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	95.06 1	97.73 1	98.39 2	96.95 1	96.33 51	96.94 35	98.30 22	94.90 15	98.61 2	97.73 3	97.97 24	98.57 23	95.74 4	99.24 1	98.70 4	98.72 8	98.70 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
TDRefinement		97.59 2	98.32 3	96.73 4	95.90 67	98.10 2	99.08 2	93.92 32	98.24 4	96.44 13	98.12 20	97.86 52	96.06 2	99.24 1	98.93 1	99.00 2	97.77 4

WR-MVS		97.53 3	98.20 4	96.76 3	96.93 29	98.17 1	98.60 10	96.67 7	96.39 14	94.46 32	99.14 1	98.92 11	94.57 15	99.06 3	98.80 2	99.32 1	96.92 26

SixPastTwentyTwo		97.36 4	97.73 10	96.92 2	97.36 13	96.15 55	98.29 23	94.43 24	96.50 12	96.96 7	98.74 6	98.74 18	96.04 3	99.03 5	97.74 18	98.44 23	97.22 14

PS-CasMVS		97.22 5	97.84 7	96.50 5	97.08 25	97.92 6	98.17 31	97.02 2	94.71 26	95.32 21	98.52 13	98.97 9	92.91 40	99.04 4	98.47 6	98.49 19	97.24 13

PEN-MVS		97.16 6	97.87 6	96.33 12	97.20 21	97.97 4	98.25 26	96.86 6	95.09 24	94.93 26	98.66 8	99.16 6	92.27 50	98.98 6	98.39 8	98.49 19	96.83 30

DTE-MVSNet		97.16 6	97.75 9	96.47 6	97.40 12	97.95 5	98.20 29	96.89 5	95.30 19	95.15 24	98.66 8	98.80 16	92.77 44	98.97 7	98.27 10	98.44 23	96.28 41

COLMAP_ROB	93.74 2	97.09 8	97.98 5	96.05 18	95.97 63	97.78 9	98.56 11	91.72 81	97.53 8	96.01 15	98.14 19	98.76 17	95.28 5	98.76 12	98.23 11	98.77 6	96.67 35
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WR-MVS_H		97.06 9	97.78 8	96.23 14	96.74 37	98.04 3	98.25 26	97.32 1	94.40 33	93.71 52	98.55 11	98.89 12	92.97 37	98.91 9	98.45 7	98.38 28	97.19 15

CP-MVSNet		96.97 10	97.42 14	96.44 7	97.06 26	97.82 8	98.12 33	96.98 3	93.50 46	95.21 23	97.98 23	98.44 25	92.83 43	98.93 8	98.37 9	98.46 22	96.91 27

test_part1		96.91 11	98.63 1	94.90 45	94.62 97	97.75 11	98.33 21	93.88 34	98.92 1	93.11 67	99.06 2	99.66 1	90.49 89	98.84 11	98.61 5	98.97 3	97.60 7

ACMH	90.17 8	96.61 12	97.69 12	95.35 31	95.29 83	96.94 35	98.43 15	92.05 70	98.04 5	95.38 19	98.07 21	99.25 5	93.23 33	98.35 17	97.16 40	97.72 50	96.00 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		96.56 13	96.73 24	96.36 10	98.99 1	97.90 7	97.79 43	95.64 10	92.78 60	92.54 74	96.23 67	95.02 124	94.31 18	98.43 16	98.12 12	98.89 4	98.58 2

ACMMPR		96.54 14	96.71 25	96.35 11	97.55 9	97.63 12	98.62 9	94.54 19	94.45 30	94.19 38	95.04 92	97.35 64	94.92 10	97.85 30	97.50 27	98.26 29	97.17 16

v7n		96.49 15	97.20 18	95.65 23	95.57 77	96.04 57	97.93 38	92.49 55	96.40 13	97.13 6	98.99 3	99.41 4	93.79 26	97.84 32	96.15 61	97.00 77	95.60 55

DeepC-MVS	92.47 4	96.44 16	96.75 23	96.08 17	97.57 7	97.19 31	97.96 37	94.28 25	95.29 20	94.92 27	98.31 18	96.92 75	93.69 27	96.81 62	96.50 51	98.06 39	96.27 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMM	90.06 9	96.31 17	96.42 32	96.19 15	97.21 20	97.16 33	98.71 5	93.79 38	94.35 34	93.81 46	92.80 122	98.23 33	95.11 6	98.07 22	97.45 29	98.51 18	96.86 29
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	89.90 10	96.27 18	97.52 13	94.81 46	95.19 86	97.18 32	97.97 36	92.52 53	96.72 10	90.50 120	97.31 44	99.11 7	94.10 20	98.67 13	97.90 15	98.56 16	95.79 51

APDe-MVS		96.23 19	97.22 17	95.08 40	96.66 41	97.56 15	98.63 8	93.69 42	94.62 27	89.80 129	97.73 32	98.13 37	93.84 25	97.79 34	97.63 20	97.87 46	97.08 21

CP-MVS		96.21 20	96.16 43	96.27 13	97.56 8	97.13 34	98.43 15	94.70 18	92.62 63	94.13 41	92.71 123	98.03 43	94.54 16	98.00 26	97.60 22	98.23 31	97.05 22

zzz-MVS		96.18 21	96.01 46	96.38 8	98.30 2	96.18 54	98.51 13	94.48 23	94.56 28	94.81 30	91.73 132	96.96 73	94.30 19	98.09 20	97.83 16	97.91 45	96.73 32

HFP-MVS		96.18 21	96.53 29	95.77 21	97.34 16	97.26 28	98.16 32	94.54 19	94.45 30	92.52 75	95.05 90	96.95 74	93.89 23	97.28 45	97.46 28	98.19 32	97.25 11

UniMVSNet_ETH3D		96.15 23	97.71 11	94.33 53	97.31 17	96.71 40	95.06 106	96.91 4	97.86 6	90.42 121	98.55 11	99.60 2	88.01 116	98.51 14	97.81 17	98.26 29	94.95 66

MP-MVS		96.13 24	95.93 49	96.37 9	98.19 4	97.31 27	98.49 14	94.53 22	91.39 91	94.38 35	94.32 105	96.43 88	94.59 14	97.75 36	97.44 30	98.04 40	96.88 28
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		96.12 25	96.27 39	95.93 19	97.20 21	97.60 13	98.64 7	93.74 39	92.47 65	93.13 66	93.23 117	98.06 40	94.51 17	97.99 27	97.57 24	98.39 27	96.99 23
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
DVP-MVS		96.10 26	97.23 16	94.79 48	96.28 54	97.49 16	97.90 39	93.60 44	95.47 17	89.57 135	97.32 43	97.72 55	93.89 23	97.74 37	97.53 25	97.51 55	97.34 9

LGP-MVS_train		96.10 26	96.29 36	95.87 20	96.72 38	97.35 26	98.43 15	93.83 36	90.81 105	92.67 73	95.05 90	98.86 14	95.01 7	98.11 19	97.37 36	98.52 17	96.50 37

CSCG		96.07 28	97.15 19	94.81 46	96.06 61	97.58 14	96.52 71	90.98 93	96.51 11	93.60 54	97.13 50	98.55 24	93.01 35	97.17 49	95.36 77	98.68 10	97.78 3

DPE-MVS		96.00 29	96.80 22	95.06 41	95.87 70	97.47 21	98.25 26	93.73 40	92.38 67	91.57 102	97.55 38	97.97 45	92.98 36	97.49 43	97.61 21	97.96 44	97.16 17

SMA-MVS		95.99 30	96.48 30	95.41 30	97.43 11	97.36 24	97.55 48	93.70 41	94.05 41	93.79 47	97.02 53	94.53 129	92.28 49	97.53 42	97.19 38	97.73 49	97.67 6
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
TSAR-MVS + MP.		95.99 30	96.57 28	95.31 33	96.87 30	96.50 47	98.71 5	91.58 82	93.25 51	92.71 70	96.86 55	96.57 86	93.92 21	98.09 20	97.91 14	98.08 37	96.81 31
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		95.96 32	96.59 27	95.23 36	96.67 40	96.52 46	97.86 41	93.28 47	95.27 22	93.46 56	96.26 64	98.85 15	92.89 41	97.09 50	96.37 56	97.22 70	95.78 52

SteuartSystems-ACMMP		95.96 32	96.13 45	95.76 22	97.06 26	97.36 24	98.40 19	94.24 27	91.49 85	91.91 93	94.50 101	96.89 76	94.99 8	98.01 25	97.44 30	97.97 43	97.25 11
Skip Steuart: Steuart Systems R&D Blog.
ACMP	89.62 11	95.96 32	96.28 37	95.59 24	96.58 43	97.23 30	98.26 25	93.22 48	92.33 70	92.31 82	94.29 106	98.73 19	94.68 12	98.04 23	97.14 41	98.47 21	96.17 44
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PGM-MVS		95.90 35	95.72 53	96.10 16	97.53 10	97.45 22	98.55 12	94.12 29	90.25 108	93.71 52	93.20 118	97.18 67	94.63 13	97.68 39	97.34 37	98.08 37	96.97 24

PMVS	87.16 16	95.88 36	96.47 31	95.19 38	97.00 28	96.02 58	96.70 62	91.57 83	94.43 32	95.33 20	97.16 49	95.37 113	92.39 46	98.89 10	98.72 3	98.17 34	94.71 71
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_NAP		95.86 37	96.18 40	95.47 29	97.11 24	97.26 28	98.37 20	93.48 46	93.49 47	93.99 44	95.61 76	94.11 134	92.49 45	97.87 29	97.44 30	97.40 61	97.52 8

Gipuma		95.86 37	96.17 41	95.50 28	95.92 66	94.59 101	94.77 114	92.50 54	97.82 7	97.90 2	95.56 79	97.88 50	94.71 11	98.02 24	94.81 91	97.23 69	94.48 78
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LS3D		95.83 39	96.35 34	95.22 37	96.47 47	97.49 16	97.99 34	92.35 58	94.92 25	94.58 31	94.88 96	95.11 122	91.52 61	98.48 15	98.05 13	98.42 25	95.49 56

SD-MVS		95.77 40	96.17 41	95.30 34	96.72 38	96.19 53	97.01 53	93.04 49	94.03 42	92.71 70	96.45 62	96.78 83	93.91 22	96.79 63	95.89 67	98.42 25	97.09 20
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
SED-MVS		95.73 41	96.98 20	94.28 55	96.08 59	97.39 23	98.18 30	93.80 37	94.20 36	89.61 134	97.29 45	97.49 61	90.69 80	97.74 37	97.41 33	97.32 66	97.34 9

TranMVSNet+NR-MVSNet		95.72 42	96.42 32	94.91 44	96.21 55	96.77 39	96.90 58	94.99 13	92.62 63	91.92 92	98.51 14	98.63 21	90.82 77	97.27 46	96.83 45	98.63 13	94.31 79

DU-MVS		95.51 43	95.68 54	95.33 32	96.45 48	96.44 49	96.61 68	95.32 11	89.97 113	93.78 48	97.46 40	98.07 39	91.19 68	97.03 52	96.53 49	98.61 14	94.22 80

UniMVSNet (Re)		95.46 44	95.86 51	95.00 43	96.09 57	96.60 41	96.68 66	94.99 13	90.36 107	92.13 85	97.64 36	98.13 37	91.38 62	96.90 57	96.74 46	98.73 7	94.63 74

RPSCF		95.46 44	96.95 21	93.73 77	95.72 74	95.94 61	95.58 97	88.08 138	95.31 18	91.34 104	96.26 64	98.04 42	93.63 28	98.28 18	97.67 19	98.01 41	97.13 18

anonymousdsp		95.45 46	96.70 26	93.99 66	88.43 194	92.05 144	99.18 1	85.42 172	94.29 35	96.10 14	98.63 10	99.08 8	96.11 1	97.77 35	97.41 33	98.70 9	97.69 5

APD-MVS		95.38 47	95.68 54	95.03 42	97.30 18	96.90 37	97.83 42	93.92 32	89.40 120	90.35 122	95.41 83	97.69 57	92.97 37	97.24 48	97.17 39	97.83 47	95.96 48
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet_NR-MVSNet		95.34 48	95.51 58	95.14 39	95.80 72	96.55 42	96.61 68	94.79 16	90.04 112	93.78 48	97.51 39	97.25 65	91.19 68	96.68 65	96.31 58	98.65 12	94.22 80

X-MVS		95.33 49	95.13 66	95.57 26	97.35 14	97.48 18	98.43 15	94.28 25	92.30 71	93.28 59	86.89 178	96.82 79	91.87 55	97.85 30	97.59 23	98.19 32	96.95 25

MSP-MVS		95.32 50	96.28 37	94.19 58	96.87 30	97.77 10	98.27 24	93.88 34	94.15 40	89.63 133	95.36 84	98.37 28	90.73 78	94.37 110	97.53 25	95.77 115	96.40 38

3Dnovator+	92.82 3	95.22 51	95.16 64	95.29 35	96.17 56	96.55 42	97.64 45	94.02 31	94.16 39	94.29 37	92.09 129	93.71 139	91.90 53	96.68 65	96.51 50	97.70 52	96.40 38

HPM-MVS++		95.21 52	94.89 69	95.59 24	97.79 6	95.39 79	97.68 44	94.05 30	91.91 78	94.35 36	93.38 116	95.07 123	92.94 39	96.01 78	95.88 68	96.73 80	96.61 36

TSAR-MVS + ACMM		95.17 53	95.95 47	94.26 56	96.07 60	96.46 48	95.67 94	94.21 28	93.84 44	90.99 112	97.18 48	95.24 121	93.55 29	96.60 69	95.61 74	95.06 134	96.69 34

xxxxxxxxxxxxxcwj		95.03 54	96.14 44	93.73 77	95.30 80	95.93 62	94.80 112	91.76 78	93.11 55	91.93 90	95.83 72	98.96 10	91.11 71	96.62 67	96.44 53	97.46 56	96.13 45

CPTT-MVS		95.00 55	94.52 77	95.57 26	96.84 34	96.78 38	97.88 40	93.67 43	92.20 72	92.35 81	85.87 186	97.56 60	94.98 9	96.96 55	96.07 64	97.70 52	96.18 43

SF-MVS		94.88 56	95.87 50	93.73 77	95.30 80	95.93 62	94.80 112	91.76 78	93.11 55	91.93 90	95.83 72	97.07 70	91.11 71	96.62 67	96.44 53	97.46 56	96.13 45

Baseline_NR-MVSNet		94.85 57	95.35 62	94.26 56	96.45 48	93.86 116	96.70 62	94.54 19	90.07 111	90.17 126	98.77 5	97.89 47	90.64 83	97.03 52	96.16 60	97.04 76	93.67 92

EG-PatchMatch MVS		94.81 58	95.53 57	93.97 67	95.89 69	94.62 99	95.55 99	88.18 136	92.77 61	94.88 28	97.04 52	98.61 22	93.31 30	96.89 58	95.19 81	95.99 108	93.56 95

OMC-MVS		94.74 59	95.46 60	93.91 70	94.62 97	96.26 52	96.64 67	89.36 124	94.20 36	94.15 40	94.02 111	97.73 54	91.34 64	96.15 76	95.04 85	97.37 63	94.80 68

DeepC-MVS_fast	91.38 6	94.73 60	94.98 67	94.44 49	96.83 36	96.12 56	96.69 64	92.17 64	92.98 58	93.72 50	94.14 107	95.45 111	90.49 89	95.73 85	95.30 78	96.71 81	95.13 64
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		94.65 61	94.84 71	94.44 49	94.95 91	96.55 42	96.46 74	91.10 91	88.96 123	96.00 16	94.55 100	95.32 116	90.67 81	96.97 54	96.69 48	97.44 59	94.84 67

pmmvs6		94.58 62	97.30 15	91.40 117	94.84 93	94.61 100	93.40 142	92.43 57	98.51 3	85.61 160	98.73 7	99.53 3	84.40 138	97.88 28	97.03 42	97.72 50	94.79 69

DeepPCF-MVS	90.68 7	94.56 63	94.92 68	94.15 59	94.11 110	95.71 70	97.03 52	90.65 97	93.39 50	94.08 42	95.29 87	94.15 133	93.21 34	95.22 96	94.92 89	95.82 114	95.75 53

NR-MVSNet		94.55 64	95.66 56	93.25 90	94.26 106	96.44 49	96.69 64	95.32 11	89.97 113	91.79 98	97.46 40	98.39 27	82.85 147	96.87 60	96.48 52	98.57 15	93.98 86

Vis-MVSNet		94.39 65	95.85 52	92.68 98	90.91 178	95.88 65	97.62 47	91.41 84	91.95 77	89.20 137	97.29 45	96.26 91	90.60 88	96.95 56	95.91 65	96.32 96	96.71 33
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TSAR-MVS + GP.		94.25 66	94.81 72	93.60 80	96.52 46	95.80 68	94.37 122	92.47 56	90.89 101	88.92 139	95.34 85	94.38 131	92.85 42	96.36 74	95.62 73	96.47 88	95.28 61

CNVR-MVS		94.24 67	94.47 78	93.96 68	96.56 44	95.67 71	96.43 75	91.95 73	92.08 75	91.28 106	90.51 140	95.35 114	91.20 67	96.34 75	95.50 76	96.34 94	95.88 50

v1192		93.98 68	93.94 90	94.01 64	93.91 118	94.63 98	97.00 54	89.75 114	91.01 99	96.50 10	97.93 25	98.26 32	91.74 57	92.06 140	92.05 130	95.18 129	91.66 132

v10		93.96 69	94.12 87	93.77 76	93.37 130	95.45 75	96.83 60	91.13 90	89.70 117	95.02 25	97.88 28	98.23 33	91.27 65	92.39 135	92.18 128	94.99 136	93.00 103

CDPH-MVS		93.96 69	93.86 92	94.08 61	96.31 52	95.84 66	96.92 56	91.85 76	87.21 139	91.25 108	92.83 120	96.06 99	91.05 74	95.57 87	94.81 91	97.12 71	94.72 70

MVS_0304		93.92 71	93.81 96	94.05 63	96.06 61	96.00 59	96.43 75	92.76 51	85.99 149	94.43 34	94.04 110	97.08 69	88.12 115	94.65 106	94.20 104	96.47 88	94.71 71

MSLP-MVS++		93.91 72	94.30 84	93.45 82	95.51 78	95.83 67	93.12 148	91.93 75	91.45 88	91.40 103	87.42 173	96.12 98	93.27 31	96.57 70	96.40 55	95.49 119	96.29 40

v1921920		93.90 73	93.82 94	94.00 65	93.74 124	94.31 104	97.12 49	89.33 125	91.13 96	96.77 9	97.90 26	98.06 40	91.95 52	91.93 144	91.54 139	95.10 132	91.85 126

train_agg		93.89 74	93.46 105	94.40 51	97.35 14	93.78 118	97.63 46	92.19 63	88.12 130	90.52 119	93.57 115	95.78 104	92.31 48	94.78 103	93.46 114	96.36 92	94.70 73

v144192		93.89 74	93.85 93	93.94 69	93.50 128	94.33 103	97.12 49	89.49 119	90.89 101	96.49 11	97.78 30	98.27 31	91.89 54	92.17 139	91.70 136	95.19 128	91.78 129

v1240		93.89 74	93.72 97	94.09 60	93.98 115	94.31 104	97.12 49	89.37 123	90.74 106	96.92 8	98.05 22	97.89 47	92.15 51	91.53 150	91.60 137	94.99 136	91.93 125

NCCC		93.87 77	93.42 106	94.40 51	96.84 34	95.42 76	96.47 73	92.62 52	92.36 69	92.05 87	83.83 193	95.55 107	91.84 56	95.89 80	95.23 80	96.56 85	95.63 54

v1144		93.83 78	93.87 91	93.78 75	93.72 125	94.57 102	96.85 59	89.98 109	91.31 93	95.90 17	97.89 27	98.40 26	91.13 70	92.01 143	92.01 131	95.10 132	90.94 136

MVS_111021_HR		93.82 79	94.26 86	93.31 85	95.01 89	93.97 114	95.73 91	89.75 114	92.06 76	92.49 76	94.01 112	96.05 100	90.61 87	95.95 79	94.78 94	96.28 97	93.04 102

thisisatest0515		93.79 80	94.41 80	93.06 95	94.14 107	92.50 137	95.56 98	88.55 133	91.61 82	92.45 77	96.84 56	95.71 105	90.62 85	94.58 107	95.07 83	97.05 74	94.58 75

TAPA-MVS	88.94 13	93.78 81	94.31 83	93.18 92	94.14 107	95.99 60	95.74 90	86.98 155	93.43 49	93.88 45	90.16 147	96.88 77	91.05 74	94.33 111	93.95 105	97.28 67	95.40 57
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPP-MVSNet		93.63 82	93.95 89	93.26 88	95.15 87	96.54 45	96.18 83	91.97 72	91.74 79	85.76 158	94.95 94	84.27 181	91.60 60	97.61 41	97.38 35	98.87 5	95.18 63

v8		93.60 83	93.82 94	93.34 83	93.13 137	95.06 85	96.39 77	90.75 95	89.90 115	94.03 43	97.70 34	98.21 35	91.08 73	92.36 136	91.47 140	94.63 143	92.07 120

MCST-MVS		93.60 83	93.40 108	93.83 71	95.30 80	95.40 78	96.49 72	90.87 94	90.08 110	91.72 99	90.28 145	95.99 101	91.69 58	93.94 119	92.99 119	96.93 78	95.13 64

PVSNet_Blended_VisFu		93.60 83	93.41 107	93.83 71	96.31 52	95.65 72	95.71 92	90.58 99	88.08 132	93.17 64	95.29 87	92.20 148	90.72 79	94.69 105	93.41 116	96.51 87	94.54 76

TransMVSNet (Re)		93.55 86	96.32 35	90.32 133	94.38 103	94.05 109	93.30 145	89.53 118	97.15 9	85.12 163	98.83 4	97.89 47	82.21 153	96.75 64	96.14 62	97.35 64	93.46 96

DCV-MVSNet		93.49 87	95.15 65	91.55 113	94.05 111	95.92 64	95.15 104	91.21 87	92.76 62	87.01 154	89.71 150	97.16 68	83.90 142	97.65 40	96.87 44	97.99 42	95.95 49

v2v482		93.42 88	93.49 104	93.32 84	93.44 129	94.05 109	96.36 80	89.76 113	91.41 90	95.24 22	97.63 37	98.34 29	90.44 91	91.65 148	91.76 135	94.69 140	89.62 146

canonicalmvs		93.38 89	94.36 81	92.24 104	93.94 117	96.41 51	94.18 129	90.47 100	93.07 57	88.47 145	88.66 160	93.78 138	88.80 105	95.74 84	95.75 71	97.57 54	97.13 18

3Dnovator	91.81 5	93.36 90	94.27 85	92.29 103	92.99 141	95.03 86	95.76 89	87.79 141	93.82 45	92.38 80	92.19 128	93.37 143	88.14 114	95.26 95	94.85 90	96.69 82	95.40 57

pm-mvs1		93.27 91	95.94 48	90.16 134	94.13 109	93.66 119	92.61 158	89.91 111	95.73 16	84.28 172	98.51 14	98.29 30	82.80 148	96.44 72	95.76 70	97.25 68	93.21 100

Anonymous20231211		93.19 92	95.50 59	90.49 130	93.77 122	95.29 81	94.36 126	90.04 108	91.44 89	84.59 167	96.72 58	97.65 58	82.45 152	97.25 47	96.32 57	97.74 48	93.79 89

TinyColmap		93.17 93	93.33 109	93.00 96	93.84 120	92.76 132	94.75 116	88.90 129	93.97 43	97.48 4	95.28 89	95.29 117	88.37 110	95.31 94	91.58 138	94.65 142	89.10 150

MVS_111021_LR		93.15 94	93.65 99	92.56 99	93.89 119	92.28 139	95.09 105	86.92 157	91.26 95	92.99 69	94.46 103	96.22 94	90.64 83	95.11 99	93.45 115	95.85 112	92.74 109

CNLPA		93.14 95	93.67 98	92.53 100	94.62 97	94.73 95	95.00 108	86.57 162	92.85 59	92.43 78	90.94 135	94.67 126	90.35 92	95.41 89	93.70 111	96.23 100	93.37 98

PLC	87.27 15	93.08 96	92.92 113	93.26 88	94.67 94	95.03 86	94.38 121	90.10 103	91.69 80	92.14 84	87.24 174	93.91 136	91.61 59	95.05 100	94.73 97	96.67 83	92.80 106
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		93.07 97	93.05 112	93.10 93	95.90 67	95.41 77	95.88 86	91.94 74	84.77 156	93.36 57	94.05 109	95.25 120	86.25 127	94.33 111	93.94 106	95.30 122	93.58 94

TSAR-MVS + COLMAP		93.06 98	93.65 99	92.36 101	94.62 97	94.28 106	95.36 103	89.46 121	92.18 73	91.64 100	95.55 80	95.27 119	88.60 108	93.24 125	92.50 124	94.46 145	92.55 115

Effi-MVS+		92.93 99	92.16 124	93.83 71	94.29 104	93.53 126	95.04 107	92.98 50	85.27 153	94.46 32	90.24 146	95.34 115	89.99 96	93.72 120	94.23 103	96.22 101	92.79 107

Fast-Effi-MVS+		92.93 99	92.64 117	93.27 87	93.81 121	93.88 115	95.90 85	90.61 98	83.98 162	92.71 70	92.81 121	96.22 94	90.67 81	94.90 102	93.92 107	95.92 110	92.77 108

HQP-MVS		92.87 101	92.49 118	93.31 85	95.75 73	95.01 89	95.64 95	91.06 92	88.54 127	91.62 101	88.16 165	96.25 92	89.47 100	92.26 138	91.81 133	96.34 94	95.40 57

FMVSNet1		92.86 102	95.26 63	90.06 136	92.40 155	95.16 82	94.37 122	92.22 60	93.18 54	82.16 182	96.76 57	97.48 62	81.85 157	95.32 91	94.98 86	97.34 65	93.93 87

CLD-MVS		92.81 103	94.32 82	91.05 121	95.39 79	95.31 80	95.82 88	81.44 195	89.40 120	91.94 89	95.86 70	97.36 63	85.83 129	95.35 90	94.59 99	95.85 112	92.34 117
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		92.76 104	93.25 110	92.19 105	94.91 92	95.56 73	95.86 87	92.12 66	88.10 131	82.71 177	93.15 119	88.30 169	88.86 104	97.29 44	96.95 43	98.66 11	93.38 97

FC-MVSNet-train		92.75 105	95.40 61	89.66 144	95.21 85	94.82 93	97.00 54	89.40 122	91.13 96	81.71 183	97.72 33	96.43 88	77.57 180	96.89 58	96.72 47	97.05 74	94.09 83

V42		92.67 106	93.50 103	91.71 111	91.41 169	92.96 130	95.71 92	85.00 173	89.67 118	93.22 62	97.67 35	98.01 44	91.02 76	92.65 131	92.12 129	93.86 153	91.42 133

PM-MVS		92.65 107	93.20 111	92.00 107	92.11 163	90.16 165	95.99 84	84.81 177	91.31 93	92.41 79	95.87 69	96.64 85	92.35 47	93.65 122	92.91 120	94.34 148	91.85 126

QAPM		92.57 108	93.51 102	91.47 115	92.91 143	94.82 93	93.01 150	87.51 145	91.49 85	91.21 109	92.24 126	91.70 151	88.74 106	94.54 108	94.39 102	95.41 120	95.37 60

MIMVSNet1		92.52 109	94.88 70	89.77 140	96.09 57	91.99 145	96.92 56	89.68 116	95.92 15	84.55 168	96.64 60	98.21 35	78.44 174	96.08 77	95.10 82	92.91 167	90.22 143

tfpnnormal		92.45 110	94.77 73	89.74 141	93.95 116	93.44 128	93.25 146	88.49 135	95.27 22	83.20 175	96.51 61	96.23 93	83.17 146	95.47 88	94.52 100	96.38 91	91.97 124

PCF-MVS	87.46 14	92.44 111	91.80 126	93.19 91	94.66 95	95.80 68	96.37 78	90.19 102	87.57 136	92.23 83	89.26 155	93.97 135	89.24 101	91.32 152	90.82 148	96.46 90	93.86 88
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		92.42 112	93.99 88	90.60 128	93.25 133	93.82 117	94.28 128	88.73 131	91.53 84	84.53 170	97.74 31	98.64 20	86.60 124	93.21 127	91.20 143	96.21 102	91.76 131

AdaColmap		92.41 113	91.49 130	93.48 81	95.96 64	95.02 88	95.37 102	91.73 80	87.97 134	91.28 106	82.82 197	91.04 155	90.62 85	95.82 83	95.07 83	95.95 109	92.67 110

v148		92.38 114	92.78 115	91.91 108	92.86 144	92.13 142	94.84 110	87.03 154	91.47 87	93.07 68	96.92 54	98.89 12	90.10 95	92.05 141	89.69 156	93.56 156	88.27 159

pmmvs-eth3d		92.34 115	92.33 119	92.34 102	92.67 148	90.67 159	96.37 78	89.06 126	90.98 100	93.60 54	97.13 50	97.02 72	88.29 111	90.20 159	91.42 141	94.07 151	88.89 154

DELS-MVS		92.33 116	93.61 101	90.83 124	92.84 145	95.13 84	94.76 115	87.22 153	87.78 135	88.42 147	95.78 74	95.28 118	85.71 132	94.44 109	93.91 108	96.01 107	92.97 104
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
Effi-MVS+-dtu		92.32 117	91.66 128	93.09 94	95.13 88	94.73 95	94.57 119	92.14 65	81.74 173	90.33 123	88.13 166	95.91 102	89.24 101	94.23 116	93.65 113	97.12 71	93.23 99

UGNet		92.31 118	94.70 74	89.53 146	90.99 177	95.53 74	96.19 82	92.10 68	91.35 92	85.76 158	95.31 86	95.48 110	76.84 185	95.22 96	94.79 93	95.32 121	95.19 62
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
USDC		92.17 119	92.17 123	92.18 106	92.93 142	92.22 140	93.66 136	87.41 148	93.49 47	97.99 1	94.10 108	96.68 84	86.46 125	92.04 142	89.18 162	94.61 144	87.47 162

ETV-MVS		92.12 120	90.44 137	94.08 61	96.36 50	93.63 121	96.27 81	92.00 71	78.90 193	92.13 85	85.29 188	89.85 162	90.26 94	97.07 51	96.29 59	97.46 56	92.04 122

IterMVS-LS		92.10 121	92.33 119	91.82 110	93.18 134	93.66 119	92.80 156	92.27 59	90.82 103	90.59 118	97.19 47	90.97 156	87.76 117	89.60 166	90.94 147	94.34 148	93.16 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		92.09 122	92.84 114	91.22 120	92.55 150	92.97 129	93.42 141	85.43 171	90.24 109	91.83 95	94.70 97	94.59 127	88.48 109	94.91 101	93.31 118	95.59 118	89.15 149

CS-MVS		92.04 123	90.08 143	94.32 54	95.94 65	94.95 92	96.72 61	91.36 85	80.81 179	94.18 39	81.09 201	90.28 160	90.30 93	95.84 82	95.57 75	97.41 60	92.05 121

EIA-MVS		91.95 124	90.36 139	93.81 74	96.54 45	94.65 97	95.38 101	90.40 101	78.01 198	93.72 50	86.70 181	91.95 150	89.93 97	95.67 86	94.72 98	96.89 79	90.79 137

MAR-MVS		91.86 125	91.14 133	92.71 97	94.29 104	94.24 107	94.91 109	91.82 77	81.66 174	93.32 58	84.51 191	93.42 142	86.86 122	95.16 98	94.44 101	95.05 135	94.53 77
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
EU-MVSNet		91.63 126	92.73 116	90.35 132	88.36 195	87.89 176	96.53 70	81.51 194	92.45 66	91.82 96	96.44 63	97.05 71	93.26 32	94.10 117	88.94 167	90.61 174	92.24 118

FC-MVSNet-test		91.49 127	94.43 79	88.07 162	94.97 90	90.53 162	95.42 100	91.18 89	93.24 52	72.94 203	98.37 16	93.86 137	78.78 168	97.82 33	96.13 63	95.13 130	91.05 134

OpenMVS	89.22 12	91.09 128	91.42 131	90.71 126	92.79 147	93.61 123	92.74 157	85.47 170	86.10 148	90.73 113	85.71 187	93.07 146	86.69 123	94.07 118	93.34 117	95.86 111	94.02 85

FPMVS		90.81 129	91.60 129	89.88 139	92.52 151	88.18 172	93.31 144	83.62 183	91.59 83	88.45 146	88.96 158	89.73 164	86.96 120	96.42 73	95.69 72	94.43 146	90.65 138

DI_MVS_plusplus_trai		90.68 130	90.40 138	91.00 122	92.43 154	92.61 136	94.17 130	88.98 127	88.32 129	88.76 143	93.67 114	87.58 171	86.44 126	89.74 164	90.33 151	95.24 125	90.56 141

Vis-MVSNet (Re-imp)		90.68 130	92.18 122	88.92 151	94.63 96	92.75 133	92.91 152	91.20 88	89.21 122	75.01 200	93.96 113	89.07 167	82.72 150	95.88 81	95.30 78	97.08 73	89.08 151

DPM-MVS		90.67 132	89.86 144	91.63 112	95.29 83	94.16 108	94.52 120	89.63 117	89.59 119	89.67 132	81.95 199	88.64 168	85.75 131	90.46 157	90.43 150	94.91 138	93.77 90

diffmvs		90.44 133	92.23 121	88.35 158	91.36 171	91.38 151	92.45 162	84.84 176	89.88 116	85.09 164	96.69 59	97.71 56	83.33 145	90.01 163	88.96 166	93.03 165	91.00 135

FMVSNet2		90.28 134	92.04 125	88.23 160	91.22 173	94.05 109	92.88 153	90.69 96	86.53 144	79.89 189	94.38 104	92.73 147	78.54 171	91.64 149	92.26 127	96.17 103	92.67 110

IterMVS-SCA-FT		90.24 135	89.37 150	91.26 119	92.50 152	92.11 143	91.69 172	87.48 146	87.05 141	91.82 96	95.76 75	87.25 172	91.36 63	89.02 171	85.53 182	92.68 168	88.90 153

MVS_Test		90.19 136	90.58 134	89.74 141	92.12 162	91.74 147	92.51 159	88.54 134	82.80 168	87.50 151	94.62 98	95.02 124	83.97 140	88.69 174	89.32 160	93.79 154	91.85 126

EPNet		90.17 137	89.07 152	91.45 116	97.25 19	90.62 161	94.84 110	93.54 45	80.96 176	91.85 94	86.98 177	85.88 177	77.79 177	92.30 137	92.58 123	93.41 158	94.20 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		90.09 138	90.12 141	90.05 137	92.40 155	92.74 134	91.74 168	85.89 166	80.54 180	90.30 124	88.54 161	95.51 108	84.69 136	92.64 132	90.25 152	95.28 123	90.61 139

PVSNet_Blended		90.09 138	90.12 141	90.05 137	92.40 155	92.74 134	91.74 168	85.89 166	80.54 180	90.30 124	88.54 161	95.51 108	84.69 136	92.64 132	90.25 152	95.28 123	90.61 139

pmmvs4		89.95 140	89.32 151	90.69 127	91.60 168	89.17 169	94.37 122	87.63 142	88.07 133	91.02 111	94.50 101	90.50 159	86.13 128	86.33 188	89.40 159	93.39 159	87.29 165

MDA-MVSNet-bldmvs		89.75 141	91.67 127	87.50 167	74.25 213	90.88 156	94.68 117	85.89 166	91.64 81	91.03 110	95.86 70	94.35 132	89.10 103	96.87 60	86.37 178	90.04 175	85.72 170

tttt0517		89.64 142	88.05 163	91.49 114	93.52 127	91.65 148	93.67 135	87.53 143	82.77 169	89.39 136	90.37 144	70.05 205	88.21 112	93.71 121	93.79 109	96.63 84	94.04 84

PatchMatch-RL		89.59 143	88.80 156	90.51 129	92.20 161	88.00 175	91.72 170	86.64 159	84.75 157	88.25 148	87.10 176	90.66 158	89.85 99	93.23 126	92.28 126	94.41 147	85.60 171

Fast-Effi-MVS+-dtu		89.57 144	88.42 160	90.92 123	93.35 131	91.57 149	93.01 150	95.71 9	78.94 192	87.65 150	84.68 190	93.14 145	82.00 155	90.84 155	91.01 146	93.78 155	88.77 155

thisisatest0530		89.54 145	87.99 165	91.35 118	93.17 135	91.31 152	93.45 140	87.53 143	82.96 167	89.17 138	90.45 141	70.32 204	88.21 112	93.37 124	93.79 109	96.54 86	93.71 91

GBi-Net		89.35 146	90.58 134	87.91 163	91.22 173	94.05 109	92.88 153	90.05 105	79.40 184	78.60 191	90.58 137	87.05 173	78.54 171	95.32 91	94.98 86	96.17 103	92.67 110

test1		89.35 146	90.58 134	87.91 163	91.22 173	94.05 109	92.88 153	90.05 105	79.40 184	78.60 191	90.58 137	87.05 173	78.54 171	95.32 91	94.98 86	96.17 103	92.67 110

thres600view7		89.14 148	88.83 154	89.51 147	93.71 126	93.55 124	93.93 133	88.02 139	87.30 138	82.40 178	81.18 200	80.63 191	82.69 151	94.27 113	95.90 66	96.27 98	88.94 152

CVMVSNet		88.97 149	89.73 146	88.10 161	87.33 201	85.22 185	94.68 117	78.68 196	88.94 124	86.98 155	95.55 80	85.71 178	89.87 98	91.19 153	89.69 156	91.05 172	91.78 129

CANet_DTU		88.95 150	89.51 149	88.29 159	93.12 138	91.22 154	93.61 137	83.47 186	80.07 183	90.71 117	89.19 156	93.68 140	76.27 189	91.44 151	91.17 145	92.59 169	89.83 145

GA-MVS		88.76 151	88.04 164	89.59 145	92.32 158	91.46 150	92.28 164	86.62 160	83.82 164	89.84 128	92.51 125	81.94 186	83.53 144	89.41 168	89.27 161	92.95 166	87.90 160

pmmvs5		88.63 152	89.70 147	87.39 168	89.24 188	90.64 160	91.87 167	82.13 190	83.34 165	87.86 149	94.58 99	96.15 97	79.87 165	87.33 183	89.07 165	93.39 159	86.76 166

thres400		88.54 153	88.15 162	88.98 149	93.17 135	92.84 131	93.56 138	86.93 156	86.45 145	82.37 179	79.96 203	81.46 189	81.83 158	93.21 127	94.76 95	96.04 106	88.39 157

CDS-MVSNet		88.41 154	89.79 145	86.79 172	94.55 101	90.82 157	92.50 160	89.85 112	83.26 166	80.52 187	91.05 133	89.93 161	69.11 199	93.17 129	92.71 122	94.21 150	87.63 161
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gg-mvs-nofinetune		88.32 155	88.81 155	87.75 165	93.07 139	89.37 168	89.06 191	95.94 8	95.29 20	87.15 152	97.38 42	76.38 194	68.05 202	91.04 154	89.10 164	93.24 161	83.10 179

IterMVS		88.32 155	88.25 161	88.41 157	90.83 179	91.24 153	93.07 149	81.69 192	86.77 142	88.55 144	95.61 76	86.91 176	87.01 119	87.38 182	83.77 184	89.29 177	86.06 169
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres200		88.29 157	87.88 166	88.76 153	92.50 152	93.55 124	92.47 161	88.02 139	84.80 155	81.44 184	79.28 205	82.20 185	81.83 158	94.27 113	93.67 112	96.27 98	87.40 163

IB-MVS	86.01 17	88.24 158	87.63 168	88.94 150	92.03 164	91.77 146	92.40 163	85.58 169	78.24 195	84.85 165	71.99 209	93.45 141	83.96 141	93.48 123	92.33 125	94.84 139	92.15 119
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
MDTV_nov1_ep13_2view		88.22 159	87.85 167	88.65 155	91.40 170	86.75 180	94.07 131	84.97 174	88.86 126	93.20 63	96.11 68	96.21 96	83.70 143	87.29 184	80.29 191	84.56 195	79.46 191

test20.03		88.20 160	91.26 132	84.63 184	96.64 42	89.39 167	90.73 179	89.97 110	91.07 98	72.02 205	94.98 93	95.45 111	69.35 198	92.70 130	91.19 144	89.06 179	84.02 173

HyFIR lowres test		88.19 161	86.56 174	90.09 135	91.24 172	92.17 141	94.30 127	88.79 130	84.06 159	85.45 161	89.52 153	85.64 179	88.64 107	85.40 191	87.28 172	92.14 171	81.87 182

ET-MVSNet_ETH3D		88.06 162	85.75 178	90.74 125	92.82 146	90.68 158	93.77 134	88.59 132	81.22 175	89.78 130	89.15 157	66.79 212	84.29 139	91.72 147	91.34 142	95.22 126	89.36 148

tfpn200view9		87.94 163	87.51 170	88.44 156	92.28 159	93.63 121	93.35 143	88.11 137	80.90 177	80.89 185	78.25 206	82.25 183	79.65 167	94.27 113	94.76 95	96.36 92	88.48 156

FMVSNet3		87.90 164	88.63 158	87.04 169	89.78 186	93.46 127	91.62 173	90.05 105	79.40 184	78.60 191	90.58 137	87.05 173	77.07 184	88.03 179	89.86 155	95.12 131	92.04 122

MS-PatchMatch		87.72 165	88.62 159	86.66 173	90.81 180	88.18 172	90.92 176	82.25 189	85.86 150	80.40 188	90.14 148	89.29 166	84.93 133	89.39 169	89.12 163	90.67 173	88.34 158

Anonymous20231206		87.45 166	89.66 148	84.87 181	94.00 112	87.73 178	91.36 174	86.41 164	88.89 125	75.03 199	92.59 124	96.82 79	72.48 196	89.72 165	88.06 169	89.93 176	83.81 175

EPNet_dtu		87.40 167	86.27 175	88.72 154	95.68 75	83.37 191	92.09 166	90.08 104	78.11 197	91.29 105	86.33 182	89.74 163	75.39 191	89.07 170	87.89 170	87.81 184	89.38 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline1		86.96 168	87.58 169	86.24 175	93.07 139	90.44 163	89.24 190	86.85 158	85.14 154	77.26 197	90.45 141	76.09 196	75.79 190	91.80 146	91.81 133	95.20 127	87.35 164

baseline		86.71 169	88.89 153	84.16 186	87.85 197	85.23 184	89.82 184	77.69 198	84.03 161	84.75 166	94.91 95	94.59 127	77.19 183	86.57 187	86.51 177	87.66 187	90.36 142

CHOSEN 1792x2688		86.64 170	86.62 173	86.65 174	90.33 183	87.86 177	93.19 147	83.30 187	83.95 163	82.32 180	87.93 168	89.34 165	86.92 121	85.64 190	84.95 183	83.85 198	86.68 167

testgi		86.49 171	90.31 140	82.03 189	95.63 76	88.18 172	93.47 139	84.89 175	93.23 53	69.54 209	87.16 175	97.96 46	60.66 206	91.90 145	89.90 154	87.99 182	83.84 174

thres100view900		86.46 172	86.00 177	86.99 170	92.28 159	91.03 155	91.09 175	84.49 179	80.90 177	80.89 185	78.25 206	82.25 183	77.57 180	90.17 160	92.84 121	95.63 116	86.57 168

gm-plane-assit		86.15 173	82.51 186	90.40 131	95.81 71	92.29 138	97.99 34	84.66 178	92.15 74	93.15 65	97.84 29	44.65 219	78.60 170	88.02 180	85.95 179	92.20 170	76.69 199

CMPMVS	66.55 18	85.55 174	87.46 171	83.32 187	84.99 203	81.97 196	79.19 210	75.93 200	79.32 187	88.82 141	85.09 189	91.07 154	82.12 154	92.56 134	89.63 158	88.84 180	92.56 114
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		85.32 175	81.58 188	89.69 143	90.36 182	84.79 187	86.72 201	92.22 60	75.38 203	90.73 113	90.41 143	67.88 209	84.86 134	83.76 193	85.74 180	93.24 161	83.14 177

baseline2		84.95 176	82.68 185	87.59 166	92.64 149	88.41 171	90.09 181	84.25 180	75.88 201	85.23 162	82.49 198	71.15 202	80.14 164	88.21 178	87.21 175	93.21 164	85.39 172

MVSTER		84.79 177	83.79 181	85.96 177	89.14 189	89.80 166	89.39 188	82.99 188	74.16 207	82.78 176	85.97 185	66.81 211	76.84 185	90.77 156	88.83 168	94.66 141	90.19 144

MIMVSNet		84.76 178	86.75 172	82.44 188	91.71 167	85.95 182	89.74 186	89.49 119	85.28 152	69.69 208	87.93 168	90.88 157	64.85 204	88.26 177	87.74 171	89.18 178	81.24 183

SCA		84.69 179	81.10 189	88.87 152	89.02 190	90.31 164	92.21 165	92.09 69	82.72 170	89.68 131	86.83 179	73.08 198	85.80 130	80.50 201	77.51 197	84.45 197	76.80 198

new-patchmatchnet		84.45 180	88.75 157	79.43 195	93.28 132	81.87 197	81.68 207	83.48 185	94.47 29	71.53 206	98.33 17	97.88 50	58.61 209	90.35 158	77.33 198	87.99 182	81.05 185

PatchT		83.44 181	81.10 189	86.18 176	77.92 211	82.58 195	89.87 183	87.39 149	75.88 201	90.73 113	89.86 149	66.71 213	84.86 134	83.76 193	85.74 180	86.33 192	83.14 177

RPMNet		83.42 182	78.40 198	89.28 148	89.79 185	84.79 187	90.64 180	92.11 67	75.38 203	87.10 153	79.80 204	61.99 218	82.79 149	81.88 199	82.07 188	93.23 163	82.87 180

TAMVS		82.96 183	86.15 176	79.24 198	90.57 181	83.12 194	87.29 197	75.12 202	84.06 159	65.81 210	92.22 127	88.27 170	69.11 199	88.72 172	87.26 174	87.56 188	79.38 192

PatchmatchNet		82.44 184	78.69 197	86.83 171	89.81 184	81.55 198	90.78 178	87.27 152	82.39 172	88.85 140	88.31 164	70.96 203	81.90 156	78.58 205	74.33 205	82.35 202	74.69 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		82.33 185	79.66 192	85.45 179	88.83 192	83.88 189	90.09 181	81.98 191	79.07 191	88.82 141	88.70 159	73.77 197	78.41 175	80.29 203	76.08 200	84.56 195	75.83 200

CostFormer		82.15 186	79.54 193	85.20 180	88.92 191	85.70 183	90.87 177	86.26 165	79.19 190	83.87 173	87.89 170	69.20 207	76.62 187	77.50 208	75.28 202	84.69 194	82.02 181

PMMVS		81.93 187	83.48 183	80.12 194	72.35 214	75.05 207	88.54 193	64.01 207	77.02 200	82.22 181	87.51 172	91.12 153	79.70 166	86.59 185	86.64 176	93.88 152	80.41 186

pmmvs3		81.69 188	83.83 180	79.19 199	78.33 210	78.57 201	89.53 187	58.71 210	78.88 194	84.34 171	88.36 163	91.96 149	77.69 179	87.48 181	82.42 187	86.54 191	79.18 193

tpm		81.58 189	78.84 195	84.79 183	91.11 176	79.50 199	89.79 185	83.75 181	79.30 188	92.05 87	90.98 134	64.78 215	74.54 192	80.50 201	76.67 199	77.49 207	80.15 189

test0.0.03 1		81.51 190	83.30 184	79.42 196	93.99 113	86.50 181	85.93 205	87.32 150	78.16 196	61.62 211	80.78 202	81.78 187	59.87 207	88.40 176	87.27 173	87.78 186	80.19 188

dps		81.42 191	77.88 203	85.56 178	87.67 199	85.17 186	88.37 195	87.46 147	74.37 206	84.55 168	86.80 180	62.18 217	80.20 163	81.13 200	77.52 196	85.10 193	77.98 196

test-LLR		80.62 192	77.20 206	84.62 185	93.99 113	75.11 205	87.04 198	87.32 150	70.11 210	78.59 194	83.17 195	71.60 200	73.88 194	82.32 197	79.20 193	86.91 189	78.87 194

tpm cat1		80.03 193	75.93 209	84.81 182	89.31 187	83.26 193	88.86 192	86.55 163	79.24 189	86.10 157	84.22 192	63.62 216	77.37 182	73.43 209	70.88 208	80.67 203	76.87 197

N_pmnet		79.33 194	84.22 179	73.62 205	91.72 166	73.72 208	86.11 203	76.36 199	92.38 67	53.38 212	95.54 82	95.62 106	59.14 208	84.23 192	74.84 204	75.03 210	73.25 206

EPMVS		79.26 195	78.20 201	80.49 192	87.04 202	78.86 200	86.08 204	83.51 184	82.63 171	73.94 202	89.59 151	68.67 208	72.03 197	78.17 206	75.08 203	80.37 204	74.37 203

CHOSEN 280x420		79.24 196	78.26 200	80.38 193	79.60 209	68.80 213	89.32 189	75.38 201	77.25 199	78.02 196	75.57 208	76.17 195	81.19 161	88.61 175	81.39 189	78.79 205	80.03 190

ADS-MVSNet		79.11 197	79.38 194	78.80 201	81.90 207	75.59 204	84.36 206	83.69 182	87.31 137	76.76 198	87.58 171	76.90 193	68.55 201	78.70 204	75.56 201	77.53 206	74.07 204

FMVSNet5		79.08 198	78.83 196	79.38 197	87.52 200	86.78 179	87.64 196	78.15 197	69.54 212	70.64 207	65.97 212	65.44 214	63.87 205	90.17 160	90.46 149	88.48 181	83.45 176

tpmrst		78.81 199	76.18 208	81.87 190	88.56 193	77.45 202	86.74 200	81.52 193	80.08 182	83.48 174	90.84 136	66.88 210	74.54 192	73.04 210	71.02 207	76.38 208	73.95 205

test-mter		78.71 200	78.35 199	79.12 200	84.03 204	76.58 203	88.51 194	59.06 209	71.06 208	78.87 190	83.73 194	71.83 199	76.44 188	83.41 196	80.61 190	87.79 185	81.24 183

MVS-HIRNet		78.28 201	75.28 210	81.79 191	80.33 208	69.38 212	76.83 211	86.59 161	70.76 209	86.66 156	89.57 152	81.04 190	77.74 178	77.81 207	71.65 206	82.62 200	66.73 210

E-PMN		77.81 202	77.88 203	77.73 204	88.26 196	70.48 211	80.19 209	71.20 204	86.66 143	72.89 204	88.09 167	81.74 188	78.75 169	90.02 162	68.30 209	75.10 209	59.85 211

EMVS		77.65 203	77.49 205	77.83 202	87.75 198	71.02 210	81.13 208	70.54 205	86.38 146	74.52 201	89.38 154	80.19 192	78.22 176	89.48 167	67.13 210	74.83 211	58.84 212

TESTMET0.1,1		77.47 204	77.20 206	77.78 203	81.94 206	75.11 205	87.04 198	58.33 211	70.11 210	78.59 194	83.17 195	71.60 200	73.88 194	82.32 197	79.20 193	86.91 189	78.87 194

new_pmnet		76.65 205	83.52 182	68.63 206	82.60 205	72.08 209	76.76 212	64.17 206	84.41 158	49.73 214	91.77 130	91.53 152	56.16 210	86.59 185	83.26 186	82.37 201	75.02 201

MVE	60.41 19	73.21 206	80.84 191	64.30 207	56.34 215	57.24 215	75.28 214	72.76 203	87.14 140	41.39 215	86.31 183	85.30 180	80.66 162	86.17 189	83.36 185	59.35 213	80.38 187
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		69.86 207	82.14 187	55.52 208	75.19 212	63.08 214	75.52 213	60.97 208	88.50 128	25.11 217	91.77 130	96.44 87	25.43 211	88.70 173	79.34 192	70.93 212	67.17 209

GG-mvs-BLEND		54.28 208	77.89 202	26.72 210	0.37 219	83.31 192	70.04 215	0.39 216	74.71 205	5.36 218	68.78 210	83.06 182	0.62 215	83.73 195	78.99 195	83.55 199	72.68 208

testmvs		2.38 209	3.35 211	1.26 212	0.83 217	0.96 219	1.53 219	0.83 214	3.59 215	1.63 220	6.03 214	2.93 221	1.55 214	3.49 213	2.51 213	1.21 217	3.92 214

test123		2.16 210	2.82 212	1.41 211	0.62 218	1.18 218	1.53 219	0.82 215	2.78 216	2.27 219	4.18 215	1.98 222	1.64 213	2.58 214	3.01 212	1.56 216	4.00 213

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

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

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

RE-MVS-def										97.21 5

9.14												93.19 144

SR-MVS					97.13 23			94.77 17				97.77 53

Anonymous202405211			94.63 75		94.51 102	94.96 91	93.94 132	91.35 86	90.82 103		95.60 78	95.85 103	81.74 160	96.47 71	95.84 69	97.39 62	92.85 105

our_test_3					91.78 165	88.87 170	94.37 122

ambc			94.61 76		98.09 5	95.14 83	91.71 171		94.18 38	96.46 12	96.26 64	96.30 90	91.26 66	94.70 104	92.00 132	93.45 157	93.67 92

MTAPA										94.88 28		96.88 77

MTMP										95.43 18		97.25 65

Patchmatch-RL test							8.96 218

tmp_tt				28.44 209	36.05 216	15.86 217	21.29 217	6.40 213	54.52 214	51.96 213	50.37 213	38.68 220	9.55 212	61.75 212	59.66 211	45.36 215

XVS					96.86 32	97.48 18	98.73 3			93.28 59		96.82 79				98.17 34

X-MVStestdata					96.86 32	97.48 18	98.73 3			93.28 59		96.82 79				98.17 34

abl_6				91.88 109	93.76 123	94.98 90	95.64 95	88.97 128	86.20 147	90.00 127	86.31 183	94.50 130	87.31 118			95.60 117	92.48 116

mPP-MVS					98.24 3							97.65 58

NP-MVS									85.48 151

Patchmtry						83.74 190	86.72 201	92.22 60		90.73 113

DeepMVS_CX						47.68 216	53.20 216	19.21 212	63.24 213	26.96 216	66.50 211	69.82 206	66.91 203	64.27 211		54.91 214	72.72 207

High-resolution multi-view benchmark