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		99.36 1	99.64 1	99.03 9	99.22 34	99.53 6	99.38 15	99.55 1	99.70 1	98.74 19	99.74 6	99.96 1	97.48 72	99.75 1	99.63 1	99.80 2	99.19 3

LTVRE_ROB	97.71 1	99.33 2	99.47 2	99.16 7	99.16 40	99.11 11	99.39 14	99.16 11	99.26 3	99.22 4	99.51 32	99.75 4	98.54 19	99.71 2	99.47 4	99.52 13	99.46 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
pmmvs6		98.77 16	99.35 3	98.09 50	98.32 96	98.92 21	98.57 81	99.03 12	99.36 2	96.86 95	99.77 5	99.86 2	96.20 111	99.56 5	99.39 7	99.59 6	98.61 22

SixPastTwentyTwo		99.25 3	99.20 4	99.32 1	99.53 14	99.32 8	99.64 2	99.19 10	98.05 13	99.19 5	99.74 6	98.96 56	99.03 5	99.69 3	99.58 2	99.32 24	99.06 6

WR-MVS		99.22 4	99.15 5	99.30 2	99.54 11	99.62 1	99.63 4	99.45 2	97.75 17	98.47 25	99.71 8	99.05 44	98.88 7	99.54 6	99.49 3	99.81 1	98.87 10

TDRefinement		99.00 9	99.13 6	98.86 12	98.99 57	99.05 16	99.58 7	98.29 49	98.96 5	97.96 47	99.40 46	98.67 87	98.87 8	99.60 4	99.46 5	99.46 19	98.74 18

v52		98.98 10	99.10 7	98.85 13	98.91 60	99.03 17	99.41 12	97.77 94	98.12 10	99.07 8	99.84 3	99.60 6	99.15 2	99.29 16	98.99 20	98.79 61	98.79 12

V4		98.98 10	99.10 7	98.85 13	98.91 60	99.03 17	99.41 12	97.77 94	98.12 10	99.06 9	99.85 2	99.60 6	99.15 2	99.30 15	98.99 20	98.80 59	98.79 12

v7n		99.03 7	99.03 9	99.02 10	99.09 51	99.11 11	99.57 9	98.82 18	98.21 9	99.25 2	99.84 3	99.59 8	98.76 9	99.23 20	98.83 29	98.63 69	98.40 36

v748		98.92 13	98.95 10	98.87 11	98.54 81	98.69 50	99.33 17	98.64 23	98.07 12	99.06 9	99.66 12	99.76 3	98.68 11	99.25 19	98.72 33	99.01 36	98.54 25

PEN-MVS		99.08 5	98.95 10	99.23 5	99.65 3	99.59 2	99.64 2	99.34 6	96.68 29	98.65 20	99.43 42	99.33 17	98.47 21	99.50 8	99.32 9	99.60 5	98.79 12

ACMH	95.26 7	98.75 17	98.93 12	98.54 27	98.86 64	99.01 19	99.58 7	98.10 68	98.67 6	97.30 73	99.18 57	99.42 12	98.40 23	99.19 22	98.86 27	98.99 40	98.19 43
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PS-CasMVS		99.08 5	98.90 13	99.28 3	99.65 3	99.56 4	99.59 6	99.39 4	96.36 36	98.83 16	99.46 39	99.09 36	98.62 14	99.51 7	99.36 8	99.63 3	98.97 7

anonymousdsp		98.85 15	98.88 14	98.83 15	98.69 77	98.20 80	99.68 1	97.35 136	97.09 24	98.98 12	99.86 1	99.43 11	98.94 6	99.28 17	99.19 14	99.33 22	99.08 5

DTE-MVSNet		99.03 7	98.88 14	99.21 6	99.66 2	99.59 2	99.62 5	99.34 6	96.92 26	98.52 22	99.36 49	98.98 51	98.57 17	99.49 9	99.23 13	99.56 10	98.55 24

Anonymous20240521		98.69 19	98.84 16	98.52 28	98.83 69	99.14 10	99.22 24	98.76 20	96.99 25	96.73 97	99.49 34	99.14 34	98.01 36	99.42 11	99.27 12	99.57 8	98.43 34

WR-MVS_H		98.97 12	98.82 17	99.14 8	99.56 9	99.56 4	99.54 11	99.42 3	96.07 43	98.37 27	99.34 50	99.09 36	98.43 22	99.45 10	99.41 6	99.53 11	98.86 11

COLMAP_ROB	96.84 2	98.75 17	98.82 17	98.66 23	99.14 44	98.79 32	99.30 19	97.67 98	98.33 8	97.82 50	99.20 56	99.18 32	98.76 9	99.27 18	98.96 22	99.29 26	98.03 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TransMVSNet (Re)		98.23 28	98.72 19	97.66 87	98.22 108	98.73 46	98.66 78	98.03 74	98.60 7	96.40 113	99.60 21	98.24 110	95.26 127	99.19 22	99.05 18	99.36 20	97.64 62

ACMH+	94.90 8	98.40 25	98.71 20	98.04 61	98.93 59	98.84 26	99.30 19	97.86 86	97.78 16	94.19 175	98.77 74	99.39 14	98.61 15	99.33 14	99.07 15	99.33 22	97.81 55

pm-mvs1		98.14 36	98.66 21	97.53 95	97.93 140	98.49 67	98.14 102	98.19 59	97.95 14	96.17 125	99.63 18	98.85 68	95.41 125	98.91 31	98.89 26	99.34 21	97.86 54

CP-MVSNet		98.91 14	98.61 22	99.25 4	99.63 5	99.50 7	99.55 10	99.36 5	95.53 69	98.77 18	99.11 59	98.64 90	98.57 17	99.42 11	99.28 11	99.61 4	98.78 15

CSCG		98.45 22	98.61 22	98.26 39	99.11 48	99.06 14	98.17 101	97.49 113	97.93 15	97.37 70	98.88 65	99.29 18	98.10 31	98.40 55	97.51 84	99.32 24	99.16 4

UA-Net		98.66 20	98.60 24	98.73 19	99.83 1	99.28 9	98.56 83	99.24 8	96.04 44	97.12 81	98.44 86	98.95 57	98.17 30	99.15 24	99.00 19	99.48 18	99.33 2

MIMVSNet1		98.22 31	98.51 25	97.87 74	99.40 25	98.82 29	99.31 18	98.53 25	97.39 20	96.59 104	99.31 52	99.23 28	94.76 137	98.93 30	98.67 35	98.63 69	97.25 85

DeepC-MVS	96.08 5	98.58 21	98.49 26	98.68 21	99.37 26	98.52 64	99.01 38	98.17 63	97.17 23	98.25 31	99.56 25	99.62 5	98.29 26	98.40 55	98.09 64	98.97 43	98.08 46
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		98.29 27	98.42 27	98.14 45	99.45 22	98.90 22	99.18 27	98.30 45	95.96 49	95.13 154	98.79 72	99.25 25	97.92 44	98.80 34	98.71 34	98.85 56	98.54 25

Vis-MVSNet		98.01 43	98.42 27	97.54 94	96.89 191	98.82 29	99.14 28	97.59 102	96.30 37	97.04 84	99.26 54	98.83 70	96.01 116	98.73 36	98.21 57	98.58 71	98.75 17
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
SMA-MVS		98.22 31	98.31 29	98.11 48	99.46 21	98.77 34	98.34 93	97.92 79	95.27 80	96.97 88	98.82 70	99.39 14	97.10 85	98.69 41	98.47 40	98.84 58	98.77 16

RPSCF		97.83 58	98.27 30	97.31 105	98.23 105	98.06 96	97.44 148	95.79 183	96.90 27	95.81 136	98.76 75	98.61 94	97.70 56	98.90 32	98.36 49	98.90 50	98.29 38

FC-MVSNet-test		97.54 69	98.26 31	96.70 132	98.87 63	97.79 130	98.49 85	98.56 24	96.04 44	90.39 210	99.65 14	98.67 87	95.15 130	99.23 20	99.07 15	98.73 63	97.39 78

TSAR-MVS + MP.		98.15 35	98.23 32	98.06 59	98.47 84	98.16 86	99.23 22	96.87 151	95.58 64	96.72 98	98.41 87	99.06 41	98.05 34	98.99 28	98.90 25	99.00 38	98.51 29

TranMVSNet+NR-MVSNet		98.45 22	98.22 33	98.72 20	99.32 30	99.06 14	98.99 40	98.89 14	95.52 70	97.53 61	99.42 44	98.83 70	98.01 36	98.55 49	98.34 50	99.57 8	97.80 56

FC-MVSNet-train		97.65 64	98.16 34	97.05 117	98.85 65	98.85 25	99.34 16	98.08 69	94.50 110	94.41 169	99.21 55	98.80 74	92.66 165	98.98 29	98.85 28	98.96 45	97.94 50

Gipuma		98.43 24	98.15 35	98.76 18	99.00 56	98.29 77	97.91 116	98.06 71	99.02 4	99.50 1	96.33 135	98.67 87	99.22 1	99.02 27	98.02 74	98.88 55	97.66 61
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FMVSNet1		97.40 85	98.09 36	96.60 137	97.80 154	98.76 39	98.26 98	98.50 26	96.79 28	93.13 197	99.28 53	98.64 90	92.90 163	97.67 78	97.86 79	99.02 34	97.64 62

ACMMP_Plus		98.12 38	98.08 37	98.18 43	99.34 28	98.74 44	98.97 44	98.00 75	95.13 84	96.90 90	97.54 111	99.27 22	97.18 83	98.72 38	98.45 43	98.68 67	98.69 19

ACMMPR		98.31 26	98.07 38	98.60 24	99.58 6	98.83 27	99.09 30	98.48 27	96.25 39	97.03 85	96.81 125	99.09 36	98.39 24	98.55 49	98.45 43	99.01 36	98.53 28

HFP-MVS		98.17 33	98.02 39	98.35 37	99.36 27	98.62 55	98.79 61	98.46 32	96.24 40	96.53 106	97.13 122	98.98 51	98.02 35	98.20 63	98.42 45	98.95 47	98.54 25

OPM-MVS		98.01 43	98.01 40	98.00 64	99.11 48	98.12 91	98.68 77	97.72 96	96.65 30	96.68 102	98.40 88	99.28 21	97.44 74	98.20 63	97.82 82	98.40 90	97.58 67

PMVS	90.51 17	97.77 60	97.98 41	97.53 95	98.68 78	98.14 90	97.67 126	97.03 146	96.43 32	98.38 26	98.72 77	97.03 143	94.44 143	99.37 13	99.30 10	98.98 42	96.86 103
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EG-PatchMatch MVS		97.98 48	97.92 42	98.04 61	98.84 66	98.04 99	97.90 117	96.83 155	95.07 86	98.79 17	99.07 60	99.37 16	97.88 47	98.74 35	98.16 62	98.01 110	96.96 96

Baseline_NR-MVSNet		98.17 33	97.90 43	98.48 31	99.23 32	98.59 57	98.83 59	98.73 22	93.97 129	96.95 89	99.66 12	98.23 112	97.90 45	98.40 55	99.06 17	99.25 27	97.42 77

NR-MVSNet		98.00 45	97.88 44	98.13 46	98.33 93	98.77 34	98.83 59	98.88 15	94.10 122	97.46 66	98.87 67	98.58 96	95.78 118	99.13 25	98.16 62	99.52 13	97.53 69

v13		98.04 42	97.86 45	98.24 40	98.36 91	98.77 34	99.04 32	98.47 29	95.93 50	98.20 35	99.67 11	99.11 35	98.00 38	97.11 100	96.93 103	97.40 136	97.53 69

UniMVSNet (Re)		98.23 28	97.85 46	98.67 22	99.15 41	98.87 24	98.74 73	98.84 17	94.27 121	97.94 48	99.01 61	98.39 104	97.82 49	98.35 60	98.29 54	99.51 16	97.78 57

UGNet		96.79 114	97.82 47	95.58 170	97.57 163	98.39 73	98.48 86	97.84 89	95.85 55	94.68 164	97.91 102	99.07 40	87.12 211	97.71 75	97.51 84	97.80 117	98.29 38
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
LS3D		97.93 54	97.80 48	98.08 55	99.20 37	98.77 34	98.89 55	97.92 79	96.59 31	96.99 87	96.71 128	97.14 140	96.39 107	99.04 26	98.96 22	99.10 33	97.39 78

tfpnnormal		97.66 63	97.79 49	97.52 97	98.32 96	98.53 63	98.45 88	97.69 97	97.59 19	96.12 126	97.79 105	96.70 145	95.69 121	98.35 60	98.34 50	98.85 56	97.22 90

ESAPD		97.71 62	97.79 49	97.62 88	99.21 35	98.80 31	98.31 96	98.30 45	93.60 135	94.74 163	97.94 100	99.24 27	96.58 99	98.42 54	98.27 55	98.56 72	98.28 41

TSAR-MVS + ACMM		97.54 69	97.79 49	97.26 106	98.23 105	98.10 94	97.71 125	97.88 85	95.97 48	95.57 147	98.71 78	98.57 97	97.36 77	97.74 74	96.81 109	96.83 173	98.59 23

zzz-MVS		98.14 36	97.78 52	98.55 26	99.58 6	98.58 58	98.98 42	98.48 27	95.98 47	97.39 68	94.73 164	99.27 22	97.98 41	98.81 33	98.64 37	98.90 50	98.46 31

v12		97.98 48	97.78 52	98.21 41	98.33 93	98.74 44	99.01 38	98.44 34	95.82 57	98.13 36	99.64 15	99.08 39	97.95 42	96.97 112	96.82 106	97.39 138	97.38 81

SD-MVS		97.84 57	97.78 52	97.90 68	98.33 93	98.06 96	97.95 113	97.80 91	96.03 46	96.72 98	97.57 109	99.18 32	97.50 71	97.88 69	97.08 98	99.11 32	98.68 20

SteuartSystems-ACMMP		98.06 41	97.78 52	98.39 35	99.54 11	98.79 32	98.94 49	98.42 35	93.98 128	95.85 134	96.66 130	99.25 25	98.61 15	98.71 40	98.38 47	98.97 43	98.67 21
Skip Steuart: Steuart Systems R&D Blog.
DU-MVS		98.23 28	97.74 56	98.81 16	99.23 32	98.77 34	98.76 64	98.88 15	94.10 122	98.50 23	98.87 67	98.32 107	97.99 39	98.40 55	98.08 71	99.49 17	97.64 62

v11		97.94 53	97.72 57	98.20 42	98.37 90	98.69 50	98.96 47	98.30 45	95.68 60	98.35 28	99.70 9	99.19 31	97.93 43	96.76 119	96.82 106	97.28 150	97.23 88

ACMM	94.29 11	98.12 38	97.71 58	98.59 25	99.51 16	98.58 58	99.24 21	98.25 51	96.22 41	96.90 90	95.01 160	98.89 62	98.52 20	98.66 44	98.32 53	99.13 30	98.28 41
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
V9		97.91 55	97.70 59	98.17 44	98.30 100	98.70 49	98.98 42	98.40 36	95.72 59	98.07 40	99.64 15	99.04 45	97.90 45	96.82 116	96.71 113	97.37 141	97.23 88

ACMP	94.03 12	97.97 51	97.61 60	98.39 35	99.43 24	98.51 65	98.97 44	98.06 71	94.63 100	96.10 127	96.12 140	99.20 30	98.63 13	98.68 42	98.20 60	99.14 29	97.93 51
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
V14		97.85 56	97.60 61	98.13 46	98.27 102	98.66 53	98.94 49	98.36 40	95.62 61	98.04 43	99.62 19	98.99 49	97.84 48	96.65 124	96.59 119	97.34 144	97.07 93

ACMMP		97.99 47	97.60 61	98.45 33	99.53 14	98.83 27	99.13 29	98.30 45	94.57 102	96.39 117	95.32 154	98.95 57	98.37 25	98.61 47	98.47 40	99.00 38	98.45 32
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
CP-MVS		98.00 45	97.57 63	98.50 29	99.47 20	98.56 61	98.91 52	98.38 38	94.71 96	97.01 86	95.20 156	99.06 41	98.20 28	98.61 47	98.46 42	99.02 34	98.40 36

no-one		97.16 100	97.57 63	96.68 134	96.30 202	95.74 185	98.40 92	94.04 213	96.28 38	96.30 119	97.95 99	99.45 10	99.06 4	96.93 114	98.19 61	95.99 188	98.48 30

UniMVSNet_NR-MVSNet		98.12 38	97.56 65	98.78 17	99.13 46	98.89 23	98.76 64	98.78 19	93.81 132	98.50 23	98.81 71	97.64 129	97.99 39	98.18 66	97.92 76	99.53 11	97.64 62

MP-MVS		97.98 48	97.53 66	98.50 29	99.56 9	98.58 58	98.97 44	98.39 37	93.49 137	97.14 78	96.08 141	99.23 28	98.06 33	98.50 52	98.38 47	98.90 50	98.44 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LGP-MVS_train		97.96 52	97.53 66	98.45 33	99.45 22	98.64 54	99.09 30	98.27 50	92.99 150	96.04 129	96.57 131	99.29 18	98.66 12	98.73 36	98.42 45	99.19 28	98.09 45

v15		97.77 60	97.50 68	98.09 50	98.23 105	98.62 55	98.90 53	98.32 44	95.51 72	98.01 45	99.60 21	98.95 57	97.78 50	96.47 130	96.45 124	97.32 145	96.90 98

TSAR-MVS + GP.		97.26 96	97.33 69	97.18 111	98.21 109	98.06 96	96.38 185	97.66 99	93.92 131	95.23 151	98.48 84	98.33 106	97.41 75	97.63 83	97.35 90	98.18 103	97.57 68

DeepC-MVS_fast	95.38 6	97.53 72	97.30 70	97.79 79	98.83 69	97.64 133	98.18 99	97.14 142	95.57 65	97.83 49	97.10 123	98.80 74	96.53 103	97.41 89	97.32 92	98.24 99	97.26 84
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v10		97.64 65	97.26 71	98.08 55	98.07 125	98.56 61	98.86 57	98.18 62	94.48 112	98.24 32	99.56 25	98.98 51	97.72 54	96.05 148	96.26 131	97.42 134	96.93 97

PGM-MVS		97.82 59	97.25 72	98.48 31	99.54 11	98.75 43	99.02 34	98.35 42	92.41 155	96.84 96	95.39 153	98.99 49	98.24 27	98.43 53	98.34 50	98.90 50	98.41 35

DELS-MVS		96.90 108	97.24 73	96.50 143	97.85 145	98.18 82	97.88 119	95.92 176	93.48 138	95.34 149	98.86 69	98.94 60	94.03 153	97.33 93	97.04 99	98.00 111	96.85 105
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
v17		97.54 69	97.21 74	97.92 66	98.02 128	98.50 66	98.79 61	98.24 52	94.39 116	97.60 59	99.45 41	98.72 85	97.68 58	96.29 137	96.28 129	97.19 159	96.86 103

OMC-MVS		97.23 97	97.21 74	97.25 109	97.85 145	97.52 142	97.92 115	95.77 184	95.83 56	97.09 83	97.86 103	98.52 99	96.62 96	97.51 84	96.65 115	98.26 96	96.57 113

v16		97.51 74	97.19 76	97.89 70	97.99 132	98.49 67	98.77 63	98.23 55	94.29 118	97.48 63	99.42 44	98.68 86	97.69 57	96.28 138	96.29 128	97.18 160	96.85 105

3Dnovator	96.31 3	97.22 98	97.19 76	97.25 109	98.14 118	97.95 108	98.03 107	96.77 157	96.42 33	97.14 78	95.11 157	97.59 130	95.14 132	97.79 72	97.72 83	98.26 96	97.76 59

PHI-MVS		97.44 81	97.17 78	97.74 86	98.14 118	98.41 72	98.03 107	97.50 110	92.07 161	98.01 45	97.33 116	98.62 93	96.02 115	98.34 62	98.21 57	98.76 62	97.24 87

v8		97.51 74	97.16 79	97.91 67	97.99 132	98.48 69	98.76 64	98.17 63	94.54 106	97.69 53	99.48 35	98.76 80	97.63 65	96.10 144	96.14 137	97.20 155	96.64 112

APD-MVS		97.47 79	97.16 79	97.84 76	99.32 30	98.39 73	98.47 87	98.21 56	92.08 160	95.23 151	96.68 129	98.90 61	96.99 88	98.20 63	98.21 57	98.80 59	97.67 60
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PVSNet_Blended_VisFu		97.44 81	97.14 81	97.79 79	99.15 41	98.44 70	98.32 95	97.66 99	93.74 134	97.73 52	98.79 72	96.93 144	95.64 124	97.69 76	96.91 104	98.25 98	97.50 73

QAPM		97.04 105	97.14 81	96.93 122	97.78 157	98.02 101	97.36 153	96.72 158	94.68 98	96.23 120	97.21 120	97.68 127	95.70 120	97.37 91	97.24 97	97.78 119	97.77 58

3Dnovator+	96.20 4	97.58 67	97.14 81	98.10 49	98.98 58	97.85 124	98.60 80	98.33 43	96.41 34	97.23 77	94.66 166	97.26 136	96.91 90	97.91 68	97.87 78	98.53 76	98.03 47

HSP-MVS		97.44 81	97.13 84	97.79 79	99.34 28	98.99 20	99.23 22	98.12 66	93.43 139	95.95 130	97.45 112	99.50 9	96.44 106	96.35 133	95.33 163	97.65 126	98.89 9

DeepPCF-MVS	94.55 10	97.05 104	97.13 84	96.95 120	96.06 204	97.12 157	98.01 110	95.44 190	95.18 82	97.50 62	97.86 103	98.08 116	97.31 81	97.23 95	97.00 100	97.36 142	97.45 75

HPM-MVS++		97.56 68	97.11 86	98.09 50	99.18 39	97.95 108	98.57 81	98.20 57	94.08 124	97.25 76	95.96 145	98.81 73	97.13 84	97.51 84	97.30 95	98.21 101	98.15 44

MVS_111021_HR		97.27 95	97.11 86	97.46 99	98.46 85	97.82 127	97.50 139	96.86 152	94.97 89	97.13 80	96.99 124	98.39 104	96.82 92	97.65 82	97.38 89	98.02 109	96.56 115

v1144		97.51 74	97.05 88	98.04 61	98.26 103	97.98 105	98.88 56	97.42 126	95.38 75	98.56 21	99.59 24	99.01 48	97.65 60	95.77 158	96.06 143	97.47 131	95.56 139

v18		97.40 85	97.04 89	97.81 78	97.90 143	98.42 71	98.71 76	98.17 63	94.06 126	97.34 72	99.40 46	98.59 95	97.60 66	96.05 148	96.12 140	97.14 163	96.67 110

v1192		97.52 73	97.03 90	98.09 50	98.31 99	98.01 102	98.96 47	97.25 139	95.22 81	98.89 14	99.64 15	98.83 70	97.68 58	95.63 160	95.91 149	97.47 131	95.97 128

v7		97.45 80	97.01 91	97.97 65	98.07 125	97.96 106	98.86 57	97.50 110	94.46 113	98.24 32	99.56 25	98.98 51	97.72 54	96.05 148	96.26 131	97.42 134	95.79 132

v1921920		97.50 77	97.00 92	98.07 57	98.20 110	97.94 111	99.03 33	97.06 144	95.29 79	99.01 11	99.62 19	98.73 84	97.74 53	95.52 163	95.78 154	97.39 138	96.12 125

X-MVS		97.60 66	97.00 92	98.29 38	99.50 17	98.76 39	98.90 53	98.37 39	94.67 99	96.40 113	91.47 198	98.78 76	97.60 66	98.55 49	98.50 39	98.96 45	98.29 38

v144192		97.49 78	96.99 94	98.07 57	98.11 123	97.95 108	99.02 34	97.21 140	94.90 92	98.88 15	99.53 31	98.89 62	97.75 52	95.59 161	95.90 150	97.43 133	96.16 123

V42		97.10 102	96.97 95	97.26 106	97.64 159	97.60 135	98.45 88	95.99 173	94.44 114	97.35 71	99.40 46	98.63 92	97.34 79	96.33 136	96.38 127	96.82 175	96.00 127

v1141		97.36 89	96.92 96	97.88 73	98.18 113	97.90 118	98.76 64	97.42 126	95.38 75	98.07 40	99.56 25	98.87 65	97.59 68	95.78 155	95.98 144	97.29 147	94.97 152

divwei89l23v2f112		97.37 87	96.92 96	97.89 70	98.18 113	97.90 118	98.76 64	97.42 126	95.38 75	98.09 38	99.56 25	98.87 65	97.59 68	95.78 155	95.98 144	97.29 147	94.97 152

v1		97.37 87	96.92 96	97.89 70	98.18 113	97.91 117	98.76 64	97.42 126	95.38 75	98.09 38	99.55 30	98.88 64	97.59 68	95.78 155	95.98 144	97.29 147	94.98 151

CLD-MVS		96.73 117	96.92 96	96.51 142	98.70 74	97.57 138	97.64 129	92.07 217	93.10 148	96.31 118	98.29 90	99.02 47	95.99 117	97.20 97	96.47 123	98.37 92	96.81 107
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
canonicalmvs		97.11 101	96.88 100	97.38 100	98.34 92	98.72 48	97.52 138	97.94 78	95.60 62	95.01 159	94.58 167	94.50 169	96.59 98	97.84 70	98.03 73	98.90 50	98.91 8

v1neww		97.30 91	96.88 100	97.78 82	97.99 132	97.87 121	98.75 70	97.46 118	94.54 106	97.62 56	99.48 35	98.76 80	97.65 60	96.09 145	96.15 133	97.20 155	95.28 147

v7new		97.30 91	96.88 100	97.78 82	97.99 132	97.87 121	98.75 70	97.46 118	94.54 106	97.62 56	99.48 35	98.76 80	97.65 60	96.09 145	96.15 133	97.20 155	95.28 147

v6		97.30 91	96.88 100	97.78 82	97.99 132	97.87 121	98.75 70	97.46 118	94.54 106	97.61 58	99.48 35	98.77 79	97.65 60	96.09 145	96.15 133	97.21 154	95.28 147

EPP-MVSNet		97.29 94	96.88 100	97.76 85	98.70 74	99.10 13	98.92 51	98.36 40	95.12 85	93.36 193	97.39 114	91.00 187	97.65 60	98.72 38	98.91 24	99.58 7	97.92 52

v1240		97.43 84	96.87 105	98.09 50	98.25 104	97.92 114	99.02 34	97.06 144	94.77 95	99.09 7	99.68 10	98.51 100	97.78 50	95.25 168	95.81 152	97.32 145	96.13 124

MVS_0304		97.18 99	96.84 106	97.58 91	99.15 41	98.19 81	98.11 103	97.81 90	92.36 156	98.06 42	97.43 113	99.06 41	94.24 147	96.80 118	96.54 121	98.12 105	97.52 71

v2v482		97.33 90	96.84 106	97.90 68	98.19 111	97.83 125	98.74 73	97.44 125	95.42 74	98.23 34	99.46 39	98.84 69	97.46 73	95.51 164	96.10 141	97.36 142	94.72 157

test20.03		96.08 131	96.80 108	95.25 179	99.19 38	97.58 136	97.24 161	97.56 106	94.95 90	91.91 206	98.58 81	98.03 118	87.88 207	97.43 88	96.94 102	97.69 123	94.05 172

ambc			96.78 109		99.01 55	97.11 158	95.73 201		95.91 51	99.25 2	98.56 82	97.17 138	97.04 87	96.76 119	95.22 165	96.72 177	96.73 109

CNVR-MVS		97.03 106	96.77 110	97.34 102	98.89 62	97.67 132	97.64 129	97.17 141	94.40 115	95.70 142	94.02 175	98.76 80	96.49 105	97.78 73	97.29 96	98.12 105	97.47 74

MVS_111021_LR		96.86 109	96.72 111	97.03 118	97.80 154	97.06 160	97.04 169	95.51 189	94.55 103	97.47 64	97.35 115	97.68 127	96.66 94	97.11 100	96.73 111	97.69 123	96.57 113

v148		96.99 107	96.70 112	97.34 102	97.89 144	97.23 149	98.33 94	96.96 147	95.57 65	97.12 81	98.99 62	99.40 13	97.23 82	96.22 141	95.45 159	96.50 180	94.02 173

TAPA-MVS	93.96 13	96.79 114	96.70 112	96.90 125	97.64 159	97.58 136	97.54 137	94.50 210	95.14 83	96.64 103	96.76 127	97.90 121	96.63 95	95.98 151	96.14 137	98.45 86	97.39 78
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PM-MVS		96.85 110	96.62 114	97.11 113	97.13 185	96.51 169	98.29 97	94.65 206	94.84 93	98.12 37	98.59 80	97.20 137	97.41 75	96.24 140	96.41 126	97.09 164	96.56 115

CANet		96.81 112	96.50 115	97.17 112	99.10 50	97.96 106	97.86 121	97.51 108	91.30 168	97.75 51	97.64 107	97.89 122	93.39 159	96.98 111	96.73 111	97.40 136	96.99 95

Vis-MVSNet (Re-imp)		96.29 127	96.50 115	96.05 157	97.96 139	97.83 125	97.30 155	97.86 86	93.14 145	88.90 220	96.80 126	95.28 161	95.15 130	98.37 59	98.25 56	99.12 31	95.84 129

IS_MVSNet		96.62 122	96.48 117	96.78 129	98.46 85	98.68 52	98.61 79	98.24 52	92.23 157	89.63 216	95.90 146	94.40 170	96.23 109	98.65 45	98.77 30	99.52 13	96.76 108

MSLP-MVS++		96.66 120	96.46 118	96.89 126	98.02 128	97.71 131	95.57 203	96.96 147	94.36 117	96.19 124	91.37 200	98.24 110	97.07 86	97.69 76	97.89 77	97.52 129	97.95 49

CPTT-MVS		97.08 103	96.25 119	98.05 60	99.21 35	98.30 76	98.54 84	97.98 76	94.28 119	95.89 133	89.57 210	98.54 98	98.18 29	97.82 71	97.32 92	98.54 74	97.91 53

EU-MVSNet		96.03 133	96.23 120	95.80 164	95.48 218	94.18 196	98.99 40	91.51 219	97.22 22	97.66 54	99.15 58	98.51 100	98.08 32	95.92 152	92.88 193	93.09 204	95.72 136

pmmvs-eth3d		96.84 111	96.22 121	97.56 92	97.63 161	96.38 176	98.74 73	96.91 150	94.63 100	98.26 30	99.43 42	98.28 108	96.58 99	94.52 181	95.54 157	97.24 152	94.75 156

FMVSNet2		95.77 139	96.20 122	95.27 177	96.77 194	98.18 82	97.28 156	97.90 81	93.12 146	91.37 207	98.25 92	96.05 156	90.04 191	94.96 175	95.94 148	98.28 93	96.90 98

MSDG		96.27 128	96.17 123	96.38 149	97.85 145	96.27 179	96.55 181	94.41 211	94.55 103	95.62 144	97.56 110	97.80 123	96.22 110	97.17 99	96.27 130	97.67 125	93.60 176

MCST-MVS		96.79 114	96.08 124	97.62 88	98.78 72	97.52 142	98.01 110	97.32 137	93.20 143	95.84 135	93.97 177	98.12 114	97.34 79	96.34 134	95.88 151	98.45 86	97.51 72

TinyColmap		96.64 121	96.07 125	97.32 104	97.84 150	96.40 173	97.63 131	96.25 167	95.86 54	98.98 12	97.94 100	96.34 151	96.17 112	97.30 94	95.38 162	97.04 166	93.24 180

testgi		94.81 160	96.05 126	93.35 200	99.06 53	96.87 165	97.57 136	96.70 160	95.77 58	88.60 222	93.19 185	98.87 65	81.21 227	97.03 109	96.64 116	96.97 170	93.99 174

CDPH-MVS		96.68 118	95.99 127	97.48 98	99.13 46	97.64 133	98.08 104	97.46 118	90.56 178	95.13 154	94.87 162	98.27 109	96.56 101	97.09 102	96.45 124	98.54 74	97.08 92

CNLPA		96.24 129	95.97 128	96.57 139	97.48 171	97.10 159	96.75 175	94.95 200	94.92 91	96.20 123	94.81 163	96.61 147	96.25 108	96.94 113	95.64 155	97.79 118	95.74 135

TSAR-MVS + COLMAP		96.05 132	95.94 129	96.18 152	97.46 172	96.41 172	97.26 160	95.83 180	94.69 97	95.30 150	98.31 89	96.52 148	94.71 138	95.48 165	94.87 169	96.54 179	95.33 142

train_agg		96.68 118	95.93 130	97.56 92	99.08 52	97.16 152	98.44 90	97.37 134	91.12 171	95.18 153	95.43 152	98.48 102	97.36 77	96.48 129	95.52 158	97.95 114	97.34 83

Fast-Effi-MVS+		96.80 113	95.92 131	97.84 76	98.57 80	97.46 144	98.06 105	98.24 52	89.64 188	97.57 60	96.45 133	97.35 134	96.73 93	97.22 96	96.64 116	97.86 116	96.65 111

IterMVS-LS		96.35 125	95.85 132	96.93 122	97.53 164	98.00 103	97.37 151	97.97 77	95.49 73	96.71 101	98.94 64	93.23 176	94.82 135	93.15 199	95.05 167	97.17 161	97.12 91
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous20231206		95.69 142	95.68 133	95.70 166	98.32 96	96.95 161	97.37 151	96.65 163	93.33 140	93.61 185	98.70 79	98.03 118	91.04 181	95.07 171	94.59 175	97.20 155	93.09 183

NCCC		96.56 123	95.68 133	97.59 90	99.04 54	97.54 141	97.67 126	97.56 106	94.84 93	96.10 127	87.91 213	98.09 115	96.98 89	97.20 97	96.80 110	98.21 101	97.38 81

USDC		96.30 126	95.64 135	97.07 115	97.62 162	96.35 178	97.17 164	95.71 185	95.52 70	99.17 6	98.11 97	97.46 131	95.67 122	95.44 166	93.60 185	97.09 164	92.99 185

PLC	92.55 15	96.10 130	95.36 136	96.96 119	98.13 121	96.88 163	96.49 182	96.67 162	94.07 125	95.71 141	91.14 201	96.09 155	96.84 91	96.70 122	96.58 120	97.92 115	96.03 126
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GBi-Net		95.21 150	95.35 137	95.04 180	96.77 194	98.18 82	97.28 156	97.58 103	88.43 196	90.28 212	96.01 142	92.43 179	90.04 191	97.67 78	97.86 79	98.28 93	96.90 98

test1		95.21 150	95.35 137	95.04 180	96.77 194	98.18 82	97.28 156	97.58 103	88.43 196	90.28 212	96.01 142	92.43 179	90.04 191	97.67 78	97.86 79	98.28 93	96.90 98

Effi-MVS+		96.46 124	95.28 139	97.85 75	98.64 79	97.16 152	97.15 166	98.75 21	90.27 181	98.03 44	93.93 178	96.21 152	96.55 102	96.34 134	96.69 114	97.97 113	96.33 120

pmmvs5		95.70 141	95.22 140	96.26 150	96.55 199	97.24 148	97.50 139	94.99 199	90.95 173	96.87 92	98.47 85	97.40 132	94.45 142	92.86 201	94.98 168	97.23 153	94.64 159

MDA-MVSNet-bldmvs		95.45 145	95.20 141	95.74 165	94.24 226	96.38 176	97.93 114	94.80 201	95.56 68	96.87 92	98.29 90	95.24 162	96.50 104	98.65 45	90.38 206	94.09 197	91.93 190

CDS-MVSNet		94.91 158	95.17 142	94.60 188	97.85 145	96.21 180	96.90 171	96.39 166	90.81 175	93.40 191	97.24 119	94.54 168	85.78 217	96.25 139	96.15 133	97.26 151	95.01 150
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_BlendedMVS		95.44 146	95.09 143	95.86 162	97.31 179	97.13 155	96.31 189	95.01 197	88.55 194	96.23 120	94.55 170	97.75 124	92.56 171	96.42 131	95.44 160	97.71 120	95.81 130

PVSNet_Blended		95.44 146	95.09 143	95.86 162	97.31 179	97.13 155	96.31 189	95.01 197	88.55 194	96.23 120	94.55 170	97.75 124	92.56 171	96.42 131	95.44 160	97.71 120	95.81 130

Effi-MVS+-dtu		95.94 136	95.08 145	96.94 121	98.54 81	97.38 145	96.66 178	97.89 84	88.68 191	95.92 131	92.90 187	97.28 135	94.18 152	96.68 123	96.13 139	98.45 86	96.51 117

PCF-MVS	92.69 14	95.98 134	95.05 146	97.06 116	98.43 87	97.56 139	97.76 123	96.65 163	89.95 186	95.70 142	96.18 139	98.48 102	95.74 119	93.64 193	93.35 189	98.09 108	96.18 122
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OpenMVS	94.63 9	95.75 140	95.04 147	96.58 138	97.85 145	97.55 140	96.71 177	96.07 171	90.15 184	96.47 108	90.77 206	95.95 157	94.41 144	97.01 110	96.95 101	98.00 111	96.90 98

HQP-MVS		95.97 135	95.01 148	97.08 114	98.72 73	97.19 151	97.07 168	96.69 161	91.49 166	95.77 138	92.19 193	97.93 120	96.15 113	94.66 177	94.16 176	98.10 107	97.45 75

FPMVS		94.70 162	94.99 149	94.37 190	95.84 211	93.20 202	96.00 194	91.93 218	95.03 87	94.64 166	94.68 165	93.29 175	90.95 182	98.07 67	97.34 91	96.85 171	93.29 178

MVS_Test		95.34 149	94.88 150	95.89 160	96.93 190	96.84 166	96.66 178	97.08 143	90.06 185	94.02 177	97.61 108	96.64 146	93.59 158	92.73 203	94.02 180	97.03 167	96.24 121

MS-PatchMatch		94.84 159	94.76 151	94.94 183	96.38 201	94.69 195	95.90 195	94.03 214	92.49 154	93.81 181	95.79 147	96.38 150	94.54 140	94.70 176	94.85 170	94.97 194	94.43 164

CHOSEN 1792x2688		94.98 155	94.69 152	95.31 175	97.27 181	95.58 186	97.90 117	95.56 188	95.03 87	93.77 184	95.65 149	99.29 18	95.30 126	91.51 211	91.28 203	92.05 213	94.50 162

conf0.05thres1000		95.91 137	94.67 153	97.37 101	98.54 81	98.73 46	98.41 91	98.07 70	96.10 42	94.93 161	92.83 188	80.67 213	95.26 127	98.68 42	98.65 36	98.99 40	97.02 94

AdaColmap		95.85 138	94.65 154	97.26 106	98.70 74	97.20 150	97.33 154	97.30 138	91.28 169	95.90 132	88.16 212	96.17 154	96.60 97	97.34 92	96.82 106	97.71 120	95.60 138

CANet_DTU		94.96 156	94.62 155	95.35 174	98.03 127	96.11 181	96.92 170	95.60 187	88.59 193	97.27 75	95.27 155	96.50 149	88.77 202	95.53 162	95.59 156	95.54 191	94.78 155

DI_MVS_plusplus_trai		95.48 144	94.51 156	96.61 136	97.13 185	97.30 146	98.05 106	96.79 156	93.75 133	95.08 157	96.38 134	89.76 190	94.95 133	93.97 192	94.82 172	97.64 127	95.63 137

MAR-MVS		95.51 143	94.49 157	96.71 131	97.92 141	96.40 173	96.72 176	98.04 73	86.74 213	96.72 98	92.52 191	95.14 163	94.02 154	96.81 117	96.54 121	96.85 171	97.25 85
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
CVMVSNet		94.01 177	94.25 158	93.73 197	94.36 225	92.44 207	97.45 147	88.56 224	95.59 63	93.06 200	98.88 65	90.03 189	94.84 134	94.08 190	93.45 186	94.09 197	95.31 143

pmmvs4		95.37 148	94.25 158	96.67 135	97.01 188	95.28 189	97.60 132	96.07 171	93.11 147	97.29 74	98.09 98	94.23 172	95.21 129	91.56 210	93.91 182	96.82 175	93.59 177

new-patchmatchnet		94.48 164	94.02 160	95.02 182	97.51 170	95.00 191	95.68 202	94.26 212	97.32 21	95.73 139	99.60 21	98.22 113	91.30 177	94.13 189	84.41 218	95.65 190	89.45 202

MIMVSNet		93.68 182	93.96 161	93.35 200	97.82 151	96.08 182	96.34 186	98.46 32	91.28 169	86.67 231	94.95 161	94.87 165	84.39 222	94.53 179	94.65 174	96.45 182	91.34 192

gg-mvs-nofinetune		94.13 174	93.93 162	94.37 190	97.99 132	95.86 184	95.45 210	99.22 9	97.61 18	95.10 156	99.50 33	84.50 195	81.73 226	95.31 167	94.12 178	96.71 178	90.59 195

tfpn_n400		95.11 152	93.86 163	96.57 139	98.16 116	97.92 114	97.59 133	97.90 81	95.90 52	92.83 202	89.94 207	83.01 204	94.23 149	97.50 86	97.43 87	98.73 63	95.30 145

tfpnconf		95.11 152	93.86 163	96.57 139	98.16 116	97.92 114	97.59 133	97.90 81	95.90 52	92.83 202	89.94 207	83.01 204	94.23 149	97.50 86	97.43 87	98.73 63	95.30 145

FMVSNet3		94.06 175	93.85 165	94.31 193	95.46 219	97.80 129	96.34 186	97.58 103	88.43 196	90.28 212	96.01 142	92.43 179	88.67 203	91.82 208	93.96 181	97.53 128	96.50 118

diffmvs		94.34 169	93.83 166	94.93 184	96.41 200	94.88 193	96.41 183	96.09 170	93.24 142	93.79 183	98.12 96	92.20 182	91.98 174	90.79 216	92.20 197	94.91 196	95.35 141

PatchMatch-RL		94.79 161	93.75 167	96.00 158	96.80 193	95.00 191	95.47 207	95.25 194	90.68 177	95.80 137	92.97 186	93.64 174	95.67 122	96.13 143	95.81 152	96.99 169	92.01 189

tfpnview11		94.92 157	93.56 168	96.50 143	98.12 122	97.99 104	97.48 141	97.86 86	94.50 110	92.83 202	89.94 207	83.01 204	94.19 151	96.91 115	98.07 72	98.50 80	94.53 160

HyFIR lowres test		95.05 154	93.54 169	96.81 128	97.81 153	96.88 163	98.18 99	97.46 118	94.28 119	94.98 160	96.57 131	92.89 178	96.15 113	90.90 215	91.87 200	96.28 185	91.35 191

EPNet		94.33 172	93.52 170	95.27 177	98.81 71	94.71 194	96.77 174	98.20 57	88.12 199	96.53 106	92.53 190	91.19 185	85.25 221	95.22 169	95.26 164	96.09 187	97.63 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS		94.48 164	93.46 171	95.66 167	97.52 165	96.43 170	97.20 162	94.73 204	92.91 152	96.44 109	98.75 76	91.10 186	94.53 141	92.10 207	90.10 208	93.51 199	92.84 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDTV_nov1_ep13_2view		94.39 166	93.34 172	95.63 168	97.23 182	95.33 188	97.76 123	96.84 154	94.55 103	97.47 64	98.96 63	97.70 126	93.88 155	92.27 205	86.81 216	90.56 216	87.73 212

TAMVS		92.46 188	93.34 172	91.44 217	97.03 187	93.84 200	94.68 220	90.60 221	90.44 179	85.31 232	97.14 121	93.03 177	85.78 217	94.34 186	93.67 184	95.22 193	90.93 194

tfpn1000		94.36 167	93.33 174	95.56 172	98.09 124	98.07 95	97.08 167	97.78 93	94.02 127	89.16 219	91.38 199	80.56 214	92.54 173	96.76 119	98.09 64	98.69 66	94.40 167

Fast-Effi-MVS+-dtu		94.34 169	93.26 175	95.62 169	97.82 151	95.97 183	95.86 196	99.01 13	86.88 211	93.39 192	90.83 204	95.46 160	90.61 186	94.46 184	94.68 173	97.01 168	94.51 161

GA-MVS		94.18 173	92.98 176	95.58 170	97.36 176	96.42 171	96.21 192	95.86 177	90.29 180	95.08 157	96.19 138	85.37 194	92.82 164	94.01 191	94.14 177	96.16 186	94.41 165

CMPMVS	71.81 19	92.34 192	92.85 177	91.75 215	92.70 231	90.43 224	88.84 236	88.56 224	85.87 220	94.35 171	90.98 202	95.89 158	91.14 179	96.14 142	94.83 171	94.93 195	95.78 133
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PMMVS2		86.47 228	92.62 178	79.29 232	92.01 232	85.63 235	93.74 226	86.37 227	93.95 130	54.18 241	98.19 93	97.39 133	58.46 235	96.57 126	93.07 190	90.99 215	83.55 228

view800		94.54 163	92.55 179	96.86 127	98.28 101	98.22 79	97.97 112	97.62 101	92.10 159	94.19 175	85.52 219	81.33 212	94.61 139	97.41 89	98.51 38	98.50 80	94.72 157

test1235678		92.36 190	92.55 179	92.13 211	97.16 183	92.69 205	96.32 188	94.62 207	86.69 214	88.16 225	97.28 117	97.13 141	83.28 224	94.54 178	93.40 187	93.26 200	86.11 218

testmv		92.35 191	92.53 181	92.13 211	97.16 183	92.68 206	96.31 189	94.61 209	86.68 215	88.16 225	97.27 118	97.09 142	83.28 224	94.52 181	93.39 188	93.26 200	86.10 219

EPNet_dtu		93.45 183	92.51 182	94.55 189	98.39 89	91.67 217	95.46 208	97.50 110	86.56 216	97.38 69	93.52 180	94.20 173	85.82 216	93.31 196	92.53 194	92.72 207	95.76 134
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
N_pmnet		92.46 188	92.38 183	92.55 207	97.91 142	93.47 201	97.42 149	94.01 215	96.40 35	88.48 223	98.50 83	98.07 117	88.14 206	91.04 214	84.30 219	89.35 222	84.85 221

view600		94.36 167	92.33 184	96.73 130	98.14 118	98.03 100	97.88 119	97.36 135	91.61 163	94.29 172	84.38 221	82.08 209	94.31 146	97.05 103	98.75 32	98.42 89	94.41 165

thres600view7		94.34 169	92.31 185	96.70 132	98.19 111	98.12 91	97.85 122	97.45 123	91.49 166	93.98 179	84.27 222	82.02 210	94.24 147	97.04 104	98.76 31	98.49 82	94.47 163

new_pmnet		90.85 205	92.26 186	89.21 226	93.68 229	89.05 230	93.20 230	84.16 233	92.99 150	84.25 233	97.72 106	94.60 167	86.80 214	93.20 197	91.30 202	93.21 202	86.94 216

IB-MVS	92.44 16	93.33 184	92.15 187	94.70 186	97.42 173	96.39 175	95.57 203	94.67 205	86.40 219	93.59 186	78.28 235	95.76 159	89.59 197	95.88 154	95.98 144	97.39 138	96.34 119
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
tfpn_ndepth		93.27 185	92.11 188	94.61 187	96.96 189	97.93 113	96.87 172	97.49 113	90.91 174	87.89 227	85.98 217	83.53 201	89.77 195	95.91 153	97.31 94	98.67 68	93.25 179

thres400		94.04 176	91.94 189	96.50 143	97.98 138	97.82 127	97.66 128	96.96 147	90.96 172	94.20 173	83.24 224	82.82 207	93.80 156	96.50 128	98.09 64	98.38 91	94.15 171

thres200		93.98 178	91.90 190	96.40 148	97.66 158	98.12 91	97.20 162	97.45 123	90.16 183	93.82 180	83.08 225	83.74 200	93.80 156	97.04 104	97.48 86	98.49 82	93.70 175

conf200view11		93.79 180	91.75 191	96.17 153	97.52 165	98.15 87	97.48 141	97.48 115	87.65 202	93.42 189	83.03 226	84.12 196	92.62 166	97.04 104	98.09 64	98.52 77	94.17 168

tfpn200view9		93.80 179	91.75 191	96.20 151	97.52 165	98.15 87	97.48 141	97.47 117	87.65 202	93.56 187	83.03 226	84.12 196	92.62 166	97.04 104	98.09 64	98.52 77	94.17 168

tfpn111		93.73 181	91.63 193	96.17 153	97.52 165	98.15 87	97.48 141	97.48 115	87.65 202	93.42 189	82.19 229	84.12 196	92.62 166	97.04 104	98.09 64	98.52 77	94.17 168

test0.0.03 1		91.17 203	91.50 194	90.80 220	98.01 130	95.46 187	94.22 221	95.80 181	86.55 217	81.75 237	90.83 204	87.93 191	78.48 230	94.51 183	94.11 179	96.50 180	91.08 193

PMMVS		91.67 199	91.47 195	91.91 214	89.43 237	88.61 232	94.99 217	85.67 230	87.50 208	93.80 182	94.42 173	94.88 164	90.71 185	92.26 206	92.96 192	96.83 173	89.65 200

pmmvs3		91.20 202	91.40 196	90.96 219	91.71 235	91.08 220	95.41 211	81.34 234	87.36 209	94.57 167	95.02 159	94.30 171	90.42 187	94.28 187	89.26 210	92.30 212	88.49 208

thres100view900		92.93 186	90.89 197	95.31 175	97.52 165	96.82 167	96.41 183	95.08 195	87.65 202	93.56 187	83.03 226	84.12 196	91.12 180	94.53 179	96.91 104	98.17 104	93.21 181

MVSTER		91.97 194	90.31 198	93.91 195	96.81 192	96.91 162	94.22 221	95.64 186	84.98 222	92.98 201	93.42 181	72.56 226	86.64 215	95.11 170	93.89 183	97.16 162	95.31 143

CHOSEN 280x420		91.55 200	90.27 199	93.05 203	94.61 223	88.01 233	96.56 180	94.62 207	88.04 200	94.20 173	92.66 189	86.60 192	90.82 183	95.06 172	91.89 199	87.49 229	89.61 201

LP		92.03 193	90.19 200	94.17 194	94.52 224	93.87 199	96.79 173	95.05 196	93.58 136	95.62 144	95.68 148	83.37 203	91.78 175	90.73 217	86.99 215	91.27 214	87.09 215

testus		90.01 208	90.03 201	89.98 222	95.89 209	91.43 219	93.88 224	89.30 223	83.54 228	89.68 215	87.81 214	94.62 166	78.31 231	92.87 200	92.01 198	92.85 206	87.91 211

test12356		88.21 222	89.73 202	86.43 230	91.94 233	89.52 229	91.79 231	86.07 229	85.51 221	81.97 236	95.56 151	96.20 153	79.11 229	94.14 188	90.94 204	87.70 228	76.23 233

MVE	72.99 18	85.37 230	89.43 203	80.63 231	74.43 238	71.94 240	88.25 237	89.81 222	93.27 141	67.32 239	96.32 136	91.83 184	90.40 188	93.36 194	90.79 205	73.55 237	88.49 208
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tfpn		92.86 187	89.37 204	96.93 122	98.40 88	98.34 75	98.02 109	97.80 91	92.54 153	93.99 178	86.54 216	57.58 237	94.82 135	97.66 81	97.99 75	98.56 72	94.95 154

PatchT		91.40 201	88.54 205	94.74 185	91.48 236	92.18 210	97.42 149	97.51 108	84.96 223	96.44 109	94.16 174	75.47 221	92.92 161	90.22 218	92.22 195	92.66 210	90.56 196

CR-MVSNet		91.94 195	88.50 206	95.94 159	96.14 203	92.08 211	95.23 214	98.47 29	84.30 226	96.44 109	94.58 167	75.57 220	92.92 161	90.22 218	92.22 195	96.43 183	90.56 196

conf0.01		91.86 196	88.22 207	96.10 155	97.40 174	97.94 111	97.48 141	97.41 130	87.65 202	93.22 195	80.39 231	63.83 233	92.62 166	96.63 125	98.09 64	98.47 84	93.03 184

thresconf0.02		91.75 198	88.21 208	95.87 161	97.38 175	97.14 154	97.27 159	96.85 153	93.04 149	92.39 205	82.19 229	63.31 234	93.10 160	94.43 185	95.06 166	98.23 100	92.32 188

test-mter		89.16 215	88.14 209	90.37 221	94.79 222	91.05 221	93.60 227	85.26 231	81.65 231	88.32 224	92.22 192	79.35 218	87.03 212	92.28 204	90.12 207	93.19 203	90.29 198

gm-plane-assit		91.85 197	87.91 210	96.44 147	99.14 44	98.25 78	99.02 34	97.38 132	95.57 65	98.31 29	99.34 50	51.00 242	88.93 200	93.16 198	91.57 201	95.85 189	86.50 217

ADS-MVSNet		89.89 210	87.70 211	92.43 209	95.52 216	90.91 222	95.57 203	95.33 192	93.19 144	91.21 208	93.41 182	82.12 208	89.05 198	86.21 226	83.77 221	87.92 226	84.31 223

1111		88.65 219	87.69 212	89.78 225	98.84 66	94.02 197	95.79 198	98.19 59	91.57 164	82.27 234	98.19 93	53.19 240	74.80 232	94.98 173	93.04 191	88.80 224	88.82 204

FMVSNet5		89.65 213	87.60 213	92.04 213	95.63 215	96.61 168	94.82 219	94.75 202	80.11 235	87.72 228	77.73 236	73.81 224	83.81 223	95.64 159	96.08 142	95.49 192	93.21 181

test-LLR		89.77 212	87.47 214	92.45 208	98.01 130	89.77 226	93.25 228	95.80 181	81.56 232	89.19 217	92.08 194	79.59 216	85.77 219	91.47 212	89.04 213	92.69 208	88.75 205

TESTMET0.1,1		88.60 220	87.47 214	89.93 224	94.23 227	89.77 226	93.25 228	84.47 232	81.56 232	89.19 217	92.08 194	79.59 216	85.77 219	91.47 212	89.04 213	92.69 208	88.75 205

MDTV_nov1_ep13		90.30 207	87.32 216	93.78 196	96.00 206	92.97 203	95.46 208	95.39 191	88.61 192	95.41 148	94.45 172	80.39 215	89.87 194	86.58 225	83.54 222	90.56 216	84.71 222

GG-mvs-BLEND		61.03 233	87.02 217	30.71 235	0.74 242	90.01 225	78.90 240	0.74 239	84.56 224	9.46 242	79.17 234	90.69 188	1.37 239	91.74 209	89.13 212	93.04 205	83.83 227

conf0.002		91.12 204	86.87 218	96.08 156	97.35 177	97.89 120	97.48 141	97.38 132	87.65 202	93.19 196	79.38 233	57.48 238	92.62 166	96.56 127	96.64 116	98.46 85	92.50 187

tpm		89.84 211	86.81 219	93.36 199	96.60 197	91.92 215	95.02 216	97.39 131	86.79 212	96.54 105	95.03 158	69.70 229	87.66 208	88.79 221	86.19 217	86.95 231	89.27 203

MVS-HIRNet		88.72 217	86.49 220	91.33 218	91.81 234	85.66 234	87.02 238	96.25 167	81.48 234	94.82 162	96.31 137	92.14 183	90.32 189	87.60 223	83.82 220	87.74 227	78.42 232

EPMVS		89.28 214	86.28 221	92.79 206	96.01 205	92.00 214	95.83 197	95.85 179	90.78 176	91.00 209	94.58 167	74.65 222	88.93 200	85.00 229	82.88 225	89.09 223	84.09 225

RPMNet		90.52 206	86.27 222	95.48 173	95.95 208	92.08 211	95.55 206	98.12 66	84.30 226	95.60 146	87.49 215	72.78 225	91.24 178	87.93 222	89.34 209	96.41 184	89.98 199

PatchmatchNet		89.98 209	86.23 223	94.36 192	96.56 198	91.90 216	96.07 193	96.72 158	90.18 182	96.87 92	93.36 184	78.06 219	91.46 176	84.71 231	81.40 227	88.45 225	83.97 226
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CostFormer		89.06 216	85.65 224	93.03 205	95.88 210	92.40 208	95.30 213	95.86 177	86.49 218	93.12 199	93.40 183	74.18 223	88.25 205	82.99 232	81.46 226	89.77 220	88.66 207

E-PMN		86.94 225	85.10 225	89.09 228	95.77 212	83.54 237	89.89 234	86.55 226	92.18 158	87.34 229	94.02 175	83.42 202	89.63 196	93.32 195	77.11 232	85.33 232	72.09 234

tpmp4_e23		88.68 218	84.61 226	93.43 198	96.00 206	91.46 218	95.40 212	96.60 165	87.71 201	94.67 165	88.54 211	69.81 228	88.41 204	85.50 228	81.08 228	89.52 221	88.18 210

EMVS		86.63 227	84.48 227	89.15 227	95.51 217	83.66 236	90.19 233	86.14 228	91.78 162	88.68 221	93.83 179	81.97 211	89.05 198	92.76 202	76.09 233	85.31 233	71.28 235

dps		88.36 221	84.32 228	93.07 202	93.86 228	92.29 209	94.89 218	95.93 175	83.50 229	93.13 197	91.87 196	67.79 231	90.32 189	85.99 227	83.22 223	90.28 219	85.56 220

tpmrst		87.60 223	84.13 229	91.66 216	95.65 214	89.73 228	93.77 225	94.74 203	88.85 190	93.35 194	95.60 150	72.37 227	87.40 209	81.24 234	78.19 230	85.02 234	82.90 230

tpm cat1		87.19 224	82.78 230	92.33 210	95.66 213	90.61 223	94.19 223	95.27 193	86.97 210	94.38 170	90.91 203	69.40 230	87.21 210	79.57 235	77.82 231	87.25 230	84.18 224

test2356		85.48 229	81.66 231	89.94 223	95.36 220	88.71 231	91.69 232	92.78 216	78.28 237	86.79 230	85.80 218	58.29 236	80.44 228	89.39 220	89.17 211	92.60 211	81.98 231

DWT-MVSNet_training		86.69 226	81.24 232	93.05 203	95.31 221	92.06 213	95.75 200	91.51 219	84.32 225	94.49 168	83.46 223	55.37 239	90.81 184	82.76 233	83.19 224	90.45 218	87.52 213

testpf		81.59 231	76.31 233	87.75 229	93.50 230	83.16 238	89.19 235	95.94 174	73.85 238	90.39 210	80.32 232	61.17 235	73.99 234	76.52 236	75.82 234	83.50 235	83.33 229

.test1245		69.06 232	63.57 234	75.47 233	98.84 66	94.02 197	95.79 198	98.19 59	91.57 164	82.27 234	98.19 93	53.19 240	74.80 232	94.98 173	5.51 236	2.94 239	7.51 236

testmvs		4.99 234	6.88 235	2.78 237	1.73 240	2.04 243	3.10 243	1.71 237	7.27 239	3.92 244	12.18 238	6.71 243	3.31 238	6.94 237	5.51 236	2.94 239	7.51 236

test123		4.41 235	5.71 236	2.88 236	1.28 241	2.21 242	3.09 244	1.65 238	6.35 240	4.98 243	8.53 239	3.88 244	3.46 237	5.79 238	5.71 235	2.85 241	7.50 238

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

our_test_3					97.32 178	95.13 190	97.59 133

MTAPA										97.43 67		99.27 22

MTMP										97.63 55		99.03 46

Patchmatch-RL test							17.42 242

tmp_tt				45.72 234	60.00 239	38.74 241	45.50 241	12.18 236	79.58 236	68.42 238	67.62 237	65.04 232	22.12 236	84.83 230	78.72 229	66.08 238

XVS					99.48 18	98.76 39	99.22 24			96.40 113		98.78 76				98.94 48

X-MVStestdata					99.48 18	98.76 39	99.22 24			96.40 113		98.78 76				98.94 48

abl_6				96.45 146	97.79 156	97.28 147	97.16 165	96.16 169	89.92 187	95.72 140	91.59 197	97.16 139	94.37 145			97.51 130	95.49 140

mPP-MVS					99.58 6							98.98 51

NP-MVS									89.27 189

Patchmtry						92.70 204	95.23 214	98.47 29		96.44 109

DeepMVS_CX						72.99 239	80.14 239	37.34 235	83.46 230	60.13 240	84.40 220	85.48 193	86.93 213	87.22 224		79.61 236	87.32 214

High-resolution multi-view benchmark