High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

Anonymous20231211		99.79 1	99.80 1	99.77 7	99.97 1	99.87 1	99.90 1	99.92 1	99.76 1	99.25 60	99.79 37	99.98 1	99.63 12	99.84 3	99.78 3	99.94 1	99.61 6

UA-Net		99.30 24	99.22 23	99.39 46	99.94 2	99.66 16	98.91 123	99.86 9	97.74 55	98.74 123	99.00 101	99.60 38	99.17 71	99.50 23	99.39 25	99.70 23	99.64 2

DTE-MVSNet		99.52 13	99.27 19	99.82 2	99.93 3	99.77 4	99.79 10	99.87 7	97.89 45	99.70 10	99.55 62	99.21 79	99.77 2	99.65 10	99.43 23	99.90 3	99.36 21

SixPastTwentyTwo		99.70 4	99.59 7	99.82 2	99.93 3	99.80 2	99.86 3	99.87 7	98.87 14	99.79 4	99.85 27	99.33 63	99.74 7	99.85 2	99.82 1	99.74 22	99.63 4

pmmvs6		99.74 3	99.75 2	99.73 15	99.92 5	99.67 15	99.76 14	99.84 11	99.59 2	99.52 26	99.87 18	99.91 2	99.43 39	99.87 1	99.81 2	99.89 6	99.52 10

PS-CasMVS		99.50 14	99.23 21	99.82 2	99.92 5	99.75 7	99.78 11	99.89 2	97.30 85	99.71 5	99.60 53	99.23 75	99.71 9	99.65 10	99.55 18	99.90 3	99.56 8

PEN-MVS		99.54 11	99.30 18	99.83 1	99.92 5	99.76 5	99.80 8	99.88 4	97.60 66	99.71 5	99.59 55	99.52 43	99.75 6	99.64 12	99.51 19	99.90 3	99.46 17

WR-MVS		99.61 10	99.44 11	99.82 2	99.92 5	99.80 2	99.80 8	99.89 2	98.54 19	99.66 14	99.78 40	99.16 87	99.68 10	99.70 6	99.63 6	99.94 1	99.49 16

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

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

CP-MVSNet		99.39 20	99.04 29	99.80 6	99.91 9	99.70 10	99.75 15	99.88 4	96.82 108	99.68 11	99.32 76	98.86 121	99.68 10	99.57 21	99.47 21	99.89 6	99.52 10

WR-MVS_H		99.48 15	99.23 21	99.76 9	99.91 9	99.76 5	99.75 15	99.88 4	97.27 88	99.58 19	99.56 59	99.24 73	99.56 18	99.60 15	99.60 14	99.88 8	99.58 7

gm-plane-assit		94.62 210	91.39 219	98.39 158	99.90 13	99.47 35	99.40 66	99.65 43	97.44 77	99.56 22	99.68 44	59.40 243	94.23 211	96.17 198	94.77 208	97.61 194	92.79 218

v7n		99.68 5	99.61 4	99.76 9	99.89 14	99.74 8	99.87 2	99.82 14	99.20 6	99.71 5	99.96 1	99.73 12	99.76 5	99.58 17	99.59 15	99.52 44	99.46 17

NR-MVSNet		99.10 39	98.68 56	99.58 21	99.89 14	99.23 59	99.35 72	99.63 47	96.58 122	99.36 37	99.05 95	98.67 133	99.46 32	99.63 13	98.73 74	99.80 15	98.88 68

TransMVSNet (Re)		99.45 18	99.32 16	99.61 17	99.88 16	99.60 18	99.75 15	99.63 47	99.11 10	99.28 56	99.83 31	98.35 141	99.27 62	99.70 6	99.62 10	99.84 10	99.03 48

anonymousdsp		99.64 9	99.55 9	99.74 14	99.87 17	99.56 22	99.82 7	99.73 28	98.54 19	99.71 5	99.92 6	99.84 7	99.61 13	99.70 6	99.63 6	99.69 26	99.64 2

FC-MVSNet-train		99.13 37	99.05 28	99.21 74	99.87 17	99.57 21	99.67 20	99.60 54	96.75 115	98.28 157	99.48 67	99.52 43	98.10 135	99.47 26	99.37 27	99.76 21	99.21 32

FC-MVSNet-test		99.32 23	99.33 14	99.31 65	99.87 17	99.65 17	99.63 29	99.75 25	97.76 50	97.29 204	99.87 18	99.63 33	99.52 24	99.66 9	99.63 6	99.77 19	99.12 37

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

LTVRE_ROB	98.82 1	99.76 2	99.75 2	99.77 7	99.87 17	99.71 9	99.77 12	99.76 22	99.52 3	99.80 2	99.79 37	99.91 2	99.56 18	99.83 4	99.75 4	99.86 9	99.75 1
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
EPP-MVSNet		98.61 95	98.19 104	99.11 87	99.86 22	99.60 18	99.44 63	99.53 71	97.37 83	96.85 212	98.69 111	93.75 188	99.18 68	99.22 36	99.35 29	99.82 13	99.32 23

MIMVSNet1		99.46 17	99.34 13	99.60 19	99.83 23	99.68 13	99.74 18	99.71 33	98.20 27	99.41 34	99.86 22	99.66 27	99.41 42	99.50 23	99.39 25	99.50 50	99.10 41

CSCG		99.23 27	99.15 25	99.32 64	99.83 23	99.45 36	98.97 115	99.21 140	98.83 15	99.04 93	99.43 71	99.64 31	99.26 63	98.85 66	98.20 104	99.62 30	99.62 5

conf0.05thres1000		97.44 162	95.93 183	99.20 77	99.82 25	99.56 22	99.41 64	99.61 52	97.42 79	98.01 172	94.34 207	82.73 222	98.68 100	99.33 32	99.42 24	99.67 27	98.74 85

pm-mvs1		99.47 16	99.38 12	99.57 22	99.82 25	99.49 32	99.63 29	99.65 43	98.88 13	99.31 46	99.85 27	99.02 113	99.23 65	99.60 15	99.58 16	99.80 15	99.22 31

IS_MVSNet		98.20 127	98.00 118	98.44 154	99.82 25	99.48 33	99.25 86	99.56 58	95.58 155	93.93 234	97.56 150	96.52 175	98.27 127	99.08 46	99.20 36	99.80 15	98.56 103

tfpnnormal		99.19 31	98.90 39	99.54 26	99.81 28	99.55 26	99.60 35	99.54 66	98.53 21	99.23 61	98.40 120	98.23 144	99.40 43	99.29 33	99.36 28	99.63 29	98.95 61

TranMVSNet+NR-MVSNet		99.23 27	98.91 38	99.61 17	99.81 28	99.45 36	99.47 58	99.68 37	97.28 87	99.39 35	99.54 63	99.08 108	99.45 34	99.09 44	98.84 61	99.83 11	99.04 46

Vis-MVSNet (Re-imp)		98.46 111	98.23 102	98.73 131	99.81 28	99.29 53	98.79 136	99.50 77	96.20 141	96.03 217	98.29 125	96.98 171	98.54 112	99.11 41	99.08 43	99.70 23	98.62 95

ACMH+	97.53 7	99.29 25	99.20 24	99.40 45	99.81 28	99.22 62	99.59 36	99.50 77	98.64 18	98.29 156	99.21 85	99.69 19	99.57 16	99.53 22	99.33 30	99.66 28	98.81 75

ACMH	97.81 6	99.44 19	99.33 14	99.56 23	99.81 28	99.42 38	99.73 19	99.58 55	99.02 11	99.10 81	99.41 73	99.69 19	99.60 14	99.45 27	99.26 35	99.55 37	99.05 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMPR		99.05 42	98.72 51	99.44 36	99.79 33	99.12 81	99.35 72	99.56 58	97.74 55	99.21 62	97.72 145	99.55 41	99.29 60	98.90 65	98.81 64	99.41 65	99.19 33

SteuartSystems-ACMMP		98.94 53	98.52 69	99.43 39	99.79 33	99.13 79	99.33 76	99.55 60	96.17 142	99.04 93	97.53 151	99.65 30	99.46 32	99.04 52	98.76 70	99.44 57	99.35 22
Skip Steuart: Steuart Systems R&D Blog.
testgi		98.18 130	98.44 83	97.89 183	99.78 35	99.23 59	98.78 138	99.21 140	97.26 90	97.41 193	97.39 155	99.36 61	92.85 221	98.82 69	98.66 81	99.31 82	98.35 116

LGP-MVS_train		98.84 73	98.33 94	99.44 36	99.78 35	98.98 106	99.39 67	99.55 60	95.41 157	98.90 109	97.51 152	99.68 22	99.44 37	99.03 53	98.81 64	99.57 36	98.91 65

zzz-MVS		98.94 53	98.57 64	99.37 53	99.77 37	99.15 77	99.24 87	99.55 60	97.38 82	99.16 71	96.64 175	99.69 19	99.15 75	99.09 44	98.92 55	99.37 72	99.11 38

XVS					99.77 37	99.07 85	99.46 60			98.95 102		99.37 57				99.33 78

X-MVStestdata					99.77 37	99.07 85	99.46 60			98.95 102		99.37 57				99.33 78

APDe-MVS		99.15 36	98.95 32	99.39 46	99.77 37	99.28 54	99.52 51	99.54 66	97.22 93	99.06 88	99.20 86	99.64 31	99.05 82	99.14 38	99.02 52	99.39 70	99.17 35

test20.03		98.84 73	98.74 49	98.95 107	99.77 37	99.33 48	99.21 92	99.46 86	97.29 86	98.88 113	99.65 49	99.10 102	97.07 169	99.11 41	98.76 70	99.32 81	97.98 148

ACMMP		98.82 80	98.33 94	99.39 46	99.77 37	99.14 78	99.37 69	99.54 66	96.47 132	99.03 95	96.26 184	99.52 43	99.28 61	98.92 63	98.80 67	99.37 72	99.16 36
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
PGM-MVS		98.69 86	98.09 110	99.39 46	99.76 43	99.07 85	99.30 78	99.51 74	94.76 174	99.18 67	96.70 173	99.51 46	99.20 66	98.79 72	98.71 77	99.39 70	99.11 38

UniMVSNet_NR-MVSNet		98.97 49	98.46 73	99.56 23	99.76 43	99.34 46	99.29 79	99.61 52	96.55 126	99.55 23	99.05 95	97.96 155	99.36 55	98.84 67	98.50 91	99.81 14	98.97 55

PVSNet_Blended_VisFu		98.98 48	98.79 46	99.21 74	99.76 43	99.34 46	99.35 72	99.35 117	97.12 99	99.46 31	99.56 59	98.89 119	98.08 138	99.05 48	98.58 86	99.27 87	98.98 54

X-MVS		98.59 98	97.99 119	99.30 66	99.75 46	99.07 85	99.17 96	99.50 77	96.62 119	98.95 102	93.95 208	99.37 57	99.11 78	98.94 59	98.86 57	99.35 76	99.09 42

MP-MVS		98.78 83	98.30 96	99.34 62	99.75 46	98.95 115	99.26 84	99.46 86	95.78 153	99.17 68	96.98 168	99.72 14	99.06 81	98.84 67	98.74 73	99.33 78	99.11 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
UniMVSNet (Re)		99.08 41	98.69 55	99.54 26	99.75 46	99.33 48	99.29 79	99.64 46	96.75 115	99.48 30	99.30 78	98.69 129	99.26 63	98.94 59	98.76 70	99.78 18	99.02 51

mPP-MVS					99.75 46							99.49 50

DU-MVS		99.04 43	98.59 61	99.56 23	99.74 50	99.23 59	99.29 79	99.63 47	96.58 122	99.55 23	99.05 95	98.68 131	99.36 55	99.03 53	98.60 84	99.77 19	98.97 55

Baseline_NR-MVSNet		99.18 34	98.87 41	99.54 26	99.74 50	99.56 22	99.36 71	99.62 51	96.53 128	99.29 51	99.85 27	98.64 135	99.40 43	99.03 53	99.63 6	99.83 11	98.86 69

ACMP	96.54 13	98.87 65	98.40 88	99.41 42	99.74 50	98.88 127	99.29 79	99.50 77	96.85 104	98.96 100	97.05 164	99.66 27	99.43 39	98.98 57	98.60 84	99.52 44	98.81 75
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	96.66 11	98.90 60	98.44 83	99.44 36	99.74 50	98.95 115	99.47 58	99.55 60	97.66 62	99.09 85	96.43 180	99.41 51	99.35 58	98.95 58	98.67 79	99.45 55	99.03 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	98.29 2	99.37 21	99.25 20	99.51 30	99.74 50	99.12 81	99.56 40	99.39 94	98.96 12	99.17 68	99.44 70	99.63 33	99.58 15	99.48 25	99.27 33	99.60 34	98.81 75
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpn		94.97 206	91.60 218	98.90 110	99.73 55	99.33 48	99.11 103	99.51 74	95.05 164	97.19 209	89.03 223	62.62 240	98.37 120	98.53 81	98.97 54	99.48 53	97.70 156

DeepC-MVS	97.88 4	99.33 22	99.15 25	99.53 29	99.73 55	99.05 89	99.49 56	99.40 92	98.42 22	99.55 23	99.71 43	99.89 4	99.49 29	99.14 38	98.81 64	99.54 38	99.02 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		98.87 65	98.73 50	99.04 96	99.72 57	99.05 89	98.64 152	99.17 146	96.31 137	98.80 118	99.07 93	99.70 18	98.67 101	98.93 62	98.82 62	99.23 92	99.23 30

MVS_0304		98.57 100	98.36 91	98.82 124	99.72 57	98.94 119	98.92 121	99.14 151	96.76 113	99.33 42	98.30 124	99.73 12	96.74 172	98.05 123	97.79 130	99.08 103	98.97 55

HyFIR lowres test		98.08 132	97.16 151	99.14 84	99.72 57	98.91 122	99.41 64	99.58 55	97.93 39	98.82 116	99.24 80	95.81 182	98.73 98	95.16 213	95.13 205	98.60 164	97.94 149

1111		94.22 217	92.26 214	96.51 218	99.71 60	98.75 135	99.03 107	99.83 12	95.01 166	93.39 236	99.54 63	60.23 241	89.58 231	97.90 133	97.62 150	97.50 197	96.75 184

.test1245		74.10 233	68.09 235	81.11 234	99.71 60	98.75 135	99.03 107	99.83 12	95.01 166	93.39 236	99.54 63	60.23 241	89.58 231	97.90 133	10.38 237	5.14 241	14.81 237

view800		96.48 182	94.42 194	98.87 114	99.70 62	99.26 55	99.05 106	99.45 90	94.77 173	97.32 200	88.21 224	83.40 220	98.28 126	98.37 93	99.33 30	99.44 57	97.58 161

TDRefinement		99.54 11	99.50 10	99.60 19	99.70 62	99.35 45	99.77 12	99.58 55	99.40 5	99.28 56	99.66 45	99.41 51	99.55 20	99.74 5	99.65 5	99.70 23	99.25 26

Gipuma		99.22 29	98.86 42	99.64 16	99.70 62	99.24 57	99.17 96	99.63 47	99.52 3	99.89 1	96.54 179	99.14 93	99.93 1	99.42 29	99.15 38	99.52 44	99.04 46
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Fast-Effi-MVS+		98.42 113	97.79 126	99.15 81	99.69 65	98.66 143	98.94 118	99.68 37	94.49 178	99.05 90	98.06 136	98.86 121	98.48 115	98.18 112	97.78 131	99.05 109	98.54 104

v148		98.77 84	98.45 77	99.15 81	99.68 66	98.94 119	99.49 56	99.31 127	97.95 38	98.91 108	99.65 49	99.62 35	99.18 68	97.99 126	97.64 149	98.33 177	97.38 168

v13		99.22 29	98.99 31	99.49 31	99.68 66	99.58 20	99.67 20	99.77 21	98.10 29	99.36 37	99.88 13	99.37 57	99.54 22	98.50 83	98.51 90	98.92 123	99.03 48

CP-MVS		98.86 70	98.43 85	99.36 55	99.68 66	98.97 113	99.19 95	99.46 86	96.60 121	99.20 63	97.11 163	99.51 46	99.15 75	98.92 63	98.82 62	99.45 55	99.08 43

Anonymous20240521		98.42 113	98.05 114	98.86 117	99.67 69	99.16 75	98.79 136	99.21 140	95.11 163	98.54 135	97.87 141	97.47 163	97.39 162	99.14 38	99.01 53	99.54 38	98.60 96

HSP-MVS		98.50 106	98.05 114	99.03 97	99.67 69	99.33 48	99.51 52	99.26 132	95.28 159	98.51 138	98.19 129	99.74 11	98.29 125	97.69 153	96.70 181	98.96 116	99.41 20

ACMMP_Plus		98.94 53	98.72 51	99.21 74	99.67 69	99.08 84	99.26 84	99.39 94	96.84 105	98.88 113	98.22 127	99.68 22	98.82 92	99.06 47	98.90 56	99.25 89	99.25 26

HFP-MVS		98.97 49	98.70 53	99.29 69	99.67 69	98.98 106	99.13 100	99.53 71	97.76 50	98.90 109	98.07 134	99.50 48	99.14 77	98.64 78	98.78 68	99.37 72	99.18 34

v12		99.19 31	98.95 32	99.48 32	99.67 69	99.56 22	99.66 22	99.76 22	98.06 31	99.33 42	99.88 13	99.34 62	99.53 23	98.42 90	98.43 95	98.91 126	98.97 55

tfpn_n400		97.59 154	96.36 171	99.01 101	99.66 74	99.19 68	99.21 92	99.55 60	97.62 63	97.77 179	94.60 202	87.78 201	98.27 127	98.44 86	98.72 75	99.62 30	98.21 130

tfpnconf		97.59 154	96.36 171	99.01 101	99.66 74	99.19 68	99.21 92	99.55 60	97.62 63	97.77 179	94.60 202	87.78 201	98.27 127	98.44 86	98.72 75	99.62 30	98.21 130

gg-mvs-nofinetune		96.77 178	96.52 167	97.06 201	99.66 74	97.82 190	97.54 215	99.86 9	98.69 17	98.61 128	99.94 4	89.62 196	88.37 235	97.55 164	96.67 183	98.30 178	95.35 201

v11		99.19 31	98.95 32	99.47 33	99.66 74	99.54 28	99.65 23	99.73 28	98.06 31	99.38 36	99.92 6	99.40 54	99.55 20	98.29 103	98.50 91	98.88 131	98.92 64

V9		99.16 35	98.90 39	99.46 34	99.66 74	99.54 28	99.65 23	99.75 25	98.01 34	99.31 46	99.87 18	99.31 67	99.51 25	98.34 97	98.34 98	98.90 128	98.91 65

thres600view7		96.35 187	94.27 196	98.79 127	99.66 74	99.18 70	98.94 118	99.38 101	94.37 187	97.21 206	87.19 227	84.10 219	98.10 135	98.16 113	99.47 21	99.42 62	97.43 165

view600		96.39 186	94.30 195	98.82 124	99.65 80	99.16 75	98.98 113	99.36 112	94.46 181	97.39 196	87.28 225	84.16 218	98.16 134	98.16 113	99.48 20	99.40 67	97.42 166

CANet		98.47 109	98.30 96	98.67 139	99.65 80	98.87 128	98.82 134	99.01 166	96.14 143	99.29 51	98.86 106	99.01 114	96.54 176	98.36 95	98.08 109	98.72 155	98.80 79

v1141		98.87 65	98.45 77	99.36 55	99.65 80	99.04 94	99.56 40	99.38 101	97.83 46	99.29 51	99.86 22	99.16 87	99.40 43	97.68 154	97.78 131	98.86 136	97.82 152

divwei89l23v2f112		98.87 65	98.45 77	99.36 55	99.65 80	99.04 94	99.56 40	99.38 101	97.83 46	99.29 51	99.86 22	99.15 91	99.40 43	97.68 154	97.78 131	98.86 136	97.82 152

V14		99.13 37	98.85 44	99.45 35	99.65 80	99.52 30	99.63 29	99.74 27	97.97 36	99.30 49	99.87 18	99.27 71	99.49 29	98.23 109	98.24 101	98.88 131	98.83 70

v2v482		98.85 72	98.40 88	99.38 51	99.65 80	98.98 106	99.55 43	99.39 94	97.92 40	99.35 40	99.85 27	99.14 93	99.39 53	97.50 166	97.78 131	98.98 115	97.60 159

v1		98.87 65	98.45 77	99.36 55	99.65 80	99.04 94	99.55 43	99.38 101	97.83 46	99.30 49	99.86 22	99.17 84	99.40 43	97.68 154	97.77 138	98.86 136	97.82 152

Vis-MVSNet		99.25 26	99.32 16	99.17 79	99.65 80	99.55 26	99.63 29	99.33 120	98.16 28	99.29 51	99.65 49	99.77 8	97.56 154	99.44 28	99.14 39	99.58 35	99.51 12
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tfpn1000		97.10 170	95.97 181	98.41 156	99.64 88	99.30 52	98.89 127	99.49 81	96.49 129	95.97 219	95.31 194	85.62 215	96.92 171	97.86 137	99.13 41	99.53 43	98.11 139

v15		99.09 40	98.79 46	99.43 39	99.64 88	99.50 31	99.61 33	99.73 28	97.92 40	99.28 56	99.86 22	99.24 73	99.47 31	98.12 120	98.14 106	98.87 133	98.76 82

APD-MVS		98.47 109	97.97 120	99.05 94	99.64 88	98.91 122	98.94 118	99.45 90	94.40 185	98.77 119	97.26 157	99.41 51	98.21 132	98.67 76	98.57 88	99.31 82	98.57 100
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EG-PatchMatch MVS		99.01 45	98.77 48	99.28 73	99.64 88	98.90 125	98.81 135	99.27 131	96.55 126	99.71 5	99.31 77	99.66 27	99.17 71	99.28 35	99.11 42	99.10 98	98.57 100

LS3D		98.79 82	98.52 69	99.12 85	99.64 88	99.09 83	99.24 87	99.46 86	97.75 53	98.93 106	97.47 153	98.23 144	97.98 141	99.36 30	99.30 32	99.46 54	98.42 112

tfpnview11		97.49 159	96.22 175	98.97 105	99.63 93	99.24 57	99.12 102	99.54 66	96.76 113	97.77 179	94.60 202	87.78 201	98.25 130	97.93 130	99.14 39	99.52 44	98.08 142

IterMVS-LS		98.23 124	97.66 131	98.90 110	99.63 93	99.38 43	99.07 105	99.48 82	97.75 53	98.81 117	99.37 75	94.57 187	97.88 145	96.54 194	97.04 175	98.53 169	98.97 55
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+		98.11 131	97.29 143	99.06 91	99.62 95	98.55 152	98.16 189	99.80 15	94.64 175	99.15 74	96.59 176	97.43 164	98.44 116	97.46 168	97.90 120	99.17 95	98.45 109

v1144		98.94 53	98.53 67	99.42 41	99.62 95	99.03 99	99.58 37	99.36 112	97.99 35	99.49 29	99.91 11	99.20 81	99.51 25	97.61 160	97.85 128	98.95 118	98.10 140

3Dnovator+	97.85 5	98.61 95	98.14 106	99.15 81	99.62 95	98.37 163	99.10 104	99.51 74	98.04 33	98.98 97	96.07 188	98.75 127	98.55 110	98.51 82	98.40 96	99.17 95	98.82 72

v1192		98.91 58	98.48 72	99.41 42	99.61 98	99.03 99	99.64 26	99.25 135	97.91 42	99.58 19	99.92 6	99.07 110	99.45 34	97.55 164	97.68 145	98.93 120	98.23 127

v1240		98.86 70	98.41 86	99.38 51	99.59 99	99.05 89	99.65 23	99.14 151	97.68 61	99.66 14	99.93 5	98.72 128	99.45 34	97.38 175	97.72 143	98.79 149	98.35 116

3Dnovator	98.16 3	98.65 90	98.35 92	99.00 103	99.59 99	98.70 139	98.90 126	99.36 112	97.97 36	99.09 85	96.55 178	99.09 106	97.97 142	98.70 75	98.65 82	99.12 97	98.81 75

v1921920		98.89 62	98.46 73	99.39 46	99.58 101	99.04 94	99.64 26	99.17 146	97.91 42	99.64 16	99.92 6	98.99 117	99.44 37	97.44 171	97.57 155	98.84 140	98.35 116

thres400		96.22 192	94.08 199	98.72 132	99.58 101	99.05 89	98.83 131	99.22 138	94.01 194	97.40 194	86.34 233	84.91 217	97.93 143	97.85 140	99.08 43	99.37 72	97.28 171

CDPH-MVS		97.99 133	97.23 147	98.87 114	99.58 101	98.29 165	98.83 131	99.20 144	93.76 197	98.11 165	96.11 186	99.16 87	98.23 131	97.80 145	97.22 170	99.29 85	98.28 122

UGNet		98.52 105	99.00 30	97.96 182	99.58 101	99.26 55	99.27 83	99.40 92	98.07 30	98.28 157	98.76 109	99.71 17	92.24 225	98.94 59	98.85 59	99.00 114	99.43 19
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
QAPM		98.62 94	98.40 88	98.89 112	99.57 105	98.80 130	98.63 153	99.35 117	96.82 108	98.60 129	98.85 108	99.08 108	98.09 137	98.31 101	98.21 102	99.08 103	98.72 86

ESAPD		98.60 97	98.41 86	98.83 121	99.56 106	99.21 63	98.66 150	99.47 83	95.22 160	98.35 151	98.48 118	99.67 26	97.84 148	98.80 71	98.57 88	99.10 98	98.93 63

v144192		98.88 64	98.46 73	99.37 53	99.56 106	99.03 99	99.61 33	99.26 132	97.79 49	99.58 19	99.88 13	99.11 101	99.43 39	97.38 175	97.61 151	98.80 147	98.43 111

new-patchmatchnet		97.26 165	96.12 178	98.58 147	99.55 108	98.63 145	99.14 99	97.04 224	98.80 16	99.19 65	99.92 6	99.19 82	98.92 86	95.51 208	87.04 224	97.66 193	93.73 213

DI_MVS_plusplus_trai		97.57 157	96.55 166	98.77 128	99.55 108	98.76 133	99.22 90	99.00 167	97.08 100	97.95 175	97.78 144	91.35 195	98.02 140	96.20 197	96.81 180	98.87 133	97.87 151

CHOSEN 1792x2688		98.31 119	98.02 117	98.66 141	99.55 108	98.57 151	99.38 68	99.25 135	98.42 22	98.48 144	99.58 57	99.85 6	98.31 124	95.75 204	95.71 197	96.96 204	98.27 124

v1neww		98.84 73	98.45 77	99.29 69	99.54 111	98.98 106	99.54 47	99.37 109	97.48 73	99.10 81	99.80 35	99.12 97	99.40 43	97.85 140	97.89 122	98.81 142	98.04 143

v7new		98.84 73	98.45 77	99.29 69	99.54 111	98.98 106	99.54 47	99.37 109	97.48 73	99.10 81	99.80 35	99.12 97	99.40 43	97.85 140	97.89 122	98.81 142	98.04 143

v17		98.96 51	98.63 58	99.35 60	99.54 111	99.41 39	99.55 43	99.70 34	97.40 80	99.10 81	99.79 37	99.10 102	99.40 43	97.96 127	97.99 114	98.80 147	98.77 81

v8		98.94 53	98.60 60	99.35 60	99.54 111	99.39 41	99.55 43	99.67 40	97.48 73	99.13 76	99.81 32	99.10 102	99.39 53	97.86 137	97.89 122	98.81 142	98.66 93

v6		98.84 73	98.46 73	99.30 66	99.54 111	98.98 106	99.54 47	99.37 109	97.49 72	99.11 80	99.81 32	99.13 96	99.40 43	97.86 137	97.89 122	98.81 142	98.04 143

CPTT-MVS		98.28 120	97.51 138	99.16 80	99.54 111	98.78 132	98.96 116	99.36 112	96.30 138	98.89 112	93.10 213	99.30 68	99.20 66	98.35 96	97.96 119	99.03 111	98.82 72

FMVSNet1		98.90 60	99.10 27	98.67 139	99.54 111	99.48 33	99.22 90	99.66 41	98.39 25	97.50 191	99.66 45	99.04 111	96.58 175	99.05 48	99.03 49	99.52 44	99.08 43

HPM-MVS++		98.56 103	98.08 111	99.11 87	99.53 118	98.61 147	99.02 111	99.32 125	96.29 139	99.06 88	97.23 158	99.50 48	98.77 95	98.15 116	97.90 120	98.96 116	98.90 67

v16		98.95 52	98.62 59	99.34 62	99.53 118	99.41 39	99.54 47	99.70 34	97.34 84	99.07 87	99.76 41	99.10 102	99.40 43	97.96 127	98.00 113	98.79 149	98.76 82

v7		98.91 58	98.53 67	99.36 55	99.53 118	98.99 105	99.57 38	99.36 112	97.58 69	99.32 44	99.88 13	99.23 75	99.50 27	97.77 148	97.98 116	98.91 126	98.26 125

MCST-MVS		98.25 123	97.57 136	99.06 91	99.53 118	98.24 171	98.63 153	99.17 146	95.88 150	98.58 131	96.11 186	99.09 106	99.18 68	97.58 163	97.31 166	99.25 89	98.75 84

CLD-MVS		98.48 108	98.15 105	98.86 117	99.53 118	98.35 164	98.55 164	97.83 216	96.02 147	98.97 98	99.08 92	99.75 9	99.03 83	98.10 122	97.33 165	99.28 86	98.44 110
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OPM-MVS		98.84 73	98.59 61	99.12 85	99.52 123	98.50 157	99.13 100	99.22 138	97.76 50	98.76 120	98.70 110	99.61 36	98.90 87	98.67 76	98.37 97	99.19 94	98.57 100

v10		99.01 45	98.66 57	99.41 42	99.52 123	99.39 41	99.57 38	99.66 41	97.59 67	99.32 44	99.88 13	99.23 75	99.50 27	97.77 148	97.98 116	98.92 123	98.78 80

V42		98.81 81	98.49 71	99.18 78	99.52 123	98.92 121	99.50 55	99.29 128	97.43 78	98.97 98	99.81 32	99.00 116	99.30 59	97.93 130	98.01 112	98.51 172	98.34 120

Anonymous20231206		98.50 106	98.03 116	99.05 94	99.50 126	99.01 103	99.15 98	99.26 132	96.38 134	99.12 78	99.50 66	99.12 97	98.60 105	97.68 154	97.24 169	98.66 158	97.30 170

v18		98.89 62	98.54 65	99.30 66	99.50 126	99.37 44	99.51 52	99.68 37	97.25 92	99.00 96	99.76 41	99.04 111	99.36 55	97.81 144	97.86 127	98.77 152	98.68 92

OpenMVS	97.26 9	97.88 139	97.17 150	98.70 135	99.50 126	98.55 152	98.34 179	99.11 156	93.92 195	98.90 109	95.04 198	98.23 144	97.38 163	98.11 121	98.12 107	98.95 118	98.23 127

pmmvs-eth3d		98.68 87	98.14 106	99.29 69	99.49 129	98.45 160	99.45 62	99.38 101	97.21 94	99.50 28	99.65 49	99.21 79	99.16 73	97.11 183	97.56 156	98.79 149	97.82 152

PHI-MVS		98.57 100	98.20 103	99.00 103	99.48 130	98.91 122	98.68 143	99.17 146	94.97 169	99.27 59	98.33 122	99.33 63	98.05 139	98.82 69	98.62 83	99.34 77	98.38 114

pmmvs5		98.37 116	97.81 125	99.03 97	99.46 131	98.97 113	99.03 107	98.96 170	95.85 151	99.05 90	99.45 69	98.66 134	98.79 94	96.02 201	97.52 157	98.87 133	98.21 130

ambc			97.89 123		99.45 132	97.88 188	97.78 202		97.27 88	99.80 2	98.99 102	98.48 139	98.55 110	97.80 145	96.68 182	98.54 168	98.10 140

canonicalmvs		98.34 118	97.92 122	98.83 121	99.45 132	99.21 63	98.37 176	99.53 71	97.06 102	97.74 183	96.95 170	95.05 185	98.36 121	98.77 73	98.85 59	99.51 49	99.53 9

IterMVS		97.40 163	96.67 161	98.25 165	99.45 132	98.66 143	98.87 129	98.73 181	96.40 133	98.94 105	99.56 59	95.26 184	97.58 153	95.38 209	94.70 210	95.90 213	96.72 185
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres200		96.23 191	94.13 197	98.69 137	99.44 135	99.18 70	98.58 162	99.38 101	93.52 200	97.35 198	86.33 234	85.83 214	97.93 143	98.16 113	98.78 68	99.42 62	97.10 180

PCF-MVS	95.58 16	97.60 152	96.67 161	98.69 137	99.44 135	98.23 172	98.37 176	98.81 177	93.01 207	98.22 159	97.97 140	99.59 39	98.20 133	95.72 206	95.08 206	99.08 103	97.09 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
train_agg		97.99 133	97.26 144	98.83 121	99.43 137	98.22 173	98.91 123	99.07 160	94.43 183	97.96 174	96.42 181	99.30 68	98.81 93	97.39 173	96.62 184	98.82 141	98.47 106

N_pmnet		96.68 180	95.70 188	97.84 185	99.42 138	98.00 183	99.35 72	98.21 204	98.40 24	98.13 164	99.42 72	99.30 68	97.44 161	94.00 223	88.79 221	94.47 219	91.96 221

USDC		98.26 122	97.57 136	99.06 91	99.42 138	97.98 186	98.83 131	98.85 174	97.57 70	99.59 18	99.15 88	98.59 136	98.99 84	97.42 172	96.08 196	98.69 157	96.23 192

NCCC		97.84 141	96.96 157	98.87 114	99.39 140	98.27 168	98.46 169	99.02 165	96.78 111	98.73 124	91.12 219	98.91 118	98.57 108	97.83 143	97.49 159	99.04 110	98.33 121

tfpn111		96.48 182	94.67 193	98.59 145	99.37 141	99.18 70	98.68 143	99.39 94	92.02 216	97.21 206	90.63 220	86.34 209	97.45 156	98.15 116	99.08 43	99.43 59	97.28 171

conf200view11		96.16 195	94.08 199	98.59 145	99.37 141	99.18 70	98.68 143	99.39 94	92.02 216	97.21 206	86.53 230	86.34 209	97.45 156	98.15 116	99.08 43	99.43 59	97.28 171

thres100view900		95.74 199	93.66 207	98.17 171	99.37 141	98.59 148	98.10 190	98.33 200	92.02 216	97.30 202	86.53 230	86.34 209	96.69 173	96.77 190	98.47 93	99.24 91	96.89 183

tfpn200view9		96.17 193	94.08 199	98.60 144	99.37 141	99.18 70	98.68 143	99.39 94	92.02 216	97.30 202	86.53 230	86.34 209	97.45 156	98.15 116	99.08 43	99.43 59	97.28 171

testmv		97.48 161	96.83 160	98.24 168	99.37 141	97.79 192	98.59 160	99.07 160	92.40 210	97.59 186	99.24 80	98.11 148	97.66 151	97.64 158	97.11 172	97.17 200	95.54 200

test1235678		97.49 159	96.84 159	98.24 168	99.37 141	97.79 192	98.59 160	99.07 160	92.41 209	97.59 186	99.24 80	98.15 147	97.66 151	97.64 158	97.12 171	97.17 200	95.55 199

casdiffmvs		98.43 112	98.08 111	98.85 120	99.37 141	98.89 126	98.66 150	99.54 66	97.07 101	98.68 125	98.53 117	99.33 63	97.83 149	96.89 187	97.11 172	99.01 113	98.70 88

RPSCF		98.84 73	98.81 45	98.89 112	99.37 141	98.95 115	98.51 166	98.85 174	97.73 57	98.33 153	98.97 103	99.14 93	98.95 85	99.18 37	98.68 78	99.31 82	98.99 53

MDA-MVSNet-bldmvs		97.75 143	97.26 144	98.33 161	99.35 149	98.45 160	99.32 77	97.21 222	97.90 44	99.05 90	99.01 100	96.86 173	99.08 79	99.36 30	92.97 216	95.97 212	96.25 191

CNVR-MVS		98.22 126	97.76 127	98.76 129	99.33 150	98.26 169	98.48 167	98.88 173	96.22 140	98.47 146	95.79 190	99.33 63	98.35 122	98.37 93	97.99 114	99.03 111	98.38 114

DeepC-MVS_fast	97.38 8	98.65 90	98.34 93	99.02 100	99.33 150	98.29 165	98.99 112	98.71 183	97.40 80	99.31 46	98.20 128	99.40 54	98.54 112	98.33 100	98.18 105	99.23 92	98.58 98
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSDG		98.20 127	97.88 124	98.56 149	99.33 150	97.74 195	98.27 183	98.10 207	97.20 96	98.06 167	98.59 115	99.16 87	98.76 96	98.39 92	97.71 144	98.86 136	96.38 189

thresconf0.02		95.49 201	92.74 212	98.70 135	99.32 153	98.70 139	98.87 129	99.21 140	95.95 148	97.57 188	90.63 220	73.55 234	97.86 147	96.09 200	97.03 176	99.40 67	97.22 176

EPNet		96.44 185	96.08 179	96.86 208	99.32 153	97.15 205	97.69 209	99.32 125	93.67 198	98.11 165	95.64 192	93.44 190	89.07 233	96.86 188	96.83 179	97.67 192	98.97 55
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_HR		98.58 99	98.26 99	98.96 106	99.32 153	98.81 129	98.48 167	98.99 168	96.81 110	99.16 71	98.07 134	99.23 75	98.89 89	98.43 89	98.27 100	98.90 128	98.24 126

PVSNet_BlendedMVS		97.93 137	97.66 131	98.25 165	99.30 156	98.67 141	98.31 180	97.95 211	94.30 188	98.75 121	97.63 147	98.76 125	96.30 183	98.29 103	97.78 131	98.93 120	98.18 134

PVSNet_Blended		97.93 137	97.66 131	98.25 165	99.30 156	98.67 141	98.31 180	97.95 211	94.30 188	98.75 121	97.63 147	98.76 125	96.30 183	98.29 103	97.78 131	98.93 120	98.18 134

HQP-MVS		97.58 156	96.65 164	98.66 141	99.30 156	97.99 184	97.88 200	98.65 186	94.58 176	98.66 126	94.65 201	99.15 91	98.59 106	96.10 199	95.59 198	98.90 128	98.50 105

our_test_3					99.29 159	97.72 196	98.98 113

conf0.01		94.53 213	91.09 221	98.53 152	99.29 159	99.05 89	98.68 143	99.35 117	92.02 216	97.04 210	84.45 236	68.52 236	97.45 156	97.79 147	99.08 43	99.41 65	96.70 186

TinyColmap		98.27 121	97.62 135	99.03 97	99.29 159	97.79 192	98.92 121	98.95 171	97.48 73	99.52 26	98.65 113	97.86 157	98.90 87	98.34 97	97.27 167	98.64 161	95.97 195

TSAR-MVS + GP.		98.54 104	98.29 98	98.82 124	99.28 162	98.59 148	97.73 205	99.24 137	95.93 149	98.59 130	99.07 93	99.17 84	98.86 90	98.44 86	98.10 108	99.26 88	98.72 86

MVS_Test		97.69 147	97.15 152	98.33 161	99.27 163	98.43 162	98.25 184	99.29 128	95.00 168	97.39 196	98.86 106	98.00 153	97.14 167	95.38 209	96.22 190	98.62 162	98.15 138

conf0.002		93.97 218	90.06 225	98.52 153	99.26 164	99.02 102	98.68 143	99.33 120	92.02 216	97.01 211	83.82 237	63.41 239	97.45 156	97.73 151	97.98 116	99.40 67	96.47 188

PM-MVS		98.57 100	98.24 101	98.95 107	99.26 164	98.59 148	99.03 107	98.74 180	96.84 105	99.44 33	99.13 89	98.31 143	98.75 97	98.03 124	98.21 102	98.48 173	98.58 98

PMVS	92.51 17	98.66 89	98.86 42	98.43 155	99.26 164	98.98 106	98.60 159	98.59 190	97.73 57	99.45 32	99.38 74	98.54 138	95.24 196	99.62 14	99.61 11	99.42 62	98.17 136
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Effi-MVS+-dtu		97.78 142	97.37 141	98.26 164	99.25 167	98.50 157	97.89 199	99.19 145	94.51 177	98.16 162	95.93 189	98.80 124	95.97 187	98.27 108	97.38 162	99.10 98	98.23 127

AdaColmap		97.57 157	96.57 165	98.74 130	99.25 167	98.01 181	98.36 178	98.98 169	94.44 182	98.47 146	92.44 217	97.91 156	98.62 104	98.19 111	97.74 140	98.73 154	97.28 171

DELS-MVS		98.63 93	98.70 53	98.55 150	99.24 169	99.04 94	98.96 116	98.52 193	96.83 107	98.38 149	99.58 57	99.68 22	97.06 170	98.74 74	98.44 94	99.10 98	98.59 97
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
TSAR-MVS + MP.		99.02 44	98.95 32	99.11 87	99.23 170	98.79 131	99.51 52	98.73 181	97.50 71	98.56 132	99.03 98	99.59 39	99.16 73	99.29 33	99.17 37	99.50 50	99.24 29

pmmvs4		97.87 140	97.02 155	98.86 117	99.20 171	97.68 198	98.89 127	99.03 164	96.57 124	99.12 78	99.03 98	97.26 168	98.42 118	95.16 213	96.34 188	98.53 169	97.10 180

test-LLR		94.79 208	93.71 205	96.06 222	99.20 171	96.16 215	96.31 230	98.50 194	89.98 233	94.08 232	97.01 165	86.43 207	92.20 226	96.76 191	95.31 201	96.05 210	94.31 209

test0.0.03 1		95.81 198	95.77 187	95.85 226	99.20 171	98.15 176	97.49 219	98.50 194	92.24 211	92.74 239	96.82 172	92.70 192	88.60 234	97.31 179	97.01 178	98.57 167	96.19 193

CANet_DTU		97.65 150	97.50 139	97.82 187	99.19 174	98.08 178	98.41 172	98.67 185	94.40 185	99.16 71	98.32 123	98.69 129	93.96 214	97.87 136	97.61 151	97.51 196	97.56 162

EPNet_dtu		96.31 188	95.96 182	96.72 212	99.18 175	95.39 229	97.03 226	99.13 155	93.02 206	99.35 40	97.23 158	97.07 170	90.70 230	95.74 205	95.08 206	94.94 216	98.16 137
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn_ndepth		96.69 179	95.49 190	98.09 176	99.17 176	99.13 79	98.61 158	99.38 101	94.90 172	95.85 221	92.85 215	88.19 200	96.07 186	97.28 180	98.67 79	99.49 52	97.44 164

TSAR-MVS + ACMM		98.64 92	98.58 63	98.72 132	99.17 176	98.63 145	98.69 142	99.10 158	97.69 60	98.30 155	99.12 91	99.38 56	98.70 99	98.45 85	97.51 158	98.35 176	99.25 26

GA-MVS		96.84 176	95.86 185	97.98 180	99.16 178	98.29 165	97.91 197	98.64 188	95.14 162	97.71 184	98.04 138	88.90 198	96.50 178	96.41 195	96.61 185	97.97 190	97.60 159

SD-MVS		98.73 85	98.54 65	98.95 107	99.14 179	98.76 133	98.46 169	99.14 151	97.71 59	98.56 132	98.06 136	99.61 36	98.85 91	98.56 80	97.74 140	99.54 38	99.32 23

EU-MVSNet		98.68 87	98.94 36	98.37 160	99.14 179	98.74 137	99.64 26	98.20 206	98.21 26	99.17 68	99.66 45	99.18 83	99.08 79	99.11 41	98.86 57	95.00 215	98.83 70

testus		96.13 196	95.13 191	97.28 196	99.13 181	97.00 206	96.84 228	97.89 215	90.48 232	97.40 194	93.60 210	96.47 176	95.39 194	96.21 196	96.19 192	97.05 202	95.99 194

MDTV_nov1_ep13_2view		97.12 168	96.19 177	98.22 170	99.13 181	98.05 179	99.24 87	99.47 83	97.61 65	99.15 74	99.59 55	99.01 114	98.40 119	94.87 215	90.14 219	93.91 220	94.04 212

PLC	95.63 15	97.73 146	97.01 156	98.57 148	99.10 183	97.80 191	97.72 206	98.77 179	96.34 135	98.38 149	93.46 212	98.06 150	98.66 102	97.90 133	97.65 148	98.77 152	97.90 150
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		96.80 177	96.30 173	97.39 193	99.09 184	96.52 209	98.76 139	99.29 128	93.88 196	97.65 185	98.34 121	93.66 189	96.29 185	98.28 106	97.73 142	93.27 224	95.70 197

IB-MVS	95.85 14	95.87 197	94.88 192	97.02 204	99.09 184	98.25 170	97.16 222	97.38 220	91.97 223	97.77 179	83.61 238	97.29 167	92.03 228	97.16 182	97.66 146	98.66 158	98.20 133
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
diffmvs		97.05 171	96.20 176	98.04 178	99.08 186	98.01 181	98.20 187	99.10 158	94.48 180	97.31 201	95.15 196	97.82 158	96.53 177	94.32 220	94.76 209	98.05 187	98.82 72

CDS-MVSNet		97.75 143	97.68 130	97.83 186	99.08 186	98.20 174	98.68 143	98.61 189	95.63 154	97.80 178	99.24 80	96.93 172	94.09 212	97.96 127	97.82 129	98.71 156	97.99 146
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_111021_LR		98.39 115	98.11 108	98.71 134	99.08 186	98.54 155	98.23 186	98.56 192	96.57 124	99.13 76	98.41 119	98.86 121	98.65 103	98.23 109	97.87 126	98.65 160	98.28 122

TAMVS		96.95 174	96.94 158	96.97 207	99.07 189	97.67 199	97.98 195	97.12 223	95.04 165	95.41 226	99.27 79	95.57 183	94.09 212	97.32 177	97.11 172	98.16 185	96.59 187

abl_6				98.38 159	99.03 190	98.04 180	98.08 192	98.65 186	93.23 203	98.56 132	94.58 205	98.57 137	97.17 166			98.81 142	97.42 166

MIMVSNet		97.24 166	97.15 152	97.36 195	99.03 190	98.52 156	98.55 164	99.73 28	94.94 171	94.94 231	97.98 139	97.37 166	93.66 216	97.60 161	97.34 164	98.23 182	96.29 190

Fast-Effi-MVS+-dtu		96.99 172	96.46 168	97.61 191	98.98 192	97.89 187	97.54 215	99.76 22	93.43 201	96.55 215	94.93 199	98.06 150	94.32 210	96.93 186	96.50 187	98.53 169	97.47 163

no-one		99.01 45	98.94 36	99.09 90	98.97 193	98.55 152	99.37 69	99.04 163	97.59 67	99.36 37	99.66 45	99.75 9	99.57 16	98.47 84	99.27 33	98.21 183	99.30 25

MAR-MVS		97.12 168	96.28 174	98.11 175	98.94 194	97.22 203	97.65 210	99.38 101	90.93 231	98.15 163	95.17 195	97.13 169	96.48 179	97.71 152	97.40 161	98.06 186	98.40 113
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
MSLP-MVS++		97.99 133	97.64 134	98.40 157	98.91 195	98.47 159	97.12 224	98.78 178	96.49 129	98.48 144	93.57 211	99.12 97	98.51 114	98.31 101	98.58 86	98.58 166	98.95 61

ADS-MVSNet		94.41 216	92.13 216	97.07 200	98.86 196	96.60 208	98.38 175	98.47 197	96.13 145	98.02 169	96.98 168	87.50 205	95.87 189	89.89 228	87.58 223	92.79 228	90.27 227

OMC-MVS		98.35 117	98.10 109	98.64 143	98.85 197	97.99 184	98.56 163	98.21 204	97.26 90	98.87 115	98.54 116	99.27 71	98.43 117	98.34 97	97.66 146	98.92 123	97.65 158

MVSTER		95.38 203	93.99 203	97.01 205	98.83 198	98.95 115	96.62 229	99.14 151	92.17 213	97.44 192	97.29 156	77.88 229	91.63 229	97.45 169	96.18 193	98.41 175	97.99 146

TAPA-MVS	96.65 12	98.23 124	97.96 121	98.55 150	98.81 199	98.16 175	98.40 173	97.94 213	96.68 117	98.49 142	98.61 114	98.89 119	98.57 108	97.45 169	97.59 153	99.09 102	98.35 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CostFormer		92.75 223	89.49 226	96.55 216	98.78 200	95.83 226	97.55 214	98.59 190	91.83 224	97.34 199	96.31 183	78.53 228	94.50 206	86.14 233	84.92 229	92.54 229	92.84 217

PatchmatchNet		93.88 220	91.08 222	97.14 199	98.75 201	96.01 221	98.25 184	99.39 94	94.95 170	98.96 100	96.32 182	85.35 216	95.50 193	88.89 230	85.89 228	91.99 232	90.15 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
GBi-Net		97.69 147	97.75 128	97.62 189	98.71 202	99.21 63	98.62 155	99.33 120	94.09 191	95.60 223	98.17 131	95.97 179	94.39 207	99.05 48	99.03 49	99.08 103	98.70 88

test1		97.69 147	97.75 128	97.62 189	98.71 202	99.21 63	98.62 155	99.33 120	94.09 191	95.60 223	98.17 131	95.97 179	94.39 207	99.05 48	99.03 49	99.08 103	98.70 88

FMVSNet2		97.94 136	98.08 111	97.77 188	98.71 202	99.21 63	98.62 155	99.47 83	96.62 119	96.37 216	99.20 86	97.70 160	94.39 207	97.39 173	97.75 139	99.08 103	98.70 88

PatchMatch-RL		97.24 166	96.45 169	98.17 171	98.70 205	97.57 200	97.31 220	98.48 196	94.42 184	98.39 148	95.74 191	96.35 178	97.88 145	97.75 150	97.48 160	98.24 181	95.87 196

LP		95.33 205	93.45 208	97.54 192	98.68 206	97.40 201	98.73 140	98.41 198	96.33 136	98.92 107	97.84 143	88.30 199	95.92 188	92.98 224	89.38 220	94.56 218	91.90 222

MS-PatchMatch		97.60 152	97.22 148	98.04 178	98.67 207	97.18 204	97.91 197	98.28 201	95.82 152	98.34 152	97.66 146	98.38 140	97.77 150	97.10 184	97.25 168	97.27 199	97.18 178

PatchT		95.49 201	93.29 209	98.06 177	98.65 208	96.20 214	98.91 123	99.73 28	92.00 222	98.50 139	96.67 174	83.25 221	96.34 181	94.40 218	95.50 199	96.21 208	95.04 204

CR-MVSNet		95.38 203	93.01 210	98.16 173	98.63 209	95.85 224	97.64 211	99.78 19	91.27 227	98.50 139	96.84 171	82.16 223	96.34 181	94.40 218	95.50 199	98.05 187	95.04 204

EPMVS		93.67 221	90.82 223	96.99 206	98.62 210	96.39 213	98.40 173	99.11 156	95.54 156	97.87 177	97.14 161	81.27 227	94.97 200	88.54 232	86.80 225	92.95 226	90.06 229

CVMVSNet		97.38 164	97.39 140	97.37 194	98.58 211	97.72 196	98.70 141	97.42 219	97.21 94	95.95 220	99.46 68	93.31 191	97.38 163	97.60 161	97.78 131	96.18 209	98.66 93

CNLPA		97.75 143	97.26 144	98.32 163	98.58 211	97.86 189	97.80 201	98.09 208	96.49 129	98.49 142	96.15 185	98.08 149	98.35 122	98.00 125	97.03 176	98.61 163	97.21 177

E-PMN		92.28 228	90.12 224	94.79 230	98.56 213	90.90 237	95.16 235	93.68 234	95.36 158	95.10 230	96.56 177	89.05 197	95.24 196	95.21 212	81.84 234	90.98 234	81.94 235

RPMNet		94.72 209	92.01 217	97.88 184	98.56 213	95.85 224	97.78 202	99.70 34	91.27 227	98.33 153	93.69 209	81.88 224	94.91 201	92.60 226	94.34 212	98.01 189	94.46 208

MDTV_nov1_ep13		94.47 214	92.15 215	97.17 198	98.54 215	96.42 212	98.10 190	98.89 172	94.49 178	98.02 169	97.41 154	86.49 206	95.56 192	90.85 227	87.95 222	93.91 220	91.45 225

test2356		92.46 224	88.72 231	96.82 209	98.48 216	95.34 230	96.22 233	98.09 208	87.46 238	96.01 218	92.82 216	64.42 237	95.10 198	94.08 221	94.05 213	97.02 203	92.87 216

TSAR-MVS + COLMAP		97.62 151	97.31 142	97.98 180	98.47 217	97.39 202	98.29 182	98.25 202	96.68 117	97.54 190	98.87 105	98.04 152	97.08 168	96.78 189	96.26 189	98.26 180	97.12 179

tpmp4_e23		92.43 226	88.82 229	96.64 215	98.46 218	95.17 231	97.61 213	98.85 174	92.42 208	98.18 160	93.03 214	74.92 232	93.80 215	88.91 229	84.60 230	92.95 226	92.66 219

EMVS		91.84 229	89.39 228	94.70 231	98.44 219	90.84 238	95.27 234	93.53 235	95.18 161	95.26 228	95.62 193	87.59 204	94.77 203	94.87 215	80.72 235	90.95 235	80.88 236

tpmrst		92.45 225	89.48 227	95.92 224	98.43 220	95.03 232	97.14 223	97.92 214	94.16 190	97.56 189	97.86 142	81.63 226	93.56 217	85.89 235	82.86 231	90.91 236	88.95 234

tpm		93.89 219	91.21 220	97.03 203	98.36 221	96.07 219	97.53 218	99.65 43	92.24 211	98.64 127	97.23 158	74.67 233	94.64 205	92.68 225	90.73 218	93.37 223	94.82 207

tpm cat1		91.52 230	87.70 232	95.97 223	98.33 222	94.98 233	97.06 225	98.03 210	92.11 215	98.03 168	94.77 200	77.19 230	92.71 222	83.56 236	82.24 233	91.67 233	89.04 233

PMMVS2		96.29 190	97.05 154	95.40 227	98.32 223	96.16 215	98.18 188	97.46 218	97.20 96	84.51 241	99.60 53	98.68 131	96.37 180	98.59 79	97.38 162	97.58 195	91.76 223

DWT-MVSNet_training		91.07 231	86.55 233	96.35 219	98.28 224	95.82 227	98.00 193	95.03 231	91.24 229	97.99 173	90.35 222	63.43 238	95.25 195	86.06 234	86.62 226	93.55 222	92.30 220

test12356		95.71 200	95.55 189	95.89 225	98.27 225	96.48 210	96.90 227	97.35 221	92.13 214	95.64 222	99.13 89	97.97 154	92.34 224	96.94 185	96.55 186	94.87 217	89.61 230

new_pmnet		96.59 181	96.40 170	96.81 210	98.24 226	95.46 228	97.71 208	94.75 232	96.92 103	96.80 214	99.23 84	97.81 159	96.69 173	96.58 193	95.16 204	96.69 205	93.64 214

dps		92.35 227	88.78 230	96.52 217	98.21 227	95.94 223	97.78 202	98.38 199	89.88 235	96.81 213	95.07 197	75.31 231	94.70 204	88.62 231	86.21 227	93.21 225	90.41 226

FMVSNet5		94.57 212	92.77 211	96.67 214	97.88 228	98.72 138	97.54 215	98.70 184	88.64 237	95.11 229	86.90 228	81.77 225	93.27 218	97.92 132	98.07 110	97.50 197	97.34 169

TESTMET0.1,1		94.44 215	93.71 205	95.30 229	97.84 229	96.16 215	96.31 230	95.32 230	89.98 233	94.08 232	97.01 165	86.43 207	92.20 226	96.76 191	95.31 201	96.05 210	94.31 209

FPMVS		96.97 173	97.20 149	96.70 213	97.75 230	96.11 218	97.72 206	95.47 228	97.13 98	98.02 169	97.57 149	96.67 174	92.97 220	99.00 56	98.34 98	98.28 179	95.58 198

test-mter		94.62 210	94.02 202	95.32 228	97.72 231	96.75 207	96.23 232	95.67 227	89.83 236	93.23 238	96.99 167	85.94 213	92.66 223	97.32 177	96.11 195	96.44 206	95.22 203

pmmvs3		96.30 189	95.87 184	96.80 211	97.66 232	96.48 210	97.93 196	93.80 233	93.40 202	98.54 135	98.27 126	97.50 162	97.37 165	97.49 167	93.11 215	95.52 214	94.85 206

FMVSNet3		96.85 175	96.67 161	97.06 201	97.56 233	99.01 103	97.99 194	99.33 120	94.09 191	95.60 223	98.17 131	95.97 179	93.26 219	94.76 217	96.22 190	98.59 165	98.46 107

CMPMVS	74.71 19	96.17 193	96.06 180	96.30 220	97.41 234	94.52 234	94.83 236	95.46 229	91.57 225	97.26 205	94.45 206	98.33 142	94.98 199	98.28 106	97.59 153	97.86 191	97.68 157
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PMMVS		96.47 184	95.81 186	97.23 197	97.38 235	95.96 222	97.31 220	96.91 225	93.21 204	97.93 176	97.14 161	97.64 161	95.70 190	95.24 211	96.18 193	98.17 184	95.33 202

MVS-HIRNet		94.86 207	93.83 204	96.07 221	97.07 236	94.00 235	94.31 237	99.17 146	91.23 230	98.17 161	98.69 111	97.43 164	95.66 191	94.05 222	91.92 217	92.04 231	89.46 231

DeepPCF-MVS	96.68 10	98.20 127	98.26 99	98.12 174	97.03 237	98.11 177	98.44 171	97.70 217	96.77 112	98.52 137	98.91 104	99.17 84	98.58 107	98.41 91	98.02 111	98.46 174	98.46 107

testpf		87.81 232	83.90 234	92.37 232	96.76 238	88.65 239	93.04 239	98.24 203	85.20 239	95.28 227	86.82 229	72.43 235	82.35 236	82.62 237	82.30 232	88.55 237	89.29 232

MVE	82.47 18	93.12 222	94.09 198	91.99 233	90.79 239	82.50 241	93.93 238	96.30 226	96.06 146	88.81 240	98.19 129	96.38 177	97.56 154	97.24 181	95.18 203	84.58 238	93.07 215
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				65.28 235	82.24 240	71.50 242	70.81 242	23.21 237	96.14 143	81.70 242	85.98 235	92.44 193	49.84 237	95.81 203	94.36 211	83.86 239

testmvs		9.73 235	13.38 236	5.48 238	3.62 241	4.12 243	6.40 244	3.19 239	14.92 240	7.68 245	22.10 239	13.89 245	6.83 238	13.47 238	10.38 237	5.14 241	14.81 237

test123		9.37 236	12.26 237	6.00 237	3.32 242	4.06 244	6.39 245	3.41 238	13.20 241	10.48 244	16.43 240	16.22 244	6.76 239	11.37 239	10.40 236	5.62 240	14.10 239

GG-mvs-BLEND		65.66 234	92.62 213	34.20 236	1.45 243	93.75 236	85.40 241	1.64 240	91.37 226	17.21 243	87.25 226	94.78 186	3.25 240	95.64 207	93.80 214	96.27 207	91.74 224

sosnet-low-res		0.00 237	0.00 238	0.00 239	0.00 244	0.00 245	0.00 246	0.00 241	0.00 242	0.00 246	0.00 241	0.00 246	0.00 241	0.00 240	0.00 239	0.00 243	0.00 240

sosnet		0.00 237	0.00 238	0.00 239	0.00 244	0.00 245	0.00 246	0.00 241	0.00 242	0.00 246	0.00 241	0.00 246	0.00 241	0.00 240	0.00 239	0.00 243	0.00 240

MTAPA										99.19 65		99.68 22

MTMP										99.20 63		99.54 42

Patchmatch-RL test							32.47 243

NP-MVS									93.07 205

Patchmtry						96.05 220	97.64 211	99.78 19		98.50 139

DeepMVS_CX						87.86 240	92.27 240	61.98 236	93.64 199	93.62 235	91.17 218	91.67 194	94.90 202	95.99 202		92.48 230	94.18 211

High-resolution multi-view benchmark