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 42	99.11 15	99.39 13	99.16 11	99.26 3	99.22 5	99.51 19	99.75 4	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 7

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 10

test_part1		99.20 4	99.62 1	98.72 17	98.92 66	99.62 1	99.52 12	99.01 13	99.39 1	97.87 39	99.74 4	99.75 4	97.29 64	99.73 1	99.71 1	99.69 2	99.41 2

PS-CasMVS		99.08 5	98.90 12	99.28 3	99.65 3	99.56 5	99.59 6	99.39 3	96.36 36	98.83 14	99.46 22	99.09 35	98.62 10	99.51 8	99.36 9	99.63 4	98.97 8

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 28	98.65 17	99.43 24	99.33 17	98.47 17	99.50 9	99.32 10	99.60 6	98.79 12

v7n		99.03 7	99.03 8	99.02 9	99.09 53	99.11 15	99.57 9	98.82 20	98.21 10	99.25 3	99.84 2	99.59 7	98.76 6	99.23 20	98.83 33	98.63 72	98.40 35

DTE-MVSNet		99.03 7	98.88 13	99.21 6	99.66 2	99.59 3	99.62 5	99.34 6	96.92 24	98.52 19	99.36 30	98.98 47	98.57 13	99.49 10	99.23 13	99.56 10	98.55 26

TDRefinement		99.00 9	99.13 7	98.86 10	98.99 63	99.05 20	99.58 7	98.29 51	98.96 5	97.96 37	99.40 27	98.67 77	98.87 5	99.60 4	99.46 5	99.46 19	98.74 15

WR-MVS_H		98.97 10	98.82 15	99.14 8	99.56 9	99.56 5	99.54 11	99.42 2	96.07 41	98.37 24	99.34 32	99.09 35	98.43 18	99.45 11	99.41 6	99.53 11	98.86 11

UniMVSNet_ETH3D		98.93 11	99.20 4	98.63 23	99.54 11	99.33 8	98.73 65	99.37 4	98.87 6	97.86 40	99.27 36	99.78 2	96.59 87	99.52 7	99.40 7	99.67 3	98.21 43

CP-MVSNet		98.91 12	98.61 20	99.25 4	99.63 5	99.50 7	99.55 10	99.36 5	95.53 67	98.77 16	99.11 43	98.64 80	98.57 13	99.42 12	99.28 12	99.61 5	98.78 13

anonymousdsp		98.85 13	98.88 13	98.83 11	98.69 85	98.20 79	99.68 1	97.35 125	97.09 23	98.98 10	99.86 1	99.43 11	98.94 3	99.28 15	99.19 14	99.33 24	99.08 6

pmmvs6		98.77 14	99.35 3	98.09 45	98.32 104	98.92 26	98.57 72	99.03 12	99.36 2	96.86 85	99.77 3	99.86 1	96.20 102	99.56 5	99.39 8	99.59 7	98.61 23

ACMH	95.26 7	98.75 15	98.93 10	98.54 27	98.86 69	99.01 22	99.58 7	98.10 70	98.67 7	97.30 63	99.18 40	99.42 12	98.40 19	99.19 22	98.86 31	98.99 48	98.19 44
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 15	98.66 21	99.14 46	98.79 40	99.30 18	97.67 97	98.33 9	97.82 42	99.20 39	99.18 33	98.76 6	99.27 18	98.96 23	99.29 28	98.03 48
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 23	98.73 15	99.83 1	99.28 10	98.56 74	99.24 8	96.04 42	97.12 72	98.44 78	98.95 52	98.17 28	99.15 25	99.00 22	99.48 18	99.33 4

DeepC-MVS	96.08 5	98.58 18	98.49 25	98.68 19	99.37 27	98.52 66	99.01 36	98.17 65	97.17 22	98.25 27	99.56 16	99.62 6	98.29 22	98.40 64	98.09 72	98.97 50	98.08 47
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 32	98.72 17	99.32 32	99.06 18	98.99 37	98.89 15	95.52 68	97.53 51	99.42 26	98.83 64	98.01 34	98.55 56	98.34 59	99.57 9	97.80 59

CSCG		98.45 19	98.61 20	98.26 39	99.11 50	99.06 18	98.17 93	97.49 110	97.93 13	97.37 60	98.88 55	99.29 20	98.10 29	98.40 64	97.51 89	99.32 26	99.16 5

DVP-MVS++		98.44 21	98.92 11	97.88 65	99.17 40	99.00 23	98.89 48	98.26 53	97.54 18	96.05 119	99.35 31	99.76 3	96.34 97	98.79 39	98.65 42	98.56 79	99.35 3

Gipuma		98.43 22	98.15 35	98.76 14	99.00 62	98.29 76	97.91 108	98.06 72	99.02 4	99.50 1	96.33 129	98.67 77	99.22 1	99.02 28	98.02 77	98.88 63	97.66 68
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ACMH+	94.90 8	98.40 23	98.71 18	98.04 55	98.93 65	98.84 33	99.30 18	97.86 89	97.78 14	94.19 176	98.77 65	99.39 14	98.61 11	99.33 14	99.07 15	99.33 24	97.81 58

ACMMPR		98.31 24	98.07 39	98.60 24	99.58 6	98.83 34	99.09 28	98.48 32	96.25 38	97.03 76	96.81 117	99.09 35	98.39 20	98.55 56	98.45 51	99.01 45	98.53 29

APDe-MVS		98.29 25	98.42 27	98.14 42	99.45 22	98.90 27	99.18 24	98.30 49	95.96 49	95.13 152	98.79 62	99.25 28	97.92 39	98.80 37	98.71 37	98.85 65	98.54 27

DVP-MVS		98.27 26	98.61 20	97.87 66	99.17 40	99.03 21	99.07 30	98.17 65	96.75 27	94.35 171	98.92 51	99.58 8	97.86 42	98.67 48	98.70 38	98.63 72	98.63 21
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 17	97.66 79	98.22 113	98.73 51	98.66 68	98.03 77	98.60 8	96.40 104	99.60 13	98.24 101	95.26 124	99.19 22	99.05 18	99.36 21	97.64 69

DU-MVS		98.23 27	97.74 56	98.81 12	99.23 34	98.77 42	98.76 59	98.88 16	94.10 116	98.50 20	98.87 57	98.32 98	97.99 35	98.40 64	98.08 75	99.49 17	97.64 69

UniMVSNet (Re)		98.23 27	97.85 48	98.67 20	99.15 43	98.87 29	98.74 62	98.84 18	94.27 114	97.94 38	99.01 45	98.39 94	97.82 43	98.35 69	98.29 64	99.51 16	97.78 60

MIMVSNet1		98.22 30	98.51 24	97.87 66	99.40 26	98.82 38	99.31 17	98.53 29	97.39 19	96.59 95	99.31 34	99.23 30	94.76 134	98.93 33	98.67 40	98.63 72	97.25 93

HFP-MVS		98.17 31	98.02 40	98.35 37	99.36 28	98.62 57	98.79 58	98.46 36	96.24 39	96.53 97	97.13 113	98.98 47	98.02 33	98.20 72	98.42 53	98.95 54	98.54 27

Baseline_NR-MVSNet		98.17 31	97.90 45	98.48 31	99.23 34	98.59 58	98.83 55	98.73 25	93.97 121	96.95 79	99.66 8	98.23 103	97.90 40	98.40 64	99.06 17	99.25 30	97.42 85

TSAR-MVS + MP.		98.15 33	98.23 31	98.06 53	98.47 95	98.16 85	99.23 21	96.87 140	95.58 62	96.72 88	98.41 79	99.06 39	98.05 32	98.99 30	98.90 27	99.00 46	98.51 30
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 26	99.58 6	98.58 60	98.98 39	98.48 32	95.98 47	97.39 58	94.73 160	99.27 24	97.98 38	98.81 36	98.64 45	98.90 58	98.46 31

pm-mvs1		98.14 34	98.66 19	97.53 88	97.93 135	98.49 68	98.14 95	98.19 61	97.95 12	96.17 115	99.63 11	98.85 60	95.41 122	98.91 34	98.89 28	99.34 23	97.86 57

SMA-MVS		98.13 36	98.22 32	98.02 58	99.44 24	98.73 51	98.24 90	97.87 88	95.22 75	96.76 87	98.66 71	99.35 16	97.03 72	98.53 59	98.39 55	98.80 67	98.69 17
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 41	99.34 29	98.74 50	98.97 40	98.00 79	95.13 79	96.90 80	97.54 101	99.27 24	97.18 66	98.72 44	98.45 51	98.68 71	98.69 17

UniMVSNet_NR-MVSNet		98.12 37	97.56 63	98.78 13	99.13 48	98.89 28	98.76 59	98.78 21	93.81 124	98.50 20	98.81 61	97.64 124	97.99 35	98.18 75	97.92 80	99.53 11	97.64 69

ACMM	94.29 11	98.12 37	97.71 57	98.59 25	99.51 17	98.58 60	99.24 20	98.25 54	96.22 40	96.90 80	95.01 153	98.89 57	98.52 16	98.66 49	98.32 62	99.13 37	98.28 41
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 35	99.54 11	98.79 40	98.94 44	98.42 38	93.98 120	95.85 126	96.66 123	99.25 28	98.61 11	98.71 46	98.38 56	98.97 50	98.67 20
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS		98.05 41	98.46 26	97.57 84	99.01 59	98.99 24	98.82 57	98.24 55	95.76 57	94.70 163	98.96 47	99.49 10	96.19 103	98.74 40	98.65 42	98.46 87	98.63 21

OPM-MVS		98.01 42	98.01 41	98.00 60	99.11 50	98.12 88	98.68 66	97.72 95	96.65 30	96.68 92	98.40 80	99.28 23	97.44 56	98.20 72	97.82 86	98.40 93	97.58 74
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		98.01 42	98.42 27	97.54 87	96.89 182	98.82 38	99.14 25	97.59 100	96.30 37	97.04 75	99.26 37	98.83 64	96.01 108	98.73 42	98.21 66	98.58 78	98.75 14
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		98.00 44	97.38 69	98.73 15	98.72 80	99.15 12	99.12 27	98.76 22	91.58 154	98.15 31	96.70 121	98.72 76	98.20 24	98.64 52	98.92 25	99.43 20	97.97 51

NR-MVSNet		98.00 44	97.88 46	98.13 43	98.33 102	98.77 42	98.83 55	98.88 16	94.10 116	97.46 56	98.87 57	98.58 85	95.78 111	99.13 26	98.16 70	99.52 13	97.53 77

CP-MVS		98.00 44	97.57 62	98.50 28	99.47 21	98.56 63	98.91 46	98.38 44	94.71 95	97.01 77	95.20 149	99.06 39	98.20 24	98.61 53	98.46 48	99.02 43	98.40 35

DPE-MVS		97.99 47	98.12 36	97.84 69	98.65 89	98.86 30	98.86 52	98.05 75	94.18 115	95.49 145	98.90 53	99.33 17	97.11 68	98.53 59	98.65 42	98.86 64	98.39 37
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMMP		97.99 47	97.60 61	98.45 33	99.53 15	98.83 34	99.13 26	98.30 49	94.57 101	96.39 108	95.32 147	98.95 52	98.37 21	98.61 53	98.47 47	99.00 46	98.45 32
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
MP-MVS		97.98 49	97.53 65	98.50 28	99.56 9	98.58 60	98.97 40	98.39 43	93.49 127	97.14 69	96.08 136	99.23 30	98.06 31	98.50 61	98.38 56	98.90 58	98.44 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EG-PatchMatch MVS		97.98 49	97.92 43	98.04 55	98.84 72	98.04 96	97.90 109	96.83 143	95.07 81	98.79 15	99.07 44	99.37 15	97.88 41	98.74 40	98.16 70	98.01 115	96.96 101

ACMP	94.03 12	97.97 51	97.61 60	98.39 35	99.43 25	98.51 67	98.97 40	98.06 72	94.63 99	96.10 117	96.12 135	99.20 32	98.63 9	98.68 47	98.20 69	99.14 34	97.93 54
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CS-MVS-test		97.96 52	97.38 69	98.64 22	98.57 91	99.13 13	99.36 14	98.66 26	91.67 153	98.17 30	96.91 116	98.84 62	97.99 35	98.80 37	98.88 29	99.08 42	97.43 84

LGP-MVS_train		97.96 52	97.53 65	98.45 33	99.45 22	98.64 56	99.09 28	98.27 52	92.99 139	96.04 120	96.57 124	99.29 20	98.66 8	98.73 42	98.42 53	99.19 32	98.09 46

LS3D		97.93 54	97.80 50	98.08 49	99.20 37	98.77 42	98.89 48	97.92 84	96.59 31	96.99 78	96.71 120	97.14 137	96.39 96	99.04 27	98.96 23	99.10 41	97.39 86

SD-MVS		97.84 55	97.78 53	97.90 63	98.33 102	98.06 93	97.95 105	97.80 94	96.03 46	96.72 88	97.57 99	99.18 33	97.50 54	97.88 78	97.08 102	99.11 39	98.68 19
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 56	98.27 29	97.31 99	98.23 111	98.06 93	97.44 134	95.79 173	96.90 25	95.81 128	98.76 66	98.61 84	97.70 48	98.90 35	98.36 58	98.90 58	98.29 38

thisisatest0515		97.82 57	97.67 58	97.99 61	98.49 94	98.07 92	98.48 79	98.06 72	95.35 73	97.74 44	98.83 60	97.61 125	96.74 79	97.53 96	98.30 63	98.43 92	98.01 50

PGM-MVS		97.82 57	97.25 75	98.48 31	99.54 11	98.75 49	99.02 32	98.35 47	92.41 143	96.84 86	95.39 146	98.99 46	98.24 23	98.43 63	98.34 59	98.90 58	98.41 34

PMVS	90.51 17	97.77 59	97.98 42	97.53 88	98.68 86	98.14 87	97.67 119	97.03 135	96.43 32	98.38 23	98.72 68	97.03 139	94.44 139	99.37 13	99.30 11	98.98 49	96.86 108
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MSP-MVS		97.67 60	97.88 46	97.43 94	99.34 29	98.99 24	98.87 51	98.12 68	95.63 59	94.16 177	97.45 102	99.50 9	96.44 95	96.35 133	98.70 38	97.65 131	98.57 25
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 61	97.79 51	97.52 90	98.32 104	98.53 65	98.45 82	97.69 96	97.59 17	96.12 116	97.79 94	96.70 143	95.69 116	98.35 69	98.34 59	98.85 65	97.22 96

FC-MVSNet-train		97.65 62	98.16 34	97.05 111	98.85 70	98.85 31	99.34 15	98.08 71	94.50 106	94.41 169	99.21 38	98.80 68	92.66 165	98.98 31	98.85 32	98.96 52	97.94 53

v10		97.64 63	97.26 74	98.08 49	98.07 124	98.56 63	98.86 52	98.18 63	94.48 107	98.24 28	99.56 16	98.98 47	97.72 47	96.05 143	96.26 130	97.42 140	96.93 102

DROMVSNet		97.63 64	96.88 97	98.50 28	98.74 79	99.16 11	99.33 16	98.83 19	88.77 184	96.62 94	96.48 126	97.75 117	98.19 26	99.00 29	98.76 35	99.29 28	98.27 42

X-MVS		97.60 65	97.00 92	98.29 38	99.50 18	98.76 45	98.90 47	98.37 45	94.67 98	96.40 104	91.47 199	98.78 70	97.60 53	98.55 56	98.50 46	98.96 52	98.29 38

3Dnovator+	96.20 4	97.58 66	97.14 83	98.10 44	98.98 64	97.85 108	98.60 71	98.33 48	96.41 34	97.23 67	94.66 163	97.26 133	96.91 76	97.91 77	97.87 82	98.53 82	98.03 48

DCV-MVSNet		97.56 67	97.63 59	97.47 92	98.41 99	99.12 14	98.63 69	98.57 27	95.71 58	95.60 142	93.79 178	98.01 112	94.25 142	99.16 24	98.88 29	99.35 22	98.74 15

HPM-MVS++		97.56 67	97.11 87	98.09 45	99.18 39	97.95 103	98.57 72	98.20 59	94.08 118	97.25 66	95.96 140	98.81 67	97.13 67	97.51 97	97.30 99	98.21 103	98.15 45

FC-MVSNet-test		97.54 69	98.26 30	96.70 128	98.87 68	97.79 116	98.49 78	98.56 28	96.04 42	90.39 205	99.65 9	98.67 77	95.15 126	99.23 20	99.07 15	98.73 70	97.39 86

TSAR-MVS + ACMM		97.54 69	97.79 51	97.26 100	98.23 111	98.10 91	97.71 117	97.88 87	95.97 48	95.57 144	98.71 69	98.57 86	97.36 59	97.74 85	96.81 111	96.83 166	98.59 24

DeepC-MVS_fast	95.38 6	97.53 71	97.30 73	97.79 74	98.83 73	97.64 119	98.18 91	97.14 131	95.57 63	97.83 41	97.10 114	98.80 68	96.53 92	97.41 100	97.32 97	98.24 102	97.26 92
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 72	97.03 91	98.09 45	98.31 107	98.01 98	98.96 43	97.25 128	95.22 75	98.89 12	99.64 10	98.83 64	97.68 49	95.63 150	95.91 140	97.47 136	95.97 135

v1144		97.51 73	97.05 90	98.04 55	98.26 109	97.98 100	98.88 50	97.42 119	95.38 72	98.56 18	99.59 15	99.01 45	97.65 50	95.77 147	96.06 137	97.47 136	95.56 147

v8		97.51 73	97.16 81	97.91 62	97.99 131	98.48 69	98.76 59	98.17 65	94.54 105	97.69 46	99.48 21	98.76 73	97.63 52	96.10 142	96.14 132	97.20 150	96.64 115

v1921920		97.50 75	97.00 92	98.07 51	98.20 115	97.94 106	99.03 31	97.06 133	95.29 74	99.01 9	99.62 12	98.73 75	97.74 46	95.52 153	95.78 145	97.39 142	96.12 131

Anonymous20231211		97.49 76	97.91 44	97.00 115	98.31 107	98.72 53	98.27 88	97.84 91	94.76 94	94.77 162	98.14 87	98.38 96	93.60 152	98.96 32	98.66 41	99.22 31	97.77 63

v144192		97.49 76	96.99 94	98.07 51	98.11 123	97.95 103	99.02 32	97.21 129	94.90 90	98.88 13	99.53 18	98.89 57	97.75 45	95.59 151	95.90 141	97.43 139	96.16 129

test1111		97.48 78	97.20 78	97.81 73	98.78 77	98.85 31	98.68 66	98.40 39	96.68 28	94.84 160	99.13 42	90.32 190	97.01 73	99.27 18	99.05 18	99.19 32	97.10 98

GeoE		97.48 78	96.84 101	98.22 40	99.01 59	98.39 72	98.85 54	98.76 22	92.37 144	97.53 51	97.58 98	98.23 103	97.11 68	97.57 95	96.98 105	98.10 111	96.78 111

APD-MVS		97.47 80	97.16 81	97.84 69	99.32 32	98.39 72	98.47 81	98.21 58	92.08 149	95.23 149	96.68 122	98.90 55	96.99 74	98.20 72	98.21 66	98.80 67	97.67 67
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PVSNet_Blended_VisFu		97.44 81	97.14 83	97.79 74	99.15 43	98.44 70	98.32 86	97.66 98	93.74 126	97.73 45	98.79 62	96.93 142	95.64 121	97.69 87	96.91 108	98.25 101	97.50 80

PHI-MVS		97.44 81	97.17 80	97.74 77	98.14 120	98.41 71	98.03 101	97.50 108	92.07 150	98.01 36	97.33 107	98.62 83	96.02 107	98.34 71	98.21 66	98.76 69	97.24 95

v1240		97.43 83	96.87 100	98.09 45	98.25 110	97.92 107	99.02 32	97.06 133	94.77 93	99.09 8	99.68 7	98.51 89	97.78 44	95.25 158	95.81 143	97.32 146	96.13 130

ECVR-MVS		97.40 84	97.11 87	97.73 78	98.66 87	98.83 34	98.50 76	98.40 39	96.04 42	95.00 158	98.95 49	91.07 187	96.70 81	99.28 15	99.04 20	99.14 34	96.58 116

FMVSNet1		97.40 84	98.09 37	96.60 133	97.80 149	98.76 45	98.26 89	98.50 31	96.79 26	93.13 193	99.28 35	98.64 80	92.90 163	97.67 89	97.86 83	99.02 43	97.64 69

v2v482		97.33 86	96.84 101	97.90 63	98.19 116	97.83 109	98.74 62	97.44 116	95.42 71	98.23 29	99.46 22	98.84 62	97.46 55	95.51 154	96.10 135	97.36 144	94.72 157

xxxxxxxxxxxxxcwj		97.32 87	97.55 64	97.05 111	98.80 75	97.83 109	96.02 183	97.44 116	94.98 84	95.74 132	97.16 110	99.30 19	95.72 113	97.85 79	97.97 78	98.60 75	97.78 60

EPP-MVSNet		97.29 88	96.88 97	97.76 76	98.70 82	99.10 17	98.92 45	98.36 46	95.12 80	93.36 191	97.39 104	91.00 188	97.65 50	98.72 44	98.91 26	99.58 8	97.92 55

MVS_111021_HR		97.27 89	97.11 87	97.46 93	98.46 96	97.82 113	97.50 130	96.86 141	94.97 86	97.13 71	96.99 115	98.39 94	96.82 78	97.65 92	97.38 92	98.02 114	96.56 119

SF-MVS		97.26 90	97.43 67	97.05 111	98.80 75	97.83 109	96.02 183	97.44 116	94.98 84	95.74 132	97.16 110	98.45 93	95.72 113	97.85 79	97.97 78	98.60 75	97.78 60

TSAR-MVS + GP.		97.26 90	97.33 72	97.18 105	98.21 114	98.06 93	96.38 174	97.66 98	93.92 123	95.23 149	98.48 76	98.33 97	97.41 57	97.63 93	97.35 93	98.18 105	97.57 75

OMC-MVS		97.23 92	97.21 77	97.25 103	97.85 140	97.52 128	97.92 107	95.77 174	95.83 53	97.09 74	97.86 92	98.52 88	96.62 85	97.51 97	96.65 117	98.26 99	96.57 117

3Dnovator	96.31 3	97.22 93	97.19 79	97.25 103	98.14 120	97.95 103	98.03 101	96.77 146	96.42 33	97.14 69	95.11 150	97.59 126	95.14 128	97.79 83	97.72 87	98.26 99	97.76 65

MVS_0304		97.18 94	96.84 101	97.58 83	99.15 43	98.19 80	98.11 96	97.81 93	92.36 145	98.06 34	97.43 103	99.06 39	94.24 143	96.80 121	96.54 121	98.12 109	97.52 78

canonicalmvs		97.11 95	96.88 97	97.38 95	98.34 101	98.72 53	97.52 129	97.94 82	95.60 60	95.01 157	94.58 164	94.50 167	96.59 87	97.84 81	98.03 76	98.90 58	98.91 9

V42		97.10 96	96.97 95	97.26 100	97.64 155	97.60 121	98.45 82	95.99 163	94.44 108	97.35 61	99.40 27	98.63 82	97.34 61	96.33 136	96.38 127	96.82 168	96.00 133

CPTT-MVS		97.08 97	96.25 115	98.05 54	99.21 36	98.30 75	98.54 75	97.98 80	94.28 112	95.89 125	89.57 208	98.54 87	98.18 27	97.82 82	97.32 97	98.54 80	97.91 56

DeepPCF-MVS	94.55 10	97.05 98	97.13 86	96.95 117	96.06 196	97.12 146	98.01 103	95.44 180	95.18 77	97.50 53	97.86 92	98.08 108	97.31 63	97.23 105	97.00 104	97.36 144	97.45 82

QAPM		97.04 99	97.14 83	96.93 119	97.78 152	98.02 97	97.36 139	96.72 147	94.68 97	96.23 110	97.21 109	97.68 122	95.70 115	97.37 101	97.24 101	97.78 124	97.77 63

CNVR-MVS		97.03 100	96.77 106	97.34 96	98.89 67	97.67 118	97.64 122	97.17 130	94.40 110	95.70 138	94.02 173	98.76 73	96.49 94	97.78 84	97.29 100	98.12 109	97.47 81

casdiffmvs		97.00 101	97.36 71	96.59 134	97.65 154	97.98 100	98.06 98	96.81 144	95.78 55	92.77 199	99.40 27	99.26 27	95.65 120	96.70 125	96.39 126	98.59 77	95.99 134

v148		96.99 102	96.70 108	97.34 96	97.89 138	97.23 138	98.33 85	96.96 136	95.57 63	97.12 72	98.99 46	99.40 13	97.23 65	96.22 139	95.45 150	96.50 173	94.02 169

DELS-MVS		96.90 103	97.24 76	96.50 139	97.85 140	98.18 81	97.88 112	95.92 166	93.48 128	95.34 147	98.86 59	98.94 54	94.03 146	97.33 103	97.04 103	98.00 116	96.85 109
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		96.86 104	96.72 107	97.03 114	97.80 149	97.06 149	97.04 154	95.51 179	94.55 102	97.47 54	97.35 106	97.68 122	96.66 83	97.11 110	96.73 113	97.69 128	96.57 117

PM-MVS		96.85 105	96.62 110	97.11 107	97.13 177	96.51 162	98.29 87	94.65 197	94.84 91	98.12 32	98.59 72	97.20 134	97.41 57	96.24 138	96.41 125	97.09 155	96.56 119

pmmvs-eth3d		96.84 106	96.22 117	97.56 85	97.63 157	96.38 169	98.74 62	96.91 139	94.63 99	98.26 26	99.43 24	98.28 99	96.58 89	94.52 168	95.54 148	97.24 148	94.75 156

CANet		96.81 107	96.50 111	97.17 106	99.10 52	97.96 102	97.86 113	97.51 106	91.30 157	97.75 43	97.64 96	97.89 115	93.39 156	96.98 117	96.73 113	97.40 141	96.99 100

Fast-Effi-MVS+		96.80 108	95.92 128	97.84 69	98.57 91	97.46 131	98.06 98	98.24 55	89.64 179	97.57 50	96.45 127	97.35 131	96.73 80	97.22 106	96.64 118	97.86 121	96.65 114

MCST-MVS		96.79 109	96.08 121	97.62 81	98.78 77	97.52 128	98.01 103	97.32 126	93.20 131	95.84 127	93.97 175	98.12 106	97.34 61	96.34 134	95.88 142	98.45 88	97.51 79

UGNet		96.79 109	97.82 49	95.58 162	97.57 160	98.39 72	98.48 79	97.84 91	95.85 52	94.68 164	97.91 91	99.07 38	87.12 204	97.71 86	97.51 89	97.80 122	98.29 38
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
TAPA-MVS	93.96 13	96.79 109	96.70 108	96.90 121	97.64 155	97.58 122	97.54 128	94.50 199	95.14 78	96.64 93	96.76 119	97.90 114	96.63 84	95.98 144	96.14 132	98.45 88	97.39 86
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CLD-MVS		96.73 112	96.92 96	96.51 138	98.70 82	97.57 124	97.64 122	92.07 206	93.10 137	96.31 109	98.29 82	99.02 44	95.99 109	97.20 107	96.47 123	98.37 95	96.81 110
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
train_agg		96.68 113	95.93 127	97.56 85	99.08 54	97.16 142	98.44 84	97.37 122	91.12 161	95.18 151	95.43 145	98.48 91	97.36 59	96.48 130	95.52 149	97.95 119	97.34 90

CDPH-MVS		96.68 113	95.99 124	97.48 91	99.13 48	97.64 119	98.08 97	97.46 112	90.56 167	95.13 152	94.87 158	98.27 100	96.56 90	97.09 111	96.45 124	98.54 80	97.08 99

MSLP-MVS++		96.66 115	96.46 114	96.89 122	98.02 126	97.71 117	95.57 191	96.96 136	94.36 111	96.19 114	91.37 200	98.24 101	97.07 70	97.69 87	97.89 81	97.52 134	97.95 52

TinyColmap		96.64 116	96.07 122	97.32 98	97.84 145	96.40 166	97.63 124	96.25 157	95.86 51	98.98 10	97.94 90	96.34 150	96.17 104	97.30 104	95.38 153	97.04 157	93.24 176

IS_MVSNet		96.62 117	96.48 113	96.78 126	98.46 96	98.68 55	98.61 70	98.24 55	92.23 146	89.63 209	95.90 141	94.40 168	96.23 99	98.65 50	98.77 34	99.52 13	96.76 112

NCCC		96.56 118	95.68 130	97.59 82	99.04 58	97.54 127	97.67 119	97.56 104	94.84 91	96.10 117	87.91 211	98.09 107	96.98 75	97.20 107	96.80 112	98.21 103	97.38 89

ETV-MVS		96.54 119	95.27 138	98.02 58	99.07 56	97.48 130	98.16 94	98.19 61	87.33 199	97.58 49	92.67 187	95.93 156	96.22 100	98.49 62	98.46 48	98.91 57	96.50 122

Effi-MVS+		96.46 120	95.28 137	97.85 68	98.64 90	97.16 142	97.15 152	98.75 24	90.27 171	98.03 35	93.93 176	96.21 151	96.55 91	96.34 134	96.69 116	97.97 118	96.33 125

IterMVS-LS		96.35 121	95.85 129	96.93 119	97.53 161	98.00 99	97.37 137	97.97 81	95.49 70	96.71 91	98.94 50	93.23 175	94.82 133	93.15 187	95.05 156	97.17 152	97.12 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
USDC		96.30 122	95.64 132	97.07 109	97.62 158	96.35 171	97.17 150	95.71 175	95.52 68	99.17 7	98.11 88	97.46 128	95.67 117	95.44 156	93.60 176	97.09 155	92.99 180

Vis-MVSNet (Re-imp)		96.29 123	96.50 111	96.05 148	97.96 134	97.83 109	97.30 142	97.86 89	93.14 133	88.90 212	96.80 118	95.28 160	95.15 126	98.37 68	98.25 65	99.12 38	95.84 137

MSDG		96.27 124	96.17 120	96.38 144	97.85 140	96.27 172	96.55 171	94.41 200	94.55 102	95.62 141	97.56 100	97.80 116	96.22 100	97.17 109	96.27 129	97.67 130	93.60 173

CNLPA		96.24 125	95.97 125	96.57 136	97.48 166	97.10 148	96.75 164	94.95 191	94.92 89	96.20 113	94.81 159	96.61 145	96.25 98	96.94 118	95.64 146	97.79 123	95.74 143

EIA-MVS		96.23 126	94.85 150	97.84 69	99.08 54	98.21 78	97.69 118	98.03 77	85.68 209	98.09 33	91.75 197	97.07 138	95.66 119	97.58 94	97.72 87	98.47 86	95.91 136

PLC	92.55 15	96.10 127	95.36 134	96.96 116	98.13 122	96.88 153	96.49 172	96.67 151	94.07 119	95.71 137	91.14 201	96.09 153	96.84 77	96.70 125	96.58 120	97.92 120	96.03 132
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test20.03		96.08 128	96.80 104	95.25 171	99.19 38	97.58 122	97.24 147	97.56 104	94.95 88	91.91 200	98.58 73	98.03 110	87.88 200	97.43 99	96.94 107	97.69 128	94.05 168

FA-MVS(training)		96.07 129	95.59 133	96.63 131	98.00 130	97.44 132	97.36 139	98.53 29	92.21 147	95.97 122	96.18 133	94.22 171	92.98 160	96.79 122	96.70 115	96.95 162	95.56 147

TSAR-MVS + COLMAP		96.05 130	95.94 126	96.18 147	97.46 167	96.41 165	97.26 146	95.83 170	94.69 96	95.30 148	98.31 81	96.52 146	94.71 135	95.48 155	94.87 158	96.54 172	95.33 151

EU-MVSNet		96.03 131	96.23 116	95.80 156	95.48 209	94.18 190	98.99 37	91.51 208	97.22 21	97.66 47	99.15 41	98.51 89	98.08 30	95.92 145	92.88 183	93.09 196	95.72 144

PCF-MVS	92.69 14	95.98 132	95.05 145	97.06 110	98.43 98	97.56 125	97.76 115	96.65 152	89.95 176	95.70 138	96.18 133	98.48 91	95.74 112	93.64 179	93.35 180	98.09 113	96.18 128
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		95.97 133	95.01 147	97.08 108	98.72 80	97.19 140	97.07 153	96.69 150	91.49 155	95.77 131	92.19 193	97.93 113	96.15 105	94.66 165	94.16 167	98.10 111	97.45 82

Effi-MVS+-dtu		95.94 134	95.08 144	96.94 118	98.54 93	97.38 133	96.66 168	97.89 86	88.68 185	95.92 123	92.90 186	97.28 132	94.18 145	96.68 127	96.13 134	98.45 88	96.51 121

diffmvs		95.86 135	96.21 118	95.44 165	97.25 175	96.85 156	96.99 156	95.23 185	94.96 87	92.82 198	98.89 54	98.85 60	93.52 154	94.21 174	94.25 166	96.84 165	95.49 149

AdaColmap		95.85 136	94.65 153	97.26 100	98.70 82	97.20 139	97.33 141	97.30 127	91.28 159	95.90 124	88.16 210	96.17 152	96.60 86	97.34 102	96.82 110	97.71 125	95.60 146

FMVSNet2		95.77 137	96.20 119	95.27 169	96.77 185	98.18 81	97.28 143	97.90 85	93.12 134	91.37 202	98.25 84	96.05 154	90.04 185	94.96 163	95.94 139	98.28 96	96.90 103

OpenMVS	94.63 9	95.75 138	95.04 146	96.58 135	97.85 140	97.55 126	96.71 166	96.07 160	90.15 174	96.47 99	90.77 206	95.95 155	94.41 140	97.01 116	96.95 106	98.00 116	96.90 103

pmmvs5		95.70 139	95.22 139	96.26 145	96.55 191	97.24 137	97.50 130	94.99 190	90.95 163	96.87 82	98.47 77	97.40 129	94.45 138	92.86 188	94.98 157	97.23 149	94.64 159

Anonymous20231206		95.69 140	95.68 130	95.70 158	98.32 104	96.95 151	97.37 137	96.65 152	93.33 129	93.61 185	98.70 70	98.03 110	91.04 174	95.07 161	94.59 165	97.20 150	93.09 179

MAR-MVS		95.51 141	94.49 157	96.71 127	97.92 136	96.40 166	96.72 165	98.04 76	86.74 203	96.72 88	92.52 190	95.14 162	94.02 147	96.81 120	96.54 121	96.85 163	97.25 93
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		95.48 142	94.51 155	96.61 132	97.13 177	97.30 135	98.05 100	96.79 145	93.75 125	95.08 155	96.38 128	89.76 192	94.95 129	93.97 178	94.82 162	97.64 132	95.63 145

MDA-MVSNet-bldmvs		95.45 143	95.20 140	95.74 157	94.24 214	96.38 169	97.93 106	94.80 192	95.56 66	96.87 82	98.29 82	95.24 161	96.50 93	98.65 50	90.38 195	94.09 190	91.93 184

PVSNet_BlendedMVS		95.44 144	95.09 142	95.86 154	97.31 172	97.13 144	96.31 177	95.01 188	88.55 188	96.23 110	94.55 167	97.75 117	92.56 167	96.42 131	95.44 151	97.71 125	95.81 138

PVSNet_Blended		95.44 144	95.09 142	95.86 154	97.31 172	97.13 144	96.31 177	95.01 188	88.55 188	96.23 110	94.55 167	97.75 117	92.56 167	96.42 131	95.44 151	97.71 125	95.81 138

pmmvs4		95.37 146	94.25 158	96.67 130	97.01 180	95.28 184	97.60 125	96.07 160	93.11 135	97.29 64	98.09 89	94.23 170	95.21 125	91.56 199	93.91 173	96.82 168	93.59 174

MVS_Test		95.34 147	94.88 149	95.89 153	96.93 181	96.84 157	96.66 168	97.08 132	90.06 175	94.02 178	97.61 97	96.64 144	93.59 153	92.73 191	94.02 171	97.03 158	96.24 126

GBi-Net		95.21 148	95.35 135	95.04 174	96.77 185	98.18 81	97.28 143	97.58 101	88.43 190	90.28 206	96.01 137	92.43 178	90.04 185	97.67 89	97.86 83	98.28 96	96.90 103

test1		95.21 148	95.35 135	95.04 174	96.77 185	98.18 81	97.28 143	97.58 101	88.43 190	90.28 206	96.01 137	92.43 178	90.04 185	97.67 89	97.86 83	98.28 96	96.90 103

IterMVS-SCA-FT		95.16 150	93.95 162	96.56 137	97.89 138	96.69 159	96.94 158	96.05 162	93.06 138	97.35 61	98.79 62	91.45 183	95.93 110	92.78 189	91.00 193	95.22 186	93.91 171

HyFIR lowres test		95.05 151	93.54 167	96.81 125	97.81 148	96.88 153	98.18 91	97.46 112	94.28 112	94.98 159	96.57 124	92.89 177	96.15 105	90.90 204	91.87 189	96.28 178	91.35 185

CHOSEN 1792x2688		94.98 152	94.69 152	95.31 167	97.27 174	95.58 181	97.90 109	95.56 178	95.03 82	93.77 184	95.65 143	99.29 20	95.30 123	91.51 200	91.28 192	92.05 204	94.50 161

CANet_DTU		94.96 153	94.62 154	95.35 166	98.03 125	96.11 174	96.92 160	95.60 177	88.59 187	97.27 65	95.27 148	96.50 147	88.77 196	95.53 152	95.59 147	95.54 184	94.78 155

CDS-MVSNet		94.91 154	95.17 141	94.60 182	97.85 140	96.21 173	96.90 162	96.39 155	90.81 164	93.40 189	97.24 108	94.54 166	85.78 210	96.25 137	96.15 131	97.26 147	95.01 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DPM-MVS		94.86 155	93.90 164	95.99 150	98.19 116	96.52 161	96.29 179	95.95 164	93.11 135	94.61 166	88.17 209	96.44 148	93.77 151	93.33 182	93.54 178	97.11 154	96.22 127

MS-PatchMatch		94.84 156	94.76 151	94.94 177	96.38 192	94.69 189	95.90 186	94.03 202	92.49 142	93.81 182	95.79 142	96.38 149	94.54 136	94.70 164	94.85 159	94.97 188	94.43 163

thisisatest0530		94.81 157	93.06 173	96.85 124	98.01 127	97.18 141	96.93 159	97.36 123	89.73 178	95.80 129	94.98 154	77.88 213	94.89 130	96.73 124	97.35 93	98.13 108	97.54 76

tttt0517		94.81 157	93.04 174	96.88 123	98.15 119	97.37 134	96.99 156	97.36 123	89.51 180	95.74 132	94.89 156	77.53 215	94.89 130	96.94 118	97.35 93	98.17 106	97.70 66

testgi		94.81 157	96.05 123	93.35 193	99.06 57	96.87 155	97.57 127	96.70 149	95.77 56	88.60 214	93.19 184	98.87 59	81.21 218	97.03 115	96.64 118	96.97 161	93.99 170

PatchMatch-RL		94.79 160	93.75 166	96.00 149	96.80 184	95.00 186	95.47 196	95.25 184	90.68 166	95.80 129	92.97 185	93.64 173	95.67 117	96.13 141	95.81 143	96.99 160	92.01 183

FPMVS		94.70 161	94.99 148	94.37 184	95.84 202	93.20 195	96.00 185	91.93 207	95.03 82	94.64 165	94.68 161	93.29 174	90.95 175	98.07 76	97.34 96	96.85 163	93.29 175

new-patchmatchnet		94.48 162	94.02 160	95.02 176	97.51 165	95.00 186	95.68 190	94.26 201	97.32 20	95.73 135	99.60 13	98.22 105	91.30 170	94.13 175	84.41 205	95.65 183	89.45 196

IterMVS		94.48 162	93.46 169	95.66 159	97.52 162	96.43 163	97.20 148	94.73 195	92.91 141	96.44 100	98.75 67	91.10 185	94.53 137	92.10 195	90.10 197	93.51 193	92.84 182
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDTV_nov1_ep13_2view		94.39 164	93.34 170	95.63 160	97.23 176	95.33 183	97.76 115	96.84 142	94.55 102	97.47 54	98.96 47	97.70 120	93.88 148	92.27 193	86.81 203	90.56 206	87.73 204

Fast-Effi-MVS+-dtu		94.34 165	93.26 172	95.62 161	97.82 146	95.97 177	95.86 187	99.01 13	86.88 201	93.39 190	90.83 204	95.46 159	90.61 179	94.46 170	94.68 163	97.01 159	94.51 160

thres600view7		94.34 165	92.31 182	96.70 128	98.19 116	98.12 88	97.85 114	97.45 114	91.49 155	93.98 180	84.27 214	82.02 204	94.24 143	97.04 112	98.76 35	98.49 84	94.47 162

EPNet		94.33 167	93.52 168	95.27 169	98.81 74	94.71 188	96.77 163	98.20 59	88.12 193	96.53 97	92.53 189	91.19 184	85.25 214	95.22 159	95.26 154	96.09 181	97.63 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2506		94.29 168	91.43 190	97.64 80	98.66 87	98.83 34	98.50 76	98.40 39	96.04 42	94.45 168	94.88 157	55.05 229	96.70 81	99.28 15	99.04 20	99.14 34	96.87 107

GA-MVS		94.18 169	92.98 175	95.58 162	97.36 169	96.42 164	96.21 180	95.86 167	90.29 170	95.08 155	96.19 132	85.37 196	92.82 164	94.01 177	94.14 168	96.16 180	94.41 164

gg-mvs-nofinetune		94.13 170	93.93 163	94.37 184	97.99 131	95.86 178	95.45 199	99.22 9	97.61 16	95.10 154	99.50 20	84.50 197	81.73 217	95.31 157	94.12 169	96.71 171	90.59 189

baseline		94.07 171	94.50 156	93.57 191	96.34 193	93.40 194	95.56 194	92.39 205	92.07 150	94.00 179	98.24 85	97.51 127	89.19 191	91.75 197	92.72 184	93.96 192	95.79 140

FMVSNet3		94.06 172	93.85 165	94.31 187	95.46 210	97.80 115	96.34 175	97.58 101	88.43 190	90.28 206	96.01 137	92.43 178	88.67 197	91.82 196	93.96 172	97.53 133	96.50 122

thres400		94.04 173	91.94 185	96.50 139	97.98 133	97.82 113	97.66 121	96.96 136	90.96 162	94.20 174	83.24 215	82.82 202	93.80 149	96.50 129	98.09 72	98.38 94	94.15 166

CVMVSNet		94.01 174	94.25 158	93.73 190	94.36 213	92.44 198	97.45 133	88.56 211	95.59 61	93.06 196	98.88 55	90.03 191	94.84 132	94.08 176	93.45 179	94.09 190	95.31 152

thres200		93.98 175	91.90 186	96.40 143	97.66 153	98.12 88	97.20 148	97.45 114	90.16 173	93.82 181	83.08 216	83.74 200	93.80 149	97.04 112	97.48 91	98.49 84	93.70 172

baseline1		93.89 176	92.82 177	95.14 173	97.62 158	96.97 150	96.12 181	96.36 156	91.30 157	91.53 201	94.68 161	80.72 206	90.80 177	95.71 148	96.29 128	98.44 91	94.09 167

tfpn200view9		93.80 177	91.75 187	96.20 146	97.52 162	98.15 86	97.48 132	97.47 111	87.65 195	93.56 187	83.03 217	84.12 198	92.62 166	97.04 112	98.09 72	98.52 83	94.17 165

MIMVSNet		93.68 178	93.96 161	93.35 193	97.82 146	96.08 175	96.34 175	98.46 36	91.28 159	86.67 219	94.95 155	94.87 164	84.39 215	94.53 166	94.65 164	96.45 175	91.34 186

pmnet_mix02		93.59 179	92.65 178	94.69 180	96.76 188	94.16 191	97.03 155	93.00 204	95.79 54	96.03 121	98.91 52	97.69 121	92.99 159	90.03 207	84.10 207	92.35 202	87.89 203

EPNet_dtu		93.45 180	92.51 180	94.55 183	98.39 100	91.67 207	95.46 197	97.50 108	86.56 204	97.38 59	93.52 179	94.20 172	85.82 209	93.31 184	92.53 185	92.72 198	95.76 142
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	92.44 16	93.33 181	92.15 184	94.70 179	97.42 168	96.39 168	95.57 191	94.67 196	86.40 207	93.59 186	78.28 221	95.76 158	89.59 190	95.88 146	95.98 138	97.39 142	96.34 124
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		93.18 182	90.80 193	95.95 151	96.05 197	96.07 176	96.92 160	96.51 154	89.34 181	95.63 140	94.08 172	72.31 224	93.13 157	94.33 172	94.83 160	97.44 138	94.65 158

thres100view900		92.93 183	90.89 192	95.31 167	97.52 162	96.82 158	96.41 173	95.08 186	87.65 195	93.56 187	83.03 217	84.12 198	91.12 173	94.53 166	96.91 108	98.17 106	93.21 177

N_pmnet		92.46 184	92.38 181	92.55 199	97.91 137	93.47 193	97.42 135	94.01 203	96.40 35	88.48 215	98.50 75	98.07 109	88.14 199	91.04 203	84.30 206	89.35 211	84.85 210

TAMVS		92.46 184	93.34 170	91.44 207	97.03 179	93.84 192	94.68 209	90.60 209	90.44 169	85.31 220	97.14 112	93.03 176	85.78 210	94.34 171	93.67 175	95.22 186	90.93 188

CMPMVS	71.81 19	92.34 186	92.85 176	91.75 205	92.70 218	90.43 212	88.84 221	88.56 211	85.87 208	94.35 171	90.98 202	95.89 157	91.14 172	96.14 140	94.83 160	94.93 189	95.78 141
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline2		92.06 187	89.82 196	94.68 181	97.32 170	95.72 179	94.97 206	95.08 186	84.75 212	94.34 173	90.68 207	77.75 214	90.13 184	93.38 180	93.58 177	96.25 179	92.90 181

MVSTER		91.97 188	90.31 194	93.91 188	96.81 183	96.91 152	94.22 210	95.64 176	84.98 210	92.98 197	93.42 180	72.56 222	86.64 208	95.11 160	93.89 174	97.16 153	95.31 152

CR-MVSNet		91.94 189	88.50 199	95.94 152	96.14 195	92.08 202	95.23 202	98.47 34	84.30 214	96.44 100	94.58 164	75.57 216	92.92 161	90.22 205	92.22 186	96.43 176	90.56 190

gm-plane-assit		91.85 190	87.91 201	96.44 142	99.14 46	98.25 77	99.02 32	97.38 121	95.57 63	98.31 25	99.34 32	51.00 230	88.93 194	93.16 186	91.57 190	95.85 182	86.50 207

PMMVS		91.67 191	91.47 189	91.91 204	89.43 223	88.61 218	94.99 205	85.67 216	87.50 197	93.80 183	94.42 170	94.88 163	90.71 178	92.26 194	92.96 182	96.83 166	89.65 194

CHOSEN 280x420		91.55 192	90.27 195	93.05 196	94.61 212	88.01 219	96.56 170	94.62 198	88.04 194	94.20 174	92.66 188	86.60 194	90.82 176	95.06 162	91.89 188	87.49 216	89.61 195

PatchT		91.40 193	88.54 198	94.74 178	91.48 222	92.18 201	97.42 135	97.51 106	84.96 211	96.44 100	94.16 171	75.47 217	92.92 161	90.22 205	92.22 186	92.66 201	90.56 190

pmmvs3		91.20 194	91.40 191	90.96 209	91.71 221	91.08 208	95.41 200	81.34 220	87.36 198	94.57 167	95.02 152	94.30 169	90.42 180	94.28 173	89.26 199	92.30 203	88.49 201

test0.0.03 1		91.17 195	91.50 188	90.80 210	98.01 127	95.46 182	94.22 210	95.80 171	86.55 205	81.75 222	90.83 204	87.93 193	78.48 219	94.51 169	94.11 170	96.50 173	91.08 187

SCA		91.15 196	87.65 203	95.23 172	96.15 194	95.68 180	96.68 167	98.18 63	90.46 168	97.21 68	92.44 191	80.17 208	93.51 155	86.04 214	83.58 210	89.68 210	85.21 209

new_pmnet		90.85 197	92.26 183	89.21 213	93.68 217	89.05 217	93.20 218	84.16 219	92.99 139	84.25 221	97.72 95	94.60 165	86.80 207	93.20 185	91.30 191	93.21 194	86.94 206

RPMNet		90.52 198	86.27 212	95.48 164	95.95 200	92.08 202	95.55 195	98.12 68	84.30 214	95.60 142	87.49 212	72.78 221	91.24 171	87.93 209	89.34 198	96.41 177	89.98 193

MDTV_nov1_ep13		90.30 199	87.32 207	93.78 189	96.00 199	92.97 196	95.46 197	95.39 181	88.61 186	95.41 146	94.45 169	80.39 207	89.87 188	86.58 212	83.54 211	90.56 206	84.71 211

PatchmatchNet		89.98 200	86.23 213	94.36 186	96.56 190	91.90 206	96.07 182	96.72 147	90.18 172	96.87 82	93.36 183	78.06 212	91.46 169	84.71 218	81.40 215	88.45 213	83.97 215
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ADS-MVSNet		89.89 201	87.70 202	92.43 201	95.52 207	90.91 210	95.57 191	95.33 182	93.19 132	91.21 203	93.41 181	82.12 203	89.05 192	86.21 213	83.77 209	87.92 214	84.31 212

tpm		89.84 202	86.81 209	93.36 192	96.60 189	91.92 205	95.02 204	97.39 120	86.79 202	96.54 96	95.03 151	69.70 225	87.66 201	88.79 208	86.19 204	86.95 218	89.27 197

test-LLR		89.77 203	87.47 205	92.45 200	98.01 127	89.77 214	93.25 216	95.80 171	81.56 219	89.19 210	92.08 194	79.59 209	85.77 212	91.47 201	89.04 201	92.69 199	88.75 198

FMVSNet5		89.65 204	87.60 204	92.04 203	95.63 206	96.61 160	94.82 208	94.75 193	80.11 223	87.72 217	77.73 222	73.81 220	83.81 216	95.64 149	96.08 136	95.49 185	93.21 177

EPMVS		89.28 205	86.28 211	92.79 198	96.01 198	92.00 204	95.83 188	95.85 169	90.78 165	91.00 204	94.58 164	74.65 218	88.93 194	85.00 216	82.88 213	89.09 212	84.09 214

test-mter		89.16 206	88.14 200	90.37 211	94.79 211	91.05 209	93.60 215	85.26 217	81.65 218	88.32 216	92.22 192	79.35 211	87.03 205	92.28 192	90.12 196	93.19 195	90.29 192

CostFormer		89.06 207	85.65 214	93.03 197	95.88 201	92.40 199	95.30 201	95.86 167	86.49 206	93.12 195	93.40 182	74.18 219	88.25 198	82.99 219	81.46 214	89.77 209	88.66 200

MVS-HIRNet		88.72 208	86.49 210	91.33 208	91.81 220	85.66 220	87.02 223	96.25 157	81.48 221	94.82 161	96.31 131	92.14 181	90.32 182	87.60 210	83.82 208	87.74 215	78.42 219

TESTMET0.1,1		88.60 209	87.47 205	89.93 212	94.23 215	89.77 214	93.25 216	84.47 218	81.56 219	89.19 210	92.08 194	79.59 209	85.77 212	91.47 201	89.04 201	92.69 199	88.75 198

dps		88.36 210	84.32 217	93.07 195	93.86 216	92.29 200	94.89 207	95.93 165	83.50 216	93.13 193	91.87 196	67.79 227	90.32 182	85.99 215	83.22 212	90.28 208	85.56 208

tpmrst		87.60 211	84.13 218	91.66 206	95.65 205	89.73 216	93.77 213	94.74 194	88.85 183	93.35 192	95.60 144	72.37 223	87.40 202	81.24 220	78.19 217	85.02 221	82.90 218

tpm cat1		87.19 212	82.78 219	92.33 202	95.66 204	90.61 211	94.19 212	95.27 183	86.97 200	94.38 170	90.91 203	69.40 226	87.21 203	79.57 222	77.82 218	87.25 217	84.18 213

E-PMN		86.94 213	85.10 215	89.09 215	95.77 203	83.54 223	89.89 220	86.55 213	92.18 148	87.34 218	94.02 173	83.42 201	89.63 189	93.32 183	77.11 219	85.33 219	72.09 220

EMVS		86.63 214	84.48 216	89.15 214	95.51 208	83.66 222	90.19 219	86.14 215	91.78 152	88.68 213	93.83 177	81.97 205	89.05 192	92.76 190	76.09 220	85.31 220	71.28 221

PMMVS2		86.47 215	92.62 179	79.29 217	92.01 219	85.63 221	93.74 214	86.37 214	93.95 122	54.18 227	98.19 86	97.39 130	58.46 220	96.57 128	93.07 181	90.99 205	83.55 217

MVE	72.99 18	85.37 216	89.43 197	80.63 216	74.43 224	71.94 225	88.25 222	89.81 210	93.27 130	67.32 225	96.32 130	91.83 182	90.40 181	93.36 181	90.79 194	73.55 224	88.49 201
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		61.30 217	70.45 220	50.62 218	22.69 226	30.92 227	68.31 226	25.76 222	80.56 222	68.71 223	82.80 219	91.08 186	44.64 221	80.50 221	56.70 221	73.64 223	70.58 222

GG-mvs-BLEND		61.03 218	87.02 208	30.71 220	0.74 229	90.01 213	78.90 225	0.74 226	84.56 213	9.46 228	79.17 220	90.69 189	1.37 225	91.74 198	89.13 200	93.04 197	83.83 216

testmvs		4.99 219	6.88 221	2.78 222	1.73 227	2.04 229	3.10 229	1.71 224	7.27 225	3.92 230	12.18 224	6.71 231	3.31 224	6.94 223	5.51 223	2.94 226	7.51 223

test123		4.41 220	5.71 222	2.88 221	1.28 228	2.21 228	3.09 230	1.65 225	6.35 226	4.98 229	8.53 225	3.88 232	3.46 223	5.79 224	5.71 222	2.85 227	7.50 224

uanet_test		0.00 221	0.00 223	0.00 223	0.00 230	0.00 230	0.00 231	0.00 227	0.00 227	0.00 231	0.00 226	0.00 233	0.00 226	0.00 225	0.00 224	0.00 228	0.00 225

sosnet-low-res		0.00 221	0.00 223	0.00 223	0.00 230	0.00 230	0.00 231	0.00 227	0.00 227	0.00 231	0.00 226	0.00 233	0.00 226	0.00 225	0.00 224	0.00 228	0.00 225

sosnet		0.00 221	0.00 223	0.00 223	0.00 230	0.00 230	0.00 231	0.00 227	0.00 227	0.00 231	0.00 226	0.00 233	0.00 226	0.00 225	0.00 224	0.00 228	0.00 225

RE-MVS-def										99.38 2

9.14												96.98 141

SR-MVS					99.33 31			98.40 39				98.90 55

Anonymous202405211			97.39 68		98.85 70	98.59 58	97.89 111	97.93 83	94.41 109		97.37 105	96.99 140	93.09 158	98.61 53	98.46 48	99.11 39	97.27 91

our_test_3					97.32 170	95.13 185	97.59 126

ambc			96.78 105		99.01 59	97.11 147	95.73 189		95.91 50	99.25 3	98.56 74	97.17 135	97.04 71	96.76 123	95.22 155	96.72 170	96.73 113

MTAPA										97.43 57		99.27 24

MTMP										97.63 48		99.03 43

Patchmatch-RL test							17.42 228

tmp_tt				45.72 219	60.00 225	38.74 226	45.50 227	12.18 223	79.58 224	68.42 224	67.62 223	65.04 228	22.12 222	84.83 217	78.72 216	66.08 225

XVS					99.48 19	98.76 45	99.22 22			96.40 104		98.78 70				98.94 55

X-MVStestdata					99.48 19	98.76 45	99.22 22			96.40 104		98.78 70				98.94 55

abl_6				96.45 141	97.79 151	97.28 136	97.16 151	96.16 159	89.92 177	95.72 136	91.59 198	97.16 136	94.37 141			97.51 135	95.49 149

mPP-MVS					99.58 6							98.98 47

NP-MVS									89.27 182

Patchmtry						92.70 197	95.23 202	98.47 34		96.44 100

DeepMVS_CX						72.99 224	80.14 224	37.34 221	83.46 217	60.13 226	84.40 213	85.48 195	86.93 206	87.22 211		79.61 222	87.32 205

High-resolution multi-view benchmark