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.

TDRefinement		93.16 1	95.57 1	90.36 1	88.79 49	93.57 1	97.27 1	78.23 20	95.55 2	93.00 1	93.98 18	96.01 50	87.53 1	97.69 1	96.81 1	97.33 1	95.34 4

XVS					91.28 24	91.23 8	96.89 2			87.14 28		94.53 85				95.84 15

X-MVStestdata					91.28 24	91.23 8	96.89 2			87.14 28		94.53 85				95.84 15

X-MVS		89.36 26	90.73 43	87.77 18	91.50 19	91.23 8	96.76 4	78.88 16	87.29 53	87.14 28	78.98 151	94.53 85	76.47 54	95.25 19	94.28 12	95.85 14	93.55 15

ACMMPR		91.30 4	92.88 10	89.46 4	91.92 11	91.61 5	96.60 5	79.46 12	90.08 30	88.53 14	89.54 78	95.57 62	84.25 7	95.24 20	94.27 13	95.97 11	93.85 8

MP-MVS		90.84 6	91.95 30	89.55 3	92.92 5	90.90 18	96.56 6	79.60 9	86.83 58	88.75 13	89.00 86	94.38 89	84.01 9	94.94 25	94.34 11	95.45 24	93.24 21
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		90.42 10	91.58 35	89.05 6	91.77 14	91.06 13	96.51 7	78.94 15	85.41 72	87.67 19	87.02 104	95.26 70	83.62 13	95.01 24	93.94 16	95.79 19	93.40 19

SteuartSystems-ACMMP		90.00 17	91.73 32	87.97 13	91.21 28	90.29 28	96.51 7	78.00 22	86.33 62	85.32 42	88.23 91	94.67 84	82.08 24	95.13 22	93.88 17	94.72 35	93.59 12
Skip Steuart: Steuart Systems R&D Blog.
ACMMP		90.63 8	92.40 19	88.56 9	91.24 27	91.60 6	96.49 9	77.53 25	87.89 46	86.87 32	87.24 101	96.46 33	82.87 19	95.59 15	94.50 9	96.35 6	93.51 16
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
CP-MVS		91.09 5	92.33 22	89.65 2	92.16 10	90.41 26	96.46 10	80.38 6	88.26 44	89.17 11	87.00 105	96.34 39	83.95 10	95.77 11	94.72 8	95.81 17	93.78 10

zzz-MVS		90.38 11	91.35 38	89.25 5	93.08 3	86.59 59	96.45 11	79.00 14	90.23 27	89.30 10	85.87 115	94.97 80	82.54 21	95.05 23	94.83 7	95.14 27	91.94 33

ACMM	80.67 7	90.67 7	92.46 18	88.57 8	91.35 21	89.93 31	96.34 12	77.36 31	90.17 28	86.88 31	87.32 99	96.63 29	83.32 14	95.79 10	94.49 10	96.19 9	92.91 24
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
anonymousdsp		85.62 58	90.53 45	79.88 94	64.64 217	76.35 149	96.28 13	53.53 205	85.63 69	81.59 82	92.81 30	97.71 16	86.88 2	94.56 26	92.83 25	96.35 6	93.84 9

HSP-MVS		88.32 40	90.71 44	85.53 34	90.63 35	92.01 4	96.15 14	77.52 26	86.02 65	81.39 84	90.21 71	96.08 47	76.38 56	88.30 90	86.70 82	91.12 79	95.64 1

TSAR-MVS + MP.		89.67 23	92.25 24	86.65 25	91.53 17	90.98 16	96.15 14	73.30 53	87.88 47	81.83 76	92.92 29	95.15 74	82.23 22	93.58 34	92.25 34	94.87 30	93.01 23

APDe-MVS		89.85 20	92.91 9	86.29 27	90.47 37	91.34 7	96.04 16	76.41 38	91.11 15	78.50 100	93.44 21	95.82 54	81.55 27	93.16 38	91.90 38	94.77 33	93.58 14

ACMMP_Plus		89.86 19	91.96 29	87.42 20	91.00 29	90.08 29	96.00 17	76.61 35	89.28 34	87.73 18	90.04 72	91.80 122	78.71 39	94.36 29	93.82 18	94.48 36	94.32 6

CPTT-MVS		89.63 24	90.52 46	88.59 7	90.95 30	90.74 20	95.71 18	79.13 13	87.70 48	85.68 40	80.05 147	95.74 57	84.77 6	94.28 31	92.68 27	95.28 26	92.45 28

HFP-MVS		90.32 14	92.37 21	87.94 14	91.46 20	90.91 17	95.69 19	79.49 10	89.94 33	83.50 63	89.06 85	94.44 88	81.68 26	94.17 32	94.19 14	95.81 17	93.87 7

COLMAP_ROB	85.66 2	91.85 2	95.01 2	88.16 12	88.98 48	92.86 2	95.51 20	72.17 56	94.95 5	91.27 3	94.11 17	97.77 14	84.22 8	96.49 4	95.27 5	96.79 2	93.60 11
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
APD-MVS		89.14 28	91.25 40	86.67 24	91.73 15	91.02 15	95.50 21	77.74 23	84.04 84	79.47 95	91.48 50	94.85 81	81.14 28	92.94 41	92.20 36	94.47 37	92.24 29
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SMA-MVS		90.37 12	92.54 17	87.83 15	91.78 13	90.56 25	95.35 22	77.47 27	90.80 20	88.51 15	91.24 59	92.22 114	79.16 36	94.32 30	93.72 19	94.75 34	94.93 5

LGP-MVS_train		90.56 9	92.38 20	88.43 10	90.88 31	91.15 11	95.35 22	77.65 24	86.26 64	87.23 25	90.45 70	97.35 20	83.20 15	95.44 16	93.41 21	96.28 8	92.63 25

HPM-MVS++		88.74 38	89.54 53	87.80 17	92.58 7	85.69 67	95.10 24	78.01 21	87.08 55	87.66 20	87.89 94	92.07 118	80.28 32	90.97 69	91.41 42	93.17 51	91.69 35

ESAPD		89.27 27	91.76 31	86.36 26	90.60 36	90.40 27	95.08 25	77.43 29	87.49 50	80.35 89	92.38 39	94.32 90	80.59 29	92.69 47	91.58 41	94.13 39	93.44 17

OPM-MVS		89.82 21	92.24 25	86.99 23	90.86 32	89.35 35	95.07 26	75.91 40	91.16 14	86.87 32	91.07 62	97.29 21	79.13 37	93.32 35	91.99 37	94.12 40	91.49 40

ACMP	80.00 8	90.12 16	92.30 23	87.58 19	90.83 33	91.10 12	94.96 27	76.06 39	87.47 51	85.33 41	88.91 88	97.65 18	82.13 23	95.31 17	93.44 20	96.14 10	92.22 30
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		89.02 32	91.44 37	86.20 28	94.88 1	89.84 32	94.76 28	77.45 28	85.41 72	74.79 115	88.83 89	88.90 144	78.67 41	96.06 7	95.45 4	96.66 3	95.58 2

DeepC-MVS	83.59 4	90.37 12	92.56 16	87.82 16	91.26 26	92.33 3	94.72 29	80.04 7	90.01 31	84.61 45	93.33 22	94.22 91	80.59 29	92.90 42	92.52 29	95.69 21	92.57 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
train_agg		86.67 50	87.73 70	85.43 35	91.51 18	82.72 82	94.47 30	74.22 50	81.71 106	81.54 83	89.20 84	92.87 105	78.33 43	90.12 76	88.47 65	92.51 63	89.04 59

SD-MVS		89.91 18	92.23 26	87.19 22	91.31 23	89.79 33	94.31 31	75.34 43	89.26 35	81.79 77	92.68 31	95.08 76	83.88 11	93.10 39	92.69 26	96.54 4	93.02 22

3Dnovator+	83.71 3	88.13 42	90.00 49	85.94 29	86.82 65	91.06 13	94.26 32	75.39 42	88.85 40	85.76 39	85.74 117	86.92 154	78.02 44	93.03 40	92.21 35	95.39 25	92.21 31

DeepPCF-MVS	81.61 6	87.95 45	90.29 48	85.22 38	87.48 60	90.01 30	93.79 33	73.54 51	88.93 37	83.89 54	89.40 80	90.84 131	80.26 33	90.62 73	90.19 50	92.36 64	92.03 32

PMVS	79.51 9	90.23 15	92.67 12	87.39 21	90.16 38	88.75 39	93.64 34	75.78 41	90.00 32	83.70 57	92.97 28	92.22 114	86.13 4	97.01 3	96.79 2	94.94 29	90.96 44
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LTVRE_ROB	86.82 1	91.55 3	94.43 3	88.19 11	83.19 104	86.35 62	93.60 35	78.79 17	95.48 4	91.79 2	93.08 26	97.21 23	86.34 3	97.06 2	96.27 3	95.46 23	95.56 3
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
WR-MVS		89.79 22	93.66 4	85.27 37	91.32 22	88.27 43	93.49 36	79.86 8	92.75 8	75.37 111	96.86 1	98.38 6	75.10 67	95.93 8	94.07 15	96.46 5	89.39 56

NCCC		86.74 49	87.97 69	85.31 36	90.64 34	87.25 54	93.27 37	74.59 46	86.50 60	83.72 56	75.92 178	92.39 112	77.08 51	91.72 52	90.68 45	92.57 62	91.30 42

CNVR-MVS		86.93 48	88.98 57	84.54 42	90.11 39	87.41 53	93.23 38	73.47 52	86.31 63	82.25 71	82.96 133	92.15 116	76.04 59	91.69 53	90.69 44	92.17 66	91.64 38

OMC-MVS		88.16 41	91.34 39	84.46 44	86.85 64	90.63 22	93.01 39	67.00 91	90.35 26	87.40 22	86.86 107	96.35 38	77.66 47	92.63 48	90.84 43	94.84 31	91.68 36

CDPH-MVS		86.66 51	88.52 60	84.48 43	89.61 43	88.27 43	92.86 40	72.69 55	80.55 122	82.71 67	86.92 106	93.32 101	75.55 64	91.00 67	89.85 52	93.47 45	89.71 53

DeepC-MVS_fast	81.78 5	87.38 46	89.64 51	84.75 39	89.89 41	90.70 21	92.74 41	74.45 47	86.02 65	82.16 74	86.05 113	91.99 121	75.84 62	91.16 62	90.44 46	93.41 46	91.09 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		89.02 32	91.69 33	85.91 30	89.72 42	90.81 19	92.56 42	71.69 58	90.83 19	87.24 23	89.71 76	92.07 118	78.37 42	94.43 28	92.59 28	95.86 13	91.35 41

ACMH+	79.05 11	89.62 25	93.08 7	85.58 32	88.58 51	89.26 36	92.18 43	74.23 49	93.55 7	82.66 68	92.32 41	98.35 8	80.29 31	95.28 18	92.34 32	95.52 22	90.43 47

WR-MVS_H		88.99 34	93.28 5	83.99 50	91.92 11	89.13 37	91.95 44	83.23 1	90.14 29	71.92 131	95.85 4	98.01 13	71.83 104	95.82 9	93.19 23	93.07 53	90.83 46

MVS_0304		84.73 70	86.19 83	83.02 54	88.32 53	86.71 58	91.55 45	70.87 62	73.79 164	82.88 66	85.13 122	93.35 100	72.55 99	88.62 86	87.69 71	91.93 68	88.05 68

ACMH	78.40 12	88.94 36	92.62 14	84.65 40	86.45 67	87.16 55	91.47 46	68.79 78	95.49 3	89.74 6	93.55 20	98.50 3	77.96 45	94.14 33	89.57 57	93.49 44	89.94 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP-MVS		85.02 66	86.41 80	83.40 51	89.19 46	86.59 59	91.28 47	71.60 59	82.79 92	83.48 64	78.65 154	93.54 98	72.55 99	86.49 103	85.89 89	92.28 65	90.95 45

PCF-MVS	76.59 14	84.11 74	85.27 97	82.76 61	86.12 70	88.30 42	91.24 48	69.10 75	82.36 98	84.45 46	77.56 159	90.40 135	72.91 98	85.88 110	83.88 105	92.72 58	88.53 64
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TSAR-MVS + ACMM		89.14 28	92.11 28	85.67 31	89.27 45	90.61 23	90.98 49	79.48 11	88.86 39	79.80 91	93.01 27	93.53 99	83.17 16	92.75 46	92.45 30	91.32 75	93.59 12

DTE-MVSNet		88.99 34	92.77 11	84.59 41	93.31 2	88.10 46	90.96 50	83.09 2	91.38 12	76.21 105	96.03 2	98.04 11	70.78 113	95.65 14	92.32 33	93.18 50	87.84 69

CNLPA		85.50 60	88.58 58	81.91 65	84.55 82	87.52 52	90.89 51	63.56 136	88.18 45	84.06 50	83.85 130	91.34 128	76.46 55	91.27 59	89.00 62	91.96 67	88.88 61

PEN-MVS		88.86 37	92.92 8	84.11 49	92.92 5	88.05 48	90.83 52	82.67 5	91.04 16	74.83 114	95.97 3	98.47 4	70.38 114	95.70 13	92.43 31	93.05 54	88.78 62

PHI-MVS		86.37 53	88.14 66	84.30 45	86.65 66	87.56 51	90.76 53	70.16 65	82.55 94	89.65 7	84.89 125	92.40 111	75.97 60	90.88 71	89.70 54	92.58 60	89.03 60

TAPA-MVS	78.00 13	85.88 57	88.37 62	82.96 57	84.69 79	88.62 40	90.62 54	64.22 127	89.15 36	88.05 16	78.83 152	93.71 94	76.20 58	90.11 77	88.22 68	94.00 41	89.97 51
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Vis-MVSNet		83.32 85	88.12 67	77.71 115	77.91 164	83.44 79	90.58 55	69.49 71	81.11 118	67.10 154	89.85 74	91.48 126	71.71 105	91.34 58	89.37 58	89.48 94	90.26 48
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CSCG		88.12 43	91.45 36	84.23 46	88.12 57	90.59 24	90.57 56	68.60 80	91.37 13	83.45 65	89.94 73	95.14 75	78.71 39	91.45 57	88.21 69	95.96 12	93.44 17

AdaColmap		84.15 73	85.14 100	83.00 56	89.08 47	87.14 56	90.56 57	70.90 61	82.40 97	80.41 87	73.82 190	84.69 162	75.19 66	91.58 55	89.90 51	91.87 69	86.48 77

v52		86.26 55	90.85 41	80.91 74	72.49 191	81.25 105	90.55 58	60.30 172	90.43 25	87.24 23	94.64 11	98.30 10	83.16 18	92.86 44	86.82 80	91.69 70	91.65 37

V4		86.26 55	90.85 41	80.91 74	72.49 191	81.25 105	90.55 58	60.31 171	90.44 24	87.23 25	94.64 11	98.31 9	83.17 16	92.87 43	86.82 80	91.69 70	91.64 38

PS-CasMVS		89.07 31	93.23 6	84.21 47	92.44 8	88.23 45	90.54 60	82.95 3	90.50 22	75.31 112	95.80 5	98.37 7	71.16 107	96.30 5	93.32 22	92.88 55	90.11 50

SixPastTwentyTwo		89.14 28	92.19 27	85.58 32	84.62 80	82.56 85	90.53 61	71.93 57	91.95 10	85.89 37	94.22 14	97.25 22	85.42 5	95.73 12	91.71 40	95.08 28	91.89 34

MVS_111021_HR		83.95 75	86.10 85	81.44 70	84.62 80	80.29 117	90.51 62	68.05 86	84.07 83	80.38 88	84.74 126	91.37 127	74.23 76	90.37 75	87.25 73	90.86 82	84.59 87

TSAR-MVS + COLMAP		85.51 59	88.36 63	82.19 63	86.05 71	87.69 50	90.50 63	70.60 64	86.40 61	82.33 69	89.69 77	92.52 109	74.01 82	87.53 94	86.84 79	89.63 91	87.80 70

abl_6				79.30 102	84.98 78	85.78 65	90.50 63	66.88 92	77.08 150	74.02 120	73.29 193	89.34 139	68.94 122			90.49 84	85.98 80

CP-MVSNet		88.71 39	92.63 13	84.13 48	92.39 9	88.09 47	90.47 65	82.86 4	88.79 41	75.16 113	94.87 7	97.68 17	71.05 109	96.16 6	93.18 24	92.85 56	89.64 54

MCST-MVS		84.79 69	86.48 78	82.83 60	87.30 61	87.03 57	90.46 66	69.33 74	83.14 88	82.21 73	81.69 141	92.14 117	75.09 68	87.27 97	84.78 98	92.58 60	89.30 57

gm-plane-assit		71.56 177	69.99 190	73.39 137	84.43 84	73.21 171	90.42 67	51.36 212	84.08 82	76.00 107	91.30 57	37.09 238	59.01 159	73.65 198	70.24 195	79.09 188	60.37 211

CANet		82.84 94	84.60 108	80.78 78	87.30 61	85.20 69	90.23 68	69.00 76	72.16 172	78.73 99	84.49 127	90.70 133	69.54 119	87.65 93	86.17 85	89.87 89	85.84 82

CLD-MVS		82.75 98	87.22 75	77.54 119	88.01 58	85.76 66	90.23 68	54.52 198	82.28 99	82.11 75	88.48 90	95.27 69	63.95 141	89.41 80	88.29 67	86.45 150	81.01 127
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RPSCF		88.05 44	92.61 15	82.73 62	84.24 85	88.40 41	90.04 70	66.29 95	91.46 11	82.29 70	88.93 87	96.01 50	79.38 34	95.15 21	94.90 6	94.15 38	93.40 19

v7n		87.11 47	90.46 47	83.19 53	85.22 76	83.69 76	90.03 71	68.20 85	91.01 17	86.71 35	94.80 8	98.46 5	77.69 46	91.10 64	85.98 87	91.30 76	88.19 65

MVS_111021_LR		83.20 89	85.33 95	80.73 82	82.88 109	78.23 129	89.61 72	65.23 113	82.08 101	81.19 85	85.31 120	92.04 120	75.22 65	89.50 79	85.90 88	90.24 85	84.23 91

v1192		83.61 79	85.23 98	81.72 67	84.05 87	82.15 89	89.54 73	66.20 96	81.38 114	86.76 34	91.79 47	96.03 49	74.88 70	81.81 162	80.92 134	88.91 99	82.50 113

v1921920		83.49 82	84.94 104	81.80 66	83.78 92	81.20 108	89.50 74	65.91 102	81.64 108	87.18 27	91.70 48	95.39 68	75.85 61	81.56 165	80.27 142	88.60 103	82.80 109

v1240		83.57 81	84.94 104	81.97 64	84.05 87	81.27 104	89.46 75	66.06 99	81.31 116	87.50 21	91.88 46	95.46 67	76.25 57	81.16 167	80.51 139	88.52 105	82.98 107

v144192		83.43 83	84.97 103	81.63 69	83.43 96	81.23 107	89.42 76	66.04 100	81.45 113	86.40 36	91.46 52	95.70 61	75.76 63	82.14 158	80.23 143	88.74 100	82.57 112

MAR-MVS		81.98 108	82.92 133	80.88 77	85.18 77	85.85 64	89.13 77	69.52 69	71.21 176	82.25 71	71.28 200	88.89 145	69.69 116	88.71 85	86.96 75	89.52 93	87.57 72
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
TSAR-MVS + GP.		85.32 62	87.41 74	82.89 59	90.07 40	85.69 67	89.07 78	72.99 54	82.45 96	74.52 118	85.09 123	87.67 151	79.24 35	91.11 63	90.41 47	91.45 73	89.45 55

v1144		83.22 88	85.01 101	81.14 71	83.76 93	81.60 98	88.95 79	65.58 108	81.89 102	85.80 38	91.68 49	95.84 53	74.04 81	82.12 159	80.56 138	88.70 102	81.41 123

PLC	76.06 15	85.38 61	87.46 72	82.95 58	85.79 73	88.84 38	88.86 80	68.70 79	87.06 56	83.60 59	79.02 150	90.05 136	77.37 50	90.88 71	89.66 55	93.37 47	86.74 76
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7		82.76 96	84.65 107	80.55 87	83.27 101	81.77 93	88.66 81	65.10 114	79.23 138	83.60 59	91.47 51	95.47 65	74.12 77	82.61 152	80.66 135	88.52 105	81.35 124

v10		83.17 90	85.22 99	80.78 78	83.26 102	82.99 81	88.66 81	66.49 94	79.24 137	83.60 59	91.46 52	95.47 65	74.12 77	82.60 153	80.66 135	88.53 104	84.11 93

v13		83.75 78	86.20 82	80.89 76	83.38 98	81.93 91	88.58 83	66.09 98	83.55 85	84.28 47	92.67 32	96.79 27	74.67 72	84.42 132	79.72 147	88.36 107	84.31 90

v12		83.59 80	86.00 88	80.77 81	83.30 100	81.83 92	88.45 84	65.95 101	83.20 87	84.15 48	92.54 37	96.71 28	74.50 74	84.19 134	79.64 148	88.30 108	83.93 95

Effi-MVS+-dtu		82.04 107	83.39 131	80.48 89	85.48 75	86.57 61	88.40 85	68.28 84	69.04 184	73.13 125	76.26 170	91.11 130	74.74 71	88.40 88	87.76 70	92.84 57	84.57 88

EPNet		79.36 128	79.44 145	79.27 103	89.51 44	77.20 139	88.35 86	77.35 32	68.27 186	74.29 119	76.31 168	79.22 176	59.63 156	85.02 125	85.45 92	86.49 149	84.61 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v11		83.30 86	85.58 93	80.64 84	83.53 95	81.74 94	88.30 87	65.46 110	82.75 93	84.63 44	92.49 38	96.17 45	73.90 83	82.69 151	79.59 149	88.04 116	83.66 97

V9		83.42 84	85.81 90	80.63 85	83.20 103	81.73 95	88.29 88	65.78 105	82.87 91	83.99 53	92.38 39	96.60 30	74.30 75	83.93 135	79.58 150	88.24 111	83.55 99

V14		83.23 87	85.59 92	80.48 89	83.09 106	81.63 97	88.13 89	65.61 107	82.53 95	83.81 55	92.17 42	96.50 31	74.07 80	83.66 137	79.51 152	88.17 113	83.16 103

UniMVSNet (Re)		84.95 67	88.53 59	80.78 78	87.82 59	84.21 72	88.03 90	76.50 36	81.18 117	69.29 141	92.63 35	96.83 26	69.07 121	91.23 61	89.60 56	93.97 42	84.00 94

PVSNet_Blended_VisFu		83.00 92	84.16 119	81.65 68	82.17 123	86.01 63	88.03 90	71.23 60	76.05 156	79.54 94	83.88 129	83.44 163	77.49 49	87.38 95	84.93 97	91.41 74	87.40 74

v15		83.06 91	85.39 94	80.35 92	83.01 107	81.53 99	87.98 92	65.47 109	82.19 100	83.66 58	92.00 43	96.40 37	73.87 84	83.39 139	79.44 153	88.10 115	82.76 110

Baseline_NR-MVSNet		82.79 95	86.51 77	78.44 112	88.30 54	75.62 160	87.81 93	74.97 44	81.53 110	66.84 155	94.71 10	96.46 33	66.90 132	91.79 50	83.37 113	85.83 162	82.09 118

Fast-Effi-MVS+		81.42 116	83.82 126	78.62 110	82.24 122	80.62 110	87.72 94	63.51 137	73.01 165	74.75 116	83.80 131	92.70 107	73.44 87	88.15 92	85.26 93	90.05 86	83.17 102

v2v482		82.20 104	84.26 116	79.81 98	82.67 113	80.18 119	87.67 95	63.96 133	81.69 107	84.73 43	91.27 58	96.33 40	72.05 103	81.94 161	79.56 151	87.79 122	78.84 144

v1141		82.26 101	84.32 112	79.85 96	82.86 110	80.31 115	87.58 96	63.48 138	81.86 105	84.03 52	91.33 55	96.28 43	73.23 94	82.39 154	79.08 164	87.93 119	78.97 142

divwei89l23v2f112		82.26 101	84.32 112	79.85 96	82.86 110	80.31 115	87.58 96	63.48 138	81.88 103	84.05 51	91.33 55	96.27 44	73.23 94	82.39 154	79.08 164	87.93 119	78.97 142

v1		82.27 100	84.32 112	79.87 95	82.86 110	80.32 114	87.57 98	63.47 140	81.87 104	84.13 49	91.34 54	96.29 42	73.23 94	82.39 154	79.08 164	87.94 118	78.98 141

Effi-MVS+		82.33 99	83.87 125	80.52 88	84.51 83	81.32 101	87.53 99	68.05 86	74.94 161	79.67 93	82.37 137	92.31 113	72.21 101	85.06 119	86.91 77	91.18 77	84.20 92

pmmvs-eth3d		79.64 125	82.06 138	76.83 120	80.05 139	72.64 173	87.47 100	66.59 93	80.83 119	73.50 122	89.32 82	93.20 102	67.78 127	80.78 170	81.64 128	85.58 165	76.01 153

TranMVSNet+NR-MVSNet		85.23 63	89.38 54	80.39 91	88.78 50	83.77 75	87.40 101	76.75 33	85.47 70	68.99 144	95.18 6	97.55 19	67.13 131	91.61 54	89.13 61	93.26 48	82.95 108

v1neww		81.76 111	83.95 123	79.21 105	82.41 115	80.46 111	87.26 102	62.93 146	79.28 135	81.62 80	91.06 63	95.72 59	73.31 90	82.83 145	79.22 158	87.73 124	79.07 138

v7new		81.76 111	83.95 123	79.21 105	82.41 115	80.46 111	87.26 102	62.93 146	79.28 135	81.62 80	91.06 63	95.72 59	73.31 90	82.83 145	79.22 158	87.73 124	79.07 138

v17		82.09 106	84.45 110	79.33 101	82.41 115	81.31 102	87.26 102	64.50 124	78.72 140	80.73 86	90.90 66	95.57 62	73.37 88	83.06 140	79.25 157	87.70 127	82.35 116

v8		82.20 104	84.56 109	79.45 99	82.42 114	81.65 96	87.26 102	64.27 125	79.36 133	81.70 78	91.04 65	95.75 56	73.30 92	82.82 147	79.18 161	87.74 123	82.09 118

v6		81.77 110	83.96 122	79.22 104	82.41 115	80.45 113	87.26 102	62.91 150	79.29 134	81.65 79	91.08 61	95.74 57	73.32 89	82.84 144	79.21 160	87.73 124	79.07 138

v16		81.92 109	84.32 112	79.12 107	82.31 120	81.29 103	87.20 107	64.51 123	78.16 144	79.76 92	90.86 67	95.23 71	73.29 93	83.05 141	79.29 156	87.63 128	82.34 117

3Dnovator	79.41 10	82.21 103	86.07 86	77.71 115	79.31 148	84.61 70	87.18 108	61.02 168	85.65 68	76.11 106	85.07 124	85.38 160	70.96 111	87.22 98	86.47 83	91.66 72	88.12 67

PM-MVS		80.42 122	83.63 128	76.67 122	78.04 159	72.37 175	87.14 109	60.18 174	80.13 125	71.75 132	86.12 112	93.92 93	77.08 51	86.56 102	85.12 95	85.83 162	81.18 125

NR-MVSNet		82.89 93	87.43 73	77.59 118	83.91 90	83.59 77	87.10 110	78.35 18	80.64 120	68.85 145	92.67 32	96.50 31	54.19 181	87.19 100	88.68 63	93.16 52	82.75 111

UniMVSNet_NR-MVSNet		84.62 71	88.00 68	80.68 83	88.18 56	83.83 74	87.06 111	76.47 37	81.46 112	70.49 137	93.24 23	95.56 64	68.13 125	90.43 74	88.47 65	93.78 43	83.02 105

DU-MVS		84.88 68	88.27 65	80.92 73	88.30 54	83.59 77	87.06 111	78.35 18	80.64 120	70.49 137	92.67 32	96.91 25	68.13 125	91.79 50	89.29 60	93.20 49	83.02 105

MSLP-MVS++		86.29 54	89.10 56	83.01 55	85.71 74	89.79 33	87.04 113	74.39 48	85.17 74	78.92 98	77.59 158	93.57 97	82.60 20	93.23 36	91.88 39	89.42 95	92.46 27

v18		81.62 114	83.99 121	78.86 108	82.08 124	81.12 109	86.93 114	64.24 126	77.44 146	79.47 95	90.53 68	94.99 79	72.99 97	82.72 150	79.18 161	87.48 131	81.91 121

EG-PatchMatch MVS		84.35 72	87.55 71	80.62 86	86.38 68	82.24 88	86.75 115	64.02 131	84.24 80	78.17 102	89.38 81	95.03 78	78.78 38	89.95 78	86.33 84	89.59 92	85.65 83

UGNet		79.62 126	85.91 89	72.28 142	73.52 184	83.91 73	86.64 116	69.51 70	79.85 128	62.57 167	85.82 116	89.63 137	53.18 188	88.39 89	87.35 72	88.28 110	86.43 78
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
V42		79.59 127	83.59 129	74.93 132	69.61 203	77.05 142	86.59 117	55.84 194	78.42 143	77.29 103	89.84 75	95.08 76	74.12 77	83.05 141	80.11 145	86.12 154	81.59 122

FPMVS		81.56 115	84.04 120	78.66 109	82.92 108	75.96 155	86.48 118	65.66 106	84.67 77	71.47 133	77.78 156	83.22 165	77.57 48	91.24 60	90.21 49	87.84 121	85.21 85

Anonymous20240521		83.87 76	89.96 50	76.76 121	87.27 63	82.39 87	86.26 119	69.89 67	88.91 38	60.94 170	94.18 16	97.20 24	63.54 144	93.18 37	88.64 64	92.63 59	85.22 84

FC-MVSNet-train		79.20 130	86.29 81	70.94 149	84.06 86	77.67 133	85.68 120	64.11 130	82.90 90	52.22 197	92.57 36	93.69 95	49.52 205	88.30 90	86.93 76	90.03 87	81.95 120

IS_MVSNet		81.72 113	85.01 101	77.90 114	86.19 69	82.64 84	85.56 121	70.02 66	80.11 126	63.52 162	87.28 100	81.18 172	67.26 129	91.08 66	89.33 59	94.82 32	83.42 101

TinyColmap		83.79 77	86.12 84	81.07 72	83.42 97	81.44 100	85.42 122	68.55 81	88.71 42	89.46 8	87.60 96	92.72 106	70.34 115	89.29 81	81.94 126	89.20 96	81.12 126

EPP-MVSNet		82.76 96	86.47 79	78.45 111	86.00 72	84.47 71	85.39 123	68.42 82	84.17 81	62.97 165	89.26 83	76.84 187	72.13 102	92.56 49	90.40 48	95.76 20	87.56 73

Gipuma		86.47 52	89.25 55	83.23 52	83.88 91	78.78 125	85.35 124	68.42 82	92.69 9	89.03 12	91.94 44	96.32 41	81.80 25	94.45 27	86.86 78	90.91 80	83.69 96
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FC-MVSNet-test		75.91 148	83.59 129	66.95 184	76.63 179	69.07 186	85.33 125	64.97 117	84.87 76	41.95 219	93.17 24	87.04 153	47.78 208	91.09 65	85.56 91	85.06 170	74.34 159

MSDG		81.39 117	84.23 118	78.09 113	82.40 119	82.47 86	85.31 126	60.91 169	79.73 129	80.26 90	86.30 110	88.27 149	69.67 117	87.20 99	84.98 96	89.97 88	80.67 128

v748		85.21 64	89.62 52	80.08 93	80.71 133	80.27 118	85.05 127	63.79 134	90.47 23	83.54 62	94.21 15	98.52 2	76.84 53	90.97 69	84.25 102	90.53 83	88.62 63

Fast-Effi-MVS+-dtu		76.92 139	77.18 160	76.62 123	79.55 145	79.17 122	84.80 128	77.40 30	64.46 206	68.75 147	70.81 206	86.57 155	63.36 148	81.74 163	81.76 127	85.86 161	75.78 155

DELS-MVS		79.71 124	83.74 127	75.01 130	79.31 148	82.68 83	84.79 129	60.06 175	75.43 159	69.09 143	86.13 111	89.38 138	67.16 130	85.12 118	83.87 106	89.65 90	83.57 98
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
MIMVSNet1		73.40 159	81.85 139	63.55 198	72.90 188	64.37 201	84.58 130	53.60 204	90.84 18	53.92 186	87.75 95	96.10 46	45.31 211	85.37 116	79.32 155	70.98 205	69.18 188

ambc			88.38 61		91.62 16	87.97 49	84.48 131		88.64 43	87.93 17	87.38 98	94.82 83	74.53 73	89.14 83	83.86 107	85.94 160	86.84 75

pmmvs4		75.92 147	77.48 158	74.10 135	78.21 158	70.94 177	84.06 132	64.78 118	75.13 160	68.47 149	84.12 128	83.32 164	64.74 140	75.93 189	79.14 163	84.31 173	73.77 167

MDA-MVSNet-bldmvs		76.51 141	82.87 134	69.09 165	50.71 235	74.72 165	84.05 133	60.27 173	81.62 109	71.16 135	88.21 92	91.58 123	69.62 118	92.78 45	77.48 173	78.75 189	73.69 169

CANet_DTU		75.04 151	78.45 148	71.07 145	77.27 169	77.96 130	83.88 134	58.00 186	64.11 207	68.67 148	75.65 180	88.37 148	53.92 183	82.05 160	81.11 131	84.67 171	79.88 135

our_test_3					73.27 185	70.91 178	83.26 135

Anonymous20231211		85.16 65	91.64 34	77.61 117	88.54 52	79.81 121	83.12 136	74.68 45	98.37 1	66.79 156	94.56 13	99.60 1	61.64 151	91.49 56	89.82 53	90.91 80	87.80 70

HyFIR lowres test		73.29 160	74.14 181	72.30 141	73.08 187	78.33 128	83.12 136	62.41 158	63.81 208	62.13 168	76.67 167	78.50 179	71.09 108	74.13 193	77.47 174	81.98 182	70.10 182

IB-MVS	71.28 17	75.21 150	77.00 163	73.12 140	76.76 173	77.45 135	83.05 138	58.92 181	63.01 211	64.31 161	59.99 229	87.57 152	68.64 123	86.26 107	82.34 124	87.05 142	82.36 115
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
CVMVSNet		75.65 149	77.62 157	73.35 139	71.95 195	69.89 182	83.04 139	60.84 170	69.12 182	68.76 146	79.92 148	78.93 178	73.64 86	81.02 168	81.01 133	81.86 183	83.43 100

USDC		81.39 117	83.07 132	79.43 100	81.48 129	78.95 124	82.62 140	66.17 97	87.45 52	90.73 4	82.40 136	93.65 96	66.57 134	83.63 138	77.97 168	89.00 98	77.45 151

EPNet_dtu		71.90 175	73.03 185	70.59 152	78.28 156	61.64 206	82.44 141	64.12 129	63.26 210	69.74 139	71.47 198	82.41 167	51.89 201	78.83 177	78.01 167	77.07 191	75.60 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDTV_nov1_ep13_2view		72.96 168	75.59 173	69.88 159	71.15 200	64.86 200	82.31 142	54.45 199	76.30 154	78.32 101	86.52 108	91.58 123	61.35 153	76.80 183	66.83 203	71.70 200	66.26 195

v148		79.33 129	82.32 137	75.84 126	80.14 138	75.74 157	81.98 143	57.06 190	81.51 111	79.36 97	89.42 79	96.42 35	71.32 106	81.54 166	75.29 182	85.20 168	76.32 152

EU-MVSNet		76.48 142	80.53 142	71.75 143	67.62 208	70.30 180	81.74 144	54.06 201	75.47 158	71.01 136	80.10 145	93.17 104	73.67 85	83.73 136	77.85 169	82.40 181	83.07 104

Vis-MVSNet (Re-imp)		76.15 145	80.84 141	70.68 151	83.66 94	74.80 164	81.66 145	69.59 68	80.48 123	46.94 213	87.44 97	80.63 174	53.14 189	86.87 101	84.56 100	89.12 97	71.12 178

DI_MVS_plusplus_trai		77.64 136	79.64 144	75.31 129	79.87 143	76.89 143	81.55 146	63.64 135	76.21 155	72.03 130	85.59 119	82.97 166	66.63 133	79.27 175	77.78 170	88.14 114	78.76 145

testgi		68.20 189	76.05 171	59.04 207	79.99 140	67.32 194	81.16 147	51.78 210	84.91 75	39.36 227	73.42 191	95.19 72	32.79 224	76.54 187	70.40 194	69.14 209	64.55 199

PatchMatch-RL		76.05 146	76.64 166	75.36 128	77.84 165	69.87 183	81.09 148	63.43 141	71.66 174	68.34 150	71.70 196	81.76 171	74.98 69	84.83 128	83.44 109	86.45 150	73.22 171

tpmp4_e23		68.32 188	66.04 203	70.98 147	77.52 168	69.23 185	80.99 149	65.46 110	68.09 187	69.25 142	70.77 208	54.03 227	59.35 157	69.01 213	63.02 210	73.34 197	68.15 190

IterMVS		73.62 158	76.53 167	70.23 156	71.83 196	77.18 140	80.69 150	53.22 206	72.23 171	66.62 157	85.21 121	78.96 177	69.54 119	76.28 188	71.63 191	79.45 186	74.25 162
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
canonicalmvs		81.22 119	86.04 87	75.60 127	83.17 105	83.18 80	80.29 151	65.82 104	85.97 67	67.98 152	77.74 157	91.51 125	65.17 137	88.62 86	86.15 86	91.17 78	89.09 58

GA-MVS		75.01 152	76.39 168	73.39 137	78.37 155	75.66 159	80.03 152	58.40 184	70.51 178	75.85 108	83.24 132	76.14 191	63.75 142	77.28 182	76.62 177	83.97 174	75.30 158

OpenMVS	75.38 16	78.44 133	81.39 140	74.99 131	80.46 135	79.85 120	79.99 153	58.31 185	77.34 148	73.85 121	77.19 163	82.33 170	68.60 124	84.67 129	81.95 125	88.72 101	86.40 79

MVS_Test		76.72 140	79.40 146	73.60 136	78.85 153	74.99 162	79.91 154	61.56 165	69.67 180	72.44 126	85.98 114	90.78 132	63.50 146	78.30 178	75.74 181	85.33 167	80.31 133

IterMVS-LS		79.79 123	82.56 135	76.56 124	81.83 127	77.85 132	79.90 155	69.42 73	78.93 139	71.21 134	90.47 69	85.20 161	70.86 112	80.54 172	80.57 137	86.15 153	84.36 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
conf0.05thres1000		77.12 138	82.38 136	70.98 147	82.30 121	77.95 131	79.86 156	64.74 119	86.63 59	53.93 185	85.74 117	75.63 197	56.85 165	88.98 84	84.10 103	88.20 112	77.61 150

QAPM		80.43 121	84.34 111	75.86 125	79.40 147	82.06 90	79.86 156	61.94 163	83.28 86	74.73 117	81.74 140	85.44 159	70.97 110	84.99 126	84.71 99	88.29 109	88.14 66

PVSNet_BlendedMVS		76.45 143	78.12 151	74.49 133	76.76 173	78.46 126	79.65 158	63.26 143	65.42 202	73.15 123	75.05 184	88.96 142	66.51 135	82.73 148	77.66 171	87.61 129	78.60 146

PVSNet_Blended		76.45 143	78.12 151	74.49 133	76.76 173	78.46 126	79.65 158	63.26 143	65.42 202	73.15 123	75.05 184	88.96 142	66.51 135	82.73 148	77.66 171	87.61 129	78.60 146

CDS-MVSNet		73.07 166	77.02 162	68.46 168	81.62 128	72.89 172	79.56 160	70.78 63	69.56 181	52.52 194	77.37 162	81.12 173	42.60 214	84.20 133	83.93 104	83.65 176	70.07 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CostFormer		66.81 194	66.94 201	66.67 186	72.79 189	68.25 190	79.55 161	55.57 195	65.52 201	62.77 166	76.98 164	60.09 216	56.73 167	65.69 223	62.35 211	72.59 198	69.71 184

diffmvs		73.65 157	77.10 161	69.63 161	73.21 186	69.52 184	79.35 162	57.48 187	73.80 163	68.08 151	87.10 103	82.39 168	61.36 152	74.27 192	74.51 183	78.31 190	78.14 148

tfpn		72.99 167	75.25 176	70.36 155	81.87 126	77.09 141	79.28 163	64.16 128	79.58 131	53.14 189	76.97 165	48.75 231	56.35 169	87.31 96	82.75 118	87.35 136	74.31 160

CHOSEN 1792x2688		68.80 186	71.09 188	66.13 190	69.11 205	68.89 188	78.98 164	54.68 196	61.63 218	56.69 175	71.56 197	78.39 180	67.69 128	72.13 204	72.01 190	69.63 208	73.02 172

view800		74.68 153	78.74 147	69.94 158	81.12 131	76.59 144	78.94 165	63.24 145	78.56 142	53.06 190	75.61 181	76.26 190	56.07 170	86.32 105	83.75 108	87.18 141	74.10 163

thres600view7		74.34 155	78.43 149	69.56 162	80.47 134	76.28 150	78.65 166	62.56 155	77.39 147	52.53 193	74.03 189	76.78 188	55.90 172	85.06 119	85.19 94	87.25 139	74.29 161

view600		74.08 156	78.15 150	69.32 164	80.27 137	75.82 156	78.27 167	62.20 159	77.26 149	52.80 192	74.07 188	76.86 186	55.57 175	84.90 127	84.43 101	86.84 143	73.71 168

FMVSNet1		78.20 135	84.83 106	70.46 154	78.62 154	79.03 123	77.90 168	67.53 90	83.02 89	55.10 181	87.19 102	93.18 103	55.65 173	85.57 111	83.39 110	87.98 117	82.40 114

pmmvs6		80.46 120	88.34 64	71.26 144	81.96 125	77.51 134	77.54 169	68.83 77	93.72 6	55.92 178	93.94 19	98.03 12	55.94 171	89.21 82	85.61 90	87.36 135	80.38 129

TransMVSNet (Re)		79.05 131	86.66 76	70.18 157	83.32 99	75.99 154	77.54 169	63.98 132	90.68 21	55.84 179	94.80 8	96.06 48	53.73 187	86.27 106	83.22 114	86.65 144	79.61 136

thres400		73.13 165	76.99 164	68.62 167	79.46 146	74.93 163	77.23 171	61.23 166	75.54 157	52.31 196	72.20 195	77.10 185	54.89 177	82.92 143	82.62 123	86.57 147	73.66 170

MS-PatchMatch		71.18 180	73.99 182	67.89 178	77.16 170	71.76 176	77.18 172	56.38 193	67.35 188	55.04 182	74.63 186	75.70 192	62.38 150	76.62 185	75.97 180	79.22 187	75.90 154

tfpn111		71.60 176	74.66 179	68.04 172	77.97 160	76.44 146	77.04 173	62.68 151	66.81 192	50.69 204	62.10 224	75.67 193	52.46 196	85.06 119	82.64 119	87.42 132	73.87 164

conf0.01		69.59 183	71.01 189	67.95 174	77.74 166	76.09 152	77.04 173	62.58 154	66.81 192	50.54 206	63.00 222	51.78 230	52.46 196	84.53 130	82.64 119	87.32 137	72.19 175

conf0.002		68.60 187	69.17 194	67.92 177	77.66 167	76.01 153	77.04 173	62.56 155	66.81 192	50.51 207	61.21 227	44.01 235	52.46 196	84.44 131	80.29 141	87.31 138	71.44 177

conf200view11		72.00 174	75.40 174	68.04 172	77.97 160	76.44 146	77.04 173	62.68 151	66.81 192	50.69 204	67.30 213	75.67 193	52.46 196	85.06 119	82.64 119	87.42 132	73.87 164

tfpn200view9		72.01 173	75.40 174	68.06 171	77.97 160	76.44 146	77.04 173	62.67 153	66.81 192	50.82 202	67.30 213	75.67 193	52.46 196	85.06 119	82.64 119	87.41 134	73.86 166

DWT-MVSNet_training		63.07 202	60.04 224	66.61 187	71.64 197	65.27 199	76.80 178	53.82 202	55.90 227	63.07 164	62.23 223	41.87 237	62.54 149	64.32 226	63.71 208	71.78 199	66.97 192

no-one		78.59 132	85.28 96	70.79 150	59.01 224	68.77 189	76.62 179	46.06 216	80.25 124	75.75 109	81.85 139	97.75 15	83.63 12	90.99 68	87.20 74	83.67 175	90.14 49

tfpn1000		72.27 172	76.88 165	66.88 185	79.01 152	74.04 166	76.60 180	61.15 167	79.65 130	45.52 215	77.41 161	67.98 210	52.47 195	85.22 117	82.99 115	86.54 148	70.89 179

tfpnnormal		77.16 137	84.26 116	68.88 166	81.02 132	75.02 161	76.52 181	63.30 142	87.29 53	52.40 195	91.24 59	93.97 92	54.85 179	85.46 114	81.08 132	85.18 169	75.76 156

tfpn_n400		73.26 161	77.94 153	67.79 179	79.91 141	73.32 168	76.38 182	62.04 160	84.26 78	48.53 209	76.23 171	71.50 204	53.83 184	86.22 108	81.59 129	86.05 155	72.47 173

tfpnconf		73.26 161	77.94 153	67.79 179	79.91 141	73.32 168	76.38 182	62.04 160	84.26 78	48.53 209	76.23 171	71.50 204	53.83 184	86.22 108	81.59 129	86.05 155	72.47 173

pm-mvs1		78.21 134	85.68 91	69.50 163	80.38 136	75.73 158	76.25 184	65.04 115	87.59 49	54.47 184	93.16 25	95.99 52	54.20 180	86.37 104	82.98 116	86.64 145	77.96 149

tfpnview11		72.88 169	77.37 159	67.65 181	79.81 144	73.43 167	76.23 185	61.97 162	81.37 115	48.53 209	76.23 171	71.50 204	53.78 186	85.45 115	82.77 117	85.56 166	70.87 181

tpm cat1		64.79 201	62.74 217	67.17 182	74.61 183	65.91 197	76.18 186	59.32 178	64.88 205	66.41 158	71.21 201	53.56 228	59.17 158	61.53 229	58.16 222	67.33 212	63.95 200

RPMNet		67.02 193	63.99 211	70.56 153	71.55 198	67.63 191	75.81 187	69.44 72	59.93 221	63.24 163	64.32 218	47.51 232	59.68 155	70.37 210	69.64 197	83.64 177	68.49 189

MDTV_nov1_ep13		64.96 199	64.77 208	65.18 196	67.08 211	62.46 205	75.80 188	51.10 213	62.27 217	69.74 139	74.12 187	62.65 213	55.64 174	68.19 215	62.16 215	71.70 200	61.57 210

GBi-Net		73.17 163	77.64 155	67.95 174	76.76 173	77.36 136	75.77 189	64.57 120	62.99 212	51.83 198	76.05 174	77.76 182	52.73 192	85.57 111	83.39 110	86.04 157	80.37 130

test1		73.17 163	77.64 155	67.95 174	76.76 173	77.36 136	75.77 189	64.57 120	62.99 212	51.83 198	76.05 174	77.76 182	52.73 192	85.57 111	83.39 110	86.04 157	80.37 130

FMVSNet2		74.43 154	79.70 143	68.27 169	76.76 173	77.36 136	75.77 189	65.36 112	72.28 170	52.97 191	81.92 138	85.61 158	52.73 192	80.66 171	79.73 146	86.04 157	80.37 130

MVSTER		68.08 191	69.73 192	66.16 189	66.33 215	70.06 181	75.71 192	52.36 208	55.18 230	58.64 172	70.23 210	56.72 222	57.34 164	79.68 174	76.03 179	86.61 146	80.20 134

test20.03		69.91 181	76.20 170	62.58 200	84.01 89	67.34 193	75.67 193	65.88 103	79.98 127	40.28 224	82.65 134	89.31 140	39.63 218	77.41 181	73.28 186	69.98 206	63.40 203

thres200		72.41 171	76.00 172	68.21 170	78.28 156	76.28 150	74.94 194	62.56 155	72.14 173	51.35 201	69.59 211	76.51 189	54.89 177	85.06 119	80.51 139	87.25 139	71.92 176

FMVSNet3		71.40 179	75.20 178	66.97 183	75.00 181	76.59 144	74.29 195	64.57 120	62.99 212	51.83 198	76.05 174	77.76 182	51.49 202	76.58 186	77.03 176	84.62 172	79.43 137

Anonymous20231206		67.28 192	73.41 184	60.12 206	76.45 180	63.61 204	74.21 196	56.52 192	76.35 153	42.23 218	75.81 179	90.47 134	41.51 217	74.52 190	69.97 196	69.83 207	63.17 204

testmv		60.72 212	68.44 198	51.71 222	61.76 219	56.70 217	73.40 197	42.24 221	67.31 190	39.54 226	70.88 204	92.49 110	28.75 227	73.83 196	66.00 204	64.56 218	51.89 225

test1235678		60.73 211	68.46 197	51.71 222	61.76 219	56.73 216	73.40 197	42.24 221	67.34 189	39.55 225	70.90 203	92.54 108	28.75 227	73.84 195	66.00 204	64.57 217	51.90 224

thres100view900		69.86 182	72.97 186	66.24 188	77.97 160	72.49 174	73.29 199	59.12 179	66.81 192	50.82 202	67.30 213	75.67 193	50.54 204	78.24 179	79.40 154	85.71 164	70.88 180

PatchmatchNet		64.81 200	63.74 213	66.06 192	69.21 204	58.62 211	73.16 200	60.01 176	65.92 198	66.19 159	76.27 169	59.09 217	60.45 154	66.58 220	61.47 218	67.33 212	58.24 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-mter		59.39 215	61.59 219	56.82 211	53.21 230	54.82 218	73.12 201	26.57 234	53.19 231	56.31 176	64.71 216	60.47 215	56.36 168	68.69 214	64.27 207	75.38 193	65.00 196

tfpn_ndepth		68.20 189	72.18 187	63.55 198	74.64 182	73.24 170	72.41 202	59.76 177	70.54 177	41.93 220	60.96 228	68.69 209	46.23 210	82.16 157	80.14 144	86.34 152	69.56 185

testpf		55.64 224	50.84 233	61.24 203	67.03 212	54.45 219	72.29 203	65.04 115	37.23 236	54.99 183	53.99 231	43.12 236	44.34 212	55.22 234	51.59 232	63.76 219	60.25 212

pmmvs5		68.91 185	74.35 180	62.56 201	67.45 210	66.78 195	71.70 204	51.47 211	67.17 191	56.25 177	82.41 135	88.59 146	47.21 209	73.21 202	74.23 184	81.30 184	68.03 191

tpm		62.79 205	63.25 214	62.26 202	70.09 202	53.78 220	71.65 205	47.31 215	65.72 200	76.70 104	80.62 142	56.40 224	48.11 207	64.20 227	58.54 220	59.70 224	63.47 202

thresconf0.02		66.71 195	68.28 200	64.89 197	76.83 172	70.38 179	71.62 206	58.90 182	77.64 145	47.04 212	62.10 224	46.01 233	51.32 203	78.85 176	76.09 178	83.62 178	66.85 193

dps		65.14 198	64.50 209	65.89 193	71.41 199	65.81 198	71.44 207	61.59 164	58.56 224	61.43 169	75.45 182	52.70 229	58.06 162	69.57 212	64.65 206	71.39 203	64.77 197

gg-mvs-nofinetune		72.68 170	75.21 177	69.73 160	81.48 129	69.04 187	70.48 208	76.67 34	86.92 57	67.80 153	88.06 93	64.67 212	42.12 216	77.60 180	73.65 185	79.81 185	66.57 194

PatchT		66.25 196	66.76 202	65.67 194	55.87 229	60.75 208	70.17 209	59.00 180	59.80 223	72.30 127	78.68 153	54.12 226	65.04 138	71.64 206	72.91 187	71.63 202	69.40 186

pmmvs3		62.72 206	68.71 196	55.74 213	50.74 234	57.10 213	70.05 210	28.82 232	61.57 220	57.39 174	71.19 202	85.73 156	53.96 182	73.36 201	69.43 198	73.47 196	62.55 206

MIMVSNet		63.02 203	69.02 195	56.01 212	68.20 206	59.26 210	70.01 211	53.79 203	71.56 175	41.26 223	71.38 199	82.38 169	36.38 220	71.43 208	67.32 201	66.45 214	59.83 213

1111		55.38 225	59.51 227	50.57 224	72.41 193	48.16 230	69.76 212	57.08 188	76.79 151	32.10 232	80.12 143	35.41 239	25.87 229	67.23 216	57.74 223	46.17 234	51.09 227

.test1245		43.71 232	44.35 234	42.95 229	72.41 193	48.16 230	69.76 212	57.08 188	76.79 151	32.10 232	80.12 143	35.41 239	25.87 229	67.23 216	1.08 236	0.48 239	1.68 236

TAMVS		63.02 203	69.30 193	55.70 214	70.12 201	56.89 214	69.63 214	45.13 217	70.23 179	38.00 229	77.79 155	75.15 198	42.60 214	74.48 191	72.81 189	68.70 210	57.75 218

LP		65.71 197	69.91 191	60.81 205	56.75 228	61.37 207	69.55 215	56.80 191	73.01 165	60.48 171	79.76 149	70.57 207	55.47 176	72.77 203	67.19 202	65.81 215	64.71 198

PMMVS		61.98 209	65.61 205	57.74 209	45.03 236	51.76 227	69.54 216	35.05 227	55.49 229	55.32 180	68.23 212	78.39 180	58.09 161	70.21 211	71.56 192	83.42 180	63.66 201

test-LLR		62.15 208	59.46 228	65.29 195	79.07 150	52.66 223	69.46 217	62.93 146	50.76 234	53.81 187	63.11 220	58.91 218	52.87 190	66.54 221	62.34 212	73.59 194	61.87 208

TESTMET0.1,1		57.21 219	59.46 228	54.60 217	50.95 233	52.66 223	69.46 217	26.91 233	50.76 234	53.81 187	63.11 220	58.91 218	52.87 190	66.54 221	62.34 212	73.59 194	61.87 208

CHOSEN 280x420		56.32 223	58.85 230	53.36 218	51.63 232	39.91 237	69.12 219	38.61 226	56.29 226	36.79 230	48.84 234	62.59 214	63.39 147	73.61 199	67.66 200	60.61 222	63.07 205

testus		57.41 218	64.98 207	48.58 227	59.39 223	57.17 212	68.81 220	32.86 229	62.32 216	43.25 217	57.59 230	88.49 147	24.19 233	71.68 205	63.20 209	62.99 220	54.42 221

test0.0.03 1		61.79 210	65.33 206	57.65 210	79.07 150	64.09 202	68.51 221	62.93 146	61.59 219	33.71 231	61.58 226	71.58 203	33.43 223	70.95 209	68.68 199	68.26 211	58.82 214

tpmrst		59.42 214	60.02 225	58.71 208	67.56 209	53.10 222	66.99 222	51.88 209	63.80 209	57.68 173	76.73 166	56.49 223	48.73 206	56.47 233	55.55 225	59.43 225	58.02 217

FMVSNet5		56.37 222	60.14 223	51.98 221	60.83 221	59.58 209	66.85 223	42.37 220	52.68 232	41.33 222	47.09 235	54.68 225	35.28 221	73.88 194	70.77 193	65.24 216	62.26 207

test12356		54.63 227	63.78 212	43.96 228	51.77 231	51.90 226	65.92 224	30.12 230	62.44 215	30.38 234	64.65 217	89.07 141	30.62 225	73.53 200	62.11 216	54.92 228	42.78 232

CR-MVSNet		69.56 184	68.34 199	70.99 146	72.78 190	67.63 191	64.47 225	67.74 88	59.93 221	72.30 127	80.10 145	56.77 221	65.04 138	71.64 206	72.91 187	83.61 179	69.40 186

Patchmtry						56.88 215	64.47 225	67.74 88		72.30 127

test2356		51.28 230	53.40 232	48.80 226	58.53 226	52.10 225	63.63 227	40.83 224	51.94 233	39.35 228	53.46 232	45.22 234	28.78 226	64.39 225	60.77 219	61.70 221	45.92 230

CMPMVS	55.74 18	71.56 177	76.26 169	66.08 191	68.11 207	63.91 203	63.17 228	50.52 214	68.79 185	75.49 110	70.78 207	85.67 157	63.54 144	81.58 164	77.20 175	75.63 192	85.86 81
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EMVS		58.97 217	62.63 218	54.70 216	66.26 216	48.71 229	61.74 229	42.71 219	72.80 169	46.00 214	73.01 194	71.66 201	57.91 163	80.41 173	50.68 233	53.55 231	41.11 234

EPMVS		56.62 221	59.77 226	52.94 219	62.41 218	50.55 228	60.66 230	52.83 207	65.15 204	41.80 221	77.46 160	57.28 220	42.68 213	59.81 231	54.82 226	57.23 227	53.35 222

E-PMN		59.07 216	62.79 216	54.72 215	67.01 213	47.81 233	60.44 231	43.40 218	72.95 167	44.63 216	70.42 209	73.17 200	58.73 160	80.97 169	51.98 230	54.14 230	42.26 233

ADS-MVSNet		56.89 220	61.09 220	52.00 220	59.48 222	48.10 232	58.02 232	54.37 200	72.82 168	49.19 208	75.32 183	65.97 211	37.96 219	59.34 232	54.66 227	52.99 232	51.42 226

N_pmnet		54.95 226	65.90 204	42.18 230	66.37 214	43.86 236	57.92 233	39.79 225	79.54 132	17.24 239	86.31 109	87.91 150	25.44 231	64.68 224	51.76 231	46.33 233	47.23 229

MVS-HIRNet		59.74 213	58.74 231	60.92 204	57.74 227	45.81 234	56.02 234	58.69 183	55.69 228	65.17 160	70.86 205	71.66 201	56.75 166	61.11 230	53.74 228	71.17 204	52.28 223

GG-mvs-BLEND		41.63 233	60.36 222	19.78 234	0.14 242	66.04 196	55.66 235	0.17 239	57.64 225	2.42 242	51.82 233	69.42 208	0.28 239	64.11 228	58.29 221	60.02 223	55.18 220

new-patchmatchnet		62.59 207	73.79 183	49.53 225	76.98 171	53.57 221	53.46 236	54.64 197	85.43 71	28.81 235	91.94 44	96.41 36	25.28 232	76.80 183	53.66 229	57.99 226	58.69 215

PMMVS2		48.13 231	64.06 210	29.55 233	44.06 237	36.69 238	51.95 237	29.97 231	74.75 162	8.90 241	76.02 177	91.24 129	7.53 235	73.78 197	55.91 224	34.87 236	40.01 235

new_pmnet		52.29 228	63.16 215	39.61 232	58.89 225	44.70 235	48.78 238	34.73 228	65.88 199	17.85 238	73.42 191	80.00 175	23.06 234	67.00 219	62.28 214	54.36 229	48.81 228

MVE	41.12 19	51.80 229	60.92 221	41.16 231	35.21 238	34.14 239	48.45 239	41.39 223	69.11 183	19.53 237	63.33 219	73.80 199	63.56 143	67.19 218	61.51 217	38.85 235	57.38 219
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX						17.78 240	20.40 240	6.69 235	31.41 237	9.80 240	38.61 236	34.88 241	33.78 222	28.41 236		23.59 237	45.77 231

tmp_tt				13.54 235	16.73 239	6.42 241	8.49 241	2.36 236	28.69 238	27.44 236	18.40 237	13.51 242	3.70 236	33.23 235	36.26 234	22.54 238

Patchmatch-RL test							4.13 242

testmvs		0.93 235	1.37 236	0.41 237	0.36 241	0.36 243	0.62 243	0.39 237	1.48 239	0.18 244	2.41 238	1.31 244	0.41 238	1.25 238	1.08 236	0.48 239	1.68 236

test123		1.06 234	1.41 235	0.64 236	0.39 240	0.48 242	0.52 244	0.25 238	1.11 240	1.37 243	2.01 239	1.98 243	0.87 237	1.43 237	1.27 235	0.46 241	1.62 238

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

MTAPA										89.37 9		94.85 81

MTMP										90.54 5		95.16 73

mPP-MVS					93.05 4							95.77 55

NP-MVS									78.65 141

High-resolution multi-view benchmark