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	99.52 163	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 45	99.81 3	96.38 57	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 55	98.54 20	99.22 10	96.23 110	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	99.59 144	97.21 62	99.76 50	99.40 91

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	97.87 137	99.67 3	96.47 74	99.92 4	97.88 34	99.98 3	99.85 4

v52		98.85 8	99.01 5	98.37 56	99.61 15	95.53 83	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 56	99.61 15	95.53 83	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 43	99.55 21	96.12 64	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 55	98.45 25	99.12 22	95.83 125	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 50	99.58 18	95.67 77	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 82	99.64 12	94.20 126	98.67 12	99.14 20	99.08 9	99.42 16	99.23 29	96.53 69	99.91 12	99.27 4	99.93 21	99.73 16

PS-CasMVS		98.73 13	98.85 13	98.39 55	99.55 21	95.47 85	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 60	98.67 12	99.02 51	96.50 99	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	95.62 131	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	95.81 126	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 51	98.55 19	99.17 15	99.05 12	99.17 31	98.79 60	95.47 105	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 58	99.17 6	99.05 38	98.05 41	99.61 11	99.52 5	93.72 164	99.88 19	98.72 20	99.88 34	99.65 24

v748		98.58 20	98.89 10	97.67 99	99.61 15	93.53 149	98.59 16	98.90 75	98.97 17	99.43 15	99.15 40	96.53 69	99.85 24	98.88 11	99.91 27	99.64 27

nrg030		98.54 21	98.62 23	98.32 61	99.22 56	95.66 78	97.90 56	99.08 30	98.31 32	99.02 37	98.74 65	97.68 24	99.61 134	97.77 40	99.85 39	99.70 19

PS-MVSNAJss		98.53 22	98.63 21	98.21 69	99.68 9	94.82 105	98.10 44	99.21 11	96.91 87	99.75 4	99.45 9	95.82 91	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	95.44 107	99.84 28	96.47 81	99.80 46	99.47 64

pm-mvs1		98.47 24	98.67 19	97.86 86	99.52 25	94.58 113	98.28 31	99.00 62	97.57 63	99.27 26	99.22 30	98.32 10	99.50 175	97.09 68	99.75 54	99.50 50

ACMH	93.61 9	98.44 25	98.76 16	97.51 109	99.43 37	93.54 148	98.23 34	99.05 38	97.40 79	99.37 19	99.08 46	98.79 6	99.47 182	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 64	99.46 32	95.22 93	98.27 33	98.84 87	99.05 12	99.01 38	98.65 73	95.37 108	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 57	98.27 104	97.88 21	99.80 37	95.67 105	99.50 112	99.38 96

TransMVSNet (Re)		98.38 28	98.67 19	97.51 109	99.51 26	93.39 153	98.20 39	98.87 81	98.23 35	99.48 12	99.27 25	98.47 9	99.55 156	96.52 78	99.53 105	99.60 34

TranMVSNet+NR-MVSNet		98.33 29	98.30 39	98.43 51	99.07 81	95.87 70	96.73 122	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	95.88 122	97.88 132	98.22 109	98.15 13	99.74 59	96.50 80	99.62 79	99.42 86

ANet_high		98.31 31	98.94 8	96.41 177	99.33 47	89.64 214	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 95	99.06 82	93.80 138	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 74	99.33 47	95.21 95	98.04 48	99.46 6	97.32 82	97.82 141	99.11 43	96.75 59	99.86 23	97.84 36	99.36 155	99.15 134
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	99.45 191	94.08 168	99.67 73	99.13 137
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 81	99.39 42	95.22 93	97.55 81	99.20 13	98.21 36	99.25 27	98.51 82	98.21 12	99.40 210	94.79 145	99.72 59	99.32 107

FC-MVSNet-test		98.16 36	98.37 33	97.56 104	99.49 30	93.10 157	98.35 28	99.21 11	98.43 28	98.89 44	98.83 59	94.30 143	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	98.73 113	97.69 57	97.90 128	97.96 138	95.81 95	99.82 31	96.13 88	99.61 84	99.45 71

APDe-MVS		98.14 37	98.03 50	98.47 49	98.72 111	96.04 67	98.07 46	99.10 25	95.96 119	98.59 61	98.69 69	96.94 48	99.81 33	96.64 74	99.58 92	99.57 40

APD-MVS_3200maxsize		98.13 39	97.90 54	98.79 28	98.79 104	97.31 33	97.55 81	98.92 73	97.72 55	98.25 89	98.13 121	97.10 43	99.75 54	95.44 117	99.24 176	99.32 107

HPM-MVS		98.11 40	97.83 58	98.92 19	99.42 39	97.46 28	98.57 17	99.05 38	95.43 140	97.41 156	97.50 180	97.98 17	99.79 38	95.58 114	99.57 95	99.50 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Gipuma		98.07 41	98.31 37	97.36 126	99.76 5	96.28 61	98.51 21	99.10 25	98.76 20	96.79 185	99.34 20	96.61 66	98.82 291	96.38 83	99.50 112	96.98 290
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	95.75 128	97.91 127	98.06 130	96.89 50	99.76 48	95.32 122	99.57 95	99.43 84
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
ACMM	93.33 11	98.05 42	97.79 59	98.85 24	99.15 67	97.55 23	96.68 124	98.83 95	95.21 148	98.36 77	98.13 121	98.13 16	99.62 128	96.04 92	99.54 103	99.39 94
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v13		98.02 44	98.52 27	96.51 170	99.02 88	90.14 205	98.07 46	99.09 29	98.10 40	99.13 32	99.35 18	94.84 122	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 98	98.08 107	97.87 149	97.02 47	99.76 48	95.25 124	99.59 89	99.40 91
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		98.01 46	97.66 69	99.06 5	99.44 34	97.90 8	95.66 177	98.73 113	97.69 57	97.90 128	97.96 138	95.81 95	99.82 31	96.13 88	99.61 84	99.45 71

v12		97.97 47	98.47 28	96.46 174	98.98 92	90.01 209	97.97 51	99.08 30	98.00 43	99.11 34	99.34 20	94.70 125	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	96.32 206	97.64 167	96.49 72	99.72 70	95.66 107	99.37 152	99.45 71

NR-MVSNet		97.96 48	97.86 56	98.26 66	98.73 109	95.54 81	98.14 42	98.73 113	97.79 48	99.42 16	97.83 150	94.40 140	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	96.05 113	97.46 154	97.63 168	96.77 58	99.76 48	95.61 111	99.46 126	99.49 58

FMVSNet1		97.95 50	98.08 46	97.56 104	99.14 75	93.67 142	98.23 34	98.66 130	97.41 78	99.00 40	99.19 32	95.47 105	99.73 64	95.83 101	99.76 50	99.30 111

HFP-MVS		97.94 52	97.64 72	98.83 25	99.15 67	97.50 25	97.59 78	98.84 87	96.05 113	97.49 149	97.54 175	97.07 45	99.70 88	95.61 111	99.46 126	99.30 111

LPG-MVS_test		97.94 52	97.67 68	98.74 32	99.15 67	97.02 38	97.09 105	99.02 51	95.15 153	98.34 79	98.23 106	97.91 19	99.70 88	94.41 156	99.73 56	99.50 50

FIs		97.93 54	98.07 47	97.48 116	99.38 43	92.95 159	98.03 50	99.11 23	98.04 42	98.62 57	98.66 71	93.75 163	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	96.00 117	97.22 161	97.62 169	96.87 53	99.76 48	95.48 115	99.43 140	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	96.11 112	96.89 183	97.45 183	96.85 54	99.78 39	95.19 127	99.63 78	99.38 96

mPP-MVS		97.91 57	97.53 82	99.04 7	99.22 56	97.87 11	97.74 67	98.78 105	96.04 115	97.10 168	97.73 162	96.53 69	99.78 39	95.16 131	99.50 112	99.46 66

V9		97.90 58	98.40 32	96.40 178	98.93 94	89.86 211	97.86 58	99.07 34	97.88 47	99.05 36	99.30 23	94.53 136	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	96.36 135	98.79 102	95.07 160	97.88 132	98.35 93	97.24 40	99.72 70	96.05 91	99.58 92	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	95.45 138	97.55 145	97.94 142	97.11 42	99.78 39	94.77 147	99.46 126	99.48 61

DP-MVS		97.87 61	97.89 55	97.81 89	98.62 127	94.82 105	97.13 99	98.79 102	98.98 16	98.74 52	98.49 83	95.80 97	99.49 177	95.04 138	99.44 131	99.11 145

RPSCF		97.87 61	97.51 84	98.95 14	99.15 67	98.43 3	97.56 80	99.06 36	96.19 111	98.48 69	98.70 68	94.72 124	99.24 244	94.37 159	99.33 165	99.17 130

test_0402		97.84 63	97.97 51	97.47 117	99.19 62	94.07 129	96.71 123	98.73 113	98.66 22	98.56 63	98.41 88	96.84 55	99.69 97	94.82 142	99.81 43	98.64 201

V14		97.83 64	98.33 36	96.35 179	98.88 100	89.72 212	97.75 65	99.05 38	97.74 51	99.01 38	99.27 25	94.35 141	99.71 80	98.95 9	99.97 8	99.62 31

UniMVSNet_NR-MVSNet		97.83 64	97.65 70	98.37 56	98.72 111	95.78 72	95.66 177	99.02 51	98.11 39	98.31 84	97.69 166	94.65 130	99.85 24	97.02 70	99.71 63	99.48 61

UniMVSNet (Re)		97.83 64	97.65 70	98.35 60	98.80 103	95.86 71	95.92 165	99.04 45	97.51 68	98.22 91	97.81 154	94.68 128	99.78 39	97.14 67	99.75 54	99.41 88

v11		97.82 67	98.36 34	96.17 195	98.93 94	89.16 231	97.79 61	99.08 30	97.64 60	99.19 29	99.32 22	94.28 144	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 70	98.78 105	95.72 74	96.23 144	99.02 51	93.92 197	98.62 57	98.99 49	97.69 23	99.62 128	96.18 87	99.87 36	99.15 134
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 59	98.73 109	95.78 72	95.65 179	98.87 81	97.57 63	98.31 84	97.83 150	94.69 126	99.85 24	97.02 70	99.71 63	99.46 66

v15		97.77 70	98.26 40	96.30 184	98.81 102	89.59 219	97.62 74	99.04 45	97.59 62	98.97 42	99.24 27	94.19 148	99.70 88	98.88 11	99.97 8	99.61 33

LS3D		97.77 70	97.50 85	98.57 44	96.24 299	97.58 21	98.45 25	98.85 84	98.58 24	97.51 147	97.94 142	95.74 98	99.63 122	95.19 127	98.97 201	98.51 212

3Dnovator+	96.13 3	97.73 72	97.59 78	98.15 71	98.11 196	95.60 79	98.04 48	98.70 122	98.13 38	96.93 181	98.45 86	95.30 112	99.62 128	95.64 109	98.96 202	99.24 123

tfpnnormal		97.72 73	97.97 51	96.94 147	99.26 51	92.23 170	97.83 60	98.45 152	98.25 34	99.13 32	98.66 71	96.65 63	99.69 97	93.92 175	99.62 79	98.91 173

Baseline_NR-MVSNet		97.72 73	97.79 59	97.50 112	99.56 19	93.29 154	95.44 188	98.86 83	98.20 37	98.37 76	99.24 27	94.69 126	99.55 156	95.98 97	99.79 47	99.65 24

v17		97.70 75	98.17 42	96.28 187	98.77 106	89.59 219	97.62 74	99.01 60	97.54 65	98.72 54	99.18 35	94.06 152	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	95.38 197	98.99 65	92.45 237	98.11 101	98.31 97	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	96.29 186	98.75 107	89.60 217	97.62 74	99.01 60	97.53 67	98.69 56	99.18 35	94.05 153	99.68 103	98.73 17	99.88 34	99.58 36

EG-PatchMatch MVS		97.69 76	97.79 59	97.40 124	99.06 82	93.52 150	95.96 161	98.97 69	94.55 177	98.82 46	98.76 63	97.31 35	99.29 238	97.20 64	99.44 131	99.38 96

MP-MVS		97.64 79	97.18 106	99.00 9	99.32 49	97.77 14	97.49 84	98.73 113	96.27 107	95.59 234	97.75 159	96.30 79	99.78 39	93.70 181	99.48 122	99.45 71
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
#test#		97.62 80	97.22 103	98.83 25	99.15 67	97.50 25	96.81 119	98.84 87	94.25 188	97.49 149	97.54 175	97.07 45	99.70 88	94.37 159	99.46 126	99.30 111

3Dnovator	96.53 2	97.61 81	97.64 72	97.50 112	97.74 238	93.65 146	98.49 22	98.88 79	96.86 91	97.11 167	98.55 79	95.82 91	99.73 64	95.94 98	99.42 143	99.13 137

v18		97.60 82	98.06 48	96.23 188	98.68 121	89.46 222	97.48 85	98.98 67	97.33 81	98.60 60	99.13 42	93.86 156	99.67 110	98.62 21	99.87 36	99.56 41

v8		97.60 82	98.06 48	96.23 188	98.71 114	89.44 223	97.43 87	98.82 99	97.29 83	98.74 52	99.10 44	93.86 156	99.68 103	98.61 22	99.94 19	99.56 41

XVG-ACMP-BASELINE		97.58 84	97.28 95	98.49 47	99.16 64	96.90 42	96.39 130	98.98 67	95.05 161	98.06 110	98.02 133	95.86 87	99.56 152	94.37 159	99.64 77	99.00 158

SMA-MVS		97.55 85	97.19 105	98.61 42	98.83 101	96.71 46	96.74 121	98.81 101	91.81 249	98.78 48	98.36 92	96.63 65	99.68 103	95.17 129	99.59 89	99.45 71

v10		97.55 85	97.97 51	96.31 183	98.60 129	89.64 214	97.44 86	99.02 51	96.60 96	98.72 54	99.16 39	93.48 168	99.72 70	98.76 15	99.92 24	99.58 36

OPM-MVS		97.54 87	97.25 96	98.41 52	99.11 77	96.61 51	95.24 209	98.46 151	94.58 176	98.10 104	98.07 127	97.09 44	99.39 216	95.16 131	99.44 131	99.21 125

XXY-MVS		97.54 87	97.70 65	97.07 140	99.46 32	92.21 171	97.22 95	99.00 62	94.93 164	98.58 62	98.92 56	97.31 35	99.41 207	94.44 154	99.43 140	99.59 35

Regformer-4		97.53 89	97.47 87	97.71 94	97.35 266	93.91 134	95.26 207	98.14 198	97.97 44	98.34 79	97.89 147	95.49 103	99.71 80	97.41 57	99.42 143	99.51 49

SixPastTwentyTwo		97.49 90	97.57 80	97.26 132	99.56 19	92.33 167	98.28 31	96.97 259	98.30 33	99.45 14	99.35 18	88.43 256	99.89 17	98.01 31	99.76 50	99.54 45

ACMP	92.54 13	97.47 91	97.10 113	98.55 46	99.04 85	96.70 48	96.24 143	98.89 77	93.71 207	97.97 119	97.75 159	97.44 29	99.63 122	93.22 189	99.70 66	99.32 107
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testing_2		97.43 92	97.71 64	96.60 163	98.91 97	90.85 195	96.01 154	98.54 144	94.78 169	98.78 48	98.96 52	96.35 78	99.54 158	97.25 60	99.82 42	99.40 91

TSAR-MVS + MP.		97.42 93	97.23 102	98.00 80	99.38 43	95.00 99	97.63 73	98.20 189	93.00 223	98.16 96	98.06 130	95.89 86	99.72 70	95.67 105	99.10 190	99.28 118

Regformer-2		97.41 94	97.24 98	97.93 83	97.21 275	94.72 108	94.85 230	98.27 181	97.74 51	98.11 101	97.50 180	95.58 101	99.69 97	96.57 77	99.31 167	99.37 101

CSCG		97.40 95	97.30 92	97.69 97	98.95 93	94.83 104	97.28 91	98.99 65	96.35 106	98.13 100	95.95 266	95.99 84	99.66 115	94.36 162	99.73 56	98.59 206

XVG-OURS-SEG-HR		97.38 96	97.07 116	98.30 64	99.01 89	97.41 31	94.66 235	99.02 51	95.20 149	98.15 98	97.52 178	98.83 5	98.43 319	94.87 140	96.41 313	99.07 152

HSP-MVS		97.37 97	96.85 126	98.92 19	99.26 51	97.70 15	97.66 70	98.23 185	95.65 129	98.51 66	96.46 242	92.15 204	99.81 33	95.14 133	98.58 237	99.26 122

VDD-MVS		97.37 97	97.25 96	97.74 93	98.69 120	94.50 116	97.04 107	95.61 285	98.59 23	98.51 66	98.72 66	92.54 196	99.58 146	96.02 94	99.49 119	99.12 142

SD-MVS		97.37 97	97.70 65	96.35 179	98.14 192	95.13 96	96.54 125	98.92 73	95.94 120	99.19 29	98.08 126	97.74 22	95.06 351	95.24 125	99.54 103	98.87 183

PM-MVS		97.36 100	97.10 113	98.14 72	98.91 97	96.77 45	96.20 145	98.63 137	93.82 204	98.54 64	98.33 95	93.98 154	99.05 263	95.99 96	99.45 130	98.61 205

LCM-MVSNet-Re		97.33 101	97.33 91	97.32 128	98.13 195	93.79 139	96.99 109	99.65 2	96.74 94	99.47 13	98.93 55	96.91 49	99.84 28	90.11 249	99.06 196	98.32 229

EI-MVSNet-UG-set		97.32 102	97.40 88	97.09 139	97.34 269	92.01 179	95.33 201	97.65 231	97.74 51	98.30 86	98.14 120	95.04 118	99.69 97	97.55 49	99.52 109	99.58 36

EI-MVSNet-Vis-set		97.32 102	97.39 89	97.11 137	97.36 265	92.08 177	95.34 200	97.65 231	97.74 51	98.29 87	98.11 124	95.05 116	99.68 103	97.50 53	99.50 112	99.56 41

Regformer-1		97.27 104	97.16 108	97.61 102	97.21 275	93.86 136	94.85 230	98.04 209	97.62 61	98.03 113	97.50 180	95.34 109	99.63 122	96.52 78	99.31 167	99.35 105

VPNet		97.26 105	97.49 86	96.59 165	99.47 31	90.58 201	96.27 139	98.53 145	97.77 49	98.46 71	98.41 88	94.59 132	99.68 103	94.61 150	99.29 171	99.52 48

Regformer-3		97.25 106	97.29 93	97.11 137	97.35 266	92.32 168	95.26 207	97.62 236	97.67 59	98.17 95	97.89 147	95.05 116	99.56 152	97.16 66	99.42 143	99.46 66

canonicalmvs		97.23 107	97.21 104	97.30 129	97.65 247	94.39 118	97.84 59	99.05 38	97.42 71	96.68 188	93.85 306	97.63 26	99.33 231	96.29 85	98.47 242	98.18 244

ESAPD		97.22 108	96.82 129	98.40 54	99.03 86	96.07 65	95.64 181	98.84 87	94.84 165	98.08 107	97.60 171	96.69 61	99.76 48	91.22 221	99.44 131	99.37 101

AllTest		97.20 109	96.92 124	98.06 75	99.08 79	96.16 62	97.14 98	99.16 16	94.35 185	97.78 142	98.07 127	95.84 88	99.12 253	91.41 215	99.42 143	98.91 173

XVG-OURS		97.12 110	96.74 134	98.26 66	98.99 90	97.45 29	93.82 271	99.05 38	95.19 150	98.32 82	97.70 165	95.22 114	98.41 320	94.27 164	98.13 254	98.93 170

V42		97.04 111	97.16 108	96.68 161	98.59 131	91.05 192	96.33 137	98.36 166	94.60 173	97.99 115	98.30 100	93.32 174	99.62 128	97.40 58	99.53 105	99.38 96

APD-MVS		97.00 112	96.53 146	98.41 52	98.55 136	96.31 59	96.32 138	98.77 106	92.96 229	97.44 155	97.58 174	95.84 88	99.74 59	91.96 203	99.35 158	99.19 127
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		96.99 113	96.38 151	98.81 27	98.64 122	97.59 20	95.97 157	98.20 189	95.51 136	95.06 242	96.53 238	94.10 151	99.70 88	94.29 163	99.15 183	99.13 137

GBi-Net		96.99 113	96.80 131	97.56 104	97.96 209	93.67 142	98.23 34	98.66 130	95.59 133	97.99 115	99.19 32	89.51 247	99.73 64	94.60 151	99.44 131	99.30 111

test1		96.99 113	96.80 131	97.56 104	97.96 209	93.67 142	98.23 34	98.66 130	95.59 133	97.99 115	99.19 32	89.51 247	99.73 64	94.60 151	99.44 131	99.30 111

VDDNet		96.98 116	96.84 127	97.41 123	99.40 41	93.26 155	97.94 53	95.31 287	99.26 6	98.39 75	99.18 35	87.85 263	99.62 128	95.13 134	99.09 191	99.35 105

v1neww		96.97 117	97.24 98	96.15 196	98.70 116	89.44 223	95.97 157	98.33 171	95.25 145	97.88 132	98.15 117	93.83 159	99.61 134	97.50 53	99.50 112	99.41 88

v7new		96.97 117	97.24 98	96.15 196	98.70 116	89.44 223	95.97 157	98.33 171	95.25 145	97.88 132	98.15 117	93.83 159	99.61 134	97.50 53	99.50 112	99.41 88

v6		96.97 117	97.24 98	96.15 196	98.71 114	89.44 223	95.97 157	98.33 171	95.25 145	97.89 130	98.15 117	93.86 156	99.61 134	97.51 52	99.50 112	99.42 86

PHI-MVS		96.96 120	96.53 146	98.25 68	97.48 256	96.50 54	96.76 120	98.85 84	93.52 210	96.19 215	96.85 217	95.94 85	99.42 196	93.79 179	99.43 140	98.83 187

v7		96.93 121	97.17 107	96.23 188	98.59 131	89.64 214	95.96 161	98.66 130	94.41 181	97.87 137	98.38 91	93.47 169	99.64 119	97.93 33	99.24 176	99.43 84

IS-MVSNet		96.93 121	96.68 136	97.70 95	99.25 54	94.00 132	98.57 17	96.74 267	98.36 30	98.14 99	97.98 137	88.23 257	99.71 80	93.10 192	99.72 59	99.38 96

CNVR-MVS		96.92 123	96.55 143	98.03 79	98.00 206	95.54 81	94.87 228	98.17 194	94.60 173	96.38 200	97.05 205	95.67 99	99.36 225	95.12 135	99.08 192	99.19 127

IterMVS-LS		96.92 123	97.29 93	95.79 218	98.51 143	88.13 254	95.10 213	98.66 130	96.99 84	98.46 71	98.68 70	92.55 194	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		96.90 125	96.81 130	97.16 134	98.56 135	92.20 173	94.33 243	98.12 200	97.34 80	98.20 93	97.33 193	92.81 185	99.75 54	94.79 145	99.81 43	99.54 45

DeepPCF-MVS	94.58 5	96.90 125	96.43 150	98.31 63	97.48 256	97.23 35	92.56 304	98.60 140	92.84 231	98.54 64	97.40 186	96.64 64	98.78 295	94.40 158	99.41 149	98.93 170

v1141		96.86 127	97.14 110	96.04 203	98.55 136	89.06 234	95.44 188	98.33 171	95.14 155	97.93 125	98.19 111	93.36 172	99.62 128	97.61 45	99.69 67	99.44 80

divwei89l23v2f112		96.86 127	97.14 110	96.04 203	98.54 139	89.06 234	95.44 188	98.33 171	95.14 155	97.93 125	98.19 111	93.36 172	99.61 134	97.61 45	99.68 71	99.44 80

v1		96.86 127	97.14 110	96.04 203	98.55 136	89.06 234	95.44 188	98.33 171	95.14 155	97.94 122	98.18 115	93.39 171	99.61 134	97.61 45	99.69 67	99.44 80

v1144		96.84 130	97.08 115	96.13 200	98.42 153	89.28 229	95.41 195	98.67 128	94.21 190	97.97 119	98.31 97	93.06 179	99.65 116	98.06 30	99.62 79	99.45 71

VNet		96.84 130	96.83 128	96.88 151	98.06 198	92.02 178	96.35 136	97.57 238	97.70 56	97.88 132	97.80 155	92.40 201	99.54 158	94.73 149	98.96 202	99.08 150

EPP-MVSNet		96.84 130	96.58 140	97.65 100	99.18 63	93.78 140	98.68 11	96.34 270	97.91 46	97.30 159	98.06 130	88.46 255	99.85 24	93.85 177	99.40 150	99.32 107

v1192		96.83 133	97.06 117	96.15 196	98.28 163	89.29 228	95.36 198	98.77 106	93.73 206	98.11 101	98.34 94	93.02 183	99.67 110	98.35 26	99.58 92	99.50 50

MVS_111021_LR		96.82 134	96.55 143	97.62 101	98.27 165	95.34 88	93.81 272	98.33 171	94.59 175	96.56 192	96.63 233	96.61 66	98.73 299	94.80 144	99.34 160	98.78 192

Effi-MVS+-dtu		96.81 135	96.09 160	98.99 10	96.90 287	98.69 2	96.42 129	98.09 202	95.86 123	95.15 241	95.54 276	94.26 145	99.81 33	94.06 169	98.51 240	98.47 214

UGNet		96.81 135	96.56 142	97.58 103	96.64 290	93.84 137	97.75 65	97.12 254	96.47 102	93.62 290	98.88 58	93.22 177	99.53 160	95.61 111	99.69 67	99.36 104
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		96.78 137	97.06 117	95.95 211	98.57 134	88.77 244	95.36 198	98.26 183	95.18 151	97.85 139	98.23 106	92.58 193	99.63 122	97.80 38	99.69 67	99.45 71

v1240		96.74 138	97.02 119	95.91 214	98.18 185	88.52 246	95.39 196	98.88 79	93.15 220	98.46 71	98.40 90	92.80 186	99.71 80	98.45 25	99.49 119	99.49 58

DeepC-MVS_fast	94.34 7	96.74 138	96.51 148	97.44 121	97.69 242	94.15 127	96.02 153	98.43 156	93.17 219	97.30 159	97.38 191	95.48 104	99.28 239	93.74 180	99.34 160	98.88 181
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		96.73 140	96.54 145	97.27 130	98.35 157	93.66 145	93.42 285	98.36 166	94.74 170	96.58 190	96.76 226	96.54 68	98.99 271	94.87 140	99.27 174	99.15 134

v1921920		96.72 141	96.96 122	95.99 207	98.21 178	88.79 243	95.42 193	98.79 102	93.22 214	98.19 94	98.26 105	92.68 189	99.70 88	98.34 27	99.55 101	99.49 58

FMVSNet2		96.72 141	96.67 137	96.87 152	97.96 209	91.88 181	97.15 96	98.06 207	95.59 133	98.50 68	98.62 74	89.51 247	99.65 116	94.99 139	99.60 87	99.07 152

PMVS	89.60 17	96.71 143	96.97 120	95.95 211	99.51 26	97.81 13	97.42 88	97.49 239	97.93 45	95.95 221	98.58 75	96.88 52	96.91 345	89.59 256	99.36 155	93.12 344
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v144192		96.69 144	96.90 125	96.03 206	98.25 174	88.92 237	95.49 186	98.77 106	93.05 222	98.09 105	98.29 101	92.51 198	99.70 88	98.11 29	99.56 97	99.47 64

CPTT-MVS		96.69 144	96.08 161	98.49 47	98.89 99	96.64 50	97.25 92	98.77 106	92.89 230	96.01 220	97.13 200	92.23 203	99.67 110	92.24 201	99.34 160	99.17 130

HQP_MVS		96.66 146	96.33 154	97.68 98	98.70 116	94.29 121	96.50 127	98.75 110	96.36 104	96.16 216	96.77 224	91.91 216	99.46 187	92.59 197	99.20 179	99.28 118

EI-MVSNet		96.63 147	96.93 123	95.74 219	97.26 273	88.13 254	95.29 205	97.65 231	96.99 84	97.94 122	98.19 111	92.55 194	99.58 146	96.91 72	99.56 97	99.50 50

ab-mvs		96.59 148	96.59 139	96.60 163	98.64 122	92.21 171	98.35 28	97.67 227	94.45 178	96.99 173	98.79 60	94.96 120	99.49 177	90.39 246	99.07 194	98.08 247

v148		96.58 149	96.97 120	95.42 229	98.63 126	87.57 269	95.09 215	97.90 212	95.91 121	98.24 90	97.96 138	93.42 170	99.39 216	96.04 92	99.52 109	99.29 117

test20.03		96.58 149	96.61 138	96.48 173	98.49 145	91.72 185	95.68 176	97.69 226	96.81 92	98.27 88	97.92 145	94.18 149	98.71 301	90.78 233	99.66 75	99.00 158

NCCC		96.52 151	95.99 165	98.10 73	97.81 223	95.68 76	95.00 224	98.20 189	95.39 141	95.40 237	96.36 249	93.81 161	99.45 191	93.55 184	98.42 243	99.17 130

pmmvs-eth3d		96.49 152	96.18 157	97.42 122	98.25 174	94.29 121	94.77 234	98.07 206	89.81 268	97.97 119	98.33 95	93.11 178	99.08 260	95.46 116	99.84 40	98.89 177

OMC-MVS		96.48 153	96.00 164	97.91 84	98.30 159	96.01 69	94.86 229	98.60 140	91.88 247	97.18 163	97.21 197	96.11 82	99.04 264	90.49 244	99.34 160	98.69 199

TSAR-MVS + GP.		96.47 154	96.12 158	97.49 115	97.74 238	95.23 90	94.15 256	96.90 261	93.26 213	98.04 112	96.70 229	94.41 139	98.89 283	94.77 147	99.14 184	98.37 222

Fast-Effi-MVS+-dtu		96.44 155	96.12 158	97.39 125	97.18 277	94.39 118	95.46 187	98.73 113	96.03 116	94.72 250	94.92 288	96.28 81	99.69 97	93.81 178	97.98 258	98.09 246

K. test v3		96.44 155	96.28 155	96.95 146	99.41 40	91.53 187	97.65 71	90.31 334	98.89 18	98.93 43	99.36 16	84.57 281	99.92 4	97.81 37	99.56 97	99.39 94

MSLP-MVS++		96.42 157	96.71 135	95.57 224	97.82 222	90.56 203	95.71 172	98.84 87	94.72 171	96.71 187	97.39 189	94.91 121	98.10 335	95.28 123	99.02 198	98.05 252

MVS_Test		96.27 158	96.79 133	94.73 252	96.94 285	86.63 285	96.18 146	98.33 171	94.94 162	96.07 218	98.28 102	95.25 113	99.26 242	97.21 62	97.90 265	98.30 232

MCST-MVS		96.24 159	95.80 171	97.56 104	98.75 107	94.13 128	94.66 235	98.17 194	90.17 265	96.21 214	96.10 260	95.14 115	99.43 195	94.13 167	98.85 217	99.13 137

MVS_0304		96.22 160	95.94 169	97.04 142	97.07 281	92.54 163	94.19 252	99.04 45	95.17 152	93.74 285	96.92 214	91.77 218	99.73 64	95.76 103	99.81 43	98.85 186

mvs-test1		96.20 161	95.50 179	98.32 61	96.90 287	98.16 4	95.07 218	98.09 202	95.86 123	93.63 289	94.32 302	94.26 145	99.71 80	94.06 169	97.27 300	97.07 287

Effi-MVS+		96.19 162	96.01 163	96.71 158	97.43 262	92.19 174	96.12 149	99.10 25	95.45 138	93.33 303	94.71 290	97.23 41	99.56 152	93.21 190	97.54 288	98.37 222

DELS-MVS		96.17 163	96.23 156	95.99 207	97.55 254	90.04 207	92.38 308	98.52 146	94.13 193	96.55 195	97.06 204	94.99 119	99.58 146	95.62 110	99.28 172	98.37 222
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		96.14 164	96.36 152	95.49 228	97.68 243	87.81 266	98.67 12	99.02 51	96.50 99	94.48 264	96.15 257	86.90 268	99.92 4	98.73 17	99.13 186	98.74 195

testgi		96.07 165	96.50 149	94.80 249	99.26 51	87.69 268	95.96 161	98.58 143	95.08 159	98.02 114	96.25 253	97.92 18	97.60 341	88.68 271	98.74 223	99.11 145

LF4IMVS		96.07 165	95.63 176	97.36 126	98.19 182	95.55 80	95.44 188	98.82 99	92.29 239	95.70 232	96.55 236	92.63 192	98.69 303	91.75 212	99.33 165	97.85 262

alignmvs		96.01 167	95.52 178	97.50 112	97.77 237	94.71 109	96.07 150	96.84 262	97.48 69	96.78 186	94.28 303	85.50 274	99.40 210	96.22 86	98.73 226	98.40 219

TinyColmap		96.00 168	96.34 153	94.96 243	97.90 214	87.91 263	94.13 258	98.49 149	94.41 181	98.16 96	97.76 156	96.29 80	98.68 306	90.52 241	99.42 143	98.30 232

PVSNet_Blended_VisFu		95.95 169	95.80 171	96.42 176	99.28 50	90.62 200	95.31 203	99.08 30	88.40 279	96.97 179	98.17 116	92.11 206	99.78 39	93.64 182	99.21 178	98.86 184

test_prior3		95.91 170	95.39 182	97.46 118	97.79 232	94.26 124	93.33 289	98.42 159	94.21 190	94.02 276	96.25 253	93.64 165	99.34 228	91.90 204	98.96 202	98.79 190

UnsupCasMVSNet_eth		95.91 170	95.73 173	96.44 175	98.48 147	91.52 188	95.31 203	98.45 152	95.76 127	97.48 152	97.54 175	89.53 246	98.69 303	94.43 155	94.61 330	99.13 137

QAPM		95.88 172	95.57 177	96.80 153	97.90 214	91.84 183	98.18 41	98.73 113	88.41 278	96.42 198	98.13 121	94.73 123	99.75 54	88.72 269	98.94 206	98.81 188

CANet		95.86 173	95.65 175	96.49 172	96.41 297	90.82 197	94.36 242	98.41 161	94.94 162	92.62 315	96.73 227	92.68 189	99.71 80	95.12 135	99.60 87	98.94 166

MVP-Stereo		95.69 174	95.28 184	96.92 148	98.15 191	93.03 158	95.64 181	98.20 189	90.39 262	96.63 189	97.73 162	91.63 219	99.10 258	91.84 208	97.31 298	98.63 203
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MDA-MVSNet-bldmvs		95.69 174	95.67 174	95.74 219	98.48 147	88.76 245	92.84 296	97.25 247	96.00 117	97.59 144	97.95 141	91.38 224	99.46 187	93.16 191	96.35 314	98.99 161

new-patchmatchnet		95.67 176	96.58 140	92.94 302	97.48 256	80.21 329	92.96 295	98.19 193	94.83 167	98.82 46	98.79 60	93.31 175	99.51 173	95.83 101	99.04 197	99.12 142

xiu_mvs_v1_base_debu		95.62 177	95.96 166	94.60 257	98.01 203	88.42 247	93.99 263	98.21 186	92.98 224	95.91 222	94.53 292	96.39 75	99.72 70	95.43 119	98.19 251	95.64 323

xiu_mvs_v1_base		95.62 177	95.96 166	94.60 257	98.01 203	88.42 247	93.99 263	98.21 186	92.98 224	95.91 222	94.53 292	96.39 75	99.72 70	95.43 119	98.19 251	95.64 323

xiu_mvs_v1_base_debi		95.62 177	95.96 166	94.60 257	98.01 203	88.42 247	93.99 263	98.21 186	92.98 224	95.91 222	94.53 292	96.39 75	99.72 70	95.43 119	98.19 251	95.64 323

DP-MVS Recon		95.55 180	95.13 188	96.80 153	98.51 143	93.99 133	94.60 237	98.69 123	90.20 264	95.78 228	96.21 256	92.73 188	98.98 273	90.58 240	98.86 215	97.42 277

test_normal		95.51 181	95.46 180	95.68 223	97.97 208	89.12 233	93.73 274	95.86 279	91.98 243	97.17 164	96.94 211	91.55 220	99.42 196	95.21 126	98.73 226	98.51 212

testmv		95.51 181	95.33 183	96.05 202	98.23 176	89.51 221	93.50 283	98.63 137	94.25 188	98.22 91	97.73 162	92.51 198	99.47 182	85.22 310	99.72 59	99.17 130

Fast-Effi-MVS+		95.49 183	95.07 190	96.75 156	97.67 246	92.82 160	94.22 250	98.60 140	91.61 250	93.42 300	92.90 317	96.73 60	99.70 88	92.60 196	97.89 266	97.74 267

TAMVS		95.49 183	94.94 195	97.16 134	98.31 158	93.41 152	95.07 218	96.82 264	91.09 255	97.51 147	97.82 153	89.96 241	99.42 196	88.42 274	99.44 131	98.64 201

OpenMVS	94.22 8	95.48 185	95.20 186	96.32 182	97.16 278	91.96 180	97.74 67	98.84 87	87.26 290	94.36 266	98.01 134	93.95 155	99.67 110	90.70 237	98.75 222	97.35 284

CLD-MVS		95.47 186	95.07 190	96.69 160	98.27 165	92.53 164	91.36 322	98.67 128	91.22 254	95.78 228	94.12 304	95.65 100	98.98 273	90.81 231	99.72 59	98.57 207
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
train_agg		95.46 187	94.66 206	97.88 85	97.84 220	95.23 90	93.62 278	98.39 162	87.04 294	93.78 282	95.99 261	94.58 133	99.52 163	91.76 210	98.90 208	98.89 177

DI_MVS_plusplus_test		95.46 187	95.43 181	95.55 225	98.05 199	88.84 241	94.18 253	95.75 281	91.92 246	97.32 158	96.94 211	91.44 222	99.39 216	94.81 143	98.48 241	98.43 218

CDPH-MVS		95.45 189	94.65 207	97.84 88	98.28 163	94.96 101	93.73 274	98.33 171	85.03 315	95.44 235	96.60 234	95.31 111	99.44 194	90.01 251	99.13 186	99.11 145

IterMVS		95.42 190	95.83 170	94.20 269	97.52 255	83.78 317	92.41 307	97.47 244	95.49 137	98.06 110	98.49 83	87.94 259	99.58 146	96.02 94	99.02 198	99.23 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
agg_prior1		95.39 191	94.60 210	97.75 92	97.80 227	94.96 101	93.39 286	98.36 166	87.20 292	93.49 295	95.97 264	94.65 130	99.53 160	91.69 213	98.86 215	98.77 193

Test4		95.39 191	95.24 185	95.82 217	98.07 197	89.60 217	94.40 241	98.49 149	91.39 253	97.40 157	96.32 251	87.32 267	99.41 207	95.09 137	98.71 228	98.44 217

mvs_anonymous		95.36 193	96.07 162	93.21 294	96.29 298	81.56 323	94.60 237	97.66 229	93.30 212	96.95 180	98.91 57	93.03 182	99.38 221	96.60 75	97.30 299	98.69 199

MSDG		95.33 194	95.13 188	95.94 213	97.40 264	91.85 182	91.02 325	98.37 165	95.30 143	96.31 208	95.99 261	94.51 137	98.38 324	89.59 256	97.65 284	97.60 272

LFMVS		95.32 195	94.88 199	96.62 162	98.03 200	91.47 189	97.65 71	90.72 329	99.11 8	97.89 130	98.31 97	79.20 294	99.48 180	93.91 176	99.12 189	98.93 170

agg_prior3		95.30 196	94.46 219	97.80 90	97.80 227	95.00 99	93.63 277	98.34 170	86.33 300	93.40 302	95.84 268	94.15 150	99.50 175	91.76 210	98.90 208	98.89 177

F-COLMAP		95.30 196	94.38 221	98.05 78	98.64 122	96.04 67	95.61 185	98.66 130	89.00 273	93.22 304	96.40 248	92.90 184	99.35 227	87.45 293	97.53 289	98.77 193

Anonymous20231206		95.27 198	95.06 192	95.88 215	98.72 111	89.37 227	95.70 173	97.85 215	88.00 286	96.98 174	97.62 169	91.95 212	99.34 228	89.21 261	99.53 105	98.94 166

FMVSNet3		95.26 199	94.94 195	96.22 192	96.53 293	90.06 206	95.99 155	97.66 229	94.11 194	97.99 115	97.91 146	80.22 292	99.63 122	94.60 151	99.44 131	98.96 163

N_pmnet		95.18 200	94.23 224	98.06 75	97.85 216	96.55 53	92.49 305	91.63 321	89.34 270	98.09 105	97.41 185	90.33 235	99.06 262	91.58 214	99.31 167	98.56 208

HQP-MVS		95.17 201	94.58 212	96.92 148	97.85 216	92.47 165	94.26 244	98.43 156	93.18 216	92.86 308	95.08 282	90.33 235	99.23 246	90.51 242	98.74 223	99.05 155

Vis-MVSNet (Re-imp)		95.11 202	94.85 200	95.87 216	99.12 76	89.17 230	97.54 83	94.92 289	96.50 99	96.58 190	97.27 195	83.64 282	99.48 180	88.42 274	99.67 73	98.97 162

AdaColmap		95.11 202	94.62 209	96.58 166	97.33 270	94.45 117	94.92 226	98.08 204	93.15 220	93.98 279	95.53 277	94.34 142	99.10 258	85.69 305	98.61 234	96.20 316

API-MVS		95.09 204	95.01 193	95.31 232	96.61 291	94.02 131	96.83 118	97.18 251	95.60 132	95.79 227	94.33 301	94.54 135	98.37 326	85.70 304	98.52 238	93.52 341

CNLPA		95.04 205	94.47 216	96.75 156	97.81 223	95.25 89	94.12 259	97.89 213	94.41 181	94.57 259	95.69 270	90.30 238	98.35 327	86.72 299	98.76 221	96.64 304

Patchmtry		95.03 206	94.59 211	96.33 181	94.83 326	90.82 197	96.38 134	97.20 249	96.59 97	97.49 149	98.57 76	77.67 300	99.38 221	92.95 195	99.62 79	98.80 189

PVSNet_BlendedMVS		95.02 207	94.93 197	95.27 233	97.79 232	87.40 274	94.14 257	98.68 125	88.94 274	94.51 262	98.01 134	93.04 180	99.30 235	89.77 254	99.49 119	99.11 145

diffmvs		95.00 208	95.00 194	95.01 242	96.53 293	87.96 262	95.73 170	98.32 180	90.67 260	91.89 322	97.43 184	92.07 209	98.90 280	95.44 117	96.88 303	98.16 245

TAPA-MVS	93.32 12	94.93 209	94.23 224	97.04 142	98.18 185	94.51 114	95.22 210	98.73 113	81.22 332	96.25 212	95.95 266	93.80 162	98.98 273	89.89 252	98.87 213	97.62 270
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CDS-MVSNet		94.88 210	94.12 229	97.14 136	97.64 248	93.57 147	93.96 266	97.06 256	90.05 266	96.30 209	96.55 236	86.10 271	99.47 182	90.10 250	99.31 167	98.40 219
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
no-one		94.84 211	94.76 204	95.09 239	98.29 160	87.49 271	91.82 316	97.49 239	88.21 282	97.84 140	98.75 64	91.51 221	99.27 240	88.96 266	99.99 2	98.52 211

MS-PatchMatch		94.83 212	94.91 198	94.57 260	96.81 289	87.10 280	94.23 249	97.34 245	88.74 276	97.14 165	97.11 202	91.94 213	98.23 331	92.99 194	97.92 263	98.37 222

pmmvs4		94.82 213	94.19 227	96.70 159	97.42 263	92.75 162	92.09 313	96.76 265	86.80 297	95.73 231	97.22 196	89.28 250	98.89 283	93.28 187	99.14 184	98.46 216

YYNet1		94.73 214	94.84 201	94.41 264	97.47 260	85.09 299	90.29 332	95.85 280	92.52 234	97.53 146	97.76 156	91.97 211	99.18 249	93.31 186	96.86 304	98.95 164

MDA-MVSNet_test_wron		94.73 214	94.83 203	94.42 263	97.48 256	85.15 297	90.28 333	95.87 278	92.52 234	97.48 152	97.76 156	91.92 215	99.17 251	93.32 185	96.80 307	98.94 166

UnsupCasMVSNet_bld		94.72 216	94.26 223	96.08 201	98.62 127	90.54 204	93.38 287	98.05 208	90.30 263	97.02 171	96.80 222	89.54 244	99.16 252	88.44 273	96.18 316	98.56 208

BH-untuned		94.69 217	94.75 205	94.52 262	97.95 213	87.53 270	94.07 260	97.01 257	93.99 195	97.10 168	95.65 272	92.65 191	98.95 278	87.60 290	96.74 308	97.09 286

Patchmatch-RL test		94.66 218	94.49 215	95.19 235	98.54 139	88.91 238	92.57 303	98.74 112	91.46 252	98.32 82	97.75 159	77.31 305	98.81 293	96.06 90	99.61 84	97.85 262

CANet_DTU		94.65 219	94.21 226	95.96 209	95.90 310	89.68 213	93.92 267	97.83 218	93.19 215	90.12 336	95.64 273	88.52 254	99.57 151	93.27 188	99.47 124	98.62 204

pmmvs5		94.63 220	94.34 222	95.50 227	97.63 249	88.34 250	94.02 261	97.13 253	87.15 293	95.22 240	97.15 199	87.50 264	99.27 240	93.99 173	99.26 175	98.88 181

PAPM_NR		94.61 221	94.17 228	95.96 209	98.36 156	91.23 190	95.93 164	97.95 210	92.98 224	93.42 300	94.43 300	90.53 232	98.38 324	87.60 290	96.29 315	98.27 235

PatchMatch-RL		94.61 221	93.81 235	97.02 145	98.19 182	95.72 74	93.66 276	97.23 248	88.17 283	94.94 246	95.62 274	91.43 223	98.57 311	87.36 294	97.68 281	96.76 300

BH-RMVSNet		94.56 223	94.44 220	94.91 244	97.57 251	87.44 273	93.78 273	96.26 271	93.69 208	96.41 199	96.50 241	92.10 207	99.00 270	85.96 302	97.71 278	98.31 230

USDC		94.56 223	94.57 214	94.55 261	97.78 236	86.43 287	92.75 299	98.65 136	85.96 303	96.91 182	97.93 144	90.82 230	98.74 298	90.71 236	99.59 89	98.47 214

ppachtmachnet_test		94.49 225	94.84 201	93.46 288	96.16 304	82.10 322	90.59 329	97.48 241	90.53 261	97.01 172	97.59 173	91.01 227	99.36 225	93.97 174	99.18 182	98.94 166

jason		94.39 226	94.04 231	95.41 231	98.29 160	87.85 265	92.74 301	96.75 266	85.38 313	95.29 238	96.15 257	88.21 258	99.65 116	94.24 165	99.34 160	98.74 195
jason: jason.
1121		94.26 227	93.26 243	97.27 130	98.26 173	94.73 107	95.86 166	97.71 225	77.96 345	94.53 261	96.71 228	91.93 214	99.40 210	87.71 280	98.64 232	97.69 268

EU-MVSNet		94.25 228	94.47 216	93.60 284	98.14 192	82.60 320	97.24 94	92.72 313	85.08 314	98.48 69	98.94 54	82.59 285	98.76 297	97.47 56	99.53 105	99.44 80

xiu_mvs_v2_base		94.22 229	94.63 208	92.99 301	97.32 271	84.84 302	92.12 311	97.84 216	91.96 244	94.17 269	93.43 307	96.07 83	99.71 80	91.27 218	97.48 291	94.42 333

RPMNet		94.22 229	94.03 232	94.78 250	95.44 319	88.15 252	96.18 146	93.73 297	97.43 70	94.10 272	98.49 83	79.40 293	99.39 216	95.69 104	95.81 318	96.81 298

sss		94.22 229	93.72 236	95.74 219	97.71 241	89.95 210	93.84 270	96.98 258	88.38 281	93.75 284	95.74 269	87.94 259	98.89 283	91.02 224	98.10 255	98.37 222

MVSTER		94.21 232	93.93 234	95.05 241	95.83 312	86.46 286	95.18 211	97.65 231	92.41 238	97.94 122	98.00 136	72.39 331	99.58 146	96.36 84	99.56 97	99.12 142

MAR-MVS		94.21 232	93.03 247	97.76 91	96.94 285	97.44 30	96.97 116	97.15 252	87.89 288	92.00 320	92.73 322	92.14 205	99.12 253	83.92 318	97.51 290	96.73 301
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
our_test_3		94.20 234	94.58 212	93.07 297	96.16 304	81.20 325	90.42 331	96.84 262	90.72 259	97.14 165	97.13 200	90.47 233	99.11 256	94.04 172	98.25 250	98.91 173

1112_ss		94.12 235	93.42 240	96.23 188	98.59 131	90.85 195	94.24 248	98.85 84	85.49 308	92.97 306	94.94 286	86.01 272	99.64 119	91.78 209	97.92 263	98.20 241

PS-MVSNAJ		94.10 236	94.47 216	93.00 300	97.35 266	84.88 301	91.86 315	97.84 216	91.96 244	94.17 269	92.50 324	95.82 91	99.71 80	91.27 218	97.48 291	94.40 334

CHOSEN 1792x2688		94.10 236	93.41 241	96.18 194	99.16 64	90.04 207	92.15 310	98.68 125	79.90 337	96.22 213	97.83 150	87.92 262	99.42 196	89.18 262	99.65 76	99.08 150

MG-MVS		94.08 238	94.00 233	94.32 266	97.09 280	85.89 288	93.19 293	95.96 276	92.52 234	94.93 247	97.51 179	89.54 244	98.77 296	87.52 292	97.71 278	98.31 230

PLC	91.02 16	94.05 239	92.90 249	97.51 109	98.00 206	95.12 97	94.25 247	98.25 184	86.17 301	91.48 325	95.25 280	91.01 227	99.19 248	85.02 312	96.69 309	98.22 239
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		93.96 240	93.22 245	96.19 193	99.06 82	90.97 194	95.99 155	98.94 72	73.88 351	93.43 299	96.93 213	92.38 202	99.37 224	89.09 263	99.28 172	98.25 237

PVSNet_Blended		93.96 240	93.65 237	94.91 244	97.79 232	87.40 274	91.43 321	98.68 125	84.50 319	94.51 262	94.48 295	93.04 180	99.30 235	89.77 254	98.61 234	98.02 257

lupinMVS		93.77 242	93.28 242	95.24 234	97.68 243	87.81 266	92.12 311	96.05 273	84.52 318	94.48 264	95.06 284	86.90 268	99.63 122	93.62 183	99.13 186	98.27 235

PatchT		93.75 243	93.57 239	94.29 268	95.05 324	87.32 276	96.05 151	92.98 308	97.54 65	94.25 267	98.72 66	75.79 313	99.24 244	95.92 99	95.81 318	96.32 314

EPNet		93.72 244	92.62 256	97.03 144	87.61 358	92.25 169	96.27 139	91.28 323	96.74 94	87.65 346	97.39 189	85.00 278	99.64 119	92.14 202	99.48 122	99.20 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		93.72 244	92.65 255	96.91 150	98.93 94	91.81 184	91.23 324	98.52 146	82.69 325	96.46 197	96.52 240	80.38 291	99.90 13	90.36 247	98.79 218	99.03 156

PMMVS2		93.66 246	94.07 230	92.45 309	97.57 251	80.67 328	86.46 345	96.00 274	93.99 195	97.10 168	97.38 191	89.90 242	97.82 338	88.76 268	99.47 124	98.86 184

OpenMVS_ROB	91.80 14	93.64 247	93.05 246	95.42 229	97.31 272	91.21 191	95.08 217	96.68 269	81.56 329	96.88 184	96.41 246	90.44 234	99.25 243	85.39 309	97.67 282	95.80 321

Patchmatch-test		93.60 248	93.25 244	94.63 255	96.14 306	87.47 272	96.04 152	94.50 293	93.57 209	96.47 196	96.97 209	76.50 308	98.61 309	90.67 238	98.41 244	97.81 265

WTY-MVS		93.55 249	93.00 248	95.19 235	97.81 223	87.86 264	93.89 268	96.00 274	89.02 272	94.07 274	95.44 278	86.27 270	99.33 231	87.69 282	96.82 305	98.39 221

Test_1112_low_res		93.53 250	92.86 250	95.54 226	98.60 129	88.86 240	92.75 299	98.69 123	82.66 326	92.65 313	96.92 214	84.75 279	99.56 152	90.94 227	97.76 267	98.19 242

MIMVSNet		93.42 251	92.86 250	95.10 238	98.17 187	88.19 251	98.13 43	93.69 298	92.07 240	95.04 244	98.21 110	80.95 289	99.03 267	81.42 330	98.06 256	98.07 249

FMVSNet5		93.39 252	92.35 258	96.50 171	95.83 312	90.81 199	97.31 89	98.27 181	92.74 232	96.27 210	98.28 102	62.23 352	99.67 110	90.86 229	99.36 155	99.03 156

Patchmatch-test1		93.38 253	93.59 238	92.73 305	96.24 299	81.40 324	93.24 291	94.00 296	91.58 251	94.57 259	96.67 231	87.94 259	99.03 267	90.42 245	97.66 283	97.77 266

CR-MVSNet		93.29 254	92.79 252	94.78 250	95.44 319	88.15 252	96.18 146	97.20 249	84.94 316	94.10 272	98.57 76	77.67 300	99.39 216	95.17 129	95.81 318	96.81 298

wuyk23d		93.25 255	95.20 186	87.40 336	96.07 307	95.38 86	97.04 107	94.97 288	95.33 142	99.70 6	98.11 124	98.14 14	91.94 353	77.76 342	99.68 71	74.89 353

LP		93.12 256	92.78 254	94.14 270	94.50 331	85.48 292	95.73 170	95.68 283	92.97 228	95.05 243	97.17 198	81.93 286	99.40 210	93.06 193	88.96 345	97.55 273

test1235678		92.95 257	92.40 257	94.61 256	96.95 284	86.87 282	90.75 327	97.75 221	91.00 257	96.33 202	95.38 279	85.21 276	98.92 279	79.00 336	99.20 179	98.03 255

X-MVStestdata		92.86 258	90.83 292	98.94 15	99.15 67	97.66 16	97.77 62	98.83 95	97.42 71	96.32 206	36.50 355	96.49 72	99.72 70	95.66 107	99.37 152	99.45 71

GA-MVS		92.83 259	92.15 261	94.87 247	96.97 283	87.27 277	90.03 334	96.12 272	91.83 248	94.05 275	94.57 291	76.01 312	98.97 277	92.46 199	97.34 297	98.36 227

CMPMVS	73.10 23	92.74 260	91.39 272	96.77 155	93.57 343	94.67 111	94.21 251	97.67 227	80.36 336	93.61 291	96.60 234	82.85 284	97.35 342	84.86 313	98.78 219	98.29 234
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HY-MVS	91.43 15	92.58 261	91.81 269	94.90 246	96.49 295	88.87 239	97.31 89	94.62 291	85.92 304	90.50 333	96.84 218	85.05 277	99.40 210	83.77 321	95.78 321	96.43 313

view600		92.56 262	92.11 262	93.91 275	98.45 149	84.76 304	97.10 101	90.23 335	97.42 71	96.98 174	94.48 295	73.62 321	99.60 140	82.49 325	98.28 246	97.36 278

view800		92.56 262	92.11 262	93.91 275	98.45 149	84.76 304	97.10 101	90.23 335	97.42 71	96.98 174	94.48 295	73.62 321	99.60 140	82.49 325	98.28 246	97.36 278

conf0.05thres1000		92.56 262	92.11 262	93.91 275	98.45 149	84.76 304	97.10 101	90.23 335	97.42 71	96.98 174	94.48 295	73.62 321	99.60 140	82.49 325	98.28 246	97.36 278

tfpn		92.56 262	92.11 262	93.91 275	98.45 149	84.76 304	97.10 101	90.23 335	97.42 71	96.98 174	94.48 295	73.62 321	99.60 140	82.49 325	98.28 246	97.36 278

TR-MVS		92.54 266	92.20 260	93.57 285	96.49 295	86.66 284	93.51 282	94.73 290	89.96 267	94.95 245	93.87 305	90.24 240	98.61 309	81.18 331	94.88 327	95.45 327

PMMVS		92.39 267	91.08 280	96.30 184	93.12 346	92.81 161	90.58 330	95.96 276	79.17 340	91.85 323	92.27 325	90.29 239	98.66 308	89.85 253	96.68 310	97.43 276

1314		92.38 268	92.30 259	92.64 307	95.42 321	85.15 297	95.86 166	96.97 259	85.40 312	90.62 329	93.06 315	91.12 226	97.80 339	86.74 298	95.49 326	94.97 331

new_pmnet		92.34 269	91.69 270	94.32 266	96.23 301	89.16 231	92.27 309	92.88 310	84.39 321	95.29 238	96.35 250	85.66 273	96.74 348	84.53 315	97.56 287	97.05 288

CVMVSNet		92.33 270	92.79 252	90.95 322	97.26 273	75.84 344	95.29 205	92.33 316	81.86 327	96.27 210	98.19 111	81.44 287	98.46 318	94.23 166	98.29 245	98.55 210

PAPR		92.22 271	91.27 276	95.07 240	95.73 315	88.81 242	91.97 314	97.87 214	85.80 306	90.91 327	92.73 322	91.16 225	98.33 328	79.48 334	95.76 322	98.08 247

DSMNet-mixed		92.19 272	91.83 268	93.25 293	96.18 303	83.68 318	96.27 139	93.68 300	76.97 348	92.54 316	99.18 35	89.20 252	98.55 314	83.88 319	98.60 236	97.51 275

BH-w/o		92.14 273	91.94 266	92.73 305	97.13 279	85.30 294	92.46 306	95.64 284	89.33 271	94.21 268	92.74 321	89.60 243	98.24 330	81.68 329	94.66 329	94.66 332

PCF-MVS	89.43 18	92.12 274	90.64 295	96.57 168	97.80 227	93.48 151	89.88 338	98.45 152	74.46 350	96.04 219	95.68 271	90.71 231	99.31 233	73.73 346	99.01 200	96.91 294
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thres600view7		92.03 275	91.43 271	93.82 280	98.19 182	84.61 308	96.27 139	90.39 330	96.81 92	96.37 201	93.11 310	73.44 327	99.49 177	80.32 332	97.95 259	97.36 278

PatchmatchNet		91.98 276	91.87 267	92.30 311	94.60 329	79.71 330	95.12 212	93.59 303	89.52 269	93.61 291	97.02 207	77.94 298	99.18 249	90.84 230	94.57 331	98.01 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tfpn111		91.92 277	91.39 272	93.49 287	98.21 178	84.50 309	96.39 130	90.39 330	96.87 88	96.33 202	93.08 312	73.44 327	99.51 173	79.87 333	97.94 262	96.46 309

conf0.01		91.90 278	90.98 283	94.67 253	98.27 165	88.03 256	96.98 110	88.58 343	93.90 198	94.64 253	91.45 331	69.62 340	99.52 163	87.62 284	97.74 268	96.46 309

conf0.002		91.90 278	90.98 283	94.67 253	98.27 165	88.03 256	96.98 110	88.58 343	93.90 198	94.64 253	91.45 331	69.62 340	99.52 163	87.62 284	97.74 268	96.46 309

cascas		91.89 280	91.35 274	93.51 286	94.27 334	85.60 290	88.86 341	98.61 139	79.32 339	92.16 319	91.44 337	89.22 251	98.12 334	90.80 232	97.47 293	96.82 297

tfpn1000		91.88 281	91.20 279	93.89 279	97.96 209	87.13 279	97.13 99	88.16 350	94.41 181	94.87 248	92.77 319	68.34 347	99.47 182	89.24 260	97.95 259	95.06 329

conf200view11		91.81 282	91.26 277	93.46 288	98.21 178	84.50 309	96.39 130	90.39 330	96.87 88	96.33 202	93.08 312	73.44 327	99.42 196	78.85 338	97.74 268	96.46 309

JIA-IIPM		91.79 283	90.69 294	95.11 237	93.80 340	90.98 193	94.16 255	91.78 320	96.38 103	90.30 335	99.30 23	72.02 333	98.90 280	88.28 276	90.17 342	95.45 327

thres100view900		91.76 284	91.26 277	93.26 292	98.21 178	84.50 309	96.39 130	90.39 330	96.87 88	96.33 202	93.08 312	73.44 327	99.42 196	78.85 338	97.74 268	95.85 319

thresconf0.02		91.72 285	90.98 283	93.97 271	98.27 165	88.03 256	96.98 110	88.58 343	93.90 198	94.64 253	91.45 331	69.62 340	99.52 163	87.62 284	97.74 268	94.35 335

tfpn_n400		91.72 285	90.98 283	93.97 271	98.27 165	88.03 256	96.98 110	88.58 343	93.90 198	94.64 253	91.45 331	69.62 340	99.52 163	87.62 284	97.74 268	94.35 335

tfpnconf		91.72 285	90.98 283	93.97 271	98.27 165	88.03 256	96.98 110	88.58 343	93.90 198	94.64 253	91.45 331	69.62 340	99.52 163	87.62 284	97.74 268	94.35 335

tfpnview11		91.72 285	90.98 283	93.97 271	98.27 165	88.03 256	96.98 110	88.58 343	93.90 198	94.64 253	91.45 331	69.62 340	99.52 163	87.62 284	97.74 268	94.35 335

thres400		91.68 289	91.00 281	93.71 282	98.02 201	84.35 313	95.70 173	90.79 327	96.26 108	95.90 225	92.13 327	73.62 321	99.42 196	78.85 338	97.74 268	97.36 278

tfpn200view9		91.55 290	91.00 281	93.21 294	98.02 201	84.35 313	95.70 173	90.79 327	96.26 108	95.90 225	92.13 327	73.62 321	99.42 196	78.85 338	97.74 268	95.85 319

ADS-MVSNet2		91.47 291	90.51 297	94.36 265	95.51 317	85.63 289	95.05 221	95.70 282	83.46 323	92.69 311	96.84 218	79.15 295	99.41 207	85.66 306	90.52 340	98.04 253

EPNet_dtu		91.39 292	90.75 293	93.31 291	90.48 356	82.61 319	94.80 232	92.88 310	93.39 211	81.74 354	94.90 289	81.36 288	99.11 256	88.28 276	98.87 213	98.21 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet	86.72 19	91.10 293	90.97 289	91.49 316	97.56 253	78.04 337	87.17 343	94.60 292	84.65 317	92.34 317	92.20 326	87.37 266	98.47 317	85.17 311	97.69 280	97.96 259

tpm		91.08 294	90.85 291	91.75 315	95.33 322	78.09 335	95.03 223	91.27 324	88.75 275	93.53 294	97.40 186	71.24 334	99.30 235	91.25 220	93.87 332	97.87 261

thres200		91.00 295	90.42 299	92.77 304	97.47 260	83.98 316	94.01 262	91.18 325	95.12 158	95.44 235	91.21 339	73.93 317	99.31 233	77.76 342	97.63 286	95.01 330

tfpn_ndepth		90.98 296	90.24 301	93.20 296	97.72 240	87.18 278	96.52 126	88.20 349	92.63 233	93.69 288	90.70 344	68.22 348	99.42 196	86.98 296	97.47 293	93.00 345

ADS-MVSNet		90.95 297	90.26 300	93.04 298	95.51 317	82.37 321	95.05 221	93.41 304	83.46 323	92.69 311	96.84 218	79.15 295	98.70 302	85.66 306	90.52 340	98.04 253

testus		90.90 298	90.51 297	92.06 313	96.07 307	79.45 331	88.99 339	98.44 155	85.46 310	94.15 271	90.77 341	89.12 253	98.01 337	73.66 347	97.95 259	98.71 198

tpmvs		90.79 299	90.87 290	90.57 325	92.75 350	76.30 342	95.79 169	93.64 301	91.04 256	91.91 321	96.26 252	77.19 306	98.86 289	89.38 259	89.85 343	96.56 307

tpmrst		90.31 300	90.61 296	89.41 329	94.06 338	72.37 351	95.06 220	93.69 298	88.01 285	92.32 318	96.86 216	77.45 302	98.82 291	91.04 223	87.01 348	97.04 289

test0.0.03 1		90.11 301	89.21 308	92.83 303	93.89 339	86.87 282	91.74 317	88.74 342	92.02 241	94.71 251	91.14 340	73.92 318	94.48 352	83.75 322	92.94 334	97.16 285

MVS		90.02 302	89.20 309	92.47 308	94.71 327	86.90 281	95.86 166	96.74 267	64.72 353	90.62 329	92.77 319	92.54 196	98.39 322	79.30 335	95.56 325	92.12 346

pmmvs3		90.00 303	88.90 312	93.32 290	94.20 337	85.34 293	91.25 323	92.56 315	78.59 342	93.82 281	95.17 281	67.36 350	98.69 303	89.08 264	98.03 257	95.92 317

CHOSEN 280x420		89.98 304	89.19 310	92.37 310	95.60 316	81.13 326	86.22 346	97.09 255	81.44 331	87.44 347	93.15 309	73.99 316	99.47 182	88.69 270	99.07 194	96.52 308

test-LLR		89.97 305	89.90 303	90.16 326	94.24 335	74.98 345	89.89 335	89.06 340	92.02 241	89.97 337	90.77 341	73.92 318	98.57 311	91.88 206	97.36 295	96.92 292

FPMVS		89.92 306	88.63 313	93.82 280	98.37 155	96.94 41	91.58 318	93.34 305	88.00 286	90.32 334	97.10 203	70.87 336	91.13 354	71.91 350	96.16 317	93.39 343

CostFormer		89.75 307	89.25 306	91.26 319	94.69 328	78.00 338	95.32 202	91.98 318	81.50 330	90.55 331	96.96 210	71.06 335	98.89 283	88.59 272	92.63 337	96.87 295

PatchFormer-LS_test		89.62 308	89.12 311	91.11 321	93.62 341	78.42 334	94.57 239	93.62 302	88.39 280	90.54 332	88.40 349	72.33 332	99.03 267	92.41 200	88.20 346	95.89 318

E-PMN		89.52 309	89.78 304	88.73 331	93.14 345	77.61 339	83.26 350	92.02 317	94.82 168	93.71 286	93.11 310	75.31 314	96.81 346	85.81 303	96.81 306	91.77 348

EPMVS		89.26 310	88.55 314	91.39 317	92.36 351	79.11 332	95.65 179	79.86 355	88.60 277	93.12 305	96.53 238	70.73 337	98.10 335	90.75 234	89.32 344	96.98 290

EMVS		89.06 311	89.22 307	88.61 332	93.00 347	77.34 340	82.91 351	90.92 326	94.64 172	92.63 314	91.81 330	76.30 310	97.02 344	83.83 320	96.90 302	91.48 349

1111		88.78 312	89.39 305	86.96 337	98.53 141	62.84 356	91.49 319	97.48 241	94.45 178	96.56 192	96.45 243	43.83 362	98.87 287	86.33 300	99.40 150	99.18 129

IB-MVS	85.98 20	88.63 313	86.95 322	93.68 283	95.12 323	84.82 303	90.85 326	90.17 339	87.55 289	88.48 343	91.34 338	58.01 354	99.59 144	87.24 295	93.80 333	96.63 306
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		88.47 314	87.69 317	90.79 323	94.98 325	77.34 340	95.09 215	91.83 319	77.51 347	89.40 339	96.41 246	67.83 349	98.73 299	83.58 323	92.60 338	96.29 315

tpmp4_e23		88.46 315	87.54 318	91.22 320	94.56 330	78.08 336	95.63 184	93.17 306	79.08 341	85.85 349	96.80 222	65.86 351	98.85 290	84.10 317	92.85 335	96.72 302

MVS-HIRNet		88.40 316	90.20 302	82.99 340	97.01 282	60.04 358	93.11 294	85.61 352	84.45 320	88.72 342	99.09 45	84.72 280	98.23 331	82.52 324	96.59 311	90.69 351

gg-mvs-nofinetune		88.28 317	86.96 321	92.23 312	92.84 349	84.44 312	98.19 40	74.60 357	99.08 9	87.01 348	99.47 8	56.93 355	98.23 331	78.91 337	95.61 324	94.01 339

dp		88.08 318	88.05 316	88.16 335	92.85 348	68.81 353	94.17 254	92.88 310	85.47 309	91.38 326	96.14 259	68.87 346	98.81 293	86.88 297	83.80 352	96.87 295

tpm cat1		88.01 319	87.33 319	90.05 328	94.48 332	76.28 343	94.47 240	94.35 295	73.84 352	89.26 340	95.61 275	73.64 320	98.30 329	84.13 316	86.20 349	95.57 326

test12356		87.98 320	88.41 315	86.69 338	95.84 311	63.49 355	87.15 344	97.32 246	87.21 291	91.78 324	93.36 308	70.66 338	98.39 322	74.70 345	97.64 285	98.19 242

test-mter		87.92 321	87.17 320	90.16 326	94.24 335	74.98 345	89.89 335	89.06 340	86.44 299	89.97 337	90.77 341	54.96 358	98.57 311	91.88 206	97.36 295	96.92 292

DWT-MVSNet_test		87.92 321	86.77 323	91.39 317	93.18 344	78.62 333	95.10 213	91.42 322	85.58 307	88.00 344	88.73 348	60.60 353	98.90 280	90.60 239	87.70 347	96.65 303

PAPM		87.64 323	85.84 326	93.04 298	96.54 292	84.99 300	88.42 342	95.57 286	79.52 338	83.82 351	93.05 316	80.57 290	98.41 320	62.29 354	92.79 336	95.71 322

TESTMET0.1,1		87.20 324	86.57 324	89.07 330	93.62 341	72.84 350	89.89 335	87.01 351	85.46 310	89.12 341	90.20 346	56.00 357	97.72 340	90.91 228	96.92 301	96.64 304

MVE	73.61 22	86.48 325	85.92 325	88.18 334	96.23 301	85.28 295	81.78 353	75.79 356	86.01 302	82.53 353	91.88 329	92.74 187	87.47 356	71.42 351	94.86 328	91.78 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2356		85.45 326	83.26 329	92.01 314	91.12 353	80.76 327	85.16 347	92.90 309	83.90 322	90.63 328	87.71 351	53.10 359	97.24 343	69.20 352	95.65 323	98.03 255

PVSNet_0	81.89 21	84.49 327	83.21 330	88.34 333	95.76 314	74.97 347	83.49 349	92.70 314	78.47 343	87.94 345	86.90 352	83.38 283	96.63 349	73.44 348	66.86 355	93.40 342

PNet_i23d		83.82 328	83.39 328	85.10 339	96.07 307	65.16 354	81.87 352	94.37 294	90.87 258	93.92 280	92.89 318	52.80 360	96.44 350	77.52 344	70.22 354	93.70 340

testpf		82.70 329	84.35 327	77.74 341	88.97 357	73.23 349	93.85 269	84.33 353	88.10 284	85.06 350	90.42 345	52.62 361	91.05 355	91.00 225	84.82 351	68.93 354

.test1245		73.49 330	79.27 331	56.15 343	98.53 141	62.84 356	91.49 319	97.48 241	94.45 178	96.56 192	96.45 243	43.83 362	98.87 287	86.33 300	8.32 357	6.75 357

tmp_tt		57.23 331	62.50 332	41.44 344	34.77 359	49.21 360	83.93 348	60.22 361	15.31 355	71.11 356	79.37 354	70.09 339	44.86 358	64.76 353	82.93 353	30.25 355

pcd1.5k->3k		41.47 332	44.19 333	33.29 345	99.65 11	0.00 363	0.00 354	99.07 34	0.00 358	0.00 359	0.00 360	99.04 4	0.00 361	0.00 358	99.96 11	99.87 2

cdsmvs_eth3d_5k		24.22 333	32.30 334	0.00 348	0.00 362	0.00 363	0.00 354	98.10 201	0.00 358	0.00 359	95.06 284	97.54 28	0.00 361	0.00 358	0.00 359	0.00 359

test123		12.59 334	15.49 335	3.87 346	6.07 360	2.55 361	90.75 327	2.59 363	2.52 356	5.20 358	13.02 357	4.96 364	1.85 360	5.20 356	9.09 356	7.23 356

testmvs		12.33 335	15.23 336	3.64 347	5.77 361	2.23 362	88.99 339	3.62 362	2.30 357	5.29 357	13.09 356	4.52 365	1.95 359	5.16 357	8.32 357	6.75 357

pcd_1.5k_mvsjas		7.98 336	10.65 337	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	95.82 91	0.00 361	0.00 358	0.00 359	0.00 359

ab-mvs-re		7.91 337	10.55 338	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	94.94 286	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

sosnet-low-res		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

sosnet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uncertanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

Regformer		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

uanet		0.00 338	0.00 339	0.00 348	0.00 362	0.00 363	0.00 354	0.00 364	0.00 358	0.00 359	0.00 360	0.00 366	0.00 361	0.00 358	0.00 359	0.00 359

GSMVS																98.06 250

test_part3							95.64 181		94.84 165		97.60 171		99.76 48	91.22 221

test_part2					99.03 86	96.07 65				98.08 107

test_part1								98.84 87				96.69 61			99.44 131	99.37 101

sam_mvs1												77.80 299				98.06 250

sam_mvs												77.38 303

semantic-postprocess				94.85 248	97.68 243	85.53 291		97.63 235	96.99 84	98.36 77	98.54 80	87.44 265	99.75 54	97.07 69	99.08 192	99.27 121

ambc				96.56 169	98.23 176	91.68 186	97.88 57	98.13 199		98.42 74	98.56 78	94.22 147	99.04 264	94.05 171	99.35 158	98.95 164

MTGPA								98.73 113

test_post1							94.98 225				10.37 359	76.21 311	99.04 264	89.47 258

test_post											10.87 358	76.83 307	99.07 261

patchmatchnet-post											96.84 218	77.36 304	99.42 196

GG-mvs-BLEND				90.60 324	91.00 354	84.21 315	98.23 34	72.63 360		82.76 352	84.11 353	56.14 356	96.79 347	72.20 349	92.09 339	90.78 350

MTMP								74.60 357

gm-plane-assit					91.79 352	71.40 352			81.67 328		90.11 347		98.99 271	84.86 313

test9_res														91.29 217	98.89 212	99.00 158

TEST9					97.84 220	95.23 90	93.62 278	98.39 162	86.81 296	93.78 282	95.99 261	94.68 128	99.52 163

test_8					97.81 223	95.07 98	93.54 281	98.38 164	87.04 294	93.71 286	95.96 265	94.58 133	99.52 163

agg_prior2														90.34 248	98.90 208	99.10 149

agg_prior					97.80 227	94.96 101		98.36 166		93.49 295			99.53 160

TestCases				98.06 75	99.08 79	96.16 62		99.16 16	94.35 185	97.78 142	98.07 127	95.84 88	99.12 253	91.41 215	99.42 143	98.91 173

test_prior4						95.38 86	93.61 280

test_prior2							93.33 289		94.21 190	94.02 276	96.25 253	93.64 165		91.90 204	98.96 202

test_prior				97.46 118	97.79 232	94.26 124		98.42 159					99.34 228			98.79 190

旧先验2							93.35 288		77.95 346	95.77 230			98.67 307	90.74 235

新几何2							93.43 284

新几何1				97.25 133	98.29 160	94.70 110		97.73 223	77.98 344	94.83 249	96.67 231	92.08 208	99.45 191	88.17 278	98.65 231	97.61 271

旧先验1					97.80 227	93.87 135		97.75 221			97.04 206	93.57 167			98.68 229	98.72 197

无先验							93.20 292	97.91 211	80.78 333				99.40 210	87.71 280		97.94 260

原ACMM2							92.82 297

原ACMM1				96.58 166	98.16 189	92.12 175		98.15 197	85.90 305	93.49 295	96.43 245	92.47 200	99.38 221	87.66 283	98.62 233	98.23 238

test222					98.17 187	93.24 156	92.74 301	97.61 237	75.17 349	94.65 252	96.69 230	90.96 229			98.66 230	97.66 269

testdata2													99.46 187	87.84 279

segment_acmp												95.34 109

testdata				95.70 222	98.16 189	90.58 201		97.72 224	80.38 335	95.62 233	97.02 207	92.06 210	98.98 273	89.06 265	98.52 238	97.54 274

testdata1							92.77 298		93.78 205

test12				97.46 118	97.61 250	94.07 129		97.78 220		93.57 293		93.31 175	99.42 196		98.78 219	98.89 177

plane_prior7					98.70 116	94.67 111

plane_prior6					98.38 154	94.37 120						91.91 216

plane_prior5								98.75 110					99.46 187	92.59 197	99.20 179	99.28 118

plane_prior4											96.77 224

plane_prior3						94.51 114			95.29 144	96.16 216

plane_prior2							96.50 127		96.36 104

plane_prior1					98.49 145

plane_prior						94.29 121	95.42 193		94.31 187						98.93 207

n2								0.00 364

nn								0.00 364

door-mid								98.17 194

lessismore_v0				97.05 141	99.36 45	92.12 175		84.07 354		98.77 51	98.98 50	85.36 275	99.74 59	97.34 59	99.37 152	99.30 111

LGP-MVS_train				98.74 32	99.15 67	97.02 38		99.02 51	95.15 153	98.34 79	98.23 106	97.91 19	99.70 88	94.41 156	99.73 56	99.50 50

test11								98.08 204

door								97.81 219

HQP5-MVS						92.47 165

HQP-NCC					97.85 216		94.26 244		93.18 216	92.86 308

ACMP_Plane					97.85 216		94.26 244		93.18 216	92.86 308

BP-MVS														90.51 242

HQP4-MVS										92.87 307			99.23 246			99.06 154

HQP3-MVS								98.43 156							98.74 223

HQP2-MVS												90.33 235

NP-MVS					98.14 192	93.72 141					95.08 282

MDTV_nov1_ep13_2view						57.28 359	94.89 227		80.59 334	94.02 276		78.66 297		85.50 308		97.82 264

MDTV_nov1_ep13			91.28 275		94.31 333	73.51 348	94.80 232	93.16 307	86.75 298	93.45 298	97.40 186	76.37 309	98.55 314	88.85 267	96.43 312

ACMMP++_ref															99.52 109

ACMMP++															99.55 101

Test By Simon												94.51 137

ITE_SJBPF				97.85 87	98.64 122	96.66 49		98.51 148	95.63 130	97.22 161	97.30 194	95.52 102	98.55 314	90.97 226	98.90 208	98.34 228

DeepMVS_CX				77.17 342	90.94 355	85.28 295		74.08 359	52.51 354	80.87 355	88.03 350	75.25 315	70.63 357	59.23 355	84.94 350	75.62 352

High-resolution multi-view benchmark