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.

LTVRE_ROB	98.82 1	99.76 1	99.75 2	99.77 7	99.87 16	99.71 9	99.77 8	99.76 17	99.52 3	99.80 3	99.79 21	99.91 1	99.56 13	99.83 4	99.75 5	99.86 10	99.75 2
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		99.74 2	99.75 2	99.73 11	99.92 6	99.67 13	99.76 10	99.84 11	99.59 1	99.52 24	99.87 11	99.91 1	99.43 27	99.87 1	99.81 3	99.89 6	99.52 10

test_part1		99.72 3	99.79 1	99.64 12	99.95 2	99.88 1	99.71 16	99.83 12	99.58 2	99.48 28	99.79 21	99.78 9	98.98 66	99.86 2	99.85 1	99.88 8	99.82 1

SixPastTwentyTwo		99.70 4	99.59 5	99.82 2	99.93 4	99.80 2	99.86 2	99.87 6	98.87 12	99.79 5	99.85 14	99.33 64	99.74 5	99.85 3	99.82 2	99.74 23	99.63 5

v7n		99.68 5	99.61 4	99.76 8	99.89 13	99.74 8	99.87 1	99.82 13	99.20 7	99.71 6	99.96 1	99.73 13	99.76 3	99.58 18	99.59 14	99.52 42	99.46 15

anonymousdsp		99.64 6	99.55 7	99.74 10	99.87 16	99.56 20	99.82 3	99.73 20	98.54 17	99.71 6	99.92 4	99.84 6	99.61 9	99.70 7	99.63 7	99.69 28	99.64 3

UniMVSNet_ETH3D		99.61 7	99.59 5	99.63 14	99.96 1	99.70 10	99.53 33	99.86 8	99.28 6	99.48 28	99.44 51	99.86 4	99.01 64	99.78 5	99.76 4	99.90 2	99.33 20

WR-MVS		99.61 7	99.44 9	99.82 2	99.92 6	99.80 2	99.80 4	99.89 1	98.54 17	99.66 13	99.78 23	99.16 83	99.68 7	99.70 7	99.63 7	99.94 1	99.49 13

PEN-MVS		99.54 9	99.30 16	99.83 1	99.92 6	99.76 5	99.80 4	99.88 3	97.60 57	99.71 6	99.59 36	99.52 44	99.75 4	99.64 13	99.51 17	99.90 2	99.46 15

TDRefinement		99.54 9	99.50 8	99.60 17	99.70 62	99.35 39	99.77 8	99.58 44	99.40 5	99.28 48	99.66 27	99.41 54	99.55 15	99.74 6	99.65 6	99.70 25	99.25 24

DTE-MVSNet		99.52 11	99.27 17	99.82 2	99.93 4	99.77 4	99.79 6	99.87 6	97.89 39	99.70 11	99.55 44	99.21 75	99.77 2	99.65 11	99.43 20	99.90 2	99.36 18

PS-CasMVS		99.50 12	99.23 19	99.82 2	99.92 6	99.75 7	99.78 7	99.89 1	97.30 69	99.71 6	99.60 34	99.23 72	99.71 6	99.65 11	99.55 16	99.90 2	99.56 8

WR-MVS_H		99.48 13	99.23 19	99.76 8	99.91 10	99.76 5	99.75 11	99.88 3	97.27 72	99.58 17	99.56 40	99.24 71	99.56 13	99.60 16	99.60 13	99.88 8	99.58 7

pm-mvs1		99.47 14	99.38 10	99.57 20	99.82 24	99.49 24	99.63 22	99.65 32	98.88 11	99.31 42	99.85 14	99.02 101	99.23 44	99.60 16	99.58 15	99.80 16	99.22 31

MIMVSNet1		99.46 15	99.34 11	99.60 17	99.83 22	99.68 12	99.74 14	99.71 23	98.20 25	99.41 34	99.86 13	99.66 26	99.41 30	99.50 22	99.39 22	99.50 47	99.10 42

TransMVSNet (Re)		99.45 16	99.32 14	99.61 15	99.88 15	99.60 17	99.75 11	99.63 36	99.11 8	99.28 48	99.83 18	98.35 137	99.27 41	99.70 7	99.62 11	99.84 11	99.03 50

ACMH	97.81 6	99.44 17	99.33 12	99.56 21	99.81 27	99.42 32	99.73 15	99.58 44	99.02 9	99.10 73	99.41 55	99.69 19	99.60 10	99.45 26	99.26 32	99.55 38	99.05 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CP-MVSNet		99.39 18	99.04 28	99.80 6	99.91 10	99.70 10	99.75 11	99.88 3	96.82 93	99.68 12	99.32 58	98.86 110	99.68 7	99.57 19	99.47 18	99.89 6	99.52 10

COLMAP_ROB	98.29 2	99.37 19	99.25 18	99.51 28	99.74 52	99.12 67	99.56 30	99.39 81	98.96 10	99.17 60	99.44 51	99.63 34	99.58 11	99.48 24	99.27 31	99.60 34	98.81 74
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepC-MVS	97.88 4	99.33 20	99.15 23	99.53 27	99.73 57	99.05 75	99.49 37	99.40 79	98.42 20	99.55 21	99.71 25	99.89 3	99.49 19	99.14 38	98.81 60	99.54 39	99.02 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		99.32 21	99.33 12	99.31 52	99.87 16	99.65 16	99.63 22	99.75 19	97.76 41	97.29 189	99.87 11	99.63 34	99.52 16	99.66 10	99.63 7	99.77 20	99.12 37

UA-Net		99.30 22	99.22 21	99.39 39	99.94 3	99.66 15	98.91 103	99.86 8	97.74 47	98.74 110	99.00 85	99.60 39	99.17 50	99.50 22	99.39 22	99.70 25	99.64 3

ACMH+	97.53 7	99.29 23	99.20 22	99.40 38	99.81 27	99.22 55	99.59 27	99.50 62	98.64 16	98.29 143	99.21 70	99.69 19	99.57 12	99.53 21	99.33 27	99.66 29	98.81 74

Vis-MVSNet		99.25 24	99.32 14	99.17 62	99.65 73	99.55 22	99.63 22	99.33 97	98.16 26	99.29 45	99.65 30	99.77 10	97.56 136	99.44 28	99.14 37	99.58 35	99.51 12
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TranMVSNet+NR-MVSNet		99.23 25	98.91 35	99.61 15	99.81 27	99.45 28	99.47 39	99.68 26	97.28 71	99.39 35	99.54 45	99.08 97	99.45 22	99.09 44	98.84 57	99.83 12	99.04 48

CSCG		99.23 25	99.15 23	99.32 51	99.83 22	99.45 28	98.97 95	99.21 117	98.83 13	99.04 82	99.43 53	99.64 32	99.26 42	98.85 70	98.20 98	99.62 32	99.62 6

Gipuma		99.22 27	98.86 38	99.64 12	99.70 62	99.24 50	99.17 79	99.63 36	99.52 3	99.89 1	96.54 167	99.14 87	99.93 1	99.42 29	99.15 36	99.52 42	99.04 48
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tfpnnormal		99.19 28	98.90 36	99.54 24	99.81 27	99.55 22	99.60 26	99.54 53	98.53 19	99.23 52	98.40 103	98.23 140	99.40 31	99.29 33	99.36 25	99.63 31	98.95 62

Baseline_NR-MVSNet		99.18 29	98.87 37	99.54 24	99.74 52	99.56 20	99.36 51	99.62 41	96.53 113	99.29 45	99.85 14	98.64 127	99.40 31	99.03 55	99.63 7	99.83 12	98.86 70

thisisatest0515		99.16 30	98.94 33	99.41 34	99.75 46	99.43 31	99.36 51	99.63 36	97.68 53	99.35 37	99.31 59	98.90 107	99.09 58	98.95 60	99.20 33	99.27 79	99.11 38

APDe-MVS		99.15 31	98.95 30	99.39 39	99.77 37	99.28 47	99.52 34	99.54 53	97.22 76	99.06 77	99.20 71	99.64 32	99.05 62	99.14 38	99.02 46	99.39 60	99.17 35

FC-MVSNet-train		99.13 32	99.05 27	99.21 57	99.87 16	99.57 19	99.67 17	99.60 43	96.75 99	98.28 144	99.48 48	99.52 44	98.10 115	99.47 25	99.37 24	99.76 22	99.21 32

NR-MVSNet		99.10 33	98.68 53	99.58 19	99.89 13	99.23 52	99.35 54	99.63 36	96.58 107	99.36 36	99.05 79	98.67 125	99.46 20	99.63 14	98.73 71	99.80 16	98.88 69

DVP-MVS		99.09 34	99.07 26	99.12 70	99.55 95	99.40 34	99.36 51	99.44 78	97.75 44	98.23 147	99.23 67	99.80 7	98.97 67	99.08 46	98.96 47	99.19 87	99.25 24

UniMVSNet (Re)		99.08 35	98.69 51	99.54 24	99.75 46	99.33 42	99.29 62	99.64 35	96.75 99	99.48 28	99.30 61	98.69 121	99.26 42	98.94 62	98.76 67	99.78 19	99.02 52

ACMMPR		99.05 36	98.72 47	99.44 29	99.79 32	99.12 67	99.35 54	99.56 47	97.74 47	99.21 53	97.72 129	99.55 42	99.29 39	98.90 68	98.81 60	99.41 59	99.19 33

DU-MVS		99.04 37	98.59 57	99.56 21	99.74 52	99.23 52	99.29 62	99.63 36	96.58 107	99.55 21	99.05 79	98.68 123	99.36 35	99.03 55	98.60 78	99.77 20	98.97 57

TSAR-MVS + MP.		99.02 38	98.95 30	99.11 73	99.23 150	98.79 111	99.51 35	98.73 158	97.50 61	98.56 120	99.03 82	99.59 40	99.16 52	99.29 33	99.17 35	99.50 47	99.24 28
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v10		99.01 39	98.66 54	99.41 34	99.52 106	99.39 35	99.57 29	99.66 30	97.59 58	99.32 41	99.88 9	99.23 72	99.50 18	97.77 133	97.98 107	98.92 120	98.78 79

EG-PatchMatch MVS		99.01 39	98.77 43	99.28 56	99.64 76	98.90 104	98.81 115	99.27 108	96.55 111	99.71 6	99.31 59	99.66 26	99.17 50	99.28 35	99.11 38	99.10 94	98.57 95

PVSNet_Blended_VisFu		98.98 41	98.79 41	99.21 57	99.76 43	99.34 40	99.35 54	99.35 93	97.12 82	99.46 31	99.56 40	98.89 108	98.08 118	99.05 49	98.58 80	99.27 79	98.98 56

HFP-MVS		98.97 42	98.70 49	99.29 54	99.67 67	98.98 87	99.13 84	99.53 56	97.76 41	98.90 96	98.07 117	99.50 50	99.14 56	98.64 81	98.78 64	99.37 62	99.18 34

UniMVSNet_NR-MVSNet		98.97 42	98.46 68	99.56 21	99.76 43	99.34 40	99.29 62	99.61 42	96.55 111	99.55 21	99.05 79	97.96 148	99.36 35	98.84 71	98.50 86	99.81 15	98.97 57

SED-MVS		98.94 44	98.95 30	98.91 95	99.43 122	99.38 37	99.12 86	99.46 72	97.05 85	98.43 135	99.23 67	99.79 8	97.99 121	99.05 49	98.94 49	99.05 106	99.23 29

ACMMP_NAP		98.94 44	98.72 47	99.21 57	99.67 67	99.08 70	99.26 67	99.39 81	96.84 90	98.88 100	98.22 110	99.68 22	98.82 75	99.06 48	98.90 52	99.25 82	99.25 24

zzz-MVS		98.94 44	98.57 60	99.37 46	99.77 37	99.15 64	99.24 70	99.55 49	97.38 67	99.16 63	96.64 163	99.69 19	99.15 54	99.09 44	98.92 51	99.37 62	99.11 38

v1144		98.94 44	98.53 63	99.42 33	99.62 80	99.03 81	99.58 28	99.36 90	97.99 30	99.49 27	99.91 8	99.20 77	99.51 17	97.61 138	97.85 114	98.95 115	98.10 134

v8		98.94 44	98.60 55	99.35 49	99.54 99	99.39 35	99.55 31	99.67 29	97.48 62	99.13 69	99.81 19	99.10 93	99.39 33	97.86 128	97.89 112	98.81 129	98.66 86

SteuartSystems-ACMMP		98.94 44	98.52 64	99.43 32	99.79 32	99.13 66	99.33 58	99.55 49	96.17 129	99.04 82	97.53 135	99.65 30	99.46 20	99.04 54	98.76 67	99.44 54	99.35 19
Skip Steuart: Steuart Systems R&D Blog.
v1192		98.91 50	98.48 67	99.41 34	99.61 84	99.03 81	99.64 19	99.25 112	97.91 36	99.58 17	99.92 4	99.07 99	99.45 22	97.55 142	97.68 128	98.93 117	98.23 124

FMVSNet1		98.90 51	99.10 25	98.67 119	99.54 99	99.48 25	99.22 73	99.66 30	98.39 23	97.50 177	99.66 27	99.04 100	96.58 158	99.05 49	99.03 43	99.52 42	99.08 44

ACMM	96.66 11	98.90 51	98.44 72	99.44 29	99.74 52	98.95 93	99.47 39	99.55 49	97.66 55	99.09 74	96.43 169	99.41 54	99.35 37	98.95 60	98.67 74	99.45 52	99.03 50
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		98.89 53	98.79 41	98.99 88	99.82 24	99.41 33	99.18 78	99.31 103	96.92 87	98.54 123	98.58 100	98.84 113	97.46 138	99.45 26	99.29 29	99.65 30	99.08 44

v1921920		98.89 53	98.46 68	99.39 39	99.58 88	99.04 79	99.64 19	99.17 123	97.91 36	99.64 15	99.92 4	98.99 105	99.44 25	97.44 149	97.57 137	98.84 127	98.35 114

v144192		98.88 55	98.46 68	99.37 46	99.56 94	99.03 81	99.61 25	99.26 109	97.79 40	99.58 17	99.88 9	99.11 92	99.43 27	97.38 154	97.61 133	98.80 131	98.43 109

SMA-MVS		98.87 56	98.73 46	99.04 81	99.72 58	99.05 75	98.64 126	99.17 123	96.31 124	98.80 105	99.07 77	99.70 18	98.67 83	98.93 65	98.82 58	99.23 85	99.23 29
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ACMP	96.54 13	98.87 56	98.40 77	99.41 34	99.74 52	98.88 105	99.29 62	99.50 62	96.85 89	98.96 88	97.05 150	99.66 26	99.43 27	98.98 59	98.60 78	99.52 42	98.81 74
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DCV-MVSNet		98.86 58	98.57 60	99.19 60	99.86 20	99.67 13	99.39 47	99.71 23	97.53 60	98.69 113	95.85 180	98.48 131	97.75 130	99.57 19	99.41 21	99.72 24	99.48 14

v1240		98.86 58	98.41 76	99.38 44	99.59 86	99.05 75	99.65 18	99.14 128	97.68 53	99.66 13	99.93 3	98.72 120	99.45 22	97.38 154	97.72 126	98.79 132	98.35 114

CP-MVS		98.86 58	98.43 75	99.36 48	99.68 65	98.97 91	99.19 76	99.46 72	96.60 105	99.20 54	97.11 149	99.51 48	99.15 54	98.92 66	98.82 58	99.45 52	99.08 44

v2v482		98.85 61	98.40 77	99.38 44	99.65 73	98.98 87	99.55 31	99.39 81	97.92 35	99.35 37	99.85 14	99.14 87	99.39 33	97.50 144	97.78 117	98.98 112	97.60 148

DPE-MVS		98.84 62	98.69 51	99.00 85	99.05 168	99.26 48	99.19 76	99.35 93	95.85 137	98.74 110	99.27 63	99.66 26	98.30 107	98.90 68	98.93 50	99.37 62	99.00 54

OPM-MVS		98.84 62	98.59 57	99.12 70	99.52 106	98.50 135	99.13 84	99.22 115	97.76 41	98.76 107	98.70 94	99.61 37	98.90 70	98.67 79	98.37 92	99.19 87	98.57 95

test20.03		98.84 62	98.74 45	98.95 91	99.77 37	99.33 42	99.21 75	99.46 72	97.29 70	98.88 100	99.65 30	99.10 93	97.07 150	99.11 41	98.76 67	99.32 72	97.98 138

casdiffmvs		98.84 62	98.75 44	98.94 94	99.75 46	99.21 56	99.33 58	99.04 139	98.04 28	97.46 180	99.72 24	99.72 15	98.60 87	98.30 103	98.37 92	99.48 49	97.92 140

LGP-MVS_train		98.84 62	98.33 83	99.44 29	99.78 35	98.98 87	99.39 47	99.55 49	95.41 145	98.90 96	97.51 136	99.68 22	99.44 25	99.03 55	98.81 60	99.57 36	98.91 65

RPSCF		98.84 62	98.81 40	98.89 97	99.37 129	98.95 93	98.51 138	98.85 151	97.73 49	98.33 140	98.97 87	99.14 87	98.95 68	99.18 37	98.68 73	99.31 73	98.99 55

ACMMP		98.82 68	98.33 83	99.39 39	99.77 37	99.14 65	99.37 50	99.54 53	96.47 117	99.03 84	96.26 173	99.52 44	99.28 40	98.92 66	98.80 63	99.37 62	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
V42		98.81 69	98.49 66	99.18 61	99.52 106	98.92 99	99.50 36	99.29 105	97.43 65	98.97 86	99.81 19	99.00 104	99.30 38	97.93 124	98.01 105	98.51 155	98.34 118

LS3D		98.79 70	98.52 64	99.12 70	99.64 76	99.09 69	99.24 70	99.46 72	97.75 44	98.93 94	97.47 137	98.23 140	97.98 122	99.36 30	99.30 28	99.46 50	98.42 110

MP-MVS		98.78 71	98.30 85	99.34 50	99.75 46	98.95 93	99.26 67	99.46 72	95.78 140	99.17 60	96.98 154	99.72 15	99.06 61	98.84 71	98.74 70	99.33 69	99.11 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.77 72	98.45 71	99.15 66	99.68 65	98.94 97	99.49 37	99.31 103	97.95 32	98.91 95	99.65 30	99.62 36	99.18 47	97.99 122	97.64 132	98.33 160	97.38 154

SD-MVS		98.73 73	98.54 62	98.95 91	99.14 159	98.76 114	98.46 142	99.14 128	97.71 51	98.56 120	98.06 119	99.61 37	98.85 74	98.56 83	97.74 123	99.54 39	99.32 21
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MSP-MVS		98.72 74	98.60 55	98.87 99	99.67 67	99.33 42	99.15 81	99.26 109	96.99 86	97.90 167	98.19 112	99.74 12	98.29 108	97.69 136	98.96 47	98.96 113	99.27 23

PGM-MVS		98.69 75	98.09 102	99.39 39	99.76 43	99.07 71	99.30 61	99.51 60	94.76 156	99.18 59	96.70 161	99.51 48	99.20 45	98.79 75	98.71 72	99.39 60	99.11 38

pmmvs-eth3d		98.68 76	98.14 98	99.29 54	99.49 111	98.45 138	99.45 43	99.38 86	97.21 77	99.50 26	99.65 30	99.21 75	99.16 52	97.11 161	97.56 138	98.79 132	97.82 144

EU-MVSNet		98.68 76	98.94 33	98.37 139	99.14 159	98.74 116	99.64 19	98.20 184	98.21 24	99.17 60	99.66 27	99.18 80	99.08 59	99.11 41	98.86 53	95.00 195	98.83 71

PMVS	92.51 17	98.66 78	98.86 38	98.43 134	99.26 145	98.98 87	98.60 132	98.59 168	97.73 49	99.45 32	99.38 56	98.54 130	95.24 176	99.62 15	99.61 12	99.42 56	98.17 131
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepC-MVS_fast	97.38 8	98.65 79	98.34 82	99.02 84	99.33 133	98.29 145	98.99 93	98.71 160	97.40 66	99.31 42	98.20 111	99.40 57	98.54 95	98.33 100	98.18 99	99.23 85	98.58 93
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	98.16 3	98.65 79	98.35 81	99.00 85	99.59 86	98.70 119	98.90 107	99.36 90	97.97 31	99.09 74	96.55 166	99.09 95	97.97 123	98.70 78	98.65 76	99.12 93	98.81 74

TSAR-MVS + ACMM		98.64 81	98.58 59	98.72 113	99.17 157	98.63 124	98.69 122	99.10 135	97.69 52	98.30 142	99.12 75	99.38 59	98.70 82	98.45 86	97.51 140	98.35 159	99.25 24

DELS-MVS		98.63 82	98.70 49	98.55 130	99.24 149	99.04 79	98.96 96	98.52 171	96.83 92	98.38 137	99.58 38	99.68 22	97.06 151	98.74 77	98.44 88	99.10 94	98.59 92
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
QAPM		98.62 83	98.40 77	98.89 97	99.57 93	98.80 110	98.63 127	99.35 93	96.82 93	98.60 117	98.85 92	99.08 97	98.09 117	98.31 101	98.21 96	99.08 99	98.72 81

EPP-MVSNet		98.61 84	98.19 94	99.11 73	99.86 20	99.60 17	99.44 44	99.53 56	97.37 68	96.85 193	98.69 95	93.75 180	99.18 47	99.22 36	99.35 26	99.82 14	99.32 21

3Dnovator+	97.85 5	98.61 84	98.14 98	99.15 66	99.62 80	98.37 143	99.10 87	99.51 60	98.04 28	98.98 85	96.07 177	98.75 119	98.55 93	98.51 85	98.40 89	99.17 89	98.82 72

X-MVS		98.59 86	97.99 108	99.30 53	99.75 46	99.07 71	99.17 79	99.50 62	96.62 103	98.95 90	93.95 195	99.37 60	99.11 57	98.94 62	98.86 53	99.35 67	99.09 43

MVS_111021_HR		98.58 87	98.26 88	98.96 90	99.32 136	98.81 108	98.48 140	98.99 144	96.81 95	99.16 63	98.07 117	99.23 72	98.89 72	98.43 88	98.27 95	98.90 122	98.24 123

MVS_0304		98.57 88	98.36 80	98.82 106	99.72 58	98.94 97	98.92 101	99.14 128	96.76 98	99.33 40	98.30 107	99.73 13	96.74 154	98.05 119	97.79 116	99.08 99	98.97 57

PM-MVS		98.57 88	98.24 90	98.95 91	99.26 145	98.59 127	99.03 90	98.74 157	96.84 90	99.44 33	99.13 74	98.31 139	98.75 80	98.03 120	98.21 96	98.48 156	98.58 93

PHI-MVS		98.57 88	98.20 93	99.00 85	99.48 113	98.91 101	98.68 123	99.17 123	94.97 152	99.27 50	98.33 105	99.33 64	98.05 119	98.82 73	98.62 77	99.34 68	98.38 112

HPM-MVS++		98.56 91	98.08 103	99.11 73	99.53 102	98.61 126	99.02 92	99.32 101	96.29 126	99.06 77	97.23 144	99.50 50	98.77 78	98.15 115	97.90 110	98.96 113	98.90 66

TSAR-MVS + GP.		98.54 92	98.29 87	98.82 106	99.28 143	98.59 127	97.73 181	99.24 114	95.93 135	98.59 118	99.07 77	99.17 81	98.86 73	98.44 87	98.10 101	99.26 81	98.72 81

UGNet		98.52 93	99.00 29	97.96 160	99.58 88	99.26 48	99.27 66	99.40 79	98.07 27	98.28 144	98.76 93	99.71 17	92.24 203	98.94 62	98.85 55	99.00 111	99.43 17
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
Anonymous20231206		98.50 94	98.03 105	99.05 79	99.50 109	99.01 84	99.15 81	99.26 109	96.38 122	99.12 71	99.50 47	99.12 90	98.60 87	97.68 137	97.24 151	98.66 140	97.30 158

CLD-MVS		98.48 95	98.15 97	98.86 102	99.53 102	98.35 144	98.55 135	97.83 193	96.02 134	98.97 86	99.08 76	99.75 11	99.03 63	98.10 118	97.33 147	99.28 77	98.44 108
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CANet		98.47 96	98.30 85	98.67 119	99.65 73	98.87 106	98.82 114	99.01 142	96.14 130	99.29 45	98.86 90	99.01 102	96.54 159	98.36 94	98.08 102	98.72 136	98.80 78

APD-MVS		98.47 96	97.97 109	99.05 79	99.64 76	98.91 101	98.94 98	99.45 77	94.40 167	98.77 106	97.26 143	99.41 54	98.21 111	98.67 79	98.57 83	99.31 73	98.57 95
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Vis-MVSNet (Re-imp)		98.46 98	98.23 91	98.73 112	99.81 27	99.29 46	98.79 117	99.50 62	96.20 128	96.03 199	98.29 108	96.98 163	98.54 95	99.11 41	99.08 39	99.70 25	98.62 89

Fast-Effi-MVS+		98.42 99	97.79 115	99.15 66	99.69 64	98.66 122	98.94 98	99.68 26	94.49 161	99.05 79	98.06 119	98.86 110	98.48 98	98.18 112	97.78 117	99.05 106	98.54 100

ETV-MVS		98.41 100	97.76 116	99.17 62	99.58 88	99.01 84	98.91 103	99.50 62	93.33 186	99.31 42	96.82 158	98.42 135	98.17 114	99.13 40	99.08 39	99.54 39	98.56 98

MVS_111021_LR		98.39 101	98.11 100	98.71 115	99.08 165	98.54 133	98.23 162	98.56 170	96.57 109	99.13 69	98.41 102	98.86 110	98.65 85	98.23 110	97.87 113	98.65 142	98.28 120

pmmvs5		98.37 102	97.81 114	99.03 82	99.46 115	98.97 91	99.03 90	98.96 146	95.85 137	99.05 79	99.45 50	98.66 126	98.79 77	96.02 178	97.52 139	98.87 124	98.21 127

OMC-MVS		98.35 103	98.10 101	98.64 125	98.85 176	97.99 165	98.56 134	98.21 182	97.26 74	98.87 102	98.54 101	99.27 70	98.43 100	98.34 98	97.66 129	98.92 120	97.65 147

canonicalmvs		98.34 104	97.92 111	98.83 104	99.45 117	99.21 56	98.37 149	99.53 56	97.06 84	97.74 171	96.95 156	95.05 177	98.36 103	98.77 76	98.85 55	99.51 46	99.53 9

CHOSEN 1792x2688		98.31 105	98.02 106	98.66 121	99.55 95	98.57 130	99.38 49	99.25 112	98.42 20	98.48 131	99.58 38	99.85 5	98.31 106	95.75 181	95.71 176	96.96 183	98.27 122

xxxxxxxxxxxxxcwj		98.28 106	98.23 91	98.35 140	99.43 122	98.42 141	97.05 203	99.09 136	96.42 119	98.13 153	97.73 127	99.65 30	97.22 144	98.36 94	98.38 90	99.16 91	98.62 89

CPTT-MVS		98.28 106	97.51 128	99.16 64	99.54 99	98.78 112	98.96 96	99.36 90	96.30 125	98.89 99	93.10 200	99.30 67	99.20 45	98.35 97	97.96 108	99.03 109	98.82 72

TinyColmap		98.27 108	97.62 125	99.03 82	99.29 141	97.79 174	98.92 101	98.95 147	97.48 62	99.52 24	98.65 97	97.86 150	98.90 70	98.34 98	97.27 149	98.64 143	95.97 178

diffmvs		98.26 109	98.16 95	98.39 136	99.61 84	98.78 112	98.79 117	98.61 166	97.94 33	97.11 192	99.51 46	99.52 44	97.61 134	96.55 170	96.93 157	98.61 145	97.87 142

USDC		98.26 109	97.57 126	99.06 76	99.42 126	97.98 167	98.83 111	98.85 151	97.57 59	99.59 16	99.15 73	98.59 128	98.99 65	97.42 150	96.08 175	98.69 139	96.23 176

SF-MVS		98.25 111	98.16 95	98.35 140	99.43 122	98.42 141	97.05 203	99.09 136	96.42 119	98.13 153	97.73 127	99.20 77	97.22 144	98.36 94	98.38 90	99.16 91	98.62 89

MCST-MVS		98.25 111	97.57 126	99.06 76	99.53 102	98.24 151	98.63 127	99.17 123	95.88 136	98.58 119	96.11 175	99.09 95	99.18 47	97.58 141	97.31 148	99.25 82	98.75 80

IterMVS-LS		98.23 113	97.66 121	98.90 96	99.63 79	99.38 37	99.07 88	99.48 68	97.75 44	98.81 104	99.37 57	94.57 179	97.88 127	96.54 171	97.04 154	98.53 152	98.97 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAPA-MVS	96.65 12	98.23 113	97.96 110	98.55 130	98.81 178	98.16 155	98.40 146	97.94 191	96.68 101	98.49 129	98.61 98	98.89 108	98.57 91	97.45 147	97.59 135	99.09 98	98.35 114
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNVR-MVS		98.22 115	97.76 116	98.76 110	99.33 133	98.26 149	98.48 140	98.88 150	96.22 127	98.47 133	95.79 181	99.33 64	98.35 104	98.37 93	97.99 106	99.03 109	98.38 112

IS_MVSNet		98.20 116	98.00 107	98.44 133	99.82 24	99.48 25	99.25 69	99.56 47	95.58 142	93.93 211	97.56 134	96.52 167	98.27 109	99.08 46	99.20 33	99.80 16	98.56 98

DeepPCF-MVS	96.68 10	98.20 116	98.26 88	98.12 154	97.03 214	98.11 158	98.44 144	97.70 195	96.77 97	98.52 125	98.91 88	99.17 81	98.58 90	98.41 90	98.02 104	98.46 157	98.46 105

MSDG		98.20 116	97.88 113	98.56 129	99.33 133	97.74 175	98.27 159	98.10 185	97.20 79	98.06 158	98.59 99	99.16 83	98.76 79	98.39 91	97.71 127	98.86 126	96.38 173

testgi		98.18 119	98.44 72	97.89 162	99.78 35	99.23 52	98.78 119	99.21 117	97.26 74	97.41 182	97.39 140	99.36 63	92.85 200	98.82 73	98.66 75	99.31 73	98.35 114

CS-MVS		98.13 120	97.25 138	99.16 64	99.71 61	99.44 30	98.80 116	99.49 67	93.16 189	99.19 56	93.95 195	98.47 133	98.19 113	98.30 103	98.78 64	99.56 37	98.66 86

Effi-MVS+		98.11 121	97.29 134	99.06 76	99.62 80	98.55 131	98.16 165	99.80 14	94.64 157	99.15 67	96.59 164	97.43 156	98.44 99	97.46 146	97.90 110	99.17 89	98.45 107

HyFIR lowres test		98.08 122	97.16 144	99.14 69	99.72 58	98.91 101	99.41 45	99.58 44	97.93 34	98.82 103	99.24 65	95.81 173	98.73 81	95.16 192	95.13 185	98.60 147	97.94 139

EIA-MVS		98.03 123	97.20 141	98.99 88	99.66 70	99.24 50	98.53 137	99.52 59	91.56 203	99.25 51	95.34 185	98.78 116	97.72 131	98.38 92	98.58 80	99.28 77	98.54 100

train_agg		97.99 124	97.26 135	98.83 104	99.43 122	98.22 153	98.91 103	99.07 138	94.43 165	97.96 164	96.42 170	99.30 67	98.81 76	97.39 152	96.62 163	98.82 128	98.47 103

MSLP-MVS++		97.99 124	97.64 124	98.40 135	98.91 174	98.47 137	97.12 201	98.78 155	96.49 115	98.48 131	93.57 198	99.12 90	98.51 97	98.31 101	98.58 80	98.58 149	98.95 62

CDPH-MVS		97.99 124	97.23 139	98.87 99	99.58 88	98.29 145	98.83 111	99.20 119	93.76 180	98.11 156	96.11 175	99.16 83	98.23 110	97.80 131	97.22 152	99.29 76	98.28 120

FMVSNet2		97.94 127	98.08 103	97.77 168	98.71 182	99.21 56	98.62 129	99.47 69	96.62 103	96.37 198	99.20 71	97.70 152	94.39 187	97.39 152	97.75 122	99.08 99	98.70 83

PVSNet_BlendedMVS		97.93 128	97.66 121	98.25 147	99.30 138	98.67 120	98.31 154	97.95 189	94.30 171	98.75 108	97.63 131	98.76 117	96.30 166	98.29 105	97.78 117	98.93 117	98.18 129

PVSNet_Blended		97.93 128	97.66 121	98.25 147	99.30 138	98.67 120	98.31 154	97.95 189	94.30 171	98.75 108	97.63 131	98.76 117	96.30 166	98.29 105	97.78 117	98.93 117	98.18 129

OpenMVS	97.26 9	97.88 130	97.17 143	98.70 116	99.50 109	98.55 131	98.34 152	99.11 133	93.92 178	98.90 96	95.04 189	98.23 140	97.38 141	98.11 117	98.12 100	98.95 115	98.23 124

pmmvs4		97.87 131	97.02 148	98.86 102	99.20 151	97.68 178	98.89 108	99.03 140	96.57 109	99.12 71	99.03 82	97.26 160	98.42 101	95.16 192	96.34 167	98.53 152	97.10 165

NCCC		97.84 132	96.96 150	98.87 99	99.39 128	98.27 148	98.46 142	99.02 141	96.78 96	98.73 112	91.12 203	98.91 106	98.57 91	97.83 130	97.49 141	99.04 108	98.33 119

Effi-MVS+-dtu		97.78 133	97.37 132	98.26 145	99.25 147	98.50 135	97.89 175	99.19 122	94.51 159	98.16 151	95.93 178	98.80 115	95.97 169	98.27 109	97.38 144	99.10 94	98.23 124

MDA-MVSNet-bldmvs		97.75 134	97.26 135	98.33 142	99.35 132	98.45 138	99.32 60	97.21 199	97.90 38	99.05 79	99.01 84	96.86 165	99.08 59	99.36 30	92.97 195	95.97 192	96.25 175

CDS-MVSNet		97.75 134	97.68 120	97.83 166	99.08 165	98.20 154	98.68 123	98.61 166	95.63 141	97.80 169	99.24 65	96.93 164	94.09 192	97.96 123	97.82 115	98.71 137	97.99 136
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		97.75 134	97.26 135	98.32 144	98.58 190	97.86 170	97.80 177	98.09 186	96.49 115	98.49 129	96.15 174	98.08 143	98.35 104	98.00 121	97.03 155	98.61 145	97.21 162

PLC	95.63 15	97.73 137	97.01 149	98.57 128	99.10 162	97.80 173	97.72 182	98.77 156	96.34 123	98.38 137	93.46 199	98.06 144	98.66 84	97.90 126	97.65 131	98.77 134	97.90 141
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_Test		97.69 138	97.15 145	98.33 142	99.27 144	98.43 140	98.25 160	99.29 105	95.00 151	97.39 184	98.86 90	98.00 147	97.14 148	95.38 187	96.22 169	98.62 144	98.15 133

GBi-Net		97.69 138	97.75 118	97.62 169	98.71 182	99.21 56	98.62 129	99.33 97	94.09 174	95.60 201	98.17 114	95.97 170	94.39 187	99.05 49	99.03 43	99.08 99	98.70 83

test1		97.69 138	97.75 118	97.62 169	98.71 182	99.21 56	98.62 129	99.33 97	94.09 174	95.60 201	98.17 114	95.97 170	94.39 187	99.05 49	99.03 43	99.08 99	98.70 83

CANet_DTU		97.65 141	97.50 130	97.82 167	99.19 154	98.08 160	98.41 145	98.67 162	94.40 167	99.16 63	98.32 106	98.69 121	93.96 194	97.87 127	97.61 133	97.51 179	97.56 150

IterMVS-SCA-FT		97.63 142	96.86 152	98.52 132	99.48 113	98.71 118	98.84 110	98.91 148	96.44 118	99.16 63	99.56 40	95.54 175	97.95 124	95.68 184	95.07 188	96.76 184	97.03 168

TSAR-MVS + COLMAP		97.62 143	97.31 133	97.98 158	98.47 196	97.39 181	98.29 156	98.25 181	96.68 101	97.54 176	98.87 89	98.04 146	97.08 149	96.78 165	96.26 168	98.26 163	97.12 164

MS-PatchMatch		97.60 144	97.22 140	98.04 157	98.67 186	97.18 185	97.91 173	98.28 180	95.82 139	98.34 139	97.66 130	98.38 136	97.77 129	97.10 162	97.25 150	97.27 181	97.18 163

PCF-MVS	95.58 16	97.60 144	96.67 153	98.69 117	99.44 120	98.23 152	98.37 149	98.81 153	93.01 192	98.22 148	97.97 123	99.59 40	98.20 112	95.72 183	95.08 186	99.08 99	97.09 167
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		97.58 146	96.65 156	98.66 121	99.30 138	97.99 165	97.88 176	98.65 163	94.58 158	98.66 114	94.65 192	99.15 86	98.59 89	96.10 176	95.59 177	98.90 122	98.50 102

DI_MVS_plusplus_trai		97.57 147	96.55 158	98.77 109	99.55 95	98.76 114	99.22 73	99.00 143	97.08 83	97.95 165	97.78 126	91.35 187	98.02 120	96.20 174	96.81 159	98.87 124	97.87 142

AdaColmap		97.57 147	96.57 157	98.74 111	99.25 147	98.01 163	98.36 151	98.98 145	94.44 164	98.47 133	92.44 201	97.91 149	98.62 86	98.19 111	97.74 123	98.73 135	97.28 159

baseline		97.50 149	97.51 128	97.50 173	99.18 155	97.38 182	98.00 169	98.00 188	96.52 114	97.49 178	99.28 62	99.43 53	95.31 175	95.27 189	96.22 169	96.99 182	98.47 103

IterMVS		97.40 150	96.67 153	98.25 147	99.45 117	98.66 122	98.87 109	98.73 158	96.40 121	98.94 93	99.56 40	95.26 176	97.58 135	95.38 187	94.70 190	95.90 193	96.72 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CVMVSNet		97.38 151	97.39 131	97.37 176	98.58 190	97.72 176	98.70 121	97.42 197	97.21 77	95.95 200	99.46 49	93.31 183	97.38 141	97.60 139	97.78 117	96.18 189	98.66 86

new-patchmatchnet		97.26 152	96.12 166	98.58 127	99.55 95	98.63 124	99.14 83	97.04 201	98.80 14	99.19 56	99.92 4	99.19 79	98.92 69	95.51 186	87.04 203	97.66 176	93.73 194

MIMVSNet		97.24 153	97.15 145	97.36 177	99.03 169	98.52 134	98.55 135	99.73 20	94.94 155	94.94 208	97.98 122	97.37 158	93.66 195	97.60 139	97.34 146	98.23 166	96.29 174

PatchMatch-RL		97.24 153	96.45 161	98.17 151	98.70 185	97.57 180	97.31 196	98.48 174	94.42 166	98.39 136	95.74 182	96.35 169	97.88 127	97.75 134	97.48 142	98.24 165	95.87 179

thisisatest0530		97.20 155	95.95 170	98.66 121	99.46 115	98.84 107	98.29 156	99.20 119	94.51 159	98.25 146	97.42 138	85.03 202	97.68 132	98.43 88	98.56 84	99.08 99	98.89 68

tttt0517		97.18 156	95.92 171	98.65 124	99.49 111	98.92 99	98.29 156	99.20 119	94.37 169	98.17 149	97.37 141	84.72 205	97.68 132	98.55 84	98.56 84	99.10 94	98.95 62

MDTV_nov1_ep13_2view		97.12 157	96.19 165	98.22 150	99.13 161	98.05 161	99.24 70	99.47 69	97.61 56	99.15 67	99.59 36	99.01 102	98.40 102	94.87 195	90.14 198	93.91 198	94.04 193

MAR-MVS		97.12 157	96.28 164	98.11 155	98.94 172	97.22 184	97.65 186	99.38 86	90.93 209	98.15 152	95.17 187	97.13 161	96.48 162	97.71 135	97.40 143	98.06 169	98.40 111
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
Fast-Effi-MVS+-dtu		96.99 159	96.46 160	97.61 171	98.98 171	97.89 168	97.54 190	99.76 17	93.43 184	96.55 197	94.93 190	98.06 144	94.32 190	96.93 163	96.50 165	98.53 152	97.47 151

FPMVS		96.97 160	97.20 141	96.70 193	97.75 206	96.11 197	97.72 182	95.47 205	97.13 81	98.02 160	97.57 133	96.67 166	92.97 199	99.00 58	98.34 94	98.28 162	95.58 181

TAMVS		96.95 161	96.94 151	96.97 188	99.07 167	97.67 179	97.98 171	97.12 200	95.04 150	95.41 204	99.27 63	95.57 174	94.09 192	97.32 156	97.11 153	98.16 168	96.59 172

FMVSNet3		96.85 162	96.67 153	97.06 182	97.56 209	99.01 84	97.99 170	99.33 97	94.09 174	95.60 201	98.17 114	95.97 170	93.26 198	94.76 197	96.22 169	98.59 148	98.46 105

GA-MVS		96.84 163	95.86 173	97.98 158	99.16 158	98.29 145	97.91 173	98.64 165	95.14 148	97.71 172	98.04 121	88.90 190	96.50 161	96.41 173	96.61 164	97.97 173	97.60 148

CHOSEN 280x420		96.80 164	96.30 163	97.39 174	99.09 163	96.52 189	98.76 120	99.29 105	93.88 179	97.65 173	98.34 104	93.66 181	96.29 168	98.28 107	97.73 125	93.27 201	95.70 180

gg-mvs-nofinetune		96.77 165	96.52 159	97.06 182	99.66 70	97.82 172	97.54 190	99.86 8	98.69 15	98.61 116	99.94 2	89.62 188	88.37 211	97.55 142	96.67 161	98.30 161	95.35 182

DPM-MVS		96.73 166	95.70 176	97.95 161	98.93 173	97.26 183	97.39 195	98.44 176	95.47 144	97.62 174	90.71 204	98.47 133	97.03 152	95.02 194	95.27 182	98.26 163	97.67 146

baseline1		96.72 167	95.40 178	98.26 145	99.53 102	98.81 108	98.32 153	98.80 154	94.96 153	96.78 196	96.50 168	84.87 204	96.68 157	97.42 150	97.91 109	99.46 50	97.33 157

N_pmnet		96.68 168	95.70 176	97.84 165	99.42 126	98.00 164	99.35 54	98.21 182	98.40 22	98.13 153	99.42 54	99.30 67	97.44 140	94.00 201	88.79 199	94.47 197	91.96 199

new_pmnet		96.59 169	96.40 162	96.81 190	98.24 202	95.46 206	97.71 184	94.75 208	96.92 87	96.80 195	99.23 67	97.81 151	96.69 155	96.58 169	95.16 184	96.69 185	93.64 195

PMMVS		96.47 170	95.81 174	97.23 178	97.38 211	95.96 201	97.31 196	96.91 202	93.21 188	97.93 166	97.14 147	97.64 154	95.70 171	95.24 190	96.18 172	98.17 167	95.33 183

EPNet		96.44 171	96.08 167	96.86 189	99.32 136	97.15 186	97.69 185	99.32 101	93.67 181	98.11 156	95.64 183	93.44 182	89.07 209	96.86 164	96.83 158	97.67 175	98.97 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres600view7		96.35 172	94.27 180	98.79 108	99.66 70	99.18 61	98.94 98	99.38 86	94.37 169	97.21 191	87.19 206	84.10 206	98.10 115	98.16 113	99.47 18	99.42 56	97.43 152

EPNet_dtu		96.31 173	95.96 169	96.72 192	99.18 155	95.39 207	97.03 205	99.13 132	93.02 191	99.35 37	97.23 144	97.07 162	90.70 208	95.74 182	95.08 186	94.94 196	98.16 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs3		96.30 174	95.87 172	96.80 191	97.66 208	96.48 190	97.93 172	93.80 209	93.40 185	98.54 123	98.27 109	97.50 155	97.37 143	97.49 145	93.11 194	95.52 194	94.85 187

PMMVS2		96.29 175	97.05 147	95.40 203	98.32 201	96.16 194	98.18 164	97.46 196	97.20 79	84.51 216	99.60 34	98.68 123	96.37 163	98.59 82	97.38 144	97.58 178	91.76 200

thres200		96.23 176	94.13 181	98.69 117	99.44 120	99.18 61	98.58 133	99.38 86	93.52 183	97.35 185	86.33 211	85.83 200	97.93 125	98.16 113	98.78 64	99.42 56	97.10 165

thres400		96.22 177	94.08 183	98.72 113	99.58 88	99.05 75	98.83 111	99.22 115	94.01 177	97.40 183	86.34 210	84.91 203	97.93 125	97.85 129	99.08 39	99.37 62	97.28 159

tfpn200view9		96.17 178	94.08 183	98.60 126	99.37 129	99.18 61	98.68 123	99.39 81	92.02 197	97.30 187	86.53 208	86.34 197	97.45 139	98.15 115	99.08 39	99.43 55	97.28 159

CMPMVS	74.71 19	96.17 178	96.06 168	96.30 197	97.41 210	94.52 210	94.83 212	95.46 206	91.57 202	97.26 190	94.45 194	98.33 138	94.98 178	98.28 107	97.59 135	97.86 174	97.68 145
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
IB-MVS	95.85 14	95.87 180	94.88 179	97.02 185	99.09 163	98.25 150	97.16 198	97.38 198	91.97 200	97.77 170	83.61 213	97.29 159	92.03 206	97.16 160	97.66 129	98.66 140	98.20 128
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
test0.0.03 1		95.81 181	95.77 175	95.85 202	99.20 151	98.15 157	97.49 194	98.50 172	92.24 193	92.74 214	96.82 158	92.70 184	88.60 210	97.31 158	97.01 156	98.57 150	96.19 177

thres100view900		95.74 182	93.66 192	98.17 151	99.37 129	98.59 127	98.10 166	98.33 179	92.02 197	97.30 187	86.53 208	86.34 197	96.69 155	96.77 166	98.47 87	99.24 84	96.89 169

ET-MVSNet_ETH3D		95.72 183	93.85 188	97.89 162	97.30 212	98.09 159	98.19 163	98.40 177	94.46 163	98.01 163	96.71 160	77.85 216	96.76 153	96.08 177	96.39 166	98.70 138	97.36 155

baseline2		95.58 184	94.04 185	97.38 175	98.80 179	98.16 155	97.14 199	97.80 194	91.45 204	97.49 178	95.22 186	83.63 207	94.98 178	96.42 172	96.66 162	98.06 169	96.76 170

PatchT		95.49 185	93.29 193	98.06 156	98.65 187	96.20 193	98.91 103	99.73 20	92.00 199	98.50 126	96.67 162	83.25 208	96.34 164	94.40 198	95.50 178	96.21 188	95.04 185

CR-MVSNet		95.38 186	93.01 194	98.16 153	98.63 188	95.85 203	97.64 187	99.78 15	91.27 206	98.50 126	96.84 157	82.16 209	96.34 164	94.40 198	95.50 178	98.05 171	95.04 185

MVSTER		95.38 186	93.99 187	97.01 186	98.83 177	98.95 93	96.62 206	99.14 128	92.17 195	97.44 181	97.29 142	77.88 215	91.63 207	97.45 147	96.18 172	98.41 158	97.99 136

MVS-HIRNet		94.86 188	93.83 189	96.07 198	97.07 213	94.00 211	94.31 213	99.17 123	91.23 208	98.17 149	98.69 95	97.43 156	95.66 172	94.05 200	91.92 196	92.04 208	89.46 208

test-LLR		94.79 189	93.71 190	96.06 199	99.20 151	96.16 194	96.31 207	98.50 172	89.98 210	94.08 209	97.01 151	86.43 195	92.20 204	96.76 167	95.31 180	96.05 190	94.31 190

RPMNet		94.72 190	92.01 199	97.88 164	98.56 193	95.85 203	97.78 178	99.70 25	91.27 206	98.33 140	93.69 197	81.88 210	94.91 181	92.60 203	94.34 192	98.01 172	94.46 189

gm-plane-assit		94.62 191	91.39 201	98.39 136	99.90 12	99.47 27	99.40 46	99.65 32	97.44 64	99.56 20	99.68 26	59.40 220	94.23 191	96.17 175	94.77 189	97.61 177	92.79 198

test-mter		94.62 191	94.02 186	95.32 204	97.72 207	96.75 187	96.23 209	95.67 204	89.83 213	93.23 213	96.99 153	85.94 199	92.66 202	97.32 156	96.11 174	96.44 186	95.22 184

FMVSNet5		94.57 193	92.77 195	96.67 194	97.88 204	98.72 117	97.54 190	98.70 161	88.64 214	95.11 206	86.90 207	81.77 211	93.27 197	97.92 125	98.07 103	97.50 180	97.34 156

SCA		94.53 194	91.95 200	97.55 172	98.58 190	97.86 170	98.49 139	99.68 26	95.11 149	99.07 76	95.87 179	87.24 193	96.53 160	89.77 206	87.08 202	92.96 203	90.69 203

MDTV_nov1_ep13		94.47 195	92.15 197	97.17 179	98.54 195	96.42 191	98.10 166	98.89 149	94.49 161	98.02 160	97.41 139	86.49 194	95.56 173	90.85 204	87.95 200	93.91 198	91.45 202

TESTMET0.1,1		94.44 196	93.71 190	95.30 205	97.84 205	96.16 194	96.31 207	95.32 207	89.98 210	94.08 209	97.01 151	86.43 195	92.20 204	96.76 167	95.31 180	96.05 190	94.31 190

ADS-MVSNet		94.41 197	92.13 198	97.07 181	98.86 175	96.60 188	98.38 148	98.47 175	96.13 132	98.02 160	96.98 154	87.50 192	95.87 170	89.89 205	87.58 201	92.79 205	90.27 205

tpm		93.89 198	91.21 202	97.03 184	98.36 199	96.07 198	97.53 193	99.65 32	92.24 193	98.64 115	97.23 144	74.67 219	94.64 185	92.68 202	90.73 197	93.37 200	94.82 188

PatchmatchNet		93.88 199	91.08 203	97.14 180	98.75 181	96.01 200	98.25 160	99.39 81	94.95 154	98.96 88	96.32 171	85.35 201	95.50 174	88.89 207	85.89 206	91.99 209	90.15 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		93.67 200	90.82 204	96.99 187	98.62 189	96.39 192	98.40 146	99.11 133	95.54 143	97.87 168	97.14 147	81.27 213	94.97 180	88.54 209	86.80 204	92.95 204	90.06 207

MVE	82.47 18	93.12 201	94.09 182	91.99 208	90.79 215	82.50 216	93.93 214	96.30 203	96.06 133	88.81 215	98.19 112	96.38 168	97.56 136	97.24 159	95.18 183	84.58 214	93.07 196
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CostFormer		92.75 202	89.49 206	96.55 195	98.78 180	95.83 205	97.55 189	98.59 168	91.83 201	97.34 186	96.31 172	78.53 214	94.50 186	86.14 210	84.92 207	92.54 206	92.84 197

tpmrst		92.45 203	89.48 207	95.92 201	98.43 198	95.03 208	97.14 199	97.92 192	94.16 173	97.56 175	97.86 125	81.63 212	93.56 196	85.89 211	82.86 208	90.91 213	88.95 210

dps		92.35 204	88.78 209	96.52 196	98.21 203	95.94 202	97.78 178	98.38 178	89.88 212	96.81 194	95.07 188	75.31 218	94.70 184	88.62 208	86.21 205	93.21 202	90.41 204

E-PMN		92.28 205	90.12 205	94.79 206	98.56 193	90.90 213	95.16 211	93.68 210	95.36 146	95.10 207	96.56 165	89.05 189	95.24 176	95.21 191	81.84 210	90.98 211	81.94 211

EMVS		91.84 206	89.39 208	94.70 207	98.44 197	90.84 214	95.27 210	93.53 211	95.18 147	95.26 205	95.62 184	87.59 191	94.77 183	94.87 195	80.72 211	90.95 212	80.88 212

tpm cat1		91.52 207	87.70 210	95.97 200	98.33 200	94.98 209	97.06 202	98.03 187	92.11 196	98.03 159	94.77 191	77.19 217	92.71 201	83.56 212	82.24 209	91.67 210	89.04 209

GG-mvs-BLEND		65.66 208	92.62 196	34.20 210	1.45 219	93.75 212	85.40 216	1.64 216	91.37 205	17.21 218	87.25 205	94.78 178	3.25 215	95.64 185	93.80 193	96.27 187	91.74 201

testmvs		9.73 209	13.38 211	5.48 212	3.62 217	4.12 218	6.40 219	3.19 215	14.92 215	7.68 220	22.10 214	13.89 222	6.83 213	13.47 213	10.38 213	5.14 217	14.81 213

test123		9.37 210	12.26 212	6.00 211	3.32 218	4.06 219	6.39 220	3.41 214	13.20 216	10.48 219	16.43 215	16.22 221	6.76 214	11.37 214	10.40 212	5.62 216	14.10 214

uanet_test		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet-low-res		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

sosnet		0.00 211	0.00 213	0.00 213	0.00 220	0.00 220	0.00 221	0.00 217	0.00 217	0.00 221	0.00 216	0.00 223	0.00 216	0.00 215	0.00 214	0.00 218	0.00 215

RE-MVS-def										99.88 2

9.14												98.83 114

SR-MVS					99.62 80			99.47 69				99.40 57

Anonymous202405211			98.44 72		99.79 32	99.32 45	99.05 89	99.34 96	96.59 106		97.95 124	97.68 153	97.16 147	99.36 30	99.28 30	99.61 33	98.90 66

our_test_3					99.29 141	97.72 176	98.98 94

ambc			97.89 112		99.45 117	97.88 169	97.78 178		97.27 72	99.80 3	98.99 86	98.48 131	98.55 93	97.80 131	96.68 160	98.54 151	98.10 134

MTAPA										99.19 56		99.68 22

MTMP										99.20 54		99.54 43

Patchmatch-RL test							32.47 218

tmp_tt				65.28 209	82.24 216	71.50 217	70.81 217	23.21 213	96.14 130	81.70 217	85.98 212	92.44 185	49.84 212	95.81 180	94.36 191	83.86 215

XVS					99.77 37	99.07 71	99.46 41			98.95 90		99.37 60				99.33 69

X-MVStestdata					99.77 37	99.07 71	99.46 41			98.95 90		99.37 60				99.33 69

abl_6				98.38 138	99.03 169	98.04 162	98.08 168	98.65 163	93.23 187	98.56 120	94.58 193	98.57 129	97.17 146			98.81 129	97.42 153

mPP-MVS					99.75 46							99.49 52

NP-MVS									93.07 190

Patchmtry						96.05 199	97.64 187	99.78 15		98.50 126

DeepMVS_CX						87.86 215	92.27 215	61.98 212	93.64 182	93.62 212	91.17 202	91.67 186	94.90 182	95.99 179		92.48 207	94.18 192

High-resolution multi-view benchmark