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 1	93.00 1	93.98 16	96.01 48	87.53 1	97.69 1	96.81 1	97.33 1	95.34 4

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 55	94.95 4	91.27 3	94.11 15	97.77 13	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
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 3	91.79 2	93.08 24	97.21 22	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
ACMMPR		91.30 4	92.88 10	89.46 4	91.92 11	91.61 5	96.60 5	79.46 12	90.08 29	88.53 14	89.54 77	95.57 60	84.25 7	95.24 20	94.27 13	95.97 11	93.85 8

CP-MVS		91.09 5	92.33 21	89.65 2	92.16 10	90.41 26	96.46 10	80.38 6	88.26 42	89.17 11	87.00 104	96.34 37	83.95 10	95.77 11	94.72 8	95.81 17	93.78 10

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 56	88.75 13	89.00 85	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.
ACMM	80.67 7	90.67 7	92.46 17	88.57 8	91.35 21	89.93 31	96.34 12	77.36 30	90.17 27	86.88 31	87.32 99	96.63 27	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
ACMMP		90.63 8	92.40 18	88.56 9	91.24 27	91.60 6	96.49 9	77.53 25	87.89 44	86.87 32	87.24 101	96.46 31	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
LGP-MVS_train		90.56 9	92.38 19	88.43 10	90.88 31	91.15 11	95.35 22	77.65 24	86.26 62	87.23 25	90.45 68	97.35 19	83.20 15	95.44 16	93.41 21	96.28 8	92.63 25

PGM-MVS		90.42 10	91.58 34	89.05 6	91.77 13	91.06 13	96.51 7	78.94 15	85.41 70	87.67 19	87.02 103	95.26 68	83.62 13	95.01 24	93.94 16	95.79 19	93.40 19

zzz-MVS		90.38 11	91.35 37	89.25 5	93.08 3	86.59 59	96.45 11	79.00 14	90.23 26	89.30 10	85.87 115	94.97 78	82.54 21	95.05 23	94.83 7	95.14 27	91.94 33

DeepC-MVS	83.59 4	90.37 12	92.56 16	87.82 15	91.26 26	92.33 3	94.72 29	80.04 7	90.01 30	84.61 45	93.33 20	94.22 91	80.59 29	92.90 41	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
HFP-MVS		90.32 13	92.37 20	87.94 14	91.46 20	90.91 17	95.69 19	79.49 10	89.94 32	83.50 63	89.06 84	94.44 87	81.68 26	94.17 31	94.19 14	95.81 17	93.87 7

PMVS	79.51 9	90.23 14	92.67 12	87.39 21	90.16 38	88.75 39	93.64 34	75.78 41	90.00 31	83.70 57	92.97 26	92.22 117	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)
SMA-MVS		90.13 15	92.26 23	87.64 18	91.68 15	90.44 25	95.22 23	77.34 32	90.79 19	87.80 17	90.42 69	92.05 122	79.05 37	93.89 33	93.59 19	94.77 33	94.62 5

ACMP	80.00 8	90.12 16	92.30 22	87.58 19	90.83 33	91.10 12	94.96 27	76.06 39	87.47 49	85.33 41	88.91 87	97.65 17	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
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 60	85.32 42	88.23 91	94.67 82	82.08 24	95.13 22	93.88 17	94.72 35	93.59 12
Skip Steuart: Steuart Systems R&D Blog.
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 34	81.79 77	92.68 29	95.08 74	83.88 11	93.10 38	92.69 26	96.54 4	93.02 22

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 33	87.73 18	90.04 71	91.80 125	78.71 39	94.36 29	93.82 18	94.48 36	94.32 6

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 14	78.50 100	93.44 19	95.82 52	81.55 27	93.16 37	91.90 38	94.77 33	93.58 14

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 13	86.87 32	91.07 59	97.29 20	79.13 36	93.32 35	91.99 37	94.12 40	91.49 40

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 7	75.37 112	96.86 1	98.38 5	75.10 67	95.93 8	94.07 15	96.46 5	89.39 56

TSAR-MVS + MP.		89.67 23	92.25 24	86.65 25	91.53 17	90.98 16	96.15 14	73.30 52	87.88 45	81.83 76	92.92 27	95.15 72	82.23 22	93.58 34	92.25 34	94.87 30	93.01 23

CPTT-MVS		89.63 24	90.52 45	88.59 7	90.95 30	90.74 20	95.71 18	79.13 13	87.70 46	85.68 40	80.05 148	95.74 55	84.77 6	94.28 30	92.68 27	95.28 26	92.45 28

ACMH+	79.05 11	89.62 25	93.08 7	85.58 32	88.58 51	89.26 36	92.18 43	74.23 48	93.55 6	82.66 68	92.32 39	98.35 7	80.29 31	95.28 18	92.34 32	95.52 22	90.43 47

X-MVS		89.36 26	90.73 42	87.77 17	91.50 19	91.23 8	96.76 4	78.88 16	87.29 51	87.14 28	78.98 152	94.53 84	76.47 54	95.25 19	94.28 12	95.85 14	93.55 15

ESAPD		89.27 27	91.76 31	86.36 26	90.60 36	90.40 27	95.08 25	77.43 28	87.49 48	80.35 89	92.38 37	94.32 90	80.59 29	92.69 46	91.58 41	94.13 39	93.44 17

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 37	79.80 91	93.01 25	93.53 101	83.17 16	92.75 45	92.45 30	91.32 74	93.59 12

SixPastTwentyTwo		89.14 28	92.19 27	85.58 32	84.62 79	82.56 85	90.53 62	71.93 56	91.95 9	85.89 37	94.22 13	97.25 21	85.42 5	95.73 12	91.71 40	95.08 28	91.89 34

APD-MVS		89.14 28	91.25 39	86.67 24	91.73 14	91.02 15	95.50 21	77.74 23	84.04 83	79.47 95	91.48 48	94.85 79	81.14 28	92.94 40	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
PS-CasMVS		89.07 31	93.23 6	84.21 47	92.44 8	88.23 45	90.54 61	82.95 3	90.50 21	75.31 113	95.80 5	98.37 6	71.16 108	96.30 5	93.32 22	92.88 55	90.11 50

UA-Net		89.02 32	91.44 36	86.20 28	94.88 1	89.84 32	94.76 28	77.45 27	85.41 70	74.79 116	88.83 88	88.90 147	78.67 41	96.06 7	95.45 4	96.66 3	95.58 2

LS3D		89.02 32	91.69 33	85.91 30	89.72 42	90.81 19	92.56 42	71.69 57	90.83 18	87.24 23	89.71 75	92.07 120	78.37 42	94.43 28	92.59 28	95.86 13	91.35 41

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 11	76.21 106	96.03 2	98.04 10	70.78 114	95.65 14	92.32 33	93.18 50	87.84 69

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 28	71.92 132	95.85 4	98.01 12	71.83 104	95.82 9	93.19 23	93.07 53	90.83 46

ACMH	78.40 12	88.94 36	92.62 14	84.65 40	86.45 65	87.16 55	91.47 46	68.79 77	95.49 2	89.74 6	93.55 18	98.50 2	77.96 45	94.14 32	89.57 56	93.49 44	89.94 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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 15	74.83 115	95.97 3	98.47 3	70.38 115	95.70 13	92.43 31	93.05 54	88.78 62

HPM-MVS++		88.74 38	89.54 51	87.80 16	92.58 7	85.69 67	95.10 24	78.01 21	87.08 53	87.66 20	87.89 94	92.07 120	80.28 32	90.97 67	91.41 42	93.17 51	91.69 35

CP-MVSNet		88.71 39	92.63 13	84.13 48	92.39 9	88.09 47	90.47 66	82.86 4	88.79 39	75.16 114	94.87 7	97.68 16	71.05 110	96.16 6	93.18 24	92.85 56	89.64 54

HSP-MVS		88.32 40	90.71 43	85.53 34	90.63 35	92.01 4	96.15 14	77.52 26	86.02 63	81.39 84	90.21 70	96.08 45	76.38 56	88.30 89	86.70 80	91.12 78	95.64 1

OMC-MVS		88.16 41	91.34 38	84.46 44	86.85 62	90.63 22	93.01 39	67.00 91	90.35 25	87.40 22	86.86 107	96.35 36	77.66 47	92.63 47	90.84 43	94.84 31	91.68 36

3Dnovator+	83.71 3	88.13 42	90.00 48	85.94 29	86.82 63	91.06 13	94.26 32	75.39 42	88.85 38	85.76 39	85.74 117	86.92 157	78.02 44	93.03 39	92.21 35	95.39 25	92.21 31

CSCG		88.12 43	91.45 35	84.23 46	88.12 56	90.59 24	90.57 57	68.60 79	91.37 12	83.45 65	89.94 72	95.14 73	78.71 39	91.45 55	88.21 67	95.96 12	93.44 17

RPSCF		88.05 44	92.61 15	82.73 62	84.24 84	88.40 41	90.04 71	66.29 95	91.46 10	82.29 70	88.93 86	96.01 48	79.38 34	95.15 21	94.90 6	94.15 38	93.40 19

DeepPCF-MVS	81.61 6	87.95 45	90.29 47	85.22 38	87.48 59	90.01 30	93.79 33	73.54 50	88.93 36	83.89 54	89.40 79	90.84 134	80.26 33	90.62 71	90.19 50	92.36 63	92.03 32

DeepC-MVS_fast	81.78 5	87.38 46	89.64 49	84.75 39	89.89 41	90.70 21	92.74 41	74.45 46	86.02 63	82.16 74	86.05 113	91.99 124	75.84 62	91.16 60	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
v7n		87.11 47	90.46 46	83.19 53	85.22 75	83.69 76	90.03 72	68.20 84	91.01 16	86.71 35	94.80 8	98.46 4	77.69 46	91.10 62	85.98 85	91.30 75	88.19 65

CNVR-MVS		86.93 48	88.98 55	84.54 42	90.11 39	87.41 53	93.23 38	73.47 51	86.31 61	82.25 71	82.96 134	92.15 118	76.04 59	91.69 52	90.69 44	92.17 65	91.64 38

NCCC		86.74 49	87.97 67	85.31 36	90.64 34	87.25 54	93.27 37	74.59 45	86.50 58	83.72 56	75.92 180	92.39 115	77.08 51	91.72 51	90.68 45	92.57 61	91.30 42

train_agg		86.67 50	87.73 68	85.43 35	91.51 18	82.72 82	94.47 30	74.22 49	81.71 108	81.54 83	89.20 83	92.87 107	78.33 43	90.12 74	88.47 63	92.51 62	89.04 59

CDPH-MVS		86.66 51	88.52 58	84.48 43	89.61 43	88.27 43	92.86 40	72.69 54	80.55 124	82.71 67	86.92 105	93.32 103	75.55 64	91.00 65	89.85 52	93.47 45	89.71 53

Gipuma		86.47 52	89.25 53	83.23 52	83.88 91	78.78 127	85.35 124	68.42 81	92.69 8	89.03 12	91.94 42	96.32 39	81.80 25	94.45 27	86.86 76	90.91 79	83.69 97
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PHI-MVS		86.37 53	88.14 64	84.30 45	86.65 64	87.56 51	90.76 54	70.16 65	82.55 95	89.65 7	84.89 126	92.40 114	75.97 60	90.88 69	89.70 53	92.58 59	89.03 60

MSLP-MVS++		86.29 54	89.10 54	83.01 55	85.71 72	89.79 33	87.04 114	74.39 47	85.17 72	78.92 98	77.59 160	93.57 99	82.60 20	93.23 36	91.88 39	89.42 96	92.46 27

v52		86.26 55	90.85 40	80.91 75	72.49 193	81.25 104	90.55 59	60.30 175	90.43 24	87.24 23	94.64 11	98.30 9	83.16 18	92.86 43	86.82 78	91.69 69	91.65 37

V4		86.26 55	90.85 40	80.91 75	72.49 193	81.25 104	90.55 59	60.31 174	90.44 23	87.23 25	94.64 11	98.31 8	83.17 16	92.87 42	86.82 78	91.69 69	91.64 38

TAPA-MVS	78.00 13	85.88 57	88.37 60	82.96 57	84.69 78	88.62 40	90.62 55	64.22 128	89.15 35	88.05 15	78.83 154	93.71 96	76.20 58	90.11 75	88.22 66	94.00 41	89.97 51
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
anonymousdsp		85.62 58	90.53 44	79.88 95	64.64 219	76.35 152	96.28 13	53.53 207	85.63 67	81.59 82	92.81 28	97.71 15	86.88 2	94.56 26	92.83 25	96.35 6	93.84 9

TSAR-MVS + COLMAP		85.51 59	88.36 61	82.19 63	86.05 69	87.69 50	90.50 64	70.60 64	86.40 59	82.33 69	89.69 76	92.52 112	74.01 82	87.53 93	86.84 77	89.63 92	87.80 70

CNLPA		85.50 60	88.58 56	81.91 65	84.55 81	87.52 52	90.89 51	63.56 138	88.18 43	84.06 50	83.85 131	91.34 131	76.46 55	91.27 57	89.00 61	91.96 66	88.88 61

PLC	76.06 15	85.38 61	87.46 70	82.95 58	85.79 71	88.84 38	88.86 81	68.70 78	87.06 54	83.60 59	79.02 151	90.05 139	77.37 50	90.88 69	89.66 54	93.37 47	86.74 75
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + GP.		85.32 62	87.41 72	82.89 59	90.07 40	85.69 67	89.07 79	72.99 53	82.45 97	74.52 119	85.09 123	87.67 154	79.24 35	91.11 61	90.41 47	91.45 72	89.45 55

TranMVSNet+NR-MVSNet		85.23 63	89.38 52	80.39 92	88.78 50	83.77 75	87.40 102	76.75 33	85.47 68	68.99 146	95.18 6	97.55 18	67.13 132	91.61 53	89.13 60	93.26 48	82.95 109

v748		85.21 64	89.62 50	80.08 94	80.71 135	80.27 119	85.05 127	63.79 136	90.47 22	83.54 62	94.21 14	98.52 1	76.84 53	90.97 67	84.25 102	90.53 81	88.62 63

HQP-MVS		85.02 65	86.41 78	83.40 51	89.19 46	86.59 59	91.28 47	71.60 58	82.79 93	83.48 64	78.65 156	93.54 100	72.55 99	86.49 104	85.89 88	92.28 64	90.95 45

UniMVSNet (Re)		84.95 66	88.53 57	80.78 79	87.82 58	84.21 72	88.03 91	76.50 36	81.18 119	69.29 143	92.63 33	96.83 24	69.07 122	91.23 59	89.60 55	93.97 42	84.00 95

DU-MVS		84.88 67	88.27 63	80.92 74	88.30 53	83.59 77	87.06 112	78.35 18	80.64 122	70.49 138	92.67 30	96.91 23	68.13 126	91.79 49	89.29 59	93.20 49	83.02 106

MCST-MVS		84.79 68	86.48 76	82.83 60	87.30 60	87.03 57	90.46 67	69.33 73	83.14 89	82.21 73	81.69 142	92.14 119	75.09 68	87.27 97	84.78 97	92.58 59	89.30 57

MVS_0304		84.73 69	86.19 81	83.02 54	88.32 52	86.71 58	91.55 45	70.87 62	73.79 165	82.88 66	85.13 122	93.35 102	72.55 99	88.62 85	87.69 69	91.93 67	88.05 68

UniMVSNet_NR-MVSNet		84.62 70	88.00 66	80.68 84	88.18 55	83.83 74	87.06 112	76.47 37	81.46 114	70.49 138	93.24 21	95.56 62	68.13 126	90.43 72	88.47 63	93.78 43	83.02 106

EG-PatchMatch MVS		84.35 71	87.55 69	80.62 87	86.38 66	82.24 87	86.75 116	64.02 132	84.24 79	78.17 102	89.38 80	95.03 76	78.78 38	89.95 76	86.33 82	89.59 93	85.65 84

AdaColmap		84.15 72	85.14 101	83.00 56	89.08 47	87.14 56	90.56 58	70.90 61	82.40 98	80.41 87	73.82 192	84.69 165	75.19 66	91.58 54	89.90 51	91.87 68	86.48 77

PCF-MVS	76.59 14	84.11 73	85.27 98	82.76 61	86.12 68	88.30 42	91.24 48	69.10 74	82.36 99	84.45 46	77.56 161	90.40 138	72.91 98	85.88 112	83.88 107	92.72 58	88.53 64
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_HR		83.95 74	86.10 83	81.44 70	84.62 79	80.29 118	90.51 63	68.05 85	84.07 82	80.38 88	84.74 127	91.37 130	74.23 76	90.37 73	87.25 71	90.86 80	84.59 87

casdiffmvs		83.82 75	86.06 85	81.19 71	85.51 73	80.80 110	90.78 53	70.96 60	84.04 83	78.01 103	86.87 106	93.80 95	71.73 105	86.25 109	84.03 105	90.18 85	86.62 76

TinyColmap		83.79 76	86.12 82	81.07 73	83.42 97	81.44 99	85.42 122	68.55 80	88.71 40	89.46 8	87.60 96	92.72 108	70.34 116	89.29 79	81.94 128	89.20 97	81.12 127

v13		83.75 77	86.20 80	80.89 77	83.38 98	81.93 90	88.58 84	66.09 98	83.55 85	84.28 47	92.67 30	96.79 25	74.67 72	84.42 134	79.72 149	88.36 109	84.31 91

v1192		83.61 78	85.23 99	81.72 67	84.05 86	82.15 88	89.54 74	66.20 96	81.38 116	86.76 34	91.79 45	96.03 47	74.88 70	81.81 164	80.92 136	88.91 101	82.50 114

v12		83.59 79	86.00 87	80.77 82	83.30 100	81.83 91	88.45 85	65.95 102	83.20 88	84.15 48	92.54 35	96.71 26	74.50 74	84.19 136	79.64 150	88.30 110	83.93 96

v1240		83.57 80	84.94 105	81.97 64	84.05 86	81.27 103	89.46 76	66.06 99	81.31 118	87.50 21	91.88 44	95.46 65	76.25 57	81.16 169	80.51 141	88.52 107	82.98 108

v1921920		83.49 81	84.94 105	81.80 66	83.78 92	81.20 107	89.50 75	65.91 103	81.64 110	87.18 27	91.70 46	95.39 66	75.85 61	81.56 167	80.27 144	88.60 105	82.80 110

v144192		83.43 82	84.97 104	81.63 69	83.43 96	81.23 106	89.42 77	66.04 101	81.45 115	86.40 36	91.46 50	95.70 59	75.76 63	82.14 160	80.23 145	88.74 102	82.57 113

V9		83.42 83	85.81 89	80.63 86	83.20 103	81.73 94	88.29 89	65.78 106	82.87 92	83.99 53	92.38 37	96.60 28	74.30 75	83.93 137	79.58 152	88.24 113	83.55 100

Vis-MVSNet		83.32 84	88.12 65	77.71 116	77.91 166	83.44 79	90.58 56	69.49 70	81.11 120	67.10 155	89.85 73	91.48 129	71.71 106	91.34 56	89.37 57	89.48 95	90.26 48
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v11		83.30 85	85.58 94	80.64 85	83.53 95	81.74 93	88.30 88	65.46 111	82.75 94	84.63 44	92.49 36	96.17 43	73.90 83	82.69 153	79.59 151	88.04 118	83.66 98

V14		83.23 86	85.59 93	80.48 90	83.09 106	81.63 96	88.13 90	65.61 108	82.53 96	83.81 55	92.17 40	96.50 29	74.07 80	83.66 139	79.51 154	88.17 115	83.16 104

v1144		83.22 87	85.01 102	81.14 72	83.76 93	81.60 97	88.95 80	65.58 109	81.89 104	85.80 38	91.68 47	95.84 51	74.04 81	82.12 161	80.56 140	88.70 104	81.41 124

MVS_111021_LR		83.20 88	85.33 96	80.73 83	82.88 110	78.23 131	89.61 73	65.23 114	82.08 102	81.19 85	85.31 120	92.04 123	75.22 65	89.50 77	85.90 87	90.24 84	84.23 92

v10		83.17 89	85.22 100	80.78 79	83.26 102	82.99 81	88.66 82	66.49 94	79.24 139	83.60 59	91.46 50	95.47 63	74.12 77	82.60 155	80.66 137	88.53 106	84.11 94

v15		83.06 90	85.39 95	80.35 93	83.01 107	81.53 98	87.98 93	65.47 110	82.19 101	83.66 58	92.00 41	96.40 35	73.87 84	83.39 141	79.44 155	88.10 117	82.76 111

PVSNet_Blended_VisFu		83.00 91	84.16 121	81.65 68	82.17 125	86.01 63	88.03 91	71.23 59	76.05 158	79.54 94	83.88 130	83.44 166	77.49 49	87.38 94	84.93 96	91.41 73	87.40 73

NR-MVSNet		82.89 92	87.43 71	77.59 118	83.91 90	83.59 77	87.10 111	78.35 18	80.64 122	68.85 147	92.67 30	96.50 29	54.19 183	87.19 100	88.68 62	93.16 52	82.75 112

CANet		82.84 93	84.60 110	80.78 79	87.30 60	85.20 69	90.23 69	69.00 75	72.16 174	78.73 99	84.49 128	90.70 136	69.54 120	87.65 92	86.17 83	89.87 90	85.84 82

Baseline_NR-MVSNet		82.79 94	86.51 75	78.44 113	88.30 53	75.62 163	87.81 94	74.97 44	81.53 112	66.84 156	94.71 10	96.46 31	66.90 133	91.79 49	83.37 115	85.83 164	82.09 119

v7		82.76 95	84.65 109	80.55 88	83.27 101	81.77 92	88.66 82	65.10 115	79.23 140	83.60 59	91.47 49	95.47 63	74.12 77	82.61 154	80.66 137	88.52 107	81.35 125

EPP-MVSNet		82.76 95	86.47 77	78.45 112	86.00 70	84.47 71	85.39 123	68.42 81	84.17 80	62.97 165	89.26 82	76.84 189	72.13 102	92.56 48	90.40 48	95.76 20	87.56 72

CLD-MVS		82.75 97	87.22 73	77.54 119	88.01 57	85.76 66	90.23 69	54.52 200	82.28 100	82.11 75	88.48 90	95.27 67	63.95 142	89.41 78	88.29 65	86.45 152	81.01 128
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		82.33 98	83.87 127	80.52 89	84.51 82	81.32 100	87.53 100	68.05 85	74.94 163	79.67 93	82.37 138	92.31 116	72.21 101	85.06 121	86.91 75	91.18 76	84.20 93

v1		82.27 99	84.32 114	79.87 96	82.86 112	80.32 115	87.57 99	63.47 142	81.87 106	84.13 49	91.34 52	96.29 40	73.23 94	82.39 156	79.08 166	87.94 120	78.98 143

v1141		82.26 100	84.32 114	79.85 97	82.86 112	80.31 116	87.58 97	63.48 140	81.86 107	84.03 52	91.33 53	96.28 41	73.23 94	82.39 156	79.08 166	87.93 121	78.97 145

divwei89l23v2f112		82.26 100	84.32 114	79.85 97	82.86 112	80.31 116	87.58 97	63.48 140	81.88 105	84.05 51	91.33 53	96.27 42	73.23 94	82.39 156	79.08 166	87.93 121	78.97 145

3Dnovator	79.41 10	82.21 102	86.07 84	77.71 116	79.31 150	84.61 70	87.18 109	61.02 171	85.65 66	76.11 107	85.07 124	85.38 163	70.96 112	87.22 98	86.47 81	91.66 71	88.12 67

v8		82.20 103	84.56 111	79.45 100	82.42 116	81.65 95	87.26 103	64.27 126	79.36 135	81.70 78	91.04 62	95.75 54	73.30 92	82.82 149	79.18 163	87.74 125	82.09 119

v2v482		82.20 103	84.26 118	79.81 99	82.67 115	80.18 120	87.67 96	63.96 134	81.69 109	84.73 43	91.27 56	96.33 38	72.05 103	81.94 163	79.56 153	87.79 124	78.84 147

v17		82.09 105	84.45 112	79.33 102	82.41 117	81.31 101	87.26 103	64.50 125	78.72 142	80.73 86	90.90 63	95.57 60	73.37 88	83.06 142	79.25 159	87.70 129	82.35 117

Effi-MVS+-dtu		82.04 106	83.39 133	80.48 90	85.48 74	86.57 61	88.40 86	68.28 83	69.04 186	73.13 126	76.26 172	91.11 133	74.74 71	88.40 87	87.76 68	92.84 57	84.57 88

MAR-MVS		81.98 107	82.92 135	80.88 78	85.18 76	85.85 64	89.13 78	69.52 68	71.21 178	82.25 71	71.28 202	88.89 148	69.69 117	88.71 83	86.96 73	89.52 94	87.57 71
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
v16		81.92 108	84.32 114	79.12 108	82.31 122	81.29 102	87.20 108	64.51 124	78.16 146	79.76 92	90.86 64	95.23 69	73.29 93	83.05 143	79.29 158	87.63 130	82.34 118

v6		81.77 109	83.96 124	79.22 105	82.41 117	80.45 114	87.26 103	62.91 152	79.29 136	81.65 79	91.08 58	95.74 55	73.32 89	82.84 146	79.21 162	87.73 126	79.07 140

v1neww		81.76 110	83.95 125	79.21 106	82.41 117	80.46 112	87.26 103	62.93 148	79.28 137	81.62 80	91.06 60	95.72 57	73.31 90	82.83 147	79.22 160	87.73 126	79.07 140

v7new		81.76 110	83.95 125	79.21 106	82.41 117	80.46 112	87.26 103	62.93 148	79.28 137	81.62 80	91.06 60	95.72 57	73.31 90	82.83 147	79.22 160	87.73 126	79.07 140

IS_MVSNet		81.72 112	85.01 102	77.90 115	86.19 67	82.64 84	85.56 121	70.02 66	80.11 128	63.52 162	87.28 100	81.18 174	67.26 130	91.08 64	89.33 58	94.82 32	83.42 102

v18		81.62 113	83.99 123	78.86 109	82.08 126	81.12 108	86.93 115	64.24 127	77.44 148	79.47 95	90.53 66	94.99 77	72.99 97	82.72 152	79.18 163	87.48 133	81.91 122

FPMVS		81.56 114	84.04 122	78.66 110	82.92 108	75.96 158	86.48 119	65.66 107	84.67 76	71.47 134	77.78 158	83.22 168	77.57 48	91.24 58	90.21 49	87.84 123	85.21 85

Fast-Effi-MVS+		81.42 115	83.82 128	78.62 111	82.24 124	80.62 111	87.72 95	63.51 139	73.01 167	74.75 117	83.80 132	92.70 109	73.44 87	88.15 91	85.26 92	90.05 86	83.17 103

USDC		81.39 116	83.07 134	79.43 101	81.48 131	78.95 126	82.62 139	66.17 97	87.45 50	90.73 4	82.40 137	93.65 98	66.57 135	83.63 140	77.97 170	89.00 99	77.45 153

MSDG		81.39 116	84.23 120	78.09 114	82.40 121	82.47 86	85.31 126	60.91 172	79.73 131	80.26 90	86.30 110	88.27 152	69.67 118	87.20 99	84.98 95	89.97 88	80.67 130

canonicalmvs		81.22 118	86.04 86	75.60 126	83.17 105	83.18 80	80.29 152	65.82 105	85.97 65	67.98 153	77.74 159	91.51 128	65.17 138	88.62 85	86.15 84	91.17 77	89.09 58

pmmvs6		80.46 119	88.34 62	71.26 146	81.96 127	77.51 136	77.54 171	68.83 76	93.72 5	55.92 179	93.94 17	98.03 11	55.94 172	89.21 80	85.61 89	87.36 137	80.38 131

QAPM		80.43 120	84.34 113	75.86 124	79.40 149	82.06 89	79.86 157	61.94 166	83.28 87	74.73 118	81.74 141	85.44 162	70.97 111	84.99 128	84.71 98	88.29 111	88.14 66

PM-MVS		80.42 121	83.63 130	76.67 121	78.04 161	72.37 178	87.14 110	60.18 177	80.13 127	71.75 133	86.12 112	93.92 93	77.08 51	86.56 103	85.12 94	85.83 164	81.18 126

Anonymous20240521		80.04 122	85.67 92	73.48 136	82.91 109	81.11 109	80.44 151	66.06 99	85.01 73	62.53 168	78.84 153	94.43 88	58.51 160	88.66 84	85.91 86	90.41 83	85.73 83

IterMVS-LS		79.79 123	82.56 137	76.56 123	81.83 129	77.85 134	79.90 156	69.42 72	78.93 141	71.21 135	90.47 67	85.20 164	70.86 113	80.54 175	80.57 139	86.15 155	84.36 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		79.71 124	83.74 129	75.01 129	79.31 150	82.68 83	84.79 129	60.06 178	75.43 161	69.09 145	86.13 111	89.38 141	67.16 131	85.12 120	83.87 108	89.65 91	83.57 99
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
pmmvs-eth3d		79.64 125	82.06 140	76.83 120	80.05 141	72.64 176	87.47 101	66.59 93	80.83 121	73.50 123	89.32 81	93.20 104	67.78 128	80.78 173	81.64 130	85.58 167	76.01 155

UGNet		79.62 126	85.91 88	72.28 143	73.52 187	83.91 73	86.64 117	69.51 69	79.85 130	62.57 167	85.82 116	89.63 140	53.18 190	88.39 88	87.35 70	88.28 112	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	83.59 131	74.93 131	69.61 205	77.05 145	86.59 118	55.84 196	78.42 145	77.29 104	89.84 74	95.08 74	74.12 77	83.05 143	80.11 147	86.12 156	81.59 123

Anonymous20231211		79.37 128	85.78 90	71.89 144	82.87 111	79.66 122	78.77 167	63.93 135	83.36 86	59.39 172	90.54 65	94.66 83	56.46 168	87.38 94	84.12 103	89.92 89	80.74 129

EPNet		79.36 129	79.44 148	79.27 104	89.51 44	77.20 141	88.35 87	77.35 31	68.27 188	74.29 120	76.31 170	79.22 178	59.63 155	85.02 127	85.45 91	86.49 151	84.61 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v148		79.33 130	82.32 139	75.84 125	80.14 140	75.74 160	81.98 142	57.06 192	81.51 113	79.36 97	89.42 78	96.42 33	71.32 107	81.54 168	75.29 185	85.20 170	76.32 154

FC-MVSNet-train		79.20 131	86.29 79	70.94 151	84.06 85	77.67 135	85.68 120	64.11 131	82.90 91	52.22 198	92.57 34	93.69 97	49.52 207	88.30 89	86.93 74	90.03 87	81.95 121

TransMVSNet (Re)		79.05 132	86.66 74	70.18 159	83.32 99	75.99 157	77.54 171	63.98 133	90.68 20	55.84 180	94.80 8	96.06 46	53.73 189	86.27 107	83.22 116	86.65 146	79.61 138

no-one		78.59 133	85.28 97	70.79 152	59.01 226	68.77 191	76.62 181	46.06 218	80.25 126	75.75 110	81.85 140	97.75 14	83.63 12	90.99 66	87.20 72	83.67 177	90.14 49

OpenMVS	75.38 16	78.44 134	81.39 143	74.99 130	80.46 137	79.85 121	79.99 154	58.31 188	77.34 150	73.85 122	77.19 165	82.33 172	68.60 125	84.67 131	81.95 127	88.72 103	86.40 79

pm-mvs1		78.21 135	85.68 91	69.50 164	80.38 138	75.73 161	76.25 186	65.04 116	87.59 47	54.47 185	93.16 23	95.99 50	54.20 182	86.37 105	82.98 118	86.64 147	77.96 151

FMVSNet1		78.20 136	84.83 107	70.46 156	78.62 156	79.03 125	77.90 170	67.53 90	83.02 90	55.10 182	87.19 102	93.18 105	55.65 175	85.57 113	83.39 112	87.98 119	82.40 115

diffmvs		77.65 137	81.71 142	72.92 141	77.79 168	77.13 143	80.70 149	62.82 153	73.16 166	70.22 140	84.92 125	93.82 94	63.41 147	81.10 170	77.40 177	82.58 183	84.42 89

DI_MVS_plusplus_trai		77.64 138	79.64 147	75.31 128	79.87 145	76.89 146	81.55 145	63.64 137	76.21 157	72.03 131	85.59 119	82.97 169	66.63 134	79.27 178	77.78 172	88.14 116	78.76 148

tfpnnormal		77.16 139	84.26 118	68.88 167	81.02 134	75.02 164	76.52 183	63.30 144	87.29 51	52.40 196	91.24 57	93.97 92	54.85 181	85.46 116	81.08 134	85.18 171	75.76 158

conf0.05thres1000		77.12 140	82.38 138	70.98 149	82.30 123	77.95 133	79.86 157	64.74 120	86.63 57	53.93 186	85.74 117	75.63 199	56.85 165	88.98 82	84.10 104	88.20 114	77.61 152

Fast-Effi-MVS+-dtu		76.92 141	77.18 163	76.62 122	79.55 147	79.17 124	84.80 128	77.40 29	64.46 208	68.75 149	70.81 208	86.57 158	63.36 149	81.74 165	81.76 129	85.86 163	75.78 157

MVS_Test		76.72 142	79.40 149	73.60 135	78.85 155	74.99 165	79.91 155	61.56 168	69.67 182	72.44 127	85.98 114	90.78 135	63.50 146	78.30 181	75.74 184	85.33 169	80.31 135

MDA-MVSNet-bldmvs		76.51 143	82.87 136	69.09 166	50.71 237	74.72 168	84.05 133	60.27 176	81.62 111	71.16 136	88.21 92	91.58 126	69.62 119	92.78 44	77.48 175	78.75 192	73.69 171

EU-MVSNet		76.48 144	80.53 145	71.75 145	67.62 210	70.30 183	81.74 143	54.06 203	75.47 160	71.01 137	80.10 146	93.17 106	73.67 85	83.73 138	77.85 171	82.40 184	83.07 105

PVSNet_BlendedMVS		76.45 145	78.12 154	74.49 132	76.76 176	78.46 128	79.65 159	63.26 145	65.42 204	73.15 124	75.05 186	88.96 145	66.51 136	82.73 150	77.66 173	87.61 131	78.60 149

PVSNet_Blended		76.45 145	78.12 154	74.49 132	76.76 176	78.46 128	79.65 159	63.26 145	65.42 204	73.15 124	75.05 186	88.96 145	66.51 136	82.73 150	77.66 173	87.61 131	78.60 149

Vis-MVSNet (Re-imp)		76.15 147	80.84 144	70.68 153	83.66 94	74.80 167	81.66 144	69.59 67	80.48 125	46.94 214	87.44 97	80.63 176	53.14 191	86.87 101	84.56 99	89.12 98	71.12 180

PatchMatch-RL		76.05 148	76.64 168	75.36 127	77.84 167	69.87 186	81.09 147	63.43 143	71.66 176	68.34 152	71.70 198	81.76 173	74.98 69	84.83 130	83.44 111	86.45 152	73.22 173

pmmvs4		75.92 149	77.48 161	74.10 134	78.21 160	70.94 180	84.06 132	64.78 119	75.13 162	68.47 151	84.12 129	83.32 167	64.74 141	75.93 192	79.14 165	84.31 175	73.77 169

FC-MVSNet-test		75.91 150	83.59 131	66.95 185	76.63 182	69.07 188	85.33 125	64.97 118	84.87 75	41.95 220	93.17 22	87.04 156	47.78 210	91.09 63	85.56 90	85.06 172	74.34 161

CVMVSNet		75.65 151	77.62 160	73.35 139	71.95 197	69.89 185	83.04 138	60.84 173	69.12 184	68.76 148	79.92 149	78.93 180	73.64 86	81.02 171	81.01 135	81.86 186	83.43 101

IB-MVS	71.28 17	75.21 152	77.00 165	73.12 140	76.76 176	77.45 137	83.05 137	58.92 184	63.01 213	64.31 161	59.99 231	87.57 155	68.64 124	86.26 108	82.34 126	87.05 144	82.36 116
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
CANet_DTU		75.04 153	78.45 151	71.07 147	77.27 172	77.96 132	83.88 134	58.00 189	64.11 209	68.67 150	75.65 182	88.37 151	53.92 185	82.05 162	81.11 133	84.67 173	79.88 137

GA-MVS		75.01 154	76.39 170	73.39 137	78.37 157	75.66 162	80.03 153	58.40 187	70.51 180	75.85 109	83.24 133	76.14 193	63.75 143	77.28 185	76.62 180	83.97 176	75.30 160

view800		74.68 155	78.74 150	69.94 160	81.12 133	76.59 147	78.94 166	63.24 147	78.56 144	53.06 191	75.61 183	76.26 192	56.07 171	86.32 106	83.75 110	87.18 143	74.10 165

FMVSNet2		74.43 156	79.70 146	68.27 170	76.76 176	77.36 138	75.77 191	65.36 113	72.28 172	52.97 192	81.92 139	85.61 161	52.73 194	80.66 174	79.73 148	86.04 159	80.37 132

thres600view7		74.34 157	78.43 152	69.56 163	80.47 136	76.28 153	78.65 168	62.56 158	77.39 149	52.53 194	74.03 191	76.78 190	55.90 174	85.06 121	85.19 93	87.25 141	74.29 163

view600		74.08 158	78.15 153	69.32 165	80.27 139	75.82 159	78.27 169	62.20 162	77.26 151	52.80 193	74.07 190	76.86 188	55.57 177	84.90 129	84.43 100	86.84 145	73.71 170

IterMVS		73.62 159	76.53 169	70.23 158	71.83 198	77.18 142	80.69 150	53.22 208	72.23 173	66.62 157	85.21 121	78.96 179	69.54 120	76.28 191	71.63 193	79.45 189	74.25 164
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet1		73.40 160	81.85 141	63.55 199	72.90 190	64.37 203	84.58 130	53.60 206	90.84 17	53.92 187	87.75 95	96.10 44	45.31 213	85.37 118	79.32 157	70.98 207	69.18 190

HyFIR lowres test		73.29 161	74.14 183	72.30 142	73.08 189	78.33 130	83.12 136	62.41 161	63.81 210	62.13 169	76.67 169	78.50 181	71.09 109	74.13 195	77.47 176	81.98 185	70.10 184

tfpn_n400		73.26 162	77.94 156	67.79 180	79.91 143	73.32 171	76.38 184	62.04 163	84.26 77	48.53 210	76.23 173	71.50 206	53.83 186	86.22 110	81.59 131	86.05 157	72.47 175

tfpnconf		73.26 162	77.94 156	67.79 180	79.91 143	73.32 171	76.38 184	62.04 163	84.26 77	48.53 210	76.23 173	71.50 206	53.83 186	86.22 110	81.59 131	86.05 157	72.47 175

GBi-Net		73.17 164	77.64 158	67.95 175	76.76 176	77.36 138	75.77 191	64.57 121	62.99 214	51.83 199	76.05 176	77.76 184	52.73 194	85.57 113	83.39 112	86.04 159	80.37 132

test1		73.17 164	77.64 158	67.95 175	76.76 176	77.36 138	75.77 191	64.57 121	62.99 214	51.83 199	76.05 176	77.76 184	52.73 194	85.57 113	83.39 112	86.04 159	80.37 132

thres400		73.13 166	76.99 166	68.62 168	79.46 148	74.93 166	77.23 173	61.23 169	75.54 159	52.31 197	72.20 197	77.10 187	54.89 179	82.92 145	82.62 125	86.57 149	73.66 172

CDS-MVSNet		73.07 167	77.02 164	68.46 169	81.62 130	72.89 175	79.56 161	70.78 63	69.56 183	52.52 195	77.37 164	81.12 175	42.60 216	84.20 135	83.93 106	83.65 178	70.07 185
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpn		72.99 168	75.25 178	70.36 157	81.87 128	77.09 144	79.28 163	64.16 129	79.58 133	53.14 190	76.97 167	48.75 233	56.35 170	87.31 96	82.75 120	87.35 138	74.31 162

MDTV_nov1_ep13_2view		72.96 169	75.59 175	69.88 161	71.15 202	64.86 202	82.31 141	54.45 201	76.30 156	78.32 101	86.52 108	91.58 126	61.35 152	76.80 186	66.83 205	71.70 202	66.26 197

tfpnview11		72.88 170	77.37 162	67.65 182	79.81 146	73.43 170	76.23 187	61.97 165	81.37 117	48.53 210	76.23 173	71.50 206	53.78 188	85.45 117	82.77 119	85.56 168	70.87 183

gg-mvs-nofinetune		72.68 171	75.21 179	69.73 162	81.48 131	69.04 189	70.48 210	76.67 34	86.92 55	67.80 154	88.06 93	64.67 214	42.12 218	77.60 183	73.65 187	79.81 188	66.57 196

thres200		72.41 172	76.00 174	68.21 171	78.28 158	76.28 153	74.94 196	62.56 158	72.14 175	51.35 202	69.59 213	76.51 191	54.89 179	85.06 121	80.51 141	87.25 141	71.92 178

tfpn1000		72.27 173	76.88 167	66.88 186	79.01 154	74.04 169	76.60 182	61.15 170	79.65 132	45.52 216	77.41 163	67.98 212	52.47 197	85.22 119	82.99 117	86.54 150	70.89 181

tfpn200view9		72.01 174	75.40 176	68.06 172	77.97 162	76.44 149	77.04 175	62.67 156	66.81 194	50.82 203	67.30 215	75.67 195	52.46 198	85.06 121	82.64 121	87.41 136	73.86 168

conf200view11		72.00 175	75.40 176	68.04 173	77.97 162	76.44 149	77.04 175	62.68 154	66.81 194	50.69 205	67.30 215	75.67 195	52.46 198	85.06 121	82.64 121	87.42 134	73.87 166

EPNet_dtu		71.90 176	73.03 187	70.59 154	78.28 158	61.64 208	82.44 140	64.12 130	63.26 212	69.74 141	71.47 200	82.41 170	51.89 203	78.83 180	78.01 169	77.07 193	75.60 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn111		71.60 177	74.66 181	68.04 173	77.97 162	76.44 149	77.04 175	62.68 154	66.81 194	50.69 205	62.10 226	75.67 195	52.46 198	85.06 121	82.64 121	87.42 134	73.87 166

gm-plane-assit		71.56 178	69.99 192	73.39 137	84.43 83	73.21 174	90.42 68	51.36 214	84.08 81	76.00 108	91.30 55	37.09 240	59.01 158	73.65 200	70.24 197	79.09 191	60.37 213

CMPMVS	55.74 18	71.56 178	76.26 171	66.08 192	68.11 209	63.91 205	63.17 230	50.52 216	68.79 187	75.49 111	70.78 209	85.67 160	63.54 145	81.58 166	77.20 178	75.63 194	85.86 81
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		71.40 180	75.20 180	66.97 184	75.00 184	76.59 147	74.29 197	64.57 121	62.99 214	51.83 199	76.05 176	77.76 184	51.49 204	76.58 189	77.03 179	84.62 174	79.43 139

MS-PatchMatch		71.18 181	73.99 184	67.89 179	77.16 173	71.76 179	77.18 174	56.38 195	67.35 190	55.04 183	74.63 188	75.70 194	62.38 151	76.62 188	75.97 183	79.22 190	75.90 156

test20.03		69.91 182	76.20 172	62.58 201	84.01 88	67.34 195	75.67 195	65.88 104	79.98 129	40.28 225	82.65 135	89.31 143	39.63 220	77.41 184	73.28 188	69.98 208	63.40 205

thres100view900		69.86 183	72.97 188	66.24 189	77.97 162	72.49 177	73.29 201	59.12 182	66.81 194	50.82 203	67.30 215	75.67 195	50.54 206	78.24 182	79.40 156	85.71 166	70.88 182

conf0.01		69.59 184	71.01 191	67.95 175	77.74 169	76.09 155	77.04 175	62.58 157	66.81 194	50.54 207	63.00 224	51.78 232	52.46 198	84.53 132	82.64 121	87.32 139	72.19 177

CR-MVSNet		69.56 185	68.34 201	70.99 148	72.78 192	67.63 193	64.47 227	67.74 88	59.93 223	72.30 128	80.10 146	56.77 223	65.04 139	71.64 208	72.91 189	83.61 181	69.40 188

pmmvs5		68.91 186	74.35 182	62.56 202	67.45 212	66.78 197	71.70 206	51.47 213	67.17 193	56.25 178	82.41 136	88.59 149	47.21 211	73.21 204	74.23 186	81.30 187	68.03 193

CHOSEN 1792x2688		68.80 187	71.09 190	66.13 191	69.11 207	68.89 190	78.98 165	54.68 198	61.63 220	56.69 176	71.56 199	78.39 182	67.69 129	72.13 206	72.01 192	69.63 210	73.02 174

conf0.002		68.60 188	69.17 196	67.92 178	77.66 170	76.01 156	77.04 175	62.56 158	66.81 194	50.51 208	61.21 229	44.01 237	52.46 198	84.44 133	80.29 143	87.31 140	71.44 179

tpmp4_e23		68.32 189	66.04 205	70.98 149	77.52 171	69.23 187	80.99 148	65.46 111	68.09 189	69.25 144	70.77 210	54.03 229	59.35 156	69.01 215	63.02 212	73.34 199	68.15 192

tfpn_ndepth		68.20 190	72.18 189	63.55 199	74.64 185	73.24 173	72.41 204	59.76 180	70.54 179	41.93 221	60.96 230	68.69 211	46.23 212	82.16 159	80.14 146	86.34 154	69.56 187

testgi		68.20 190	76.05 173	59.04 208	79.99 142	67.32 196	81.16 146	51.78 212	84.91 74	39.36 228	73.42 193	95.19 70	32.79 226	76.54 190	70.40 196	69.14 211	64.55 201

MVSTER		68.08 192	69.73 194	66.16 190	66.33 217	70.06 184	75.71 194	52.36 210	55.18 232	58.64 173	70.23 212	56.72 224	57.34 164	79.68 177	76.03 182	86.61 148	80.20 136

Anonymous20231206		67.28 193	73.41 186	60.12 207	76.45 183	63.61 206	74.21 198	56.52 194	76.35 155	42.23 219	75.81 181	90.47 137	41.51 219	74.52 193	69.97 198	69.83 209	63.17 206

RPMNet		67.02 194	63.99 213	70.56 155	71.55 200	67.63 193	75.81 189	69.44 71	59.93 223	63.24 163	64.32 220	47.51 234	59.68 154	70.37 212	69.64 199	83.64 179	68.49 191

CostFormer		66.81 195	66.94 203	66.67 187	72.79 191	68.25 192	79.55 162	55.57 197	65.52 203	62.77 166	76.98 166	60.09 218	56.73 167	65.69 225	62.35 213	72.59 200	69.71 186

thresconf0.02		66.71 196	68.28 202	64.89 198	76.83 175	70.38 182	71.62 208	58.90 185	77.64 147	47.04 213	62.10 226	46.01 235	51.32 205	78.85 179	76.09 181	83.62 180	66.85 195

PatchT		66.25 197	66.76 204	65.67 195	55.87 231	60.75 210	70.17 211	59.00 183	59.80 225	72.30 128	78.68 155	54.12 228	65.04 139	71.64 208	72.91 189	71.63 204	69.40 188

LP		65.71 198	69.91 193	60.81 206	56.75 230	61.37 209	69.55 217	56.80 193	73.01 167	60.48 171	79.76 150	70.57 209	55.47 178	72.77 205	67.19 204	65.81 217	64.71 200

dps		65.14 199	64.50 211	65.89 194	71.41 201	65.81 200	71.44 209	61.59 167	58.56 226	61.43 170	75.45 184	52.70 231	58.06 162	69.57 214	64.65 208	71.39 205	64.77 199

MDTV_nov1_ep13		64.96 200	64.77 210	65.18 197	67.08 213	62.46 207	75.80 190	51.10 215	62.27 219	69.74 141	74.12 189	62.65 215	55.64 176	68.19 217	62.16 217	71.70 202	61.57 212

PatchmatchNet		64.81 201	63.74 215	66.06 193	69.21 206	58.62 213	73.16 202	60.01 179	65.92 200	66.19 159	76.27 171	59.09 219	60.45 153	66.58 222	61.47 220	67.33 214	58.24 218
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		64.79 202	62.74 219	67.17 183	74.61 186	65.91 199	76.18 188	59.32 181	64.88 207	66.41 158	71.21 203	53.56 230	59.17 157	61.53 231	58.16 224	67.33 214	63.95 202

DWT-MVSNet_training		63.07 203	60.04 226	66.61 188	71.64 199	65.27 201	76.80 180	53.82 204	55.90 229	63.07 164	62.23 225	41.87 239	62.54 150	64.32 228	63.71 210	71.78 201	66.97 194

MIMVSNet		63.02 204	69.02 197	56.01 213	68.20 208	59.26 212	70.01 213	53.79 205	71.56 177	41.26 224	71.38 201	82.38 171	36.38 222	71.43 210	67.32 203	66.45 216	59.83 215

TAMVS		63.02 204	69.30 195	55.70 215	70.12 203	56.89 216	69.63 216	45.13 219	70.23 181	38.00 230	77.79 157	75.15 200	42.60 216	74.48 194	72.81 191	68.70 212	57.75 220

tpm		62.79 206	63.25 216	62.26 203	70.09 204	53.78 222	71.65 207	47.31 217	65.72 202	76.70 105	80.62 143	56.40 226	48.11 209	64.20 229	58.54 222	59.70 226	63.47 204

pmmvs3		62.72 207	68.71 198	55.74 214	50.74 236	57.10 215	70.05 212	28.82 234	61.57 222	57.39 175	71.19 204	85.73 159	53.96 184	73.36 203	69.43 200	73.47 198	62.55 208

new-patchmatchnet		62.59 208	73.79 185	49.53 226	76.98 174	53.57 223	53.46 238	54.64 199	85.43 69	28.81 236	91.94 42	96.41 34	25.28 234	76.80 186	53.66 231	57.99 228	58.69 217

test-LLR		62.15 209	59.46 230	65.29 196	79.07 152	52.66 225	69.46 219	62.93 148	50.76 236	53.81 188	63.11 222	58.91 220	52.87 192	66.54 223	62.34 214	73.59 196	61.87 210

PMMVS		61.98 210	65.61 207	57.74 210	45.03 238	51.76 229	69.54 218	35.05 229	55.49 231	55.32 181	68.23 214	78.39 182	58.09 161	70.21 213	71.56 194	83.42 182	63.66 203

test0.0.03 1		61.79 211	65.33 208	57.65 211	79.07 152	64.09 204	68.51 223	62.93 148	61.59 221	33.71 232	61.58 228	71.58 205	33.43 225	70.95 211	68.68 201	68.26 213	58.82 216

test1235678		60.73 212	68.46 199	51.71 223	61.76 221	56.73 218	73.40 199	42.24 223	67.34 191	39.55 226	70.90 205	92.54 111	28.75 229	73.84 197	66.00 206	64.57 219	51.90 226

testmv		60.72 213	68.44 200	51.71 223	61.76 221	56.70 219	73.40 199	42.24 223	67.31 192	39.54 227	70.88 206	92.49 113	28.75 229	73.83 198	66.00 206	64.56 220	51.89 227

MVS-HIRNet		59.74 214	58.74 233	60.92 205	57.74 229	45.81 236	56.02 236	58.69 186	55.69 230	65.17 160	70.86 207	71.66 203	56.75 166	61.11 232	53.74 230	71.17 206	52.28 225

tpmrst		59.42 215	60.02 227	58.71 209	67.56 211	53.10 224	66.99 224	51.88 211	63.80 211	57.68 174	76.73 168	56.49 225	48.73 208	56.47 235	55.55 227	59.43 227	58.02 219

test-mter		59.39 216	61.59 221	56.82 212	53.21 232	54.82 220	73.12 203	26.57 236	53.19 233	56.31 177	64.71 218	60.47 217	56.36 169	68.69 216	64.27 209	75.38 195	65.00 198

E-PMN		59.07 217	62.79 218	54.72 216	67.01 215	47.81 235	60.44 233	43.40 220	72.95 169	44.63 217	70.42 211	73.17 202	58.73 159	80.97 172	51.98 232	54.14 232	42.26 235

EMVS		58.97 218	62.63 220	54.70 217	66.26 218	48.71 231	61.74 231	42.71 221	72.80 171	46.00 215	73.01 196	71.66 203	57.91 163	80.41 176	50.68 235	53.55 233	41.11 236

testus		57.41 219	64.98 209	48.58 228	59.39 225	57.17 214	68.81 222	32.86 231	62.32 218	43.25 218	57.59 232	88.49 150	24.19 235	71.68 207	63.20 211	62.99 222	54.42 223

TESTMET0.1,1		57.21 220	59.46 230	54.60 218	50.95 235	52.66 225	69.46 219	26.91 235	50.76 236	53.81 188	63.11 222	58.91 220	52.87 192	66.54 223	62.34 214	73.59 196	61.87 210

ADS-MVSNet		56.89 221	61.09 222	52.00 221	59.48 224	48.10 234	58.02 234	54.37 202	72.82 170	49.19 209	75.32 185	65.97 213	37.96 221	59.34 234	54.66 229	52.99 234	51.42 228

EPMVS		56.62 222	59.77 228	52.94 220	62.41 220	50.55 230	60.66 232	52.83 209	65.15 206	41.80 222	77.46 162	57.28 222	42.68 215	59.81 233	54.82 228	57.23 229	53.35 224

FMVSNet5		56.37 223	60.14 225	51.98 222	60.83 223	59.58 211	66.85 225	42.37 222	52.68 234	41.33 223	47.09 237	54.68 227	35.28 223	73.88 196	70.77 195	65.24 218	62.26 209

CHOSEN 280x420		56.32 224	58.85 232	53.36 219	51.63 234	39.91 239	69.12 221	38.61 228	56.29 228	36.79 231	48.84 236	62.59 216	63.39 148	73.61 201	67.66 202	60.61 224	63.07 207

testpf		55.64 225	50.84 235	61.24 204	67.03 214	54.45 221	72.29 205	65.04 116	37.23 238	54.99 184	53.99 233	43.12 238	44.34 214	55.22 236	51.59 234	63.76 221	60.25 214

1111		55.38 226	59.51 229	50.57 225	72.41 195	48.16 232	69.76 214	57.08 190	76.79 153	32.10 233	80.12 144	35.41 241	25.87 231	67.23 218	57.74 225	46.17 236	51.09 229

N_pmnet		54.95 227	65.90 206	42.18 231	66.37 216	43.86 238	57.92 235	39.79 227	79.54 134	17.24 240	86.31 109	87.91 153	25.44 233	64.68 226	51.76 233	46.33 235	47.23 231

test12356		54.63 228	63.78 214	43.96 229	51.77 233	51.90 228	65.92 226	30.12 232	62.44 217	30.38 235	64.65 219	89.07 144	30.62 227	73.53 202	62.11 218	54.92 230	42.78 234

new_pmnet		52.29 229	63.16 217	39.61 233	58.89 227	44.70 237	48.78 240	34.73 230	65.88 201	17.85 239	73.42 193	80.00 177	23.06 236	67.00 221	62.28 216	54.36 231	48.81 230

MVE	41.12 19	51.80 230	60.92 223	41.16 232	35.21 240	34.14 241	48.45 241	41.39 225	69.11 185	19.53 238	63.33 221	73.80 201	63.56 144	67.19 220	61.51 219	38.85 237	57.38 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2356		51.28 231	53.40 234	48.80 227	58.53 228	52.10 227	63.63 229	40.83 226	51.94 235	39.35 229	53.46 234	45.22 236	28.78 228	64.39 227	60.77 221	61.70 223	45.92 232

PMMVS2		48.13 232	64.06 212	29.55 234	44.06 239	36.69 240	51.95 239	29.97 233	74.75 164	8.90 242	76.02 179	91.24 132	7.53 237	73.78 199	55.91 226	34.87 238	40.01 237

.test1245		43.71 233	44.35 236	42.95 230	72.41 195	48.16 232	69.76 214	57.08 190	76.79 153	32.10 233	80.12 144	35.41 241	25.87 231	67.23 218	1.08 238	0.48 241	1.68 238

GG-mvs-BLEND		41.63 234	60.36 224	19.78 235	0.14 244	66.04 198	55.66 237	0.17 241	57.64 227	2.42 243	51.82 235	69.42 210	0.28 241	64.11 230	58.29 223	60.02 225	55.18 222

test123		1.06 235	1.41 237	0.64 237	0.39 242	0.48 244	0.52 246	0.25 240	1.11 242	1.37 244	2.01 241	1.98 245	0.87 239	1.43 239	1.27 237	0.46 243	1.62 240

testmvs		0.93 236	1.37 238	0.41 238	0.36 243	0.36 245	0.62 245	0.39 239	1.48 241	0.18 245	2.41 240	1.31 246	0.41 240	1.25 240	1.08 238	0.48 241	1.68 238

sosnet-low-res		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

sosnet		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

Anonymous202405211			84.68 108		83.92 89	79.45 123	79.03 164	67.79 87	82.01 103		88.77 89	92.58 110	55.93 173	86.68 102	84.26 101	88.92 100	78.98 143

our_test_3					73.27 188	70.91 181	83.26 135

ambc			88.38 59		91.62 16	87.97 49	84.48 131		88.64 41	87.93 16	87.38 98	94.82 81	74.53 73	89.14 81	83.86 109	85.94 162	86.84 74

MTAPA										89.37 9		94.85 79

MTMP										90.54 5		95.16 71

Patchmatch-RL test							4.13 244

tmp_tt				13.54 236	16.73 241	6.42 243	8.49 243	2.36 238	28.69 240	27.44 237	18.40 239	13.51 244	3.70 238	33.23 237	36.26 236	22.54 240

XVS					91.28 24	91.23 8	96.89 2			87.14 28		94.53 84				95.84 15

X-MVStestdata					91.28 24	91.23 8	96.89 2			87.14 28		94.53 84				95.84 15

abl_6				79.30 103	84.98 77	85.78 65	90.50 64	66.88 92	77.08 152	74.02 121	73.29 195	89.34 142	68.94 123			90.49 82	85.98 80

mPP-MVS					93.05 4							95.77 53

NP-MVS									78.65 143

Patchmtry						56.88 217	64.47 227	67.74 88		72.30 128

DeepMVS_CX						17.78 242	20.40 242	6.69 237	31.41 239	9.80 241	38.61 238	34.88 243	33.78 224	28.41 238		23.59 239	45.77 233

High-resolution multi-view benchmark