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 18	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 21	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 52	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 84	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 58	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 63	99.35 39	99.77 8	99.58 45	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 40	99.70 11	99.55 44	99.21 76	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 70	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 73	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 25	99.49 24	99.63 22	99.65 33	98.88 11	99.31 42	99.85 14	99.02 103	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 24	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 37	99.11 8	99.28 48	99.83 18	98.35 139	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 28	99.42 32	99.73 15	99.58 45	99.02 9	99.10 74	99.41 56	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 94	99.68 12	99.32 59	98.86 112	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 53	99.12 68	99.56 30	99.39 82	98.96 10	99.17 61	99.44 52	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 58	99.05 76	99.49 38	99.40 80	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 53	99.87 16	99.65 16	99.63 22	99.75 20	97.76 42	97.29 191	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 40	99.94 3	99.66 15	98.91 105	99.86 8	97.74 48	98.74 112	99.00 86	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 39	99.81 28	99.22 56	99.59 27	99.50 63	98.64 16	98.29 145	99.21 71	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 63	99.65 74	99.55 22	99.63 22	99.33 98	98.16 26	99.29 45	99.65 30	99.77 10	97.56 138	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 28	99.45 28	99.47 40	99.68 27	97.28 72	99.39 35	99.54 45	99.08 99	99.45 22	99.09 44	98.84 57	99.83 12	99.04 48

CSCG		99.23 25	99.15 23	99.32 52	99.83 22	99.45 28	98.97 97	99.21 118	98.83 13	99.04 84	99.43 54	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 63	99.24 50	99.17 80	99.63 37	99.52 3	99.89 1	96.54 169	99.14 88	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 28	99.55 22	99.60 26	99.54 54	98.53 19	99.23 52	98.40 105	98.23 142	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 53	99.56 20	99.36 52	99.62 42	96.53 115	99.29 45	99.85 14	98.64 129	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 47	99.43 31	99.36 52	99.63 37	97.68 54	99.35 37	99.31 60	98.90 109	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 40	99.77 38	99.28 47	99.52 34	99.54 54	97.22 77	99.06 78	99.20 72	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 58	99.87 16	99.57 19	99.67 17	99.60 44	96.75 100	98.28 146	99.48 48	99.52 44	98.10 117	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 53	99.35 55	99.63 37	96.58 108	99.36 36	99.05 80	98.67 127	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 71	99.55 96	99.40 34	99.36 52	99.44 79	97.75 45	98.23 149	99.23 68	99.80 7	98.97 67	99.08 46	98.96 47	99.19 87	99.25 24
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
UniMVSNet (Re)		99.08 35	98.69 51	99.54 24	99.75 47	99.33 42	99.29 63	99.64 36	96.75 100	99.48 28	99.30 62	98.69 123	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 33	99.12 68	99.35 55	99.56 48	97.74 48	99.21 54	97.72 131	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 53	99.23 53	99.29 63	99.63 37	96.58 108	99.55 21	99.05 80	98.68 125	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 74	99.23 152	98.79 112	99.51 35	98.73 159	97.50 62	98.56 122	99.03 83	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	99.52 107	99.39 35	99.57 29	99.66 31	97.59 59	99.32 41	99.88 9	99.23 72	99.50 18	97.77 134	97.98 108	98.92 121	98.78 79

EG-PatchMatch MVS		99.01 39	98.77 43	99.28 57	99.64 77	98.90 105	98.81 117	99.27 109	96.55 112	99.71 6	99.31 60	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 58	99.76 44	99.34 40	99.35 55	99.35 94	97.12 83	99.46 31	99.56 40	98.89 110	98.08 120	99.05 49	98.58 80	99.27 79	98.98 56

HFP-MVS		98.97 42	98.70 49	99.29 55	99.67 68	98.98 88	99.13 85	99.53 57	97.76 42	98.90 98	98.07 119	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 44	99.34 40	99.29 63	99.61 43	96.55 112	99.55 21	99.05 80	97.96 150	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 96	99.43 123	99.38 37	99.12 87	99.46 73	97.05 86	98.43 137	99.23 68	99.79 8	97.99 123	99.05 49	98.94 49	99.05 107	99.23 29

ACMMP_NAP		98.94 44	98.72 47	99.21 58	99.67 68	99.08 71	99.26 68	99.39 82	96.84 91	98.88 102	98.22 112	99.68 22	98.82 76	99.06 48	98.90 52	99.25 82	99.25 24

zzz-MVS		98.94 44	98.57 60	99.37 47	99.77 38	99.15 65	99.24 71	99.55 50	97.38 68	99.16 64	96.64 165	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 81	99.03 82	99.58 28	99.36 91	97.99 31	99.49 27	99.91 8	99.20 78	99.51 17	97.61 139	97.85 115	98.95 116	98.10 135

v8		98.94 44	98.60 55	99.35 50	99.54 100	99.39 35	99.55 31	99.67 30	97.48 63	99.13 70	99.81 19	99.10 95	99.39 33	97.86 129	97.89 113	98.81 130	98.66 86

SteuartSystems-ACMMP		98.94 44	98.52 64	99.43 32	99.79 33	99.13 67	99.33 59	99.55 50	96.17 131	99.04 84	97.53 137	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 85	99.03 82	99.64 19	99.25 113	97.91 37	99.58 17	99.92 4	99.07 101	99.45 22	97.55 143	97.68 129	98.93 118	98.23 125

FMVSNet1		98.90 51	99.10 25	98.67 120	99.54 100	99.48 25	99.22 74	99.66 31	98.39 23	97.50 179	99.66 27	99.04 102	96.58 160	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 53	98.95 94	99.47 40	99.55 50	97.66 56	99.09 75	96.43 171	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 89	99.82 25	99.41 33	99.18 79	99.31 104	96.92 88	98.54 125	98.58 102	98.84 115	97.46 140	99.45 26	99.29 29	99.65 30	99.08 44

v1921920		98.89 53	98.46 68	99.39 40	99.58 89	99.04 80	99.64 19	99.17 124	97.91 37	99.64 15	99.92 4	98.99 107	99.44 25	97.44 150	97.57 138	98.84 128	98.35 115

GeoE		98.88 55	98.43 75	99.41 34	99.83 22	99.24 50	99.51 35	99.82 13	96.55 112	99.22 53	98.76 94	99.22 75	98.96 68	98.55 84	98.15 100	99.10 94	98.56 98

v144192		98.88 55	98.46 68	99.37 47	99.56 95	99.03 82	99.61 25	99.26 110	97.79 41	99.58 17	99.88 9	99.11 93	99.43 27	97.38 155	97.61 134	98.80 132	98.43 110

SMA-MVS		98.87 57	98.73 46	99.04 82	99.72 59	99.05 76	98.64 128	99.17 124	96.31 126	98.80 107	99.07 78	99.70 18	98.67 84	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 57	98.40 78	99.41 34	99.74 53	98.88 106	99.29 63	99.50 63	96.85 90	98.96 90	97.05 152	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 59	98.57 60	99.19 61	99.86 20	99.67 13	99.39 48	99.71 24	97.53 61	98.69 115	95.85 182	98.48 133	97.75 132	99.57 19	99.41 21	99.72 24	99.48 14

v1240		98.86 59	98.41 77	99.38 45	99.59 87	99.05 76	99.65 18	99.14 129	97.68 54	99.66 13	99.93 3	98.72 122	99.45 22	97.38 155	97.72 127	98.79 133	98.35 115

CP-MVS		98.86 59	98.43 75	99.36 49	99.68 66	98.97 92	99.19 77	99.46 73	96.60 106	99.20 55	97.11 151	99.51 48	99.15 54	98.92 66	98.82 58	99.45 52	99.08 44

v2v482		98.85 62	98.40 78	99.38 45	99.65 74	98.98 88	99.55 31	99.39 82	97.92 36	99.35 37	99.85 14	99.14 88	99.39 33	97.50 145	97.78 118	98.98 113	97.60 149

DPE-MVS		98.84 63	98.69 51	99.00 86	99.05 170	99.26 48	99.19 77	99.35 94	95.85 139	98.74 112	99.27 64	99.66 26	98.30 109	98.90 68	98.93 50	99.37 62	99.00 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
OPM-MVS		98.84 63	98.59 57	99.12 71	99.52 107	98.50 136	99.13 85	99.22 116	97.76 42	98.76 109	98.70 96	99.61 37	98.90 71	98.67 79	98.37 92	99.19 87	98.57 95

test20.03		98.84 63	98.74 45	98.95 92	99.77 38	99.33 42	99.21 76	99.46 73	97.29 71	98.88 102	99.65 30	99.10 95	97.07 152	99.11 41	98.76 67	99.32 72	97.98 139

casdiffmvs		98.84 63	98.75 44	98.94 95	99.75 47	99.21 57	99.33 59	99.04 140	98.04 29	97.46 182	99.72 24	99.72 15	98.60 88	98.30 104	98.37 92	99.48 49	97.92 141

LGP-MVS_train		98.84 63	98.33 84	99.44 29	99.78 36	98.98 88	99.39 48	99.55 50	95.41 147	98.90 98	97.51 138	99.68 22	99.44 25	99.03 55	98.81 60	99.57 36	98.91 65

RPSCF		98.84 63	98.81 40	98.89 98	99.37 131	98.95 94	98.51 140	98.85 152	97.73 50	98.33 142	98.97 88	99.14 88	98.95 69	99.18 37	98.68 73	99.31 73	98.99 55

ACMMP		98.82 69	98.33 84	99.39 40	99.77 38	99.14 66	99.37 51	99.54 54	96.47 119	99.03 86	96.26 175	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 70	98.49 66	99.18 62	99.52 107	98.92 100	99.50 37	99.29 106	97.43 66	98.97 88	99.81 19	99.00 106	99.30 38	97.93 125	98.01 106	98.51 156	98.34 119

LS3D		98.79 71	98.52 64	99.12 71	99.64 77	99.09 70	99.24 71	99.46 73	97.75 45	98.93 96	97.47 139	98.23 142	97.98 124	99.36 30	99.30 28	99.46 50	98.42 111

MP-MVS		98.78 72	98.30 86	99.34 51	99.75 47	98.95 94	99.26 68	99.46 73	95.78 142	99.17 61	96.98 156	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 73	98.45 71	99.15 67	99.68 66	98.94 98	99.49 38	99.31 104	97.95 33	98.91 97	99.65 30	99.62 36	99.18 47	97.99 123	97.64 133	98.33 161	97.38 155

SD-MVS		98.73 74	98.54 62	98.95 92	99.14 161	98.76 115	98.46 144	99.14 129	97.71 52	98.56 122	98.06 121	99.61 37	98.85 75	98.56 83	97.74 124	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 75	98.60 55	98.87 100	99.67 68	99.33 42	99.15 82	99.26 110	96.99 87	97.90 169	98.19 114	99.74 12	98.29 110	97.69 137	98.96 47	98.96 114	99.27 23
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PGM-MVS		98.69 76	98.09 103	99.39 40	99.76 44	99.07 72	99.30 62	99.51 61	94.76 158	99.18 60	96.70 163	99.51 48	99.20 45	98.79 75	98.71 72	99.39 60	99.11 38

pmmvs-eth3d		98.68 77	98.14 99	99.29 55	99.49 112	98.45 139	99.45 44	99.38 87	97.21 78	99.50 26	99.65 30	99.21 76	99.16 52	97.11 162	97.56 139	98.79 133	97.82 145

EU-MVSNet		98.68 77	98.94 33	98.37 140	99.14 161	98.74 117	99.64 19	98.20 185	98.21 24	99.17 61	99.66 27	99.18 81	99.08 59	99.11 41	98.86 53	95.00 197	98.83 71

PMVS	92.51 17	98.66 79	98.86 38	98.43 135	99.26 147	98.98 88	98.60 134	98.59 169	97.73 50	99.45 32	99.38 57	98.54 132	95.24 178	99.62 15	99.61 12	99.42 56	98.17 132
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepC-MVS_fast	97.38 8	98.65 80	98.34 83	99.02 85	99.33 135	98.29 146	98.99 95	98.71 161	97.40 67	99.31 42	98.20 113	99.40 57	98.54 96	98.33 101	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 80	98.35 82	99.00 86	99.59 87	98.70 120	98.90 109	99.36 91	97.97 32	99.09 75	96.55 168	99.09 97	97.97 125	98.70 78	98.65 76	99.12 93	98.81 74

TSAR-MVS + ACMM		98.64 82	98.58 59	98.72 114	99.17 159	98.63 125	98.69 124	99.10 136	97.69 53	98.30 144	99.12 76	99.38 59	98.70 83	98.45 87	97.51 141	98.35 160	99.25 24

DELS-MVS		98.63 83	98.70 49	98.55 131	99.24 151	99.04 80	98.96 98	98.52 172	96.83 93	98.38 139	99.58 38	99.68 22	97.06 153	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 84	98.40 78	98.89 98	99.57 94	98.80 111	98.63 129	99.35 94	96.82 94	98.60 119	98.85 93	99.08 99	98.09 119	98.31 102	98.21 96	99.08 100	98.72 81

EPP-MVSNet		98.61 85	98.19 95	99.11 74	99.86 20	99.60 17	99.44 45	99.53 57	97.37 69	96.85 195	98.69 97	93.75 183	99.18 47	99.22 36	99.35 26	99.82 14	99.32 21

3Dnovator+	97.85 5	98.61 85	98.14 99	99.15 67	99.62 81	98.37 144	99.10 88	99.51 61	98.04 29	98.98 87	96.07 179	98.75 121	98.55 94	98.51 86	98.40 89	99.17 89	98.82 72

X-MVS		98.59 87	97.99 109	99.30 54	99.75 47	99.07 72	99.17 80	99.50 63	96.62 104	98.95 92	93.95 198	99.37 60	99.11 57	98.94 62	98.86 53	99.35 67	99.09 43

MVS_111021_HR		98.58 88	98.26 89	98.96 91	99.32 138	98.81 109	98.48 142	98.99 145	96.81 96	99.16 64	98.07 119	99.23 72	98.89 73	98.43 89	98.27 95	98.90 123	98.24 124

MVS_0304		98.57 89	98.36 81	98.82 107	99.72 59	98.94 98	98.92 103	99.14 129	96.76 99	99.33 40	98.30 109	99.73 13	96.74 156	98.05 120	97.79 117	99.08 100	98.97 57

PM-MVS		98.57 89	98.24 91	98.95 92	99.26 147	98.59 128	99.03 92	98.74 158	96.84 91	99.44 33	99.13 75	98.31 141	98.75 81	98.03 121	98.21 96	98.48 157	98.58 93

PHI-MVS		98.57 89	98.20 94	99.00 86	99.48 114	98.91 102	98.68 125	99.17 124	94.97 154	99.27 50	98.33 107	99.33 64	98.05 121	98.82 73	98.62 77	99.34 68	98.38 113

HPM-MVS++		98.56 92	98.08 104	99.11 74	99.53 103	98.61 127	99.02 94	99.32 102	96.29 128	99.06 78	97.23 146	99.50 50	98.77 79	98.15 116	97.90 111	98.96 114	98.90 66

TSAR-MVS + GP.		98.54 93	98.29 88	98.82 107	99.28 145	98.59 128	97.73 183	99.24 115	95.93 137	98.59 120	99.07 78	99.17 82	98.86 74	98.44 88	98.10 102	99.26 81	98.72 81

UGNet		98.52 94	99.00 29	97.96 162	99.58 89	99.26 48	99.27 67	99.40 80	98.07 28	98.28 146	98.76 94	99.71 17	92.24 205	98.94 62	98.85 55	99.00 112	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 95	98.03 106	99.05 80	99.50 110	99.01 85	99.15 82	99.26 110	96.38 124	99.12 72	99.50 47	99.12 91	98.60 88	97.68 138	97.24 152	98.66 141	97.30 159

CLD-MVS		98.48 96	98.15 98	98.86 103	99.53 103	98.35 145	98.55 137	97.83 194	96.02 136	98.97 88	99.08 77	99.75 11	99.03 63	98.10 119	97.33 148	99.28 77	98.44 109
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 97	98.30 86	98.67 120	99.65 74	98.87 107	98.82 116	99.01 143	96.14 132	99.29 45	98.86 91	99.01 104	96.54 161	98.36 95	98.08 103	98.72 137	98.80 78

APD-MVS		98.47 97	97.97 110	99.05 80	99.64 77	98.91 102	98.94 100	99.45 78	94.40 169	98.77 108	97.26 145	99.41 54	98.21 113	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 99	98.23 92	98.73 113	99.81 28	99.29 46	98.79 119	99.50 63	96.20 130	96.03 201	98.29 110	96.98 165	98.54 96	99.11 41	99.08 39	99.70 25	98.62 89

Fast-Effi-MVS+		98.42 100	97.79 116	99.15 67	99.69 65	98.66 123	98.94 100	99.68 27	94.49 163	99.05 80	98.06 121	98.86 112	98.48 99	98.18 113	97.78 118	99.05 107	98.54 101

ETV-MVS		98.41 101	97.76 117	99.17 63	99.58 89	99.01 85	98.91 105	99.50 63	93.33 188	99.31 42	96.82 160	98.42 137	98.17 116	99.13 40	99.08 39	99.54 39	98.56 98

MVS_111021_LR		98.39 102	98.11 101	98.71 116	99.08 167	98.54 134	98.23 164	98.56 171	96.57 110	99.13 70	98.41 104	98.86 112	98.65 86	98.23 111	97.87 114	98.65 143	98.28 121

pmmvs5		98.37 103	97.81 115	99.03 83	99.46 116	98.97 92	99.03 92	98.96 147	95.85 139	99.05 80	99.45 51	98.66 128	98.79 78	96.02 179	97.52 140	98.87 125	98.21 128

OMC-MVS		98.35 104	98.10 102	98.64 126	98.85 178	97.99 166	98.56 136	98.21 183	97.26 75	98.87 104	98.54 103	99.27 70	98.43 101	98.34 99	97.66 130	98.92 121	97.65 148

canonicalmvs		98.34 105	97.92 112	98.83 105	99.45 118	99.21 57	98.37 151	99.53 57	97.06 85	97.74 173	96.95 158	95.05 180	98.36 104	98.77 76	98.85 55	99.51 46	99.53 9

CHOSEN 1792x2688		98.31 106	98.02 107	98.66 122	99.55 96	98.57 131	99.38 50	99.25 113	98.42 20	98.48 133	99.58 38	99.85 5	98.31 108	95.75 182	95.71 177	96.96 184	98.27 123

xxxxxxxxxxxxxcwj		98.28 107	98.23 92	98.35 141	99.43 123	98.42 142	97.05 205	99.09 137	96.42 121	98.13 155	97.73 129	99.65 30	97.22 146	98.36 95	98.38 90	99.16 91	98.62 89

CPTT-MVS		98.28 107	97.51 129	99.16 65	99.54 100	98.78 113	98.96 98	99.36 91	96.30 127	98.89 101	93.10 203	99.30 67	99.20 45	98.35 98	97.96 109	99.03 110	98.82 72

TinyColmap		98.27 109	97.62 126	99.03 83	99.29 143	97.79 175	98.92 103	98.95 148	97.48 63	99.52 24	98.65 99	97.86 152	98.90 71	98.34 99	97.27 150	98.64 144	95.97 179

diffmvs		98.26 110	98.16 96	98.39 137	99.61 85	98.78 113	98.79 119	98.61 167	97.94 34	97.11 194	99.51 46	99.52 44	97.61 136	96.55 171	96.93 158	98.61 146	97.87 143

USDC		98.26 110	97.57 127	99.06 77	99.42 127	97.98 168	98.83 113	98.85 152	97.57 60	99.59 16	99.15 74	98.59 130	98.99 65	97.42 151	96.08 176	98.69 140	96.23 177

SF-MVS		98.25 112	98.16 96	98.35 141	99.43 123	98.42 142	97.05 205	99.09 137	96.42 121	98.13 155	97.73 129	99.20 78	97.22 146	98.36 95	98.38 90	99.16 91	98.62 89

MCST-MVS		98.25 112	97.57 127	99.06 77	99.53 103	98.24 152	98.63 129	99.17 124	95.88 138	98.58 121	96.11 177	99.09 97	99.18 47	97.58 142	97.31 149	99.25 82	98.75 80

IterMVS-LS		98.23 114	97.66 122	98.90 97	99.63 80	99.38 37	99.07 89	99.48 69	97.75 45	98.81 106	99.37 58	94.57 182	97.88 129	96.54 172	97.04 155	98.53 153	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	98.23 114	97.96 111	98.55 131	98.81 180	98.16 156	98.40 148	97.94 192	96.68 102	98.49 131	98.61 100	98.89 110	98.57 92	97.45 148	97.59 136	99.09 99	98.35 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNVR-MVS		98.22 116	97.76 117	98.76 111	99.33 135	98.26 150	98.48 142	98.88 151	96.22 129	98.47 135	95.79 183	99.33 64	98.35 105	98.37 94	97.99 107	99.03 110	98.38 113

IS_MVSNet		98.20 117	98.00 108	98.44 134	99.82 25	99.48 25	99.25 70	99.56 48	95.58 144	93.93 213	97.56 136	96.52 169	98.27 111	99.08 46	99.20 33	99.80 16	98.56 98

DeepPCF-MVS	96.68 10	98.20 117	98.26 89	98.12 155	97.03 216	98.11 159	98.44 146	97.70 196	96.77 98	98.52 127	98.91 89	99.17 82	98.58 91	98.41 91	98.02 105	98.46 158	98.46 106

MSDG		98.20 117	97.88 114	98.56 130	99.33 135	97.74 176	98.27 161	98.10 186	97.20 80	98.06 160	98.59 101	99.16 84	98.76 80	98.39 92	97.71 128	98.86 127	96.38 174

testgi		98.18 120	98.44 72	97.89 164	99.78 36	99.23 53	98.78 121	99.21 118	97.26 75	97.41 184	97.39 142	99.36 63	92.85 202	98.82 73	98.66 75	99.31 73	98.35 115

CS-MVS		98.13 121	97.25 139	99.16 65	99.71 62	99.44 30	98.80 118	99.49 68	93.16 191	99.19 57	93.95 198	98.47 135	98.19 115	98.30 104	98.78 64	99.56 37	98.66 86

Effi-MVS+		98.11 122	97.29 135	99.06 77	99.62 81	98.55 132	98.16 167	99.80 15	94.64 159	99.15 68	96.59 166	97.43 158	98.44 100	97.46 147	97.90 111	99.17 89	98.45 108

HyFIR lowres test		98.08 123	97.16 145	99.14 70	99.72 59	98.91 102	99.41 46	99.58 45	97.93 35	98.82 105	99.24 66	95.81 175	98.73 82	95.16 193	95.13 186	98.60 148	97.94 140

EIA-MVS		98.03 124	97.20 142	98.99 89	99.66 71	99.24 50	98.53 139	99.52 60	91.56 206	99.25 51	95.34 187	98.78 118	97.72 133	98.38 93	98.58 80	99.28 77	98.54 101

train_agg		97.99 125	97.26 136	98.83 105	99.43 123	98.22 154	98.91 105	99.07 139	94.43 167	97.96 166	96.42 172	99.30 67	98.81 77	97.39 153	96.62 164	98.82 129	98.47 104

MSLP-MVS++		97.99 125	97.64 125	98.40 136	98.91 176	98.47 138	97.12 203	98.78 156	96.49 117	98.48 133	93.57 201	99.12 91	98.51 98	98.31 102	98.58 80	98.58 150	98.95 62

CDPH-MVS		97.99 125	97.23 140	98.87 100	99.58 89	98.29 146	98.83 113	99.20 120	93.76 182	98.11 158	96.11 177	99.16 84	98.23 112	97.80 132	97.22 153	99.29 76	98.28 121

FMVSNet2		97.94 128	98.08 104	97.77 170	98.71 184	99.21 57	98.62 131	99.47 70	96.62 104	96.37 200	99.20 72	97.70 154	94.39 189	97.39 153	97.75 123	99.08 100	98.70 83

PVSNet_BlendedMVS		97.93 129	97.66 122	98.25 148	99.30 140	98.67 121	98.31 156	97.95 190	94.30 173	98.75 110	97.63 133	98.76 119	96.30 168	98.29 106	97.78 118	98.93 118	98.18 130

PVSNet_Blended		97.93 129	97.66 122	98.25 148	99.30 140	98.67 121	98.31 156	97.95 190	94.30 173	98.75 110	97.63 133	98.76 119	96.30 168	98.29 106	97.78 118	98.93 118	98.18 130

OpenMVS	97.26 9	97.88 131	97.17 144	98.70 117	99.50 110	98.55 132	98.34 154	99.11 134	93.92 180	98.90 98	95.04 191	98.23 142	97.38 143	98.11 118	98.12 101	98.95 116	98.23 125

pmmvs4		97.87 132	97.02 149	98.86 103	99.20 153	97.68 179	98.89 110	99.03 141	96.57 110	99.12 72	99.03 83	97.26 162	98.42 102	95.16 193	96.34 168	98.53 153	97.10 166

NCCC		97.84 133	96.96 151	98.87 100	99.39 130	98.27 149	98.46 144	99.02 142	96.78 97	98.73 114	91.12 206	98.91 108	98.57 92	97.83 131	97.49 142	99.04 109	98.33 120

Effi-MVS+-dtu		97.78 134	97.37 133	98.26 146	99.25 149	98.50 136	97.89 177	99.19 123	94.51 161	98.16 153	95.93 180	98.80 117	95.97 171	98.27 110	97.38 145	99.10 94	98.23 125

MDA-MVSNet-bldmvs		97.75 135	97.26 136	98.33 143	99.35 134	98.45 139	99.32 61	97.21 201	97.90 39	99.05 80	99.01 85	96.86 167	99.08 59	99.36 30	92.97 196	95.97 193	96.25 176

CDS-MVSNet		97.75 135	97.68 121	97.83 168	99.08 167	98.20 155	98.68 125	98.61 167	95.63 143	97.80 171	99.24 66	96.93 166	94.09 194	97.96 124	97.82 116	98.71 138	97.99 137
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		97.75 135	97.26 136	98.32 145	98.58 192	97.86 171	97.80 179	98.09 187	96.49 117	98.49 131	96.15 176	98.08 145	98.35 105	98.00 122	97.03 156	98.61 146	97.21 163

PLC	95.63 15	97.73 138	97.01 150	98.57 129	99.10 164	97.80 174	97.72 184	98.77 157	96.34 125	98.38 139	93.46 202	98.06 146	98.66 85	97.90 127	97.65 132	98.77 135	97.90 142
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_Test		97.69 139	97.15 146	98.33 143	99.27 146	98.43 141	98.25 162	99.29 106	95.00 153	97.39 186	98.86 91	98.00 149	97.14 150	95.38 188	96.22 170	98.62 145	98.15 134

GBi-Net		97.69 139	97.75 119	97.62 171	98.71 184	99.21 57	98.62 131	99.33 98	94.09 176	95.60 203	98.17 116	95.97 172	94.39 189	99.05 49	99.03 43	99.08 100	98.70 83

test1		97.69 139	97.75 119	97.62 171	98.71 184	99.21 57	98.62 131	99.33 98	94.09 176	95.60 203	98.17 116	95.97 172	94.39 189	99.05 49	99.03 43	99.08 100	98.70 83

CANet_DTU		97.65 142	97.50 131	97.82 169	99.19 156	98.08 161	98.41 147	98.67 163	94.40 169	99.16 64	98.32 108	98.69 123	93.96 196	97.87 128	97.61 134	97.51 180	97.56 151

IterMVS-SCA-FT		97.63 143	96.86 153	98.52 133	99.48 114	98.71 119	98.84 112	98.91 149	96.44 120	99.16 64	99.56 40	95.54 177	97.95 126	95.68 185	95.07 189	96.76 185	97.03 169

TSAR-MVS + COLMAP		97.62 144	97.31 134	97.98 160	98.47 198	97.39 183	98.29 158	98.25 182	96.68 102	97.54 178	98.87 90	98.04 148	97.08 151	96.78 166	96.26 169	98.26 164	97.12 165

MS-PatchMatch		97.60 145	97.22 141	98.04 159	98.67 188	97.18 187	97.91 175	98.28 181	95.82 141	98.34 141	97.66 132	98.38 138	97.77 131	97.10 163	97.25 151	97.27 182	97.18 164

PCF-MVS	95.58 16	97.60 145	96.67 154	98.69 118	99.44 121	98.23 153	98.37 151	98.81 154	93.01 194	98.22 150	97.97 125	99.59 40	98.20 114	95.72 184	95.08 187	99.08 100	97.09 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		97.58 147	96.65 157	98.66 122	99.30 140	97.99 166	97.88 178	98.65 164	94.58 160	98.66 116	94.65 195	99.15 87	98.59 90	96.10 177	95.59 178	98.90 123	98.50 103

DI_MVS_plusplus_trai		97.57 148	96.55 159	98.77 110	99.55 96	98.76 115	99.22 74	99.00 144	97.08 84	97.95 167	97.78 128	91.35 190	98.02 122	96.20 175	96.81 160	98.87 125	97.87 143

AdaColmap		97.57 148	96.57 158	98.74 112	99.25 149	98.01 164	98.36 153	98.98 146	94.44 166	98.47 135	92.44 204	97.91 151	98.62 87	98.19 112	97.74 124	98.73 136	97.28 160

baseline		97.50 150	97.51 129	97.50 175	99.18 157	97.38 184	98.00 171	98.00 189	96.52 116	97.49 180	99.28 63	99.43 53	95.31 177	95.27 190	96.22 170	96.99 183	98.47 104

IterMVS		97.40 151	96.67 154	98.25 148	99.45 118	98.66 123	98.87 111	98.73 159	96.40 123	98.94 95	99.56 40	95.26 179	97.58 137	95.38 188	94.70 191	95.90 194	96.72 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CVMVSNet		97.38 152	97.39 132	97.37 178	98.58 192	97.72 177	98.70 123	97.42 199	97.21 78	95.95 202	99.46 50	93.31 186	97.38 143	97.60 140	97.78 118	96.18 190	98.66 86

new-patchmatchnet		97.26 153	96.12 167	98.58 128	99.55 96	98.63 125	99.14 84	97.04 203	98.80 14	99.19 57	99.92 4	99.19 80	98.92 70	95.51 187	87.04 205	97.66 177	93.73 195

MIMVSNet		97.24 154	97.15 146	97.36 179	99.03 171	98.52 135	98.55 137	99.73 21	94.94 157	94.94 210	97.98 124	97.37 160	93.66 197	97.60 140	97.34 147	98.23 167	96.29 175

PatchMatch-RL		97.24 154	96.45 162	98.17 152	98.70 187	97.57 182	97.31 198	98.48 175	94.42 168	98.39 138	95.74 184	96.35 171	97.88 129	97.75 135	97.48 143	98.24 166	95.87 180

thisisatest0530		97.20 156	95.95 171	98.66 122	99.46 116	98.84 108	98.29 158	99.20 120	94.51 161	98.25 148	97.42 140	85.03 205	97.68 134	98.43 89	98.56 84	99.08 100	98.89 68

tttt0517		97.18 157	95.92 172	98.65 125	99.49 112	98.92 100	98.29 158	99.20 120	94.37 171	98.17 151	97.37 143	84.72 208	97.68 134	98.55 84	98.56 84	99.10 94	98.95 62

MDTV_nov1_ep13_2view		97.12 158	96.19 166	98.22 151	99.13 163	98.05 162	99.24 71	99.47 70	97.61 57	99.15 68	99.59 36	99.01 104	98.40 103	94.87 196	90.14 199	93.91 200	94.04 194

MAR-MVS		97.12 158	96.28 165	98.11 156	98.94 174	97.22 186	97.65 188	99.38 87	90.93 212	98.15 154	95.17 189	97.13 163	96.48 164	97.71 136	97.40 144	98.06 170	98.40 112
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		96.99 160	96.46 161	97.61 173	98.98 173	97.89 169	97.54 192	99.76 18	93.43 186	96.55 199	94.93 192	98.06 146	94.32 192	96.93 164	96.50 166	98.53 153	97.47 152

FPMVS		96.97 161	97.20 142	96.70 195	97.75 208	96.11 199	97.72 184	95.47 207	97.13 82	98.02 162	97.57 135	96.67 168	92.97 201	99.00 58	98.34 94	98.28 163	95.58 182

TAMVS		96.95 162	96.94 152	96.97 190	99.07 169	97.67 181	97.98 173	97.12 202	95.04 152	95.41 206	99.27 64	95.57 176	94.09 194	97.32 157	97.11 154	98.16 169	96.59 173

FMVSNet3		96.85 163	96.67 154	97.06 184	97.56 211	99.01 85	97.99 172	99.33 98	94.09 176	95.60 203	98.17 116	95.97 172	93.26 200	94.76 198	96.22 170	98.59 149	98.46 106

GA-MVS		96.84 164	95.86 174	97.98 160	99.16 160	98.29 146	97.91 175	98.64 166	95.14 150	97.71 174	98.04 123	88.90 193	96.50 163	96.41 174	96.61 165	97.97 174	97.60 149

CHOSEN 280x420		96.80 165	96.30 164	97.39 176	99.09 165	96.52 191	98.76 122	99.29 106	93.88 181	97.65 175	98.34 106	93.66 184	96.29 170	98.28 108	97.73 126	93.27 203	95.70 181

gg-mvs-nofinetune		96.77 166	96.52 160	97.06 184	99.66 71	97.82 173	97.54 192	99.86 8	98.69 15	98.61 118	99.94 2	89.62 191	88.37 213	97.55 143	96.67 162	98.30 162	95.35 183

DPM-MVS		96.73 167	95.70 177	97.95 163	98.93 175	97.26 185	97.39 197	98.44 177	95.47 146	97.62 176	90.71 207	98.47 135	97.03 154	95.02 195	95.27 183	98.26 164	97.67 147

baseline1		96.72 168	95.40 179	98.26 146	99.53 103	98.81 109	98.32 155	98.80 155	94.96 155	96.78 198	96.50 170	84.87 207	96.68 159	97.42 151	97.91 110	99.46 50	97.33 158

N_pmnet		96.68 169	95.70 177	97.84 167	99.42 127	98.00 165	99.35 55	98.21 183	98.40 22	98.13 155	99.42 55	99.30 67	97.44 142	94.00 202	88.79 200	94.47 199	91.96 201

pmnet_mix02		96.61 170	95.32 180	98.11 156	99.41 129	97.68 179	99.05 90	97.59 197	98.16 26	99.05 80	99.48 48	99.11 93	98.32 107	92.36 206	87.67 202	95.26 196	92.80 199

new_pmnet		96.59 171	96.40 163	96.81 192	98.24 204	95.46 208	97.71 186	94.75 210	96.92 88	96.80 197	99.23 68	97.81 153	96.69 157	96.58 170	95.16 185	96.69 186	93.64 196

PMMVS		96.47 172	95.81 175	97.23 180	97.38 213	95.96 203	97.31 198	96.91 204	93.21 190	97.93 168	97.14 149	97.64 156	95.70 173	95.24 191	96.18 173	98.17 168	95.33 184

EPNet		96.44 173	96.08 168	96.86 191	99.32 138	97.15 188	97.69 187	99.32 102	93.67 183	98.11 158	95.64 185	93.44 185	89.07 211	96.86 165	96.83 159	97.67 176	98.97 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres600view7		96.35 174	94.27 182	98.79 109	99.66 71	99.18 62	98.94 100	99.38 87	94.37 171	97.21 193	87.19 209	84.10 209	98.10 117	98.16 114	99.47 18	99.42 56	97.43 153

EPNet_dtu		96.31 175	95.96 170	96.72 194	99.18 157	95.39 209	97.03 207	99.13 133	93.02 193	99.35 37	97.23 146	97.07 164	90.70 210	95.74 183	95.08 187	94.94 198	98.16 133
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs3		96.30 176	95.87 173	96.80 193	97.66 210	96.48 192	97.93 174	93.80 211	93.40 187	98.54 125	98.27 111	97.50 157	97.37 145	97.49 146	93.11 195	95.52 195	94.85 188

PMMVS2		96.29 177	97.05 148	95.40 205	98.32 203	96.16 196	98.18 166	97.46 198	97.20 80	84.51 219	99.60 34	98.68 125	96.37 165	98.59 82	97.38 145	97.58 179	91.76 202

thres200		96.23 178	94.13 183	98.69 118	99.44 121	99.18 62	98.58 135	99.38 87	93.52 185	97.35 187	86.33 214	85.83 203	97.93 127	98.16 114	98.78 64	99.42 56	97.10 166

thres400		96.22 179	94.08 185	98.72 114	99.58 89	99.05 76	98.83 113	99.22 116	94.01 179	97.40 185	86.34 213	84.91 206	97.93 127	97.85 130	99.08 39	99.37 62	97.28 160

tfpn200view9		96.17 180	94.08 185	98.60 127	99.37 131	99.18 62	98.68 125	99.39 82	92.02 200	97.30 189	86.53 211	86.34 200	97.45 141	98.15 116	99.08 39	99.43 55	97.28 160

CMPMVS	74.71 19	96.17 180	96.06 169	96.30 199	97.41 212	94.52 212	94.83 214	95.46 208	91.57 205	97.26 192	94.45 197	98.33 140	94.98 180	98.28 108	97.59 136	97.86 175	97.68 146
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
IB-MVS	95.85 14	95.87 182	94.88 181	97.02 187	99.09 165	98.25 151	97.16 200	97.38 200	91.97 203	97.77 172	83.61 216	97.29 161	92.03 208	97.16 161	97.66 130	98.66 141	98.20 129
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		95.81 183	95.77 176	95.85 204	99.20 153	98.15 158	97.49 196	98.50 173	92.24 196	92.74 216	96.82 160	92.70 187	88.60 212	97.31 159	97.01 157	98.57 151	96.19 178

thres100view900		95.74 184	93.66 194	98.17 152	99.37 131	98.59 128	98.10 168	98.33 180	92.02 200	97.30 189	86.53 211	86.34 200	96.69 157	96.77 167	98.47 87	99.24 84	96.89 170

ET-MVSNet_ETH3D		95.72 185	93.85 190	97.89 164	97.30 214	98.09 160	98.19 165	98.40 178	94.46 165	98.01 165	96.71 162	77.85 219	96.76 155	96.08 178	96.39 167	98.70 139	97.36 156

baseline2		95.58 186	94.04 187	97.38 177	98.80 181	98.16 156	97.14 201	97.80 195	91.45 207	97.49 180	95.22 188	83.63 210	94.98 180	96.42 173	96.66 163	98.06 170	96.76 171

PatchT		95.49 187	93.29 195	98.06 158	98.65 189	96.20 195	98.91 105	99.73 21	92.00 202	98.50 128	96.67 164	83.25 211	96.34 166	94.40 199	95.50 179	96.21 189	95.04 186

CR-MVSNet		95.38 188	93.01 196	98.16 154	98.63 190	95.85 205	97.64 189	99.78 16	91.27 209	98.50 128	96.84 159	82.16 212	96.34 166	94.40 199	95.50 179	98.05 172	95.04 186

MVSTER		95.38 188	93.99 189	97.01 188	98.83 179	98.95 94	96.62 208	99.14 129	92.17 198	97.44 183	97.29 144	77.88 218	91.63 209	97.45 148	96.18 173	98.41 159	97.99 137

MVS-HIRNet		94.86 190	93.83 191	96.07 200	97.07 215	94.00 213	94.31 215	99.17 124	91.23 211	98.17 151	98.69 97	97.43 158	95.66 174	94.05 201	91.92 197	92.04 210	89.46 210

test-LLR		94.79 191	93.71 192	96.06 201	99.20 153	96.16 196	96.31 209	98.50 173	89.98 213	94.08 211	97.01 153	86.43 198	92.20 206	96.76 168	95.31 181	96.05 191	94.31 191

RPMNet		94.72 192	92.01 201	97.88 166	98.56 195	95.85 205	97.78 180	99.70 26	91.27 209	98.33 142	93.69 200	81.88 213	94.91 183	92.60 204	94.34 193	98.01 173	94.46 190

gm-plane-assit		94.62 193	91.39 203	98.39 137	99.90 12	99.47 27	99.40 47	99.65 33	97.44 65	99.56 20	99.68 26	59.40 223	94.23 193	96.17 176	94.77 190	97.61 178	92.79 200

test-mter		94.62 193	94.02 188	95.32 206	97.72 209	96.75 189	96.23 211	95.67 206	89.83 216	93.23 215	96.99 155	85.94 202	92.66 204	97.32 157	96.11 175	96.44 187	95.22 185

FMVSNet5		94.57 195	92.77 197	96.67 196	97.88 206	98.72 118	97.54 192	98.70 162	88.64 217	95.11 208	86.90 210	81.77 214	93.27 199	97.92 126	98.07 104	97.50 181	97.34 157

SCA		94.53 196	91.95 202	97.55 174	98.58 192	97.86 171	98.49 141	99.68 27	95.11 151	99.07 77	95.87 181	87.24 196	96.53 162	89.77 209	87.08 204	92.96 205	90.69 205

MDTV_nov1_ep13		94.47 197	92.15 199	97.17 181	98.54 197	96.42 193	98.10 168	98.89 150	94.49 163	98.02 162	97.41 141	86.49 197	95.56 175	90.85 207	87.95 201	93.91 200	91.45 204

TESTMET0.1,1		94.44 198	93.71 192	95.30 207	97.84 207	96.16 196	96.31 209	95.32 209	89.98 213	94.08 211	97.01 153	86.43 198	92.20 206	96.76 168	95.31 181	96.05 191	94.31 191

ADS-MVSNet		94.41 199	92.13 200	97.07 183	98.86 177	96.60 190	98.38 150	98.47 176	96.13 134	98.02 162	96.98 156	87.50 195	95.87 172	89.89 208	87.58 203	92.79 207	90.27 207

tpm		93.89 200	91.21 204	97.03 186	98.36 201	96.07 200	97.53 195	99.65 33	92.24 196	98.64 117	97.23 146	74.67 222	94.64 187	92.68 203	90.73 198	93.37 202	94.82 189

PatchmatchNet		93.88 201	91.08 205	97.14 182	98.75 183	96.01 202	98.25 162	99.39 82	94.95 156	98.96 90	96.32 173	85.35 204	95.50 176	88.89 210	85.89 208	91.99 211	90.15 208
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		93.67 202	90.82 206	96.99 189	98.62 191	96.39 194	98.40 148	99.11 134	95.54 145	97.87 170	97.14 149	81.27 216	94.97 182	88.54 212	86.80 206	92.95 206	90.06 209

MVE	82.47 18	93.12 203	94.09 184	91.99 210	90.79 217	82.50 218	93.93 216	96.30 205	96.06 135	88.81 217	98.19 114	96.38 170	97.56 138	97.24 160	95.18 184	84.58 217	93.07 197
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CostFormer		92.75 204	89.49 208	96.55 197	98.78 182	95.83 207	97.55 191	98.59 169	91.83 204	97.34 188	96.31 174	78.53 217	94.50 188	86.14 213	84.92 209	92.54 208	92.84 198

tpmrst		92.45 205	89.48 209	95.92 203	98.43 200	95.03 210	97.14 201	97.92 193	94.16 175	97.56 177	97.86 127	81.63 215	93.56 198	85.89 214	82.86 210	90.91 215	88.95 212

dps		92.35 206	88.78 211	96.52 198	98.21 205	95.94 204	97.78 180	98.38 179	89.88 215	96.81 196	95.07 190	75.31 221	94.70 186	88.62 211	86.21 207	93.21 204	90.41 206

E-PMN		92.28 207	90.12 207	94.79 208	98.56 195	90.90 215	95.16 213	93.68 212	95.36 148	95.10 209	96.56 167	89.05 192	95.24 178	95.21 192	81.84 212	90.98 213	81.94 214

EMVS		91.84 208	89.39 210	94.70 209	98.44 199	90.84 216	95.27 212	93.53 213	95.18 149	95.26 207	95.62 186	87.59 194	94.77 185	94.87 196	80.72 213	90.95 214	80.88 215

tpm cat1		91.52 209	87.70 212	95.97 202	98.33 202	94.98 211	97.06 204	98.03 188	92.11 199	98.03 161	94.77 194	77.19 220	92.71 203	83.56 215	82.24 211	91.67 212	89.04 211

test_method		77.69 210	85.40 213	68.69 211	42.66 219	55.39 220	82.17 219	52.05 215	92.83 195	84.52 218	94.88 193	95.41 178	65.37 214	92.49 205	79.32 214	85.36 216	87.50 213

GG-mvs-BLEND		65.66 211	92.62 198	34.20 213	1.45 222	93.75 214	85.40 218	1.64 219	91.37 208	17.21 221	87.25 208	94.78 181	3.25 218	95.64 186	93.80 194	96.27 188	91.74 203

testmvs		9.73 212	13.38 214	5.48 215	3.62 220	4.12 221	6.40 222	3.19 218	14.92 218	7.68 223	22.10 217	13.89 225	6.83 216	13.47 216	10.38 216	5.14 220	14.81 216

test123		9.37 213	12.26 215	6.00 214	3.32 221	4.06 222	6.39 223	3.41 217	13.20 219	10.48 222	16.43 218	16.22 224	6.76 217	11.37 217	10.40 215	5.62 219	14.10 217

uanet_test		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet-low-res		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

RE-MVS-def										99.88 2

9.14												98.83 116

SR-MVS					99.62 81			99.47 70				99.40 57

Anonymous202405211			98.44 72		99.79 33	99.32 45	99.05 90	99.34 97	96.59 107		97.95 126	97.68 155	97.16 149	99.36 30	99.28 30	99.61 33	98.90 66

our_test_3					99.29 143	97.72 177	98.98 96

ambc			97.89 113		99.45 118	97.88 170	97.78 180		97.27 73	99.80 3	98.99 87	98.48 133	98.55 94	97.80 132	96.68 161	98.54 152	98.10 135

MTAPA										99.19 57		99.68 22

MTMP										99.20 55		99.54 43

Patchmatch-RL test							32.47 221

tmp_tt				65.28 212	82.24 218	71.50 219	70.81 220	23.21 216	96.14 132	81.70 220	85.98 215	92.44 188	49.84 215	95.81 181	94.36 192	83.86 218

XVS					99.77 38	99.07 72	99.46 42			98.95 92		99.37 60				99.33 69

X-MVStestdata					99.77 38	99.07 72	99.46 42			98.95 92		99.37 60				99.33 69

abl_6				98.38 139	99.03 171	98.04 163	98.08 170	98.65 164	93.23 189	98.56 122	94.58 196	98.57 131	97.17 148			98.81 130	97.42 154

mPP-MVS					99.75 47							99.49 52

NP-MVS									93.07 192

Patchmtry						96.05 201	97.64 189	99.78 16		98.50 128

DeepMVS_CX						87.86 217	92.27 217	61.98 214	93.64 184	93.62 214	91.17 205	91.67 189	94.90 184	95.99 180		92.48 209	94.18 193

High-resolution multi-view benchmark