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 8	99.91 3	99.92 37	99.75 4	99.93 5	99.89 30	100.00 1	99.87 2	99.93 3	99.82 10	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 4	99.93 1	99.96 12	99.77 3	99.89 17	99.99 1	99.86 76	99.84 5	99.89 11	99.81 11	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 4	99.94 11	99.74 107	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 21	99.89 5	99.96 12	99.82 2	99.90 15	99.92 16	99.95 25	99.68 31	99.93 3	99.88 3	99.95 7	99.86 11

anonymousdsp		99.87 5	99.86 3	99.88 12	99.95 10	99.75 27	99.90 4	99.96 12	99.69 7	99.83 51	99.96 4	99.99 3	99.74 21	99.95 2	99.83 7	99.91 24	99.88 6

FC-MVSNet-test		99.84 6	99.80 6	99.89 9	99.96 7	99.83 4	99.84 16	99.95 23	99.37 48	99.77 68	99.95 6	99.96 14	99.85 3	99.93 3	99.83 7	99.95 7	99.72 39

UniMVSNet_ETH3D		99.81 7	99.79 7	99.85 18	99.98 1	99.76 21	99.73 47	99.96 12	99.68 9	99.87 29	99.59 84	99.91 56	99.58 51	99.90 10	99.85 6	99.96 3	99.81 19

TDRefinement		99.81 7	99.76 9	99.86 15	99.83 88	99.53 62	99.89 5	99.91 43	99.73 5	99.88 23	99.83 45	99.96 14	99.76 16	99.91 9	99.81 11	99.86 41	99.59 68

WR-MVS		99.79 9	99.68 13	99.91 5	99.95 10	99.83 4	99.87 9	99.96 12	99.39 46	99.93 5	99.87 35	99.29 150	99.77 14	99.83 22	99.72 20	99.97 1	99.82 16

MIMVSNet1		99.79 9	99.75 10	99.84 21	99.89 42	99.83 4	99.84 16	99.89 52	99.31 54	99.93 5	99.92 16	99.97 9	99.68 31	99.89 11	99.64 27	99.82 55	99.66 53

pm-mvs1		99.77 11	99.69 12	99.86 15	99.94 23	99.68 36	99.84 16	99.93 27	99.59 21	99.87 29	99.92 16	99.21 153	99.65 37	99.88 15	99.77 16	99.93 20	99.78 26

PEN-MVS		99.77 11	99.65 18	99.91 5	99.95 10	99.80 15	99.86 10	99.97 1	99.08 82	99.89 17	99.69 67	99.68 116	99.84 5	99.81 27	99.64 27	99.95 7	99.81 19

EU-MVSNet		99.76 13	99.74 11	99.78 41	99.82 93	99.81 12	99.88 7	99.87 57	99.31 54	99.75 76	99.91 23	99.76 106	99.78 12	99.84 21	99.74 19	99.56 136	99.81 19

Vis-MVSNet		99.76 13	99.78 8	99.75 51	99.92 30	99.77 20	99.83 19	99.85 68	99.43 40	99.85 42	99.84 42	100.00 1	99.13 117	99.83 22	99.66 24	99.90 28	99.90 2
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		99.75 15	99.66 17	99.85 18	99.87 53	99.86 2	99.83 19	99.91 43	98.84 116	99.92 9	99.57 86	99.85 82	99.60 46	99.82 25	99.79 13	99.94 15	99.87 9

CS-MVS-test		99.75 15	99.67 14	99.84 21	99.91 34	99.85 3	99.85 13	99.92 37	98.75 126	99.89 17	99.64 74	99.95 25	99.55 54	99.89 11	99.79 13	99.92 21	99.83 14

DTE-MVSNet		99.75 15	99.61 24	99.92 4	99.95 10	99.81 12	99.86 10	99.96 12	99.18 71	99.92 9	99.66 70	99.45 135	99.85 3	99.80 28	99.56 33	99.96 3	99.79 25

tfpnnormal		99.74 18	99.63 21	99.86 15	99.93 27	99.75 27	99.80 28	99.89 52	99.31 54	99.88 23	99.43 107	99.66 119	99.77 14	99.80 28	99.71 21	99.92 21	99.76 30

DeepC-MVS	99.05 5	99.74 18	99.64 19	99.84 21	99.90 39	99.39 93	99.79 29	99.81 97	99.69 7	99.90 15	99.87 35	99.98 5	99.81 7	99.62 54	99.32 61	99.83 52	99.65 56
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 20	99.67 14	99.81 31	99.93 27	99.74 29	99.68 56	99.91 43	99.59 21	99.88 23	99.73 56	99.81 92	99.55 54	99.59 55	99.53 38	99.89 31	99.70 47

PS-CasMVS		99.73 20	99.59 30	99.90 8	99.95 10	99.80 15	99.85 13	99.97 1	98.95 100	99.86 35	99.73 56	99.36 142	99.81 7	99.83 22	99.67 23	99.95 7	99.83 14

WR-MVS_H		99.73 20	99.61 24	99.88 12	99.95 10	99.82 8	99.83 19	99.96 12	99.01 92	99.84 46	99.71 64	99.41 141	99.74 21	99.77 33	99.70 22	99.95 7	99.82 16

TransMVSNet (Re)		99.72 23	99.59 30	99.88 12	99.95 10	99.76 21	99.88 7	99.94 24	99.58 23	99.92 9	99.90 27	98.55 170	99.65 37	99.89 11	99.76 17	99.95 7	99.70 47

ACMH	99.11 4	99.72 23	99.63 21	99.84 21	99.87 53	99.59 49	99.83 19	99.88 56	99.46 37	99.87 29	99.66 70	99.95 25	99.76 16	99.73 38	99.47 47	99.84 47	99.52 100
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		99.70 25	99.67 14	99.74 57	99.94 23	99.71 32	99.82 24	99.91 43	99.14 79	99.53 134	99.70 65	99.88 68	99.33 89	99.88 15	99.61 32	99.94 15	99.77 27

EC-MVSNet		99.70 25	99.57 33	99.85 18	99.95 10	99.81 12	99.85 13	99.93 27	98.39 164	99.76 71	99.48 104	99.94 35	99.70 29	99.85 19	99.66 24	99.91 24	99.87 9

COLMAP_ROB	99.18 2	99.70 25	99.60 28	99.81 31	99.84 82	99.37 100	99.76 35	99.84 77	99.54 29	99.82 54	99.64 74	99.95 25	99.75 18	99.79 30	99.56 33	99.83 52	99.37 130
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 28	99.59 30	99.81 31	99.88 48	99.41 90	99.75 39	99.86 61	99.43 40	99.80 58	99.54 90	99.97 9	99.73 24	99.82 25	99.52 40	99.85 44	99.43 116

test20.03		99.68 29	99.60 28	99.76 47	99.91 34	99.70 35	99.68 56	99.87 57	99.05 89	99.88 23	99.92 16	99.88 68	99.50 67	99.77 33	99.42 54	99.75 76	99.49 102

CP-MVSNet		99.68 29	99.51 42	99.89 9	99.95 10	99.76 21	99.83 19	99.96 12	98.83 120	99.84 46	99.65 73	99.09 156	99.80 10	99.78 31	99.62 31	99.95 7	99.82 16

casdiffmvs_mvg		99.67 31	99.61 24	99.74 57	99.94 23	99.60 45	99.62 70	99.77 120	99.54 29	99.67 109	99.82 47	99.80 98	99.52 61	99.40 76	99.51 41	99.91 24	99.59 68
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		99.66 32	99.64 19	99.67 69	99.91 34	99.71 32	99.61 71	99.79 108	99.41 42	99.91 13	99.85 40	99.61 122	99.00 127	99.67 45	99.42 54	99.81 58	99.81 19

v10		99.65 33	99.51 42	99.81 31	99.83 88	99.61 44	99.75 39	99.94 24	99.56 25	99.76 71	99.94 11	99.60 124	99.73 24	99.11 132	99.01 102	99.85 44	99.74 34

CHOSEN 1792x2688		99.65 33	99.55 36	99.77 46	99.93 27	99.60 45	99.79 29	99.92 37	99.73 5	99.74 82	99.93 14	99.98 5	99.80 10	98.83 172	99.01 102	99.45 154	99.76 30

UA-Net		99.64 35	99.62 23	99.66 71	99.97 2	99.82 8	99.14 159	99.96 12	98.95 100	99.52 140	99.38 116	99.86 76	99.55 54	99.72 39	99.66 24	99.80 62	99.94 1

GeoE		99.63 36	99.51 42	99.78 41	99.91 34	99.57 52	99.78 31	99.97 1	99.23 62	99.72 92	99.72 60	99.80 98	99.50 67	99.45 73	99.10 88	99.79 65	99.71 45

Baseline_NR-MVSNet		99.62 37	99.48 47	99.78 41	99.85 76	99.76 21	99.59 76	99.82 89	98.84 116	99.88 23	99.91 23	99.04 157	99.61 44	99.46 66	99.78 15	99.94 15	99.60 66

pmmvs-eth3d		99.61 38	99.48 47	99.75 51	99.87 53	99.30 116	99.75 39	99.89 52	99.23 62	99.85 42	99.88 34	99.97 9	99.49 72	99.46 66	99.01 102	99.68 96	99.52 100

v1144		99.61 38	99.43 55	99.82 26	99.88 48	99.41 90	99.76 35	99.86 61	99.64 15	99.84 46	99.95 6	99.49 133	99.74 21	99.00 142	98.93 114	99.84 47	99.58 77

v8		99.61 38	99.45 53	99.79 40	99.80 99	99.59 49	99.73 47	99.93 27	99.48 35	99.77 68	99.90 27	99.48 134	99.67 34	99.11 132	98.89 118	99.84 47	99.73 36

casdiffmvs		99.61 38	99.55 36	99.68 68	99.89 42	99.53 62	99.64 64	99.68 148	99.51 32	99.62 118	99.90 27	99.96 14	99.37 83	99.28 101	99.25 64	99.88 33	99.44 113
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CSCG		99.61 38	99.52 41	99.71 62	99.89 42	99.62 42	99.52 92	99.76 129	99.61 19	99.69 101	99.73 56	99.96 14	99.57 52	99.27 104	98.62 148	99.81 58	99.85 13

v1192		99.60 43	99.41 59	99.82 26	99.89 42	99.43 85	99.81 26	99.84 77	99.63 17	99.85 42	99.95 6	99.35 145	99.72 26	99.01 140	98.90 117	99.82 55	99.58 77

APDe-MVS		99.60 43	99.48 47	99.73 60	99.85 76	99.51 73	99.75 39	99.85 68	99.17 72	99.81 57	99.56 88	99.94 35	99.44 79	99.42 75	99.22 65	99.67 98	99.54 92

v1921920		99.59 45	99.40 62	99.82 26	99.88 48	99.45 80	99.81 26	99.83 82	99.65 13	99.86 35	99.95 6	99.29 150	99.75 18	98.98 146	98.86 122	99.78 67	99.59 68

TranMVSNet+NR-MVSNet		99.59 45	99.42 58	99.80 36	99.87 53	99.55 56	99.64 64	99.86 61	99.05 89	99.88 23	99.72 60	99.33 148	99.64 41	99.47 65	99.14 74	99.91 24	99.67 52

EG-PatchMatch MVS		99.59 45	99.49 46	99.70 65	99.82 93	99.26 123	99.39 121	99.83 82	98.99 94	99.93 5	99.54 90	99.92 50	99.51 63	99.78 31	99.50 42	99.73 85	99.41 120

pmmvs5		99.58 48	99.47 50	99.70 65	99.84 82	99.50 74	99.58 80	99.80 105	98.98 97	99.73 89	99.92 16	99.81 92	99.49 72	99.28 101	99.05 96	99.77 71	99.73 36

v144192		99.58 48	99.39 63	99.80 36	99.87 53	99.44 82	99.77 32	99.84 77	99.64 15	99.86 35	99.93 14	99.35 145	99.72 26	98.92 152	98.82 126	99.74 81	99.66 53

v148		99.58 48	99.43 55	99.76 47	99.87 53	99.40 92	99.76 35	99.85 68	99.48 35	99.83 51	99.82 47	99.83 87	99.51 63	99.20 118	98.82 126	99.75 76	99.45 110

v1240		99.58 48	99.38 66	99.82 26	99.89 42	99.49 75	99.82 24	99.83 82	99.63 17	99.86 35	99.96 4	98.92 163	99.75 18	99.15 128	98.96 111	99.76 73	99.56 84

V42		99.57 52	99.41 59	99.75 51	99.84 82	99.37 100	99.73 47	99.83 82	99.41 42	99.75 76	99.89 30	99.42 139	99.60 46	99.15 128	98.96 111	99.76 73	99.65 56

TSAR-MVS + MP.		99.56 53	99.54 39	99.58 87	99.69 144	99.14 145	99.73 47	99.45 185	99.50 33	99.35 171	99.60 82	99.93 42	99.50 67	99.56 57	99.37 58	99.77 71	99.64 59
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 53	99.35 68	99.81 31	99.87 53	99.35 106	99.75 39	99.85 68	99.56 25	99.87 29	99.95 6	99.44 137	99.66 35	98.91 155	98.76 132	99.86 41	99.45 110

Gipuma		99.55 55	99.23 87	99.91 5	99.87 53	99.52 69	99.86 10	99.93 27	99.87 1	99.96 2	96.72 209	99.55 129	99.97 1	99.77 33	99.46 49	99.87 39	99.74 34
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DVP-MVS		99.53 56	99.51 42	99.55 96	99.82 93	99.58 51	99.54 88	99.78 113	99.28 60	99.21 181	99.70 65	99.97 9	99.32 92	99.32 89	99.14 74	99.64 110	99.58 77
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 57	99.29 77	99.80 36	99.96 7	99.38 96	99.55 84	99.81 97	98.86 113	99.87 29	99.51 100	98.81 165	99.72 26	99.86 18	99.04 98	99.89 31	99.54 92

ACMMPR		99.51 58	99.32 72	99.72 61	99.87 53	99.33 109	99.61 71	99.85 68	99.19 69	99.73 89	98.73 165	99.95 25	99.61 44	99.35 83	99.14 74	99.66 100	99.58 77

UniMVSNet (Re)		99.50 59	99.29 77	99.75 51	99.86 68	99.47 78	99.51 95	99.82 89	98.90 108	99.89 17	99.64 74	99.00 158	99.55 54	99.32 89	99.08 91	99.90 28	99.59 68

FMVSNet1		99.50 59	99.57 33	99.42 118	99.67 151	99.65 39	99.60 75	99.91 43	99.40 44	99.39 164	99.83 45	99.27 152	98.14 166	99.68 42	99.50 42	99.81 58	99.68 49

HyFIR lowres test		99.50 59	99.26 81	99.80 36	99.95 10	99.62 42	99.76 35	99.97 1	99.67 10	99.56 130	99.94 11	98.40 173	99.78 12	98.84 171	98.59 151	99.76 73	99.72 39

PM-MVS		99.49 62	99.43 55	99.57 91	99.76 121	99.34 108	99.53 89	99.77 120	98.93 104	99.75 76	99.46 105	99.83 87	99.11 119	99.72 39	99.29 63	99.49 149	99.46 109

Anonymous20231206		99.48 63	99.31 74	99.69 67	99.79 103	99.57 52	99.63 68	99.79 108	98.88 110	99.91 13	99.72 60	99.93 42	99.59 48	99.24 107	98.63 147	99.43 158	99.18 147

DU-MVS		99.48 63	99.26 81	99.75 51	99.85 76	99.38 96	99.50 99	99.81 97	98.86 113	99.89 17	99.51 100	98.98 159	99.59 48	99.46 66	98.97 109	99.87 39	99.63 60

RPSCF		99.48 63	99.45 53	99.52 103	99.73 137	99.33 109	99.13 160	99.77 120	99.33 52	99.47 151	99.39 115	99.92 50	99.36 84	99.63 51	99.13 82	99.63 113	99.41 120

ACMMP_NAP		99.47 66	99.33 70	99.63 79	99.85 76	99.28 121	99.56 83	99.83 82	98.75 126	99.48 148	99.03 152	99.95 25	99.47 78	99.48 62	99.19 67	99.57 132	99.59 68

Anonymous20231211		99.47 66	99.39 63	99.57 91	99.89 42	99.60 45	99.50 99	99.69 142	98.91 107	99.62 118	99.17 138	99.35 145	98.86 140	99.63 51	99.46 49	99.84 47	99.62 63

SteuartSystems-ACMMP		99.47 66	99.22 90	99.76 47	99.88 48	99.36 102	99.65 63	99.84 77	98.47 151	99.80 58	98.68 168	99.96 14	99.68 31	99.37 80	99.06 93	99.72 89	99.66 53
Skip Steuart: Steuart Systems R&D Blog.
ACMM	98.37 12	99.47 66	99.23 87	99.74 57	99.86 68	99.19 139	99.68 56	99.86 61	99.16 76	99.71 98	98.52 178	99.95 25	99.62 43	99.35 83	99.02 100	99.74 81	99.42 119
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DVP-MVS++		99.46 70	99.57 33	99.33 139	99.75 125	99.57 52	99.44 112	99.81 97	99.38 47	98.56 210	99.81 51	99.99 3	98.79 144	99.33 87	99.13 82	99.62 119	99.81 19

HFP-MVS		99.46 70	99.30 75	99.65 73	99.82 93	99.25 127	99.50 99	99.82 89	99.23 62	99.58 128	98.86 156	99.94 35	99.56 53	99.14 130	99.12 86	99.63 113	99.56 84

LGP-MVS_train		99.46 70	99.18 99	99.78 41	99.87 53	99.25 127	99.71 54	99.87 57	98.02 183	99.79 62	98.90 155	99.96 14	99.66 35	99.49 61	99.17 70	99.79 65	99.49 102

SED-MVS		99.45 73	99.46 52	99.42 118	99.77 116	99.57 52	99.42 115	99.80 105	99.06 86	99.38 165	99.66 70	99.96 14	98.65 152	99.31 91	99.14 74	99.53 141	99.55 89

ETV-MVS		99.45 73	99.32 72	99.60 84	99.79 103	99.60 45	99.40 120	99.78 113	97.88 189	99.83 51	99.33 119	99.70 114	98.97 130	99.74 36	99.43 53	99.84 47	99.58 77

ACMP	98.32 13	99.44 75	99.18 99	99.75 51	99.83 88	99.18 140	99.64 64	99.83 82	98.81 122	99.79 62	98.42 185	99.96 14	99.64 41	99.46 66	98.98 108	99.74 81	99.44 113
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DCV-MVSNet		99.43 76	99.23 87	99.67 69	99.92 30	99.76 21	99.64 64	99.93 27	99.06 86	99.68 108	97.77 196	98.97 160	98.97 130	99.72 39	99.54 37	99.88 33	99.81 19

SMA-MVS		99.43 76	99.41 59	99.45 114	99.82 93	99.31 114	99.02 174	99.59 164	99.06 86	99.34 174	99.53 96	99.96 14	99.38 82	99.29 96	99.13 82	99.53 141	99.59 68
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 76	99.47 50	99.38 127	99.90 39	99.67 38	99.30 139	99.73 137	98.64 139	99.53 134	99.52 98	99.90 59	98.08 169	99.65 49	99.40 57	99.75 76	99.55 89

DELS-MVS		99.42 79	99.53 40	99.29 142	99.52 179	99.43 85	99.42 115	99.28 200	99.16 76	99.72 92	99.82 47	99.97 9	98.17 163	99.56 57	99.16 71	99.65 102	99.59 68
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 79	99.22 90	99.65 73	99.78 108	99.13 149	99.50 99	99.85 68	99.40 44	99.80 58	98.59 174	99.79 102	99.30 96	99.20 118	99.06 93	99.71 92	99.35 133

DPE-MVS		99.41 81	99.36 67	99.47 110	99.66 152	99.48 76	99.46 110	99.75 134	98.65 135	99.41 161	99.67 68	99.95 25	98.82 141	99.21 115	99.14 74	99.72 89	99.40 125
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 81	99.12 111	99.76 47	99.86 68	99.48 76	99.50 99	99.81 97	98.84 116	99.89 17	99.45 106	98.32 176	99.59 48	99.22 111	98.89 118	99.90 28	99.63 60

CP-MVS		99.41 81	99.20 95	99.65 73	99.80 99	99.23 134	99.44 112	99.75 134	98.60 144	99.74 82	98.66 169	99.93 42	99.48 75	99.33 87	99.16 71	99.73 85	99.48 105

QAPM		99.41 81	99.21 94	99.64 78	99.78 108	99.16 142	99.51 95	99.85 68	99.20 66	99.72 92	99.43 107	99.81 92	99.25 101	98.87 161	98.71 139	99.71 92	99.30 138

UGNet		99.40 85	99.61 24	99.16 161	99.88 48	99.64 40	99.61 71	99.77 120	99.31 54	99.63 117	99.33 119	99.93 42	96.46 203	99.63 51	99.53 38	99.63 113	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 85	99.28 79	99.55 96	99.92 30	99.68 36	99.31 134	99.87 57	98.69 132	99.16 183	99.08 147	98.64 169	99.20 105	99.65 49	99.46 49	99.83 52	99.72 39

OPM-MVS		99.39 87	99.22 90	99.59 85	99.76 121	98.82 173	99.51 95	99.79 108	99.17 72	99.53 134	99.31 124	99.95 25	99.35 85	99.22 111	98.79 131	99.60 124	99.27 141
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Fast-Effi-MVS+		99.39 87	99.18 99	99.63 79	99.86 68	99.28 121	99.45 111	99.91 43	98.47 151	99.61 121	99.50 102	99.57 126	99.17 106	99.24 107	98.66 144	99.78 67	99.59 68

LS3D		99.39 87	99.28 79	99.52 103	99.77 116	99.39 93	99.55 84	99.82 89	98.93 104	99.64 115	98.52 178	99.67 118	98.58 156	99.74 36	99.63 29	99.75 76	99.06 163

diffmvs		99.38 90	99.33 70	99.45 114	99.87 53	99.39 93	99.28 143	99.58 167	99.55 27	99.50 144	99.85 40	99.85 82	98.94 135	98.58 184	98.68 142	99.51 146	99.39 127
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CANet		99.36 91	99.39 63	99.34 138	99.80 99	99.35 106	99.41 119	99.47 183	99.20 66	99.74 82	99.54 90	99.68 116	98.05 171	99.23 109	98.97 109	99.57 132	99.73 36

MVS_0304		99.36 91	99.35 68	99.37 133	99.85 76	99.36 102	99.39 121	99.56 169	99.36 50	99.75 76	99.23 130	99.90 59	97.97 177	99.00 142	98.83 125	99.69 95	99.77 27

ACMMP		99.36 91	99.06 119	99.71 62	99.86 68	99.36 102	99.63 68	99.85 68	98.33 166	99.72 92	97.73 198	99.94 35	99.53 58	99.37 80	99.13 82	99.65 102	99.56 84
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 94	99.26 81	99.46 112	99.66 152	99.15 144	98.92 183	99.67 152	99.55 27	99.35 171	98.83 158	99.91 56	99.35 85	99.19 121	98.53 153	99.78 67	99.68 49
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 94	99.09 117	99.65 73	99.84 82	99.22 135	99.59 76	99.78 113	98.13 175	99.67 109	98.44 182	99.93 42	99.43 81	99.31 91	99.09 90	99.60 124	99.49 102
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
pmmvs4		99.34 96	99.15 106	99.57 91	99.77 116	98.90 166	99.51 95	99.77 120	99.07 84	99.73 89	99.72 60	99.84 85	99.07 121	98.85 166	98.39 162	99.55 139	99.27 141

EPP-MVSNet		99.34 96	99.10 115	99.62 83	99.94 23	99.74 29	99.66 62	99.80 105	99.07 84	98.93 193	99.61 79	96.13 191	99.49 72	99.67 45	99.63 29	99.92 21	99.86 11

TSAR-MVS + GP.		99.33 98	99.17 103	99.51 105	99.71 142	99.00 161	98.84 191	99.71 139	98.23 172	99.74 82	99.53 96	99.90 59	99.35 85	99.38 79	98.85 123	99.72 89	99.31 136

PHI-MVS		99.33 98	99.19 97	99.49 108	99.69 144	99.25 127	99.27 144	99.59 164	98.44 155	99.78 66	99.15 139	99.92 50	98.95 134	99.39 77	99.04 98	99.64 110	99.18 147

MSP-MVS		99.32 100	99.26 81	99.38 127	99.76 121	99.54 59	99.42 115	99.72 138	98.92 106	98.84 200	98.96 154	99.96 14	98.91 136	98.72 179	99.14 74	99.63 113	99.58 77
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		99.32 100	98.99 128	99.71 62	99.86 68	99.31 114	99.59 76	99.86 61	97.51 198	99.75 76	98.23 188	99.94 35	99.53 58	99.29 96	99.08 91	99.65 102	99.54 92

DeepC-MVS_fast	98.69 9	99.32 100	99.13 109	99.53 99	99.63 158	98.78 176	99.53 89	99.33 198	99.08 82	99.77 68	99.18 137	99.89 62	99.29 97	99.00 142	98.70 140	99.65 102	99.30 138
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSDG		99.32 100	99.09 117	99.58 87	99.75 125	98.74 180	99.36 126	99.54 172	99.14 79	99.72 92	99.24 128	99.89 62	99.51 63	99.30 93	98.76 132	99.62 119	98.54 182

TSAR-MVS + ACMM		99.31 104	99.26 81	99.37 133	99.66 152	98.97 164	99.20 152	99.56 169	99.33 52	99.19 182	99.54 90	99.91 56	99.32 92	99.12 131	98.34 165	99.29 172	99.65 56

3Dnovator+	98.92 7	99.31 104	99.03 123	99.63 79	99.77 116	98.90 166	99.52 92	99.81 97	99.37 48	99.72 92	98.03 193	99.73 110	99.32 92	98.99 145	98.81 129	99.67 98	99.36 131

X-MVS		99.30 106	98.99 128	99.66 71	99.85 76	99.30 116	99.49 106	99.82 89	98.32 167	99.69 101	97.31 207	99.93 42	99.50 67	99.37 80	99.16 71	99.60 124	99.53 95

MVS_111021_HR		99.30 106	99.14 107	99.48 109	99.58 175	99.25 127	99.27 144	99.61 159	98.74 128	99.66 112	99.02 153	99.84 85	99.33 89	99.20 118	98.76 132	99.44 155	99.18 147

TAPA-MVS	98.54 10	99.30 106	99.24 86	99.36 137	99.44 194	98.77 178	99.00 176	99.41 189	99.23 62	99.60 123	99.50 102	99.86 76	99.15 113	99.29 96	98.95 113	99.56 136	99.08 159
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		99.30 106	99.01 127	99.63 79	99.75 125	98.89 169	99.35 129	99.60 161	98.53 149	99.86 35	99.57 86	99.94 35	99.52 61	98.96 147	98.10 178	99.70 94	99.08 159
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
USDC		99.29 110	98.98 130	99.65 73	99.72 139	98.87 171	99.47 108	99.66 155	99.35 51	99.87 29	99.58 85	99.87 75	99.51 63	98.85 166	97.93 184	99.65 102	98.38 186

PMVS	94.32 17	99.27 111	99.55 36	98.94 178	99.60 167	99.43 85	99.39 121	99.54 172	98.99 94	99.69 101	99.60 82	99.81 92	95.68 208	99.88 15	99.83 7	99.73 85	99.31 136
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FA-MVS(training)		99.26 112	99.12 111	99.44 116	99.60 167	99.26 123	99.24 149	99.97 1	98.84 116	99.76 71	99.43 107	98.74 166	98.47 159	99.39 77	99.10 88	99.57 132	99.07 162

MVS_111021_LR		99.25 113	99.13 109	99.39 123	99.50 187	99.14 145	99.23 150	99.50 180	98.67 133	99.61 121	99.12 143	99.81 92	99.16 109	99.28 101	98.67 143	99.35 168	99.21 146

ECVR-MVS		99.24 114	98.74 153	99.82 26	99.95 10	99.78 17	99.67 60	99.93 27	99.45 38	99.80 58	99.86 38	92.58 207	99.65 37	99.93 3	99.88 3	99.94 15	99.71 45

baseline		99.24 114	99.30 75	99.17 160	99.78 108	99.14 145	99.10 164	99.69 142	98.97 98	99.49 146	99.84 42	99.88 68	97.99 176	98.85 166	98.73 137	98.98 187	99.72 39

EIA-MVS		99.23 116	99.03 123	99.47 110	99.83 88	99.64 40	99.16 156	99.81 97	97.11 205	99.65 114	98.44 182	99.78 105	98.61 155	99.46 66	99.22 65	99.75 76	99.59 68

HPM-MVS++		99.23 116	98.98 130	99.53 99	99.75 125	99.02 159	99.44 112	99.77 120	98.65 135	99.52 140	98.72 166	99.92 50	99.33 89	98.77 177	98.40 161	99.40 162	99.36 131

PMMVS2		99.23 116	99.22 90	99.24 149	99.80 99	99.14 145	99.50 99	99.82 89	99.12 81	98.41 216	99.91 23	99.98 5	98.51 157	99.48 62	98.76 132	99.38 164	98.14 194

test1111		99.21 119	98.67 157	99.84 21	99.96 7	99.82 8	99.72 51	99.94 24	99.54 29	99.78 66	99.89 30	91.89 210	99.69 30	99.93 3	99.89 1	99.95 7	99.75 32

CPTT-MVS		99.21 119	98.89 140	99.58 87	99.72 139	99.12 152	99.30 139	99.76 129	98.62 140	99.66 112	97.51 203	99.89 62	99.48 75	99.01 140	98.64 146	99.58 131	99.40 125

TinyColmap		99.21 119	98.89 140	99.59 85	99.61 163	98.61 188	99.47 108	99.67 152	99.02 91	99.82 54	99.15 139	99.74 107	99.35 85	99.17 126	98.33 166	99.63 113	98.22 192

Effi-MVS+		99.20 122	98.93 135	99.50 107	99.79 103	99.26 123	98.82 194	99.96 12	98.37 165	99.60 123	99.12 143	98.36 174	99.05 124	98.93 150	98.82 126	99.78 67	99.68 49

PVSNet_BlendedMVS		99.20 122	99.17 103	99.23 150	99.69 144	99.33 109	99.04 169	99.13 203	98.41 160	99.79 62	99.33 119	99.36 142	98.10 167	99.29 96	98.87 120	99.65 102	99.56 84

PVSNet_Blended		99.20 122	99.17 103	99.23 150	99.69 144	99.33 109	99.04 169	99.13 203	98.41 160	99.79 62	99.33 119	99.36 142	98.10 167	99.29 96	98.87 120	99.65 102	99.56 84

MCST-MVS		99.17 125	98.82 148	99.57 91	99.75 125	98.70 184	99.25 148	99.69 142	98.62 140	99.59 125	98.54 176	99.79 102	99.53 58	98.48 188	98.15 174	99.64 110	99.43 116

APD-MVS		99.17 125	98.92 136	99.46 112	99.78 108	99.24 132	99.34 130	99.78 113	97.79 192	99.48 148	98.25 187	99.88 68	98.77 145	99.18 124	98.92 115	99.63 113	99.18 147
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	98.82 8	99.17 125	98.85 144	99.53 99	99.75 125	99.06 157	99.36 126	99.82 89	98.28 169	99.76 71	98.47 180	99.61 122	98.91 136	98.80 174	98.70 140	99.60 124	99.04 167

IterMVS-LS		99.16 128	98.82 148	99.57 91	99.87 53	99.71 32	99.58 80	99.92 37	99.24 61	99.71 98	99.73 56	95.79 192	98.91 136	98.82 173	98.66 144	99.43 158	99.77 27
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepPCF-MVS	98.38 11	99.16 128	99.20 95	99.12 165	99.20 211	98.71 183	98.85 190	99.06 206	99.17 72	98.96 192	99.61 79	99.86 76	99.29 97	99.17 126	98.72 138	99.36 166	99.15 155

IterMVS-SCA-FT		99.15 130	98.96 132	99.38 127	99.87 53	99.54 59	99.53 89	99.79 108	98.94 102	99.82 54	99.92 16	97.65 183	98.82 141	98.95 149	98.26 168	98.45 197	99.47 108

CDS-MVSNet		99.15 130	99.10 115	99.21 156	99.59 172	99.22 135	99.48 107	99.47 183	98.89 109	99.41 161	99.84 42	98.11 179	97.76 180	99.26 106	99.01 102	99.57 132	99.38 128
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS_MVSNet		99.15 130	99.12 111	99.19 158	99.92 30	99.73 31	99.55 84	99.86 61	98.45 154	96.91 222	98.74 164	98.33 175	99.02 126	99.54 59	99.47 47	99.88 33	99.61 65

dmvs_re		99.14 133	98.76 151	99.58 87	99.75 125	99.38 96	99.30 139	99.68 148	96.94 210	99.74 82	97.70 199	99.20 154	99.29 97	99.22 111	99.35 59	99.73 85	99.55 89

MDA-MVSNet-bldmvs		99.11 134	99.11 114	99.12 165	99.91 34	99.38 96	99.77 32	98.72 210	99.31 54	99.85 42	99.43 107	98.26 177	99.48 75	99.85 19	98.47 156	96.99 208	99.08 159

OMC-MVS		99.11 134	98.95 133	99.29 142	99.37 200	98.57 190	99.19 153	99.20 202	98.87 112	99.58 128	99.13 141	99.88 68	99.00 127	99.19 121	98.46 157	99.43 158	98.57 181

MVS_Test		99.09 136	98.92 136	99.29 142	99.61 163	99.07 156	99.04 169	99.81 97	98.58 146	99.37 168	99.74 54	98.87 164	98.41 161	98.61 183	98.01 182	99.50 148	99.57 83

CNVR-MVS		99.08 137	98.83 145	99.37 133	99.61 163	98.74 180	99.15 157	99.54 172	98.59 145	99.37 168	98.15 190	99.88 68	99.08 120	98.91 155	98.46 157	99.48 150	99.06 163

IterMVS		99.08 137	98.90 139	99.29 142	99.87 53	99.53 62	99.52 92	99.77 120	98.94 102	99.75 76	99.91 23	97.52 187	98.72 149	98.86 164	98.14 175	98.09 200	99.43 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		99.07 139	99.19 97	98.93 180	99.02 216	99.53 62	99.31 134	99.84 77	98.86 113	98.88 196	99.64 74	98.44 172	96.92 197	99.35 83	99.00 106	99.61 121	99.53 95

CVMVSNet		99.06 140	98.88 143	99.28 146	99.52 179	99.53 62	99.42 115	99.69 142	98.74 128	98.27 218	99.89 30	95.48 195	99.44 79	99.46 66	99.33 60	99.32 171	99.75 32

CDPH-MVS		99.05 141	98.63 158	99.54 98	99.75 125	98.78 176	99.59 76	99.68 148	97.79 192	99.37 168	98.20 189	99.86 76	99.14 115	98.58 184	98.01 182	99.68 96	99.16 153

TAMVS		99.05 141	99.02 126	99.08 170	99.69 144	99.22 135	99.33 131	99.32 199	99.16 76	98.97 191	99.87 35	97.36 188	97.76 180	99.21 115	99.00 106	99.44 155	99.33 134

CANet_DTU		99.03 143	99.18 99	98.87 183	99.58 175	99.03 158	99.18 154	99.41 189	98.65 135	99.74 82	99.55 89	99.71 111	96.13 206	99.19 121	98.92 115	99.17 181	99.18 147

Effi-MVS+-dtu		99.01 144	99.05 120	98.98 174	99.60 167	99.13 149	99.03 173	99.61 159	98.52 150	99.01 188	98.53 177	99.83 87	96.95 196	99.48 62	98.59 151	99.66 100	99.25 145

canonicalmvs		99.00 145	98.68 156	99.37 133	99.68 150	99.42 89	98.94 182	99.89 52	99.00 93	98.99 189	98.43 184	95.69 193	98.96 133	99.18 124	99.18 68	99.74 81	99.88 6

MIMVSNet		99.00 145	99.03 123	98.97 177	99.32 206	99.32 113	99.39 121	99.91 43	98.41 160	98.76 203	99.24 128	99.17 155	97.13 190	99.30 93	98.80 130	99.29 172	99.01 168

CHOSEN 280x420		98.99 147	98.91 138	99.07 171	99.77 116	99.26 123	99.55 84	99.92 37	98.62 140	98.67 207	99.62 78	97.20 189	98.44 160	99.50 60	99.18 68	98.08 201	98.99 171

SF-MVS		98.96 148	98.95 133	98.98 174	99.64 157	98.89 169	98.00 220	99.58 167	98.42 158	99.08 187	98.63 171	99.83 87	98.04 173	99.02 139	98.76 132	99.52 143	99.13 156

GBi-Net		98.96 148	99.05 120	98.85 184	99.02 216	99.53 62	99.31 134	99.78 113	98.13 175	98.48 212	99.43 107	97.58 184	96.92 197	99.68 42	99.50 42	99.61 121	99.53 95

test1		98.96 148	99.05 120	98.85 184	99.02 216	99.53 62	99.31 134	99.78 113	98.13 175	98.48 212	99.43 107	97.58 184	96.92 197	99.68 42	99.50 42	99.61 121	99.53 95

PCF-MVS	97.86 15	98.95 151	98.53 163	99.44 116	99.70 143	98.80 175	98.96 178	99.69 142	98.65 135	99.59 125	99.33 119	99.94 35	99.12 118	98.01 198	97.11 195	99.59 130	97.83 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		98.94 152	98.71 155	99.21 156	99.52 179	98.22 206	98.97 177	99.53 177	98.76 124	99.50 144	98.59 174	99.56 128	98.68 150	98.63 182	98.45 159	99.05 184	98.73 178

AdaColmap		98.93 153	98.53 163	99.39 123	99.52 179	98.65 187	99.11 163	99.59 164	98.08 179	99.44 154	97.46 205	99.45 135	99.24 102	98.92 152	98.44 160	99.44 155	98.73 178

MSLP-MVS++		98.92 154	98.73 154	99.14 162	99.44 194	99.00 161	98.36 210	99.35 195	98.82 121	99.38 165	96.06 211	99.79 102	99.07 121	98.88 160	99.05 96	99.27 174	99.53 95

new_pmnet		98.91 155	98.89 140	98.94 178	99.51 185	98.27 202	99.15 157	98.66 211	99.17 72	99.48 148	99.79 52	99.80 98	98.49 158	99.23 109	98.20 172	98.34 198	97.74 202

train_agg		98.89 156	98.48 168	99.38 127	99.69 144	98.76 179	99.31 134	99.60 161	97.71 194	98.98 190	97.89 194	99.89 62	99.29 97	98.32 189	97.59 191	99.42 161	99.16 153

NCCC		98.88 157	98.42 169	99.42 118	99.62 159	98.81 174	99.10 164	99.54 172	98.76 124	99.53 134	95.97 212	99.80 98	99.16 109	98.49 187	98.06 181	99.55 139	99.05 165

PLC	97.83 16	98.88 157	98.52 165	99.30 141	99.45 192	98.60 189	98.65 200	99.49 181	98.66 134	99.59 125	96.33 210	99.59 125	99.17 106	98.87 161	98.53 153	99.46 152	99.05 165
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs3		98.85 159	98.60 159	99.13 163	99.66 152	98.72 182	99.37 125	99.06 206	98.44 155	99.76 71	99.74 54	99.55 129	99.15 113	99.04 137	96.00 203	97.80 202	98.72 180

Fast-Effi-MVS+-dtu		98.82 160	98.80 150	98.84 186	99.51 185	98.90 166	98.96 178	99.91 43	98.29 168	99.11 186	98.47 180	99.63 121	96.03 207	99.21 115	98.12 176	99.52 143	99.01 168

CNLPA		98.82 160	98.52 165	99.18 159	99.21 210	98.50 194	98.73 198	99.34 197	98.73 130	99.56 130	97.55 202	99.42 139	99.06 123	98.93 150	98.10 178	99.21 180	98.38 186

PatchMatch-RL		98.80 162	98.52 165	99.12 165	99.38 199	98.70 184	98.56 203	99.55 171	97.81 191	99.34 174	97.57 201	99.31 149	98.67 151	99.27 104	98.62 148	99.22 179	98.35 188

thisisatest0530		98.78 163	98.26 172	99.39 123	99.78 108	99.43 85	99.07 166	99.64 157	98.44 155	99.42 159	99.22 131	92.68 206	98.63 153	99.30 93	99.14 74	99.80 62	99.60 66

tttt0517		98.77 164	98.25 174	99.38 127	99.79 103	99.46 79	99.07 166	99.64 157	98.40 163	99.38 165	99.21 133	92.54 208	98.63 153	99.34 86	99.14 74	99.80 62	99.62 63

DI_MVS_plusplus_trai		98.74 165	98.08 182	99.51 105	99.79 103	99.29 120	99.61 71	99.60 161	99.20 66	99.46 152	99.09 146	92.93 200	98.97 130	98.27 192	98.35 164	99.65 102	99.45 110

TSAR-MVS + COLMAP		98.74 165	98.58 161	98.93 180	99.29 207	98.23 203	99.04 169	99.24 201	98.79 123	98.80 202	99.37 117	99.71 111	98.06 170	98.02 197	97.46 193	99.16 182	98.48 184

MDTV_nov1_ep13_2view		98.73 167	98.31 171	99.22 153	99.75 125	99.24 132	99.75 39	99.93 27	99.31 54	99.84 46	99.86 38	99.81 92	99.31 95	97.40 206	94.77 205	96.73 210	97.81 199

PMMVS		98.71 168	98.55 162	98.90 182	99.28 208	98.45 196	98.53 206	99.45 185	97.67 196	99.15 185	98.76 162	99.54 131	97.79 179	98.77 177	98.23 170	99.16 182	98.46 185

HQP-MVS		98.70 169	98.19 178	99.28 146	99.61 163	98.52 192	98.71 199	99.35 195	97.97 186	99.53 134	97.38 206	99.85 82	99.14 115	97.53 202	96.85 199	99.36 166	99.26 144

N_pmnet		98.64 170	98.23 177	99.11 168	99.78 108	99.25 127	99.75 39	99.39 193	99.65 13	99.70 100	99.78 53	99.89 62	98.81 143	97.60 201	94.28 206	97.24 207	97.15 206

CMPMVS	76.62 19	98.64 170	98.60 159	98.68 191	99.33 204	97.07 219	98.11 218	98.50 212	97.69 195	99.26 177	98.35 186	99.66 119	97.62 183	99.43 74	99.02 100	99.24 177	99.01 168
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		98.63 172	98.75 152	98.49 197	98.10 222	99.44 82	99.02 174	99.78 113	98.13 175	98.48 212	99.43 107	97.58 184	96.16 205	98.85 166	98.39 162	99.40 162	99.41 120

GA-MVS		98.59 173	98.15 179	99.09 169	99.59 172	99.13 149	98.84 191	99.52 179	98.61 143	99.35 171	99.67 68	93.03 199	97.73 182	98.90 159	98.26 168	99.51 146	99.48 105

MAR-MVS		98.54 174	98.15 179	98.98 174	99.37 200	98.09 209	98.56 203	99.65 156	96.11 220	99.27 176	97.16 208	99.50 132	98.03 174	98.87 161	98.23 170	99.01 185	99.13 156
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		98.49 175	97.60 184	99.53 99	99.90 39	99.55 56	99.77 32	99.48 182	99.67 10	99.86 35	99.98 3	99.98 5	99.50 67	96.90 208	91.52 212	98.67 194	95.62 212

FPMVS		98.48 176	98.83 145	98.07 207	99.09 214	97.98 212	99.07 166	98.04 218	98.99 94	99.22 180	98.85 157	99.43 138	93.79 216	99.66 47	99.11 87	99.24 177	97.76 200

MVS-HIRNet		98.45 177	98.25 174	98.69 190	99.12 212	97.81 218	98.55 205	99.85 68	98.58 146	99.67 109	99.61 79	99.86 76	97.46 186	97.95 199	96.37 201	97.49 204	97.56 203

test0.0.03 1		98.41 178	98.41 170	98.40 201	99.62 159	99.16 142	98.87 188	99.41 189	97.15 203	96.60 224	99.31 124	97.00 190	96.55 202	98.91 155	98.51 155	99.37 165	98.82 175

gg-mvs-nofinetune		98.40 179	98.26 172	98.57 195	99.83 88	98.86 172	98.77 197	99.97 1	99.57 24	99.99 1	99.99 1	93.81 197	93.50 217	98.91 155	98.20 172	99.33 170	98.52 183

baseline1		98.39 180	97.59 185	99.31 140	99.78 108	99.45 80	99.13 160	99.53 177	98.06 181	98.87 197	98.63 171	90.04 214	98.76 146	98.85 166	98.84 124	99.81 58	99.28 140

pmnet_mix02		98.28 181	97.48 187	99.22 153	99.78 108	99.12 152	99.68 56	99.39 193	99.49 34	99.86 35	99.82 47	99.89 62	99.23 103	95.54 211	92.36 209	97.38 205	96.14 210

PatchT		98.11 182	97.12 193	99.26 148	99.65 156	98.34 200	99.57 82	99.97 1	97.48 199	99.43 156	99.04 151	90.84 212	98.15 164	98.04 195	97.78 185	98.82 191	98.30 189

DPM-MVS		98.10 183	97.32 191	99.01 173	99.52 179	97.92 213	98.47 208	99.45 185	98.25 170	98.91 194	93.99 216	99.69 115	98.73 148	96.29 210	96.32 202	99.00 186	98.77 176

EPNet_dtu		98.09 184	98.25 174	97.91 209	99.58 175	98.02 211	98.19 215	99.67 152	97.94 187	99.74 82	99.07 149	98.71 168	93.40 218	97.50 203	97.09 196	96.89 209	99.44 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.06 185	98.11 181	98.00 208	99.60 167	98.99 163	98.38 209	99.68 148	98.18 174	98.85 199	97.89 194	95.60 194	92.72 219	98.30 190	98.10 178	98.76 192	99.72 39
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 186	96.80 196	99.22 153	99.60 167	98.23 203	98.91 184	99.97 1	96.89 213	99.43 156	99.10 145	89.24 217	98.15 164	98.04 195	97.78 185	99.26 175	98.30 189

thres200		97.87 187	96.56 198	99.39 123	99.76 121	99.52 69	99.13 160	99.76 129	96.88 215	98.66 208	92.87 220	88.77 220	99.16 109	99.11 132	99.42 54	99.88 33	99.33 134

baseline2		97.87 187	97.18 192	98.67 192	99.34 203	99.17 141	98.48 207	98.82 209	97.08 206	98.83 201	98.75 163	89.47 216	97.03 195	98.67 181	98.27 167	99.52 143	98.83 174

thres600view7		97.86 189	96.53 201	99.41 121	99.84 82	99.52 69	99.36 126	99.76 129	97.32 201	98.38 217	93.24 217	87.25 222	99.23 103	99.11 132	99.75 18	99.88 33	99.48 105

tfpn200view9		97.85 190	96.54 199	99.38 127	99.74 135	99.52 69	99.17 155	99.76 129	96.10 221	98.70 205	92.99 218	89.10 218	99.00 127	99.11 132	99.56 33	99.88 33	99.41 120

thres400		97.82 191	96.47 202	99.40 122	99.81 98	99.44 82	99.29 142	99.69 142	97.15 203	98.57 209	92.82 221	87.96 221	99.16 109	98.96 147	99.55 36	99.86 41	99.41 120

IB-MVS	98.10 14	97.76 192	97.40 190	98.18 203	99.62 159	99.11 154	98.24 213	98.35 214	96.56 217	99.44 154	91.28 222	98.96 162	93.84 215	98.09 194	98.62 148	99.56 136	99.18 147
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		97.74 193	97.46 188	98.08 205	99.62 159	98.37 198	98.26 211	99.41 189	97.03 207	97.38 220	99.54 90	92.89 201	95.12 212	98.78 175	97.68 189	98.65 195	97.90 196

RPMNet		97.70 194	96.54 199	99.06 172	99.57 178	98.23 203	98.95 181	99.97 1	96.89 213	99.49 146	99.13 141	89.63 215	97.09 192	96.68 209	97.02 197	99.26 175	98.19 193

thres100view900		97.69 195	96.37 203	99.23 150	99.74 135	99.21 138	98.81 195	99.43 188	96.10 221	98.70 205	92.99 218	89.10 218	98.88 139	98.58 184	99.31 62	99.82 55	99.27 141

FMVSNet5		97.69 195	96.98 194	98.53 196	98.53 220	99.36 102	98.90 187	99.54 172	96.38 218	98.44 215	95.38 214	90.08 213	97.05 194	99.46 66	99.06 93	98.73 193	99.12 158

MVE	91.08 18	97.68 197	97.65 183	97.71 215	98.46 221	91.62 225	97.92 221	98.86 208	98.73 130	97.99 219	98.64 170	99.96 14	99.17 106	99.59 55	97.75 187	93.87 224	97.27 204
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-mter		97.65 198	97.57 186	97.75 213	98.90 219	98.56 191	98.15 216	98.45 213	96.92 212	96.84 223	99.52 98	92.53 209	95.24 211	99.04 137	98.12 176	98.90 189	98.29 191

TESTMET0.1,1		97.62 199	97.46 188	97.81 211	99.07 215	98.37 198	98.26 211	98.35 214	97.03 207	97.38 220	99.54 90	92.89 201	95.12 212	98.78 175	97.68 189	98.65 195	97.90 196

test2506		97.57 200	95.67 209	99.78 41	99.95 10	99.78 17	99.67 60	99.93 27	99.45 38	99.55 133	99.20 134	71.73 229	99.65 37	99.93 3	99.88 3	99.94 15	99.72 39

MVSTER		97.55 201	96.75 197	98.48 198	99.46 191	99.54 59	98.24 213	99.77 120	97.56 197	99.41 161	99.31 124	84.86 224	94.66 214	98.86 164	97.75 187	99.34 169	99.38 128

ET-MVSNet_ETH3D		97.44 202	96.29 204	98.78 187	97.93 223	98.95 165	98.91 184	99.09 205	98.00 184	99.24 178	98.83 158	84.62 225	98.02 175	97.43 205	97.38 194	99.48 150	98.84 173

MDTV_nov1_ep13		97.41 203	96.26 205	98.76 188	99.47 189	98.43 197	99.26 147	99.82 89	98.06 181	99.23 179	99.22 131	92.86 203	98.05 171	95.33 213	93.66 208	96.73 210	96.26 209

ADS-MVSNet		97.29 204	96.17 206	98.59 194	99.59 172	98.70 184	99.32 132	99.86 61	98.47 151	99.56 130	99.08 147	98.16 178	97.34 188	92.92 215	91.17 213	95.91 213	94.72 215

SCA		97.25 205	96.05 207	98.64 193	99.36 202	99.02 159	99.27 144	99.96 12	98.25 170	99.69 101	98.71 167	94.66 196	97.95 178	93.95 214	92.35 210	95.64 214	95.40 214

gm-plane-assit		96.82 206	94.84 214	99.13 163	99.95 10	99.78 17	99.69 55	99.92 37	99.19 69	99.84 46	99.92 16	72.93 228	96.44 204	98.21 193	97.01 198	98.92 188	96.87 208

PatchmatchNet		96.81 207	95.41 211	98.43 200	99.43 196	98.30 201	99.23 150	99.93 27	98.19 173	99.64 115	98.81 161	93.50 198	97.43 187	92.89 216	90.78 215	94.94 219	95.41 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		96.76 208	95.30 213	98.46 199	99.42 197	98.47 195	99.32 132	99.91 43	98.42 158	99.51 142	99.07 149	92.81 204	97.12 191	92.39 217	91.71 211	95.51 215	94.20 217

E-PMN		96.72 209	95.78 208	97.81 211	99.45 192	95.46 222	98.14 217	98.33 216	97.99 185	98.73 204	98.09 191	98.97 160	97.54 185	97.45 204	91.09 214	94.70 221	91.40 220

tpm		96.56 210	94.68 215	98.74 189	99.12 212	97.90 214	98.79 196	99.93 27	96.79 216	99.69 101	99.19 136	81.48 227	97.56 184	95.46 212	93.97 207	97.37 206	97.99 195

EMVS		96.47 211	95.38 212	97.74 214	99.42 197	95.37 223	98.07 219	98.27 217	97.85 190	98.90 195	97.48 204	98.73 167	97.20 189	97.21 207	90.39 216	94.59 223	90.65 221

tpmrst		96.18 212	94.47 216	98.18 203	99.52 179	97.89 215	98.96 178	99.79 108	98.07 180	99.16 183	99.30 127	92.69 205	96.69 200	90.76 219	88.85 219	94.96 218	93.69 218

CostFormer		95.61 213	93.35 219	98.24 202	99.48 188	98.03 210	98.65 200	99.83 82	96.93 211	99.42 159	98.83 158	83.65 226	97.08 193	90.39 220	89.54 218	94.94 219	96.11 211

dps		95.59 214	93.46 218	98.08 205	99.33 204	98.22 206	98.87 188	99.70 140	96.17 219	98.87 197	97.75 197	86.85 223	96.60 201	91.24 218	89.62 217	95.10 217	94.34 216

tpm cat1		95.52 215	93.49 217	97.88 210	99.28 208	97.87 216	98.65 200	99.77 120	97.27 202	99.46 152	98.04 192	90.99 211	95.46 209	88.57 221	88.14 220	94.64 222	93.54 219

test_method		91.96 216	95.51 210	87.82 217	70.84 225	82.79 226	92.13 225	87.74 220	98.88 110	95.40 225	99.20 134	98.04 180	85.65 221	97.71 200	94.95 204	95.13 216	97.00 207

GG-mvs-BLEND		70.44 217	96.91 195	39.57 218	3.32 228	96.51 220	91.01 226	4.05 224	97.03 207	33.20 227	94.67 215	97.75 182	7.59 224	98.28 191	96.85 199	98.24 199	97.26 205

testmvs		22.33 218	29.66 220	13.79 219	8.97 226	10.35 227	15.53 229	8.09 223	32.51 223	19.87 228	45.18 223	30.56 231	17.05 223	29.96 222	24.74 221	13.21 225	34.30 222

test123		21.52 219	28.47 221	13.42 220	7.29 227	10.12 228	15.70 228	8.31 222	31.54 224	19.34 229	36.33 224	37.40 230	17.14 222	27.45 223	23.17 222	12.73 226	33.30 223

uanet_test		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

TPM-MVS					99.47 189	97.86 217	97.79 222			98.49 211	97.62 200	99.83 87	95.33 210			98.90 189	98.77 176

RE-MVS-def										99.96 2

9.14												99.57 126

SR-MVS					99.73 137			99.74 136				99.88 68

Anonymous202405211			99.14 107		99.87 53	99.55 56	99.50 99	99.70 140	98.55 148		98.61 173	98.46 171	98.76 146	99.66 47	99.50 42	99.85 44	99.63 60

our_test_3					99.75 125	99.11 154	99.74 46

ambc			98.83 145		99.72 139	98.52 192	98.84 191		98.96 99	99.92 9	99.34 118	99.74 107	99.04 125	98.68 180	97.57 192	99.46 152	98.99 171

MTAPA										99.62 118		99.95 25

MTMP										99.53 134		99.92 50

Patchmatch-RL test							65.75 227

tmp_tt				88.14 216	96.68 224	91.91 224	93.70 224	61.38 221	99.61 19	90.51 226	99.40 114	99.71 111	90.32 220	99.22 111	99.44 52	96.25 212

XVS					99.86 68	99.30 116	99.72 51			99.69 101		99.93 42				99.60 124

X-MVStestdata					99.86 68	99.30 116	99.72 51			99.69 101		99.93 42				99.60 124

mPP-MVS					99.84 82							99.92 50

NP-MVS									97.37 200

Patchmtry						98.19 208	98.91 184	99.97 1		99.43 156

DeepMVS_CX						96.39 221	97.15 223	88.89 219	97.94 187	99.51 142	95.71 213	97.88 181	98.19 162	98.92 152		97.73 203	97.75 201

High-resolution multi-view benchmark