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.

Anonymous20231211		99.83 1	99.80 1	99.86 1	99.97 1	99.87 1	99.90 1	99.92 1	99.76 1	99.82 2	99.79 37	99.98 1	99.63 12	99.84 3	99.78 3	99.94 1	99.61 6

LTVRE_ROB	98.82 1	99.76 2	99.75 2	99.77 8	99.87 17	99.71 9	99.77 12	99.76 22	99.52 3	99.80 3	99.79 37	99.91 2	99.56 18	99.83 4	99.75 4	99.86 9	99.75 1
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
pmmvs6		99.74 3	99.75 2	99.73 15	99.92 5	99.67 15	99.76 14	99.84 11	99.59 2	99.52 27	99.87 18	99.91 2	99.43 39	99.87 1	99.81 2	99.89 6	99.52 10

SixPastTwentyTwo		99.70 4	99.59 7	99.82 3	99.93 3	99.80 2	99.86 3	99.87 7	98.87 14	99.79 5	99.85 27	99.33 62	99.74 7	99.85 2	99.82 1	99.74 22	99.63 4

v7n		99.68 5	99.61 4	99.76 9	99.89 14	99.74 8	99.87 2	99.82 14	99.20 6	99.71 6	99.96 1	99.73 12	99.76 5	99.58 17	99.59 15	99.52 43	99.46 17

v748		99.67 6	99.61 4	99.75 13	99.87 17	99.68 13	99.84 6	99.79 16	99.14 7	99.64 17	99.89 12	99.88 5	99.72 8	99.58 17	99.57 17	99.62 30	99.50 13

v52		99.67 6	99.59 7	99.76 9	99.91 9	99.69 11	99.85 4	99.79 16	99.12 9	99.68 12	99.95 2	99.72 14	99.77 2	99.58 17	99.61 11	99.54 38	99.50 13

V4		99.67 6	99.60 6	99.76 9	99.91 9	99.69 11	99.85 4	99.79 16	99.13 8	99.68 12	99.95 2	99.72 14	99.77 2	99.58 17	99.61 11	99.54 38	99.50 13

anonymousdsp		99.64 9	99.55 9	99.74 14	99.87 17	99.56 22	99.82 7	99.73 28	98.54 19	99.71 6	99.92 6	99.84 7	99.61 13	99.70 6	99.63 6	99.69 26	99.64 2

WR-MVS		99.61 10	99.44 11	99.82 3	99.92 5	99.80 2	99.80 8	99.89 2	98.54 19	99.66 15	99.78 40	99.16 85	99.68 10	99.70 6	99.63 6	99.94 1	99.49 16

PEN-MVS		99.54 11	99.30 18	99.83 2	99.92 5	99.76 5	99.80 8	99.88 4	97.60 66	99.71 6	99.59 55	99.52 42	99.75 6	99.64 12	99.51 19	99.90 3	99.46 17

TDRefinement		99.54 11	99.50 10	99.60 19	99.70 61	99.35 45	99.77 12	99.58 55	99.40 5	99.28 57	99.66 45	99.41 50	99.55 20	99.74 5	99.65 5	99.70 23	99.25 26

DTE-MVSNet		99.52 13	99.27 19	99.82 3	99.93 3	99.77 4	99.79 10	99.87 7	97.89 45	99.70 11	99.55 62	99.21 77	99.77 2	99.65 10	99.43 23	99.90 3	99.36 21

PS-CasMVS		99.50 14	99.23 21	99.82 3	99.92 5	99.75 7	99.78 11	99.89 2	97.30 85	99.71 6	99.60 53	99.23 73	99.71 9	99.65 10	99.55 18	99.90 3	99.56 8

WR-MVS_H		99.48 15	99.23 21	99.76 9	99.91 9	99.76 5	99.75 15	99.88 4	97.27 88	99.58 20	99.56 59	99.24 71	99.56 18	99.60 15	99.60 14	99.88 8	99.58 7

pm-mvs1		99.47 16	99.38 12	99.57 22	99.82 25	99.49 32	99.63 29	99.65 43	98.88 13	99.31 47	99.85 27	99.02 111	99.23 65	99.60 15	99.58 16	99.80 15	99.22 30

MIMVSNet1		99.46 17	99.34 13	99.60 19	99.83 23	99.68 13	99.74 18	99.71 33	98.20 27	99.41 35	99.86 22	99.66 26	99.41 42	99.50 23	99.39 25	99.50 49	99.10 40

TransMVSNet (Re)		99.45 18	99.32 16	99.61 17	99.88 16	99.60 18	99.75 15	99.63 47	99.11 10	99.28 57	99.83 31	98.35 139	99.27 62	99.70 6	99.62 10	99.84 10	99.03 47

ACMH	97.81 6	99.44 19	99.33 14	99.56 23	99.81 28	99.42 38	99.73 19	99.58 55	99.02 11	99.10 81	99.41 73	99.69 18	99.60 14	99.45 27	99.26 35	99.55 37	99.05 44
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		99.39 20	99.04 29	99.80 7	99.91 9	99.70 10	99.75 15	99.88 4	96.82 107	99.68 12	99.32 76	98.86 119	99.68 10	99.57 21	99.47 21	99.89 6	99.52 10

COLMAP_ROB	98.29 2	99.37 21	99.25 20	99.51 30	99.74 50	99.12 80	99.56 40	99.39 93	98.96 12	99.17 68	99.44 70	99.63 32	99.58 15	99.48 25	99.27 33	99.60 34	98.81 73
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	97.88 4	99.33 22	99.15 25	99.53 29	99.73 55	99.05 88	99.49 56	99.40 91	98.42 22	99.55 24	99.71 43	99.89 4	99.49 29	99.14 38	98.81 62	99.54 38	99.02 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		99.32 23	99.33 14	99.31 65	99.87 17	99.65 17	99.63 29	99.75 25	97.76 50	97.29 201	99.87 18	99.63 32	99.52 24	99.66 9	99.63 6	99.77 19	99.12 36

UA-Net		99.30 24	99.22 23	99.39 46	99.94 2	99.66 16	98.91 122	99.86 9	97.74 55	98.74 122	99.00 101	99.60 37	99.17 71	99.50 23	99.39 25	99.70 23	99.64 2

ACMH+	97.53 7	99.29 25	99.20 24	99.40 45	99.81 28	99.22 62	99.59 36	99.50 76	98.64 18	98.29 153	99.21 85	99.69 18	99.57 16	99.53 22	99.33 30	99.66 28	98.81 73

Vis-MVSNet		99.25 26	99.32 16	99.17 79	99.65 78	99.55 26	99.63 29	99.33 119	98.16 28	99.29 52	99.65 49	99.77 8	97.56 152	99.44 28	99.14 39	99.58 35	99.51 12
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TranMVSNet+NR-MVSNet		99.23 27	98.91 38	99.61 17	99.81 28	99.45 36	99.47 58	99.68 37	97.28 87	99.39 36	99.54 63	99.08 106	99.45 34	99.09 43	98.84 60	99.83 11	99.04 45

CSCG		99.23 27	99.15 25	99.32 64	99.83 23	99.45 36	98.97 114	99.21 139	98.83 15	99.04 93	99.43 71	99.64 30	99.26 63	98.85 64	98.20 102	99.62 30	99.62 5

v13		99.22 29	98.99 31	99.49 31	99.68 65	99.58 20	99.67 20	99.77 21	98.10 29	99.36 38	99.88 13	99.37 56	99.54 22	98.50 81	98.51 88	98.92 120	99.03 47

Gipuma		99.22 29	98.86 42	99.64 16	99.70 61	99.24 57	99.17 96	99.63 47	99.52 3	99.89 1	96.54 177	99.14 91	99.93 1	99.42 29	99.15 38	99.52 43	99.04 45
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tfpnnormal		99.19 31	98.90 39	99.54 26	99.81 28	99.55 26	99.60 35	99.54 66	98.53 21	99.23 61	98.40 119	98.23 142	99.40 43	99.29 33	99.36 28	99.63 29	98.95 60

v12		99.19 31	98.95 32	99.48 32	99.67 68	99.56 22	99.66 22	99.76 22	98.06 31	99.33 43	99.88 13	99.34 61	99.53 23	98.42 88	98.43 93	98.91 123	98.97 54

v11		99.19 31	98.95 32	99.47 33	99.66 72	99.54 28	99.65 23	99.73 28	98.06 31	99.38 37	99.92 6	99.40 53	99.55 20	98.29 101	98.50 89	98.88 128	98.92 63

Baseline_NR-MVSNet		99.18 34	98.87 41	99.54 26	99.74 50	99.56 22	99.36 71	99.62 51	96.53 128	99.29 52	99.85 27	98.64 133	99.40 43	99.03 52	99.63 6	99.83 11	98.86 68

V9		99.16 35	98.90 39	99.46 34	99.66 72	99.54 28	99.65 23	99.75 25	98.01 34	99.31 47	99.87 18	99.31 65	99.51 25	98.34 95	98.34 96	98.90 125	98.91 64

APDe-MVS		99.15 36	98.95 32	99.39 46	99.77 37	99.28 54	99.52 51	99.54 66	97.22 93	99.06 88	99.20 86	99.64 30	99.05 82	99.14 38	99.02 52	99.39 69	99.17 34

FC-MVSNet-train		99.13 37	99.05 28	99.21 74	99.87 17	99.57 21	99.67 20	99.60 54	96.75 114	98.28 154	99.48 67	99.52 42	98.10 134	99.47 26	99.37 27	99.76 21	99.21 31

V14		99.13 37	98.85 44	99.45 35	99.65 78	99.52 30	99.63 29	99.74 27	97.97 36	99.30 50	99.87 18	99.27 69	99.49 29	98.23 107	98.24 99	98.88 128	98.83 69

NR-MVSNet		99.10 39	98.68 55	99.58 21	99.89 14	99.23 59	99.35 72	99.63 47	96.58 122	99.36 38	99.05 95	98.67 131	99.46 32	99.63 13	98.73 72	99.80 15	98.88 67

v15		99.09 40	98.79 46	99.43 39	99.64 86	99.50 31	99.61 33	99.73 28	97.92 40	99.28 57	99.86 22	99.24 71	99.47 31	98.12 118	98.14 104	98.87 130	98.76 80

UniMVSNet (Re)		99.08 41	98.69 54	99.54 26	99.75 46	99.33 48	99.29 79	99.64 46	96.75 114	99.48 31	99.30 78	98.69 127	99.26 63	98.94 58	98.76 68	99.78 18	99.02 50

ACMMPR		99.05 42	98.72 50	99.44 36	99.79 33	99.12 80	99.35 72	99.56 58	97.74 55	99.21 62	97.72 143	99.55 40	99.29 60	98.90 63	98.81 62	99.41 64	99.19 32

DU-MVS		99.04 43	98.59 60	99.56 23	99.74 50	99.23 59	99.29 79	99.63 47	96.58 122	99.55 24	99.05 95	98.68 129	99.36 55	99.03 52	98.60 82	99.77 19	98.97 54

TSAR-MVS + MP.		99.02 44	98.95 32	99.11 87	99.23 166	98.79 128	99.51 52	98.73 178	97.50 71	98.56 130	99.03 98	99.59 38	99.16 73	99.29 33	99.17 37	99.50 49	99.24 29

v10		99.01 45	98.66 56	99.41 42	99.52 121	99.39 41	99.57 38	99.66 41	97.59 67	99.32 45	99.88 13	99.23 73	99.50 27	97.77 146	97.98 114	98.92 120	98.78 78

EG-PatchMatch MVS		99.01 45	98.77 48	99.28 73	99.64 86	98.90 123	98.81 134	99.27 130	96.55 126	99.71 6	99.31 77	99.66 26	99.17 71	99.28 35	99.11 42	99.10 96	98.57 96

no-one		99.01 45	98.94 36	99.09 90	98.97 189	98.55 149	99.37 69	99.04 160	97.59 67	99.36 38	99.66 45	99.75 9	99.57 16	98.47 82	99.27 33	98.21 180	99.30 25

PVSNet_Blended_VisFu		98.98 48	98.79 46	99.21 74	99.76 43	99.34 46	99.35 72	99.35 116	97.12 99	99.46 32	99.56 59	98.89 117	98.08 137	99.05 47	98.58 84	99.27 86	98.98 53

HFP-MVS		98.97 49	98.70 52	99.29 69	99.67 68	98.98 104	99.13 100	99.53 70	97.76 50	98.90 109	98.07 133	99.50 47	99.14 77	98.64 76	98.78 66	99.37 71	99.18 33

UniMVSNet_NR-MVSNet		98.97 49	98.46 72	99.56 23	99.76 43	99.34 46	99.29 79	99.61 52	96.55 126	99.55 24	99.05 95	97.96 153	99.36 55	98.84 65	98.50 89	99.81 14	98.97 54

v17		98.96 51	98.63 57	99.35 60	99.54 109	99.41 39	99.55 43	99.70 34	97.40 80	99.10 81	99.79 37	99.10 100	99.40 43	97.96 125	97.99 112	98.80 144	98.77 79

v16		98.95 52	98.62 58	99.34 62	99.53 116	99.41 39	99.54 47	99.70 34	97.34 84	99.07 87	99.76 41	99.10 100	99.40 43	97.96 125	98.00 111	98.79 146	98.76 80

ACMMP_Plus		98.94 53	98.72 50	99.21 74	99.67 68	99.08 83	99.26 84	99.39 93	96.84 104	98.88 113	98.22 126	99.68 21	98.82 92	99.06 46	98.90 55	99.25 88	99.25 26

MPTG		98.94 53	98.57 63	99.37 53	99.77 37	99.15 76	99.24 87	99.55 60	97.38 82	99.16 71	96.64 173	99.69 18	99.15 75	99.09 43	98.92 54	99.37 71	99.11 37

v1144		98.94 53	98.53 66	99.42 41	99.62 93	99.03 97	99.58 37	99.36 111	97.99 35	99.49 30	99.91 11	99.20 79	99.51 25	97.61 158	97.85 126	98.95 115	98.10 137

v8		98.94 53	98.60 59	99.35 60	99.54 109	99.39 41	99.55 43	99.67 40	97.48 73	99.13 76	99.81 32	99.10 100	99.39 53	97.86 135	97.89 120	98.81 139	98.66 90

SteuartSystems-ACMMP		98.94 53	98.52 68	99.43 39	99.79 33	99.13 78	99.33 76	99.55 60	96.17 141	99.04 93	97.53 149	99.65 29	99.46 32	99.04 51	98.76 68	99.44 56	99.35 22
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v1192		98.91 58	98.48 71	99.41 42	99.61 96	99.03 97	99.64 26	99.25 134	97.91 42	99.58 20	99.92 6	99.07 108	99.45 34	97.55 162	97.68 143	98.93 117	98.23 124

v7		98.91 58	98.53 66	99.36 55	99.53 116	98.99 103	99.57 38	99.36 111	97.58 69	99.32 45	99.88 13	99.23 73	99.50 27	97.77 146	97.98 114	98.91 123	98.26 121

FMVSNet1		98.90 60	99.10 27	98.67 136	99.54 109	99.48 33	99.22 90	99.66 41	98.39 25	97.50 189	99.66 45	99.04 109	96.58 172	99.05 47	99.03 49	99.52 43	99.08 42

ACMM	96.66 11	98.90 60	98.44 82	99.44 36	99.74 50	98.95 113	99.47 58	99.55 60	97.66 62	99.09 85	96.43 178	99.41 50	99.35 58	98.95 57	98.67 77	99.45 54	99.03 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1921920		98.89 62	98.46 72	99.39 46	99.58 99	99.04 92	99.64 26	99.17 145	97.91 42	99.64 17	99.92 6	98.99 115	99.44 37	97.44 169	97.57 153	98.84 137	98.35 112

v18		98.89 62	98.54 64	99.30 66	99.50 124	99.37 44	99.51 52	99.68 37	97.25 92	99.00 96	99.76 41	99.04 109	99.36 55	97.81 142	97.86 125	98.77 149	98.68 89

v144192		98.88 64	98.46 72	99.37 53	99.56 104	99.03 97	99.61 33	99.26 131	97.79 49	99.58 20	99.88 13	99.11 99	99.43 39	97.38 173	97.61 149	98.80 144	98.43 107

v1141		98.87 65	98.45 76	99.36 55	99.65 78	99.04 92	99.56 40	99.38 100	97.83 46	99.29 52	99.86 22	99.16 85	99.40 43	97.68 152	97.78 129	98.86 133	97.82 149

divwei89l23v2f112		98.87 65	98.45 76	99.36 55	99.65 78	99.04 92	99.56 40	99.38 100	97.83 46	99.29 52	99.86 22	99.15 89	99.40 43	97.68 152	97.78 129	98.86 133	97.82 149

v1		98.87 65	98.45 76	99.36 55	99.65 78	99.04 92	99.55 43	99.38 100	97.83 46	99.30 50	99.86 22	99.17 82	99.40 43	97.68 152	97.77 136	98.86 133	97.82 149

ACMP	96.54 13	98.87 65	98.40 87	99.41 42	99.74 50	98.88 124	99.29 79	99.50 76	96.85 103	98.96 100	97.05 162	99.66 26	99.43 39	98.98 56	98.60 82	99.52 43	98.81 73
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v1240		98.86 69	98.41 85	99.38 51	99.59 97	99.05 88	99.65 23	99.14 149	97.68 61	99.66 15	99.93 5	98.72 126	99.45 34	97.38 173	97.72 141	98.79 146	98.35 112

CP-MVS		98.86 69	98.43 84	99.36 55	99.68 65	98.97 111	99.19 95	99.46 85	96.60 121	99.20 63	97.11 161	99.51 45	99.15 75	98.92 61	98.82 61	99.45 54	99.08 42

v2v482		98.85 71	98.40 87	99.38 51	99.65 78	98.98 104	99.55 43	99.39 93	97.92 40	99.35 41	99.85 27	99.14 91	99.39 53	97.50 164	97.78 129	98.98 112	97.60 156

OPM-MVS		98.84 72	98.59 60	99.12 85	99.52 121	98.50 154	99.13 100	99.22 137	97.76 50	98.76 119	98.70 110	99.61 35	98.90 87	98.67 74	98.37 95	99.19 92	98.57 96

v1neww		98.84 72	98.45 76	99.29 69	99.54 109	98.98 104	99.54 47	99.37 108	97.48 73	99.10 81	99.80 35	99.12 95	99.40 43	97.85 138	97.89 120	98.81 139	98.04 140

v7new		98.84 72	98.45 76	99.29 69	99.54 109	98.98 104	99.54 47	99.37 108	97.48 73	99.10 81	99.80 35	99.12 95	99.40 43	97.85 138	97.89 120	98.81 139	98.04 140

v6		98.84 72	98.46 72	99.30 66	99.54 109	98.98 104	99.54 47	99.37 108	97.49 72	99.11 80	99.81 32	99.13 94	99.40 43	97.86 135	97.89 120	98.81 139	98.04 140

test20.03		98.84 72	98.74 49	98.95 106	99.77 37	99.33 48	99.21 92	99.46 85	97.29 86	98.88 113	99.65 49	99.10 100	97.07 166	99.11 40	98.76 68	99.32 80	97.98 145

LGP-MVS_train		98.84 72	98.33 93	99.44 36	99.78 35	98.98 104	99.39 67	99.55 60	95.41 156	98.90 109	97.51 150	99.68 21	99.44 37	99.03 52	98.81 62	99.57 36	98.91 64

RPSCF		98.84 72	98.81 45	98.89 111	99.37 139	98.95 113	98.51 162	98.85 171	97.73 57	98.33 150	98.97 103	99.14 91	98.95 85	99.18 37	98.68 76	99.31 81	98.99 52

ACMMP		98.82 79	98.33 93	99.39 46	99.77 37	99.14 77	99.37 69	99.54 66	96.47 132	99.03 95	96.26 182	99.52 42	99.28 61	98.92 61	98.80 65	99.37 71	99.16 35
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
V42		98.81 80	98.49 70	99.18 78	99.52 121	98.92 119	99.50 55	99.29 127	97.43 78	98.97 98	99.81 32	99.00 114	99.30 59	97.93 128	98.01 110	98.51 169	98.34 116

LS3D		98.79 81	98.52 68	99.12 85	99.64 86	99.09 82	99.24 87	99.46 85	97.75 53	98.93 106	97.47 151	98.23 142	97.98 140	99.36 30	99.30 32	99.46 53	98.42 108

MP-MVS		98.78 82	98.30 95	99.34 62	99.75 46	98.95 113	99.26 84	99.46 85	95.78 152	99.17 68	96.98 166	99.72 14	99.06 81	98.84 65	98.74 71	99.33 77	99.11 37

v148		98.77 83	98.45 76	99.15 81	99.68 65	98.94 117	99.49 56	99.31 126	97.95 38	98.91 108	99.65 49	99.62 34	99.18 68	97.99 124	97.64 147	98.33 174	97.38 165

SD-MVS		98.73 84	98.54 64	98.95 106	99.14 175	98.76 130	98.46 165	99.14 149	97.71 59	98.56 130	98.06 135	99.61 35	98.85 91	98.56 78	97.74 138	99.54 38	99.32 23

PGM-MVS		98.69 85	98.09 109	99.39 46	99.76 43	99.07 84	99.30 78	99.51 73	94.76 172	99.18 67	96.70 171	99.51 45	99.20 66	98.79 70	98.71 75	99.39 69	99.11 37

pmmvs-eth3d		98.68 86	98.14 105	99.29 69	99.49 127	98.45 157	99.45 62	99.38 100	97.21 94	99.50 29	99.65 49	99.21 77	99.16 73	97.11 181	97.56 154	98.79 146	97.82 149

EU-MVSNet		98.68 86	98.94 36	98.37 157	99.14 175	98.74 134	99.64 26	98.20 203	98.21 26	99.17 68	99.66 45	99.18 81	99.08 79	99.11 40	98.86 56	95.00 212	98.83 69

PMVS	92.51 17	98.66 88	98.86 42	98.43 152	99.26 160	98.98 104	98.60 155	98.59 187	97.73 57	99.45 33	99.38 74	98.54 136	95.24 193	99.62 14	99.61 11	99.42 61	98.17 133
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepC-MVS_fast	97.38 8	98.65 89	98.34 92	99.02 99	99.33 147	98.29 162	98.99 112	98.71 180	97.40 80	99.31 47	98.20 127	99.40 53	98.54 111	98.33 98	98.18 103	99.23 91	98.58 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	98.16 3	98.65 89	98.35 91	99.00 102	99.59 97	98.70 136	98.90 125	99.36 111	97.97 36	99.09 85	96.55 176	99.09 104	97.97 141	98.70 73	98.65 80	99.12 95	98.81 73

TSAR-MVS + ACMM		98.64 91	98.58 62	98.72 129	99.17 172	98.63 142	98.69 140	99.10 156	97.69 60	98.30 152	99.12 91	99.38 55	98.70 99	98.45 83	97.51 156	98.35 173	99.25 26

DELS-MVS		98.63 92	98.70 52	98.55 147	99.24 165	99.04 92	98.96 115	98.52 190	96.83 106	98.38 146	99.58 57	99.68 21	97.06 167	98.74 72	98.44 92	99.10 96	98.59 93
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
QAPM		98.62 93	98.40 87	98.89 111	99.57 103	98.80 127	98.63 149	99.35 116	96.82 107	98.60 127	98.85 108	99.08 106	98.09 136	98.31 99	98.21 100	99.08 101	98.72 84

EPP-MVSNet		98.61 94	98.19 103	99.11 87	99.86 22	99.60 18	99.44 63	99.53 70	97.37 83	96.85 209	98.69 111	93.75 185	99.18 68	99.22 36	99.35 29	99.82 13	99.32 23

3Dnovator+	97.85 5	98.61 94	98.14 105	99.15 81	99.62 93	98.37 160	99.10 104	99.51 73	98.04 33	98.98 97	96.07 186	98.75 125	98.55 109	98.51 80	98.40 94	99.17 93	98.82 71

ESAPD		98.60 96	98.41 85	98.83 118	99.56 104	99.21 63	98.66 148	99.47 82	95.22 159	98.35 148	98.48 117	99.67 25	97.84 147	98.80 69	98.57 86	99.10 96	98.93 62

X-MVS		98.59 97	97.99 116	99.30 66	99.75 46	99.07 84	99.17 96	99.50 76	96.62 119	98.95 102	93.95 205	99.37 56	99.11 78	98.94 58	98.86 56	99.35 75	99.09 41

MVS_111021_HR		98.58 98	98.26 98	98.96 105	99.32 150	98.81 126	98.48 163	98.99 165	96.81 109	99.16 71	98.07 133	99.23 73	98.89 89	98.43 87	98.27 98	98.90 125	98.24 123

MVS_0304		98.57 99	98.36 90	98.82 121	99.72 57	98.94 117	98.92 120	99.14 149	96.76 112	99.33 43	98.30 123	99.73 12	96.74 169	98.05 121	97.79 128	99.08 101	98.97 54

PM-MVS		98.57 99	98.24 100	98.95 106	99.26 160	98.59 145	99.03 107	98.74 177	96.84 104	99.44 34	99.13 89	98.31 141	98.75 97	98.03 122	98.21 100	98.48 170	98.58 94

PHI-MVS		98.57 99	98.20 102	99.00 102	99.48 128	98.91 120	98.68 141	99.17 145	94.97 167	99.27 60	98.33 121	99.33 62	98.05 138	98.82 67	98.62 81	99.34 76	98.38 110

HPM-MVS++		98.56 102	98.08 110	99.11 87	99.53 116	98.61 144	99.02 111	99.32 124	96.29 138	99.06 88	97.23 156	99.50 47	98.77 95	98.15 114	97.90 118	98.96 113	98.90 66

TSAR-MVS + GP.		98.54 103	98.29 97	98.82 121	99.28 158	98.59 145	97.73 201	99.24 136	95.93 148	98.59 128	99.07 94	99.17 82	98.86 90	98.44 84	98.10 106	99.26 87	98.72 84

UGNet		98.52 104	99.00 30	97.96 179	99.58 99	99.26 55	99.27 83	99.40 91	98.07 30	98.28 154	98.76 109	99.71 17	92.24 222	98.94 58	98.85 58	99.00 111	99.43 19
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
HSP-MVS		98.50 105	98.05 112	99.03 96	99.67 68	99.33 48	99.51 52	99.26 131	95.28 158	98.51 135	98.19 128	99.74 11	98.29 124	97.69 151	96.70 178	98.96 113	99.41 20

Anonymous20231206		98.50 105	98.03 113	99.05 94	99.50 124	99.01 101	99.15 98	99.26 131	96.38 134	99.12 78	99.50 66	99.12 95	98.60 104	97.68 152	97.24 167	98.66 155	97.30 167

CLD-MVS		98.48 107	98.15 104	98.86 116	99.53 116	98.35 161	98.55 160	97.83 213	96.02 146	98.97 98	99.08 92	99.75 9	99.03 83	98.10 120	97.33 163	99.28 85	98.44 106
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CANet		98.47 108	98.30 95	98.67 136	99.65 78	98.87 125	98.82 133	99.01 163	96.14 142	99.29 52	98.86 106	99.01 112	96.54 173	98.36 93	98.08 107	98.72 152	98.80 77

APD-MVS		98.47 108	97.97 117	99.05 94	99.64 86	98.91 120	98.94 117	99.45 89	94.40 182	98.77 118	97.26 155	99.41 50	98.21 131	98.67 74	98.57 86	99.31 81	98.57 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Vis-MVSNet (Re-imp)		98.46 110	98.23 101	98.73 128	99.81 28	99.29 53	98.79 135	99.50 76	96.20 140	96.03 214	98.29 124	96.98 167	98.54 111	99.11 40	99.08 43	99.70 23	98.62 92

Fast-Effi-MVS+		98.42 111	97.79 123	99.15 81	99.69 64	98.66 140	98.94 117	99.68 37	94.49 176	99.05 90	98.06 135	98.86 119	98.48 114	98.18 110	97.78 129	99.05 107	98.54 100

MVS_111021_LR		98.39 112	98.11 107	98.71 131	99.08 182	98.54 152	98.23 183	98.56 189	96.57 124	99.13 76	98.41 118	98.86 119	98.65 102	98.23 107	97.87 124	98.65 157	98.28 118

pmmvs5		98.37 113	97.81 122	99.03 96	99.46 129	98.97 111	99.03 107	98.96 167	95.85 150	99.05 90	99.45 69	98.66 132	98.79 94	96.02 198	97.52 155	98.87 130	98.21 127

OMC-MVS		98.35 114	98.10 108	98.64 140	98.85 193	97.99 180	98.56 159	98.21 201	97.26 90	98.87 115	98.54 116	99.27 69	98.43 116	98.34 95	97.66 144	98.92 120	97.65 155

canonicalmvs		98.34 115	97.92 119	98.83 118	99.45 130	99.21 63	98.37 172	99.53 70	97.06 101	97.74 180	96.95 168	95.05 181	98.36 120	98.77 71	98.85 58	99.51 48	99.53 9

CHOSEN 1792x2688		98.31 116	98.02 114	98.66 138	99.55 106	98.57 148	99.38 68	99.25 134	98.42 22	98.48 141	99.58 57	99.85 6	98.31 123	95.75 201	95.71 194	96.96 201	98.27 120

CPTT-MVS		98.28 117	97.51 135	99.16 80	99.54 109	98.78 129	98.96 115	99.36 111	96.30 137	98.89 112	93.10 210	99.30 66	99.20 66	98.35 94	97.96 117	99.03 109	98.82 71

TinyColmap		98.27 118	97.62 132	99.03 96	99.29 156	97.79 189	98.92 120	98.95 168	97.48 73	99.52 27	98.65 113	97.86 155	98.90 87	98.34 95	97.27 165	98.64 158	95.97 192

USDC		98.26 119	97.57 133	99.06 91	99.42 136	97.98 182	98.83 130	98.85 171	97.57 70	99.59 19	99.15 88	98.59 134	98.99 84	97.42 170	96.08 193	98.69 154	96.23 189

MCST-MVS		98.25 120	97.57 133	99.06 91	99.53 116	98.24 168	98.63 149	99.17 145	95.88 149	98.58 129	96.11 184	99.09 104	99.18 68	97.58 161	97.31 164	99.25 88	98.75 82

IterMVS-LS		98.23 121	97.66 128	98.90 109	99.63 91	99.38 43	99.07 105	99.48 81	97.75 53	98.81 117	99.37 75	94.57 183	97.88 144	96.54 191	97.04 172	98.53 166	98.97 54
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAPA-MVS	96.65 12	98.23 121	97.96 118	98.55 147	98.81 195	98.16 172	98.40 169	97.94 210	96.68 117	98.49 139	98.61 114	98.89 117	98.57 107	97.45 167	97.59 151	99.09 100	98.35 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNVR-MVS		98.22 123	97.76 124	98.76 126	99.33 147	98.26 166	98.48 163	98.88 170	96.22 139	98.47 143	95.79 188	99.33 62	98.35 121	98.37 91	97.99 112	99.03 109	98.38 110

IS_MVSNet		98.20 124	98.00 115	98.44 151	99.82 25	99.48 33	99.25 86	99.56 58	95.58 154	93.93 231	97.56 148	96.52 171	98.27 126	99.08 45	99.20 36	99.80 15	98.56 99

DeepPCF-MVS	96.68 10	98.20 124	98.26 98	98.12 171	97.03 233	98.11 174	98.44 167	97.70 214	96.77 111	98.52 134	98.91 104	99.17 82	98.58 106	98.41 89	98.02 109	98.46 171	98.46 103

MSDG		98.20 124	97.88 121	98.56 146	99.33 147	97.74 192	98.27 180	98.10 204	97.20 96	98.06 164	98.59 115	99.16 85	98.76 96	98.39 90	97.71 142	98.86 133	96.38 186

testgi		98.18 127	98.44 82	97.89 180	99.78 35	99.23 59	98.78 136	99.21 139	97.26 90	97.41 191	97.39 153	99.36 60	92.85 218	98.82 67	98.66 79	99.31 81	98.35 112

Effi-MVS+		98.11 128	97.29 140	99.06 91	99.62 93	98.55 149	98.16 185	99.80 15	94.64 173	99.15 74	96.59 174	97.43 160	98.44 115	97.46 166	97.90 118	99.17 93	98.45 105

HyFIR lowres test		98.08 129	97.16 148	99.14 84	99.72 57	98.91 120	99.41 64	99.58 55	97.93 39	98.82 116	99.24 80	95.81 178	98.73 98	95.16 210	95.13 203	98.60 161	97.94 146

train_agg		97.99 130	97.26 141	98.83 118	99.43 135	98.22 170	98.91 122	99.07 157	94.43 180	97.96 171	96.42 179	99.30 66	98.81 93	97.39 171	96.62 181	98.82 138	98.47 102

MSLP-MVS++		97.99 130	97.64 131	98.40 154	98.91 191	98.47 156	97.12 220	98.78 175	96.49 129	98.48 141	93.57 208	99.12 95	98.51 113	98.31 99	98.58 84	98.58 163	98.95 60

CDPH-MVS		97.99 130	97.23 144	98.87 113	99.58 99	98.29 162	98.83 130	99.20 142	93.76 194	98.11 162	96.11 184	99.16 85	98.23 130	97.80 143	97.22 168	99.29 84	98.28 118

FMVSNet2		97.94 133	98.08 110	97.77 185	98.71 198	99.21 63	98.62 151	99.47 82	96.62 119	96.37 213	99.20 86	97.70 157	94.39 204	97.39 171	97.75 137	99.08 101	98.70 86

PVSNet_BlendedMVS		97.93 134	97.66 128	98.25 162	99.30 153	98.67 138	98.31 177	97.95 208	94.30 185	98.75 120	97.63 145	98.76 123	96.30 180	98.29 101	97.78 129	98.93 117	98.18 131

PVSNet_Blended		97.93 134	97.66 128	98.25 162	99.30 153	98.67 138	98.31 177	97.95 208	94.30 185	98.75 120	97.63 145	98.76 123	96.30 180	98.29 101	97.78 129	98.93 117	98.18 131

OpenMVS	97.26 9	97.88 136	97.17 147	98.70 132	99.50 124	98.55 149	98.34 176	99.11 154	93.92 192	98.90 109	95.04 195	98.23 142	97.38 160	98.11 119	98.12 105	98.95 115	98.23 124

pmmvs4		97.87 137	97.02 152	98.86 116	99.20 167	97.68 194	98.89 126	99.03 161	96.57 124	99.12 78	99.03 98	97.26 164	98.42 117	95.16 210	96.34 185	98.53 166	97.10 177

NCCC		97.84 138	96.96 154	98.87 113	99.39 138	98.27 165	98.46 165	99.02 162	96.78 110	98.73 123	91.12 216	98.91 116	98.57 107	97.83 141	97.49 157	99.04 108	98.33 117

Effi-MVS+-dtu		97.78 139	97.37 138	98.26 161	99.25 163	98.50 154	97.89 195	99.19 143	94.51 175	98.16 159	95.93 187	98.80 122	95.97 184	98.27 106	97.38 160	99.10 96	98.23 124

MDA-MVSNet-bldmvs		97.75 140	97.26 141	98.33 158	99.35 146	98.45 157	99.32 77	97.21 219	97.90 44	99.05 90	99.01 100	96.86 169	99.08 79	99.36 30	92.97 213	95.97 209	96.25 188

CDS-MVSNet		97.75 140	97.68 127	97.83 183	99.08 182	98.20 171	98.68 141	98.61 186	95.63 153	97.80 175	99.24 80	96.93 168	94.09 209	97.96 125	97.82 127	98.71 153	97.99 143
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		97.75 140	97.26 141	98.32 160	98.58 207	97.86 185	97.80 197	98.09 205	96.49 129	98.49 139	96.15 183	98.08 147	98.35 121	98.00 123	97.03 173	98.61 160	97.21 174

PLC	95.63 15	97.73 143	97.01 153	98.57 145	99.10 179	97.80 188	97.72 202	98.77 176	96.34 135	98.38 146	93.46 209	98.06 148	98.66 101	97.90 131	97.65 146	98.77 149	97.90 147
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_Test		97.69 144	97.15 149	98.33 158	99.27 159	98.43 159	98.25 181	99.29 127	95.00 166	97.39 194	98.86 106	98.00 151	97.14 164	95.38 206	96.22 187	98.62 159	98.15 135

GBi-Net		97.69 144	97.75 125	97.62 186	98.71 198	99.21 63	98.62 151	99.33 119	94.09 188	95.60 220	98.17 130	95.97 175	94.39 204	99.05 47	99.03 49	99.08 101	98.70 86

test1		97.69 144	97.75 125	97.62 186	98.71 198	99.21 63	98.62 151	99.33 119	94.09 188	95.60 220	98.17 130	95.97 175	94.39 204	99.05 47	99.03 49	99.08 101	98.70 86

CANet_DTU		97.65 147	97.50 136	97.82 184	99.19 170	98.08 175	98.41 168	98.67 182	94.40 182	99.16 71	98.32 122	98.69 127	93.96 211	97.87 134	97.61 149	97.51 192	97.56 159

TSAR-MVS + COLMAP		97.62 148	97.31 139	97.98 177	98.47 213	97.39 198	98.29 179	98.25 199	96.68 117	97.54 188	98.87 105	98.04 150	97.08 165	96.78 186	96.26 186	98.26 177	97.12 176

MS-PatchMatch		97.60 149	97.22 145	98.04 175	98.67 203	97.18 200	97.91 193	98.28 198	95.82 151	98.34 149	97.66 144	98.38 138	97.77 148	97.10 182	97.25 166	97.27 196	97.18 175

PCF-MVS	95.58 16	97.60 149	96.67 158	98.69 134	99.44 133	98.23 169	98.37 172	98.81 174	93.01 204	98.22 156	97.97 139	99.59 38	98.20 132	95.72 203	95.08 204	99.08 101	97.09 179
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tfpn_n400		97.59 151	96.36 169	99.01 100	99.66 72	99.19 68	99.21 92	99.55 60	97.62 63	97.77 176	94.60 199	87.78 198	98.27 126	98.44 84	98.72 73	99.62 30	98.21 127

tfpnconf		97.59 151	96.36 169	99.01 100	99.66 72	99.19 68	99.21 92	99.55 60	97.62 63	97.77 176	94.60 199	87.78 198	98.27 126	98.44 84	98.72 73	99.62 30	98.21 127

HQP-MVS		97.58 153	96.65 162	98.66 138	99.30 153	97.99 180	97.88 196	98.65 183	94.58 174	98.66 124	94.65 198	99.15 89	98.59 105	96.10 196	95.59 196	98.90 125	98.50 101

DI_MVS_plusplus_trai		97.57 154	96.55 164	98.77 125	99.55 106	98.76 130	99.22 90	99.00 164	97.08 100	97.95 172	97.78 142	91.35 192	98.02 139	96.20 194	96.81 177	98.87 130	97.87 148

AdaColmap		97.57 154	96.57 163	98.74 127	99.25 163	98.01 178	98.36 175	98.98 166	94.44 179	98.47 143	92.44 214	97.91 154	98.62 103	98.19 109	97.74 138	98.73 151	97.28 168

tfpnview11		97.49 156	96.22 173	98.97 104	99.63 91	99.24 57	99.12 102	99.54 66	96.76 112	97.77 176	94.60 199	87.78 198	98.25 129	97.93 128	99.14 39	99.52 43	98.08 139

test1235678		97.49 156	96.84 156	98.24 165	99.37 139	97.79 189	98.59 156	99.07 157	92.41 206	97.59 184	99.24 80	98.15 145	97.66 149	97.64 156	97.12 169	97.17 197	95.55 196

testmv		97.48 158	96.83 157	98.24 165	99.37 139	97.79 189	98.59 156	99.07 157	92.40 207	97.59 184	99.24 80	98.11 146	97.66 149	97.64 156	97.11 170	97.17 197	95.54 197

conf0.05thres1000		97.44 159	95.93 180	99.20 77	99.82 25	99.56 22	99.41 64	99.61 52	97.42 79	98.01 169	94.34 204	82.73 219	98.68 100	99.33 32	99.42 24	99.67 27	98.74 83

IterMVS		97.40 160	96.67 158	98.25 162	99.45 130	98.66 140	98.87 128	98.73 178	96.40 133	98.94 105	99.56 59	95.26 180	97.58 151	95.38 206	94.70 207	95.90 210	96.72 182
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CVMVSNet		97.38 161	97.39 137	97.37 191	98.58 207	97.72 193	98.70 139	97.42 216	97.21 94	95.95 217	99.46 68	93.31 188	97.38 160	97.60 159	97.78 129	96.18 206	98.66 90

diffmvs		97.29 162	96.67 158	98.01 176	99.00 187	97.82 186	98.37 172	99.18 144	96.73 116	97.74 180	99.08 92	94.26 184	96.50 174	94.86 214	95.67 195	97.29 195	98.25 122

new-patchmatchnet		97.26 163	96.12 175	98.58 144	99.55 106	98.63 142	99.14 99	97.04 221	98.80 16	99.19 65	99.92 6	99.19 80	98.92 86	95.51 205	87.04 221	97.66 189	93.73 210

MIMVSNet		97.24 164	97.15 149	97.36 192	99.03 185	98.52 153	98.55 160	99.73 28	94.94 169	94.94 228	97.98 138	97.37 162	93.66 213	97.60 159	97.34 162	98.23 179	96.29 187

PatchMatch-RL		97.24 164	96.45 167	98.17 168	98.70 201	97.57 196	97.31 216	98.48 193	94.42 181	98.39 145	95.74 189	96.35 174	97.88 144	97.75 148	97.48 158	98.24 178	95.87 193

MDTV_nov1_ep13_2view		97.12 166	96.19 174	98.22 167	99.13 177	98.05 176	99.24 87	99.47 82	97.61 65	99.15 74	99.59 55	99.01 112	98.40 118	94.87 212	90.14 216	93.91 217	94.04 209

MAR-MVS		97.12 166	96.28 172	98.11 172	98.94 190	97.22 199	97.65 206	99.38 100	90.93 228	98.15 160	95.17 193	97.13 165	96.48 176	97.71 150	97.40 159	98.06 183	98.40 109
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
tfpn1000		97.10 168	95.97 178	98.41 153	99.64 86	99.30 52	98.89 126	99.49 80	96.49 129	95.97 216	95.31 192	85.62 212	96.92 168	97.86 135	99.13 41	99.53 42	98.11 136

Fast-Effi-MVS+-dtu		96.99 169	96.46 166	97.61 188	98.98 188	97.89 183	97.54 211	99.76 22	93.43 198	96.55 212	94.93 196	98.06 148	94.32 207	96.93 184	96.50 184	98.53 166	97.47 160

FPMVS		96.97 170	97.20 146	96.70 210	97.75 226	96.11 214	97.72 202	95.47 225	97.13 98	98.02 166	97.57 147	96.67 170	92.97 217	99.00 55	98.34 96	98.28 176	95.58 195

TAMVS		96.95 171	96.94 155	96.97 204	99.07 184	97.67 195	97.98 191	97.12 220	95.04 163	95.41 223	99.27 79	95.57 179	94.09 209	97.32 175	97.11 170	98.16 182	96.59 184

FMVSNet3		96.85 172	96.67 158	97.06 198	97.56 229	99.01 101	97.99 190	99.33 119	94.09 188	95.60 220	98.17 130	95.97 175	93.26 216	94.76 215	96.22 187	98.59 162	98.46 103

GA-MVS		96.84 173	95.86 182	97.98 177	99.16 174	98.29 162	97.91 193	98.64 185	95.14 161	97.71 182	98.04 137	88.90 195	96.50 174	96.41 192	96.61 182	97.97 186	97.60 156

CHOSEN 280x420		96.80 174	96.30 171	97.39 190	99.09 180	96.52 205	98.76 137	99.29 127	93.88 193	97.65 183	98.34 120	93.66 186	96.29 182	98.28 104	97.73 140	93.27 221	95.70 194

gg-mvs-nofinetune		96.77 175	96.52 165	97.06 198	99.66 72	97.82 186	97.54 211	99.86 9	98.69 17	98.61 126	99.94 4	89.62 193	88.37 232	97.55 162	96.67 180	98.30 175	95.35 198

tfpn_ndepth		96.69 176	95.49 187	98.09 173	99.17 172	99.13 78	98.61 154	99.38 100	94.90 170	95.85 218	92.85 212	88.19 197	96.07 183	97.28 178	98.67 77	99.49 51	97.44 161

N_pmnet		96.68 177	95.70 185	97.84 182	99.42 136	98.00 179	99.35 72	98.21 201	98.40 24	98.13 161	99.42 72	99.30 66	97.44 159	94.00 220	88.79 218	94.47 216	91.96 218

new_pmnet		96.59 178	96.40 168	96.81 207	98.24 222	95.46 224	97.71 204	94.75 229	96.92 102	96.80 211	99.23 84	97.81 156	96.69 170	96.58 190	95.16 202	96.69 202	93.64 211

tfpn111		96.48 179	94.67 190	98.59 142	99.37 139	99.18 70	98.68 141	99.39 93	92.02 213	97.21 203	90.63 217	86.34 206	97.45 154	98.15 114	99.08 43	99.43 58	97.28 168

view800		96.48 179	94.42 191	98.87 113	99.70 61	99.26 55	99.05 106	99.45 89	94.77 171	97.32 198	88.21 221	83.40 217	98.28 125	98.37 91	99.33 30	99.44 56	97.58 158

PMMVS		96.47 181	95.81 183	97.23 194	97.38 231	95.96 218	97.31 216	96.91 222	93.21 201	97.93 173	97.14 159	97.64 158	95.70 187	95.24 208	96.18 190	98.17 181	95.33 199

EPNet		96.44 182	96.08 176	96.86 205	99.32 150	97.15 201	97.69 205	99.32 124	93.67 195	98.11 162	95.64 190	93.44 187	89.07 230	96.86 185	96.83 176	97.67 188	98.97 54
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
view600		96.39 183	94.30 192	98.82 121	99.65 78	99.16 75	98.98 113	99.36 111	94.46 178	97.39 194	87.28 222	84.16 215	98.16 133	98.16 111	99.48 20	99.40 66	97.42 163

thres600view7		96.35 184	94.27 193	98.79 124	99.66 72	99.18 70	98.94 117	99.38 100	94.37 184	97.21 203	87.19 224	84.10 216	98.10 134	98.16 111	99.47 21	99.42 61	97.43 162

EPNet_dtu		96.31 185	95.96 179	96.72 209	99.18 171	95.39 225	97.03 222	99.13 153	93.02 203	99.35 41	97.23 156	97.07 166	90.70 227	95.74 202	95.08 204	94.94 213	98.16 134
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs3		96.30 186	95.87 181	96.80 208	97.66 228	96.48 206	97.93 192	93.80 230	93.40 199	98.54 133	98.27 125	97.50 159	97.37 162	97.49 165	93.11 212	95.52 211	94.85 203

PMMVS2		96.29 187	97.05 151	95.40 224	98.32 219	96.16 211	98.18 184	97.46 215	97.20 96	84.51 238	99.60 53	98.68 129	96.37 177	98.59 77	97.38 160	97.58 191	91.76 220

thres200		96.23 188	94.13 194	98.69 134	99.44 133	99.18 70	98.58 158	99.38 100	93.52 197	97.35 196	86.33 231	85.83 211	97.93 142	98.16 111	98.78 66	99.42 61	97.10 177

thres400		96.22 189	94.08 196	98.72 129	99.58 99	99.05 88	98.83 130	99.22 137	94.01 191	97.40 192	86.34 230	84.91 214	97.93 142	97.85 138	99.08 43	99.37 71	97.28 168

tfpn200view9		96.17 190	94.08 196	98.60 141	99.37 139	99.18 70	98.68 141	99.39 93	92.02 213	97.30 199	86.53 227	86.34 206	97.45 154	98.15 114	99.08 43	99.43 58	97.28 168

CMPMVS	74.71 19	96.17 190	96.06 177	96.30 217	97.41 230	94.52 230	94.83 232	95.46 226	91.57 222	97.26 202	94.45 203	98.33 140	94.98 196	98.28 104	97.59 151	97.86 187	97.68 154
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
conf200view11		96.16 192	94.08 196	98.59 142	99.37 139	99.18 70	98.68 141	99.39 93	92.02 213	97.21 203	86.53 227	86.34 206	97.45 154	98.15 114	99.08 43	99.43 58	97.28 168

testus		96.13 193	95.13 188	97.28 193	99.13 177	97.00 202	96.84 224	97.89 212	90.48 229	97.40 192	93.60 207	96.47 172	95.39 191	96.21 193	96.19 189	97.05 199	95.99 191

IB-MVS	95.85 14	95.87 194	94.88 189	97.02 201	99.09 180	98.25 167	97.16 218	97.38 217	91.97 220	97.77 176	83.61 235	97.29 163	92.03 225	97.16 180	97.66 144	98.66 155	98.20 130
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
test0.0.03 1		95.81 195	95.77 184	95.85 223	99.20 167	98.15 173	97.49 215	98.50 191	92.24 208	92.74 236	96.82 170	92.70 189	88.60 231	97.31 177	97.01 175	98.57 164	96.19 190

thres100view900		95.74 196	93.66 204	98.17 168	99.37 139	98.59 145	98.10 186	98.33 197	92.02 213	97.30 199	86.53 227	86.34 206	96.69 170	96.77 187	98.47 91	99.24 90	96.89 180

test12356		95.71 197	95.55 186	95.89 222	98.27 221	96.48 206	96.90 223	97.35 218	92.13 211	95.64 219	99.13 89	97.97 152	92.34 221	96.94 183	96.55 183	94.87 214	89.61 227

thresconf0.02		95.49 198	92.74 209	98.70 132	99.32 150	98.70 136	98.87 128	99.21 139	95.95 147	97.57 186	90.63 217	73.55 231	97.86 146	96.09 197	97.03 173	99.40 66	97.22 173

PatchT		95.49 198	93.29 206	98.06 174	98.65 204	96.20 210	98.91 122	99.73 28	92.00 219	98.50 136	96.67 172	83.25 218	96.34 178	94.40 216	95.50 197	96.21 205	95.04 201

CR-MVSNet		95.38 200	93.01 207	98.16 170	98.63 205	95.85 220	97.64 207	99.78 19	91.27 224	98.50 136	96.84 169	82.16 220	96.34 178	94.40 216	95.50 197	98.05 184	95.04 201

MVSTER		95.38 200	93.99 200	97.01 202	98.83 194	98.95 113	96.62 225	99.14 149	92.17 210	97.44 190	97.29 154	77.88 226	91.63 226	97.45 167	96.18 190	98.41 172	97.99 143

LP		95.33 202	93.45 205	97.54 189	98.68 202	97.40 197	98.73 138	98.41 195	96.33 136	98.92 107	97.84 141	88.30 196	95.92 185	92.98 221	89.38 217	94.56 215	91.90 219

tfpn		94.97 203	91.60 215	98.90 109	99.73 55	99.33 48	99.11 103	99.51 73	95.05 162	97.19 206	89.03 220	62.62 237	98.37 119	98.53 79	98.97 53	99.48 52	97.70 153

MVS-HIRNet		94.86 204	93.83 201	96.07 218	97.07 232	94.00 231	94.31 233	99.17 145	91.23 227	98.17 158	98.69 111	97.43 160	95.66 188	94.05 219	91.92 214	92.04 228	89.46 228

test-LLR		94.79 205	93.71 202	96.06 219	99.20 167	96.16 211	96.31 226	98.50 191	89.98 230	94.08 229	97.01 163	86.43 204	92.20 223	96.76 188	95.31 199	96.05 207	94.31 206

RPMNet		94.72 206	92.01 214	97.88 181	98.56 209	95.85 220	97.78 198	99.70 34	91.27 224	98.33 150	93.69 206	81.88 221	94.91 198	92.60 223	94.34 209	98.01 185	94.46 205

gm-plane-assit		94.62 207	91.39 216	98.39 155	99.90 13	99.47 35	99.40 66	99.65 43	97.44 77	99.56 23	99.68 44	59.40 240	94.23 208	96.17 195	94.77 206	97.61 190	92.79 215

test-mter		94.62 207	94.02 199	95.32 225	97.72 227	96.75 203	96.23 228	95.67 224	89.83 233	93.23 235	96.99 165	85.94 210	92.66 220	97.32 175	96.11 192	96.44 203	95.22 200

FMVSNet5		94.57 209	92.77 208	96.67 211	97.88 224	98.72 135	97.54 211	98.70 181	88.64 234	95.11 226	86.90 225	81.77 222	93.27 215	97.92 130	98.07 108	97.50 193	97.34 166

conf0.01		94.53 210	91.09 218	98.53 149	99.29 156	99.05 88	98.68 141	99.35 116	92.02 213	97.04 207	84.45 233	68.52 233	97.45 154	97.79 145	99.08 43	99.41 64	96.70 183

MDTV_nov1_ep13		94.47 211	92.15 212	97.17 195	98.54 211	96.42 208	98.10 186	98.89 169	94.49 176	98.02 166	97.41 152	86.49 203	95.56 189	90.85 224	87.95 219	93.91 217	91.45 222

TESTMET0.1,1		94.44 212	93.71 202	95.30 226	97.84 225	96.16 211	96.31 226	95.32 227	89.98 230	94.08 229	97.01 163	86.43 204	92.20 223	96.76 188	95.31 199	96.05 207	94.31 206

ADS-MVSNet		94.41 213	92.13 213	97.07 197	98.86 192	96.60 204	98.38 171	98.47 194	96.13 144	98.02 166	96.98 166	87.50 202	95.87 186	89.89 225	87.58 220	92.79 225	90.27 224

1111		94.22 214	92.26 211	96.51 215	99.71 59	98.75 132	99.03 107	99.83 12	95.01 164	93.39 233	99.54 63	60.23 238	89.58 228	97.90 131	97.62 148	97.50 193	96.75 181

conf0.002		93.97 215	90.06 222	98.52 150	99.26 160	99.02 100	98.68 141	99.33 119	92.02 213	97.01 208	83.82 234	63.41 236	97.45 154	97.73 149	97.98 114	99.40 66	96.47 185

tpm		93.89 216	91.21 217	97.03 200	98.36 217	96.07 215	97.53 214	99.65 43	92.24 208	98.64 125	97.23 156	74.67 230	94.64 202	92.68 222	90.73 215	93.37 220	94.82 204

PatchmatchNet		93.88 217	91.08 219	97.14 196	98.75 197	96.01 217	98.25 181	99.39 93	94.95 168	98.96 100	96.32 180	85.35 213	95.50 190	88.89 227	85.89 225	91.99 229	90.15 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		93.67 218	90.82 220	96.99 203	98.62 206	96.39 209	98.40 169	99.11 154	95.54 155	97.87 174	97.14 159	81.27 224	94.97 197	88.54 229	86.80 222	92.95 223	90.06 226

MVE	82.47 18	93.12 219	94.09 195	91.99 230	90.79 235	82.50 237	93.93 234	96.30 223	96.06 145	88.81 237	98.19 128	96.38 173	97.56 152	97.24 179	95.18 201	84.58 235	93.07 212
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CostFormer		92.75 220	89.49 223	96.55 213	98.78 196	95.83 222	97.55 210	98.59 187	91.83 221	97.34 197	96.31 181	78.53 225	94.50 203	86.14 230	84.92 226	92.54 226	92.84 214

test2356		92.46 221	88.72 228	96.82 206	98.48 212	95.34 226	96.22 229	98.09 205	87.46 235	96.01 215	92.82 213	64.42 234	95.10 195	94.08 218	94.05 210	97.02 200	92.87 213

tpmrst		92.45 222	89.48 224	95.92 221	98.43 216	95.03 228	97.14 219	97.92 211	94.16 187	97.56 187	97.86 140	81.63 223	93.56 214	85.89 232	82.86 228	90.91 233	88.95 231

tpmp4_e23		92.43 223	88.82 226	96.64 212	98.46 214	95.17 227	97.61 209	98.85 171	92.42 205	98.18 157	93.03 211	74.92 229	93.80 212	88.91 226	84.60 227	92.95 223	92.66 216

dps		92.35 224	88.78 227	96.52 214	98.21 223	95.94 219	97.78 198	98.38 196	89.88 232	96.81 210	95.07 194	75.31 228	94.70 201	88.62 228	86.21 224	93.21 222	90.41 223

E-PMN		92.28 225	90.12 221	94.79 227	98.56 209	90.90 233	95.16 231	93.68 231	95.36 157	95.10 227	96.56 175	89.05 194	95.24 193	95.21 209	81.84 231	90.98 231	81.94 232

EMVS		91.84 226	89.39 225	94.70 228	98.44 215	90.84 234	95.27 230	93.53 232	95.18 160	95.26 225	95.62 191	87.59 201	94.77 200	94.87 212	80.72 232	90.95 232	80.88 233

tpm cat1		91.52 227	87.70 229	95.97 220	98.33 218	94.98 229	97.06 221	98.03 207	92.11 212	98.03 165	94.77 197	77.19 227	92.71 219	83.56 233	82.24 230	91.67 230	89.04 230

DWT-MVSNet_training		91.07 228	86.55 230	96.35 216	98.28 220	95.82 223	98.00 189	95.03 228	91.24 226	97.99 170	90.35 219	63.43 235	95.25 192	86.06 231	86.62 223	93.55 219	92.30 217

testpf		87.81 229	83.90 231	92.37 229	96.76 234	88.65 235	93.04 235	98.24 200	85.20 236	95.28 224	86.82 226	72.43 232	82.35 233	82.62 234	82.30 229	88.55 234	89.29 229

.test1245		74.10 230	68.09 232	81.11 231	99.71 59	98.75 132	99.03 107	99.83 12	95.01 164	93.39 233	99.54 63	60.23 238	89.58 228	97.90 131	10.38 234	5.14 238	14.81 234

GG-mvs-BLEND		65.66 231	92.62 210	34.20 233	1.45 239	93.75 232	85.40 237	1.64 237	91.37 223	17.21 240	87.25 223	94.78 182	3.25 237	95.64 204	93.80 211	96.27 204	91.74 221

testmvs		9.73 232	13.38 233	5.48 235	3.62 237	4.12 239	6.40 240	3.19 236	14.92 237	7.68 242	22.10 236	13.89 242	6.83 235	13.47 235	10.38 234	5.14 238	14.81 234

test123		9.37 233	12.26 234	6.00 234	3.32 238	4.06 240	6.39 241	3.41 235	13.20 238	10.48 241	16.43 237	16.22 241	6.76 236	11.37 236	10.40 233	5.62 237	14.10 236

sosnet-low-res		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 243	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

sosnet		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 243	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

ambc			97.89 120		99.45 130	97.88 184	97.78 198		97.27 88	99.80 3	98.99 102	98.48 137	98.55 109	97.80 143	96.68 179	98.54 165	98.10 137

MTAPA										99.19 65		99.68 21

MTMP										99.20 63		99.54 41

Patchmatch-RL test							32.47 239

tmp_tt				65.28 232	82.24 236	71.50 238	70.81 238	23.21 234	96.14 142	81.70 239	85.98 232	92.44 190	49.84 234	95.81 200	94.36 208	83.86 236

XVS					99.77 37	99.07 84	99.46 60			98.95 102		99.37 56				99.33 77

X-MVStestdata					99.77 37	99.07 84	99.46 60			98.95 102		99.37 56				99.33 77

abl_6				98.38 156	99.03 185	98.04 177	98.08 188	98.65 183	93.23 200	98.56 130	94.58 202	98.57 135	97.17 163			98.81 139	97.42 163

mPP-MVS					99.75 46							99.49 49

NP-MVS									93.07 202

Patchmtry						96.05 216	97.64 207	99.78 19		98.50 136

DeepMVS_CX						87.86 236	92.27 236	61.98 233	93.64 196	93.62 232	91.17 215	91.67 191	94.90 199	95.99 199		92.48 227	94.18 208

High-resolution multi-view benchmark