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		99.90 1	99.87 1	99.92 4	99.98 1	99.91 1	99.92 2	99.97 1	99.86 2	99.98 2	99.82 64	100.00 1	99.70 39	99.86 17	99.79 12	99.96 3	99.87 12

LTVRE_ROB	99.39 1	99.90 1	99.87 1	99.93 1	99.97 2	99.82 7	99.91 6	99.92 43	99.75 8	99.93 5	99.89 42	100.00 1	99.87 2	99.93 4	99.82 6	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
v748		99.89 3	99.87 1	99.92 4	99.96 9	99.80 12	99.91 6	99.95 29	99.77 6	99.92 9	99.96 5	99.93 38	99.81 9	99.92 7	99.82 6	99.96 3	99.90 2

v7n		99.89 3	99.86 5	99.93 1	99.97 2	99.83 3	99.93 1	99.96 15	99.77 6	99.89 19	99.99 1	99.86 69	99.84 5	99.89 9	99.81 10	99.97 1	99.88 9

v52		99.89 3	99.85 7	99.92 4	99.97 2	99.80 12	99.92 2	99.97 1	99.78 4	99.90 15	99.96 5	99.85 75	99.82 7	99.88 12	99.82 6	99.96 3	99.89 5

V4		99.89 3	99.85 7	99.92 4	99.97 2	99.80 12	99.92 2	99.97 1	99.78 4	99.90 15	99.96 5	99.84 77	99.82 7	99.88 12	99.82 6	99.96 3	99.89 5

SixPastTwentyTwo		99.89 3	99.85 7	99.93 1	99.97 2	99.88 2	99.92 2	99.97 1	99.66 15	99.94 4	99.94 15	99.74 94	99.81 9	99.97 2	99.89 1	99.96 3	99.89 5

pmmvs6		99.88 8	99.87 1	99.89 13	99.97 2	99.76 19	99.89 9	99.96 15	99.82 3	99.90 15	99.92 26	99.95 22	99.68 40	99.93 4	99.88 2	99.95 10	99.86 13

anonymousdsp		99.87 9	99.86 5	99.88 16	99.95 12	99.75 23	99.90 8	99.96 15	99.69 11	99.83 54	99.96 5	99.99 4	99.74 26	99.95 3	99.83 3	99.91 25	99.88 9

FC-MVSNet-test		99.84 10	99.80 10	99.89 13	99.96 9	99.83 3	99.84 18	99.95 29	99.37 58	99.77 76	99.95 10	99.96 14	99.85 3	99.93 4	99.83 3	99.95 10	99.72 46

TDRefinement		99.81 11	99.76 12	99.86 19	99.83 98	99.53 66	99.89 9	99.91 48	99.73 9	99.88 24	99.83 62	99.96 14	99.76 19	99.91 8	99.81 10	99.86 53	99.59 70

WR-MVS		99.79 12	99.68 17	99.91 9	99.95 12	99.83 3	99.87 13	99.96 15	99.39 57	99.93 5	99.87 50	99.29 152	99.77 17	99.83 21	99.72 21	99.97 1	99.82 17

MIMVSNet1		99.79 12	99.75 13	99.84 25	99.89 39	99.83 3	99.84 18	99.89 56	99.31 64	99.93 5	99.92 26	99.97 10	99.68 40	99.89 9	99.64 28	99.82 74	99.66 57

pm-mvs1		99.77 14	99.69 16	99.86 19	99.94 22	99.68 36	99.84 18	99.93 36	99.59 29	99.87 29	99.92 26	99.21 155	99.65 55	99.88 12	99.77 14	99.93 19	99.78 27

PEN-MVS		99.77 14	99.65 19	99.91 9	99.95 12	99.80 12	99.86 14	99.97 1	99.08 94	99.89 19	99.69 81	99.68 102	99.84 5	99.81 25	99.64 28	99.95 10	99.81 20

EU-MVSNet		99.76 16	99.74 14	99.78 49	99.82 103	99.81 10	99.88 11	99.87 62	99.31 64	99.75 86	99.91 35	99.76 93	99.78 15	99.84 20	99.74 18	99.56 149	99.81 20

Vis-MVSNet		99.76 16	99.78 11	99.75 63	99.92 27	99.77 18	99.83 21	99.85 78	99.43 50	99.85 41	99.84 60	100.00 1	99.13 136	99.83 21	99.66 26	99.90 28	99.90 2
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DTE-MVSNet		99.75 18	99.61 27	99.92 4	99.95 12	99.81 10	99.86 14	99.96 15	99.18 81	99.92 9	99.66 83	99.45 136	99.85 3	99.80 26	99.56 34	99.96 3	99.79 24

tfpnnormal		99.74 19	99.63 22	99.86 19	99.93 25	99.75 23	99.80 30	99.89 56	99.31 64	99.88 24	99.43 115	99.66 105	99.77 17	99.80 26	99.71 22	99.92 23	99.76 33

DeepC-MVS	99.05 5	99.74 19	99.64 20	99.84 25	99.90 34	99.39 99	99.79 32	99.81 118	99.69 11	99.90 15	99.87 50	99.98 5	99.81 9	99.62 49	99.32 62	99.83 70	99.65 61
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v13		99.73 21	99.63 22	99.85 22	99.87 50	99.71 28	99.80 30	99.96 15	99.62 23	99.83 54	99.93 19	99.66 105	99.75 21	99.41 70	99.26 67	99.89 33	99.80 23

PS-CasMVS		99.73 21	99.59 33	99.90 12	99.95 12	99.80 12	99.85 17	99.97 1	98.95 109	99.86 34	99.73 73	99.36 145	99.81 9	99.83 21	99.67 25	99.95 10	99.83 16

WR-MVS_H		99.73 21	99.61 27	99.88 16	99.95 12	99.82 7	99.83 21	99.96 15	99.01 102	99.84 45	99.71 79	99.41 142	99.74 26	99.77 31	99.70 23	99.95 10	99.82 17

no-one		99.73 21	99.70 15	99.76 57	99.77 126	99.58 53	99.76 40	99.90 55	99.08 94	99.86 34	99.90 39	99.98 5	99.66 52	99.98 1	99.73 19	99.59 143	99.67 55

v12		99.72 25	99.61 27	99.85 22	99.86 66	99.70 33	99.79 32	99.96 15	99.61 24	99.83 54	99.93 19	99.61 109	99.74 26	99.38 72	99.22 69	99.89 33	99.79 24

v11		99.72 25	99.62 25	99.85 22	99.87 50	99.71 28	99.81 27	99.96 15	99.63 20	99.83 54	99.97 4	99.58 116	99.75 21	99.33 83	99.33 60	99.87 47	99.79 24

TransMVSNet (Re)		99.72 25	99.59 33	99.88 16	99.95 12	99.76 19	99.88 11	99.94 32	99.58 31	99.92 9	99.90 39	98.55 170	99.65 55	99.89 9	99.76 15	99.95 10	99.70 50

ACMH	99.11 4	99.72 25	99.63 22	99.84 25	99.87 50	99.59 51	99.83 21	99.88 60	99.46 47	99.87 29	99.66 83	99.95 22	99.76 19	99.73 36	99.47 49	99.84 60	99.52 100
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
V9		99.71 29	99.59 33	99.84 25	99.86 66	99.69 35	99.78 35	99.96 15	99.61 24	99.84 45	99.93 19	99.61 109	99.73 30	99.34 82	99.17 75	99.88 37	99.78 27

FC-MVSNet-train		99.70 30	99.67 18	99.74 69	99.94 22	99.71 28	99.82 25	99.91 48	99.14 90	99.53 141	99.70 80	99.88 63	99.33 107	99.88 12	99.61 33	99.94 17	99.77 29

COLMAP_ROB	99.18 2	99.70 30	99.60 31	99.81 35	99.84 87	99.37 110	99.76 40	99.84 90	99.54 40	99.82 61	99.64 87	99.95 22	99.75 21	99.79 28	99.56 34	99.83 70	99.37 139
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
V14		99.69 32	99.56 38	99.84 25	99.86 66	99.68 36	99.78 35	99.96 15	99.60 28	99.83 54	99.93 19	99.58 116	99.72 34	99.28 96	99.11 86	99.88 37	99.77 29

ACMH+	98.94 6	99.69 32	99.59 33	99.81 35	99.88 43	99.41 96	99.75 46	99.86 66	99.43 50	99.80 66	99.54 100	99.97 10	99.73 30	99.82 24	99.52 44	99.85 57	99.43 121

test20.03		99.68 34	99.60 31	99.76 57	99.91 31	99.70 33	99.68 71	99.87 62	99.05 99	99.88 24	99.92 26	99.88 63	99.50 85	99.77 31	99.42 55	99.75 92	99.49 106

CP-MVSNet		99.68 34	99.51 45	99.89 13	99.95 12	99.76 19	99.83 21	99.96 15	98.83 125	99.84 45	99.65 86	99.09 158	99.80 13	99.78 29	99.62 32	99.95 10	99.82 17

v15		99.67 36	99.54 41	99.83 30	99.86 66	99.67 39	99.76 40	99.95 29	99.59 29	99.83 54	99.93 19	99.55 120	99.71 38	99.23 105	99.05 94	99.87 47	99.75 36

PVSNet_Blended_VisFu		99.66 37	99.64 20	99.67 83	99.91 31	99.71 28	99.61 84	99.79 127	99.41 53	99.91 13	99.85 57	99.61 109	99.00 146	99.67 42	99.42 55	99.81 78	99.81 20

v10		99.65 38	99.51 45	99.81 35	99.83 98	99.61 46	99.75 46	99.94 32	99.56 36	99.76 79	99.94 15	99.60 113	99.73 30	99.11 133	99.01 101	99.85 57	99.74 39

CHOSEN 1792x2688		99.65 38	99.55 39	99.77 53	99.93 25	99.60 48	99.79 32	99.92 43	99.73 9	99.74 92	99.93 19	99.98 5	99.80 13	98.83 181	99.01 101	99.45 162	99.76 33

UA-Net		99.64 40	99.62 25	99.66 84	99.97 2	99.82 7	99.14 174	99.96 15	98.95 109	99.52 147	99.38 123	99.86 69	99.55 72	99.72 37	99.66 26	99.80 81	99.94 1

v17		99.62 41	99.48 48	99.79 46	99.80 107	99.60 48	99.73 57	99.94 32	99.46 47	99.73 98	99.88 48	99.52 125	99.67 44	99.16 126	98.96 111	99.84 60	99.75 36

Baseline_NR-MVSNet		99.62 41	99.48 48	99.78 49	99.85 79	99.76 19	99.59 89	99.82 106	98.84 123	99.88 24	99.91 35	99.04 159	99.61 63	99.46 61	99.78 13	99.94 17	99.60 69

pmmvs-eth3d		99.61 43	99.48 48	99.75 63	99.87 50	99.30 129	99.75 46	99.89 56	99.23 71	99.85 41	99.88 48	99.97 10	99.49 89	99.46 61	99.01 101	99.68 110	99.52 100

v1144		99.61 43	99.43 59	99.82 31	99.88 43	99.41 96	99.76 40	99.86 66	99.64 18	99.84 45	99.95 10	99.49 132	99.74 26	99.00 149	98.93 116	99.84 60	99.58 79

v16		99.61 43	99.47 52	99.78 49	99.79 115	99.60 48	99.72 62	99.94 32	99.45 49	99.70 109	99.85 57	99.54 123	99.67 44	99.15 127	98.96 111	99.83 70	99.76 33

v8		99.61 43	99.45 56	99.79 46	99.80 107	99.59 51	99.73 57	99.93 36	99.48 45	99.77 76	99.90 39	99.48 134	99.67 44	99.11 133	98.89 120	99.84 60	99.73 42

v7		99.61 43	99.46 55	99.79 46	99.83 98	99.37 110	99.75 46	99.84 90	99.56 36	99.76 79	99.94 15	99.60 113	99.73 30	99.11 133	99.01 101	99.85 57	99.63 65

CSCG		99.61 43	99.52 44	99.71 73	99.89 39	99.62 43	99.52 105	99.76 147	99.61 24	99.69 111	99.73 73	99.96 14	99.57 70	99.27 99	98.62 158	99.81 78	99.85 15

v1192		99.60 49	99.41 63	99.82 31	99.89 39	99.43 92	99.81 27	99.84 90	99.63 20	99.85 41	99.95 10	99.35 148	99.72 34	99.01 147	98.90 119	99.82 74	99.58 79

APDe-MVS		99.60 49	99.48 48	99.73 71	99.85 79	99.51 79	99.75 46	99.85 78	99.17 82	99.81 64	99.56 98	99.94 32	99.44 97	99.42 69	99.22 69	99.67 112	99.54 90

v1921920		99.59 51	99.40 65	99.82 31	99.88 43	99.45 86	99.81 27	99.83 98	99.65 16	99.86 34	99.95 10	99.29 152	99.75 21	98.98 153	98.86 128	99.78 83	99.59 70

v18		99.59 51	99.44 58	99.76 57	99.78 120	99.57 55	99.70 69	99.93 36	99.43 50	99.69 111	99.85 57	99.51 127	99.65 55	99.08 144	98.87 125	99.82 74	99.74 39

TranMVSNet+NR-MVSNet		99.59 51	99.42 62	99.80 39	99.87 50	99.55 62	99.64 76	99.86 66	99.05 99	99.88 24	99.72 76	99.33 150	99.64 58	99.47 59	99.14 80	99.91 25	99.67 55

EG-PatchMatch MVS		99.59 51	99.49 47	99.70 76	99.82 103	99.26 137	99.39 132	99.83 98	98.99 104	99.93 5	99.54 100	99.92 47	99.51 81	99.78 29	99.50 45	99.73 100	99.41 126

pmmvs5		99.58 55	99.47 52	99.70 76	99.84 87	99.50 80	99.58 93	99.80 124	98.98 107	99.73 98	99.92 26	99.81 83	99.49 89	99.28 96	99.05 94	99.77 87	99.73 42

v144192		99.58 55	99.39 69	99.80 39	99.87 50	99.44 88	99.77 37	99.84 90	99.64 18	99.86 34	99.93 19	99.35 148	99.72 34	98.92 161	98.82 134	99.74 96	99.66 57

v148		99.58 55	99.43 59	99.76 57	99.87 50	99.40 98	99.76 40	99.85 78	99.48 45	99.83 54	99.82 64	99.83 80	99.51 81	99.20 114	98.82 134	99.75 92	99.45 114

v1141		99.58 55	99.39 69	99.80 39	99.87 50	99.39 99	99.74 54	99.85 78	99.58 31	99.84 45	99.92 26	99.49 132	99.68 40	98.98 153	98.83 131	99.84 60	99.52 100

v1240		99.58 55	99.38 74	99.82 31	99.89 39	99.49 82	99.82 25	99.83 98	99.63 20	99.86 34	99.96 5	98.92 164	99.75 21	99.15 127	98.96 111	99.76 89	99.56 84

divwei89l23v2f112		99.58 55	99.39 69	99.80 39	99.87 50	99.39 99	99.74 54	99.85 78	99.57 34	99.84 45	99.92 26	99.48 134	99.67 44	98.98 153	98.83 131	99.84 60	99.52 100

v1		99.58 55	99.39 69	99.80 39	99.87 50	99.39 99	99.74 54	99.85 78	99.58 31	99.84 45	99.92 26	99.51 127	99.67 44	98.98 153	98.82 134	99.84 60	99.52 100

v1neww		99.57 62	99.40 65	99.77 53	99.80 107	99.34 119	99.72 62	99.82 106	99.49 42	99.76 79	99.89 42	99.50 129	99.67 44	99.10 141	98.89 120	99.84 60	99.59 70

v7new		99.57 62	99.40 65	99.77 53	99.80 107	99.34 119	99.72 62	99.82 106	99.49 42	99.76 79	99.89 42	99.50 129	99.67 44	99.10 141	98.89 120	99.84 60	99.59 70

v6		99.57 62	99.40 65	99.77 53	99.80 107	99.34 119	99.72 62	99.82 106	99.49 42	99.76 79	99.89 42	99.52 125	99.67 44	99.10 141	98.89 120	99.84 60	99.59 70

V42		99.57 62	99.41 63	99.75 63	99.84 87	99.37 110	99.73 57	99.83 98	99.41 53	99.75 86	99.89 42	99.42 140	99.60 65	99.15 127	98.96 111	99.76 89	99.65 61

TSAR-MVS + MP.		99.56 66	99.54 41	99.58 102	99.69 156	99.14 157	99.73 57	99.45 201	99.50 41	99.35 179	99.60 94	99.93 38	99.50 85	99.56 51	99.37 59	99.77 87	99.64 64

v2v482		99.56 66	99.35 76	99.81 35	99.87 50	99.35 117	99.75 46	99.85 78	99.56 36	99.87 29	99.95 10	99.44 138	99.66 52	98.91 164	98.76 143	99.86 53	99.45 114

Gipuma		99.55 68	99.23 91	99.91 9	99.87 50	99.52 72	99.86 14	99.93 36	99.87 1	99.96 3	96.72 216	99.55 120	99.97 1	99.77 31	99.46 51	99.87 47	99.74 39
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
NR-MVSNet		99.52 69	99.29 82	99.80 39	99.96 9	99.38 104	99.55 98	99.81 118	98.86 119	99.87 29	99.51 109	98.81 166	99.72 34	99.86 17	99.04 97	99.89 33	99.54 90

MPTG		99.51 70	99.36 75	99.68 81	99.88 43	99.38 104	99.53 102	99.84 90	99.11 93	99.59 132	98.93 161	99.95 22	99.58 69	99.44 67	99.21 71	99.65 116	99.52 100

ACMMPR		99.51 70	99.32 79	99.72 72	99.87 50	99.33 123	99.61 84	99.85 78	99.19 79	99.73 98	98.73 170	99.95 22	99.61 63	99.35 79	99.14 80	99.66 114	99.58 79

UniMVSNet (Re)		99.50 72	99.29 82	99.75 63	99.86 66	99.47 84	99.51 108	99.82 106	98.90 115	99.89 19	99.64 87	99.00 160	99.55 72	99.32 85	99.08 89	99.90 28	99.59 70

FMVSNet1		99.50 72	99.57 37	99.42 131	99.67 164	99.65 41	99.60 88	99.91 48	99.40 55	99.39 170	99.83 62	99.27 154	98.14 178	99.68 39	99.50 45	99.81 78	99.68 52

HyFIR lowres test		99.50 72	99.26 86	99.80 39	99.95 12	99.62 43	99.76 40	99.97 1	99.67 13	99.56 138	99.94 15	98.40 173	99.78 15	98.84 180	98.59 161	99.76 89	99.72 46

PM-MVS		99.49 75	99.43 59	99.57 106	99.76 131	99.34 119	99.53 102	99.77 139	98.93 113	99.75 86	99.46 113	99.83 80	99.11 138	99.72 37	99.29 64	99.49 158	99.46 113

Anonymous20231206		99.48 76	99.31 80	99.69 80	99.79 115	99.57 55	99.63 78	99.79 127	98.88 117	99.91 13	99.72 76	99.93 38	99.59 66	99.24 102	98.63 157	99.43 167	99.18 159

DU-MVS		99.48 76	99.26 86	99.75 63	99.85 79	99.38 104	99.50 112	99.81 118	98.86 119	99.89 19	99.51 109	98.98 161	99.59 66	99.46 61	98.97 109	99.87 47	99.63 65

RPSCF		99.48 76	99.45 56	99.52 116	99.73 146	99.33 123	99.13 175	99.77 139	99.33 62	99.47 159	99.39 122	99.92 47	99.36 101	99.63 47	99.13 83	99.63 127	99.41 126

ACMMP_Plus		99.47 79	99.33 78	99.63 92	99.85 79	99.28 134	99.56 96	99.83 98	98.75 131	99.48 156	99.03 155	99.95 22	99.47 96	99.48 56	99.19 72	99.57 146	99.59 70

SteuartSystems-ACMMP		99.47 79	99.22 93	99.76 57	99.88 43	99.36 113	99.65 75	99.84 90	98.47 159	99.80 66	98.68 173	99.96 14	99.68 40	99.37 74	99.06 91	99.72 104	99.66 57
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ACMM	98.37 12	99.47 79	99.23 91	99.74 69	99.86 66	99.19 152	99.68 71	99.86 66	99.16 86	99.71 107	98.52 180	99.95 22	99.62 62	99.35 79	99.02 99	99.74 96	99.42 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HFP-MVS		99.46 82	99.30 81	99.65 86	99.82 103	99.25 140	99.50 112	99.82 106	99.23 71	99.58 136	98.86 163	99.94 32	99.56 71	99.14 130	99.12 85	99.63 127	99.56 84

LGP-MVS_train		99.46 82	99.18 104	99.78 49	99.87 50	99.25 140	99.71 68	99.87 62	98.02 188	99.79 69	98.90 162	99.96 14	99.66 52	99.49 55	99.17 75	99.79 82	99.49 106

ACMP	98.32 13	99.44 84	99.18 104	99.75 63	99.83 98	99.18 153	99.64 76	99.83 98	98.81 127	99.79 69	98.42 186	99.96 14	99.64 58	99.46 61	98.98 108	99.74 96	99.44 117
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testgi		99.43 85	99.47 52	99.38 139	99.90 34	99.67 39	99.30 153	99.73 156	98.64 145	99.53 141	99.52 107	99.90 55	98.08 181	99.65 45	99.40 58	99.75 92	99.55 89

DELS-MVS		99.42 86	99.53 43	99.29 153	99.52 191	99.43 92	99.42 127	99.28 215	99.16 86	99.72 102	99.82 64	99.97 10	98.17 175	99.56 51	99.16 77	99.65 116	99.59 70
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 86	99.22 93	99.65 86	99.78 120	99.13 160	99.50 112	99.85 78	99.40 55	99.80 66	98.59 176	99.79 90	99.30 114	99.20 114	99.06 91	99.71 106	99.35 142

UniMVSNet_NR-MVSNet		99.41 88	99.12 116	99.76 57	99.86 66	99.48 83	99.50 112	99.81 118	98.84 123	99.89 19	99.45 114	98.32 176	99.59 66	99.22 108	98.89 120	99.90 28	99.63 65

CP-MVS		99.41 88	99.20 98	99.65 86	99.80 107	99.23 147	99.44 125	99.75 155	98.60 150	99.74 92	98.66 174	99.93 38	99.48 93	99.33 83	99.16 77	99.73 100	99.48 109

QAPM		99.41 88	99.21 97	99.64 91	99.78 120	99.16 154	99.51 108	99.85 78	99.20 76	99.72 102	99.43 115	99.81 83	99.25 119	98.87 170	98.71 148	99.71 106	99.30 148

UGNet		99.40 91	99.61 27	99.16 175	99.88 43	99.64 42	99.61 84	99.77 139	99.31 64	99.63 124	99.33 126	99.93 38	96.46 214	99.63 47	99.53 43	99.63 127	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 91	99.28 84	99.55 110	99.92 27	99.68 36	99.31 148	99.87 62	98.69 137	99.16 189	99.08 150	98.64 169	99.20 123	99.65 45	99.46 51	99.83 70	99.72 46

OPM-MVS		99.39 93	99.22 93	99.59 100	99.76 131	98.82 183	99.51 108	99.79 127	99.17 82	99.53 141	99.31 130	99.95 22	99.35 102	99.22 108	98.79 142	99.60 137	99.27 152

Fast-Effi-MVS+		99.39 93	99.18 104	99.63 92	99.86 66	99.28 134	99.45 124	99.91 48	98.47 159	99.61 126	99.50 111	99.57 118	99.17 124	99.24 102	98.66 154	99.78 83	99.59 70

testmv		99.39 93	99.19 101	99.62 97	99.84 87	99.38 104	99.37 138	99.86 66	98.47 159	99.79 69	99.82 64	99.39 144	99.63 60	99.30 88	98.70 150	99.21 189	99.28 150

test1235678		99.39 93	99.20 98	99.62 97	99.84 87	99.38 104	99.38 136	99.86 66	98.47 159	99.79 69	99.82 64	99.41 142	99.63 60	99.30 88	98.71 148	99.21 189	99.28 150

LS3D		99.39 93	99.28 84	99.52 116	99.77 126	99.39 99	99.55 98	99.82 106	98.93 113	99.64 122	98.52 180	99.67 104	98.58 167	99.74 35	99.63 30	99.75 92	99.06 174

CANet		99.36 98	99.39 69	99.34 149	99.80 107	99.35 117	99.41 130	99.47 199	99.20 76	99.74 92	99.54 100	99.68 102	98.05 185	99.23 105	98.97 109	99.57 146	99.73 42

MVS_0304		99.36 98	99.35 76	99.37 142	99.85 79	99.36 113	99.39 132	99.56 185	99.36 60	99.75 86	99.23 136	99.90 55	97.97 188	99.00 149	98.83 131	99.69 109	99.77 29

ACMMP		99.36 98	99.06 124	99.71 73	99.86 66	99.36 113	99.63 78	99.85 78	98.33 172	99.72 102	97.73 203	99.94 32	99.53 77	99.37 74	99.13 83	99.65 116	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		99.35 101	99.26 86	99.46 125	99.66 165	99.15 156	98.92 196	99.67 171	99.55 39	99.35 179	98.83 165	99.91 53	99.35 102	99.19 119	98.53 163	99.78 83	99.68 52

MP-MVS		99.35 101	99.09 121	99.65 86	99.84 87	99.22 148	99.59 89	99.78 133	98.13 181	99.67 118	98.44 184	99.93 38	99.43 99	99.31 87	99.09 88	99.60 137	99.49 106

pmmvs4		99.34 103	99.15 111	99.57 106	99.77 126	98.90 177	99.51 108	99.77 139	99.07 97	99.73 98	99.72 76	99.84 77	99.07 140	98.85 176	98.39 172	99.55 153	99.27 152

EPP-MVSNet		99.34 103	99.10 119	99.62 97	99.94 22	99.74 25	99.66 73	99.80 124	99.07 97	98.93 201	99.61 91	96.13 189	99.49 89	99.67 42	99.63 30	99.92 23	99.86 13

TSAR-MVS + GP.		99.33 105	99.17 108	99.51 118	99.71 150	99.00 170	98.84 204	99.71 161	98.23 177	99.74 92	99.53 106	99.90 55	99.35 102	99.38 72	98.85 129	99.72 104	99.31 146

PHI-MVS		99.33 105	99.19 101	99.49 123	99.69 156	99.25 140	99.27 157	99.59 183	98.44 165	99.78 75	99.15 141	99.92 47	98.95 155	99.39 71	99.04 97	99.64 125	99.18 159

PGM-MVS		99.32 107	98.99 133	99.71 73	99.86 66	99.31 128	99.59 89	99.86 66	97.51 203	99.75 86	98.23 190	99.94 32	99.53 77	99.29 92	99.08 89	99.65 116	99.54 90

DeepC-MVS_fast	98.69 9	99.32 107	99.13 114	99.53 112	99.63 170	98.78 186	99.53 102	99.33 213	99.08 94	99.77 76	99.18 140	99.89 58	99.29 115	99.00 149	98.70 150	99.65 116	99.30 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSDG		99.32 107	99.09 121	99.58 102	99.75 135	98.74 190	99.36 140	99.54 188	99.14 90	99.72 102	99.24 134	99.89 58	99.51 81	99.30 88	98.76 143	99.62 133	98.54 193

TSAR-MVS + ACMM		99.31 110	99.26 86	99.37 142	99.66 165	98.97 174	99.20 163	99.56 185	99.33 62	99.19 188	99.54 100	99.91 53	99.32 110	99.12 132	98.34 175	99.29 181	99.65 61

3Dnovator+	98.92 7	99.31 110	99.03 128	99.63 92	99.77 126	98.90 177	99.52 105	99.81 118	99.37 58	99.72 102	98.03 198	99.73 97	99.32 110	98.99 152	98.81 139	99.67 112	99.36 140

X-MVS		99.30 112	98.99 133	99.66 84	99.85 79	99.30 129	99.49 117	99.82 106	98.32 173	99.69 111	97.31 212	99.93 38	99.50 85	99.37 74	99.16 77	99.60 137	99.53 95

MVS_111021_HR		99.30 112	99.14 112	99.48 124	99.58 187	99.25 140	99.27 157	99.61 177	98.74 132	99.66 120	99.02 156	99.84 77	99.33 107	99.20 114	98.76 143	99.44 164	99.18 159

TAPA-MVS	98.54 10	99.30 112	99.24 90	99.36 148	99.44 205	98.77 188	99.00 188	99.41 205	99.23 71	99.60 130	99.50 111	99.86 69	99.15 132	99.29 92	98.95 115	99.56 149	99.08 171
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		99.30 112	99.01 132	99.63 92	99.75 135	98.89 180	99.35 143	99.60 179	98.53 156	99.86 34	99.57 97	99.94 32	99.52 80	98.96 157	98.10 188	99.70 108	99.08 171
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
USDC		99.29 116	98.98 135	99.65 86	99.72 147	98.87 181	99.47 121	99.66 175	99.35 61	99.87 29	99.58 96	99.87 68	99.51 81	98.85 176	97.93 195	99.65 116	98.38 197

HSP-MVS		99.27 117	99.07 123	99.50 120	99.76 131	99.54 64	99.73 57	99.72 158	98.94 111	99.23 185	98.96 157	99.96 14	98.91 156	98.72 190	97.71 200	99.63 127	99.66 57

PMVS	94.32 17	99.27 117	99.55 39	98.94 192	99.60 180	99.43 92	99.39 132	99.54 188	98.99 104	99.69 111	99.60 94	99.81 83	95.68 222	99.88 12	99.83 3	99.73 100	99.31 146
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS_111021_LR		99.25 119	99.13 114	99.39 136	99.50 198	99.14 157	99.23 161	99.50 196	98.67 139	99.61 126	99.12 145	99.81 83	99.16 128	99.28 96	98.67 153	99.35 177	99.21 157

HPM-MVS++		99.23 120	98.98 135	99.53 112	99.75 135	99.02 169	99.44 125	99.77 139	98.65 141	99.52 147	98.72 171	99.92 47	99.33 107	98.77 188	98.40 171	99.40 171	99.36 140

PMMVS2		99.23 120	99.22 93	99.24 161	99.80 107	99.14 157	99.50 112	99.82 106	99.12 92	98.41 225	99.91 35	99.98 5	98.51 168	99.48 56	98.76 143	99.38 173	98.14 205

ESAPD		99.21 122	99.14 112	99.29 153	99.79 115	99.44 88	99.02 186	99.79 127	97.96 191	99.12 193	99.22 137	99.95 22	98.50 169	99.21 111	98.84 130	99.56 149	99.34 143

CPTT-MVS		99.21 122	98.89 143	99.58 102	99.72 147	99.12 163	99.30 153	99.76 147	98.62 146	99.66 120	97.51 206	99.89 58	99.48 93	99.01 147	98.64 156	99.58 145	99.40 133

TinyColmap		99.21 122	98.89 143	99.59 100	99.61 176	98.61 199	99.47 121	99.67 171	99.02 101	99.82 61	99.15 141	99.74 94	99.35 102	99.17 124	98.33 176	99.63 127	98.22 203

Effi-MVS+		99.20 125	98.93 138	99.50 120	99.79 115	99.26 137	98.82 207	99.96 15	98.37 171	99.60 130	99.12 145	98.36 174	99.05 143	98.93 159	98.82 134	99.78 83	99.68 52

PVSNet_BlendedMVS		99.20 125	99.17 108	99.23 162	99.69 156	99.33 123	99.04 181	99.13 218	98.41 168	99.79 69	99.33 126	99.36 145	98.10 179	99.29 92	98.87 125	99.65 116	99.56 84

PVSNet_Blended		99.20 125	99.17 108	99.23 162	99.69 156	99.33 123	99.04 181	99.13 218	98.41 168	99.79 69	99.33 126	99.36 145	98.10 179	99.29 92	98.87 125	99.65 116	99.56 84

MCST-MVS		99.17 128	98.82 151	99.57 106	99.75 135	98.70 194	99.25 160	99.69 165	98.62 146	99.59 132	98.54 178	99.79 90	99.53 77	98.48 198	98.15 184	99.64 125	99.43 121

APD-MVS		99.17 128	98.92 139	99.46 125	99.78 120	99.24 145	99.34 144	99.78 133	97.79 196	99.48 156	98.25 189	99.88 63	98.77 162	99.18 122	98.92 117	99.63 127	99.18 159
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	98.82 8	99.17 128	98.85 147	99.53 112	99.75 135	99.06 166	99.36 140	99.82 106	98.28 175	99.76 79	98.47 182	99.61 109	98.91 156	98.80 184	98.70 150	99.60 137	99.04 179

IterMVS-LS		99.16 131	98.82 151	99.57 106	99.87 50	99.71 28	99.58 93	99.92 43	99.24 70	99.71 107	99.73 73	95.79 190	98.91 156	98.82 182	98.66 154	99.43 167	99.77 29
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepPCF-MVS	98.38 11	99.16 131	99.20 98	99.12 179	99.20 223	98.71 193	98.85 203	99.06 220	99.17 82	98.96 200	99.61 91	99.86 69	99.29 115	99.17 124	98.72 147	99.36 175	99.15 167

CDS-MVSNet		99.15 133	99.10 119	99.21 169	99.59 184	99.22 148	99.48 119	99.47 199	98.89 116	99.41 168	99.84 60	98.11 179	97.76 190	99.26 101	99.01 101	99.57 146	99.38 135
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS_MVSNet		99.15 133	99.12 116	99.19 172	99.92 27	99.73 27	99.55 98	99.86 66	98.45 164	96.91 236	98.74 169	98.33 175	99.02 145	99.54 53	99.47 49	99.88 37	99.61 68

test12356		99.12 135	99.03 128	99.23 162	99.78 120	98.95 175	99.10 178	99.72 158	98.26 176	99.81 64	99.87 50	99.20 156	98.06 183	99.47 59	98.80 140	98.91 201	98.67 190

MDA-MVSNet-bldmvs		99.11 136	99.11 118	99.12 179	99.91 31	99.38 104	99.77 37	98.72 223	99.31 64	99.85 41	99.43 115	98.26 177	99.48 93	99.85 19	98.47 166	96.99 218	99.08 171

OMC-MVS		99.11 136	98.95 137	99.29 153	99.37 212	98.57 201	99.19 164	99.20 217	98.87 118	99.58 136	99.13 143	99.88 63	99.00 146	99.19 119	98.46 167	99.43 167	98.57 191

MVS_Test		99.09 138	98.92 139	99.29 153	99.61 176	99.07 165	99.04 181	99.81 118	98.58 152	99.37 173	99.74 71	98.87 165	98.41 172	98.61 193	98.01 193	99.50 157	99.57 83

tfpn_n400		99.08 139	98.56 162	99.70 76	99.85 79	99.56 60	99.63 78	99.86 66	99.21 74	99.37 173	98.95 158	94.24 196	99.55 72	99.20 114	99.29 64	99.93 19	99.44 117

tfpnconf		99.08 139	98.56 162	99.70 76	99.85 79	99.56 60	99.63 78	99.86 66	99.21 74	99.37 173	98.95 158	94.24 196	99.55 72	99.20 114	99.29 64	99.93 19	99.44 117

CNVR-MVS		99.08 139	98.83 148	99.37 142	99.61 176	98.74 190	99.15 172	99.54 188	98.59 151	99.37 173	98.15 194	99.88 63	99.08 139	98.91 164	98.46 167	99.48 159	99.06 174

IterMVS		99.08 139	98.90 142	99.29 153	99.87 50	99.53 66	99.52 105	99.77 139	98.94 111	99.75 86	99.91 35	97.52 185	98.72 164	98.86 174	98.14 185	98.09 211	99.43 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		99.07 143	99.19 101	98.93 194	99.02 228	99.53 66	99.31 148	99.84 90	98.86 119	98.88 204	99.64 87	98.44 172	96.92 208	99.35 79	99.00 106	99.61 134	99.53 95

CVMVSNet		99.06 144	98.88 146	99.28 158	99.52 191	99.53 66	99.42 127	99.69 165	98.74 132	98.27 228	99.89 42	95.48 194	99.44 97	99.46 61	99.33 60	99.32 180	99.75 36

CDPH-MVS		99.05 145	98.63 158	99.54 111	99.75 135	98.78 186	99.59 89	99.68 169	97.79 196	99.37 173	98.20 193	99.86 69	99.14 134	98.58 194	98.01 193	99.68 110	99.16 165

TAMVS		99.05 145	99.02 131	99.08 184	99.69 156	99.22 148	99.33 145	99.32 214	99.16 86	98.97 199	99.87 50	97.36 186	97.76 190	99.21 111	99.00 106	99.44 164	99.33 144

tfpnview11		99.04 147	98.49 171	99.68 81	99.84 87	99.58 53	99.56 96	99.86 66	98.86 119	99.37 173	98.95 158	94.24 196	99.54 76	98.87 170	99.54 42	99.91 25	99.39 134

CANet_DTU		99.03 148	99.18 104	98.87 197	99.58 187	99.03 167	99.18 165	99.41 205	98.65 141	99.74 92	99.55 99	99.71 99	96.13 220	99.19 119	98.92 117	99.17 192	99.18 159

Effi-MVS+-dtu		99.01 149	99.05 125	98.98 188	99.60 180	99.13 160	99.03 185	99.61 177	98.52 158	99.01 196	98.53 179	99.83 80	96.95 207	99.48 56	98.59 161	99.66 114	99.25 156

canonicalmvs		99.00 150	98.68 157	99.37 142	99.68 163	99.42 95	98.94 195	99.89 56	99.00 103	98.99 197	98.43 185	95.69 191	98.96 154	99.18 122	99.18 73	99.74 96	99.88 9

MIMVSNet		99.00 150	99.03 128	98.97 190	99.32 217	99.32 127	99.39 132	99.91 48	98.41 168	98.76 208	99.24 134	99.17 157	97.13 201	99.30 88	98.80 140	99.29 181	99.01 180

CHOSEN 280x420		98.99 152	98.91 141	99.07 185	99.77 126	99.26 137	99.55 98	99.92 43	98.62 146	98.67 213	99.62 90	97.20 187	98.44 171	99.50 54	99.18 73	98.08 212	98.99 183

GBi-Net		98.96 153	99.05 125	98.85 198	99.02 228	99.53 66	99.31 148	99.78 133	98.13 181	98.48 221	99.43 115	97.58 182	96.92 208	99.68 39	99.50 45	99.61 134	99.53 95

test1		98.96 153	99.05 125	98.85 198	99.02 228	99.53 66	99.31 148	99.78 133	98.13 181	98.48 221	99.43 115	97.58 182	96.92 208	99.68 39	99.50 45	99.61 134	99.53 95

PCF-MVS	97.86 15	98.95 155	98.53 165	99.44 129	99.70 154	98.80 185	98.96 191	99.69 165	98.65 141	99.59 132	99.33 126	99.94 32	99.12 137	98.01 210	97.11 206	99.59 143	97.83 209
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		98.94 156	98.71 156	99.21 169	99.52 191	98.22 219	98.97 190	99.53 193	98.76 129	99.50 154	98.59 176	99.56 119	98.68 165	98.63 192	98.45 169	99.05 197	98.73 187

AdaColmap		98.93 157	98.53 165	99.39 136	99.52 191	98.65 197	99.11 177	99.59 183	98.08 185	99.44 162	97.46 209	99.45 136	99.24 120	98.92 161	98.44 170	99.44 164	98.73 187

MSLP-MVS++		98.92 158	98.73 155	99.14 176	99.44 205	99.00 170	98.36 222	99.35 210	98.82 126	99.38 172	96.06 218	99.79 90	99.07 140	98.88 169	99.05 94	99.27 183	99.53 95

new_pmnet		98.91 159	98.89 143	98.94 192	99.51 196	98.27 215	99.15 172	98.66 224	99.17 82	99.48 156	99.79 69	99.80 88	98.49 170	99.23 105	98.20 181	98.34 209	97.74 213

train_agg		98.89 160	98.48 172	99.38 139	99.69 156	98.76 189	99.31 148	99.60 179	97.71 198	98.98 198	97.89 200	99.89 58	99.29 115	98.32 200	97.59 203	99.42 170	99.16 165

NCCC		98.88 161	98.42 173	99.42 131	99.62 171	98.81 184	99.10 178	99.54 188	98.76 129	99.53 141	95.97 219	99.80 88	99.16 128	98.49 196	98.06 191	99.55 153	99.05 176

PLC	97.83 16	98.88 161	98.52 167	99.30 152	99.45 203	98.60 200	98.65 214	99.49 197	98.66 140	99.59 132	96.33 217	99.59 115	99.17 124	98.87 170	98.53 163	99.46 160	99.05 176
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs3		98.85 163	98.60 159	99.13 177	99.66 165	98.72 192	99.37 138	99.06 220	98.44 165	99.76 79	99.74 71	99.55 120	99.15 132	99.04 145	96.00 214	97.80 213	98.72 189

diffmvs		98.83 164	98.51 170	99.19 172	99.62 171	98.98 173	99.18 165	99.82 106	99.15 89	99.51 151	99.66 83	95.37 195	98.07 182	98.49 196	98.22 180	98.96 199	99.73 42

Fast-Effi-MVS+-dtu		98.82 165	98.80 153	98.84 200	99.51 196	98.90 177	98.96 191	99.91 48	98.29 174	99.11 194	98.47 182	99.63 108	96.03 221	99.21 111	98.12 186	99.52 155	99.01 180

CNLPA		98.82 165	98.52 167	99.18 174	99.21 222	98.50 205	98.73 212	99.34 212	98.73 134	99.56 138	97.55 205	99.42 140	99.06 142	98.93 159	98.10 188	99.21 189	98.38 197

PatchMatch-RL		98.80 167	98.52 167	99.12 179	99.38 211	98.70 194	98.56 217	99.55 187	97.81 195	99.34 182	97.57 204	99.31 151	98.67 166	99.27 99	98.62 158	99.22 188	98.35 199

DI_MVS_plusplus_trai		98.74 168	98.08 185	99.51 118	99.79 115	99.29 133	99.61 84	99.60 179	99.20 76	99.46 160	99.09 149	92.93 203	98.97 153	98.27 204	98.35 174	99.65 116	99.45 114

TSAR-MVS + COLMAP		98.74 168	98.58 161	98.93 194	99.29 219	98.23 216	99.04 181	99.24 216	98.79 128	98.80 207	99.37 124	99.71 99	98.06 183	98.02 209	97.46 205	99.16 193	98.48 195

testus		98.74 168	98.33 175	99.23 162	99.71 150	99.03 167	98.17 228	99.60 179	97.18 210	99.52 147	98.07 196	98.45 171	99.21 122	98.30 201	98.06 191	99.14 195	99.21 157

tfpn1000		98.73 171	98.07 186	99.50 120	99.84 87	99.61 46	99.48 119	99.84 90	98.71 136	98.74 209	98.71 172	91.70 210	99.17 124	98.81 183	99.55 40	99.90 28	99.43 121

MDTV_nov1_ep13_2view		98.73 171	98.31 176	99.22 167	99.75 135	99.24 145	99.75 46	99.93 36	99.31 64	99.84 45	99.86 56	99.81 83	99.31 112	97.40 217	94.77 215	96.73 220	97.81 210

PMMVS		98.71 173	98.55 164	98.90 196	99.28 220	98.45 207	98.53 220	99.45 201	97.67 200	99.15 192	98.76 168	99.54 123	97.79 189	98.77 188	98.23 178	99.16 193	98.46 196

HQP-MVS		98.70 174	98.19 181	99.28 158	99.61 176	98.52 203	98.71 213	99.35 210	97.97 190	99.53 141	97.38 210	99.85 75	99.14 134	97.53 214	96.85 211	99.36 175	99.26 155

tfpn_ndepth		98.67 175	98.03 187	99.42 131	99.65 168	99.50 80	99.29 155	99.78 133	98.17 180	99.04 195	98.36 187	93.29 201	98.88 159	98.46 199	99.26 67	99.88 37	99.14 168

N_pmnet		98.64 176	98.23 180	99.11 182	99.78 120	99.25 140	99.75 46	99.39 209	99.65 16	99.70 109	99.78 70	99.89 58	98.81 161	97.60 213	94.28 216	97.24 217	97.15 217

CMPMVS	76.62 19	98.64 176	98.60 159	98.68 205	99.33 215	97.07 231	98.11 232	98.50 226	97.69 199	99.26 184	98.35 188	99.66 105	97.62 193	99.43 68	99.02 99	99.24 186	99.01 180
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		98.63 178	98.75 154	98.49 209	98.10 235	99.44 88	99.02 186	99.78 133	98.13 181	98.48 221	99.43 115	97.58 182	96.16 219	98.85 176	98.39 172	99.40 171	99.41 126

GA-MVS		98.59 179	98.15 182	99.09 183	99.59 184	99.13 160	98.84 204	99.52 194	98.61 149	99.35 179	99.67 82	93.03 202	97.73 192	98.90 168	98.26 177	99.51 156	99.48 109

MAR-MVS		98.54 180	98.15 182	98.98 188	99.37 212	98.09 222	98.56 217	99.65 176	96.11 229	99.27 183	97.16 215	99.50 129	98.03 187	98.87 170	98.23 178	99.01 198	99.13 169
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		98.49 181	97.60 189	99.53 112	99.90 34	99.55 62	99.77 37	99.48 198	99.67 13	99.86 34	99.98 3	99.98 5	99.50 85	96.90 220	91.52 221	98.67 206	95.62 222

FPMVS		98.48 182	98.83 148	98.07 221	99.09 226	97.98 225	99.07 180	98.04 232	98.99 104	99.22 187	98.85 164	99.43 139	93.79 229	99.66 44	99.11 86	99.24 186	97.76 211

MVS-HIRNet		98.45 183	98.25 178	98.69 204	99.12 224	97.81 229	98.55 219	99.85 78	98.58 152	99.67 118	99.61 91	99.86 69	97.46 196	97.95 211	96.37 213	97.49 215	97.56 214

test0.0.03 1		98.41 184	98.41 174	98.40 213	99.62 171	99.16 154	98.87 201	99.41 205	97.15 211	96.60 238	99.31 130	97.00 188	96.55 213	98.91 164	98.51 165	99.37 174	98.82 186

gg-mvs-nofinetune		98.40 185	98.26 177	98.57 207	99.83 98	98.86 182	98.77 210	99.97 1	99.57 34	99.99 1	99.99 1	93.81 199	93.50 230	98.91 164	98.20 181	99.33 179	98.52 194

conf0.05thres1000		98.36 186	97.28 195	99.63 92	99.92 27	99.74 25	99.66 73	99.88 60	98.68 138	98.92 202	97.30 213	86.02 228	99.49 89	99.77 31	99.73 19	99.93 19	99.69 51

tfpn111		98.25 187	97.29 194	99.37 142	99.74 142	99.52 72	99.17 167	99.76 147	96.10 230	98.65 215	98.23 190	89.10 216	99.00 146	99.11 133	99.56 34	99.88 37	99.41 126

PatchT		98.11 188	97.12 196	99.26 160	99.65 168	98.34 212	99.57 95	99.97 1	97.48 205	99.43 164	99.04 154	90.84 212	98.15 176	98.04 207	97.78 196	98.82 203	98.30 200

thresconf0.02		98.10 189	96.83 199	99.58 102	99.71 150	99.28 134	99.40 131	99.72 158	98.65 141	99.39 170	98.23 190	86.73 226	99.43 99	97.73 212	98.17 183	99.86 53	99.05 176

EPNet_dtu		98.09 190	98.25 178	97.91 223	99.58 187	98.02 224	98.19 227	99.67 171	97.94 192	99.74 92	99.07 152	98.71 168	93.40 231	97.50 215	97.09 207	96.89 219	99.44 117
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet		98.06 191	98.11 184	98.00 222	99.60 180	98.99 172	98.38 221	99.68 169	98.18 179	98.85 206	97.89 200	95.60 193	92.72 232	98.30 201	98.10 188	98.76 204	99.72 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		97.91 192	96.80 200	99.22 167	99.60 180	98.23 216	98.91 197	99.97 1	96.89 220	99.43 164	99.10 148	89.24 215	98.15 176	98.04 207	97.78 196	99.26 184	98.30 200

view800		97.89 193	96.56 202	99.45 127	99.86 66	99.57 55	99.42 127	99.80 124	97.50 204	98.40 226	93.78 225	86.63 227	99.31 112	99.24 102	99.68 24	99.89 33	99.54 90

view600		97.88 194	96.55 204	99.44 129	99.84 87	99.52 72	99.38 136	99.76 147	97.36 207	98.50 220	93.29 226	87.31 223	99.26 118	99.13 131	99.76 15	99.88 37	99.48 109

thres200		97.87 195	96.56 202	99.39 136	99.76 131	99.52 72	99.13 175	99.76 147	96.88 222	98.66 214	92.87 231	88.77 220	99.16 128	99.11 133	99.42 55	99.88 37	99.33 144

thres600view7		97.86 196	96.53 208	99.41 134	99.84 87	99.52 72	99.36 140	99.76 147	97.32 208	98.38 227	93.24 227	87.25 224	99.23 121	99.11 133	99.75 17	99.88 37	99.48 109

conf200view11		97.85 197	96.54 205	99.37 142	99.74 142	99.52 72	99.17 167	99.76 147	96.10 230	98.65 215	92.99 228	89.10 216	99.00 146	99.11 133	99.56 34	99.88 37	99.41 126

tfpn200view9		97.85 197	96.54 205	99.38 139	99.74 142	99.52 72	99.17 167	99.76 147	96.10 230	98.70 211	92.99 228	89.10 216	99.00 146	99.11 133	99.56 34	99.88 37	99.41 126

thres400		97.82 199	96.47 209	99.40 135	99.81 106	99.44 88	99.29 155	99.69 165	97.15 211	98.57 217	92.82 232	87.96 221	99.16 128	98.96 157	99.55 40	99.86 53	99.41 126

IB-MVS	98.10 14	97.76 200	97.40 193	98.18 216	99.62 171	99.11 164	98.24 225	98.35 228	96.56 225	99.44 162	91.28 235	98.96 163	93.84 228	98.09 206	98.62 158	99.56 149	99.18 159
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		97.74 201	97.46 191	98.08 219	99.62 171	98.37 210	98.26 223	99.41 205	97.03 215	97.38 234	99.54 100	92.89 204	95.12 225	98.78 186	97.68 201	98.65 207	97.90 207

RPMNet		97.70 202	96.54 205	99.06 186	99.57 190	98.23 216	98.95 194	99.97 1	96.89 220	99.49 155	99.13 143	89.63 214	97.09 203	96.68 221	97.02 208	99.26 184	98.19 204

thres100view900		97.69 203	96.37 210	99.23 162	99.74 142	99.21 151	98.81 208	99.43 204	96.10 230	98.70 211	92.99 228	89.10 216	98.88 159	98.58 194	99.31 63	99.82 74	99.27 152

FMVSNet5		97.69 203	96.98 197	98.53 208	98.53 233	99.36 113	98.90 199	99.54 188	96.38 226	98.44 224	95.38 221	90.08 213	97.05 206	99.46 61	99.06 91	98.73 205	99.12 170

MVE	91.08 18	97.68 205	97.65 188	97.71 229	98.46 234	91.62 238	97.92 234	98.86 222	98.73 134	97.99 232	98.64 175	99.96 14	99.17 124	99.59 50	97.75 198	93.87 235	97.27 215
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-mter		97.65 206	97.57 190	97.75 227	98.90 231	98.56 202	98.15 229	98.45 227	96.92 219	96.84 237	99.52 107	92.53 209	95.24 224	99.04 145	98.12 186	98.90 202	98.29 202

TESTMET0.1,1		97.62 207	97.46 191	97.81 225	99.07 227	98.37 210	98.26 223	98.35 228	97.03 215	97.38 234	99.54 100	92.89 204	95.12 225	98.78 186	97.68 201	98.65 207	97.90 207

MVSTER		97.55 208	96.75 201	98.48 210	99.46 202	99.54 64	98.24 225	99.77 139	97.56 202	99.41 168	99.31 130	84.86 229	94.66 227	98.86 174	97.75 198	99.34 178	99.38 135

LP		97.43 209	96.28 211	98.77 201	99.69 156	98.92 176	99.49 117	99.70 163	98.53 156	99.82 61	99.12 145	95.67 192	97.30 199	94.65 224	91.76 219	96.65 222	95.34 224

MDTV_nov1_ep13		97.41 210	96.26 212	98.76 202	99.47 201	98.43 208	99.26 159	99.82 106	98.06 187	99.23 185	99.22 137	92.86 206	98.05 185	95.33 223	93.66 218	96.73 220	96.26 219

ADS-MVSNet		97.29 211	96.17 213	98.59 206	99.59 184	98.70 194	99.32 146	99.86 66	98.47 159	99.56 138	99.08 150	98.16 178	97.34 198	92.92 225	91.17 222	95.91 224	94.72 226

1111		96.83 212	95.02 218	98.95 191	99.90 34	99.57 55	99.62 82	99.97 1	98.58 152	98.06 230	99.87 50	69.04 239	96.43 216	99.36 77	99.14 80	99.73 100	99.54 90

gm-plane-assit		96.82 213	94.84 219	99.13 177	99.95 12	99.78 17	99.69 70	99.92 43	99.19 79	99.84 45	99.92 26	72.93 236	96.44 215	98.21 205	97.01 209	98.92 200	96.87 218

PatchmatchNet		96.81 214	95.41 215	98.43 212	99.43 207	98.30 213	99.23 161	99.93 36	98.19 178	99.64 122	98.81 167	93.50 200	97.43 197	92.89 227	90.78 224	94.94 230	95.41 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tfpn		96.77 215	94.47 221	99.45 127	99.88 43	99.62 43	99.46 123	99.83 98	97.61 201	98.27 228	94.22 224	71.45 238	99.34 106	99.32 85	99.46 51	99.90 28	99.58 79

EPMVS		96.76 216	95.30 217	98.46 211	99.42 208	98.47 206	99.32 146	99.91 48	98.42 167	99.51 151	99.07 152	92.81 207	97.12 202	92.39 228	91.71 220	95.51 226	94.20 228

E-PMN		96.72 217	95.78 214	97.81 225	99.45 203	95.46 234	98.14 231	98.33 230	97.99 189	98.73 210	98.09 195	98.97 162	97.54 195	97.45 216	91.09 223	94.70 232	91.40 232

conf0.01		96.70 218	94.44 223	99.34 149	99.71 150	99.46 85	99.17 167	99.73 156	96.10 230	98.53 218	91.96 233	75.75 234	99.00 146	98.85 176	99.56 34	99.87 47	99.38 135

tpm		96.56 219	94.68 220	98.74 203	99.12 224	97.90 226	98.79 209	99.93 36	96.79 223	99.69 111	99.19 139	81.48 231	97.56 194	95.46 222	93.97 217	97.37 216	97.99 206

EMVS		96.47 220	95.38 216	97.74 228	99.42 208	95.37 235	98.07 233	98.27 231	97.85 194	98.90 203	97.48 207	98.73 167	97.20 200	97.21 218	90.39 225	94.59 234	90.65 233

conf0.002		96.39 221	93.87 225	99.33 151	99.70 154	99.45 86	99.17 167	99.71 161	96.10 230	98.51 219	91.88 234	72.65 237	99.00 146	98.80 184	98.82 134	99.87 47	99.38 135

test2356		96.34 222	94.05 224	99.00 187	99.39 210	98.28 214	98.15 229	99.51 195	96.23 227	99.16 189	97.95 199	73.39 235	98.75 163	97.07 219	96.86 210	99.06 196	98.57 191

tpmrst		96.18 223	94.47 221	98.18 216	99.52 191	97.89 227	98.96 191	99.79 127	98.07 186	99.16 189	99.30 133	92.69 208	96.69 211	90.76 230	88.85 229	94.96 229	93.69 230

CostFormer		95.61 224	93.35 228	98.24 215	99.48 200	98.03 223	98.65 214	99.83 98	96.93 218	99.42 167	98.83 165	83.65 230	97.08 204	90.39 231	89.54 228	94.94 230	96.11 221

dps		95.59 225	93.46 227	98.08 219	99.33 215	98.22 219	98.87 201	99.70 163	96.17 228	98.87 205	97.75 202	86.85 225	96.60 212	91.24 229	89.62 227	95.10 228	94.34 227

tpm cat1		95.52 226	93.49 226	97.88 224	99.28 220	97.87 228	98.65 214	99.77 139	97.27 209	99.46 160	98.04 197	90.99 211	95.46 223	88.57 234	88.14 232	94.64 233	93.54 231

tpmp4_e23		95.42 227	92.99 229	98.27 214	99.32 217	97.77 230	98.74 211	99.79 127	97.11 213	99.61 126	97.47 208	80.64 232	96.36 218	92.92 225	88.79 230	95.80 225	96.19 220

DWT-MVSNet_training		94.92 228	92.14 230	98.15 218	99.37 212	98.43 208	98.99 189	98.51 225	96.76 224	99.52 147	97.35 211	77.20 233	97.08 204	89.76 232	90.38 226	95.43 227	95.13 225

testpf		93.65 229	91.79 231	95.82 230	98.71 232	93.25 236	96.38 236	99.67 171	95.38 236	97.83 233	94.48 223	87.69 222	89.61 234	88.96 233	88.79 230	92.71 236	93.97 229

.test1245		79.44 230	75.07 232	84.53 232	99.90 34	99.57 55	99.62 82	99.97 1	98.58 152	98.06 230	99.87 50	69.04 239	96.43 216	99.36 77	24.74 233	13.21 237	34.30 234

GG-mvs-BLEND		70.44 231	96.91 198	39.57 233	3.32 239	96.51 232	91.01 238	4.05 237	97.03 215	33.20 240	94.67 222	97.75 181	7.59 237	98.28 203	96.85 211	98.24 210	97.26 216

testmvs		22.33 232	29.66 233	13.79 234	8.97 237	10.35 239	15.53 241	8.09 236	32.51 237	19.87 241	45.18 236	30.56 242	17.05 236	29.96 235	24.74 233	13.21 237	34.30 234

test123		21.52 233	28.47 234	13.42 235	7.29 238	10.12 240	15.70 240	8.31 235	31.54 238	19.34 242	36.33 237	37.40 241	17.14 235	27.45 236	23.17 235	12.73 239	33.30 236

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			98.83 148		99.72 147	98.52 203	98.84 204		98.96 108	99.92 9	99.34 125	99.74 94	99.04 144	98.68 191	97.57 204	99.46 160	98.99 183

MTAPA										99.62 125		99.95 22

MTMP										99.53 141		99.92 47

Patchmatch-RL test							65.75 239

tmp_tt				88.14 231	96.68 236	91.91 237	93.70 237	61.38 234	99.61 24	90.51 239	99.40 121	99.71 99	90.32 233	99.22 108	99.44 54	96.25 223

XVS					99.86 66	99.30 129	99.72 62			99.69 111		99.93 38				99.60 137

X-MVStestdata					99.86 66	99.30 129	99.72 62			99.69 111		99.93 38				99.60 137

abl_6				99.21 169	99.49 199	98.62 198	98.90 199	99.44 203	97.08 214	99.61 126	97.19 214	99.73 97	98.35 173			99.45 162	98.84 185

mPP-MVS					99.84 87							99.92 47

NP-MVS									97.37 206

Patchmtry						98.19 221	98.91 197	99.97 1		99.43 164

DeepMVS_CX						96.39 233	97.15 235	88.89 233	97.94 192	99.51 151	95.71 220	97.88 180	98.19 174	98.92 161		97.73 214	97.75 212

High-resolution multi-view benchmark