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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 2	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 3	100.00 1	99.85 9

Anonymous20231211		99.71 2	99.70 3	99.74 2	99.97 2	99.52 2	99.74 4	99.82 4	99.73 6	99.91 4	99.89 2	99.27 9	99.94 20	99.02 49	99.94 33	99.75 21

pmmvs6		99.67 3	99.70 3	99.60 12	99.90 5	99.27 16	99.53 9	99.76 7	99.64 10	99.84 9	99.83 3	99.50 5	99.87 72	99.36 28	99.92 49	99.64 40

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 18	99.85 18	99.11 41	99.90 1	99.78 5	99.63 12	99.78 10	99.67 21	99.48 6	99.81 142	99.30 31	99.97 23	99.77 16
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
mvs_tets		99.63 5	99.67 5	99.49 43	99.88 8	98.61 71	99.34 15	99.71 12	99.27 45	99.90 5	99.74 8	99.68 3	99.97 3	99.55 20	99.99 11	99.88 5

v52		99.59 6	99.60 8	99.55 20	99.87 12	99.00 47	99.59 7	99.56 49	99.56 22	99.68 20	99.72 11	98.57 34	99.93 26	99.85 1	99.99 11	99.72 24

V4		99.59 6	99.60 8	99.55 20	99.87 12	99.00 47	99.59 7	99.56 49	99.56 22	99.68 20	99.72 11	98.57 34	99.93 26	99.85 1	99.99 11	99.72 24

jajsoiax		99.58 8	99.61 7	99.48 44	99.87 12	98.61 71	99.28 29	99.66 19	99.09 68	99.89 8	99.68 19	99.53 4	99.97 3	99.50 22	99.99 11	99.87 6

ANet_high		99.57 9	99.67 5	99.28 70	99.89 7	98.09 104	99.14 44	99.93 1	99.82 2	99.93 2	99.81 4	99.17 14	99.94 20	99.31 30	100.00 1	99.82 10

v7n		99.53 10	99.57 10	99.41 52	99.88 8	98.54 79	99.45 10	99.61 30	99.66 9	99.68 20	99.66 22	98.44 42	99.95 13	99.73 8	99.96 28	99.75 21

test_djsdf		99.52 11	99.51 11	99.53 32	99.86 16	98.74 60	99.39 13	99.56 49	99.11 61	99.70 15	99.73 10	99.00 17	99.97 3	99.26 32	99.98 19	99.89 3

anonymousdsp		99.51 12	99.47 15	99.62 6	99.88 8	99.08 46	99.34 15	99.69 15	98.93 83	99.65 23	99.72 11	98.93 20	99.95 13	99.11 44	100.00 1	99.82 10

UA-Net		99.47 13	99.40 17	99.70 3	99.49 92	99.29 13	99.80 3	99.72 11	99.82 2	99.04 116	99.81 4	98.05 64	99.96 8	98.85 56	99.99 11	99.86 8

PS-MVSNAJss		99.46 14	99.49 12	99.35 61	99.90 5	98.15 100	99.20 35	99.65 20	99.48 25	99.92 3	99.71 14	98.07 61	99.96 8	99.53 21	100.00 1	99.93 1

v748		99.44 15	99.48 13	99.33 66	99.88 8	98.43 86	99.42 11	99.53 59	99.63 12	99.69 17	99.60 34	97.99 69	99.91 43	99.60 14	99.96 28	99.66 33

pm-mvs1		99.44 15	99.48 13	99.33 66	99.80 22	98.63 68	99.29 25	99.63 25	99.30 42	99.65 23	99.60 34	99.16 16	99.82 129	99.07 46	99.83 79	99.56 75

TransMVSNet (Re)		99.44 15	99.47 15	99.36 56	99.80 22	98.58 74	99.27 31	99.57 43	99.39 33	99.75 12	99.62 28	99.17 14	99.83 117	99.06 47	99.62 155	99.66 33

DTE-MVSNet		99.43 18	99.35 22	99.66 4	99.71 34	99.30 12	99.31 20	99.51 64	99.64 10	99.56 33	99.46 52	98.23 50	99.97 3	98.78 59	99.93 39	99.72 24

TDRefinement		99.42 19	99.38 19	99.55 20	99.76 26	99.33 11	99.68 5	99.71 12	99.38 35	99.53 37	99.61 30	98.64 29	99.80 154	98.24 85	99.84 73	99.52 96

PEN-MVS		99.41 20	99.34 24	99.62 6	99.73 28	99.14 34	99.29 25	99.54 58	99.62 16	99.56 33	99.42 59	98.16 57	99.96 8	98.78 59	99.93 39	99.77 16

nrg030		99.40 21	99.35 22	99.54 25	99.58 57	99.13 37	98.98 62	99.48 74	99.68 7	99.46 50	99.26 79	98.62 30	99.73 217	99.17 43	99.92 49	99.76 19

PS-CasMVS		99.40 21	99.33 26	99.62 6	99.71 34	99.10 42	99.29 25	99.53 59	99.53 24	99.46 50	99.41 61	98.23 50	99.95 13	98.89 55	99.95 30	99.81 12

MIMVSNet1		99.38 23	99.32 27	99.55 20	99.86 16	99.19 24	99.41 12	99.59 34	99.59 19	99.71 14	99.57 39	97.12 124	99.90 47	99.21 38	99.87 68	99.54 86

OurMVSNet-221017-0		99.37 24	99.31 28	99.53 32	99.91 4	98.98 49	99.63 6	99.58 36	99.44 30	99.78 10	99.76 6	96.39 172	99.92 34	99.44 26	99.92 49	99.68 30

wuykxyi23d		99.36 25	99.31 28	99.50 41	99.81 21	98.67 67	98.08 134	99.75 8	98.03 126	99.90 5	99.60 34	99.18 12	99.94 20	99.46 25	99.98 19	99.89 3

Vis-MVSNet		99.34 26	99.36 21	99.27 73	99.73 28	98.26 93	99.17 41	99.78 5	99.11 61	99.27 82	99.48 50	98.82 22	99.95 13	98.94 52	99.93 39	99.59 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
WR-MVS_H		99.33 27	99.22 36	99.65 5	99.71 34	99.24 19	99.32 17	99.55 54	99.46 28	99.50 44	99.34 70	97.30 109	99.93 26	98.90 53	99.93 39	99.77 16

VPA-MVSNet		99.30 28	99.30 31	99.28 70	99.49 92	98.36 91	99.00 59	99.45 85	99.63 12	99.52 39	99.44 57	98.25 48	99.88 63	99.09 45	99.84 73	99.62 45

FC-MVSNet-test		99.27 29	99.25 34	99.34 64	99.77 25	98.37 90	99.30 24	99.57 43	99.61 18	99.40 60	99.50 46	97.12 124	99.85 88	99.02 49	99.94 33	99.80 13

ACMH	96.65 7	99.25 30	99.24 35	99.26 75	99.72 33	98.38 89	99.07 52	99.55 54	98.30 115	99.65 23	99.45 56	99.22 10	99.76 197	98.44 76	99.77 104	99.64 40
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v13		99.24 31	99.39 18	98.77 140	99.63 52	96.79 184	99.24 33	99.65 20	99.39 33	99.62 27	99.70 16	97.50 95	99.84 103	99.78 5	100.00 1	99.67 31

v12		99.21 32	99.37 20	98.74 148	99.60 55	96.72 189	99.19 39	99.65 20	99.35 39	99.62 27	99.69 17	97.43 102	99.83 117	99.76 6	100.00 1	99.66 33

CP-MVSNet		99.21 32	99.09 45	99.56 18	99.65 47	98.96 53	99.13 46	99.34 121	99.42 31	99.33 72	99.26 79	97.01 132	99.94 20	98.74 63	99.93 39	99.79 14

V9		99.18 34	99.34 24	98.70 149	99.58 57	96.63 192	99.14 44	99.64 24	99.30 42	99.61 29	99.68 19	97.33 107	99.83 117	99.75 7	100.00 1	99.65 37

TranMVSNet+NR-MVSNet		99.17 35	99.07 47	99.46 49	99.37 120	98.87 55	98.39 114	99.42 96	99.42 31	99.36 66	99.06 117	98.38 44	99.95 13	98.34 81	99.90 57	99.57 70

FMVSNet1		99.17 35	99.17 39	99.17 81	99.55 73	98.24 94	99.20 35	99.44 88	99.21 47	99.43 55	99.55 41	97.82 79	99.86 77	98.42 78	99.89 63	99.41 144

FIs		99.14 37	99.09 45	99.29 69	99.70 40	98.28 92	99.13 46	99.52 63	99.48 25	99.24 90	99.41 61	96.79 149	99.82 129	98.69 65	99.88 64	99.76 19

V14		99.14 37	99.30 31	98.66 152	99.56 69	96.53 193	99.08 49	99.63 25	99.24 46	99.60 30	99.66 22	97.23 119	99.82 129	99.73 8	100.00 1	99.65 37

XXY-MVS		99.14 37	99.15 43	99.10 92	99.76 26	97.74 143	98.85 72	99.62 28	98.48 106	99.37 64	99.49 49	98.75 25	99.86 77	98.20 88	99.80 92	99.71 27

v11		99.12 40	99.31 28	98.53 177	99.59 56	96.11 212	99.08 49	99.65 20	99.15 56	99.60 30	99.69 17	97.26 115	99.83 117	99.81 3	100.00 1	99.66 33

v15		99.11 41	99.27 33	98.62 158	99.52 81	96.43 197	99.01 55	99.63 25	99.18 55	99.59 32	99.64 26	97.13 123	99.81 142	99.71 10	100.00 1	99.64 40

ACMH+	96.62 9	99.08 42	99.00 49	99.33 66	99.71 34	98.83 56	98.60 83	99.58 36	99.11 61	99.53 37	99.18 92	98.81 23	99.67 244	96.71 162	99.77 104	99.50 103

v17		99.07 43	99.22 36	98.61 161	99.50 86	96.42 198	99.01 55	99.60 32	99.15 56	99.48 46	99.61 30	97.05 127	99.81 142	99.64 12	99.98 19	99.61 49

v16		99.07 43	99.22 36	98.61 161	99.50 86	96.42 198	99.01 55	99.60 32	99.15 56	99.46 50	99.61 30	97.04 128	99.81 142	99.64 12	99.97 23	99.61 49

Gipuma		99.03 45	99.16 41	98.64 154	99.94 3	98.51 81	99.32 17	99.75 8	99.58 21	98.60 172	99.62 28	98.22 52	99.51 298	97.70 112	99.73 118	97.89 290
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v18		99.02 46	99.17 39	98.57 168	99.45 106	96.31 204	98.94 64	99.58 36	99.06 70	99.43 55	99.58 38	96.91 137	99.80 154	99.60 14	99.97 23	99.59 58

v8		99.01 47	99.16 41	98.57 168	99.47 99	96.31 204	98.90 67	99.47 80	99.03 72	99.52 39	99.57 39	96.93 136	99.81 142	99.60 14	99.98 19	99.60 52

HPM-MVS_fast		99.01 47	98.82 56	99.57 16	99.71 34	99.35 9	99.00 59	99.50 65	97.33 183	98.94 133	98.86 159	98.75 25	99.82 129	97.53 119	99.71 127	99.56 75

APDe-MVS		98.99 49	98.79 59	99.60 12	99.21 150	99.15 33	98.87 69	99.48 74	97.57 160	99.35 68	99.24 82	97.83 76	99.89 56	97.88 102	99.70 130	99.75 21

abl_6		98.99 49	98.78 60	99.61 9	99.45 106	99.46 4	98.60 83	99.50 65	98.59 99	99.24 90	99.04 124	98.54 37	99.89 56	96.45 182	99.62 155	99.50 103

EG-PatchMatch MVS		98.99 49	99.01 48	98.94 117	99.50 86	97.47 156	98.04 140	99.59 34	98.15 125	99.40 60	99.36 67	98.58 33	99.76 197	98.78 59	99.68 142	99.59 58

COLMAP_ROB	96.50 10	98.99 49	98.85 54	99.41 52	99.58 57	99.10 42	98.74 75	99.56 49	99.09 68	99.33 72	99.19 90	98.40 43	99.72 226	95.98 202	99.76 113	99.42 142
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Baseline_NR-MVSNet		98.98 53	98.86 53	99.36 56	99.82 20	98.55 76	97.47 206	99.57 43	99.37 36	99.21 94	99.61 30	96.76 152	99.83 117	98.06 93	99.83 79	99.71 27

v10		98.97 54	99.11 44	98.55 173	99.44 109	96.21 210	98.90 67	99.55 54	98.73 93	99.48 46	99.60 34	96.63 158	99.83 117	99.70 11	99.99 11	99.61 49

DeepC-MVS	97.60 4	98.97 54	98.93 51	99.10 92	99.35 125	97.98 118	98.01 149	99.46 82	97.56 162	99.54 35	99.50 46	98.97 18	99.84 103	98.06 93	99.92 49	99.49 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NR-MVSNet		98.95 56	98.82 56	99.36 56	99.16 167	98.72 65	99.22 34	99.20 163	99.10 65	99.72 13	98.76 175	96.38 173	99.86 77	98.00 98	99.82 82	99.50 103

testing_2		98.93 57	98.99 50	98.76 142	99.57 62	97.03 176	97.85 165	99.13 186	98.46 107	99.44 54	99.44 57	98.22 52	99.74 212	98.85 56	99.94 33	99.51 98

DP-MVS		98.93 57	98.81 58	99.28 70	99.21 150	98.45 85	98.46 109	99.33 126	99.63 12	99.48 46	99.15 102	97.23 119	99.75 203	97.17 133	99.66 151	99.63 44

ACMM	96.08 12	98.91 59	98.73 67	99.48 44	99.55 73	99.14 34	98.07 136	99.37 107	97.62 154	99.04 116	98.96 141	98.84 21	99.79 174	97.43 125	99.65 152	99.49 110
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnnormal		98.90 60	98.90 52	98.91 121	99.67 44	97.82 135	99.00 59	99.44 88	99.45 29	99.51 43	99.24 82	98.20 55	99.86 77	95.92 204	99.69 137	99.04 222

MTAPA		98.88 61	98.64 84	99.61 9	99.67 44	99.36 7	98.43 111	99.20 163	98.83 87	98.89 138	98.90 150	96.98 134	99.92 34	97.16 134	99.70 130	99.56 75

VPNet		98.87 62	98.83 55	99.01 109	99.70 40	97.62 151	98.43 111	99.35 117	99.47 27	99.28 80	99.05 122	96.72 154	99.82 129	98.09 91	99.36 204	99.59 58

UniMVSNet (Re)		98.87 62	98.71 71	99.35 61	99.24 138	98.73 63	97.73 176	99.38 103	98.93 83	99.12 102	98.73 177	96.77 150	99.86 77	98.63 67	99.80 92	99.46 128

UniMVSNet_NR-MVSNet		98.86 64	98.68 79	99.40 54	99.17 165	98.74 60	97.68 180	99.40 98	99.14 59	99.06 108	98.59 203	96.71 155	99.93 26	98.57 70	99.77 104	99.53 91

APD-MVS_3200maxsize		98.84 65	98.61 89	99.53 32	99.19 160	99.27 16	98.49 97	99.33 126	98.64 95	99.03 118	98.98 136	97.89 74	99.85 88	96.54 176	99.42 199	99.46 128

PM-MVS		98.82 66	98.72 70	99.12 89	99.64 50	98.54 79	97.98 152	99.68 16	97.62 154	99.34 71	99.18 92	97.54 93	99.77 192	97.79 105	99.74 115	99.04 222

DU-MVS		98.82 66	98.63 85	99.39 55	99.16 167	98.74 60	97.54 200	99.25 152	98.84 86	99.06 108	98.76 175	96.76 152	99.93 26	98.57 70	99.77 104	99.50 103

3Dnovator	98.27 2	98.81 68	98.73 67	99.05 102	98.76 242	97.81 137	99.25 32	99.30 138	98.57 103	98.55 178	99.33 72	97.95 73	99.90 47	97.16 134	99.67 147	99.44 134

MPTG		98.79 69	98.52 96	99.61 9	99.67 44	99.36 7	97.33 211	99.20 163	98.83 87	98.89 138	98.90 150	96.98 134	99.92 34	97.16 134	99.70 130	99.56 75

HPM-MVS		98.79 69	98.53 95	99.59 15	99.65 47	99.29 13	99.16 42	99.43 93	96.74 214	98.61 170	98.38 223	98.62 30	99.87 72	96.47 180	99.67 147	99.59 58

SteuartSystems-ACMMP		98.79 69	98.54 94	99.54 25	99.73 28	99.16 28	98.23 120	99.31 131	97.92 130	98.90 136	98.90 150	98.00 67	99.88 63	96.15 196	99.72 123	99.58 65
Skip Steuart: Steuart Systems R&D Blog.
V42		98.78 72	98.78 60	98.76 142	99.44 109	97.04 175	98.27 118	99.19 169	97.87 142	99.25 89	99.16 98	96.84 144	99.78 183	99.21 38	99.84 73	99.46 128

test20.03		98.78 72	98.77 62	98.78 138	99.46 103	97.20 168	97.78 169	99.24 156	99.04 71	99.41 58	98.90 150	97.65 84	99.76 197	97.70 112	99.79 96	99.39 150

test_0402		98.76 74	98.71 71	98.93 118	99.56 69	98.14 102	98.45 110	99.34 121	99.28 44	98.95 129	98.91 147	98.34 46	99.79 174	95.63 219	99.91 54	98.86 244

ACMMP_Plus		98.75 75	98.48 103	99.57 16	99.58 57	99.29 13	97.82 168	99.25 152	96.94 206	98.78 153	99.12 106	98.02 65	99.84 103	97.13 138	99.67 147	99.59 58

SixPastTwentyTwo		98.75 75	98.62 86	99.16 84	99.83 19	97.96 121	99.28 29	98.20 272	99.37 36	99.70 15	99.65 25	92.65 263	99.93 26	99.04 48	99.84 73	99.60 52

ACMMP		98.75 75	98.50 99	99.52 37	99.56 69	99.16 28	98.87 69	99.37 107	97.16 200	98.82 150	99.01 130	97.71 82	99.87 72	96.29 188	99.69 137	99.54 86
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
Regformer-4		98.73 78	98.68 79	98.89 124	99.02 196	97.22 167	97.17 226	99.06 195	99.21 47	99.17 99	98.85 161	97.45 100	99.86 77	98.48 75	99.70 130	99.60 52

XVS		98.72 79	98.45 110	99.53 32	99.46 103	99.21 21	98.65 78	99.34 121	98.62 97	97.54 246	98.63 197	97.50 95	99.83 117	96.79 154	99.53 186	99.56 75

HFP-MVS		98.71 80	98.44 112	99.51 39	99.49 92	99.16 28	98.52 91	99.31 131	97.47 169	98.58 175	98.50 216	97.97 71	99.85 88	96.57 171	99.59 162	99.53 91

LPG-MVS_test		98.71 80	98.46 108	99.47 47	99.57 62	98.97 50	98.23 120	99.48 74	96.60 223	99.10 105	99.06 117	98.71 27	99.83 117	95.58 222	99.78 100	99.62 45

v1neww		98.70 82	98.76 63	98.52 178	99.47 99	96.30 206	98.03 141	99.18 173	97.92 130	99.26 87	99.08 111	96.91 137	99.78 183	99.19 40	99.82 82	99.47 124

v7new		98.70 82	98.76 63	98.52 178	99.47 99	96.30 206	98.03 141	99.18 173	97.92 130	99.26 87	99.08 111	96.91 137	99.78 183	99.19 40	99.82 82	99.47 124

v6		98.70 82	98.76 63	98.52 178	99.47 99	96.30 206	98.03 141	99.18 173	97.92 130	99.27 82	99.08 111	96.91 137	99.78 183	99.19 40	99.82 82	99.48 116

ACMMPR		98.70 82	98.42 115	99.54 25	99.52 81	99.14 34	98.52 91	99.31 131	97.47 169	98.56 177	98.54 211	97.75 81	99.88 63	96.57 171	99.59 162	99.58 65

CP-MVS		98.70 82	98.42 115	99.52 37	99.36 121	99.12 39	98.72 77	99.36 111	97.54 164	98.30 192	98.40 222	97.86 75	99.89 56	96.53 177	99.72 123	99.56 75

region2R		98.69 87	98.40 117	99.54 25	99.53 79	99.17 26	98.52 91	99.31 131	97.46 174	98.44 184	98.51 213	97.83 76	99.88 63	96.46 181	99.58 168	99.58 65

EI-MVSNet-UG-set		98.69 87	98.71 71	98.62 158	99.10 174	96.37 202	97.23 218	98.87 228	99.20 50	99.19 95	98.99 133	97.30 109	99.85 88	98.77 62	99.79 96	99.65 37

3Dnovator+	97.89 3	98.69 87	98.51 97	99.24 77	98.81 238	98.40 87	99.02 54	99.19 169	98.99 75	98.07 201	99.28 75	97.11 126	99.84 103	96.84 152	99.32 211	99.47 124

EI-MVSNet-Vis-set		98.68 90	98.70 74	98.63 156	99.09 177	96.40 200	97.23 218	98.86 232	99.20 50	99.18 98	98.97 138	97.29 111	99.85 88	98.72 64	99.78 100	99.64 40

CSCG		98.68 90	98.50 99	99.20 80	99.45 106	98.63 68	98.56 87	99.57 43	97.87 142	98.85 144	98.04 251	97.66 83	99.84 103	96.72 159	99.81 88	99.13 215

v7		98.67 92	98.73 67	98.50 183	99.43 113	96.21 210	98.00 150	99.31 131	97.58 158	99.17 99	99.18 92	96.63 158	99.80 154	99.42 27	99.88 64	99.48 116

PGM-MVS		98.66 93	98.37 122	99.55 20	99.53 79	99.18 25	98.23 120	99.49 71	97.01 204	98.69 160	98.88 156	98.00 67	99.89 56	95.87 208	99.59 162	99.58 65

GBi-Net		98.65 94	98.47 105	99.17 81	98.90 218	98.24 94	99.20 35	99.44 88	98.59 99	98.95 129	99.55 41	94.14 240	99.86 77	97.77 107	99.69 137	99.41 144

test1		98.65 94	98.47 105	99.17 81	98.90 218	98.24 94	99.20 35	99.44 88	98.59 99	98.95 129	99.55 41	94.14 240	99.86 77	97.77 107	99.69 137	99.41 144

LCM-MVSNet-Re		98.64 96	98.48 103	99.11 90	98.85 228	98.51 81	98.49 97	99.83 3	98.37 108	99.69 17	99.46 52	98.21 54	99.92 34	94.13 253	99.30 214	98.91 239

mPP-MVS		98.64 96	98.34 126	99.54 25	99.54 77	99.17 26	98.63 80	99.24 156	97.47 169	98.09 200	98.68 184	97.62 88	99.89 56	96.22 190	99.62 155	99.57 70

TSAR-MVS + MP.		98.63 98	98.49 102	99.06 101	99.64 50	97.90 127	98.51 95	98.94 216	96.96 205	99.24 90	98.89 155	97.83 76	99.81 142	96.88 149	99.49 195	99.48 116

v1141		98.63 98	98.70 74	98.41 192	99.39 117	95.96 219	97.64 185	99.21 159	97.92 130	99.35 68	99.08 111	96.61 161	99.78 183	99.25 34	99.90 57	99.50 103

divwei89l23v2f112		98.63 98	98.70 74	98.41 192	99.39 117	95.96 219	97.64 185	99.21 159	97.92 130	99.35 68	99.08 111	96.61 161	99.78 183	99.25 34	99.90 57	99.50 103

v1		98.63 98	98.70 74	98.41 192	99.39 117	95.96 219	97.64 185	99.20 163	97.92 130	99.36 66	99.07 116	96.63 158	99.78 183	99.25 34	99.90 57	99.50 103

LS3D		98.63 98	98.38 121	99.36 56	97.25 330	99.38 6	99.12 48	99.32 129	99.21 47	98.44 184	98.88 156	97.31 108	99.80 154	96.58 169	99.34 208	98.92 237

RPSCF		98.62 103	98.36 123	99.42 50	99.65 47	99.42 5	98.55 89	99.57 43	97.72 149	98.90 136	99.26 79	96.12 180	99.52 293	95.72 215	99.71 127	99.32 174

Regformer-3		98.61 104	98.61 89	98.63 156	99.02 196	96.53 193	97.17 226	98.84 234	99.13 60	99.10 105	98.85 161	97.24 117	99.79 174	98.41 79	99.70 130	99.57 70

v1192		98.60 105	98.66 82	98.41 192	99.27 134	95.88 223	97.52 201	99.36 111	97.41 177	99.33 72	99.20 89	96.37 174	99.82 129	99.57 18	99.92 49	99.55 83

v1144		98.60 105	98.66 82	98.41 192	99.36 121	95.90 222	97.58 195	99.34 121	97.51 165	99.27 82	99.15 102	96.34 175	99.80 154	99.47 24	99.93 39	99.51 98

Regformer-2		98.60 105	98.46 108	99.02 108	98.85 228	97.71 145	96.91 240	99.09 192	98.98 77	99.01 119	98.64 193	97.37 106	99.84 103	97.75 111	99.57 172	99.52 96

MP-MVS-pluss		98.57 108	98.23 134	99.60 12	99.69 42	99.35 9	97.16 228	99.38 103	94.87 270	98.97 126	98.99 133	98.01 66	99.88 63	97.29 130	99.70 130	99.58 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		98.56 109	98.32 130	99.25 76	99.41 115	98.73 63	97.13 230	99.18 173	97.10 203	98.75 157	98.92 146	98.18 56	99.65 257	96.68 164	99.56 179	99.37 157

VDD-MVS		98.56 109	98.39 119	99.07 96	99.13 172	98.07 109	98.59 85	97.01 297	99.59 19	99.11 103	99.27 77	94.82 223	99.79 174	98.34 81	99.63 154	99.34 169

v2v482		98.56 109	98.62 86	98.37 199	99.42 114	95.81 226	97.58 195	99.16 182	97.90 138	99.28 80	99.01 130	95.98 189	99.79 174	99.33 29	99.90 57	99.51 98

XVG-ACMP-BASELINE		98.56 109	98.34 126	99.22 79	99.54 77	98.59 73	97.71 177	99.46 82	97.25 191	98.98 124	98.99 133	97.54 93	99.84 103	95.88 205	99.74 115	99.23 195

Regformer-1		98.55 113	98.44 112	98.87 126	98.85 228	97.29 162	96.91 240	98.99 215	98.97 78	98.99 122	98.64 193	97.26 115	99.81 142	97.79 105	99.57 172	99.51 98

v1240		98.55 113	98.62 86	98.32 202	99.22 144	95.58 231	97.51 203	99.45 85	97.16 200	99.45 53	99.24 82	96.12 180	99.85 88	99.60 14	99.88 64	99.55 83

IterMVS-LS		98.55 113	98.70 74	98.09 216	99.48 97	94.73 249	97.22 221	99.39 100	98.97 78	99.38 62	99.31 74	96.00 185	99.93 26	98.58 68	99.97 23	99.60 52
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		98.54 116	98.57 93	98.45 189	99.21 150	95.98 217	97.63 188	99.36 111	97.15 202	99.32 77	99.18 92	95.84 196	99.84 103	99.50 22	99.91 54	99.54 86

v1921920		98.54 116	98.60 91	98.38 198	99.20 159	95.76 227	97.56 197	99.36 111	97.23 196	99.38 62	99.17 97	96.02 183	99.84 103	99.57 18	99.90 57	99.54 86

XVG-OURS		98.53 118	98.34 126	99.11 90	99.50 86	98.82 58	95.97 285	99.50 65	97.30 187	99.05 113	98.98 136	99.35 7	99.32 322	95.72 215	99.68 142	99.18 207

UGNet		98.53 118	98.45 110	98.79 135	97.94 305	96.96 179	99.08 49	98.54 260	99.10 65	96.82 285	99.47 51	96.55 164	99.84 103	98.56 73	99.94 33	99.55 83
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
testmv		98.51 120	98.47 105	98.61 161	99.24 138	96.53 193	96.66 255	99.73 10	98.56 105	99.50 44	99.23 86	97.24 117	99.87 72	96.16 195	99.93 39	99.44 134

#test#		98.50 121	98.16 142	99.51 39	99.49 92	99.16 28	98.03 141	99.31 131	96.30 232	98.58 175	98.50 216	97.97 71	99.85 88	95.68 218	99.59 162	99.53 91

XVG-OURS-SEG-HR		98.49 122	98.28 132	99.14 87	99.49 92	98.83 56	96.54 261	99.48 74	97.32 185	99.11 103	98.61 201	99.33 8	99.30 325	96.23 189	98.38 283	99.28 185

FMVSNet2		98.49 122	98.40 117	98.75 144	98.90 218	97.14 174	98.61 82	99.13 186	98.59 99	99.19 95	99.28 75	94.14 240	99.82 129	97.97 99	99.80 92	99.29 184

pmmvs-eth3d		98.47 124	98.34 126	98.86 128	99.30 132	97.76 140	97.16 228	99.28 141	95.54 257	99.42 57	99.19 90	97.27 112	99.63 260	97.89 100	99.97 23	99.20 201

MP-MVS		98.46 125	98.09 150	99.54 25	99.57 62	99.22 20	98.50 96	99.19 169	97.61 156	97.58 242	98.66 188	97.40 104	99.88 63	94.72 236	99.60 161	99.54 86

v148		98.45 126	98.60 91	98.00 225	99.44 109	94.98 245	97.44 207	99.06 195	98.30 115	99.32 77	98.97 138	96.65 157	99.62 262	98.37 80	99.85 71	99.39 150

AllTest		98.44 127	98.20 136	99.16 84	99.50 86	98.55 76	98.25 119	99.58 36	96.80 212	98.88 141	99.06 117	97.65 84	99.57 280	94.45 242	99.61 159	99.37 157

VNet		98.42 128	98.30 131	98.79 135	98.79 241	97.29 162	98.23 120	98.66 255	99.31 41	98.85 144	98.80 169	94.80 226	99.78 183	98.13 90	99.13 241	99.31 178

ab-mvs		98.41 129	98.36 123	98.59 165	99.19 160	97.23 165	99.32 17	98.81 240	97.66 151	98.62 168	99.40 64	96.82 146	99.80 154	95.88 205	99.51 189	98.75 258

ACMP	95.32 15	98.41 129	98.09 150	99.36 56	99.51 84	98.79 59	97.68 180	99.38 103	95.76 248	98.81 152	98.82 167	98.36 45	99.82 129	94.75 233	99.77 104	99.48 116
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SD-MVS		98.40 131	98.68 79	97.54 247	98.96 205	97.99 114	97.88 161	99.36 111	98.20 121	99.63 26	99.04 124	98.76 24	95.33 353	96.56 174	99.74 115	99.31 178

EI-MVSNet		98.40 131	98.51 97	98.04 223	99.10 174	94.73 249	97.20 222	98.87 228	98.97 78	99.06 108	99.02 128	96.00 185	99.80 154	98.58 68	99.82 82	99.60 52

WR-MVS		98.40 131	98.19 138	99.03 105	99.00 199	97.65 148	96.85 244	98.94 216	98.57 103	98.89 138	98.50 216	95.60 201	99.85 88	97.54 118	99.85 71	99.59 58

new-patchmatchnet		98.35 134	98.74 66	97.18 259	99.24 138	92.23 299	96.42 268	99.48 74	98.30 115	99.69 17	99.53 44	97.44 101	99.82 129	98.84 58	99.77 104	99.49 110

HSP-MVS		98.34 135	97.94 162	99.54 25	99.57 62	99.25 18	98.57 86	98.84 234	97.55 163	99.31 79	97.71 265	94.61 231	99.88 63	96.14 197	99.19 231	99.48 116

canonicalmvs		98.34 135	98.26 133	98.58 166	98.46 279	97.82 135	98.96 63	99.46 82	99.19 54	97.46 252	95.46 326	98.59 32	99.46 306	98.08 92	98.71 268	98.46 272

testgi		98.32 137	98.39 119	98.13 214	99.57 62	95.54 232	97.78 169	99.49 71	97.37 180	99.19 95	97.65 269	98.96 19	99.49 300	96.50 179	98.99 254	99.34 169

DeepPCF-MVS	96.93 5	98.32 137	98.01 157	99.23 78	98.39 284	98.97 50	95.03 321	99.18 173	96.88 209	99.33 72	98.78 171	98.16 57	99.28 328	96.74 158	99.62 155	99.44 134

MVS_111021_LR		98.30 139	98.12 147	98.83 131	99.16 167	98.03 112	96.09 282	99.30 138	97.58 158	98.10 199	98.24 235	98.25 48	99.34 319	96.69 163	99.65 152	99.12 216

EPP-MVSNet		98.30 139	98.04 156	99.07 96	99.56 69	97.83 132	99.29 25	98.07 276	99.03 72	98.59 173	99.13 105	92.16 267	99.90 47	96.87 150	99.68 142	99.49 110

DeepC-MVS_fast	96.85 6	98.30 139	98.15 144	98.75 144	98.61 266	97.23 165	97.76 173	99.09 192	97.31 186	98.75 157	98.66 188	97.56 90	99.64 259	96.10 198	99.55 181	99.39 150
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		98.29 142	97.95 160	99.34 64	98.44 281	99.16 28	98.12 130	99.38 103	96.01 244	98.06 202	98.43 220	97.80 80	99.67 244	95.69 217	99.58 168	99.20 201

Fast-Effi-MVS+-dtu		98.27 143	98.09 150	98.81 133	98.43 282	98.11 103	97.61 191	99.50 65	98.64 95	97.39 260	97.52 276	98.12 60	99.95 13	96.90 148	98.71 268	98.38 278

DELS-MVS		98.27 143	98.20 136	98.48 185	98.86 225	96.70 190	95.60 307	99.20 163	97.73 148	98.45 183	98.71 179	97.50 95	99.82 129	98.21 87	99.59 162	98.93 236
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
Effi-MVS+-dtu		98.26 145	97.90 166	99.35 61	98.02 302	99.49 3	98.02 148	99.16 182	98.29 118	97.64 237	97.99 253	96.44 170	99.95 13	96.66 165	98.93 259	98.60 267

MVSFormer		98.26 145	98.43 114	97.77 232	98.88 223	93.89 277	99.39 13	99.56 49	99.11 61	98.16 195	98.13 240	93.81 247	99.97 3	99.26 32	99.57 172	99.43 139

ESAPD		98.25 147	97.83 170	99.50 41	99.36 121	99.10 42	97.25 216	99.28 141	96.66 220	99.05 113	98.71 179	97.56 90	99.86 77	93.00 279	99.57 172	99.53 91

MVS_111021_HR		98.25 147	98.08 153	98.75 144	99.09 177	97.46 157	95.97 285	99.27 146	97.60 157	97.99 206	98.25 234	98.15 59	99.38 316	96.87 150	99.57 172	99.42 142

TAMVS		98.24 149	98.05 155	98.80 134	99.07 181	97.18 170	97.88 161	98.81 240	96.66 220	99.17 99	99.21 87	94.81 225	99.77 192	96.96 145	99.88 64	99.44 134

Anonymous20231206		98.21 150	98.21 135	98.20 211	99.51 84	95.43 237	98.13 128	99.32 129	96.16 238	98.93 134	98.82 167	96.00 185	99.83 117	97.32 129	99.73 118	99.36 163

VDDNet		98.21 150	97.95 160	99.01 109	99.58 57	97.74 143	99.01 55	97.29 292	99.67 8	98.97 126	99.50 46	90.45 275	99.80 154	97.88 102	99.20 227	99.48 116

IS-MVSNet		98.19 152	97.90 166	99.08 95	99.57 62	97.97 119	99.31 20	98.32 268	99.01 74	98.98 124	99.03 127	91.59 270	99.79 174	95.49 224	99.80 92	99.48 116

MVS_Test		98.18 153	98.36 123	97.67 237	98.48 277	94.73 249	98.18 124	99.02 207	97.69 150	98.04 204	99.11 107	97.22 121	99.56 283	98.57 70	98.90 260	98.71 260

TSAR-MVS + GP.		98.18 153	97.98 158	98.77 140	98.71 248	97.88 128	96.32 272	98.66 255	96.33 229	99.23 93	98.51 213	97.48 99	99.40 312	97.16 134	99.46 196	99.02 225

CNVR-MVS		98.17 155	97.87 169	99.07 96	98.67 258	98.24 94	97.01 233	98.93 219	97.25 191	97.62 238	98.34 227	97.27 112	99.57 280	96.42 184	99.33 209	99.39 150

PVSNet_Blended_VisFu		98.17 155	98.15 144	98.22 210	99.73 28	95.15 242	97.36 210	99.68 16	94.45 279	98.99 122	99.27 77	96.87 143	99.94 20	97.13 138	99.91 54	99.57 70

HPM-MVS++		98.10 157	97.64 180	99.48 44	99.09 177	99.13 37	97.52 201	98.75 248	97.46 174	96.90 280	97.83 260	96.01 184	99.84 103	95.82 212	99.35 206	99.46 128

APD-MVS		98.10 157	97.67 175	99.42 50	99.11 173	98.93 54	97.76 173	99.28 141	94.97 267	98.72 159	98.77 173	97.04 128	99.85 88	93.79 263	99.54 182	99.49 110
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVP-Stereo		98.08 159	97.92 164	98.57 168	98.96 205	96.79 184	97.90 160	99.18 173	96.41 228	98.46 182	98.95 142	95.93 191	99.60 269	96.51 178	98.98 256	99.31 178
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PMMVS2		98.07 160	98.08 153	98.04 223	99.41 115	94.59 255	94.59 329	99.40 98	97.50 166	98.82 150	98.83 164	96.83 145	99.84 103	97.50 121	99.81 88	99.71 27

MVS_0304		98.02 161	97.88 168	98.46 187	98.22 295	96.39 201	96.50 262	99.49 71	98.03 126	97.24 266	98.33 229	94.80 226	99.90 47	98.31 84	99.95 30	99.08 217

Effi-MVS+		98.02 161	97.82 171	98.62 158	98.53 276	97.19 169	97.33 211	99.68 16	97.30 187	96.68 288	97.46 281	98.56 36	99.80 154	96.63 167	98.20 289	98.86 244

MSLP-MVS++		98.02 161	98.14 146	97.64 241	98.58 269	95.19 241	97.48 204	99.23 158	97.47 169	97.90 210	98.62 199	97.04 128	98.81 345	97.55 117	99.41 200	98.94 235

MCST-MVS		98.00 164	97.63 181	99.10 92	99.24 138	98.17 99	96.89 242	98.73 251	95.66 249	97.92 207	97.70 266	97.17 122	99.66 252	96.18 194	99.23 223	99.47 124

K. test v3		98.00 164	97.66 178	99.03 105	99.79 24	97.56 152	99.19 39	92.47 344	99.62 16	99.52 39	99.66 22	89.61 278	99.96 8	99.25 34	99.81 88	99.56 75

HQP_MVS		97.99 166	97.67 175	98.93 118	99.19 160	97.65 148	97.77 171	99.27 146	98.20 121	97.79 229	97.98 254	94.90 218	99.70 229	94.42 244	99.51 189	99.45 132

no-one		97.98 167	98.10 149	97.61 242	99.55 73	93.82 279	96.70 252	98.94 216	96.18 234	99.52 39	99.41 61	95.90 194	99.81 142	96.72 159	99.99 11	99.20 201

MDA-MVSNet-bldmvs		97.94 168	97.91 165	98.06 221	99.44 109	94.96 246	96.63 257	99.15 185	98.35 109	98.83 147	99.11 107	94.31 237	99.85 88	96.60 168	98.72 265	99.37 157

LF4IMVS		97.90 169	97.69 174	98.52 178	99.17 165	97.66 147	97.19 225	99.47 80	96.31 231	97.85 215	98.20 239	96.71 155	99.52 293	94.62 237	99.72 123	98.38 278

UnsupCasMVSNet_eth		97.89 170	97.60 183	98.75 144	99.31 130	97.17 171	97.62 189	99.35 117	98.72 94	98.76 156	98.68 184	92.57 264	99.74 212	97.76 110	95.60 337	99.34 169

TinyColmap		97.89 170	97.98 158	97.60 243	98.86 225	94.35 263	96.21 277	99.44 88	97.45 176	99.06 108	98.88 156	97.99 69	99.28 328	94.38 248	99.58 168	99.18 207

OMC-MVS		97.88 172	97.49 187	99.04 104	98.89 222	98.63 68	96.94 236	99.25 152	95.02 265	98.53 180	98.51 213	97.27 112	99.47 304	93.50 272	99.51 189	99.01 226

CANet		97.87 173	97.76 172	98.19 212	97.75 310	95.51 234	96.76 248	99.05 199	97.74 147	96.93 275	98.21 238	95.59 202	99.89 56	97.86 104	99.93 39	99.19 206

xiu_mvs_v1_base_debu		97.86 174	98.17 139	96.92 268	98.98 202	93.91 274	96.45 265	99.17 179	97.85 144	98.41 187	97.14 294	98.47 39	99.92 34	98.02 95	99.05 246	96.92 320

xiu_mvs_v1_base		97.86 174	98.17 139	96.92 268	98.98 202	93.91 274	96.45 265	99.17 179	97.85 144	98.41 187	97.14 294	98.47 39	99.92 34	98.02 95	99.05 246	96.92 320

xiu_mvs_v1_base_debi		97.86 174	98.17 139	96.92 268	98.98 202	93.91 274	96.45 265	99.17 179	97.85 144	98.41 187	97.14 294	98.47 39	99.92 34	98.02 95	99.05 246	96.92 320

NCCC		97.86 174	97.47 191	99.05 102	98.61 266	98.07 109	96.98 234	98.90 225	97.63 153	97.04 272	97.93 257	95.99 188	99.66 252	95.31 225	98.82 262	99.43 139

PMVS	91.26 20	97.86 174	97.94 162	97.65 239	99.71 34	97.94 124	98.52 91	98.68 254	98.99 75	97.52 248	99.35 68	97.41 103	98.18 348	91.59 302	99.67 147	96.82 327
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CPTT-MVS		97.84 179	97.36 197	99.27 73	99.31 130	98.46 84	98.29 116	99.27 146	94.90 269	97.83 220	98.37 224	94.90 218	99.84 103	93.85 262	99.54 182	99.51 98

mvs-test1		97.83 180	97.48 190	98.89 124	98.02 302	99.20 23	97.20 222	99.16 182	98.29 118	96.46 299	97.17 291	96.44 170	99.92 34	96.66 165	97.90 309	97.54 313

mvs_anonymous		97.83 180	98.16 142	96.87 271	98.18 297	91.89 301	97.31 213	98.90 225	97.37 180	98.83 147	99.46 52	96.28 176	99.79 174	98.90 53	98.16 292	98.95 233

IterMVS		97.73 182	98.11 148	96.57 281	99.24 138	90.28 320	95.52 310	99.21 159	98.86 85	99.33 72	99.33 72	93.11 255	99.94 20	98.49 74	99.94 33	99.48 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		97.71 183	97.52 186	98.28 207	98.91 217	96.82 183	94.42 330	99.37 107	97.65 152	98.37 191	98.29 232	97.40 104	99.33 321	94.09 254	99.22 224	98.68 266

CDS-MVSNet		97.69 184	97.35 199	98.69 150	98.73 245	97.02 178	96.92 239	98.75 248	95.89 246	98.59 173	98.67 186	92.08 269	99.74 212	96.72 159	99.81 88	99.32 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		97.68 185	97.75 173	97.45 251	98.23 294	93.78 280	97.29 214	98.84 234	96.10 240	98.64 164	98.65 190	96.04 182	99.36 317	96.84 152	99.14 238	99.20 201

Fast-Effi-MVS+		97.67 186	97.38 196	98.57 168	98.71 248	97.43 159	97.23 218	99.45 85	94.82 272	96.13 303	96.51 302	98.52 38	99.91 43	96.19 192	98.83 261	98.37 280

EU-MVSNet		97.66 187	98.50 99	95.13 313	99.63 52	85.84 335	98.35 115	98.21 271	98.23 120	99.54 35	99.46 52	95.02 216	99.68 238	98.24 85	99.87 68	99.87 6

pmmvs5		97.64 188	97.49 187	98.08 219	99.14 171	95.12 244	96.70 252	99.05 199	93.77 289	98.62 168	98.83 164	93.23 252	99.75 203	98.33 83	99.76 113	99.36 163

N_pmnet		97.63 189	97.17 205	98.99 112	99.27 134	97.86 130	95.98 284	93.41 336	95.25 262	99.47 49	98.90 150	95.63 200	99.85 88	96.91 146	99.73 118	99.27 186

YYNet1		97.60 190	97.67 175	97.39 255	99.04 191	93.04 291	95.27 315	98.38 267	97.25 191	98.92 135	98.95 142	95.48 207	99.73 217	96.99 143	98.74 264	99.41 144

MDA-MVSNet_test_wron		97.60 190	97.66 178	97.41 254	99.04 191	93.09 288	95.27 315	98.42 265	97.26 190	98.88 141	98.95 142	95.43 208	99.73 217	97.02 142	98.72 265	99.41 144

test_normal		97.58 192	97.41 192	98.10 215	99.03 194	95.72 228	96.21 277	97.05 296	96.71 217	98.65 162	98.12 244	93.87 244	99.69 233	97.68 116	99.35 206	98.88 242

pmmvs4		97.58 192	97.28 201	98.51 182	98.84 231	96.93 181	95.40 314	98.52 261	93.60 291	98.61 170	98.65 190	95.10 215	99.60 269	96.97 144	99.79 96	98.99 228

DI_MVS_plusplus_test		97.57 194	97.40 193	98.07 220	99.06 184	95.71 229	96.58 260	96.96 298	96.71 217	98.69 160	98.13 240	93.81 247	99.68 238	97.45 123	99.19 231	98.80 252

PVSNet_BlendedMVS		97.55 195	97.53 185	97.60 243	98.92 214	93.77 281	96.64 256	99.43 93	94.49 275	97.62 238	99.18 92	96.82 146	99.67 244	94.73 234	99.93 39	99.36 163

FMVSNet3		97.50 196	97.24 202	98.29 206	98.08 300	95.83 225	97.86 164	98.91 224	97.89 139	98.95 129	98.95 142	87.06 287	99.81 142	97.77 107	99.69 137	99.23 195

diffmvs		97.49 197	97.36 197	97.91 227	98.38 285	95.70 230	97.95 155	99.31 131	94.87 270	96.14 302	98.78 171	94.84 222	99.43 310	97.69 114	98.26 285	98.59 268

CHOSEN 1792x2688		97.49 197	97.14 208	98.54 176	99.68 43	96.09 215	96.50 262	99.62 28	91.58 315	98.84 146	98.97 138	92.36 265	99.88 63	96.76 157	99.95 30	99.67 31

CLD-MVS		97.49 197	97.16 206	98.48 185	99.07 181	97.03 176	94.71 327	99.21 159	94.46 277	98.06 202	97.16 292	97.57 89	99.48 303	94.46 241	99.78 100	98.95 233
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_prior3		97.48 200	97.00 211	98.95 115	98.69 253	97.95 122	95.74 302	99.03 203	96.48 225	96.11 304	97.63 270	95.92 192	99.59 273	94.16 249	99.20 227	99.30 181

Vis-MVSNet (Re-imp)		97.46 201	97.16 206	98.34 201	99.55 73	96.10 213	98.94 64	98.44 264	98.32 114	98.16 195	98.62 199	88.76 283	99.73 217	93.88 260	99.79 96	99.18 207

jason		97.45 202	97.35 199	97.76 233	99.24 138	93.93 273	95.86 296	98.42 265	94.24 284	98.50 181	98.13 240	94.82 223	99.91 43	97.22 132	99.73 118	99.43 139
jason: jason.
Test4		97.43 203	97.18 204	98.18 213	99.05 189	96.02 216	96.62 258	99.09 192	96.25 233	98.63 167	97.70 266	90.49 274	99.68 238	97.50 121	99.30 214	98.83 246

DSMNet-mixed		97.42 204	97.60 183	96.87 271	99.15 170	91.46 306	98.54 90	99.12 188	92.87 299	97.58 242	99.63 27	96.21 177	99.90 47	95.74 214	99.54 182	99.27 186

USDC		97.41 205	97.40 193	97.44 252	98.94 208	93.67 283	95.17 318	99.53 59	94.03 287	98.97 126	99.10 109	95.29 210	99.34 319	95.84 211	99.73 118	99.30 181

alignmvs		97.35 206	96.88 217	98.78 138	98.54 274	98.09 104	97.71 177	97.69 286	99.20 50	97.59 241	95.90 316	88.12 286	99.55 286	98.18 89	98.96 257	98.70 262

Patchmtry		97.35 206	96.97 212	98.50 183	97.31 329	96.47 196	98.18 124	98.92 222	98.95 82	98.78 153	99.37 65	85.44 300	99.85 88	95.96 203	99.83 79	99.17 211

DP-MVS Recon		97.33 208	96.92 214	98.57 168	99.09 177	97.99 114	96.79 245	99.35 117	93.18 295	97.71 233	98.07 250	95.00 217	99.31 323	93.97 256	99.13 241	98.42 276

QAPM		97.31 209	96.81 221	98.82 132	98.80 240	97.49 155	99.06 53	99.19 169	90.22 327	97.69 235	99.16 98	96.91 137	99.90 47	90.89 316	99.41 200	99.07 219

UnsupCasMVSNet_bld		97.30 210	96.92 214	98.45 189	99.28 133	96.78 188	96.20 279	99.27 146	95.42 260	98.28 193	98.30 231	93.16 254	99.71 227	94.99 229	97.37 317	98.87 243

F-COLMAP		97.30 210	96.68 229	99.14 87	99.19 160	98.39 88	97.27 215	99.30 138	92.93 297	96.62 290	98.00 252	95.73 198	99.68 238	92.62 289	98.46 282	99.35 168

1112_ss		97.29 212	96.86 218	98.58 166	99.34 127	96.32 203	96.75 249	99.58 36	93.14 296	96.89 281	97.48 279	92.11 268	99.86 77	96.91 146	99.54 182	99.57 70

CANet_DTU		97.26 213	97.06 209	97.84 229	97.57 317	94.65 253	96.19 280	98.79 243	97.23 196	95.14 327	98.24 235	93.22 253	99.84 103	97.34 128	99.84 73	99.04 222

Patchmatch-RL test		97.26 213	97.02 210	97.99 226	99.52 81	95.53 233	96.13 281	99.71 12	97.47 169	99.27 82	99.16 98	84.30 308	99.62 262	97.89 100	99.77 104	98.81 249

CDPH-MVS		97.26 213	96.66 232	99.07 96	99.00 199	98.15 100	96.03 283	99.01 210	91.21 321	97.79 229	97.85 259	96.89 142	99.69 233	92.75 287	99.38 203	99.39 150

PatchMatch-RL		97.24 216	96.78 222	98.61 161	99.03 194	97.83 132	96.36 270	99.06 195	93.49 294	97.36 263	97.78 262	95.75 197	99.49 300	93.44 273	98.77 263	98.52 270

sss		97.21 217	96.93 213	98.06 221	98.83 233	95.22 240	96.75 249	98.48 263	94.49 275	97.27 265	97.90 258	92.77 261	99.80 154	96.57 171	99.32 211	99.16 214

LFMVS		97.20 218	96.72 225	98.64 154	98.72 246	96.95 180	98.93 66	94.14 334	99.74 5	98.78 153	99.01 130	84.45 305	99.73 217	97.44 124	99.27 219	99.25 191

HyFIR lowres test		97.19 219	96.60 235	98.96 114	99.62 54	97.28 164	95.17 318	99.50 65	94.21 285	99.01 119	98.32 230	86.61 289	99.99 2	97.10 141	99.84 73	99.60 52

CNLPA		97.17 220	96.71 227	98.55 173	98.56 271	98.05 111	96.33 271	98.93 219	96.91 208	97.06 271	97.39 285	94.38 236	99.45 308	91.66 298	99.18 233	98.14 284

xiu_mvs_v2_base		97.16 221	97.49 187	96.17 293	98.54 274	92.46 295	95.45 312	98.84 234	97.25 191	97.48 251	96.49 303	98.31 47	99.90 47	96.34 187	98.68 270	96.15 336

AdaColmap		97.14 222	96.71 227	98.46 187	98.34 287	97.80 138	96.95 235	98.93 219	95.58 256	96.92 276	97.66 268	95.87 195	99.53 289	90.97 313	99.14 238	98.04 287

train_agg		97.10 223	96.45 241	99.07 96	98.71 248	98.08 107	95.96 289	99.03 203	91.64 312	95.85 310	97.53 274	96.47 168	99.76 197	93.67 265	99.16 234	99.36 163

OpenMVS	96.65 7	97.09 224	96.68 229	98.32 202	98.32 288	97.16 172	98.86 71	99.37 107	89.48 331	96.29 301	99.15 102	96.56 163	99.90 47	92.90 281	99.20 227	97.89 290

PS-MVSNAJ		97.08 225	97.39 195	96.16 295	98.56 271	92.46 295	95.24 317	98.85 233	97.25 191	97.49 250	95.99 311	98.07 61	99.90 47	96.37 185	98.67 271	96.12 337

agg_prior1		97.06 226	96.40 242	99.03 105	98.68 255	97.99 114	95.76 300	99.01 210	91.73 311	95.59 314	97.50 277	96.49 167	99.77 192	93.71 264	99.14 238	99.34 169

test1235678		97.06 226	96.84 220	97.73 235	98.55 273	94.46 262	94.80 325	99.36 111	96.85 211	98.83 147	98.26 233	92.72 262	99.82 129	92.49 292	99.70 130	98.91 239

lupinMVS		97.06 226	96.86 218	97.65 239	98.88 223	93.89 277	95.48 311	97.97 278	93.53 292	98.16 195	97.58 272	93.81 247	99.91 43	96.77 156	99.57 172	99.17 211

API-MVS		97.04 229	96.91 216	97.42 253	97.88 309	98.23 98	98.18 124	98.50 262	97.57 160	97.39 260	96.75 299	96.77 150	99.15 334	90.16 320	99.02 250	94.88 346

HQP-MVS		97.00 230	96.49 240	98.55 173	98.67 258	96.79 184	96.29 273	99.04 201	96.05 241	95.55 318	96.84 297	93.84 245	99.54 287	92.82 284	99.26 221	99.32 174

new_pmnet		96.99 231	96.76 223	97.67 237	98.72 246	94.89 247	95.95 292	98.20 272	92.62 302	98.55 178	98.54 211	94.88 221	99.52 293	93.96 257	99.44 198	98.59 268

Test_1112_low_res		96.99 231	96.55 238	98.31 204	99.35 125	95.47 236	95.84 299	99.53 59	91.51 317	96.80 286	98.48 219	91.36 271	99.83 117	96.58 169	99.53 186	99.62 45

agg_prior3		96.95 233	96.27 246	99.00 111	98.68 255	97.91 125	95.96 289	99.01 210	90.74 324	95.60 313	97.45 282	96.14 178	99.74 212	93.67 265	99.16 234	99.36 163

PVSNet_Blended		96.88 234	96.68 229	97.47 250	98.92 214	93.77 281	94.71 327	99.43 93	90.98 322	97.62 238	97.36 288	96.82 146	99.67 244	94.73 234	99.56 179	98.98 229

MVSTER		96.86 235	96.55 238	97.79 231	97.91 307	94.21 266	97.56 197	98.87 228	97.49 168	99.06 108	99.05 122	80.72 320	99.80 154	98.44 76	99.82 82	99.37 157

BH-untuned		96.83 236	96.75 224	97.08 261	98.74 244	93.33 287	96.71 251	98.26 270	96.72 215	98.44 184	97.37 287	95.20 212	99.47 304	91.89 296	97.43 316	98.44 274

BH-RMVSNet		96.83 236	96.58 236	97.58 245	98.47 278	94.05 269	96.67 254	97.36 290	96.70 219	97.87 212	97.98 254	95.14 214	99.44 309	90.47 319	98.58 276	99.25 191

RPMNet		96.82 238	96.66 232	97.28 256	97.71 312	94.22 264	98.11 131	96.90 303	99.37 36	96.91 278	99.34 70	86.72 288	99.81 142	97.53 119	97.36 319	97.81 296

PAPM_NR		96.82 238	96.32 245	98.30 205	99.07 181	96.69 191	97.48 204	98.76 245	95.81 247	96.61 291	96.47 305	94.12 243	99.17 332	90.82 318	97.78 311	99.06 220

MG-MVS		96.77 240	96.61 234	97.26 258	98.31 289	93.06 289	95.93 293	98.12 275	96.45 227	97.92 207	98.73 177	93.77 250	99.39 314	91.19 312	99.04 249	99.33 173

1121		96.73 241	96.00 249	98.91 121	98.95 207	97.76 140	98.07 136	98.73 251	87.65 338	96.54 292	98.13 240	94.52 233	99.73 217	92.38 293	99.02 250	99.24 194

WTY-MVS		96.67 242	96.27 246	97.87 228	98.81 238	94.61 254	96.77 247	97.92 280	94.94 268	97.12 267	97.74 264	91.11 272	99.82 129	93.89 259	98.15 293	99.18 207

PatchT		96.65 243	96.35 243	97.54 247	97.40 326	95.32 239	97.98 152	96.64 309	99.33 40	96.89 281	99.42 59	84.32 307	99.81 142	97.69 114	97.49 314	97.48 314

TAPA-MVS	96.21 11	96.63 244	95.95 251	98.65 153	98.93 210	98.09 104	96.93 237	99.28 141	83.58 346	98.13 198	97.78 262	96.13 179	99.40 312	93.52 270	99.29 217	98.45 273
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		96.62 245	96.25 248	97.71 236	99.04 191	94.66 252	99.16 42	96.92 302	97.23 196	97.87 212	99.10 109	86.11 293	99.65 257	91.65 299	99.21 226	98.82 248

LP		96.60 246	96.57 237	96.68 276	97.64 316	91.70 303	98.11 131	97.74 283	97.29 189	97.91 209	99.24 82	88.35 284	99.85 88	97.11 140	95.76 336	98.49 271

Patchmatch-test		96.55 247	96.34 244	97.17 260	98.35 286	93.06 289	98.40 113	97.79 281	97.33 183	98.41 187	98.67 186	83.68 312	99.69 233	95.16 226	99.31 213	98.77 255

PMMVS		96.51 248	95.98 250	98.09 216	97.53 320	95.84 224	94.92 323	98.84 234	91.58 315	96.05 308	95.58 318	95.68 199	99.66 252	95.59 221	98.09 302	98.76 257

PLC	94.65 16	96.51 248	95.73 254	98.85 129	98.75 243	97.91 125	96.42 268	99.06 195	90.94 323	95.59 314	97.38 286	94.41 235	99.59 273	90.93 314	98.04 307	99.05 221
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		96.50 250	95.77 253	98.69 150	99.48 97	97.43 159	97.84 166	99.55 54	81.42 348	96.51 295	98.58 204	95.53 203	99.67 244	93.41 274	99.58 168	98.98 229

MAR-MVS		96.47 251	95.70 255	98.79 135	97.92 306	99.12 39	98.28 117	98.60 259	92.16 309	95.54 321	96.17 309	94.77 229	99.52 293	89.62 322	98.23 286	97.72 302
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
Patchmatch-test1		96.44 252	96.72 225	95.60 308	98.24 292	88.35 326	95.85 298	96.88 304	96.11 239	97.67 236	98.57 205	93.10 256	99.69 233	94.79 232	99.22 224	98.77 255

CMPMVS	75.91 23	96.29 253	95.44 262	98.84 130	96.25 345	98.69 66	97.02 232	99.12 188	88.90 334	97.83 220	98.86 159	89.51 279	98.90 342	91.92 295	99.51 189	98.92 237
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		96.28 254	95.95 251	97.28 256	97.71 312	94.22 264	98.11 131	98.92 222	92.31 306	96.91 278	99.37 65	85.44 300	99.81 142	97.39 127	97.36 319	97.81 296

CVMVSNet		96.25 255	97.21 203	93.38 333	99.10 174	80.56 353	97.20 222	98.19 274	96.94 206	99.00 121	99.02 128	89.50 280	99.80 154	96.36 186	99.59 162	99.78 15

EPNet		96.14 256	95.44 262	98.25 208	90.76 355	95.50 235	97.92 157	94.65 321	98.97 78	92.98 342	98.85 161	89.12 282	99.87 72	95.99 201	99.68 142	99.39 150
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		96.06 257	97.62 182	91.38 336	98.65 264	98.57 75	98.85 72	96.95 300	96.86 210	99.90 5	99.16 98	99.18 12	98.40 347	89.23 323	99.77 104	77.18 352

FMVSNet5		96.01 258	95.20 269	98.41 192	97.53 320	96.10 213	98.74 75	99.50 65	97.22 199	98.03 205	99.04 124	69.80 351	99.88 63	97.27 131	99.71 127	99.25 191

HY-MVS	95.94 13	95.90 259	95.35 264	97.55 246	97.95 304	94.79 248	98.81 74	96.94 301	92.28 307	95.17 326	98.57 205	89.90 277	99.75 203	91.20 311	97.33 321	98.10 285

GA-MVS		95.86 260	95.32 265	97.49 249	98.60 268	94.15 268	93.83 337	97.93 279	95.49 258	96.68 288	97.42 284	83.21 313	99.30 325	96.22 190	98.55 277	99.01 226

OpenMVS_ROB	95.38 14	95.84 261	95.18 270	97.81 230	98.41 283	97.15 173	97.37 209	98.62 258	83.86 345	98.65 162	98.37 224	94.29 238	99.68 238	88.41 325	98.62 274	96.60 330

1314		95.74 262	95.60 259	96.17 293	97.53 320	92.75 292	98.07 136	98.31 269	91.22 320	94.25 334	96.68 300	95.53 203	99.03 336	91.64 300	97.18 322	96.74 328

PVSNet	93.40 17	95.67 263	95.70 255	95.57 309	98.83 233	88.57 324	92.50 343	97.72 284	92.69 301	96.49 298	96.44 306	93.72 251	99.43 310	93.61 267	99.28 218	98.71 260

PatchmatchNet		95.58 264	95.67 257	95.30 312	97.34 328	87.32 330	97.65 184	96.65 308	95.30 261	97.07 270	98.69 182	84.77 302	99.75 203	94.97 230	98.64 272	98.83 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TR-MVS		95.55 265	95.12 271	96.86 274	97.54 319	93.94 272	96.49 264	96.53 311	94.36 282	97.03 273	96.61 301	94.26 239	99.16 333	86.91 330	96.31 332	97.47 315

testus		95.52 266	95.32 265	96.13 297	97.91 307	89.49 323	93.62 338	99.61 30	92.41 304	97.38 262	95.42 328	94.72 230	99.63 260	88.06 327	98.72 265	99.26 189

JIA-IIPM		95.52 266	95.03 273	97.00 265	96.85 337	94.03 270	96.93 237	95.82 316	99.20 50	94.63 331	99.71 14	83.09 314	99.60 269	94.42 244	94.64 341	97.36 316

CHOSEN 280x420		95.51 268	95.47 260	95.65 307	98.25 290	88.27 327	93.25 340	98.88 227	93.53 292	94.65 330	97.15 293	86.17 291	99.93 26	97.41 126	99.93 39	98.73 259

ADS-MVSNet2		95.43 269	94.98 274	96.76 275	98.14 298	91.74 302	97.92 157	97.76 282	90.23 325	96.51 295	98.91 147	85.61 297	99.85 88	92.88 282	96.90 325	98.69 263

PAPR		95.29 270	94.47 278	97.75 234	97.50 324	95.14 243	94.89 324	98.71 253	91.39 319	95.35 325	95.48 325	94.57 232	99.14 335	84.95 337	97.37 317	98.97 232

ADS-MVSNet		95.24 271	94.93 275	96.18 292	98.14 298	90.10 321	97.92 157	97.32 291	90.23 325	96.51 295	98.91 147	85.61 297	99.74 212	92.88 282	96.90 325	98.69 263

BH-w/o		95.13 272	94.89 276	95.86 302	98.20 296	91.31 316	95.65 305	97.37 289	93.64 290	96.52 294	95.70 317	93.04 257	99.02 337	88.10 326	95.82 335	97.24 318

tpmrst		95.07 273	95.46 261	93.91 327	97.11 332	84.36 345	97.62 189	96.96 298	94.98 266	96.35 300	98.80 169	85.46 299	99.59 273	95.60 220	96.23 333	97.79 299

pmmvs3		95.03 274	94.40 283	96.93 267	97.70 314	92.53 294	95.08 320	97.71 285	88.57 335	97.71 233	98.08 249	79.39 333	99.82 129	96.19 192	99.11 244	98.43 275

tpmvs		95.02 275	95.25 267	94.33 321	96.39 344	85.87 334	98.08 134	96.83 305	95.46 259	95.51 322	98.69 182	85.91 294	99.53 289	94.16 249	96.23 333	97.58 311

EPNet_dtu		94.93 276	94.78 277	95.38 311	93.58 354	87.68 329	96.78 246	95.69 318	97.35 182	89.14 350	98.09 248	88.15 285	99.49 300	94.95 231	99.30 214	98.98 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
view600		94.87 277	94.41 279	96.26 287	99.22 144	91.37 309	98.49 97	94.45 323	98.75 89	97.85 215	95.98 312	80.38 322	99.75 203	86.06 333	98.49 278	97.66 303

view800		94.87 277	94.41 279	96.26 287	99.22 144	91.37 309	98.49 97	94.45 323	98.75 89	97.85 215	95.98 312	80.38 322	99.75 203	86.06 333	98.49 278	97.66 303

conf0.05thres1000		94.87 277	94.41 279	96.26 287	99.22 144	91.37 309	98.49 97	94.45 323	98.75 89	97.85 215	95.98 312	80.38 322	99.75 203	86.06 333	98.49 278	97.66 303

tfpn		94.87 277	94.41 279	96.26 287	99.22 144	91.37 309	98.49 97	94.45 323	98.75 89	97.85 215	95.98 312	80.38 322	99.75 203	86.06 333	98.49 278	97.66 303

test12356		94.85 281	95.12 271	94.03 326	98.25 290	83.12 348	93.85 336	99.33 126	94.17 286	97.28 264	97.20 289	85.83 295	99.75 203	90.85 317	99.33 209	99.22 199

conf0.01		94.82 282	94.07 288	97.06 263	99.21 150	94.53 256	98.47 103	92.69 338	95.61 250	97.81 223	95.54 319	77.71 339	99.80 154	91.49 304	98.11 295	96.86 323

conf0.002		94.82 282	94.07 288	97.06 263	99.21 150	94.53 256	98.47 103	92.69 338	95.61 250	97.81 223	95.54 319	77.71 339	99.80 154	91.49 304	98.11 295	96.86 323

tfpn1000		94.81 284	94.25 287	96.47 284	99.01 198	93.47 286	98.56 87	92.30 347	96.17 235	97.90 210	96.29 308	76.70 345	99.77 192	93.02 278	98.29 284	96.16 334

cascas		94.79 285	94.33 286	96.15 296	96.02 348	92.36 298	92.34 345	99.26 151	85.34 344	95.08 328	94.96 337	92.96 258	98.53 346	94.41 247	98.59 275	97.56 312

thresconf0.02		94.70 286	94.07 288	96.58 277	99.21 150	94.53 256	98.47 103	92.69 338	95.61 250	97.81 223	95.54 319	77.71 339	99.80 154	91.49 304	98.11 295	95.42 342

tfpn_n400		94.70 286	94.07 288	96.58 277	99.21 150	94.53 256	98.47 103	92.69 338	95.61 250	97.81 223	95.54 319	77.71 339	99.80 154	91.49 304	98.11 295	95.42 342

tfpnconf		94.70 286	94.07 288	96.58 277	99.21 150	94.53 256	98.47 103	92.69 338	95.61 250	97.81 223	95.54 319	77.71 339	99.80 154	91.49 304	98.11 295	95.42 342

tfpnview11		94.70 286	94.07 288	96.58 277	99.21 150	94.53 256	98.47 103	92.69 338	95.61 250	97.81 223	95.54 319	77.71 339	99.80 154	91.49 304	98.11 295	95.42 342

tpm		94.67 290	94.34 285	95.66 306	97.68 315	88.42 325	97.88 161	94.90 320	94.46 277	96.03 309	98.56 208	78.66 334	99.79 174	95.88 205	95.01 340	98.78 254

test0.0.03 1		94.51 291	93.69 301	96.99 266	96.05 346	93.61 284	94.97 322	93.49 335	96.17 235	97.57 244	94.88 338	82.30 317	99.01 339	93.60 268	94.17 346	98.37 280

thres600view7		94.45 292	93.83 297	96.29 285	99.06 184	91.53 305	97.99 151	94.24 330	98.34 110	97.44 254	95.01 332	79.84 327	99.67 244	84.33 339	98.23 286	97.66 303

PCF-MVS	92.86 18	94.36 293	93.00 311	98.42 191	98.70 252	97.56 152	93.16 341	99.11 190	79.59 349	97.55 245	97.43 283	92.19 266	99.73 217	79.85 349	99.45 197	97.97 289
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tfpn111		94.33 294	93.78 298	95.96 299	99.06 184	91.35 313	98.03 141	94.24 330	98.33 111	97.40 257	94.98 334	79.84 327	99.68 238	83.94 340	98.22 288	96.86 323

X-MVStestdata		94.32 295	92.59 312	99.53 32	99.46 103	99.21 21	98.65 78	99.34 121	98.62 97	97.54 246	45.85 352	97.50 95	99.83 117	96.79 154	99.53 186	99.56 75

MVS-HIRNet		94.32 295	95.62 258	90.42 337	98.46 279	75.36 354	96.29 273	89.13 352	95.25 262	95.38 324	99.75 7	92.88 260	99.19 331	94.07 255	99.39 202	96.72 329

conf200view11		94.24 297	93.67 302	95.94 300	99.06 184	91.35 313	98.03 141	94.24 330	98.33 111	97.40 257	94.98 334	79.84 327	99.62 262	83.05 342	98.08 303	96.86 323

thres100view900		94.19 298	93.67 302	95.75 305	99.06 184	91.35 313	98.03 141	94.24 330	98.33 111	97.40 257	94.98 334	79.84 327	99.62 262	83.05 342	98.08 303	96.29 331

E-PMN		94.17 299	94.37 284	93.58 330	96.86 336	85.71 337	90.11 348	97.07 295	98.17 124	97.82 222	97.19 290	84.62 304	98.94 340	89.77 321	97.68 313	96.09 338

thres400		94.14 300	93.44 306	96.24 291	98.93 210	91.44 307	97.60 192	94.29 328	97.94 128	97.10 268	94.31 343	79.67 331	99.62 262	83.05 342	98.08 303	97.66 303

tfpn_ndepth		94.12 301	93.51 305	95.94 300	98.86 225	93.60 285	98.16 127	91.90 349	94.66 274	97.41 256	95.24 329	76.24 346	99.73 217	91.21 310	97.88 310	94.50 347

PatchFormer-LS_test		94.08 302	93.91 295	94.59 319	96.93 334	86.86 332	97.55 199	96.57 310	94.27 283	94.38 333	93.64 348	80.96 319	99.59 273	96.44 183	94.48 344	97.31 317

tfpn200view9		94.03 303	93.44 306	95.78 304	98.93 210	91.44 307	97.60 192	94.29 328	97.94 128	97.10 268	94.31 343	79.67 331	99.62 262	83.05 342	98.08 303	96.29 331

1111		93.99 304	93.72 300	94.80 316	99.33 128	85.20 339	95.97 285	99.39 100	97.88 140	98.64 164	98.56 208	57.79 359	99.80 154	96.02 199	99.87 68	99.40 149

CostFormer		93.97 305	93.78 298	94.51 320	97.53 320	85.83 336	97.98 152	95.96 315	89.29 333	94.99 329	98.63 197	78.63 335	99.62 262	94.54 239	96.50 330	98.09 286

test-LLR		93.90 306	93.85 296	94.04 324	96.53 340	84.62 343	94.05 333	92.39 345	96.17 235	94.12 336	95.07 330	82.30 317	99.67 244	95.87 208	98.18 290	97.82 294

EMVS		93.83 307	94.02 294	93.23 334	96.83 338	84.96 341	89.77 349	96.32 313	97.92 130	97.43 255	96.36 307	86.17 291	98.93 341	87.68 328	97.73 312	95.81 339

thres200		93.72 308	93.14 309	95.46 310	98.66 263	91.29 317	96.61 259	94.63 322	97.39 179	96.83 284	93.71 346	79.88 326	99.56 283	82.40 346	98.13 294	95.54 341

EPMVS		93.72 308	93.27 308	95.09 314	96.04 347	87.76 328	98.13 128	85.01 354	94.69 273	96.92 276	98.64 193	78.47 337	99.31 323	95.04 227	96.46 331	98.20 282

dp		93.47 310	93.59 304	93.13 335	96.64 339	81.62 352	97.66 182	96.42 312	92.80 300	96.11 304	98.64 193	78.55 336	99.59 273	93.31 275	92.18 350	98.16 283

FPMVS		93.44 311	92.23 316	97.08 261	99.25 137	97.86 130	95.61 306	97.16 294	92.90 298	93.76 341	98.65 190	75.94 348	95.66 351	79.30 350	97.49 314	97.73 301

tpm cat1		93.29 312	93.13 310	93.75 328	97.39 327	84.74 342	97.39 208	97.65 287	83.39 347	94.16 335	98.41 221	82.86 316	99.39 314	91.56 303	95.35 339	97.14 319

MVS		93.19 313	92.09 317	96.50 283	96.91 335	94.03 270	98.07 136	98.06 277	68.01 350	94.56 332	96.48 304	95.96 190	99.30 325	83.84 341	96.89 327	96.17 333

tpm2		93.09 314	92.58 313	94.62 318	97.56 318	86.53 333	97.66 182	95.79 317	86.15 342	94.07 338	98.23 237	75.95 347	99.53 289	90.91 315	96.86 328	97.81 296

tpmp4_e23		92.91 315	92.45 314	94.29 322	97.41 325	85.62 338	97.95 155	96.77 306	87.55 340	91.33 347	98.57 205	74.21 349	99.59 273	91.62 301	96.64 329	97.65 310

DWT-MVSNet_test		92.75 316	92.05 318	94.85 315	96.48 342	87.21 331	97.83 167	94.99 319	92.22 308	92.72 343	94.11 345	70.75 350	99.46 306	95.01 228	94.33 345	97.87 292

MVE	83.40 22	92.50 317	91.92 319	94.25 323	98.83 233	91.64 304	92.71 342	83.52 355	95.92 245	86.46 353	95.46 326	95.20 212	95.40 352	80.51 348	98.64 272	95.73 340
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
gg-mvs-nofinetune		92.37 318	91.20 322	95.85 303	95.80 349	92.38 297	99.31 20	81.84 356	99.75 4	91.83 345	99.74 8	68.29 352	99.02 337	87.15 329	97.12 323	96.16 334

test-mter		92.33 319	91.76 321	94.04 324	96.53 340	84.62 343	94.05 333	92.39 345	94.00 288	94.12 336	95.07 330	65.63 358	99.67 244	95.87 208	98.18 290	97.82 294

IB-MVS	91.63 19	92.24 320	90.90 323	96.27 286	97.22 331	91.24 318	94.36 331	93.33 337	92.37 305	92.24 344	94.58 342	66.20 356	99.89 56	93.16 277	94.63 342	97.66 303
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
TESTMET0.1,1		92.19 321	91.77 320	93.46 331	96.48 342	82.80 350	94.05 333	91.52 350	94.45 279	94.00 339	94.88 338	66.65 355	99.56 283	95.78 213	98.11 295	98.02 288

PAPM		91.88 322	90.34 324	96.51 282	98.06 301	92.56 293	92.44 344	97.17 293	86.35 341	90.38 349	96.01 310	86.61 289	99.21 330	70.65 352	95.43 338	97.75 300

PNet_i23d		91.80 323	92.35 315	90.14 338	98.65 264	73.10 357	89.22 350	99.02 207	95.23 264	97.87 212	97.82 261	78.45 338	98.89 343	88.73 324	86.14 351	98.42 276

test2356		91.64 324	90.19 327	96.00 298	94.30 352	89.58 322	90.84 346	96.68 307	91.76 310	95.48 323	93.69 347	67.05 354	99.52 293	84.83 338	97.08 324	98.91 239

PVSNet_0	89.98 21	91.15 325	90.30 325	93.70 329	97.72 311	84.34 346	90.24 347	97.42 288	90.20 328	93.79 340	93.09 349	90.90 273	98.89 343	86.57 331	72.76 352	97.87 292

testpf		89.08 326	90.27 326	85.50 339	94.03 353	82.85 349	96.87 243	91.09 351	91.61 314	90.96 348	94.86 341	66.15 357	95.83 350	94.58 238	92.27 349	77.82 351

.test1245		79.71 327	84.30 328	65.96 341	99.33 128	85.20 339	95.97 285	99.39 100	97.88 140	98.64 164	98.56 208	57.79 359	99.80 154	96.02 199	15.07 353	12.86 354

tmp_tt		78.77 328	78.73 329	78.90 340	58.45 356	74.76 356	94.20 332	78.26 358	39.16 352	86.71 352	92.82 350	80.50 321	75.19 355	86.16 332	92.29 348	86.74 350

pcd1.5k->3k		41.59 329	44.35 330	33.30 342	99.87 12	0.00 360	0.00 351	99.58 36	0.00 355	0.00 356	0.00 357	99.70 2	0.00 358	0.00 355	99.99 11	99.91 2

cdsmvs_eth3d_5k		24.66 330	32.88 331	0.00 345	0.00 359	0.00 360	0.00 351	99.10 191	0.00 355	0.00 356	97.58 272	99.21 11	0.00 358	0.00 355	0.00 356	0.00 356

testmvs		17.12 331	20.53 332	6.87 344	12.05 357	4.20 359	93.62 338	6.73 359	4.62 354	10.41 354	24.33 353	8.28 362	3.56 357	9.69 354	15.07 353	12.86 354

test123		17.04 332	20.11 333	7.82 343	10.25 358	4.91 358	94.80 325	4.47 360	4.93 353	10.00 355	24.28 354	9.69 361	3.64 356	10.14 353	12.43 355	14.92 353

pcd_1.5k_mvsjas		8.17 333	10.90 334	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	98.07 61	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		8.12 334	10.83 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	97.48 279	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

GSMVS																98.81 249

test_part3							97.25 216		96.66 220		98.71 179		99.86 77	93.00 279

test_part2					99.36 121	99.10 42				99.05 113

test_part1								99.28 141				97.56 90			99.57 172	99.53 91

sam_mvs1												84.74 303				98.81 249

sam_mvs												84.29 309

semantic-postprocess				96.87 271	99.27 134	91.16 319		99.25 152	99.10 65	99.41 58	99.35 68	92.91 259	99.96 8	98.65 66	99.94 33	99.49 110

ambc				98.24 209	98.82 236	95.97 218	98.62 81	99.00 214		99.27 82	99.21 87	96.99 133	99.50 299	96.55 175	99.50 194	99.26 189

MTGPA								99.20 163

test_post1							97.59 194				20.48 356	83.07 315	99.66 252	94.16 249

test_post											21.25 355	83.86 311	99.70 229

patchmatchnet-post											98.77 173	84.37 306	99.85 88

GG-mvs-BLEND				94.76 317	94.54 351	92.13 300	99.31 20	80.47 357		88.73 351	91.01 351	67.59 353	98.16 349	82.30 347	94.53 343	93.98 348

MTMP								91.91 348

gm-plane-assit					94.83 350	81.97 351			88.07 337		94.99 333		99.60 269	91.76 297

test9_res														93.28 276	99.15 237	99.38 156

TEST9					98.71 248	98.08 107	95.96 289	99.03 203	91.40 318	95.85 310	97.53 274	96.52 165	99.76 197

test_8					98.67 258	98.01 113	95.91 295	99.02 207	91.64 312	95.79 312	97.50 277	96.47 168	99.76 197

agg_prior2														92.50 291	99.16 234	99.37 157

agg_prior					98.68 255	97.99 114		99.01 210		95.59 314			99.77 192

TestCases				99.16 84	99.50 86	98.55 76		99.58 36	96.80 212	98.88 141	99.06 117	97.65 84	99.57 280	94.45 242	99.61 159	99.37 157

test_prior4						97.97 119	95.86 296

test_prior2							95.74 302		96.48 225	96.11 304	97.63 270	95.92 192		94.16 249	99.20 227

test_prior				98.95 115	98.69 253	97.95 122		99.03 203					99.59 273			99.30 181

旧先验2							95.76 300		88.56 336	97.52 248			99.66 252	94.48 240

新几何2							95.93 293

新几何1				98.91 121	98.94 208	97.76 140		98.76 245	87.58 339	96.75 287	98.10 246	94.80 226	99.78 183	92.73 288	99.00 253	99.20 201

旧先验1					98.82 236	97.45 158		98.76 245			98.34 227	95.50 206			99.01 252	99.23 195

无先验							95.74 302	98.74 250	89.38 332				99.73 217	92.38 293		99.22 199

原ACMM2							95.53 309

原ACMM1				98.35 200	98.90 218	96.25 209		98.83 239	92.48 303	96.07 307	98.10 246	95.39 209	99.71 227	92.61 290	98.99 254	99.08 217

test222					98.92 214	96.93 181	95.54 308	98.78 244	85.72 343	96.86 283	98.11 245	94.43 234			99.10 245	99.23 195

testdata2													99.79 174	92.80 286

segment_acmp												97.02 131

testdata				98.09 216	98.93 210	95.40 238		98.80 242	90.08 329	97.45 253	98.37 224	95.26 211	99.70 229	93.58 269	98.95 258	99.17 211

testdata1							95.44 313		96.32 230

test12				98.93 118	98.58 269	97.83 132		98.66 255		96.53 293		95.51 205	99.69 233		99.13 241	99.27 186

plane_prior7					99.19 160	97.87 129

plane_prior6					98.99 201	97.70 146						94.90 218

plane_prior5								99.27 146					99.70 229	94.42 244	99.51 189	99.45 132

plane_prior4											97.98 254

plane_prior3						97.78 139			97.41 177	97.79 229

plane_prior2							97.77 171		98.20 121

plane_prior1					99.05 189

plane_prior						97.65 148	97.07 231		96.72 215						99.36 204

n2								0.00 361

nn								0.00 361

door-mid								99.57 43

lessismore_v0				98.97 113	99.73 28	97.53 154		86.71 353		99.37 64	99.52 45	89.93 276	99.92 34	98.99 51	99.72 123	99.44 134

LGP-MVS_train				99.47 47	99.57 62	98.97 50		99.48 74	96.60 223	99.10 105	99.06 117	98.71 27	99.83 117	95.58 222	99.78 100	99.62 45

test11								98.87 228

door								99.41 97

HQP5-MVS						96.79 184

HQP-NCC					98.67 258		96.29 273		96.05 241	95.55 318

ACMP_Plane					98.67 258		96.29 273		96.05 241	95.55 318

BP-MVS														92.82 284

HQP4-MVS										95.56 317			99.54 287			99.32 174

HQP3-MVS								99.04 201							99.26 221

HQP2-MVS												93.84 245

NP-MVS					98.84 231	97.39 161					96.84 297

MDTV_nov1_ep13_2view						74.92 355	97.69 179		90.06 330	97.75 232		85.78 296		93.52 270		98.69 263

MDTV_nov1_ep13			95.22 268		97.06 333	83.20 347	97.74 175	96.16 314	94.37 281	96.99 274	98.83 164	83.95 310	99.53 289	93.90 258	97.95 308

ACMMP++_ref															99.77 104

ACMMP++															99.68 142

Test By Simon												96.52 165

ITE_SJBPF				98.87 126	99.22 144	98.48 83		99.35 117	97.50 166	98.28 193	98.60 202	97.64 87	99.35 318	93.86 261	99.27 219	98.79 253

DeepMVS_CX				93.44 332	98.24 292	94.21 266		94.34 327	64.28 351	91.34 346	94.87 340	89.45 281	92.77 354	77.54 351	93.14 347	93.35 349

High-resolution multi-view benchmark