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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

Anonymous20231211		97.78 3	98.31 2	96.16 46	99.55 2	89.37 80	98.40 5	98.89 4	98.75 2	99.48 3	99.62 2	98.70 2	99.40 36	91.60 105	99.84 5	99.71 3

LTVRE_ROB	93.87 1	97.93 2	98.16 3	97.26 23	98.81 23	93.86 27	99.07 2	98.98 3	97.01 11	98.92 5	98.78 14	95.22 32	98.61 157	96.85 4	99.77 12	99.31 38
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
TDRefinement		97.68 4	97.60 4	97.93 2	99.02 11	95.95 6	98.61 3	98.81 5	97.41 8	97.28 47	98.46 28	94.62 46	98.84 121	94.64 26	99.53 43	98.99 70

PS-CasMVS		96.69 20	97.43 5	94.49 108	99.13 5	84.09 162	96.61 25	97.97 48	97.91 5	98.64 13	98.13 40	95.24 31	99.65 3	93.39 59	99.84 5	99.72 2

DTE-MVSNet		96.74 18	97.43 5	94.67 96	99.13 5	84.68 154	96.51 30	97.94 54	98.14 3	98.67 12	98.32 35	95.04 36	99.69 2	93.27 63	99.82 10	99.62 11

ACMH	88.36 12	96.59 26	97.43 5	94.07 121	98.56 35	85.33 149	96.33 39	98.30 16	94.66 35	98.72 9	98.30 36	97.51 5	98.00 210	94.87 21	99.59 34	98.86 85
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		96.69 20	97.39 8	94.61 98	99.16 3	84.50 155	96.54 29	98.05 37	98.06 4	98.64 13	98.25 38	95.01 39	99.65 3	92.95 72	99.83 8	99.68 5

pmmvs6		96.80 14	97.36 9	95.15 85	99.12 7	87.82 111	96.68 23	97.86 57	96.10 24	98.14 25	99.28 3	97.94 4	98.21 199	91.38 112	99.69 15	99.42 27

v7n		96.82 11	97.31 10	95.33 78	98.54 39	86.81 124	96.83 19	98.07 35	96.59 17	98.46 19	98.43 32	92.91 74	99.52 17	96.25 8	99.76 13	99.65 9

v52		96.93 8	97.29 11	95.86 58	98.12 66	88.48 99	97.69 7	97.74 67	94.90 33	98.55 15	98.72 17	93.39 63	99.49 21	96.92 2	99.62 29	99.61 12

V4		96.93 8	97.29 11	95.86 58	98.11 67	88.47 100	97.69 7	97.74 67	94.91 31	98.55 15	98.72 17	93.37 64	99.49 21	96.92 2	99.62 29	99.61 12

UA-Net		97.35 5	97.24 13	97.69 5	98.22 60	93.87 26	98.42 4	98.19 24	96.95 12	95.46 123	99.23 4	93.45 59	99.57 13	95.34 17	99.89 4	99.63 10

abl_6		97.31 6	97.12 14	97.86 3	98.54 39	95.32 8	96.61 25	98.35 12	95.81 29	97.55 38	97.44 72	96.51 10	99.40 36	94.06 41	99.23 78	98.85 88

v748		96.51 28	97.05 15	94.89 90	98.35 55	85.82 143	96.58 27	97.47 92	96.25 21	98.46 19	98.35 33	93.27 67	99.33 52	95.13 19	99.59 34	99.52 20

WR-MVS_H		96.60 25	97.05 15	95.24 81	99.02 11	86.44 130	96.78 22	98.08 32	97.42 7	98.48 18	97.86 55	91.76 96	99.63 6	94.23 37	99.84 5	99.66 7

HPM-MVS_fast		97.01 7	96.89 17	97.39 18	99.12 7	93.92 24	97.16 12	98.17 26	93.11 62	96.48 75	97.36 78	96.92 7	99.34 49	94.31 33	99.38 64	98.92 82

ACMH+	88.43 11	96.48 30	96.82 18	95.47 74	98.54 39	89.06 83	95.65 61	98.61 7	96.10 24	98.16 24	97.52 68	96.90 8	98.62 156	90.30 125	99.60 32	98.72 100

CP-MVSNet		96.19 44	96.80 19	94.38 114	98.99 13	83.82 164	96.31 41	97.53 86	97.60 6	98.34 22	97.52 68	91.98 92	99.63 6	93.08 70	99.81 11	99.70 4

OurMVSNet-221017-0		96.80 14	96.75 20	96.96 32	99.03 10	91.85 52	97.98 6	98.01 43	94.15 44	98.93 4	99.07 5	88.07 168	99.57 13	95.86 11	99.69 15	99.46 25

mvs_tets		96.83 10	96.71 21	97.17 25	98.83 21	92.51 43	96.58 27	97.61 77	87.57 196	98.80 8	98.90 9	96.50 11	99.59 12	96.15 9	99.47 48	99.40 31

APD-MVS_3200maxsize		96.82 11	96.65 22	97.32 22	97.95 79	93.82 29	96.31 41	98.25 19	95.51 30	96.99 59	97.05 93	95.63 20	99.39 41	93.31 62	98.88 108	98.75 96

APDe-MVS		96.46 32	96.64 23	95.93 55	97.68 94	89.38 79	96.90 18	98.41 11	92.52 76	97.43 44	97.92 50	95.11 34	99.50 18	94.45 30	99.30 70	98.92 82

HPM-MVS		96.81 13	96.62 24	97.36 20	98.89 18	93.53 34	97.51 9	98.44 8	92.35 81	95.95 102	96.41 125	96.71 9	99.42 28	93.99 42	99.36 65	99.13 50

COLMAP_ROB	91.06 5	96.75 17	96.62 24	97.13 26	98.38 50	94.31 12	96.79 21	98.32 13	96.69 15	96.86 61	97.56 65	95.48 22	98.77 137	90.11 131	99.44 54	98.31 119
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
wuykxyi23d		96.76 16	96.57 26	97.34 21	97.75 85	96.73 3	94.37 105	96.48 163	91.00 122	99.72 2	98.99 6	96.06 15	98.21 199	94.86 22	99.90 2	97.09 190

nrg030		96.32 40	96.55 27	95.62 69	97.83 82	88.55 96	95.77 58	98.29 18	92.68 70	98.03 27	97.91 52	95.13 33	98.95 99	93.85 43	99.49 47	99.36 35

test_djsdf		96.62 23	96.49 28	97.01 30	98.55 38	91.77 54	97.15 13	97.37 99	88.98 157	98.26 23	98.86 10	93.35 66	99.60 8	96.41 6	99.45 52	99.66 7

anonymousdsp		96.74 18	96.42 29	97.68 7	98.00 75	94.03 21	96.97 16	97.61 77	87.68 195	98.45 21	98.77 15	94.20 52	99.50 18	96.70 5	99.40 61	99.53 17

jajsoiax		96.59 26	96.42 29	97.12 27	98.76 25	92.49 44	96.44 35	97.42 95	86.96 206	98.71 10	98.72 17	95.36 26	99.56 16	95.92 10	99.45 52	99.32 37

MTAPA		96.65 22	96.38 31	97.47 11	98.95 15	94.05 18	95.88 55	97.62 74	94.46 40	96.29 83	96.94 94	93.56 57	99.37 45	94.29 35	99.42 56	98.99 70

ACMMP		96.61 24	96.34 32	97.43 15	98.61 31	93.88 25	96.95 17	98.18 25	92.26 84	96.33 79	96.84 103	95.10 35	99.40 36	93.47 55	99.33 68	99.02 67
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
SteuartSystems-ACMMP		96.40 37	96.30 33	96.71 38	98.63 28	91.96 50	95.70 59	98.01 43	93.34 60	96.64 70	96.57 116	94.99 40	99.36 47	93.48 54	99.34 66	98.82 90
Skip Steuart: Steuart Systems R&D Blog.
ANet_high		94.83 90	96.28 34	90.47 232	96.65 137	73.16 305	94.33 107	98.74 6	96.39 20	98.09 26	98.93 8	93.37 64	98.70 149	90.38 120	99.68 18	99.53 17

TranMVSNet+NR-MVSNet		96.07 48	96.26 35	95.50 73	98.26 58	87.69 112	93.75 126	97.86 57	95.96 28	97.48 41	97.14 87	95.33 27	99.44 24	90.79 115	99.76 13	99.38 32

LPG-MVS_test		96.38 39	96.23 36	96.84 36	98.36 53	92.13 47	95.33 70	98.25 19	91.78 104	97.07 53	97.22 83	96.38 12	99.28 55	92.07 93	99.59 34	99.11 52

test_0402		95.73 52	96.22 37	94.26 117	98.19 63	85.77 144	93.24 142	97.24 116	96.88 14	97.69 36	97.77 58	94.12 53	99.13 71	91.54 109	99.29 72	97.88 149

XVS		96.49 29	96.18 38	97.44 13	98.56 35	93.99 22	96.50 31	97.95 51	94.58 36	94.38 157	96.49 118	94.56 47	99.39 41	93.57 50	99.05 95	98.93 79

HFP-MVS		96.39 38	96.17 39	97.04 28	98.51 43	93.37 35	96.30 43	97.98 45	92.35 81	95.63 117	96.47 120	95.37 24	99.27 57	93.78 45	99.14 86	98.48 111

MPTG		96.47 31	96.14 40	97.47 11	98.95 15	94.05 18	93.69 128	97.62 74	94.46 40	96.29 83	96.94 94	93.56 57	99.37 45	94.29 35	99.42 56	98.99 70

ACMMPR		96.46 32	96.14 40	97.41 17	98.60 32	93.82 29	96.30 43	97.96 49	92.35 81	95.57 119	96.61 114	94.93 42	99.41 32	93.78 45	99.15 85	99.00 68

ACMMP_Plus		96.21 43	96.12 42	96.49 45	98.90 17	91.42 57	94.57 98	98.03 40	90.42 135	96.37 78	97.35 79	95.68 19	99.25 59	94.44 31	99.34 66	98.80 92

v13		95.39 63	96.12 42	93.18 148	97.22 109	80.81 196	95.55 64	97.57 81	93.42 58	98.02 29	98.49 26	89.62 141	99.18 64	95.54 12	99.68 18	99.54 16

region2R		96.41 36	96.09 44	97.38 19	98.62 29	93.81 31	96.32 40	97.96 49	92.26 84	95.28 128	96.57 116	95.02 38	99.41 32	93.63 49	99.11 89	98.94 78

CP-MVS		96.44 35	96.08 45	97.54 9	98.29 56	94.62 10	96.80 20	98.08 32	92.67 72	95.08 139	96.39 130	94.77 43	99.42 28	93.17 66	99.44 54	98.58 110

ACMM	88.83 9	96.30 42	96.07 46	96.97 31	98.39 49	92.95 41	94.74 89	98.03 40	90.82 125	97.15 51	96.85 101	96.25 14	99.00 91	93.10 68	99.33 68	98.95 77
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mPP-MVS		96.46 32	96.05 47	97.69 5	98.62 29	94.65 9	96.45 33	97.74 67	92.59 75	95.47 121	96.68 112	94.50 49	99.42 28	93.10 68	99.26 74	98.99 70

PS-MVSNAJss		96.01 49	96.04 48	95.89 57	98.82 22	88.51 98	95.57 63	97.88 56	88.72 169	98.81 7	98.86 10	90.77 118	99.60 8	95.43 14	99.53 43	99.57 15

TransMVSNet (Re)		95.27 72	96.04 48	92.97 156	98.37 52	81.92 182	95.07 78	96.76 150	93.97 47	97.77 34	98.57 21	95.72 18	97.90 213	88.89 156	99.23 78	99.08 59

v12		95.29 69	96.02 50	93.10 150	97.14 115	80.63 197	95.39 68	97.55 85	93.19 61	97.98 30	98.44 30	89.40 144	99.16 65	95.38 16	99.67 21	99.52 20

pm-mvs1		95.43 60	95.94 51	93.93 127	98.38 50	85.08 151	95.46 67	97.12 124	91.84 99	97.28 47	98.46 28	95.30 29	97.71 238	90.17 129	99.42 56	98.99 70

PGM-MVS		96.32 40	95.94 51	97.43 15	98.59 34	93.84 28	95.33 70	98.30 16	91.40 114	95.76 113	96.87 100	95.26 30	99.45 23	92.77 74	99.21 80	99.00 68

MP-MVS-pluss		96.08 47	95.92 53	96.57 41	99.06 9	91.21 59	93.25 141	98.32 13	87.89 190	96.86 61	97.38 75	95.55 21	99.39 41	95.47 13	99.47 48	99.11 52
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
V9		95.17 75	95.89 54	93.02 153	97.04 118	80.42 199	95.22 74	97.53 86	92.92 68	97.90 31	98.35 33	89.15 148	99.14 69	95.21 18	99.65 25	99.50 22

FC-MVSNet-test		95.32 66	95.88 55	93.62 134	98.49 46	81.77 183	95.90 54	98.32 13	93.93 48	97.53 39	97.56 65	88.48 154	99.40 36	92.91 73	99.83 8	99.68 5

v11		95.10 77	95.88 55	92.76 168	96.98 120	79.64 225	95.12 76	97.60 79	92.64 73	98.03 27	98.44 30	89.06 149	99.15 67	95.42 15	99.67 21	99.50 22

DP-MVS		95.62 55	95.84 57	94.97 88	97.16 112	88.62 93	94.54 102	97.64 73	96.94 13	96.58 73	97.32 80	93.07 71	98.72 143	90.45 117	98.84 113	97.57 169

LS3D		96.11 46	95.83 58	96.95 33	94.75 248	94.20 14	97.34 11	97.98 45	97.31 9	95.32 126	96.77 104	93.08 70	99.20 63	91.79 100	98.16 177	97.44 175

Gipuma		95.31 68	95.80 59	93.81 132	97.99 78	90.91 63	96.42 36	97.95 51	96.69 15	91.78 220	98.85 12	91.77 95	95.49 300	91.72 101	99.08 92	95.02 261
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
3Dnovator+	92.74 2	95.86 51	95.77 60	96.13 48	96.81 130	90.79 66	96.30 43	97.82 61	96.13 23	94.74 149	97.23 82	91.33 104	99.16 65	93.25 64	98.30 163	98.46 113

V14		95.05 78	95.75 61	92.94 159	96.94 122	80.21 202	95.03 80	97.50 90	92.62 74	97.84 33	98.28 37	88.87 151	99.13 71	95.03 20	99.64 26	99.48 24

SD-MVS		95.19 73	95.73 62	93.55 137	96.62 143	88.88 89	94.67 91	98.05 37	91.26 116	97.25 50	96.40 126	95.42 23	94.36 317	92.72 78	99.19 81	97.40 178

MP-MVS		96.14 45	95.68 63	97.51 10	98.81 23	94.06 16	96.10 47	97.78 66	92.73 69	93.48 179	96.72 110	94.23 51	99.42 28	91.99 95	99.29 72	99.05 63

VPA-MVSNet		95.14 76	95.67 64	93.58 136	97.76 84	83.15 171	94.58 97	97.58 80	93.39 59	97.05 57	98.04 42	93.25 68	98.51 174	89.75 138	99.59 34	99.08 59

v15		94.93 83	95.62 65	92.86 164	96.83 128	80.01 215	94.84 87	97.48 91	92.36 79	97.76 35	98.20 39	88.61 152	99.11 74	94.86 22	99.62 29	99.46 25

#test#		95.89 50	95.51 66	97.04 28	98.51 43	93.37 35	95.14 75	97.98 45	89.34 151	95.63 117	96.47 120	95.37 24	99.27 57	91.99 95	99.14 86	98.48 111

Vis-MVSNet		95.50 59	95.48 67	95.56 72	98.11 67	89.40 78	95.35 69	98.22 23	92.36 79	94.11 165	98.07 41	92.02 89	99.44 24	93.38 60	97.67 206	97.85 152
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v17		94.80 91	95.46 68	92.83 165	96.76 133	80.02 213	94.85 85	97.40 97	92.23 86	97.45 43	98.04 42	88.46 156	99.06 79	94.56 27	99.40 61	99.41 28

OPM-MVS		95.61 56	95.45 69	96.08 49	98.49 46	91.00 61	92.65 157	97.33 108	90.05 140	96.77 65	96.85 101	95.04 36	98.56 165	92.77 74	99.06 93	98.70 101

MIMVSNet1		95.52 58	95.45 69	95.72 65	99.14 4	89.02 84	96.23 46	96.87 144	93.73 51	97.87 32	98.49 26	90.73 122	99.05 81	86.43 192	99.60 32	99.10 55

v16		94.79 93	95.44 71	92.83 165	96.73 134	80.03 211	94.85 85	97.41 96	92.23 86	97.41 46	98.04 42	88.40 158	99.06 79	94.56 27	99.30 70	99.41 28

ACMP	88.15 13	95.71 53	95.43 72	96.54 42	98.17 64	91.73 55	94.24 109	98.08 32	89.46 149	96.61 72	96.47 120	95.85 17	99.12 73	90.45 117	99.56 41	98.77 95
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FIs		94.90 85	95.35 73	93.55 137	98.28 57	81.76 184	95.33 70	98.14 28	93.05 63	97.07 53	97.18 85	87.65 174	99.29 54	91.72 101	99.69 15	99.61 12

ESAPD		95.42 62	95.34 74	95.68 68	98.21 61	89.41 76	93.92 121	98.14 28	91.83 101	96.72 66	96.39 130	94.69 44	99.44 24	89.00 153	99.10 90	98.17 127

XVG-ACMP-BASELINE		95.68 54	95.34 74	96.69 39	98.40 48	93.04 38	94.54 102	98.05 37	90.45 134	96.31 81	96.76 106	92.91 74	98.72 143	91.19 113	99.42 56	98.32 117

DeepC-MVS	91.39 4	95.43 60	95.33 76	95.71 66	97.67 95	90.17 67	93.86 124	98.02 42	87.35 198	96.22 89	97.99 47	94.48 50	99.05 81	92.73 77	99.68 18	97.93 143
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PMVS	87.21 14	94.97 81	95.33 76	93.91 128	98.97 14	97.16 2	95.54 65	95.85 192	96.47 18	93.40 182	97.46 71	95.31 28	95.47 301	86.18 195	98.78 124	89.11 335
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v8		94.65 98	95.29 78	92.74 169	96.65 137	79.77 221	94.59 95	97.17 120	91.86 98	97.47 42	97.93 49	88.16 162	99.08 76	94.32 32	99.47 48	99.38 32

NR-MVSNet		95.28 70	95.28 79	95.26 80	97.75 85	87.21 118	95.08 77	97.37 99	93.92 49	97.65 37	95.90 157	90.10 136	99.33 52	90.11 131	99.66 23	99.26 40

v10		94.68 97	95.27 80	92.90 162	96.57 146	80.15 204	94.65 93	97.57 81	90.68 128	97.43 44	98.00 46	88.18 160	99.15 67	94.84 24	99.55 42	99.41 28

v18		94.63 99	95.26 81	92.74 169	96.60 144	79.81 219	94.64 94	97.37 99	91.87 97	97.26 49	97.91 52	88.13 163	99.04 84	94.30 34	99.24 76	99.38 32

UniMVSNet_NR-MVSNet		95.35 65	95.21 82	95.76 63	97.69 93	88.59 94	92.26 176	97.84 60	94.91 31	96.80 63	95.78 165	90.42 128	99.41 32	91.60 105	99.58 39	99.29 39

SixPastTwentyTwo		94.91 84	95.21 82	93.98 123	98.52 42	83.19 170	95.93 52	94.84 215	94.86 34	98.49 17	98.74 16	81.45 240	99.60 8	94.69 25	99.39 63	99.15 48

UniMVSNet (Re)		95.32 66	95.15 84	95.80 61	97.79 83	88.91 86	92.91 150	98.07 35	93.46 57	96.31 81	95.97 156	90.14 132	99.34 49	92.11 90	99.64 26	99.16 47

FMVSNet1		94.84 89	95.13 85	93.97 124	97.60 97	84.29 156	95.99 48	96.56 157	92.38 78	97.03 58	98.53 23	90.12 133	98.98 92	88.78 158	99.16 84	98.65 102

DU-MVS		95.28 70	95.12 86	95.75 64	97.75 85	88.59 94	92.58 158	97.81 62	93.99 45	96.80 63	95.90 157	90.10 136	99.41 32	91.60 105	99.58 39	99.26 40

Baseline_NR-MVSNet		94.47 105	95.09 87	92.60 177	98.50 45	80.82 195	92.08 181	96.68 153	93.82 50	96.29 83	98.56 22	90.10 136	97.75 236	90.10 133	99.66 23	99.24 42

XVG-OURS-SEG-HR		95.38 64	95.00 88	96.51 43	98.10 69	94.07 15	92.46 167	98.13 31	90.69 127	93.75 173	96.25 140	98.03 3	97.02 263	92.08 92	95.55 264	98.45 114

3Dnovator	92.54 3	94.80 91	94.90 89	94.47 109	95.47 225	87.06 120	96.63 24	97.28 114	91.82 103	94.34 160	97.41 73	90.60 126	98.65 155	92.47 85	98.11 183	97.70 161

RPSCF		95.58 57	94.89 90	97.62 8	97.58 98	96.30 5	95.97 51	97.53 86	92.42 77	93.41 180	97.78 56	91.21 110	97.77 233	91.06 114	97.06 227	98.80 92

tfpnnormal		94.27 111	94.87 91	92.48 183	97.71 90	80.88 194	94.55 101	95.41 207	93.70 52	96.67 69	97.72 59	91.40 102	98.18 204	87.45 176	99.18 83	98.36 115

TSAR-MVS + MP.		94.96 82	94.75 92	95.57 71	98.86 20	88.69 90	96.37 38	96.81 146	85.23 223	94.75 148	97.12 89	91.85 94	99.40 36	93.45 56	98.33 158	98.62 106

CSCG		94.69 96	94.75 92	94.52 106	97.55 100	87.87 109	95.01 81	97.57 81	92.68 70	96.20 91	93.44 244	91.92 93	98.78 133	89.11 152	99.24 76	96.92 197

canonicalmvs		94.59 100	94.69 94	94.30 116	95.60 221	87.03 121	95.59 62	98.24 22	91.56 112	95.21 133	92.04 273	94.95 41	98.66 153	91.45 110	97.57 210	97.20 188

APD-MVS		95.00 80	94.69 94	95.93 55	97.38 105	90.88 64	94.59 95	97.81 62	89.22 155	95.46 123	96.17 150	93.42 62	99.34 49	89.30 144	98.87 111	97.56 171
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-4		94.90 85	94.67 96	95.59 70	92.78 289	89.02 84	92.39 170	95.91 189	94.50 38	96.41 76	95.56 173	92.10 88	99.01 90	94.23 37	98.14 179	98.74 97

EG-PatchMatch MVS		94.54 103	94.67 96	94.14 119	97.87 81	86.50 126	92.00 184	96.74 151	88.16 186	96.93 60	97.61 63	93.04 72	97.90 213	91.60 105	98.12 182	98.03 136

LCM-MVSNet-Re		94.20 114	94.58 98	93.04 151	95.91 205	83.13 172	93.79 125	99.19 2	92.00 93	98.84 6	98.04 42	93.64 56	99.02 88	81.28 238	98.54 139	96.96 195

Regformer-2		94.86 88	94.55 99	95.77 62	92.83 287	89.98 69	91.87 193	96.40 167	94.38 42	96.19 93	95.04 193	92.47 85	99.04 84	93.49 53	98.31 160	98.28 121

AllTest		94.88 87	94.51 100	96.00 50	98.02 73	92.17 45	95.26 73	98.43 9	90.48 132	95.04 140	96.74 108	92.54 82	97.86 224	85.11 205	98.98 101	97.98 139

HSP-MVS		95.18 74	94.49 101	97.23 24	98.67 27	94.05 18	96.41 37	97.00 128	91.26 116	95.12 134	95.15 186	86.60 202	99.50 18	93.43 58	96.81 234	98.13 132

HPM-MVS++		95.02 79	94.39 102	96.91 34	97.88 80	93.58 33	94.09 112	96.99 130	91.05 121	92.40 207	95.22 185	91.03 116	99.25 59	92.11 90	98.69 131	97.90 147

testing_2		94.03 117	94.38 103	93.00 154	96.79 132	81.41 189	92.87 152	96.96 132	85.88 218	97.06 56	97.92 50	91.18 114	98.71 148	91.72 101	99.04 98	98.87 84

VDD-MVS		94.37 106	94.37 104	94.40 113	97.49 103	86.07 138	93.97 116	93.28 247	94.49 39	96.24 87	97.78 56	87.99 170	98.79 130	88.92 155	99.14 86	98.34 116

IS-MVSNet		94.49 104	94.35 105	94.92 89	98.25 59	86.46 129	97.13 15	94.31 229	96.24 22	96.28 86	96.36 135	82.88 227	99.35 48	88.19 167	99.52 45	98.96 76

Regformer-1		94.55 102	94.33 106	95.19 83	92.83 287	88.54 97	91.87 193	95.84 193	93.99 45	95.95 102	95.04 193	92.00 90	98.79 130	93.14 67	98.31 160	98.23 123

CNVR-MVS		94.58 101	94.29 107	95.46 75	96.94 122	89.35 81	91.81 202	96.80 147	89.66 147	93.90 171	95.44 178	92.80 78	98.72 143	92.74 76	98.52 141	98.32 117

EI-MVSNet-Vis-set		94.36 107	94.28 108	94.61 98	92.55 291	85.98 140	92.44 168	94.69 222	93.70 52	96.12 96	95.81 162	91.24 108	98.86 118	93.76 48	98.22 172	98.98 75

IterMVS-LS		93.78 121	94.28 108	92.27 189	96.27 173	79.21 238	91.87 193	96.78 148	91.77 106	96.57 74	97.07 91	87.15 186	98.74 141	91.99 95	99.03 99	98.86 85
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet-UG-set		94.35 108	94.27 110	94.59 103	92.46 292	85.87 141	92.42 169	94.69 222	93.67 56	96.13 95	95.84 161	91.20 111	98.86 118	93.78 45	98.23 170	99.03 66

VDDNet		94.03 117	94.27 110	93.31 145	98.87 19	82.36 178	95.51 66	91.78 273	97.19 10	96.32 80	98.60 20	84.24 220	98.75 138	87.09 181	98.83 116	98.81 91

Regformer-3		94.28 110	94.23 112	94.46 110	92.78 289	86.28 134	92.39 170	94.70 221	93.69 55	95.97 100	95.56 173	91.34 103	98.48 178	93.45 56	98.14 179	98.62 106

XVG-OURS		94.72 95	94.12 113	96.50 44	98.00 75	94.23 13	91.48 210	98.17 26	90.72 126	95.30 127	96.47 120	87.94 171	96.98 264	91.41 111	97.61 209	98.30 120

CPTT-MVS		94.74 94	94.12 113	96.60 40	98.15 65	93.01 39	95.84 56	97.66 72	89.21 156	93.28 186	95.46 176	88.89 150	98.98 92	89.80 137	98.82 119	97.80 156

v7		93.66 123	93.97 115	92.73 171	96.55 147	80.15 204	92.54 159	96.99 130	87.36 197	95.99 99	96.48 119	88.18 160	98.94 102	93.35 61	98.31 160	99.09 56

v6		93.59 126	93.93 116	92.56 179	96.65 137	79.77 221	92.50 164	96.40 167	88.55 174	95.94 104	96.23 143	88.13 163	98.87 115	92.46 86	98.50 144	99.06 62

HQP_MVS		94.26 112	93.93 116	95.23 82	97.71 90	88.12 105	94.56 99	97.81 62	91.74 108	93.31 183	95.59 168	86.93 193	98.95 99	89.26 148	98.51 142	98.60 108

v1neww		93.58 127	93.92 118	92.56 179	96.64 141	79.77 221	92.50 164	96.41 165	88.55 174	95.93 105	96.24 141	88.08 165	98.87 115	92.45 87	98.50 144	99.05 63

v7new		93.58 127	93.92 118	92.56 179	96.64 141	79.77 221	92.50 164	96.41 165	88.55 174	95.93 105	96.24 141	88.08 165	98.87 115	92.45 87	98.50 144	99.05 63

MSLP-MVS++		93.25 143	93.88 120	91.37 215	96.34 168	82.81 175	93.11 143	97.74 67	89.37 150	94.08 167	95.29 184	90.40 131	96.35 288	90.35 123	98.25 168	94.96 262

v1144		93.50 129	93.81 121	92.57 178	96.28 172	79.61 227	91.86 197	96.96 132	86.95 207	95.91 108	96.32 136	87.65 174	98.96 97	93.51 52	98.88 108	99.13 50

PHI-MVS		94.34 109	93.80 122	95.95 52	95.65 217	91.67 56	94.82 88	97.86 57	87.86 191	93.04 194	94.16 224	91.58 98	98.78 133	90.27 126	98.96 104	97.41 176

v1192		93.49 130	93.78 123	92.62 176	96.16 182	79.62 226	91.83 201	97.22 118	86.07 214	96.10 97	96.38 133	87.22 184	99.02 88	94.14 40	98.88 108	99.22 43

v1		93.43 132	93.77 124	92.41 185	96.37 159	79.24 233	91.84 198	96.38 170	88.33 181	95.87 109	96.22 146	87.45 178	98.89 105	92.61 81	98.83 116	99.09 56

v1141		93.42 134	93.76 125	92.40 187	96.37 159	79.24 233	91.84 198	96.38 170	88.33 181	95.86 110	96.23 143	87.41 180	98.89 105	92.61 81	98.82 119	99.08 59

divwei89l23v2f112		93.42 134	93.76 125	92.41 185	96.37 159	79.24 233	91.84 198	96.38 170	88.33 181	95.86 110	96.23 143	87.41 180	98.89 105	92.61 81	98.83 116	99.09 56

VPNet		93.08 147	93.76 125	91.03 223	98.60 32	75.83 278	91.51 209	95.62 197	91.84 99	95.74 114	97.10 90	89.31 145	98.32 190	85.07 207	99.06 93	98.93 79

WR-MVS		93.49 130	93.72 128	92.80 167	97.57 99	80.03 211	90.14 248	95.68 196	93.70 52	96.62 71	95.39 182	87.21 185	99.04 84	87.50 175	99.64 26	99.33 36

v1240		93.29 138	93.71 129	92.06 196	96.01 192	77.89 255	91.81 202	97.37 99	85.12 226	96.69 68	96.40 126	86.67 199	99.07 78	94.51 29	98.76 126	99.22 43

OMC-MVS		94.22 113	93.69 130	95.81 60	97.25 108	91.27 58	92.27 175	97.40 97	87.10 204	94.56 153	95.42 179	93.74 55	98.11 207	86.62 188	98.85 112	98.06 134

EPP-MVSNet		93.91 119	93.68 131	94.59 103	98.08 70	85.55 147	97.44 10	94.03 234	94.22 43	94.94 143	96.19 148	82.07 235	99.57 13	87.28 180	98.89 106	98.65 102

v2v482		93.29 138	93.63 132	92.29 188	96.35 167	78.82 244	91.77 205	96.28 175	88.45 177	95.70 116	96.26 139	86.02 209	98.90 103	93.02 71	98.81 122	99.14 49

v1921920		93.26 141	93.61 133	92.19 191	96.04 191	78.31 250	91.88 192	97.24 116	85.17 224	96.19 93	96.19 148	86.76 198	99.05 81	94.18 39	98.84 113	99.22 43

V42		93.43 132	93.58 134	92.97 156	95.34 232	81.22 190	92.67 156	96.49 162	87.25 200	96.20 91	96.37 134	87.32 183	98.85 120	92.39 89	98.21 173	98.85 88

DeepPCF-MVS	90.46 6	94.20 114	93.56 135	96.14 47	95.96 201	92.96 40	89.48 268	97.46 93	85.14 225	96.23 88	95.42 179	93.19 69	98.08 208	90.37 121	98.76 126	97.38 181

v144192		93.20 146	93.54 136	92.16 193	96.05 188	78.26 251	91.95 185	97.14 121	84.98 230	95.96 101	96.11 151	87.08 188	99.04 84	93.79 44	98.84 113	99.17 46

NCCC		94.08 116	93.54 136	95.70 67	96.49 150	89.90 71	92.39 170	96.91 140	90.64 129	92.33 212	94.60 209	90.58 127	98.96 97	90.21 128	97.70 204	98.23 123

DeepC-MVS_fast	89.96 7	93.73 122	93.44 138	94.60 102	96.14 183	87.90 108	93.36 134	97.14 121	85.53 222	93.90 171	95.45 177	91.30 106	98.59 161	89.51 141	98.62 133	97.31 184
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		93.63 125	93.42 139	94.26 117	96.65 137	86.96 122	89.30 274	96.23 179	88.36 180	93.57 177	94.60 209	93.45 59	97.77 233	90.23 127	98.38 151	98.03 136

v148		92.87 155	93.29 140	91.62 207	96.25 176	77.72 257	91.28 215	95.05 211	89.69 146	95.93 105	96.04 153	87.34 182	98.38 186	90.05 134	97.99 192	98.78 94

MVS_Test		92.57 165	93.29 140	90.40 234	93.53 278	75.85 276	92.52 161	96.96 132	88.73 168	92.35 209	96.70 111	90.77 118	98.37 189	92.53 84	95.49 266	96.99 194

MVS_111021_LR		93.66 123	93.28 142	94.80 93	96.25 176	90.95 62	90.21 244	95.43 206	87.91 188	93.74 175	94.40 215	92.88 76	96.38 286	90.39 119	98.28 164	97.07 191

K. test v3		93.37 136	93.27 143	93.66 133	98.05 71	82.62 176	94.35 106	86.62 304	96.05 26	97.51 40	98.85 12	76.59 273	99.65 3	93.21 65	98.20 175	98.73 99

EI-MVSNet		92.99 151	93.26 144	92.19 191	92.12 300	79.21 238	92.32 173	94.67 224	91.77 106	95.24 131	95.85 159	87.14 187	98.49 175	91.99 95	98.26 166	98.86 85

XXY-MVS		92.58 164	93.16 145	90.84 228	97.75 85	79.84 218	91.87 193	96.22 181	85.94 216	95.53 120	97.68 60	92.69 79	94.48 313	83.21 222	97.51 211	98.21 125

VNet		92.67 161	92.96 146	91.79 202	96.27 173	80.15 204	91.95 185	94.98 212	92.19 89	94.52 155	96.07 152	87.43 179	97.39 251	84.83 209	98.38 151	97.83 153

GBi-Net		93.21 144	92.96 146	93.97 124	95.40 227	84.29 156	95.99 48	96.56 157	88.63 170	95.10 136	98.53 23	81.31 243	98.98 92	86.74 184	98.38 151	98.65 102

test1		93.21 144	92.96 146	93.97 124	95.40 227	84.29 156	95.99 48	96.56 157	88.63 170	95.10 136	98.53 23	81.31 243	98.98 92	86.74 184	98.38 151	98.65 102

alignmvs		93.26 141	92.85 149	94.50 107	95.70 213	87.45 113	93.45 132	95.76 194	91.58 111	95.25 130	92.42 266	81.96 237	98.72 143	91.61 104	97.87 198	97.33 183

test_prior3		93.29 138	92.85 149	94.61 98	95.95 202	87.23 116	90.21 244	97.36 105	89.33 152	90.77 240	94.81 200	90.41 129	98.68 151	88.21 165	98.55 137	97.93 143

QAPM		92.88 154	92.77 151	93.22 147	95.82 207	83.31 168	96.45 33	97.35 107	83.91 238	93.75 173	96.77 104	89.25 146	98.88 109	84.56 212	97.02 229	97.49 173

TinyColmap		92.00 176	92.76 152	89.71 248	95.62 220	77.02 265	90.72 229	96.17 183	87.70 194	95.26 129	96.29 137	92.54 82	96.45 282	81.77 233	98.77 125	95.66 244

Effi-MVS+		92.79 156	92.74 153	92.94 159	95.10 238	83.30 169	94.00 114	97.53 86	91.36 115	89.35 270	90.65 297	94.01 54	98.66 153	87.40 178	95.30 272	96.88 200

FMVSNet2		92.78 157	92.73 154	92.95 158	95.40 227	81.98 181	94.18 111	95.53 204	88.63 170	96.05 98	97.37 76	81.31 243	98.81 128	87.38 179	98.67 132	98.06 134

PM-MVS		93.33 137	92.67 155	95.33 78	96.58 145	94.06 16	92.26 176	92.18 265	85.92 217	96.22 89	96.61 114	85.64 214	95.99 294	90.35 123	98.23 170	95.93 236

ab-mvs		92.40 168	92.62 156	91.74 203	97.02 119	81.65 185	95.84 56	95.50 205	86.95 207	92.95 197	97.56 65	90.70 124	97.50 245	79.63 257	97.43 218	96.06 232

Effi-MVS+-dtu		93.90 120	92.60 157	97.77 4	94.74 249	96.67 4	94.00 114	95.41 207	89.94 142	91.93 219	92.13 271	90.12 133	98.97 96	87.68 173	97.48 216	97.67 164

MVS_0304		92.99 151	92.54 158	94.35 115	94.67 254	86.06 139	91.16 217	97.92 55	90.01 141	88.33 286	94.41 213	87.02 189	99.22 61	90.36 122	99.00 100	97.76 157

MCST-MVS		92.91 153	92.51 159	94.10 120	97.52 101	85.72 145	91.36 214	97.13 123	80.33 267	92.91 198	94.24 220	91.23 109	98.72 143	89.99 135	97.93 195	97.86 151

UGNet		93.08 147	92.50 160	94.79 94	93.87 272	87.99 107	95.07 78	94.26 231	90.64 129	87.33 298	97.67 61	86.89 196	98.49 175	88.10 169	98.71 129	97.91 146
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
TSAR-MVS + GP.		93.07 149	92.41 161	95.06 87	95.82 207	90.87 65	90.97 222	92.61 260	88.04 187	94.61 152	93.79 236	88.08 165	97.81 229	89.41 143	98.39 150	96.50 216

MVSFormer		92.18 173	92.23 162	92.04 197	94.74 249	80.06 209	97.15 13	97.37 99	88.98 157	88.83 274	92.79 253	77.02 268	99.60 8	96.41 6	96.75 237	96.46 218

Fast-Effi-MVS+-dtu		92.77 158	92.16 163	94.58 105	94.66 255	88.25 103	92.05 182	96.65 154	89.62 148	90.08 253	91.23 283	92.56 81	98.60 159	86.30 194	96.27 252	96.90 198

DELS-MVS		92.05 175	92.16 163	91.72 204	94.44 261	80.13 207	87.62 294	97.25 115	87.34 199	92.22 214	93.18 249	89.54 143	98.73 142	89.67 140	98.20 175	96.30 224
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	89.45 8	92.27 172	92.13 165	92.68 173	94.53 260	84.10 161	95.70 59	97.03 126	82.44 254	91.14 237	96.42 124	88.47 155	98.38 186	85.95 196	97.47 217	95.55 251

LF4IMVS		92.72 159	92.02 166	94.84 92	95.65 217	91.99 49	92.92 149	96.60 156	85.08 228	92.44 206	93.62 238	86.80 197	96.35 288	86.81 183	98.25 168	96.18 228

CANet		92.38 169	91.99 167	93.52 141	93.82 274	83.46 167	91.14 218	97.00 128	89.81 145	86.47 303	94.04 228	87.90 172	99.21 62	89.50 142	98.27 165	97.90 147

DP-MVS Recon		92.31 170	91.88 168	93.60 135	97.18 111	86.87 123	91.10 220	97.37 99	84.92 231	92.08 216	94.08 227	88.59 153	98.20 201	83.50 219	98.14 179	95.73 241

train_agg		92.71 160	91.83 169	95.35 76	96.45 156	89.46 73	90.60 233	96.92 137	79.37 275	90.49 247	94.39 216	91.20 111	98.88 109	88.66 161	98.43 147	97.72 159

CDPH-MVS		92.67 161	91.83 169	95.18 84	96.94 122	88.46 101	90.70 230	97.07 125	77.38 289	92.34 211	95.08 190	92.67 80	98.88 109	85.74 197	98.57 136	98.20 126

mvs-test1		93.07 149	91.80 171	96.89 35	94.74 249	95.83 7	92.17 179	95.41 207	89.94 142	89.85 261	90.59 298	90.12 133	98.88 109	87.68 173	95.66 262	95.97 234

agg_prior1		92.60 163	91.76 172	95.10 86	96.20 178	88.89 87	90.37 239	96.88 142	79.67 272	90.21 250	94.41 213	91.30 106	98.78 133	88.46 164	98.37 156	97.64 166

TAPA-MVS	88.58 10	92.49 167	91.75 173	94.73 95	96.50 149	89.69 72	92.91 150	97.68 71	78.02 286	92.79 199	94.10 226	90.85 117	97.96 212	84.76 210	98.16 177	96.54 207
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
agg_prior3		92.56 166	91.62 174	95.35 76	96.39 158	89.45 75	90.61 232	96.82 145	78.82 282	90.03 255	94.14 225	90.72 123	98.88 109	88.66 161	98.43 147	97.72 159

API-MVS		91.52 182	91.61 175	91.26 219	94.16 266	86.26 135	94.66 92	94.82 216	91.17 119	92.13 215	91.08 286	90.03 139	97.06 262	79.09 262	97.35 222	90.45 332

xiu_mvs_v1_base_debu		91.47 184	91.52 176	91.33 216	95.69 214	81.56 186	89.92 256	96.05 185	83.22 242	91.26 227	90.74 292	91.55 99	98.82 123	89.29 145	95.91 257	93.62 296

xiu_mvs_v1_base		91.47 184	91.52 176	91.33 216	95.69 214	81.56 186	89.92 256	96.05 185	83.22 242	91.26 227	90.74 292	91.55 99	98.82 123	89.29 145	95.91 257	93.62 296

xiu_mvs_v1_base_debi		91.47 184	91.52 176	91.33 216	95.69 214	81.56 186	89.92 256	96.05 185	83.22 242	91.26 227	90.74 292	91.55 99	98.82 123	89.29 145	95.91 257	93.62 296

HQP-MVS		92.09 174	91.49 179	93.88 129	96.36 164	84.89 152	91.37 211	97.31 109	87.16 201	88.81 276	93.40 245	84.76 217	98.60 159	86.55 190	97.73 201	98.14 131

test_normal		91.49 183	91.44 180	91.62 207	95.21 235	79.44 229	90.08 251	93.84 238	82.60 250	94.37 159	94.74 205	86.66 200	98.46 181	88.58 163	96.92 232	96.95 196

DI_MVS_plusplus_test		91.42 187	91.41 181	91.46 212	95.34 232	79.06 240	90.58 235	93.74 240	82.59 251	94.69 151	94.76 204	86.54 203	98.44 183	87.93 171	96.49 250	96.87 201

CLD-MVS		91.82 177	91.41 181	93.04 151	96.37 159	83.65 166	86.82 307	97.29 112	84.65 234	92.27 213	89.67 307	92.20 86	97.85 227	83.95 216	99.47 48	97.62 167
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
AdaColmap		91.63 179	91.36 183	92.47 184	95.56 222	86.36 133	92.24 178	96.27 176	88.88 161	89.90 260	92.69 257	91.65 97	98.32 190	77.38 281	97.64 207	92.72 311

testgi		90.38 203	91.34 184	87.50 292	97.49 103	71.54 315	89.43 269	95.16 210	88.38 179	94.54 154	94.68 208	92.88 76	93.09 327	71.60 316	97.85 199	97.88 149

mvs_anonymous		90.37 204	91.30 185	87.58 291	92.17 299	68.00 324	89.84 261	94.73 220	83.82 240	93.22 192	97.40 74	87.54 176	97.40 250	87.94 170	95.05 277	97.34 182

Test4		91.41 188	91.25 186	91.89 199	95.35 231	80.32 200	90.97 222	96.92 137	81.96 257	95.11 135	93.81 235	81.34 242	98.48 178	88.71 160	97.08 226	96.87 201

PVSNet_Blended_VisFu		91.63 179	91.20 187	92.94 159	97.73 89	83.95 163	92.14 180	97.46 93	78.85 281	92.35 209	94.98 196	84.16 221	99.08 76	86.36 193	96.77 236	95.79 239

CNLPA		91.72 178	91.20 187	93.26 146	96.17 181	91.02 60	91.14 218	95.55 203	90.16 139	90.87 239	93.56 241	86.31 205	94.40 316	79.92 256	97.12 225	94.37 276

LFMVS		91.33 189	91.16 189	91.82 201	96.27 173	79.36 231	95.01 81	85.61 314	96.04 27	94.82 146	97.06 92	72.03 281	98.46 181	84.96 208	98.70 130	97.65 165

F-COLMAP		92.28 171	91.06 190	95.95 52	97.52 101	91.90 51	93.53 130	97.18 119	83.98 237	88.70 282	94.04 228	88.41 157	98.55 171	80.17 251	95.99 256	97.39 179

BH-untuned		90.68 197	90.90 191	90.05 245	95.98 200	79.57 228	90.04 252	94.94 214	87.91 188	94.07 168	93.00 250	87.76 173	97.78 232	79.19 261	95.17 275	92.80 309

MDA-MVSNet-bldmvs		91.04 191	90.88 192	91.55 210	94.68 253	80.16 203	85.49 316	92.14 268	90.41 136	94.93 144	95.79 163	85.10 215	96.93 266	85.15 203	94.19 294	97.57 169

Fast-Effi-MVS+		91.28 190	90.86 193	92.53 182	95.45 226	82.53 177	89.25 277	96.52 161	85.00 229	89.91 259	88.55 314	92.94 73	98.84 121	84.72 211	95.44 269	96.22 227

test20.03		90.80 194	90.85 194	90.63 230	95.63 219	79.24 233	89.81 262	92.87 253	89.90 144	94.39 156	96.40 126	85.77 210	95.27 308	73.86 301	99.05 95	97.39 179

PAPM_NR		91.03 192	90.81 195	91.68 206	96.73 134	81.10 192	93.72 127	96.35 174	88.19 185	88.77 280	92.12 272	85.09 216	97.25 255	82.40 230	93.90 296	96.68 206

new-patchmatchnet		88.97 225	90.79 196	83.50 320	94.28 265	55.83 350	85.34 317	93.56 243	86.18 212	95.47 121	95.73 166	83.10 225	96.51 279	85.40 200	98.06 187	98.16 129

wuyk23d		87.83 246	90.79 196	78.96 331	90.46 316	88.63 92	92.72 154	90.67 280	91.65 110	98.68 11	97.64 62	96.06 15	77.53 352	59.84 342	99.41 60	70.73 349

pmmvs-eth3d		91.54 181	90.73 198	93.99 122	95.76 211	87.86 110	90.83 226	93.98 236	78.23 285	94.02 169	96.22 146	82.62 232	96.83 270	86.57 189	98.33 158	97.29 185

MSDG		90.82 193	90.67 199	91.26 219	94.16 266	83.08 173	86.63 310	96.19 182	90.60 131	91.94 218	91.89 274	89.16 147	95.75 297	80.96 245	94.51 287	94.95 263

diffmvs		90.45 200	90.49 200	90.34 235	92.25 295	77.09 264	91.80 204	95.96 188	82.68 249	85.83 307	95.07 191	87.01 190	97.09 260	89.68 139	94.10 295	96.83 203

BH-RMVSNet		90.47 199	90.44 201	90.56 231	95.21 235	78.65 248	89.15 278	93.94 237	88.21 184	92.74 200	94.22 221	86.38 204	97.88 221	78.67 270	95.39 270	95.14 258

UnsupCasMVSNet_eth		90.33 206	90.34 202	90.28 237	94.64 256	80.24 201	89.69 264	95.88 190	85.77 220	93.94 170	95.69 167	81.99 236	92.98 328	84.21 214	91.30 323	97.62 167

FMVSNet3		90.78 195	90.32 203	92.16 193	93.03 285	79.92 217	92.54 159	94.95 213	86.17 213	95.10 136	96.01 154	69.97 287	98.75 138	86.74 184	98.38 151	97.82 155

IterMVS		90.18 209	90.16 204	90.21 242	93.15 283	75.98 275	87.56 297	92.97 252	86.43 211	94.09 166	96.40 126	78.32 258	97.43 247	87.87 172	94.69 284	97.23 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		90.42 201	90.16 204	91.20 221	97.66 96	77.32 261	94.33 107	87.66 297	91.20 118	92.99 195	95.13 188	75.40 275	98.28 192	77.86 274	99.19 81	97.99 138

PVSNet_BlendedMVS		90.35 205	89.96 206	91.54 211	94.81 245	78.80 246	90.14 248	96.93 135	79.43 273	88.68 283	95.06 192	86.27 206	98.15 205	80.27 248	98.04 189	97.68 163

Patchmtry		90.11 211	89.92 207	90.66 229	90.35 318	77.00 266	92.96 148	92.81 254	90.25 138	94.74 149	96.93 96	67.11 293	97.52 244	85.17 201	98.98 101	97.46 174

114514_t		90.51 198	89.80 208	92.63 175	98.00 75	82.24 179	93.40 133	97.29 112	65.84 339	89.40 269	94.80 203	86.99 191	98.75 138	83.88 217	98.61 134	96.89 199

MG-MVS		89.54 216	89.80 208	88.76 272	94.88 241	72.47 312	89.60 265	92.44 263	85.82 219	89.48 268	95.98 155	82.85 228	97.74 237	81.87 232	95.27 273	96.08 231

1121		90.26 208	89.23 210	93.34 143	97.15 114	87.40 114	91.94 187	94.39 227	67.88 333	91.02 238	94.91 198	86.91 195	98.59 161	81.17 241	97.71 203	94.02 285

xiu_mvs_v2_base		89.00 224	89.19 211	88.46 282	94.86 243	74.63 289	86.97 304	95.60 198	80.88 263	87.83 292	88.62 313	91.04 115	98.81 128	82.51 229	94.38 288	91.93 322

CANet_DTU		89.85 213	89.17 212	91.87 200	92.20 298	80.02 213	90.79 227	95.87 191	86.02 215	82.53 328	91.77 276	80.01 251	98.57 164	85.66 198	97.70 204	97.01 193

USDC		89.02 223	89.08 213	88.84 271	95.07 239	74.50 292	88.97 281	96.39 169	73.21 309	93.27 187	96.28 138	82.16 234	96.39 285	77.55 278	98.80 123	95.62 246

TAMVS		90.16 210	89.05 214	93.49 142	96.49 150	86.37 132	90.34 241	92.55 261	80.84 265	92.99 195	94.57 211	81.94 238	98.20 201	73.51 302	98.21 173	95.90 237

OpenMVS_ROB	85.12 16	89.52 217	89.05 214	90.92 227	94.58 259	81.21 191	91.10 220	93.41 246	77.03 292	93.41 180	93.99 232	83.23 224	97.80 230	79.93 255	94.80 282	93.74 293

RPMNet		89.30 219	89.00 216	90.22 240	91.01 307	78.93 241	92.52 161	87.85 296	91.91 95	89.10 271	96.89 99	68.84 288	97.64 241	90.17 129	92.70 312	94.08 280

PS-MVSNAJ		88.86 228	88.99 217	88.48 281	94.88 241	74.71 287	86.69 308	95.60 198	80.88 263	87.83 292	87.37 326	90.77 118	98.82 123	82.52 228	94.37 289	91.93 322

MVP-Stereo		90.07 212	88.92 218	93.54 139	96.31 170	86.49 127	90.93 224	95.59 201	79.80 269	91.48 222	95.59 168	80.79 248	97.39 251	78.57 271	91.19 324	96.76 205
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PLC	85.34 15	90.40 202	88.92 218	94.85 91	96.53 148	90.02 68	91.58 207	96.48 163	80.16 268	86.14 305	92.18 270	85.73 211	98.25 197	76.87 284	94.61 286	96.30 224
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		90.32 207	88.87 220	94.66 97	94.82 244	91.85 52	94.22 110	94.75 219	80.91 262	87.52 297	88.07 318	86.63 201	97.87 223	76.67 285	96.21 254	94.25 278
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
MVSTER		89.32 218	88.75 221	91.03 223	90.10 320	76.62 268	90.85 225	94.67 224	82.27 255	95.24 131	95.79 163	61.09 328	98.49 175	90.49 116	98.26 166	97.97 142

Patchmatch-RL test		88.81 229	88.52 222	89.69 251	95.33 234	79.94 216	86.22 312	92.71 258	78.46 283	95.80 112	94.18 223	66.25 301	95.33 306	89.22 150	98.53 140	93.78 291

X-MVStestdata		90.70 196	88.45 223	97.44 13	98.56 35	93.99 22	96.50 31	97.95 51	94.58 36	94.38 157	26.89 352	94.56 47	99.39 41	93.57 50	99.05 95	98.93 79

jason		89.17 221	88.32 224	91.70 205	95.73 212	80.07 208	88.10 291	93.22 249	71.98 315	90.09 252	92.79 253	78.53 257	98.56 165	87.43 177	97.06 227	96.46 218
jason: jason.
Anonymous20231206		88.77 230	88.29 225	90.20 243	96.31 170	78.81 245	89.56 267	93.49 245	74.26 303	92.38 208	95.58 171	82.21 233	95.43 303	72.07 311	98.75 128	96.34 222

EPNet		89.80 214	88.25 226	94.45 111	83.91 352	86.18 136	93.87 123	87.07 302	91.16 120	80.64 339	94.72 206	78.83 254	98.89 105	85.17 201	98.89 106	98.28 121
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
YYNet1		88.17 240	88.24 227	87.93 287	92.21 297	73.62 297	80.75 336	88.77 286	82.51 253	94.99 142	95.11 189	82.70 230	93.70 322	83.33 220	93.83 297	96.48 217

MDA-MVSNet_test_wron		88.16 241	88.23 228	87.93 287	92.22 296	73.71 296	80.71 337	88.84 285	82.52 252	94.88 145	95.14 187	82.70 230	93.61 323	83.28 221	93.80 298	96.46 218

CDS-MVSNet		89.55 215	88.22 229	93.53 140	95.37 230	86.49 127	89.26 275	93.59 242	79.76 270	91.15 236	92.31 268	77.12 267	98.38 186	77.51 279	97.92 196	95.71 242
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PatchT		87.51 253	88.17 230	85.55 305	90.64 311	66.91 328	92.02 183	86.09 307	92.20 88	89.05 273	97.16 86	64.15 310	96.37 287	89.21 151	92.98 310	93.37 302

PVSNet_Blended		88.74 231	88.16 231	90.46 233	94.81 245	78.80 246	86.64 309	96.93 135	74.67 298	88.68 283	89.18 311	86.27 206	98.15 205	80.27 248	96.00 255	94.44 275

testmv		88.46 233	88.11 232	89.48 252	96.00 193	76.14 272	86.20 313	93.75 239	84.48 235	93.57 177	95.52 175	80.91 247	95.09 309	63.97 338	98.61 134	97.22 187

UnsupCasMVSNet_bld		88.50 232	88.03 233	89.90 246	95.52 224	78.88 243	87.39 299	94.02 235	79.32 278	93.06 193	94.02 230	80.72 249	94.27 318	75.16 298	93.08 308	96.54 207

PatchMatch-RL		89.18 220	88.02 234	92.64 174	95.90 206	92.87 42	88.67 287	91.06 277	80.34 266	90.03 255	91.67 278	83.34 223	94.42 315	76.35 288	94.84 281	90.64 331

view600		88.32 236	87.94 235	89.46 254	96.49 150	73.31 300	93.95 117	84.46 327	93.02 64	94.18 161	92.68 258	63.33 318	98.56 165	75.87 292	97.50 212	96.51 209

view800		88.32 236	87.94 235	89.46 254	96.49 150	73.31 300	93.95 117	84.46 327	93.02 64	94.18 161	92.68 258	63.33 318	98.56 165	75.87 292	97.50 212	96.51 209

conf0.05thres1000		88.32 236	87.94 235	89.46 254	96.49 150	73.31 300	93.95 117	84.46 327	93.02 64	94.18 161	92.68 258	63.33 318	98.56 165	75.87 292	97.50 212	96.51 209

tfpn		88.32 236	87.94 235	89.46 254	96.49 150	73.31 300	93.95 117	84.46 327	93.02 64	94.18 161	92.68 258	63.33 318	98.56 165	75.87 292	97.50 212	96.51 209

MS-PatchMatch		88.05 242	87.75 239	88.95 269	93.28 280	77.93 253	87.88 293	92.49 262	75.42 297	92.57 204	93.59 240	80.44 250	94.24 320	81.28 238	92.75 311	94.69 269

PCF-MVS	84.52 17	89.12 222	87.71 240	93.34 143	96.06 187	85.84 142	86.58 311	97.31 109	68.46 331	93.61 176	93.89 233	87.51 177	98.52 173	67.85 329	98.11 183	95.66 244
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
pmmvs4		88.95 226	87.70 241	92.70 172	94.30 264	85.60 146	87.22 301	92.16 267	74.62 299	89.75 265	94.19 222	77.97 261	96.41 284	82.71 226	96.36 251	96.09 230

1112_ss		88.42 234	87.41 242	91.45 213	96.69 136	80.99 193	89.72 263	96.72 152	73.37 308	87.00 301	90.69 295	77.38 265	98.20 201	81.38 237	93.72 299	95.15 257

lupinMVS		88.34 235	87.31 243	91.45 213	94.74 249	80.06 209	87.23 300	92.27 264	71.10 319	88.83 274	91.15 284	77.02 268	98.53 172	86.67 187	96.75 237	95.76 240

Patchmatch-test1		87.28 258	87.30 244	87.22 294	92.01 302	71.98 314	89.43 269	88.11 294	82.26 256	88.71 281	92.20 269	78.65 256	95.81 296	80.99 244	93.30 303	93.87 290

N_pmnet		88.90 227	87.25 245	93.83 131	94.40 263	93.81 31	84.73 320	87.09 301	79.36 277	93.26 188	92.43 265	79.29 253	91.68 333	77.50 280	97.22 224	96.00 233

no-one		87.84 245	87.21 246	89.74 247	93.58 277	78.64 249	81.28 335	92.69 259	74.36 301	92.05 217	97.14 87	81.86 239	96.07 292	72.03 312	99.90 2	94.52 272

TR-MVS		87.70 248	87.17 247	89.27 263	94.11 268	79.26 232	88.69 286	91.86 271	81.94 258	90.69 243	89.79 304	82.82 229	97.42 248	72.65 309	91.98 320	91.14 327

pmmvs5		87.87 244	87.14 248	90.07 244	93.26 282	76.97 267	88.89 283	92.18 265	73.71 307	88.36 285	93.89 233	76.86 271	96.73 273	80.32 247	96.81 234	96.51 209

tfpn111		87.60 252	87.12 249	89.04 267	96.14 183	73.09 306	93.00 145	85.31 317	92.13 90	93.26 188	90.96 288	63.42 314	98.48 178	72.87 307	96.98 231	95.56 247

CR-MVSNet		87.89 243	87.12 249	90.22 240	91.01 307	78.93 241	92.52 161	92.81 254	73.08 310	89.10 271	96.93 96	67.11 293	97.64 241	88.80 157	92.70 312	94.08 280

thres600view7		87.66 250	87.10 251	89.36 261	96.05 188	73.17 304	92.72 154	85.31 317	91.89 96	93.29 185	90.97 287	63.42 314	98.39 184	73.23 304	96.99 230	96.51 209

BH-w/o		87.21 261	87.02 252	87.79 290	94.77 247	77.27 262	87.90 292	93.21 251	81.74 259	89.99 257	88.39 316	83.47 222	96.93 266	71.29 318	92.43 314	89.15 334

conf200view11		87.41 255	86.89 253	88.97 268	96.14 183	73.09 306	93.00 145	85.31 317	92.13 90	93.26 188	90.96 288	63.42 314	98.28 192	71.27 319	96.54 245	95.56 247

thres100view900		87.35 257	86.89 253	88.72 273	96.14 183	73.09 306	93.00 145	85.31 317	92.13 90	93.26 188	90.96 288	63.42 314	98.28 192	71.27 319	96.54 245	94.79 265

GA-MVS		87.70 248	86.82 255	90.31 236	93.27 281	77.22 263	84.72 322	92.79 256	85.11 227	89.82 262	90.07 299	66.80 296	97.76 235	84.56 212	94.27 292	95.96 235

sss		87.23 260	86.82 255	88.46 282	93.96 269	77.94 252	86.84 306	92.78 257	77.59 287	87.61 296	91.83 275	78.75 255	91.92 332	77.84 275	94.20 293	95.52 252

PAPR		87.65 251	86.77 257	90.27 238	92.85 286	77.38 260	88.56 288	96.23 179	76.82 294	84.98 312	89.75 306	86.08 208	97.16 258	72.33 310	93.35 302	96.26 226

EU-MVSNet		87.39 256	86.71 258	89.44 258	93.40 279	76.11 273	94.93 84	90.00 283	57.17 348	95.71 115	97.37 76	64.77 308	97.68 240	92.67 79	94.37 289	94.52 272

Test_1112_low_res		87.50 254	86.58 259	90.25 239	96.80 131	77.75 256	87.53 298	96.25 177	69.73 327	86.47 303	93.61 239	75.67 274	97.88 221	79.95 253	93.20 304	95.11 259

FMVSNet5		87.82 247	86.56 260	91.62 207	92.31 294	79.81 219	93.49 131	94.81 218	83.26 241	91.36 225	96.93 96	52.77 347	97.49 246	76.07 289	98.03 190	97.55 172

MIMVSNet		87.13 265	86.54 261	88.89 270	96.05 188	76.11 273	94.39 104	88.51 288	81.37 261	88.27 288	96.75 107	72.38 279	95.52 299	65.71 336	95.47 268	95.03 260

tfpn200view9		87.05 266	86.52 262	88.67 274	95.77 209	72.94 309	91.89 190	86.00 309	90.84 123	92.61 202	89.80 302	63.93 311	98.28 192	71.27 319	96.54 245	94.79 265

thres400		87.20 262	86.52 262	89.24 265	95.77 209	72.94 309	91.89 190	86.00 309	90.84 123	92.61 202	89.80 302	63.93 311	98.28 192	71.27 319	96.54 245	96.51 209

WTY-MVS		86.93 270	86.50 264	88.24 284	94.96 240	74.64 288	87.19 302	92.07 270	78.29 284	88.32 287	91.59 281	78.06 260	94.27 318	74.88 299	93.15 306	95.80 238

1314		86.46 277	86.33 265	86.87 297	91.65 303	74.54 290	91.94 187	94.10 233	74.28 302	84.78 314	87.33 327	83.03 226	95.00 310	78.72 269	91.16 325	91.06 328

cascas		87.02 267	86.28 266	89.25 264	91.56 304	76.45 269	84.33 324	96.78 148	71.01 320	86.89 302	85.91 333	81.35 241	96.94 265	83.09 223	95.60 263	94.35 277

tfpn1000		86.83 271	86.23 267	88.64 276	95.53 223	75.25 286	93.57 129	82.28 341	89.27 154	91.46 223	89.24 310	57.22 341	97.86 224	80.63 246	96.88 233	92.81 308

conf0.01		86.95 268	86.04 268	89.70 249	95.99 194	75.66 279	93.28 135	82.70 334	88.81 162	91.26 227	88.01 319	58.77 333	97.89 215	78.93 263	96.60 239	95.56 247

conf0.002		86.95 268	86.04 268	89.70 249	95.99 194	75.66 279	93.28 135	82.70 334	88.81 162	91.26 227	88.01 319	58.77 333	97.89 215	78.93 263	96.60 239	95.56 247

thresconf0.02		86.69 273	86.04 268	88.64 276	95.99 194	75.66 279	93.28 135	82.70 334	88.81 162	91.26 227	88.01 319	58.77 333	97.89 215	78.93 263	96.60 239	92.36 315

tfpn_n400		86.69 273	86.04 268	88.64 276	95.99 194	75.66 279	93.28 135	82.70 334	88.81 162	91.26 227	88.01 319	58.77 333	97.89 215	78.93 263	96.60 239	92.36 315

tfpnconf		86.69 273	86.04 268	88.64 276	95.99 194	75.66 279	93.28 135	82.70 334	88.81 162	91.26 227	88.01 319	58.77 333	97.89 215	78.93 263	96.60 239	92.36 315

tfpnview11		86.69 273	86.04 268	88.64 276	95.99 194	75.66 279	93.28 135	82.70 334	88.81 162	91.26 227	88.01 319	58.77 333	97.89 215	78.93 263	96.60 239	92.36 315

Patchmatch-test		86.10 279	86.01 274	86.38 301	90.63 312	74.22 295	89.57 266	86.69 303	85.73 221	89.81 263	92.83 252	65.24 306	91.04 335	77.82 277	95.78 261	93.88 289

HY-MVS	82.50 18	86.81 272	85.93 275	89.47 253	93.63 276	77.93 253	94.02 113	91.58 274	75.68 295	83.64 321	93.64 237	77.40 264	97.42 248	71.70 315	92.07 319	93.05 305

CHOSEN 1792x2688		87.19 263	85.92 276	91.00 226	97.13 116	79.41 230	84.51 323	95.60 198	64.14 342	90.07 254	94.81 200	78.26 259	97.14 259	73.34 303	95.38 271	96.46 218

CMPMVS	68.83 22	87.28 258	85.67 277	92.09 195	88.77 333	85.42 148	90.31 242	94.38 228	70.02 326	88.00 290	93.30 247	73.78 277	94.03 321	75.96 291	96.54 245	96.83 203
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HyFIR lowres test		87.19 263	85.51 278	92.24 190	97.12 117	80.51 198	85.03 318	96.06 184	66.11 338	91.66 221	92.98 251	70.12 286	99.14 69	75.29 297	95.23 274	97.07 191

LP		86.29 278	85.35 279	89.10 266	87.80 335	76.21 271	89.92 256	90.99 278	84.86 232	87.66 294	92.32 267	70.40 285	96.48 280	81.94 231	82.24 344	94.63 270

thres200		85.85 280	85.18 280	87.88 289	94.44 261	72.52 311	89.08 279	86.21 306	88.57 173	91.44 224	88.40 315	64.22 309	98.00 210	68.35 328	95.88 260	93.12 304

tfpn_ndepth		85.85 280	85.15 281	87.98 286	95.19 237	75.36 285	92.79 153	83.18 333	86.97 205	89.92 258	86.43 331	57.44 340	97.85 227	78.18 272	96.22 253	90.72 330

CVMVSNet		85.16 284	84.72 282	86.48 299	92.12 300	70.19 318	92.32 173	88.17 293	56.15 349	90.64 244	95.85 159	67.97 291	96.69 274	88.78 158	90.52 327	92.56 312

PatchmatchNet		85.22 283	84.64 283	86.98 296	89.51 326	69.83 321	90.52 236	87.34 300	78.87 280	87.22 299	92.74 255	66.91 295	96.53 277	81.77 233	86.88 335	94.58 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		85.63 282	84.37 284	89.40 260	86.30 345	74.33 294	91.64 206	88.26 290	84.84 233	72.96 350	89.85 300	71.27 283	97.69 239	76.60 286	97.62 208	96.18 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS		84.98 286	84.30 285	87.01 295	91.03 306	77.69 258	91.94 187	94.16 232	59.36 347	84.23 318	87.50 325	85.66 212	96.80 271	71.79 313	93.05 309	86.54 340

tpm		84.38 289	84.08 286	85.30 310	90.47 315	63.43 343	89.34 272	85.63 313	77.24 291	87.62 295	95.03 195	61.00 329	97.30 254	79.26 260	91.09 326	95.16 256

tpmvs		84.22 290	83.97 287	84.94 311	87.09 342	65.18 335	91.21 216	88.35 289	82.87 248	85.21 309	90.96 288	65.24 306	96.75 272	79.60 259	85.25 336	92.90 307

MDTV_nov1_ep13			83.88 288		89.42 327	61.52 344	88.74 285	87.41 299	73.99 305	84.96 313	94.01 231	65.25 305	95.53 298	78.02 273	93.16 305

test1235678		84.54 287	83.85 289	86.59 298	93.81 275	73.41 299	82.38 330	91.79 272	79.43 273	89.50 267	91.61 280	70.59 284	92.94 329	58.14 344	97.40 220	93.44 300

PMMVS2		81.31 307	83.44 290	74.92 335	90.52 314	46.49 352	69.19 348	85.23 322	84.30 236	87.95 291	94.71 207	76.95 270	84.36 350	64.07 337	98.09 185	93.89 288

FPMVS		84.50 288	83.28 291	88.16 285	96.32 169	94.49 11	85.76 314	85.47 315	83.09 245	85.20 310	94.26 219	63.79 313	86.58 348	63.72 339	91.88 322	83.40 343

test-LLR		83.58 292	83.17 292	84.79 313	89.68 323	66.86 330	83.08 327	84.52 325	83.07 246	82.85 326	84.78 337	62.86 323	93.49 324	82.85 224	94.86 279	94.03 283

JIA-IIPM		85.08 285	83.04 293	91.19 222	87.56 337	86.14 137	89.40 271	84.44 331	88.98 157	82.20 330	97.95 48	56.82 343	96.15 290	76.55 287	83.45 340	91.30 326

tpmrst		82.85 297	82.93 294	82.64 324	87.65 336	58.99 347	90.14 248	87.90 295	75.54 296	83.93 319	91.63 279	66.79 298	95.36 304	81.21 240	81.54 345	93.57 299

PVSNet	76.22 20	82.89 296	82.37 295	84.48 315	93.96 269	64.38 340	78.60 340	88.61 287	71.50 317	84.43 317	86.36 332	74.27 276	94.60 312	69.87 326	93.69 300	94.46 274

CostFormer		83.09 294	82.21 296	85.73 304	89.27 329	67.01 327	90.35 240	86.47 305	70.42 324	83.52 323	93.23 248	61.18 327	96.85 269	77.21 282	88.26 333	93.34 303

ADS-MVSNet2		84.01 291	82.20 297	89.41 259	89.04 330	76.37 270	87.57 295	90.98 279	72.71 313	84.46 315	92.45 262	68.08 289	96.48 280	70.58 324	83.97 337	95.38 254

testus		82.09 303	81.78 298	83.03 322	92.35 293	64.37 341	79.44 338	93.27 248	73.08 310	87.06 300	85.21 336	76.80 272	89.27 343	53.30 347	95.48 267	95.46 253

PatchFormer-LS_test		82.62 298	81.71 299	85.32 309	87.92 334	67.31 326	89.03 280	88.20 292	77.58 288	83.79 320	80.50 347	60.96 330	96.42 283	83.86 218	83.59 339	92.23 319

DSMNet-mixed		82.21 301	81.56 300	84.16 317	89.57 325	70.00 320	90.65 231	77.66 350	54.99 350	83.30 324	97.57 64	77.89 262	90.50 339	66.86 332	95.54 265	91.97 321

ADS-MVSNet		82.25 300	81.55 301	84.34 316	89.04 330	65.30 334	87.57 295	85.13 323	72.71 313	84.46 315	92.45 262	68.08 289	92.33 331	70.58 324	83.97 337	95.38 254

test0.0.03 1		82.48 299	81.47 302	85.48 306	89.70 322	73.57 298	84.73 320	81.64 343	83.07 246	88.13 289	86.61 328	62.86 323	89.10 345	66.24 335	90.29 328	93.77 292

1111		80.36 315	81.32 303	77.48 332	94.61 257	44.56 353	81.59 333	90.66 281	86.78 209	90.60 245	93.52 242	30.37 358	90.67 336	66.36 333	97.42 219	97.20 188

PMMVS		83.00 295	81.11 304	88.66 275	83.81 353	86.44 130	82.24 332	85.65 312	61.75 346	82.07 331	85.64 334	79.75 252	91.59 334	75.99 290	93.09 307	87.94 339

IB-MVS	77.21 19	83.11 293	81.05 305	89.29 262	91.15 305	75.85 276	85.66 315	86.00 309	79.70 271	82.02 333	86.61 328	48.26 351	98.39 184	77.84 275	92.22 317	93.63 295
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
gg-mvs-nofinetune		82.10 302	81.02 306	85.34 308	87.46 340	71.04 316	94.74 89	67.56 353	96.44 19	79.43 342	98.99 6	45.24 352	96.15 290	67.18 331	92.17 318	88.85 336

new_pmnet		81.22 308	81.01 307	81.86 326	90.92 309	70.15 319	84.03 325	80.25 348	70.83 322	85.97 306	89.78 305	67.93 292	84.65 349	67.44 330	91.90 321	90.78 329

E-PMN		80.72 313	80.86 308	80.29 329	85.11 349	68.77 323	72.96 344	81.97 342	87.76 193	83.25 325	83.01 343	62.22 326	89.17 344	77.15 283	94.31 291	82.93 344

MVS-HIRNet		78.83 320	80.60 309	73.51 336	93.07 284	47.37 351	87.10 303	78.00 349	68.94 329	77.53 345	97.26 81	71.45 282	94.62 311	63.28 340	88.74 330	78.55 348

tpmp4_e23		81.87 305	80.41 310	86.27 302	89.29 328	67.84 325	91.58 207	87.61 298	67.42 334	78.60 343	92.71 256	56.42 344	96.87 268	71.44 317	88.63 331	94.10 279

EPMVS		81.17 310	80.37 311	83.58 319	85.58 348	65.08 337	90.31 242	71.34 352	77.31 290	85.80 308	91.30 282	59.38 331	92.70 330	79.99 252	82.34 343	92.96 306

tpm2		81.46 306	80.35 312	84.80 312	89.90 321	65.14 336	90.44 238	85.36 316	65.82 340	82.05 332	92.44 264	57.94 339	96.69 274	70.71 323	88.49 332	92.56 312

EMVS		80.35 316	80.28 313	80.54 328	84.73 351	69.07 322	72.54 346	80.73 345	87.80 192	81.66 335	81.73 344	62.89 322	89.84 341	75.79 296	94.65 285	82.71 345

PAPM		81.91 304	80.11 314	87.31 293	93.87 272	72.32 313	84.02 326	93.22 249	69.47 328	76.13 347	89.84 301	72.15 280	97.23 256	53.27 348	89.02 329	92.37 314

test-mter		81.21 309	80.01 315	84.79 313	89.68 323	66.86 330	83.08 327	84.52 325	73.85 306	82.85 326	84.78 337	43.66 355	93.49 324	82.85 224	94.86 279	94.03 283

tpm cat1		80.61 314	79.46 316	84.07 318	88.78 332	65.06 338	89.26 275	88.23 291	62.27 345	81.90 334	89.66 308	62.70 325	95.29 307	71.72 314	80.60 346	91.86 324

DWT-MVSNet_test		80.74 312	79.18 317	85.43 307	87.51 339	66.87 329	89.87 260	86.01 308	74.20 304	80.86 337	80.62 346	48.84 350	96.68 276	81.54 235	83.14 342	92.75 310

pmmvs3		80.83 311	78.96 318	86.45 300	87.23 341	77.48 259	84.87 319	82.31 340	63.83 343	85.03 311	89.50 309	49.66 349	93.10 326	73.12 306	95.10 276	88.78 338

dp		79.28 318	78.62 319	81.24 327	85.97 347	56.45 349	86.91 305	85.26 321	72.97 312	81.45 336	89.17 312	56.01 346	95.45 302	73.19 305	76.68 348	91.82 325

TESTMET0.1,1		79.09 319	78.04 320	82.25 325	87.52 338	64.03 342	83.08 327	80.62 346	70.28 325	80.16 341	83.22 342	44.13 354	90.56 338	79.95 253	93.36 301	92.15 320

CHOSEN 280x420		80.04 317	77.97 321	86.23 303	90.13 319	74.53 291	72.87 345	89.59 284	66.38 337	76.29 346	85.32 335	56.96 342	95.36 304	69.49 327	94.72 283	88.79 337

test12356		76.35 321	77.41 322	73.19 337	90.70 310	38.86 356	74.56 342	91.14 276	74.55 300	80.54 340	88.18 317	52.36 348	90.49 340	52.38 349	92.26 316	90.21 333

testpf		74.01 324	76.37 323	66.95 338	80.56 354	60.00 345	88.43 290	75.07 351	81.54 260	75.75 348	83.73 339	38.93 356	83.09 351	84.01 215	79.32 347	57.75 350

test2356		75.58 322	73.13 324	82.95 323	86.10 346	66.42 332	75.07 341	84.87 324	70.91 321	80.85 338	80.66 345	38.02 357	88.98 346	49.32 350	92.35 315	93.44 300

PVSNet_0	70.34 21	74.58 323	72.96 325	79.47 330	90.63 312	66.24 333	73.26 343	83.40 332	63.67 344	78.02 344	78.35 348	72.53 278	89.59 342	56.68 345	60.05 351	82.57 346

MVE	59.87 23	73.86 325	72.65 326	77.47 333	87.00 344	74.35 293	61.37 350	60.93 355	67.27 335	69.69 351	86.49 330	81.24 246	72.33 353	56.45 346	83.45 340	85.74 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		72.03 326	70.91 327	75.38 334	90.46 316	57.84 348	71.73 347	81.53 344	83.86 239	82.21 329	83.49 341	29.97 360	87.80 347	60.78 341	54.12 352	80.51 347

.test1245		64.72 327	70.88 328	46.22 340	94.61 257	44.56 353	81.59 333	90.66 281	86.78 209	90.60 245	93.52 242	30.37 358	90.67 336	66.36 333	3.45 354	3.44 354

tmp_tt		37.97 329	44.33 329	18.88 342	11.80 356	21.54 357	63.51 349	45.66 358	4.23 352	51.34 353	50.48 351	59.08 332	22.11 355	44.50 351	68.35 350	13.00 352

pcd1.5k->3k		41.03 328	43.65 330	33.18 341	98.74 26	0.00 360	0.00 351	97.57 81	0.00 355	0.00 356	0.00 357	97.01 6	0.00 358	0.00 355	99.52 45	99.53 17

cdsmvs_eth3d_5k		23.35 330	31.13 331	0.00 345	0.00 359	0.00 360	0.00 351	95.58 202	0.00 355	0.00 356	91.15 284	93.43 61	0.00 358	0.00 355	0.00 356	0.00 356

test123		9.49 331	12.01 332	1.91 343	2.87 357	1.30 358	82.38 330	1.34 360	1.36 353	2.84 354	6.56 354	2.45 361	0.97 356	2.73 353	5.56 353	3.47 353

testmvs		9.02 332	11.42 333	1.81 344	2.77 358	1.13 359	79.44 338	1.90 359	1.18 354	2.65 355	6.80 353	1.95 362	0.87 357	2.62 354	3.45 354	3.44 354

pcd_1.5k_mvsjas		7.56 333	10.09 334	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	90.77 118	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		7.56 333	10.08 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	90.69 295	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

GSMVS																94.75 267

test_part3							93.92 121		91.83 101		96.39 130		99.44 24	89.00 153

test_part2					98.21 61	89.41 76				96.72 66

test_part1								98.14 28				94.69 44			99.10 90	98.17 127

sam_mvs1												66.64 299				94.75 267

sam_mvs												66.41 300

semantic-postprocess				91.94 198	93.89 271	79.22 237		93.51 244	91.53 113	95.37 125	96.62 113	77.17 266	98.90 103	91.89 99	94.95 278	97.70 161

ambc				92.98 155	96.88 126	83.01 174	95.92 53	96.38 170		96.41 76	97.48 70	88.26 159	97.80 230	89.96 136	98.93 105	98.12 133

MTGPA								97.62 74

test_post1							90.21 244				5.85 356	65.36 304	96.00 293	79.61 258

test_post											6.07 355	65.74 303	95.84 295

patchmatchnet-post											91.71 277	66.22 302	97.59 243

GG-mvs-BLEND				83.24 321	85.06 350	71.03 317	94.99 83	65.55 354		74.09 349	75.51 349	44.57 353	94.46 314	59.57 343	87.54 334	84.24 342

MTMP								54.62 356

gm-plane-assit					87.08 343	59.33 346			71.22 318		83.58 340		97.20 257	73.95 300

test9_res														88.16 168	98.40 149	97.83 153

TEST9					96.45 156	89.46 73	90.60 233	96.92 137	79.09 279	90.49 247	94.39 216	91.31 105	98.88 109

test_8					96.37 159	89.14 82	90.51 237	96.89 141	79.37 275	90.42 249	94.36 218	91.20 111	98.82 123

agg_prior2														87.06 182	98.36 157	97.98 139

agg_prior					96.20 178	88.89 87		96.88 142		90.21 250			98.78 133

TestCases				96.00 50	98.02 73	92.17 45		98.43 9	90.48 132	95.04 140	96.74 108	92.54 82	97.86 224	85.11 205	98.98 101	97.98 139

test_prior4						89.91 70	90.74 228

test_prior2							90.21 244		89.33 152	90.77 240	94.81 200	90.41 129		88.21 165	98.55 137

test_prior				94.61 98	95.95 202	87.23 116		97.36 105					98.68 151			97.93 143

旧先验2							90.00 254		68.65 330	92.71 201			96.52 278	85.15 203

新几何2							90.02 253

新几何1				93.17 149	97.16 112	87.29 115		94.43 226	67.95 332	91.29 226	94.94 197	86.97 192	98.23 198	81.06 243	97.75 200	93.98 286

旧先验1					96.20 178	84.17 160		94.82 216			95.57 172	89.57 142			97.89 197	96.32 223

无先验							89.94 255	95.75 195	70.81 323				98.59 161	81.17 241		94.81 264

原ACMM2							89.34 272

原ACMM1				92.87 163	96.91 125	84.22 159		97.01 127	76.84 293	89.64 266	94.46 212	88.00 169	98.70 149	81.53 236	98.01 191	95.70 243

test222					96.95 121	85.27 150	88.83 284	93.61 241	65.09 341	90.74 242	94.85 199	84.62 219			97.36 221	93.91 287

testdata2													98.03 209	80.24 250

segment_acmp												92.14 87

testdata				91.03 223	96.87 127	82.01 180		94.28 230	71.55 316	92.46 205	95.42 179	85.65 213	97.38 253	82.64 227	97.27 223	93.70 294

testdata1							88.96 282		88.44 178

test12				94.43 112	95.95 202	86.75 125		96.24 178		89.76 264		89.79 140	98.79 130		97.95 194	97.75 158

plane_prior7					97.71 90	88.68 91

plane_prior6					97.21 110	88.23 104						86.93 193

plane_prior5								97.81 62					98.95 99	89.26 148	98.51 142	98.60 108

plane_prior4											95.59 168

plane_prior3						88.43 102			90.35 137	93.31 183

plane_prior2							94.56 99		91.74 108

plane_prior1					97.38 105

plane_prior						88.12 105	93.01 144		88.98 157						98.06 187

n2								0.00 361

nn								0.00 361

door-mid								92.13 269

lessismore_v0				93.87 130	98.05 71	83.77 165		80.32 347		97.13 52	97.91 52	77.49 263	99.11 74	92.62 80	98.08 186	98.74 97

LGP-MVS_train				96.84 36	98.36 53	92.13 47		98.25 19	91.78 104	97.07 53	97.22 83	96.38 12	99.28 55	92.07 93	99.59 34	99.11 52

test11								96.65 154

door								91.26 275

HQP5-MVS						84.89 152

HQP-NCC					96.36 164		91.37 211		87.16 201	88.81 276

ACMP_Plane					96.36 164		91.37 211		87.16 201	88.81 276

BP-MVS														86.55 190

HQP4-MVS										88.81 276			98.61 157			98.15 130

HQP3-MVS								97.31 109							97.73 201

HQP2-MVS												84.76 217

NP-MVS					96.82 129	87.10 119					93.40 245

MDTV_nov1_ep13_2view						42.48 355	88.45 289		67.22 336	83.56 322		66.80 296		72.86 308		94.06 282

ACMMP++_ref															98.82 119

ACMMP++															99.25 75

Test By Simon												90.61 125

ITE_SJBPF				95.95 52	97.34 107	93.36 37		96.55 160	91.93 94	94.82 146	95.39 182	91.99 91	97.08 261	85.53 199	97.96 193	97.41 176

DeepMVS_CX				53.83 339	70.38 355	64.56 339		48.52 357	33.01 351	65.50 352	74.21 350	56.19 345	46.64 354	38.45 352	70.07 349	50.30 351

High-resolution multi-view benchmark