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 32	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 11	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	99.74 103	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 11	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 11	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 22	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 44	99.96 11	99.68 10	99.87 28	99.59 79	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 83	99.53 55	99.89 5	99.91 37	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 62

WR-MVS		99.79 10	99.68 14	99.91 5	99.95 10	99.83 3	99.87 9	99.96 11	99.39 43	99.93 5	99.87 35	99.29 148	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 36	99.83 3	99.84 14	99.89 45	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 46

pm-mvs1		99.77 12	99.69 13	99.86 16	99.94 20	99.68 31	99.84 14	99.93 25	99.59 22	99.87 28	99.92 16	99.21 151	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 78	99.89 17	99.69 63	99.68 114	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 88	99.81 10	99.88 7	99.87 50	99.31 50	99.75 71	99.91 23	99.76 102	99.78 12	99.84 17	99.74 15	99.56 128	99.81 17

Vis-MVSNet		99.76 14	99.78 9	99.75 45	99.92 26	99.77 15	99.83 17	99.85 61	99.43 37	99.85 41	99.84 41	100.00 1	99.13 110	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 11	99.18 66	99.92 10	99.66 66	99.45 133	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 45	99.31 50	99.88 22	99.43 100	99.66 117	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 33	99.39 86	99.79 27	99.81 91	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 49
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 52	99.91 37	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 40

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 96	99.86 34	99.73 53	99.36 140	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 11	99.01 88	99.84 45	99.71 60	99.41 139	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 23	99.58 24	99.92 10	99.90 28	98.55 166	99.65 35	99.89 8	99.76 13	99.95 8	99.70 40

ACMH	99.11 4	99.72 22	99.63 20	99.84 20	99.87 48	99.59 44	99.83 17	99.88 49	99.46 36	99.87 28	99.66 66	99.95 25	99.76 16	99.73 33	99.47 41	99.84 42	99.52 92
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 51	99.94 20	99.71 27	99.82 22	99.91 37	99.14 74	99.53 128	99.70 61	99.88 67	99.33 83	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 77	99.37 93	99.76 32	99.84 70	99.54 30	99.82 53	99.64 70	99.95 25	99.75 18	99.79 25	99.56 28	99.83 47	99.37 123
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 42	99.41 83	99.75 36	99.86 54	99.43 37	99.80 57	99.54 84	99.97 8	99.73 24	99.82 21	99.52 35	99.85 39	99.43 109

test20.03		99.68 27	99.60 26	99.76 41	99.91 30	99.70 30	99.68 52	99.87 50	99.05 85	99.88 22	99.92 16	99.88 67	99.50 62	99.77 28	99.42 48	99.75 70	99.49 95

CP-MVSNet		99.68 27	99.51 38	99.89 9	99.95 10	99.76 16	99.83 17	99.96 11	98.83 113	99.84 45	99.65 69	99.09 153	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 63	99.91 30	99.71 27	99.61 64	99.79 101	99.41 39	99.91 13	99.85 39	99.61 120	99.00 121	99.67 40	99.42 48	99.81 53	99.81 17

v10		99.65 30	99.51 38	99.81 27	99.83 83	99.61 40	99.75 36	99.94 23	99.56 26	99.76 68	99.94 11	99.60 122	99.73 24	99.11 124	99.01 94	99.85 39	99.74 30

CHOSEN 1792x2688		99.65 30	99.55 32	99.77 40	99.93 23	99.60 41	99.79 27	99.92 32	99.73 6	99.74 78	99.93 14	99.98 4	99.80 10	98.83 165	99.01 94	99.45 147	99.76 27

UA-Net		99.64 32	99.62 22	99.66 65	99.97 2	99.82 7	99.14 151	99.96 11	98.95 96	99.52 134	99.38 108	99.86 75	99.55 51	99.72 34	99.66 20	99.80 57	99.94 2

Baseline_NR-MVSNet		99.62 33	99.48 42	99.78 37	99.85 71	99.76 16	99.59 69	99.82 83	98.84 111	99.88 22	99.91 23	99.04 154	99.61 41	99.46 62	99.78 11	99.94 15	99.60 60

pmmvs-eth3d		99.61 34	99.48 42	99.75 45	99.87 48	99.30 109	99.75 36	99.89 45	99.23 58	99.85 41	99.88 34	99.97 8	99.49 66	99.46 62	99.01 94	99.68 89	99.52 92

v1144		99.61 34	99.43 50	99.82 23	99.88 42	99.41 83	99.76 32	99.86 54	99.64 16	99.84 45	99.95 6	99.49 131	99.74 21	99.00 135	98.93 106	99.84 42	99.58 70

v8		99.61 34	99.45 48	99.79 36	99.80 94	99.59 44	99.73 44	99.93 25	99.48 34	99.77 65	99.90 28	99.48 132	99.67 32	99.11 124	98.89 110	99.84 42	99.73 32

casdiffmvs		99.61 34	99.55 32	99.68 61	99.89 36	99.53 55	99.64 58	99.68 141	99.51 31	99.62 111	99.90 28	99.96 13	99.37 77	99.28 94	99.25 58	99.88 27	99.44 106

CSCG		99.61 34	99.52 37	99.71 55	99.89 36	99.62 38	99.52 86	99.76 122	99.61 20	99.69 95	99.73 53	99.96 13	99.57 49	99.27 97	98.62 141	99.81 53	99.85 12

v1192		99.60 39	99.41 54	99.82 23	99.89 36	99.43 78	99.81 24	99.84 70	99.63 18	99.85 41	99.95 6	99.35 143	99.72 26	99.01 133	98.90 109	99.82 50	99.58 70

APDe-MVS		99.60 39	99.48 42	99.73 53	99.85 71	99.51 66	99.75 36	99.85 61	99.17 67	99.81 56	99.56 82	99.94 35	99.44 73	99.42 71	99.22 59	99.67 91	99.54 84

v1921920		99.59 41	99.40 57	99.82 23	99.88 42	99.45 73	99.81 24	99.83 76	99.65 14	99.86 34	99.95 6	99.29 148	99.75 18	98.98 139	98.86 114	99.78 61	99.59 62

TranMVSNet+NR-MVSNet		99.59 41	99.42 53	99.80 32	99.87 48	99.55 49	99.64 58	99.86 54	99.05 85	99.88 22	99.72 57	99.33 146	99.64 37	99.47 61	99.14 69	99.91 20	99.67 45

EG-PatchMatch MVS		99.59 41	99.49 41	99.70 58	99.82 88	99.26 116	99.39 114	99.83 76	98.99 90	99.93 5	99.54 84	99.92 49	99.51 58	99.78 26	99.50 36	99.73 79	99.41 113

pmmvs5		99.58 44	99.47 45	99.70 58	99.84 77	99.50 67	99.58 73	99.80 98	98.98 93	99.73 84	99.92 16	99.81 90	99.49 66	99.28 94	99.05 88	99.77 65	99.73 32

v144192		99.58 44	99.39 58	99.80 32	99.87 48	99.44 75	99.77 29	99.84 70	99.64 16	99.86 34	99.93 14	99.35 143	99.72 26	98.92 145	98.82 118	99.74 75	99.66 46

v148		99.58 44	99.43 50	99.76 41	99.87 48	99.40 85	99.76 32	99.85 61	99.48 34	99.83 50	99.82 46	99.83 85	99.51 58	99.20 110	98.82 118	99.75 70	99.45 103

v1240		99.58 44	99.38 61	99.82 23	99.89 36	99.49 68	99.82 22	99.83 76	99.63 18	99.86 34	99.96 4	98.92 160	99.75 18	99.15 120	98.96 103	99.76 67	99.56 77

V42		99.57 48	99.41 54	99.75 45	99.84 77	99.37 93	99.73 44	99.83 76	99.41 39	99.75 71	99.89 31	99.42 137	99.60 43	99.15 120	98.96 103	99.76 67	99.65 49

TSAR-MVS + MP.		99.56 49	99.54 35	99.58 82	99.69 137	99.14 137	99.73 44	99.45 178	99.50 32	99.35 165	99.60 77	99.93 41	99.50 62	99.56 53	99.37 53	99.77 65	99.64 52
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 49	99.35 64	99.81 27	99.87 48	99.35 99	99.75 36	99.85 61	99.56 26	99.87 28	99.95 6	99.44 135	99.66 33	98.91 148	98.76 124	99.86 36	99.45 103

Gipuma		99.55 51	99.23 83	99.91 5	99.87 48	99.52 62	99.86 10	99.93 25	99.87 1	99.96 2	96.72 201	99.55 127	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 52	99.51 38	99.55 90	99.82 88	99.58 46	99.54 81	99.78 106	99.28 56	99.21 175	99.70 61	99.97 8	99.32 86	99.32 82	99.14 69	99.64 104	99.58 70
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 53	99.29 73	99.80 32	99.96 7	99.38 89	99.55 77	99.81 91	98.86 108	99.87 28	99.51 94	98.81 162	99.72 26	99.86 15	99.04 90	99.89 25	99.54 84

zzz-MVS		99.51 54	99.36 62	99.68 61	99.88 42	99.38 89	99.53 82	99.84 70	99.11 77	99.59 119	98.93 145	99.95 25	99.58 47	99.44 69	99.21 61	99.65 95	99.52 92

ACMMPR		99.51 54	99.32 68	99.72 54	99.87 48	99.33 102	99.61 64	99.85 61	99.19 64	99.73 84	98.73 156	99.95 25	99.61 41	99.35 77	99.14 69	99.66 93	99.58 70

UniMVSNet (Re)		99.50 56	99.29 73	99.75 45	99.86 62	99.47 71	99.51 89	99.82 83	98.90 104	99.89 17	99.64 70	99.00 155	99.55 51	99.32 82	99.08 83	99.90 22	99.59 62

FMVSNet1		99.50 56	99.57 31	99.42 111	99.67 144	99.65 34	99.60 68	99.91 37	99.40 41	99.39 158	99.83 44	99.27 150	98.14 159	99.68 37	99.50 36	99.81 53	99.68 42

HyFIR lowres test		99.50 56	99.26 77	99.80 32	99.95 10	99.62 38	99.76 32	99.97 1	99.67 11	99.56 125	99.94 11	98.40 169	99.78 12	98.84 164	98.59 144	99.76 67	99.72 35

PM-MVS		99.49 59	99.43 50	99.57 85	99.76 116	99.34 101	99.53 82	99.77 113	98.93 100	99.75 71	99.46 98	99.83 85	99.11 112	99.72 34	99.29 57	99.49 142	99.46 102

Anonymous20231206		99.48 60	99.31 70	99.69 60	99.79 98	99.57 47	99.63 62	99.79 101	98.88 106	99.91 13	99.72 57	99.93 41	99.59 44	99.24 100	98.63 140	99.43 152	99.18 140

DU-MVS		99.48 60	99.26 77	99.75 45	99.85 71	99.38 89	99.50 93	99.81 91	98.86 108	99.89 17	99.51 94	98.98 156	99.59 44	99.46 62	98.97 101	99.87 33	99.63 53

RPSCF		99.48 60	99.45 48	99.52 97	99.73 130	99.33 102	99.13 152	99.77 113	99.33 48	99.47 145	99.39 107	99.92 49	99.36 78	99.63 46	99.13 77	99.63 107	99.41 113

ACMMP_NAP		99.47 63	99.33 66	99.63 73	99.85 71	99.28 114	99.56 76	99.83 76	98.75 119	99.48 142	99.03 142	99.95 25	99.47 72	99.48 58	99.19 62	99.57 125	99.59 62

Anonymous20231211		99.47 63	99.39 58	99.57 85	99.89 36	99.60 41	99.50 93	99.69 135	98.91 103	99.62 111	99.17 128	99.35 143	98.86 134	99.63 46	99.46 43	99.84 42	99.62 56

SteuartSystems-ACMMP		99.47 63	99.22 86	99.76 41	99.88 42	99.36 95	99.65 57	99.84 70	98.47 143	99.80 57	98.68 159	99.96 13	99.68 29	99.37 74	99.06 85	99.72 82	99.66 46
Skip Steuart: Steuart Systems R&D Blog.
ACMM	98.37 12	99.47 63	99.23 83	99.74 51	99.86 62	99.19 131	99.68 52	99.86 54	99.16 71	99.71 92	98.52 170	99.95 25	99.62 40	99.35 77	99.02 92	99.74 75	99.42 112
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HFP-MVS		99.46 67	99.30 71	99.65 67	99.82 88	99.25 119	99.50 93	99.82 83	99.23 58	99.58 123	98.86 147	99.94 35	99.56 50	99.14 122	99.12 80	99.63 107	99.56 77

LGP-MVS_train		99.46 67	99.18 96	99.78 37	99.87 48	99.25 119	99.71 50	99.87 50	98.02 175	99.79 60	98.90 146	99.96 13	99.66 33	99.49 57	99.17 65	99.79 60	99.49 95

SED-MVS		99.45 69	99.46 47	99.42 111	99.77 111	99.57 47	99.42 108	99.80 98	99.06 82	99.38 159	99.66 66	99.96 13	98.65 145	99.31 84	99.14 69	99.53 133	99.55 82

ETV-MVS		99.45 69	99.32 68	99.60 78	99.79 98	99.60 41	99.40 113	99.78 106	97.88 182	99.83 50	99.33 111	99.70 111	98.97 124	99.74 31	99.43 47	99.84 42	99.58 70

ACMP	98.32 13	99.44 71	99.18 96	99.75 45	99.83 83	99.18 132	99.64 58	99.83 76	98.81 115	99.79 60	98.42 177	99.96 13	99.64 37	99.46 62	98.98 100	99.74 75	99.44 106
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DCV-MVSNet		99.43 72	99.23 83	99.67 63	99.92 26	99.76 16	99.64 58	99.93 25	99.06 82	99.68 102	97.77 189	98.97 157	98.97 124	99.72 34	99.54 32	99.88 27	99.81 17

SMA-MVS		99.43 72	99.41 54	99.45 108	99.82 88	99.31 107	99.02 166	99.59 156	99.06 82	99.34 168	99.53 90	99.96 13	99.38 76	99.29 89	99.13 77	99.53 133	99.59 62
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 72	99.47 45	99.38 120	99.90 33	99.67 33	99.30 132	99.73 130	98.64 131	99.53 128	99.52 92	99.90 58	98.08 162	99.65 44	99.40 52	99.75 70	99.55 82

DELS-MVS		99.42 75	99.53 36	99.29 134	99.52 172	99.43 78	99.42 108	99.28 194	99.16 71	99.72 87	99.82 46	99.97 8	98.17 156	99.56 53	99.16 66	99.65 95	99.59 62
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
3Dnovator	99.16 3	99.42 75	99.22 86	99.65 67	99.78 103	99.13 141	99.50 93	99.85 61	99.40 41	99.80 57	98.59 166	99.79 98	99.30 90	99.20 110	99.06 85	99.71 85	99.35 126

DPE-MVS		99.41 77	99.36 62	99.47 104	99.66 145	99.48 69	99.46 104	99.75 127	98.65 127	99.41 155	99.67 64	99.95 25	98.82 135	99.21 107	99.14 69	99.72 82	99.40 118

UniMVSNet_NR-MVSNet		99.41 77	99.12 108	99.76 41	99.86 62	99.48 69	99.50 93	99.81 91	98.84 111	99.89 17	99.45 99	98.32 172	99.59 44	99.22 104	98.89 110	99.90 22	99.63 53

CP-MVS		99.41 77	99.20 91	99.65 67	99.80 94	99.23 126	99.44 106	99.75 127	98.60 136	99.74 78	98.66 160	99.93 41	99.48 69	99.33 81	99.16 66	99.73 79	99.48 98

QAPM		99.41 77	99.21 90	99.64 72	99.78 103	99.16 134	99.51 89	99.85 61	99.20 61	99.72 87	99.43 100	99.81 90	99.25 94	98.87 154	98.71 132	99.71 85	99.30 131

UGNet		99.40 81	99.61 23	99.16 154	99.88 42	99.64 36	99.61 64	99.77 113	99.31 50	99.63 110	99.33 111	99.93 41	96.46 197	99.63 46	99.53 33	99.63 107	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 81	99.28 75	99.55 90	99.92 26	99.68 31	99.31 127	99.87 50	98.69 124	99.16 177	99.08 137	98.64 165	99.20 98	99.65 44	99.46 43	99.83 47	99.72 35

OPM-MVS		99.39 83	99.22 86	99.59 79	99.76 116	98.82 166	99.51 89	99.79 101	99.17 67	99.53 128	99.31 116	99.95 25	99.35 79	99.22 104	98.79 123	99.60 117	99.27 134

Fast-Effi-MVS+		99.39 83	99.18 96	99.63 73	99.86 62	99.28 114	99.45 105	99.91 37	98.47 143	99.61 114	99.50 96	99.57 124	99.17 99	99.24 100	98.66 137	99.78 61	99.59 62

LS3D		99.39 83	99.28 75	99.52 97	99.77 111	99.39 86	99.55 77	99.82 83	98.93 100	99.64 108	98.52 170	99.67 116	98.58 149	99.74 31	99.63 24	99.75 70	99.06 156

CS-MVS		99.38 86	99.19 93	99.59 79	99.86 62	99.65 34	99.28 135	99.77 113	97.97 178	99.75 71	98.42 177	99.70 111	99.03 119	99.57 52	99.42 48	99.87 33	99.61 58

diffmvs		99.38 86	99.33 66	99.45 108	99.87 48	99.39 86	99.28 135	99.58 159	99.55 28	99.50 138	99.85 39	99.85 81	98.94 129	98.58 177	98.68 135	99.51 139	99.39 120

CANet		99.36 88	99.39 58	99.34 131	99.80 94	99.35 99	99.41 112	99.47 176	99.20 61	99.74 78	99.54 84	99.68 114	98.05 164	99.23 102	98.97 101	99.57 125	99.73 32

MVS_0304		99.36 88	99.35 64	99.37 126	99.85 71	99.36 95	99.39 114	99.56 162	99.36 46	99.75 71	99.23 122	99.90 58	97.97 171	99.00 135	98.83 117	99.69 88	99.77 24

ACMMP		99.36 88	99.06 115	99.71 55	99.86 62	99.36 95	99.63 62	99.85 61	98.33 158	99.72 87	97.73 191	99.94 35	99.53 54	99.37 74	99.13 77	99.65 95	99.56 77
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 91	99.26 77	99.46 106	99.66 145	99.15 136	98.92 175	99.67 144	99.55 28	99.35 165	98.83 149	99.91 55	99.35 79	99.19 113	98.53 146	99.78 61	99.68 42
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 91	99.09 113	99.65 67	99.84 77	99.22 127	99.59 69	99.78 106	98.13 167	99.67 103	98.44 174	99.93 41	99.43 75	99.31 84	99.09 82	99.60 117	99.49 95
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
pmmvs4		99.34 93	99.15 103	99.57 85	99.77 111	98.90 158	99.51 89	99.77 113	99.07 80	99.73 84	99.72 57	99.84 83	99.07 114	98.85 159	98.39 155	99.55 131	99.27 134

EPP-MVSNet		99.34 93	99.10 111	99.62 77	99.94 20	99.74 24	99.66 56	99.80 98	99.07 80	98.93 188	99.61 74	96.13 186	99.49 66	99.67 40	99.63 24	99.92 18	99.86 10

TSAR-MVS + GP.		99.33 95	99.17 100	99.51 99	99.71 135	99.00 153	98.84 184	99.71 132	98.23 164	99.74 78	99.53 90	99.90 58	99.35 79	99.38 73	98.85 115	99.72 82	99.31 129

PHI-MVS		99.33 95	99.19 93	99.49 102	99.69 137	99.25 119	99.27 137	99.59 156	98.44 147	99.78 64	99.15 129	99.92 49	98.95 128	99.39 72	99.04 90	99.64 104	99.18 140

MSP-MVS		99.32 97	99.26 77	99.38 120	99.76 116	99.54 52	99.42 108	99.72 131	98.92 102	98.84 195	98.96 144	99.96 13	98.91 130	98.72 172	99.14 69	99.63 107	99.58 70
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 97	98.99 124	99.71 55	99.86 62	99.31 107	99.59 69	99.86 54	97.51 191	99.75 71	98.23 181	99.94 35	99.53 54	99.29 89	99.08 83	99.65 95	99.54 84

DeepC-MVS_fast	98.69 9	99.32 97	99.13 106	99.53 93	99.63 152	98.78 169	99.53 82	99.33 192	99.08 78	99.77 65	99.18 127	99.89 61	99.29 91	99.00 135	98.70 133	99.65 95	99.30 131
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 97	99.09 113	99.58 82	99.75 120	98.74 173	99.36 119	99.54 165	99.14 74	99.72 87	99.24 120	99.89 61	99.51 58	99.30 86	98.76 124	99.62 113	98.54 175

TSAR-MVS + ACMM		99.31 101	99.26 77	99.37 126	99.66 145	98.97 156	99.20 144	99.56 162	99.33 48	99.19 176	99.54 84	99.91 55	99.32 86	99.12 123	98.34 158	99.29 166	99.65 49

3Dnovator+	98.92 7	99.31 101	99.03 119	99.63 73	99.77 111	98.90 158	99.52 86	99.81 91	99.37 44	99.72 87	98.03 186	99.73 106	99.32 86	98.99 138	98.81 121	99.67 91	99.36 124

X-MVS		99.30 103	98.99 124	99.66 65	99.85 71	99.30 109	99.49 100	99.82 83	98.32 159	99.69 95	97.31 198	99.93 41	99.50 62	99.37 74	99.16 66	99.60 117	99.53 87

MVS_111021_HR		99.30 103	99.14 104	99.48 103	99.58 168	99.25 119	99.27 137	99.61 151	98.74 120	99.66 105	99.02 143	99.84 83	99.33 83	99.20 110	98.76 124	99.44 149	99.18 140

TAPA-MVS	98.54 10	99.30 103	99.24 82	99.36 130	99.44 187	98.77 171	99.00 168	99.41 183	99.23 58	99.60 117	99.50 96	99.86 75	99.15 106	99.29 89	98.95 105	99.56 128	99.08 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		99.30 103	99.01 123	99.63 73	99.75 120	98.89 161	99.35 122	99.60 153	98.53 141	99.86 34	99.57 81	99.94 35	99.52 57	98.96 140	98.10 171	99.70 87	99.08 153
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
USDC		99.29 107	98.98 126	99.65 67	99.72 132	98.87 164	99.47 102	99.66 147	99.35 47	99.87 28	99.58 80	99.87 74	99.51 58	98.85 159	97.93 177	99.65 95	98.38 179

PMVS	94.32 17	99.27 108	99.55 32	98.94 172	99.60 161	99.43 78	99.39 114	99.54 165	98.99 90	99.69 95	99.60 77	99.81 90	95.68 202	99.88 12	99.83 5	99.73 79	99.31 129
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS_111021_LR		99.25 109	99.13 106	99.39 116	99.50 180	99.14 137	99.23 142	99.50 173	98.67 125	99.61 114	99.12 133	99.81 90	99.16 102	99.28 94	98.67 136	99.35 162	99.21 139

baseline		99.24 110	99.30 71	99.17 153	99.78 103	99.14 137	99.10 156	99.69 135	98.97 94	99.49 140	99.84 41	99.88 67	97.99 170	98.85 159	98.73 130	98.98 181	99.72 35

EIA-MVS		99.23 111	99.03 119	99.47 104	99.83 83	99.64 36	99.16 148	99.81 91	97.11 198	99.65 107	98.44 174	99.78 101	98.61 148	99.46 62	99.22 59	99.75 70	99.59 62

HPM-MVS++		99.23 111	98.98 126	99.53 93	99.75 120	99.02 151	99.44 106	99.77 113	98.65 127	99.52 134	98.72 157	99.92 49	99.33 83	98.77 170	98.40 154	99.40 156	99.36 124

PMMVS2		99.23 111	99.22 86	99.24 141	99.80 94	99.14 137	99.50 93	99.82 83	99.12 76	98.41 209	99.91 23	99.98 4	98.51 150	99.48 58	98.76 124	99.38 158	98.14 187

CPTT-MVS		99.21 114	98.89 137	99.58 82	99.72 132	99.12 144	99.30 132	99.76 122	98.62 132	99.66 105	97.51 194	99.89 61	99.48 69	99.01 133	98.64 139	99.58 124	99.40 118

TinyColmap		99.21 114	98.89 137	99.59 79	99.61 157	98.61 182	99.47 102	99.67 144	99.02 87	99.82 53	99.15 129	99.74 103	99.35 79	99.17 118	98.33 159	99.63 107	98.22 185

Effi-MVS+		99.20 116	98.93 132	99.50 101	99.79 98	99.26 116	98.82 187	99.96 11	98.37 157	99.60 117	99.12 133	98.36 170	99.05 117	98.93 143	98.82 118	99.78 61	99.68 42

PVSNet_BlendedMVS		99.20 116	99.17 100	99.23 142	99.69 137	99.33 102	99.04 161	99.13 197	98.41 153	99.79 60	99.33 111	99.36 140	98.10 160	99.29 89	98.87 112	99.65 95	99.56 77

PVSNet_Blended		99.20 116	99.17 100	99.23 142	99.69 137	99.33 102	99.04 161	99.13 197	98.41 153	99.79 60	99.33 111	99.36 140	98.10 160	99.29 89	98.87 112	99.65 95	99.56 77

MCST-MVS		99.17 119	98.82 145	99.57 85	99.75 120	98.70 177	99.25 141	99.69 135	98.62 132	99.59 119	98.54 168	99.79 98	99.53 54	98.48 181	98.15 167	99.64 104	99.43 109

APD-MVS		99.17 119	98.92 133	99.46 106	99.78 103	99.24 124	99.34 123	99.78 106	97.79 185	99.48 142	98.25 180	99.88 67	98.77 138	99.18 116	98.92 107	99.63 107	99.18 140
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	98.82 8	99.17 119	98.85 141	99.53 93	99.75 120	99.06 149	99.36 119	99.82 83	98.28 161	99.76 68	98.47 172	99.61 120	98.91 130	98.80 167	98.70 133	99.60 117	99.04 160

IterMVS-LS		99.16 122	98.82 145	99.57 85	99.87 48	99.71 27	99.58 73	99.92 32	99.24 57	99.71 92	99.73 53	95.79 187	98.91 130	98.82 166	98.66 137	99.43 152	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	99.16 122	99.20 91	99.12 158	99.20 204	98.71 176	98.85 183	99.06 200	99.17 67	98.96 187	99.61 74	99.86 75	99.29 91	99.17 118	98.72 131	99.36 160	99.15 148

IterMVS-SCA-FT		99.15 124	98.96 129	99.38 120	99.87 48	99.54 52	99.53 82	99.79 101	98.94 98	99.82 53	99.92 16	97.65 178	98.82 135	98.95 142	98.26 161	98.45 190	99.47 101

CDS-MVSNet		99.15 124	99.10 111	99.21 148	99.59 165	99.22 127	99.48 101	99.47 176	98.89 105	99.41 155	99.84 41	98.11 175	97.76 174	99.26 99	99.01 94	99.57 125	99.38 121
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS_MVSNet		99.15 124	99.12 108	99.19 151	99.92 26	99.73 26	99.55 77	99.86 54	98.45 146	96.91 215	98.74 155	98.33 171	99.02 120	99.54 55	99.47 41	99.88 27	99.61 58

MDA-MVSNet-bldmvs		99.11 127	99.11 110	99.12 158	99.91 30	99.38 89	99.77 29	98.72 204	99.31 50	99.85 41	99.43 100	98.26 173	99.48 69	99.85 16	98.47 149	96.99 201	99.08 153

OMC-MVS		99.11 127	98.95 130	99.29 134	99.37 193	98.57 184	99.19 145	99.20 196	98.87 107	99.58 123	99.13 131	99.88 67	99.00 121	99.19 113	98.46 150	99.43 152	98.57 174

MVS_Test		99.09 129	98.92 133	99.29 134	99.61 157	99.07 148	99.04 161	99.81 91	98.58 138	99.37 162	99.74 51	98.87 161	98.41 153	98.61 176	98.01 175	99.50 141	99.57 76

CNVR-MVS		99.08 130	98.83 142	99.37 126	99.61 157	98.74 173	99.15 149	99.54 165	98.59 137	99.37 162	98.15 183	99.88 67	99.08 113	98.91 148	98.46 150	99.48 143	99.06 156

IterMVS		99.08 130	98.90 136	99.29 134	99.87 48	99.53 55	99.52 86	99.77 113	98.94 98	99.75 71	99.91 23	97.52 182	98.72 142	98.86 157	98.14 168	98.09 193	99.43 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		99.07 132	99.19 93	98.93 174	99.02 209	99.53 55	99.31 127	99.84 70	98.86 108	98.88 191	99.64 70	98.44 168	96.92 191	99.35 77	99.00 98	99.61 114	99.53 87

CVMVSNet		99.06 133	98.88 140	99.28 138	99.52 172	99.53 55	99.42 108	99.69 135	98.74 120	98.27 211	99.89 31	95.48 190	99.44 73	99.46 62	99.33 54	99.32 165	99.75 29

CDPH-MVS		99.05 134	98.63 152	99.54 92	99.75 120	98.78 169	99.59 69	99.68 141	97.79 185	99.37 162	98.20 182	99.86 75	99.14 108	98.58 177	98.01 175	99.68 89	99.16 146

TAMVS		99.05 134	99.02 122	99.08 163	99.69 137	99.22 127	99.33 124	99.32 193	99.16 71	98.97 186	99.87 35	97.36 183	97.76 174	99.21 107	99.00 98	99.44 149	99.33 127

CANet_DTU		99.03 136	99.18 96	98.87 177	99.58 168	99.03 150	99.18 146	99.41 183	98.65 127	99.74 78	99.55 83	99.71 108	96.13 200	99.19 113	98.92 107	99.17 175	99.18 140

Effi-MVS+-dtu		99.01 137	99.05 116	98.98 167	99.60 161	99.13 141	99.03 165	99.61 151	98.52 142	99.01 183	98.53 169	99.83 85	96.95 190	99.48 58	98.59 144	99.66 93	99.25 138

canonicalmvs		99.00 138	98.68 151	99.37 126	99.68 143	99.42 82	98.94 174	99.89 45	99.00 89	98.99 184	98.43 176	95.69 188	98.96 127	99.18 116	99.18 63	99.74 75	99.88 7

MIMVSNet		99.00 138	99.03 119	98.97 171	99.32 199	99.32 106	99.39 114	99.91 37	98.41 153	98.76 198	99.24 120	99.17 152	97.13 184	99.30 86	98.80 122	99.29 166	99.01 161

CHOSEN 280x420		98.99 140	98.91 135	99.07 164	99.77 111	99.26 116	99.55 77	99.92 32	98.62 132	98.67 202	99.62 73	97.20 184	98.44 152	99.50 56	99.18 63	98.08 194	98.99 164

xxxxxxxxxxxxxcwj		98.97 141	98.97 128	98.98 167	99.64 150	98.89 161	98.00 213	99.58 159	98.42 150	99.08 181	98.63 162	99.96 13	98.04 166	99.02 131	98.76 124	99.52 135	99.13 149

SF-MVS		98.96 142	98.95 130	98.98 167	99.64 150	98.89 161	98.00 213	99.58 159	98.42 150	99.08 181	98.63 162	99.83 85	98.04 166	99.02 131	98.76 124	99.52 135	99.13 149

GBi-Net		98.96 142	99.05 116	98.85 178	99.02 209	99.53 55	99.31 127	99.78 106	98.13 167	98.48 205	99.43 100	97.58 179	96.92 191	99.68 37	99.50 36	99.61 114	99.53 87

test1		98.96 142	99.05 116	98.85 178	99.02 209	99.53 55	99.31 127	99.78 106	98.13 167	98.48 205	99.43 100	97.58 179	96.92 191	99.68 37	99.50 36	99.61 114	99.53 87

PCF-MVS	97.86 15	98.95 145	98.53 157	99.44 110	99.70 136	98.80 168	98.96 170	99.69 135	98.65 127	99.59 119	99.33 111	99.94 35	99.12 111	98.01 191	97.11 188	99.59 123	97.83 191
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		98.94 146	98.71 150	99.21 148	99.52 172	98.22 200	98.97 169	99.53 170	98.76 117	99.50 138	98.59 166	99.56 126	98.68 143	98.63 175	98.45 152	99.05 178	98.73 171

AdaColmap		98.93 147	98.53 157	99.39 116	99.52 172	98.65 180	99.11 155	99.59 156	98.08 171	99.44 148	97.46 196	99.45 133	99.24 95	98.92 145	98.44 153	99.44 149	98.73 171

MSLP-MVS++		98.92 148	98.73 149	99.14 155	99.44 187	99.00 153	98.36 203	99.35 189	98.82 114	99.38 159	96.06 203	99.79 98	99.07 114	98.88 153	99.05 88	99.27 168	99.53 87

new_pmnet		98.91 149	98.89 137	98.94 172	99.51 178	98.27 196	99.15 149	98.66 205	99.17 67	99.48 142	99.79 49	99.80 96	98.49 151	99.23 102	98.20 165	98.34 191	97.74 195

train_agg		98.89 150	98.48 162	99.38 120	99.69 137	98.76 172	99.31 127	99.60 153	97.71 187	98.98 185	97.89 187	99.89 61	99.29 91	98.32 182	97.59 184	99.42 155	99.16 146

NCCC		98.88 151	98.42 163	99.42 111	99.62 153	98.81 167	99.10 156	99.54 165	98.76 117	99.53 128	95.97 204	99.80 96	99.16 102	98.49 180	98.06 174	99.55 131	99.05 158

PLC	97.83 16	98.88 151	98.52 159	99.30 133	99.45 185	98.60 183	98.65 193	99.49 174	98.66 126	99.59 119	96.33 202	99.59 123	99.17 99	98.87 154	98.53 146	99.46 145	99.05 158
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs3		98.85 153	98.60 153	99.13 156	99.66 145	98.72 175	99.37 118	99.06 200	98.44 147	99.76 68	99.74 51	99.55 127	99.15 106	99.04 129	96.00 196	97.80 195	98.72 173

Fast-Effi-MVS+-dtu		98.82 154	98.80 147	98.84 180	99.51 178	98.90 158	98.96 170	99.91 37	98.29 160	99.11 180	98.47 172	99.63 119	96.03 201	99.21 107	98.12 169	99.52 135	99.01 161

CNLPA		98.82 154	98.52 159	99.18 152	99.21 203	98.50 188	98.73 191	99.34 191	98.73 122	99.56 125	97.55 193	99.42 137	99.06 116	98.93 143	98.10 171	99.21 174	98.38 179

PatchMatch-RL		98.80 156	98.52 159	99.12 158	99.38 192	98.70 177	98.56 196	99.55 164	97.81 184	99.34 168	97.57 192	99.31 147	98.67 144	99.27 97	98.62 141	99.22 173	98.35 181

thisisatest0530		98.78 157	98.26 166	99.39 116	99.78 103	99.43 78	99.07 158	99.64 149	98.44 147	99.42 153	99.22 123	92.68 201	98.63 146	99.30 86	99.14 69	99.80 57	99.60 60

tttt0517		98.77 158	98.25 168	99.38 120	99.79 98	99.46 72	99.07 158	99.64 149	98.40 156	99.38 159	99.21 125	92.54 202	98.63 146	99.34 80	99.14 69	99.80 57	99.62 56

DI_MVS_plusplus_trai		98.74 159	98.08 176	99.51 99	99.79 98	99.29 113	99.61 64	99.60 153	99.20 61	99.46 146	99.09 136	92.93 195	98.97 124	98.27 185	98.35 157	99.65 95	99.45 103

TSAR-MVS + COLMAP		98.74 159	98.58 155	98.93 174	99.29 200	98.23 197	99.04 161	99.24 195	98.79 116	98.80 197	99.37 109	99.71 108	98.06 163	98.02 190	97.46 186	99.16 176	98.48 177

MDTV_nov1_ep13_2view		98.73 161	98.31 165	99.22 145	99.75 120	99.24 124	99.75 36	99.93 25	99.31 50	99.84 45	99.86 38	99.81 90	99.31 89	97.40 198	94.77 197	96.73 203	97.81 192

PMMVS		98.71 162	98.55 156	98.90 176	99.28 201	98.45 190	98.53 199	99.45 178	97.67 189	99.15 179	98.76 153	99.54 129	97.79 173	98.77 170	98.23 163	99.16 176	98.46 178

HQP-MVS		98.70 163	98.19 172	99.28 138	99.61 157	98.52 186	98.71 192	99.35 189	97.97 178	99.53 128	97.38 197	99.85 81	99.14 108	97.53 194	96.85 192	99.36 160	99.26 137

N_pmnet		98.64 164	98.23 171	99.11 161	99.78 103	99.25 119	99.75 36	99.39 187	99.65 14	99.70 94	99.78 50	99.89 61	98.81 137	97.60 193	94.28 198	97.24 200	97.15 199

CMPMVS	76.62 19	98.64 164	98.60 153	98.68 185	99.33 197	97.07 212	98.11 211	98.50 206	97.69 188	99.26 171	98.35 179	99.66 117	97.62 177	99.43 70	99.02 92	99.24 171	99.01 161
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		98.63 166	98.75 148	98.49 191	98.10 215	99.44 75	99.02 166	99.78 106	98.13 167	98.48 205	99.43 100	97.58 179	96.16 199	98.85 159	98.39 155	99.40 156	99.41 113

GA-MVS		98.59 167	98.15 173	99.09 162	99.59 165	99.13 141	98.84 184	99.52 172	98.61 135	99.35 165	99.67 64	93.03 194	97.73 176	98.90 152	98.26 161	99.51 139	99.48 98

MAR-MVS		98.54 168	98.15 173	98.98 167	99.37 193	98.09 203	98.56 196	99.65 148	96.11 213	99.27 170	97.16 200	99.50 130	98.03 168	98.87 154	98.23 163	99.01 179	99.13 149
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		98.49 169	97.60 178	99.53 93	99.90 33	99.55 49	99.77 29	99.48 175	99.67 11	99.86 34	99.98 3	99.98 4	99.50 62	96.90 200	91.52 204	98.67 187	95.62 204

FPMVS		98.48 170	98.83 142	98.07 201	99.09 207	97.98 206	99.07 158	98.04 212	98.99 90	99.22 174	98.85 148	99.43 136	93.79 209	99.66 42	99.11 81	99.24 171	97.76 193

MVS-HIRNet		98.45 171	98.25 168	98.69 184	99.12 205	97.81 211	98.55 198	99.85 61	98.58 138	99.67 103	99.61 74	99.86 75	97.46 180	97.95 192	96.37 194	97.49 197	97.56 196

test0.0.03 1		98.41 172	98.41 164	98.40 195	99.62 153	99.16 134	98.87 181	99.41 183	97.15 196	96.60 217	99.31 116	97.00 185	96.55 196	98.91 148	98.51 148	99.37 159	98.82 169

gg-mvs-nofinetune		98.40 173	98.26 166	98.57 189	99.83 83	98.86 165	98.77 190	99.97 1	99.57 25	99.99 1	99.99 1	93.81 192	93.50 210	98.91 148	98.20 165	99.33 164	98.52 176

baseline1		98.39 174	97.59 179	99.31 132	99.78 103	99.45 73	99.13 152	99.53 170	98.06 173	98.87 192	98.63 162	90.04 207	98.76 139	98.85 159	98.84 116	99.81 53	99.28 133

pmnet_mix02		98.28 175	97.48 181	99.22 145	99.78 103	99.12 144	99.68 52	99.39 187	99.49 33	99.86 34	99.82 46	99.89 61	99.23 96	95.54 203	92.36 201	97.38 198	96.14 202

PatchT		98.11 176	97.12 187	99.26 140	99.65 149	98.34 194	99.57 75	99.97 1	97.48 192	99.43 150	99.04 141	90.84 205	98.15 157	98.04 188	97.78 178	98.82 184	98.30 182

DPM-MVS		98.10 177	97.32 185	99.01 166	99.52 172	97.92 207	98.47 201	99.45 178	98.25 162	98.91 189	93.99 208	99.69 113	98.73 141	96.29 202	96.32 195	99.00 180	98.77 170

EPNet_dtu		98.09 178	98.25 168	97.91 203	99.58 168	98.02 205	98.19 208	99.67 144	97.94 180	99.74 78	99.07 139	98.71 164	93.40 211	97.50 195	97.09 189	96.89 202	99.44 106
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.06 179	98.11 175	98.00 202	99.60 161	98.99 155	98.38 202	99.68 141	98.18 166	98.85 194	97.89 187	95.60 189	92.72 212	98.30 183	98.10 171	98.76 185	99.72 35
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 180	96.80 190	99.22 145	99.60 161	98.23 197	98.91 176	99.97 1	96.89 206	99.43 150	99.10 135	89.24 210	98.15 157	98.04 188	97.78 178	99.26 169	98.30 182

thres200		97.87 181	96.56 192	99.39 116	99.76 116	99.52 62	99.13 152	99.76 122	96.88 208	98.66 203	92.87 212	88.77 213	99.16 102	99.11 124	99.42 48	99.88 27	99.33 127

baseline2		97.87 181	97.18 186	98.67 186	99.34 196	99.17 133	98.48 200	98.82 203	97.08 199	98.83 196	98.75 154	89.47 209	97.03 189	98.67 174	98.27 160	99.52 135	98.83 168

thres600view7		97.86 183	96.53 195	99.41 114	99.84 77	99.52 62	99.36 119	99.76 122	97.32 194	98.38 210	93.24 209	87.25 215	99.23 96	99.11 124	99.75 14	99.88 27	99.48 98

tfpn200view9		97.85 184	96.54 193	99.38 120	99.74 128	99.52 62	99.17 147	99.76 122	96.10 214	98.70 200	92.99 210	89.10 211	99.00 121	99.11 124	99.56 28	99.88 27	99.41 113

thres400		97.82 185	96.47 196	99.40 115	99.81 93	99.44 75	99.29 134	99.69 135	97.15 196	98.57 204	92.82 213	87.96 214	99.16 102	98.96 140	99.55 31	99.86 36	99.41 113

IB-MVS	98.10 14	97.76 186	97.40 184	98.18 197	99.62 153	99.11 146	98.24 206	98.35 208	96.56 210	99.44 148	91.28 214	98.96 159	93.84 208	98.09 187	98.62 141	99.56 128	99.18 140
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		97.74 187	97.46 182	98.08 199	99.62 153	98.37 192	98.26 204	99.41 183	97.03 201	97.38 213	99.54 84	92.89 196	95.12 205	98.78 168	97.68 182	98.65 188	97.90 189

RPMNet		97.70 188	96.54 193	99.06 165	99.57 171	98.23 197	98.95 173	99.97 1	96.89 206	99.49 140	99.13 131	89.63 208	97.09 186	96.68 201	97.02 190	99.26 169	98.19 186

thres100view900		97.69 189	96.37 197	99.23 142	99.74 128	99.21 130	98.81 188	99.43 182	96.10 214	98.70 200	92.99 210	89.10 211	98.88 133	98.58 177	99.31 56	99.82 50	99.27 134

FMVSNet5		97.69 189	96.98 188	98.53 190	98.53 213	99.36 95	98.90 179	99.54 165	96.38 211	98.44 208	95.38 206	90.08 206	97.05 188	99.46 62	99.06 85	98.73 186	99.12 152

MVE	91.08 18	97.68 191	97.65 177	97.71 209	98.46 214	91.62 218	97.92 215	98.86 202	98.73 122	97.99 212	98.64 161	99.96 13	99.17 99	99.59 50	97.75 180	93.87 216	97.27 197
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-mter		97.65 192	97.57 180	97.75 207	98.90 212	98.56 185	98.15 209	98.45 207	96.92 205	96.84 216	99.52 92	92.53 203	95.24 204	99.04 129	98.12 169	98.90 183	98.29 184

TESTMET0.1,1		97.62 193	97.46 182	97.81 205	99.07 208	98.37 192	98.26 204	98.35 208	97.03 201	97.38 213	99.54 84	92.89 196	95.12 205	98.78 168	97.68 182	98.65 188	97.90 189

MVSTER		97.55 194	96.75 191	98.48 192	99.46 184	99.54 52	98.24 206	99.77 113	97.56 190	99.41 155	99.31 116	84.86 217	94.66 207	98.86 157	97.75 180	99.34 163	99.38 121

ET-MVSNet_ETH3D		97.44 195	96.29 198	98.78 181	97.93 216	98.95 157	98.91 176	99.09 199	98.00 176	99.24 172	98.83 149	84.62 218	98.02 169	97.43 197	97.38 187	99.48 143	98.84 166

MDTV_nov1_ep13		97.41 196	96.26 199	98.76 182	99.47 183	98.43 191	99.26 140	99.82 83	98.06 173	99.23 173	99.22 123	92.86 198	98.05 164	95.33 205	93.66 200	96.73 203	96.26 201

ADS-MVSNet		97.29 197	96.17 200	98.59 188	99.59 165	98.70 177	99.32 125	99.86 54	98.47 143	99.56 125	99.08 137	98.16 174	97.34 182	92.92 207	91.17 205	95.91 206	94.72 207

SCA		97.25 198	96.05 201	98.64 187	99.36 195	99.02 151	99.27 137	99.96 11	98.25 162	99.69 95	98.71 158	94.66 191	97.95 172	93.95 206	92.35 202	95.64 207	95.40 206

gm-plane-assit		96.82 199	94.84 206	99.13 156	99.95 10	99.78 14	99.69 51	99.92 32	99.19 64	99.84 45	99.92 16	72.93 221	96.44 198	98.21 186	97.01 191	98.92 182	96.87 200

PatchmatchNet		96.81 200	95.41 203	98.43 194	99.43 189	98.30 195	99.23 142	99.93 25	98.19 165	99.64 108	98.81 152	93.50 193	97.43 181	92.89 208	90.78 207	94.94 211	95.41 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		96.76 201	95.30 205	98.46 193	99.42 190	98.47 189	99.32 125	99.91 37	98.42 150	99.51 136	99.07 139	92.81 199	97.12 185	92.39 209	91.71 203	95.51 208	94.20 209

E-PMN		96.72 202	95.78 202	97.81 205	99.45 185	95.46 215	98.14 210	98.33 210	97.99 177	98.73 199	98.09 184	98.97 157	97.54 179	97.45 196	91.09 206	94.70 213	91.40 212

tpm		96.56 203	94.68 207	98.74 183	99.12 205	97.90 208	98.79 189	99.93 25	96.79 209	99.69 95	99.19 126	81.48 220	97.56 178	95.46 204	93.97 199	97.37 199	97.99 188

EMVS		96.47 204	95.38 204	97.74 208	99.42 190	95.37 216	98.07 212	98.27 211	97.85 183	98.90 190	97.48 195	98.73 163	97.20 183	97.21 199	90.39 208	94.59 215	90.65 213

tpmrst		96.18 205	94.47 208	98.18 197	99.52 172	97.89 209	98.96 170	99.79 101	98.07 172	99.16 177	99.30 119	92.69 200	96.69 194	90.76 211	88.85 211	94.96 210	93.69 210

CostFormer		95.61 206	93.35 211	98.24 196	99.48 182	98.03 204	98.65 193	99.83 76	96.93 204	99.42 153	98.83 149	83.65 219	97.08 187	90.39 212	89.54 210	94.94 211	96.11 203

dps		95.59 207	93.46 210	98.08 199	99.33 197	98.22 200	98.87 181	99.70 133	96.17 212	98.87 192	97.75 190	86.85 216	96.60 195	91.24 210	89.62 209	95.10 209	94.34 208

tpm cat1		95.52 208	93.49 209	97.88 204	99.28 201	97.87 210	98.65 193	99.77 113	97.27 195	99.46 146	98.04 185	90.99 204	95.46 203	88.57 213	88.14 212	94.64 214	93.54 211

GG-mvs-BLEND		70.44 209	96.91 189	39.57 211	3.32 220	96.51 213	91.01 218	4.05 217	97.03 201	33.20 219	94.67 207	97.75 177	7.59 216	98.28 184	96.85 192	98.24 192	97.26 198

testmvs		22.33 210	29.66 212	13.79 212	8.97 218	10.35 219	15.53 221	8.09 216	32.51 216	19.87 220	45.18 215	30.56 223	17.05 215	29.96 214	24.74 213	13.21 217	34.30 214

test123		21.52 211	28.47 213	13.42 213	7.29 219	10.12 220	15.70 220	8.31 215	31.54 217	19.34 221	36.33 216	37.40 222	17.14 214	27.45 215	23.17 214	12.73 218	33.30 215

uanet_test		0.00 212	0.00 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 218	0.00 222	0.00 217	0.00 224	0.00 217	0.00 216	0.00 215	0.00 219	0.00 216

sosnet-low-res		0.00 212	0.00 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 218	0.00 222	0.00 217	0.00 224	0.00 217	0.00 216	0.00 215	0.00 219	0.00 216

sosnet		0.00 212	0.00 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 218	0.00 222	0.00 217	0.00 224	0.00 217	0.00 216	0.00 215	0.00 219	0.00 216

RE-MVS-def										99.96 2

9.14												99.57 124

SR-MVS					99.73 130			99.74 129				99.88 67

Anonymous202405211			99.14 104		99.87 48	99.55 49	99.50 93	99.70 133	98.55 140		98.61 165	98.46 167	98.76 139	99.66 42	99.50 36	99.85 39	99.63 53

our_test_3					99.75 120	99.11 146	99.74 43

ambc			98.83 142		99.72 132	98.52 186	98.84 184		98.96 95	99.92 10	99.34 110	99.74 103	99.04 118	98.68 173	97.57 185	99.46 145	98.99 164

MTAPA										99.62 111		99.95 25

MTMP										99.53 128		99.92 49

Patchmatch-RL test							65.75 219

tmp_tt				88.14 210	96.68 217	91.91 217	93.70 217	61.38 214	99.61 20	90.51 218	99.40 106	99.71 108	90.32 213	99.22 104	99.44 46	96.25 205

XVS					99.86 62	99.30 109	99.72 48			99.69 95		99.93 41				99.60 117

X-MVStestdata					99.86 62	99.30 109	99.72 48			99.69 95		99.93 41				99.60 117

abl_6				99.21 148	99.49 181	98.62 181	98.90 179	99.44 181	97.08 199	99.61 114	97.19 199	99.73 106	98.35 154			99.45 147	98.84 166

mPP-MVS					99.84 77							99.92 49

NP-MVS									97.37 193

Patchmtry						98.19 202	98.91 176	99.97 1		99.43 150

DeepMVS_CX						96.39 214	97.15 216	88.89 213	97.94 180	99.51 136	95.71 205	97.88 176	98.19 155	98.92 145		97.73 196	97.75 194

High-resolution multi-view benchmark