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 47	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 46	92.86 2	95.51 20	72.17 54	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 10
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	83.19 101	86.35 60	93.60 33	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 29	88.53 14	89.54 75	95.57 61	84.25 7	95.24 20	94.27 13	95.97 11	93.85 7

CP-MVS		91.09 5	92.33 21	89.65 2	92.16 10	90.41 25	96.46 10	80.38 6	88.26 42	89.17 11	87.00 102	96.34 38	83.95 10	95.77 11	94.72 8	95.81 17	93.78 9

MP-MVS		90.84 6	91.95 29	89.55 3	92.92 5	90.90 18	96.56 6	79.60 9	86.83 55	88.75 13	89.00 83	94.38 88	84.01 9	94.94 25	94.34 11	95.45 24	93.24 19

ACMM	80.67 7	90.67 7	92.46 17	88.57 8	91.35 20	89.93 29	96.34 12	77.36 29	90.17 27	86.88 30	87.32 96	96.63 28	83.32 14	95.79 10	94.49 10	96.19 9	92.91 22
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 26	91.60 6	96.49 9	77.53 25	87.89 44	86.87 31	87.24 98	96.46 32	82.87 19	95.59 15	94.50 9	96.35 6	93.51 15
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 30	91.15 11	95.35 22	77.65 24	86.26 61	87.23 24	90.45 67	97.35 20	83.20 15	95.44 16	93.41 20	96.28 8	92.63 23

PGM-MVS		90.42 10	91.58 33	89.05 6	91.77 13	91.06 13	96.51 7	78.94 15	85.41 69	87.67 18	87.02 101	95.26 69	83.62 13	95.01 24	93.94 16	95.79 19	93.40 17

MPTG		90.38 11	91.35 36	89.25 5	93.08 3	86.59 57	96.45 11	79.00 14	90.23 26	89.30 10	85.87 112	94.97 79	82.54 21	95.05 23	94.83 7	95.14 27	91.94 31

DeepC-MVS	83.59 4	90.37 12	92.56 16	87.82 15	91.26 25	92.33 3	94.72 27	80.04 7	90.01 30	84.61 44	93.33 21	94.22 89	80.59 29	92.90 40	92.52 28	95.69 21	92.57 24
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 19	90.91 17	95.69 19	79.49 10	89.94 32	83.50 62	89.06 82	94.44 87	81.68 26	94.17 31	94.19 14	95.81 17	93.87 6

PMVS	79.51 9	90.23 14	92.67 12	87.39 20	90.16 36	88.75 37	93.64 32	75.78 39	90.00 31	83.70 56	92.97 27	92.22 112	86.13 4	97.01 3	96.79 2	94.94 29	90.96 42
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMP	80.00 8	90.12 15	92.30 22	87.58 18	90.83 32	91.10 12	94.96 25	76.06 37	87.47 48	85.33 40	88.91 85	97.65 18	82.13 23	95.31 17	93.44 19	96.14 10	92.22 28
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SteuartSystems-ACMMP		90.00 16	91.73 30	87.97 13	91.21 27	90.29 26	96.51 7	78.00 22	86.33 59	85.32 41	88.23 88	94.67 83	82.08 24	95.13 22	93.88 17	94.72 34	93.59 11
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		89.91 17	92.23 25	87.19 21	91.31 22	89.79 31	94.31 29	75.34 41	89.26 34	81.79 76	92.68 30	95.08 75	83.88 11	93.10 37	92.69 25	96.54 4	93.02 20

ACMMP_Plus		89.86 18	91.96 28	87.42 19	91.00 28	90.08 27	96.00 17	76.61 33	89.28 33	87.73 17	90.04 69	91.80 119	78.71 37	94.36 29	93.82 18	94.48 35	94.32 5

APDe-MVS		89.85 19	92.91 9	86.29 25	90.47 35	91.34 7	96.04 16	76.41 36	91.11 15	78.50 98	93.44 20	95.82 53	81.55 27	93.16 36	91.90 37	94.77 33	93.58 13

OPM-MVS		89.82 20	92.24 24	86.99 22	90.86 31	89.35 33	95.07 24	75.91 38	91.16 14	86.87 31	91.07 59	97.29 21	79.13 35	93.32 34	91.99 36	94.12 38	91.49 38

WR-MVS		89.79 21	93.66 4	85.27 35	91.32 21	88.27 41	93.49 34	79.86 8	92.75 8	75.37 109	96.86 1	98.38 6	75.10 65	95.93 8	94.07 15	96.46 5	89.39 54

TSAR-MVS + MP.		89.67 22	92.25 23	86.65 24	91.53 16	90.98 16	96.15 14	73.30 51	87.88 45	81.83 75	92.92 28	95.15 73	82.23 22	93.58 33	92.25 33	94.87 30	93.01 21

CPTT-MVS		89.63 23	90.52 44	88.59 7	90.95 29	90.74 20	95.71 18	79.13 13	87.70 46	85.68 39	80.05 144	95.74 56	84.77 6	94.28 30	92.68 26	95.28 26	92.45 26

ACMH+	79.05 11	89.62 24	93.08 7	85.58 30	88.58 49	89.26 34	92.18 41	74.23 47	93.55 7	82.66 67	92.32 39	98.35 8	80.29 30	95.28 18	92.34 31	95.52 22	90.43 45

X-MVS		89.36 25	90.73 41	87.77 17	91.50 18	91.23 8	96.76 4	78.88 16	87.29 50	87.14 27	78.98 148	94.53 84	76.47 52	95.25 19	94.28 12	95.85 14	93.55 14

TSAR-MVS + ACMM		89.14 26	92.11 27	85.67 29	89.27 43	90.61 23	90.98 47	79.48 11	88.86 37	79.80 89	93.01 26	93.53 97	83.17 16	92.75 44	92.45 29	91.32 72	93.59 11

SixPastTwentyTwo		89.14 26	92.19 26	85.58 30	84.62 77	82.56 83	90.53 59	71.93 55	91.95 10	85.89 36	94.22 14	97.25 22	85.42 5	95.73 12	91.71 39	95.08 28	91.89 32

APD-MVS		89.14 26	91.25 38	86.67 23	91.73 14	91.02 15	95.50 21	77.74 23	84.04 81	79.47 93	91.48 48	94.85 80	81.14 28	92.94 39	92.20 35	94.47 36	92.24 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PS-CasMVS		89.07 29	93.23 6	84.21 45	92.44 8	88.23 43	90.54 58	82.95 3	90.50 21	75.31 110	95.80 5	98.37 7	71.16 105	96.30 5	93.32 21	92.88 53	90.11 48

UA-Net		89.02 30	91.44 35	86.20 26	94.88 1	89.84 30	94.76 26	77.45 27	85.41 69	74.79 113	88.83 86	88.90 141	78.67 39	96.06 7	95.45 4	96.66 3	95.58 2

LS3D		89.02 30	91.69 31	85.91 28	89.72 40	90.81 19	92.56 40	71.69 56	90.83 19	87.24 22	89.71 73	92.07 115	78.37 40	94.43 28	92.59 27	95.86 13	91.35 39

DTE-MVSNet		88.99 32	92.77 11	84.59 39	93.31 2	88.10 44	90.96 48	83.09 2	91.38 12	76.21 103	96.03 2	98.04 11	70.78 111	95.65 14	92.32 32	93.18 48	87.84 67

WR-MVS_H		88.99 32	93.28 5	83.99 48	91.92 11	89.13 35	91.95 42	83.23 1	90.14 28	71.92 129	95.85 4	98.01 13	71.83 102	95.82 9	93.19 22	93.07 51	90.83 44

ACMH	78.40 12	88.94 34	92.62 14	84.65 38	86.45 64	87.16 53	91.47 44	68.79 75	95.49 3	89.74 6	93.55 19	98.50 3	77.96 43	94.14 32	89.57 55	93.49 42	89.94 50
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		88.86 35	92.92 8	84.11 47	92.92 5	88.05 46	90.83 50	82.67 5	91.04 16	74.83 112	95.97 3	98.47 4	70.38 112	95.70 13	92.43 30	93.05 52	88.78 60

HPM-MVS++		88.74 36	89.54 50	87.80 16	92.58 7	85.69 65	95.10 23	78.01 21	87.08 52	87.66 19	87.89 91	92.07 115	80.28 31	90.97 66	91.41 40	93.17 49	91.69 33

CP-MVSNet		88.71 37	92.63 13	84.13 46	92.39 9	88.09 45	90.47 63	82.86 4	88.79 39	75.16 111	94.87 7	97.68 17	71.05 107	96.16 6	93.18 23	92.85 54	89.64 52

HSP-MVS		88.32 38	90.71 42	85.53 32	90.63 34	92.01 4	96.15 14	77.52 26	86.02 62	81.39 83	90.21 68	96.08 46	76.38 54	88.30 87	86.70 79	91.12 76	95.64 1

OMC-MVS		88.16 39	91.34 37	84.46 42	86.85 61	90.63 22	93.01 37	67.00 88	90.35 25	87.40 21	86.86 104	96.35 37	77.66 45	92.63 45	90.84 41	94.84 31	91.68 34

3Dnovator+	83.71 3	88.13 40	90.00 47	85.94 27	86.82 62	91.06 13	94.26 30	75.39 40	88.85 38	85.76 38	85.74 114	86.92 151	78.02 42	93.03 38	92.21 34	95.39 25	92.21 29

CSCG		88.12 41	91.45 34	84.23 44	88.12 55	90.59 24	90.57 54	68.60 77	91.37 13	83.45 64	89.94 70	95.14 74	78.71 37	91.45 54	88.21 66	95.96 12	93.44 16

RPSCF		88.05 42	92.61 15	82.73 60	84.24 82	88.40 39	90.04 68	66.29 92	91.46 11	82.29 69	88.93 84	96.01 49	79.38 33	95.15 21	94.90 6	94.15 37	93.40 17

DeepPCF-MVS	81.61 6	87.95 43	90.29 46	85.22 36	87.48 58	90.01 28	93.79 31	73.54 49	88.93 36	83.89 53	89.40 77	90.84 128	80.26 32	90.62 70	90.19 48	92.36 61	92.03 30

DeepC-MVS_fast	81.78 5	87.38 44	89.64 48	84.75 37	89.89 39	90.70 21	92.74 39	74.45 45	86.02 62	82.16 73	86.05 110	91.99 118	75.84 60	91.16 59	90.44 44	93.41 44	91.09 41
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 45	90.46 45	83.19 51	85.22 73	83.69 74	90.03 69	68.20 82	91.01 17	86.71 34	94.80 8	98.46 5	77.69 44	91.10 61	85.98 84	91.30 73	88.19 63

CNVR-MVS		86.93 46	88.98 54	84.54 40	90.11 37	87.41 51	93.23 36	73.47 50	86.31 60	82.25 70	82.96 130	92.15 113	76.04 57	91.69 50	90.69 42	92.17 63	91.64 36

NCCC		86.74 47	87.97 66	85.31 34	90.64 33	87.25 52	93.27 35	74.59 44	86.50 57	83.72 55	75.92 175	92.39 110	77.08 49	91.72 49	90.68 43	92.57 59	91.30 40

train_agg		86.67 48	87.73 67	85.43 33	91.51 17	82.72 80	94.47 28	74.22 48	81.71 103	81.54 82	89.20 81	92.87 103	78.33 41	90.12 73	88.47 62	92.51 60	89.04 57

CDPH-MVS		86.66 49	88.52 57	84.48 41	89.61 41	88.27 41	92.86 38	72.69 53	80.55 119	82.71 66	86.92 103	93.32 99	75.55 62	91.00 64	89.85 50	93.47 43	89.71 51

Gipuma		86.47 50	89.25 52	83.23 50	83.88 88	78.78 122	85.35 121	68.42 79	92.69 9	89.03 12	91.94 42	96.32 40	81.80 25	94.45 27	86.86 75	90.91 77	83.69 93
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PHI-MVS		86.37 51	88.14 63	84.30 43	86.65 63	87.56 49	90.76 51	70.16 63	82.55 91	89.65 7	84.89 122	92.40 109	75.97 58	90.88 68	89.70 52	92.58 57	89.03 58

MSLP-MVS++		86.29 52	89.10 53	83.01 53	85.71 71	89.79 31	87.04 111	74.39 46	85.17 71	78.92 96	77.59 155	93.57 95	82.60 20	93.23 35	91.88 38	89.42 92	92.46 25

v52		86.26 53	90.85 39	80.91 72	72.49 184	81.25 102	90.55 56	60.30 166	90.43 24	87.24 22	94.64 11	98.30 10	83.16 18	92.86 42	86.82 77	91.69 67	91.65 35

V4		86.26 53	90.85 39	80.91 72	72.49 184	81.25 102	90.55 56	60.31 165	90.44 23	87.23 24	94.64 11	98.31 9	83.17 16	92.87 41	86.82 77	91.69 67	91.64 36

TAPA-MVS	78.00 13	85.88 55	88.37 59	82.96 55	84.69 76	88.62 38	90.62 52	64.22 124	89.15 35	88.05 15	78.83 149	93.71 92	76.20 56	90.11 74	88.22 65	94.00 39	89.97 49
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
anonymousdsp		85.62 56	90.53 43	79.88 92	64.64 210	76.35 145	96.28 13	53.53 199	85.63 66	81.59 81	92.81 29	97.71 16	86.88 2	94.56 26	92.83 24	96.35 6	93.84 8

TSAR-MVS + COLMAP		85.51 57	88.36 60	82.19 61	86.05 68	87.69 48	90.50 61	70.60 62	86.40 58	82.33 68	89.69 74	92.52 107	74.01 80	87.53 91	86.84 76	89.63 88	87.80 68

CNLPA		85.50 58	88.58 55	81.91 63	84.55 79	87.52 50	90.89 49	63.56 133	88.18 43	84.06 49	83.85 127	91.34 125	76.46 53	91.27 56	89.00 60	91.96 64	88.88 59

PLC	76.06 15	85.38 59	87.46 69	82.95 56	85.79 70	88.84 36	88.86 78	68.70 76	87.06 53	83.60 58	79.02 147	90.05 133	77.37 48	90.88 68	89.66 53	93.37 45	86.74 74
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + GP.		85.32 60	87.41 71	82.89 57	90.07 38	85.69 65	89.07 76	72.99 52	82.45 93	74.52 116	85.09 120	87.67 148	79.24 34	91.11 60	90.41 45	91.45 70	89.45 53

TranMVSNet+NR-MVSNet		85.23 61	89.38 51	80.39 89	88.78 48	83.77 73	87.40 99	76.75 31	85.47 67	68.99 142	95.18 6	97.55 19	67.13 129	91.61 51	89.13 59	93.26 46	82.95 105

v748		85.21 62	89.62 49	80.08 91	80.71 130	80.27 115	85.05 124	63.79 131	90.47 22	83.54 61	94.21 15	98.52 2	76.84 51	90.97 66	84.25 99	90.53 80	88.62 61

Anonymous20231211		85.16 63	91.64 32	77.61 115	88.54 50	79.81 118	83.12 132	74.68 43	98.37 1	66.79 154	94.56 13	99.60 1	61.64 148	91.49 53	89.82 51	90.91 77	87.80 68

HQP-MVS		85.02 64	86.41 77	83.40 49	89.19 44	86.59 57	91.28 45	71.60 57	82.79 89	83.48 63	78.65 151	93.54 96	72.55 97	86.49 100	85.89 86	92.28 62	90.95 43

UniMVSNet (Re)		84.95 65	88.53 56	80.78 76	87.82 57	84.21 70	88.03 88	76.50 34	81.18 114	69.29 139	92.63 34	96.83 25	69.07 119	91.23 58	89.60 54	93.97 40	84.00 91

DU-MVS		84.88 66	88.27 62	80.92 71	88.30 52	83.59 75	87.06 109	78.35 18	80.64 117	70.49 135	92.67 31	96.91 24	68.13 123	91.79 47	89.29 58	93.20 47	83.02 102

MCST-MVS		84.79 67	86.48 75	82.83 58	87.30 59	87.03 55	90.46 64	69.33 71	83.14 85	82.21 72	81.69 138	92.14 114	75.09 66	87.27 94	84.78 95	92.58 57	89.30 55

MVS_0304		84.73 68	86.19 80	83.02 52	88.32 51	86.71 56	91.55 43	70.87 60	73.79 161	82.88 65	85.13 119	93.35 98	72.55 97	88.62 83	87.69 68	91.93 65	88.05 66

UniMVSNet_NR-MVSNet		84.62 69	88.00 65	80.68 81	88.18 54	83.83 72	87.06 109	76.47 35	81.46 109	70.49 135	93.24 22	95.56 63	68.13 123	90.43 71	88.47 62	93.78 41	83.02 102

EG-PatchMatch MVS		84.35 70	87.55 68	80.62 84	86.38 65	82.24 85	86.75 113	64.02 128	84.24 77	78.17 100	89.38 78	95.03 77	78.78 36	89.95 75	86.33 81	89.59 89	85.65 81

AdaColmap		84.15 71	85.14 97	83.00 54	89.08 45	87.14 54	90.56 55	70.90 59	82.40 94	80.41 86	73.82 187	84.69 159	75.19 64	91.58 52	89.90 49	91.87 66	86.48 75

PCF-MVS	76.59 14	84.11 72	85.27 94	82.76 59	86.12 67	88.30 40	91.24 46	69.10 72	82.36 95	84.45 45	77.56 156	90.40 132	72.91 96	85.88 107	83.88 102	92.72 56	88.53 62
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_HR		83.95 73	86.10 82	81.44 68	84.62 77	80.29 114	90.51 60	68.05 83	84.07 80	80.38 87	84.74 123	91.37 124	74.23 74	90.37 72	87.25 70	90.86 79	84.59 84

TinyColmap		83.79 74	86.12 81	81.07 70	83.42 94	81.44 97	85.42 119	68.55 78	88.71 40	89.46 8	87.60 93	92.72 104	70.34 113	89.29 78	81.94 121	89.20 93	81.12 123

v13		83.75 75	86.20 79	80.89 74	83.38 95	81.93 88	88.58 81	66.09 95	83.55 82	84.28 46	92.67 31	96.79 26	74.67 70	84.42 126	79.72 141	88.36 104	84.31 87

v1192		83.61 76	85.23 95	81.72 65	84.05 84	82.15 86	89.54 71	66.20 93	81.38 111	86.76 33	91.79 45	96.03 48	74.88 68	81.81 156	80.92 129	88.91 96	82.50 110

v12		83.59 77	86.00 85	80.77 79	83.30 97	81.83 89	88.45 82	65.95 98	83.20 84	84.15 47	92.54 36	96.71 27	74.50 72	84.19 128	79.64 142	88.30 105	83.93 92

v1240		83.57 78	84.94 101	81.97 62	84.05 84	81.27 101	89.46 73	66.06 96	81.31 113	87.50 20	91.88 44	95.46 66	76.25 55	81.16 161	80.51 134	88.52 102	82.98 104

v1921920		83.49 79	84.94 101	81.80 64	83.78 89	81.20 105	89.50 72	65.91 99	81.64 105	87.18 26	91.70 46	95.39 67	75.85 59	81.56 159	80.27 136	88.60 100	82.80 106

v144192		83.43 80	84.97 100	81.63 67	83.43 93	81.23 104	89.42 74	66.04 97	81.45 110	86.40 35	91.46 50	95.70 60	75.76 61	82.14 152	80.23 137	88.74 97	82.57 109

V9		83.42 81	85.81 87	80.63 83	83.20 100	81.73 92	88.29 86	65.78 102	82.87 88	83.99 52	92.38 38	96.60 29	74.30 73	83.93 129	79.58 144	88.24 108	83.55 96

Vis-MVSNet		83.32 82	88.12 64	77.71 113	77.91 160	83.44 77	90.58 53	69.49 68	81.11 115	67.10 152	89.85 71	91.48 123	71.71 103	91.34 55	89.37 56	89.48 91	90.26 46
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v11		83.30 83	85.58 90	80.64 82	83.53 92	81.74 91	88.30 85	65.46 107	82.75 90	84.63 43	92.49 37	96.17 44	73.90 81	82.69 145	79.59 143	88.04 113	83.66 94

V14		83.23 84	85.59 89	80.48 87	83.09 103	81.63 94	88.13 87	65.61 104	82.53 92	83.81 54	92.17 40	96.50 30	74.07 78	83.66 131	79.51 146	88.17 110	83.16 100

v1144		83.22 85	85.01 98	81.14 69	83.76 90	81.60 95	88.95 77	65.58 105	81.89 99	85.80 37	91.68 47	95.84 52	74.04 79	82.12 153	80.56 133	88.70 99	81.41 120

MVS_111021_LR		83.20 86	85.33 92	80.73 80	82.88 106	78.23 126	89.61 70	65.23 110	82.08 98	81.19 84	85.31 117	92.04 117	75.22 63	89.50 76	85.90 85	90.24 82	84.23 88

v10		83.17 87	85.22 96	80.78 76	83.26 99	82.99 79	88.66 79	66.49 91	79.24 134	83.60 58	91.46 50	95.47 64	74.12 75	82.60 147	80.66 130	88.53 101	84.11 90

v15		83.06 88	85.39 91	80.35 90	83.01 104	81.53 96	87.98 90	65.47 106	82.19 97	83.66 57	92.00 41	96.40 36	73.87 82	83.39 133	79.44 147	88.10 112	82.76 107

PVSNet_Blended_VisFu		83.00 89	84.16 116	81.65 66	82.17 120	86.01 61	88.03 88	71.23 58	76.05 153	79.54 92	83.88 126	83.44 160	77.49 47	87.38 92	84.93 94	91.41 71	87.40 72

NR-MVSNet		82.89 90	87.43 70	77.59 116	83.91 87	83.59 75	87.10 108	78.35 18	80.64 117	68.85 143	92.67 31	96.50 30	54.19 178	87.19 97	88.68 61	93.16 50	82.75 108

CANet		82.84 91	84.60 105	80.78 76	87.30 59	85.20 67	90.23 66	69.00 73	72.16 169	78.73 97	84.49 124	90.70 130	69.54 117	87.65 90	86.17 82	89.87 86	85.84 80

Baseline_NR-MVSNet		82.79 92	86.51 74	78.44 110	88.30 52	75.62 154	87.81 91	74.97 42	81.53 107	66.84 153	94.71 10	96.46 32	66.90 130	91.79 47	83.37 110	85.83 156	82.09 115

v7		82.76 93	84.65 104	80.55 85	83.27 98	81.77 90	88.66 79	65.10 111	79.23 135	83.60 58	91.47 49	95.47 64	74.12 75	82.61 146	80.66 130	88.52 102	81.35 121

EPP-MVSNet		82.76 93	86.47 76	78.45 109	86.00 69	84.47 69	85.39 120	68.42 79	84.17 78	62.97 163	89.26 80	76.84 184	72.13 100	92.56 46	90.40 46	95.76 20	87.56 71

CLD-MVS		82.75 95	87.22 72	77.54 117	88.01 56	85.76 64	90.23 66	54.52 192	82.28 96	82.11 74	88.48 87	95.27 68	63.95 139	89.41 77	88.29 64	86.45 144	81.01 124
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 96	83.87 122	80.52 86	84.51 80	81.32 98	87.53 97	68.05 83	74.94 158	79.67 91	82.37 134	92.31 111	72.21 99	85.06 116	86.91 74	91.18 74	84.20 89

v1		82.27 97	84.32 109	79.87 93	82.86 107	80.32 111	87.57 96	63.47 137	81.87 101	84.13 48	91.34 52	96.29 41	73.23 92	82.39 148	79.08 158	87.94 115	78.98 138

v1141		82.26 98	84.32 109	79.85 94	82.86 107	80.31 112	87.58 94	63.48 135	81.86 102	84.03 51	91.33 53	96.28 42	73.23 92	82.39 148	79.08 158	87.93 116	78.97 139

divwei89l23v2f112		82.26 98	84.32 109	79.85 94	82.86 107	80.31 112	87.58 94	63.48 135	81.88 100	84.05 50	91.33 53	96.27 43	73.23 92	82.39 148	79.08 158	87.93 116	78.97 139

3Dnovator	79.41 10	82.21 100	86.07 83	77.71 113	79.31 145	84.61 68	87.18 106	61.02 162	85.65 65	76.11 104	85.07 121	85.38 157	70.96 109	87.22 95	86.47 80	91.66 69	88.12 65

v8		82.20 101	84.56 106	79.45 97	82.42 111	81.65 93	87.26 100	64.27 122	79.36 130	81.70 77	91.04 62	95.75 55	73.30 90	82.82 141	79.18 155	87.74 120	82.09 115

v2v482		82.20 101	84.26 113	79.81 96	82.67 110	80.18 116	87.67 93	63.96 130	81.69 104	84.73 42	91.27 56	96.33 39	72.05 101	81.94 155	79.56 145	87.79 119	78.84 141

v17		82.09 103	84.45 107	79.33 99	82.41 112	81.31 99	87.26 100	64.50 121	78.72 137	80.73 85	90.90 63	95.57 61	73.37 86	83.06 134	79.25 151	87.70 124	82.35 113

Effi-MVS+-dtu		82.04 104	83.39 128	80.48 87	85.48 72	86.57 59	88.40 83	68.28 81	69.04 181	73.13 123	76.26 167	91.11 127	74.74 69	88.40 85	87.76 67	92.84 55	84.57 85

MAR-MVS		81.98 105	82.92 130	80.88 75	85.18 74	85.85 62	89.13 75	69.52 66	71.21 173	82.25 70	71.28 197	88.89 142	69.69 114	88.71 82	86.96 72	89.52 90	87.57 70
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		81.92 106	84.32 109	79.12 105	82.31 117	81.29 100	87.20 105	64.51 120	78.16 141	79.76 90	90.86 64	95.23 70	73.29 91	83.05 135	79.29 150	87.63 125	82.34 114

v6		81.77 107	83.96 119	79.22 102	82.41 112	80.45 110	87.26 100	62.91 147	79.29 131	81.65 78	91.08 58	95.74 56	73.32 87	82.84 138	79.21 154	87.73 121	79.07 135

v1neww		81.76 108	83.95 120	79.21 103	82.41 112	80.46 108	87.26 100	62.93 143	79.28 132	81.62 79	91.06 60	95.72 58	73.31 88	82.83 139	79.22 152	87.73 121	79.07 135

v7new		81.76 108	83.95 120	79.21 103	82.41 112	80.46 108	87.26 100	62.93 143	79.28 132	81.62 79	91.06 60	95.72 58	73.31 88	82.83 139	79.22 152	87.73 121	79.07 135

IS_MVSNet		81.72 110	85.01 98	77.90 112	86.19 66	82.64 82	85.56 118	70.02 64	80.11 123	63.52 160	87.28 97	81.18 169	67.26 127	91.08 63	89.33 57	94.82 32	83.42 98

v18		81.62 111	83.99 118	78.86 106	82.08 121	81.12 106	86.93 112	64.24 123	77.44 143	79.47 93	90.53 65	94.99 78	72.99 95	82.72 144	79.18 155	87.48 128	81.91 118

FPMVS		81.56 112	84.04 117	78.66 107	82.92 105	75.96 149	86.48 116	65.66 103	84.67 74	71.47 131	77.78 153	83.22 162	77.57 46	91.24 57	90.21 47	87.84 118	85.21 82

Fast-Effi-MVS+		81.42 113	83.82 123	78.62 108	82.24 119	80.62 107	87.72 92	63.51 134	73.01 162	74.75 114	83.80 128	92.70 105	73.44 85	88.15 89	85.26 90	90.05 83	83.17 99

USDC		81.39 114	83.07 129	79.43 98	81.48 126	78.95 121	82.62 136	66.17 94	87.45 49	90.73 4	82.40 133	93.65 94	66.57 132	83.63 132	77.97 162	89.00 95	77.45 148

MSDG		81.39 114	84.23 115	78.09 111	82.40 116	82.47 84	85.31 123	60.91 163	79.73 126	80.26 88	86.30 107	88.27 146	69.67 115	87.20 96	84.98 93	89.97 85	80.67 125

canonicalmvs		81.22 116	86.04 84	75.60 124	83.17 102	83.18 78	80.29 147	65.82 101	85.97 64	67.98 150	77.74 154	91.51 122	65.17 135	88.62 83	86.15 83	91.17 75	89.09 56

pmmvs6		80.46 117	88.34 61	71.26 141	81.96 122	77.51 131	77.54 165	68.83 74	93.72 6	55.92 175	93.94 18	98.03 12	55.94 168	89.21 79	85.61 87	87.36 131	80.38 126

QAPM		80.43 118	84.34 108	75.86 122	79.40 144	82.06 87	79.86 152	61.94 157	83.28 83	74.73 115	81.74 137	85.44 156	70.97 108	84.99 122	84.71 96	88.29 106	88.14 64

PM-MVS		80.42 119	83.63 125	76.67 119	78.04 156	72.37 169	87.14 107	60.18 168	80.13 122	71.75 130	86.12 109	93.92 91	77.08 49	86.56 99	85.12 92	85.83 156	81.18 122

IterMVS-LS		79.79 120	82.56 132	76.56 121	81.83 124	77.85 129	79.90 151	69.42 70	78.93 136	71.21 132	90.47 66	85.20 158	70.86 110	80.54 166	80.57 132	86.15 147	84.36 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		79.71 121	83.74 124	75.01 127	79.31 145	82.68 81	84.79 126	60.06 169	75.43 156	69.09 141	86.13 108	89.38 135	67.16 128	85.12 115	83.87 103	89.65 87	83.57 95
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		79.64 122	82.06 135	76.83 118	80.05 136	72.64 167	87.47 98	66.59 90	80.83 116	73.50 120	89.32 79	93.20 100	67.78 125	80.78 164	81.64 123	85.58 159	76.01 150

UGNet		79.62 123	85.91 86	72.28 139	73.52 178	83.91 71	86.64 114	69.51 67	79.85 125	62.57 165	85.82 113	89.63 134	53.18 185	88.39 86	87.35 69	88.28 107	86.43 76
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		79.59 124	83.59 126	74.93 129	69.61 196	77.05 139	86.59 115	55.84 188	78.42 140	77.29 101	89.84 72	95.08 75	74.12 75	83.05 135	80.11 139	86.12 148	81.59 119

EPNet		79.36 125	79.44 142	79.27 101	89.51 42	77.20 136	88.35 84	77.35 30	68.27 183	74.29 117	76.31 165	79.22 173	59.63 153	85.02 121	85.45 89	86.49 143	84.61 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v148		79.33 126	82.32 134	75.84 123	80.14 135	75.74 151	81.98 139	57.06 184	81.51 108	79.36 95	89.42 76	96.42 34	71.32 104	81.54 160	75.29 176	85.20 162	76.32 149

FC-MVSNet-train		79.20 127	86.29 78	70.94 146	84.06 83	77.67 130	85.68 117	64.11 127	82.90 87	52.22 194	92.57 35	93.69 93	49.52 199	88.30 87	86.93 73	90.03 84	81.95 117

TransMVSNet (Re)		79.05 128	86.66 73	70.18 154	83.32 96	75.99 148	77.54 165	63.98 129	90.68 20	55.84 176	94.80 8	96.06 47	53.73 184	86.27 103	83.22 111	86.65 138	79.61 133

no-one		78.59 129	85.28 93	70.79 147	59.01 217	68.77 182	76.62 172	46.06 210	80.25 121	75.75 107	81.85 136	97.75 15	83.63 12	90.99 65	87.20 71	83.67 169	90.14 47

OpenMVS	75.38 16	78.44 130	81.39 137	74.99 128	80.46 132	79.85 117	79.99 149	58.31 179	77.34 145	73.85 119	77.19 160	82.33 167	68.60 122	84.67 125	81.95 120	88.72 98	86.40 77

pm-mvs1		78.21 131	85.68 88	69.50 160	80.38 133	75.73 152	76.25 177	65.04 112	87.59 47	54.47 181	93.16 24	95.99 51	54.20 177	86.37 101	82.98 113	86.64 139	77.96 146

FMVSNet1		78.20 132	84.83 103	70.46 151	78.62 151	79.03 120	77.90 164	67.53 87	83.02 86	55.10 178	87.19 99	93.18 101	55.65 170	85.57 108	83.39 107	87.98 114	82.40 111

DI_MVS_plusplus_trai		77.64 133	79.64 141	75.31 126	79.87 140	76.89 140	81.55 142	63.64 132	76.21 152	72.03 128	85.59 116	82.97 163	66.63 131	79.27 169	77.78 164	88.14 111	78.76 142

tfpnnormal		77.16 134	84.26 113	68.88 163	81.02 129	75.02 155	76.52 174	63.30 139	87.29 50	52.40 192	91.24 57	93.97 90	54.85 176	85.46 111	81.08 127	85.18 163	75.76 153

conf0.05thres1000		77.12 135	82.38 133	70.98 144	82.30 118	77.95 128	79.86 152	64.74 116	86.63 56	53.93 182	85.74 114	75.63 193	56.85 162	88.98 81	84.10 100	88.20 109	77.61 147

Fast-Effi-MVS+-dtu		76.92 136	77.18 157	76.62 120	79.55 142	79.17 119	84.80 125	77.40 28	64.46 200	68.75 145	70.81 203	86.57 152	63.36 145	81.74 157	81.76 122	85.86 155	75.78 152

MVS_Test		76.72 137	79.40 143	73.60 133	78.85 150	74.99 156	79.91 150	61.56 159	69.67 177	72.44 124	85.98 111	90.78 129	63.50 143	78.30 172	75.74 175	85.33 161	80.31 130

MDA-MVSNet-bldmvs		76.51 138	82.87 131	69.09 162	50.71 228	74.72 159	84.05 130	60.27 167	81.62 106	71.16 133	88.21 89	91.58 120	69.62 116	92.78 43	77.48 167	78.75 183	73.69 165

EU-MVSNet		76.48 139	80.53 139	71.75 140	67.62 201	70.30 173	81.74 140	54.06 195	75.47 155	71.01 134	80.10 142	93.17 102	73.67 83	83.73 130	77.85 163	82.40 175	83.07 101

PVSNet_BlendedMVS		76.45 140	78.12 148	74.49 130	76.76 167	78.46 123	79.65 154	63.26 140	65.42 196	73.15 121	75.05 181	88.96 139	66.51 133	82.73 142	77.66 165	87.61 126	78.60 143

PVSNet_Blended		76.45 140	78.12 148	74.49 130	76.76 167	78.46 123	79.65 154	63.26 140	65.42 196	73.15 121	75.05 181	88.96 139	66.51 133	82.73 142	77.66 165	87.61 126	78.60 143

Vis-MVSNet (Re-imp)		76.15 142	80.84 138	70.68 148	83.66 91	74.80 158	81.66 141	69.59 65	80.48 120	46.94 207	87.44 94	80.63 171	53.14 186	86.87 98	84.56 97	89.12 94	71.12 172

PatchMatch-RL		76.05 143	76.64 163	75.36 125	77.84 161	69.87 176	81.09 144	63.43 138	71.66 171	68.34 148	71.70 193	81.76 168	74.98 67	84.83 124	83.44 106	86.45 144	73.22 167

pmmvs4		75.92 144	77.48 155	74.10 132	78.21 155	70.94 171	84.06 129	64.78 115	75.13 157	68.47 147	84.12 125	83.32 161	64.74 138	75.93 183	79.14 157	84.31 167	73.77 163

FC-MVSNet-test		75.91 145	83.59 126	66.95 178	76.63 173	69.07 179	85.33 122	64.97 114	84.87 73	41.95 213	93.17 23	87.04 150	47.78 202	91.09 62	85.56 88	85.06 164	74.34 156

CVMVSNet		75.65 146	77.62 154	73.35 136	71.95 188	69.89 175	83.04 135	60.84 164	69.12 179	68.76 144	79.92 145	78.93 175	73.64 84	81.02 162	81.01 128	81.86 177	83.43 97

IB-MVS	71.28 17	75.21 147	77.00 160	73.12 137	76.76 167	77.45 132	83.05 134	58.92 175	63.01 205	64.31 159	59.99 223	87.57 149	68.64 121	86.26 104	82.34 119	87.05 136	82.36 112
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		75.04 148	78.45 145	71.07 142	77.27 163	77.96 127	83.88 131	58.00 180	64.11 201	68.67 146	75.65 177	88.37 145	53.92 180	82.05 154	81.11 126	84.67 165	79.88 132

GA-MVS		75.01 149	76.39 165	73.39 134	78.37 152	75.66 153	80.03 148	58.40 178	70.51 175	75.85 106	83.24 129	76.14 188	63.75 140	77.28 176	76.62 171	83.97 168	75.30 155

view800		74.68 150	78.74 144	69.94 155	81.12 128	76.59 141	78.94 161	63.24 142	78.56 139	53.06 187	75.61 178	76.26 187	56.07 167	86.32 102	83.75 105	87.18 135	74.10 160

FMVSNet2		74.43 151	79.70 140	68.27 166	76.76 167	77.36 133	75.77 182	65.36 109	72.28 167	52.97 188	81.92 135	85.61 155	52.73 189	80.66 165	79.73 140	86.04 151	80.37 127

thres600view7		74.34 152	78.43 146	69.56 159	80.47 131	76.28 146	78.65 162	62.56 150	77.39 144	52.53 190	74.03 186	76.78 185	55.90 169	85.06 116	85.19 91	87.25 133	74.29 158

view600		74.08 153	78.15 147	69.32 161	80.27 134	75.82 150	78.27 163	62.20 153	77.26 146	52.80 189	74.07 185	76.86 183	55.57 172	84.90 123	84.43 98	86.84 137	73.71 164

diffmvs		73.65 154	77.10 158	69.63 158	73.21 179	69.52 177	79.35 158	57.48 181	73.80 160	68.08 149	87.10 100	82.39 165	61.36 149	74.27 186	74.51 177	78.31 184	78.14 145

IterMVS		73.62 155	76.53 164	70.23 153	71.83 189	77.18 137	80.69 146	53.22 200	72.23 168	66.62 155	85.21 118	78.96 174	69.54 117	76.28 182	71.63 185	79.45 180	74.25 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet1		73.40 156	81.85 136	63.55 192	72.90 181	64.37 194	84.58 127	53.60 198	90.84 18	53.92 183	87.75 92	96.10 45	45.31 205	85.37 113	79.32 149	70.98 199	69.18 182

HyFIR lowres test		73.29 157	74.14 177	72.30 138	73.08 180	78.33 125	83.12 132	62.41 152	63.81 202	62.13 166	76.67 164	78.50 176	71.09 106	74.13 187	77.47 168	81.98 176	70.10 176

tfpn_n400		73.26 158	77.94 150	67.79 173	79.91 138	73.32 162	76.38 175	62.04 154	84.26 75	48.53 203	76.23 168	71.50 200	53.83 181	86.22 105	81.59 124	86.05 149	72.47 169

tfpnconf		73.26 158	77.94 150	67.79 173	79.91 138	73.32 162	76.38 175	62.04 154	84.26 75	48.53 203	76.23 168	71.50 200	53.83 181	86.22 105	81.59 124	86.05 149	72.47 169

GBi-Net		73.17 160	77.64 152	67.95 170	76.76 167	77.36 133	75.77 182	64.57 117	62.99 206	51.83 195	76.05 171	77.76 179	52.73 189	85.57 108	83.39 107	86.04 151	80.37 127

test1		73.17 160	77.64 152	67.95 170	76.76 167	77.36 133	75.77 182	64.57 117	62.99 206	51.83 195	76.05 171	77.76 179	52.73 189	85.57 108	83.39 107	86.04 151	80.37 127

thres400		73.13 162	76.99 161	68.62 164	79.46 143	74.93 157	77.23 167	61.23 160	75.54 154	52.31 193	72.20 192	77.10 182	54.89 174	82.92 137	82.62 118	86.57 141	73.66 166

CDS-MVSNet		73.07 163	77.02 159	68.46 165	81.62 125	72.89 166	79.56 156	70.78 61	69.56 178	52.52 191	77.37 159	81.12 170	42.60 208	84.20 127	83.93 101	83.65 170	70.07 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpn		72.99 164	75.25 173	70.36 152	81.87 123	77.09 138	79.28 159	64.16 125	79.58 128	53.14 186	76.97 162	48.75 226	56.35 166	87.31 93	82.75 115	87.35 132	74.31 157

MDTV_nov1_ep13_2view		72.96 165	75.59 170	69.88 156	71.15 193	64.86 193	82.31 138	54.45 193	76.30 151	78.32 99	86.52 105	91.58 120	61.35 150	76.80 177	66.83 197	71.70 194	66.26 189

tfpnview11		72.88 166	77.37 156	67.65 175	79.81 141	73.43 161	76.23 178	61.97 156	81.37 112	48.53 203	76.23 168	71.50 200	53.78 183	85.45 112	82.77 114	85.56 160	70.87 175

gg-mvs-nofinetune		72.68 167	75.21 174	69.73 157	81.48 126	69.04 180	70.48 201	76.67 32	86.92 54	67.80 151	88.06 90	64.67 208	42.12 210	77.60 174	73.65 179	79.81 179	66.57 188

thres200		72.41 168	76.00 169	68.21 167	78.28 153	76.28 146	74.94 187	62.56 150	72.14 170	51.35 198	69.59 208	76.51 186	54.89 174	85.06 116	80.51 134	87.25 133	71.92 171

tfpn1000		72.27 169	76.88 162	66.88 179	79.01 149	74.04 160	76.60 173	61.15 161	79.65 127	45.52 209	77.41 158	67.98 206	52.47 192	85.22 114	82.99 112	86.54 142	70.89 173

tfpn200view9		72.01 170	75.40 171	68.06 168	77.97 157	76.44 143	77.04 169	62.67 149	66.81 189	50.82 199	67.30 210	75.67 190	52.46 193	85.06 116	82.64 116	87.41 130	73.86 162

conf200view11		72.00 171	75.40 171	68.04 169	77.97 157	76.44 143	77.04 169	62.68 148	66.81 189	50.69 201	67.30 210	75.67 190	52.46 193	85.06 116	82.64 116	87.42 129	73.87 161

EPNet_dtu		71.90 172	73.03 181	70.59 149	78.28 153	61.64 199	82.44 137	64.12 126	63.26 204	69.74 137	71.47 195	82.41 164	51.89 195	78.83 171	78.01 161	77.07 185	75.60 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
gm-plane-assit		71.56 173	69.99 185	73.39 134	84.43 81	73.21 165	90.42 65	51.36 206	84.08 79	76.00 105	91.30 55	37.09 232	59.01 156	73.65 192	70.24 189	79.09 182	60.37 205

CMPMVS	55.74 18	71.56 173	76.26 166	66.08 185	68.11 200	63.91 196	63.17 221	50.52 208	68.79 182	75.49 108	70.78 204	85.67 154	63.54 142	81.58 158	77.20 169	75.63 186	85.86 79
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet3		71.40 175	75.20 175	66.97 177	75.00 175	76.59 141	74.29 188	64.57 117	62.99 206	51.83 195	76.05 171	77.76 179	51.49 196	76.58 180	77.03 170	84.62 166	79.43 134

MS-PatchMatch		71.18 176	73.99 178	67.89 172	77.16 164	71.76 170	77.18 168	56.38 187	67.35 185	55.04 179	74.63 183	75.70 189	62.38 147	76.62 179	75.97 174	79.22 181	75.90 151

test20.03		69.91 177	76.20 167	62.58 194	84.01 86	67.34 186	75.67 186	65.88 100	79.98 124	40.28 218	82.65 131	89.31 137	39.63 212	77.41 175	73.28 180	69.98 200	63.40 197

thres100view900		69.86 178	72.97 182	66.24 182	77.97 157	72.49 168	73.29 192	59.12 173	66.81 189	50.82 199	67.30 210	75.67 190	50.54 198	78.24 173	79.40 148	85.71 158	70.88 174

CR-MVSNet		69.56 179	68.34 193	70.99 143	72.78 183	67.63 184	64.47 218	67.74 85	59.93 215	72.30 125	80.10 142	56.77 217	65.04 136	71.64 200	72.91 181	83.61 173	69.40 180

pmmvs5		68.91 180	74.35 176	62.56 195	67.45 203	66.78 188	71.70 197	51.47 205	67.17 188	56.25 174	82.41 132	88.59 143	47.21 203	73.21 196	74.23 178	81.30 178	68.03 185

CHOSEN 1792x2688		68.80 181	71.09 184	66.13 184	69.11 198	68.89 181	78.98 160	54.68 190	61.63 212	56.69 172	71.56 194	78.39 177	67.69 126	72.13 198	72.01 184	69.63 202	73.02 168

tpmp4_e23		68.32 182	66.04 197	70.98 144	77.52 162	69.23 178	80.99 145	65.46 107	68.09 184	69.25 140	70.77 205	54.03 223	59.35 154	69.01 207	63.02 204	73.34 191	68.15 184

tfpn_ndepth		68.20 183	72.18 183	63.55 192	74.64 176	73.24 164	72.41 195	59.76 171	70.54 174	41.93 214	60.96 222	68.69 205	46.23 204	82.16 151	80.14 138	86.34 146	69.56 179

testgi		68.20 183	76.05 168	59.04 201	79.99 137	67.32 187	81.16 143	51.78 204	84.91 72	39.36 221	73.42 188	95.19 71	32.79 218	76.54 181	70.40 188	69.14 203	64.55 193

MVSTER		68.08 185	69.73 187	66.16 183	66.33 208	70.06 174	75.71 185	52.36 202	55.18 224	58.64 169	70.23 207	56.72 218	57.34 161	79.68 168	76.03 173	86.61 140	80.20 131

Anonymous20231206		67.28 186	73.41 180	60.12 200	76.45 174	63.61 197	74.21 189	56.52 186	76.35 150	42.23 212	75.81 176	90.47 131	41.51 211	74.52 184	69.97 190	69.83 201	63.17 198

RPMNet		67.02 187	63.99 205	70.56 150	71.55 191	67.63 184	75.81 180	69.44 69	59.93 215	63.24 161	64.32 215	47.51 227	59.68 152	70.37 204	69.64 191	83.64 171	68.49 183

CostFormer		66.81 188	66.94 195	66.67 180	72.79 182	68.25 183	79.55 157	55.57 189	65.52 195	62.77 164	76.98 161	60.09 212	56.73 164	65.69 217	62.35 205	72.59 192	69.71 178

thresconf0.02		66.71 189	68.28 194	64.89 191	76.83 166	70.38 172	71.62 199	58.90 176	77.64 142	47.04 206	62.10 220	46.01 228	51.32 197	78.85 170	76.09 172	83.62 172	66.85 187

PatchT		66.25 190	66.76 196	65.67 188	55.87 222	60.75 201	70.17 202	59.00 174	59.80 217	72.30 125	78.68 150	54.12 222	65.04 136	71.64 200	72.91 181	71.63 196	69.40 180

LP		65.71 191	69.91 186	60.81 199	56.75 221	61.37 200	69.55 208	56.80 185	73.01 162	60.48 168	79.76 146	70.57 203	55.47 173	72.77 197	67.19 196	65.81 209	64.71 192

dps		65.14 192	64.50 203	65.89 187	71.41 192	65.81 191	71.44 200	61.59 158	58.56 218	61.43 167	75.45 179	52.70 225	58.06 159	69.57 206	64.65 200	71.39 197	64.77 191

MDTV_nov1_ep13		64.96 193	64.77 202	65.18 190	67.08 204	62.46 198	75.80 181	51.10 207	62.27 211	69.74 137	74.12 184	62.65 209	55.64 171	68.19 209	62.16 209	71.70 194	61.57 204

PatchmatchNet		64.81 194	63.74 207	66.06 186	69.21 197	58.62 204	73.16 193	60.01 170	65.92 192	66.19 157	76.27 166	59.09 213	60.45 151	66.58 214	61.47 212	67.33 206	58.24 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		64.79 195	62.74 211	67.17 176	74.61 177	65.91 190	76.18 179	59.32 172	64.88 199	66.41 156	71.21 198	53.56 224	59.17 155	61.53 223	58.16 216	67.33 206	63.95 194

DWT-MVSNet_training		63.07 196	60.04 218	66.61 181	71.64 190	65.27 192	76.80 171	53.82 196	55.90 221	63.07 162	62.23 219	41.87 231	62.54 146	64.32 220	63.71 202	71.78 193	66.97 186

MIMVSNet		63.02 197	69.02 189	56.01 206	68.20 199	59.26 203	70.01 204	53.79 197	71.56 172	41.26 217	71.38 196	82.38 166	36.38 214	71.43 202	67.32 195	66.45 208	59.83 207

TAMVS		63.02 197	69.30 188	55.70 208	70.12 194	56.89 207	69.63 207	45.13 211	70.23 176	38.00 223	77.79 152	75.15 194	42.60 208	74.48 185	72.81 183	68.70 204	57.75 212

tpm		62.79 199	63.25 208	62.26 196	70.09 195	53.78 213	71.65 198	47.31 209	65.72 194	76.70 102	80.62 139	56.40 220	48.11 201	64.20 221	58.54 214	59.70 218	63.47 196

pmmvs3		62.72 200	68.71 190	55.74 207	50.74 227	57.10 206	70.05 203	28.82 226	61.57 214	57.39 171	71.19 199	85.73 153	53.96 179	73.36 195	69.43 192	73.47 190	62.55 200

new-patchmatchnet		62.59 201	73.79 179	49.53 219	76.98 165	53.57 214	53.46 229	54.64 191	85.43 68	28.81 229	91.94 42	96.41 35	25.28 226	76.80 177	53.66 223	57.99 220	58.69 209

test-LLR		62.15 202	59.46 222	65.29 189	79.07 147	52.66 216	69.46 210	62.93 143	50.76 228	53.81 184	63.11 217	58.91 214	52.87 187	66.54 215	62.34 206	73.59 188	61.87 202

PMMVS		61.98 203	65.61 199	57.74 203	45.03 229	51.76 220	69.54 209	35.05 221	55.49 223	55.32 177	68.23 209	78.39 177	58.09 158	70.21 205	71.56 186	83.42 174	63.66 195

test0.0.03 1		61.79 204	65.33 200	57.65 204	79.07 147	64.09 195	68.51 214	62.93 143	61.59 213	33.71 225	61.58 221	71.58 199	33.43 217	70.95 203	68.68 193	68.26 205	58.82 208

test1235678		60.73 205	68.46 191	51.71 216	61.76 212	56.73 209	73.40 190	42.24 215	67.34 186	39.55 219	70.90 200	92.54 106	28.75 221	73.84 189	66.00 198	64.57 211	51.90 218

testmv		60.72 206	68.44 192	51.71 216	61.76 212	56.70 210	73.40 190	42.24 215	67.31 187	39.54 220	70.88 201	92.49 108	28.75 221	73.83 190	66.00 198	64.56 212	51.89 219

MVS-HIRNet		59.74 207	58.74 225	60.92 198	57.74 220	45.81 227	56.02 227	58.69 177	55.69 222	65.17 158	70.86 202	71.66 197	56.75 163	61.11 224	53.74 222	71.17 198	52.28 217

tpmrst		59.42 208	60.02 219	58.71 202	67.56 202	53.10 215	66.99 215	51.88 203	63.80 203	57.68 170	76.73 163	56.49 219	48.73 200	56.47 227	55.55 219	59.43 219	58.02 211

test-mter		59.39 209	61.59 213	56.82 205	53.21 223	54.82 211	73.12 194	26.57 228	53.19 225	56.31 173	64.71 213	60.47 211	56.36 165	68.69 208	64.27 201	75.38 187	65.00 190

E-PMN		59.07 210	62.79 210	54.72 209	67.01 206	47.81 226	60.44 224	43.40 212	72.95 164	44.63 210	70.42 206	73.17 196	58.73 157	80.97 163	51.98 224	54.14 224	42.26 227

EMVS		58.97 211	62.63 212	54.70 210	66.26 209	48.71 222	61.74 222	42.71 213	72.80 166	46.00 208	73.01 191	71.66 197	57.91 160	80.41 167	50.68 227	53.55 225	41.11 228

testus		57.41 212	64.98 201	48.58 221	59.39 216	57.17 205	68.81 213	32.86 223	62.32 210	43.25 211	57.59 224	88.49 144	24.19 227	71.68 199	63.20 203	62.99 214	54.42 215

TESTMET0.1,1		57.21 213	59.46 222	54.60 211	50.95 226	52.66 216	69.46 210	26.91 227	50.76 228	53.81 184	63.11 217	58.91 214	52.87 187	66.54 215	62.34 206	73.59 188	61.87 202

ADS-MVSNet		56.89 214	61.09 214	52.00 214	59.48 215	48.10 225	58.02 225	54.37 194	72.82 165	49.19 202	75.32 180	65.97 207	37.96 213	59.34 226	54.66 221	52.99 226	51.42 220

EPMVS		56.62 215	59.77 220	52.94 213	62.41 211	50.55 221	60.66 223	52.83 201	65.15 198	41.80 215	77.46 157	57.28 216	42.68 207	59.81 225	54.82 220	57.23 221	53.35 216

FMVSNet5		56.37 216	60.14 217	51.98 215	60.83 214	59.58 202	66.85 216	42.37 214	52.68 226	41.33 216	47.09 229	54.68 221	35.28 215	73.88 188	70.77 187	65.24 210	62.26 201

CHOSEN 280x420		56.32 217	58.85 224	53.36 212	51.63 225	39.91 230	69.12 212	38.61 220	56.29 220	36.79 224	48.84 228	62.59 210	63.39 144	73.61 193	67.66 194	60.61 216	63.07 199

testpf		55.64 218	50.84 227	61.24 197	67.03 205	54.45 212	72.29 196	65.04 112	37.23 230	54.99 180	53.99 225	43.12 230	44.34 206	55.22 228	51.59 226	63.76 213	60.25 206

1111		55.38 219	59.51 221	50.57 218	72.41 186	48.16 223	69.76 205	57.08 182	76.79 148	32.10 226	80.12 140	35.41 233	25.87 223	67.23 210	57.74 217	46.17 228	51.09 221

N_pmnet		54.95 220	65.90 198	42.18 224	66.37 207	43.86 229	57.92 226	39.79 219	79.54 129	17.24 233	86.31 106	87.91 147	25.44 225	64.68 218	51.76 225	46.33 227	47.23 223

test12356		54.63 221	63.78 206	43.96 222	51.77 224	51.90 219	65.92 217	30.12 224	62.44 209	30.38 228	64.65 214	89.07 138	30.62 219	73.53 194	62.11 210	54.92 222	42.78 226

new_pmnet		52.29 222	63.16 209	39.61 226	58.89 218	44.70 228	48.78 231	34.73 222	65.88 193	17.85 232	73.42 188	80.00 172	23.06 228	67.00 213	62.28 208	54.36 223	48.81 222

MVE	41.12 19	51.80 223	60.92 215	41.16 225	35.21 231	34.14 232	48.45 232	41.39 217	69.11 180	19.53 231	63.33 216	73.80 195	63.56 141	67.19 212	61.51 211	38.85 229	57.38 213
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2356		51.28 224	53.40 226	48.80 220	58.53 219	52.10 218	63.63 220	40.83 218	51.94 227	39.35 222	53.46 226	45.22 229	28.78 220	64.39 219	60.77 213	61.70 215	45.92 224

PMMVS2		48.13 225	64.06 204	29.55 227	44.06 230	36.69 231	51.95 230	29.97 225	74.75 159	8.90 235	76.02 174	91.24 126	7.53 229	73.78 191	55.91 218	34.87 230	40.01 229

.test1245		43.71 226	44.35 228	42.95 223	72.41 186	48.16 223	69.76 205	57.08 182	76.79 148	32.10 226	80.12 140	35.41 233	25.87 223	67.23 210	1.08 230	0.48 233	1.68 230

GG-mvs-BLEND		41.63 227	60.36 216	19.78 228	0.14 235	66.04 189	55.66 228	0.17 233	57.64 219	2.42 236	51.82 227	69.42 204	0.28 233	64.11 222	58.29 215	60.02 217	55.18 214

test123		1.06 228	1.41 229	0.64 230	0.39 233	0.48 235	0.52 237	0.25 232	1.11 234	1.37 237	2.01 233	1.98 237	0.87 231	1.43 231	1.27 229	0.46 235	1.62 232

testmvs		0.93 229	1.37 230	0.41 231	0.36 234	0.36 236	0.62 236	0.39 231	1.48 233	0.18 238	2.41 232	1.31 238	0.41 232	1.25 232	1.08 230	0.48 233	1.68 230

test_all		0.00 230	0.00 231	0.00 232	0.00 236	0.00 237	0.00 238	0.00 234	0.00 235	0.00 239	0.00 234	0.00 239	0.00 234	0.00 233	0.00 232	0.00 236	0.00 233

sosnet-low-res		0.00 230	0.00 231	0.00 232	0.00 236	0.00 237	0.00 238	0.00 234	0.00 235	0.00 239	0.00 234	0.00 239	0.00 234	0.00 233	0.00 232	0.00 236	0.00 233

sosnet		0.00 230	0.00 231	0.00 232	0.00 236	0.00 237	0.00 238	0.00 234	0.00 235	0.00 239	0.00 234	0.00 239	0.00 234	0.00 233	0.00 232	0.00 236	0.00 233

ambc			88.38 58		91.62 15	87.97 47	84.48 128		88.64 41	87.93 16	87.38 95	94.82 82	74.53 71	89.14 80	83.86 104	85.94 154	86.84 73

MTAPA										89.37 9		94.85 80

MTMP										90.54 5		95.16 72

Patchmatch-RL test							4.13 235

tmp_tt				13.54 229	16.73 232	6.42 234	8.49 234	2.36 230	28.69 232	27.44 230	18.40 231	13.51 236	3.70 230	33.23 229	36.26 228	22.54 232

XVS					91.28 23	91.23 8	96.89 2			87.14 27		94.53 84				95.84 15

X-MVStestdata					91.28 23	91.23 8	96.89 2			87.14 27		94.53 84				95.84 15

abl_6				79.30 100	84.98 75	85.78 63	90.50 61	66.88 89	77.08 147	74.02 118	73.29 190	89.34 136	68.94 120			90.49 81	85.98 78

mPP-MVS					93.05 4							95.77 54

NP-MVS									78.65 138

Patchmtry						56.88 208	64.47 218	67.74 85		72.30 125

DeepMVS_CX						17.78 233	20.40 233	6.69 229	31.41 231	9.80 234	38.61 230	34.88 235	33.78 216	28.41 230		23.59 231	45.77 225

High-resolution multi-view benchmark