178	98.52 160	99.01 198	98.53 181	99.83 81	96.95 209	99.48 57	98.59 163	99.66 115	99.25 158

EPNet		98.06 193	98.11 186	98.00 224	99.60 183	98.99 175	98.38 224	99.68 170	98.18 181	98.85 208	97.89 202	95.60 195	92.72 234	98.30 203	98.10 190	98.76 206	99.72 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 194	96.80 202	99.22 169	99.60 183	98.23 219	98.91 200	99.97 1	96.89 222	99.43 165	99.10 150	89.24 217	98.15 178	98.04 209	97.78 198	99.26 186	98.30 202

PMVS	94.32 17	99.27 119	99.55 40	98.94 194	99.60 183	99.43 93	99.39 134	99.54 190	98.99 106	99.69 112	99.60 95	99.81 84	95.68 224	99.88 13	99.83 3	99.73 101	99.31 148
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GA-MVS		98.59 181	98.15 184	99.09 185	99.59 187	99.13 162	98.84 207	99.52 196	98.61 151	99.35 181	99.67 83	93.03 204	97.73 194	98.90 170	98.26 179	99.51 158	99.48 111

ADS-MVSNet		97.29 213	96.17 215	98.59 208	99.59 187	98.70 197	99.32 148	99.86 67	98.47 161	99.56 139	99.08 152	98.16 180	97.34 200	92.92 227	91.17 224	95.91 226	94.72 228

CDS-MVSNet		99.15 135	99.10 121	99.21 171	99.59 187	99.22 150	99.48 121	99.47 201	98.89 118	99.41 169	99.84 60	98.11 181	97.76 192	99.26 103	99.01 103	99.57 148	99.38 137
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		99.03 150	99.18 106	98.87 199	99.58 190	99.03 170	99.18 167	99.41 207	98.65 143	99.74 93	99.55 101	99.71 101	96.13 222	99.19 121	98.92 119	99.17 194	99.18 161

EPNet_dtu		98.09 192	98.25 180	97.91 225	99.58 190	98.02 227	98.19 230	99.67 172	97.94 194	99.74 93	99.07 154	98.71 170	93.40 233	97.50 217	97.09 209	96.89 221	99.44 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_HR		99.30 114	99.14 114	99.48 126	99.58 190	99.25 142	99.27 159	99.61 178	98.74 134	99.66 121	99.02 158	99.84 78	99.33 109	99.20 116	98.76 145	99.44 166	99.18 161

RPMNet		97.70 204	96.54 207	99.06 188	99.57 193	98.23 219	98.95 197	99.97 1	96.89 222	99.49 156	99.13 145	89.63 216	97.09 205	96.68 223	97.02 210	99.26 186	98.19 206

MS-PatchMatch		98.94 158	98.71 158	99.21 171	99.52 194	98.22 222	98.97 193	99.53 195	98.76 131	99.50 155	98.59 178	99.56 121	98.68 167	98.63 194	98.45 171	99.05 199	98.73 189

CVMVSNet		99.06 146	98.88 148	99.28 160	99.52 194	99.53 67	99.42 129	99.69 166	98.74 134	98.27 230	99.89 42	95.48 196	99.44 99	99.46 62	99.33 61	99.32 182	99.75 37

tpmrst		96.18 225	94.47 223	98.18 218	99.52 194	97.89 230	98.96 194	99.79 128	98.07 188	99.16 191	99.30 135	92.69 210	96.69 213	90.76 232	88.85 231	94.96 231	93.69 232

DELS-MVS		99.42 88	99.53 44	99.29 155	99.52 194	99.43 93	99.42 129	99.28 217	99.16 87	99.72 103	99.82 64	99.97 10	98.17 177	99.56 52	99.16 78	99.65 117	99.59 72
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		98.93 159	98.53 167	99.39 138	99.52 194	98.65 200	99.11 180	99.59 185	98.08 187	99.44 163	97.46 211	99.45 138	99.24 122	98.92 163	98.44 172	99.44 166	98.73 189

Fast-Effi-MVS+-dtu		98.82 167	98.80 155	98.84 202	99.51 199	98.90 180	98.96 194	99.91 49	98.29 176	99.11 196	98.47 184	99.63 110	96.03 223	99.21 113	98.12 188	99.52 157	99.01 182

new_pmnet		98.91 161	98.89 145	98.94 194	99.51 199	98.27 218	99.15 174	98.66 226	99.17 83	99.48 157	99.79 69	99.80 89	98.49 172	99.23 107	98.20 183	98.34 211	97.74 215

MVS_111021_LR		99.25 121	99.13 116	99.39 138	99.50 201	99.14 159	99.23 163	99.50 198	98.67 141	99.61 127	99.12 147	99.81 84	99.16 130	99.28 98	98.67 155	99.35 179	99.21 159

abl_6				99.21 171	99.49 202	98.62 201	98.90 202	99.44 205	97.08 216	99.61 127	97.19 216	99.73 99	98.35 175			99.45 164	98.84 187

CostFormer		95.61 226	93.35 230	98.24 217	99.48 203	98.03 226	98.65 217	99.83 99	96.93 220	99.42 168	98.83 167	83.65 232	97.08 206	90.39 233	89.54 230	94.94 232	96.11 223

MDTV_nov1_ep13		97.41 212	96.26 214	98.76 204	99.47 204	98.43 211	99.26 161	99.82 107	98.06 189	99.23 187	99.22 139	92.86 208	98.05 187	95.33 225	93.66 220	96.73 222	96.26 221

MVSTER		97.55 210	96.75 203	98.48 212	99.46 205	99.54 65	98.24 228	99.77 140	97.56 204	99.41 169	99.31 132	84.86 231	94.66 229	98.86 176	97.75 200	99.34 180	99.38 137

E-PMN		96.72 219	95.78 216	97.81 227	99.45 206	95.46 237	98.14 234	98.33 232	97.99 191	98.73 212	98.09 197	98.97 164	97.54 197	97.45 218	91.09 225	94.70 234	91.40 234

PLC	97.83 16	98.88 163	98.52 169	99.30 154	99.45 206	98.60 203	98.65 217	99.49 199	98.66 142	99.59 133	96.33 219	99.59 117	99.17 126	98.87 172	98.53 165	99.46 162	99.05 178
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MSLP-MVS++		98.92 160	98.73 157	99.14 178	99.44 208	99.00 173	98.36 225	99.35 212	98.82 128	99.38 173	96.06 220	99.79 91	99.07 142	98.88 171	99.05 96	99.27 185	99.53 97

TAPA-MVS	98.54 10	99.30 114	99.24 92	99.36 150	99.44 208	98.77 191	99.00 191	99.41 207	99.23 72	99.60 131	99.50 113	99.86 70	99.15 134	99.29 94	98.95 117	99.56 151	99.08 173
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PatchmatchNet		96.81 216	95.41 217	98.43 214	99.43 210	98.30 216	99.23 163	99.93 37	98.19 180	99.64 123	98.81 169	93.50 202	97.43 199	92.89 229	90.78 226	94.94 232	95.41 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EMVS		96.47 222	95.38 218	97.74 230	99.42 211	95.37 238	98.07 236	98.27 233	97.85 196	98.90 205	97.48 209	98.73 169	97.20 202	97.21 220	90.39 227	94.59 236	90.65 235

EPMVS		96.76 218	95.30 219	98.46 213	99.42 211	98.47 209	99.32 148	99.91 49	98.42 169	99.51 152	99.07 154	92.81 209	97.12 204	92.39 230	91.71 222	95.51 228	94.20 230

test2356		96.34 224	94.05 226	99.00 189	99.39 213	98.28 217	98.15 232	99.51 197	96.23 229	99.16 191	97.95 201	73.39 237	98.75 165	97.07 221	96.86 212	99.06 198	98.57 193

PatchMatch-RL		98.80 169	98.52 169	99.12 181	99.38 214	98.70 197	98.56 220	99.55 189	97.81 197	99.34 184	97.57 206	99.31 153	98.67 168	99.27 101	98.62 160	99.22 190	98.35 201

DWT-MVSNet_training		94.92 230	92.14 232	98.15 220	99.37 215	98.43 211	98.99 192	98.51 227	96.76 226	99.52 148	97.35 213	77.20 235	97.08 206	89.76 234	90.38 228	95.43 229	95.13 227

OMC-MVS		99.11 138	98.95 139	99.29 155	99.37 215	98.57 204	99.19 166	99.20 219	98.87 120	99.58 137	99.13 145	99.88 64	99.00 148	99.19 121	98.46 169	99.43 169	98.57 193

MAR-MVS		98.54 182	98.15 184	98.98 190	99.37 215	98.09 225	98.56 220	99.65 177	96.11 231	99.27 185	97.16 217	99.50 131	98.03 189	98.87 172	98.23 180	99.01 200	99.13 171
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
dps		95.59 227	93.46 229	98.08 221	99.33 218	98.22 222	98.87 204	99.70 164	96.17 230	98.87 207	97.75 204	86.85 227	96.60 214	91.24 231	89.62 229	95.10 230	94.34 229

CMPMVS	76.62 19	98.64 178	98.60 161	98.68 207	99.33 218	97.07 234	98.11 235	98.50 228	97.69 201	99.26 186	98.35 190	99.66 107	97.62 195	99.43 69	99.02 101	99.24 188	99.01 182
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpmp4_e23		95.42 229	92.99 231	98.27 216	99.32 220	97.77 233	98.74 214	99.79 128	97.11 215	99.61 127	97.47 210	80.64 234	96.36 220	92.92 227	88.79 232	95.80 227	96.19 222

MIMVSNet		99.00 152	99.03 130	98.97 192	99.32 220	99.32 129	99.39 134	99.91 49	98.41 170	98.76 210	99.24 136	99.17 159	97.13 203	99.30 90	98.80 142	99.29 183	99.01 182

TSAR-MVS + COLMAP		98.74 170	98.58 163	98.93 196	99.29 222	98.23 219	99.04 184	99.24 218	98.79 130	98.80 209	99.37 126	99.71 101	98.06 185	98.02 211	97.46 207	99.16 195	98.48 197

tpm cat1		95.52 228	93.49 228	97.88 226	99.28 223	97.87 231	98.65 217	99.77 140	97.27 211	99.46 161	98.04 199	90.99 213	95.46 225	88.57 236	88.14 234	94.64 235	93.54 233

PMMVS		98.71 175	98.55 166	98.90 198	99.28 223	98.45 210	98.53 223	99.45 203	97.67 202	99.15 194	98.76 170	99.54 125	97.79 191	98.77 190	98.23 180	99.16 195	98.46 198

CNLPA		98.82 167	98.52 169	99.18 176	99.21 225	98.50 208	98.73 215	99.34 214	98.73 136	99.56 139	97.55 207	99.42 142	99.06 144	98.93 161	98.10 190	99.21 191	98.38 199

DeepPCF-MVS	98.38 11	99.16 133	99.20 100	99.12 181	99.20 226	98.71 196	98.85 206	99.06 222	99.17 83	98.96 202	99.61 92	99.86 70	99.29 117	99.17 126	98.72 149	99.36 177	99.15 169

tpm		96.56 221	94.68 222	98.74 205	99.12 227	97.90 229	98.79 212	99.93 37	96.79 225	99.69 112	99.19 141	81.48 233	97.56 196	95.46 224	93.97 219	97.37 218	97.99 208

MVS-HIRNet		98.45 185	98.25 180	98.69 206	99.12 227	97.81 232	98.55 222	99.85 79	98.58 154	99.67 119	99.61 92	99.86 70	97.46 198	97.95 213	96.37 215	97.49 217	97.56 216

FPMVS		98.48 184	98.83 150	98.07 223	99.09 229	97.98 228	99.07 183	98.04 234	98.99 106	99.22 189	98.85 166	99.43 141	93.79 231	99.66 45	99.11 88	99.24 188	97.76 213

TESTMET0.1,1		97.62 209	97.46 193	97.81 227	99.07 230	98.37 213	98.26 226	98.35 230	97.03 217	97.38 236	99.54 102	92.89 206	95.12 227	98.78 188	97.68 203	98.65 209	97.90 209

GBi-Net		98.96 155	99.05 127	98.85 200	99.02 231	99.53 67	99.31 150	99.78 134	98.13 183	98.48 223	99.43 117	97.58 184	96.92 210	99.68 40	99.50 46	99.61 135	99.53 97

test1		98.96 155	99.05 127	98.85 200	99.02 231	99.53 67	99.31 150	99.78 134	98.13 183	98.48 223	99.43 117	97.58 184	96.92 210	99.68 40	99.50 46	99.61 135	99.53 97

FMVSNet2		99.07 145	99.19 103	98.93 196	99.02 231	99.53 67	99.31 150	99.84 91	98.86 121	98.88 206	99.64 88	98.44 174	96.92 210	99.35 81	99.00 108	99.61 135	99.53 97

test-mter		97.65 208	97.57 192	97.75 229	98.90 234	98.56 205	98.15 232	98.45 229	96.92 221	96.84 239	99.52 109	92.53 211	95.24 226	99.04 147	98.12 188	98.90 204	98.29 204

testpf		93.65 231	91.79 233	95.82 232	98.71 235	93.25 239	96.38 239	99.67 172	95.38 238	97.83 235	94.48 225	87.69 224	89.61 236	88.96 235	88.79 232	92.71 238	93.97 231

FMVSNet5		97.69 205	96.98 199	98.53 210	98.53 236	99.36 114	98.90 202	99.54 190	96.38 228	98.44 226	95.38 223	90.08 215	97.05 208	99.46 62	99.06 93	98.73 207	99.12 172

MVE	91.08 18	97.68 207	97.65 190	97.71 231	98.46 237	91.62 241	97.92 237	98.86 224	98.73 136	97.99 234	98.64 177	99.96 14	99.17 126	99.59 51	97.75 200	93.87 237	97.27 217
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
FMVSNet3		98.63 180	98.75 156	98.49 211	98.10 238	99.44 89	99.02 189	99.78 134	98.13 183	98.48 223	99.43 117	97.58 184	96.16 221	98.85 178	98.39 174	99.40 173	99.41 128

tmp_tt				88.14 233	96.68 239	91.91 240	93.70 240	61.38 236	99.61 24	90.51 241	99.40 123	99.71 101	90.32 235	99.22 110	99.44 55	96.25 225

testmvs		22.33 234	29.66 235	13.79 236	8.97 240	10.35 242	15.53 244	8.09 238	32.51 239	19.87 243	45.18 238	30.56 244	17.05 238	29.96 237	24.74 235	13.21 239	34.30 236

test123		21.52 235	28.47 236	13.42 237	7.29 241	10.12 243	15.70 243	8.31 237	31.54 240	19.34 244	36.33 239	37.40 243	17.14 237	27.45 238	23.17 237	12.73 241	33.30 238

GG-mvs-BLEND		70.44 233	96.91 200	39.57 235	3.32 242	96.51 235	91.01 241	4.05 239	97.03 217	33.20 242	94.67 224	97.75 183	7.59 239	98.28 205	96.85 213	98.24 212	97.26 218

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

MTAPA										99.62 126		99.95 23

MTMP										99.53 142		99.92 48

Patchmatch-RL test							65.75 242

NP-MVS									97.37 208

Patchmtry						98.19 224	98.91 200	99.97 1		99.43 165

DeepMVS_CX						96.39 236	97.15 238	88.89 235	97.94 194	99.51 152	95.71 222	97.88 182	98.19 176	98.92 163		97.73 216	97.75 214

High-resolution multi-view benchmark