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 48	96.94 32	98.30 22	94.90 15	98.61 2	97.73 3	97.97 33	98.57 29	95.74 7	99.24 1	98.70 4	98.72 7	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 62	98.10 2	99.08 2	93.92 32	98.24 4	96.44 15	98.12 28	97.86 69	96.06 2	99.24 1	98.93 1	99.00 2	97.77 6

WR-MVS		97.53 3	98.20 4	96.76 3	96.93 27	98.17 1	98.60 11	96.67 6	96.39 13	94.46 44	99.14 1	98.92 13	94.57 18	99.06 3	98.80 2	99.32 1	96.92 27

SixPastTwentyTwo		97.36 4	97.73 10	96.92 2	97.36 13	96.15 51	98.29 23	94.43 23	96.50 11	96.96 8	98.74 8	98.74 20	96.04 3	99.03 5	97.74 17	98.44 24	97.22 13

PS-CasMVS		97.22 5	97.84 7	96.50 5	97.08 23	97.92 6	98.17 28	97.02 2	94.71 28	95.32 24	98.52 15	98.97 12	92.91 42	99.04 4	98.47 6	98.49 19	97.24 12

PEN-MVS		97.16 6	97.87 6	96.33 12	97.20 20	97.97 4	98.25 25	96.86 5	95.09 25	94.93 36	98.66 11	99.16 8	92.27 53	98.98 6	98.39 8	98.49 19	96.83 31

DTE-MVSNet		97.16 6	97.75 9	96.47 6	97.40 12	97.95 5	98.20 27	96.89 4	95.30 20	95.15 28	98.66 11	98.80 18	92.77 46	98.97 7	98.27 10	98.44 24	96.28 41

COLMAP_ROB	93.74 2	97.09 8	97.98 5	96.05 18	95.97 59	97.78 9	98.56 12	91.72 74	97.53 7	96.01 17	98.14 27	98.76 19	95.28 8	98.76 11	98.23 11	98.77 5	96.67 36
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 36	98.04 3	98.25 25	97.32 1	94.40 35	93.71 65	98.55 14	98.89 14	92.97 39	98.91 9	98.45 7	98.38 29	97.19 14

CP-MVSNet		96.97 10	97.42 13	96.44 7	97.06 24	97.82 8	98.12 30	96.98 3	93.50 47	95.21 26	97.98 32	98.44 33	92.83 45	98.93 8	98.37 9	98.46 22	96.91 28

ACMH	90.17 8	96.61 11	97.69 11	95.35 31	95.29 76	96.94 32	98.43 16	92.05 66	98.04 5	95.38 22	98.07 30	99.25 7	93.23 36	98.35 17	97.16 37	97.72 46	96.00 46
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		96.59 12	98.43 1	94.44 51	95.89 64	96.12 52	95.23 117	95.91 8	99.42 1	92.75 85	98.87 5	99.94 1	88.19 123	98.64 13	98.50 5	98.66 10	97.49 9

UA-Net		96.56 13	96.73 24	96.36 10	98.99 1	97.90 7	97.79 41	95.64 10	92.78 62	92.54 90	96.23 80	95.02 135	94.31 21	98.43 15	98.12 12	98.89 3	98.58 2

ACMMPR		96.54 14	96.71 25	96.35 11	97.55 9	97.63 11	98.62 10	94.54 18	94.45 32	94.19 50	95.04 105	97.35 76	94.92 13	97.85 33	97.50 23	98.26 30	97.17 15

v7n		96.49 15	97.20 18	95.65 23	95.57 72	96.04 54	97.93 35	92.49 52	96.40 12	97.13 7	98.99 4	99.41 4	93.79 28	97.84 35	96.15 55	97.00 70	95.60 54

DeepC-MVS	92.47 4	96.44 16	96.75 23	96.08 17	97.57 7	97.19 28	97.96 34	94.28 24	95.29 21	94.92 37	98.31 23	96.92 86	93.69 29	96.81 60	96.50 47	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
v52		96.35 17	97.40 14	95.12 40	93.83 120	95.54 69	97.82 39	88.95 140	96.27 14	97.22 5	99.11 2	99.40 5	95.80 5	98.16 20	96.37 50	97.10 65	96.96 23

V4		96.35 17	97.40 14	95.12 40	93.83 120	95.54 69	97.82 39	88.95 140	96.27 14	97.21 6	99.10 3	99.40 5	95.79 6	98.17 19	96.37 50	97.10 65	96.96 23

ACMM	90.06 9	96.31 19	96.42 32	96.19 15	97.21 19	97.16 30	98.71 5	93.79 36	94.35 36	93.81 61	92.80 134	98.23 45	95.11 9	98.07 24	97.45 25	98.51 18	96.86 30
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	89.90 10	96.27 20	97.52 12	94.81 47	95.19 78	97.18 29	97.97 33	92.52 50	96.72 9	90.50 131	97.31 60	99.11 9	94.10 23	98.67 12	97.90 15	98.56 16	95.79 50

APDe-MVS		96.23 21	97.22 17	95.08 42	96.66 40	97.56 14	98.63 9	93.69 39	94.62 29	89.80 140	97.73 44	98.13 53	93.84 27	97.79 37	97.63 19	97.87 44	97.08 19

CP-MVS		96.21 22	96.16 43	96.27 13	97.56 8	97.13 31	98.43 16	94.70 17	92.62 64	94.13 53	92.71 135	98.03 59	94.54 19	98.00 28	97.60 20	98.23 31	97.05 20

zzz-MVS		96.18 23	96.01 45	96.38 8	98.30 2	96.18 50	98.51 14	94.48 22	94.56 30	94.81 42	91.73 144	96.96 84	94.30 22	98.09 22	97.83 16	97.91 43	96.73 33

HFP-MVS		96.18 23	96.53 30	95.77 21	97.34 16	97.26 25	98.16 29	94.54 18	94.45 32	92.52 91	95.05 103	96.95 85	93.89 26	97.28 45	97.46 24	98.19 32	97.25 10

MP-MVS		96.13 25	95.93 48	96.37 9	98.19 4	97.31 23	98.49 15	94.53 21	91.39 98	94.38 47	94.32 117	96.43 101	94.59 17	97.75 39	97.44 26	98.04 40	96.88 29
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		96.12 26	96.27 39	95.93 19	97.20 20	97.60 12	98.64 8	93.74 38	92.47 66	93.13 80	93.23 129	98.06 56	94.51 20	97.99 29	97.57 22	98.39 28	96.99 21
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
SMA-MVS		96.11 27	96.61 27	95.53 28	97.49 11	97.41 20	97.62 46	93.78 37	94.14 41	94.18 51	97.16 64	94.67 138	92.42 49	97.74 40	97.33 34	97.70 48	97.79 4

LGP-MVS_train		96.10 28	96.29 37	95.87 20	96.72 37	97.35 22	98.43 16	93.83 35	90.81 113	92.67 89	95.05 103	98.86 16	95.01 10	98.11 21	97.37 31	98.52 17	96.50 38

CSCG		96.07 29	97.15 19	94.81 47	96.06 56	97.58 13	96.52 77	90.98 90	96.51 10	93.60 68	97.13 66	98.55 31	93.01 38	97.17 48	95.36 73	98.68 9	97.78 5

v748		96.05 30	97.00 21	94.95 45	94.41 94	94.77 98	96.72 66	91.03 89	96.12 16	96.71 11	98.74 8	99.59 2	93.55 31	97.97 30	95.96 59	97.28 58	95.84 49

TSAR-MVS + MP.		95.99 31	96.57 29	95.31 33	96.87 28	96.50 43	98.71 5	91.58 77	93.25 53	92.71 86	96.86 70	96.57 97	93.92 24	98.09 22	97.91 14	98.08 37	96.81 32

OPM-MVS		95.96 32	96.59 28	95.23 36	96.67 39	96.52 42	97.86 37	93.28 43	95.27 23	93.46 70	96.26 77	98.85 17	92.89 43	97.09 49	96.37 50	97.22 62	95.78 51

SteuartSystems-ACMMP		95.96 32	96.13 44	95.76 22	97.06 24	97.36 21	98.40 20	94.24 26	91.49 90	91.91 105	94.50 113	96.89 87	94.99 11	98.01 27	97.44 26	97.97 42	97.25 10
Skip Steuart: Steuart Systems R&D Blog.
ACMP	89.62 11	95.96 32	96.28 38	95.59 24	96.58 42	97.23 27	98.26 24	93.22 44	92.33 71	92.31 97	94.29 118	98.73 21	94.68 15	98.04 25	97.14 38	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 51	96.10 16	97.53 10	97.45 19	98.55 13	94.12 29	90.25 116	93.71 65	93.20 130	97.18 80	94.63 16	97.68 41	97.34 33	98.08 37	96.97 22

PMVS	87.16 16	95.88 36	96.47 31	95.19 38	97.00 26	96.02 55	96.70 67	91.57 78	94.43 34	95.33 23	97.16 64	95.37 124	92.39 50	98.89 10	98.72 3	98.17 34	94.71 70
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_Plus		95.86 37	96.18 40	95.47 30	97.11 22	97.26 25	98.37 21	93.48 42	93.49 48	93.99 56	95.61 87	94.11 145	92.49 47	97.87 32	97.44 26	97.40 54	97.52 8

Gipuma		95.86 37	96.17 41	95.50 29	95.92 61	94.59 105	94.77 124	92.50 51	97.82 6	97.90 2	95.56 89	97.88 67	94.71 14	98.02 26	94.81 87	97.23 61	94.48 76
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 45	97.49 15	97.99 31	92.35 55	94.92 26	94.58 43	94.88 108	95.11 133	91.52 67	98.48 14	98.05 13	98.42 26	95.49 56

SD-MVS		95.77 40	96.17 41	95.30 34	96.72 37	96.19 49	97.01 52	93.04 45	94.03 42	92.71 86	96.45 75	96.78 94	93.91 25	96.79 61	95.89 62	98.42 26	97.09 18

TranMVSNet+NR-MVSNet		95.72 41	96.42 32	94.91 46	96.21 51	96.77 36	96.90 59	94.99 13	92.62 64	91.92 104	98.51 16	98.63 26	90.82 93	97.27 46	96.83 41	98.63 13	94.31 77

ESAPD		95.63 42	96.35 34	94.80 49	96.76 35	97.29 24	97.74 42	94.15 28	91.69 85	90.01 137	96.65 72	97.29 77	92.45 48	97.41 43	97.18 35	97.67 51	96.95 25

Anonymous20240521		95.52 43	97.08 20	93.69 81	96.01 58	95.99 57	96.24 96	92.87 47	94.91 27	88.51 149	98.51 16	98.72 23	90.09 107	98.43 15	97.37 31	98.46 22	95.60 54

DU-MVS		95.51 44	95.68 52	95.33 32	96.45 46	96.44 45	96.61 74	95.32 11	89.97 122	93.78 62	97.46 57	98.07 55	91.19 77	97.03 50	96.53 45	98.61 14	94.22 78

UniMVSNet (Re)		95.46 45	95.86 49	95.00 44	96.09 53	96.60 37	96.68 71	94.99 13	90.36 115	92.13 100	97.64 52	98.13 53	91.38 70	96.90 55	96.74 42	98.73 6	94.63 73

RPSCF		95.46 45	96.95 22	93.73 80	95.72 69	95.94 59	95.58 112	88.08 154	95.31 19	91.34 115	96.26 77	98.04 58	93.63 30	98.28 18	97.67 18	98.01 41	97.13 16

anonymousdsp		95.45 47	96.70 26	93.99 66	88.43 210	92.05 162	99.18 1	85.42 190	94.29 37	96.10 16	98.63 13	99.08 11	96.11 1	97.77 38	97.41 29	98.70 8	97.69 7

APD-MVS		95.38 48	95.68 52	95.03 43	97.30 17	96.90 34	97.83 38	93.92 32	89.40 132	90.35 132	95.41 93	97.69 71	92.97 39	97.24 47	97.17 36	97.83 45	95.96 47
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet_NR-MVSNet		95.34 49	95.51 56	95.14 39	95.80 67	96.55 38	96.61 74	94.79 16	90.04 121	93.78 62	97.51 55	97.25 78	91.19 77	96.68 63	96.31 53	98.65 12	94.22 78

X-MVS		95.33 50	95.13 63	95.57 26	97.35 14	97.48 16	98.43 16	94.28 24	92.30 72	93.28 73	86.89 190	96.82 90	91.87 58	97.85 33	97.59 21	98.19 32	96.95 25

3Dnovator+	92.82 3	95.22 51	95.16 62	95.29 35	96.17 52	96.55 38	97.64 44	94.02 31	94.16 40	94.29 49	92.09 141	93.71 150	91.90 56	96.68 63	96.51 46	97.70 48	96.40 39

HPM-MVS++		95.21 52	94.89 67	95.59 24	97.79 6	95.39 77	97.68 43	94.05 30	91.91 82	94.35 48	93.38 128	95.07 134	92.94 41	96.01 74	95.88 63	96.73 73	96.61 37

TSAR-MVS + ACMM		95.17 53	95.95 46	94.26 56	96.07 55	96.46 44	95.67 109	94.21 27	93.84 44	90.99 123	97.18 63	95.24 132	93.55 31	96.60 66	95.61 69	95.06 135	96.69 35

HSP-MVS		95.04 54	95.45 58	94.57 50	96.87 28	97.77 10	98.71 5	93.88 34	91.21 103	91.48 113	95.36 94	98.37 39	90.73 94	94.37 108	92.98 122	95.77 117	98.08 3

CPTT-MVS		95.00 55	94.52 78	95.57 26	96.84 32	96.78 35	97.88 36	93.67 40	92.20 74	92.35 96	85.87 197	97.56 73	94.98 12	96.96 53	96.07 58	97.70 48	96.18 43

Baseline_NR-MVSNet		94.85 56	95.35 60	94.26 56	96.45 46	93.86 127	96.70 67	94.54 18	90.07 120	90.17 136	98.77 7	97.89 64	90.64 98	97.03 50	96.16 54	97.04 69	93.67 86

EG-PatchMatch MVS		94.81 57	95.53 55	93.97 67	95.89 64	94.62 102	95.55 113	88.18 150	92.77 63	94.88 39	97.04 68	98.61 27	93.31 33	96.89 56	95.19 78	95.99 109	93.56 90

OMC-MVS		94.74 58	95.46 57	93.91 72	94.62 89	96.26 48	96.64 73	89.36 130	94.20 38	94.15 52	94.02 123	97.73 70	91.34 72	96.15 72	95.04 81	97.37 55	94.80 67

DeepC-MVS_fast	91.38 6	94.73 59	94.98 64	94.44 51	96.83 34	96.12 52	96.69 69	92.17 61	92.98 58	93.72 64	94.14 119	95.45 122	90.49 103	95.73 80	95.30 74	96.71 74	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 60	94.84 69	94.44 51	94.95 83	96.55 38	96.46 80	91.10 87	88.96 136	96.00 18	94.55 112	95.32 127	90.67 96	96.97 52	96.69 44	97.44 53	94.84 66

pmmvs6		94.58 61	97.30 16	91.40 125	94.84 85	94.61 103	93.40 150	92.43 54	98.51 3	85.61 167	98.73 10	99.53 3	84.40 144	97.88 31	97.03 39	97.72 46	94.79 68

DeepPCF-MVS	90.68 7	94.56 62	94.92 66	94.15 58	94.11 104	95.71 65	97.03 51	90.65 96	93.39 52	94.08 54	95.29 97	94.15 144	93.21 37	95.22 92	94.92 85	95.82 116	95.75 52

NR-MVSNet		94.55 63	95.66 54	93.25 97	94.26 99	96.44 45	96.69 69	95.32 11	89.97 122	91.79 109	97.46 57	98.39 38	82.85 153	96.87 58	96.48 48	98.57 15	93.98 83

v13		94.54 64	94.93 65	94.09 59	93.81 122	95.44 73	96.99 55	91.67 75	92.43 68	95.20 27	98.33 20	98.73 21	91.87 58	93.67 126	92.26 131	95.00 137	93.63 88

v12		94.44 65	94.79 71	94.02 63	93.75 125	95.37 78	96.92 56	91.61 76	92.21 73	95.10 29	98.27 24	98.69 24	91.73 62	93.49 128	92.15 136	94.97 141	93.37 93

Vis-MVSNet		94.39 66	95.85 50	92.68 109	90.91 192	95.88 60	97.62 46	91.41 81	91.95 80	89.20 142	97.29 61	96.26 104	90.60 102	96.95 54	95.91 60	96.32 92	96.71 34
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V9		94.33 67	94.66 74	93.94 70	93.69 129	95.31 79	96.84 61	91.53 79	92.04 79	95.00 33	98.22 25	98.64 25	91.62 64	93.29 130	92.05 138	94.93 142	93.10 98

v11		94.32 68	94.62 75	93.97 67	93.95 112	95.31 79	96.83 62	91.30 83	91.95 80	95.51 20	98.32 22	98.61 27	91.44 69	92.83 137	92.23 133	94.77 146	93.08 99

TSAR-MVS + GP.		94.25 69	94.81 70	93.60 82	96.52 44	95.80 63	94.37 131	92.47 53	90.89 110	88.92 143	95.34 95	94.38 142	92.85 44	96.36 70	95.62 68	96.47 79	95.28 61

CNVR-MVS		94.24 70	94.47 80	93.96 69	96.56 43	95.67 66	96.43 82	91.95 68	92.08 77	91.28 117	90.51 156	95.35 125	91.20 76	96.34 71	95.50 71	96.34 89	95.88 48

V14		94.21 71	94.52 78	93.85 73	93.62 130	95.25 82	96.76 65	91.42 80	91.83 83	94.91 38	98.15 26	98.57 29	91.49 68	93.06 135	91.93 142	94.90 143	92.82 103

v15		94.09 72	94.37 82	93.77 78	93.56 132	95.18 83	96.68 71	91.34 82	91.64 87	94.83 41	98.09 29	98.51 32	91.37 71	92.84 136	91.80 144	94.85 144	92.53 114

v1192		93.98 73	93.94 91	94.01 64	93.91 116	94.63 101	97.00 53	89.75 114	91.01 107	96.50 12	97.93 34	98.26 43	91.74 61	92.06 156	92.05 138	95.18 130	91.66 132

v10		93.96 74	94.12 89	93.77 78	93.37 139	95.45 72	96.83 62	91.13 86	89.70 128	95.02 31	97.88 38	98.23 45	91.27 73	92.39 146	92.18 134	94.99 138	93.00 101

CDPH-MVS		93.96 74	93.86 93	94.08 61	96.31 49	95.84 61	96.92 56	91.85 71	87.21 161	91.25 119	92.83 132	96.06 112	91.05 86	95.57 81	94.81 87	97.12 63	94.72 69

MVS_0304		93.92 76	93.81 98	94.05 62	96.06 56	96.00 56	96.43 82	92.76 48	85.99 170	94.43 46	94.04 122	97.08 81	88.12 125	94.65 105	94.20 105	96.47 79	94.71 70

MSLP-MVS++		93.91 77	94.30 86	93.45 85	95.51 73	95.83 62	93.12 160	91.93 70	91.45 95	91.40 114	87.42 185	96.12 111	93.27 34	96.57 67	96.40 49	95.49 121	96.29 40

v1921920		93.90 78	93.82 96	94.00 65	93.74 126	94.31 109	97.12 48	89.33 131	91.13 104	96.77 10	97.90 35	98.06 56	91.95 55	91.93 163	91.54 151	95.10 133	91.85 127

train_agg		93.89 79	93.46 113	94.40 54	97.35 14	93.78 128	97.63 45	92.19 60	88.12 146	90.52 130	93.57 127	95.78 116	92.31 52	94.78 102	93.46 115	96.36 84	94.70 72

v144192		93.89 79	93.85 94	93.94 70	93.50 133	94.33 108	97.12 48	89.49 125	90.89 110	96.49 13	97.78 43	98.27 42	91.89 57	92.17 155	91.70 146	95.19 129	91.78 130

v1240		93.89 79	93.72 101	94.09 59	93.98 109	94.31 109	97.12 48	89.37 129	90.74 114	96.92 9	98.05 31	97.89 64	92.15 54	91.53 167	91.60 149	94.99 138	91.93 126

NCCC		93.87 82	93.42 114	94.40 54	96.84 32	95.42 74	96.47 79	92.62 49	92.36 70	92.05 101	83.83 206	95.55 118	91.84 60	95.89 76	95.23 77	96.56 77	95.63 53

v1144		93.83 83	93.87 92	93.78 77	93.72 127	94.57 106	96.85 60	89.98 107	91.31 100	95.90 19	97.89 36	98.40 37	91.13 81	92.01 159	92.01 140	95.10 133	90.94 136

MVS_111021_HR		93.82 84	94.26 88	93.31 91	95.01 81	93.97 124	95.73 106	89.75 114	92.06 78	92.49 92	94.01 124	96.05 113	90.61 101	95.95 75	94.78 90	96.28 94	93.04 100

TAPA-MVS	88.94 13	93.78 85	94.31 85	93.18 100	94.14 102	95.99 57	95.74 105	86.98 172	93.43 50	93.88 60	90.16 160	96.88 88	91.05 86	94.33 109	93.95 106	97.28 58	95.40 57
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v7		93.65 86	93.73 100	93.57 83	93.38 138	94.60 104	96.83 62	89.92 110	89.69 129	95.02 31	97.89 36	98.24 44	91.27 73	92.38 147	92.18 134	94.99 138	91.12 134

EPP-MVSNet		93.63 87	93.95 90	93.26 95	95.15 79	96.54 41	96.18 98	91.97 67	91.74 84	85.76 164	94.95 107	84.27 188	91.60 66	97.61 42	97.38 30	98.87 4	95.18 63

v17		93.60 88	93.85 94	93.30 93	93.15 144	94.99 92	96.46 80	90.81 92	89.58 131	93.61 67	97.66 51	98.15 52	91.19 77	92.60 143	91.61 148	94.61 157	92.37 116

v8		93.60 88	93.82 96	93.34 89	93.13 145	95.06 87	96.39 88	90.75 94	89.90 124	94.03 55	97.70 49	98.21 48	91.08 85	92.36 148	91.47 156	94.63 153	92.07 122

MCST-MVS		93.60 88	93.40 117	93.83 74	95.30 75	95.40 76	96.49 78	90.87 91	90.08 119	91.72 110	90.28 158	95.99 114	91.69 63	93.94 122	92.99 121	96.93 72	95.13 64

PVSNet_Blended_VisFu		93.60 88	93.41 115	93.83 74	96.31 49	95.65 67	95.71 107	90.58 99	88.08 149	93.17 78	95.29 97	92.20 159	90.72 95	94.69 104	93.41 118	96.51 78	94.54 74

TransMVSNet (Re)		93.55 92	96.32 36	90.32 137	94.38 95	94.05 119	93.30 157	89.53 123	97.15 8	85.12 169	98.83 6	97.89 64	82.21 159	96.75 62	96.14 56	97.35 56	93.46 91

v16		93.53 93	93.80 99	93.20 98	93.10 150	94.98 93	96.43 82	90.81 92	89.39 134	93.12 81	97.63 53	98.01 60	91.19 77	92.60 143	91.65 147	94.58 159	92.36 117

v1		93.48 94	93.57 107	93.37 86	93.48 134	94.18 116	96.41 87	89.61 119	91.46 93	95.03 30	97.82 40	98.43 34	90.95 90	92.00 160	91.37 160	94.75 147	89.70 148

v1141		93.47 95	93.56 108	93.36 88	93.48 134	94.17 117	96.42 85	89.62 117	91.44 96	94.99 35	97.81 41	98.42 35	90.94 91	92.00 160	91.38 158	94.74 149	89.69 150

divwei89l23v2f112		93.47 95	93.56 108	93.37 86	93.48 134	94.17 117	96.42 85	89.62 117	91.46 93	95.00 33	97.81 41	98.42 35	90.94 91	92.00 160	91.38 158	94.75 147	89.70 148

v2v482		93.42 97	93.49 112	93.32 90	93.44 137	94.05 119	96.36 94	89.76 113	91.41 97	95.24 25	97.63 53	98.34 40	90.44 104	91.65 165	91.76 145	94.69 150	89.62 151

canonicalmvs		93.38 98	94.36 83	92.24 115	93.94 114	96.41 47	94.18 138	90.47 100	93.07 57	88.47 150	88.66 171	93.78 149	88.80 116	95.74 79	95.75 65	97.57 52	97.13 16

3Dnovator	91.81 5	93.36 99	94.27 87	92.29 114	92.99 152	95.03 88	95.76 104	87.79 159	93.82 45	92.38 95	92.19 140	93.37 154	88.14 124	95.26 91	94.85 86	96.69 75	95.40 57

v18		93.33 100	93.59 106	93.04 106	92.94 153	94.87 95	96.31 95	90.59 98	88.96 136	92.89 84	97.51 55	97.90 63	91.01 89	92.33 152	91.48 155	94.50 160	92.05 123

v1neww		93.27 101	93.40 117	93.12 101	93.13 145	94.20 113	96.39 88	89.56 120	89.87 126	93.95 57	97.71 47	98.21 48	91.09 83	92.36 148	91.49 152	94.62 155	89.96 143

v7new		93.27 101	93.40 117	93.12 101	93.13 145	94.20 113	96.39 88	89.56 120	89.87 126	93.95 57	97.71 47	98.21 48	91.09 83	92.36 148	91.49 152	94.62 155	89.96 143

pm-mvs1		93.27 101	95.94 47	90.16 138	94.13 103	93.66 130	92.61 170	89.91 111	95.73 18	84.28 175	98.51 16	98.29 41	82.80 154	96.44 68	95.76 64	97.25 60	93.21 96

v6		93.27 101	93.41 115	93.12 101	93.13 145	94.20 113	96.39 88	89.55 122	89.89 125	93.93 59	97.72 45	98.22 47	91.10 82	92.36 148	91.49 152	94.63 153	89.95 145

TinyColmap		93.17 105	93.33 120	93.00 107	93.84 119	92.76 147	94.75 126	88.90 142	93.97 43	97.48 4	95.28 99	95.29 128	88.37 121	95.31 90	91.58 150	94.65 152	89.10 156

MVS_111021_LR		93.15 106	93.65 103	92.56 110	93.89 118	92.28 158	95.09 118	86.92 174	91.26 102	92.99 83	94.46 115	96.22 107	90.64 98	95.11 95	93.45 116	95.85 114	92.74 107

CNLPA		93.14 107	93.67 102	92.53 111	94.62 89	94.73 99	95.00 120	86.57 178	92.85 61	92.43 93	90.94 149	94.67 138	90.35 105	95.41 83	93.70 110	96.23 99	93.37 93

PLC	87.27 15	93.08 108	92.92 124	93.26 95	94.67 86	95.03 88	94.38 130	90.10 102	91.69 85	92.14 99	87.24 186	93.91 147	91.61 65	95.05 96	94.73 96	96.67 76	92.80 104
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		93.07 109	93.05 123	93.10 104	95.90 62	95.41 75	95.88 101	91.94 69	84.77 177	93.36 71	94.05 121	95.25 131	86.25 135	94.33 109	93.94 107	95.30 124	93.58 89

TSAR-MVS + COLMAP		93.06 110	93.65 103	92.36 112	94.62 89	94.28 111	95.36 116	89.46 127	92.18 75	91.64 111	95.55 90	95.27 130	88.60 119	93.24 131	92.50 128	94.46 161	92.55 113

Effi-MVS+		92.93 111	92.16 134	93.83 74	94.29 97	93.53 138	95.04 119	92.98 46	85.27 174	94.46 44	90.24 159	95.34 126	89.99 108	93.72 124	94.23 104	96.22 100	92.79 105

Fast-Effi-MVS+		92.93 111	92.64 128	93.27 94	93.81 122	93.88 126	95.90 100	90.61 97	83.98 182	92.71 86	92.81 133	96.22 107	90.67 96	94.90 101	93.92 108	95.92 111	92.77 106

HQP-MVS		92.87 113	92.49 129	93.31 91	95.75 68	95.01 91	95.64 110	91.06 88	88.54 143	91.62 112	88.16 176	96.25 105	89.47 111	92.26 154	91.81 143	96.34 89	95.40 57

FMVSNet1		92.86 114	95.26 61	90.06 140	92.40 164	95.16 84	94.37 131	92.22 57	93.18 56	82.16 189	96.76 71	97.48 74	81.85 164	95.32 87	94.98 82	97.34 57	93.93 84

CLD-MVS		92.81 115	94.32 84	91.05 127	95.39 74	95.31 79	95.82 103	81.44 212	89.40 132	91.94 103	95.86 84	97.36 75	85.83 137	95.35 85	94.59 99	95.85 114	92.34 119
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		92.76 116	93.25 121	92.19 116	94.91 84	95.56 68	95.86 102	92.12 63	88.10 147	82.71 184	93.15 131	88.30 177	88.86 115	97.29 44	96.95 40	98.66 10	93.38 92

FC-MVSNet-train		92.75 117	95.40 59	89.66 150	95.21 77	94.82 96	97.00 53	89.40 128	91.13 104	81.71 190	97.72 45	96.43 101	77.57 197	96.89 56	96.72 43	97.05 68	94.09 81

V42		92.67 118	93.50 111	91.71 122	91.41 183	92.96 145	95.71 107	85.00 191	89.67 130	93.22 76	97.67 50	98.01 60	91.02 88	92.65 140	92.12 137	93.86 169	91.42 133

PM-MVS		92.65 119	93.20 122	92.00 118	92.11 175	90.16 178	95.99 99	84.81 194	91.31 100	92.41 94	95.87 83	96.64 96	92.35 51	93.65 127	92.91 123	94.34 164	91.85 127

QAPM		92.57 120	93.51 110	91.47 123	92.91 155	94.82 96	93.01 162	87.51 163	91.49 90	91.21 120	92.24 138	91.70 161	88.74 117	94.54 106	94.39 103	95.41 122	95.37 60

MIMVSNet1		92.52 121	94.88 68	89.77 146	96.09 53	91.99 163	96.92 56	89.68 116	95.92 17	84.55 172	96.64 73	98.21 48	78.44 190	96.08 73	95.10 79	92.91 181	90.22 141

tfpnnormal		92.45 122	94.77 72	89.74 147	93.95 112	93.44 140	93.25 158	88.49 148	95.27 23	83.20 179	96.51 74	96.23 106	83.17 151	95.47 82	94.52 101	96.38 83	91.97 125

PCF-MVS	87.46 14	92.44 123	91.80 137	93.19 99	94.66 87	95.80 63	96.37 92	90.19 101	87.57 154	92.23 98	89.26 167	93.97 146	89.24 112	91.32 169	90.82 165	96.46 81	93.86 85
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
AdaColmap		92.41 124	91.49 141	93.48 84	95.96 60	95.02 90	95.37 115	91.73 73	87.97 152	91.28 117	82.82 211	91.04 165	90.62 100	95.82 78	95.07 80	95.95 110	92.67 108

v148		92.38 125	92.78 126	91.91 119	92.86 156	92.13 161	94.84 122	87.03 171	91.47 92	93.07 82	96.92 69	98.89 14	90.10 106	92.05 157	89.69 173	93.56 172	88.27 169

pmmvs-eth3d		92.34 126	92.33 130	92.34 113	92.67 159	90.67 174	96.37 92	89.06 134	90.98 108	93.60 68	97.13 66	97.02 83	88.29 122	90.20 176	91.42 157	94.07 167	88.89 159

DELS-MVS		92.33 127	93.61 105	90.83 130	92.84 157	95.13 86	94.76 125	87.22 170	87.78 153	88.42 152	95.78 86	95.28 129	85.71 138	94.44 107	93.91 109	96.01 108	92.97 102
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		92.32 128	91.66 139	93.09 105	95.13 80	94.73 99	94.57 129	92.14 62	81.74 190	90.33 133	88.13 177	95.91 115	89.24 112	94.23 118	93.65 114	97.12 63	93.23 95

UGNet		92.31 129	94.70 73	89.53 152	90.99 191	95.53 71	96.19 97	92.10 65	91.35 99	85.76 164	95.31 96	95.48 121	76.84 201	95.22 92	94.79 89	95.32 123	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		92.17 130	92.17 133	92.18 117	92.93 154	92.22 159	93.66 145	87.41 165	93.49 48	97.99 1	94.10 120	96.68 95	86.46 133	92.04 158	89.18 179	94.61 157	87.47 172

IterMVS-LS		92.10 131	92.33 130	91.82 121	93.18 142	93.66 130	92.80 168	92.27 56	90.82 112	90.59 129	97.19 62	90.97 166	87.76 126	89.60 182	90.94 164	94.34 164	93.16 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		92.09 132	92.84 125	91.22 126	92.55 160	92.97 144	93.42 149	85.43 189	90.24 117	91.83 107	94.70 109	94.59 140	88.48 120	94.91 100	93.31 120	95.59 120	89.15 155

no-one		92.05 133	94.57 77	89.12 156	85.55 222	87.65 192	94.21 137	77.34 218	93.43 50	89.64 141	95.11 102	99.11 9	95.86 4	95.38 84	95.24 76	92.08 185	96.11 45

MAR-MVS		91.86 134	91.14 144	92.71 108	94.29 97	94.24 112	94.91 121	91.82 72	81.66 191	93.32 72	84.51 204	93.42 153	86.86 131	95.16 94	94.44 102	95.05 136	94.53 75
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		91.63 135	92.73 127	90.35 136	88.36 211	87.89 189	96.53 76	81.51 211	92.45 67	91.82 108	96.44 76	97.05 82	93.26 35	94.10 120	88.94 183	90.61 188	92.24 120

FC-MVSNet-test		91.49 136	94.43 81	88.07 175	94.97 82	90.53 177	95.42 114	91.18 85	93.24 54	72.94 220	98.37 19	93.86 148	78.78 184	97.82 36	96.13 57	95.13 131	91.05 135

conf0.05thres1000		91.24 137	91.85 136	90.53 133	94.59 92	94.56 107	94.33 135	89.52 124	93.67 46	83.77 177	91.04 147	79.10 205	83.98 145	96.66 65	95.56 70	96.98 71	92.36 117

OpenMVS	89.22 12	91.09 138	91.42 142	90.71 131	92.79 158	93.61 135	92.74 169	85.47 188	86.10 169	90.73 124	85.71 198	93.07 157	86.69 132	94.07 121	93.34 119	95.86 112	94.02 82

FPMVS		90.81 139	91.60 140	89.88 144	92.52 161	88.18 184	93.31 156	83.62 200	91.59 89	88.45 151	88.96 169	89.73 172	86.96 129	96.42 69	95.69 67	94.43 162	90.65 137

DI_MVS_plusplus_trai		90.68 140	90.40 148	91.00 128	92.43 163	92.61 152	94.17 139	88.98 136	88.32 145	88.76 147	93.67 126	87.58 179	86.44 134	89.74 180	90.33 167	95.24 128	90.56 140

Vis-MVSNet (Re-imp)		90.68 140	92.18 132	88.92 159	94.63 88	92.75 148	92.91 164	91.20 84	89.21 135	75.01 215	93.96 125	89.07 175	82.72 156	95.88 77	95.30 74	97.08 67	89.08 157

FMVSNet2		90.28 142	92.04 135	88.23 173	91.22 187	94.05 119	92.88 165	90.69 95	86.53 165	79.89 200	94.38 116	92.73 158	78.54 187	91.64 166	92.26 131	96.17 103	92.67 108

MVS_Test		90.19 143	90.58 145	89.74 147	92.12 174	91.74 165	92.51 171	88.54 147	82.80 187	87.50 156	94.62 110	95.02 135	83.97 146	88.69 189	89.32 177	93.79 170	91.85 127

EPNet		90.17 144	89.07 161	91.45 124	97.25 18	90.62 176	94.84 122	93.54 41	80.96 193	91.85 106	86.98 189	85.88 184	77.79 194	92.30 153	92.58 127	93.41 174	94.20 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		90.09 145	90.12 150	90.05 141	92.40 164	92.74 149	91.74 181	85.89 183	80.54 200	90.30 134	88.54 172	95.51 119	84.69 142	92.64 141	90.25 169	95.28 126	90.61 138

PVSNet_Blended		90.09 145	90.12 150	90.05 141	92.40 164	92.74 149	91.74 181	85.89 183	80.54 200	90.30 134	88.54 172	95.51 119	84.69 142	92.64 141	90.25 169	95.28 126	90.61 138

pmmvs4		89.95 147	89.32 159	90.69 132	91.60 182	89.17 182	94.37 131	87.63 162	88.07 150	91.02 122	94.50 113	90.50 169	86.13 136	86.33 204	89.40 176	93.39 175	87.29 174

MDA-MVSNet-bldmvs		89.75 148	91.67 138	87.50 180	74.25 236	90.88 172	94.68 127	85.89 183	91.64 87	91.03 121	95.86 84	94.35 143	89.10 114	96.87 58	86.37 193	90.04 189	85.72 183

PatchMatch-RL		89.59 149	88.80 166	90.51 134	92.20 173	88.00 188	91.72 183	86.64 175	84.75 178	88.25 153	87.10 188	90.66 168	89.85 110	93.23 132	92.28 130	94.41 163	85.60 185

Fast-Effi-MVS+-dtu		89.57 150	88.42 172	90.92 129	93.35 140	91.57 166	93.01 162	95.71 9	78.94 214	87.65 155	84.68 203	93.14 156	82.00 161	90.84 172	91.01 163	93.78 171	88.77 160

view800		89.42 151	89.11 160	89.78 145	94.00 105	93.71 129	93.96 141	88.47 149	88.10 147	82.91 180	82.61 212	79.85 203	83.10 152	94.92 99	95.38 72	96.26 98	89.19 154

GBi-Net		89.35 152	90.58 145	87.91 176	91.22 187	94.05 119	92.88 165	90.05 104	79.40 206	78.60 206	90.58 153	87.05 180	78.54 187	95.32 87	94.98 82	96.17 103	92.67 108

test1		89.35 152	90.58 145	87.91 176	91.22 187	94.05 119	92.88 165	90.05 104	79.40 206	78.60 206	90.58 153	87.05 180	78.54 187	95.32 87	94.98 82	96.17 103	92.67 108

thres600view7		89.14 154	88.83 164	89.51 153	93.71 128	93.55 136	93.93 142	88.02 155	87.30 158	82.40 185	81.18 215	80.63 201	82.69 157	94.27 113	95.90 61	96.27 96	88.94 158

view600		89.09 155	88.78 167	89.46 154	93.59 131	93.33 142	93.92 143	87.76 160	87.40 155	82.79 181	81.29 214	80.71 200	82.59 158	94.28 112	95.72 66	96.12 106	88.70 161

tfpn_n400		89.03 156	89.39 157	88.61 163	93.98 109	92.33 155	91.83 179	88.97 137	92.97 59	78.90 202	84.93 200	78.24 207	81.77 167	95.00 97	93.67 111	96.22 100	88.59 162

tfpnconf		89.03 156	89.39 157	88.61 163	93.98 109	92.33 155	91.83 179	88.97 137	92.97 59	78.90 202	84.93 200	78.24 207	81.77 167	95.00 97	93.67 111	96.22 100	88.59 162

CVMVSNet		88.97 158	89.73 153	88.10 174	87.33 218	85.22 198	94.68 127	78.68 214	88.94 138	86.98 160	95.55 90	85.71 185	89.87 109	91.19 170	89.69 173	91.05 186	91.78 130

CANet_DTU		88.95 159	89.51 156	88.29 172	93.12 149	91.22 169	93.61 146	83.47 203	80.07 205	90.71 128	89.19 168	93.68 151	76.27 205	91.44 168	91.17 162	92.59 182	89.83 146

GA-MVS		88.76 160	88.04 175	89.59 151	92.32 167	91.46 167	92.28 175	86.62 176	83.82 184	89.84 139	92.51 137	81.94 195	83.53 149	89.41 184	89.27 178	92.95 180	87.90 170

tfpnview11		88.74 161	88.95 162	88.50 165	93.91 116	92.43 154	91.70 185	88.90 142	90.93 109	78.90 202	84.93 200	78.24 207	81.71 169	94.32 111	94.60 98	95.86 112	87.23 175

pmmvs5		88.63 162	89.70 154	87.39 182	89.24 204	90.64 175	91.87 178	82.13 207	83.34 185	87.86 154	94.58 111	96.15 110	79.87 176	87.33 200	89.07 182	93.39 175	86.76 178

thres400		88.54 163	88.15 174	88.98 157	93.17 143	92.84 146	93.56 147	86.93 173	86.45 166	82.37 186	79.96 217	81.46 198	81.83 165	93.21 133	94.76 91	96.04 107	88.39 167

CDS-MVSNet		88.41 164	89.79 152	86.79 187	94.55 93	90.82 173	92.50 172	89.85 112	83.26 186	80.52 196	91.05 146	89.93 170	69.11 215	93.17 134	92.71 126	94.21 166	87.63 171
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gg-mvs-nofinetune		88.32 165	88.81 165	87.75 178	93.07 151	89.37 181	89.06 209	95.94 7	95.29 21	87.15 157	97.38 59	76.38 211	68.05 218	91.04 171	89.10 181	93.24 177	83.10 193

IterMVS		88.32 165	88.25 173	88.41 167	90.83 193	91.24 168	93.07 161	81.69 209	86.77 163	88.55 148	95.61 87	86.91 183	87.01 128	87.38 199	83.77 200	89.29 192	86.06 182
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres200		88.29 167	87.88 176	88.76 160	92.50 162	93.55 136	92.47 173	88.02 155	84.80 175	81.44 191	79.28 219	82.20 194	81.83 165	94.27 113	93.67 111	96.27 96	87.40 173

diffmvs		88.28 168	88.88 163	87.58 179	89.51 202	88.07 187	91.88 177	85.83 186	87.31 156	86.34 162	96.01 82	88.90 176	81.90 162	85.49 211	86.61 192	90.04 189	89.77 147

IB-MVS	86.01 17	88.24 169	87.63 178	88.94 158	92.03 178	91.77 164	92.40 174	85.58 187	78.24 216	84.85 170	71.99 232	93.45 152	83.96 147	93.48 129	92.33 129	94.84 145	92.15 121
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		88.22 170	87.85 177	88.65 162	91.40 184	86.75 194	94.07 140	84.97 192	88.86 140	93.20 77	96.11 81	96.21 109	83.70 148	87.29 201	80.29 211	84.56 210	79.46 208

test20.03		88.20 171	91.26 143	84.63 201	96.64 41	89.39 180	90.73 196	89.97 108	91.07 106	72.02 222	94.98 106	95.45 122	69.35 214	92.70 139	91.19 161	89.06 194	84.02 186

HyFIR lowres test		88.19 172	86.56 185	90.09 139	91.24 186	92.17 160	94.30 136	88.79 144	84.06 180	85.45 168	89.52 165	85.64 186	88.64 118	85.40 212	87.28 188	92.14 184	81.87 196

tfpn1000		88.13 173	88.68 169	87.49 181	93.94 114	92.64 151	91.50 187	88.70 146	90.12 118	74.35 217	86.74 192	75.27 213	80.14 175	94.16 119	94.66 97	96.33 91	87.16 176

tfpn200view9		87.94 174	87.51 179	88.44 166	92.28 168	93.63 134	93.35 155	88.11 153	80.90 194	80.89 192	78.25 220	82.25 190	79.65 179	94.27 113	94.76 91	96.36 84	88.48 164

conf200view11		87.93 175	87.51 179	88.41 167	92.28 168	93.64 132	93.36 151	88.12 151	80.90 194	80.71 194	78.25 220	82.25 190	79.65 179	94.27 113	94.76 91	96.36 84	88.48 164

FMVSNet3		87.90 176	88.63 170	87.04 184	89.78 201	93.46 139	91.62 186	90.05 104	79.40 206	78.60 206	90.58 153	87.05 180	77.07 200	88.03 196	89.86 172	95.12 132	92.04 124

MS-PatchMatch		87.72 177	88.62 171	86.66 189	90.81 194	88.18 184	90.92 192	82.25 206	85.86 171	80.40 199	90.14 161	89.29 174	84.93 139	89.39 185	89.12 180	90.67 187	88.34 168

tfpn		87.65 178	85.66 190	89.96 143	94.36 96	93.94 125	93.85 144	89.02 135	88.71 142	82.78 182	83.79 207	53.79 234	83.43 150	95.35 85	94.54 100	96.35 88	89.51 152

tfpn111		87.59 179	86.89 182	88.41 167	92.28 168	93.64 132	93.36 151	88.12 151	80.90 194	80.71 194	73.93 229	82.25 190	79.65 179	94.27 113	94.76 91	96.36 84	88.48 164

Anonymous20231206		87.45 180	89.66 155	84.87 198	94.00 105	87.73 191	91.36 188	86.41 181	88.89 139	75.03 214	92.59 136	96.82 90	72.48 212	89.72 181	88.06 185	89.93 191	83.81 188

EPNet_dtu		87.40 181	86.27 187	88.72 161	95.68 70	83.37 207	92.09 176	90.08 103	78.11 219	91.29 116	86.33 193	89.74 171	75.39 206	89.07 186	87.89 186	87.81 199	89.38 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		86.64 182	86.62 184	86.65 190	90.33 197	87.86 190	93.19 159	83.30 204	83.95 183	82.32 187	87.93 179	89.34 173	86.92 130	85.64 210	84.95 197	83.85 216	86.68 179

testgi		86.49 183	90.31 149	82.03 206	95.63 71	88.18 184	93.47 148	84.89 193	93.23 55	69.54 229	87.16 187	97.96 62	60.66 223	91.90 164	89.90 171	87.99 197	83.84 187

thres100view900		86.46 184	86.00 189	86.99 185	92.28 168	91.03 170	91.09 190	84.49 196	80.90 194	80.89 192	78.25 220	82.25 190	77.57 197	90.17 177	92.84 124	95.63 118	86.57 180

gm-plane-assit		86.15 185	82.51 201	90.40 135	95.81 66	92.29 157	97.99 31	84.66 195	92.15 76	93.15 79	97.84 39	44.65 239	78.60 186	88.02 197	85.95 194	92.20 183	76.69 215

tfpn_ndepth		85.89 186	86.40 186	85.30 196	91.31 185	92.47 153	90.78 194	87.75 161	84.79 176	71.04 224	76.95 224	78.80 206	74.52 209	92.72 138	93.43 117	96.39 82	85.65 184

conf0.01		85.72 187	83.49 198	88.32 170	92.11 175	93.35 141	93.36 151	88.02 155	80.90 194	80.51 197	74.83 227	59.86 232	79.65 179	93.80 123	94.76 91	96.29 93	86.94 177

CMPMVS	66.55 18	85.55 188	87.46 181	83.32 204	84.99 223	81.97 212	79.19 234	75.93 220	79.32 209	88.82 145	85.09 199	91.07 164	82.12 160	92.56 145	89.63 175	88.84 195	92.56 112
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		85.32 189	81.58 205	89.69 149	90.36 196	84.79 201	86.72 220	92.22 57	75.38 224	90.73 124	90.41 157	67.88 222	84.86 140	83.76 214	85.74 195	93.24 177	83.14 191

conf0.002		84.82 190	81.84 204	88.30 171	92.05 177	93.28 143	93.36 151	88.00 158	80.90 194	80.48 198	73.43 231	52.48 237	79.65 179	93.72 124	92.82 125	96.28 94	86.22 181

MVSTER		84.79 191	83.79 196	85.96 192	89.14 205	89.80 179	89.39 207	82.99 205	74.16 228	82.78 182	85.97 196	66.81 224	76.84 201	90.77 173	88.83 184	94.66 151	90.19 142

MIMVSNet		84.76 192	86.75 183	82.44 205	91.71 181	85.95 196	89.74 205	89.49 125	85.28 173	69.69 228	87.93 179	90.88 167	64.85 220	88.26 194	87.74 187	89.18 193	81.24 197

new-patchmatchnet		84.45 193	88.75 168	79.43 213	93.28 141	81.87 213	81.68 231	83.48 202	94.47 31	71.53 223	98.33 20	97.88 67	58.61 226	90.35 175	77.33 218	87.99 197	81.05 199

thresconf0.02		84.34 194	82.02 203	87.06 183	92.23 172	90.93 171	91.05 191	86.43 180	88.83 141	77.65 212	73.93 229	55.81 233	79.68 178	90.62 174	90.28 168	95.30 124	83.73 189

LP		84.09 195	84.31 193	83.85 203	79.40 231	84.34 204	90.26 199	84.02 197	87.99 151	84.66 171	91.61 145	79.13 204	80.58 173	85.90 209	81.59 206	84.16 215	79.59 207

PatchT		83.44 196	81.10 207	86.18 191	77.92 233	82.58 211	89.87 203	87.39 166	75.88 223	90.73 124	89.86 162	66.71 225	84.86 140	83.76 214	85.74 195	86.33 206	83.14 191

RPMNet		83.42 197	78.40 216	89.28 155	89.79 200	84.79 201	90.64 197	92.11 64	75.38 224	87.10 158	79.80 218	61.99 231	82.79 155	81.88 220	82.07 205	93.23 179	82.87 194

TAMVS		82.96 198	86.15 188	79.24 216	90.57 195	83.12 210	87.29 215	75.12 223	84.06 180	65.81 231	92.22 139	88.27 178	69.11 215	88.72 187	87.26 190	87.56 202	79.38 209

PatchmatchNet		82.44 199	78.69 215	86.83 186	89.81 199	81.55 214	90.78 194	87.27 169	82.39 189	88.85 144	88.31 175	70.96 218	81.90 162	78.58 227	74.33 227	82.35 222	74.69 219
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		82.33 200	79.66 210	85.45 194	88.83 207	83.88 205	90.09 202	81.98 208	79.07 213	88.82 145	88.70 170	73.77 214	78.41 191	80.29 224	76.08 221	84.56 210	75.83 216

tpmp4_e23		82.16 201	78.26 218	86.70 188	89.92 198	84.82 200	91.17 189	89.95 109	81.21 192	87.10 158	81.91 213	64.01 228	77.88 193	79.89 225	74.99 225	84.18 214	81.00 200

CostFormer		82.15 202	79.54 211	85.20 197	88.92 206	85.70 197	90.87 193	86.26 182	79.19 212	83.87 176	87.89 181	69.20 220	76.62 203	77.50 230	75.28 223	84.69 209	82.02 195

PMMVS		81.93 203	83.48 199	80.12 211	72.35 237	75.05 230	88.54 211	64.01 229	77.02 221	82.22 188	87.51 184	91.12 163	79.70 177	86.59 202	86.64 191	93.88 168	80.41 201

pmmvs3		81.69 204	83.83 195	79.19 217	78.33 232	78.57 220	89.53 206	58.71 233	78.88 215	84.34 174	88.36 174	91.96 160	77.69 196	87.48 198	82.42 204	86.54 205	79.18 210

tpm		81.58 205	78.84 213	84.79 200	91.11 190	79.50 217	89.79 204	83.75 198	79.30 210	92.05 101	90.98 148	64.78 227	74.54 207	80.50 223	76.67 220	77.49 227	80.15 204

test0.0.03 1		81.51 206	83.30 200	79.42 214	93.99 107	86.50 195	85.93 226	87.32 167	78.16 217	61.62 232	80.78 216	81.78 196	59.87 224	88.40 193	87.27 189	87.78 201	80.19 203

test1235678		81.50 207	84.78 191	77.67 223	87.67 214	80.27 215	90.12 200	77.62 216	80.36 202	69.71 226	90.93 150	96.51 98	56.57 228	88.60 191	84.93 198	84.34 212	71.87 228

testmv		81.49 208	84.76 192	77.67 223	87.67 214	80.25 216	90.12 200	77.62 216	80.34 203	69.71 226	90.92 151	96.47 99	56.57 228	88.58 192	84.92 199	84.33 213	71.86 229

dps		81.42 209	77.88 223	85.56 193	87.67 214	85.17 199	88.37 213	87.46 164	74.37 227	84.55 172	86.80 191	62.18 230	80.20 174	81.13 222	77.52 217	85.10 207	77.98 213

test-LLR		80.62 210	77.20 226	84.62 202	93.99 107	75.11 228	87.04 216	87.32 167	70.11 232	78.59 209	83.17 209	71.60 216	73.88 210	82.32 218	79.20 214	86.91 203	78.87 211

tpm cat1		80.03 211	75.93 229	84.81 199	89.31 203	83.26 209	88.86 210	86.55 179	79.24 211	86.10 163	84.22 205	63.62 229	77.37 199	73.43 232	70.88 230	80.67 223	76.87 214

N_pmnet		79.33 212	84.22 194	73.62 228	91.72 180	73.72 232	86.11 224	76.36 219	92.38 69	53.38 236	95.54 92	95.62 117	59.14 225	84.23 213	74.84 226	75.03 232	73.25 225

EPMVS		79.26 213	78.20 220	80.49 209	87.04 219	78.86 219	86.08 225	83.51 201	82.63 188	73.94 218	89.59 163	68.67 221	72.03 213	78.17 228	75.08 224	80.37 224	74.37 221

CHOSEN 280x420		79.24 214	78.26 218	80.38 210	79.60 230	68.80 237	89.32 208	75.38 221	77.25 220	78.02 211	75.57 226	76.17 212	81.19 171	88.61 190	81.39 207	78.79 225	80.03 205

DWT-MVSNet_training		79.22 215	73.99 231	85.33 195	88.57 208	84.41 203	90.56 198	80.96 213	73.90 229	85.72 166	75.62 225	50.09 238	81.30 170	76.91 231	77.02 219	84.88 208	79.97 206

ADS-MVSNet		79.11 216	79.38 212	78.80 219	81.90 228	75.59 226	84.36 227	83.69 199	87.31 156	76.76 213	87.58 183	76.90 210	68.55 217	78.70 226	75.56 222	77.53 226	74.07 223

FMVSNet5		79.08 217	78.83 214	79.38 215	87.52 217	86.78 193	87.64 214	78.15 215	69.54 234	70.64 225	65.97 236	65.44 226	63.87 221	90.17 177	90.46 166	88.48 196	83.45 190

tpmrst		78.81 218	76.18 228	81.87 207	88.56 209	77.45 223	86.74 219	81.52 210	80.08 204	83.48 178	90.84 152	66.88 223	74.54 207	73.04 233	71.02 229	76.38 229	73.95 224

test-mter		78.71 219	78.35 217	79.12 218	84.03 225	76.58 224	88.51 212	59.06 232	71.06 230	78.87 205	83.73 208	71.83 215	76.44 204	83.41 217	80.61 209	87.79 200	81.24 197

MVS-HIRNet		78.28 220	75.28 230	81.79 208	80.33 229	69.38 236	76.83 235	86.59 177	70.76 231	86.66 161	89.57 164	81.04 199	77.74 195	77.81 229	71.65 228	82.62 219	66.73 231

testus		78.20 221	81.50 206	74.36 227	85.59 221	79.36 218	86.99 218	65.76 227	76.01 222	73.00 219	77.98 223	93.35 155	51.30 234	86.33 204	82.79 203	83.50 218	74.68 220

E-PMN		77.81 222	77.88 223	77.73 222	88.26 212	70.48 235	80.19 233	71.20 225	86.66 164	72.89 221	88.09 178	81.74 197	78.75 185	90.02 179	68.30 231	75.10 231	59.85 234

EMVS		77.65 223	77.49 225	77.83 220	87.75 213	71.02 234	81.13 232	70.54 226	86.38 167	74.52 216	89.38 166	80.19 202	78.22 192	89.48 183	67.13 232	74.83 233	58.84 235

TESTMET0.1,1		77.47 224	77.20 226	77.78 221	81.94 227	75.11 228	87.04 216	58.33 234	70.11 232	78.59 209	83.17 209	71.60 216	73.88 210	82.32 218	79.20 214	86.91 203	78.87 211

1111		76.85 225	78.03 221	75.46 225	94.16 100	78.29 221	86.40 222	89.12 132	87.23 159	61.26 233	95.15 100	44.14 240	51.46 232	86.04 207	81.00 208	70.40 235	74.37 221

new_pmnet		76.65 226	83.52 197	68.63 230	82.60 226	72.08 233	76.76 236	64.17 228	84.41 179	49.73 238	91.77 142	91.53 162	56.16 230	86.59 202	83.26 202	82.37 221	75.02 217

test12356		75.40 227	80.89 208	69.01 229	77.43 234	75.75 225	83.03 229	61.48 230	78.13 218	59.08 235	87.69 182	94.95 137	57.37 227	88.18 195	80.59 210	75.65 230	60.93 233

MVE	60.41 19	73.21 228	80.84 209	64.30 231	56.34 238	57.24 239	75.28 238	72.76 224	87.14 162	41.39 239	86.31 194	85.30 187	80.66 172	86.17 206	83.36 201	59.35 236	80.38 202
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2356		72.95 229	71.24 232	74.95 226	84.89 224	75.49 227	82.67 230	75.38 221	68.02 235	68.65 230	74.40 228	52.81 236	55.61 231	81.50 221	79.80 212	82.50 220	66.70 232

testpf		72.68 230	66.81 233	79.53 212	86.52 220	73.89 231	83.56 228	88.74 145	58.70 237	79.68 201	71.31 233	53.64 235	62.23 222	68.68 234	66.64 233	76.46 228	74.82 218

PMMVS2		69.86 231	82.14 202	55.52 233	75.19 235	63.08 238	75.52 237	60.97 231	88.50 144	25.11 241	91.77 142	96.44 100	25.43 235	88.70 188	79.34 213	70.93 234	67.17 230

.test1245		60.07 232	56.75 234	63.93 232	94.16 100	78.29 221	86.40 222	89.12 132	87.23 159	61.26 233	95.15 100	44.14 240	51.46 232	86.04 207	2.51 236	1.21 240	3.92 237

GG-mvs-BLEND		54.28 233	77.89 222	26.72 235	0.37 242	83.31 208	70.04 239	0.39 239	74.71 226	5.36 242	68.78 234	83.06 189	0.62 239	83.73 216	78.99 216	83.55 217	72.68 227

testmvs		2.38 234	3.35 235	1.26 237	0.83 240	0.96 243	1.53 243	0.83 237	3.59 239	1.63 244	6.03 238	2.93 243	1.55 238	3.49 237	2.51 236	1.21 240	3.92 237

test123		2.16 235	2.82 236	1.41 236	0.62 241	1.18 242	1.53 243	0.82 238	2.78 240	2.27 243	4.18 239	1.98 244	1.64 237	2.58 238	3.01 235	1.56 239	4.00 236

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

our_test_3					91.78 179	88.87 183	94.37 131

ambc			94.61 76		98.09 5	95.14 85	91.71 184		94.18 39	96.46 14	96.26 77	96.30 103	91.26 75	94.70 103	92.00 141	93.45 173	93.67 86

MTAPA										94.88 39		96.88 88

MTMP										95.43 21		97.25 78

Patchmatch-RL test							8.96 242

tmp_tt				28.44 234	36.05 239	15.86 241	21.29 241	6.40 236	54.52 238	51.96 237	50.37 237	38.68 242	9.55 236	61.75 236	59.66 234	45.36 238

XVS					96.86 30	97.48 16	98.73 3			93.28 73		96.82 90				98.17 34

X-MVStestdata					96.86 30	97.48 16	98.73 3			93.28 73		96.82 90				98.17 34

abl_6				91.88 120	93.76 124	94.98 93	95.64 110	88.97 137	86.20 168	90.00 138	86.31 194	94.50 141	87.31 127			95.60 119	92.48 115

mPP-MVS					98.24 3							97.65 72

NP-MVS									85.48 172

Patchmtry						83.74 206	86.72 220	92.22 57		90.73 124

DeepMVS_CX						47.68 240	53.20 240	19.21 235	63.24 236	26.96 240	66.50 235	69.82 219	66.91 219	64.27 235		54.91 237	72.72 226

High-resolution multi-view benchmark