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 17	96.01 49	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 56	94.95 5	91.27 3	94.11 16	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
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 25	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
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 77	95.57 61	84.25 7	95.24 20	94.27 13	95.97 11	93.85 8

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 43	89.17 11	87.00 105	96.34 38	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 57	88.75 13	89.00 85	94.38 88	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 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 28	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 19	88.56 9	91.24 27	91.60 6	96.49 9	77.53 25	87.89 45	86.87 32	87.24 101	96.46 32	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 20	88.43 10	90.88 31	91.15 11	95.35 22	77.65 24	86.26 63	87.23 25	90.45 69	97.35 20	83.20 15	95.44 16	93.41 21	96.28 8	92.63 25

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 71	87.67 19	87.02 104	95.26 69	83.62 13	95.01 24	93.94 16	95.79 19	93.40 19

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 79	82.54 21	95.05 23	94.83 7	95.14 27	91.94 33

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 58	92.22 113	79.16 36	94.32 30	93.72 19	94.75 34	94.93 5

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 21	94.22 90	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 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 84	94.44 87	81.68 26	94.17 32	94.19 14	95.81 17	93.87 7

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 27	92.22 113	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)
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 50	85.33 41	88.91 87	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
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 61	85.32 42	88.23 91	94.67 83	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 35	81.79 77	92.68 30	95.08 75	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 34	87.73 18	90.04 71	91.80 121	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 15	78.50 100	93.44 20	95.82 53	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 14	86.87 32	91.07 61	97.29 21	79.13 37	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 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

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 46	81.83 76	92.92 28	95.15 73	82.23 22	93.58 34	92.25 34	94.87 30	93.01 23

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 47	85.68 40	80.05 147	95.74 56	84.77 6	94.28 31	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 49	93.55 7	82.66 68	92.32 40	98.35 8	80.29 31	95.28 18	92.34 32	95.52 22	90.43 47

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 52	87.14 28	78.98 151	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 29	87.49 49	80.35 89	92.38 38	94.32 89	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 38	79.80 91	93.01 26	93.53 98	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 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

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 83	79.47 95	91.48 49	94.85 80	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 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

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 71	74.79 115	88.83 88	88.90 144	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 58	90.83 19	87.24 23	89.71 75	92.07 117	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 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

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

ACMH	78.40 12	88.94 36	92.62 14	84.65 40	86.45 66	87.16 55	91.47 46	68.79 77	95.49 3	89.74 6	93.55 19	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
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

HPM-MVS++		88.74 38	89.54 52	87.80 17	92.58 7	85.69 67	95.10 24	78.01 21	87.08 54	87.66 20	87.89 94	92.07 117	80.28 32	90.97 68	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 65	82.86 4	88.79 40	75.16 113	94.87 7	97.68 17	71.05 109	96.16 6	93.18 24	92.85 56	89.64 54

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 64	81.39 84	90.21 70	96.08 46	76.38 56	88.30 89	86.70 81	91.12 78	95.64 1

OMC-MVS		88.16 41	91.34 39	84.46 44	86.85 63	90.63 22	93.01 39	67.00 90	90.35 26	87.40 22	86.86 107	96.35 37	77.66 47	92.63 47	90.84 43	94.84 31	91.68 36

3Dnovator+	83.71 3	88.13 42	90.00 49	85.94 29	86.82 64	91.06 13	94.26 32	75.39 42	88.85 39	85.76 39	85.74 117	86.92 154	78.02 44	93.03 39	92.21 35	95.39 25	92.21 31

CSCG		88.12 43	91.45 36	84.23 46	88.12 57	90.59 24	90.57 56	68.60 79	91.37 13	83.45 65	89.94 72	95.14 74	78.71 39	91.45 56	88.21 68	95.96 12	93.44 17

RPSCF		88.05 44	92.61 15	82.73 62	84.24 84	88.40 41	90.04 70	66.29 94	91.46 11	82.29 70	88.93 86	96.01 49	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 48	85.22 38	87.48 60	90.01 30	93.79 33	73.54 51	88.93 37	83.89 54	89.40 79	90.84 131	80.26 33	90.62 72	90.19 50	92.36 63	92.03 32

DeepC-MVS_fast	81.78 5	87.38 46	89.64 50	84.75 39	89.89 41	90.70 21	92.74 41	74.45 47	86.02 64	82.16 74	86.05 113	91.99 120	75.84 62	91.16 61	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 47	83.19 53	85.22 75	83.69 76	90.03 71	68.20 84	91.01 17	86.71 35	94.80 8	98.46 5	77.69 46	91.10 63	85.98 86	91.30 75	88.19 65

CNVR-MVS		86.93 48	88.98 56	84.54 42	90.11 39	87.41 53	93.23 38	73.47 52	86.31 62	82.25 71	82.96 133	92.15 115	76.04 59	91.69 52	90.69 44	92.17 65	91.64 38

NCCC		86.74 49	87.97 68	85.31 36	90.64 34	87.25 54	93.27 37	74.59 46	86.50 59	83.72 56	75.92 178	92.39 111	77.08 51	91.72 51	90.68 45	92.57 61	91.30 42

train_agg		86.67 50	87.73 69	85.43 35	91.51 18	82.72 82	94.47 30	74.22 50	81.71 105	81.54 83	89.20 83	92.87 104	78.33 43	90.12 75	88.47 64	92.51 62	89.04 59

CDPH-MVS		86.66 51	88.52 59	84.48 43	89.61 43	88.27 43	92.86 40	72.69 55	80.55 121	82.71 67	86.92 106	93.32 100	75.55 64	91.00 66	89.85 52	93.47 45	89.71 53

Gipuma		86.47 52	89.25 54	83.23 52	83.88 90	78.78 124	85.35 123	68.42 81	92.69 9	89.03 12	91.94 43	96.32 40	81.80 25	94.45 27	86.86 77	90.91 79	83.69 95
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PHI-MVS		86.37 53	88.14 65	84.30 45	86.65 65	87.56 51	90.76 53	70.16 65	82.55 93	89.65 7	84.89 125	92.40 110	75.97 60	90.88 70	89.70 54	92.58 59	89.03 60

MSLP-MVS++		86.29 54	89.10 55	83.01 55	85.71 73	89.79 33	87.04 113	74.39 48	85.17 73	78.92 98	77.59 158	93.57 96	82.60 20	93.23 36	91.88 39	89.42 94	92.46 27

v52		86.26 55	90.85 41	80.91 74	72.49 191	81.25 104	90.55 58	60.30 172	90.43 25	87.24 23	94.64 11	98.30 10	83.16 18	92.86 43	86.82 79	91.69 69	91.65 37

V4		86.26 55	90.85 41	80.91 74	72.49 191	81.25 104	90.55 58	60.31 171	90.44 24	87.23 25	94.64 11	98.31 9	83.17 16	92.87 42	86.82 79	91.69 69	91.64 38

TAPA-MVS	78.00 13	85.88 57	88.37 61	82.96 57	84.69 78	88.62 40	90.62 54	64.22 126	89.15 36	88.05 16	78.83 152	93.71 93	76.20 58	90.11 76	88.22 67	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 45	79.88 94	64.64 217	76.35 148	96.28 13	53.53 205	85.63 68	81.59 82	92.81 29	97.71 16	86.88 2	94.56 26	92.83 25	96.35 6	93.84 9

TSAR-MVS + COLMAP		85.51 59	88.36 62	82.19 63	86.05 70	87.69 50	90.50 63	70.60 64	86.40 60	82.33 69	89.69 76	92.52 108	74.01 82	87.53 93	86.84 78	89.63 90	87.80 70

CNLPA		85.50 60	88.58 57	81.91 65	84.55 81	87.52 52	90.89 51	63.56 136	88.18 44	84.06 50	83.85 130	91.34 128	76.46 55	91.27 58	89.00 62	91.96 66	88.88 61

PLC	76.06 15	85.38 61	87.46 71	82.95 58	85.79 72	88.84 38	88.86 80	68.70 78	87.06 55	83.60 59	79.02 150	90.05 136	77.37 50	90.88 70	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
TSAR-MVS + GP.		85.32 62	87.41 73	82.89 59	90.07 40	85.69 67	89.07 78	72.99 54	82.45 95	74.52 118	85.09 123	87.67 151	79.24 35	91.11 62	90.41 47	91.45 72	89.45 55

TranMVSNet+NR-MVSNet		85.23 63	89.38 53	80.39 91	88.78 50	83.77 75	87.40 101	76.75 33	85.47 69	68.99 144	95.18 6	97.55 19	67.13 131	91.61 53	89.13 61	93.26 48	82.95 107

v748		85.21 64	89.62 51	80.08 93	80.71 133	80.27 117	85.05 126	63.79 134	90.47 23	83.54 62	94.21 15	98.52 2	76.84 53	90.97 68	84.25 101	90.53 82	88.62 63

HQP-MVS		85.02 65	86.41 79	83.40 51	89.19 46	86.59 59	91.28 47	71.60 59	82.79 91	83.48 64	78.65 154	93.54 97	72.55 99	86.49 102	85.89 88	92.28 64	90.95 45

UniMVSNet (Re)		84.95 66	88.53 58	80.78 78	87.82 59	84.21 72	88.03 90	76.50 36	81.18 116	69.29 141	92.63 34	96.83 25	69.07 121	91.23 60	89.60 56	93.97 42	84.00 93

DU-MVS		84.88 67	88.27 64	80.92 73	88.30 54	83.59 77	87.06 111	78.35 18	80.64 119	70.49 137	92.67 31	96.91 24	68.13 125	91.79 49	89.29 60	93.20 49	83.02 104

MCST-MVS		84.79 68	86.48 77	82.83 60	87.30 61	87.03 57	90.46 66	69.33 73	83.14 87	82.21 73	81.69 141	92.14 116	75.09 68	87.27 96	84.78 97	92.58 59	89.30 57

MVS_0304		84.73 69	86.19 82	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 99	72.55 99	88.62 85	87.69 70	91.93 67	88.05 68

UniMVSNet_NR-MVSNet		84.62 70	88.00 67	80.68 83	88.18 56	83.83 74	87.06 111	76.47 37	81.46 111	70.49 137	93.24 22	95.56 63	68.13 125	90.43 73	88.47 64	93.78 43	83.02 104

EG-PatchMatch MVS		84.35 71	87.55 70	80.62 86	86.38 67	82.24 87	86.75 115	64.02 131	84.24 79	78.17 102	89.38 80	95.03 77	78.78 38	89.95 77	86.33 83	89.59 91	85.65 83

AdaColmap		84.15 72	85.14 99	83.00 56	89.08 47	87.14 56	90.56 57	70.90 61	82.40 96	80.41 87	73.82 190	84.69 162	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 96	82.76 61	86.12 69	88.30 42	91.24 48	69.10 74	82.36 97	84.45 46	77.56 159	90.40 135	72.91 98	85.88 109	83.88 104	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 84	81.44 70	84.62 79	80.29 116	90.51 62	68.05 85	84.07 82	80.38 88	84.74 126	91.37 127	74.23 76	90.37 74	87.25 72	90.86 81	84.59 86

TinyColmap		83.79 75	86.12 83	81.07 72	83.42 97	81.44 99	85.42 121	68.55 80	88.71 41	89.46 8	87.60 96	92.72 105	70.34 115	89.29 80	81.94 125	89.20 95	81.12 125

Anonymous20231211		83.75 76	91.64 34	74.55 131	88.54 52	79.81 120	83.12 135	74.68 45	98.37 1	48.44 210	94.56 13	99.60 1	61.64 150	91.49 55	89.82 53	90.91 79	87.80 70

v13		83.75 76	86.20 81	80.89 76	83.38 98	81.93 90	88.58 83	66.09 97	83.55 84	84.28 47	92.67 31	96.79 26	74.67 72	84.42 131	79.72 146	88.36 106	84.31 89

v1192		83.61 78	85.23 97	81.72 67	84.05 86	82.15 88	89.54 73	66.20 95	81.38 113	86.76 34	91.79 46	96.03 48	74.88 70	81.81 162	80.92 133	88.91 98	82.50 112

v12		83.59 79	86.00 87	80.77 81	83.30 100	81.83 91	88.45 84	65.95 100	83.20 86	84.15 48	92.54 36	96.71 27	74.50 74	84.19 133	79.64 147	88.30 107	83.93 94

v1240		83.57 80	84.94 103	81.97 64	84.05 86	81.27 103	89.46 75	66.06 98	81.31 115	87.50 21	91.88 45	95.46 66	76.25 57	81.16 167	80.51 138	88.52 104	82.98 106

v1921920		83.49 81	84.94 103	81.80 66	83.78 91	81.20 107	89.50 74	65.91 101	81.64 107	87.18 27	91.70 47	95.39 67	75.85 61	81.56 165	80.27 141	88.60 102	82.80 108

v144192		83.43 82	84.97 102	81.63 69	83.43 96	81.23 106	89.42 76	66.04 99	81.45 112	86.40 36	91.46 51	95.70 60	75.76 63	82.14 158	80.23 142	88.74 99	82.57 111

V9		83.42 83	85.81 89	80.63 85	83.20 103	81.73 94	88.29 88	65.78 104	82.87 90	83.99 53	92.38 38	96.60 29	74.30 75	83.93 134	79.58 149	88.24 110	83.55 98

Vis-MVSNet		83.32 84	88.12 66	77.71 115	77.91 164	83.44 79	90.58 55	69.49 70	81.11 117	67.10 154	89.85 73	91.48 126	71.71 105	91.34 57	89.37 58	89.48 93	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 92	80.64 84	83.53 95	81.74 93	88.30 87	65.46 109	82.75 92	84.63 44	92.49 37	96.17 44	73.90 83	82.69 151	79.59 148	88.04 115	83.66 96

V14		83.23 86	85.59 91	80.48 89	83.09 106	81.63 96	88.13 89	65.61 106	82.53 94	83.81 55	92.17 41	96.50 30	74.07 80	83.66 136	79.51 151	88.17 112	83.16 102

v1144		83.22 87	85.01 100	81.14 71	83.76 92	81.60 97	88.95 79	65.58 107	81.89 101	85.80 38	91.68 48	95.84 52	74.04 81	82.12 159	80.56 137	88.70 101	81.41 122

MVS_111021_LR		83.20 88	85.33 94	80.73 82	82.88 109	78.23 128	89.61 72	65.23 112	82.08 100	81.19 85	85.31 120	92.04 119	75.22 65	89.50 78	85.90 87	90.24 84	84.23 90

v10		83.17 89	85.22 98	80.78 78	83.26 102	82.99 81	88.66 81	66.49 93	79.24 136	83.60 59	91.46 51	95.47 64	74.12 77	82.60 153	80.66 134	88.53 103	84.11 92

v15		83.06 90	85.39 93	80.35 92	83.01 107	81.53 98	87.98 92	65.47 108	82.19 99	83.66 58	92.00 42	96.40 36	73.87 84	83.39 138	79.44 152	88.10 114	82.76 109

PVSNet_Blended_VisFu		83.00 91	84.16 118	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 94	84.93 96	91.41 73	87.40 74

NR-MVSNet		82.89 92	87.43 72	77.59 117	83.91 89	83.59 77	87.10 110	78.35 18	80.64 119	68.85 145	92.67 31	96.50 30	54.19 180	87.19 99	88.68 63	93.16 52	82.75 110

CANet		82.84 93	84.60 107	80.78 78	87.30 61	85.20 69	90.23 68	69.00 75	72.16 172	78.73 99	84.49 127	90.70 133	69.54 119	87.65 92	86.17 84	89.87 88	85.84 82

Baseline_NR-MVSNet		82.79 94	86.51 76	78.44 112	88.30 54	75.62 159	87.81 93	74.97 44	81.53 109	66.84 155	94.71 10	96.46 32	66.90 132	91.79 49	83.37 112	85.83 161	82.09 117

v7		82.76 95	84.65 106	80.55 87	83.27 101	81.77 92	88.66 81	65.10 113	79.23 137	83.60 59	91.47 50	95.47 64	74.12 77	82.61 152	80.66 134	88.52 104	81.35 123

EPP-MVSNet		82.76 95	86.47 78	78.45 111	86.00 71	84.47 71	85.39 122	68.42 81	84.17 80	62.97 164	89.26 82	76.84 187	72.13 102	92.56 48	90.40 48	95.76 20	87.56 73

CLD-MVS		82.75 97	87.22 74	77.54 118	88.01 58	85.76 66	90.23 68	54.52 198	82.28 98	82.11 75	88.48 90	95.27 68	63.95 141	89.41 79	88.29 66	86.45 149	81.01 126
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	83.87 124	80.52 88	84.51 82	81.32 100	87.53 99	68.05 85	74.94 161	79.67 93	82.37 137	92.31 112	72.21 101	85.06 118	86.91 76	91.18 76	84.20 91

v1		82.27 99	84.32 111	79.87 95	82.86 110	80.32 113	87.57 98	63.47 140	81.87 103	84.13 49	91.34 53	96.29 41	73.23 94	82.39 154	79.08 163	87.94 117	78.98 140

v1141		82.26 100	84.32 111	79.85 96	82.86 110	80.31 114	87.58 96	63.48 138	81.86 104	84.03 52	91.33 54	96.28 42	73.23 94	82.39 154	79.08 163	87.93 118	78.97 141

divwei89l23v2f112		82.26 100	84.32 111	79.85 96	82.86 110	80.31 114	87.58 96	63.48 138	81.88 102	84.05 51	91.33 54	96.27 43	73.23 94	82.39 154	79.08 163	87.93 118	78.97 141

3Dnovator	79.41 10	82.21 102	86.07 85	77.71 115	79.31 148	84.61 70	87.18 108	61.02 168	85.65 67	76.11 106	85.07 124	85.38 160	70.96 111	87.22 97	86.47 82	91.66 71	88.12 67

v8		82.20 103	84.56 108	79.45 99	82.42 114	81.65 95	87.26 102	64.27 124	79.36 132	81.70 78	91.04 64	95.75 55	73.30 92	82.82 147	79.18 160	87.74 122	82.09 117

v2v482		82.20 103	84.26 115	79.81 98	82.67 113	80.18 118	87.67 95	63.96 133	81.69 106	84.73 43	91.27 57	96.33 39	72.05 103	81.94 161	79.56 150	87.79 121	78.84 143

v17		82.09 105	84.45 109	79.33 101	82.41 115	81.31 101	87.26 102	64.50 123	78.72 139	80.73 86	90.90 65	95.57 61	73.37 88	83.06 139	79.25 156	87.70 126	82.35 115

Effi-MVS+-dtu		82.04 106	83.39 130	80.48 89	85.48 74	86.57 61	88.40 85	68.28 83	69.04 184	73.13 125	76.26 170	91.11 130	74.74 71	88.40 87	87.76 69	92.84 57	84.57 87

MAR-MVS		81.98 107	82.92 132	80.88 77	85.18 76	85.85 64	89.13 77	69.52 68	71.21 176	82.25 71	71.28 200	88.89 145	69.69 116	88.71 84	86.96 74	89.52 92	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
v16		81.92 108	84.32 111	79.12 107	82.31 120	81.29 102	87.20 107	64.51 122	78.16 143	79.76 92	90.86 66	95.23 70	73.29 93	83.05 140	79.29 155	87.63 127	82.34 116

v6		81.77 109	83.96 121	79.22 104	82.41 115	80.45 112	87.26 102	62.91 150	79.29 133	81.65 79	91.08 60	95.74 56	73.32 89	82.84 144	79.21 159	87.73 123	79.07 137

v1neww		81.76 110	83.95 122	79.21 105	82.41 115	80.46 110	87.26 102	62.93 146	79.28 134	81.62 80	91.06 62	95.72 58	73.31 90	82.83 145	79.22 157	87.73 123	79.07 137

v7new		81.76 110	83.95 122	79.21 105	82.41 115	80.46 110	87.26 102	62.93 146	79.28 134	81.62 80	91.06 62	95.72 58	73.31 90	82.83 145	79.22 157	87.73 123	79.07 137

IS_MVSNet		81.72 112	85.01 100	77.90 114	86.19 68	82.64 84	85.56 120	70.02 66	80.11 125	63.52 161	87.28 100	81.18 172	67.26 129	91.08 65	89.33 59	94.82 32	83.42 100

v18		81.62 113	83.99 120	78.86 108	82.08 124	81.12 108	86.93 114	64.24 125	77.44 145	79.47 95	90.53 67	94.99 78	72.99 97	82.72 150	79.18 160	87.48 130	81.91 120

FPMVS		81.56 114	84.04 119	78.66 109	82.92 108	75.96 154	86.48 118	65.66 105	84.67 76	71.47 133	77.78 156	83.22 165	77.57 48	91.24 59	90.21 49	87.84 120	85.21 84

Fast-Effi-MVS+		81.42 115	83.82 125	78.62 110	82.24 122	80.62 109	87.72 94	63.51 137	73.01 165	74.75 116	83.80 131	92.70 106	73.44 87	88.15 91	85.26 92	90.05 85	83.17 101

USDC		81.39 116	83.07 131	79.43 100	81.48 129	78.95 123	82.62 139	66.17 96	87.45 51	90.73 4	82.40 136	93.65 95	66.57 134	83.63 137	77.97 168	89.00 97	77.45 150

MSDG		81.39 116	84.23 117	78.09 113	82.40 119	82.47 86	85.31 125	60.91 169	79.73 128	80.26 90	86.30 110	88.27 149	69.67 117	87.20 98	84.98 95	89.97 87	80.67 127

canonicalmvs		81.22 118	86.04 86	75.60 125	83.17 105	83.18 80	80.29 150	65.82 103	85.97 66	67.98 152	77.74 157	91.51 125	65.17 137	88.62 85	86.15 85	91.17 77	89.09 58

pmmvs6		80.46 119	88.34 63	71.26 143	81.96 125	77.51 133	77.54 168	68.83 76	93.72 6	55.92 176	93.94 18	98.03 12	55.94 170	89.21 81	85.61 89	87.36 134	80.38 128

QAPM		80.43 120	84.34 110	75.86 123	79.40 147	82.06 89	79.86 155	61.94 163	83.28 85	74.73 117	81.74 140	85.44 159	70.97 110	84.99 125	84.71 98	88.29 108	88.14 66

PM-MVS		80.42 121	83.63 127	76.67 120	78.04 159	72.37 175	87.14 109	60.18 174	80.13 124	71.75 132	86.12 112	93.92 92	77.08 51	86.56 101	85.12 94	85.83 161	81.18 124

IterMVS-LS		79.79 122	82.56 134	76.56 122	81.83 127	77.85 131	79.90 154	69.42 72	78.93 138	71.21 134	90.47 68	85.20 161	70.86 112	80.54 172	80.57 136	86.15 152	84.36 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		79.71 123	83.74 126	75.01 128	79.31 148	82.68 83	84.79 128	60.06 175	75.43 159	69.09 143	86.13 111	89.38 138	67.16 130	85.12 117	83.87 105	89.65 89	83.57 97
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		79.64 124	82.06 137	76.83 119	80.05 139	72.64 173	87.47 100	66.59 92	80.83 118	73.50 122	89.32 81	93.20 101	67.78 127	80.78 170	81.64 127	85.58 164	76.01 152

UGNet		79.62 125	85.91 88	72.28 141	73.52 184	83.91 73	86.64 116	69.51 69	79.85 127	62.57 166	85.82 116	89.63 137	53.18 187	88.39 88	87.35 71	88.28 109	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		79.59 126	83.59 128	74.93 130	69.61 203	77.05 141	86.59 117	55.84 194	78.42 142	77.29 103	89.84 74	95.08 75	74.12 77	83.05 140	80.11 144	86.12 153	81.59 121

EPNet		79.36 127	79.44 145	79.27 103	89.51 44	77.20 138	88.35 86	77.35 32	68.27 186	74.29 119	76.31 168	79.22 176	59.63 155	85.02 124	85.45 91	86.49 148	84.61 85
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v148		79.33 128	82.32 136	75.84 124	80.14 138	75.74 156	81.98 142	57.06 190	81.51 110	79.36 97	89.42 78	96.42 34	71.32 106	81.54 166	75.29 182	85.20 167	76.32 151

FC-MVSNet-train		79.20 129	86.29 80	70.94 148	84.06 85	77.67 132	85.68 119	64.11 130	82.90 89	52.22 195	92.57 35	93.69 94	49.52 204	88.30 89	86.93 75	90.03 86	81.95 119

TransMVSNet (Re)		79.05 130	86.66 75	70.18 156	83.32 99	75.99 153	77.54 168	63.98 132	90.68 21	55.84 177	94.80 8	96.06 47	53.73 186	86.27 105	83.22 113	86.65 143	79.61 135

no-one		78.59 131	85.28 95	70.79 149	59.01 224	68.77 189	76.62 178	46.06 216	80.25 123	75.75 109	81.85 139	97.75 15	83.63 12	90.99 67	87.20 73	83.67 174	90.14 49

OpenMVS	75.38 16	78.44 132	81.39 139	74.99 129	80.46 135	79.85 119	79.99 152	58.31 185	77.34 147	73.85 121	77.19 163	82.33 170	68.60 124	84.67 128	81.95 124	88.72 100	86.40 79

pm-mvs1		78.21 133	85.68 90	69.50 162	80.38 136	75.73 157	76.25 183	65.04 114	87.59 48	54.47 182	93.16 24	95.99 51	54.20 179	86.37 103	82.98 115	86.64 144	77.96 148

FMVSNet1		78.20 134	84.83 105	70.46 153	78.62 154	79.03 122	77.90 167	67.53 89	83.02 88	55.10 179	87.19 102	93.18 102	55.65 172	85.57 110	83.39 109	87.98 116	82.40 113

DI_MVS_plusplus_trai		77.64 135	79.64 144	75.31 127	79.87 143	76.89 142	81.55 145	63.64 135	76.21 155	72.03 130	85.59 119	82.97 166	66.63 133	79.27 175	77.78 170	88.14 113	78.76 144

tfpnnormal		77.16 136	84.26 115	68.88 165	81.02 132	75.02 160	76.52 180	63.30 142	87.29 52	52.40 193	91.24 58	93.97 91	54.85 178	85.46 113	81.08 131	85.18 168	75.76 155

conf0.05thres1000		77.12 137	82.38 135	70.98 146	82.30 121	77.95 130	79.86 155	64.74 118	86.63 58	53.93 183	85.74 117	75.63 197	56.85 164	88.98 83	84.10 102	88.20 111	77.61 149

Fast-Effi-MVS+-dtu		76.92 138	77.18 160	76.62 121	79.55 145	79.17 121	84.80 127	77.40 30	64.46 206	68.75 147	70.81 206	86.57 155	63.36 147	81.74 163	81.76 126	85.86 160	75.78 154

MVS_Test		76.72 139	79.40 146	73.60 135	78.85 153	74.99 161	79.91 153	61.56 165	69.67 180	72.44 126	85.98 114	90.78 132	63.50 145	78.30 178	75.74 181	85.33 166	80.31 132

MDA-MVSNet-bldmvs		76.51 140	82.87 133	69.09 164	50.71 235	74.72 165	84.05 132	60.27 173	81.62 108	71.16 135	88.21 92	91.58 123	69.62 118	92.78 44	77.48 173	78.75 189	73.69 169

EU-MVSNet		76.48 141	80.53 142	71.75 142	67.62 208	70.30 180	81.74 143	54.06 201	75.47 158	71.01 136	80.10 145	93.17 103	73.67 85	83.73 135	77.85 169	82.40 180	83.07 103

PVSNet_BlendedMVS		76.45 142	78.12 151	74.49 132	76.76 173	78.46 125	79.65 157	63.26 143	65.42 202	73.15 123	75.05 184	88.96 142	66.51 135	82.73 148	77.66 171	87.61 128	78.60 145

PVSNet_Blended		76.45 142	78.12 151	74.49 132	76.76 173	78.46 125	79.65 157	63.26 143	65.42 202	73.15 123	75.05 184	88.96 142	66.51 135	82.73 148	77.66 171	87.61 128	78.60 145

Vis-MVSNet (Re-imp)		76.15 144	80.84 141	70.68 150	83.66 93	74.80 164	81.66 144	69.59 67	80.48 122	46.94 212	87.44 97	80.63 174	53.14 188	86.87 100	84.56 99	89.12 96	71.12 178

PatchMatch-RL		76.05 145	76.64 166	75.36 126	77.84 165	69.87 183	81.09 147	63.43 141	71.66 174	68.34 150	71.70 196	81.76 171	74.98 69	84.83 127	83.44 108	86.45 149	73.22 171

pmmvs4		75.92 146	77.48 158	74.10 134	78.21 158	70.94 177	84.06 131	64.78 117	75.13 160	68.47 149	84.12 128	83.32 164	64.74 140	75.93 189	79.14 162	84.31 172	73.77 167

FC-MVSNet-test		75.91 147	83.59 128	66.95 183	76.63 179	69.07 186	85.33 124	64.97 116	84.87 75	41.95 218	93.17 23	87.04 153	47.78 208	91.09 64	85.56 90	85.06 169	74.34 158

CVMVSNet		75.65 148	77.62 157	73.35 138	71.95 195	69.89 182	83.04 138	60.84 170	69.12 182	68.76 146	79.92 148	78.93 178	73.64 86	81.02 168	81.01 132	81.86 183	83.43 99

IB-MVS	71.28 17	75.21 149	77.00 163	73.12 139	76.76 173	77.45 134	83.05 137	58.92 181	63.01 211	64.31 160	59.99 229	87.57 152	68.64 123	86.26 106	82.34 123	87.05 141	82.36 114
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		75.04 150	78.45 148	71.07 144	77.27 169	77.96 129	83.88 133	58.00 186	64.11 207	68.67 148	75.65 180	88.37 148	53.92 182	82.05 160	81.11 130	84.67 170	79.88 134

GA-MVS		75.01 151	76.39 168	73.39 136	78.37 155	75.66 158	80.03 151	58.40 184	70.51 178	75.85 108	83.24 132	76.14 191	63.75 142	77.28 182	76.62 177	83.97 173	75.30 157

view800		74.68 152	78.74 147	69.94 157	81.12 131	76.59 143	78.94 164	63.24 145	78.56 141	53.06 188	75.61 181	76.26 190	56.07 169	86.32 104	83.75 107	87.18 140	74.10 162

FMVSNet2		74.43 153	79.70 143	68.27 168	76.76 173	77.36 135	75.77 188	65.36 111	72.28 170	52.97 189	81.92 138	85.61 158	52.73 191	80.66 171	79.73 145	86.04 156	80.37 129

thres600view7		74.34 154	78.43 149	69.56 161	80.47 134	76.28 149	78.65 165	62.56 155	77.39 146	52.53 191	74.03 189	76.78 188	55.90 171	85.06 118	85.19 93	87.25 138	74.29 160

view600		74.08 155	78.15 150	69.32 163	80.27 137	75.82 155	78.27 166	62.20 159	77.26 148	52.80 190	74.07 188	76.86 186	55.57 174	84.90 126	84.43 100	86.84 142	73.71 168

diffmvs		73.65 156	77.10 161	69.63 160	73.21 186	69.52 184	79.35 161	57.48 187	73.80 163	68.08 151	87.10 103	82.39 168	61.36 151	74.27 192	74.51 183	78.31 190	78.14 147

IterMVS		73.62 157	76.53 167	70.23 155	71.83 196	77.18 139	80.69 149	53.22 206	72.23 171	66.62 156	85.21 121	78.96 177	69.54 119	76.28 188	71.63 191	79.45 186	74.25 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet1		73.40 158	81.85 138	63.55 198	72.90 188	64.37 201	84.58 129	53.60 204	90.84 18	53.92 184	87.75 95	96.10 45	45.31 211	85.37 115	79.32 154	70.98 205	69.18 188

HyFIR lowres test		73.29 159	74.14 181	72.30 140	73.08 187	78.33 127	83.12 135	62.41 158	63.81 208	62.13 167	76.67 167	78.50 179	71.09 108	74.13 193	77.47 174	81.98 181	70.10 182

Anonymous20240521		73.28 160	81.15 140	64.11 197	83.66 93	74.98 162	72.96 201	64.19 127	76.63 152	33.27 231	88.78 89	91.59 122	48.70 206	82.93 142	79.06 166	81.88 182	74.06 163

tfpn_n400		73.26 161	77.94 153	67.79 178	79.91 141	73.32 168	76.38 181	62.04 160	84.26 77	48.53 207	76.23 171	71.50 204	53.83 183	86.22 107	81.59 128	86.05 154	72.47 173

tfpnconf		73.26 161	77.94 153	67.79 178	79.91 141	73.32 168	76.38 181	62.04 160	84.26 77	48.53 207	76.23 171	71.50 204	53.83 183	86.22 107	81.59 128	86.05 154	72.47 173

GBi-Net		73.17 163	77.64 155	67.95 173	76.76 173	77.36 135	75.77 188	64.57 119	62.99 212	51.83 196	76.05 174	77.76 182	52.73 191	85.57 110	83.39 109	86.04 156	80.37 129

test1		73.17 163	77.64 155	67.95 173	76.76 173	77.36 135	75.77 188	64.57 119	62.99 212	51.83 196	76.05 174	77.76 182	52.73 191	85.57 110	83.39 109	86.04 156	80.37 129

thres400		73.13 165	76.99 164	68.62 166	79.46 146	74.93 163	77.23 170	61.23 166	75.54 157	52.31 194	72.20 195	77.10 185	54.89 176	82.92 143	82.62 122	86.57 146	73.66 170

CDS-MVSNet		73.07 166	77.02 162	68.46 167	81.62 128	72.89 172	79.56 159	70.78 63	69.56 181	52.52 192	77.37 162	81.12 173	42.60 214	84.20 132	83.93 103	83.65 175	70.07 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpn		72.99 167	75.25 176	70.36 154	81.87 126	77.09 140	79.28 162	64.16 128	79.58 130	53.14 187	76.97 165	48.75 231	56.35 168	87.31 95	82.75 117	87.35 135	74.31 159

MDTV_nov1_ep13_2view		72.96 168	75.59 173	69.88 158	71.15 200	64.86 200	82.31 141	54.45 199	76.30 154	78.32 101	86.52 108	91.58 123	61.35 152	76.80 183	66.83 203	71.70 200	66.26 195

tfpnview11		72.88 169	77.37 159	67.65 180	79.81 144	73.43 167	76.23 184	61.97 162	81.37 114	48.53 207	76.23 171	71.50 204	53.78 185	85.45 114	82.77 116	85.56 165	70.87 181

gg-mvs-nofinetune		72.68 170	75.21 177	69.73 159	81.48 129	69.04 187	70.48 208	76.67 34	86.92 56	67.80 153	88.06 93	64.67 212	42.12 216	77.60 180	73.65 185	79.81 185	66.57 194

thres200		72.41 171	76.00 172	68.21 169	78.28 156	76.28 149	74.94 193	62.56 155	72.14 173	51.35 199	69.59 211	76.51 189	54.89 176	85.06 118	80.51 138	87.25 138	71.92 176

tfpn1000		72.27 172	76.88 165	66.88 184	79.01 152	74.04 166	76.60 179	61.15 167	79.65 129	45.52 214	77.41 161	67.98 210	52.47 194	85.22 116	82.99 114	86.54 147	70.89 179

tfpn200view9		72.01 173	75.40 174	68.06 170	77.97 160	76.44 145	77.04 172	62.67 153	66.81 192	50.82 200	67.30 213	75.67 193	52.46 195	85.06 118	82.64 118	87.41 133	73.86 166

conf200view11		72.00 174	75.40 174	68.04 171	77.97 160	76.44 145	77.04 172	62.68 151	66.81 192	50.69 202	67.30 213	75.67 193	52.46 195	85.06 118	82.64 118	87.42 131	73.87 164

EPNet_dtu		71.90 175	73.03 185	70.59 151	78.28 156	61.64 206	82.44 140	64.12 129	63.26 210	69.74 139	71.47 198	82.41 167	51.89 200	78.83 177	78.01 167	77.07 191	75.60 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn111		71.60 176	74.66 179	68.04 171	77.97 160	76.44 145	77.04 172	62.68 151	66.81 192	50.69 202	62.10 224	75.67 193	52.46 195	85.06 118	82.64 118	87.42 131	73.87 164

gm-plane-assit		71.56 177	69.99 190	73.39 136	84.43 83	73.21 171	90.42 67	51.36 212	84.08 81	76.00 107	91.30 56	37.09 238	59.01 158	73.65 198	70.24 195	79.09 188	60.37 211

CMPMVS	55.74 18	71.56 177	76.26 169	66.08 190	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
FMVSNet3		71.40 179	75.20 178	66.97 182	75.00 181	76.59 143	74.29 194	64.57 119	62.99 212	51.83 196	76.05 174	77.76 182	51.49 201	76.58 186	77.03 176	84.62 171	79.43 136

MS-PatchMatch		71.18 180	73.99 182	67.89 177	77.16 170	71.76 176	77.18 171	56.38 193	67.35 188	55.04 180	74.63 186	75.70 192	62.38 149	76.62 185	75.97 180	79.22 187	75.90 153

test20.03		69.91 181	76.20 170	62.58 200	84.01 88	67.34 193	75.67 192	65.88 102	79.98 126	40.28 223	82.65 134	89.31 140	39.63 218	77.41 181	73.28 186	69.98 206	63.40 203

thres100view900		69.86 182	72.97 186	66.24 187	77.97 160	72.49 174	73.29 198	59.12 179	66.81 192	50.82 200	67.30 213	75.67 193	50.54 203	78.24 179	79.40 153	85.71 163	70.88 180

conf0.01		69.59 183	71.01 189	67.95 173	77.74 166	76.09 151	77.04 172	62.58 154	66.81 192	50.54 204	63.00 222	51.78 230	52.46 195	84.53 129	82.64 118	87.32 136	72.19 175

CR-MVSNet		69.56 184	68.34 199	70.99 145	72.78 190	67.63 191	64.47 225	67.74 87	59.93 221	72.30 127	80.10 145	56.77 221	65.04 138	71.64 206	72.91 187	83.61 178	69.40 186

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	56.25 175	82.41 135	88.59 146	47.21 209	73.21 202	74.23 184	81.30 184	68.03 191

CHOSEN 1792x2688		68.80 186	71.09 188	66.13 189	69.11 205	68.89 188	78.98 163	54.68 196	61.63 218	56.69 173	71.56 197	78.39 180	67.69 128	72.13 204	72.01 190	69.63 208	73.02 172

conf0.002		68.60 187	69.17 194	67.92 176	77.66 167	76.01 152	77.04 172	62.56 155	66.81 192	50.51 205	61.21 227	44.01 235	52.46 195	84.44 130	80.29 140	87.31 137	71.44 177

tpmp4_e23		68.32 188	66.04 203	70.98 146	77.52 168	69.23 185	80.99 148	65.46 109	68.09 187	69.25 142	70.77 208	54.03 227	59.35 156	69.01 213	63.02 210	73.34 197	68.15 190

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	41.93 219	60.96 228	68.69 209	46.23 210	82.16 157	80.14 143	86.34 151	69.56 185

testgi		68.20 189	76.05 171	59.04 207	79.99 140	67.32 194	81.16 146	51.78 210	84.91 74	39.36 226	73.42 191	95.19 71	32.79 224	76.54 187	70.40 194	69.14 209	64.55 199

MVSTER		68.08 191	69.73 192	66.16 188	66.33 215	70.06 181	75.71 191	52.36 208	55.18 230	58.64 170	70.23 210	56.72 222	57.34 163	79.68 174	76.03 179	86.61 145	80.20 133

Anonymous20231206		67.28 192	73.41 184	60.12 206	76.45 180	63.61 204	74.21 195	56.52 192	76.35 153	42.23 217	75.81 179	90.47 134	41.51 217	74.52 190	69.97 196	69.83 207	63.17 204

RPMNet		67.02 193	63.99 211	70.56 152	71.55 198	67.63 191	75.81 186	69.44 71	59.93 221	63.24 162	64.32 218	47.51 232	59.68 154	70.37 210	69.64 197	83.64 176	68.49 189

CostFormer		66.81 194	66.94 201	66.67 185	72.79 189	68.25 190	79.55 160	55.57 195	65.52 201	62.77 165	76.98 164	60.09 216	56.73 166	65.69 223	62.35 211	72.59 198	69.71 184

thresconf0.02		66.71 195	68.28 200	64.89 196	76.83 172	70.38 179	71.62 206	58.90 182	77.64 144	47.04 211	62.10 224	46.01 233	51.32 202	78.85 176	76.09 178	83.62 177	66.85 193

PatchT		66.25 196	66.76 202	65.67 193	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

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	60.48 169	79.76 149	70.57 207	55.47 175	72.77 203	67.19 202	65.81 215	64.71 198

dps		65.14 198	64.50 209	65.89 192	71.41 199	65.81 198	71.44 207	61.59 164	58.56 224	61.43 168	75.45 182	52.70 229	58.06 161	69.57 212	64.65 206	71.39 203	64.77 197

MDTV_nov1_ep13		64.96 199	64.77 208	65.18 195	67.08 211	62.46 205	75.80 187	51.10 213	62.27 217	69.74 139	74.12 187	62.65 213	55.64 173	68.19 215	62.16 215	71.70 200	61.57 210

PatchmatchNet		64.81 200	63.74 213	66.06 191	69.21 204	58.62 211	73.16 199	60.01 176	65.92 198	66.19 158	76.27 169	59.09 217	60.45 153	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.
tpm cat1		64.79 201	62.74 217	67.17 181	74.61 183	65.91 197	76.18 185	59.32 178	64.88 205	66.41 157	71.21 201	53.56 228	59.17 157	61.53 229	58.16 222	67.33 212	63.95 200

DWT-MVSNet_training		63.07 202	60.04 224	66.61 186	71.64 197	65.27 199	76.80 177	53.82 202	55.90 227	63.07 163	62.23 223	41.87 237	62.54 148	64.32 226	63.71 208	71.78 199	66.97 192

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	41.26 222	71.38 199	82.38 169	36.38 220	71.43 208	67.32 201	66.45 214	59.83 213

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	38.00 228	77.79 155	75.15 198	42.60 214	74.48 191	72.81 189	68.70 210	57.75 218

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

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	57.39 172	71.19 202	85.73 156	53.96 181	73.36 201	69.43 198	73.47 196	62.55 206

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 70	28.81 235	91.94 43	96.41 35	25.28 232	76.80 183	53.66 229	57.99 226	58.69 215

test-LLR		62.15 208	59.46 228	65.29 194	79.07 150	52.66 223	69.46 217	62.93 146	50.76 234	53.81 185	63.11 220	58.91 218	52.87 189	66.54 221	62.34 212	73.59 194	61.87 208

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	55.32 178	68.23 212	78.39 180	58.09 160	70.21 211	71.56 192	83.42 179	63.66 201

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	33.71 230	61.58 226	71.58 203	33.43 223	70.95 209	68.68 199	68.26 211	58.82 214

test1235678		60.73 211	68.46 197	51.71 222	61.76 219	56.73 216	73.40 196	42.24 221	67.34 189	39.55 224	70.90 203	92.54 107	28.75 227	73.84 195	66.00 204	64.57 217	51.90 224

testmv		60.72 212	68.44 198	51.71 222	61.76 219	56.70 217	73.40 196	42.24 221	67.31 190	39.54 225	70.88 204	92.49 109	28.75 227	73.83 196	66.00 204	64.56 218	51.89 225

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	65.17 159	70.86 205	71.66 201	56.75 165	61.11 230	53.74 228	71.17 204	52.28 223

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	57.68 171	76.73 166	56.49 223	48.73 205	56.47 233	55.55 225	59.43 225	58.02 217

test-mter		59.39 215	61.59 219	56.82 211	53.21 230	54.82 218	73.12 200	26.57 234	53.19 231	56.31 174	64.71 216	60.47 215	56.36 167	68.69 214	64.27 207	75.38 193	65.00 196

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	44.63 215	70.42 209	73.17 200	58.73 159	80.97 169	51.98 230	54.14 230	42.26 233

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	46.00 213	73.01 194	71.66 201	57.91 162	80.41 173	50.68 233	53.55 231	41.11 234

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	43.25 216	57.59 230	88.49 147	24.19 233	71.68 205	63.20 209	62.99 220	54.42 221

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	53.81 185	63.11 220	58.91 218	52.87 189	66.54 221	62.34 212	73.59 194	61.87 208

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	49.19 206	75.32 183	65.97 211	37.96 219	59.34 232	54.66 227	52.99 232	51.42 226

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	41.80 220	77.46 160	57.28 220	42.68 213	59.81 231	54.82 226	57.23 227	53.35 222

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	41.33 221	47.09 235	54.68 225	35.28 221	73.88 194	70.77 193	65.24 216	62.26 207

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	36.79 229	48.84 234	62.59 214	63.39 146	73.61 199	67.66 200	60.61 222	63.07 205

testpf		55.64 224	50.84 233	61.24 203	67.03 212	54.45 219	72.29 203	65.04 114	37.23 236	54.99 181	53.99 231	43.12 236	44.34 212	55.22 234	51.59 232	63.76 219	60.25 212

1111		55.38 225	59.51 227	50.57 224	72.41 193	48.16 230	69.76 212	57.08 188	76.79 150	32.10 232	80.12 143	35.41 239	25.87 229	67.23 216	57.74 223	46.17 234	51.09 227

N_pmnet		54.95 226	65.90 204	42.18 230	66.37 214	43.86 236	57.92 233	39.79 225	79.54 131	17.24 239	86.31 109	87.91 150	25.44 231	64.68 224	51.76 231	46.33 233	47.23 229

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

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)
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	39.35 227	53.46 232	45.22 234	28.78 226	64.39 225	60.77 219	61.70 221	45.92 230

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

.test1245		43.71 232	44.35 234	42.95 229	72.41 193	48.16 230	69.76 212	57.08 188	76.79 150	32.10 232	80.12 143	35.41 239	25.87 229	67.23 216	1.08 236	0.48 239	1.68 236

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

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

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

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					73.27 185	70.91 178	83.26 134

ambc			88.38 60		91.62 16	87.97 49	84.48 130		88.64 42	87.93 17	87.38 98	94.82 82	74.53 73	89.14 82	83.86 106	85.94 159	86.84 75

MTAPA										89.37 9		94.85 80

MTMP										90.54 5		95.16 72

Patchmatch-RL test							4.13 242

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

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				79.30 102	84.98 77	85.78 65	90.50 63	66.88 91	77.08 149	74.02 120	73.29 193	89.34 139	68.94 122			90.49 83	85.98 80

mPP-MVS					93.05 4							95.77 54

NP-MVS									78.65 140

Patchmtry						56.88 215	64.47 225	67.74 87		72.30 127

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

High-resolution multi-view benchmark