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 17	99.71 10	99.77 9	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 7	99.67 14	99.76 11	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 13	99.95 2	99.88 1	99.71 17	99.83 12	99.58 2	99.48 28	99.79 21	99.78 9	98.98 67	99.86 2	99.85 1	99.88 8	99.82 1

SixPastTwentyTwo		99.70 4	99.59 5	99.82 2	99.93 5	99.80 3	99.86 2	99.87 6	98.87 12	99.79 5	99.85 14	99.33 65	99.74 5	99.85 3	99.82 2	99.74 24	99.63 5

v7n		99.68 5	99.61 4	99.76 8	99.89 14	99.74 9	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 43	99.46 15

anonymousdsp		99.64 6	99.55 7	99.74 10	99.87 17	99.56 21	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 29	99.64 3

UniMVSNet_ETH3D		99.61 7	99.59 5	99.63 15	99.96 1	99.70 11	99.53 34	99.86 8	99.28 6	99.48 28	99.44 52	99.86 4	99.01 65	99.78 5	99.76 4	99.90 2	99.33 21

WR-MVS		99.61 7	99.44 9	99.82 2	99.92 7	99.80 3	99.80 5	99.89 1	98.54 17	99.66 13	99.78 23	99.16 86	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 7	99.76 6	99.80 5	99.88 3	97.60 58	99.71 6	99.59 36	99.52 45	99.75 4	99.64 13	99.51 17	99.90 2	99.46 15

TDRefinement		99.54 9	99.50 8	99.60 18	99.70 64	99.35 40	99.77 9	99.58 47	99.40 5	99.28 49	99.66 27	99.41 55	99.55 15	99.74 6	99.65 6	99.70 26	99.25 25

DTE-MVSNet		99.52 11	99.27 17	99.82 2	99.93 5	99.77 5	99.79 7	99.87 6	97.89 40	99.70 11	99.55 44	99.21 78	99.77 2	99.65 11	99.43 21	99.90 2	99.36 19

PS-CasMVS		99.50 12	99.23 19	99.82 2	99.92 7	99.75 8	99.78 8	99.89 1	97.30 71	99.71 6	99.60 34	99.23 74	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 11	99.76 6	99.75 12	99.88 3	97.27 74	99.58 17	99.56 40	99.24 73	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 21	99.82 26	99.49 25	99.63 23	99.65 35	98.88 11	99.31 43	99.85 14	99.02 105	99.23 45	99.60 16	99.58 15	99.80 16	99.22 32

MIMVSNet1		99.46 15	99.34 11	99.60 18	99.83 23	99.68 13	99.74 15	99.71 24	98.20 25	99.41 35	99.86 13	99.66 27	99.41 30	99.50 22	99.39 23	99.50 48	99.10 43

TransMVSNet (Re)		99.45 16	99.32 14	99.61 16	99.88 16	99.60 18	99.75 12	99.63 39	99.11 8	99.28 49	99.83 18	98.35 141	99.27 42	99.70 7	99.62 11	99.84 11	99.03 51

ACMH	97.81 6	99.44 17	99.33 12	99.56 22	99.81 29	99.42 33	99.73 16	99.58 47	99.02 9	99.10 74	99.41 56	99.69 20	99.60 10	99.45 26	99.26 33	99.55 39	99.05 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CS-MVS-test		99.39 18	99.14 25	99.68 12	99.94 3	99.81 2	99.81 4	99.67 31	97.33 70	99.46 31	99.08 77	99.70 18	99.39 33	99.18 37	99.50 18	99.80 16	99.38 18

CP-MVSNet		99.39 18	99.04 29	99.80 6	99.91 11	99.70 11	99.75 12	99.88 3	96.82 95	99.68 12	99.32 59	98.86 114	99.68 7	99.57 19	99.47 19	99.89 6	99.52 10

COLMAP_ROB	98.29 2	99.37 20	99.25 18	99.51 29	99.74 55	99.12 69	99.56 31	99.39 83	98.96 10	99.17 61	99.44 52	99.63 35	99.58 11	99.48 24	99.27 32	99.60 35	98.81 76
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 21	99.15 23	99.53 28	99.73 60	99.05 77	99.49 39	99.40 81	98.42 20	99.55 21	99.71 25	99.89 3	99.49 19	99.14 39	98.81 62	99.54 40	99.02 53
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 22	99.33 12	99.31 54	99.87 17	99.65 17	99.63 23	99.75 20	97.76 42	97.29 193	99.87 11	99.63 35	99.52 16	99.66 10	99.63 7	99.77 21	99.12 38

UA-Net		99.30 23	99.22 21	99.39 41	99.94 3	99.66 16	98.91 108	99.86 8	97.74 48	98.74 114	99.00 87	99.60 40	99.17 51	99.50 22	99.39 23	99.70 26	99.64 3

ACMH+	97.53 7	99.29 24	99.20 22	99.40 40	99.81 29	99.22 57	99.59 28	99.50 65	98.64 16	98.29 147	99.21 71	99.69 20	99.57 12	99.53 21	99.33 28	99.66 30	98.81 76

Vis-MVSNet		99.25 25	99.32 14	99.17 65	99.65 76	99.55 23	99.63 23	99.33 99	98.16 26	99.29 46	99.65 30	99.77 10	97.56 140	99.44 28	99.14 38	99.58 36	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 26	98.91 36	99.61 16	99.81 29	99.45 30	99.47 41	99.68 27	97.28 73	99.39 36	99.54 45	99.08 101	99.45 22	99.09 45	98.84 59	99.83 12	99.04 49

CSCG		99.23 26	99.15 23	99.32 53	99.83 23	99.45 30	98.97 99	99.21 120	98.83 13	99.04 85	99.43 54	99.64 33	99.26 43	98.85 71	98.20 99	99.62 33	99.62 6

Gipuma		99.22 28	98.86 39	99.64 13	99.70 64	99.24 51	99.17 82	99.63 39	99.52 3	99.89 1	96.54 172	99.14 90	99.93 1	99.42 29	99.15 37	99.52 43	99.04 49
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tfpnnormal		99.19 29	98.90 37	99.54 25	99.81 29	99.55 23	99.60 27	99.54 56	98.53 19	99.23 53	98.40 106	98.23 144	99.40 31	99.29 33	99.36 26	99.63 32	98.95 63

Baseline_NR-MVSNet		99.18 30	98.87 38	99.54 25	99.74 55	99.56 21	99.36 53	99.62 44	96.53 116	99.29 46	99.85 14	98.64 132	99.40 31	99.03 56	99.63 7	99.83 12	98.86 71

thisisatest0515		99.16 31	98.94 34	99.41 35	99.75 49	99.43 32	99.36 53	99.63 39	97.68 54	99.35 38	99.31 60	98.90 111	99.09 59	98.95 61	99.20 34	99.27 80	99.11 39

APDe-MVS		99.15 32	98.95 31	99.39 41	99.77 39	99.28 48	99.52 35	99.54 56	97.22 78	99.06 78	99.20 72	99.64 33	99.05 63	99.14 39	99.02 47	99.39 61	99.17 36

FC-MVSNet-train		99.13 33	99.05 28	99.21 60	99.87 17	99.57 20	99.67 18	99.60 46	96.75 101	98.28 148	99.48 48	99.52 45	98.10 119	99.47 25	99.37 25	99.76 23	99.21 33

NR-MVSNet		99.10 34	98.68 54	99.58 20	99.89 14	99.23 54	99.35 56	99.63 39	96.58 109	99.36 37	99.05 81	98.67 130	99.46 20	99.63 14	98.73 72	99.80 16	98.88 70

DVP-MVS		99.09 35	99.07 27	99.12 73	99.55 98	99.40 35	99.36 53	99.44 80	97.75 45	98.23 151	99.23 68	99.80 7	98.97 68	99.08 47	98.96 48	99.19 88	99.25 25
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 36	98.69 52	99.54 25	99.75 49	99.33 43	99.29 64	99.64 38	96.75 101	99.48 28	99.30 62	98.69 126	99.26 43	98.94 63	98.76 68	99.78 20	99.02 53

ACMMPR		99.05 37	98.72 48	99.44 30	99.79 34	99.12 69	99.35 56	99.56 50	97.74 48	99.21 55	97.72 133	99.55 43	99.29 40	98.90 69	98.81 62	99.41 60	99.19 34

DU-MVS		99.04 38	98.59 58	99.56 22	99.74 55	99.23 54	99.29 64	99.63 39	96.58 109	99.55 21	99.05 81	98.68 128	99.36 36	99.03 56	98.60 79	99.77 21	98.97 58

TSAR-MVS + MP.		99.02 39	98.95 31	99.11 76	99.23 154	98.79 113	99.51 36	98.73 161	97.50 62	98.56 124	99.03 84	99.59 41	99.16 53	99.29 33	99.17 36	99.50 48	99.24 29
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 40	98.66 55	99.41 35	99.52 109	99.39 36	99.57 30	99.66 33	97.59 59	99.32 42	99.88 9	99.23 74	99.50 18	97.77 136	97.98 109	98.92 123	98.78 81

EG-PatchMatch MVS		99.01 40	98.77 44	99.28 58	99.64 79	98.90 106	98.81 120	99.27 111	96.55 113	99.71 6	99.31 60	99.66 27	99.17 51	99.28 35	99.11 39	99.10 95	98.57 96

PVSNet_Blended_VisFu		98.98 42	98.79 42	99.21 60	99.76 45	99.34 41	99.35 56	99.35 95	97.12 84	99.46 31	99.56 40	98.89 112	98.08 122	99.05 50	98.58 81	99.27 80	98.98 57

HFP-MVS		98.97 43	98.70 50	99.29 56	99.67 70	98.98 89	99.13 87	99.53 59	97.76 42	98.90 100	98.07 120	99.50 51	99.14 57	98.64 82	98.78 66	99.37 63	99.18 35

UniMVSNet_NR-MVSNet		98.97 43	98.46 69	99.56 22	99.76 45	99.34 41	99.29 64	99.61 45	96.55 113	99.55 21	99.05 81	97.96 152	99.36 36	98.84 72	98.50 87	99.81 15	98.97 58

SED-MVS		98.94 45	98.95 31	98.91 98	99.43 125	99.38 38	99.12 89	99.46 74	97.05 87	98.43 139	99.23 68	99.79 8	97.99 125	99.05 50	98.94 50	99.05 108	99.23 30

ACMMP_NAP		98.94 45	98.72 48	99.21 60	99.67 70	99.08 72	99.26 69	99.39 83	96.84 92	98.88 104	98.22 113	99.68 23	98.82 77	99.06 49	98.90 53	99.25 83	99.25 25

zzz-MVS		98.94 45	98.57 61	99.37 48	99.77 39	99.15 66	99.24 72	99.55 52	97.38 68	99.16 64	96.64 168	99.69 20	99.15 55	99.09 45	98.92 52	99.37 63	99.11 39

v1144		98.94 45	98.53 64	99.42 34	99.62 83	99.03 83	99.58 29	99.36 92	97.99 31	99.49 27	99.91 8	99.20 80	99.51 17	97.61 141	97.85 116	98.95 118	98.10 137

v8		98.94 45	98.60 56	99.35 51	99.54 102	99.39 36	99.55 32	99.67 31	97.48 63	99.13 70	99.81 19	99.10 97	99.39 33	97.86 131	97.89 114	98.81 132	98.66 88

SteuartSystems-ACMMP		98.94 45	98.52 65	99.43 33	99.79 34	99.13 68	99.33 60	99.55 52	96.17 132	99.04 85	97.53 139	99.65 31	99.46 20	99.04 55	98.76 68	99.44 55	99.35 20
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v1192		98.91 51	98.48 68	99.41 35	99.61 87	99.03 83	99.64 20	99.25 115	97.91 37	99.58 17	99.92 4	99.07 103	99.45 22	97.55 145	97.68 131	98.93 120	98.23 127

FMVSNet1		98.90 52	99.10 26	98.67 122	99.54 102	99.48 27	99.22 75	99.66 33	98.39 23	97.50 181	99.66 27	99.04 104	96.58 162	99.05 50	99.03 44	99.52 43	99.08 45

ACMM	96.66 11	98.90 52	98.44 73	99.44 30	99.74 55	98.95 95	99.47 41	99.55 52	97.66 56	99.09 75	96.43 174	99.41 55	99.35 38	98.95 61	98.67 75	99.45 53	99.03 51
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		98.89 54	98.79 42	98.99 91	99.82 26	99.41 34	99.18 80	99.31 105	96.92 89	98.54 127	98.58 103	98.84 118	97.46 142	99.45 26	99.29 30	99.65 31	99.08 45

v1921920		98.89 54	98.46 69	99.39 41	99.58 91	99.04 81	99.64 20	99.17 126	97.91 37	99.64 15	99.92 4	98.99 109	99.44 25	97.44 152	97.57 140	98.84 130	98.35 117

GeoE		98.88 56	98.43 76	99.41 35	99.83 23	99.24 51	99.51 36	99.82 13	96.55 113	99.22 54	98.76 95	99.22 77	98.96 69	98.55 85	98.15 101	99.10 95	98.56 99

v144192		98.88 56	98.46 69	99.37 48	99.56 97	99.03 83	99.61 26	99.26 112	97.79 41	99.58 17	99.88 9	99.11 95	99.43 27	97.38 157	97.61 136	98.80 134	98.43 112

SMA-MVS		98.87 58	98.73 47	99.04 84	99.72 61	99.05 77	98.64 130	99.17 126	96.31 127	98.80 109	99.07 79	99.70 18	98.67 86	98.93 66	98.82 60	99.23 86	99.23 30
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 58	98.40 79	99.41 35	99.74 55	98.88 107	99.29 64	99.50 65	96.85 91	98.96 92	97.05 155	99.66 27	99.43 27	98.98 60	98.60 79	99.52 43	98.81 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DCV-MVSNet		98.86 60	98.57 61	99.19 63	99.86 21	99.67 14	99.39 49	99.71 24	97.53 61	98.69 117	95.85 185	98.48 136	97.75 134	99.57 19	99.41 22	99.72 25	99.48 14

v1240		98.86 60	98.41 78	99.38 46	99.59 89	99.05 77	99.65 19	99.14 131	97.68 54	99.66 13	99.93 3	98.72 125	99.45 22	97.38 157	97.72 129	98.79 135	98.35 117

CP-MVS		98.86 60	98.43 76	99.36 50	99.68 68	98.97 93	99.19 78	99.46 74	96.60 107	99.20 56	97.11 154	99.51 49	99.15 55	98.92 67	98.82 60	99.45 53	99.08 45

v2v482		98.85 63	98.40 79	99.38 46	99.65 76	98.98 89	99.55 32	99.39 83	97.92 36	99.35 38	99.85 14	99.14 90	99.39 33	97.50 147	97.78 119	98.98 115	97.60 151

DPE-MVS		98.84 64	98.69 52	99.00 88	99.05 172	99.26 49	99.19 78	99.35 95	95.85 140	98.74 114	99.27 64	99.66 27	98.30 112	98.90 69	98.93 51	99.37 63	99.00 55
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
OPM-MVS		98.84 64	98.59 58	99.12 73	99.52 109	98.50 138	99.13 87	99.22 118	97.76 42	98.76 111	98.70 97	99.61 38	98.90 72	98.67 80	98.37 93	99.19 88	98.57 96

test20.03		98.84 64	98.74 46	98.95 94	99.77 39	99.33 43	99.21 77	99.46 74	97.29 72	98.88 104	99.65 30	99.10 97	97.07 154	99.11 42	98.76 68	99.32 73	97.98 141

casdiffmvs		98.84 64	98.75 45	98.94 97	99.75 49	99.21 58	99.33 60	99.04 142	98.04 29	97.46 184	99.72 24	99.72 15	98.60 90	98.30 105	98.37 93	99.48 50	97.92 143

LGP-MVS_train		98.84 64	98.33 85	99.44 30	99.78 37	98.98 89	99.39 49	99.55 52	95.41 148	98.90 100	97.51 140	99.68 23	99.44 25	99.03 56	98.81 62	99.57 37	98.91 66

RPSCF		98.84 64	98.81 41	98.89 100	99.37 133	98.95 95	98.51 142	98.85 154	97.73 50	98.33 144	98.97 89	99.14 90	98.95 70	99.18 37	98.68 74	99.31 74	98.99 56

ACMMP		98.82 70	98.33 85	99.39 41	99.77 39	99.14 67	99.37 52	99.54 56	96.47 120	99.03 87	96.26 178	99.52 45	99.28 41	98.92 67	98.80 65	99.37 63	99.16 37
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 71	98.49 67	99.18 64	99.52 109	98.92 101	99.50 38	99.29 107	97.43 66	98.97 89	99.81 19	99.00 108	99.30 39	97.93 127	98.01 107	98.51 158	98.34 121

LS3D		98.79 72	98.52 65	99.12 73	99.64 79	99.09 71	99.24 72	99.46 74	97.75 45	98.93 98	97.47 141	98.23 144	97.98 126	99.36 30	99.30 29	99.46 51	98.42 113

MP-MVS		98.78 73	98.30 87	99.34 52	99.75 49	98.95 95	99.26 69	99.46 74	95.78 143	99.17 61	96.98 159	99.72 15	99.06 62	98.84 72	98.74 71	99.33 70	99.11 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.77 74	98.45 72	99.15 68	99.68 68	98.94 99	99.49 39	99.31 105	97.95 33	98.91 99	99.65 30	99.62 37	99.18 48	97.99 125	97.64 135	98.33 163	97.38 157

SD-MVS		98.73 75	98.54 63	98.95 94	99.14 163	98.76 116	98.46 146	99.14 131	97.71 52	98.56 124	98.06 122	99.61 38	98.85 76	98.56 84	97.74 126	99.54 40	99.32 22
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 76	98.60 56	98.87 102	99.67 70	99.33 43	99.15 84	99.26 112	96.99 88	97.90 171	98.19 115	99.74 12	98.29 113	97.69 139	98.96 48	98.96 116	99.27 24
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 77	98.09 104	99.39 41	99.76 45	99.07 73	99.30 63	99.51 63	94.76 159	99.18 60	96.70 166	99.51 49	99.20 46	98.79 76	98.71 73	99.39 61	99.11 39

pmmvs-eth3d		98.68 78	98.14 100	99.29 56	99.49 114	98.45 141	99.45 45	99.38 88	97.21 79	99.50 26	99.65 30	99.21 78	99.16 53	97.11 164	97.56 141	98.79 135	97.82 147

EU-MVSNet		98.68 78	98.94 34	98.37 142	99.14 163	98.74 118	99.64 20	98.20 187	98.21 24	99.17 61	99.66 27	99.18 83	99.08 60	99.11 42	98.86 54	95.00 199	98.83 73

PMVS	92.51 17	98.66 80	98.86 39	98.43 137	99.26 149	98.98 89	98.60 136	98.59 171	97.73 50	99.45 33	99.38 57	98.54 135	95.24 180	99.62 15	99.61 12	99.42 57	98.17 134
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepC-MVS_fast	97.38 8	98.65 81	98.34 84	99.02 87	99.33 137	98.29 148	98.99 97	98.71 163	97.40 67	99.31 43	98.20 114	99.40 58	98.54 98	98.33 102	98.18 100	99.23 86	98.58 94
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 81	98.35 83	99.00 88	99.59 89	98.70 121	98.90 112	99.36 92	97.97 32	99.09 75	96.55 171	99.09 99	97.97 127	98.70 79	98.65 77	99.12 94	98.81 76

TSAR-MVS + ACMM		98.64 83	98.58 60	98.72 116	99.17 161	98.63 127	98.69 126	99.10 138	97.69 53	98.30 146	99.12 76	99.38 60	98.70 85	98.45 88	97.51 143	98.35 162	99.25 25

DELS-MVS		98.63 84	98.70 50	98.55 133	99.24 153	99.04 81	98.96 100	98.52 174	96.83 94	98.38 141	99.58 38	99.68 23	97.06 155	98.74 78	98.44 89	99.10 95	98.59 93
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 85	98.40 79	98.89 100	99.57 96	98.80 112	98.63 131	99.35 95	96.82 95	98.60 121	98.85 94	99.08 101	98.09 121	98.31 103	98.21 97	99.08 101	98.72 83

EPP-MVSNet		98.61 86	98.19 96	99.11 76	99.86 21	99.60 18	99.44 46	99.53 59	97.37 69	96.85 197	98.69 98	93.75 185	99.18 48	99.22 36	99.35 27	99.82 14	99.32 22

3Dnovator+	97.85 5	98.61 86	98.14 100	99.15 68	99.62 83	98.37 146	99.10 90	99.51 63	98.04 29	98.98 88	96.07 182	98.75 124	98.55 96	98.51 87	98.40 90	99.17 90	98.82 74

X-MVS		98.59 88	97.99 110	99.30 55	99.75 49	99.07 73	99.17 82	99.50 65	96.62 105	98.95 94	93.95 201	99.37 61	99.11 58	98.94 63	98.86 54	99.35 68	99.09 44

MVS_111021_HR		98.58 89	98.26 90	98.96 93	99.32 140	98.81 110	98.48 144	98.99 147	96.81 97	99.16 64	98.07 120	99.23 74	98.89 74	98.43 90	98.27 96	98.90 125	98.24 126

MVS_0304		98.57 90	98.36 82	98.82 109	99.72 61	98.94 99	98.92 106	99.14 131	96.76 100	99.33 41	98.30 110	99.73 13	96.74 158	98.05 122	97.79 118	99.08 101	98.97 58

PM-MVS		98.57 90	98.24 92	98.95 94	99.26 149	98.59 130	99.03 94	98.74 160	96.84 92	99.44 34	99.13 75	98.31 143	98.75 82	98.03 123	98.21 97	98.48 159	98.58 94

PHI-MVS		98.57 90	98.20 95	99.00 88	99.48 116	98.91 103	98.68 127	99.17 126	94.97 155	99.27 51	98.33 108	99.33 65	98.05 123	98.82 74	98.62 78	99.34 69	98.38 115

HPM-MVS++		98.56 93	98.08 105	99.11 76	99.53 105	98.61 129	99.02 96	99.32 103	96.29 129	99.06 78	97.23 148	99.50 51	98.77 80	98.15 118	97.90 112	98.96 116	98.90 67

TSAR-MVS + GP.		98.54 94	98.29 89	98.82 109	99.28 147	98.59 130	97.73 185	99.24 117	95.93 138	98.59 122	99.07 79	99.17 84	98.86 75	98.44 89	98.10 103	99.26 82	98.72 83

CS-MVS		98.52 95	97.90 114	99.25 59	99.76 45	99.49 25	99.18 80	99.28 110	93.34 190	98.97 89	97.21 151	99.30 68	98.75 82	98.24 111	98.86 54	99.57 37	98.84 72

UGNet		98.52 95	99.00 30	97.96 164	99.58 91	99.26 49	99.27 68	99.40 81	98.07 28	98.28 148	98.76 95	99.71 17	92.24 207	98.94 63	98.85 57	99.00 114	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 97	98.03 107	99.05 82	99.50 112	99.01 86	99.15 84	99.26 112	96.38 125	99.12 72	99.50 47	99.12 93	98.60 90	97.68 140	97.24 154	98.66 143	97.30 161

CLD-MVS		98.48 98	98.15 99	98.86 105	99.53 105	98.35 147	98.55 139	97.83 196	96.02 137	98.97 89	99.08 77	99.75 11	99.03 64	98.10 121	97.33 150	99.28 78	98.44 111
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 99	98.30 87	98.67 122	99.65 76	98.87 108	98.82 119	99.01 145	96.14 133	99.29 46	98.86 92	99.01 106	96.54 163	98.36 96	98.08 104	98.72 139	98.80 80

APD-MVS		98.47 99	97.97 111	99.05 82	99.64 79	98.91 103	98.94 102	99.45 79	94.40 171	98.77 110	97.26 147	99.41 55	98.21 116	98.67 80	98.57 84	99.31 74	98.57 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Vis-MVSNet (Re-imp)		98.46 101	98.23 93	98.73 115	99.81 29	99.29 47	98.79 121	99.50 65	96.20 131	96.03 203	98.29 111	96.98 167	98.54 98	99.11 42	99.08 40	99.70 26	98.62 90

Fast-Effi-MVS+		98.42 102	97.79 118	99.15 68	99.69 66	98.66 124	98.94 102	99.68 27	94.49 164	99.05 80	98.06 122	98.86 114	98.48 101	98.18 114	97.78 119	99.05 108	98.54 102

DROMVSNet		98.42 102	97.79 118	99.15 68	99.69 66	98.66 124	98.94 102	99.68 27	94.49 164	99.05 80	98.06 122	98.86 114	98.48 101	98.18 114	97.78 119	99.05 108	98.54 102

ETV-MVS		98.41 104	97.76 120	99.17 65	99.58 91	99.01 86	98.91 108	99.50 65	93.33 191	99.31 43	96.82 163	98.42 139	98.17 118	99.13 41	99.08 40	99.54 40	98.56 99

MVS_111021_LR		98.39 105	98.11 102	98.71 118	99.08 169	98.54 136	98.23 166	98.56 173	96.57 111	99.13 70	98.41 105	98.86 114	98.65 88	98.23 112	97.87 115	98.65 145	98.28 123

pmmvs5		98.37 106	97.81 117	99.03 85	99.46 118	98.97 93	99.03 94	98.96 149	95.85 140	99.05 80	99.45 51	98.66 131	98.79 79	96.02 181	97.52 142	98.87 127	98.21 130

OMC-MVS		98.35 107	98.10 103	98.64 128	98.85 180	97.99 168	98.56 138	98.21 185	97.26 76	98.87 106	98.54 104	99.27 72	98.43 104	98.34 100	97.66 132	98.92 123	97.65 150

canonicalmvs		98.34 108	97.92 113	98.83 107	99.45 120	99.21 58	98.37 153	99.53 59	97.06 86	97.74 175	96.95 161	95.05 182	98.36 107	98.77 77	98.85 57	99.51 47	99.53 9

CHOSEN 1792x2688		98.31 109	98.02 108	98.66 124	99.55 98	98.57 133	99.38 51	99.25 115	98.42 20	98.48 135	99.58 38	99.85 5	98.31 111	95.75 184	95.71 179	96.96 186	98.27 125

xxxxxxxxxxxxxcwj		98.28 110	98.23 93	98.35 143	99.43 125	98.42 144	97.05 207	99.09 139	96.42 122	98.13 157	97.73 131	99.65 31	97.22 148	98.36 96	98.38 91	99.16 92	98.62 90

CPTT-MVS		98.28 110	97.51 132	99.16 67	99.54 102	98.78 114	98.96 100	99.36 92	96.30 128	98.89 103	93.10 205	99.30 68	99.20 46	98.35 99	97.96 110	99.03 112	98.82 74

TinyColmap		98.27 112	97.62 129	99.03 85	99.29 145	97.79 177	98.92 106	98.95 150	97.48 63	99.52 24	98.65 100	97.86 154	98.90 72	98.34 100	97.27 152	98.64 146	95.97 181

diffmvs		98.26 113	98.16 97	98.39 139	99.61 87	98.78 114	98.79 121	98.61 169	97.94 34	97.11 196	99.51 46	99.52 45	97.61 138	96.55 173	96.93 160	98.61 148	97.87 145

USDC		98.26 113	97.57 130	99.06 79	99.42 129	97.98 170	98.83 116	98.85 154	97.57 60	99.59 16	99.15 74	98.59 133	98.99 66	97.42 153	96.08 178	98.69 142	96.23 179

SF-MVS		98.25 115	98.16 97	98.35 143	99.43 125	98.42 144	97.05 207	99.09 139	96.42 122	98.13 157	97.73 131	99.20 80	97.22 148	98.36 96	98.38 91	99.16 92	98.62 90

MCST-MVS		98.25 115	97.57 130	99.06 79	99.53 105	98.24 154	98.63 131	99.17 126	95.88 139	98.58 123	96.11 180	99.09 99	99.18 48	97.58 144	97.31 151	99.25 83	98.75 82

IterMVS-LS		98.23 117	97.66 125	98.90 99	99.63 82	99.38 38	99.07 91	99.48 70	97.75 45	98.81 108	99.37 58	94.57 184	97.88 131	96.54 174	97.04 157	98.53 155	98.97 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAPA-MVS	96.65 12	98.23 117	97.96 112	98.55 133	98.81 182	98.16 158	98.40 150	97.94 194	96.68 103	98.49 133	98.61 101	98.89 112	98.57 94	97.45 150	97.59 138	99.09 100	98.35 117
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNVR-MVS		98.22 119	97.76 120	98.76 113	99.33 137	98.26 152	98.48 144	98.88 153	96.22 130	98.47 137	95.79 186	99.33 65	98.35 108	98.37 95	97.99 108	99.03 112	98.38 115

IS_MVSNet		98.20 120	98.00 109	98.44 136	99.82 26	99.48 27	99.25 71	99.56 50	95.58 145	93.93 215	97.56 138	96.52 171	98.27 114	99.08 47	99.20 34	99.80 16	98.56 99

DeepPCF-MVS	96.68 10	98.20 120	98.26 90	98.12 157	97.03 218	98.11 161	98.44 148	97.70 198	96.77 99	98.52 129	98.91 90	99.17 84	98.58 93	98.41 92	98.02 106	98.46 160	98.46 108

MSDG		98.20 120	97.88 116	98.56 132	99.33 137	97.74 178	98.27 163	98.10 188	97.20 81	98.06 162	98.59 102	99.16 86	98.76 81	98.39 93	97.71 130	98.86 129	96.38 176

testgi		98.18 123	98.44 73	97.89 166	99.78 37	99.23 54	98.78 123	99.21 120	97.26 76	97.41 186	97.39 144	99.36 64	92.85 204	98.82 74	98.66 76	99.31 74	98.35 117

Effi-MVS+		98.11 124	97.29 138	99.06 79	99.62 83	98.55 134	98.16 169	99.80 15	94.64 160	99.15 68	96.59 169	97.43 160	98.44 103	97.46 149	97.90 112	99.17 90	98.45 110

HyFIR lowres test		98.08 125	97.16 147	99.14 72	99.72 61	98.91 103	99.41 47	99.58 47	97.93 35	98.82 107	99.24 66	95.81 177	98.73 84	95.16 195	95.13 188	98.60 150	97.94 142

EIA-MVS		98.03 126	97.20 144	98.99 91	99.66 73	99.24 51	98.53 141	99.52 62	91.56 208	99.25 52	95.34 190	98.78 121	97.72 135	98.38 94	98.58 81	99.28 78	98.54 102

train_agg		97.99 127	97.26 139	98.83 107	99.43 125	98.22 156	98.91 108	99.07 141	94.43 169	97.96 168	96.42 175	99.30 68	98.81 78	97.39 155	96.62 166	98.82 131	98.47 106

MSLP-MVS++		97.99 127	97.64 128	98.40 138	98.91 178	98.47 140	97.12 205	98.78 158	96.49 118	98.48 135	93.57 203	99.12 93	98.51 100	98.31 103	98.58 81	98.58 152	98.95 63

CDPH-MVS		97.99 127	97.23 142	98.87 102	99.58 91	98.29 148	98.83 116	99.20 122	93.76 184	98.11 160	96.11 180	99.16 86	98.23 115	97.80 134	97.22 155	99.29 77	98.28 123

FMVSNet2		97.94 130	98.08 105	97.77 172	98.71 186	99.21 58	98.62 133	99.47 71	96.62 105	96.37 202	99.20 72	97.70 156	94.39 191	97.39 155	97.75 125	99.08 101	98.70 85

PVSNet_BlendedMVS		97.93 131	97.66 125	98.25 150	99.30 142	98.67 122	98.31 158	97.95 192	94.30 175	98.75 112	97.63 135	98.76 122	96.30 170	98.29 106	97.78 119	98.93 120	98.18 132

PVSNet_Blended		97.93 131	97.66 125	98.25 150	99.30 142	98.67 122	98.31 158	97.95 192	94.30 175	98.75 112	97.63 135	98.76 122	96.30 170	98.29 106	97.78 119	98.93 120	98.18 132

OpenMVS	97.26 9	97.88 133	97.17 146	98.70 119	99.50 112	98.55 134	98.34 156	99.11 136	93.92 182	98.90 100	95.04 194	98.23 144	97.38 145	98.11 120	98.12 102	98.95 118	98.23 127

pmmvs4		97.87 134	97.02 151	98.86 105	99.20 155	97.68 181	98.89 113	99.03 143	96.57 111	99.12 72	99.03 84	97.26 164	98.42 105	95.16 195	96.34 170	98.53 155	97.10 168

NCCC		97.84 135	96.96 153	98.87 102	99.39 132	98.27 151	98.46 146	99.02 144	96.78 98	98.73 116	91.12 208	98.91 110	98.57 94	97.83 133	97.49 144	99.04 111	98.33 122

Effi-MVS+-dtu		97.78 136	97.37 136	98.26 148	99.25 151	98.50 138	97.89 179	99.19 125	94.51 162	98.16 155	95.93 183	98.80 120	95.97 173	98.27 110	97.38 147	99.10 95	98.23 127

MDA-MVSNet-bldmvs		97.75 137	97.26 139	98.33 145	99.35 136	98.45 141	99.32 62	97.21 203	97.90 39	99.05 80	99.01 86	96.86 169	99.08 60	99.36 30	92.97 198	95.97 195	96.25 178

CDS-MVSNet		97.75 137	97.68 124	97.83 170	99.08 169	98.20 157	98.68 127	98.61 169	95.63 144	97.80 173	99.24 66	96.93 168	94.09 196	97.96 126	97.82 117	98.71 140	97.99 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CNLPA		97.75 137	97.26 139	98.32 147	98.58 194	97.86 173	97.80 181	98.09 189	96.49 118	98.49 133	96.15 179	98.08 147	98.35 108	98.00 124	97.03 158	98.61 148	97.21 165

PLC	95.63 15	97.73 140	97.01 152	98.57 131	99.10 166	97.80 176	97.72 186	98.77 159	96.34 126	98.38 141	93.46 204	98.06 148	98.66 87	97.90 129	97.65 134	98.77 137	97.90 144
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_Test		97.69 141	97.15 148	98.33 145	99.27 148	98.43 143	98.25 164	99.29 107	95.00 154	97.39 188	98.86 92	98.00 151	97.14 152	95.38 190	96.22 172	98.62 147	98.15 136

GBi-Net		97.69 141	97.75 122	97.62 173	98.71 186	99.21 58	98.62 133	99.33 99	94.09 178	95.60 205	98.17 117	95.97 174	94.39 191	99.05 50	99.03 44	99.08 101	98.70 85

test1		97.69 141	97.75 122	97.62 173	98.71 186	99.21 58	98.62 133	99.33 99	94.09 178	95.60 205	98.17 117	95.97 174	94.39 191	99.05 50	99.03 44	99.08 101	98.70 85

CANet_DTU		97.65 144	97.50 134	97.82 171	99.19 158	98.08 163	98.41 149	98.67 165	94.40 171	99.16 64	98.32 109	98.69 126	93.96 198	97.87 130	97.61 136	97.51 182	97.56 153

IterMVS-SCA-FT		97.63 145	96.86 155	98.52 135	99.48 116	98.71 120	98.84 115	98.91 151	96.44 121	99.16 64	99.56 40	95.54 179	97.95 128	95.68 187	95.07 191	96.76 187	97.03 171

TSAR-MVS + COLMAP		97.62 146	97.31 137	97.98 162	98.47 200	97.39 185	98.29 160	98.25 184	96.68 103	97.54 180	98.87 91	98.04 150	97.08 153	96.78 168	96.26 171	98.26 166	97.12 167

MS-PatchMatch		97.60 147	97.22 143	98.04 161	98.67 190	97.18 189	97.91 177	98.28 183	95.82 142	98.34 143	97.66 134	98.38 140	97.77 133	97.10 165	97.25 153	97.27 184	97.18 166

PCF-MVS	95.58 16	97.60 147	96.67 156	98.69 120	99.44 123	98.23 155	98.37 153	98.81 156	93.01 196	98.22 152	97.97 127	99.59 41	98.20 117	95.72 186	95.08 189	99.08 101	97.09 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		97.58 149	96.65 159	98.66 124	99.30 142	97.99 168	97.88 180	98.65 166	94.58 161	98.66 118	94.65 198	99.15 89	98.59 92	96.10 179	95.59 180	98.90 125	98.50 105

DI_MVS_plusplus_trai		97.57 150	96.55 161	98.77 112	99.55 98	98.76 116	99.22 75	99.00 146	97.08 85	97.95 169	97.78 130	91.35 192	98.02 124	96.20 177	96.81 162	98.87 127	97.87 145

AdaColmap		97.57 150	96.57 160	98.74 114	99.25 151	98.01 166	98.36 155	98.98 148	94.44 168	98.47 137	92.44 206	97.91 153	98.62 89	98.19 113	97.74 126	98.73 138	97.28 162

baseline		97.50 152	97.51 132	97.50 177	99.18 159	97.38 186	98.00 173	98.00 191	96.52 117	97.49 182	99.28 63	99.43 54	95.31 179	95.27 192	96.22 172	96.99 185	98.47 106

IterMVS		97.40 153	96.67 156	98.25 150	99.45 120	98.66 124	98.87 114	98.73 161	96.40 124	98.94 97	99.56 40	95.26 181	97.58 139	95.38 190	94.70 193	95.90 196	96.72 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CVMVSNet		97.38 154	97.39 135	97.37 180	98.58 194	97.72 179	98.70 125	97.42 201	97.21 79	95.95 204	99.46 50	93.31 188	97.38 145	97.60 142	97.78 119	96.18 192	98.66 88

new-patchmatchnet		97.26 155	96.12 169	98.58 130	99.55 98	98.63 127	99.14 86	97.04 205	98.80 14	99.19 58	99.92 4	99.19 82	98.92 71	95.51 189	87.04 207	97.66 179	93.73 197

MIMVSNet		97.24 156	97.15 148	97.36 181	99.03 173	98.52 137	98.55 139	99.73 21	94.94 158	94.94 212	97.98 126	97.37 162	93.66 199	97.60 142	97.34 149	98.23 169	96.29 177

PatchMatch-RL		97.24 156	96.45 164	98.17 154	98.70 189	97.57 184	97.31 200	98.48 177	94.42 170	98.39 140	95.74 187	96.35 173	97.88 131	97.75 137	97.48 145	98.24 168	95.87 182

thisisatest0530		97.20 158	95.95 173	98.66 124	99.46 118	98.84 109	98.29 160	99.20 122	94.51 162	98.25 150	97.42 142	85.03 207	97.68 136	98.43 90	98.56 85	99.08 101	98.89 69

tttt0517		97.18 159	95.92 174	98.65 127	99.49 114	98.92 101	98.29 160	99.20 122	94.37 173	98.17 153	97.37 145	84.72 210	97.68 136	98.55 85	98.56 85	99.10 95	98.95 63

MDTV_nov1_ep13_2view		97.12 160	96.19 168	98.22 153	99.13 165	98.05 164	99.24 72	99.47 71	97.61 57	99.15 68	99.59 36	99.01 106	98.40 106	94.87 198	90.14 201	93.91 202	94.04 196

MAR-MVS		97.12 160	96.28 167	98.11 158	98.94 176	97.22 188	97.65 190	99.38 88	90.93 214	98.15 156	95.17 192	97.13 165	96.48 166	97.71 138	97.40 146	98.06 172	98.40 114
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		96.99 162	96.46 163	97.61 175	98.98 175	97.89 171	97.54 194	99.76 18	93.43 188	96.55 201	94.93 195	98.06 148	94.32 194	96.93 166	96.50 168	98.53 155	97.47 154

FPMVS		96.97 163	97.20 144	96.70 197	97.75 210	96.11 201	97.72 186	95.47 209	97.13 83	98.02 164	97.57 137	96.67 170	92.97 203	99.00 59	98.34 95	98.28 165	95.58 184

TAMVS		96.95 164	96.94 154	96.97 192	99.07 171	97.67 183	97.98 175	97.12 204	95.04 153	95.41 208	99.27 64	95.57 178	94.09 196	97.32 159	97.11 156	98.16 171	96.59 175

FMVSNet3		96.85 165	96.67 156	97.06 186	97.56 213	99.01 86	97.99 174	99.33 99	94.09 178	95.60 205	98.17 117	95.97 174	93.26 202	94.76 200	96.22 172	98.59 151	98.46 108

GA-MVS		96.84 166	95.86 176	97.98 162	99.16 162	98.29 148	97.91 177	98.64 168	95.14 151	97.71 176	98.04 125	88.90 195	96.50 165	96.41 176	96.61 167	97.97 176	97.60 151

CHOSEN 280x420		96.80 167	96.30 166	97.39 178	99.09 167	96.52 193	98.76 124	99.29 107	93.88 183	97.65 177	98.34 107	93.66 186	96.29 172	98.28 108	97.73 128	93.27 205	95.70 183

gg-mvs-nofinetune		96.77 168	96.52 162	97.06 186	99.66 73	97.82 175	97.54 194	99.86 8	98.69 15	98.61 120	99.94 2	89.62 193	88.37 215	97.55 145	96.67 164	98.30 164	95.35 185

DPM-MVS		96.73 169	95.70 179	97.95 165	98.93 177	97.26 187	97.39 199	98.44 179	95.47 147	97.62 178	90.71 209	98.47 138	97.03 156	95.02 197	95.27 185	98.26 166	97.67 149

baseline1		96.72 170	95.40 181	98.26 148	99.53 105	98.81 110	98.32 157	98.80 157	94.96 156	96.78 200	96.50 173	84.87 209	96.68 161	97.42 153	97.91 111	99.46 51	97.33 160

N_pmnet		96.68 171	95.70 179	97.84 169	99.42 129	98.00 167	99.35 56	98.21 185	98.40 22	98.13 157	99.42 55	99.30 68	97.44 144	94.00 204	88.79 202	94.47 201	91.96 203

pmnet_mix02		96.61 172	95.32 182	98.11 158	99.41 131	97.68 181	99.05 92	97.59 199	98.16 26	99.05 80	99.48 48	99.11 95	98.32 110	92.36 208	87.67 204	95.26 198	92.80 201

new_pmnet		96.59 173	96.40 165	96.81 194	98.24 206	95.46 210	97.71 188	94.75 212	96.92 89	96.80 199	99.23 68	97.81 155	96.69 159	96.58 172	95.16 187	96.69 188	93.64 198

PMMVS		96.47 174	95.81 177	97.23 182	97.38 215	95.96 205	97.31 200	96.91 206	93.21 193	97.93 170	97.14 152	97.64 158	95.70 175	95.24 193	96.18 175	98.17 170	95.33 186

EPNet		96.44 175	96.08 170	96.86 193	99.32 140	97.15 190	97.69 189	99.32 103	93.67 185	98.11 160	95.64 188	93.44 187	89.07 213	96.86 167	96.83 161	97.67 178	98.97 58
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres600view7		96.35 176	94.27 184	98.79 111	99.66 73	99.18 63	98.94 102	99.38 88	94.37 173	97.21 195	87.19 211	84.10 211	98.10 119	98.16 116	99.47 19	99.42 57	97.43 155

EPNet_dtu		96.31 177	95.96 172	96.72 196	99.18 159	95.39 211	97.03 209	99.13 135	93.02 195	99.35 38	97.23 148	97.07 166	90.70 212	95.74 185	95.08 189	94.94 200	98.16 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs3		96.30 178	95.87 175	96.80 195	97.66 212	96.48 194	97.93 176	93.80 213	93.40 189	98.54 127	98.27 112	97.50 159	97.37 147	97.49 148	93.11 197	95.52 197	94.85 190

PMMVS2		96.29 179	97.05 150	95.40 207	98.32 205	96.16 198	98.18 168	97.46 200	97.20 81	84.51 221	99.60 34	98.68 128	96.37 167	98.59 83	97.38 147	97.58 181	91.76 204

thres200		96.23 180	94.13 185	98.69 120	99.44 123	99.18 63	98.58 137	99.38 88	93.52 187	97.35 189	86.33 216	85.83 205	97.93 129	98.16 116	98.78 66	99.42 57	97.10 168

thres400		96.22 181	94.08 187	98.72 116	99.58 91	99.05 77	98.83 116	99.22 118	94.01 181	97.40 187	86.34 215	84.91 208	97.93 129	97.85 132	99.08 40	99.37 63	97.28 162

tfpn200view9		96.17 182	94.08 187	98.60 129	99.37 133	99.18 63	98.68 127	99.39 83	92.02 202	97.30 191	86.53 213	86.34 202	97.45 143	98.15 118	99.08 40	99.43 56	97.28 162

CMPMVS	74.71 19	96.17 182	96.06 171	96.30 201	97.41 214	94.52 214	94.83 216	95.46 210	91.57 207	97.26 194	94.45 200	98.33 142	94.98 182	98.28 108	97.59 138	97.86 177	97.68 148
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
IB-MVS	95.85 14	95.87 184	94.88 183	97.02 189	99.09 167	98.25 153	97.16 202	97.38 202	91.97 205	97.77 174	83.61 218	97.29 163	92.03 210	97.16 163	97.66 132	98.66 143	98.20 131
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		95.81 185	95.77 178	95.85 206	99.20 155	98.15 160	97.49 198	98.50 175	92.24 198	92.74 218	96.82 163	92.70 189	88.60 214	97.31 161	97.01 159	98.57 153	96.19 180

thres100view900		95.74 186	93.66 196	98.17 154	99.37 133	98.59 130	98.10 170	98.33 182	92.02 202	97.30 191	86.53 213	86.34 202	96.69 159	96.77 169	98.47 88	99.24 85	96.89 172

ET-MVSNet_ETH3D		95.72 187	93.85 192	97.89 166	97.30 216	98.09 162	98.19 167	98.40 180	94.46 167	98.01 167	96.71 165	77.85 221	96.76 157	96.08 180	96.39 169	98.70 141	97.36 158

baseline2		95.58 188	94.04 189	97.38 179	98.80 183	98.16 158	97.14 203	97.80 197	91.45 209	97.49 182	95.22 191	83.63 212	94.98 182	96.42 175	96.66 165	98.06 172	96.76 173

PatchT		95.49 189	93.29 197	98.06 160	98.65 191	96.20 197	98.91 108	99.73 21	92.00 204	98.50 130	96.67 167	83.25 213	96.34 168	94.40 201	95.50 181	96.21 191	95.04 188

CR-MVSNet		95.38 190	93.01 198	98.16 156	98.63 192	95.85 207	97.64 191	99.78 16	91.27 211	98.50 130	96.84 162	82.16 214	96.34 168	94.40 201	95.50 181	98.05 174	95.04 188

MVSTER		95.38 190	93.99 191	97.01 190	98.83 181	98.95 95	96.62 210	99.14 131	92.17 200	97.44 185	97.29 146	77.88 220	91.63 211	97.45 150	96.18 175	98.41 161	97.99 139

MVS-HIRNet		94.86 192	93.83 193	96.07 202	97.07 217	94.00 215	94.31 217	99.17 126	91.23 213	98.17 153	98.69 98	97.43 160	95.66 176	94.05 203	91.92 199	92.04 212	89.46 212

test-LLR		94.79 193	93.71 194	96.06 203	99.20 155	96.16 198	96.31 211	98.50 175	89.98 215	94.08 213	97.01 156	86.43 200	92.20 208	96.76 170	95.31 183	96.05 193	94.31 193

RPMNet		94.72 194	92.01 203	97.88 168	98.56 197	95.85 207	97.78 182	99.70 26	91.27 211	98.33 144	93.69 202	81.88 215	94.91 185	92.60 206	94.34 195	98.01 175	94.46 192

gm-plane-assit		94.62 195	91.39 205	98.39 139	99.90 13	99.47 29	99.40 48	99.65 35	97.44 65	99.56 20	99.68 26	59.40 225	94.23 195	96.17 178	94.77 192	97.61 180	92.79 202

test-mter		94.62 195	94.02 190	95.32 208	97.72 211	96.75 191	96.23 213	95.67 208	89.83 218	93.23 217	96.99 158	85.94 204	92.66 206	97.32 159	96.11 177	96.44 189	95.22 187

FMVSNet5		94.57 197	92.77 199	96.67 198	97.88 208	98.72 119	97.54 194	98.70 164	88.64 219	95.11 210	86.90 212	81.77 216	93.27 201	97.92 128	98.07 105	97.50 183	97.34 159

SCA		94.53 198	91.95 204	97.55 176	98.58 194	97.86 173	98.49 143	99.68 27	95.11 152	99.07 77	95.87 184	87.24 198	96.53 164	89.77 211	87.08 206	92.96 207	90.69 207

MDTV_nov1_ep13		94.47 199	92.15 201	97.17 183	98.54 199	96.42 195	98.10 170	98.89 152	94.49 164	98.02 164	97.41 143	86.49 199	95.56 177	90.85 209	87.95 203	93.91 202	91.45 206

TESTMET0.1,1		94.44 200	93.71 194	95.30 209	97.84 209	96.16 198	96.31 211	95.32 211	89.98 215	94.08 213	97.01 156	86.43 200	92.20 208	96.76 170	95.31 183	96.05 193	94.31 193

ADS-MVSNet		94.41 201	92.13 202	97.07 185	98.86 179	96.60 192	98.38 152	98.47 178	96.13 135	98.02 164	96.98 159	87.50 197	95.87 174	89.89 210	87.58 205	92.79 209	90.27 209

tpm		93.89 202	91.21 206	97.03 188	98.36 203	96.07 202	97.53 197	99.65 35	92.24 198	98.64 119	97.23 148	74.67 224	94.64 189	92.68 205	90.73 200	93.37 204	94.82 191

PatchmatchNet		93.88 203	91.08 207	97.14 184	98.75 185	96.01 204	98.25 164	99.39 83	94.95 157	98.96 92	96.32 176	85.35 206	95.50 178	88.89 212	85.89 210	91.99 213	90.15 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		93.67 204	90.82 208	96.99 191	98.62 193	96.39 196	98.40 150	99.11 136	95.54 146	97.87 172	97.14 152	81.27 218	94.97 184	88.54 214	86.80 208	92.95 208	90.06 211

MVE	82.47 18	93.12 205	94.09 186	91.99 212	90.79 219	82.50 220	93.93 218	96.30 207	96.06 136	88.81 219	98.19 115	96.38 172	97.56 140	97.24 162	95.18 186	84.58 219	93.07 199
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CostFormer		92.75 206	89.49 210	96.55 199	98.78 184	95.83 209	97.55 193	98.59 171	91.83 206	97.34 190	96.31 177	78.53 219	94.50 190	86.14 215	84.92 211	92.54 210	92.84 200

tpmrst		92.45 207	89.48 211	95.92 205	98.43 202	95.03 212	97.14 203	97.92 195	94.16 177	97.56 179	97.86 129	81.63 217	93.56 200	85.89 216	82.86 212	90.91 217	88.95 214

dps		92.35 208	88.78 213	96.52 200	98.21 207	95.94 206	97.78 182	98.38 181	89.88 217	96.81 198	95.07 193	75.31 223	94.70 188	88.62 213	86.21 209	93.21 206	90.41 208

E-PMN		92.28 209	90.12 209	94.79 210	98.56 197	90.90 217	95.16 215	93.68 214	95.36 149	95.10 211	96.56 170	89.05 194	95.24 180	95.21 194	81.84 214	90.98 215	81.94 216

EMVS		91.84 210	89.39 212	94.70 211	98.44 201	90.84 218	95.27 214	93.53 215	95.18 150	95.26 209	95.62 189	87.59 196	94.77 187	94.87 198	80.72 215	90.95 216	80.88 217

tpm cat1		91.52 211	87.70 214	95.97 204	98.33 204	94.98 213	97.06 206	98.03 190	92.11 201	98.03 163	94.77 197	77.19 222	92.71 205	83.56 217	82.24 213	91.67 214	89.04 213

test_method		77.69 212	85.40 215	68.69 213	42.66 221	55.39 222	82.17 221	52.05 217	92.83 197	84.52 220	94.88 196	95.41 180	65.37 216	92.49 207	79.32 216	85.36 218	87.50 215

GG-mvs-BLEND		65.66 213	92.62 200	34.20 215	1.45 224	93.75 216	85.40 220	1.64 221	91.37 210	17.21 223	87.25 210	94.78 183	3.25 220	95.64 188	93.80 196	96.27 190	91.74 205

testmvs		9.73 214	13.38 216	5.48 217	3.62 222	4.12 223	6.40 224	3.19 220	14.92 220	7.68 225	22.10 219	13.89 227	6.83 218	13.47 218	10.38 218	5.14 222	14.81 218

test123		9.37 215	12.26 217	6.00 216	3.32 223	4.06 224	6.39 225	3.41 219	13.20 221	10.48 224	16.43 220	16.22 226	6.76 219	11.37 219	10.40 217	5.62 221	14.10 219

uanet_test		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

RE-MVS-def										99.88 2

9.14												98.83 119

SR-MVS					99.62 83			99.47 71				99.40 58

Anonymous202405211			98.44 73		99.79 34	99.32 46	99.05 92	99.34 98	96.59 108		97.95 128	97.68 157	97.16 151	99.36 30	99.28 31	99.61 34	98.90 67

our_test_3					99.29 145	97.72 179	98.98 98

ambc			97.89 115		99.45 120	97.88 172	97.78 182		97.27 74	99.80 3	98.99 88	98.48 136	98.55 96	97.80 134	96.68 163	98.54 154	98.10 137

MTAPA										99.19 58		99.68 23

MTMP										99.20 56		99.54 44

Patchmatch-RL test							32.47 223

tmp_tt				65.28 214	82.24 220	71.50 221	70.81 222	23.21 218	96.14 133	81.70 222	85.98 217	92.44 190	49.84 217	95.81 183	94.36 194	83.86 220

XVS					99.77 39	99.07 73	99.46 43			98.95 94		99.37 61				99.33 70

X-MVStestdata					99.77 39	99.07 73	99.46 43			98.95 94		99.37 61				99.33 70

abl_6				98.38 141	99.03 173	98.04 165	98.08 172	98.65 166	93.23 192	98.56 124	94.58 199	98.57 134	97.17 150			98.81 132	97.42 156

mPP-MVS					99.75 49							99.49 53

NP-MVS									93.07 194

Patchmtry						96.05 203	97.64 191	99.78 16		98.50 130

DeepMVS_CX						87.86 219	92.27 219	61.98 216	93.64 186	93.62 216	91.17 207	91.67 191	94.90 186	95.99 182		92.48 211	94.18 195

High-resolution multi-view benchmark