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		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 4

Anonymous20231211		99.29 2	99.41 2	98.91 22	99.94 2	97.08 37	99.47 3	99.51 5	99.56 2	99.83 3	99.80 2	99.13 3	99.90 13	97.55 49	99.93 21	99.75 13

LTVRE_ROB	96.88 1	99.18 3	99.34 3	98.72 35	99.71 7	96.99 40	99.69 2	99.57 3	99.02 14	99.62 10	99.36 16	98.53 8	99.52 162	98.58 24	99.95 13	99.66 23
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
pmmvs6		99.07 4	99.24 4	98.56 44	99.81 3	96.38 56	98.87 9	99.30 9	99.01 15	99.63 9	99.66 4	99.27 2	99.68 103	97.75 41	99.89 33	99.62 31

mvs_tets		98.90 5	98.94 8	98.75 30	99.69 8	96.48 54	98.54 20	99.22 10	96.23 109	99.71 5	99.48 7	98.77 7	99.93 2	98.89 10	99.95 13	99.84 6

TDRefinement		98.90 5	98.86 11	99.02 8	99.54 23	98.06 6	99.34 5	99.44 7	98.85 19	99.00 40	99.20 31	97.42 31	99.59 143	97.21 62	99.76 50	99.40 90

UA-Net		98.88 7	98.76 16	99.22 2	99.11 77	97.89 10	99.47 3	99.32 8	99.08 9	97.87 136	99.67 3	96.47 73	99.92 4	97.88 34	99.98 3	99.85 4

v52		98.85 8	99.01 5	98.37 55	99.61 15	95.53 82	99.01 7	99.04 45	98.48 26	99.31 22	99.41 11	96.82 56	99.87 21	99.44 2	99.95 13	99.70 19

V4		98.85 8	99.01 5	98.37 55	99.61 15	95.53 82	99.01 7	99.04 45	98.48 26	99.31 22	99.41 11	96.81 57	99.87 21	99.44 2	99.95 13	99.70 19

DTE-MVSNet		98.79 10	98.86 11	98.59 42	99.55 21	96.12 63	98.48 24	99.10 25	99.36 3	99.29 25	99.06 47	97.27 37	99.93 2	97.71 43	99.91 27	99.70 19

jajsoiax		98.77 11	98.79 15	98.74 32	99.66 10	96.48 54	98.45 25	99.12 22	95.83 124	99.67 7	99.37 14	98.25 11	99.92 4	98.77 14	99.94 19	99.82 7

PEN-MVS		98.75 12	98.85 13	98.44 49	99.58 18	95.67 76	98.45 25	99.15 19	99.33 4	99.30 24	99.00 48	97.27 37	99.92 4	97.64 44	99.92 24	99.75 13

v7n		98.73 13	98.99 7	97.95 81	99.64 12	94.20 125	98.67 12	99.14 20	99.08 9	99.42 16	99.23 29	96.53 68	99.91 12	99.27 4	99.93 21	99.73 16

PS-CasMVS		98.73 13	98.85 13	98.39 54	99.55 21	95.47 84	98.49 22	99.13 21	99.22 7	99.22 28	98.96 52	97.35 33	99.92 4	97.79 39	99.93 21	99.79 8

test_djsdf		98.73 13	98.74 18	98.69 37	99.63 13	96.30 59	98.67 12	99.02 51	96.50 98	99.32 21	99.44 10	97.43 30	99.92 4	98.73 17	99.95 13	99.86 3

anonymousdsp		98.72 16	98.63 21	98.99 10	99.62 14	97.29 34	98.65 15	99.19 14	95.62 130	99.35 20	99.37 14	97.38 32	99.90 13	98.59 23	99.91 27	99.77 9

wuykxyi23d		98.68 17	98.53 26	99.13 3	99.44 34	97.97 7	96.85 117	99.02 51	95.81 125	99.88 2	99.38 13	98.14 14	99.69 97	98.32 28	99.95 13	99.73 16

WR-MVS_H		98.65 18	98.62 23	98.75 30	99.51 26	96.61 50	98.55 19	99.17 15	99.05 12	99.17 31	98.79 60	95.47 104	99.89 17	97.95 32	99.91 27	99.75 13

OurMVSNet-221017-0		98.61 19	98.61 25	98.63 41	99.77 4	96.35 57	99.17 6	99.05 38	98.05 41	99.61 11	99.52 5	93.72 163	99.88 19	98.72 20	99.88 34	99.65 24

v748		98.58 20	98.89 10	97.67 98	99.61 15	93.53 148	98.59 16	98.90 75	98.97 17	99.43 15	99.15 40	96.53 68	99.85 24	98.88 11	99.91 27	99.64 27

nrg030		98.54 21	98.62 23	98.32 60	99.22 56	95.66 77	97.90 56	99.08 30	98.31 32	99.02 37	98.74 65	97.68 24	99.61 133	97.77 40	99.85 39	99.70 19

PS-MVSNAJss		98.53 22	98.63 21	98.21 68	99.68 9	94.82 104	98.10 44	99.21 11	96.91 87	99.75 4	99.45 9	95.82 90	99.92 4	98.80 13	99.96 11	99.89 1

MIMVSNet1		98.51 23	98.45 31	98.67 38	99.72 6	96.71 46	98.76 10	98.89 77	98.49 25	99.38 18	99.14 41	95.44 106	99.84 28	96.47 81	99.80 46	99.47 64

pm-mvs1		98.47 24	98.67 19	97.86 85	99.52 25	94.58 112	98.28 31	99.00 62	97.57 63	99.27 26	99.22 30	98.32 10	99.50 173	97.09 68	99.75 54	99.50 50

ACMH	93.61 9	98.44 25	98.76 16	97.51 108	99.43 37	93.54 147	98.23 34	99.05 38	97.40 79	99.37 19	99.08 46	98.79 6	99.47 180	97.74 42	99.71 63	99.50 50
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		98.42 26	98.46 29	98.30 63	99.46 32	95.22 92	98.27 33	98.84 87	99.05 12	99.01 38	98.65 73	95.37 107	99.90 13	97.57 48	99.91 27	99.77 9

abl_6		98.42 26	98.19 41	99.09 4	99.16 64	98.10 5	97.73 69	99.11 23	97.76 50	98.62 56	98.27 103	97.88 21	99.80 37	95.67 105	99.50 111	99.38 95

TransMVSNet (Re)		98.38 28	98.67 19	97.51 108	99.51 26	93.39 152	98.20 39	98.87 81	98.23 35	99.48 12	99.27 25	98.47 9	99.55 155	96.52 78	99.53 104	99.60 34

TranMVSNet+NR-MVSNet		98.33 29	98.30 39	98.43 50	99.07 81	95.87 69	96.73 121	99.05 38	98.67 21	98.84 45	98.45 86	97.58 27	99.88 19	96.45 82	99.86 38	99.54 45

HPM-MVS_fast		98.32 30	98.13 44	98.88 23	99.54 23	97.48 27	98.35 28	99.03 50	95.88 121	97.88 131	98.22 108	98.15 13	99.74 59	96.50 80	99.62 79	99.42 85

ANet_high		98.31 31	98.94 8	96.41 176	99.33 47	89.64 213	97.92 55	99.56 4	99.27 5	99.66 8	99.50 6	97.67 25	99.83 30	97.55 49	99.98 3	99.77 9

VPA-MVSNet		98.27 32	98.46 29	97.70 94	99.06 82	93.80 137	97.76 64	99.00 62	98.40 29	99.07 35	98.98 50	96.89 50	99.75 54	97.19 65	99.79 47	99.55 44

Vis-MVSNet		98.27 32	98.34 35	98.07 73	99.33 47	95.21 94	98.04 48	99.46 6	97.32 82	97.82 140	99.11 43	96.75 59	99.86 23	97.84 36	99.36 154	99.15 133
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
COLMAP_ROB	94.48 6	98.25 34	98.11 45	98.64 40	99.21 59	97.35 32	97.96 52	99.16 16	98.34 31	98.78 48	98.52 81	97.32 34	99.45 189	94.08 167	99.67 73	99.13 136
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMH+	93.58 10	98.23 35	98.31 37	97.98 80	99.39 42	95.22 92	97.55 81	99.20 13	98.21 36	99.25 27	98.51 82	98.21 12	99.40 208	94.79 144	99.72 59	99.32 106

FC-MVSNet-test		98.16 36	98.37 33	97.56 103	99.49 30	93.10 156	98.35 28	99.21 11	98.43 28	98.89 44	98.83 59	94.30 142	99.81 33	97.87 35	99.91 27	99.77 9

MTAPA		98.14 37	97.84 57	99.06 5	99.44 34	97.90 8	97.25 92	98.73 112	97.69 57	97.90 127	97.96 137	95.81 94	99.82 31	96.13 88	99.61 84	99.45 71

APDe-MVS		98.14 37	98.03 50	98.47 48	98.72 110	96.04 66	98.07 46	99.10 25	95.96 118	98.59 60	98.69 69	96.94 48	99.81 33	96.64 74	99.58 91	99.57 40

APD-MVS_3200maxsize		98.13 39	97.90 54	98.79 28	98.79 103	97.31 33	97.55 81	98.92 73	97.72 55	98.25 88	98.13 120	97.10 43	99.75 54	95.44 117	99.24 175	99.32 106

HPM-MVS		98.11 40	97.83 58	98.92 19	99.42 39	97.46 28	98.57 17	99.05 38	95.43 139	97.41 155	97.50 178	97.98 17	99.79 38	95.58 114	99.57 94	99.50 50

Gipuma		98.07 41	98.31 37	97.36 125	99.76 5	96.28 60	98.51 21	99.10 25	98.76 20	96.79 182	99.34 20	96.61 65	98.82 287	96.38 83	99.50 111	96.98 287
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ACMMP		98.05 42	97.75 63	98.93 18	99.23 55	97.60 19	98.09 45	98.96 70	95.75 127	97.91 126	98.06 129	96.89 50	99.76 48	95.32 122	99.57 94	99.43 83
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
ACMM	93.33 11	98.05 42	97.79 59	98.85 24	99.15 67	97.55 23	96.68 123	98.83 95	95.21 147	98.36 76	98.13 120	98.13 16	99.62 127	96.04 92	99.54 102	99.39 93
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v13		98.02 44	98.52 27	96.51 169	99.02 88	90.14 204	98.07 46	99.09 29	98.10 40	99.13 32	99.35 18	94.84 121	99.74 59	99.12 5	99.98 3	99.65 24

SteuartSystems-ACMMP		98.02 44	97.76 62	98.79 28	99.43 37	97.21 36	97.15 96	98.90 75	96.58 97	98.08 106	97.87 148	97.02 47	99.76 48	95.25 124	99.59 89	99.40 90
Skip Steuart: Steuart Systems R&D Blog.
MPTG		98.01 46	97.66 69	99.06 5	99.44 34	97.90 8	95.66 175	98.73 112	97.69 57	97.90 127	97.96 137	95.81 94	99.82 31	96.13 88	99.61 84	99.45 71

v12		97.97 47	98.47 28	96.46 173	98.98 92	90.01 208	97.97 51	99.08 30	98.00 43	99.11 34	99.34 20	94.70 124	99.73 64	99.07 6	99.98 3	99.64 27

XVS		97.96 48	97.63 74	98.94 15	99.15 67	97.66 16	97.77 62	98.83 95	97.42 71	96.32 202	97.64 166	96.49 71	99.72 70	95.66 107	99.37 151	99.45 71

NR-MVSNet		97.96 48	97.86 56	98.26 65	98.73 108	95.54 80	98.14 42	98.73 112	97.79 48	99.42 16	97.83 149	94.40 139	99.78 39	95.91 100	99.76 50	99.46 66

ACMMPR		97.95 50	97.62 76	98.94 15	99.20 60	97.56 22	97.59 78	98.83 95	96.05 112	97.46 153	97.63 167	96.77 58	99.76 48	95.61 111	99.46 125	99.49 58

FMVSNet1		97.95 50	98.08 46	97.56 103	99.14 75	93.67 141	98.23 34	98.66 129	97.41 78	99.00 40	99.19 32	95.47 104	99.73 64	95.83 101	99.76 50	99.30 110

HFP-MVS		97.94 52	97.64 72	98.83 25	99.15 67	97.50 25	97.59 78	98.84 87	96.05 112	97.49 148	97.54 173	97.07 45	99.70 88	95.61 111	99.46 125	99.30 110

LPG-MVS_test		97.94 52	97.67 68	98.74 32	99.15 67	97.02 38	97.09 105	99.02 51	95.15 152	98.34 78	98.23 105	97.91 19	99.70 88	94.41 155	99.73 56	99.50 50

FIs		97.93 54	98.07 47	97.48 115	99.38 43	92.95 158	98.03 50	99.11 23	98.04 42	98.62 56	98.66 71	93.75 162	99.78 39	97.23 61	99.84 40	99.73 16

region2R		97.92 55	97.59 78	98.92 19	99.22 56	97.55 23	97.60 77	98.84 87	96.00 116	97.22 160	97.62 168	96.87 53	99.76 48	95.48 115	99.43 139	99.46 66

CP-MVS		97.92 55	97.56 81	98.99 10	98.99 90	97.82 12	97.93 54	98.96 70	96.11 111	96.89 180	97.45 181	96.85 54	99.78 39	95.19 127	99.63 78	99.38 95

mPP-MVS		97.91 57	97.53 82	99.04 7	99.22 56	97.87 11	97.74 67	98.78 104	96.04 114	97.10 166	97.73 161	96.53 68	99.78 39	95.16 130	99.50 111	99.46 66

V9		97.90 58	98.40 32	96.40 177	98.93 94	89.86 210	97.86 58	99.07 34	97.88 47	99.05 36	99.30 23	94.53 135	99.72 70	99.01 8	99.98 3	99.63 29

ACMMP_Plus		97.89 59	97.63 74	98.67 38	99.35 46	96.84 43	96.36 133	98.79 101	95.07 159	97.88 131	98.35 92	97.24 40	99.72 70	96.05 91	99.58 91	99.45 71

PGM-MVS		97.88 60	97.52 83	98.96 13	99.20 60	97.62 18	97.09 105	99.06 36	95.45 137	97.55 144	97.94 141	97.11 42	99.78 39	94.77 146	99.46 125	99.48 61

DP-MVS		97.87 61	97.89 55	97.81 88	98.62 126	94.82 104	97.13 99	98.79 101	98.98 16	98.74 51	98.49 83	95.80 96	99.49 175	95.04 137	99.44 130	99.11 144

RPSCF		97.87 61	97.51 84	98.95 14	99.15 67	98.43 3	97.56 80	99.06 36	96.19 110	98.48 68	98.70 68	94.72 123	99.24 241	94.37 158	99.33 164	99.17 129

test_0402		97.84 63	97.97 51	97.47 116	99.19 62	94.07 128	96.71 122	98.73 112	98.66 22	98.56 62	98.41 88	96.84 55	99.69 97	94.82 141	99.81 43	98.64 198

V14		97.83 64	98.33 36	96.35 178	98.88 100	89.72 211	97.75 65	99.05 38	97.74 51	99.01 38	99.27 25	94.35 140	99.71 80	98.95 9	99.97 8	99.62 31

UniMVSNet_NR-MVSNet		97.83 64	97.65 70	98.37 55	98.72 110	95.78 71	95.66 175	99.02 51	98.11 39	98.31 83	97.69 165	94.65 129	99.85 24	97.02 70	99.71 63	99.48 61

UniMVSNet (Re)		97.83 64	97.65 70	98.35 59	98.80 102	95.86 70	95.92 163	99.04 45	97.51 68	98.22 90	97.81 153	94.68 127	99.78 39	97.14 67	99.75 54	99.41 87

v11		97.82 67	98.36 34	96.17 194	98.93 94	89.16 230	97.79 61	99.08 30	97.64 60	99.19 29	99.32 22	94.28 143	99.72 70	99.07 6	99.97 8	99.63 29

DeepC-MVS	95.41 4	97.82 67	97.70 65	98.16 69	98.78 104	95.72 73	96.23 142	99.02 51	93.92 196	98.62 56	98.99 49	97.69 23	99.62 127	96.18 87	99.87 36	99.15 133
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DU-MVS		97.79 69	97.60 77	98.36 58	98.73 108	95.78 71	95.65 177	98.87 81	97.57 63	98.31 83	97.83 149	94.69 125	99.85 24	97.02 70	99.71 63	99.46 66

v15		97.77 70	98.26 40	96.30 183	98.81 101	89.59 218	97.62 74	99.04 45	97.59 62	98.97 42	99.24 27	94.19 147	99.70 88	98.88 11	99.97 8	99.61 33

LS3D		97.77 70	97.50 85	98.57 43	96.24 297	97.58 21	98.45 25	98.85 84	98.58 24	97.51 146	97.94 141	95.74 97	99.63 121	95.19 127	98.97 199	98.51 209

3Dnovator+	96.13 3	97.73 72	97.59 78	98.15 70	98.11 194	95.60 78	98.04 48	98.70 121	98.13 38	96.93 178	98.45 86	95.30 111	99.62 127	95.64 109	98.96 200	99.24 122

tfpnnormal		97.72 73	97.97 51	96.94 146	99.26 51	92.23 169	97.83 60	98.45 151	98.25 34	99.13 32	98.66 71	96.65 63	99.69 97	93.92 172	99.62 79	98.91 171

Baseline_NR-MVSNet		97.72 73	97.79 59	97.50 111	99.56 19	93.29 153	95.44 186	98.86 83	98.20 37	98.37 75	99.24 27	94.69 125	99.55 155	95.98 97	99.79 47	99.65 24

v17		97.70 75	98.17 42	96.28 186	98.77 105	89.59 218	97.62 74	99.01 60	97.54 65	98.72 53	99.18 35	94.06 151	99.68 103	98.74 16	99.92 24	99.58 36

MP-MVS-pluss		97.69 76	97.36 90	98.70 36	99.50 29	96.84 43	95.38 195	98.99 65	92.45 236	98.11 100	98.31 96	97.25 39	99.77 47	96.60 75	99.62 79	99.48 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v16		97.69 76	98.16 43	96.29 185	98.75 106	89.60 216	97.62 74	99.01 60	97.53 67	98.69 55	99.18 35	94.05 152	99.68 103	98.73 17	99.88 34	99.58 36

EG-PatchMatch MVS		97.69 76	97.79 59	97.40 123	99.06 82	93.52 149	95.96 159	98.97 69	94.55 176	98.82 46	98.76 63	97.31 35	99.29 235	97.20 64	99.44 130	99.38 95

MP-MVS		97.64 79	97.18 105	99.00 9	99.32 49	97.77 14	97.49 84	98.73 112	96.27 106	95.59 230	97.75 158	96.30 78	99.78 39	93.70 178	99.48 121	99.45 71

#test#		97.62 80	97.22 103	98.83 25	99.15 67	97.50 25	96.81 119	98.84 87	94.25 187	97.49 148	97.54 173	97.07 45	99.70 88	94.37 158	99.46 125	99.30 110

3Dnovator	96.53 2	97.61 81	97.64 72	97.50 111	97.74 236	93.65 145	98.49 22	98.88 79	96.86 90	97.11 165	98.55 79	95.82 90	99.73 64	95.94 98	99.42 142	99.13 136

v18		97.60 82	98.06 48	96.23 187	98.68 120	89.46 221	97.48 85	98.98 67	97.33 81	98.60 59	99.13 42	93.86 155	99.67 109	98.62 21	99.87 36	99.56 41

v8		97.60 82	98.06 48	96.23 187	98.71 113	89.44 222	97.43 87	98.82 99	97.29 83	98.74 51	99.10 44	93.86 155	99.68 103	98.61 22	99.94 19	99.56 41

XVG-ACMP-BASELINE		97.58 84	97.28 95	98.49 46	99.16 64	96.90 42	96.39 129	98.98 67	95.05 160	98.06 109	98.02 132	95.86 86	99.56 151	94.37 158	99.64 77	99.00 157

v10		97.55 85	97.97 51	96.31 182	98.60 128	89.64 213	97.44 86	99.02 51	96.60 95	98.72 53	99.16 39	93.48 167	99.72 70	98.76 15	99.92 24	99.58 36

OPM-MVS		97.54 86	97.25 96	98.41 51	99.11 77	96.61 50	95.24 207	98.46 150	94.58 175	98.10 103	98.07 126	97.09 44	99.39 214	95.16 130	99.44 130	99.21 124

XXY-MVS		97.54 86	97.70 65	97.07 139	99.46 32	92.21 170	97.22 95	99.00 62	94.93 163	98.58 61	98.92 56	97.31 35	99.41 205	94.44 153	99.43 139	99.59 35

Regformer-4		97.53 88	97.47 87	97.71 93	97.35 264	93.91 133	95.26 205	98.14 197	97.97 44	98.34 78	97.89 146	95.49 102	99.71 80	97.41 57	99.42 142	99.51 49

SixPastTwentyTwo		97.49 89	97.57 80	97.26 131	99.56 19	92.33 166	98.28 31	96.97 257	98.30 33	99.45 14	99.35 18	88.43 253	99.89 17	98.01 31	99.76 50	99.54 45

ACMP	92.54 13	97.47 90	97.10 112	98.55 45	99.04 85	96.70 47	96.24 141	98.89 77	93.71 206	97.97 118	97.75 158	97.44 29	99.63 121	93.22 186	99.70 66	99.32 106
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testing_2		97.43 91	97.71 64	96.60 162	98.91 97	90.85 194	96.01 152	98.54 143	94.78 168	98.78 48	98.96 52	96.35 77	99.54 157	97.25 60	99.82 42	99.40 90

TSAR-MVS + MP.		97.42 92	97.23 102	98.00 79	99.38 43	95.00 98	97.63 73	98.20 188	93.00 222	98.16 95	98.06 129	95.89 85	99.72 70	95.67 105	99.10 188	99.28 117

Regformer-2		97.41 93	97.24 98	97.93 82	97.21 273	94.72 107	94.85 228	98.27 180	97.74 51	98.11 100	97.50 178	95.58 100	99.69 97	96.57 77	99.31 166	99.37 100

CSCG		97.40 94	97.30 92	97.69 96	98.95 93	94.83 103	97.28 91	98.99 65	96.35 105	98.13 99	95.95 263	95.99 83	99.66 114	94.36 161	99.73 56	98.59 203

XVG-OURS-SEG-HR		97.38 95	97.07 115	98.30 63	99.01 89	97.41 31	94.66 233	99.02 51	95.20 148	98.15 97	97.52 176	98.83 5	98.43 315	94.87 139	96.41 309	99.07 151

HSP-MVS		97.37 96	96.85 125	98.92 19	99.26 51	97.70 15	97.66 70	98.23 184	95.65 128	98.51 65	96.46 239	92.15 203	99.81 33	95.14 132	98.58 235	99.26 121

VDD-MVS		97.37 96	97.25 96	97.74 92	98.69 119	94.50 115	97.04 107	95.61 282	98.59 23	98.51 65	98.72 66	92.54 195	99.58 145	96.02 94	99.49 118	99.12 141

SD-MVS		97.37 96	97.70 65	96.35 178	98.14 190	95.13 95	96.54 124	98.92 73	95.94 119	99.19 29	98.08 125	97.74 22	95.06 347	95.24 125	99.54 102	98.87 180

PM-MVS		97.36 99	97.10 112	98.14 71	98.91 97	96.77 45	96.20 143	98.63 136	93.82 203	98.54 63	98.33 94	93.98 153	99.05 259	95.99 96	99.45 129	98.61 202

LCM-MVSNet-Re		97.33 100	97.33 91	97.32 127	98.13 193	93.79 138	96.99 109	99.65 2	96.74 93	99.47 13	98.93 55	96.91 49	99.84 28	90.11 246	99.06 194	98.32 226

EI-MVSNet-UG-set		97.32 101	97.40 88	97.09 138	97.34 267	92.01 178	95.33 199	97.65 230	97.74 51	98.30 85	98.14 119	95.04 117	99.69 97	97.55 49	99.52 108	99.58 36

EI-MVSNet-Vis-set		97.32 101	97.39 89	97.11 136	97.36 263	92.08 176	95.34 198	97.65 230	97.74 51	98.29 86	98.11 123	95.05 115	99.68 103	97.50 53	99.50 111	99.56 41

Regformer-1		97.27 103	97.16 107	97.61 101	97.21 273	93.86 135	94.85 228	98.04 208	97.62 61	98.03 112	97.50 178	95.34 108	99.63 121	96.52 78	99.31 166	99.35 104

VPNet		97.26 104	97.49 86	96.59 164	99.47 31	90.58 200	96.27 137	98.53 144	97.77 49	98.46 70	98.41 88	94.59 131	99.68 103	94.61 149	99.29 170	99.52 48

Regformer-3		97.25 105	97.29 93	97.11 136	97.35 264	92.32 167	95.26 205	97.62 235	97.67 59	98.17 94	97.89 146	95.05 115	99.56 151	97.16 66	99.42 142	99.46 66

canonicalmvs		97.23 106	97.21 104	97.30 128	97.65 245	94.39 117	97.84 59	99.05 38	97.42 71	96.68 185	93.85 303	97.63 26	99.33 228	96.29 85	98.47 240	98.18 241

ESAPD		97.22 107	96.82 128	98.40 53	99.03 86	96.07 64	95.64 179	98.84 87	94.84 164	98.08 106	97.60 170	96.69 61	99.76 48	91.22 218	99.44 130	99.37 100

AllTest		97.20 108	96.92 123	98.06 74	99.08 79	96.16 61	97.14 98	99.16 16	94.35 184	97.78 141	98.07 126	95.84 87	99.12 250	91.41 212	99.42 142	98.91 171

XVG-OURS		97.12 109	96.74 133	98.26 65	98.99 90	97.45 29	93.82 269	99.05 38	95.19 149	98.32 81	97.70 164	95.22 113	98.41 316	94.27 163	98.13 251	98.93 168

V42		97.04 110	97.16 107	96.68 160	98.59 130	91.05 191	96.33 135	98.36 165	94.60 172	97.99 114	98.30 99	93.32 173	99.62 127	97.40 58	99.53 104	99.38 95

APD-MVS		97.00 111	96.53 145	98.41 51	98.55 135	96.31 58	96.32 136	98.77 105	92.96 228	97.44 154	97.58 172	95.84 87	99.74 59	91.96 200	99.35 157	99.19 126
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		96.99 112	96.38 150	98.81 27	98.64 121	97.59 20	95.97 155	98.20 188	95.51 135	95.06 238	96.53 235	94.10 150	99.70 88	94.29 162	99.15 181	99.13 136

GBi-Net		96.99 112	96.80 130	97.56 103	97.96 207	93.67 141	98.23 34	98.66 129	95.59 132	97.99 114	99.19 32	89.51 244	99.73 64	94.60 150	99.44 130	99.30 110

test1		96.99 112	96.80 130	97.56 103	97.96 207	93.67 141	98.23 34	98.66 129	95.59 132	97.99 114	99.19 32	89.51 244	99.73 64	94.60 150	99.44 130	99.30 110

VDDNet		96.98 115	96.84 126	97.41 122	99.40 41	93.26 154	97.94 53	95.31 284	99.26 6	98.39 74	99.18 35	87.85 260	99.62 127	95.13 133	99.09 189	99.35 104

v1neww		96.97 116	97.24 98	96.15 195	98.70 115	89.44 222	95.97 155	98.33 170	95.25 144	97.88 131	98.15 116	93.83 158	99.61 133	97.50 53	99.50 111	99.41 87

v7new		96.97 116	97.24 98	96.15 195	98.70 115	89.44 222	95.97 155	98.33 170	95.25 144	97.88 131	98.15 116	93.83 158	99.61 133	97.50 53	99.50 111	99.41 87

v6		96.97 116	97.24 98	96.15 195	98.71 113	89.44 222	95.97 155	98.33 170	95.25 144	97.89 129	98.15 116	93.86 155	99.61 133	97.51 52	99.50 111	99.42 85

PHI-MVS		96.96 119	96.53 145	98.25 67	97.48 254	96.50 53	96.76 120	98.85 84	93.52 209	96.19 211	96.85 214	95.94 84	99.42 194	93.79 176	99.43 139	98.83 184

v7		96.93 120	97.17 106	96.23 187	98.59 130	89.64 213	95.96 159	98.66 129	94.41 180	97.87 136	98.38 91	93.47 168	99.64 118	97.93 33	99.24 175	99.43 83

IS-MVSNet		96.93 120	96.68 135	97.70 94	99.25 54	94.00 131	98.57 17	96.74 264	98.36 30	98.14 98	97.98 136	88.23 254	99.71 80	93.10 189	99.72 59	99.38 95

CNVR-MVS		96.92 122	96.55 142	98.03 78	98.00 204	95.54 80	94.87 226	98.17 193	94.60 172	96.38 197	97.05 202	95.67 98	99.36 223	95.12 134	99.08 190	99.19 126

IterMVS-LS		96.92 122	97.29 93	95.79 217	98.51 142	88.13 253	95.10 211	98.66 129	96.99 84	98.46 70	98.68 70	92.55 193	99.74 59	96.91 72	99.79 47	99.50 50
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS		96.90 124	96.81 129	97.16 133	98.56 134	92.20 172	94.33 241	98.12 199	97.34 80	98.20 92	97.33 191	92.81 184	99.75 54	94.79 144	99.81 43	99.54 45

DeepPCF-MVS	94.58 5	96.90 124	96.43 149	98.31 62	97.48 254	97.23 35	92.56 302	98.60 139	92.84 230	98.54 63	97.40 184	96.64 64	98.78 291	94.40 157	99.41 148	98.93 168

v1141		96.86 126	97.14 109	96.04 202	98.55 135	89.06 233	95.44 186	98.33 170	95.14 154	97.93 124	98.19 110	93.36 171	99.62 127	97.61 45	99.69 67	99.44 79

divwei89l23v2f112		96.86 126	97.14 109	96.04 202	98.54 138	89.06 233	95.44 186	98.33 170	95.14 154	97.93 124	98.19 110	93.36 171	99.61 133	97.61 45	99.68 71	99.44 79

v1		96.86 126	97.14 109	96.04 202	98.55 135	89.06 233	95.44 186	98.33 170	95.14 154	97.94 121	98.18 114	93.39 170	99.61 133	97.61 45	99.69 67	99.44 79

v1144		96.84 129	97.08 114	96.13 199	98.42 152	89.28 228	95.41 193	98.67 127	94.21 189	97.97 118	98.31 96	93.06 178	99.65 115	98.06 30	99.62 79	99.45 71

VNet		96.84 129	96.83 127	96.88 150	98.06 196	92.02 177	96.35 134	97.57 237	97.70 56	97.88 131	97.80 154	92.40 200	99.54 157	94.73 148	98.96 200	99.08 149

EPP-MVSNet		96.84 129	96.58 139	97.65 99	99.18 63	93.78 139	98.68 11	96.34 267	97.91 46	97.30 158	98.06 129	88.46 252	99.85 24	93.85 174	99.40 149	99.32 106

v1192		96.83 132	97.06 116	96.15 195	98.28 162	89.29 227	95.36 196	98.77 105	93.73 205	98.11 100	98.34 93	93.02 182	99.67 109	98.35 26	99.58 91	99.50 50

MVS_111021_LR		96.82 133	96.55 142	97.62 100	98.27 164	95.34 87	93.81 270	98.33 170	94.59 174	96.56 189	96.63 230	96.61 65	98.73 295	94.80 143	99.34 159	98.78 189

Effi-MVS+-dtu		96.81 134	96.09 159	98.99 10	96.90 285	98.69 2	96.42 128	98.09 201	95.86 122	95.15 237	95.54 273	94.26 144	99.81 33	94.06 168	98.51 238	98.47 211

UGNet		96.81 134	96.56 141	97.58 102	96.64 288	93.84 136	97.75 65	97.12 252	96.47 101	93.62 286	98.88 58	93.22 176	99.53 159	95.61 111	99.69 67	99.36 103
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
v2v482		96.78 136	97.06 116	95.95 210	98.57 133	88.77 243	95.36 196	98.26 182	95.18 150	97.85 138	98.23 105	92.58 192	99.63 121	97.80 38	99.69 67	99.45 71

v1240		96.74 137	97.02 118	95.91 213	98.18 183	88.52 245	95.39 194	98.88 79	93.15 219	98.46 70	98.40 90	92.80 185	99.71 80	98.45 25	99.49 118	99.49 58

DeepC-MVS_fast	94.34 7	96.74 137	96.51 147	97.44 120	97.69 240	94.15 126	96.02 151	98.43 155	93.17 218	97.30 158	97.38 189	95.48 103	99.28 236	93.74 177	99.34 159	98.88 178
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		96.73 139	96.54 144	97.27 129	98.35 156	93.66 144	93.42 283	98.36 165	94.74 169	96.58 187	96.76 223	96.54 67	98.99 267	94.87 139	99.27 173	99.15 133

v1921920		96.72 140	96.96 121	95.99 206	98.21 177	88.79 242	95.42 191	98.79 101	93.22 213	98.19 93	98.26 104	92.68 188	99.70 88	98.34 27	99.55 100	99.49 58

FMVSNet2		96.72 140	96.67 136	96.87 151	97.96 207	91.88 180	97.15 96	98.06 206	95.59 132	98.50 67	98.62 74	89.51 244	99.65 115	94.99 138	99.60 87	99.07 151

PMVS	89.60 17	96.71 142	96.97 119	95.95 210	99.51 26	97.81 13	97.42 88	97.49 238	97.93 45	95.95 217	98.58 75	96.88 52	96.91 341	89.59 253	99.36 154	93.12 340
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v144192		96.69 143	96.90 124	96.03 205	98.25 173	88.92 236	95.49 184	98.77 105	93.05 221	98.09 104	98.29 100	92.51 197	99.70 88	98.11 29	99.56 96	99.47 64

CPTT-MVS		96.69 143	96.08 160	98.49 46	98.89 99	96.64 49	97.25 92	98.77 105	92.89 229	96.01 216	97.13 198	92.23 202	99.67 109	92.24 198	99.34 159	99.17 129

HQP_MVS		96.66 145	96.33 153	97.68 97	98.70 115	94.29 120	96.50 126	98.75 109	96.36 103	96.16 212	96.77 221	91.91 215	99.46 185	92.59 194	99.20 178	99.28 117

EI-MVSNet		96.63 146	96.93 122	95.74 218	97.26 271	88.13 253	95.29 203	97.65 230	96.99 84	97.94 121	98.19 110	92.55 193	99.58 145	96.91 72	99.56 96	99.50 50

ab-mvs		96.59 147	96.59 138	96.60 162	98.64 121	92.21 170	98.35 28	97.67 226	94.45 177	96.99 170	98.79 60	94.96 119	99.49 175	90.39 243	99.07 192	98.08 244

v148		96.58 148	96.97 119	95.42 228	98.63 125	87.57 268	95.09 213	97.90 211	95.91 120	98.24 89	97.96 137	93.42 169	99.39 214	96.04 92	99.52 108	99.29 116

test20.03		96.58 148	96.61 137	96.48 172	98.49 144	91.72 184	95.68 174	97.69 225	96.81 91	98.27 87	97.92 144	94.18 148	98.71 297	90.78 230	99.66 75	99.00 157

NCCC		96.52 150	95.99 164	98.10 72	97.81 221	95.68 75	95.00 222	98.20 188	95.39 140	95.40 233	96.36 246	93.81 160	99.45 189	93.55 181	98.42 241	99.17 129

pmmvs-eth3d		96.49 151	96.18 156	97.42 121	98.25 173	94.29 120	94.77 232	98.07 205	89.81 264	97.97 118	98.33 94	93.11 177	99.08 256	95.46 116	99.84 40	98.89 174

OMC-MVS		96.48 152	96.00 163	97.91 83	98.30 158	96.01 68	94.86 227	98.60 139	91.88 246	97.18 162	97.21 195	96.11 81	99.04 260	90.49 241	99.34 159	98.69 196

TSAR-MVS + GP.		96.47 153	96.12 157	97.49 114	97.74 236	95.23 89	94.15 254	96.90 259	93.26 212	98.04 111	96.70 226	94.41 138	98.89 279	94.77 146	99.14 182	98.37 219

Fast-Effi-MVS+-dtu		96.44 154	96.12 157	97.39 124	97.18 275	94.39 117	95.46 185	98.73 112	96.03 115	94.72 246	94.92 285	96.28 80	99.69 97	93.81 175	97.98 255	98.09 243

K. test v3		96.44 154	96.28 154	96.95 145	99.41 40	91.53 186	97.65 71	90.31 330	98.89 18	98.93 43	99.36 16	84.57 278	99.92 4	97.81 37	99.56 96	99.39 93

MSLP-MVS++		96.42 156	96.71 134	95.57 223	97.82 220	90.56 202	95.71 170	98.84 87	94.72 170	96.71 184	97.39 187	94.91 120	98.10 331	95.28 123	99.02 196	98.05 249

MVS_Test		96.27 157	96.79 132	94.73 251	96.94 283	86.63 284	96.18 144	98.33 170	94.94 161	96.07 214	98.28 101	95.25 112	99.26 239	97.21 62	97.90 261	98.30 229

MCST-MVS		96.24 158	95.80 170	97.56 103	98.75 106	94.13 127	94.66 233	98.17 193	90.17 261	96.21 210	96.10 257	95.14 114	99.43 193	94.13 166	98.85 215	99.13 136

MVS_0304		96.22 159	95.94 168	97.04 141	97.07 279	92.54 162	94.19 250	99.04 45	95.17 151	93.74 281	96.92 211	91.77 217	99.73 64	95.76 103	99.81 43	98.85 183

mvs-test1		96.20 160	95.50 178	98.32 60	96.90 285	98.16 4	95.07 216	98.09 201	95.86 122	93.63 285	94.32 299	94.26 144	99.71 80	94.06 168	97.27 296	97.07 284

Effi-MVS+		96.19 161	96.01 162	96.71 157	97.43 260	92.19 173	96.12 147	99.10 25	95.45 137	93.33 299	94.71 287	97.23 41	99.56 151	93.21 187	97.54 284	98.37 219

DELS-MVS		96.17 162	96.23 155	95.99 206	97.55 252	90.04 206	92.38 306	98.52 145	94.13 192	96.55 192	97.06 201	94.99 118	99.58 145	95.62 110	99.28 171	98.37 219
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
MVSFormer		96.14 163	96.36 151	95.49 227	97.68 241	87.81 265	98.67 12	99.02 51	96.50 98	94.48 260	96.15 254	86.90 265	99.92 4	98.73 17	99.13 184	98.74 192

testgi		96.07 164	96.50 148	94.80 248	99.26 51	87.69 267	95.96 159	98.58 142	95.08 158	98.02 113	96.25 250	97.92 18	97.60 337	88.68 268	98.74 221	99.11 144

LF4IMVS		96.07 164	95.63 175	97.36 125	98.19 180	95.55 79	95.44 186	98.82 99	92.29 238	95.70 228	96.55 233	92.63 191	98.69 299	91.75 209	99.33 164	97.85 259

alignmvs		96.01 166	95.52 177	97.50 111	97.77 235	94.71 108	96.07 148	96.84 260	97.48 69	96.78 183	94.28 300	85.50 271	99.40 208	96.22 86	98.73 224	98.40 216

TinyColmap		96.00 167	96.34 152	94.96 242	97.90 212	87.91 262	94.13 256	98.49 148	94.41 180	98.16 95	97.76 155	96.29 79	98.68 302	90.52 238	99.42 142	98.30 229

PVSNet_Blended_VisFu		95.95 168	95.80 170	96.42 175	99.28 50	90.62 199	95.31 201	99.08 30	88.40 275	96.97 176	98.17 115	92.11 205	99.78 39	93.64 179	99.21 177	98.86 181

test_prior3		95.91 169	95.39 181	97.46 117	97.79 230	94.26 123	93.33 287	98.42 158	94.21 189	94.02 272	96.25 250	93.64 164	99.34 225	91.90 201	98.96 200	98.79 187

UnsupCasMVSNet_eth		95.91 169	95.73 172	96.44 174	98.48 146	91.52 187	95.31 201	98.45 151	95.76 126	97.48 151	97.54 173	89.53 243	98.69 299	94.43 154	94.61 326	99.13 136

QAPM		95.88 171	95.57 176	96.80 152	97.90 212	91.84 182	98.18 41	98.73 112	88.41 274	96.42 195	98.13 120	94.73 122	99.75 54	88.72 266	98.94 204	98.81 185

CANet		95.86 172	95.65 174	96.49 171	96.41 295	90.82 196	94.36 240	98.41 160	94.94 161	92.62 311	96.73 224	92.68 188	99.71 80	95.12 134	99.60 87	98.94 165

MVP-Stereo		95.69 173	95.28 183	96.92 147	98.15 189	93.03 157	95.64 179	98.20 188	90.39 258	96.63 186	97.73 161	91.63 218	99.10 254	91.84 205	97.31 294	98.63 200
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet-bldmvs		95.69 173	95.67 173	95.74 218	98.48 146	88.76 244	92.84 294	97.25 245	96.00 116	97.59 143	97.95 140	91.38 223	99.46 185	93.16 188	96.35 310	98.99 160

new-patchmatchnet		95.67 175	96.58 139	92.94 298	97.48 254	80.21 325	92.96 293	98.19 192	94.83 166	98.82 46	98.79 60	93.31 174	99.51 172	95.83 101	99.04 195	99.12 141

xiu_mvs_v1_base_debu		95.62 176	95.96 165	94.60 256	98.01 201	88.42 246	93.99 261	98.21 185	92.98 223	95.91 218	94.53 289	96.39 74	99.72 70	95.43 119	98.19 248	95.64 319

xiu_mvs_v1_base		95.62 176	95.96 165	94.60 256	98.01 201	88.42 246	93.99 261	98.21 185	92.98 223	95.91 218	94.53 289	96.39 74	99.72 70	95.43 119	98.19 248	95.64 319

xiu_mvs_v1_base_debi		95.62 176	95.96 165	94.60 256	98.01 201	88.42 246	93.99 261	98.21 185	92.98 223	95.91 218	94.53 289	96.39 74	99.72 70	95.43 119	98.19 248	95.64 319

DP-MVS Recon		95.55 179	95.13 187	96.80 152	98.51 142	93.99 132	94.60 235	98.69 122	90.20 260	95.78 224	96.21 253	92.73 187	98.98 269	90.58 237	98.86 213	97.42 274

test_normal		95.51 180	95.46 179	95.68 222	97.97 206	89.12 232	93.73 272	95.86 276	91.98 242	97.17 163	96.94 208	91.55 219	99.42 194	95.21 126	98.73 224	98.51 209

testmv		95.51 180	95.33 182	96.05 201	98.23 175	89.51 220	93.50 281	98.63 136	94.25 187	98.22 90	97.73 161	92.51 197	99.47 180	85.22 307	99.72 59	99.17 129

Fast-Effi-MVS+		95.49 182	95.07 189	96.75 155	97.67 244	92.82 159	94.22 248	98.60 139	91.61 248	93.42 296	92.90 313	96.73 60	99.70 88	92.60 193	97.89 262	97.74 264

TAMVS		95.49 182	94.94 194	97.16 133	98.31 157	93.41 151	95.07 216	96.82 261	91.09 253	97.51 146	97.82 152	89.96 238	99.42 194	88.42 271	99.44 130	98.64 198

OpenMVS	94.22 8	95.48 184	95.20 185	96.32 181	97.16 276	91.96 179	97.74 67	98.84 87	87.26 286	94.36 262	98.01 133	93.95 154	99.67 109	90.70 234	98.75 220	97.35 281

CLD-MVS		95.47 185	95.07 189	96.69 159	98.27 164	92.53 163	91.36 320	98.67 127	91.22 252	95.78 224	94.12 301	95.65 99	98.98 269	90.81 228	99.72 59	98.57 204
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
train_agg		95.46 186	94.66 204	97.88 84	97.84 218	95.23 89	93.62 276	98.39 161	87.04 290	93.78 278	95.99 258	94.58 132	99.52 162	91.76 207	98.90 206	98.89 174

DI_MVS_plusplus_test		95.46 186	95.43 180	95.55 224	98.05 197	88.84 240	94.18 251	95.75 278	91.92 245	97.32 157	96.94 208	91.44 221	99.39 214	94.81 142	98.48 239	98.43 215

CDPH-MVS		95.45 188	94.65 205	97.84 87	98.28 162	94.96 100	93.73 272	98.33 170	85.03 311	95.44 231	96.60 231	95.31 110	99.44 192	90.01 248	99.13 184	99.11 144

IterMVS		95.42 189	95.83 169	94.20 268	97.52 253	83.78 315	92.41 305	97.47 242	95.49 136	98.06 109	98.49 83	87.94 256	99.58 145	96.02 94	99.02 196	99.23 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
agg_prior1		95.39 190	94.60 208	97.75 91	97.80 225	94.96 100	93.39 284	98.36 165	87.20 288	93.49 291	95.97 261	94.65 129	99.53 159	91.69 210	98.86 213	98.77 190

Test4		95.39 190	95.24 184	95.82 216	98.07 195	89.60 216	94.40 239	98.49 148	91.39 251	97.40 156	96.32 248	87.32 264	99.41 205	95.09 136	98.71 226	98.44 214

mvs_anonymous		95.36 192	96.07 161	93.21 291	96.29 296	81.56 320	94.60 235	97.66 228	93.30 211	96.95 177	98.91 57	93.03 181	99.38 219	96.60 75	97.30 295	98.69 196

MSDG		95.33 193	95.13 187	95.94 212	97.40 262	91.85 181	91.02 323	98.37 164	95.30 142	96.31 204	95.99 258	94.51 136	98.38 320	89.59 253	97.65 280	97.60 269

LFMVS		95.32 194	94.88 198	96.62 161	98.03 198	91.47 188	97.65 71	90.72 326	99.11 8	97.89 129	98.31 96	79.20 291	99.48 178	93.91 173	99.12 187	98.93 168

agg_prior3		95.30 195	94.46 216	97.80 89	97.80 225	95.00 98	93.63 275	98.34 169	86.33 296	93.40 298	95.84 265	94.15 149	99.50 173	91.76 207	98.90 206	98.89 174

F-COLMAP		95.30 195	94.38 218	98.05 77	98.64 121	96.04 66	95.61 183	98.66 129	89.00 269	93.22 300	96.40 245	92.90 183	99.35 224	87.45 290	97.53 285	98.77 190

Anonymous20231206		95.27 197	95.06 191	95.88 214	98.72 110	89.37 226	95.70 171	97.85 214	88.00 282	96.98 171	97.62 168	91.95 211	99.34 225	89.21 258	99.53 104	98.94 165

FMVSNet3		95.26 198	94.94 194	96.22 191	96.53 291	90.06 205	95.99 153	97.66 228	94.11 193	97.99 114	97.91 145	80.22 289	99.63 121	94.60 150	99.44 130	98.96 162

N_pmnet		95.18 199	94.23 221	98.06 74	97.85 214	96.55 52	92.49 303	91.63 318	89.34 266	98.09 104	97.41 183	90.33 232	99.06 258	91.58 211	99.31 166	98.56 205

HQP-MVS		95.17 200	94.58 210	96.92 147	97.85 214	92.47 164	94.26 242	98.43 155	93.18 215	92.86 304	95.08 279	90.33 232	99.23 243	90.51 239	98.74 221	99.05 154

Vis-MVSNet (Re-imp)		95.11 201	94.85 199	95.87 215	99.12 76	89.17 229	97.54 83	94.92 286	96.50 98	96.58 187	97.27 193	83.64 279	99.48 178	88.42 271	99.67 73	98.97 161

AdaColmap		95.11 201	94.62 207	96.58 165	97.33 268	94.45 116	94.92 224	98.08 203	93.15 219	93.98 275	95.53 274	94.34 141	99.10 254	85.69 302	98.61 232	96.20 312

API-MVS		95.09 203	95.01 192	95.31 231	96.61 289	94.02 130	96.83 118	97.18 249	95.60 131	95.79 223	94.33 298	94.54 134	98.37 322	85.70 301	98.52 236	93.52 337

CNLPA		95.04 204	94.47 213	96.75 155	97.81 221	95.25 88	94.12 257	97.89 212	94.41 180	94.57 255	95.69 267	90.30 235	98.35 323	86.72 296	98.76 219	96.64 301

Patchmtry		95.03 205	94.59 209	96.33 180	94.83 322	90.82 196	96.38 132	97.20 247	96.59 96	97.49 148	98.57 76	77.67 297	99.38 219	92.95 192	99.62 79	98.80 186

PVSNet_BlendedMVS		95.02 206	94.93 196	95.27 232	97.79 230	87.40 273	94.14 255	98.68 124	88.94 270	94.51 258	98.01 133	93.04 179	99.30 232	89.77 251	99.49 118	99.11 144

diffmvs		95.00 207	95.00 193	95.01 241	96.53 291	87.96 261	95.73 168	98.32 179	90.67 257	91.89 318	97.43 182	92.07 208	98.90 276	95.44 117	96.88 299	98.16 242

TAPA-MVS	93.32 12	94.93 208	94.23 221	97.04 141	98.18 183	94.51 113	95.22 208	98.73 112	81.22 328	96.25 208	95.95 263	93.80 161	98.98 269	89.89 249	98.87 211	97.62 267
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CDS-MVSNet		94.88 209	94.12 226	97.14 135	97.64 246	93.57 146	93.96 264	97.06 254	90.05 262	96.30 205	96.55 233	86.10 268	99.47 180	90.10 247	99.31 166	98.40 216
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
no-one		94.84 210	94.76 202	95.09 238	98.29 159	87.49 270	91.82 314	97.49 238	88.21 278	97.84 139	98.75 64	91.51 220	99.27 237	88.96 263	99.99 2	98.52 208

MS-PatchMatch		94.83 211	94.91 197	94.57 259	96.81 287	87.10 279	94.23 247	97.34 243	88.74 272	97.14 164	97.11 199	91.94 212	98.23 327	92.99 191	97.92 259	98.37 219

pmmvs4		94.82 212	94.19 224	96.70 158	97.42 261	92.75 161	92.09 311	96.76 262	86.80 293	95.73 227	97.22 194	89.28 247	98.89 279	93.28 184	99.14 182	98.46 213

YYNet1		94.73 213	94.84 200	94.41 263	97.47 258	85.09 298	90.29 328	95.85 277	92.52 233	97.53 145	97.76 155	91.97 210	99.18 246	93.31 183	96.86 300	98.95 163

MDA-MVSNet_test_wron		94.73 213	94.83 201	94.42 262	97.48 254	85.15 296	90.28 329	95.87 275	92.52 233	97.48 151	97.76 155	91.92 214	99.17 248	93.32 182	96.80 303	98.94 165

UnsupCasMVSNet_bld		94.72 215	94.26 220	96.08 200	98.62 126	90.54 203	93.38 285	98.05 207	90.30 259	97.02 169	96.80 219	89.54 241	99.16 249	88.44 270	96.18 312	98.56 205

BH-untuned		94.69 216	94.75 203	94.52 261	97.95 211	87.53 269	94.07 258	97.01 255	93.99 194	97.10 166	95.65 269	92.65 190	98.95 274	87.60 287	96.74 304	97.09 283

Patchmatch-RL test		94.66 217	94.49 212	95.19 234	98.54 138	88.91 237	92.57 301	98.74 111	91.46 250	98.32 81	97.75 158	77.31 302	98.81 289	96.06 90	99.61 84	97.85 259

CANet_DTU		94.65 218	94.21 223	95.96 208	95.90 306	89.68 212	93.92 265	97.83 217	93.19 214	90.12 332	95.64 270	88.52 251	99.57 150	93.27 185	99.47 123	98.62 201

pmmvs5		94.63 219	94.34 219	95.50 226	97.63 247	88.34 249	94.02 259	97.13 251	87.15 289	95.22 236	97.15 197	87.50 261	99.27 237	93.99 171	99.26 174	98.88 178

PAPM_NR		94.61 220	94.17 225	95.96 208	98.36 155	91.23 189	95.93 162	97.95 209	92.98 223	93.42 296	94.43 297	90.53 230	98.38 320	87.60 287	96.29 311	98.27 232

PatchMatch-RL		94.61 220	93.81 232	97.02 144	98.19 180	95.72 73	93.66 274	97.23 246	88.17 279	94.94 242	95.62 271	91.43 222	98.57 307	87.36 291	97.68 277	96.76 297

BH-RMVSNet		94.56 222	94.44 217	94.91 243	97.57 249	87.44 272	93.78 271	96.26 268	93.69 207	96.41 196	96.50 238	92.10 206	99.00 266	85.96 299	97.71 274	98.31 227

USDC		94.56 222	94.57 211	94.55 260	97.78 234	86.43 286	92.75 297	98.65 135	85.96 299	96.91 179	97.93 143	90.82 228	98.74 294	90.71 233	99.59 89	98.47 211

jason		94.39 224	94.04 228	95.41 230	98.29 159	87.85 264	92.74 299	96.75 263	85.38 309	95.29 234	96.15 254	88.21 255	99.65 115	94.24 164	99.34 159	98.74 192
jason: jason.
1121		94.26 225	93.26 240	97.27 129	98.26 172	94.73 106	95.86 164	97.71 224	77.96 341	94.53 257	96.71 225	91.93 213	99.40 208	87.71 277	98.64 230	97.69 265

EU-MVSNet		94.25 226	94.47 213	93.60 283	98.14 190	82.60 318	97.24 94	92.72 310	85.08 310	98.48 68	98.94 54	82.59 282	98.76 293	97.47 56	99.53 104	99.44 79

xiu_mvs_v2_base		94.22 227	94.63 206	92.99 297	97.32 269	84.84 301	92.12 309	97.84 215	91.96 243	94.17 265	93.43 304	96.07 82	99.71 80	91.27 215	97.48 287	94.42 329

RPMNet		94.22 227	94.03 229	94.78 249	95.44 315	88.15 251	96.18 144	93.73 294	97.43 70	94.10 268	98.49 83	79.40 290	99.39 214	95.69 104	95.81 314	96.81 295

sss		94.22 227	93.72 233	95.74 218	97.71 239	89.95 209	93.84 268	96.98 256	88.38 277	93.75 280	95.74 266	87.94 256	98.89 279	91.02 221	98.10 252	98.37 219

MVSTER		94.21 230	93.93 231	95.05 240	95.83 308	86.46 285	95.18 209	97.65 230	92.41 237	97.94 121	98.00 135	72.39 327	99.58 145	96.36 84	99.56 96	99.12 141

MAR-MVS		94.21 230	93.03 244	97.76 90	96.94 283	97.44 30	96.97 116	97.15 250	87.89 284	92.00 316	92.73 318	92.14 204	99.12 250	83.92 315	97.51 286	96.73 298
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
1112_ss		94.12 232	93.42 237	96.23 187	98.59 130	90.85 194	94.24 246	98.85 84	85.49 304	92.97 302	94.94 283	86.01 269	99.64 118	91.78 206	97.92 259	98.20 238

PS-MVSNAJ		94.10 233	94.47 213	93.00 296	97.35 264	84.88 300	91.86 313	97.84 215	91.96 243	94.17 265	92.50 320	95.82 90	99.71 80	91.27 215	97.48 287	94.40 330

CHOSEN 1792x2688		94.10 233	93.41 238	96.18 193	99.16 64	90.04 206	92.15 308	98.68 124	79.90 333	96.22 209	97.83 149	87.92 259	99.42 194	89.18 259	99.65 76	99.08 149

MG-MVS		94.08 235	94.00 230	94.32 265	97.09 278	85.89 287	93.19 291	95.96 273	92.52 233	94.93 243	97.51 177	89.54 241	98.77 292	87.52 289	97.71 274	98.31 227

PLC	91.02 16	94.05 236	92.90 246	97.51 108	98.00 204	95.12 96	94.25 245	98.25 183	86.17 297	91.48 321	95.25 277	91.01 226	99.19 245	85.02 309	96.69 305	98.22 236
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		93.96 237	93.22 242	96.19 192	99.06 82	90.97 193	95.99 153	98.94 72	73.88 347	93.43 295	96.93 210	92.38 201	99.37 222	89.09 260	99.28 171	98.25 234

PVSNet_Blended		93.96 237	93.65 234	94.91 243	97.79 230	87.40 273	91.43 319	98.68 124	84.50 315	94.51 258	94.48 292	93.04 179	99.30 232	89.77 251	98.61 232	98.02 254

lupinMVS		93.77 239	93.28 239	95.24 233	97.68 241	87.81 265	92.12 309	96.05 270	84.52 314	94.48 260	95.06 281	86.90 265	99.63 121	93.62 180	99.13 184	98.27 232

PatchT		93.75 240	93.57 236	94.29 267	95.05 320	87.32 275	96.05 149	92.98 305	97.54 65	94.25 263	98.72 66	75.79 310	99.24 241	95.92 99	95.81 314	96.32 310

EPNet		93.72 241	92.62 253	97.03 143	87.61 354	92.25 168	96.27 137	91.28 320	96.74 93	87.65 342	97.39 187	85.00 275	99.64 118	92.14 199	99.48 121	99.20 125
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		93.72 241	92.65 252	96.91 149	98.93 94	91.81 183	91.23 322	98.52 145	82.69 321	96.46 194	96.52 237	80.38 288	99.90 13	90.36 244	98.79 216	99.03 155

PMMVS2		93.66 243	94.07 227	92.45 305	97.57 249	80.67 324	86.46 341	96.00 271	93.99 194	97.10 166	97.38 189	89.90 239	97.82 334	88.76 265	99.47 123	98.86 181

OpenMVS_ROB	91.80 14	93.64 244	93.05 243	95.42 228	97.31 270	91.21 190	95.08 215	96.68 266	81.56 325	96.88 181	96.41 243	90.44 231	99.25 240	85.39 306	97.67 278	95.80 317

Patchmatch-test		93.60 245	93.25 241	94.63 254	96.14 302	87.47 271	96.04 150	94.50 290	93.57 208	96.47 193	96.97 206	76.50 305	98.61 305	90.67 235	98.41 242	97.81 262

WTY-MVS		93.55 246	93.00 245	95.19 234	97.81 221	87.86 263	93.89 266	96.00 271	89.02 268	94.07 270	95.44 275	86.27 267	99.33 228	87.69 279	96.82 301	98.39 218

Test_1112_low_res		93.53 247	92.86 247	95.54 225	98.60 128	88.86 239	92.75 297	98.69 122	82.66 322	92.65 309	96.92 211	84.75 276	99.56 151	90.94 224	97.76 263	98.19 239

MIMVSNet		93.42 248	92.86 247	95.10 237	98.17 185	88.19 250	98.13 43	93.69 295	92.07 239	95.04 240	98.21 109	80.95 286	99.03 263	81.42 327	98.06 253	98.07 246

FMVSNet5		93.39 249	92.35 255	96.50 170	95.83 308	90.81 198	97.31 89	98.27 180	92.74 231	96.27 206	98.28 101	62.23 348	99.67 109	90.86 226	99.36 154	99.03 155

Patchmatch-test1		93.38 250	93.59 235	92.73 301	96.24 297	81.40 321	93.24 289	94.00 293	91.58 249	94.57 255	96.67 228	87.94 256	99.03 263	90.42 242	97.66 279	97.77 263

CR-MVSNet		93.29 251	92.79 249	94.78 249	95.44 315	88.15 251	96.18 144	97.20 247	84.94 312	94.10 268	98.57 76	77.67 297	99.39 214	95.17 129	95.81 314	96.81 295

wuyk23d		93.25 252	95.20 185	87.40 332	96.07 303	95.38 85	97.04 107	94.97 285	95.33 141	99.70 6	98.11 123	98.14 14	91.94 349	77.76 338	99.68 71	74.89 349

LP		93.12 253	92.78 251	94.14 269	94.50 327	85.48 291	95.73 168	95.68 280	92.97 227	95.05 239	97.17 196	81.93 283	99.40 208	93.06 190	88.96 341	97.55 270

test1235678		92.95 254	92.40 254	94.61 255	96.95 282	86.87 281	90.75 325	97.75 220	91.00 255	96.33 199	95.38 276	85.21 273	98.92 275	79.00 332	99.20 178	98.03 252

X-MVStestdata		92.86 255	90.83 288	98.94 15	99.15 67	97.66 16	97.77 62	98.83 95	97.42 71	96.32 202	36.50 351	96.49 71	99.72 70	95.66 107	99.37 151	99.45 71

GA-MVS		92.83 256	92.15 258	94.87 246	96.97 281	87.27 276	90.03 330	96.12 269	91.83 247	94.05 271	94.57 288	76.01 309	98.97 273	92.46 196	97.34 293	98.36 224

CMPMVS	73.10 23	92.74 257	91.39 269	96.77 154	93.57 339	94.67 110	94.21 249	97.67 226	80.36 332	93.61 287	96.60 231	82.85 281	97.35 338	84.86 310	98.78 217	98.29 231
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HY-MVS	91.43 15	92.58 258	91.81 266	94.90 245	96.49 293	88.87 238	97.31 89	94.62 288	85.92 300	90.50 329	96.84 215	85.05 274	99.40 208	83.77 318	95.78 317	96.43 309

view600		92.56 259	92.11 259	93.91 274	98.45 148	84.76 303	97.10 101	90.23 331	97.42 71	96.98 171	94.48 292	73.62 318	99.60 139	82.49 322	98.28 244	97.36 275

view800		92.56 259	92.11 259	93.91 274	98.45 148	84.76 303	97.10 101	90.23 331	97.42 71	96.98 171	94.48 292	73.62 318	99.60 139	82.49 322	98.28 244	97.36 275

conf0.05thres1000		92.56 259	92.11 259	93.91 274	98.45 148	84.76 303	97.10 101	90.23 331	97.42 71	96.98 171	94.48 292	73.62 318	99.60 139	82.49 322	98.28 244	97.36 275

tfpn		92.56 259	92.11 259	93.91 274	98.45 148	84.76 303	97.10 101	90.23 331	97.42 71	96.98 171	94.48 292	73.62 318	99.60 139	82.49 322	98.28 244	97.36 275

TR-MVS		92.54 263	92.20 257	93.57 284	96.49 293	86.66 283	93.51 280	94.73 287	89.96 263	94.95 241	93.87 302	90.24 237	98.61 305	81.18 328	94.88 323	95.45 323

PMMVS		92.39 264	91.08 276	96.30 183	93.12 342	92.81 160	90.58 327	95.96 273	79.17 336	91.85 319	92.27 321	90.29 236	98.66 304	89.85 250	96.68 306	97.43 273

1314		92.38 265	92.30 256	92.64 303	95.42 317	85.15 296	95.86 164	96.97 257	85.40 308	90.62 325	93.06 311	91.12 225	97.80 335	86.74 295	95.49 322	94.97 327

new_pmnet		92.34 266	91.69 267	94.32 265	96.23 299	89.16 230	92.27 307	92.88 307	84.39 317	95.29 234	96.35 247	85.66 270	96.74 344	84.53 312	97.56 283	97.05 285

CVMVSNet		92.33 267	92.79 249	90.95 318	97.26 271	75.84 340	95.29 203	92.33 313	81.86 323	96.27 206	98.19 110	81.44 284	98.46 314	94.23 165	98.29 243	98.55 207

PAPR		92.22 268	91.27 272	95.07 239	95.73 311	88.81 241	91.97 312	97.87 213	85.80 302	90.91 323	92.73 318	91.16 224	98.33 324	79.48 330	95.76 318	98.08 244

DSMNet-mixed		92.19 269	91.83 265	93.25 290	96.18 301	83.68 316	96.27 137	93.68 297	76.97 344	92.54 312	99.18 35	89.20 249	98.55 310	83.88 316	98.60 234	97.51 272

BH-w/o		92.14 270	91.94 263	92.73 301	97.13 277	85.30 293	92.46 304	95.64 281	89.33 267	94.21 264	92.74 317	89.60 240	98.24 326	81.68 326	94.66 325	94.66 328

PCF-MVS	89.43 18	92.12 271	90.64 291	96.57 167	97.80 225	93.48 150	89.88 334	98.45 151	74.46 346	96.04 215	95.68 268	90.71 229	99.31 230	73.73 342	99.01 198	96.91 291
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres600view7		92.03 272	91.43 268	93.82 279	98.19 180	84.61 307	96.27 137	90.39 327	96.81 91	96.37 198	93.11 307	73.44 324	99.49 175	80.32 329	97.95 256	97.36 275

PatchmatchNet		91.98 273	91.87 264	92.30 307	94.60 325	79.71 326	95.12 210	93.59 300	89.52 265	93.61 287	97.02 204	77.94 295	99.18 246	90.84 227	94.57 327	98.01 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
conf0.01		91.90 274	90.98 279	94.67 252	98.27 164	88.03 255	96.98 110	88.58 339	93.90 197	94.64 249	91.45 327	69.62 336	99.52 162	87.62 281	97.74 264	96.46 306

conf0.002		91.90 274	90.98 279	94.67 252	98.27 164	88.03 255	96.98 110	88.58 339	93.90 197	94.64 249	91.45 327	69.62 336	99.52 162	87.62 281	97.74 264	96.46 306

cascas		91.89 276	91.35 270	93.51 285	94.27 330	85.60 289	88.86 337	98.61 138	79.32 335	92.16 315	91.44 333	89.22 248	98.12 330	90.80 229	97.47 289	96.82 294

tfpn1000		91.88 277	91.20 275	93.89 278	97.96 207	87.13 278	97.13 99	88.16 346	94.41 180	94.87 244	92.77 315	68.34 343	99.47 180	89.24 257	97.95 256	95.06 325

conf200view11		91.81 278	91.26 273	93.46 286	98.21 177	84.50 308	96.39 129	90.39 327	96.87 88	96.33 199	93.08 309	73.44 324	99.42 194	78.85 334	97.74 264	96.46 306

JIA-IIPM		91.79 279	90.69 290	95.11 236	93.80 336	90.98 192	94.16 253	91.78 317	96.38 102	90.30 331	99.30 23	72.02 329	98.90 276	88.28 273	90.17 338	95.45 323

thres100view900		91.76 280	91.26 273	93.26 289	98.21 177	84.50 308	96.39 129	90.39 327	96.87 88	96.33 199	93.08 309	73.44 324	99.42 194	78.85 334	97.74 264	95.85 315

thresconf0.02		91.72 281	90.98 279	93.97 270	98.27 164	88.03 255	96.98 110	88.58 339	93.90 197	94.64 249	91.45 327	69.62 336	99.52 162	87.62 281	97.74 264	94.35 331

tfpn_n400		91.72 281	90.98 279	93.97 270	98.27 164	88.03 255	96.98 110	88.58 339	93.90 197	94.64 249	91.45 327	69.62 336	99.52 162	87.62 281	97.74 264	94.35 331

tfpnconf		91.72 281	90.98 279	93.97 270	98.27 164	88.03 255	96.98 110	88.58 339	93.90 197	94.64 249	91.45 327	69.62 336	99.52 162	87.62 281	97.74 264	94.35 331

tfpnview11		91.72 281	90.98 279	93.97 270	98.27 164	88.03 255	96.98 110	88.58 339	93.90 197	94.64 249	91.45 327	69.62 336	99.52 162	87.62 281	97.74 264	94.35 331

thres400		91.68 285	91.00 277	93.71 281	98.02 199	84.35 311	95.70 171	90.79 324	96.26 107	95.90 221	92.13 323	73.62 318	99.42 194	78.85 334	97.74 264	97.36 275

tfpn200view9		91.55 286	91.00 277	93.21 291	98.02 199	84.35 311	95.70 171	90.79 324	96.26 107	95.90 221	92.13 323	73.62 318	99.42 194	78.85 334	97.74 264	95.85 315

ADS-MVSNet2		91.47 287	90.51 293	94.36 264	95.51 313	85.63 288	95.05 219	95.70 279	83.46 319	92.69 307	96.84 215	79.15 292	99.41 205	85.66 303	90.52 336	98.04 250

EPNet_dtu		91.39 288	90.75 289	93.31 288	90.48 352	82.61 317	94.80 230	92.88 307	93.39 210	81.74 350	94.90 286	81.36 285	99.11 253	88.28 273	98.87 211	98.21 237
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	86.72 19	91.10 289	90.97 285	91.49 312	97.56 251	78.04 333	87.17 339	94.60 289	84.65 313	92.34 313	92.20 322	87.37 263	98.47 313	85.17 308	97.69 276	97.96 256

tpm		91.08 290	90.85 287	91.75 311	95.33 318	78.09 331	95.03 221	91.27 321	88.75 271	93.53 290	97.40 184	71.24 330	99.30 232	91.25 217	93.87 328	97.87 258

thres200		91.00 291	90.42 295	92.77 300	97.47 258	83.98 314	94.01 260	91.18 322	95.12 157	95.44 231	91.21 335	73.93 314	99.31 230	77.76 338	97.63 282	95.01 326

tfpn_ndepth		90.98 292	90.24 297	93.20 293	97.72 238	87.18 277	96.52 125	88.20 345	92.63 232	93.69 284	90.70 340	68.22 344	99.42 194	86.98 293	97.47 289	93.00 341

ADS-MVSNet		90.95 293	90.26 296	93.04 294	95.51 313	82.37 319	95.05 219	93.41 301	83.46 319	92.69 307	96.84 215	79.15 292	98.70 298	85.66 303	90.52 336	98.04 250

testus		90.90 294	90.51 293	92.06 309	96.07 303	79.45 327	88.99 335	98.44 154	85.46 306	94.15 267	90.77 337	89.12 250	98.01 333	73.66 343	97.95 256	98.71 195

tpmvs		90.79 295	90.87 286	90.57 321	92.75 346	76.30 338	95.79 167	93.64 298	91.04 254	91.91 317	96.26 249	77.19 303	98.86 285	89.38 256	89.85 339	96.56 304

tpmrst		90.31 296	90.61 292	89.41 325	94.06 334	72.37 347	95.06 218	93.69 295	88.01 281	92.32 314	96.86 213	77.45 299	98.82 287	91.04 220	87.01 344	97.04 286

test0.0.03 1		90.11 297	89.21 304	92.83 299	93.89 335	86.87 281	91.74 315	88.74 338	92.02 240	94.71 247	91.14 336	73.92 315	94.48 348	83.75 319	92.94 330	97.16 282

MVS		90.02 298	89.20 305	92.47 304	94.71 323	86.90 280	95.86 164	96.74 264	64.72 349	90.62 325	92.77 315	92.54 195	98.39 318	79.30 331	95.56 321	92.12 342

pmmvs3		90.00 299	88.90 308	93.32 287	94.20 333	85.34 292	91.25 321	92.56 312	78.59 338	93.82 277	95.17 278	67.36 346	98.69 299	89.08 261	98.03 254	95.92 313

CHOSEN 280x420		89.98 300	89.19 306	92.37 306	95.60 312	81.13 322	86.22 342	97.09 253	81.44 327	87.44 343	93.15 306	73.99 313	99.47 180	88.69 267	99.07 192	96.52 305

test-LLR		89.97 301	89.90 299	90.16 322	94.24 331	74.98 341	89.89 331	89.06 336	92.02 240	89.97 333	90.77 337	73.92 315	98.57 307	91.88 203	97.36 291	96.92 289

FPMVS		89.92 302	88.63 309	93.82 279	98.37 154	96.94 41	91.58 316	93.34 302	88.00 282	90.32 330	97.10 200	70.87 332	91.13 350	71.91 346	96.16 313	93.39 339

CostFormer		89.75 303	89.25 302	91.26 315	94.69 324	78.00 334	95.32 200	91.98 315	81.50 326	90.55 327	96.96 207	71.06 331	98.89 279	88.59 269	92.63 333	96.87 292

PatchFormer-LS_test		89.62 304	89.12 307	91.11 317	93.62 337	78.42 330	94.57 237	93.62 299	88.39 276	90.54 328	88.40 345	72.33 328	99.03 263	92.41 197	88.20 342	95.89 314

E-PMN		89.52 305	89.78 300	88.73 327	93.14 341	77.61 335	83.26 346	92.02 314	94.82 167	93.71 282	93.11 307	75.31 311	96.81 342	85.81 300	96.81 302	91.77 344

EPMVS		89.26 306	88.55 310	91.39 313	92.36 347	79.11 328	95.65 177	79.86 351	88.60 273	93.12 301	96.53 235	70.73 333	98.10 331	90.75 231	89.32 340	96.98 287

EMVS		89.06 307	89.22 303	88.61 328	93.00 343	77.34 336	82.91 347	90.92 323	94.64 171	92.63 310	91.81 326	76.30 307	97.02 340	83.83 317	96.90 298	91.48 345

1111		88.78 308	89.39 301	86.96 333	98.53 140	62.84 352	91.49 317	97.48 240	94.45 177	96.56 189	96.45 240	43.83 358	98.87 283	86.33 297	99.40 149	99.18 128

IB-MVS	85.98 20	88.63 309	86.95 318	93.68 282	95.12 319	84.82 302	90.85 324	90.17 335	87.55 285	88.48 339	91.34 334	58.01 350	99.59 143	87.24 292	93.80 329	96.63 303
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
tpm2		88.47 310	87.69 313	90.79 319	94.98 321	77.34 336	95.09 213	91.83 316	77.51 343	89.40 335	96.41 243	67.83 345	98.73 295	83.58 320	92.60 334	96.29 311

tpmp4_e23		88.46 311	87.54 314	91.22 316	94.56 326	78.08 332	95.63 182	93.17 303	79.08 337	85.85 345	96.80 219	65.86 347	98.85 286	84.10 314	92.85 331	96.72 299

MVS-HIRNet		88.40 312	90.20 298	82.99 336	97.01 280	60.04 354	93.11 292	85.61 348	84.45 316	88.72 338	99.09 45	84.72 277	98.23 327	82.52 321	96.59 307	90.69 347

gg-mvs-nofinetune		88.28 313	86.96 317	92.23 308	92.84 345	84.44 310	98.19 40	74.60 353	99.08 9	87.01 344	99.47 8	56.93 351	98.23 327	78.91 333	95.61 320	94.01 335

dp		88.08 314	88.05 312	88.16 331	92.85 344	68.81 349	94.17 252	92.88 307	85.47 305	91.38 322	96.14 256	68.87 342	98.81 289	86.88 294	83.80 348	96.87 292

tpm cat1		88.01 315	87.33 315	90.05 324	94.48 328	76.28 339	94.47 238	94.35 292	73.84 348	89.26 336	95.61 272	73.64 317	98.30 325	84.13 313	86.20 345	95.57 322

test12356		87.98 316	88.41 311	86.69 334	95.84 307	63.49 351	87.15 340	97.32 244	87.21 287	91.78 320	93.36 305	70.66 334	98.39 318	74.70 341	97.64 281	98.19 239

test-mter		87.92 317	87.17 316	90.16 322	94.24 331	74.98 341	89.89 331	89.06 336	86.44 295	89.97 333	90.77 337	54.96 354	98.57 307	91.88 203	97.36 291	96.92 289

DWT-MVSNet_test		87.92 317	86.77 319	91.39 313	93.18 340	78.62 329	95.10 211	91.42 319	85.58 303	88.00 340	88.73 344	60.60 349	98.90 276	90.60 236	87.70 343	96.65 300

PAPM		87.64 319	85.84 322	93.04 294	96.54 290	84.99 299	88.42 338	95.57 283	79.52 334	83.82 347	93.05 312	80.57 287	98.41 316	62.29 350	92.79 332	95.71 318

TESTMET0.1,1		87.20 320	86.57 320	89.07 326	93.62 337	72.84 346	89.89 331	87.01 347	85.46 306	89.12 337	90.20 342	56.00 353	97.72 336	90.91 225	96.92 297	96.64 301

MVE	73.61 22	86.48 321	85.92 321	88.18 330	96.23 299	85.28 294	81.78 349	75.79 352	86.01 298	82.53 349	91.88 325	92.74 186	87.47 352	71.42 347	94.86 324	91.78 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2356		85.45 322	83.26 325	92.01 310	91.12 349	80.76 323	85.16 343	92.90 306	83.90 318	90.63 324	87.71 347	53.10 355	97.24 339	69.20 348	95.65 319	98.03 252

PVSNet_0	81.89 21	84.49 323	83.21 326	88.34 329	95.76 310	74.97 343	83.49 345	92.70 311	78.47 339	87.94 341	86.90 348	83.38 280	96.63 345	73.44 344	66.86 351	93.40 338

PNet_i23d		83.82 324	83.39 324	85.10 335	96.07 303	65.16 350	81.87 348	94.37 291	90.87 256	93.92 276	92.89 314	52.80 356	96.44 346	77.52 340	70.22 350	93.70 336

testpf		82.70 325	84.35 323	77.74 337	88.97 353	73.23 345	93.85 267	84.33 349	88.10 280	85.06 346	90.42 341	52.62 357	91.05 351	91.00 222	84.82 347	68.93 350

.test1245		73.49 326	79.27 327	56.15 339	98.53 140	62.84 352	91.49 317	97.48 240	94.45 177	96.56 189	96.45 240	43.83 358	98.87 283	86.33 297	8.32 353	6.75 353

tmp_tt		57.23 327	62.50 328	41.44 340	34.77 355	49.21 356	83.93 344	60.22 357	15.31 351	71.11 352	79.37 350	70.09 335	44.86 354	64.76 349	82.93 349	30.25 351

pcd1.5k->3k		41.47 328	44.19 329	33.29 341	99.65 11	0.00 359	0.00 350	99.07 34	0.00 354	0.00 355	0.00 356	99.04 4	0.00 357	0.00 354	99.96 11	99.87 2

cdsmvs_eth3d_5k		24.22 329	32.30 330	0.00 344	0.00 358	0.00 359	0.00 350	98.10 200	0.00 354	0.00 355	95.06 281	97.54 28	0.00 357	0.00 354	0.00 355	0.00 355

test123		12.59 330	15.49 331	3.87 342	6.07 356	2.55 357	90.75 325	2.59 359	2.52 352	5.20 354	13.02 353	4.96 360	1.85 356	5.20 352	9.09 352	7.23 352

testmvs		12.33 331	15.23 332	3.64 343	5.77 357	2.23 358	88.99 335	3.62 358	2.30 353	5.29 353	13.09 352	4.52 361	1.95 355	5.16 353	8.32 353	6.75 353

pcd_1.5k_mvsjas		7.98 332	10.65 333	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	95.82 90	0.00 357	0.00 354	0.00 355	0.00 355

ab-mvs-re		7.91 333	10.55 334	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	94.94 283	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet-low-res		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

sosnet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uncertanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

Regformer		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

uanet		0.00 334	0.00 335	0.00 344	0.00 358	0.00 359	0.00 350	0.00 360	0.00 354	0.00 355	0.00 356	0.00 362	0.00 357	0.00 354	0.00 355	0.00 355

GSMVS																98.06 247

test_part3							95.64 179		94.84 164		97.60 170		99.76 48	91.22 218

test_part2					99.03 86	96.07 64				98.08 106

test_part1								98.84 87				96.69 61			99.44 130	99.37 100

sam_mvs1												77.80 296				98.06 247

sam_mvs												77.38 300

semantic-postprocess				94.85 247	97.68 241	85.53 290		97.63 234	96.99 84	98.36 76	98.54 80	87.44 262	99.75 54	97.07 69	99.08 190	99.27 120

ambc				96.56 168	98.23 175	91.68 185	97.88 57	98.13 198		98.42 73	98.56 78	94.22 146	99.04 260	94.05 170	99.35 157	98.95 163

MTGPA								98.73 112

test_post1							94.98 223				10.37 355	76.21 308	99.04 260	89.47 255

test_post											10.87 354	76.83 304	99.07 257

patchmatchnet-post											96.84 215	77.36 301	99.42 194

GG-mvs-BLEND				90.60 320	91.00 350	84.21 313	98.23 34	72.63 356		82.76 348	84.11 349	56.14 352	96.79 343	72.20 345	92.09 335	90.78 346

MTMP								74.60 353

gm-plane-assit					91.79 348	71.40 348			81.67 324		90.11 343		98.99 267	84.86 310

test9_res														91.29 214	98.89 210	99.00 157

TEST9					97.84 218	95.23 89	93.62 276	98.39 161	86.81 292	93.78 278	95.99 258	94.68 127	99.52 162

test_8					97.81 221	95.07 97	93.54 279	98.38 163	87.04 290	93.71 282	95.96 262	94.58 132	99.52 162

agg_prior2														90.34 245	98.90 206	99.10 148

agg_prior					97.80 225	94.96 100		98.36 165		93.49 291			99.53 159

TestCases				98.06 74	99.08 79	96.16 61		99.16 16	94.35 184	97.78 141	98.07 126	95.84 87	99.12 250	91.41 212	99.42 142	98.91 171

test_prior4						95.38 85	93.61 278

test_prior2							93.33 287		94.21 189	94.02 272	96.25 250	93.64 164		91.90 201	98.96 200

test_prior				97.46 117	97.79 230	94.26 123		98.42 158					99.34 225			98.79 187

旧先验2							93.35 286		77.95 342	95.77 226			98.67 303	90.74 232

新几何2							93.43 282

新几何1				97.25 132	98.29 159	94.70 109		97.73 222	77.98 340	94.83 245	96.67 228	92.08 207	99.45 189	88.17 275	98.65 229	97.61 268

旧先验1					97.80 225	93.87 134		97.75 220			97.04 203	93.57 166			98.68 227	98.72 194

无先验							93.20 290	97.91 210	80.78 329				99.40 208	87.71 277		97.94 257

原ACMM2							92.82 295

原ACMM1				96.58 165	98.16 187	92.12 174		98.15 196	85.90 301	93.49 291	96.43 242	92.47 199	99.38 219	87.66 280	98.62 231	98.23 235

test222					98.17 185	93.24 155	92.74 299	97.61 236	75.17 345	94.65 248	96.69 227	90.96 227			98.66 228	97.66 266

testdata2													99.46 185	87.84 276

segment_acmp												95.34 108

testdata				95.70 221	98.16 187	90.58 200		97.72 223	80.38 331	95.62 229	97.02 204	92.06 209	98.98 269	89.06 262	98.52 236	97.54 271

testdata1							92.77 296		93.78 204

test12				97.46 117	97.61 248	94.07 128		97.78 219		93.57 289		93.31 174	99.42 194		98.78 217	98.89 174

plane_prior7					98.70 115	94.67 110

plane_prior6					98.38 153	94.37 119						91.91 215

plane_prior5								98.75 109					99.46 185	92.59 194	99.20 178	99.28 117

plane_prior4											96.77 221

plane_prior3						94.51 113			95.29 143	96.16 212

plane_prior2							96.50 126		96.36 103

plane_prior1					98.49 144

plane_prior						94.29 120	95.42 191		94.31 186						98.93 205

n2								0.00 360

nn								0.00 360

door-mid								98.17 193

lessismore_v0				97.05 140	99.36 45	92.12 174		84.07 350		98.77 50	98.98 50	85.36 272	99.74 59	97.34 59	99.37 151	99.30 110

LGP-MVS_train				98.74 32	99.15 67	97.02 38		99.02 51	95.15 152	98.34 78	98.23 105	97.91 19	99.70 88	94.41 155	99.73 56	99.50 50

test11								98.08 203

door								97.81 218

HQP5-MVS						92.47 164

HQP-NCC					97.85 214		94.26 242		93.18 215	92.86 304

ACMP_Plane					97.85 214		94.26 242		93.18 215	92.86 304

BP-MVS														90.51 239

HQP4-MVS										92.87 303			99.23 243			99.06 153

HQP3-MVS								98.43 155							98.74 221

HQP2-MVS												90.33 232

NP-MVS					98.14 190	93.72 140					95.08 279

MDTV_nov1_ep13_2view						57.28 355	94.89 225		80.59 330	94.02 272		78.66 294		85.50 305		97.82 261

MDTV_nov1_ep13			91.28 271		94.31 329	73.51 344	94.80 230	93.16 304	86.75 294	93.45 294	97.40 184	76.37 306	98.55 310	88.85 264	96.43 308

ACMMP++_ref															99.52 108

ACMMP++															99.55 100

Test By Simon												94.51 136

ITE_SJBPF				97.85 86	98.64 121	96.66 48		98.51 147	95.63 129	97.22 160	97.30 192	95.52 101	98.55 310	90.97 223	98.90 206	98.34 225

DeepMVS_CX				77.17 338	90.94 351	85.28 294		74.08 355	52.51 350	80.87 351	88.03 346	75.25 312	70.63 353	59.23 351	84.94 346	75.62 348

High-resolution multi-view benchmark