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	97.71 1	99.33 1	99.47 2	99.16 7	99.16 41	99.11 13	99.39 14	99.16 11	99.26 3	99.22 5	99.51 19	99.75 3	98.54 15	99.71 2	99.47 4	99.52 13	99.46 1
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
SixPastTwentyTwo		99.25 2	99.20 4	99.32 1	99.53 15	99.32 9	99.64 2	99.19 10	98.05 11	99.19 6	99.74 4	98.96 51	99.03 2	99.69 3	99.58 2	99.32 26	99.06 6

WR-MVS		99.22 3	99.15 6	99.30 2	99.54 11	99.62 1	99.63 4	99.45 1	97.75 15	98.47 22	99.71 6	99.05 42	98.88 4	99.54 6	99.49 3	99.81 1	98.87 9

test_part1		99.20 4	99.62 1	98.72 16	98.92 65	99.62 1	99.52 13	99.01 13	99.39 1	97.87 38	99.74 4	99.75 3	97.29 62	99.73 1	99.71 1	99.69 2	99.41 2

PS-CasMVS		99.08 5	98.90 11	99.28 3	99.65 3	99.56 5	99.59 6	99.39 3	96.36 34	98.83 14	99.46 22	99.09 35	98.62 10	99.51 8	99.36 9	99.63 4	98.97 7

PEN-MVS		99.08 5	98.95 9	99.23 5	99.65 3	99.59 3	99.64 2	99.34 6	96.68 27	98.65 17	99.43 24	99.33 16	98.47 17	99.50 9	99.32 10	99.60 6	98.79 11

v7n		99.03 7	99.03 8	99.02 9	99.09 52	99.11 13	99.57 10	98.82 19	98.21 10	99.25 3	99.84 2	99.59 6	98.76 6	99.23 17	98.83 29	98.63 68	98.40 34

DTE-MVSNet		99.03 7	98.88 12	99.21 6	99.66 2	99.59 3	99.62 5	99.34 6	96.92 23	98.52 19	99.36 30	98.98 47	98.57 13	99.49 10	99.23 13	99.56 10	98.55 25

TDRefinement		99.00 9	99.13 7	98.86 10	98.99 62	99.05 18	99.58 7	98.29 44	98.96 5	97.96 36	99.40 27	98.67 75	98.87 5	99.60 4	99.46 5	99.46 19	98.74 14

WR-MVS_H		98.97 10	98.82 14	99.14 8	99.56 9	99.56 5	99.54 12	99.42 2	96.07 39	98.37 24	99.34 31	99.09 35	98.43 18	99.45 11	99.41 6	99.53 11	98.86 10

UniMVSNet_ETH3D		98.93 11	99.20 4	98.63 22	99.54 11	99.33 8	98.73 62	99.37 4	98.87 6	97.86 39	99.27 35	99.78 2	96.59 84	99.52 7	99.40 7	99.67 3	98.21 42

CP-MVSNet		98.91 12	98.61 19	99.25 4	99.63 5	99.50 7	99.55 11	99.36 5	95.53 63	98.77 16	99.11 41	98.64 78	98.57 13	99.42 12	99.28 12	99.61 5	98.78 12

anonymousdsp		98.85 13	98.88 12	98.83 11	98.69 82	98.20 74	99.68 1	97.35 119	97.09 22	98.98 10	99.86 1	99.43 10	98.94 3	99.28 15	99.19 14	99.33 24	99.08 5

pmmvs6		98.77 14	99.35 3	98.09 43	98.32 99	98.92 23	98.57 69	99.03 12	99.36 2	96.86 85	99.77 3	99.86 1	96.20 98	99.56 5	99.39 8	99.59 7	98.61 22

ACMH	95.26 7	98.75 15	98.93 10	98.54 26	98.86 68	99.01 20	99.58 7	98.10 63	98.67 7	97.30 63	99.18 39	99.42 11	98.40 19	99.19 19	98.86 27	98.99 43	98.19 43
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	96.84 2	98.75 15	98.82 14	98.66 20	99.14 45	98.79 34	99.30 17	97.67 91	98.33 9	97.82 41	99.20 38	99.18 32	98.76 6	99.27 16	98.96 21	99.29 28	98.03 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UA-Net		98.66 17	98.60 22	98.73 15	99.83 1	99.28 10	98.56 71	99.24 8	96.04 40	97.12 72	98.44 75	98.95 52	98.17 26	99.15 22	99.00 20	99.48 18	99.33 3

DeepC-MVS	96.08 5	98.58 18	98.49 24	98.68 18	99.37 27	98.52 60	99.01 34	98.17 58	97.17 21	98.25 27	99.56 16	99.62 5	98.29 22	98.40 58	98.09 67	98.97 45	98.08 46
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TranMVSNet+NR-MVSNet		98.45 19	98.22 31	98.72 16	99.32 32	99.06 16	98.99 35	98.89 15	95.52 64	97.53 51	99.42 26	98.83 63	98.01 33	98.55 50	98.34 53	99.57 9	97.80 57

CSCG		98.45 19	98.61 19	98.26 37	99.11 49	99.06 16	98.17 88	97.49 104	97.93 13	97.37 60	98.88 52	99.29 19	98.10 27	98.40 58	97.51 84	99.32 26	99.16 4

Gipuma		98.43 21	98.15 35	98.76 14	99.00 61	98.29 71	97.91 104	98.06 65	99.02 4	99.50 1	96.33 125	98.67 75	99.22 1	99.02 25	98.02 72	98.88 59	97.66 66
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CS-MVS-test		98.41 22	98.22 31	98.64 21	98.75 77	99.16 11	99.58 7	97.99 73	94.54 101	98.06 32	98.30 79	99.14 34	98.07 29	98.93 29	99.18 15	99.40 20	98.27 41

ACMH+	94.90 8	98.40 23	98.71 17	98.04 53	98.93 64	98.84 29	99.30 17	97.86 83	97.78 14	94.19 171	98.77 62	99.39 13	98.61 11	99.33 14	99.07 16	99.33 24	97.81 56

ACMMPR		98.31 24	98.07 39	98.60 23	99.58 6	98.83 30	99.09 26	98.48 28	96.25 36	97.03 76	96.81 114	99.09 35	98.39 20	98.55 50	98.45 45	99.01 40	98.53 28

APDe-MVS		98.29 25	98.42 26	98.14 40	99.45 22	98.90 24	99.18 23	98.30 42	95.96 45	95.13 150	98.79 59	99.25 27	97.92 37	98.80 34	98.71 32	98.85 61	98.54 26

DVP-MVS		98.27 26	98.61 19	97.87 63	99.17 40	99.03 19	99.07 28	98.17 58	96.75 26	94.35 166	98.92 48	99.58 7	97.86 40	98.67 43	98.70 33	98.63 68	98.63 20
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
TransMVSNet (Re)		98.23 27	98.72 16	97.66 75	98.22 108	98.73 45	98.66 64	98.03 70	98.60 8	96.40 104	99.60 13	98.24 99	95.26 120	99.19 19	99.05 19	99.36 21	97.64 67

DU-MVS		98.23 27	97.74 56	98.81 12	99.23 34	98.77 36	98.76 56	98.88 16	94.10 113	98.50 20	98.87 54	98.32 96	97.99 34	98.40 58	98.08 70	99.49 17	97.64 67

UniMVSNet (Re)		98.23 27	97.85 48	98.67 19	99.15 42	98.87 26	98.74 59	98.84 18	94.27 111	97.94 37	99.01 43	98.39 92	97.82 41	98.35 63	98.29 59	99.51 16	97.78 58

MIMVSNet1		98.22 30	98.51 23	97.87 63	99.40 26	98.82 32	99.31 16	98.53 26	97.39 18	96.59 94	99.31 33	99.23 29	94.76 130	98.93 29	98.67 35	98.63 68	97.25 90

HFP-MVS		98.17 31	98.02 40	98.35 35	99.36 28	98.62 51	98.79 55	98.46 32	96.24 37	96.53 96	97.13 111	98.98 47	98.02 32	98.20 66	98.42 47	98.95 49	98.54 26

Baseline_NR-MVSNet		98.17 31	97.90 45	98.48 29	99.23 34	98.59 52	98.83 52	98.73 23	93.97 118	96.95 79	99.66 8	98.23 101	97.90 38	98.40 58	99.06 18	99.25 29	97.42 82

TSAR-MVS + MP.		98.15 33	98.23 30	98.06 51	98.47 90	98.16 80	99.23 20	96.87 135	95.58 58	96.72 88	98.41 76	99.06 39	98.05 31	98.99 26	98.90 24	99.00 41	98.51 29
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
zzz-MVS		98.14 34	97.78 53	98.55 25	99.58 6	98.58 54	98.98 37	98.48 28	95.98 43	97.39 58	94.73 154	99.27 23	97.98 36	98.81 33	98.64 39	98.90 53	98.46 30

pm-mvs1		98.14 34	98.66 18	97.53 84	97.93 130	98.49 63	98.14 90	98.19 54	97.95 12	96.17 115	99.63 11	98.85 60	95.41 118	98.91 31	98.89 25	99.34 23	97.86 55

SMA-MVS		98.13 36	98.22 31	98.02 56	99.44 24	98.73 45	98.24 85	97.87 82	95.22 71	96.76 87	98.66 68	99.35 15	97.03 70	98.53 53	98.39 49	98.80 63	98.69 16
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
ACMMP_NAP		98.12 37	98.08 38	98.18 39	99.34 29	98.74 44	98.97 38	98.00 72	95.13 75	96.90 80	97.54 99	99.27 23	97.18 64	98.72 39	98.45 45	98.68 67	98.69 16

UniMVSNet_NR-MVSNet		98.12 37	97.56 63	98.78 13	99.13 47	98.89 25	98.76 56	98.78 20	93.81 121	98.50 20	98.81 58	97.64 122	97.99 34	98.18 69	97.92 75	99.53 11	97.64 67

ACMM	94.29 11	98.12 37	97.71 57	98.59 24	99.51 17	98.58 54	99.24 19	98.25 46	96.22 38	96.90 80	95.01 148	98.89 57	98.52 16	98.66 44	98.32 56	99.13 33	98.28 40
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SteuartSystems-ACMMP		98.06 40	97.78 53	98.39 33	99.54 11	98.79 34	98.94 42	98.42 34	93.98 117	95.85 124	96.66 119	99.25 27	98.61 11	98.71 41	98.38 50	98.97 45	98.67 19
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS		98.05 41	98.46 25	97.57 80	99.01 58	98.99 21	98.82 54	98.24 47	95.76 53	94.70 159	98.96 45	99.49 9	96.19 99	98.74 35	98.65 37	98.46 82	98.63 20

OPM-MVS		98.01 42	98.01 41	98.00 58	99.11 49	98.12 83	98.68 63	97.72 89	96.65 28	96.68 92	98.40 77	99.28 22	97.44 54	98.20 66	97.82 81	98.40 88	97.58 72

Vis-MVSNet		98.01 42	98.42 26	97.54 83	96.89 177	98.82 32	99.14 24	97.59 94	96.30 35	97.04 75	99.26 36	98.83 63	96.01 104	98.73 37	98.21 61	98.58 74	98.75 13
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
NR-MVSNet		98.00 44	97.88 46	98.13 41	98.33 97	98.77 36	98.83 52	98.88 16	94.10 113	97.46 56	98.87 54	98.58 83	95.78 107	99.13 23	98.16 65	99.52 13	97.53 75

CP-MVS		98.00 44	97.57 62	98.50 27	99.47 21	98.56 57	98.91 44	98.38 37	94.71 91	97.01 77	95.20 144	99.06 39	98.20 24	98.61 47	98.46 42	99.02 38	98.40 34

DPE-MVS		97.99 46	98.12 36	97.84 66	98.65 84	98.86 27	98.86 49	98.05 68	94.18 112	95.49 143	98.90 50	99.33 16	97.11 66	98.53 53	98.65 37	98.86 60	98.39 36
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMMP		97.99 46	97.60 61	98.45 31	99.53 15	98.83 30	99.13 25	98.30 42	94.57 97	96.39 108	95.32 142	98.95 52	98.37 21	98.61 47	98.47 41	99.00 41	98.45 31
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
MP-MVS		97.98 48	97.53 65	98.50 27	99.56 9	98.58 54	98.97 38	98.39 36	93.49 124	97.14 69	96.08 131	99.23 29	98.06 30	98.50 55	98.38 50	98.90 53	98.44 32
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EG-PatchMatch MVS		97.98 48	97.92 43	98.04 53	98.84 71	98.04 91	97.90 105	96.83 138	95.07 77	98.79 15	99.07 42	99.37 14	97.88 39	98.74 35	98.16 65	98.01 110	96.96 97

ACMP	94.03 12	97.97 50	97.61 60	98.39 33	99.43 25	98.51 61	98.97 38	98.06 65	94.63 95	96.10 117	96.12 130	99.20 31	98.63 9	98.68 42	98.20 64	99.14 32	97.93 52
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		97.96 51	97.53 65	98.45 31	99.45 22	98.64 50	99.09 26	98.27 45	92.99 136	96.04 119	96.57 120	99.29 19	98.66 8	98.73 37	98.42 47	99.19 31	98.09 45

LS3D		97.93 52	97.80 50	98.08 47	99.20 37	98.77 36	98.89 46	97.92 78	96.59 29	96.99 78	96.71 117	97.14 136	96.39 93	99.04 24	98.96 21	99.10 37	97.39 83

SD-MVS		97.84 53	97.78 53	97.90 61	98.33 97	98.06 88	97.95 101	97.80 88	96.03 42	96.72 88	97.57 97	99.18 32	97.50 52	97.88 72	97.08 97	99.11 35	98.68 18
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
RPSCF		97.83 54	98.27 28	97.31 95	98.23 106	98.06 88	97.44 130	95.79 168	96.90 24	95.81 126	98.76 63	98.61 82	97.70 46	98.90 32	98.36 52	98.90 53	98.29 37

thisisatest0515		97.82 55	97.67 58	97.99 59	98.49 89	98.07 87	98.48 74	98.06 65	95.35 69	97.74 43	98.83 57	97.61 123	96.74 77	97.53 91	98.30 58	98.43 87	98.01 49

PGM-MVS		97.82 55	97.25 73	98.48 29	99.54 11	98.75 43	99.02 30	98.35 40	92.41 140	96.84 86	95.39 141	98.99 46	98.24 23	98.43 57	98.34 53	98.90 53	98.41 33

PMVS	90.51 17	97.77 57	97.98 42	97.53 84	98.68 83	98.14 82	97.67 115	97.03 129	96.43 30	98.38 23	98.72 65	97.03 138	94.44 135	99.37 13	99.30 11	98.98 44	96.86 103
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MSP-MVS		97.67 58	97.88 46	97.43 90	99.34 29	98.99 21	98.87 48	98.12 61	95.63 55	94.16 172	97.45 100	99.50 8	96.44 92	96.35 128	98.70 33	97.65 127	98.57 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
tfpnnormal		97.66 59	97.79 51	97.52 86	98.32 99	98.53 59	98.45 77	97.69 90	97.59 17	96.12 116	97.79 92	96.70 142	95.69 112	98.35 63	98.34 53	98.85 61	97.22 93

FC-MVSNet-train		97.65 60	98.16 34	97.05 107	98.85 69	98.85 28	99.34 15	98.08 64	94.50 103	94.41 164	99.21 37	98.80 67	92.66 160	98.98 27	98.85 28	98.96 47	97.94 51

v10		97.64 61	97.26 72	98.08 47	98.07 120	98.56 57	98.86 49	98.18 56	94.48 104	98.24 28	99.56 16	98.98 47	97.72 45	96.05 138	96.26 125	97.42 136	96.93 98

X-MVS		97.60 62	97.00 88	98.29 36	99.50 18	98.76 39	98.90 45	98.37 38	94.67 94	96.40 104	91.47 194	98.78 69	97.60 51	98.55 50	98.50 40	98.96 47	98.29 37

3Dnovator+	96.20 4	97.58 63	97.14 80	98.10 42	98.98 63	97.85 103	98.60 67	98.33 41	96.41 32	97.23 67	94.66 157	97.26 132	96.91 73	97.91 71	97.87 77	98.53 77	98.03 47

DCV-MVSNet		97.56 64	97.63 59	97.47 88	98.41 94	99.12 12	98.63 65	98.57 24	95.71 54	95.60 140	93.79 173	98.01 110	94.25 138	99.16 21	98.88 26	99.35 22	98.74 14

HPM-MVS++		97.56 64	97.11 84	98.09 43	99.18 39	97.95 98	98.57 69	98.20 52	94.08 115	97.25 66	95.96 135	98.81 66	97.13 65	97.51 92	97.30 94	98.21 98	98.15 44

FC-MVSNet-test		97.54 66	98.26 29	96.70 124	98.87 67	97.79 111	98.49 73	98.56 25	96.04 40	90.39 200	99.65 9	98.67 75	95.15 122	99.23 17	99.07 16	98.73 66	97.39 83

TSAR-MVS + ACMM		97.54 66	97.79 51	97.26 96	98.23 106	98.10 86	97.71 113	97.88 81	95.97 44	95.57 142	98.71 66	98.57 84	97.36 57	97.74 79	96.81 106	96.83 161	98.59 23

DeepC-MVS_fast	95.38 6	97.53 68	97.30 71	97.79 71	98.83 72	97.64 114	98.18 86	97.14 125	95.57 59	97.83 40	97.10 112	98.80 67	96.53 89	97.41 95	97.32 92	98.24 97	97.26 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v1192		97.52 69	97.03 87	98.09 43	98.31 102	98.01 93	98.96 41	97.25 122	95.22 71	98.89 12	99.64 10	98.83 63	97.68 47	95.63 145	95.91 135	97.47 132	95.97 131

v1144		97.51 70	97.05 86	98.04 53	98.26 104	97.98 95	98.88 47	97.42 113	95.38 68	98.56 18	99.59 15	99.01 45	97.65 48	95.77 142	96.06 132	97.47 132	95.56 143

v8		97.51 70	97.16 78	97.91 60	97.99 126	98.48 64	98.76 56	98.17 58	94.54 101	97.69 45	99.48 21	98.76 72	97.63 50	96.10 137	96.14 127	97.20 146	96.64 112

v1921920		97.50 72	97.00 88	98.07 49	98.20 110	97.94 101	99.03 29	97.06 127	95.29 70	99.01 9	99.62 12	98.73 74	97.74 44	95.52 148	95.78 140	97.39 138	96.12 127

Anonymous20231211		97.49 73	97.91 44	97.00 111	98.31 102	98.72 47	98.27 83	97.84 85	94.76 90	94.77 158	98.14 85	98.38 94	93.60 148	98.96 28	98.66 36	99.22 30	97.77 61

v144192		97.49 73	96.99 90	98.07 49	98.11 119	97.95 98	99.02 30	97.21 123	94.90 86	98.88 13	99.53 18	98.89 57	97.75 43	95.59 146	95.90 136	97.43 135	96.16 125

GeoE		97.48 75	96.84 96	98.22 38	99.01 58	98.39 67	98.85 51	98.76 21	92.37 141	97.53 51	97.58 96	98.23 101	97.11 66	97.57 90	96.98 100	98.10 106	96.78 106

APD-MVS		97.47 76	97.16 78	97.84 66	99.32 32	98.39 67	98.47 76	98.21 51	92.08 145	95.23 147	96.68 118	98.90 55	96.99 71	98.20 66	98.21 61	98.80 63	97.67 65
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PVSNet_Blended_VisFu		97.44 77	97.14 80	97.79 71	99.15 42	98.44 65	98.32 81	97.66 92	93.74 123	97.73 44	98.79 59	96.93 141	95.64 117	97.69 82	96.91 103	98.25 96	97.50 78

PHI-MVS		97.44 77	97.17 77	97.74 74	98.14 116	98.41 66	98.03 97	97.50 102	92.07 146	98.01 35	97.33 105	98.62 81	96.02 103	98.34 65	98.21 61	98.76 65	97.24 92

v1240		97.43 79	96.87 95	98.09 43	98.25 105	97.92 102	99.02 30	97.06 127	94.77 89	99.09 8	99.68 7	98.51 87	97.78 42	95.25 153	95.81 138	97.32 142	96.13 126

FMVSNet1		97.40 80	98.09 37	96.60 128	97.80 144	98.76 39	98.26 84	98.50 27	96.79 25	93.13 188	99.28 34	98.64 78	92.90 158	97.67 84	97.86 78	99.02 38	97.64 67

v2v482		97.33 81	96.84 96	97.90 61	98.19 111	97.83 104	98.74 59	97.44 110	95.42 67	98.23 29	99.46 22	98.84 62	97.46 53	95.51 149	96.10 130	97.36 140	94.72 152

xxxxxxxxxxxxxcwj		97.32 82	97.55 64	97.05 107	98.80 74	97.83 104	96.02 178	97.44 110	94.98 80	95.74 130	97.16 108	99.30 18	95.72 109	97.85 73	97.97 73	98.60 71	97.78 58

EPP-MVSNet		97.29 83	96.88 93	97.76 73	98.70 79	99.10 15	98.92 43	98.36 39	95.12 76	93.36 186	97.39 102	91.00 185	97.65 48	98.72 39	98.91 23	99.58 8	97.92 53

MVS_111021_HR		97.27 84	97.11 84	97.46 89	98.46 91	97.82 108	97.50 126	96.86 136	94.97 82	97.13 71	96.99 113	98.39 92	96.82 75	97.65 87	97.38 87	98.02 109	96.56 115

SF-MVS		97.26 85	97.43 67	97.05 107	98.80 74	97.83 104	96.02 178	97.44 110	94.98 80	95.74 130	97.16 108	98.45 91	95.72 109	97.85 73	97.97 73	98.60 71	97.78 58

TSAR-MVS + GP.		97.26 85	97.33 70	97.18 101	98.21 109	98.06 88	96.38 169	97.66 92	93.92 120	95.23 147	98.48 73	98.33 95	97.41 55	97.63 88	97.35 88	98.18 100	97.57 73

OMC-MVS		97.23 87	97.21 75	97.25 99	97.85 135	97.52 123	97.92 103	95.77 169	95.83 49	97.09 74	97.86 90	98.52 86	96.62 82	97.51 92	96.65 111	98.26 94	96.57 113

3Dnovator	96.31 3	97.22 88	97.19 76	97.25 99	98.14 116	97.95 98	98.03 97	96.77 141	96.42 31	97.14 69	95.11 145	97.59 124	95.14 124	97.79 77	97.72 82	98.26 94	97.76 63

MVS_0304		97.18 89	96.84 96	97.58 79	99.15 42	98.19 75	98.11 91	97.81 87	92.36 142	98.06 32	97.43 101	99.06 39	94.24 139	96.80 117	96.54 116	98.12 104	97.52 76

canonicalmvs		97.11 90	96.88 93	97.38 91	98.34 96	98.72 47	97.52 125	97.94 76	95.60 56	95.01 155	94.58 158	94.50 166	96.59 84	97.84 75	98.03 71	98.90 53	98.91 8

V42		97.10 91	96.97 91	97.26 96	97.64 150	97.60 116	98.45 77	95.99 158	94.44 105	97.35 61	99.40 27	98.63 80	97.34 59	96.33 131	96.38 122	96.82 163	96.00 129

CPTT-MVS		97.08 92	96.25 110	98.05 52	99.21 36	98.30 70	98.54 72	97.98 74	94.28 109	95.89 123	89.57 203	98.54 85	98.18 25	97.82 76	97.32 92	98.54 75	97.91 54

DeepPCF-MVS	94.55 10	97.05 93	97.13 83	96.95 113	96.06 191	97.12 141	98.01 99	95.44 175	95.18 73	97.50 53	97.86 90	98.08 106	97.31 61	97.23 100	97.00 99	97.36 140	97.45 80

QAPM		97.04 94	97.14 80	96.93 115	97.78 147	98.02 92	97.36 135	96.72 142	94.68 93	96.23 110	97.21 107	97.68 120	95.70 111	97.37 96	97.24 96	97.78 120	97.77 61

CNVR-MVS		97.03 95	96.77 101	97.34 92	98.89 66	97.67 113	97.64 118	97.17 124	94.40 107	95.70 136	94.02 168	98.76 72	96.49 91	97.78 78	97.29 95	98.12 104	97.47 79

casdiffmvs		97.00 96	97.36 69	96.59 129	97.65 149	97.98 95	98.06 93	96.81 139	95.78 51	92.77 194	99.40 27	99.26 26	95.65 116	96.70 120	96.39 121	98.59 73	95.99 130

v148		96.99 97	96.70 103	97.34 92	97.89 133	97.23 133	98.33 80	96.96 130	95.57 59	97.12 72	98.99 44	99.40 12	97.23 63	96.22 134	95.45 145	96.50 168	94.02 164

DELS-MVS		96.90 98	97.24 74	96.50 134	97.85 135	98.18 76	97.88 108	95.92 161	93.48 125	95.34 145	98.86 56	98.94 54	94.03 142	97.33 98	97.04 98	98.00 111	96.85 104
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
MVS_111021_LR		96.86 99	96.72 102	97.03 110	97.80 144	97.06 144	97.04 149	95.51 174	94.55 98	97.47 54	97.35 104	97.68 120	96.66 80	97.11 106	96.73 108	97.69 124	96.57 113

PM-MVS		96.85 100	96.62 105	97.11 103	97.13 172	96.51 157	98.29 82	94.65 192	94.84 87	98.12 30	98.59 69	97.20 133	97.41 55	96.24 133	96.41 120	97.09 151	96.56 115

pmmvs-eth3d		96.84 101	96.22 112	97.56 81	97.63 152	96.38 164	98.74 59	96.91 134	94.63 95	98.26 26	99.43 24	98.28 97	96.58 86	94.52 163	95.54 143	97.24 144	94.75 151

CANet		96.81 102	96.50 106	97.17 102	99.10 51	97.96 97	97.86 109	97.51 100	91.30 151	97.75 42	97.64 94	97.89 114	93.39 152	96.98 113	96.73 108	97.40 137	96.99 96

Fast-Effi-MVS+		96.80 103	95.92 123	97.84 66	98.57 86	97.46 126	98.06 93	98.24 47	89.64 173	97.57 49	96.45 122	97.35 129	96.73 78	97.22 101	96.64 112	97.86 116	96.65 110

DROMVSNet		96.80 103	95.92 123	97.84 66	98.57 86	97.46 126	98.06 93	98.24 47	89.64 173	97.57 49	96.45 122	97.35 129	96.73 78	97.22 101	96.64 112	97.86 116	96.65 110

MCST-MVS		96.79 105	96.08 116	97.62 77	98.78 76	97.52 123	98.01 99	97.32 120	93.20 128	95.84 125	93.97 170	98.12 104	97.34 59	96.34 129	95.88 137	98.45 83	97.51 77

UGNet		96.79 105	97.82 49	95.58 157	97.57 155	98.39 67	98.48 74	97.84 85	95.85 48	94.68 160	97.91 89	99.07 38	87.12 199	97.71 80	97.51 84	97.80 118	98.29 37
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
TAPA-MVS	93.96 13	96.79 105	96.70 103	96.90 117	97.64 150	97.58 117	97.54 124	94.50 194	95.14 74	96.64 93	96.76 116	97.90 113	96.63 81	95.98 139	96.14 127	98.45 83	97.39 83
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		96.73 108	96.92 92	96.51 133	98.70 79	97.57 119	97.64 118	92.07 201	93.10 134	96.31 109	98.29 80	99.02 44	95.99 105	97.20 103	96.47 118	98.37 90	96.81 105
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
train_agg		96.68 109	95.93 122	97.56 81	99.08 53	97.16 137	98.44 79	97.37 116	91.12 155	95.18 149	95.43 140	98.48 89	97.36 57	96.48 125	95.52 144	97.95 114	97.34 87

CDPH-MVS		96.68 109	95.99 119	97.48 87	99.13 47	97.64 114	98.08 92	97.46 106	90.56 161	95.13 150	94.87 152	98.27 98	96.56 87	97.09 107	96.45 119	98.54 75	97.08 95

MSLP-MVS++		96.66 111	96.46 109	96.89 118	98.02 122	97.71 112	95.57 186	96.96 130	94.36 108	96.19 114	91.37 195	98.24 99	97.07 68	97.69 82	97.89 76	97.52 130	97.95 50

CS-MVS		96.65 112	95.80 126	97.65 76	98.18 114	98.50 62	98.58 68	96.96 130	87.33 193	96.52 98	94.16 165	97.91 112	96.79 76	97.71 80	98.31 57	98.89 58	96.66 109

TinyColmap		96.64 113	96.07 117	97.32 94	97.84 140	96.40 161	97.63 120	96.25 152	95.86 47	98.98 10	97.94 88	96.34 149	96.17 100	97.30 99	95.38 148	97.04 153	93.24 171

IS_MVSNet		96.62 114	96.48 108	96.78 122	98.46 91	98.68 49	98.61 66	98.24 47	92.23 143	89.63 204	95.90 136	94.40 167	96.23 95	98.65 45	98.77 30	99.52 13	96.76 107

NCCC		96.56 115	95.68 127	97.59 78	99.04 57	97.54 122	97.67 115	97.56 98	94.84 87	96.10 117	87.91 206	98.09 105	96.98 72	97.20 103	96.80 107	98.21 98	97.38 86

ETV-MVS		96.54 116	95.27 134	98.02 56	99.07 55	97.48 125	98.16 89	98.19 54	87.33 193	97.58 48	92.67 182	95.93 155	96.22 96	98.49 56	98.46 42	98.91 52	96.50 118

Effi-MVS+		96.46 117	95.28 133	97.85 65	98.64 85	97.16 137	97.15 147	98.75 22	90.27 165	98.03 34	93.93 171	96.21 150	96.55 88	96.34 129	96.69 110	97.97 113	96.33 121

IterMVS-LS		96.35 118	95.85 125	96.93 115	97.53 156	98.00 94	97.37 133	97.97 75	95.49 66	96.71 91	98.94 47	93.23 173	94.82 129	93.15 182	95.05 151	97.17 148	97.12 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
USDC		96.30 119	95.64 129	97.07 105	97.62 153	96.35 166	97.17 145	95.71 170	95.52 64	99.17 7	98.11 86	97.46 126	95.67 113	95.44 151	93.60 171	97.09 151	92.99 175

Vis-MVSNet (Re-imp)		96.29 120	96.50 106	96.05 143	97.96 129	97.83 104	97.30 137	97.86 83	93.14 130	88.90 207	96.80 115	95.28 159	95.15 122	98.37 62	98.25 60	99.12 34	95.84 133

MSDG		96.27 121	96.17 115	96.38 139	97.85 135	96.27 167	96.55 166	94.41 195	94.55 98	95.62 139	97.56 98	97.80 115	96.22 96	97.17 105	96.27 124	97.67 126	93.60 168

CNLPA		96.24 122	95.97 120	96.57 131	97.48 161	97.10 143	96.75 159	94.95 186	94.92 85	96.20 113	94.81 153	96.61 144	96.25 94	96.94 114	95.64 141	97.79 119	95.74 139

EIA-MVS		96.23 123	94.85 146	97.84 66	99.08 53	98.21 73	97.69 114	98.03 70	85.68 204	98.09 31	91.75 192	97.07 137	95.66 115	97.58 89	97.72 82	98.47 81	95.91 132

PLC	92.55 15	96.10 124	95.36 130	96.96 112	98.13 118	96.88 148	96.49 167	96.67 146	94.07 116	95.71 135	91.14 196	96.09 152	96.84 74	96.70 120	96.58 115	97.92 115	96.03 128
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test20.03		96.08 125	96.80 99	95.25 166	99.19 38	97.58 117	97.24 142	97.56 98	94.95 84	91.91 195	98.58 70	98.03 108	87.88 195	97.43 94	96.94 102	97.69 124	94.05 163

TSAR-MVS + COLMAP		96.05 126	95.94 121	96.18 142	97.46 162	96.41 160	97.26 141	95.83 165	94.69 92	95.30 146	98.31 78	96.52 145	94.71 131	95.48 150	94.87 153	96.54 167	95.33 146

EU-MVSNet		96.03 127	96.23 111	95.80 151	95.48 204	94.18 185	98.99 35	91.51 203	97.22 20	97.66 46	99.15 40	98.51 87	98.08 28	95.92 140	92.88 178	93.09 191	95.72 140

PCF-MVS	92.69 14	95.98 128	95.05 141	97.06 106	98.43 93	97.56 120	97.76 111	96.65 147	89.95 170	95.70 136	96.18 129	98.48 89	95.74 108	93.64 174	93.35 175	98.09 108	96.18 124
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		95.97 129	95.01 143	97.08 104	98.72 78	97.19 135	97.07 148	96.69 145	91.49 149	95.77 129	92.19 188	97.93 111	96.15 101	94.66 160	94.16 162	98.10 106	97.45 80

Effi-MVS+-dtu		95.94 130	95.08 140	96.94 114	98.54 88	97.38 128	96.66 163	97.89 80	88.68 179	95.92 121	92.90 181	97.28 131	94.18 141	96.68 122	96.13 129	98.45 83	96.51 117

diffmvs		95.86 131	96.21 113	95.44 160	97.25 170	96.85 151	96.99 151	95.23 180	94.96 83	92.82 193	98.89 51	98.85 60	93.52 150	94.21 169	94.25 161	96.84 160	95.49 144

AdaColmap		95.85 132	94.65 149	97.26 96	98.70 79	97.20 134	97.33 136	97.30 121	91.28 153	95.90 122	88.16 205	96.17 151	96.60 83	97.34 97	96.82 105	97.71 121	95.60 142

FMVSNet2		95.77 133	96.20 114	95.27 164	96.77 180	98.18 76	97.28 138	97.90 79	93.12 131	91.37 197	98.25 82	96.05 153	90.04 180	94.96 158	95.94 134	98.28 91	96.90 99

OpenMVS	94.63 9	95.75 134	95.04 142	96.58 130	97.85 135	97.55 121	96.71 161	96.07 155	90.15 168	96.47 99	90.77 201	95.95 154	94.41 136	97.01 112	96.95 101	98.00 111	96.90 99

pmmvs5		95.70 135	95.22 135	96.26 140	96.55 186	97.24 132	97.50 126	94.99 185	90.95 157	96.87 82	98.47 74	97.40 127	94.45 134	92.86 183	94.98 152	97.23 145	94.64 154

Anonymous20231206		95.69 136	95.68 127	95.70 153	98.32 99	96.95 146	97.37 133	96.65 147	93.33 126	93.61 180	98.70 67	98.03 108	91.04 169	95.07 156	94.59 160	97.20 146	93.09 174

MAR-MVS		95.51 137	94.49 153	96.71 123	97.92 131	96.40 161	96.72 160	98.04 69	86.74 198	96.72 88	92.52 185	95.14 161	94.02 143	96.81 116	96.54 116	96.85 158	97.25 90
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
DI_MVS_plusplus_trai		95.48 138	94.51 151	96.61 127	97.13 172	97.30 130	98.05 96	96.79 140	93.75 122	95.08 153	96.38 124	89.76 188	94.95 125	93.97 173	94.82 157	97.64 128	95.63 141

MDA-MVSNet-bldmvs		95.45 139	95.20 136	95.74 152	94.24 209	96.38 164	97.93 102	94.80 187	95.56 62	96.87 82	98.29 80	95.24 160	96.50 90	98.65 45	90.38 190	94.09 185	91.93 179

PVSNet_BlendedMVS		95.44 140	95.09 138	95.86 149	97.31 167	97.13 139	96.31 172	95.01 183	88.55 182	96.23 110	94.55 161	97.75 116	92.56 162	96.42 126	95.44 146	97.71 121	95.81 134

PVSNet_Blended		95.44 140	95.09 138	95.86 149	97.31 167	97.13 139	96.31 172	95.01 183	88.55 182	96.23 110	94.55 161	97.75 116	92.56 162	96.42 126	95.44 146	97.71 121	95.81 134

pmmvs4		95.37 142	94.25 154	96.67 126	97.01 175	95.28 179	97.60 121	96.07 155	93.11 132	97.29 64	98.09 87	94.23 169	95.21 121	91.56 194	93.91 168	96.82 163	93.59 169

MVS_Test		95.34 143	94.88 145	95.89 148	96.93 176	96.84 152	96.66 163	97.08 126	90.06 169	94.02 173	97.61 95	96.64 143	93.59 149	92.73 186	94.02 166	97.03 154	96.24 122

GBi-Net		95.21 144	95.35 131	95.04 169	96.77 180	98.18 76	97.28 138	97.58 95	88.43 184	90.28 201	96.01 132	92.43 176	90.04 180	97.67 84	97.86 78	98.28 91	96.90 99

test1		95.21 144	95.35 131	95.04 169	96.77 180	98.18 76	97.28 138	97.58 95	88.43 184	90.28 201	96.01 132	92.43 176	90.04 180	97.67 84	97.86 78	98.28 91	96.90 99

IterMVS-SCA-FT		95.16 146	93.95 158	96.56 132	97.89 133	96.69 154	96.94 153	96.05 157	93.06 135	97.35 61	98.79 59	91.45 181	95.93 106	92.78 184	91.00 188	95.22 181	93.91 166

HyFIR lowres test		95.05 147	93.54 163	96.81 121	97.81 143	96.88 148	98.18 86	97.46 106	94.28 109	94.98 156	96.57 120	92.89 175	96.15 101	90.90 199	91.87 184	96.28 173	91.35 180

CHOSEN 1792x2688		94.98 148	94.69 148	95.31 162	97.27 169	95.58 176	97.90 105	95.56 173	95.03 78	93.77 179	95.65 138	99.29 19	95.30 119	91.51 195	91.28 187	92.05 199	94.50 156

CANet_DTU		94.96 149	94.62 150	95.35 161	98.03 121	96.11 169	96.92 155	95.60 172	88.59 181	97.27 65	95.27 143	96.50 146	88.77 191	95.53 147	95.59 142	95.54 179	94.78 150

CDS-MVSNet		94.91 150	95.17 137	94.60 177	97.85 135	96.21 168	96.90 157	96.39 150	90.81 158	93.40 184	97.24 106	94.54 165	85.78 205	96.25 132	96.15 126	97.26 143	95.01 149
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DPM-MVS		94.86 151	93.90 160	95.99 145	98.19 111	96.52 156	96.29 174	95.95 159	93.11 132	94.61 162	88.17 204	96.44 147	93.77 147	93.33 177	93.54 173	97.11 150	96.22 123

MS-PatchMatch		94.84 152	94.76 147	94.94 172	96.38 187	94.69 184	95.90 181	94.03 197	92.49 139	93.81 177	95.79 137	96.38 148	94.54 132	94.70 159	94.85 154	94.97 183	94.43 158

thisisatest0530		94.81 153	93.06 169	96.85 120	98.01 123	97.18 136	96.93 154	97.36 117	89.73 172	95.80 127	94.98 149	77.88 209	94.89 126	96.73 119	97.35 88	98.13 103	97.54 74

tttt0517		94.81 153	93.04 170	96.88 119	98.15 115	97.37 129	96.99 151	97.36 117	89.51 175	95.74 130	94.89 151	77.53 211	94.89 126	96.94 114	97.35 88	98.17 101	97.70 64

testgi		94.81 153	96.05 118	93.35 188	99.06 56	96.87 150	97.57 123	96.70 144	95.77 52	88.60 209	93.19 179	98.87 59	81.21 213	97.03 111	96.64 112	96.97 157	93.99 165

PatchMatch-RL		94.79 156	93.75 162	96.00 144	96.80 179	95.00 181	95.47 191	95.25 179	90.68 160	95.80 127	92.97 180	93.64 171	95.67 113	96.13 136	95.81 138	96.99 156	92.01 178

FPMVS		94.70 157	94.99 144	94.37 179	95.84 197	93.20 190	96.00 180	91.93 202	95.03 78	94.64 161	94.68 155	93.29 172	90.95 170	98.07 70	97.34 91	96.85 158	93.29 170

new-patchmatchnet		94.48 158	94.02 156	95.02 171	97.51 160	95.00 181	95.68 185	94.26 196	97.32 19	95.73 133	99.60 13	98.22 103	91.30 165	94.13 170	84.41 200	95.65 178	89.45 191

IterMVS		94.48 158	93.46 165	95.66 154	97.52 157	96.43 158	97.20 143	94.73 190	92.91 138	96.44 100	98.75 64	91.10 183	94.53 133	92.10 190	90.10 192	93.51 188	92.84 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDTV_nov1_ep13_2view		94.39 160	93.34 166	95.63 155	97.23 171	95.33 178	97.76 111	96.84 137	94.55 98	97.47 54	98.96 45	97.70 118	93.88 144	92.27 188	86.81 198	90.56 201	87.73 199

Fast-Effi-MVS+-dtu		94.34 161	93.26 168	95.62 156	97.82 141	95.97 172	95.86 182	99.01 13	86.88 196	93.39 185	90.83 199	95.46 158	90.61 174	94.46 165	94.68 158	97.01 155	94.51 155

thres600view7		94.34 161	92.31 178	96.70 124	98.19 111	98.12 83	97.85 110	97.45 108	91.49 149	93.98 175	84.27 209	82.02 200	94.24 139	97.04 108	98.76 31	98.49 79	94.47 157

EPNet		94.33 163	93.52 164	95.27 164	98.81 73	94.71 183	96.77 158	98.20 52	88.12 187	96.53 96	92.53 184	91.19 182	85.25 209	95.22 154	95.26 149	96.09 176	97.63 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GA-MVS		94.18 164	92.98 171	95.58 157	97.36 164	96.42 159	96.21 175	95.86 162	90.29 164	95.08 153	96.19 128	85.37 192	92.82 159	94.01 172	94.14 163	96.16 175	94.41 159

gg-mvs-nofinetune		94.13 165	93.93 159	94.37 179	97.99 126	95.86 173	95.45 194	99.22 9	97.61 16	95.10 152	99.50 20	84.50 193	81.73 212	95.31 152	94.12 164	96.71 166	90.59 184

baseline		94.07 166	94.50 152	93.57 186	96.34 188	93.40 189	95.56 189	92.39 200	92.07 146	94.00 174	98.24 83	97.51 125	89.19 186	91.75 192	92.72 179	93.96 187	95.79 136

FMVSNet3		94.06 167	93.85 161	94.31 182	95.46 205	97.80 110	96.34 170	97.58 95	88.43 184	90.28 201	96.01 132	92.43 176	88.67 192	91.82 191	93.96 167	97.53 129	96.50 118

thres400		94.04 168	91.94 181	96.50 134	97.98 128	97.82 108	97.66 117	96.96 130	90.96 156	94.20 169	83.24 210	82.82 198	93.80 145	96.50 124	98.09 67	98.38 89	94.15 161

CVMVSNet		94.01 169	94.25 154	93.73 185	94.36 208	92.44 193	97.45 129	88.56 206	95.59 57	93.06 191	98.88 52	90.03 187	94.84 128	94.08 171	93.45 174	94.09 185	95.31 147

thres200		93.98 170	91.90 182	96.40 138	97.66 148	98.12 83	97.20 143	97.45 108	90.16 167	93.82 176	83.08 211	83.74 196	93.80 145	97.04 108	97.48 86	98.49 79	93.70 167

baseline1		93.89 171	92.82 173	95.14 168	97.62 153	96.97 145	96.12 176	96.36 151	91.30 151	91.53 196	94.68 155	80.72 202	90.80 172	95.71 143	96.29 123	98.44 86	94.09 162

tfpn200view9		93.80 172	91.75 183	96.20 141	97.52 157	98.15 81	97.48 128	97.47 105	87.65 189	93.56 182	83.03 212	84.12 194	92.62 161	97.04 108	98.09 67	98.52 78	94.17 160

MIMVSNet		93.68 173	93.96 157	93.35 188	97.82 141	96.08 170	96.34 170	98.46 32	91.28 153	86.67 214	94.95 150	94.87 163	84.39 210	94.53 161	94.65 159	96.45 170	91.34 181

pmnet_mix02		93.59 174	92.65 174	94.69 175	96.76 183	94.16 186	97.03 150	93.00 199	95.79 50	96.03 120	98.91 49	97.69 119	92.99 155	90.03 202	84.10 202	92.35 197	87.89 198

EPNet_dtu		93.45 175	92.51 176	94.55 178	98.39 95	91.67 202	95.46 192	97.50 102	86.56 199	97.38 59	93.52 174	94.20 170	85.82 204	93.31 179	92.53 180	92.72 193	95.76 138
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	92.44 16	93.33 176	92.15 180	94.70 174	97.42 163	96.39 163	95.57 186	94.67 191	86.40 202	93.59 181	78.28 216	95.76 157	89.59 185	95.88 141	95.98 133	97.39 138	96.34 120
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
ET-MVSNet_ETH3D		93.18 177	90.80 188	95.95 146	96.05 192	96.07 171	96.92 155	96.51 149	89.34 176	95.63 138	94.08 167	72.31 220	93.13 153	94.33 167	94.83 155	97.44 134	94.65 153

thres100view900		92.93 178	90.89 187	95.31 162	97.52 157	96.82 153	96.41 168	95.08 181	87.65 189	93.56 182	83.03 212	84.12 194	91.12 168	94.53 161	96.91 103	98.17 101	93.21 172

N_pmnet		92.46 179	92.38 177	92.55 194	97.91 132	93.47 188	97.42 131	94.01 198	96.40 33	88.48 210	98.50 72	98.07 107	88.14 194	91.04 198	84.30 201	89.35 206	84.85 205

TAMVS		92.46 179	93.34 166	91.44 202	97.03 174	93.84 187	94.68 204	90.60 204	90.44 163	85.31 215	97.14 110	93.03 174	85.78 205	94.34 166	93.67 170	95.22 181	90.93 183

CMPMVS	71.81 19	92.34 181	92.85 172	91.75 200	92.70 213	90.43 207	88.84 216	88.56 206	85.87 203	94.35 166	90.98 197	95.89 156	91.14 167	96.14 135	94.83 155	94.93 184	95.78 137
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline2		92.06 182	89.82 191	94.68 176	97.32 165	95.72 174	94.97 201	95.08 181	84.75 207	94.34 168	90.68 202	77.75 210	90.13 179	93.38 175	93.58 172	96.25 174	92.90 176

MVSTER		91.97 183	90.31 189	93.91 183	96.81 178	96.91 147	94.22 205	95.64 171	84.98 205	92.98 192	93.42 175	72.56 218	86.64 203	95.11 155	93.89 169	97.16 149	95.31 147

CR-MVSNet		91.94 184	88.50 194	95.94 147	96.14 190	92.08 197	95.23 197	98.47 30	84.30 209	96.44 100	94.58 158	75.57 212	92.92 156	90.22 200	92.22 181	96.43 171	90.56 185

gm-plane-assit		91.85 185	87.91 196	96.44 137	99.14 45	98.25 72	99.02 30	97.38 115	95.57 59	98.31 25	99.34 31	51.00 225	88.93 189	93.16 181	91.57 185	95.85 177	86.50 202

PMMVS		91.67 186	91.47 185	91.91 199	89.43 218	88.61 213	94.99 200	85.67 211	87.50 191	93.80 178	94.42 164	94.88 162	90.71 173	92.26 189	92.96 177	96.83 161	89.65 189

CHOSEN 280x420		91.55 187	90.27 190	93.05 191	94.61 207	88.01 214	96.56 165	94.62 193	88.04 188	94.20 169	92.66 183	86.60 190	90.82 171	95.06 157	91.89 183	87.49 211	89.61 190

PatchT		91.40 188	88.54 193	94.74 173	91.48 217	92.18 196	97.42 131	97.51 100	84.96 206	96.44 100	94.16 165	75.47 213	92.92 156	90.22 200	92.22 181	92.66 196	90.56 185

pmmvs3		91.20 189	91.40 186	90.96 204	91.71 216	91.08 203	95.41 195	81.34 215	87.36 192	94.57 163	95.02 147	94.30 168	90.42 175	94.28 168	89.26 194	92.30 198	88.49 196

test0.0.03 1		91.17 190	91.50 184	90.80 205	98.01 123	95.46 177	94.22 205	95.80 166	86.55 200	81.75 217	90.83 199	87.93 189	78.48 214	94.51 164	94.11 165	96.50 168	91.08 182

SCA		91.15 191	87.65 198	95.23 167	96.15 189	95.68 175	96.68 162	98.18 56	90.46 162	97.21 68	92.44 186	80.17 204	93.51 151	86.04 209	83.58 205	89.68 205	85.21 204

new_pmnet		90.85 192	92.26 179	89.21 208	93.68 212	89.05 212	93.20 213	84.16 214	92.99 136	84.25 216	97.72 93	94.60 164	86.80 202	93.20 180	91.30 186	93.21 189	86.94 201

RPMNet		90.52 193	86.27 207	95.48 159	95.95 195	92.08 197	95.55 190	98.12 61	84.30 209	95.60 140	87.49 207	72.78 217	91.24 166	87.93 204	89.34 193	96.41 172	89.98 188

MDTV_nov1_ep13		90.30 194	87.32 202	93.78 184	96.00 194	92.97 191	95.46 192	95.39 176	88.61 180	95.41 144	94.45 163	80.39 203	89.87 183	86.58 207	83.54 206	90.56 201	84.71 206

PatchmatchNet		89.98 195	86.23 208	94.36 181	96.56 185	91.90 201	96.07 177	96.72 142	90.18 166	96.87 82	93.36 178	78.06 208	91.46 164	84.71 213	81.40 210	88.45 208	83.97 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ADS-MVSNet		89.89 196	87.70 197	92.43 196	95.52 202	90.91 205	95.57 186	95.33 177	93.19 129	91.21 198	93.41 176	82.12 199	89.05 187	86.21 208	83.77 204	87.92 209	84.31 207

tpm		89.84 197	86.81 204	93.36 187	96.60 184	91.92 200	95.02 199	97.39 114	86.79 197	96.54 95	95.03 146	69.70 221	87.66 196	88.79 203	86.19 199	86.95 213	89.27 192

test-LLR		89.77 198	87.47 200	92.45 195	98.01 123	89.77 209	93.25 211	95.80 166	81.56 214	89.19 205	92.08 189	79.59 205	85.77 207	91.47 196	89.04 196	92.69 194	88.75 193

FMVSNet5		89.65 199	87.60 199	92.04 198	95.63 201	96.61 155	94.82 203	94.75 188	80.11 218	87.72 212	77.73 217	73.81 216	83.81 211	95.64 144	96.08 131	95.49 180	93.21 172

EPMVS		89.28 200	86.28 206	92.79 193	96.01 193	92.00 199	95.83 183	95.85 164	90.78 159	91.00 199	94.58 158	74.65 214	88.93 189	85.00 211	82.88 208	89.09 207	84.09 209

test-mter		89.16 201	88.14 195	90.37 206	94.79 206	91.05 204	93.60 210	85.26 212	81.65 213	88.32 211	92.22 187	79.35 207	87.03 200	92.28 187	90.12 191	93.19 190	90.29 187

CostFormer		89.06 202	85.65 209	93.03 192	95.88 196	92.40 194	95.30 196	95.86 162	86.49 201	93.12 190	93.40 177	74.18 215	88.25 193	82.99 214	81.46 209	89.77 204	88.66 195

MVS-HIRNet		88.72 203	86.49 205	91.33 203	91.81 215	85.66 215	87.02 218	96.25 152	81.48 216	94.82 157	96.31 127	92.14 179	90.32 177	87.60 205	83.82 203	87.74 210	78.42 214

TESTMET0.1,1		88.60 204	87.47 200	89.93 207	94.23 210	89.77 209	93.25 211	84.47 213	81.56 214	89.19 205	92.08 189	79.59 205	85.77 207	91.47 196	89.04 196	92.69 194	88.75 193

dps		88.36 205	84.32 212	93.07 190	93.86 211	92.29 195	94.89 202	95.93 160	83.50 211	93.13 188	91.87 191	67.79 223	90.32 177	85.99 210	83.22 207	90.28 203	85.56 203

tpmrst		87.60 206	84.13 213	91.66 201	95.65 200	89.73 211	93.77 208	94.74 189	88.85 178	93.35 187	95.60 139	72.37 219	87.40 197	81.24 215	78.19 212	85.02 216	82.90 213

tpm cat1		87.19 207	82.78 214	92.33 197	95.66 199	90.61 206	94.19 207	95.27 178	86.97 195	94.38 165	90.91 198	69.40 222	87.21 198	79.57 217	77.82 213	87.25 212	84.18 208

E-PMN		86.94 208	85.10 210	89.09 210	95.77 198	83.54 218	89.89 215	86.55 208	92.18 144	87.34 213	94.02 168	83.42 197	89.63 184	93.32 178	77.11 214	85.33 214	72.09 215

EMVS		86.63 209	84.48 211	89.15 209	95.51 203	83.66 217	90.19 214	86.14 210	91.78 148	88.68 208	93.83 172	81.97 201	89.05 187	92.76 185	76.09 215	85.31 215	71.28 216

PMMVS2		86.47 210	92.62 175	79.29 212	92.01 214	85.63 216	93.74 209	86.37 209	93.95 119	54.18 222	98.19 84	97.39 128	58.46 215	96.57 123	93.07 176	90.99 200	83.55 212

MVE	72.99 18	85.37 211	89.43 192	80.63 211	74.43 219	71.94 220	88.25 217	89.81 205	93.27 127	67.32 220	96.32 126	91.83 180	90.40 176	93.36 176	90.79 189	73.55 219	88.49 196
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		61.30 212	70.45 215	50.62 213	22.69 221	30.92 222	68.31 221	25.76 217	80.56 217	68.71 218	82.80 214	91.08 184	44.64 216	80.50 216	56.70 216	73.64 218	70.58 217

GG-mvs-BLEND		61.03 213	87.02 203	30.71 215	0.74 224	90.01 208	78.90 220	0.74 221	84.56 208	9.46 223	79.17 215	90.69 186	1.37 220	91.74 193	89.13 195	93.04 192	83.83 211

testmvs		4.99 214	6.88 216	2.78 217	1.73 222	2.04 224	3.10 224	1.71 219	7.27 220	3.92 225	12.18 219	6.71 226	3.31 219	6.94 218	5.51 218	2.94 221	7.51 218

test123		4.41 215	5.71 217	2.88 216	1.28 223	2.21 223	3.09 225	1.65 220	6.35 221	4.98 224	8.53 220	3.88 227	3.46 218	5.79 219	5.71 217	2.85 222	7.50 219

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										99.38 2

9.14												96.98 140

SR-MVS					99.33 31			98.40 35				98.90 55

Anonymous202405211			97.39 68		98.85 69	98.59 52	97.89 107	97.93 77	94.41 106		97.37 103	96.99 139	93.09 154	98.61 47	98.46 42	99.11 35	97.27 88

our_test_3					97.32 165	95.13 180	97.59 122

ambc			96.78 100		99.01 58	97.11 142	95.73 184		95.91 46	99.25 3	98.56 71	97.17 134	97.04 69	96.76 118	95.22 150	96.72 165	96.73 108

MTAPA										97.43 57		99.27 23

MTMP										97.63 47		99.03 43

Patchmatch-RL test							17.42 223

tmp_tt				45.72 214	60.00 220	38.74 221	45.50 222	12.18 218	79.58 219	68.42 219	67.62 218	65.04 224	22.12 217	84.83 212	78.72 211	66.08 220

XVS					99.48 19	98.76 39	99.22 21			96.40 104		98.78 69				98.94 50

X-MVStestdata					99.48 19	98.76 39	99.22 21			96.40 104		98.78 69				98.94 50

abl_6				96.45 136	97.79 146	97.28 131	97.16 146	96.16 154	89.92 171	95.72 134	91.59 193	97.16 135	94.37 137			97.51 131	95.49 144

mPP-MVS					99.58 6							98.98 47

NP-MVS									89.27 177

Patchmtry						92.70 192	95.23 197	98.47 30		96.44 100

DeepMVS_CX						72.99 219	80.14 219	37.34 216	83.46 212	60.13 221	84.40 208	85.48 191	86.93 201	87.22 206		79.61 217	87.32 200

High-resolution multi-view benchmark