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.

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
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 73	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	99.74 100	99.81 7	99.97 1	99.89 1	99.96 3	99.89 4

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 24	99.68 29	99.93 3	99.88 2	99.95 7	99.86 9

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

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

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 54	99.58 46	99.90 7	99.85 3	99.96 3	99.81 16

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 35	99.59 61

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	99.29 145	99.77 14	99.83 17	99.72 15	99.97 1	99.82 13

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

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	99.21 148	99.65 35	99.88 11	99.77 11	99.93 16	99.78 22

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	99.68 111	99.84 5	99.81 21	99.64 21	99.95 7	99.81 16

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 99	99.78 12	99.84 16	99.74 14	99.56 127	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	99.13 108	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
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	99.45 130	99.85 3	99.80 22	99.56 27	99.96 3	99.79 21

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 97	99.66 114	99.77 14	99.80 22	99.71 16	99.92 17	99.76 26

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
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 87	99.55 50	99.59 49	99.53 32	99.89 24	99.70 39

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	99.36 137	99.81 7	99.83 17	99.67 18	99.95 7	99.83 12

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	99.41 136	99.74 21	99.77 27	99.70 17	99.95 7	99.82 13

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	98.55 163	99.65 35	99.89 8	99.76 12	99.95 7	99.70 39

ACMH	99.11 4	99.72 21	99.63 19	99.84 19	99.87 47	99.59 43	99.83 17	99.88 48	99.46 34	99.87 26	99.66 64	99.95 24	99.76 16	99.73 32	99.47 40	99.84 41	99.52 90
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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 65	99.33 82	99.88 11	99.61 26	99.94 14	99.77 23

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 50	99.64 67	99.95 24	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
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 54	99.54 81	99.97 8	99.73 24	99.82 20	99.52 34	99.85 38	99.43 107

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 65	99.50 61	99.77 27	99.42 47	99.75 69	99.49 93

CP-MVSNet		99.68 26	99.51 37	99.89 9	99.95 9	99.76 15	99.83 17	99.96 10	98.83 110	99.84 42	99.65 66	99.09 150	99.80 10	99.78 25	99.62 25	99.95 7	99.82 13

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	99.61 117	99.00 119	99.67 39	99.42 47	99.81 52	99.81 16

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	99.60 119	99.73 24	99.11 122	99.01 92	99.85 38	99.74 29

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	98.83 163	99.01 92	99.45 145	99.76 26

UA-Net		99.64 31	99.62 21	99.66 64	99.97 2	99.82 6	99.14 148	99.96 10	98.95 93	99.52 131	99.38 105	99.86 73	99.55 50	99.72 33	99.66 19	99.80 56	99.94 1

Baseline_NR-MVSNet		99.62 32	99.48 41	99.78 36	99.85 70	99.76 15	99.59 67	99.82 82	98.84 108	99.88 20	99.91 23	99.04 151	99.61 40	99.46 61	99.78 10	99.94 14	99.60 59

pmmvs-eth3d		99.61 33	99.48 41	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

v1144		99.61 33	99.43 48	99.82 22	99.88 41	99.41 81	99.76 31	99.86 53	99.64 15	99.84 42	99.95 6	99.49 128	99.74 21	99.00 133	98.93 104	99.84 41	99.58 69

v8		99.61 33	99.45 46	99.79 35	99.80 93	99.59 43	99.73 43	99.93 24	99.48 32	99.77 62	99.90 27	99.48 129	99.67 32	99.11 122	98.89 108	99.84 41	99.73 31

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

CSCG		99.61 33	99.52 36	99.71 54	99.89 35	99.62 37	99.52 84	99.76 120	99.61 19	99.69 92	99.73 51	99.96 13	99.57 48	99.27 95	98.62 139	99.81 52	99.85 11

v1192		99.60 38	99.41 52	99.82 22	99.89 35	99.43 76	99.81 23	99.84 69	99.63 17	99.85 38	99.95 6	99.35 140	99.72 26	99.01 131	98.90 107	99.82 49	99.58 69

APDe-MVS		99.60 38	99.48 41	99.73 52	99.85 70	99.51 64	99.75 35	99.85 60	99.17 65	99.81 53	99.56 79	99.94 34	99.44 72	99.42 70	99.22 58	99.67 90	99.54 82

v1921920		99.59 40	99.40 55	99.82 22	99.88 41	99.45 71	99.81 23	99.83 75	99.65 13	99.86 32	99.95 6	99.29 145	99.75 18	98.98 137	98.86 112	99.78 60	99.59 61

TranMVSNet+NR-MVSNet		99.59 40	99.42 51	99.80 31	99.87 47	99.55 47	99.64 56	99.86 53	99.05 82	99.88 20	99.72 55	99.33 143	99.64 37	99.47 60	99.14 68	99.91 19	99.67 44

EG-PatchMatch MVS		99.59 40	99.49 40	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 48	99.51 57	99.78 25	99.50 35	99.73 78	99.41 111

pmmvs5		99.58 43	99.47 44	99.70 57	99.84 76	99.50 65	99.58 71	99.80 97	98.98 90	99.73 81	99.92 16	99.81 87	99.49 65	99.28 92	99.05 86	99.77 64	99.73 31

v144192		99.58 43	99.39 56	99.80 31	99.87 47	99.44 73	99.77 28	99.84 69	99.64 15	99.86 32	99.93 14	99.35 140	99.72 26	98.92 143	98.82 116	99.74 74	99.66 45

v148		99.58 43	99.43 48	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 83	99.51 57	99.20 108	98.82 116	99.75 69	99.45 101

v1240		99.58 43	99.38 59	99.82 22	99.89 35	99.49 66	99.82 21	99.83 75	99.63 17	99.86 32	99.96 4	98.92 157	99.75 18	99.15 118	98.96 101	99.76 66	99.56 76

V42		99.57 47	99.41 52	99.75 44	99.84 76	99.37 91	99.73 43	99.83 75	99.41 37	99.75 68	99.89 30	99.42 134	99.60 42	99.15 118	98.96 101	99.76 66	99.65 48

TSAR-MVS + MP.		99.56 48	99.54 34	99.58 81	99.69 134	99.14 135	99.73 43	99.45 176	99.50 31	99.35 161	99.60 74	99.93 40	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
v2v482		99.56 48	99.35 62	99.81 26	99.87 47	99.35 97	99.75 35	99.85 60	99.56 25	99.87 26	99.95 6	99.44 132	99.66 33	98.91 146	98.76 122	99.86 35	99.45 101

Gipuma		99.55 50	99.23 81	99.91 5	99.87 47	99.52 60	99.86 10	99.93 24	99.87 1	99.96 2	96.72 198	99.55 124	99.97 1	99.77 27	99.46 42	99.87 32	99.74 29
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MSP-MVS		99.53 51	99.51 37	99.55 89	99.82 87	99.58 45	99.54 79	99.78 104	99.28 54	99.21 171	99.70 59	99.97 8	99.32 85	99.32 81	99.14 68	99.64 103	99.58 69

NR-MVSNet		99.52 52	99.29 71	99.80 31	99.96 7	99.38 87	99.55 75	99.81 90	98.86 105	99.87 26	99.51 91	98.81 159	99.72 26	99.86 14	99.04 88	99.89 24	99.54 82

zzz-MVS		99.51 53	99.36 60	99.68 60	99.88 41	99.38 87	99.53 80	99.84 69	99.11 75	99.59 116	98.93 142	99.95 24	99.58 46	99.44 68	99.21 60	99.65 94	99.52 90

ACMMPR		99.51 53	99.32 66	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 24	99.61 40	99.35 76	99.14 68	99.66 92	99.58 69

UniMVSNet (Re)		99.50 55	99.29 71	99.75 44	99.86 61	99.47 69	99.51 87	99.82 82	98.90 101	99.89 15	99.64 67	99.00 152	99.55 50	99.32 81	99.08 81	99.90 21	99.59 61

FMVSNet1		99.50 55	99.57 30	99.42 110	99.67 141	99.65 33	99.60 66	99.91 36	99.40 39	99.39 155	99.83 43	99.27 147	98.14 156	99.68 36	99.50 35	99.81 52	99.68 41

HyFIR lowres test		99.50 55	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	98.40 166	99.78 12	98.84 162	98.59 142	99.76 66	99.72 34

PM-MVS		99.49 58	99.43 48	99.57 84	99.76 113	99.34 99	99.53 80	99.77 111	98.93 97	99.75 68	99.46 95	99.83 83	99.11 110	99.72 33	99.29 56	99.49 140	99.46 100

Anonymous20231206		99.48 59	99.31 68	99.69 59	99.79 97	99.57 46	99.63 60	99.79 99	98.88 103	99.91 11	99.72 55	99.93 40	99.59 43	99.24 98	98.63 138	99.43 150	99.18 139

DU-MVS		99.48 59	99.26 75	99.75 44	99.85 70	99.38 87	99.50 91	99.81 90	98.86 105	99.89 15	99.51 91	98.98 153	99.59 43	99.46 61	98.97 99	99.87 32	99.63 52

RPSCF		99.48 59	99.45 46	99.52 96	99.73 127	99.33 100	99.13 149	99.77 111	99.33 46	99.47 142	99.39 104	99.92 48	99.36 77	99.63 45	99.13 75	99.63 106	99.41 111

ACMMP_NAP		99.47 62	99.33 64	99.63 72	99.85 70	99.28 112	99.56 74	99.83 75	98.75 116	99.48 139	99.03 139	99.95 24	99.47 71	99.48 57	99.19 61	99.57 124	99.59 61

Anonymous20231211		99.47 62	99.39 56	99.57 84	99.89 35	99.60 40	99.50 91	99.69 133	98.91 100	99.62 108	99.17 125	99.35 140	98.86 132	99.63 45	99.46 42	99.84 41	99.62 55

SteuartSystems-ACMMP		99.47 62	99.22 84	99.76 40	99.88 41	99.36 93	99.65 55	99.84 69	98.47 140	99.80 54	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.
ACMM	98.37 12	99.47 62	99.23 81	99.74 50	99.86 61	99.19 129	99.68 51	99.86 53	99.16 69	99.71 89	98.52 167	99.95 24	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
HFP-MVS		99.46 66	99.30 69	99.65 66	99.82 87	99.25 117	99.50 91	99.82 82	99.23 56	99.58 120	98.86 144	99.94 34	99.56 49	99.14 120	99.12 78	99.63 106	99.56 76

LGP-MVS_train		99.46 66	99.18 94	99.78 36	99.87 47	99.25 117	99.71 49	99.87 49	98.02 172	99.79 57	98.90 143	99.96 13	99.66 33	99.49 56	99.17 64	99.79 59	99.49 93

ETV-MVS		99.45 68	99.32 66	99.60 77	99.79 97	99.60 40	99.40 110	99.78 104	97.88 179	99.83 47	99.33 108	99.70 108	98.97 122	99.74 30	99.43 46	99.84 41	99.58 69

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	98.81 112	99.79 57	98.42 174	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
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	97.77 186	98.97 154	98.97 122	99.72 33	99.54 31	99.88 26	99.81 16

SMA-MVS		99.43 70	99.41 52	99.45 107	99.82 87	99.31 105	99.02 163	99.59 154	99.06 80	99.34 164	99.53 87	99.96 13	99.38 75	99.29 87	99.13 75	99.53 132	99.59 61

testgi		99.43 70	99.47 44	99.38 118	99.90 32	99.67 32	99.30 129	99.73 128	98.64 128	99.53 125	99.52 89	99.90 57	98.08 159	99.65 43	99.40 51	99.75 69	99.55 81

DELS-MVS		99.42 73	99.53 35	99.29 132	99.52 169	99.43 76	99.42 106	99.28 191	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 61
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
3Dnovator	99.16 3	99.42 73	99.22 84	99.65 66	99.78 102	99.13 139	99.50 91	99.85 60	99.40 39	99.80 54	98.59 163	99.79 95	99.30 89	99.20 108	99.06 83	99.71 84	99.35 124

DPE-MVS		99.41 75	99.36 60	99.47 103	99.66 142	99.48 67	99.46 102	99.75 125	98.65 124	99.41 152	99.67 62	99.95 24	98.82 133	99.21 105	99.14 68	99.72 81	99.40 116

UniMVSNet_NR-MVSNet		99.41 75	99.12 106	99.76 40	99.86 61	99.48 67	99.50 91	99.81 90	98.84 108	99.89 15	99.45 96	98.32 169	99.59 43	99.22 102	98.89 108	99.90 21	99.63 52

CP-MVS		99.41 75	99.20 89	99.65 66	99.80 93	99.23 124	99.44 104	99.75 125	98.60 133	99.74 75	98.66 157	99.93 40	99.48 68	99.33 80	99.16 65	99.73 78	99.48 96

QAPM		99.41 75	99.21 88	99.64 71	99.78 102	99.16 132	99.51 87	99.85 60	99.20 59	99.72 84	99.43 97	99.81 87	99.25 93	98.87 152	98.71 130	99.71 84	99.30 129

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	99.33 108	99.93 40	96.46 194	99.63 45	99.53 32	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
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	98.69 121	99.16 173	99.08 134	98.64 162	99.20 96	99.65 43	99.46 42	99.83 46	99.72 34

OPM-MVS		99.39 81	99.22 84	99.59 78	99.76 113	98.82 163	99.51 87	99.79 99	99.17 65	99.53 125	99.31 113	99.95 24	99.35 78	99.22 102	98.79 121	99.60 116	99.27 132

Fast-Effi-MVS+		99.39 81	99.18 94	99.63 72	99.86 61	99.28 112	99.45 103	99.91 36	98.47 140	99.61 111	99.50 93	99.57 121	99.17 97	99.24 98	98.66 135	99.78 60	99.59 61

LS3D		99.39 81	99.28 73	99.52 96	99.77 109	99.39 84	99.55 75	99.82 82	98.93 97	99.64 105	98.52 167	99.67 113	98.58 146	99.74 30	99.63 23	99.75 69	99.06 155

CS-MVS		99.38 84	99.19 91	99.59 78	99.86 61	99.65 33	99.28 132	99.77 111	97.97 175	99.75 68	98.42 174	99.70 108	99.03 117	99.57 51	99.42 47	99.87 32	99.61 57

diffmvs		99.38 84	99.33 64	99.45 107	99.87 47	99.39 84	99.28 132	99.58 157	99.55 27	99.50 135	99.85 38	99.85 79	98.94 127	98.58 175	98.68 133	99.51 137	99.39 118

CANet		99.36 86	99.39 56	99.34 129	99.80 93	99.35 97	99.41 109	99.47 174	99.20 59	99.74 75	99.54 81	99.68 111	98.05 161	99.23 100	98.97 99	99.57 124	99.73 31

MVS_0304		99.36 86	99.35 62	99.37 124	99.85 70	99.36 93	99.39 111	99.56 160	99.36 44	99.75 68	99.23 119	99.90 57	97.97 168	99.00 133	98.83 115	99.69 87	99.77 23

ACMMP		99.36 86	99.06 113	99.71 54	99.86 61	99.36 93	99.63 60	99.85 60	98.33 155	99.72 84	97.73 188	99.94 34	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
SD-MVS		99.35 89	99.26 75	99.46 105	99.66 142	99.15 134	98.92 172	99.67 142	99.55 27	99.35 161	98.83 146	99.91 54	99.35 78	99.19 111	98.53 144	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
MP-MVS		99.35 89	99.09 111	99.65 66	99.84 76	99.22 125	99.59 67	99.78 104	98.13 164	99.67 100	98.44 171	99.93 40	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.
pmmvs4		99.34 91	99.15 101	99.57 84	99.77 109	98.90 155	99.51 87	99.77 111	99.07 78	99.73 81	99.72 55	99.84 81	99.07 112	98.85 157	98.39 153	99.55 130	99.27 132

EPP-MVSNet		99.34 91	99.10 109	99.62 76	99.94 19	99.74 23	99.66 54	99.80 97	99.07 78	98.93 184	99.61 71	96.13 183	99.49 65	99.67 39	99.63 23	99.92 17	99.86 9

TSAR-MVS + GP.		99.33 93	99.17 98	99.51 98	99.71 132	99.00 150	98.84 181	99.71 130	98.23 161	99.74 75	99.53 87	99.90 57	99.35 78	99.38 72	98.85 113	99.72 81	99.31 127

PHI-MVS		99.33 93	99.19 91	99.49 101	99.69 134	99.25 117	99.27 134	99.59 154	98.44 144	99.78 61	99.15 126	99.92 48	98.95 126	99.39 71	99.04 88	99.64 103	99.18 139

DVP-MVS		99.32 95	99.26 75	99.38 118	99.76 113	99.54 50	99.42 106	99.72 129	98.92 99	98.84 191	98.96 141	99.96 13	98.91 128	98.72 170	99.14 68	99.63 106	99.58 69

PGM-MVS		99.32 95	98.99 122	99.71 54	99.86 61	99.31 105	99.59 67	99.86 53	97.51 188	99.75 68	98.23 178	99.94 34	99.53 53	99.29 87	99.08 81	99.65 94	99.54 82

DeepC-MVS_fast	98.69 9	99.32 95	99.13 104	99.53 92	99.63 149	98.78 166	99.53 80	99.33 189	99.08 76	99.77 62	99.18 124	99.89 60	99.29 90	99.00 133	98.70 131	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
MSDG		99.32 95	99.09 111	99.58 81	99.75 117	98.74 170	99.36 116	99.54 163	99.14 72	99.72 84	99.24 117	99.89 60	99.51 57	99.30 84	98.76 122	99.62 112	98.54 174

TSAR-MVS + ACMM		99.31 99	99.26 75	99.37 124	99.66 142	98.97 153	99.20 141	99.56 160	99.33 46	99.19 172	99.54 81	99.91 54	99.32 85	99.12 121	98.34 156	99.29 164	99.65 48

3Dnovator+	98.92 7	99.31 99	99.03 117	99.63 72	99.77 109	98.90 155	99.52 84	99.81 90	99.37 42	99.72 84	98.03 183	99.73 103	99.32 85	98.99 136	98.81 119	99.67 90	99.36 122

X-MVS		99.30 101	98.99 122	99.66 64	99.85 70	99.30 107	99.49 98	99.82 82	98.32 156	99.69 92	97.31 195	99.93 40	99.50 61	99.37 73	99.16 65	99.60 116	99.53 85

MVS_111021_HR		99.30 101	99.14 102	99.48 102	99.58 165	99.25 117	99.27 134	99.61 149	98.74 117	99.66 102	99.02 140	99.84 81	99.33 82	99.20 108	98.76 122	99.44 147	99.18 139

TAPA-MVS	98.54 10	99.30 101	99.24 80	99.36 128	99.44 184	98.77 168	99.00 165	99.41 181	99.23 56	99.60 114	99.50 93	99.86 73	99.15 104	99.29 87	98.95 103	99.56 127	99.08 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		99.30 101	99.01 121	99.63 72	99.75 117	98.89 158	99.35 119	99.60 151	98.53 138	99.86 32	99.57 78	99.94 34	99.52 56	98.96 138	98.10 169	99.70 86	99.08 152
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
USDC		99.29 105	98.98 124	99.65 66	99.72 129	98.87 161	99.47 100	99.66 145	99.35 45	99.87 26	99.58 77	99.87 72	99.51 57	98.85 157	97.93 175	99.65 94	98.38 178

PMVS	94.32 17	99.27 106	99.55 31	98.94 169	99.60 158	99.43 76	99.39 111	99.54 163	98.99 87	99.69 92	99.60 74	99.81 87	95.68 199	99.88 11	99.83 4	99.73 78	99.31 127
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS_111021_LR		99.25 107	99.13 104	99.39 114	99.50 177	99.14 135	99.23 139	99.50 171	98.67 122	99.61 111	99.12 130	99.81 87	99.16 100	99.28 92	98.67 134	99.35 160	99.21 138

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	99.49 137	99.84 40	99.88 65	97.99 167	98.85 157	98.73 128	98.98 179	99.72 34

EIA-MVS		99.23 109	99.03 117	99.47 103	99.83 82	99.64 35	99.16 145	99.81 90	97.11 195	99.65 104	98.44 171	99.78 98	98.61 145	99.46 61	99.22 58	99.75 69	99.59 61

HPM-MVS++		99.23 109	98.98 124	99.53 92	99.75 117	99.02 148	99.44 104	99.77 111	98.65 124	99.52 131	98.72 154	99.92 48	99.33 82	98.77 168	98.40 152	99.40 154	99.36 122

PMMVS2		99.23 109	99.22 84	99.24 139	99.80 93	99.14 135	99.50 91	99.82 82	99.12 74	98.41 205	99.91 23	99.98 4	98.51 147	99.48 57	98.76 122	99.38 156	98.14 186

CPTT-MVS		99.21 112	98.89 135	99.58 81	99.72 129	99.12 142	99.30 129	99.76 120	98.62 129	99.66 102	97.51 191	99.89 60	99.48 68	99.01 131	98.64 137	99.58 123	99.40 116

TinyColmap		99.21 112	98.89 135	99.59 78	99.61 154	98.61 179	99.47 100	99.67 142	99.02 84	99.82 50	99.15 126	99.74 100	99.35 78	99.17 116	98.33 157	99.63 106	98.22 184

Effi-MVS+		99.20 114	98.93 130	99.50 100	99.79 97	99.26 114	98.82 184	99.96 10	98.37 154	99.60 114	99.12 130	98.36 167	99.05 115	98.93 141	98.82 116	99.78 60	99.68 41

PVSNet_BlendedMVS		99.20 114	99.17 98	99.23 140	99.69 134	99.33 100	99.04 158	99.13 194	98.41 150	99.79 57	99.33 108	99.36 137	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	99.13 194	98.41 150	99.79 57	99.33 108	99.36 137	98.10 157	99.29 87	98.87 110	99.65 94	99.56 76

MCST-MVS		99.17 117	98.82 143	99.57 84	99.75 117	98.70 174	99.25 138	99.69 133	98.62 129	99.59 116	98.54 165	99.79 95	99.53 53	98.48 179	98.15 165	99.64 103	99.43 107

APD-MVS		99.17 117	98.92 131	99.46 105	99.78 102	99.24 122	99.34 120	99.78 104	97.79 182	99.48 139	98.25 177	99.88 65	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
OpenMVS	98.82 8	99.17 117	98.85 139	99.53 92	99.75 117	99.06 146	99.36 116	99.82 82	98.28 158	99.76 65	98.47 169	99.61 117	98.91 128	98.80 165	98.70 131	99.60 116	99.04 159

IterMVS-LS		99.16 120	98.82 143	99.57 84	99.87 47	99.71 26	99.58 71	99.92 31	99.24 55	99.71 89	99.73 51	95.79 184	98.91 128	98.82 164	98.66 135	99.43 150	99.77 23
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepPCF-MVS	98.38 11	99.16 120	99.20 89	99.12 155	99.20 201	98.71 173	98.85 180	99.06 197	99.17 65	98.96 183	99.61 71	99.86 73	99.29 90	99.17 116	98.72 129	99.36 158	99.15 147

IterMVS-SCA-FT		99.15 122	98.96 127	99.38 118	99.87 47	99.54 50	99.53 80	99.79 99	98.94 95	99.82 50	99.92 16	97.65 175	98.82 133	98.95 140	98.26 159	98.45 188	99.47 99

CDS-MVSNet		99.15 122	99.10 109	99.21 145	99.59 162	99.22 125	99.48 99	99.47 174	98.89 102	99.41 152	99.84 40	98.11 172	97.76 171	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
IS_MVSNet		99.15 122	99.12 106	99.19 148	99.92 25	99.73 25	99.55 75	99.86 53	98.45 143	96.91 211	98.74 152	98.33 168	99.02 118	99.54 54	99.47 40	99.88 26	99.61 57

MDA-MVSNet-bldmvs		99.11 125	99.11 108	99.12 155	99.91 29	99.38 87	99.77 28	98.72 201	99.31 48	99.85 38	99.43 97	98.26 170	99.48 68	99.85 15	98.47 147	96.99 198	99.08 152

OMC-MVS		99.11 125	98.95 128	99.29 132	99.37 190	98.57 181	99.19 142	99.20 193	98.87 104	99.58 120	99.13 128	99.88 65	99.00 119	99.19 111	98.46 148	99.43 150	98.57 173

MVS_Test		99.09 127	98.92 131	99.29 132	99.61 154	99.07 145	99.04 158	99.81 90	98.58 135	99.37 158	99.74 49	98.87 158	98.41 150	98.61 174	98.01 173	99.50 139	99.57 75

CNVR-MVS		99.08 128	98.83 140	99.37 124	99.61 154	98.74 170	99.15 146	99.54 163	98.59 134	99.37 158	98.15 180	99.88 65	99.08 111	98.91 146	98.46 148	99.48 141	99.06 155

IterMVS		99.08 128	98.90 134	99.29 132	99.87 47	99.53 53	99.52 84	99.77 111	98.94 95	99.75 68	99.91 23	97.52 179	98.72 140	98.86 155	98.14 166	98.09 191	99.43 107
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		99.07 130	99.19 91	98.93 171	99.02 206	99.53 53	99.31 124	99.84 69	98.86 105	98.88 187	99.64 67	98.44 165	96.92 188	99.35 76	99.00 96	99.61 113	99.53 85

CVMVSNet		99.06 131	98.88 138	99.28 136	99.52 169	99.53 53	99.42 106	99.69 133	98.74 117	98.27 207	99.89 30	95.48 187	99.44 72	99.46 61	99.33 53	99.32 163	99.75 28

CDPH-MVS		99.05 132	98.63 150	99.54 91	99.75 117	98.78 166	99.59 67	99.68 139	97.79 182	99.37 158	98.20 179	99.86 73	99.14 106	98.58 175	98.01 173	99.68 88	99.16 145

TAMVS		99.05 132	99.02 120	99.08 160	99.69 134	99.22 125	99.33 121	99.32 190	99.16 69	98.97 182	99.87 34	97.36 180	97.76 171	99.21 105	99.00 96	99.44 147	99.33 125

CANet_DTU		99.03 134	99.18 94	98.87 174	99.58 165	99.03 147	99.18 143	99.41 181	98.65 124	99.74 75	99.55 80	99.71 105	96.13 197	99.19 111	98.92 105	99.17 173	99.18 139

Effi-MVS+-dtu		99.01 135	99.05 114	98.98 164	99.60 158	99.13 139	99.03 162	99.61 149	98.52 139	99.01 179	98.53 166	99.83 83	96.95 187	99.48 57	98.59 142	99.66 92	99.25 136

canonicalmvs		99.00 136	98.68 149	99.37 124	99.68 140	99.42 80	98.94 171	99.89 44	99.00 86	98.99 180	98.43 173	95.69 185	98.96 125	99.18 114	99.18 62	99.74 74	99.88 6

MIMVSNet		99.00 136	99.03 117	98.97 168	99.32 196	99.32 104	99.39 111	99.91 36	98.41 150	98.76 194	99.24 117	99.17 149	97.13 181	99.30 84	98.80 120	99.29 164	99.01 160

CHOSEN 280x420		98.99 138	98.91 133	99.07 161	99.77 109	99.26 114	99.55 75	99.92 31	98.62 129	98.67 198	99.62 70	97.20 181	98.44 149	99.50 55	99.18 62	98.08 192	98.99 163

xxxxxxxxxxxxxcwj		98.97 139	98.97 126	98.98 164	99.64 147	98.89 158	98.00 210	99.58 157	98.42 147	99.08 177	98.63 159	99.96 13	98.04 163	99.02 129	98.76 122	99.52 133	99.13 148

SF-MVS		98.96 140	98.95 128	98.98 164	99.64 147	98.89 158	98.00 210	99.58 157	98.42 147	99.08 177	98.63 159	99.83 83	98.04 163	99.02 129	98.76 122	99.52 133	99.13 148

GBi-Net		98.96 140	99.05 114	98.85 175	99.02 206	99.53 53	99.31 124	99.78 104	98.13 164	98.48 201	99.43 97	97.58 176	96.92 188	99.68 36	99.50 35	99.61 113	99.53 85

test1		98.96 140	99.05 114	98.85 175	99.02 206	99.53 53	99.31 124	99.78 104	98.13 164	98.48 201	99.43 97	97.58 176	96.92 188	99.68 36	99.50 35	99.61 113	99.53 85

PCF-MVS	97.86 15	98.95 143	98.53 155	99.44 109	99.70 133	98.80 165	98.96 167	99.69 133	98.65 124	99.59 116	99.33 108	99.94 34	99.12 109	98.01 189	97.11 186	99.59 122	97.83 190
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		98.94 144	98.71 148	99.21 145	99.52 169	98.22 197	98.97 166	99.53 168	98.76 114	99.50 135	98.59 163	99.56 123	98.68 141	98.63 173	98.45 150	99.05 176	98.73 170

AdaColmap		98.93 145	98.53 155	99.39 114	99.52 169	98.65 177	99.11 152	99.59 154	98.08 168	99.44 145	97.46 193	99.45 130	99.24 94	98.92 143	98.44 151	99.44 147	98.73 170

MSLP-MVS++		98.92 146	98.73 147	99.14 152	99.44 184	99.00 150	98.36 200	99.35 186	98.82 111	99.38 156	96.06 200	99.79 95	99.07 112	98.88 151	99.05 86	99.27 166	99.53 85

new_pmnet		98.91 147	98.89 135	98.94 169	99.51 175	98.27 193	99.15 146	98.66 202	99.17 65	99.48 139	99.79 47	99.80 93	98.49 148	99.23 100	98.20 163	98.34 189	97.74 194

train_agg		98.89 148	98.48 160	99.38 118	99.69 134	98.76 169	99.31 124	99.60 151	97.71 184	98.98 181	97.89 184	99.89 60	99.29 90	98.32 180	97.59 182	99.42 153	99.16 145

NCCC		98.88 149	98.42 161	99.42 110	99.62 150	98.81 164	99.10 153	99.54 163	98.76 114	99.53 125	95.97 201	99.80 93	99.16 100	98.49 178	98.06 172	99.55 130	99.05 157

PLC	97.83 16	98.88 149	98.52 157	99.30 131	99.45 182	98.60 180	98.65 190	99.49 172	98.66 123	99.59 116	96.33 199	99.59 120	99.17 97	98.87 152	98.53 144	99.46 143	99.05 157
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs3		98.85 151	98.60 151	99.13 153	99.66 142	98.72 172	99.37 115	99.06 197	98.44 144	99.76 65	99.74 49	99.55 124	99.15 104	99.04 127	96.00 194	97.80 193	98.72 172

Fast-Effi-MVS+-dtu		98.82 152	98.80 145	98.84 177	99.51 175	98.90 155	98.96 167	99.91 36	98.29 157	99.11 176	98.47 169	99.63 116	96.03 198	99.21 105	98.12 167	99.52 133	99.01 160

CNLPA		98.82 152	98.52 157	99.18 149	99.21 200	98.50 185	98.73 188	99.34 188	98.73 119	99.56 122	97.55 190	99.42 134	99.06 114	98.93 141	98.10 169	99.21 172	98.38 178

PatchMatch-RL		98.80 154	98.52 157	99.12 155	99.38 189	98.70 174	98.56 193	99.55 162	97.81 181	99.34 164	97.57 189	99.31 144	98.67 142	99.27 95	98.62 139	99.22 171	98.35 180

thisisatest0530		98.78 155	98.26 164	99.39 114	99.78 102	99.43 76	99.07 155	99.64 147	98.44 144	99.42 150	99.22 120	92.68 198	98.63 143	99.30 84	99.14 68	99.80 56	99.60 59

tttt0517		98.77 156	98.25 166	99.38 118	99.79 97	99.46 70	99.07 155	99.64 147	98.40 153	99.38 156	99.21 122	92.54 199	98.63 143	99.34 79	99.14 68	99.80 56	99.62 55

DI_MVS_plusplus_trai		98.74 157	98.08 174	99.51 98	99.79 97	99.29 111	99.61 62	99.60 151	99.20 59	99.46 143	99.09 133	92.93 192	98.97 122	98.27 183	98.35 155	99.65 94	99.45 101

TSAR-MVS + COLMAP		98.74 157	98.58 153	98.93 171	99.29 197	98.23 194	99.04 158	99.24 192	98.79 113	98.80 193	99.37 106	99.71 105	98.06 160	98.02 188	97.46 184	99.16 174	98.48 176

MDTV_nov1_ep13_2view		98.73 159	98.31 163	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 87	99.31 88	97.40 196	94.77 195	96.73 200	97.81 191

PMMVS		98.71 160	98.55 154	98.90 173	99.28 198	98.45 187	98.53 196	99.45 176	97.67 186	99.15 175	98.76 150	99.54 126	97.79 170	98.77 168	98.23 161	99.16 174	98.46 177

HQP-MVS		98.70 161	98.19 170	99.28 136	99.61 154	98.52 183	98.71 189	99.35 186	97.97 175	99.53 125	97.38 194	99.85 79	99.14 106	97.53 192	96.85 190	99.36 158	99.26 135

N_pmnet		98.64 162	98.23 169	99.11 158	99.78 102	99.25 117	99.75 35	99.39 185	99.65 13	99.70 91	99.78 48	99.89 60	98.81 135	97.60 191	94.28 196	97.24 197	97.15 198

CMPMVS	76.62 19	98.64 162	98.60 151	98.68 182	99.33 194	97.07 209	98.11 208	98.50 203	97.69 185	99.26 167	98.35 176	99.66 114	97.62 174	99.43 69	99.02 90	99.24 169	99.01 160
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		98.63 164	98.75 146	98.49 188	98.10 212	99.44 73	99.02 163	99.78 104	98.13 164	98.48 201	99.43 97	97.58 176	96.16 196	98.85 157	98.39 153	99.40 154	99.41 111

GA-MVS		98.59 165	98.15 171	99.09 159	99.59 162	99.13 139	98.84 181	99.52 170	98.61 132	99.35 161	99.67 62	93.03 191	97.73 173	98.90 150	98.26 159	99.51 137	99.48 96

MAR-MVS		98.54 166	98.15 171	98.98 164	99.37 190	98.09 200	98.56 193	99.65 146	96.11 210	99.27 166	97.16 197	99.50 127	98.03 165	98.87 152	98.23 161	99.01 177	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
new-patchmatchnet		98.49 167	97.60 176	99.53 92	99.90 32	99.55 47	99.77 28	99.48 173	99.67 10	99.86 32	99.98 3	99.98 4	99.50 61	96.90 198	91.52 201	98.67 185	95.62 202

FPMVS		98.48 168	98.83 140	98.07 198	99.09 204	97.98 203	99.07 155	98.04 209	98.99 87	99.22 170	98.85 145	99.43 133	93.79 206	99.66 41	99.11 79	99.24 169	97.76 192

MVS-HIRNet		98.45 169	98.25 166	98.69 181	99.12 202	97.81 208	98.55 195	99.85 60	98.58 135	99.67 100	99.61 71	99.86 73	97.46 177	97.95 190	96.37 192	97.49 195	97.56 195

test0.0.03 1		98.41 170	98.41 162	98.40 192	99.62 150	99.16 132	98.87 178	99.41 181	97.15 193	96.60 213	99.31 113	97.00 182	96.55 193	98.91 146	98.51 146	99.37 157	98.82 168

gg-mvs-nofinetune		98.40 171	98.26 164	98.57 186	99.83 82	98.86 162	98.77 187	99.97 1	99.57 24	99.99 1	99.99 1	93.81 189	93.50 207	98.91 146	98.20 163	99.33 162	98.52 175

baseline1		98.39 172	97.59 177	99.31 130	99.78 102	99.45 71	99.13 149	99.53 168	98.06 170	98.87 188	98.63 159	90.04 204	98.76 137	98.85 157	98.84 114	99.81 52	99.28 131

PatchT		98.11 173	97.12 184	99.26 138	99.65 146	98.34 191	99.57 73	99.97 1	97.48 189	99.43 147	99.04 138	90.84 202	98.15 154	98.04 186	97.78 176	98.82 182	98.30 181

DPM-MVS		98.10 174	97.32 182	99.01 163	99.52 169	97.92 204	98.47 198	99.45 176	98.25 159	98.91 185	93.99 205	99.69 110	98.73 139	96.29 200	96.32 193	99.00 178	98.77 169

EPNet_dtu		98.09 175	98.25 166	97.91 200	99.58 165	98.02 202	98.19 205	99.67 142	97.94 177	99.74 75	99.07 136	98.71 161	93.40 208	97.50 193	97.09 187	96.89 199	99.44 104
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.06 176	98.11 173	98.00 199	99.60 158	98.99 152	98.38 199	99.68 139	98.18 163	98.85 190	97.89 184	95.60 186	92.72 209	98.30 181	98.10 169	98.76 183	99.72 34
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 177	96.80 187	99.22 143	99.60 158	98.23 194	98.91 173	99.97 1	96.89 203	99.43 147	99.10 132	89.24 207	98.15 154	98.04 186	97.78 176	99.26 167	98.30 181

thres200		97.87 178	96.56 189	99.39 114	99.76 113	99.52 60	99.13 149	99.76 120	96.88 205	98.66 199	92.87 209	88.77 210	99.16 100	99.11 122	99.42 47	99.88 26	99.33 125

baseline2		97.87 178	97.18 183	98.67 183	99.34 193	99.17 131	98.48 197	98.82 200	97.08 196	98.83 192	98.75 151	89.47 206	97.03 186	98.67 172	98.27 158	99.52 133	98.83 167

thres600view7		97.86 180	96.53 192	99.41 112	99.84 76	99.52 60	99.36 116	99.76 120	97.32 191	98.38 206	93.24 206	87.25 212	99.23 95	99.11 122	99.75 13	99.88 26	99.48 96

tfpn200view9		97.85 181	96.54 190	99.38 118	99.74 125	99.52 60	99.17 144	99.76 120	96.10 211	98.70 196	92.99 207	89.10 208	99.00 119	99.11 122	99.56 27	99.88 26	99.41 111

thres400		97.82 182	96.47 193	99.40 113	99.81 92	99.44 73	99.29 131	99.69 133	97.15 193	98.57 200	92.82 210	87.96 211	99.16 100	98.96 138	99.55 30	99.86 35	99.41 111

IB-MVS	98.10 14	97.76 183	97.40 181	98.18 194	99.62 150	99.11 143	98.24 203	98.35 205	96.56 207	99.44 145	91.28 211	98.96 156	93.84 205	98.09 185	98.62 139	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
test-LLR		97.74 184	97.46 179	98.08 196	99.62 150	98.37 189	98.26 201	99.41 181	97.03 198	97.38 209	99.54 81	92.89 193	95.12 202	98.78 166	97.68 180	98.65 186	97.90 188

RPMNet		97.70 185	96.54 190	99.06 162	99.57 168	98.23 194	98.95 170	99.97 1	96.89 203	99.49 137	99.13 128	89.63 205	97.09 183	96.68 199	97.02 188	99.26 167	98.19 185

thres100view900		97.69 186	96.37 194	99.23 140	99.74 125	99.21 128	98.81 185	99.43 180	96.10 211	98.70 196	92.99 207	89.10 208	98.88 131	98.58 175	99.31 55	99.82 49	99.27 132

FMVSNet5		97.69 186	96.98 185	98.53 187	98.53 210	99.36 93	98.90 176	99.54 163	96.38 208	98.44 204	95.38 203	90.08 203	97.05 185	99.46 61	99.06 83	98.73 184	99.12 151

MVE	91.08 18	97.68 188	97.65 175	97.71 206	98.46 211	91.62 215	97.92 212	98.86 199	98.73 119	97.99 208	98.64 158	99.96 13	99.17 97	99.59 49	97.75 178	93.87 213	97.27 196
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-mter		97.65 189	97.57 178	97.75 204	98.90 209	98.56 182	98.15 206	98.45 204	96.92 202	96.84 212	99.52 89	92.53 200	95.24 201	99.04 127	98.12 167	98.90 181	98.29 183

TESTMET0.1,1		97.62 190	97.46 179	97.81 202	99.07 205	98.37 189	98.26 201	98.35 205	97.03 198	97.38 209	99.54 81	92.89 193	95.12 202	98.78 166	97.68 180	98.65 186	97.90 188

MVSTER		97.55 191	96.75 188	98.48 189	99.46 181	99.54 50	98.24 203	99.77 111	97.56 187	99.41 152	99.31 113	84.86 214	94.66 204	98.86 155	97.75 178	99.34 161	99.38 119

ET-MVSNet_ETH3D		97.44 192	96.29 195	98.78 178	97.93 213	98.95 154	98.91 173	99.09 196	98.00 173	99.24 168	98.83 146	84.62 215	98.02 166	97.43 195	97.38 185	99.48 141	98.84 165

MDTV_nov1_ep13		97.41 193	96.26 196	98.76 179	99.47 180	98.43 188	99.26 137	99.82 82	98.06 170	99.23 169	99.22 120	92.86 195	98.05 161	95.33 202	93.66 198	96.73 200	96.26 200

ADS-MVSNet		97.29 194	96.17 197	98.59 185	99.59 162	98.70 174	99.32 122	99.86 53	98.47 140	99.56 122	99.08 134	98.16 171	97.34 179	92.92 204	91.17 202	95.91 203	94.72 205

SCA		97.25 195	96.05 198	98.64 184	99.36 192	99.02 148	99.27 134	99.96 10	98.25 159	99.69 92	98.71 155	94.66 188	97.95 169	93.95 203	92.35 199	95.64 204	95.40 204

gm-plane-assit		96.82 196	94.84 203	99.13 153	99.95 9	99.78 13	99.69 50	99.92 31	99.19 62	99.84 42	99.92 16	72.93 218	96.44 195	98.21 184	97.01 189	98.92 180	96.87 199

PatchmatchNet		96.81 197	95.41 200	98.43 191	99.43 186	98.30 192	99.23 139	99.93 24	98.19 162	99.64 105	98.81 149	93.50 190	97.43 178	92.89 205	90.78 204	94.94 208	95.41 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		96.76 198	95.30 202	98.46 190	99.42 187	98.47 186	99.32 122	99.91 36	98.42 147	99.51 133	99.07 136	92.81 196	97.12 182	92.39 206	91.71 200	95.51 205	94.20 207

E-PMN		96.72 199	95.78 199	97.81 202	99.45 182	95.46 212	98.14 207	98.33 207	97.99 174	98.73 195	98.09 181	98.97 154	97.54 176	97.45 194	91.09 203	94.70 210	91.40 210

tpm		96.56 200	94.68 204	98.74 180	99.12 202	97.90 205	98.79 186	99.93 24	96.79 206	99.69 92	99.19 123	81.48 217	97.56 175	95.46 201	93.97 197	97.37 196	97.99 187

EMVS		96.47 201	95.38 201	97.74 205	99.42 187	95.37 213	98.07 209	98.27 208	97.85 180	98.90 186	97.48 192	98.73 160	97.20 180	97.21 197	90.39 205	94.59 212	90.65 211

tpmrst		96.18 202	94.47 205	98.18 194	99.52 169	97.89 206	98.96 167	99.79 99	98.07 169	99.16 173	99.30 116	92.69 197	96.69 191	90.76 208	88.85 208	94.96 207	93.69 208

CostFormer		95.61 203	93.35 208	98.24 193	99.48 179	98.03 201	98.65 190	99.83 75	96.93 201	99.42 150	98.83 146	83.65 216	97.08 184	90.39 209	89.54 207	94.94 208	96.11 201

dps		95.59 204	93.46 207	98.08 196	99.33 194	98.22 197	98.87 178	99.70 131	96.17 209	98.87 188	97.75 187	86.85 213	96.60 192	91.24 207	89.62 206	95.10 206	94.34 206

tpm cat1		95.52 205	93.49 206	97.88 201	99.28 198	97.87 207	98.65 190	99.77 111	97.27 192	99.46 143	98.04 182	90.99 201	95.46 200	88.57 210	88.14 209	94.64 211	93.54 209

GG-mvs-BLEND		70.44 206	96.91 186	39.57 208	3.32 217	96.51 210	91.01 215	4.05 214	97.03 198	33.20 215	94.67 204	97.75 174	7.59 213	98.28 182	96.85 190	98.24 190	97.26 197

testmvs		22.33 207	29.66 209	13.79 209	8.97 215	10.35 216	15.53 218	8.09 213	32.51 213	19.87 216	45.18 212	30.56 220	17.05 212	29.96 211	24.74 210	13.21 214	34.30 212

test123		21.52 208	28.47 210	13.42 210	7.29 216	10.12 217	15.70 217	8.31 212	31.54 214	19.34 217	36.33 213	37.40 219	17.14 211	27.45 212	23.17 211	12.73 215	33.30 213

uanet_test		0.00 209	0.00 211	0.00 211	0.00 218	0.00 218	0.00 219	0.00 215	0.00 215	0.00 218	0.00 214	0.00 221	0.00 214	0.00 213	0.00 212	0.00 216	0.00 214

sosnet-low-res		0.00 209	0.00 211	0.00 211	0.00 218	0.00 218	0.00 219	0.00 215	0.00 215	0.00 218	0.00 214	0.00 221	0.00 214	0.00 213	0.00 212	0.00 216	0.00 214

sosnet		0.00 209	0.00 211	0.00 211	0.00 218	0.00 218	0.00 219	0.00 215	0.00 215	0.00 218	0.00 214	0.00 221	0.00 214	0.00 213	0.00 212	0.00 216	0.00 214

9.14												99.57 121

SR-MVS					99.73 127			99.74 127				99.88 65

Anonymous202405211			99.14 102		99.87 47	99.55 47	99.50 91	99.70 131	98.55 137		98.61 162	98.46 164	98.76 137	99.66 41	99.50 35	99.85 38	99.63 52

our_test_3					99.75 117	99.11 143	99.74 42

test_part1																	99.23 137

ambc			98.83 140		99.72 129	98.52 183	98.84 181		98.96 92	99.92 8	99.34 107	99.74 100	99.04 116	98.68 171	97.57 183	99.46 143	98.99 163

MTAPA										99.62 108		99.95 24

MTMP										99.53 125		99.92 48

Patchmatch-RL test							65.75 216

tmp_tt				88.14 207	96.68 214	91.91 214	93.70 214	61.38 211	99.61 19	90.51 214	99.40 103	99.71 105	90.32 210	99.22 102	99.44 45	96.25 202

XVS					99.86 61	99.30 107	99.72 47			99.69 92		99.93 40				99.60 116

X-MVStestdata					99.86 61	99.30 107	99.72 47			99.69 92		99.93 40				99.60 116

abl_6				99.21 145	99.49 178	98.62 178	98.90 176	99.44 179	97.08 196	99.61 111	97.19 196	99.73 103	98.35 151			99.45 145	98.84 165

mPP-MVS					99.84 76							99.92 48

NP-MVS									97.37 190

Patchmtry						98.19 199	98.91 173	99.97 1		99.43 147

DeepMVS_CX						96.39 211	97.15 213	88.89 210	97.94 177	99.51 133	95.71 202	97.88 173	98.19 152	98.92 143		97.73 194	97.75 193

High-resolution multi-view benchmark