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 2	99.93 1	99.97 2	99.82 7	99.91 3	99.92 33	99.75 5	99.93 5	99.89 31	100.00 1	99.87 2	99.93 3	99.82 8	99.96 3	99.90 3
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 4	99.93 1	99.97 2	99.83 3	99.93 1	99.96 12	99.77 4	99.89 17	99.99 1	99.86 75	99.84 5	99.89 8	99.81 9	99.97 1	99.88 7

SixPastTwentyTwo		99.89 2	99.85 6	99.93 1	99.97 2	99.88 2	99.92 2	99.97 1	99.66 13	99.94 4	99.94 11	99.74 104	99.81 7	99.97 1	99.89 1	99.96 3	99.89 5

test_part1		99.88 4	99.89 1	99.88 12	99.96 7	99.90 1	99.83 17	99.97 1	99.84 2	99.93 5	99.91 23	99.83 85	99.63 39	99.89 8	99.88 2	99.96 3	99.95 1

pmmvs6		99.88 4	99.87 2	99.89 9	99.97 2	99.76 16	99.89 5	99.96 12	99.82 3	99.90 15	99.92 16	99.95 25	99.68 29	99.93 3	99.88 2	99.95 8	99.86 10

anonymousdsp		99.87 6	99.86 4	99.88 12	99.95 10	99.75 22	99.90 4	99.96 12	99.69 8	99.83 50	99.96 4	99.99 3	99.74 21	99.95 2	99.83 5	99.91 20	99.88 7

FC-MVSNet-test		99.84 7	99.80 7	99.89 9	99.96 7	99.83 3	99.84 14	99.95 23	99.37 44	99.77 65	99.95 6	99.96 13	99.85 3	99.93 3	99.83 5	99.95 8	99.72 35

UniMVSNet_ETH3D		99.81 8	99.79 8	99.85 19	99.98 1	99.76 16	99.73 45	99.96 12	99.68 10	99.87 28	99.59 80	99.91 55	99.58 47	99.90 7	99.85 4	99.96 3	99.81 17

TDRefinement		99.81 8	99.76 10	99.86 16	99.83 84	99.53 56	99.89 5	99.91 38	99.73 6	99.88 22	99.83 44	99.96 13	99.76 16	99.91 6	99.81 9	99.86 36	99.59 63

WR-MVS		99.79 10	99.68 14	99.91 5	99.95 10	99.83 3	99.87 9	99.96 12	99.39 43	99.93 5	99.87 35	99.29 149	99.77 14	99.83 18	99.72 16	99.97 1	99.82 14

MIMVSNet1		99.79 10	99.75 11	99.84 20	99.89 37	99.83 3	99.84 14	99.89 46	99.31 50	99.93 5	99.92 16	99.97 8	99.68 29	99.89 8	99.64 22	99.82 50	99.66 47

pm-mvs1		99.77 12	99.69 13	99.86 16	99.94 20	99.68 31	99.84 14	99.93 26	99.59 22	99.87 28	99.92 16	99.21 152	99.65 35	99.88 12	99.77 12	99.93 17	99.78 23

PEN-MVS		99.77 12	99.65 17	99.91 5	99.95 10	99.80 12	99.86 10	99.97 1	99.08 79	99.89 17	99.69 64	99.68 115	99.84 5	99.81 22	99.64 22	99.95 8	99.81 17

EU-MVSNet		99.76 14	99.74 12	99.78 37	99.82 89	99.81 10	99.88 7	99.87 51	99.31 50	99.75 71	99.91 23	99.76 103	99.78 12	99.84 17	99.74 15	99.56 129	99.81 17

Vis-MVSNet		99.76 14	99.78 9	99.75 46	99.92 26	99.77 15	99.83 17	99.85 62	99.43 37	99.85 41	99.84 41	100.00 1	99.13 111	99.83 18	99.66 20	99.90 22	99.90 3
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DTE-MVSNet		99.75 16	99.61 23	99.92 4	99.95 10	99.81 10	99.86 10	99.96 12	99.18 67	99.92 10	99.66 67	99.45 134	99.85 3	99.80 23	99.56 28	99.96 3	99.79 22

tfpnnormal		99.74 17	99.63 20	99.86 16	99.93 23	99.75 22	99.80 26	99.89 46	99.31 50	99.88 22	99.43 101	99.66 118	99.77 14	99.80 23	99.71 17	99.92 18	99.76 27

DeepC-MVS	99.05 5	99.74 17	99.64 18	99.84 20	99.90 34	99.39 87	99.79 27	99.81 92	99.69 8	99.90 15	99.87 35	99.98 4	99.81 7	99.62 49	99.32 55	99.83 47	99.65 50
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 19	99.67 15	99.81 27	99.93 23	99.74 24	99.68 53	99.91 38	99.59 22	99.88 22	99.73 53	99.81 90	99.55 51	99.59 50	99.53 33	99.89 25	99.70 41

PS-CasMVS		99.73 19	99.59 28	99.90 8	99.95 10	99.80 12	99.85 13	99.97 1	98.95 97	99.86 34	99.73 53	99.36 141	99.81 7	99.83 18	99.67 19	99.95 8	99.83 13

WR-MVS_H		99.73 19	99.61 23	99.88 12	99.95 10	99.82 7	99.83 17	99.96 12	99.01 89	99.84 45	99.71 61	99.41 140	99.74 21	99.77 28	99.70 18	99.95 8	99.82 14

TransMVSNet (Re)		99.72 22	99.59 28	99.88 12	99.95 10	99.76 16	99.88 7	99.94 24	99.58 24	99.92 10	99.90 28	98.55 167	99.65 35	99.89 8	99.76 13	99.95 8	99.70 41

ACMH	99.11 4	99.72 22	99.63 20	99.84 20	99.87 49	99.59 44	99.83 17	99.88 50	99.46 36	99.87 28	99.66 67	99.95 25	99.76 16	99.73 33	99.47 41	99.84 42	99.52 93
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		99.70 24	99.67 15	99.74 52	99.94 20	99.71 27	99.82 22	99.91 38	99.14 75	99.53 129	99.70 62	99.88 67	99.33 84	99.88 12	99.61 27	99.94 15	99.77 24

COLMAP_ROB	99.18 2	99.70 24	99.60 26	99.81 27	99.84 78	99.37 94	99.76 33	99.84 71	99.54 30	99.82 53	99.64 71	99.95 25	99.75 18	99.79 25	99.56 28	99.83 47	99.37 124
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 26	99.59 28	99.81 27	99.88 43	99.41 84	99.75 37	99.86 55	99.43 37	99.80 57	99.54 85	99.97 8	99.73 24	99.82 21	99.52 35	99.85 39	99.43 110

test20.03		99.68 27	99.60 26	99.76 42	99.91 30	99.70 30	99.68 53	99.87 51	99.05 86	99.88 22	99.92 16	99.88 67	99.50 62	99.77 28	99.42 48	99.75 71	99.49 96

CP-MVSNet		99.68 27	99.51 38	99.89 9	99.95 10	99.76 16	99.83 17	99.96 12	98.83 115	99.84 45	99.65 70	99.09 154	99.80 10	99.78 26	99.62 26	99.95 8	99.82 14

PVSNet_Blended_VisFu		99.66 29	99.64 18	99.67 64	99.91 30	99.71 27	99.61 65	99.79 102	99.41 39	99.91 13	99.85 39	99.61 121	99.00 122	99.67 40	99.42 48	99.81 53	99.81 17

v10		99.65 30	99.51 38	99.81 27	99.83 84	99.61 40	99.75 37	99.94 24	99.56 26	99.76 68	99.94 11	99.60 123	99.73 24	99.11 125	99.01 95	99.85 39	99.74 30

CHOSEN 1792x2688		99.65 30	99.55 32	99.77 41	99.93 23	99.60 41	99.79 27	99.92 33	99.73 6	99.74 78	99.93 14	99.98 4	99.80 10	98.83 166	99.01 95	99.45 148	99.76 27

UA-Net		99.64 32	99.62 22	99.66 66	99.97 2	99.82 7	99.14 152	99.96 12	98.95 97	99.52 135	99.38 109	99.86 75	99.55 51	99.72 34	99.66 20	99.80 57	99.94 2

GeoE		99.63 33	99.51 38	99.78 37	99.91 30	99.57 47	99.78 29	99.97 1	99.23 58	99.72 87	99.72 57	99.80 96	99.50 62	99.45 69	99.10 82	99.79 60	99.71 40

Baseline_NR-MVSNet		99.62 34	99.48 43	99.78 37	99.85 72	99.76 16	99.59 70	99.82 84	98.84 113	99.88 22	99.91 23	99.04 155	99.61 41	99.46 62	99.78 11	99.94 15	99.60 61

pmmvs-eth3d		99.61 35	99.48 43	99.75 46	99.87 49	99.30 110	99.75 37	99.89 46	99.23 58	99.85 41	99.88 34	99.97 8	99.49 67	99.46 62	99.01 95	99.68 90	99.52 93

v1144		99.61 35	99.43 51	99.82 23	99.88 43	99.41 84	99.76 33	99.86 55	99.64 16	99.84 45	99.95 6	99.49 132	99.74 21	99.00 136	98.93 107	99.84 42	99.58 71

v8		99.61 35	99.45 49	99.79 36	99.80 95	99.59 44	99.73 45	99.93 26	99.48 34	99.77 65	99.90 28	99.48 133	99.67 32	99.11 125	98.89 111	99.84 42	99.73 32

casdiffmvs		99.61 35	99.55 32	99.68 62	99.89 37	99.53 56	99.64 59	99.68 142	99.51 31	99.62 112	99.90 28	99.96 13	99.37 78	99.28 95	99.25 58	99.88 27	99.44 107

CSCG		99.61 35	99.52 37	99.71 56	99.89 37	99.62 38	99.52 87	99.76 123	99.61 20	99.69 96	99.73 53	99.96 13	99.57 49	99.27 98	98.62 142	99.81 53	99.85 12

v1192		99.60 40	99.41 55	99.82 23	99.89 37	99.43 79	99.81 24	99.84 71	99.63 18	99.85 41	99.95 6	99.35 144	99.72 26	99.01 134	98.90 110	99.82 50	99.58 71

APDe-MVS		99.60 40	99.48 43	99.73 54	99.85 72	99.51 67	99.75 37	99.85 62	99.17 68	99.81 56	99.56 83	99.94 35	99.44 74	99.42 72	99.22 59	99.67 92	99.54 85

v1921920		99.59 42	99.40 58	99.82 23	99.88 43	99.45 74	99.81 24	99.83 77	99.65 14	99.86 34	99.95 6	99.29 149	99.75 18	98.98 140	98.86 115	99.78 62	99.59 63

TranMVSNet+NR-MVSNet		99.59 42	99.42 54	99.80 32	99.87 49	99.55 50	99.64 59	99.86 55	99.05 86	99.88 22	99.72 57	99.33 147	99.64 37	99.47 61	99.14 69	99.91 20	99.67 46

EG-PatchMatch MVS		99.59 42	99.49 42	99.70 59	99.82 89	99.26 117	99.39 115	99.83 77	98.99 91	99.93 5	99.54 85	99.92 49	99.51 58	99.78 26	99.50 36	99.73 80	99.41 114

pmmvs5		99.58 45	99.47 46	99.70 59	99.84 78	99.50 68	99.58 74	99.80 99	98.98 94	99.73 84	99.92 16	99.81 90	99.49 67	99.28 95	99.05 89	99.77 66	99.73 32

v144192		99.58 45	99.39 59	99.80 32	99.87 49	99.44 76	99.77 30	99.84 71	99.64 16	99.86 34	99.93 14	99.35 144	99.72 26	98.92 146	98.82 119	99.74 76	99.66 47

v148		99.58 45	99.43 51	99.76 42	99.87 49	99.40 86	99.76 33	99.85 62	99.48 34	99.83 50	99.82 46	99.83 85	99.51 58	99.20 111	98.82 119	99.75 71	99.45 104

v1240		99.58 45	99.38 62	99.82 23	99.89 37	99.49 69	99.82 22	99.83 77	99.63 18	99.86 34	99.96 4	98.92 161	99.75 18	99.15 121	98.96 104	99.76 68	99.56 78

V42		99.57 49	99.41 55	99.75 46	99.84 78	99.37 94	99.73 45	99.83 77	99.41 39	99.75 71	99.89 31	99.42 138	99.60 43	99.15 121	98.96 104	99.76 68	99.65 50

TSAR-MVS + MP.		99.56 50	99.54 35	99.58 83	99.69 138	99.14 138	99.73 45	99.45 179	99.50 32	99.35 166	99.60 78	99.93 41	99.50 62	99.56 53	99.37 53	99.77 66	99.64 53
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 50	99.35 65	99.81 27	99.87 49	99.35 100	99.75 37	99.85 62	99.56 26	99.87 28	99.95 6	99.44 136	99.66 33	98.91 149	98.76 125	99.86 36	99.45 104

Gipuma		99.55 52	99.23 84	99.91 5	99.87 49	99.52 63	99.86 10	99.93 26	99.87 1	99.96 2	96.72 203	99.55 128	99.97 1	99.77 28	99.46 43	99.87 33	99.74 30
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DVP-MVS		99.53 53	99.51 38	99.55 91	99.82 89	99.58 46	99.54 82	99.78 107	99.28 56	99.21 176	99.70 62	99.97 8	99.32 87	99.32 83	99.14 69	99.64 105	99.58 71
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
NR-MVSNet		99.52 54	99.29 74	99.80 32	99.96 7	99.38 90	99.55 78	99.81 92	98.86 110	99.87 28	99.51 95	98.81 163	99.72 26	99.86 15	99.04 91	99.89 25	99.54 85

zzz-MVS		99.51 55	99.36 63	99.68 62	99.88 43	99.38 90	99.53 83	99.84 71	99.11 78	99.59 120	98.93 147	99.95 25	99.58 47	99.44 70	99.21 61	99.65 96	99.52 93

ACMMPR		99.51 55	99.32 69	99.72 55	99.87 49	99.33 103	99.61 65	99.85 62	99.19 65	99.73 84	98.73 158	99.95 25	99.61 41	99.35 78	99.14 69	99.66 94	99.58 71

UniMVSNet (Re)		99.50 57	99.29 74	99.75 46	99.86 63	99.47 72	99.51 90	99.82 84	98.90 105	99.89 17	99.64 71	99.00 156	99.55 51	99.32 83	99.08 84	99.90 22	99.59 63

FMVSNet1		99.50 57	99.57 31	99.42 112	99.67 145	99.65 34	99.60 69	99.91 38	99.40 41	99.39 159	99.83 44	99.27 151	98.14 160	99.68 37	99.50 36	99.81 53	99.68 43

HyFIR lowres test		99.50 57	99.26 78	99.80 32	99.95 10	99.62 38	99.76 33	99.97 1	99.67 11	99.56 126	99.94 11	98.40 170	99.78 12	98.84 165	98.59 145	99.76 68	99.72 35

PM-MVS		99.49 60	99.43 51	99.57 86	99.76 117	99.34 102	99.53 83	99.77 114	98.93 101	99.75 71	99.46 99	99.83 85	99.11 113	99.72 34	99.29 57	99.49 143	99.46 103

Anonymous20231206		99.48 61	99.31 71	99.69 61	99.79 99	99.57 47	99.63 63	99.79 102	98.88 107	99.91 13	99.72 57	99.93 41	99.59 44	99.24 101	98.63 141	99.43 153	99.18 141

DU-MVS		99.48 61	99.26 78	99.75 46	99.85 72	99.38 90	99.50 94	99.81 92	98.86 110	99.89 17	99.51 95	98.98 157	99.59 44	99.46 62	98.97 102	99.87 33	99.63 54

RPSCF		99.48 61	99.45 49	99.52 98	99.73 131	99.33 103	99.13 153	99.77 114	99.33 48	99.47 146	99.39 108	99.92 49	99.36 79	99.63 46	99.13 77	99.63 108	99.41 114

ACMMP_NAP		99.47 64	99.33 67	99.63 74	99.85 72	99.28 115	99.56 77	99.83 77	98.75 121	99.48 143	99.03 144	99.95 25	99.47 73	99.48 58	99.19 62	99.57 126	99.59 63

Anonymous20231211		99.47 64	99.39 59	99.57 86	99.89 37	99.60 41	99.50 94	99.69 136	98.91 104	99.62 112	99.17 130	99.35 144	98.86 135	99.63 46	99.46 43	99.84 42	99.62 57

SteuartSystems-ACMMP		99.47 64	99.22 87	99.76 42	99.88 43	99.36 96	99.65 58	99.84 71	98.47 145	99.80 57	98.68 161	99.96 13	99.68 29	99.37 75	99.06 86	99.72 83	99.66 47
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ACMM	98.37 12	99.47 64	99.23 84	99.74 52	99.86 63	99.19 132	99.68 53	99.86 55	99.16 72	99.71 93	98.52 172	99.95 25	99.62 40	99.35 78	99.02 93	99.74 76	99.42 113
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HFP-MVS		99.46 68	99.30 72	99.65 68	99.82 89	99.25 120	99.50 94	99.82 84	99.23 58	99.58 124	98.86 149	99.94 35	99.56 50	99.14 123	99.12 80	99.63 108	99.56 78

LGP-MVS_train		99.46 68	99.18 97	99.78 37	99.87 49	99.25 120	99.71 51	99.87 51	98.02 177	99.79 60	98.90 148	99.96 13	99.66 33	99.49 57	99.17 65	99.79 60	99.49 96

SED-MVS		99.45 70	99.46 48	99.42 112	99.77 112	99.57 47	99.42 109	99.80 99	99.06 83	99.38 160	99.66 67	99.96 13	98.65 146	99.31 85	99.14 69	99.53 134	99.55 83

ETV-MVS		99.45 70	99.32 69	99.60 79	99.79 99	99.60 41	99.40 114	99.78 107	97.88 184	99.83 50	99.33 112	99.70 112	98.97 125	99.74 31	99.43 47	99.84 42	99.58 71

ACMP	98.32 13	99.44 72	99.18 97	99.75 46	99.83 84	99.18 133	99.64 59	99.83 77	98.81 117	99.79 60	98.42 179	99.96 13	99.64 37	99.46 62	98.98 101	99.74 76	99.44 107
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DCV-MVSNet		99.43 73	99.23 84	99.67 64	99.92 26	99.76 16	99.64 59	99.93 26	99.06 83	99.68 103	97.77 191	98.97 158	98.97 125	99.72 34	99.54 32	99.88 27	99.81 17

SMA-MVS		99.43 73	99.41 55	99.45 109	99.82 89	99.31 108	99.02 167	99.59 157	99.06 83	99.34 169	99.53 91	99.96 13	99.38 77	99.29 90	99.13 77	99.53 134	99.59 63
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
testgi		99.43 73	99.47 46	99.38 121	99.90 34	99.67 33	99.30 133	99.73 131	98.64 133	99.53 129	99.52 93	99.90 58	98.08 163	99.65 44	99.40 52	99.75 71	99.55 83

DELS-MVS		99.42 76	99.53 36	99.29 135	99.52 173	99.43 79	99.42 109	99.28 195	99.16 72	99.72 87	99.82 46	99.97 8	98.17 157	99.56 53	99.16 66	99.65 96	99.59 63
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 76	99.22 87	99.65 68	99.78 104	99.13 142	99.50 94	99.85 62	99.40 41	99.80 57	98.59 168	99.79 99	99.30 91	99.20 111	99.06 86	99.71 86	99.35 127

DPE-MVS		99.41 78	99.36 63	99.47 105	99.66 146	99.48 70	99.46 105	99.75 128	98.65 129	99.41 156	99.67 65	99.95 25	98.82 136	99.21 108	99.14 69	99.72 83	99.40 119
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UniMVSNet_NR-MVSNet		99.41 78	99.12 109	99.76 42	99.86 63	99.48 70	99.50 94	99.81 92	98.84 113	99.89 17	99.45 100	98.32 173	99.59 44	99.22 105	98.89 111	99.90 22	99.63 54

CP-MVS		99.41 78	99.20 92	99.65 68	99.80 95	99.23 127	99.44 107	99.75 128	98.60 138	99.74 78	98.66 162	99.93 41	99.48 70	99.33 82	99.16 66	99.73 80	99.48 99

QAPM		99.41 78	99.21 91	99.64 73	99.78 104	99.16 135	99.51 90	99.85 62	99.20 62	99.72 87	99.43 101	99.81 90	99.25 95	98.87 155	98.71 133	99.71 86	99.30 132

UGNet		99.40 82	99.61 23	99.16 155	99.88 43	99.64 36	99.61 65	99.77 114	99.31 50	99.63 111	99.33 112	99.93 41	96.46 198	99.63 46	99.53 33	99.63 108	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
Vis-MVSNet (Re-imp)		99.40 82	99.28 76	99.55 91	99.92 26	99.68 31	99.31 128	99.87 51	98.69 126	99.16 178	99.08 139	98.64 166	99.20 99	99.65 44	99.46 43	99.83 47	99.72 35

OPM-MVS		99.39 84	99.22 87	99.59 80	99.76 117	98.82 167	99.51 90	99.79 102	99.17 68	99.53 129	99.31 117	99.95 25	99.35 80	99.22 105	98.79 124	99.60 118	99.27 135

Fast-Effi-MVS+		99.39 84	99.18 97	99.63 74	99.86 63	99.28 115	99.45 106	99.91 38	98.47 145	99.61 115	99.50 97	99.57 125	99.17 100	99.24 101	98.66 138	99.78 62	99.59 63

LS3D		99.39 84	99.28 76	99.52 98	99.77 112	99.39 87	99.55 78	99.82 84	98.93 101	99.64 109	98.52 172	99.67 117	98.58 150	99.74 31	99.63 24	99.75 71	99.06 157

CS-MVS		99.38 87	99.19 94	99.59 80	99.86 63	99.65 34	99.28 136	99.77 114	97.97 180	99.75 71	98.42 179	99.70 112	99.03 120	99.57 52	99.42 48	99.87 33	99.61 59

diffmvs		99.38 87	99.33 67	99.45 109	99.87 49	99.39 87	99.28 136	99.58 160	99.55 28	99.50 139	99.85 39	99.85 81	98.94 130	98.58 178	98.68 136	99.51 140	99.39 121

CANet		99.36 89	99.39 59	99.34 132	99.80 95	99.35 100	99.41 113	99.47 177	99.20 62	99.74 78	99.54 85	99.68 115	98.05 165	99.23 103	98.97 102	99.57 126	99.73 32

MVS_0304		99.36 89	99.35 65	99.37 127	99.85 72	99.36 96	99.39 115	99.56 163	99.36 46	99.75 71	99.23 123	99.90 58	97.97 172	99.00 136	98.83 118	99.69 89	99.77 24

ACMMP		99.36 89	99.06 116	99.71 56	99.86 63	99.36 96	99.63 63	99.85 62	98.33 160	99.72 87	97.73 193	99.94 35	99.53 54	99.37 75	99.13 77	99.65 96	99.56 78
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 92	99.26 78	99.46 107	99.66 146	99.15 137	98.92 176	99.67 145	99.55 28	99.35 166	98.83 151	99.91 55	99.35 80	99.19 114	98.53 147	99.78 62	99.68 43
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 92	99.09 114	99.65 68	99.84 78	99.22 128	99.59 70	99.78 107	98.13 169	99.67 104	98.44 176	99.93 41	99.43 76	99.31 85	99.09 83	99.60 118	99.49 96
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
pmmvs4		99.34 94	99.15 104	99.57 86	99.77 112	98.90 159	99.51 90	99.77 114	99.07 81	99.73 84	99.72 57	99.84 83	99.07 115	98.85 160	98.39 156	99.55 132	99.27 135

EPP-MVSNet		99.34 94	99.10 112	99.62 78	99.94 20	99.74 24	99.66 57	99.80 99	99.07 81	98.93 189	99.61 75	96.13 188	99.49 67	99.67 40	99.63 24	99.92 18	99.86 10

TSAR-MVS + GP.		99.33 96	99.17 101	99.51 100	99.71 136	99.00 154	98.84 185	99.71 133	98.23 166	99.74 78	99.53 91	99.90 58	99.35 80	99.38 74	98.85 116	99.72 83	99.31 130

PHI-MVS		99.33 96	99.19 94	99.49 103	99.69 138	99.25 120	99.27 138	99.59 157	98.44 149	99.78 64	99.15 131	99.92 49	98.95 129	99.39 73	99.04 91	99.64 105	99.18 141

MSP-MVS		99.32 98	99.26 78	99.38 121	99.76 117	99.54 53	99.42 109	99.72 132	98.92 103	98.84 196	98.96 146	99.96 13	98.91 131	98.72 173	99.14 69	99.63 108	99.58 71
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PGM-MVS		99.32 98	98.99 125	99.71 56	99.86 63	99.31 108	99.59 70	99.86 55	97.51 193	99.75 71	98.23 183	99.94 35	99.53 54	99.29 90	99.08 84	99.65 96	99.54 85

DeepC-MVS_fast	98.69 9	99.32 98	99.13 107	99.53 94	99.63 153	98.78 170	99.53 83	99.33 193	99.08 79	99.77 65	99.18 129	99.89 61	99.29 92	99.00 136	98.70 134	99.65 96	99.30 132
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 98	99.09 114	99.58 83	99.75 121	98.74 174	99.36 120	99.54 166	99.14 75	99.72 87	99.24 121	99.89 61	99.51 58	99.30 87	98.76 125	99.62 114	98.54 176

TSAR-MVS + ACMM		99.31 102	99.26 78	99.37 127	99.66 146	98.97 157	99.20 145	99.56 163	99.33 48	99.19 177	99.54 85	99.91 55	99.32 87	99.12 124	98.34 159	99.29 167	99.65 50

3Dnovator+	98.92 7	99.31 102	99.03 120	99.63 74	99.77 112	98.90 159	99.52 87	99.81 92	99.37 44	99.72 87	98.03 188	99.73 107	99.32 87	98.99 139	98.81 122	99.67 92	99.36 125

X-MVS		99.30 104	98.99 125	99.66 66	99.85 72	99.30 110	99.49 101	99.82 84	98.32 161	99.69 96	97.31 200	99.93 41	99.50 62	99.37 75	99.16 66	99.60 118	99.53 88

MVS_111021_HR		99.30 104	99.14 105	99.48 104	99.58 169	99.25 120	99.27 138	99.61 152	98.74 122	99.66 106	99.02 145	99.84 83	99.33 84	99.20 111	98.76 125	99.44 150	99.18 141

TAPA-MVS	98.54 10	99.30 104	99.24 83	99.36 131	99.44 188	98.77 172	99.00 169	99.41 184	99.23 58	99.60 118	99.50 97	99.86 75	99.15 107	99.29 90	98.95 106	99.56 129	99.08 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		99.30 104	99.01 124	99.63 74	99.75 121	98.89 162	99.35 123	99.60 154	98.53 143	99.86 34	99.57 82	99.94 35	99.52 57	98.96 141	98.10 172	99.70 88	99.08 154
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
USDC		99.29 108	98.98 127	99.65 68	99.72 133	98.87 165	99.47 103	99.66 148	99.35 47	99.87 28	99.58 81	99.87 74	99.51 58	98.85 160	97.93 178	99.65 96	98.38 180

PMVS	94.32 17	99.27 109	99.55 32	98.94 173	99.60 162	99.43 79	99.39 115	99.54 166	98.99 91	99.69 96	99.60 78	99.81 90	95.68 203	99.88 12	99.83 5	99.73 80	99.31 130
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS_111021_LR		99.25 110	99.13 107	99.39 117	99.50 181	99.14 138	99.23 143	99.50 174	98.67 127	99.61 115	99.12 135	99.81 90	99.16 103	99.28 95	98.67 137	99.35 163	99.21 140

baseline		99.24 111	99.30 72	99.17 154	99.78 104	99.14 138	99.10 157	99.69 136	98.97 95	99.49 141	99.84 41	99.88 67	97.99 171	98.85 160	98.73 131	98.98 182	99.72 35

EIA-MVS		99.23 112	99.03 120	99.47 105	99.83 84	99.64 36	99.16 149	99.81 92	97.11 200	99.65 108	98.44 176	99.78 102	98.61 149	99.46 62	99.22 59	99.75 71	99.59 63

HPM-MVS++		99.23 112	98.98 127	99.53 94	99.75 121	99.02 152	99.44 107	99.77 114	98.65 129	99.52 135	98.72 159	99.92 49	99.33 84	98.77 171	98.40 155	99.40 157	99.36 125

PMMVS2		99.23 112	99.22 87	99.24 142	99.80 95	99.14 138	99.50 94	99.82 84	99.12 77	98.41 210	99.91 23	99.98 4	98.51 151	99.48 58	98.76 125	99.38 159	98.14 188

CPTT-MVS		99.21 115	98.89 138	99.58 83	99.72 133	99.12 145	99.30 133	99.76 123	98.62 134	99.66 106	97.51 196	99.89 61	99.48 70	99.01 134	98.64 140	99.58 125	99.40 119

TinyColmap		99.21 115	98.89 138	99.59 80	99.61 158	98.61 183	99.47 103	99.67 145	99.02 88	99.82 53	99.15 131	99.74 104	99.35 80	99.17 119	98.33 160	99.63 108	98.22 186

Effi-MVS+		99.20 117	98.93 133	99.50 102	99.79 99	99.26 117	98.82 188	99.96 12	98.37 159	99.60 118	99.12 135	98.36 171	99.05 118	98.93 144	98.82 119	99.78 62	99.68 43

PVSNet_BlendedMVS		99.20 117	99.17 101	99.23 143	99.69 138	99.33 103	99.04 162	99.13 198	98.41 155	99.79 60	99.33 112	99.36 141	98.10 161	99.29 90	98.87 113	99.65 96	99.56 78

PVSNet_Blended		99.20 117	99.17 101	99.23 143	99.69 138	99.33 103	99.04 162	99.13 198	98.41 155	99.79 60	99.33 112	99.36 141	98.10 161	99.29 90	98.87 113	99.65 96	99.56 78

MCST-MVS		99.17 120	98.82 146	99.57 86	99.75 121	98.70 178	99.25 142	99.69 136	98.62 134	99.59 120	98.54 170	99.79 99	99.53 54	98.48 182	98.15 168	99.64 105	99.43 110

APD-MVS		99.17 120	98.92 134	99.46 107	99.78 104	99.24 125	99.34 124	99.78 107	97.79 187	99.48 143	98.25 182	99.88 67	98.77 139	99.18 117	98.92 108	99.63 108	99.18 141
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	98.82 8	99.17 120	98.85 142	99.53 94	99.75 121	99.06 150	99.36 120	99.82 84	98.28 163	99.76 68	98.47 174	99.61 121	98.91 131	98.80 168	98.70 134	99.60 118	99.04 161

IterMVS-LS		99.16 123	98.82 146	99.57 86	99.87 49	99.71 27	99.58 74	99.92 33	99.24 57	99.71 93	99.73 53	95.79 189	98.91 131	98.82 167	98.66 138	99.43 153	99.77 24
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepPCF-MVS	98.38 11	99.16 123	99.20 92	99.12 159	99.20 205	98.71 177	98.85 184	99.06 201	99.17 68	98.96 188	99.61 75	99.86 75	99.29 92	99.17 119	98.72 132	99.36 161	99.15 149

IterMVS-SCA-FT		99.15 125	98.96 130	99.38 121	99.87 49	99.54 53	99.53 83	99.79 102	98.94 99	99.82 53	99.92 16	97.65 180	98.82 136	98.95 143	98.26 162	98.45 191	99.47 102

CDS-MVSNet		99.15 125	99.10 112	99.21 149	99.59 166	99.22 128	99.48 102	99.47 177	98.89 106	99.41 156	99.84 41	98.11 176	97.76 175	99.26 100	99.01 95	99.57 126	99.38 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS_MVSNet		99.15 125	99.12 109	99.19 152	99.92 26	99.73 26	99.55 78	99.86 55	98.45 148	96.91 216	98.74 157	98.33 172	99.02 121	99.54 55	99.47 41	99.88 27	99.61 59

MDA-MVSNet-bldmvs		99.11 128	99.11 111	99.12 159	99.91 30	99.38 90	99.77 30	98.72 205	99.31 50	99.85 41	99.43 101	98.26 174	99.48 70	99.85 16	98.47 150	96.99 202	99.08 154

OMC-MVS		99.11 128	98.95 131	99.29 135	99.37 194	98.57 185	99.19 146	99.20 197	98.87 109	99.58 124	99.13 133	99.88 67	99.00 122	99.19 114	98.46 151	99.43 153	98.57 175

MVS_Test		99.09 130	98.92 134	99.29 135	99.61 158	99.07 149	99.04 162	99.81 92	98.58 140	99.37 163	99.74 51	98.87 162	98.41 154	98.61 177	98.01 176	99.50 142	99.57 77

CNVR-MVS		99.08 131	98.83 143	99.37 127	99.61 158	98.74 174	99.15 150	99.54 166	98.59 139	99.37 163	98.15 185	99.88 67	99.08 114	98.91 149	98.46 151	99.48 144	99.06 157

IterMVS		99.08 131	98.90 137	99.29 135	99.87 49	99.53 56	99.52 87	99.77 114	98.94 99	99.75 71	99.91 23	97.52 184	98.72 143	98.86 158	98.14 169	98.09 194	99.43 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		99.07 133	99.19 94	98.93 175	99.02 210	99.53 56	99.31 128	99.84 71	98.86 110	98.88 192	99.64 71	98.44 169	96.92 192	99.35 78	99.00 99	99.61 115	99.53 88

CVMVSNet		99.06 134	98.88 141	99.28 139	99.52 173	99.53 56	99.42 109	99.69 136	98.74 122	98.27 212	99.89 31	95.48 192	99.44 74	99.46 62	99.33 54	99.32 166	99.75 29

CDPH-MVS		99.05 135	98.63 153	99.54 93	99.75 121	98.78 170	99.59 70	99.68 142	97.79 187	99.37 163	98.20 184	99.86 75	99.14 109	98.58 178	98.01 176	99.68 90	99.16 147

TAMVS		99.05 135	99.02 123	99.08 164	99.69 138	99.22 128	99.33 125	99.32 194	99.16 72	98.97 187	99.87 35	97.36 185	97.76 175	99.21 108	99.00 99	99.44 150	99.33 128

CANet_DTU		99.03 137	99.18 97	98.87 178	99.58 169	99.03 151	99.18 147	99.41 184	98.65 129	99.74 78	99.55 84	99.71 109	96.13 201	99.19 114	98.92 108	99.17 176	99.18 141

Effi-MVS+-dtu		99.01 138	99.05 117	98.98 168	99.60 162	99.13 142	99.03 166	99.61 152	98.52 144	99.01 184	98.53 171	99.83 85	96.95 191	99.48 58	98.59 145	99.66 94	99.25 139

canonicalmvs		99.00 139	98.68 152	99.37 127	99.68 144	99.42 83	98.94 175	99.89 46	99.00 90	98.99 185	98.43 178	95.69 190	98.96 128	99.18 117	99.18 63	99.74 76	99.88 7

MIMVSNet		99.00 139	99.03 120	98.97 172	99.32 200	99.32 107	99.39 115	99.91 38	98.41 155	98.76 199	99.24 121	99.17 153	97.13 185	99.30 87	98.80 123	99.29 167	99.01 162

CHOSEN 280x420		98.99 141	98.91 136	99.07 165	99.77 112	99.26 117	99.55 78	99.92 33	98.62 134	98.67 203	99.62 74	97.20 186	98.44 153	99.50 56	99.18 63	98.08 195	98.99 165

xxxxxxxxxxxxxcwj		98.97 142	98.97 129	98.98 168	99.64 151	98.89 162	98.00 214	99.58 160	98.42 152	99.08 182	98.63 164	99.96 13	98.04 167	99.02 132	98.76 125	99.52 136	99.13 150

SF-MVS		98.96 143	98.95 131	98.98 168	99.64 151	98.89 162	98.00 214	99.58 160	98.42 152	99.08 182	98.63 164	99.83 85	98.04 167	99.02 132	98.76 125	99.52 136	99.13 150

GBi-Net		98.96 143	99.05 117	98.85 179	99.02 210	99.53 56	99.31 128	99.78 107	98.13 169	98.48 206	99.43 101	97.58 181	96.92 192	99.68 37	99.50 36	99.61 115	99.53 88

test1		98.96 143	99.05 117	98.85 179	99.02 210	99.53 56	99.31 128	99.78 107	98.13 169	98.48 206	99.43 101	97.58 181	96.92 192	99.68 37	99.50 36	99.61 115	99.53 88

PCF-MVS	97.86 15	98.95 146	98.53 158	99.44 111	99.70 137	98.80 169	98.96 171	99.69 136	98.65 129	99.59 120	99.33 112	99.94 35	99.12 112	98.01 192	97.11 189	99.59 124	97.83 192
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		98.94 147	98.71 151	99.21 149	99.52 173	98.22 201	98.97 170	99.53 171	98.76 119	99.50 139	98.59 168	99.56 127	98.68 144	98.63 176	98.45 153	99.05 179	98.73 172

AdaColmap		98.93 148	98.53 158	99.39 117	99.52 173	98.65 181	99.11 156	99.59 157	98.08 173	99.44 149	97.46 198	99.45 134	99.24 96	98.92 146	98.44 154	99.44 150	98.73 172

MSLP-MVS++		98.92 149	98.73 150	99.14 156	99.44 188	99.00 154	98.36 204	99.35 190	98.82 116	99.38 160	96.06 205	99.79 99	99.07 115	98.88 154	99.05 89	99.27 169	99.53 88

new_pmnet		98.91 150	98.89 138	98.94 173	99.51 179	98.27 197	99.15 150	98.66 206	99.17 68	99.48 143	99.79 49	99.80 96	98.49 152	99.23 103	98.20 166	98.34 192	97.74 196

train_agg		98.89 151	98.48 163	99.38 121	99.69 138	98.76 173	99.31 128	99.60 154	97.71 189	98.98 186	97.89 189	99.89 61	99.29 92	98.32 183	97.59 185	99.42 156	99.16 147

NCCC		98.88 152	98.42 164	99.42 112	99.62 154	98.81 168	99.10 157	99.54 166	98.76 119	99.53 129	95.97 206	99.80 96	99.16 103	98.49 181	98.06 175	99.55 132	99.05 159

PLC	97.83 16	98.88 152	98.52 160	99.30 134	99.45 186	98.60 184	98.65 194	99.49 175	98.66 128	99.59 120	96.33 204	99.59 124	99.17 100	98.87 155	98.53 147	99.46 146	99.05 159
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs3		98.85 154	98.60 154	99.13 157	99.66 146	98.72 176	99.37 119	99.06 201	98.44 149	99.76 68	99.74 51	99.55 128	99.15 107	99.04 130	96.00 197	97.80 196	98.72 174

Fast-Effi-MVS+-dtu		98.82 155	98.80 148	98.84 181	99.51 179	98.90 159	98.96 171	99.91 38	98.29 162	99.11 181	98.47 174	99.63 120	96.03 202	99.21 108	98.12 170	99.52 136	99.01 162

CNLPA		98.82 155	98.52 160	99.18 153	99.21 204	98.50 189	98.73 192	99.34 192	98.73 124	99.56 126	97.55 195	99.42 138	99.06 117	98.93 144	98.10 172	99.21 175	98.38 180

PatchMatch-RL		98.80 157	98.52 160	99.12 159	99.38 193	98.70 178	98.56 197	99.55 165	97.81 186	99.34 169	97.57 194	99.31 148	98.67 145	99.27 98	98.62 142	99.22 174	98.35 182

thisisatest0530		98.78 158	98.26 167	99.39 117	99.78 104	99.43 79	99.07 159	99.64 150	98.44 149	99.42 154	99.22 124	92.68 203	98.63 147	99.30 87	99.14 69	99.80 57	99.60 61

tttt0517		98.77 159	98.25 169	99.38 121	99.79 99	99.46 73	99.07 159	99.64 150	98.40 158	99.38 160	99.21 126	92.54 204	98.63 147	99.34 81	99.14 69	99.80 57	99.62 57

DI_MVS_plusplus_trai		98.74 160	98.08 177	99.51 100	99.79 99	99.29 114	99.61 65	99.60 154	99.20 62	99.46 147	99.09 138	92.93 197	98.97 125	98.27 186	98.35 158	99.65 96	99.45 104

TSAR-MVS + COLMAP		98.74 160	98.58 156	98.93 175	99.29 201	98.23 198	99.04 162	99.24 196	98.79 118	98.80 198	99.37 110	99.71 109	98.06 164	98.02 191	97.46 187	99.16 177	98.48 178

MDTV_nov1_ep13_2view		98.73 162	98.31 166	99.22 146	99.75 121	99.24 125	99.75 37	99.93 26	99.31 50	99.84 45	99.86 38	99.81 90	99.31 90	97.40 200	94.77 199	96.73 204	97.81 193

PMMVS		98.71 163	98.55 157	98.90 177	99.28 202	98.45 191	98.53 200	99.45 179	97.67 191	99.15 180	98.76 155	99.54 130	97.79 174	98.77 171	98.23 164	99.16 177	98.46 179

HQP-MVS		98.70 164	98.19 173	99.28 139	99.61 158	98.52 187	98.71 193	99.35 190	97.97 180	99.53 129	97.38 199	99.85 81	99.14 109	97.53 196	96.85 193	99.36 161	99.26 138

N_pmnet		98.64 165	98.23 172	99.11 162	99.78 104	99.25 120	99.75 37	99.39 188	99.65 14	99.70 95	99.78 50	99.89 61	98.81 138	97.60 195	94.28 200	97.24 201	97.15 200

CMPMVS	76.62 19	98.64 165	98.60 154	98.68 186	99.33 198	97.07 213	98.11 212	98.50 207	97.69 190	99.26 172	98.35 181	99.66 118	97.62 178	99.43 71	99.02 93	99.24 172	99.01 162
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		98.63 167	98.75 149	98.49 192	98.10 216	99.44 76	99.02 167	99.78 107	98.13 169	98.48 206	99.43 101	97.58 181	96.16 200	98.85 160	98.39 156	99.40 157	99.41 114

GA-MVS		98.59 168	98.15 174	99.09 163	99.59 166	99.13 142	98.84 185	99.52 173	98.61 137	99.35 166	99.67 65	93.03 196	97.73 177	98.90 153	98.26 162	99.51 140	99.48 99

MAR-MVS		98.54 169	98.15 174	98.98 168	99.37 194	98.09 204	98.56 197	99.65 149	96.11 215	99.27 171	97.16 202	99.50 131	98.03 169	98.87 155	98.23 164	99.01 180	99.13 150
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		98.49 170	97.60 179	99.53 94	99.90 34	99.55 50	99.77 30	99.48 176	99.67 11	99.86 34	99.98 3	99.98 4	99.50 62	96.90 202	91.52 206	98.67 188	95.62 206

FPMVS		98.48 171	98.83 143	98.07 202	99.09 208	97.98 207	99.07 159	98.04 213	98.99 91	99.22 175	98.85 150	99.43 137	93.79 210	99.66 42	99.11 81	99.24 172	97.76 194

MVS-HIRNet		98.45 172	98.25 169	98.69 185	99.12 206	97.81 212	98.55 199	99.85 62	98.58 140	99.67 104	99.61 75	99.86 75	97.46 181	97.95 193	96.37 195	97.49 198	97.56 197

test0.0.03 1		98.41 173	98.41 165	98.40 196	99.62 154	99.16 135	98.87 182	99.41 184	97.15 198	96.60 218	99.31 117	97.00 187	96.55 197	98.91 149	98.51 149	99.37 160	98.82 170

gg-mvs-nofinetune		98.40 174	98.26 167	98.57 190	99.83 84	98.86 166	98.77 191	99.97 1	99.57 25	99.99 1	99.99 1	93.81 194	93.50 211	98.91 149	98.20 166	99.33 165	98.52 177

baseline1		98.39 175	97.59 180	99.31 133	99.78 104	99.45 74	99.13 153	99.53 171	98.06 175	98.87 193	98.63 164	90.04 209	98.76 140	98.85 160	98.84 117	99.81 53	99.28 134

pmnet_mix02		98.28 176	97.48 182	99.22 146	99.78 104	99.12 145	99.68 53	99.39 188	99.49 33	99.86 34	99.82 46	99.89 61	99.23 97	95.54 205	92.36 203	97.38 199	96.14 204

PatchT		98.11 177	97.12 188	99.26 141	99.65 150	98.34 195	99.57 76	99.97 1	97.48 194	99.43 151	99.04 143	90.84 207	98.15 158	98.04 189	97.78 179	98.82 185	98.30 183

DPM-MVS		98.10 178	97.32 186	99.01 167	99.52 173	97.92 208	98.47 202	99.45 179	98.25 164	98.91 190	93.99 210	99.69 114	98.73 142	96.29 204	96.32 196	99.00 181	98.77 171

EPNet_dtu		98.09 179	98.25 169	97.91 204	99.58 169	98.02 206	98.19 209	99.67 145	97.94 182	99.74 78	99.07 141	98.71 165	93.40 212	97.50 197	97.09 190	96.89 203	99.44 107
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.06 180	98.11 176	98.00 203	99.60 162	98.99 156	98.38 203	99.68 142	98.18 168	98.85 195	97.89 189	95.60 191	92.72 213	98.30 184	98.10 172	98.76 186	99.72 35
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 181	96.80 191	99.22 146	99.60 162	98.23 198	98.91 177	99.97 1	96.89 208	99.43 151	99.10 137	89.24 212	98.15 158	98.04 189	97.78 179	99.26 170	98.30 183

thres200		97.87 182	96.56 193	99.39 117	99.76 117	99.52 63	99.13 153	99.76 123	96.88 210	98.66 204	92.87 214	88.77 215	99.16 103	99.11 125	99.42 48	99.88 27	99.33 128

baseline2		97.87 182	97.18 187	98.67 187	99.34 197	99.17 134	98.48 201	98.82 204	97.08 201	98.83 197	98.75 156	89.47 211	97.03 190	98.67 175	98.27 161	99.52 136	98.83 169

thres600view7		97.86 184	96.53 196	99.41 115	99.84 78	99.52 63	99.36 120	99.76 123	97.32 196	98.38 211	93.24 211	87.25 217	99.23 97	99.11 125	99.75 14	99.88 27	99.48 99

tfpn200view9		97.85 185	96.54 194	99.38 121	99.74 129	99.52 63	99.17 148	99.76 123	96.10 216	98.70 201	92.99 212	89.10 213	99.00 122	99.11 125	99.56 28	99.88 27	99.41 114

thres400		97.82 186	96.47 197	99.40 116	99.81 94	99.44 76	99.29 135	99.69 136	97.15 198	98.57 205	92.82 215	87.96 216	99.16 103	98.96 141	99.55 31	99.86 36	99.41 114

IB-MVS	98.10 14	97.76 187	97.40 185	98.18 198	99.62 154	99.11 147	98.24 207	98.35 209	96.56 212	99.44 149	91.28 216	98.96 160	93.84 209	98.09 188	98.62 142	99.56 129	99.18 141
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		97.74 188	97.46 183	98.08 200	99.62 154	98.37 193	98.26 205	99.41 184	97.03 203	97.38 214	99.54 85	92.89 198	95.12 206	98.78 169	97.68 183	98.65 189	97.90 190

RPMNet		97.70 189	96.54 194	99.06 166	99.57 172	98.23 198	98.95 174	99.97 1	96.89 208	99.49 141	99.13 133	89.63 210	97.09 187	96.68 203	97.02 191	99.26 170	98.19 187

thres100view900		97.69 190	96.37 198	99.23 143	99.74 129	99.21 131	98.81 189	99.43 183	96.10 216	98.70 201	92.99 212	89.10 213	98.88 134	98.58 178	99.31 56	99.82 50	99.27 135

FMVSNet5		97.69 190	96.98 189	98.53 191	98.53 214	99.36 96	98.90 180	99.54 166	96.38 213	98.44 209	95.38 208	90.08 208	97.05 189	99.46 62	99.06 86	98.73 187	99.12 153

MVE	91.08 18	97.68 192	97.65 178	97.71 210	98.46 215	91.62 219	97.92 216	98.86 203	98.73 124	97.99 213	98.64 163	99.96 13	99.17 100	99.59 50	97.75 181	93.87 218	97.27 198
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-mter		97.65 193	97.57 181	97.75 208	98.90 213	98.56 186	98.15 210	98.45 208	96.92 207	96.84 217	99.52 93	92.53 205	95.24 205	99.04 130	98.12 170	98.90 184	98.29 185

TESTMET0.1,1		97.62 194	97.46 183	97.81 206	99.07 209	98.37 193	98.26 205	98.35 209	97.03 203	97.38 214	99.54 85	92.89 198	95.12 206	98.78 169	97.68 183	98.65 189	97.90 190

MVSTER		97.55 195	96.75 192	98.48 193	99.46 185	99.54 53	98.24 207	99.77 114	97.56 192	99.41 156	99.31 117	84.86 219	94.66 208	98.86 158	97.75 181	99.34 164	99.38 122

ET-MVSNet_ETH3D		97.44 196	96.29 199	98.78 182	97.93 217	98.95 158	98.91 177	99.09 200	98.00 178	99.24 173	98.83 151	84.62 220	98.02 170	97.43 199	97.38 188	99.48 144	98.84 167

MDTV_nov1_ep13		97.41 197	96.26 200	98.76 183	99.47 184	98.43 192	99.26 141	99.82 84	98.06 175	99.23 174	99.22 124	92.86 200	98.05 165	95.33 207	93.66 202	96.73 204	96.26 203

ADS-MVSNet		97.29 198	96.17 201	98.59 189	99.59 166	98.70 178	99.32 126	99.86 55	98.47 145	99.56 126	99.08 139	98.16 175	97.34 183	92.92 209	91.17 207	95.91 207	94.72 209

SCA		97.25 199	96.05 202	98.64 188	99.36 196	99.02 152	99.27 138	99.96 12	98.25 164	99.69 96	98.71 160	94.66 193	97.95 173	93.95 208	92.35 204	95.64 208	95.40 208

gm-plane-assit		96.82 200	94.84 208	99.13 157	99.95 10	99.78 14	99.69 52	99.92 33	99.19 65	99.84 45	99.92 16	72.93 223	96.44 199	98.21 187	97.01 192	98.92 183	96.87 202

PatchmatchNet		96.81 201	95.41 205	98.43 195	99.43 190	98.30 196	99.23 143	99.93 26	98.19 167	99.64 109	98.81 154	93.50 195	97.43 182	92.89 210	90.78 209	94.94 213	95.41 207
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		96.76 202	95.30 207	98.46 194	99.42 191	98.47 190	99.32 126	99.91 38	98.42 152	99.51 137	99.07 141	92.81 201	97.12 186	92.39 211	91.71 205	95.51 209	94.20 211

E-PMN		96.72 203	95.78 203	97.81 206	99.45 186	95.46 216	98.14 211	98.33 211	97.99 179	98.73 200	98.09 186	98.97 158	97.54 180	97.45 198	91.09 208	94.70 215	91.40 214

tpm		96.56 204	94.68 209	98.74 184	99.12 206	97.90 209	98.79 190	99.93 26	96.79 211	99.69 96	99.19 128	81.48 222	97.56 179	95.46 206	93.97 201	97.37 200	97.99 189

EMVS		96.47 205	95.38 206	97.74 209	99.42 191	95.37 217	98.07 213	98.27 212	97.85 185	98.90 191	97.48 197	98.73 164	97.20 184	97.21 201	90.39 210	94.59 217	90.65 215

tpmrst		96.18 206	94.47 210	98.18 198	99.52 173	97.89 210	98.96 171	99.79 102	98.07 174	99.16 178	99.30 120	92.69 202	96.69 195	90.76 213	88.85 213	94.96 212	93.69 212

CostFormer		95.61 207	93.35 213	98.24 197	99.48 183	98.03 205	98.65 194	99.83 77	96.93 206	99.42 154	98.83 151	83.65 221	97.08 188	90.39 214	89.54 212	94.94 213	96.11 205

dps		95.59 208	93.46 212	98.08 200	99.33 198	98.22 201	98.87 182	99.70 134	96.17 214	98.87 193	97.75 192	86.85 218	96.60 196	91.24 212	89.62 211	95.10 211	94.34 210

tpm cat1		95.52 209	93.49 211	97.88 205	99.28 202	97.87 211	98.65 194	99.77 114	97.27 197	99.46 147	98.04 187	90.99 206	95.46 204	88.57 215	88.14 214	94.64 216	93.54 213

test_method		91.96 210	95.51 204	87.82 212	70.84 219	82.79 220	92.13 219	87.74 215	98.88 107	95.40 219	99.20 127	98.04 177	85.65 215	97.71 194	94.95 198	95.13 210	97.00 201

GG-mvs-BLEND		70.44 211	96.91 190	39.57 213	3.32 222	96.51 214	91.01 220	4.05 219	97.03 203	33.20 221	94.67 209	97.75 179	7.59 218	98.28 185	96.85 193	98.24 193	97.26 199

testmvs		22.33 212	29.66 214	13.79 214	8.97 220	10.35 221	15.53 223	8.09 218	32.51 218	19.87 222	45.18 217	30.56 225	17.05 217	29.96 216	24.74 215	13.21 219	34.30 216

test123		21.52 213	28.47 215	13.42 215	7.29 221	10.12 222	15.70 222	8.31 217	31.54 219	19.34 223	36.33 218	37.40 224	17.14 216	27.45 217	23.17 216	12.73 220	33.30 217

uanet_test		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet-low-res		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

RE-MVS-def										99.96 2

9.14												99.57 125

SR-MVS					99.73 131			99.74 130				99.88 67

Anonymous202405211			99.14 105		99.87 49	99.55 50	99.50 94	99.70 134	98.55 142		98.61 167	98.46 168	98.76 140	99.66 42	99.50 36	99.85 39	99.63 54

our_test_3					99.75 121	99.11 147	99.74 44

ambc			98.83 143		99.72 133	98.52 187	98.84 185		98.96 96	99.92 10	99.34 111	99.74 104	99.04 119	98.68 174	97.57 186	99.46 146	98.99 165

MTAPA										99.62 112		99.95 25

MTMP										99.53 129		99.92 49

Patchmatch-RL test							65.75 221

tmp_tt				88.14 211	96.68 218	91.91 218	93.70 218	61.38 216	99.61 20	90.51 220	99.40 107	99.71 109	90.32 214	99.22 105	99.44 46	96.25 206

XVS					99.86 63	99.30 110	99.72 49			99.69 96		99.93 41				99.60 118

X-MVStestdata					99.86 63	99.30 110	99.72 49			99.69 96		99.93 41				99.60 118

abl_6				99.21 149	99.49 182	98.62 182	98.90 180	99.44 182	97.08 201	99.61 115	97.19 201	99.73 107	98.35 155			99.45 148	98.84 167

mPP-MVS					99.84 78							99.92 49

NP-MVS									97.37 195

Patchmtry						98.19 203	98.91 177	99.97 1		99.43 151

DeepMVS_CX						96.39 215	97.15 217	88.89 214	97.94 182	99.51 137	95.71 207	97.88 178	98.19 156	98.92 146		97.73 197	97.75 195

High-resolution multi-view benchmark