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 3	99.82 8	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 3	99.83 4	99.93 1	99.96 12	99.77 4	99.89 17	99.99 1	99.86 77	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 3	99.88 3	99.92 2	99.97 1	99.66 13	99.94 4	99.94 11	99.74 106	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 13	99.96 8	99.90 1	99.83 18	99.97 1	99.84 2	99.93 5	99.91 23	99.83 87	99.63 40	99.89 8	99.88 2	99.96 3	99.95 1

pmmvs6		99.88 4	99.87 2	99.89 9	99.97 3	99.76 17	99.89 6	99.96 12	99.82 3	99.90 15	99.92 16	99.95 26	99.68 30	99.93 3	99.88 2	99.95 8	99.86 10

anonymousdsp		99.87 6	99.86 4	99.88 13	99.95 11	99.75 23	99.90 4	99.96 12	99.69 8	99.83 51	99.96 4	99.99 3	99.74 21	99.95 2	99.83 5	99.91 21	99.88 7

FC-MVSNet-test		99.84 7	99.80 7	99.89 9	99.96 8	99.83 4	99.84 15	99.95 23	99.37 45	99.77 67	99.95 6	99.96 14	99.85 3	99.93 3	99.83 5	99.95 8	99.72 37

CS-MVS-test		99.83 8	99.78 9	99.89 9	99.98 1	99.90 1	99.90 4	99.92 33	99.38 44	99.89 17	99.74 51	99.97 8	99.71 29	99.82 21	99.81 9	99.94 15	99.86 10

UniMVSNet_ETH3D		99.81 9	99.79 8	99.85 20	99.98 1	99.76 17	99.73 46	99.96 12	99.68 10	99.87 29	99.59 81	99.91 57	99.58 48	99.90 7	99.85 4	99.96 3	99.81 18

TDRefinement		99.81 9	99.76 11	99.86 17	99.83 86	99.53 57	99.89 6	99.91 39	99.73 6	99.88 23	99.83 44	99.96 14	99.76 16	99.91 6	99.81 9	99.86 36	99.59 64

WR-MVS		99.79 11	99.68 15	99.91 5	99.95 11	99.83 4	99.87 10	99.96 12	99.39 43	99.93 5	99.87 35	99.29 151	99.77 14	99.83 18	99.72 17	99.97 1	99.82 15

MIMVSNet1		99.79 11	99.75 12	99.84 21	99.89 39	99.83 4	99.84 15	99.89 48	99.31 51	99.93 5	99.92 16	99.97 8	99.68 30	99.89 8	99.64 23	99.82 51	99.66 49

pm-mvs1		99.77 13	99.69 14	99.86 17	99.94 21	99.68 33	99.84 15	99.93 26	99.59 22	99.87 29	99.92 16	99.21 154	99.65 36	99.88 12	99.77 13	99.93 18	99.78 24

PEN-MVS		99.77 13	99.65 18	99.91 5	99.95 11	99.80 13	99.86 11	99.97 1	99.08 80	99.89 17	99.69 65	99.68 116	99.84 5	99.81 23	99.64 23	99.95 8	99.81 18

EU-MVSNet		99.76 15	99.74 13	99.78 38	99.82 91	99.81 11	99.88 8	99.87 53	99.31 51	99.75 73	99.91 23	99.76 105	99.78 12	99.84 17	99.74 16	99.56 131	99.81 18

Vis-MVSNet		99.76 15	99.78 9	99.75 47	99.92 27	99.77 16	99.83 18	99.85 64	99.43 37	99.85 42	99.84 41	100.00 1	99.13 114	99.83 18	99.66 21	99.90 23	99.90 3
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DTE-MVSNet		99.75 17	99.61 24	99.92 4	99.95 11	99.81 11	99.86 11	99.96 12	99.18 68	99.92 10	99.66 68	99.45 136	99.85 3	99.80 24	99.56 29	99.96 3	99.79 23

tfpnnormal		99.74 18	99.63 21	99.86 17	99.93 24	99.75 23	99.80 27	99.89 48	99.31 51	99.88 23	99.43 103	99.66 119	99.77 14	99.80 24	99.71 18	99.92 19	99.76 28

DeepC-MVS	99.05 5	99.74 18	99.64 19	99.84 21	99.90 35	99.39 88	99.79 28	99.81 94	99.69 8	99.90 15	99.87 35	99.98 4	99.81 7	99.62 50	99.32 56	99.83 48	99.65 52
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 16	99.81 28	99.93 24	99.74 25	99.68 54	99.91 39	99.59 22	99.88 23	99.73 54	99.81 92	99.55 52	99.59 51	99.53 35	99.89 26	99.70 43

PS-CasMVS		99.73 20	99.59 29	99.90 8	99.95 11	99.80 13	99.85 14	99.97 1	98.95 98	99.86 35	99.73 54	99.36 143	99.81 7	99.83 18	99.67 20	99.95 8	99.83 14

WR-MVS_H		99.73 20	99.61 24	99.88 13	99.95 11	99.82 8	99.83 18	99.96 12	99.01 90	99.84 46	99.71 62	99.41 142	99.74 21	99.77 29	99.70 19	99.95 8	99.82 15

TransMVSNet (Re)		99.72 23	99.59 29	99.88 13	99.95 11	99.76 17	99.88 8	99.94 24	99.58 24	99.92 10	99.90 28	98.55 169	99.65 36	99.89 8	99.76 14	99.95 8	99.70 43

ACMH	99.11 4	99.72 23	99.63 21	99.84 21	99.87 51	99.59 45	99.83 18	99.88 52	99.46 36	99.87 29	99.66 68	99.95 26	99.76 16	99.73 34	99.47 43	99.84 43	99.52 95
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		99.70 25	99.67 16	99.74 53	99.94 21	99.71 28	99.82 23	99.91 39	99.14 76	99.53 131	99.70 63	99.88 69	99.33 86	99.88 12	99.61 28	99.94 15	99.77 25

COLMAP_ROB	99.18 2	99.70 25	99.60 27	99.81 28	99.84 80	99.37 95	99.76 34	99.84 73	99.54 30	99.82 54	99.64 72	99.95 26	99.75 18	99.79 26	99.56 29	99.83 48	99.37 126
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 27	99.59 29	99.81 28	99.88 45	99.41 85	99.75 38	99.86 57	99.43 37	99.80 58	99.54 86	99.97 8	99.73 24	99.82 21	99.52 37	99.85 39	99.43 112

test20.03		99.68 28	99.60 27	99.76 43	99.91 31	99.70 31	99.68 54	99.87 53	99.05 87	99.88 23	99.92 16	99.88 69	99.50 63	99.77 29	99.42 50	99.75 73	99.49 98

CP-MVSNet		99.68 28	99.51 39	99.89 9	99.95 11	99.76 17	99.83 18	99.96 12	98.83 116	99.84 46	99.65 71	99.09 156	99.80 10	99.78 27	99.62 27	99.95 8	99.82 15

PVSNet_Blended_VisFu		99.66 30	99.64 19	99.67 66	99.91 31	99.71 28	99.61 66	99.79 104	99.41 39	99.91 13	99.85 39	99.61 122	99.00 124	99.67 41	99.42 50	99.81 54	99.81 18

v10		99.65 31	99.51 39	99.81 28	99.83 86	99.61 41	99.75 38	99.94 24	99.56 26	99.76 70	99.94 11	99.60 124	99.73 24	99.11 127	99.01 96	99.85 39	99.74 31

CHOSEN 1792x2688		99.65 31	99.55 33	99.77 42	99.93 24	99.60 42	99.79 28	99.92 33	99.73 6	99.74 79	99.93 14	99.98 4	99.80 10	98.83 168	99.01 96	99.45 150	99.76 28

UA-Net		99.64 33	99.62 23	99.66 68	99.97 3	99.82 8	99.14 154	99.96 12	98.95 98	99.52 137	99.38 111	99.86 77	99.55 52	99.72 35	99.66 21	99.80 58	99.94 2

GeoE		99.63 34	99.51 39	99.78 38	99.91 31	99.57 48	99.78 30	99.97 1	99.23 59	99.72 88	99.72 58	99.80 98	99.50 63	99.45 70	99.10 83	99.79 61	99.71 42

Baseline_NR-MVSNet		99.62 35	99.48 44	99.78 38	99.85 74	99.76 17	99.59 71	99.82 86	98.84 114	99.88 23	99.91 23	99.04 157	99.61 42	99.46 63	99.78 12	99.94 15	99.60 62

pmmvs-eth3d		99.61 36	99.48 44	99.75 47	99.87 51	99.30 111	99.75 38	99.89 48	99.23 59	99.85 42	99.88 34	99.97 8	99.49 68	99.46 63	99.01 96	99.68 92	99.52 95

v1144		99.61 36	99.43 52	99.82 24	99.88 45	99.41 85	99.76 34	99.86 57	99.64 16	99.84 46	99.95 6	99.49 134	99.74 21	99.00 138	98.93 108	99.84 43	99.58 73

v8		99.61 36	99.45 50	99.79 37	99.80 97	99.59 45	99.73 46	99.93 26	99.48 34	99.77 67	99.90 28	99.48 135	99.67 33	99.11 127	98.89 112	99.84 43	99.73 33

casdiffmvs		99.61 36	99.55 33	99.68 64	99.89 39	99.53 57	99.64 60	99.68 144	99.51 31	99.62 113	99.90 28	99.96 14	99.37 80	99.28 96	99.25 59	99.88 28	99.44 109

CSCG		99.61 36	99.52 38	99.71 57	99.89 39	99.62 39	99.52 89	99.76 124	99.61 20	99.69 97	99.73 54	99.96 14	99.57 50	99.27 99	98.62 144	99.81 54	99.85 13

v1192		99.60 41	99.41 56	99.82 24	99.89 39	99.43 80	99.81 25	99.84 73	99.63 18	99.85 42	99.95 6	99.35 146	99.72 26	99.01 136	98.90 111	99.82 51	99.58 73

APDe-MVS		99.60 41	99.48 44	99.73 55	99.85 74	99.51 68	99.75 38	99.85 64	99.17 69	99.81 57	99.56 84	99.94 36	99.44 75	99.42 73	99.22 60	99.67 94	99.54 87

v1921920		99.59 43	99.40 59	99.82 24	99.88 45	99.45 75	99.81 25	99.83 79	99.65 14	99.86 35	99.95 6	99.29 151	99.75 18	98.98 142	98.86 116	99.78 63	99.59 64

TranMVSNet+NR-MVSNet		99.59 43	99.42 55	99.80 33	99.87 51	99.55 51	99.64 60	99.86 57	99.05 87	99.88 23	99.72 58	99.33 149	99.64 38	99.47 62	99.14 70	99.91 21	99.67 48

EG-PatchMatch MVS		99.59 43	99.49 43	99.70 60	99.82 91	99.26 119	99.39 118	99.83 79	98.99 92	99.93 5	99.54 86	99.92 51	99.51 59	99.78 27	99.50 38	99.73 82	99.41 116

pmmvs5		99.58 46	99.47 47	99.70 60	99.84 80	99.50 69	99.58 75	99.80 101	98.98 95	99.73 85	99.92 16	99.81 92	99.49 68	99.28 96	99.05 90	99.77 68	99.73 33

v144192		99.58 46	99.39 61	99.80 33	99.87 51	99.44 77	99.77 31	99.84 73	99.64 16	99.86 35	99.93 14	99.35 146	99.72 26	98.92 148	98.82 120	99.74 78	99.66 49

v148		99.58 46	99.43 52	99.76 43	99.87 51	99.40 87	99.76 34	99.85 64	99.48 34	99.83 51	99.82 46	99.83 87	99.51 59	99.20 113	98.82 120	99.75 73	99.45 106

v1240		99.58 46	99.38 64	99.82 24	99.89 39	99.49 70	99.82 23	99.83 79	99.63 18	99.86 35	99.96 4	98.92 163	99.75 18	99.15 123	98.96 105	99.76 70	99.56 80

V42		99.57 50	99.41 56	99.75 47	99.84 80	99.37 95	99.73 46	99.83 79	99.41 39	99.75 73	99.89 31	99.42 140	99.60 44	99.15 123	98.96 105	99.76 70	99.65 52

TSAR-MVS + MP.		99.56 51	99.54 36	99.58 85	99.69 140	99.14 140	99.73 46	99.45 181	99.50 32	99.35 168	99.60 79	99.93 43	99.50 63	99.56 53	99.37 54	99.77 68	99.64 55
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 51	99.35 67	99.81 28	99.87 51	99.35 101	99.75 38	99.85 64	99.56 26	99.87 29	99.95 6	99.44 138	99.66 34	98.91 151	98.76 126	99.86 36	99.45 106

Gipuma		99.55 53	99.23 86	99.91 5	99.87 51	99.52 64	99.86 11	99.93 26	99.87 1	99.96 2	96.72 205	99.55 130	99.97 1	99.77 29	99.46 45	99.87 34	99.74 31
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CS-MVS		99.54 54	99.40 59	99.70 60	99.90 35	99.69 32	99.54 83	99.73 132	98.16 171	99.79 61	99.21 128	99.94 36	99.38 78	99.53 56	99.54 33	99.85 39	99.73 33

DVP-MVS		99.53 55	99.51 39	99.55 93	99.82 91	99.58 47	99.54 83	99.78 109	99.28 57	99.21 178	99.70 63	99.97 8	99.32 89	99.32 84	99.14 70	99.64 107	99.58 73
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 56	99.29 76	99.80 33	99.96 8	99.38 91	99.55 79	99.81 94	98.86 111	99.87 29	99.51 96	98.81 165	99.72 26	99.86 15	99.04 92	99.89 26	99.54 87

zzz-MVS		99.51 57	99.36 65	99.68 64	99.88 45	99.38 91	99.53 85	99.84 73	99.11 79	99.59 122	98.93 150	99.95 26	99.58 48	99.44 71	99.21 62	99.65 98	99.52 95

ACMMPR		99.51 57	99.32 71	99.72 56	99.87 51	99.33 104	99.61 66	99.85 64	99.19 66	99.73 85	98.73 161	99.95 26	99.61 42	99.35 79	99.14 70	99.66 96	99.58 73

UniMVSNet (Re)		99.50 59	99.29 76	99.75 47	99.86 65	99.47 73	99.51 92	99.82 86	98.90 106	99.89 17	99.64 72	99.00 158	99.55 52	99.32 84	99.08 85	99.90 23	99.59 64

FMVSNet1		99.50 59	99.57 32	99.42 114	99.67 147	99.65 36	99.60 70	99.91 39	99.40 41	99.39 161	99.83 44	99.27 153	98.14 162	99.68 38	99.50 38	99.81 54	99.68 45

HyFIR lowres test		99.50 59	99.26 80	99.80 33	99.95 11	99.62 39	99.76 34	99.97 1	99.67 11	99.56 128	99.94 11	98.40 172	99.78 12	98.84 167	98.59 147	99.76 70	99.72 37

PM-MVS		99.49 62	99.43 52	99.57 88	99.76 119	99.34 103	99.53 85	99.77 116	98.93 102	99.75 73	99.46 101	99.83 87	99.11 116	99.72 35	99.29 58	99.49 145	99.46 105

Anonymous20231206		99.48 63	99.31 73	99.69 63	99.79 101	99.57 48	99.63 64	99.79 104	98.88 108	99.91 13	99.72 58	99.93 43	99.59 45	99.24 102	98.63 143	99.43 155	99.18 143

DU-MVS		99.48 63	99.26 80	99.75 47	99.85 74	99.38 91	99.50 96	99.81 94	98.86 111	99.89 17	99.51 96	98.98 159	99.59 45	99.46 63	98.97 103	99.87 34	99.63 56

RPSCF		99.48 63	99.45 50	99.52 100	99.73 133	99.33 104	99.13 155	99.77 116	99.33 49	99.47 148	99.39 110	99.92 51	99.36 81	99.63 47	99.13 78	99.63 110	99.41 116

ACMMP_NAP		99.47 66	99.33 69	99.63 76	99.85 74	99.28 116	99.56 78	99.83 79	98.75 122	99.48 145	99.03 147	99.95 26	99.47 74	99.48 59	99.19 63	99.57 128	99.59 64

Anonymous20231211		99.47 66	99.39 61	99.57 88	99.89 39	99.60 42	99.50 96	99.69 138	98.91 105	99.62 113	99.17 133	99.35 146	98.86 137	99.63 47	99.46 45	99.84 43	99.62 59

SteuartSystems-ACMMP		99.47 66	99.22 89	99.76 43	99.88 45	99.36 97	99.65 59	99.84 73	98.47 146	99.80 58	98.68 164	99.96 14	99.68 30	99.37 76	99.06 87	99.72 85	99.66 49
Skip Steuart: Steuart Systems R&D Blog.
ACMM	98.37 12	99.47 66	99.23 86	99.74 53	99.86 65	99.19 134	99.68 54	99.86 57	99.16 73	99.71 94	98.52 175	99.95 26	99.62 41	99.35 79	99.02 94	99.74 78	99.42 115
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HFP-MVS		99.46 70	99.30 74	99.65 70	99.82 91	99.25 122	99.50 96	99.82 86	99.23 59	99.58 126	98.86 152	99.94 36	99.56 51	99.14 125	99.12 81	99.63 110	99.56 80

LGP-MVS_train		99.46 70	99.18 98	99.78 38	99.87 51	99.25 122	99.71 52	99.87 53	98.02 180	99.79 61	98.90 151	99.96 14	99.66 34	99.49 58	99.17 66	99.79 61	99.49 98

SED-MVS		99.45 72	99.46 49	99.42 114	99.77 114	99.57 48	99.42 112	99.80 101	99.06 84	99.38 162	99.66 68	99.96 14	98.65 148	99.31 86	99.14 70	99.53 136	99.55 85

ETV-MVS		99.45 72	99.32 71	99.60 82	99.79 101	99.60 42	99.40 117	99.78 109	97.88 186	99.83 51	99.33 114	99.70 114	98.97 127	99.74 32	99.43 49	99.84 43	99.58 73

ACMP	98.32 13	99.44 74	99.18 98	99.75 47	99.83 86	99.18 135	99.64 60	99.83 79	98.81 118	99.79 61	98.42 182	99.96 14	99.64 38	99.46 63	98.98 102	99.74 78	99.44 109
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DCV-MVSNet		99.43 75	99.23 86	99.67 66	99.92 27	99.76 17	99.64 60	99.93 26	99.06 84	99.68 104	97.77 193	98.97 160	98.97 127	99.72 35	99.54 33	99.88 28	99.81 18

SMA-MVS		99.43 75	99.41 56	99.45 111	99.82 91	99.31 109	99.02 169	99.59 159	99.06 84	99.34 171	99.53 92	99.96 14	99.38 78	99.29 91	99.13 78	99.53 136	99.59 64
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 75	99.47 47	99.38 123	99.90 35	99.67 35	99.30 136	99.73 132	98.64 134	99.53 131	99.52 94	99.90 60	98.08 165	99.65 45	99.40 53	99.75 73	99.55 85

DELS-MVS		99.42 78	99.53 37	99.29 137	99.52 175	99.43 80	99.42 112	99.28 197	99.16 73	99.72 88	99.82 46	99.97 8	98.17 159	99.56 53	99.16 67	99.65 98	99.59 64
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 78	99.22 89	99.65 70	99.78 106	99.13 144	99.50 96	99.85 64	99.40 41	99.80 58	98.59 171	99.79 101	99.30 93	99.20 113	99.06 87	99.71 88	99.35 129

DPE-MVS		99.41 80	99.36 65	99.47 107	99.66 148	99.48 71	99.46 107	99.75 129	98.65 130	99.41 158	99.67 66	99.95 26	98.82 138	99.21 110	99.14 70	99.72 85	99.40 121
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 80	99.12 111	99.76 43	99.86 65	99.48 71	99.50 96	99.81 94	98.84 114	99.89 17	99.45 102	98.32 175	99.59 45	99.22 107	98.89 112	99.90 23	99.63 56

CP-MVS		99.41 80	99.20 94	99.65 70	99.80 97	99.23 129	99.44 110	99.75 129	98.60 139	99.74 79	98.66 165	99.93 43	99.48 71	99.33 83	99.16 67	99.73 82	99.48 101

QAPM		99.41 80	99.21 93	99.64 75	99.78 106	99.16 137	99.51 92	99.85 64	99.20 63	99.72 88	99.43 103	99.81 92	99.25 97	98.87 157	98.71 134	99.71 88	99.30 134

UGNet		99.40 84	99.61 24	99.16 157	99.88 45	99.64 37	99.61 66	99.77 116	99.31 51	99.63 112	99.33 114	99.93 43	96.46 200	99.63 47	99.53 35	99.63 110	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 84	99.28 78	99.55 93	99.92 27	99.68 33	99.31 131	99.87 53	98.69 127	99.16 180	99.08 142	98.64 168	99.20 101	99.65 45	99.46 45	99.83 48	99.72 37

OPM-MVS		99.39 86	99.22 89	99.59 83	99.76 119	98.82 169	99.51 92	99.79 104	99.17 69	99.53 131	99.31 119	99.95 26	99.35 82	99.22 107	98.79 125	99.60 120	99.27 137

Fast-Effi-MVS+		99.39 86	99.18 98	99.63 76	99.86 65	99.28 116	99.45 108	99.91 39	98.47 146	99.61 116	99.50 98	99.57 126	99.17 102	99.24 102	98.66 139	99.78 63	99.59 64

DROMVSNet		99.39 86	99.18 98	99.63 76	99.86 65	99.28 116	99.45 108	99.91 39	98.47 146	99.61 116	99.50 98	99.57 126	99.17 102	99.24 102	98.66 139	99.78 63	99.59 64

LS3D		99.39 86	99.28 78	99.52 100	99.77 114	99.39 88	99.55 79	99.82 86	98.93 102	99.64 110	98.52 175	99.67 118	98.58 152	99.74 32	99.63 25	99.75 73	99.06 159

diffmvs		99.38 90	99.33 69	99.45 111	99.87 51	99.39 88	99.28 139	99.58 162	99.55 28	99.50 141	99.85 39	99.85 83	98.94 132	98.58 180	98.68 137	99.51 142	99.39 123

CANet		99.36 91	99.39 61	99.34 134	99.80 97	99.35 101	99.41 116	99.47 179	99.20 63	99.74 79	99.54 86	99.68 116	98.05 167	99.23 105	98.97 103	99.57 128	99.73 33

MVS_0304		99.36 91	99.35 67	99.37 129	99.85 74	99.36 97	99.39 118	99.56 165	99.36 47	99.75 73	99.23 125	99.90 60	97.97 174	99.00 138	98.83 119	99.69 91	99.77 25

ACMMP		99.36 91	99.06 118	99.71 57	99.86 65	99.36 97	99.63 64	99.85 64	98.33 162	99.72 88	97.73 195	99.94 36	99.53 55	99.37 76	99.13 78	99.65 98	99.56 80
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 80	99.46 109	99.66 148	99.15 139	98.92 178	99.67 147	99.55 28	99.35 168	98.83 154	99.91 57	99.35 82	99.19 116	98.53 149	99.78 63	99.68 45
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	99.09 116	99.65 70	99.84 80	99.22 130	99.59 71	99.78 109	98.13 172	99.67 105	98.44 179	99.93 43	99.43 77	99.31 86	99.09 84	99.60 120	99.49 98
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 88	99.77 114	98.90 161	99.51 92	99.77 116	99.07 82	99.73 85	99.72 58	99.84 85	99.07 118	98.85 162	98.39 158	99.55 134	99.27 137

EPP-MVSNet		99.34 96	99.10 114	99.62 81	99.94 21	99.74 25	99.66 58	99.80 101	99.07 82	98.93 191	99.61 76	96.13 190	99.49 68	99.67 41	99.63 25	99.92 19	99.86 10

TSAR-MVS + GP.		99.33 98	99.17 103	99.51 102	99.71 138	99.00 156	98.84 187	99.71 135	98.23 168	99.74 79	99.53 92	99.90 60	99.35 82	99.38 75	98.85 117	99.72 85	99.31 132

PHI-MVS		99.33 98	99.19 96	99.49 105	99.69 140	99.25 122	99.27 140	99.59 159	98.44 151	99.78 66	99.15 134	99.92 51	98.95 131	99.39 74	99.04 92	99.64 107	99.18 143

MSP-MVS		99.32 100	99.26 80	99.38 123	99.76 119	99.54 54	99.42 112	99.72 134	98.92 104	98.84 198	98.96 149	99.96 14	98.91 133	98.72 175	99.14 70	99.63 110	99.58 73
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	98.99 127	99.71 57	99.86 65	99.31 109	99.59 71	99.86 57	97.51 195	99.75 73	98.23 185	99.94 36	99.53 55	99.29 91	99.08 85	99.65 98	99.54 87

DeepC-MVS_fast	98.69 9	99.32 100	99.13 109	99.53 96	99.63 155	98.78 172	99.53 85	99.33 195	99.08 80	99.77 67	99.18 132	99.89 63	99.29 94	99.00 138	98.70 135	99.65 98	99.30 134
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	99.09 116	99.58 85	99.75 123	98.74 176	99.36 123	99.54 168	99.14 76	99.72 88	99.24 123	99.89 63	99.51 59	99.30 88	98.76 126	99.62 116	98.54 178

TSAR-MVS + ACMM		99.31 104	99.26 80	99.37 129	99.66 148	98.97 159	99.20 147	99.56 165	99.33 49	99.19 179	99.54 86	99.91 57	99.32 89	99.12 126	98.34 161	99.29 169	99.65 52

3Dnovator+	98.92 7	99.31 104	99.03 122	99.63 76	99.77 114	98.90 161	99.52 89	99.81 94	99.37 45	99.72 88	98.03 190	99.73 109	99.32 89	98.99 141	98.81 123	99.67 94	99.36 127

X-MVS		99.30 106	98.99 127	99.66 68	99.85 74	99.30 111	99.49 103	99.82 86	98.32 163	99.69 97	97.31 202	99.93 43	99.50 63	99.37 76	99.16 67	99.60 120	99.53 90

MVS_111021_HR		99.30 106	99.14 107	99.48 106	99.58 171	99.25 122	99.27 140	99.61 154	98.74 123	99.66 107	99.02 148	99.84 85	99.33 86	99.20 113	98.76 126	99.44 152	99.18 143

TAPA-MVS	98.54 10	99.30 106	99.24 85	99.36 133	99.44 190	98.77 174	99.00 171	99.41 186	99.23 59	99.60 120	99.50 98	99.86 77	99.15 110	99.29 91	98.95 107	99.56 131	99.08 156
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		99.30 106	99.01 126	99.63 76	99.75 123	98.89 164	99.35 126	99.60 156	98.53 144	99.86 35	99.57 83	99.94 36	99.52 58	98.96 143	98.10 174	99.70 90	99.08 156
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	98.98 129	99.65 70	99.72 135	98.87 167	99.47 105	99.66 150	99.35 48	99.87 29	99.58 82	99.87 76	99.51 59	98.85 162	97.93 180	99.65 98	98.38 182

PMVS	94.32 17	99.27 111	99.55 33	98.94 175	99.60 164	99.43 80	99.39 118	99.54 168	98.99 92	99.69 97	99.60 79	99.81 92	95.68 205	99.88 12	99.83 5	99.73 82	99.31 132
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS_111021_LR		99.25 112	99.13 109	99.39 119	99.50 183	99.14 140	99.23 145	99.50 176	98.67 128	99.61 116	99.12 138	99.81 92	99.16 106	99.28 96	98.67 138	99.35 165	99.21 142

baseline		99.24 113	99.30 74	99.17 156	99.78 106	99.14 140	99.10 159	99.69 138	98.97 96	99.49 143	99.84 41	99.88 69	97.99 173	98.85 162	98.73 132	98.98 184	99.72 37

EIA-MVS		99.23 114	99.03 122	99.47 107	99.83 86	99.64 37	99.16 151	99.81 94	97.11 202	99.65 109	98.44 179	99.78 104	98.61 151	99.46 63	99.22 60	99.75 73	99.59 64

HPM-MVS++		99.23 114	98.98 129	99.53 96	99.75 123	99.02 154	99.44 110	99.77 116	98.65 130	99.52 137	98.72 162	99.92 51	99.33 86	98.77 173	98.40 157	99.40 159	99.36 127

PMMVS2		99.23 114	99.22 89	99.24 144	99.80 97	99.14 140	99.50 96	99.82 86	99.12 78	98.41 212	99.91 23	99.98 4	98.51 153	99.48 59	98.76 126	99.38 161	98.14 190

CPTT-MVS		99.21 117	98.89 140	99.58 85	99.72 135	99.12 147	99.30 136	99.76 124	98.62 135	99.66 107	97.51 198	99.89 63	99.48 71	99.01 136	98.64 142	99.58 127	99.40 121

TinyColmap		99.21 117	98.89 140	99.59 83	99.61 160	98.61 185	99.47 105	99.67 147	99.02 89	99.82 54	99.15 134	99.74 106	99.35 82	99.17 121	98.33 162	99.63 110	98.22 188

Effi-MVS+		99.20 119	98.93 135	99.50 104	99.79 101	99.26 119	98.82 190	99.96 12	98.37 161	99.60 120	99.12 138	98.36 173	99.05 121	98.93 146	98.82 120	99.78 63	99.68 45

PVSNet_BlendedMVS		99.20 119	99.17 103	99.23 145	99.69 140	99.33 104	99.04 164	99.13 200	98.41 157	99.79 61	99.33 114	99.36 143	98.10 163	99.29 91	98.87 114	99.65 98	99.56 80

PVSNet_Blended		99.20 119	99.17 103	99.23 145	99.69 140	99.33 104	99.04 164	99.13 200	98.41 157	99.79 61	99.33 114	99.36 143	98.10 163	99.29 91	98.87 114	99.65 98	99.56 80

MCST-MVS		99.17 122	98.82 148	99.57 88	99.75 123	98.70 180	99.25 144	99.69 138	98.62 135	99.59 122	98.54 173	99.79 101	99.53 55	98.48 184	98.15 170	99.64 107	99.43 112

APD-MVS		99.17 122	98.92 136	99.46 109	99.78 106	99.24 127	99.34 127	99.78 109	97.79 189	99.48 145	98.25 184	99.88 69	98.77 141	99.18 119	98.92 109	99.63 110	99.18 143
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	98.82 8	99.17 122	98.85 144	99.53 96	99.75 123	99.06 152	99.36 123	99.82 86	98.28 165	99.76 70	98.47 177	99.61 122	98.91 133	98.80 170	98.70 135	99.60 120	99.04 163

IterMVS-LS		99.16 125	98.82 148	99.57 88	99.87 51	99.71 28	99.58 75	99.92 33	99.24 58	99.71 94	99.73 54	95.79 191	98.91 133	98.82 169	98.66 139	99.43 155	99.77 25
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepPCF-MVS	98.38 11	99.16 125	99.20 94	99.12 161	99.20 207	98.71 179	98.85 186	99.06 203	99.17 69	98.96 190	99.61 76	99.86 77	99.29 94	99.17 121	98.72 133	99.36 163	99.15 151

IterMVS-SCA-FT		99.15 127	98.96 132	99.38 123	99.87 51	99.54 54	99.53 85	99.79 104	98.94 100	99.82 54	99.92 16	97.65 182	98.82 138	98.95 145	98.26 164	98.45 193	99.47 104

CDS-MVSNet		99.15 127	99.10 114	99.21 151	99.59 168	99.22 130	99.48 104	99.47 179	98.89 107	99.41 158	99.84 41	98.11 178	97.76 177	99.26 101	99.01 96	99.57 128	99.38 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS_MVSNet		99.15 127	99.12 111	99.19 154	99.92 27	99.73 27	99.55 79	99.86 57	98.45 150	96.91 218	98.74 160	98.33 174	99.02 123	99.54 55	99.47 43	99.88 28	99.61 61

MDA-MVSNet-bldmvs		99.11 130	99.11 113	99.12 161	99.91 31	99.38 91	99.77 31	98.72 207	99.31 51	99.85 42	99.43 103	98.26 176	99.48 71	99.85 16	98.47 152	96.99 204	99.08 156

OMC-MVS		99.11 130	98.95 133	99.29 137	99.37 196	98.57 187	99.19 148	99.20 199	98.87 110	99.58 126	99.13 136	99.88 69	99.00 124	99.19 116	98.46 153	99.43 155	98.57 177

MVS_Test		99.09 132	98.92 136	99.29 137	99.61 160	99.07 151	99.04 164	99.81 94	98.58 141	99.37 165	99.74 51	98.87 164	98.41 156	98.61 179	98.01 178	99.50 144	99.57 79

CNVR-MVS		99.08 133	98.83 145	99.37 129	99.61 160	98.74 176	99.15 152	99.54 168	98.59 140	99.37 165	98.15 187	99.88 69	99.08 117	98.91 151	98.46 153	99.48 146	99.06 159

IterMVS		99.08 133	98.90 139	99.29 137	99.87 51	99.53 57	99.52 89	99.77 116	98.94 100	99.75 73	99.91 23	97.52 186	98.72 145	98.86 160	98.14 171	98.09 196	99.43 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		99.07 135	99.19 96	98.93 177	99.02 212	99.53 57	99.31 131	99.84 73	98.86 111	98.88 194	99.64 72	98.44 171	96.92 194	99.35 79	99.00 100	99.61 117	99.53 90

CVMVSNet		99.06 136	98.88 143	99.28 141	99.52 175	99.53 57	99.42 112	99.69 138	98.74 123	98.27 214	99.89 31	95.48 194	99.44 75	99.46 63	99.33 55	99.32 168	99.75 30

CDPH-MVS		99.05 137	98.63 155	99.54 95	99.75 123	98.78 172	99.59 71	99.68 144	97.79 189	99.37 165	98.20 186	99.86 77	99.14 112	98.58 180	98.01 178	99.68 92	99.16 149

TAMVS		99.05 137	99.02 125	99.08 166	99.69 140	99.22 130	99.33 128	99.32 196	99.16 73	98.97 189	99.87 35	97.36 187	97.76 177	99.21 110	99.00 100	99.44 152	99.33 130

CANet_DTU		99.03 139	99.18 98	98.87 180	99.58 171	99.03 153	99.18 149	99.41 186	98.65 130	99.74 79	99.55 85	99.71 111	96.13 203	99.19 116	98.92 109	99.17 178	99.18 143

Effi-MVS+-dtu		99.01 140	99.05 119	98.98 170	99.60 164	99.13 144	99.03 168	99.61 154	98.52 145	99.01 186	98.53 174	99.83 87	96.95 193	99.48 59	98.59 147	99.66 96	99.25 141

canonicalmvs		99.00 141	98.68 154	99.37 129	99.68 146	99.42 84	98.94 177	99.89 48	99.00 91	98.99 187	98.43 181	95.69 192	98.96 130	99.18 119	99.18 64	99.74 78	99.88 7

MIMVSNet		99.00 141	99.03 122	98.97 174	99.32 202	99.32 108	99.39 118	99.91 39	98.41 157	98.76 201	99.24 123	99.17 155	97.13 187	99.30 88	98.80 124	99.29 169	99.01 164

CHOSEN 280x420		98.99 143	98.91 138	99.07 167	99.77 114	99.26 119	99.55 79	99.92 33	98.62 135	98.67 205	99.62 75	97.20 188	98.44 155	99.50 57	99.18 64	98.08 197	98.99 167

xxxxxxxxxxxxxcwj		98.97 144	98.97 131	98.98 170	99.64 153	98.89 164	98.00 216	99.58 162	98.42 154	99.08 184	98.63 167	99.96 14	98.04 169	99.02 134	98.76 126	99.52 138	99.13 152

SF-MVS		98.96 145	98.95 133	98.98 170	99.64 153	98.89 164	98.00 216	99.58 162	98.42 154	99.08 184	98.63 167	99.83 87	98.04 169	99.02 134	98.76 126	99.52 138	99.13 152

GBi-Net		98.96 145	99.05 119	98.85 181	99.02 212	99.53 57	99.31 131	99.78 109	98.13 172	98.48 208	99.43 103	97.58 183	96.92 194	99.68 38	99.50 38	99.61 117	99.53 90

test1		98.96 145	99.05 119	98.85 181	99.02 212	99.53 57	99.31 131	99.78 109	98.13 172	98.48 208	99.43 103	97.58 183	96.92 194	99.68 38	99.50 38	99.61 117	99.53 90

PCF-MVS	97.86 15	98.95 148	98.53 160	99.44 113	99.70 139	98.80 171	98.96 173	99.69 138	98.65 130	99.59 122	99.33 114	99.94 36	99.12 115	98.01 194	97.11 191	99.59 126	97.83 194
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		98.94 149	98.71 153	99.21 151	99.52 175	98.22 203	98.97 172	99.53 173	98.76 120	99.50 141	98.59 171	99.56 129	98.68 146	98.63 178	98.45 155	99.05 181	98.73 174

AdaColmap		98.93 150	98.53 160	99.39 119	99.52 175	98.65 183	99.11 158	99.59 159	98.08 176	99.44 151	97.46 200	99.45 136	99.24 98	98.92 148	98.44 156	99.44 152	98.73 174

MSLP-MVS++		98.92 151	98.73 152	99.14 158	99.44 190	99.00 156	98.36 206	99.35 192	98.82 117	99.38 162	96.06 207	99.79 101	99.07 118	98.88 156	99.05 90	99.27 171	99.53 90

new_pmnet		98.91 152	98.89 140	98.94 175	99.51 181	98.27 199	99.15 152	98.66 208	99.17 69	99.48 145	99.79 49	99.80 98	98.49 154	99.23 105	98.20 168	98.34 194	97.74 198

train_agg		98.89 153	98.48 165	99.38 123	99.69 140	98.76 175	99.31 131	99.60 156	97.71 191	98.98 188	97.89 191	99.89 63	99.29 94	98.32 185	97.59 187	99.42 158	99.16 149

NCCC		98.88 154	98.42 166	99.42 114	99.62 156	98.81 170	99.10 159	99.54 168	98.76 120	99.53 131	95.97 208	99.80 98	99.16 106	98.49 183	98.06 177	99.55 134	99.05 161

PLC	97.83 16	98.88 154	98.52 162	99.30 136	99.45 188	98.60 186	98.65 196	99.49 177	98.66 129	99.59 122	96.33 206	99.59 125	99.17 102	98.87 157	98.53 149	99.46 148	99.05 161
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs3		98.85 156	98.60 156	99.13 159	99.66 148	98.72 178	99.37 122	99.06 203	98.44 151	99.76 70	99.74 51	99.55 130	99.15 110	99.04 132	96.00 199	97.80 198	98.72 176

Fast-Effi-MVS+-dtu		98.82 157	98.80 150	98.84 183	99.51 181	98.90 161	98.96 173	99.91 39	98.29 164	99.11 183	98.47 177	99.63 121	96.03 204	99.21 110	98.12 172	99.52 138	99.01 164

CNLPA		98.82 157	98.52 162	99.18 155	99.21 206	98.50 191	98.73 194	99.34 194	98.73 125	99.56 128	97.55 197	99.42 140	99.06 120	98.93 146	98.10 174	99.21 177	98.38 182

PatchMatch-RL		98.80 159	98.52 162	99.12 161	99.38 195	98.70 180	98.56 199	99.55 167	97.81 188	99.34 171	97.57 196	99.31 150	98.67 147	99.27 99	98.62 144	99.22 176	98.35 184

thisisatest0530		98.78 160	98.26 169	99.39 119	99.78 106	99.43 80	99.07 161	99.64 152	98.44 151	99.42 156	99.22 126	92.68 205	98.63 149	99.30 88	99.14 70	99.80 58	99.60 62

tttt0517		98.77 161	98.25 171	99.38 123	99.79 101	99.46 74	99.07 161	99.64 152	98.40 160	99.38 162	99.21 128	92.54 206	98.63 149	99.34 82	99.14 70	99.80 58	99.62 59

DI_MVS_plusplus_trai		98.74 162	98.08 179	99.51 102	99.79 101	99.29 115	99.61 66	99.60 156	99.20 63	99.46 149	99.09 141	92.93 199	98.97 127	98.27 188	98.35 160	99.65 98	99.45 106

TSAR-MVS + COLMAP		98.74 162	98.58 158	98.93 177	99.29 203	98.23 200	99.04 164	99.24 198	98.79 119	98.80 200	99.37 112	99.71 111	98.06 166	98.02 193	97.46 189	99.16 179	98.48 180

MDTV_nov1_ep13_2view		98.73 164	98.31 168	99.22 148	99.75 123	99.24 127	99.75 38	99.93 26	99.31 51	99.84 46	99.86 38	99.81 92	99.31 92	97.40 202	94.77 201	96.73 206	97.81 195

PMMVS		98.71 165	98.55 159	98.90 179	99.28 204	98.45 193	98.53 202	99.45 181	97.67 193	99.15 182	98.76 158	99.54 132	97.79 176	98.77 173	98.23 166	99.16 179	98.46 181

HQP-MVS		98.70 166	98.19 175	99.28 141	99.61 160	98.52 189	98.71 195	99.35 192	97.97 183	99.53 131	97.38 201	99.85 83	99.14 112	97.53 198	96.85 195	99.36 163	99.26 140

N_pmnet		98.64 167	98.23 174	99.11 164	99.78 106	99.25 122	99.75 38	99.39 190	99.65 14	99.70 96	99.78 50	99.89 63	98.81 140	97.60 197	94.28 202	97.24 203	97.15 202

CMPMVS	76.62 19	98.64 167	98.60 156	98.68 188	99.33 200	97.07 215	98.11 214	98.50 209	97.69 192	99.26 174	98.35 183	99.66 119	97.62 180	99.43 72	99.02 94	99.24 174	99.01 164
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		98.63 169	98.75 151	98.49 194	98.10 218	99.44 77	99.02 169	99.78 109	98.13 172	98.48 208	99.43 103	97.58 183	96.16 202	98.85 162	98.39 158	99.40 159	99.41 116

GA-MVS		98.59 170	98.15 176	99.09 165	99.59 168	99.13 144	98.84 187	99.52 175	98.61 138	99.35 168	99.67 66	93.03 198	97.73 179	98.90 155	98.26 164	99.51 142	99.48 101

MAR-MVS		98.54 171	98.15 176	98.98 170	99.37 196	98.09 206	98.56 199	99.65 151	96.11 217	99.27 173	97.16 204	99.50 133	98.03 171	98.87 157	98.23 166	99.01 182	99.13 152
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		98.49 172	97.60 181	99.53 96	99.90 35	99.55 51	99.77 31	99.48 178	99.67 11	99.86 35	99.98 3	99.98 4	99.50 63	96.90 204	91.52 208	98.67 190	95.62 208

FPMVS		98.48 173	98.83 145	98.07 204	99.09 210	97.98 209	99.07 161	98.04 215	98.99 92	99.22 177	98.85 153	99.43 139	93.79 212	99.66 43	99.11 82	99.24 174	97.76 196

MVS-HIRNet		98.45 174	98.25 171	98.69 187	99.12 208	97.81 214	98.55 201	99.85 64	98.58 141	99.67 105	99.61 76	99.86 77	97.46 183	97.95 195	96.37 197	97.49 200	97.56 199

test0.0.03 1		98.41 175	98.41 167	98.40 198	99.62 156	99.16 137	98.87 184	99.41 186	97.15 200	96.60 220	99.31 119	97.00 189	96.55 199	98.91 151	98.51 151	99.37 162	98.82 172

gg-mvs-nofinetune		98.40 176	98.26 169	98.57 192	99.83 86	98.86 168	98.77 193	99.97 1	99.57 25	99.99 1	99.99 1	93.81 196	93.50 213	98.91 151	98.20 168	99.33 167	98.52 179

baseline1		98.39 177	97.59 182	99.31 135	99.78 106	99.45 75	99.13 155	99.53 173	98.06 178	98.87 195	98.63 167	90.04 211	98.76 142	98.85 162	98.84 118	99.81 54	99.28 136

pmnet_mix02		98.28 178	97.48 184	99.22 148	99.78 106	99.12 147	99.68 54	99.39 190	99.49 33	99.86 35	99.82 46	99.89 63	99.23 99	95.54 207	92.36 205	97.38 201	96.14 206

PatchT		98.11 179	97.12 190	99.26 143	99.65 152	98.34 197	99.57 77	99.97 1	97.48 196	99.43 153	99.04 146	90.84 209	98.15 160	98.04 191	97.78 181	98.82 187	98.30 185

DPM-MVS		98.10 180	97.32 188	99.01 169	99.52 175	97.92 210	98.47 204	99.45 181	98.25 166	98.91 192	93.99 212	99.69 115	98.73 144	96.29 206	96.32 198	99.00 183	98.77 173

EPNet_dtu		98.09 181	98.25 171	97.91 206	99.58 171	98.02 208	98.19 211	99.67 147	97.94 184	99.74 79	99.07 144	98.71 167	93.40 214	97.50 199	97.09 192	96.89 205	99.44 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.06 182	98.11 178	98.00 205	99.60 164	98.99 158	98.38 205	99.68 144	98.18 170	98.85 197	97.89 191	95.60 193	92.72 215	98.30 186	98.10 174	98.76 188	99.72 37
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 183	96.80 193	99.22 148	99.60 164	98.23 200	98.91 179	99.97 1	96.89 210	99.43 153	99.10 140	89.24 214	98.15 160	98.04 191	97.78 181	99.26 172	98.30 185

thres200		97.87 184	96.56 195	99.39 119	99.76 119	99.52 64	99.13 155	99.76 124	96.88 212	98.66 206	92.87 216	88.77 217	99.16 106	99.11 127	99.42 50	99.88 28	99.33 130

baseline2		97.87 184	97.18 189	98.67 189	99.34 199	99.17 136	98.48 203	98.82 206	97.08 203	98.83 199	98.75 159	89.47 213	97.03 192	98.67 177	98.27 163	99.52 138	98.83 171

thres600view7		97.86 186	96.53 198	99.41 117	99.84 80	99.52 64	99.36 123	99.76 124	97.32 198	98.38 213	93.24 213	87.25 219	99.23 99	99.11 127	99.75 15	99.88 28	99.48 101

tfpn200view9		97.85 187	96.54 196	99.38 123	99.74 131	99.52 64	99.17 150	99.76 124	96.10 218	98.70 203	92.99 214	89.10 215	99.00 124	99.11 127	99.56 29	99.88 28	99.41 116

thres400		97.82 188	96.47 199	99.40 118	99.81 96	99.44 77	99.29 138	99.69 138	97.15 200	98.57 207	92.82 217	87.96 218	99.16 106	98.96 143	99.55 32	99.86 36	99.41 116

IB-MVS	98.10 14	97.76 189	97.40 187	98.18 200	99.62 156	99.11 149	98.24 209	98.35 211	96.56 214	99.44 151	91.28 218	98.96 162	93.84 211	98.09 190	98.62 144	99.56 131	99.18 143
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		97.74 190	97.46 185	98.08 202	99.62 156	98.37 195	98.26 207	99.41 186	97.03 205	97.38 216	99.54 86	92.89 200	95.12 208	98.78 171	97.68 185	98.65 191	97.90 192

RPMNet		97.70 191	96.54 196	99.06 168	99.57 174	98.23 200	98.95 176	99.97 1	96.89 210	99.49 143	99.13 136	89.63 212	97.09 189	96.68 205	97.02 193	99.26 172	98.19 189

thres100view900		97.69 192	96.37 200	99.23 145	99.74 131	99.21 133	98.81 191	99.43 185	96.10 218	98.70 203	92.99 214	89.10 215	98.88 136	98.58 180	99.31 57	99.82 51	99.27 137

FMVSNet5		97.69 192	96.98 191	98.53 193	98.53 216	99.36 97	98.90 182	99.54 168	96.38 215	98.44 211	95.38 210	90.08 210	97.05 191	99.46 63	99.06 87	98.73 189	99.12 155

MVE	91.08 18	97.68 194	97.65 180	97.71 212	98.46 217	91.62 221	97.92 218	98.86 205	98.73 125	97.99 215	98.64 166	99.96 14	99.17 102	99.59 51	97.75 183	93.87 220	97.27 200
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-mter		97.65 195	97.57 183	97.75 210	98.90 215	98.56 188	98.15 212	98.45 210	96.92 209	96.84 219	99.52 94	92.53 207	95.24 207	99.04 132	98.12 172	98.90 186	98.29 187

TESTMET0.1,1		97.62 196	97.46 185	97.81 208	99.07 211	98.37 195	98.26 207	98.35 211	97.03 205	97.38 216	99.54 86	92.89 200	95.12 208	98.78 171	97.68 185	98.65 191	97.90 192

MVSTER		97.55 197	96.75 194	98.48 195	99.46 187	99.54 54	98.24 209	99.77 116	97.56 194	99.41 158	99.31 119	84.86 221	94.66 210	98.86 160	97.75 183	99.34 166	99.38 124

ET-MVSNet_ETH3D		97.44 198	96.29 201	98.78 184	97.93 219	98.95 160	98.91 179	99.09 202	98.00 181	99.24 175	98.83 154	84.62 222	98.02 172	97.43 201	97.38 190	99.48 146	98.84 169

MDTV_nov1_ep13		97.41 199	96.26 202	98.76 185	99.47 186	98.43 194	99.26 143	99.82 86	98.06 178	99.23 176	99.22 126	92.86 202	98.05 167	95.33 209	93.66 204	96.73 206	96.26 205

ADS-MVSNet		97.29 200	96.17 203	98.59 191	99.59 168	98.70 180	99.32 129	99.86 57	98.47 146	99.56 128	99.08 142	98.16 177	97.34 185	92.92 211	91.17 209	95.91 209	94.72 211

SCA		97.25 201	96.05 204	98.64 190	99.36 198	99.02 154	99.27 140	99.96 12	98.25 166	99.69 97	98.71 163	94.66 195	97.95 175	93.95 210	92.35 206	95.64 210	95.40 210

gm-plane-assit		96.82 202	94.84 210	99.13 159	99.95 11	99.78 15	99.69 53	99.92 33	99.19 66	99.84 46	99.92 16	72.93 225	96.44 201	98.21 189	97.01 194	98.92 185	96.87 204

PatchmatchNet		96.81 203	95.41 207	98.43 197	99.43 192	98.30 198	99.23 145	99.93 26	98.19 169	99.64 110	98.81 157	93.50 197	97.43 184	92.89 212	90.78 211	94.94 215	95.41 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		96.76 204	95.30 209	98.46 196	99.42 193	98.47 192	99.32 129	99.91 39	98.42 154	99.51 139	99.07 144	92.81 203	97.12 188	92.39 213	91.71 207	95.51 211	94.20 213

E-PMN		96.72 205	95.78 205	97.81 208	99.45 188	95.46 218	98.14 213	98.33 213	97.99 182	98.73 202	98.09 188	98.97 160	97.54 182	97.45 200	91.09 210	94.70 217	91.40 216

tpm		96.56 206	94.68 211	98.74 186	99.12 208	97.90 211	98.79 192	99.93 26	96.79 213	99.69 97	99.19 131	81.48 224	97.56 181	95.46 208	93.97 203	97.37 202	97.99 191

EMVS		96.47 207	95.38 208	97.74 211	99.42 193	95.37 219	98.07 215	98.27 214	97.85 187	98.90 193	97.48 199	98.73 166	97.20 186	97.21 203	90.39 212	94.59 219	90.65 217

tpmrst		96.18 208	94.47 212	98.18 200	99.52 175	97.89 212	98.96 173	99.79 104	98.07 177	99.16 180	99.30 122	92.69 204	96.69 197	90.76 215	88.85 215	94.96 214	93.69 214

CostFormer		95.61 209	93.35 215	98.24 199	99.48 185	98.03 207	98.65 196	99.83 79	96.93 208	99.42 156	98.83 154	83.65 223	97.08 190	90.39 216	89.54 214	94.94 215	96.11 207

dps		95.59 210	93.46 214	98.08 202	99.33 200	98.22 203	98.87 184	99.70 136	96.17 216	98.87 195	97.75 194	86.85 220	96.60 198	91.24 214	89.62 213	95.10 213	94.34 212

tpm cat1		95.52 211	93.49 213	97.88 207	99.28 204	97.87 213	98.65 196	99.77 116	97.27 199	99.46 149	98.04 189	90.99 208	95.46 206	88.57 217	88.14 216	94.64 218	93.54 215

test_method		91.96 212	95.51 206	87.82 214	70.84 221	82.79 222	92.13 221	87.74 217	98.88 108	95.40 221	99.20 130	98.04 179	85.65 217	97.71 196	94.95 200	95.13 212	97.00 203

GG-mvs-BLEND		70.44 213	96.91 192	39.57 215	3.32 224	96.51 216	91.01 222	4.05 221	97.03 205	33.20 223	94.67 211	97.75 181	7.59 220	98.28 187	96.85 195	98.24 195	97.26 201

testmvs		22.33 214	29.66 216	13.79 216	8.97 222	10.35 223	15.53 225	8.09 220	32.51 220	19.87 224	45.18 219	30.56 227	17.05 219	29.96 218	24.74 217	13.21 221	34.30 218

test123		21.52 215	28.47 217	13.42 217	7.29 223	10.12 224	15.70 224	8.31 219	31.54 221	19.34 225	36.33 220	37.40 226	17.14 218	27.45 219	23.17 218	12.73 222	33.30 219

uanet_test		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

RE-MVS-def										99.96 2

9.14												99.57 126

SR-MVS					99.73 133			99.74 131				99.88 69

Anonymous202405211			99.14 107		99.87 51	99.55 51	99.50 96	99.70 136	98.55 143		98.61 170	98.46 170	98.76 142	99.66 43	99.50 38	99.85 39	99.63 56

our_test_3					99.75 123	99.11 149	99.74 45

ambc			98.83 145		99.72 135	98.52 189	98.84 187		98.96 97	99.92 10	99.34 113	99.74 106	99.04 122	98.68 176	97.57 188	99.46 148	98.99 167

MTAPA										99.62 113		99.95 26

MTMP										99.53 131		99.92 51

Patchmatch-RL test							65.75 223

tmp_tt				88.14 213	96.68 220	91.91 220	93.70 220	61.38 218	99.61 20	90.51 222	99.40 109	99.71 111	90.32 216	99.22 107	99.44 48	96.25 208

XVS					99.86 65	99.30 111	99.72 50			99.69 97		99.93 43				99.60 120

X-MVStestdata					99.86 65	99.30 111	99.72 50			99.69 97		99.93 43				99.60 120

abl_6				99.21 151	99.49 184	98.62 184	98.90 182	99.44 184	97.08 203	99.61 116	97.19 203	99.73 109	98.35 157			99.45 150	98.84 169

mPP-MVS					99.84 80							99.92 51

NP-MVS									97.37 197

Patchmtry						98.19 205	98.91 179	99.97 1		99.43 153

DeepMVS_CX						96.39 217	97.15 219	88.89 216	97.94 184	99.51 139	95.71 209	97.88 180	98.19 158	98.92 148		97.73 199	97.75 197

High-resolution multi-view benchmark