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.

UA-Net		96.56 12	96.73 23	96.36 10	98.99 1	97.90 7	97.79 41	95.64 10	92.78 61	92.54 89	96.23 79	95.02 136	94.31 21	98.43 15	98.12 12	98.89 3	98.58 2

zzz-MVS		96.18 22	96.01 44	96.38 8	98.30 2	96.18 50	98.51 14	94.48 22	94.56 29	94.81 42	91.73 145	96.96 84	94.30 22	98.09 21	97.83 16	97.91 42	96.73 33

mPP-MVS					98.24 3							97.65 71

MP-MVS		96.13 24	95.93 47	96.37 9	98.19 4	97.31 23	98.49 15	94.53 21	91.39 98	94.38 47	94.32 118	96.43 101	94.59 17	97.75 38	97.44 26	98.04 39	96.88 29
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ambc			94.61 75		98.09 5	95.14 84	91.71 184		94.18 38	96.46 14	96.26 76	96.30 103	91.26 75	94.70 103	92.00 142	93.45 174	93.67 86

HPM-MVS++		95.21 51	94.89 66	95.59 24	97.79 6	95.39 76	97.68 43	94.05 30	91.91 82	94.35 48	93.38 129	95.07 135	92.94 41	96.01 73	95.88 62	96.73 72	96.61 37

DeepC-MVS	92.47 4	96.44 15	96.75 22	96.08 17	97.57 7	97.19 28	97.96 34	94.28 24	95.29 21	94.92 37	98.31 22	96.92 86	93.69 29	96.81 59	96.50 46	98.06 38	96.27 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CP-MVS		96.21 21	96.16 42	96.27 13	97.56 8	97.13 31	98.43 16	94.70 17	92.62 63	94.13 53	92.71 136	98.03 58	94.54 19	98.00 27	97.60 20	98.23 30	97.05 20

ACMMPR		96.54 13	96.71 24	96.35 11	97.55 9	97.63 11	98.62 10	94.54 18	94.45 31	94.19 50	95.04 105	97.35 76	94.92 13	97.85 32	97.50 23	98.26 29	97.17 15

PGM-MVS		95.90 35	95.72 50	96.10 16	97.53 10	97.45 19	98.55 13	94.12 29	90.25 116	93.71 65	93.20 131	97.18 80	94.63 16	97.68 40	97.34 32	98.08 36	96.97 22

SMA-MVS		96.11 27	96.61 26	95.53 28	97.49 11	97.41 20	97.62 46	93.78 37	94.14 40	94.18 51	97.16 63	94.67 139	92.42 49	97.74 39	97.33 33	97.70 47	97.79 4

DTE-MVSNet		97.16 6	97.75 9	96.47 6	97.40 12	97.95 5	98.20 27	96.89 4	95.30 20	95.15 28	98.66 11	98.80 18	92.77 46	98.97 7	98.27 10	98.44 23	96.28 41

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

train_agg		93.89 78	93.46 113	94.40 53	97.35 14	93.78 128	97.63 45	92.19 59	88.12 147	90.52 130	93.57 128	95.78 117	92.31 52	94.78 102	93.46 115	96.36 83	94.70 71

X-MVS		95.33 49	95.13 62	95.57 26	97.35 14	97.48 16	98.43 16	94.28 24	92.30 71	93.28 73	86.89 191	96.82 90	91.87 58	97.85 32	97.59 21	98.19 31	96.95 25

HFP-MVS		96.18 22	96.53 29	95.77 21	97.34 16	97.26 25	98.16 29	94.54 18	94.45 31	92.52 90	95.05 103	96.95 85	93.89 26	97.28 44	97.46 24	98.19 31	97.25 10

APD-MVS		95.38 47	95.68 51	95.03 43	97.30 17	96.90 34	97.83 38	93.92 32	89.40 132	90.35 132	95.41 93	97.69 70	92.97 39	97.24 46	97.17 35	97.83 44	95.96 47
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
EPNet		90.17 145	89.07 162	91.45 124	97.25 18	90.62 177	94.84 122	93.54 41	80.96 194	91.85 105	86.98 190	85.88 185	77.79 195	92.30 154	92.58 128	93.41 175	94.20 80
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMM	90.06 9	96.31 18	96.42 31	96.19 15	97.21 19	97.16 30	98.71 5	93.79 36	94.35 35	93.81 61	92.80 135	98.23 44	95.11 9	98.07 23	97.45 25	98.51 18	96.86 30
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		97.16 6	97.87 6	96.33 12	97.20 20	97.97 4	98.25 25	96.86 5	95.09 25	94.93 36	98.66 11	99.16 8	92.27 53	98.98 6	98.39 8	98.49 19	96.83 31

ACMMP		96.12 26	96.27 38	95.93 19	97.20 20	97.60 12	98.64 8	93.74 38	92.47 65	93.13 80	93.23 130	98.06 55	94.51 20	97.99 28	97.57 22	98.39 27	96.99 21
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
ACMMP_Plus		95.86 37	96.18 39	95.47 30	97.11 22	97.26 25	98.37 21	93.48 42	93.49 47	93.99 56	95.61 86	94.11 146	92.49 47	97.87 31	97.44 26	97.40 53	97.52 8

PS-CasMVS		97.22 5	97.84 7	96.50 5	97.08 23	97.92 6	98.17 28	97.02 2	94.71 27	95.32 24	98.52 15	98.97 12	92.91 42	99.04 4	98.47 6	98.49 19	97.24 12

CP-MVSNet		96.97 10	97.42 13	96.44 7	97.06 24	97.82 8	98.12 30	96.98 3	93.50 46	95.21 26	97.98 31	98.44 32	92.83 45	98.93 8	98.37 9	98.46 22	96.91 28

SteuartSystems-ACMMP		95.96 32	96.13 43	95.76 22	97.06 24	97.36 21	98.40 20	94.24 26	91.49 90	91.91 104	94.50 114	96.89 87	94.99 11	98.01 26	97.44 26	97.97 41	97.25 10
Skip Steuart: Steuart Systems R&D Blog.
PMVS	87.16 16	95.88 36	96.47 30	95.19 38	97.00 26	96.02 55	96.70 67	91.57 78	94.43 33	95.33 23	97.16 63	95.37 125	92.39 50	98.89 10	98.72 3	98.17 33	94.71 69
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WR-MVS		97.53 3	98.20 4	96.76 3	96.93 27	98.17 1	98.60 11	96.67 6	96.39 13	94.46 44	99.14 1	98.92 13	94.57 18	99.06 3	98.80 2	99.32 1	96.92 27

HSP-MVS		95.04 53	95.45 57	94.57 50	96.87 28	97.77 10	98.71 5	93.88 34	91.21 103	91.48 112	95.36 94	98.37 38	90.73 94	94.37 108	92.98 122	95.77 117	98.08 3

TSAR-MVS + MP.		95.99 31	96.57 28	95.31 33	96.87 28	96.50 43	98.71 5	91.58 77	93.25 52	92.71 85	96.86 69	96.57 97	93.92 24	98.09 21	97.91 14	98.08 36	96.81 32

XVS					96.86 30	97.48 16	98.73 3			93.28 73		96.82 90				98.17 33

X-MVStestdata					96.86 30	97.48 16	98.73 3			93.28 73		96.82 90				98.17 33

NCCC		93.87 81	93.42 114	94.40 53	96.84 32	95.42 73	96.47 79	92.62 48	92.36 69	92.05 100	83.83 207	95.55 119	91.84 60	95.89 75	95.23 76	96.56 76	95.63 53

CPTT-MVS		95.00 54	94.52 77	95.57 26	96.84 32	96.78 35	97.88 36	93.67 40	92.20 73	92.35 95	85.87 198	97.56 72	94.98 12	96.96 52	96.07 57	97.70 47	96.18 43

DeepC-MVS_fast	91.38 6	94.73 58	94.98 63	94.44 51	96.83 34	96.12 52	96.69 69	92.17 60	92.98 57	93.72 64	94.14 120	95.45 123	90.49 103	95.73 79	95.30 73	96.71 73	95.13 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ESAPD		95.63 42	96.35 33	94.80 49	96.76 35	97.29 24	97.74 42	94.15 28	91.69 85	90.01 137	96.65 71	97.29 77	92.45 48	97.41 42	97.18 34	97.67 50	96.95 25

WR-MVS_H		97.06 9	97.78 8	96.23 14	96.74 36	98.04 3	98.25 25	97.32 1	94.40 34	93.71 65	98.55 14	98.89 14	92.97 39	98.91 9	98.45 7	98.38 28	97.19 14

SD-MVS		95.77 40	96.17 40	95.30 34	96.72 37	96.19 49	97.01 52	93.04 45	94.03 41	92.71 85	96.45 74	96.78 94	93.91 25	96.79 60	95.89 61	98.42 25	97.09 18

LGP-MVS_train		96.10 28	96.29 36	95.87 20	96.72 37	97.35 22	98.43 16	93.83 35	90.81 113	92.67 88	95.05 103	98.86 16	95.01 10	98.11 20	97.37 31	98.52 17	96.50 38

OPM-MVS		95.96 32	96.59 27	95.23 36	96.67 39	96.52 42	97.86 37	93.28 43	95.27 23	93.46 70	96.26 76	98.85 17	92.89 43	97.09 48	96.37 49	97.22 61	95.78 51

APDe-MVS		96.23 20	97.22 17	95.08 42	96.66 40	97.56 14	98.63 9	93.69 39	94.62 28	89.80 140	97.73 43	98.13 52	93.84 27	97.79 36	97.63 19	97.87 43	97.08 19

test20.03		88.20 172	91.26 144	84.63 202	96.64 41	89.39 181	90.73 196	89.97 108	91.07 106	72.02 223	94.98 106	95.45 123	69.35 215	92.70 140	91.19 162	89.06 195	84.02 187

ACMP	89.62 11	95.96 32	96.28 37	95.59 24	96.58 42	97.23 27	98.26 24	93.22 44	92.33 70	92.31 96	94.29 119	98.73 21	94.68 15	98.04 24	97.14 37	98.47 21	96.17 44
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CNVR-MVS		94.24 69	94.47 79	93.96 68	96.56 43	95.67 65	96.43 82	91.95 68	92.08 76	91.28 117	90.51 157	95.35 126	91.20 76	96.34 70	95.50 70	96.34 88	95.88 48

TSAR-MVS + GP.		94.25 68	94.81 69	93.60 80	96.52 44	95.80 62	94.37 131	92.47 52	90.89 110	88.92 143	95.34 95	94.38 143	92.85 44	96.36 69	95.62 67	96.47 78	95.28 60

LS3D		95.83 39	96.35 33	95.22 37	96.47 45	97.49 15	97.99 31	92.35 54	94.92 26	94.58 43	94.88 108	95.11 134	91.52 67	98.48 14	98.05 13	98.42 25	95.49 55

DU-MVS		95.51 43	95.68 51	95.33 32	96.45 46	96.44 45	96.61 74	95.32 11	89.97 122	93.78 62	97.46 56	98.07 54	91.19 77	97.03 49	96.53 44	98.61 14	94.22 78

Baseline_NR-MVSNet		94.85 55	95.35 59	94.26 55	96.45 46	93.86 127	96.70 67	94.54 18	90.07 120	90.17 136	98.77 7	97.89 63	90.64 98	97.03 49	96.16 53	97.04 68	93.67 86

LTVRE_ROB	95.06 1	97.73 1	98.39 2	96.95 1	96.33 48	96.94 32	98.30 22	94.90 15	98.61 2	97.73 3	97.97 32	98.57 28	95.74 7	99.24 1	98.70 4	98.72 7	98.70 1
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
CDPH-MVS		93.96 73	93.86 93	94.08 60	96.31 49	95.84 60	96.92 56	91.85 71	87.21 162	91.25 119	92.83 133	96.06 112	91.05 86	95.57 80	94.81 86	97.12 62	94.72 68

PVSNet_Blended_VisFu		93.60 88	93.41 115	93.83 73	96.31 49	95.65 66	95.71 107	90.58 99	88.08 150	93.17 78	95.29 97	92.20 160	90.72 95	94.69 104	93.41 118	96.51 77	94.54 73

TranMVSNet+NR-MVSNet		95.72 41	96.42 31	94.91 46	96.21 51	96.77 36	96.90 59	94.99 13	92.62 63	91.92 103	98.51 16	98.63 25	90.82 93	97.27 45	96.83 40	98.63 13	94.31 77

3Dnovator+	92.82 3	95.22 50	95.16 61	95.29 35	96.17 52	96.55 38	97.64 44	94.02 31	94.16 39	94.29 49	92.09 142	93.71 151	91.90 56	96.68 62	96.51 45	97.70 47	96.40 39

UniMVSNet (Re)		95.46 44	95.86 48	95.00 44	96.09 53	96.60 37	96.68 71	94.99 13	90.36 115	92.13 99	97.64 51	98.13 52	91.38 70	96.90 54	96.74 41	98.73 6	94.63 72

MIMVSNet1		92.52 121	94.88 67	89.77 146	96.09 53	91.99 164	96.92 56	89.68 116	95.92 17	84.55 172	96.64 72	98.21 47	78.44 190	96.08 72	95.10 78	92.91 182	90.22 142

TSAR-MVS + ACMM		95.17 52	95.95 45	94.26 55	96.07 55	96.46 44	95.67 109	94.21 27	93.84 43	90.99 123	97.18 62	95.24 133	93.55 31	96.60 65	95.61 68	95.06 136	96.69 35

MVS_0304		93.92 75	93.81 98	94.05 61	96.06 56	96.00 56	96.43 82	92.76 47	85.99 171	94.43 46	94.04 123	97.08 81	88.12 125	94.65 105	94.20 104	96.47 78	94.71 69

CSCG		96.07 29	97.15 19	94.81 47	96.06 56	97.58 13	96.52 77	90.98 90	96.51 10	93.60 68	97.13 65	98.55 30	93.01 38	97.17 47	95.36 72	98.68 9	97.78 5

COLMAP_ROB	93.74 2	97.09 8	97.98 5	96.05 18	95.97 58	97.78 9	98.56 12	91.72 74	97.53 7	96.01 17	98.14 26	98.76 19	95.28 8	98.76 11	98.23 11	98.77 5	96.67 36
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AdaColmap		92.41 124	91.49 142	93.48 82	95.96 59	95.02 89	95.37 115	91.73 73	87.97 153	91.28 117	82.82 212	91.04 166	90.62 100	95.82 77	95.07 79	95.95 110	92.67 108

Gipuma		95.86 37	96.17 40	95.50 29	95.92 60	94.59 105	94.77 124	92.50 50	97.82 6	97.90 2	95.56 89	97.88 66	94.71 14	98.02 25	94.81 86	97.23 60	94.48 75
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CANet		93.07 109	93.05 124	93.10 104	95.90 61	95.41 74	95.88 101	91.94 69	84.77 178	93.36 71	94.05 122	95.25 132	86.25 135	94.33 109	93.94 106	95.30 125	93.58 89

TDRefinement		97.59 2	98.32 3	96.73 4	95.90 61	98.10 2	99.08 2	93.92 32	98.24 4	96.44 15	98.12 27	97.86 68	96.06 2	99.24 1	98.93 1	99.00 2	97.77 6

Anonymous20231211		96.13 24	98.43 1	93.44 84	95.89 63	96.12 52	95.23 117	95.91 8	99.42 1	86.76 160	98.87 5	99.94 1	88.19 123	98.64 13	98.50 5	98.66 10	97.49 9

EG-PatchMatch MVS		94.81 56	95.53 54	93.97 66	95.89 63	94.62 102	95.55 113	88.18 151	92.77 62	94.88 39	97.04 67	98.61 26	93.31 33	96.89 55	95.19 77	95.99 109	93.56 90

gm-plane-assit		86.15 186	82.51 202	90.40 135	95.81 65	92.29 158	97.99 31	84.66 196	92.15 75	93.15 79	97.84 38	44.65 240	78.60 186	88.02 198	85.95 195	92.20 184	76.69 216

UniMVSNet_NR-MVSNet		95.34 48	95.51 55	95.14 39	95.80 66	96.55 38	96.61 74	94.79 16	90.04 121	93.78 62	97.51 54	97.25 78	91.19 77	96.68 62	96.31 52	98.65 12	94.22 78

HQP-MVS		92.87 113	92.49 130	93.31 90	95.75 67	95.01 90	95.64 110	91.06 88	88.54 143	91.62 111	88.16 177	96.25 105	89.47 110	92.26 155	91.81 144	96.34 88	95.40 56

RPSCF		95.46 44	96.95 21	93.73 79	95.72 68	95.94 58	95.58 112	88.08 155	95.31 19	91.34 114	96.26 76	98.04 57	93.63 30	98.28 17	97.67 18	98.01 40	97.13 16

EPNet_dtu		87.40 182	86.27 188	88.72 161	95.68 69	83.37 208	92.09 176	90.08 103	78.11 220	91.29 116	86.33 194	89.74 172	75.39 207	89.07 187	87.89 187	87.81 200	89.38 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testgi		86.49 184	90.31 150	82.03 207	95.63 70	88.18 185	93.47 148	84.89 194	93.23 54	69.54 230	87.16 188	97.96 61	60.66 224	91.90 165	89.90 172	87.99 198	83.84 188

v7n		96.49 14	97.20 18	95.65 23	95.57 71	96.04 54	97.93 35	92.49 51	96.40 12	97.13 7	98.99 4	99.41 4	93.79 28	97.84 34	96.15 54	97.00 69	95.60 54

MSLP-MVS++		93.91 76	94.30 85	93.45 83	95.51 72	95.83 61	93.12 160	91.93 70	91.45 95	91.40 113	87.42 186	96.12 111	93.27 34	96.57 66	96.40 48	95.49 122	96.29 40

CLD-MVS		92.81 115	94.32 83	91.05 127	95.39 73	95.31 78	95.82 103	81.44 213	89.40 132	91.94 102	95.86 83	97.36 75	85.83 137	95.35 84	94.59 98	95.85 114	92.34 119
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MCST-MVS		93.60 88	93.40 117	93.83 73	95.30 74	95.40 75	96.49 78	90.87 91	90.08 119	91.72 109	90.28 159	95.99 114	91.69 63	93.94 122	92.99 121	96.93 71	95.13 63

ACMH	90.17 8	96.61 11	97.69 11	95.35 31	95.29 75	96.94 32	98.43 16	92.05 66	98.04 5	95.38 22	98.07 29	99.25 7	93.23 36	98.35 16	97.16 36	97.72 45	96.00 46
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		92.75 117	95.40 58	89.66 150	95.21 76	94.82 96	97.00 53	89.40 128	91.13 104	81.71 190	97.72 44	96.43 101	77.57 198	96.89 55	96.72 42	97.05 67	94.09 81

ACMH+	89.90 10	96.27 19	97.52 12	94.81 47	95.19 77	97.18 29	97.97 33	92.52 49	96.72 9	90.50 131	97.31 59	99.11 9	94.10 23	98.67 12	97.90 15	98.56 16	95.79 50

EPP-MVSNet		93.63 87	93.95 90	93.26 94	95.15 78	96.54 41	96.18 97	91.97 67	91.74 84	85.76 164	94.95 107	84.27 189	91.60 66	97.61 41	97.38 30	98.87 4	95.18 62

Effi-MVS+-dtu		92.32 128	91.66 140	93.09 105	95.13 79	94.73 99	94.57 129	92.14 62	81.74 191	90.33 133	88.13 178	95.91 115	89.24 111	94.23 118	93.65 114	97.12 62	93.23 95

MVS_111021_HR		93.82 83	94.26 87	93.31 90	95.01 80	93.97 124	95.73 106	89.75 114	92.06 77	92.49 91	94.01 125	96.05 113	90.61 101	95.95 74	94.78 89	96.28 93	93.04 100

FC-MVSNet-test		91.49 136	94.43 80	88.07 175	94.97 81	90.53 178	95.42 114	91.18 85	93.24 53	72.94 221	98.37 18	93.86 149	78.78 184	97.82 35	96.13 56	95.13 132	91.05 136

PHI-MVS		94.65 59	94.84 68	94.44 51	94.95 82	96.55 38	96.46 80	91.10 87	88.96 136	96.00 18	94.55 113	95.32 128	90.67 96	96.97 51	96.69 43	97.44 52	94.84 65

IS_MVSNet		92.76 116	93.25 122	92.19 116	94.91 83	95.56 67	95.86 102	92.12 63	88.10 148	82.71 184	93.15 132	88.30 178	88.86 115	97.29 43	96.95 39	98.66 10	93.38 92

casdiffmvs		93.69 85	94.11 89	93.20 97	94.85 84	94.86 95	96.00 98	92.15 61	91.92 81	91.34 114	94.77 109	97.42 74	89.12 113	92.72 138	92.62 127	95.76 118	94.46 76

pmmvs6		94.58 60	97.30 16	91.40 125	94.84 85	94.61 103	93.40 150	92.43 53	98.51 3	85.61 167	98.73 10	99.53 3	84.40 144	97.88 30	97.03 38	97.72 45	94.79 67

PLC	87.27 15	93.08 108	92.92 125	93.26 94	94.67 86	95.03 87	94.38 130	90.10 102	91.69 85	92.14 98	87.24 187	93.91 148	91.61 65	95.05 96	94.73 95	96.67 75	92.80 104
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	87.46 14	92.44 123	91.80 138	93.19 99	94.66 87	95.80 62	96.37 92	90.19 101	87.57 155	92.23 97	89.26 168	93.97 147	89.24 111	91.32 170	90.82 166	96.46 80	93.86 85
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		90.68 141	92.18 133	88.92 159	94.63 88	92.75 149	92.91 164	91.20 84	89.21 135	75.01 216	93.96 126	89.07 176	82.72 156	95.88 76	95.30 73	97.08 66	89.08 158

TSAR-MVS + COLMAP		93.06 110	93.65 103	92.36 112	94.62 89	94.28 111	95.36 116	89.46 127	92.18 74	91.64 110	95.55 90	95.27 131	88.60 119	93.24 131	92.50 129	94.46 162	92.55 113

CNLPA		93.14 107	93.67 102	92.53 111	94.62 89	94.73 99	95.00 120	86.57 179	92.85 60	92.43 92	90.94 150	94.67 139	90.35 105	95.41 82	93.70 110	96.23 98	93.37 93

OMC-MVS		94.74 57	95.46 56	93.91 71	94.62 89	96.26 48	96.64 73	89.36 130	94.20 37	94.15 52	94.02 124	97.73 69	91.34 72	96.15 71	95.04 80	97.37 54	94.80 66

conf0.05thres1000		91.24 137	91.85 137	90.53 133	94.59 92	94.56 107	94.33 135	89.52 124	93.67 45	83.77 177	91.04 148	79.10 206	83.98 145	96.66 64	95.56 69	96.98 70	92.36 117

CDS-MVSNet		88.41 165	89.79 153	86.79 188	94.55 93	90.82 174	92.50 172	89.85 112	83.26 187	80.52 196	91.05 147	89.93 171	69.11 216	93.17 134	92.71 126	94.21 167	87.63 172
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v748		96.05 30	97.00 20	94.95 45	94.41 94	94.77 98	96.72 66	91.03 89	96.12 16	96.71 11	98.74 8	99.59 2	93.55 31	97.97 29	95.96 58	97.28 57	95.84 49

TransMVSNet (Re)		93.55 92	96.32 35	90.32 137	94.38 95	94.05 119	93.30 157	89.53 123	97.15 8	85.12 169	98.83 6	97.89 63	82.21 159	96.75 61	96.14 55	97.35 55	93.46 91

tfpn		87.65 179	85.66 191	89.96 143	94.36 96	93.94 125	93.85 144	89.02 136	88.71 142	82.78 182	83.79 208	53.79 235	83.43 150	95.35 84	94.54 99	96.35 87	89.51 153

Effi-MVS+		92.93 111	92.16 135	93.83 73	94.29 97	93.53 138	95.04 119	92.98 46	85.27 175	94.46 44	90.24 160	95.34 127	89.99 107	93.72 124	94.23 103	96.22 99	92.79 105

MAR-MVS		91.86 134	91.14 145	92.71 108	94.29 97	94.24 112	94.91 121	91.82 72	81.66 192	93.32 72	84.51 205	93.42 154	86.86 131	95.16 93	94.44 101	95.05 137	94.53 74
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
NR-MVSNet		94.55 62	95.66 53	93.25 96	94.26 99	96.44 45	96.69 69	95.32 11	89.97 122	91.79 108	97.46 56	98.39 37	82.85 153	96.87 57	96.48 47	98.57 15	93.98 83

1111		76.85 226	78.03 222	75.46 226	94.16 100	78.29 222	86.40 223	89.12 133	87.23 160	61.26 234	95.15 100	44.14 241	51.46 233	86.04 208	81.00 209	70.40 236	74.37 222

.test1245		60.07 233	56.75 235	63.93 233	94.16 100	78.29 222	86.40 223	89.12 133	87.23 160	61.26 234	95.15 100	44.14 241	51.46 233	86.04 208	2.51 237	1.21 241	3.92 238

TAPA-MVS	88.94 13	93.78 84	94.31 84	93.18 100	94.14 102	95.99 57	95.74 105	86.98 173	93.43 49	93.88 60	90.16 161	96.88 88	91.05 86	94.33 109	93.95 105	97.28 57	95.40 56
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pm-mvs1		93.27 101	95.94 46	90.16 138	94.13 103	93.66 130	92.61 170	89.91 111	95.73 18	84.28 175	98.51 16	98.29 40	82.80 154	96.44 67	95.76 63	97.25 59	93.21 96

DeepPCF-MVS	90.68 7	94.56 61	94.92 65	94.15 57	94.11 104	95.71 64	97.03 51	90.65 96	93.39 51	94.08 54	95.29 97	94.15 145	93.21 37	95.22 91	94.92 84	95.82 116	95.75 52

view800		89.42 152	89.11 161	89.78 145	94.00 105	93.71 129	93.96 141	88.47 150	88.10 148	82.91 180	82.61 213	79.85 204	83.10 152	94.92 99	95.38 71	96.26 97	89.19 155

Anonymous20231206		87.45 181	89.66 156	84.87 199	94.00 105	87.73 192	91.36 188	86.41 182	88.89 139	75.03 215	92.59 137	96.82 90	72.48 213	89.72 182	88.06 186	89.93 192	83.81 189

test-LLR		80.62 211	77.20 227	84.62 203	93.99 107	75.11 229	87.04 217	87.32 168	70.11 233	78.59 210	83.17 210	71.60 217	73.88 211	82.32 219	79.20 215	86.91 204	78.87 212

test0.0.03 1		81.51 207	83.30 201	79.42 215	93.99 107	86.50 196	85.93 227	87.32 168	78.16 218	61.62 233	80.78 217	81.78 197	59.87 225	88.40 194	87.27 190	87.78 202	80.19 204

tfpn_n400		89.03 157	89.39 158	88.61 163	93.98 109	92.33 156	91.83 179	88.97 138	92.97 58	78.90 203	84.93 201	78.24 208	81.77 167	95.00 97	93.67 111	96.22 99	88.59 163

tfpnconf		89.03 157	89.39 158	88.61 163	93.98 109	92.33 156	91.83 179	88.97 138	92.97 58	78.90 203	84.93 201	78.24 208	81.77 167	95.00 97	93.67 111	96.22 99	88.59 163

v1240		93.89 78	93.72 101	94.09 58	93.98 109	94.31 109	97.12 48	89.37 129	90.74 114	96.92 9	98.05 30	97.89 63	92.15 54	91.53 168	91.60 150	94.99 139	91.93 126

tfpnnormal		92.45 122	94.77 71	89.74 147	93.95 112	93.44 140	93.25 158	88.49 149	95.27 23	83.20 179	96.51 73	96.23 106	83.17 151	95.47 81	94.52 100	96.38 82	91.97 125

v11		94.32 67	94.62 74	93.97 66	93.95 112	95.31 78	96.83 62	91.30 83	91.95 79	95.51 20	98.32 21	98.61 26	91.44 69	92.83 137	92.23 134	94.77 147	93.08 99

tfpn1000		88.13 174	88.68 170	87.49 182	93.94 114	92.64 152	91.50 187	88.70 147	90.12 118	74.35 218	86.74 193	75.27 214	80.14 175	94.16 119	94.66 96	96.33 90	87.16 177

canonicalmvs		93.38 98	94.36 82	92.24 115	93.94 114	96.41 47	94.18 138	90.47 100	93.07 56	88.47 149	88.66 172	93.78 150	88.80 116	95.74 78	95.75 64	97.57 51	97.13 16

tfpnview11		88.74 162	88.95 163	88.50 165	93.91 116	92.43 155	91.70 185	88.90 143	90.93 109	78.90 203	84.93 201	78.24 208	81.71 169	94.32 111	94.60 97	95.86 112	87.23 176

v1192		93.98 72	93.94 91	94.01 63	93.91 116	94.63 101	97.00 53	89.75 114	91.01 107	96.50 12	97.93 33	98.26 42	91.74 61	92.06 157	92.05 139	95.18 131	91.66 132

MVS_111021_LR		93.15 106	93.65 103	92.56 110	93.89 118	92.28 159	95.09 118	86.92 175	91.26 102	92.99 83	94.46 116	96.22 107	90.64 98	95.11 94	93.45 116	95.85 114	92.74 107

Anonymous20240521		90.84 139	93.27 121	88.02 176	93.86 119	93.11 144	90.69 197	89.25 132	88.22 146	80.40 199	95.59 88	95.85 116	77.90 193	95.10 95	93.85 109	96.00 108	91.18 134

TinyColmap		93.17 105	93.33 120	93.00 107	93.84 120	92.76 148	94.75 126	88.90 143	93.97 42	97.48 4	95.28 99	95.29 129	88.37 121	95.31 89	91.58 151	94.65 153	89.10 157

v52		96.35 16	97.40 14	95.12 40	93.83 121	95.54 68	97.82 39	88.95 141	96.27 14	97.22 5	99.11 2	99.40 5	95.80 5	98.16 19	96.37 49	97.10 64	96.96 23

V4		96.35 16	97.40 14	95.12 40	93.83 121	95.54 68	97.82 39	88.95 141	96.27 14	97.21 6	99.10 3	99.40 5	95.79 6	98.17 18	96.37 49	97.10 64	96.96 23

Fast-Effi-MVS+		92.93 111	92.64 129	93.27 93	93.81 123	93.88 126	95.90 100	90.61 97	83.98 183	92.71 85	92.81 134	96.22 107	90.67 96	94.90 101	93.92 107	95.92 111	92.77 106

v13		94.54 63	94.93 64	94.09 58	93.81 123	95.44 72	96.99 55	91.67 75	92.43 67	95.20 27	98.33 19	98.73 21	91.87 58	93.67 126	92.26 132	95.00 138	93.63 88

abl_6				91.88 120	93.76 125	94.98 92	95.64 110	88.97 138	86.20 169	90.00 138	86.31 195	94.50 142	87.31 127			95.60 120	92.48 115

v12		94.44 64	94.79 70	94.02 62	93.75 126	95.37 77	96.92 56	91.61 76	92.21 72	95.10 29	98.27 23	98.69 23	91.73 62	93.49 128	92.15 137	94.97 142	93.37 93

v1921920		93.90 77	93.82 96	94.00 64	93.74 127	94.31 109	97.12 48	89.33 131	91.13 104	96.77 10	97.90 34	98.06 55	91.95 55	91.93 164	91.54 152	95.10 134	91.85 127

v1144		93.83 82	93.87 92	93.78 76	93.72 128	94.57 106	96.85 60	89.98 107	91.31 100	95.90 19	97.89 35	98.40 36	91.13 81	92.01 160	92.01 141	95.10 134	90.94 137

thres600view7		89.14 155	88.83 165	89.51 153	93.71 129	93.55 136	93.93 142	88.02 156	87.30 159	82.40 185	81.18 216	80.63 202	82.69 157	94.27 113	95.90 60	96.27 95	88.94 159

V9		94.33 66	94.66 73	93.94 69	93.69 130	95.31 78	96.84 61	91.53 79	92.04 78	95.00 33	98.22 24	98.64 24	91.62 64	93.29 130	92.05 139	94.93 143	93.10 98

V14		94.21 70	94.52 77	93.85 72	93.62 131	95.25 81	96.76 65	91.42 80	91.83 83	94.91 38	98.15 25	98.57 28	91.49 68	93.06 135	91.93 143	94.90 144	92.82 103

view600		89.09 156	88.78 168	89.46 154	93.59 132	93.33 142	93.92 143	87.76 161	87.40 156	82.79 181	81.29 215	80.71 201	82.59 158	94.28 112	95.72 65	96.12 105	88.70 162

v15		94.09 71	94.37 81	93.77 77	93.56 133	95.18 82	96.68 71	91.34 82	91.64 87	94.83 41	98.09 28	98.51 31	91.37 71	92.84 136	91.80 145	94.85 145	92.53 114

v144192		93.89 78	93.85 94	93.94 69	93.50 134	94.33 108	97.12 48	89.49 125	90.89 110	96.49 13	97.78 42	98.27 41	91.89 57	92.17 156	91.70 147	95.19 130	91.78 130

v1141		93.47 95	93.56 108	93.36 87	93.48 135	94.17 117	96.42 85	89.62 117	91.44 96	94.99 35	97.81 40	98.42 34	90.94 91	92.00 161	91.38 159	94.74 150	89.69 151

divwei89l23v2f112		93.47 95	93.56 108	93.37 85	93.48 135	94.17 117	96.42 85	89.62 117	91.46 93	95.00 33	97.81 40	98.42 34	90.94 91	92.00 161	91.38 159	94.75 148	89.70 149

v1		93.48 94	93.57 107	93.37 85	93.48 135	94.18 116	96.41 87	89.61 119	91.46 93	95.03 30	97.82 39	98.43 33	90.95 90	92.00 161	91.37 161	94.75 148	89.70 149

v2v482		93.42 97	93.49 112	93.32 89	93.44 138	94.05 119	96.36 94	89.76 113	91.41 97	95.24 25	97.63 52	98.34 39	90.44 104	91.65 166	91.76 146	94.69 151	89.62 152

v7		93.65 86	93.73 100	93.57 81	93.38 139	94.60 104	96.83 62	89.92 110	89.69 129	95.02 31	97.89 35	98.24 43	91.27 73	92.38 148	92.18 135	94.99 139	91.12 135

v10		93.96 73	94.12 88	93.77 77	93.37 140	95.45 71	96.83 62	91.13 86	89.70 128	95.02 31	97.88 37	98.23 44	91.27 73	92.39 147	92.18 135	94.99 139	93.00 101

Fast-Effi-MVS+-dtu		89.57 151	88.42 173	90.92 129	93.35 141	91.57 167	93.01 162	95.71 9	78.94 215	87.65 154	84.68 204	93.14 157	82.00 161	90.84 173	91.01 164	93.78 172	88.77 161

new-patchmatchnet		84.45 194	88.75 169	79.43 214	93.28 142	81.87 214	81.68 232	83.48 203	94.47 30	71.53 224	98.33 19	97.88 66	58.61 227	90.35 176	77.33 219	87.99 198	81.05 200

IterMVS-LS		92.10 131	92.33 131	91.82 121	93.18 143	93.66 130	92.80 168	92.27 55	90.82 112	90.59 129	97.19 61	90.97 167	87.76 126	89.60 183	90.94 165	94.34 165	93.16 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres400		88.54 164	88.15 175	88.98 157	93.17 144	92.84 147	93.56 147	86.93 174	86.45 167	82.37 186	79.96 218	81.46 199	81.83 165	93.21 133	94.76 90	96.04 106	88.39 168

v17		93.60 88	93.85 94	93.30 92	93.15 145	94.99 91	96.46 80	90.81 92	89.58 131	93.61 67	97.66 50	98.15 51	91.19 77	92.60 144	91.61 149	94.61 158	92.37 116

v1neww		93.27 101	93.40 117	93.12 101	93.13 146	94.20 113	96.39 88	89.56 120	89.87 126	93.95 57	97.71 46	98.21 47	91.09 83	92.36 149	91.49 153	94.62 156	89.96 144

v7new		93.27 101	93.40 117	93.12 101	93.13 146	94.20 113	96.39 88	89.56 120	89.87 126	93.95 57	97.71 46	98.21 47	91.09 83	92.36 149	91.49 153	94.62 156	89.96 144

v8		93.60 88	93.82 96	93.34 88	93.13 146	95.06 86	96.39 88	90.75 94	89.90 124	94.03 55	97.70 48	98.21 47	91.08 85	92.36 149	91.47 157	94.63 154	92.07 122

v6		93.27 101	93.41 115	93.12 101	93.13 146	94.20 113	96.39 88	89.55 122	89.89 125	93.93 59	97.72 44	98.22 46	91.10 82	92.36 149	91.49 153	94.63 154	89.95 146

CANet_DTU		88.95 160	89.51 157	88.29 172	93.12 150	91.22 170	93.61 146	83.47 204	80.07 206	90.71 128	89.19 169	93.68 152	76.27 206	91.44 169	91.17 163	92.59 183	89.83 147

v16		93.53 93	93.80 99	93.20 97	93.10 151	94.98 92	96.43 82	90.81 92	89.39 134	93.12 81	97.63 52	98.01 59	91.19 77	92.60 144	91.65 148	94.58 160	92.36 117

gg-mvs-nofinetune		88.32 166	88.81 166	87.75 179	93.07 152	89.37 182	89.06 210	95.94 7	95.29 21	87.15 156	97.38 58	76.38 212	68.05 219	91.04 172	89.10 182	93.24 178	83.10 194

3Dnovator	91.81 5	93.36 99	94.27 86	92.29 114	92.99 153	95.03 87	95.76 104	87.79 160	93.82 44	92.38 94	92.19 141	93.37 155	88.14 124	95.26 90	94.85 85	96.69 74	95.40 56

v18		93.33 100	93.59 106	93.04 106	92.94 154	94.87 94	96.31 95	90.59 98	88.96 136	92.89 84	97.51 54	97.90 62	91.01 89	92.33 153	91.48 156	94.50 161	92.05 123

USDC		92.17 130	92.17 134	92.18 117	92.93 155	92.22 160	93.66 145	87.41 166	93.49 47	97.99 1	94.10 121	96.68 95	86.46 133	92.04 159	89.18 180	94.61 158	87.47 173

QAPM		92.57 120	93.51 110	91.47 123	92.91 156	94.82 96	93.01 162	87.51 164	91.49 90	91.21 120	92.24 139	91.70 162	88.74 117	94.54 106	94.39 102	95.41 123	95.37 59

v148		92.38 125	92.78 127	91.91 119	92.86 157	92.13 162	94.84 122	87.03 172	91.47 92	93.07 82	96.92 68	98.89 14	90.10 106	92.05 158	89.69 174	93.56 173	88.27 170

DELS-MVS		92.33 127	93.61 105	90.83 130	92.84 158	95.13 85	94.76 125	87.22 171	87.78 154	88.42 151	95.78 85	95.28 130	85.71 138	94.44 107	93.91 108	96.01 107	92.97 102
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
OpenMVS	89.22 12	91.09 138	91.42 143	90.71 131	92.79 159	93.61 135	92.74 169	85.47 189	86.10 170	90.73 124	85.71 199	93.07 158	86.69 132	94.07 121	93.34 119	95.86 112	94.02 82

pmmvs-eth3d		92.34 126	92.33 131	92.34 113	92.67 160	90.67 175	96.37 92	89.06 135	90.98 108	93.60 68	97.13 65	97.02 83	88.29 122	90.20 177	91.42 158	94.07 168	88.89 160

MSDG		92.09 132	92.84 126	91.22 126	92.55 161	92.97 145	93.42 149	85.43 190	90.24 117	91.83 106	94.70 110	94.59 141	88.48 120	94.91 100	93.31 120	95.59 121	89.15 156

FPMVS		90.81 140	91.60 141	89.88 144	92.52 162	88.18 185	93.31 156	83.62 201	91.59 89	88.45 150	88.96 170	89.73 173	86.96 129	96.42 68	95.69 66	94.43 163	90.65 138

thres200		88.29 168	87.88 177	88.76 160	92.50 163	93.55 136	92.47 173	88.02 156	84.80 176	81.44 191	79.28 220	82.20 195	81.83 165	94.27 113	93.67 111	96.27 95	87.40 174

DI_MVS_plusplus_trai		90.68 141	90.40 149	91.00 128	92.43 164	92.61 153	94.17 139	88.98 137	88.32 145	88.76 147	93.67 127	87.58 180	86.44 134	89.74 181	90.33 168	95.24 129	90.56 141

PVSNet_BlendedMVS		90.09 146	90.12 151	90.05 141	92.40 165	92.74 150	91.74 181	85.89 184	80.54 201	90.30 134	88.54 173	95.51 120	84.69 142	92.64 142	90.25 170	95.28 127	90.61 139

PVSNet_Blended		90.09 146	90.12 151	90.05 141	92.40 165	92.74 150	91.74 181	85.89 184	80.54 201	90.30 134	88.54 173	95.51 120	84.69 142	92.64 142	90.25 170	95.28 127	90.61 139

FMVSNet1		92.86 114	95.26 60	90.06 140	92.40 165	95.16 83	94.37 131	92.22 56	93.18 55	82.16 189	96.76 70	97.48 73	81.85 164	95.32 86	94.98 81	97.34 56	93.93 84

GA-MVS		88.76 161	88.04 176	89.59 151	92.32 168	91.46 168	92.28 175	86.62 177	83.82 185	89.84 139	92.51 138	81.94 196	83.53 149	89.41 185	89.27 179	92.95 181	87.90 171

tfpn111		87.59 180	86.89 183	88.41 167	92.28 169	93.64 132	93.36 151	88.12 152	80.90 195	80.71 194	73.93 230	82.25 191	79.65 179	94.27 113	94.76 90	96.36 83	88.48 165

conf200view11		87.93 176	87.51 180	88.41 167	92.28 169	93.64 132	93.36 151	88.12 152	80.90 195	80.71 194	78.25 221	82.25 191	79.65 179	94.27 113	94.76 90	96.36 83	88.48 165

thres100view900		86.46 185	86.00 190	86.99 186	92.28 169	91.03 171	91.09 190	84.49 197	80.90 195	80.89 192	78.25 221	82.25 191	77.57 198	90.17 178	92.84 124	95.63 119	86.57 181

tfpn200view9		87.94 175	87.51 180	88.44 166	92.28 169	93.63 134	93.35 155	88.11 154	80.90 195	80.89 192	78.25 221	82.25 191	79.65 179	94.27 113	94.76 90	96.36 83	88.48 165

thresconf0.02		84.34 195	82.02 204	87.06 184	92.23 173	90.93 172	91.05 191	86.43 181	88.83 141	77.65 213	73.93 230	55.81 234	79.68 178	90.62 175	90.28 169	95.30 125	83.73 190

PatchMatch-RL		89.59 150	88.80 167	90.51 134	92.20 174	88.00 189	91.72 183	86.64 176	84.75 179	88.25 152	87.10 189	90.66 169	89.85 109	93.23 132	92.28 131	94.41 164	85.60 186

MVS_Test		90.19 144	90.58 146	89.74 147	92.12 175	91.74 166	92.51 171	88.54 148	82.80 188	87.50 155	94.62 111	95.02 136	83.97 146	88.69 190	89.32 178	93.79 171	91.85 127

conf0.01		85.72 188	83.49 199	88.32 170	92.11 176	93.35 141	93.36 151	88.02 156	80.90 195	80.51 197	74.83 228	59.86 233	79.65 179	93.80 123	94.76 90	96.29 92	86.94 178

PM-MVS		92.65 119	93.20 123	92.00 118	92.11 176	90.16 179	95.99 99	84.81 195	91.31 100	92.41 93	95.87 82	96.64 96	92.35 51	93.65 127	92.91 123	94.34 165	91.85 127

conf0.002		84.82 191	81.84 205	88.30 171	92.05 178	93.28 143	93.36 151	88.00 159	80.90 195	80.48 198	73.43 232	52.48 238	79.65 179	93.72 124	92.82 125	96.28 93	86.22 182

IB-MVS	86.01 17	88.24 170	87.63 179	88.94 158	92.03 179	91.77 165	92.40 174	85.58 188	78.24 217	84.85 170	71.99 233	93.45 153	83.96 147	93.48 129	92.33 130	94.84 146	92.15 121
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
our_test_3					91.78 180	88.87 184	94.37 131

N_pmnet		79.33 213	84.22 195	73.62 229	91.72 181	73.72 233	86.11 225	76.36 220	92.38 68	53.38 237	95.54 92	95.62 118	59.14 226	84.23 214	74.84 227	75.03 233	73.25 226

MIMVSNet		84.76 193	86.75 184	82.44 206	91.71 182	85.95 197	89.74 206	89.49 125	85.28 174	69.69 229	87.93 180	90.88 168	64.85 221	88.26 195	87.74 188	89.18 194	81.24 198

pmmvs4		89.95 148	89.32 160	90.69 132	91.60 183	89.17 183	94.37 131	87.63 163	88.07 151	91.02 122	94.50 114	90.50 170	86.13 136	86.33 205	89.40 177	93.39 176	87.29 175

V42		92.67 118	93.50 111	91.71 122	91.41 184	92.96 146	95.71 107	85.00 192	89.67 130	93.22 76	97.67 49	98.01 59	91.02 88	92.65 141	92.12 138	93.86 170	91.42 133

MDTV_nov1_ep13_2view		88.22 171	87.85 178	88.65 162	91.40 185	86.75 195	94.07 140	84.97 193	88.86 140	93.20 77	96.11 80	96.21 109	83.70 148	87.29 202	80.29 212	84.56 211	79.46 209

tfpn_ndepth		85.89 187	86.40 187	85.30 197	91.31 186	92.47 154	90.78 194	87.75 162	84.79 177	71.04 225	76.95 225	78.80 207	74.52 210	92.72 138	93.43 117	96.39 81	85.65 185

HyFIR lowres test		88.19 173	86.56 186	90.09 139	91.24 187	92.17 161	94.30 136	88.79 145	84.06 181	85.45 168	89.52 166	85.64 187	88.64 118	85.40 213	87.28 189	92.14 185	81.87 197

GBi-Net		89.35 153	90.58 146	87.91 177	91.22 188	94.05 119	92.88 165	90.05 104	79.40 207	78.60 207	90.58 154	87.05 181	78.54 187	95.32 86	94.98 81	96.17 102	92.67 108

test1		89.35 153	90.58 146	87.91 177	91.22 188	94.05 119	92.88 165	90.05 104	79.40 207	78.60 207	90.58 154	87.05 181	78.54 187	95.32 86	94.98 81	96.17 102	92.67 108

FMVSNet2		90.28 143	92.04 136	88.23 173	91.22 188	94.05 119	92.88 165	90.69 95	86.53 166	79.89 201	94.38 117	92.73 159	78.54 187	91.64 167	92.26 132	96.17 102	92.67 108

tpm		81.58 206	78.84 214	84.79 201	91.11 191	79.50 218	89.79 205	83.75 199	79.30 211	92.05 100	90.98 149	64.78 228	74.54 208	80.50 224	76.67 221	77.49 228	80.15 205

UGNet		92.31 129	94.70 72	89.53 152	90.99 192	95.53 70	96.19 96	92.10 65	91.35 99	85.76 164	95.31 96	95.48 122	76.84 202	95.22 91	94.79 88	95.32 124	95.19 61
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
Vis-MVSNet		94.39 65	95.85 49	92.68 109	90.91 193	95.88 59	97.62 46	91.41 81	91.95 79	89.20 142	97.29 60	96.26 104	90.60 102	96.95 53	95.91 59	96.32 91	96.71 34
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IterMVS		88.32 166	88.25 174	88.41 167	90.83 194	91.24 169	93.07 161	81.69 210	86.77 164	88.55 148	95.61 86	86.91 184	87.01 128	87.38 200	83.77 201	89.29 193	86.06 183
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		87.72 178	88.62 172	86.66 190	90.81 195	88.18 185	90.92 192	82.25 207	85.86 172	80.40 199	90.14 162	89.29 175	84.93 139	89.39 186	89.12 181	90.67 188	88.34 169

TAMVS		82.96 199	86.15 189	79.24 217	90.57 196	83.12 211	87.29 216	75.12 224	84.06 181	65.81 232	92.22 140	88.27 179	69.11 216	88.72 188	87.26 191	87.56 203	79.38 210

CR-MVSNet		85.32 190	81.58 206	89.69 149	90.36 197	84.79 202	86.72 221	92.22 56	75.38 225	90.73 124	90.41 158	67.88 223	84.86 140	83.76 215	85.74 196	93.24 178	83.14 192

CHOSEN 1792x2688		86.64 183	86.62 185	86.65 191	90.33 198	87.86 191	93.19 159	83.30 205	83.95 184	82.32 187	87.93 180	89.34 174	86.92 130	85.64 211	84.95 198	83.85 217	86.68 180

tpmp4_e23		82.16 202	78.26 219	86.70 189	89.92 199	84.82 201	91.17 189	89.95 109	81.21 193	87.10 157	81.91 214	64.01 229	77.88 194	79.89 226	74.99 226	84.18 215	81.00 201

PatchmatchNet		82.44 200	78.69 216	86.83 187	89.81 200	81.55 215	90.78 194	87.27 170	82.39 190	88.85 144	88.31 176	70.96 219	81.90 162	78.58 228	74.33 228	82.35 223	74.69 220
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
RPMNet		83.42 198	78.40 217	89.28 155	89.79 201	84.79 202	90.64 198	92.11 64	75.38 225	87.10 157	79.80 219	61.99 232	82.79 155	81.88 221	82.07 206	93.23 180	82.87 195

FMVSNet3		87.90 177	88.63 171	87.04 185	89.78 202	93.46 139	91.62 186	90.05 104	79.40 207	78.60 207	90.58 154	87.05 181	77.07 201	88.03 197	89.86 173	95.12 133	92.04 124

diffmvs		88.28 169	88.88 164	87.58 180	89.51 203	88.07 188	91.88 177	85.83 187	87.31 157	86.34 162	96.01 81	88.90 177	81.90 162	85.49 212	86.61 193	90.04 190	89.77 148

tpm cat1		80.03 212	75.93 230	84.81 200	89.31 204	83.26 210	88.86 211	86.55 180	79.24 212	86.10 163	84.22 206	63.62 230	77.37 200	73.43 233	70.88 231	80.67 224	76.87 215

pmmvs5		88.63 163	89.70 155	87.39 183	89.24 205	90.64 176	91.87 178	82.13 208	83.34 186	87.86 153	94.58 112	96.15 110	79.87 176	87.33 201	89.07 183	93.39 176	86.76 179

MVSTER		84.79 192	83.79 197	85.96 193	89.14 206	89.80 180	89.39 208	82.99 206	74.16 229	82.78 182	85.97 197	66.81 225	76.84 202	90.77 174	88.83 185	94.66 152	90.19 143

CostFormer		82.15 203	79.54 212	85.20 198	88.92 207	85.70 198	90.87 193	86.26 183	79.19 213	83.87 176	87.89 182	69.20 221	76.62 204	77.50 231	75.28 224	84.69 210	82.02 196

MDTV_nov1_ep13		82.33 201	79.66 211	85.45 195	88.83 208	83.88 206	90.09 203	81.98 209	79.07 214	88.82 145	88.70 171	73.77 215	78.41 191	80.29 225	76.08 222	84.56 211	75.83 217

DWT-MVSNet_training		79.22 216	73.99 232	85.33 196	88.57 209	84.41 204	90.56 199	80.96 214	73.90 230	85.72 166	75.62 226	50.09 239	81.30 170	76.91 232	77.02 220	84.88 209	79.97 207

tpmrst		78.81 219	76.18 229	81.87 208	88.56 210	77.45 224	86.74 220	81.52 211	80.08 205	83.48 178	90.84 153	66.88 224	74.54 208	73.04 234	71.02 230	76.38 230	73.95 225

anonymousdsp		95.45 46	96.70 25	93.99 65	88.43 211	92.05 163	99.18 1	85.42 191	94.29 36	96.10 16	98.63 13	99.08 11	96.11 1	97.77 37	97.41 29	98.70 8	97.69 7

EU-MVSNet		91.63 135	92.73 128	90.35 136	88.36 212	87.89 190	96.53 76	81.51 212	92.45 66	91.82 107	96.44 75	97.05 82	93.26 35	94.10 120	88.94 184	90.61 189	92.24 120

E-PMN		77.81 223	77.88 224	77.73 223	88.26 213	70.48 236	80.19 234	71.20 226	86.66 165	72.89 222	88.09 179	81.74 198	78.75 185	90.02 180	68.30 232	75.10 232	59.85 235

EMVS		77.65 224	77.49 226	77.83 221	87.75 214	71.02 235	81.13 233	70.54 227	86.38 168	74.52 217	89.38 167	80.19 203	78.22 192	89.48 184	67.13 233	74.83 234	58.84 236

testmv		81.49 209	84.76 193	77.67 224	87.67 215	80.25 217	90.12 201	77.62 217	80.34 204	69.71 227	90.92 152	96.47 99	56.57 229	88.58 193	84.92 200	84.33 214	71.86 230

test1235678		81.50 208	84.78 192	77.67 224	87.67 215	80.27 216	90.12 201	77.62 217	80.36 203	69.71 227	90.93 151	96.51 98	56.57 229	88.60 192	84.93 199	84.34 213	71.87 229

dps		81.42 210	77.88 224	85.56 194	87.67 215	85.17 200	88.37 214	87.46 165	74.37 228	84.55 172	86.80 192	62.18 231	80.20 174	81.13 223	77.52 218	85.10 208	77.98 214

FMVSNet5		79.08 218	78.83 215	79.38 216	87.52 218	86.78 194	87.64 215	78.15 216	69.54 235	70.64 226	65.97 237	65.44 227	63.87 222	90.17 178	90.46 167	88.48 197	83.45 191

CVMVSNet		88.97 159	89.73 154	88.10 174	87.33 219	85.22 199	94.68 127	78.68 215	88.94 138	86.98 159	95.55 90	85.71 186	89.87 108	91.19 171	89.69 174	91.05 187	91.78 130

EPMVS		79.26 214	78.20 221	80.49 210	87.04 220	78.86 220	86.08 226	83.51 202	82.63 189	73.94 219	89.59 164	68.67 222	72.03 214	78.17 229	75.08 225	80.37 225	74.37 222

testpf		72.68 231	66.81 234	79.53 213	86.52 221	73.89 232	83.56 229	88.74 146	58.70 238	79.68 202	71.31 234	53.64 236	62.23 223	68.68 235	66.64 234	76.46 229	74.82 219

testus		78.20 222	81.50 207	74.36 228	85.59 222	79.36 219	86.99 219	65.76 228	76.01 223	73.00 220	77.98 224	93.35 156	51.30 235	86.33 205	82.79 204	83.50 219	74.68 221

no-one		92.05 133	94.57 76	89.12 156	85.55 223	87.65 193	94.21 137	77.34 219	93.43 49	89.64 141	95.11 102	99.11 9	95.86 4	95.38 83	95.24 75	92.08 186	96.11 45

CMPMVS	66.55 18	85.55 189	87.46 182	83.32 205	84.99 224	81.97 213	79.19 235	75.93 221	79.32 210	88.82 145	85.09 200	91.07 165	82.12 160	92.56 146	89.63 176	88.84 196	92.56 112
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test2356		72.95 230	71.24 233	74.95 227	84.89 225	75.49 228	82.67 231	75.38 222	68.02 236	68.65 231	74.40 229	52.81 237	55.61 232	81.50 222	79.80 213	82.50 221	66.70 233

test-mter		78.71 220	78.35 218	79.12 219	84.03 226	76.58 225	88.51 213	59.06 233	71.06 231	78.87 206	83.73 209	71.83 216	76.44 205	83.41 218	80.61 210	87.79 201	81.24 198

new_pmnet		76.65 227	83.52 198	68.63 231	82.60 227	72.08 234	76.76 237	64.17 229	84.41 180	49.73 239	91.77 143	91.53 163	56.16 231	86.59 203	83.26 203	82.37 222	75.02 218

TESTMET0.1,1		77.47 225	77.20 227	77.78 222	81.94 228	75.11 229	87.04 217	58.33 235	70.11 233	78.59 210	83.17 210	71.60 217	73.88 211	82.32 219	79.20 215	86.91 204	78.87 212

ADS-MVSNet		79.11 217	79.38 213	78.80 220	81.90 229	75.59 227	84.36 228	83.69 200	87.31 157	76.76 214	87.58 184	76.90 211	68.55 218	78.70 227	75.56 223	77.53 227	74.07 224

MVS-HIRNet		78.28 221	75.28 231	81.79 209	80.33 230	69.38 237	76.83 236	86.59 178	70.76 232	86.66 161	89.57 165	81.04 200	77.74 196	77.81 230	71.65 229	82.62 220	66.73 232

CHOSEN 280x420		79.24 215	78.26 219	80.38 211	79.60 231	68.80 238	89.32 209	75.38 222	77.25 221	78.02 212	75.57 227	76.17 213	81.19 171	88.61 191	81.39 208	78.79 226	80.03 206

LP		84.09 196	84.31 194	83.85 204	79.40 232	84.34 205	90.26 200	84.02 198	87.99 152	84.66 171	91.61 146	79.13 205	80.58 173	85.90 210	81.59 207	84.16 216	79.59 208

pmmvs3		81.69 205	83.83 196	79.19 218	78.33 233	78.57 221	89.53 207	58.71 234	78.88 216	84.34 174	88.36 175	91.96 161	77.69 197	87.48 199	82.42 205	86.54 206	79.18 211

PatchT		83.44 197	81.10 208	86.18 192	77.92 234	82.58 212	89.87 204	87.39 167	75.88 224	90.73 124	89.86 163	66.71 226	84.86 140	83.76 215	85.74 196	86.33 207	83.14 192

test12356		75.40 228	80.89 209	69.01 230	77.43 235	75.75 226	83.03 230	61.48 231	78.13 219	59.08 236	87.69 183	94.95 138	57.37 228	88.18 196	80.59 211	75.65 231	60.93 234

PMMVS2		69.86 232	82.14 203	55.52 234	75.19 236	63.08 239	75.52 238	60.97 232	88.50 144	25.11 242	91.77 143	96.44 100	25.43 236	88.70 189	79.34 214	70.93 235	67.17 231

MDA-MVSNet-bldmvs		89.75 149	91.67 139	87.50 181	74.25 237	90.88 173	94.68 127	85.89 184	91.64 87	91.03 121	95.86 83	94.35 144	89.10 114	96.87 57	86.37 194	90.04 190	85.72 184

PMMVS		81.93 204	83.48 200	80.12 212	72.35 238	75.05 231	88.54 212	64.01 230	77.02 222	82.22 188	87.51 185	91.12 164	79.70 177	86.59 203	86.64 192	93.88 169	80.41 202

MVE	60.41 19	73.21 229	80.84 210	64.30 232	56.34 239	57.24 240	75.28 239	72.76 225	87.14 163	41.39 240	86.31 195	85.30 188	80.66 172	86.17 207	83.36 202	59.35 237	80.38 203
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				28.44 235	36.05 240	15.86 242	21.29 242	6.40 237	54.52 239	51.96 238	50.37 238	38.68 243	9.55 237	61.75 237	59.66 235	45.36 239

testmvs		2.38 235	3.35 236	1.26 238	0.83 241	0.96 244	1.53 244	0.83 238	3.59 240	1.63 245	6.03 239	2.93 244	1.55 239	3.49 238	2.51 237	1.21 241	3.92 238

test123		2.16 236	2.82 237	1.41 237	0.62 242	1.18 243	1.53 244	0.82 239	2.78 241	2.27 244	4.18 240	1.98 245	1.64 238	2.58 239	3.01 236	1.56 240	4.00 237

GG-mvs-BLEND		54.28 234	77.89 223	26.72 236	0.37 243	83.31 209	70.04 240	0.39 240	74.71 227	5.36 243	68.78 235	83.06 190	0.62 240	83.73 217	78.99 217	83.55 218	72.68 228

sosnet-low-res		0.00 237	0.00 238	0.00 239	0.00 244	0.00 245	0.00 246	0.00 241	0.00 242	0.00 246	0.00 241	0.00 246	0.00 241	0.00 240	0.00 239	0.00 243	0.00 240

sosnet		0.00 237	0.00 238	0.00 239	0.00 244	0.00 245	0.00 246	0.00 241	0.00 242	0.00 246	0.00 241	0.00 246	0.00 241	0.00 240	0.00 239	0.00 243	0.00 240

MTAPA										94.88 39		96.88 88

MTMP										95.43 21		97.25 78

Patchmatch-RL test							8.96 243

NP-MVS									85.48 173

Patchmtry						83.74 207	86.72 221	92.22 56		90.73 124

DeepMVS_CX						47.68 241	53.20 241	19.21 236	63.24 237	26.96 241	66.50 236	69.82 220	66.91 220	64.27 236		54.91 238	72.72 227

High-resolution multi-view benchmark