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		98.66 18	98.60 22	98.73 18	99.83 1	99.28 8	98.56 83	99.24 7	96.04 42	97.12 82	98.44 86	98.95 56	98.17 30	99.15 23	99.00 17	99.48 16	99.33 2

DTE-MVSNet		99.03 6	98.88 13	99.21 6	99.66 2	99.59 2	99.62 5	99.34 5	96.92 24	98.52 21	99.36 47	98.98 50	98.57 17	99.49 8	99.23 11	99.56 8	98.55 24

PS-CasMVS		99.08 4	98.90 12	99.28 3	99.65 3	99.56 4	99.59 6	99.39 3	96.36 34	98.83 16	99.46 37	99.09 35	98.62 14	99.51 6	99.36 7	99.63 2	98.97 6

PEN-MVS		99.08 4	98.95 9	99.23 5	99.65 3	99.59 2	99.64 2	99.34 5	96.68 27	98.65 19	99.43 40	99.33 16	98.47 21	99.50 7	99.32 8	99.60 4	98.79 11

CP-MVSNet		98.91 13	98.61 20	99.25 4	99.63 5	99.50 6	99.55 10	99.36 4	95.53 68	98.77 18	99.11 57	98.64 89	98.57 17	99.42 10	99.28 10	99.61 3	98.78 14

zzz-MVS		98.14 33	97.78 51	98.55 25	99.58 6	98.58 60	98.98 40	98.48 26	95.98 45	97.39 69	94.73 171	99.27 22	97.98 40	98.81 33	98.64 38	98.90 51	98.46 31

ACMMPR		98.31 24	98.07 37	98.60 23	99.58 6	98.83 27	99.09 28	98.48 26	96.25 37	97.03 86	96.81 128	99.09 35	98.39 24	98.55 49	98.45 44	99.01 37	98.53 28

mPP-MVS					99.58 6							98.98 50

MP-MVS		97.98 47	97.53 67	98.50 27	99.56 9	98.58 60	98.97 42	98.39 36	93.49 142	97.14 79	96.08 146	99.23 28	98.06 33	98.50 54	98.38 49	98.90 51	98.44 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
WR-MVS_H		98.97 11	98.82 15	99.14 8	99.56 9	99.56 4	99.54 11	99.42 2	96.07 41	98.37 26	99.34 48	99.09 35	98.43 22	99.45 9	99.41 5	99.53 9	98.86 10

PGM-MVS		97.82 58	97.25 74	98.48 29	99.54 11	98.75 42	99.02 32	98.35 41	92.41 160	96.84 96	95.39 158	98.99 48	98.24 27	98.43 55	98.34 52	98.90 51	98.41 34

WR-MVS		99.22 3	99.15 4	99.30 2	99.54 11	99.62 1	99.63 4	99.45 1	97.75 16	98.47 24	99.71 7	99.05 43	98.88 7	99.54 5	99.49 2	99.81 1	98.87 9

SteuartSystems-ACMMP		98.06 39	97.78 51	98.39 33	99.54 11	98.79 32	98.94 47	98.42 34	93.98 133	95.85 135	96.66 133	99.25 25	98.61 15	98.71 40	98.38 49	98.97 44	98.67 21
Skip Steuart: Steuart Systems R&D Blog.
SixPastTwentyTwo		99.25 2	99.20 3	99.32 1	99.53 14	99.32 7	99.64 2	99.19 9	98.05 12	99.19 5	99.74 6	98.96 55	99.03 5	99.69 2	99.58 1	99.32 23	99.06 5

ACMMP		97.99 45	97.60 62	98.45 31	99.53 14	98.83 27	99.13 27	98.30 44	94.57 105	96.39 118	95.32 159	98.95 56	98.37 25	98.61 46	98.47 41	99.00 39	98.45 32
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
ACMM	94.29 11	98.12 36	97.71 57	98.59 24	99.51 16	98.58 60	99.24 20	98.25 50	96.22 39	96.90 90	95.01 165	98.89 61	98.52 20	98.66 43	98.32 55	99.13 30	98.28 41
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
X-MVS		97.60 65	97.00 94	98.29 36	99.50 17	98.76 38	98.90 51	98.37 38	94.67 102	96.40 114	91.47 206	98.78 75	97.60 65	98.55 49	98.50 40	98.96 46	98.29 38

XVS					99.48 18	98.76 38	99.22 23			96.40 114		98.78 75				98.94 49

X-MVStestdata					99.48 18	98.76 38	99.22 23			96.40 114		98.78 75				98.94 49

CP-MVS		98.00 43	97.57 64	98.50 27	99.47 20	98.56 63	98.91 50	98.38 37	94.71 99	97.01 87	95.20 161	99.06 40	98.20 28	98.61 46	98.46 42	99.02 35	98.40 35

APDe-MVS		98.29 25	98.42 25	98.14 43	99.45 21	98.90 21	99.18 25	98.30 44	95.96 47	95.13 159	98.79 71	99.25 25	97.92 43	98.80 34	98.71 33	98.85 58	98.54 25

LGP-MVS_train		97.96 51	97.53 67	98.45 31	99.45 21	98.64 55	99.09 28	98.27 49	92.99 154	96.04 130	96.57 134	99.29 18	98.66 12	98.73 36	98.42 46	99.19 28	98.09 44

SMA-MVS		98.13 35	98.22 30	98.02 61	99.44 23	98.73 45	98.24 100	97.87 89	95.22 80	96.76 97	98.66 79	99.35 15	97.03 86	98.53 52	98.39 48	98.80 60	98.69 18

ACMP	94.03 12	97.97 50	97.61 61	98.39 33	99.43 24	98.51 67	98.97 42	98.06 71	94.63 103	96.10 128	96.12 144	99.20 30	98.63 13	98.68 41	98.20 63	99.14 29	97.93 51
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MIMVSNet1		98.22 29	98.51 23	97.87 73	99.40 25	98.82 29	99.31 17	98.53 24	97.39 19	96.59 105	99.31 50	99.23 28	94.76 140	98.93 30	98.67 34	98.63 71	97.25 90

DeepC-MVS	96.08 5	98.58 19	98.49 24	98.68 20	99.37 26	98.52 66	99.01 36	98.17 62	97.17 22	98.25 30	99.56 24	99.62 4	98.29 26	98.40 57	98.09 67	98.97 44	98.08 45
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		98.17 30	98.02 38	98.35 35	99.36 27	98.62 56	98.79 60	98.46 31	96.24 38	96.53 107	97.13 125	98.98 50	98.02 35	98.20 65	98.42 46	98.95 48	98.54 25

HSP-MVS		97.44 82	97.13 86	97.79 79	99.34 28	98.99 19	99.23 21	98.12 65	93.43 144	95.95 131	97.45 112	99.50 8	96.44 106	96.35 138	95.33 168	97.65 132	98.89 8

ACMMP_NAP		98.12 36	98.08 36	98.18 41	99.34 28	98.74 43	98.97 42	98.00 77	95.13 86	96.90 90	97.54 111	99.27 22	97.18 81	98.72 38	98.45 44	98.68 69	98.69 18

TranMVSNet+NR-MVSNet		98.45 20	98.22 30	98.72 19	99.32 30	99.06 13	98.99 38	98.89 13	95.52 69	97.53 61	99.42 42	98.83 69	98.01 36	98.55 49	98.34 52	99.57 7	97.80 56

APD-MVS		97.47 80	97.16 81	97.84 75	99.32 30	98.39 75	98.47 88	98.21 55	92.08 165	95.23 156	96.68 132	98.90 60	96.99 87	98.20 65	98.21 60	98.80 60	97.67 62
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DU-MVS		98.23 26	97.74 55	98.81 15	99.23 32	98.77 34	98.76 63	98.88 14	94.10 127	98.50 22	98.87 66	98.32 108	97.99 38	98.40 57	98.08 74	99.49 15	97.64 64

Baseline_NR-MVSNet		98.17 30	97.90 43	98.48 29	99.23 32	98.59 58	98.83 58	98.73 20	93.97 134	96.95 89	99.66 11	98.23 113	97.90 44	98.40 57	99.06 15	99.25 26	97.42 81

v1.0		90.15 214	83.75 237	97.62 88	99.21 34	98.80 31	98.31 96	98.30 44	93.60 140	94.74 169	97.94 100	99.24 27	96.58 99	98.42 56	98.27 58	98.56 74	0.00 246

CPTT-MVS		97.08 104	96.25 123	98.05 57	99.21 34	98.30 78	98.54 84	97.98 79	94.28 123	95.89 134	89.57 218	98.54 97	98.18 29	97.82 73	97.32 97	98.54 76	97.91 53

LS3D		97.93 53	97.80 48	98.08 52	99.20 36	98.77 34	98.89 53	97.92 83	96.59 29	96.99 88	96.71 131	97.14 145	96.39 107	99.04 25	98.96 20	99.10 34	97.39 82

test20.03		96.08 133	96.80 110	95.25 187	99.19 37	97.58 141	97.24 166	97.56 111	94.95 92	91.91 213	98.58 81	98.03 119	87.88 215	97.43 91	96.94 107	97.69 129	94.05 179

HPM-MVS++		97.56 67	97.11 88	98.09 47	99.18 38	97.95 112	98.57 81	98.20 56	94.08 129	97.25 77	95.96 150	98.81 72	97.13 82	97.51 87	97.30 100	98.21 104	98.15 43

LTVRE_ROB	97.71 1	99.33 1	99.47 1	99.16 7	99.16 39	99.11 10	99.39 14	99.16 10	99.26 2	99.22 4	99.51 31	99.75 3	98.54 19	99.71 1	99.47 3	99.52 11	99.46 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
MVS_0304		97.18 100	96.84 108	97.58 91	99.15 40	98.19 84	98.11 105	97.81 95	92.36 161	98.06 41	97.43 113	99.06 40	94.24 153	96.80 122	96.54 126	98.12 110	97.52 74

UniMVSNet (Re)		98.23 26	97.85 46	98.67 21	99.15 40	98.87 23	98.74 72	98.84 16	94.27 125	97.94 47	99.01 59	98.39 103	97.82 48	98.35 62	98.29 57	99.51 14	97.78 57

PVSNet_Blended_VisFu		97.44 82	97.14 83	97.79 79	99.15 40	98.44 72	98.32 95	97.66 104	93.74 139	97.73 52	98.79 71	96.93 150	95.64 124	97.69 78	96.91 109	98.25 101	97.50 76

gm-plane-assit		91.85 203	87.91 217	96.44 153	99.14 43	98.25 80	99.02 32	97.38 137	95.57 64	98.31 28	99.34 48	51.00 250	88.93 208	93.16 206	91.57 209	95.85 198	86.50 224

COLMAP_ROB	96.84 2	98.75 16	98.82 15	98.66 22	99.14 43	98.79 32	99.30 18	97.67 103	98.33 7	97.82 49	99.20 54	99.18 32	98.76 9	99.27 16	98.96 20	99.29 25	98.03 46
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CDPH-MVS		96.68 120	95.99 131	97.48 99	99.13 45	97.64 138	98.08 106	97.46 123	90.56 184	95.13 159	94.87 169	98.27 110	96.56 101	97.09 105	96.45 129	98.54 76	97.08 97

UniMVSNet_NR-MVSNet		98.12 36	97.56 66	98.78 16	99.13 45	98.89 22	98.76 63	98.78 18	93.81 137	98.50 22	98.81 70	97.64 133	97.99 38	98.18 68	97.92 79	99.53 9	97.64 64

OPM-MVS		98.01 41	98.01 39	98.00 62	99.11 47	98.12 94	98.68 76	97.72 101	96.65 28	96.68 102	98.40 88	99.28 21	97.44 72	98.20 65	97.82 85	98.40 93	97.58 69

CSCG		98.45 20	98.61 20	98.26 37	99.11 47	99.06 13	98.17 103	97.49 118	97.93 14	97.37 71	98.88 64	99.29 18	98.10 31	98.40 57	97.51 87	99.32 23	99.16 3

CANet		96.81 114	96.50 117	97.17 114	99.10 49	97.96 110	97.86 125	97.51 113	91.30 174	97.75 50	97.64 107	97.89 125	93.39 166	96.98 114	96.73 116	97.40 143	96.99 100

v7n		99.03 6	99.03 8	99.02 9	99.09 50	99.11 10	99.57 9	98.82 17	98.21 8	99.25 2	99.84 3	99.59 7	98.76 9	99.23 18	98.83 28	98.63 71	98.40 35

train_agg		96.68 120	95.93 134	97.56 92	99.08 51	97.16 159	98.44 91	97.37 139	91.12 177	95.18 158	95.43 157	98.48 101	97.36 75	96.48 134	95.52 163	97.95 119	97.34 87

testgi		94.81 165	96.05 130	93.35 207	99.06 52	96.87 173	97.57 141	96.70 167	95.77 56	88.60 229	93.19 193	98.87 64	81.21 235	97.03 112	96.64 121	96.97 177	93.99 181

NCCC		96.56 125	95.68 137	97.59 90	99.04 53	97.54 146	97.67 130	97.56 111	94.84 95	96.10 128	87.91 221	98.09 116	96.98 88	97.20 100	96.80 115	98.21 104	97.38 85

ambc			96.78 111		99.01 54	97.11 165	95.73 209		95.91 49	99.25 2	98.56 82	97.17 143	97.04 85	96.76 123	95.22 170	96.72 184	96.73 115

Gipuma		98.43 22	98.15 33	98.76 17	99.00 55	98.29 79	97.91 119	98.06 71	99.02 3	99.50 1	96.33 138	98.67 86	99.22 1	99.02 26	98.02 77	98.88 56	97.66 63
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
TDRefinement		99.00 8	99.13 5	98.86 11	98.99 56	99.05 15	99.58 7	98.29 48	98.96 4	97.96 46	99.40 44	98.67 86	98.87 8	99.60 3	99.46 4	99.46 17	98.74 16

3Dnovator+	96.20 4	97.58 66	97.14 83	98.10 46	98.98 57	97.85 129	98.60 80	98.33 42	96.41 32	97.23 78	94.66 173	97.26 141	96.91 89	97.91 70	97.87 81	98.53 78	98.03 46

ACMH+	94.90 8	98.40 23	98.71 18	98.04 58	98.93 58	98.84 26	99.30 18	97.86 90	97.78 15	94.19 181	98.77 73	99.39 13	98.61 15	99.33 12	99.07 13	99.33 21	97.81 55

v52		98.98 9	99.10 6	98.85 12	98.91 59	99.03 16	99.41 12	97.77 99	98.12 9	99.07 8	99.84 3	99.60 5	99.15 2	99.29 14	98.99 18	98.79 63	98.79 11

V4		98.98 9	99.10 6	98.85 12	98.91 59	99.03 16	99.41 12	97.77 99	98.12 9	99.06 9	99.85 2	99.60 5	99.15 2	99.30 13	98.99 18	98.80 60	98.79 11

CNVR-MVS		97.03 107	96.77 112	97.34 104	98.89 61	97.67 137	97.64 133	97.17 148	94.40 119	95.70 145	94.02 182	98.76 79	96.49 105	97.78 75	97.29 101	98.12 110	97.47 77

FC-MVSNet-test		97.54 69	98.26 28	96.70 138	98.87 62	97.79 135	98.49 85	98.56 23	96.04 42	90.39 217	99.65 13	98.67 86	95.15 131	99.23 18	99.07 13	98.73 65	97.39 82

ACMH	95.26 7	98.75 16	98.93 11	98.54 26	98.86 63	99.01 18	99.58 7	98.10 68	98.67 5	97.30 74	99.18 55	99.42 11	98.40 23	99.19 20	98.86 26	98.99 41	98.19 42
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous202405211			97.39 70		98.85 64	98.59 58	97.89 122	97.93 82	94.41 118		97.37 116	96.99 149	93.09 168	98.61 46	98.46 42	99.11 32	97.27 88

FC-MVSNet-train		97.65 63	98.16 32	97.05 120	98.85 64	98.85 25	99.34 15	98.08 69	94.50 113	94.41 175	99.21 53	98.80 73	92.66 174	98.98 28	98.85 27	98.96 46	97.94 50

1111		88.65 226	87.69 219	89.78 232	98.84 66	94.02 205	95.79 206	98.19 58	91.57 170	82.27 241	98.19 93	53.19 248	74.80 240	94.98 178	93.04 200	88.80 232	88.82 211

.test1245		69.06 239	63.57 242	75.47 240	98.84 66	94.02 205	95.79 206	98.19 58	91.57 170	82.27 241	98.19 93	53.19 248	74.80 240	94.98 178	5.51 244	2.94 247	7.51 243

EG-PatchMatch MVS		97.98 47	97.92 41	98.04 58	98.84 66	98.04 103	97.90 120	96.83 162	95.07 88	98.79 17	99.07 58	99.37 14	97.88 46	98.74 35	98.16 65	98.01 115	96.96 102

DeepC-MVS_fast	95.38 6	97.53 72	97.30 72	97.79 79	98.83 69	97.64 138	98.18 101	97.14 149	95.57 64	97.83 48	97.10 126	98.80 73	96.53 103	97.41 92	97.32 97	98.24 102	97.26 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPNet		94.33 178	93.52 175	95.27 185	98.81 70	94.71 202	96.77 182	98.20 56	88.12 207	96.53 107	92.53 198	91.19 191	85.25 229	95.22 174	95.26 169	96.09 196	97.63 68
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MCST-MVS		96.79 116	96.08 128	97.62 88	98.78 71	97.52 147	98.01 112	97.32 144	93.20 147	95.84 136	93.97 184	98.12 115	97.34 77	96.34 139	95.88 156	98.45 88	97.51 75

HQP-MVS		95.97 138	95.01 153	97.08 117	98.72 72	97.19 157	97.07 173	96.69 168	91.49 172	95.77 140	92.19 201	97.93 123	96.15 113	94.66 182	94.16 183	98.10 112	97.45 79

EPP-MVSNet		97.29 95	96.88 102	97.76 85	98.70 73	99.10 12	98.92 49	98.36 39	95.12 87	93.36 199	97.39 115	91.00 193	97.65 59	98.72 38	98.91 22	99.58 6	97.92 52

AdaColmap		95.85 142	94.65 160	97.26 108	98.70 73	97.20 156	97.33 159	97.30 145	91.28 175	95.90 133	88.16 220	96.17 161	96.60 97	97.34 95	96.82 111	97.71 126	95.60 146

CLD-MVS		96.73 119	96.92 98	96.51 148	98.70 73	97.57 143	97.64 133	92.07 225	93.10 152	96.31 119	98.29 90	99.02 46	95.99 117	97.20 100	96.47 128	98.37 95	96.81 113
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
anonymousdsp		98.85 14	98.88 13	98.83 14	98.69 76	98.20 83	99.68 1	97.35 143	97.09 23	98.98 12	99.86 1	99.43 10	98.94 6	99.28 15	99.19 12	99.33 21	99.08 4

PMVS	90.51 17	97.77 60	97.98 40	97.53 95	98.68 77	98.14 93	97.67 130	97.03 153	96.43 30	98.38 25	98.72 76	97.03 148	94.44 147	99.37 11	99.30 9	98.98 43	96.86 109
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
casdiffmvs1		96.90 109	96.36 121	97.53 95	98.67 78	98.24 81	98.00 114	98.11 67	95.20 82	97.40 68	97.29 119	97.83 126	95.21 129	94.08 196	94.44 182	97.82 122	97.46 78

ESAPD		97.99 45	98.12 34	97.84 75	98.65 79	98.86 24	98.86 55	98.05 74	94.18 126	95.49 152	98.90 63	99.33 16	97.11 83	98.53 52	98.65 36	98.86 57	98.39 37

Effi-MVS+		96.46 126	95.28 144	97.85 74	98.64 80	97.16 159	97.15 171	98.75 19	90.27 187	98.03 43	93.93 185	96.21 159	96.55 102	96.34 139	96.69 119	97.97 118	96.33 128

Fast-Effi-MVS+		96.80 115	95.92 135	97.84 75	98.57 81	97.46 149	98.06 107	98.24 51	89.64 195	97.57 60	96.45 136	97.35 139	96.73 93	97.22 99	96.64 121	97.86 121	96.65 118

conf0.05thres1000		95.91 141	94.67 159	97.37 103	98.54 82	98.73 45	98.41 92	98.07 70	96.10 40	94.93 166	92.83 196	80.67 219	95.26 127	98.68 41	98.65 36	98.99 41	97.02 99

Effi-MVS+-dtu		95.94 140	95.08 150	96.94 125	98.54 82	97.38 150	96.66 186	97.89 87	88.68 199	95.92 132	92.90 195	97.28 140	94.18 158	96.68 128	96.13 144	98.45 88	96.51 125

v748		98.92 12	98.95 9	98.87 10	98.54 82	98.69 51	99.33 16	98.64 21	98.07 11	99.06 9	99.66 11	99.76 2	98.68 11	99.25 17	98.72 32	99.01 37	98.54 25

thisisatest0515		97.82 58	97.67 59	97.99 63	98.49 85	98.07 98	98.48 86	98.06 71	95.35 78	97.74 51	98.83 69	97.61 134	96.74 92	97.53 86	98.30 56	98.43 91	98.01 48

TSAR-MVS + MP.		98.15 32	98.23 29	98.06 56	98.47 86	98.16 89	99.23 21	96.87 158	95.58 63	96.72 98	98.41 87	99.06 40	98.05 34	98.99 27	98.90 23	99.00 39	98.51 29

IS_MVSNet		96.62 124	96.48 119	96.78 135	98.46 87	98.68 53	98.61 79	98.24 51	92.23 162	89.63 223	95.90 151	94.40 177	96.23 109	98.65 44	98.77 29	99.52 11	96.76 114

MVS_111021_HR		97.27 96	97.11 88	97.46 101	98.46 87	97.82 132	97.50 144	96.86 159	94.97 91	97.13 81	96.99 127	98.39 103	96.82 91	97.65 84	97.38 92	98.02 114	96.56 123

PCF-MVS	92.69 14	95.98 137	95.05 151	97.06 119	98.43 89	97.56 144	97.76 127	96.65 170	89.95 192	95.70 145	96.18 143	98.48 101	95.74 119	93.64 201	93.35 197	98.09 113	96.18 130
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Anonymous20240521		97.56 67	97.63 60	97.47 100	98.41 90	99.12 9	98.63 78	98.57 22	95.71 58	95.60 149	93.79 187	98.01 121	94.25 151	99.16 22	98.88 25	99.35 19	98.74 16

tfpn		92.86 193	89.37 211	96.93 126	98.40 91	98.34 77	98.02 111	97.80 96	92.54 157	93.99 184	86.54 224	57.58 245	94.82 138	97.66 83	97.99 78	98.56 74	94.95 161

EPNet_dtu		93.45 189	92.51 189	94.55 196	98.39 92	91.67 225	95.46 216	97.50 115	86.56 224	97.38 70	93.52 188	94.20 180	85.82 224	93.31 204	92.53 203	92.72 215	95.76 142
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v11		97.94 52	97.72 56	98.20 40	98.37 93	98.69 51	98.96 45	98.30 44	95.68 59	98.35 27	99.70 8	99.19 31	97.93 42	96.76 123	96.82 111	97.28 157	97.23 93

v13		98.04 40	97.86 45	98.24 38	98.36 94	98.77 34	99.04 30	98.47 28	95.93 48	98.20 34	99.67 10	99.11 34	98.00 37	97.11 103	96.93 108	97.40 143	97.53 72

canonicalmvs		97.11 102	96.88 102	97.38 102	98.34 95	98.72 48	97.52 143	97.94 81	95.60 61	95.01 164	94.58 174	94.50 176	96.59 98	97.84 72	98.03 76	98.90 51	98.91 7

v12		97.98 47	97.78 51	98.21 39	98.33 96	98.74 43	99.01 36	98.44 33	95.82 55	98.13 35	99.64 14	99.08 38	97.95 41	96.97 115	96.82 111	97.39 145	97.38 85

SD-MVS		97.84 56	97.78 51	97.90 67	98.33 96	98.06 100	97.95 116	97.80 96	96.03 44	96.72 98	97.57 109	99.18 32	97.50 70	97.88 71	97.08 103	99.11 32	98.68 20

NR-MVSNet		98.00 43	97.88 44	98.13 44	98.33 96	98.77 34	98.83 58	98.88 14	94.10 127	97.46 66	98.87 66	98.58 95	95.78 118	99.13 24	98.16 65	99.52 11	97.53 72

tfpnnormal		97.66 62	97.79 49	97.52 98	98.32 99	98.53 65	98.45 89	97.69 102	97.59 18	96.12 127	97.79 105	96.70 151	95.69 121	98.35 62	98.34 52	98.85 58	97.22 95

pmmvs6		98.77 15	99.35 2	98.09 47	98.32 99	98.92 20	98.57 81	99.03 11	99.36 1	96.86 95	99.77 5	99.86 1	96.20 111	99.56 4	99.39 6	99.59 5	98.61 22

Anonymous20231206		95.69 146	95.68 137	95.70 172	98.32 99	96.95 168	97.37 156	96.65 170	93.33 145	93.61 191	98.70 78	98.03 119	91.04 189	95.07 176	94.59 180	97.20 162	93.09 190

Anonymous20231211		97.49 78	97.91 42	97.00 122	98.31 102	98.72 48	98.27 98	97.84 93	94.76 98	94.77 168	98.14 96	98.38 105	93.60 164	98.96 29	98.66 35	99.22 27	97.77 58

v1192		97.52 73	97.03 92	98.09 47	98.31 102	98.01 106	98.96 45	97.25 146	95.22 80	98.89 14	99.64 14	98.83 69	97.68 57	95.63 165	95.91 154	97.47 137	95.97 136

V9		97.91 54	97.70 58	98.17 42	98.30 104	98.70 50	98.98 40	98.40 35	95.72 57	98.07 39	99.64 14	99.04 44	97.90 44	96.82 120	96.71 118	97.37 148	97.23 93

view800		94.54 170	92.55 186	96.86 132	98.28 105	98.22 82	97.97 115	97.62 106	92.10 164	94.19 181	85.52 227	81.33 218	94.61 143	97.41 92	98.51 39	98.50 82	94.72 164

V14		97.85 55	97.60 62	98.13 44	98.27 106	98.66 54	98.94 47	98.36 39	95.62 60	98.04 42	99.62 18	98.99 48	97.84 47	96.65 129	96.59 124	97.34 151	97.07 98

casdiffmvs		95.95 139	94.97 155	97.09 116	98.27 106	97.87 125	97.62 136	97.99 78	91.60 169	96.60 104	96.11 145	96.58 154	94.64 142	92.69 212	93.32 198	97.45 139	96.60 120

v1144		97.51 74	97.05 90	98.04 58	98.26 108	97.98 109	98.88 54	97.42 131	95.38 74	98.56 20	99.59 23	99.01 47	97.65 59	95.77 163	96.06 148	97.47 137	95.56 147

v1240		97.43 85	96.87 107	98.09 47	98.25 109	97.92 118	99.02 32	97.06 151	94.77 97	99.09 7	99.68 9	98.51 99	97.78 49	95.25 173	95.81 157	97.32 152	96.13 132

v15		97.77 60	97.50 69	98.09 47	98.23 110	98.62 56	98.90 51	98.32 43	95.51 71	98.01 44	99.60 20	98.95 56	97.78 49	96.47 135	96.45 129	97.32 152	96.90 104

TSAR-MVS + ACMM		97.54 69	97.79 49	97.26 108	98.23 110	98.10 97	97.71 129	97.88 88	95.97 46	95.57 151	98.71 77	98.57 96	97.36 75	97.74 76	96.81 114	96.83 180	98.59 23

RPSCF		97.83 57	98.27 27	97.31 107	98.23 110	98.06 100	97.44 153	95.79 191	96.90 25	95.81 137	98.76 74	98.61 93	97.70 55	98.90 32	98.36 51	98.90 51	98.29 38

TransMVSNet (Re)		98.23 26	98.72 17	97.66 87	98.22 113	98.73 45	98.66 77	98.03 76	98.60 6	96.40 114	99.60 20	98.24 111	95.26 127	99.19 20	99.05 16	99.36 18	97.64 64

TSAR-MVS + GP.		97.26 97	97.33 71	97.18 113	98.21 114	98.06 100	96.38 192	97.66 104	93.92 136	95.23 156	98.48 84	98.33 107	97.41 73	97.63 85	97.35 93	98.18 106	97.57 70

v1921920		97.50 77	97.00 94	98.07 54	98.20 115	97.94 115	99.03 31	97.06 151	95.29 79	99.01 11	99.62 18	98.73 83	97.74 52	95.52 168	95.78 159	97.39 145	96.12 133

v2v482		97.33 91	96.84 108	97.90 67	98.19 116	97.83 130	98.74 72	97.44 130	95.42 73	98.23 33	99.46 37	98.84 68	97.46 71	95.51 169	96.10 146	97.36 149	94.72 164

thres600view7		94.34 176	92.31 192	96.70 138	98.19 116	98.12 94	97.85 126	97.45 128	91.49 172	93.98 185	84.27 230	82.02 216	94.24 153	97.04 107	98.76 30	98.49 84	94.47 170

v1141		97.36 90	96.92 98	97.88 72	98.18 118	97.90 122	98.76 63	97.42 131	95.38 74	98.07 39	99.56 24	98.87 64	97.59 67	95.78 160	95.98 149	97.29 154	94.97 159

divwei89l23v2f112		97.37 88	96.92 98	97.89 69	98.18 118	97.90 122	98.76 63	97.42 131	95.38 74	98.09 37	99.56 24	98.87 64	97.59 67	95.78 160	95.98 149	97.29 154	94.97 159

v1		97.37 88	96.92 98	97.89 69	98.18 118	97.91 121	98.76 63	97.42 131	95.38 74	98.09 37	99.55 29	98.88 63	97.59 67	95.78 160	95.98 149	97.29 154	94.98 158

tfpn_n400		95.11 157	93.86 169	96.57 145	98.16 121	97.92 118	97.59 138	97.90 84	95.90 50	92.83 209	89.94 215	83.01 210	94.23 155	97.50 89	97.43 90	98.73 65	95.30 152

tfpnconf		95.11 157	93.86 169	96.57 145	98.16 121	97.92 118	97.59 138	97.90 84	95.90 50	92.83 209	89.94 215	83.01 210	94.23 155	97.50 89	97.43 90	98.73 65	95.30 152

tttt0517		94.81 165	93.04 182	96.88 131	98.15 123	97.37 151	96.99 175	97.36 140	89.51 196	95.74 141	94.89 168	77.53 227	94.89 135	96.94 116	97.35 93	98.17 107	97.70 61

view600		94.36 174	92.33 191	96.73 136	98.14 124	98.03 104	97.88 123	97.36 140	91.61 168	94.29 178	84.38 229	82.08 215	94.31 150	97.05 106	98.75 31	98.42 92	94.41 172

PHI-MVS		97.44 82	97.17 80	97.74 86	98.14 124	98.41 74	98.03 109	97.50 115	92.07 166	98.01 44	97.33 118	98.62 92	96.02 115	98.34 64	98.21 60	98.76 64	97.24 92

3Dnovator	96.31 3	97.22 99	97.19 78	97.25 111	98.14 124	97.95 112	98.03 109	96.77 164	96.42 31	97.14 79	95.11 162	97.59 135	95.14 133	97.79 74	97.72 86	98.26 99	97.76 60

PLC	92.55 15	96.10 132	95.36 140	96.96 123	98.13 127	96.88 171	96.49 190	96.67 169	94.07 130	95.71 144	91.14 209	96.09 162	96.84 90	96.70 127	96.58 125	97.92 120	96.03 134
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tfpnview11		94.92 162	93.56 173	96.50 149	98.12 128	97.99 108	97.48 146	97.86 90	94.50 113	92.83 209	89.94 215	83.01 210	94.19 157	96.91 119	98.07 75	98.50 82	94.53 167

v144192		97.49 78	96.99 96	98.07 54	98.11 129	97.95 112	99.02 32	97.21 147	94.90 94	98.88 15	99.53 30	98.89 61	97.75 51	95.59 166	95.90 155	97.43 140	96.16 131

tfpn1000		94.36 174	93.33 179	95.56 179	98.09 130	98.07 98	97.08 172	97.78 98	94.02 132	89.16 226	91.38 207	80.56 220	92.54 182	96.76 123	98.09 67	98.69 68	94.40 174

v7		97.45 81	97.01 93	97.97 64	98.07 131	97.96 110	98.86 55	97.50 115	94.46 116	98.24 31	99.56 24	98.98 50	97.72 53	96.05 153	96.26 136	97.42 141	95.79 140

v10		97.64 64	97.26 73	98.08 52	98.07 131	98.56 63	98.86 55	98.18 61	94.48 115	98.24 31	99.56 24	98.98 50	97.72 53	96.05 153	96.26 136	97.42 141	96.93 103

CANet_DTU		94.96 161	94.62 161	95.35 182	98.03 133	96.11 190	96.92 177	95.60 195	88.59 201	97.27 76	95.27 160	96.50 156	88.77 210	95.53 167	95.59 161	95.54 200	94.78 162

v17		97.54 69	97.21 76	97.92 65	98.02 134	98.50 68	98.79 60	98.24 51	94.39 120	97.60 59	99.45 39	98.72 84	97.68 57	96.29 142	96.28 134	97.19 166	96.86 109

MSLP-MVS++		96.66 122	96.46 120	96.89 130	98.02 134	97.71 136	95.57 211	96.96 154	94.36 121	96.19 125	91.37 208	98.24 111	97.07 84	97.69 78	97.89 80	97.52 135	97.95 49

thisisatest0530		94.81 165	93.06 181	96.85 133	98.01 136	97.18 158	96.93 176	97.36 140	89.73 194	95.80 138	94.98 166	77.88 226	94.89 135	96.73 126	97.35 93	98.13 109	97.54 71

test-LLR		89.77 219	87.47 221	92.45 215	98.01 136	89.77 234	93.25 236	95.80 189	81.56 240	89.19 224	92.08 202	79.59 222	85.77 227	91.47 221	89.04 221	92.69 216	88.75 212

test0.0.03 1		91.17 209	91.50 201	90.80 227	98.01 136	95.46 196	94.22 229	95.80 189	86.55 225	81.75 244	90.83 212	87.93 197	78.48 238	94.51 189	94.11 186	96.50 188	91.08 200

gg-mvs-nofinetune		94.13 180	93.93 168	94.37 197	97.99 139	95.86 193	95.45 218	99.22 8	97.61 17	95.10 161	99.50 32	84.50 201	81.73 234	95.31 172	94.12 185	96.71 185	90.59 202

v1neww		97.30 92	96.88 102	97.78 82	97.99 139	97.87 125	98.75 69	97.46 123	94.54 109	97.62 56	99.48 33	98.76 79	97.65 59	96.09 150	96.15 138	97.20 162	95.28 154

v7new		97.30 92	96.88 102	97.78 82	97.99 139	97.87 125	98.75 69	97.46 123	94.54 109	97.62 56	99.48 33	98.76 79	97.65 59	96.09 150	96.15 138	97.20 162	95.28 154

v16		97.51 74	97.19 78	97.89 69	97.99 139	98.49 69	98.77 62	98.23 54	94.29 122	97.48 63	99.42 42	98.68 85	97.69 56	96.28 143	96.29 133	97.18 167	96.85 111

v8		97.51 74	97.16 81	97.91 66	97.99 139	98.48 71	98.76 63	98.17 62	94.54 109	97.69 53	99.48 33	98.76 79	97.63 64	96.10 149	96.14 142	97.20 162	96.64 119

v6		97.30 92	96.88 102	97.78 82	97.99 139	97.87 125	98.75 69	97.46 123	94.54 109	97.61 58	99.48 33	98.77 78	97.65 59	96.09 150	96.15 138	97.21 161	95.28 154

thres400		94.04 182	91.94 196	96.50 149	97.98 145	97.82 132	97.66 132	96.96 154	90.96 178	94.20 179	83.24 232	82.82 213	93.80 162	96.50 133	98.09 67	98.38 94	94.15 178

Vis-MVSNet (Re-imp)		96.29 129	96.50 117	96.05 163	97.96 146	97.83 130	97.30 160	97.86 90	93.14 149	88.90 227	96.80 129	95.28 168	95.15 131	98.37 61	98.25 59	99.12 31	95.84 137

pm-mvs1		98.14 33	98.66 19	97.53 95	97.93 147	98.49 69	98.14 104	98.19 58	97.95 13	96.17 126	99.63 17	98.85 67	95.41 125	98.91 31	98.89 24	99.34 20	97.86 54

MAR-MVS		95.51 147	94.49 163	96.71 137	97.92 148	96.40 182	96.72 184	98.04 75	86.74 221	96.72 98	92.52 199	95.14 170	94.02 160	96.81 121	96.54 126	96.85 178	97.25 90
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
N_pmnet		92.46 194	92.38 190	92.55 214	97.91 149	93.47 209	97.42 154	94.01 223	96.40 33	88.48 230	98.50 83	98.07 118	88.14 214	91.04 223	84.30 227	89.35 230	84.85 228

v18		97.40 86	97.04 91	97.81 78	97.90 150	98.42 73	98.71 75	98.17 62	94.06 131	97.34 73	99.40 44	98.59 94	97.60 65	96.05 153	96.12 145	97.14 170	96.67 117

v148		96.99 108	96.70 114	97.34 104	97.89 151	97.23 155	98.33 94	96.96 154	95.57 64	97.12 82	98.99 60	99.40 12	97.23 80	96.22 146	95.45 164	96.50 188	94.02 180

DELS-MVS		96.90 109	97.24 75	96.50 149	97.85 152	98.18 85	97.88 123	95.92 184	93.48 143	95.34 154	98.86 68	98.94 59	94.03 159	97.33 96	97.04 104	98.00 116	96.85 111
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
CDS-MVSNet		94.91 163	95.17 147	94.60 195	97.85 152	96.21 189	96.90 178	96.39 175	90.81 181	93.40 197	97.24 122	94.54 175	85.78 225	96.25 144	96.15 138	97.26 158	95.01 157
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OMC-MVS		97.23 98	97.21 76	97.25 111	97.85 152	97.52 147	97.92 118	95.77 192	95.83 54	97.09 84	97.86 103	98.52 98	96.62 96	97.51 87	96.65 120	98.26 99	96.57 121

OpenMVS	94.63 9	95.75 144	95.04 152	96.58 144	97.85 152	97.55 145	96.71 185	96.07 179	90.15 190	96.47 109	90.77 214	95.95 164	94.41 148	97.01 113	96.95 106	98.00 116	96.90 104

MSDG		96.27 130	96.17 127	96.38 155	97.85 152	96.27 188	96.55 189	94.41 219	94.55 106	95.62 147	97.56 110	97.80 127	96.22 110	97.17 102	96.27 135	97.67 131	93.60 183

TinyColmap		96.64 123	96.07 129	97.32 106	97.84 157	96.40 182	97.63 135	96.25 176	95.86 52	98.98 12	97.94 100	96.34 158	96.17 112	97.30 97	95.38 167	97.04 173	93.24 187

Fast-Effi-MVS+-dtu		94.34 176	93.26 180	95.62 176	97.82 158	95.97 192	95.86 204	99.01 12	86.88 219	93.39 198	90.83 212	95.46 167	90.61 194	94.46 190	94.68 178	97.01 175	94.51 168

MIMVSNet		93.68 188	93.96 167	93.35 207	97.82 158	96.08 191	96.34 193	98.46 31	91.28 175	86.67 238	94.95 167	94.87 172	84.39 230	94.53 185	94.65 179	96.45 190	91.34 199

HyFIR lowres test		95.05 159	93.54 174	96.81 134	97.81 160	96.88 171	98.18 101	97.46 123	94.28 123	94.98 165	96.57 134	92.89 185	96.15 113	90.90 224	91.87 208	96.28 194	91.35 198

FMVSNet1		97.40 86	98.09 35	96.60 143	97.80 161	98.76 38	98.26 99	98.50 25	96.79 26	93.13 203	99.28 51	98.64 89	92.90 171	97.67 80	97.86 82	99.02 35	97.64 64

MVS_111021_LR		96.86 111	96.72 113	97.03 121	97.80 161	97.06 167	97.04 174	95.51 197	94.55 106	97.47 64	97.35 117	97.68 131	96.66 94	97.11 103	96.73 116	97.69 129	96.57 121

abl_6				96.45 152	97.79 163	97.28 153	97.16 170	96.16 178	89.92 193	95.72 143	91.59 205	97.16 144	94.37 149			97.51 136	95.49 148

QAPM		97.04 106	97.14 83	96.93 126	97.78 164	98.02 105	97.36 158	96.72 165	94.68 101	96.23 121	97.21 123	97.68 131	95.70 120	97.37 94	97.24 102	97.78 125	97.77 58

thres200		93.98 184	91.90 197	96.40 154	97.66 165	98.12 94	97.20 167	97.45 128	90.16 189	93.82 186	83.08 233	83.74 206	93.80 162	97.04 107	97.48 89	98.49 84	93.70 182

V42		97.10 103	96.97 97	97.26 108	97.64 166	97.60 140	98.45 89	95.99 181	94.44 117	97.35 72	99.40 44	98.63 91	97.34 77	96.33 141	96.38 132	96.82 182	96.00 135

TAPA-MVS	93.96 13	96.79 116	96.70 114	96.90 129	97.64 166	97.58 141	97.54 142	94.50 218	95.14 85	96.64 103	96.76 130	97.90 124	96.63 95	95.98 156	96.14 142	98.45 88	97.39 82
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pmmvs-eth3d		96.84 113	96.22 125	97.56 92	97.63 168	96.38 185	98.74 72	96.91 157	94.63 103	98.26 29	99.43 40	98.28 109	96.58 99	94.52 187	95.54 162	97.24 159	94.75 163

USDC		96.30 128	95.64 139	97.07 118	97.62 169	96.35 187	97.17 169	95.71 193	95.52 69	99.17 6	98.11 97	97.46 136	95.67 122	95.44 171	93.60 192	97.09 171	92.99 192

UGNet		96.79 116	97.82 47	95.58 177	97.57 170	98.39 75	98.48 86	97.84 93	95.85 53	94.68 170	97.91 102	99.07 39	87.12 219	97.71 77	97.51 87	97.80 123	98.29 38
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
IterMVS-LS		96.35 127	95.85 136	96.93 126	97.53 171	98.00 107	97.37 156	97.97 80	95.49 72	96.71 101	98.94 62	93.23 183	94.82 138	93.15 207	95.05 172	97.17 168	97.12 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn111		93.73 187	91.63 200	96.17 159	97.52 172	98.15 90	97.48 146	97.48 120	87.65 210	93.42 195	82.19 237	84.12 202	92.62 175	97.04 107	98.09 67	98.52 79	94.17 175

conf200view11		93.79 186	91.75 198	96.17 159	97.52 172	98.15 90	97.48 146	97.48 120	87.65 210	93.42 195	83.03 234	84.12 202	92.62 175	97.04 107	98.09 67	98.52 79	94.17 175

thres100view900		92.93 192	90.89 204	95.31 183	97.52 172	96.82 175	96.41 191	95.08 203	87.65 210	93.56 193	83.03 234	84.12 202	91.12 188	94.53 185	96.91 109	98.17 107	93.21 188

tfpn200view9		93.80 185	91.75 198	96.20 157	97.52 172	98.15 90	97.48 146	97.47 122	87.65 210	93.56 193	83.03 234	84.12 202	92.62 175	97.04 107	98.09 67	98.52 79	94.17 175

IterMVS		94.48 171	93.46 176	95.66 174	97.52 172	96.43 179	97.20 167	94.73 212	92.91 156	96.44 110	98.75 75	91.10 192	94.53 145	92.10 216	90.10 216	93.51 207	92.84 193
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
new-patchmatchnet		94.48 171	94.02 166	95.02 190	97.51 177	95.00 200	95.68 210	94.26 220	97.32 20	95.73 142	99.60 20	98.22 114	91.30 185	94.13 195	84.41 226	95.65 199	89.45 209

CNLPA		96.24 131	95.97 132	96.57 145	97.48 178	97.10 166	96.75 183	94.95 208	94.92 93	96.20 124	94.81 170	96.61 153	96.25 108	96.94 116	95.64 160	97.79 124	95.74 143

TSAR-MVS + COLMAP		96.05 134	95.94 133	96.18 158	97.46 179	96.41 181	97.26 165	95.83 188	94.69 100	95.30 155	98.31 89	96.52 155	94.71 141	95.48 170	94.87 174	96.54 187	95.33 149

IB-MVS	92.44 16	93.33 190	92.15 194	94.70 193	97.42 180	96.39 184	95.57 211	94.67 213	86.40 227	93.59 192	78.28 243	95.76 166	89.59 205	95.88 159	95.98 149	97.39 145	96.34 127
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
conf0.01		91.86 202	88.22 214	96.10 161	97.40 181	97.94 115	97.48 146	97.41 135	87.65 210	93.22 201	80.39 239	63.83 241	92.62 175	96.63 130	98.09 67	98.47 86	93.03 191

thresconf0.02		91.75 204	88.21 215	95.87 167	97.38 182	97.14 161	97.27 164	96.85 160	93.04 153	92.39 212	82.19 237	63.31 242	93.10 167	94.43 191	95.06 171	98.23 103	92.32 195

GA-MVS		94.18 179	92.98 183	95.58 177	97.36 183	96.42 180	96.21 199	95.86 185	90.29 186	95.08 162	96.19 142	85.37 200	92.82 172	94.01 198	94.14 184	96.16 195	94.41 172

conf0.002		91.12 210	86.87 225	96.08 162	97.35 184	97.89 124	97.48 146	97.38 137	87.65 210	93.19 202	79.38 241	57.48 246	92.62 175	96.56 132	96.64 121	98.46 87	92.50 194

our_test_3					97.32 185	95.13 199	97.59 138

PVSNet_BlendedMVS		95.44 150	95.09 148	95.86 168	97.31 186	97.13 162	96.31 196	95.01 205	88.55 202	96.23 121	94.55 177	97.75 128	92.56 180	96.42 136	95.44 165	97.71 126	95.81 138

PVSNet_Blended		95.44 150	95.09 148	95.86 168	97.31 186	97.13 162	96.31 196	95.01 205	88.55 202	96.23 121	94.55 177	97.75 128	92.56 180	96.42 136	95.44 165	97.71 126	95.81 138

CHOSEN 1792x2688		94.98 160	94.69 158	95.31 183	97.27 188	95.58 195	97.90 120	95.56 196	95.03 89	93.77 189	95.65 154	99.29 18	95.30 126	91.51 220	91.28 211	92.05 221	94.50 169

MDTV_nov1_ep13_2view		94.39 173	93.34 177	95.63 175	97.23 189	95.33 197	97.76 127	96.84 161	94.55 106	97.47 64	98.96 61	97.70 130	93.88 161	92.27 214	86.81 224	90.56 224	87.73 219

testmv		92.35 197	92.53 188	92.13 218	97.16 190	92.68 214	96.31 196	94.61 217	86.68 223	88.16 232	97.27 121	97.09 147	83.28 232	94.52 187	93.39 195	93.26 208	86.10 226

test1235678		92.36 196	92.55 186	92.13 218	97.16 190	92.69 213	96.32 195	94.62 215	86.69 222	88.16 232	97.28 120	97.13 146	83.28 232	94.54 184	93.40 194	93.26 208	86.11 225

DI_MVS_plusplus_trai		95.48 148	94.51 162	96.61 142	97.13 192	97.30 152	98.05 108	96.79 163	93.75 138	95.08 162	96.38 137	89.76 196	94.95 134	93.97 200	94.82 177	97.64 133	95.63 145

PM-MVS		96.85 112	96.62 116	97.11 115	97.13 192	96.51 178	98.29 97	94.65 214	94.84 95	98.12 36	98.59 80	97.20 142	97.41 73	96.24 145	96.41 131	97.09 171	96.56 123

TAMVS		92.46 194	93.34 177	91.44 224	97.03 194	93.84 208	94.68 228	90.60 229	90.44 185	85.31 239	97.14 124	93.03 184	85.78 225	94.34 192	93.67 191	95.22 202	90.93 201

pmmvs4		95.37 152	94.25 164	96.67 141	97.01 195	95.28 198	97.60 137	96.07 179	93.11 151	97.29 75	98.09 98	94.23 179	95.21 129	91.56 219	93.91 189	96.82 182	93.59 184

tfpn_ndepth		93.27 191	92.11 195	94.61 194	96.96 196	97.93 117	96.87 179	97.49 118	90.91 180	87.89 234	85.98 225	83.53 207	89.77 203	95.91 158	97.31 99	98.67 70	93.25 186

MVS_Test		95.34 154	94.88 156	95.89 166	96.93 197	96.84 174	96.66 186	97.08 150	90.06 191	94.02 183	97.61 108	96.64 152	93.59 165	92.73 211	94.02 187	97.03 174	96.24 129

Vis-MVSNet		98.01 41	98.42 25	97.54 94	96.89 198	98.82 29	99.14 26	97.59 107	96.30 35	97.04 85	99.26 52	98.83 69	96.01 116	98.73 36	98.21 60	98.58 73	98.75 15
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
diffmvs1		95.99 136	96.26 122	95.68 173	96.83 199	96.90 170	96.87 179	96.47 174	95.17 84	92.97 208	97.43 113	98.35 106	94.25 151	94.56 183	94.45 181	96.69 186	96.98 101

MVSTER		91.97 200	90.31 205	93.91 202	96.81 200	96.91 169	94.22 229	95.64 194	84.98 230	92.98 207	93.42 189	72.56 234	86.64 223	95.11 175	93.89 190	97.16 169	95.31 150

PatchMatch-RL		94.79 168	93.75 172	96.00 164	96.80 201	95.00 200	95.47 215	95.25 202	90.68 183	95.80 138	92.97 194	93.64 181	95.67 122	96.13 148	95.81 157	96.99 176	92.01 196

GBi-Net		95.21 155	95.35 141	95.04 188	96.77 202	98.18 85	97.28 161	97.58 108	88.43 204	90.28 219	96.01 147	92.43 186	90.04 199	97.67 80	97.86 82	98.28 96	96.90 104

test1		95.21 155	95.35 141	95.04 188	96.77 202	98.18 85	97.28 161	97.58 108	88.43 204	90.28 219	96.01 147	92.43 186	90.04 199	97.67 80	97.86 82	98.28 96	96.90 104

FMVSNet2		95.77 143	96.20 126	95.27 185	96.77 202	98.18 85	97.28 161	97.90 84	93.12 150	91.37 214	98.25 92	96.05 163	90.04 199	94.96 180	95.94 153	98.28 96	96.90 104

diffmvs		95.36 153	95.35 141	95.37 181	96.71 205	96.73 176	96.10 200	96.56 173	92.43 159	93.69 190	96.20 141	97.94 122	92.79 173	94.00 199	93.39 195	96.38 193	96.73 115

tpm		89.84 218	86.81 226	93.36 206	96.60 206	91.92 223	95.02 224	97.39 136	86.79 220	96.54 106	95.03 163	69.70 237	87.66 216	88.79 229	86.19 225	86.95 239	89.27 210

PatchmatchNet		89.98 216	86.23 230	94.36 199	96.56 207	91.90 224	96.07 201	96.72 165	90.18 188	96.87 92	93.36 192	78.06 225	91.46 184	84.71 239	81.40 235	88.45 233	83.97 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs5		95.70 145	95.22 145	96.26 156	96.55 208	97.24 154	97.50 144	94.99 207	90.95 179	96.87 92	98.47 85	97.40 137	94.45 146	92.86 209	94.98 173	97.23 160	94.64 166

MS-PatchMatch		94.84 164	94.76 157	94.94 191	96.38 209	94.69 203	95.90 203	94.03 222	92.49 158	93.81 187	95.79 152	96.38 157	94.54 144	94.70 181	94.85 175	94.97 203	94.43 171

no-one		97.16 101	97.57 64	96.68 140	96.30 210	95.74 194	98.40 93	94.04 221	96.28 36	96.30 120	97.95 99	99.45 9	99.06 4	96.93 118	98.19 64	95.99 197	98.48 30

CR-MVSNet		91.94 201	88.50 213	95.94 165	96.14 211	92.08 219	95.23 222	98.47 28	84.30 234	96.44 110	94.58 174	75.57 228	92.92 169	90.22 226	92.22 204	96.43 191	90.56 203

DeepPCF-MVS	94.55 10	97.05 105	97.13 86	96.95 124	96.06 212	97.12 164	98.01 112	95.44 198	95.18 83	97.50 62	97.86 103	98.08 117	97.31 79	97.23 98	97.00 105	97.36 149	97.45 79

EPMVS		89.28 221	86.28 228	92.79 213	96.01 213	92.00 222	95.83 205	95.85 187	90.78 182	91.00 216	94.58 174	74.65 230	88.93 208	85.00 237	82.88 233	89.09 231	84.09 232

tpmp4_e23		88.68 225	84.61 233	93.43 205	96.00 214	91.46 226	95.40 220	96.60 172	87.71 209	94.67 171	88.54 219	69.81 236	88.41 212	85.50 236	81.08 236	89.52 229	88.18 217

MDTV_nov1_ep13		90.30 213	87.32 223	93.78 203	96.00 214	92.97 211	95.46 216	95.39 199	88.61 200	95.41 153	94.45 179	80.39 221	89.87 202	86.58 233	83.54 230	90.56 224	84.71 229

RPMNet		90.52 212	86.27 229	95.48 180	95.95 216	92.08 219	95.55 214	98.12 65	84.30 234	95.60 149	87.49 223	72.78 233	91.24 186	87.93 230	89.34 217	96.41 192	89.98 206

testus		90.01 215	90.03 208	89.98 229	95.89 217	91.43 227	93.88 232	89.30 231	83.54 236	89.68 222	87.81 222	94.62 173	78.31 239	92.87 208	92.01 206	92.85 214	87.91 218

CostFormer		89.06 223	85.65 231	93.03 212	95.88 218	92.40 216	95.30 221	95.86 185	86.49 226	93.12 205	93.40 191	74.18 231	88.25 213	82.99 240	81.46 234	89.77 228	88.66 214

FPMVS		94.70 169	94.99 154	94.37 197	95.84 219	93.20 210	96.00 202	91.93 226	95.03 89	94.64 172	94.68 172	93.29 182	90.95 190	98.07 69	97.34 96	96.85 178	93.29 185

E-PMN		86.94 232	85.10 232	89.09 235	95.77 220	83.54 245	89.89 242	86.55 234	92.18 163	87.34 236	94.02 182	83.42 208	89.63 204	93.32 203	77.11 240	85.33 240	72.09 241

tpm cat1		87.19 231	82.78 238	92.33 217	95.66 221	90.61 231	94.19 231	95.27 201	86.97 218	94.38 176	90.91 211	69.40 238	87.21 218	79.57 243	77.82 239	87.25 238	84.18 231

tpmrst		87.60 230	84.13 236	91.66 223	95.65 222	89.73 236	93.77 233	94.74 211	88.85 198	93.35 200	95.60 155	72.37 235	87.40 217	81.24 242	78.19 238	85.02 242	82.90 237

FMVSNet5		89.65 220	87.60 220	92.04 220	95.63 223	96.61 177	94.82 227	94.75 210	80.11 243	87.72 235	77.73 244	73.81 232	83.81 231	95.64 164	96.08 147	95.49 201	93.21 188

ADS-MVSNet		89.89 217	87.70 218	92.43 216	95.52 224	90.91 230	95.57 211	95.33 200	93.19 148	91.21 215	93.41 190	82.12 214	89.05 206	86.21 234	83.77 229	87.92 234	84.31 230

EMVS		86.63 234	84.48 234	89.15 234	95.51 225	83.66 244	90.19 241	86.14 236	91.78 167	88.68 228	93.83 186	81.97 217	89.05 206	92.76 210	76.09 241	85.31 241	71.28 242

EU-MVSNet		96.03 135	96.23 124	95.80 170	95.48 226	94.18 204	98.99 38	91.51 227	97.22 21	97.66 54	99.15 56	98.51 99	98.08 32	95.92 157	92.88 202	93.09 212	95.72 144

FMVSNet3		94.06 181	93.85 171	94.31 200	95.46 227	97.80 134	96.34 193	97.58 108	88.43 204	90.28 219	96.01 147	92.43 186	88.67 211	91.82 217	93.96 188	97.53 134	96.50 126

test2356		85.48 236	81.66 239	89.94 230	95.36 228	88.71 239	91.69 240	92.78 224	78.28 245	86.79 237	85.80 226	58.29 244	80.44 236	89.39 228	89.17 219	92.60 219	81.98 238

DWT-MVSNet_training		86.69 233	81.24 240	93.05 210	95.31 229	92.06 221	95.75 208	91.51 227	84.32 233	94.49 174	83.46 231	55.37 247	90.81 192	82.76 241	83.19 232	90.45 226	87.52 220

test-mter		89.16 222	88.14 216	90.37 228	94.79 230	91.05 229	93.60 235	85.26 239	81.65 239	88.32 231	92.22 200	79.35 224	87.03 220	92.28 213	90.12 215	93.19 211	90.29 205

CHOSEN 280x420		91.55 206	90.27 206	93.05 210	94.61 231	88.01 241	96.56 188	94.62 215	88.04 208	94.20 179	92.66 197	86.60 198	90.82 191	95.06 177	91.89 207	87.49 237	89.61 208

LP		92.03 199	90.19 207	94.17 201	94.52 232	93.87 207	96.79 181	95.05 204	93.58 141	95.62 147	95.68 153	83.37 209	91.78 183	90.73 225	86.99 223	91.27 222	87.09 222

CVMVSNet		94.01 183	94.25 164	93.73 204	94.36 233	92.44 215	97.45 152	88.56 232	95.59 62	93.06 206	98.88 64	90.03 195	94.84 137	94.08 196	93.45 193	94.09 205	95.31 150

MDA-MVSNet-bldmvs		95.45 149	95.20 146	95.74 171	94.24 234	96.38 185	97.93 117	94.80 209	95.56 67	96.87 92	98.29 90	95.24 169	96.50 104	98.65 44	90.38 214	94.09 205	91.93 197

TESTMET0.1,1		88.60 227	87.47 221	89.93 231	94.23 235	89.77 234	93.25 236	84.47 240	81.56 240	89.19 224	92.08 202	79.59 222	85.77 227	91.47 221	89.04 221	92.69 216	88.75 212

dps		88.36 228	84.32 235	93.07 209	93.86 236	92.29 217	94.89 226	95.93 183	83.50 237	93.13 203	91.87 204	67.79 239	90.32 197	85.99 235	83.22 231	90.28 227	85.56 227

new_pmnet		90.85 211	92.26 193	89.21 233	93.68 237	89.05 238	93.20 238	84.16 241	92.99 154	84.25 240	97.72 106	94.60 174	86.80 222	93.20 205	91.30 210	93.21 210	86.94 223

testpf		81.59 238	76.31 241	87.75 236	93.50 238	83.16 246	89.19 243	95.94 182	73.85 246	90.39 217	80.32 240	61.17 243	73.99 242	76.52 244	75.82 242	83.50 243	83.33 236

CMPMVS	71.81 19	92.34 198	92.85 184	91.75 222	92.70 239	90.43 232	88.84 244	88.56 232	85.87 228	94.35 177	90.98 210	95.89 165	91.14 187	96.14 147	94.83 176	94.93 204	95.78 141
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PMMVS2		86.47 235	92.62 185	79.29 239	92.01 240	85.63 243	93.74 234	86.37 235	93.95 135	54.18 248	98.19 93	97.39 138	58.46 243	96.57 131	93.07 199	90.99 223	83.55 235

test12356		88.21 229	89.73 209	86.43 237	91.94 241	89.52 237	91.79 239	86.07 237	85.51 229	81.97 243	95.56 156	96.20 160	79.11 237	94.14 194	90.94 212	87.70 236	76.23 240

MVS-HIRNet		88.72 224	86.49 227	91.33 225	91.81 242	85.66 242	87.02 246	96.25 176	81.48 242	94.82 167	96.31 140	92.14 189	90.32 197	87.60 231	83.82 228	87.74 235	78.42 239

pmmvs3		91.20 208	91.40 203	90.96 226	91.71 243	91.08 228	95.41 219	81.34 242	87.36 217	94.57 173	95.02 164	94.30 178	90.42 195	94.28 193	89.26 218	92.30 220	88.49 215

PatchT		91.40 207	88.54 212	94.74 192	91.48 244	92.18 218	97.42 154	97.51 113	84.96 231	96.44 110	94.16 181	75.47 229	92.92 169	90.22 226	92.22 204	92.66 218	90.56 203

PMMVS		91.67 205	91.47 202	91.91 221	89.43 245	88.61 240	94.99 225	85.67 238	87.50 216	93.80 188	94.42 180	94.88 171	90.71 193	92.26 215	92.96 201	96.83 180	89.65 207

MVE	72.99 18	85.37 237	89.43 210	80.63 238	74.43 246	71.94 248	88.25 245	89.81 230	93.27 146	67.32 246	96.32 139	91.83 190	90.40 196	93.36 202	90.79 213	73.55 245	88.49 215
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				45.72 241	60.00 247	38.74 249	45.50 249	12.18 244	79.58 244	68.42 245	67.62 245	65.04 240	22.12 244	84.83 238	78.72 237	66.08 246

testmvs		4.99 241	6.88 243	2.78 244	1.73 248	2.04 251	3.10 251	1.71 245	7.27 247	3.92 251	12.18 246	6.71 251	3.31 246	6.94 245	5.51 244	2.94 247	7.51 243

test123		4.41 242	5.71 244	2.88 243	1.28 249	2.21 250	3.09 252	1.65 246	6.35 248	4.98 250	8.53 247	3.88 252	3.46 245	5.79 246	5.71 243	2.85 249	7.50 245

GG-mvs-BLEND		61.03 240	87.02 224	30.71 242	0.74 250	90.01 233	78.90 248	0.74 247	84.56 232	9.46 249	79.17 242	90.69 194	1.37 247	91.74 218	89.13 220	93.04 213	83.83 234

sosnet-low-res		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

sosnet		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

MTAPA										97.43 67		99.27 22

MTMP										97.63 55		99.03 45

Patchmatch-RL test							17.42 250

NP-MVS									89.27 197

Patchmtry						92.70 212	95.23 222	98.47 28		96.44 110

DeepMVS_CX						72.99 247	80.14 247	37.34 243	83.46 238	60.13 247	84.40 228	85.48 199	86.93 221	87.22 232		79.61 244	87.32 221

High-resolution multi-view benchmark