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

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 23	98.81 22	93.86 27	99.07 2	98.98 3	97.01 11	98.92 4	98.78 14	95.22 31	98.61 161	96.85 4	99.77 11	99.31 39
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 3	97.60 3	97.93 2	99.02 10	95.95 6	98.61 3	98.81 4	97.41 8	97.28 50	98.46 29	94.62 45	98.84 123	94.64 26	99.53 44	98.99 71

UA-Net		97.35 4	97.24 12	97.69 5	98.22 60	93.87 26	98.42 4	98.19 24	96.95 12	95.46 127	99.23 3	93.45 59	99.57 13	95.34 17	99.89 4	99.63 9

abl_6		97.31 5	97.12 14	97.86 3	98.54 38	95.32 8	96.61 26	98.35 11	95.81 30	97.55 40	97.44 74	96.51 9	99.40 36	94.06 42	99.23 80	98.85 89

HPM-MVS_fast		97.01 6	96.89 17	97.39 18	99.12 6	93.92 24	97.16 12	98.17 26	93.11 65	96.48 78	97.36 81	96.92 6	99.34 48	94.31 33	99.38 65	98.92 83

v52		96.93 7	97.29 10	95.86 58	98.12 67	88.48 99	97.69 6	97.74 69	94.90 34	98.55 15	98.72 17	93.39 63	99.49 21	96.92 2	99.62 30	99.61 11

V4		96.93 7	97.29 10	95.86 58	98.11 68	88.47 100	97.69 6	97.74 69	94.91 32	98.55 15	98.72 17	93.37 64	99.49 21	96.92 2	99.62 30	99.61 11

mvs_tets		96.83 9	96.71 21	97.17 25	98.83 20	92.51 43	96.58 28	97.61 79	87.57 201	98.80 7	98.90 9	96.50 10	99.59 12	96.15 9	99.47 49	99.40 31

v7n		96.82 10	97.31 9	95.33 79	98.54 38	86.81 125	96.83 20	98.07 35	96.59 18	98.46 19	98.43 33	92.91 76	99.52 17	96.25 8	99.76 12	99.65 8

APD-MVS_3200maxsize		96.82 10	96.65 23	97.32 22	97.95 80	93.82 29	96.31 42	98.25 18	95.51 31	96.99 62	97.05 96	95.63 19	99.39 40	93.31 64	98.88 111	98.75 99

HPM-MVS		96.81 12	96.62 25	97.36 20	98.89 17	93.53 34	97.51 8	98.44 7	92.35 84	95.95 105	96.41 131	96.71 8	99.42 28	93.99 43	99.36 66	99.13 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs6		96.80 13	97.36 8	95.15 86	99.12 6	87.82 112	96.68 24	97.86 57	96.10 25	98.14 26	99.28 2	97.94 3	98.21 203	91.38 116	99.69 16	99.42 27

OurMVSNet-221017-0		96.80 13	96.75 20	96.96 32	99.03 9	91.85 52	97.98 5	98.01 43	94.15 46	98.93 3	99.07 4	88.07 173	99.57 13	95.86 11	99.69 16	99.46 25

wuykxyi23d		96.76 15	96.57 27	97.34 21	97.75 86	96.73 3	94.37 110	96.48 167	91.00 125	99.72 2	98.99 5	96.06 14	98.21 203	94.86 22	99.90 2	97.09 194

COLMAP_ROB	91.06 5	96.75 16	96.62 25	97.13 26	98.38 50	94.31 12	96.79 22	98.32 12	96.69 15	96.86 64	97.56 67	95.48 21	98.77 140	90.11 135	99.44 55	98.31 122
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
anonymousdsp		96.74 17	96.42 30	97.68 7	98.00 76	94.03 21	96.97 17	97.61 79	87.68 200	98.45 21	98.77 15	94.20 52	99.50 18	96.70 5	99.40 62	99.53 16

DTE-MVSNet		96.74 17	97.43 4	94.67 98	99.13 4	84.68 157	96.51 31	97.94 54	98.14 2	98.67 12	98.32 36	95.04 35	99.69 2	93.27 65	99.82 9	99.62 10

PS-CasMVS		96.69 19	97.43 4	94.49 111	99.13 4	84.09 165	96.61 26	97.97 48	97.91 4	98.64 13	98.13 41	95.24 30	99.65 3	93.39 61	99.84 5	99.72 2

PEN-MVS		96.69 19	97.39 7	94.61 100	99.16 2	84.50 158	96.54 30	98.05 37	98.06 3	98.64 13	98.25 39	95.01 38	99.65 3	92.95 76	99.83 7	99.68 4

MTAPA		96.65 21	96.38 32	97.47 11	98.95 14	94.05 18	95.88 56	97.62 76	94.46 42	96.29 86	96.94 98	93.56 57	99.37 44	94.29 35	99.42 57	98.99 71

test_djsdf		96.62 22	96.49 29	97.01 30	98.55 37	91.77 54	97.15 13	97.37 101	88.98 162	98.26 23	98.86 10	93.35 66	99.60 8	96.41 6	99.45 53	99.66 6

ACMMP		96.61 23	96.34 33	97.43 15	98.61 30	93.88 25	96.95 18	98.18 25	92.26 87	96.33 82	96.84 107	95.10 34	99.40 36	93.47 56	99.33 69	99.02 68
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
Anonymous20231211		96.60 24	97.13 13	95.00 89	97.46 106	86.35 135	97.11 16	98.24 21	97.58 6	98.72 8	98.97 7	93.15 71	99.15 68	93.18 69	99.74 14	99.50 21

WR-MVS_H		96.60 24	97.05 15	95.24 82	99.02 10	86.44 131	96.78 23	98.08 32	97.42 7	98.48 18	97.86 57	91.76 98	99.63 6	94.23 37	99.84 5	99.66 6

jajsoiax		96.59 26	96.42 30	97.12 27	98.76 24	92.49 44	96.44 36	97.42 97	86.96 211	98.71 10	98.72 17	95.36 25	99.56 16	95.92 10	99.45 53	99.32 38

ACMH	88.36 12	96.59 26	97.43 4	94.07 124	98.56 34	85.33 152	96.33 40	98.30 15	94.66 37	98.72 8	98.30 37	97.51 4	98.00 215	94.87 21	99.59 35	98.86 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v748		96.51 28	97.05 15	94.89 92	98.35 55	85.82 146	96.58 28	97.47 94	96.25 22	98.46 19	98.35 34	93.27 67	99.33 51	95.13 19	99.59 35	99.52 19

XVS		96.49 29	96.18 39	97.44 13	98.56 34	93.99 22	96.50 32	97.95 51	94.58 38	94.38 161	96.49 123	94.56 46	99.39 40	93.57 51	99.05 97	98.93 80

ACMH+	88.43 11	96.48 30	96.82 18	95.47 75	98.54 38	89.06 83	95.65 62	98.61 6	96.10 25	98.16 25	97.52 70	96.90 7	98.62 160	90.30 129	99.60 33	98.72 103

zzz-MVS		96.47 31	96.14 41	97.47 11	98.95 14	94.05 18	93.69 133	97.62 76	94.46 42	96.29 86	96.94 98	93.56 57	99.37 44	94.29 35	99.42 57	98.99 71

APDe-MVS		96.46 32	96.64 24	95.93 55	97.68 95	89.38 80	96.90 19	98.41 10	92.52 79	97.43 46	97.92 52	95.11 33	99.50 18	94.45 30	99.30 71	98.92 83

ACMMPR		96.46 32	96.14 41	97.41 17	98.60 31	93.82 29	96.30 44	97.96 49	92.35 84	95.57 123	96.61 119	94.93 41	99.41 32	93.78 46	99.15 87	99.00 69

mPP-MVS		96.46 32	96.05 48	97.69 5	98.62 28	94.65 9	96.45 34	97.74 69	92.59 78	95.47 125	96.68 116	94.50 48	99.42 28	93.10 72	99.26 76	98.99 71

CP-MVS		96.44 35	96.08 46	97.54 9	98.29 56	94.62 10	96.80 21	98.08 32	92.67 75	95.08 143	96.39 136	94.77 42	99.42 28	93.17 70	99.44 55	98.58 113

region2R		96.41 36	96.09 45	97.38 19	98.62 28	93.81 31	96.32 41	97.96 49	92.26 87	95.28 132	96.57 121	95.02 37	99.41 32	93.63 50	99.11 91	98.94 79

SteuartSystems-ACMMP		96.40 37	96.30 34	96.71 38	98.63 27	91.96 50	95.70 60	98.01 43	93.34 63	96.64 73	96.57 121	94.99 39	99.36 46	93.48 55	99.34 67	98.82 91
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 38	96.17 40	97.04 28	98.51 42	93.37 35	96.30 44	97.98 45	92.35 84	95.63 120	96.47 126	95.37 23	99.27 57	93.78 46	99.14 88	98.48 114

LPG-MVS_test		96.38 39	96.23 37	96.84 36	98.36 53	92.13 47	95.33 71	98.25 18	91.78 107	97.07 56	97.22 86	96.38 11	99.28 54	92.07 98	99.59 35	99.11 53

Anonymous20240521		96.37 40	96.66 22	95.50 73	98.49 45	87.84 111	97.47 9	97.77 68	94.75 36	98.22 24	98.49 26	90.93 119	99.28 54	94.12 41	99.74 14	99.38 32

nrg030		96.32 41	96.55 28	95.62 69	97.83 83	88.55 96	95.77 59	98.29 17	92.68 73	98.03 28	97.91 54	95.13 32	98.95 101	93.85 44	99.49 48	99.36 36

PGM-MVS		96.32 41	95.94 52	97.43 15	98.59 33	93.84 28	95.33 71	98.30 15	91.40 117	95.76 116	96.87 104	95.26 29	99.45 23	92.77 78	99.21 82	99.00 69

ACMM	88.83 9	96.30 43	96.07 47	96.97 31	98.39 49	92.95 41	94.74 93	98.03 40	90.82 129	97.15 54	96.85 105	96.25 13	99.00 93	93.10 72	99.33 69	98.95 78
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMP_Plus		96.21 44	96.12 43	96.49 45	98.90 16	91.42 57	94.57 102	98.03 40	90.42 140	96.37 81	97.35 82	95.68 18	99.25 60	94.44 31	99.34 67	98.80 93

CP-MVSNet		96.19 45	96.80 19	94.38 117	98.99 12	83.82 167	96.31 42	97.53 88	97.60 5	98.34 22	97.52 70	91.98 94	99.63 6	93.08 74	99.81 10	99.70 3

MP-MVS		96.14 46	95.68 65	97.51 10	98.81 22	94.06 16	96.10 48	97.78 67	92.73 72	93.48 183	96.72 114	94.23 51	99.42 28	91.99 100	99.29 73	99.05 64
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LS3D		96.11 47	95.83 59	96.95 33	94.75 252	94.20 14	97.34 11	97.98 45	97.31 9	95.32 130	96.77 108	93.08 72	99.20 64	91.79 105	98.16 181	97.44 179

MP-MVS-pluss		96.08 48	95.92 54	96.57 41	99.06 8	91.21 59	93.25 146	98.32 12	87.89 195	96.86 64	97.38 78	95.55 20	99.39 40	95.47 13	99.47 49	99.11 53
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		96.07 49	96.26 36	95.50 73	98.26 58	87.69 113	93.75 131	97.86 57	95.96 29	97.48 43	97.14 90	95.33 26	99.44 24	90.79 119	99.76 12	99.38 32

PS-MVSNAJss		96.01 50	96.04 49	95.89 57	98.82 21	88.51 98	95.57 64	97.88 56	88.72 174	98.81 6	98.86 10	90.77 121	99.60 8	95.43 14	99.53 44	99.57 14

#test#		95.89 51	95.51 68	97.04 28	98.51 42	93.37 35	95.14 77	97.98 45	89.34 156	95.63 120	96.47 126	95.37 23	99.27 57	91.99 100	99.14 88	98.48 114

3Dnovator+	92.74 2	95.86 52	95.77 62	96.13 48	96.81 133	90.79 67	96.30 44	97.82 61	96.13 24	94.74 153	97.23 85	91.33 106	99.16 66	93.25 66	98.30 167	98.46 116

SMA-MVS		95.73 53	95.51 68	96.41 46	98.17 64	91.19 60	95.09 79	97.79 66	86.48 216	97.42 48	97.42 75	94.47 50	99.26 59	93.42 60	99.29 73	98.79 95

test_0402		95.73 53	96.22 38	94.26 120	98.19 63	85.77 147	93.24 147	97.24 118	96.88 14	97.69 37	97.77 60	94.12 53	99.13 73	91.54 113	99.29 73	97.88 152

ACMP	88.15 13	95.71 55	95.43 75	96.54 42	98.17 64	91.73 55	94.24 114	98.08 32	89.46 154	96.61 75	96.47 126	95.85 16	99.12 75	90.45 121	99.56 42	98.77 98
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-ACMP-BASELINE		95.68 56	95.34 77	96.69 39	98.40 48	93.04 38	94.54 106	98.05 37	90.45 138	96.31 84	96.76 110	92.91 76	98.72 147	91.19 117	99.42 57	98.32 120

DP-MVS		95.62 57	95.84 58	94.97 90	97.16 115	88.62 93	94.54 106	97.64 75	96.94 13	96.58 76	97.32 83	93.07 73	98.72 147	90.45 121	98.84 116	97.57 173

OPM-MVS		95.61 58	95.45 72	96.08 49	98.49 45	91.00 62	92.65 162	97.33 110	90.05 145	96.77 68	96.85 105	95.04 35	98.56 169	92.77 78	99.06 95	98.70 104

RPSCF		95.58 59	94.89 93	97.62 8	97.58 99	96.30 5	95.97 52	97.53 88	92.42 80	93.41 184	97.78 58	91.21 112	97.77 238	91.06 118	97.06 232	98.80 93

MIMVSNet1		95.52 60	95.45 72	95.72 65	99.14 3	89.02 84	96.23 47	96.87 147	93.73 54	97.87 33	98.49 26	90.73 125	99.05 83	86.43 197	99.60 33	99.10 56

Anonymous20240529		95.50 61	95.83 59	94.50 109	97.33 110	85.93 143	95.19 76	96.77 153	96.64 17	97.61 39	98.05 43	93.23 69	98.79 132	88.60 166	99.04 100	98.78 96

Vis-MVSNet		95.50 61	95.48 70	95.56 72	98.11 68	89.40 79	95.35 70	98.22 23	92.36 82	94.11 169	98.07 42	92.02 91	99.44 24	93.38 62	97.67 210	97.85 155
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
pm-mvs1		95.43 63	95.94 52	93.93 130	98.38 50	85.08 154	95.46 68	97.12 126	91.84 102	97.28 50	98.46 29	95.30 28	97.71 243	90.17 133	99.42 57	98.99 71

DeepC-MVS	91.39 4	95.43 63	95.33 79	95.71 66	97.67 96	90.17 68	93.86 129	98.02 42	87.35 203	96.22 92	97.99 49	94.48 49	99.05 83	92.73 81	99.68 19	97.93 146
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ESAPD		95.42 65	95.34 77	95.68 68	98.21 61	89.41 77	93.92 126	98.14 28	91.83 104	96.72 69	96.39 136	94.69 43	99.44 24	89.00 156	99.10 92	98.17 130

v13		95.39 66	96.12 43	93.18 151	97.22 112	80.81 201	95.55 65	97.57 83	93.42 61	98.02 30	98.49 26	89.62 145	99.18 65	95.54 12	99.68 19	99.54 15

XVG-OURS-SEG-HR		95.38 67	95.00 91	96.51 43	98.10 70	94.07 15	92.46 173	98.13 31	90.69 131	93.75 177	96.25 146	98.03 2	97.02 270	92.08 97	95.55 271	98.45 117

UniMVSNet_NR-MVSNet		95.35 68	95.21 85	95.76 63	97.69 94	88.59 94	92.26 183	97.84 60	94.91 32	96.80 66	95.78 171	90.42 131	99.41 32	91.60 110	99.58 40	99.29 40

FC-MVSNet-test		95.32 69	95.88 56	93.62 137	98.49 45	81.77 188	95.90 55	98.32 12	93.93 51	97.53 41	97.56 67	88.48 158	99.40 36	92.91 77	99.83 7	99.68 4

UniMVSNet (Re)		95.32 69	95.15 87	95.80 61	97.79 84	88.91 86	92.91 155	98.07 35	93.46 60	96.31 84	95.97 162	90.14 135	99.34 48	92.11 95	99.64 27	99.16 48

Gipuma		95.31 71	95.80 61	93.81 135	97.99 79	90.91 64	96.42 37	97.95 51	96.69 15	91.78 227	98.85 12	91.77 97	95.49 308	91.72 106	99.08 94	95.02 269
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v12		95.29 72	96.02 51	93.10 153	97.14 118	80.63 202	95.39 69	97.55 87	93.19 64	97.98 31	98.44 31	89.40 148	99.16 66	95.38 16	99.67 22	99.52 19

DU-MVS		95.28 73	95.12 89	95.75 64	97.75 86	88.59 94	92.58 163	97.81 62	93.99 48	96.80 66	95.90 163	90.10 139	99.41 32	91.60 110	99.58 40	99.26 41

NR-MVSNet		95.28 73	95.28 82	95.26 81	97.75 86	87.21 119	95.08 80	97.37 101	93.92 52	97.65 38	95.90 163	90.10 139	99.33 51	90.11 135	99.66 24	99.26 41

TransMVSNet (Re)		95.27 75	96.04 49	92.97 160	98.37 52	81.92 187	95.07 81	96.76 154	93.97 50	97.77 35	98.57 21	95.72 17	97.90 218	88.89 159	99.23 80	99.08 60

SD-MVS		95.19 76	95.73 64	93.55 140	96.62 146	88.88 89	94.67 95	98.05 37	91.26 119	97.25 53	96.40 132	95.42 22	94.36 325	92.72 82	99.19 83	97.40 182

HSP-MVS		95.18 77	94.49 104	97.23 24	98.67 26	94.05 18	96.41 38	97.00 131	91.26 119	95.12 138	95.15 192	86.60 206	99.50 18	93.43 59	96.81 240	98.13 135

V9		95.17 78	95.89 55	93.02 156	97.04 121	80.42 204	95.22 75	97.53 88	92.92 71	97.90 32	98.35 34	89.15 152	99.14 71	95.21 18	99.65 26	99.50 21

VPA-MVSNet		95.14 79	95.67 66	93.58 139	97.76 85	83.15 176	94.58 101	97.58 82	93.39 62	97.05 60	98.04 44	93.25 68	98.51 178	89.75 142	99.59 35	99.08 60

v11		95.10 80	95.88 56	92.76 173	96.98 123	79.64 231	95.12 78	97.60 81	92.64 76	98.03 28	98.44 31	89.06 153	99.15 68	95.42 15	99.67 22	99.50 21

V14		95.05 81	95.75 63	92.94 163	96.94 125	80.21 207	95.03 83	97.50 92	92.62 77	97.84 34	98.28 38	88.87 155	99.13 73	95.03 20	99.64 27	99.48 24

HPM-MVS++		95.02 82	94.39 105	96.91 34	97.88 81	93.58 33	94.09 117	96.99 133	91.05 124	92.40 212	95.22 191	91.03 118	99.25 60	92.11 95	98.69 134	97.90 150

APD-MVS		95.00 83	94.69 97	95.93 55	97.38 107	90.88 65	94.59 99	97.81 62	89.22 160	95.46 127	96.17 156	93.42 62	99.34 48	89.30 147	98.87 114	97.56 175
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PMVS	87.21 14	94.97 84	95.33 79	93.91 131	98.97 13	97.16 2	95.54 66	95.85 195	96.47 19	93.40 186	97.46 73	95.31 27	95.47 309	86.18 200	98.78 127	89.11 343
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
TSAR-MVS + MP.		94.96 85	94.75 95	95.57 71	98.86 19	88.69 90	96.37 39	96.81 149	85.23 229	94.75 152	97.12 92	91.85 96	99.40 36	93.45 57	98.33 162	98.62 109

v15		94.93 86	95.62 67	92.86 168	96.83 131	80.01 220	94.84 90	97.48 93	92.36 82	97.76 36	98.20 40	88.61 156	99.11 76	94.86 22	99.62 30	99.46 25

SixPastTwentyTwo		94.91 87	95.21 85	93.98 126	98.52 41	83.19 175	95.93 53	94.84 219	94.86 35	98.49 17	98.74 16	81.45 245	99.60 8	94.69 25	99.39 64	99.15 49

FIs		94.90 88	95.35 76	93.55 140	98.28 57	81.76 189	95.33 71	98.14 28	93.05 66	97.07 56	97.18 88	87.65 179	99.29 53	91.72 106	99.69 16	99.61 11

Regformer-4		94.90 88	94.67 99	95.59 70	92.78 295	89.02 84	92.39 177	95.91 192	94.50 40	96.41 79	95.56 179	92.10 90	99.01 92	94.23 37	98.14 183	98.74 100

AllTest		94.88 90	94.51 103	96.00 50	98.02 74	92.17 45	95.26 74	98.43 8	90.48 136	95.04 144	96.74 112	92.54 84	97.86 229	85.11 211	98.98 104	97.98 142

Regformer-2		94.86 91	94.55 102	95.77 62	92.83 293	89.98 70	91.87 200	96.40 171	94.38 44	96.19 96	95.04 198	92.47 87	99.04 86	93.49 54	98.31 164	98.28 124

FMVSNet1		94.84 92	95.13 88	93.97 127	97.60 98	84.29 159	95.99 49	96.56 161	92.38 81	97.03 61	98.53 23	90.12 136	98.98 94	88.78 161	99.16 86	98.65 105

ANet_high		94.83 93	96.28 35	90.47 239	96.65 140	73.16 311	94.33 112	98.74 5	96.39 21	98.09 27	98.93 8	93.37 64	98.70 153	90.38 124	99.68 19	99.53 16

v17		94.80 94	95.46 71	92.83 169	96.76 136	80.02 218	94.85 88	97.40 99	92.23 89	97.45 45	98.04 44	88.46 160	99.06 81	94.56 27	99.40 62	99.41 28

3Dnovator	92.54 3	94.80 94	94.90 92	94.47 112	95.47 229	87.06 121	96.63 25	97.28 116	91.82 106	94.34 164	97.41 76	90.60 129	98.65 159	92.47 90	98.11 187	97.70 165

v16		94.79 96	95.44 74	92.83 169	96.73 137	80.03 216	94.85 88	97.41 98	92.23 89	97.41 49	98.04 44	88.40 162	99.06 81	94.56 27	99.30 71	99.41 28

CPTT-MVS		94.74 97	94.12 116	96.60 40	98.15 66	93.01 39	95.84 57	97.66 74	89.21 161	93.28 190	95.46 182	88.89 154	98.98 94	89.80 141	98.82 122	97.80 159

XVG-OURS		94.72 98	94.12 116	96.50 44	98.00 76	94.23 13	91.48 216	98.17 26	90.72 130	95.30 131	96.47 126	87.94 176	96.98 271	91.41 115	97.61 213	98.30 123

CSCG		94.69 99	94.75 95	94.52 108	97.55 101	87.87 109	95.01 84	97.57 83	92.68 73	96.20 94	93.44 250	91.92 95	98.78 136	89.11 155	99.24 78	96.92 202

v10		94.68 100	95.27 83	92.90 166	96.57 149	80.15 209	94.65 97	97.57 83	90.68 132	97.43 46	98.00 48	88.18 165	99.15 68	94.84 24	99.55 43	99.41 28

v8		94.65 101	95.29 81	92.74 174	96.65 140	79.77 227	94.59 99	97.17 122	91.86 101	97.47 44	97.93 51	88.16 167	99.08 78	94.32 32	99.47 49	99.38 32

v18		94.63 102	95.26 84	92.74 174	96.60 147	79.81 225	94.64 98	97.37 101	91.87 100	97.26 52	97.91 54	88.13 168	99.04 86	94.30 34	99.24 78	99.38 32

canonicalmvs		94.59 103	94.69 97	94.30 119	95.60 225	87.03 122	95.59 63	98.24 21	91.56 115	95.21 137	92.04 281	94.95 40	98.66 157	91.45 114	97.57 214	97.20 192

CNVR-MVS		94.58 104	94.29 110	95.46 76	96.94 125	89.35 81	91.81 209	96.80 150	89.66 152	93.90 175	95.44 184	92.80 80	98.72 147	92.74 80	98.52 144	98.32 120

Regformer-1		94.55 105	94.33 109	95.19 84	92.83 293	88.54 97	91.87 200	95.84 196	93.99 48	95.95 105	95.04 198	92.00 92	98.79 132	93.14 71	98.31 164	98.23 126

EG-PatchMatch MVS		94.54 106	94.67 99	94.14 122	97.87 82	86.50 127	92.00 191	96.74 155	88.16 191	96.93 63	97.61 65	93.04 74	97.90 218	91.60 110	98.12 186	98.03 139

IS-MVSNet		94.49 107	94.35 108	94.92 91	98.25 59	86.46 130	97.13 15	94.31 233	96.24 23	96.28 89	96.36 141	82.88 231	99.35 47	88.19 171	99.52 46	98.96 77

Baseline_NR-MVSNet		94.47 108	95.09 90	92.60 182	98.50 44	80.82 200	92.08 188	96.68 157	93.82 53	96.29 86	98.56 22	90.10 139	97.75 241	90.10 137	99.66 24	99.24 43

VDD-MVS		94.37 109	94.37 107	94.40 116	97.49 104	86.07 140	93.97 121	93.28 251	94.49 41	96.24 90	97.78 58	87.99 175	98.79 132	88.92 158	99.14 88	98.34 119

EI-MVSNet-Vis-set		94.36 110	94.28 111	94.61 100	92.55 297	85.98 142	92.44 174	94.69 226	93.70 55	96.12 99	95.81 168	91.24 110	98.86 120	93.76 49	98.22 176	98.98 76

EI-MVSNet-UG-set		94.35 111	94.27 113	94.59 105	92.46 298	85.87 144	92.42 176	94.69 226	93.67 59	96.13 98	95.84 167	91.20 113	98.86 120	93.78 46	98.23 174	99.03 67

PHI-MVS		94.34 112	93.80 125	95.95 52	95.65 221	91.67 56	94.82 91	97.86 57	87.86 196	93.04 199	94.16 229	91.58 100	98.78 136	90.27 130	98.96 107	97.41 180

Regformer-3		94.28 113	94.23 115	94.46 113	92.78 295	86.28 136	92.39 177	94.70 225	93.69 58	95.97 103	95.56 179	91.34 105	98.48 182	93.45 57	98.14 183	98.62 109

tfpnnormal		94.27 114	94.87 94	92.48 188	97.71 91	80.88 199	94.55 105	95.41 211	93.70 55	96.67 72	97.72 61	91.40 104	98.18 208	87.45 180	99.18 85	98.36 118

HQP_MVS		94.26 115	93.93 119	95.23 83	97.71 91	88.12 105	94.56 103	97.81 62	91.74 111	93.31 187	95.59 174	86.93 197	98.95 101	89.26 151	98.51 146	98.60 111

OMC-MVS		94.22 116	93.69 133	95.81 60	97.25 111	91.27 58	92.27 182	97.40 99	87.10 209	94.56 157	95.42 185	93.74 55	98.11 211	86.62 192	98.85 115	98.06 137

LCM-MVSNet-Re		94.20 117	94.58 101	93.04 154	95.91 209	83.13 177	93.79 130	99.19 2	92.00 96	98.84 5	98.04 44	93.64 56	99.02 90	81.28 246	98.54 142	96.96 200

DeepPCF-MVS	90.46 6	94.20 117	93.56 138	96.14 47	95.96 205	92.96 40	89.48 275	97.46 95	85.14 231	96.23 91	95.42 185	93.19 70	98.08 212	90.37 125	98.76 129	97.38 185

NCCC		94.08 119	93.54 139	95.70 67	96.49 154	89.90 72	92.39 177	96.91 143	90.64 133	92.33 218	94.60 214	90.58 130	98.96 99	90.21 132	97.70 208	98.23 126

testing_2		94.03 120	94.38 106	93.00 158	96.79 135	81.41 194	92.87 157	96.96 135	85.88 224	97.06 59	97.92 52	91.18 116	98.71 152	91.72 106	99.04 100	98.87 85

VDDNet		94.03 120	94.27 113	93.31 148	98.87 18	82.36 183	95.51 67	91.78 279	97.19 10	96.32 83	98.60 20	84.24 224	98.75 142	87.09 185	98.83 119	98.81 92

EPP-MVSNet		93.91 122	93.68 134	94.59 105	98.08 71	85.55 150	97.44 10	94.03 238	94.22 45	94.94 147	96.19 154	82.07 239	99.57 13	87.28 184	98.89 109	98.65 105

Effi-MVS+-dtu		93.90 123	92.60 161	97.77 4	94.74 253	96.67 4	94.00 119	95.41 211	89.94 147	91.93 226	92.13 279	90.12 136	98.97 98	87.68 177	97.48 220	97.67 168

IterMVS-LS		93.78 124	94.28 111	92.27 194	96.27 177	79.21 244	91.87 200	96.78 151	91.77 109	96.57 77	97.07 94	87.15 191	98.74 145	91.99 100	99.03 102	98.86 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS_fast	89.96 7	93.73 125	93.44 141	94.60 104	96.14 187	87.90 108	93.36 139	97.14 123	85.53 228	93.90 175	95.45 183	91.30 108	98.59 165	89.51 144	98.62 136	97.31 188
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v7		93.66 126	93.97 118	92.73 176	96.55 150	80.15 209	92.54 164	96.99 133	87.36 202	95.99 102	96.48 124	88.18 165	98.94 104	93.35 63	98.31 164	99.09 57

MVS_111021_LR		93.66 126	93.28 145	94.80 95	96.25 180	90.95 63	90.21 251	95.43 210	87.91 193	93.74 179	94.40 220	92.88 78	96.38 293	90.39 123	98.28 168	97.07 195

MVS_111021_HR		93.63 128	93.42 142	94.26 120	96.65 140	86.96 123	89.30 281	96.23 183	88.36 185	93.57 181	94.60 214	93.45 59	97.77 238	90.23 131	98.38 155	98.03 139

v6		93.59 129	93.93 119	92.56 184	96.65 140	79.77 227	92.50 170	96.40 171	88.55 179	95.94 107	96.23 149	88.13 168	98.87 117	92.46 91	98.50 148	99.06 63

v1neww		93.58 130	93.92 121	92.56 184	96.64 144	79.77 227	92.50 170	96.41 169	88.55 179	95.93 108	96.24 147	88.08 170	98.87 117	92.45 92	98.50 148	99.05 64

v7new		93.58 130	93.92 121	92.56 184	96.64 144	79.77 227	92.50 170	96.41 169	88.55 179	95.93 108	96.24 147	88.08 170	98.87 117	92.45 92	98.50 148	99.05 64

v1144		93.50 132	93.81 124	92.57 183	96.28 176	79.61 233	91.86 204	96.96 135	86.95 212	95.91 111	96.32 142	87.65 179	98.96 99	93.51 53	98.88 111	99.13 51

v1192		93.49 133	93.78 126	92.62 181	96.16 186	79.62 232	91.83 208	97.22 120	86.07 220	96.10 100	96.38 139	87.22 189	99.02 90	94.14 40	98.88 111	99.22 44

WR-MVS		93.49 133	93.72 131	92.80 172	97.57 100	80.03 216	90.14 255	95.68 200	93.70 55	96.62 74	95.39 188	87.21 190	99.04 86	87.50 179	99.64 27	99.33 37

v1		93.43 135	93.77 127	92.41 190	96.37 163	79.24 239	91.84 205	96.38 174	88.33 186	95.87 112	96.22 152	87.45 183	98.89 107	92.61 86	98.83 119	99.09 57

V42		93.43 135	93.58 137	92.97 160	95.34 236	81.22 195	92.67 161	96.49 166	87.25 205	96.20 94	96.37 140	87.32 188	98.85 122	92.39 94	98.21 177	98.85 89

v1141		93.42 137	93.76 128	92.40 192	96.37 163	79.24 239	91.84 205	96.38 174	88.33 186	95.86 113	96.23 149	87.41 185	98.89 107	92.61 86	98.82 122	99.08 60

divwei89l23v2f112		93.42 137	93.76 128	92.41 190	96.37 163	79.24 239	91.84 205	96.38 174	88.33 186	95.86 113	96.23 149	87.41 185	98.89 107	92.61 86	98.83 119	99.09 57

K. test v3		93.37 139	93.27 146	93.66 136	98.05 72	82.62 181	94.35 111	86.62 312	96.05 27	97.51 42	98.85 12	76.59 280	99.65 3	93.21 68	98.20 179	98.73 102

PM-MVS		93.33 140	92.67 159	95.33 79	96.58 148	94.06 16	92.26 183	92.18 271	85.92 223	96.22 92	96.61 119	85.64 218	95.99 301	90.35 127	98.23 174	95.93 243

v1240		93.29 141	93.71 132	92.06 201	96.01 196	77.89 261	91.81 209	97.37 101	85.12 232	96.69 71	96.40 132	86.67 203	99.07 80	94.51 29	98.76 129	99.22 44

test_prior3		93.29 141	92.85 152	94.61 100	95.95 206	87.23 117	90.21 251	97.36 107	89.33 157	90.77 247	94.81 205	90.41 132	98.68 155	88.21 169	98.55 140	97.93 146

v2v482		93.29 141	93.63 135	92.29 193	96.35 171	78.82 250	91.77 211	96.28 179	88.45 182	95.70 119	96.26 145	86.02 213	98.90 105	93.02 75	98.81 125	99.14 50

alignmvs		93.26 144	92.85 152	94.50 109	95.70 217	87.45 114	93.45 137	95.76 197	91.58 114	95.25 134	92.42 273	81.96 241	98.72 147	91.61 109	97.87 202	97.33 187

v1921920		93.26 144	93.61 136	92.19 196	96.04 195	78.31 256	91.88 199	97.24 118	85.17 230	96.19 96	96.19 154	86.76 202	99.05 83	94.18 39	98.84 116	99.22 44

MSLP-MVS++		93.25 146	93.88 123	91.37 220	96.34 172	82.81 180	93.11 148	97.74 69	89.37 155	94.08 171	95.29 190	90.40 134	96.35 295	90.35 127	98.25 172	94.96 270

GBi-Net		93.21 147	92.96 149	93.97 127	95.40 231	84.29 159	95.99 49	96.56 161	88.63 175	95.10 140	98.53 23	81.31 248	98.98 94	86.74 188	98.38 155	98.65 105

test1		93.21 147	92.96 149	93.97 127	95.40 231	84.29 159	95.99 49	96.56 161	88.63 175	95.10 140	98.53 23	81.31 248	98.98 94	86.74 188	98.38 155	98.65 105

v144192		93.20 149	93.54 139	92.16 198	96.05 192	78.26 257	91.95 192	97.14 123	84.98 236	95.96 104	96.11 157	87.08 193	99.04 86	93.79 45	98.84 116	99.17 47

VPNet		93.08 150	93.76 128	91.03 229	98.60 31	75.83 284	91.51 215	95.62 201	91.84 102	95.74 117	97.10 93	89.31 149	98.32 194	85.07 213	99.06 95	98.93 80

UGNet		93.08 150	92.50 164	94.79 96	93.87 278	87.99 107	95.07 81	94.26 235	90.64 133	87.33 307	97.67 63	86.89 200	98.49 179	88.10 173	98.71 132	97.91 149
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
mvs-test1		93.07 152	91.80 177	96.89 35	94.74 253	95.83 7	92.17 186	95.41 211	89.94 147	89.85 269	90.59 306	90.12 136	98.88 111	87.68 177	95.66 269	95.97 241

TSAR-MVS + GP.		93.07 152	92.41 166	95.06 88	95.82 211	90.87 66	90.97 228	92.61 266	88.04 192	94.61 156	93.79 241	88.08 170	97.81 234	89.41 146	98.39 154	96.50 222

MVS_0304		92.99 154	92.54 162	94.35 118	94.67 259	86.06 141	91.16 223	97.92 55	90.01 146	88.33 294	94.41 218	87.02 194	99.22 62	90.36 126	99.00 103	97.76 161

EI-MVSNet		92.99 154	93.26 147	92.19 196	92.12 306	79.21 244	92.32 180	94.67 228	91.77 109	95.24 135	95.85 165	87.14 192	98.49 179	91.99 100	98.26 170	98.86 86

MCST-MVS		92.91 156	92.51 163	94.10 123	97.52 102	85.72 148	91.36 220	97.13 125	80.33 273	92.91 203	94.24 225	91.23 111	98.72 147	89.99 139	97.93 199	97.86 154

QAPM		92.88 157	92.77 154	93.22 150	95.82 211	83.31 172	96.45 34	97.35 109	83.91 244	93.75 177	96.77 108	89.25 150	98.88 111	84.56 219	97.02 234	97.49 177

v148		92.87 158	93.29 143	91.62 212	96.25 180	77.72 263	91.28 221	95.05 215	89.69 151	95.93 108	96.04 159	87.34 187	98.38 190	90.05 138	97.99 196	98.78 96

Effi-MVS+		92.79 159	92.74 156	92.94 163	95.10 242	83.30 173	94.00 119	97.53 88	91.36 118	89.35 278	90.65 305	94.01 54	98.66 157	87.40 182	95.30 279	96.88 206

FMVSNet2		92.78 160	92.73 157	92.95 162	95.40 231	81.98 186	94.18 116	95.53 208	88.63 175	96.05 101	97.37 79	81.31 248	98.81 130	87.38 183	98.67 135	98.06 137

Fast-Effi-MVS+-dtu		92.77 161	92.16 169	94.58 107	94.66 260	88.25 103	92.05 189	96.65 158	89.62 153	90.08 260	91.23 291	92.56 83	98.60 163	86.30 199	96.27 259	96.90 204

LF4IMVS		92.72 162	92.02 172	94.84 94	95.65 221	91.99 49	92.92 154	96.60 160	85.08 234	92.44 211	93.62 243	86.80 201	96.35 295	86.81 187	98.25 172	96.18 235

train_agg		92.71 163	91.83 175	95.35 77	96.45 160	89.46 74	90.60 239	96.92 140	79.37 282	90.49 254	94.39 221	91.20 113	98.88 111	88.66 164	98.43 151	97.72 163

VNet		92.67 164	92.96 149	91.79 207	96.27 177	80.15 209	91.95 192	94.98 216	92.19 92	94.52 159	96.07 158	87.43 184	97.39 258	84.83 215	98.38 155	97.83 156

CDPH-MVS		92.67 164	91.83 175	95.18 85	96.94 125	88.46 101	90.70 236	97.07 127	77.38 297	92.34 217	95.08 196	92.67 82	98.88 111	85.74 202	98.57 139	98.20 129

agg_prior1		92.60 166	91.76 178	95.10 87	96.20 182	88.89 87	90.37 246	96.88 145	79.67 279	90.21 257	94.41 218	91.30 108	98.78 136	88.46 168	98.37 160	97.64 170

Anonymous202405211		92.58 167	92.50 164	92.83 169	96.55 150	83.22 174	92.43 175	91.64 280	94.10 47	95.59 122	96.64 117	81.88 243	97.50 250	85.12 210	98.52 144	97.77 160

XXY-MVS		92.58 167	93.16 148	90.84 234	97.75 86	79.84 223	91.87 200	96.22 185	85.94 222	95.53 124	97.68 62	92.69 81	94.48 321	83.21 229	97.51 215	98.21 128

MVS_Test		92.57 169	93.29 143	90.40 241	93.53 284	75.85 282	92.52 167	96.96 135	88.73 173	92.35 215	96.70 115	90.77 121	98.37 193	92.53 89	95.49 273	96.99 199

agg_prior3		92.56 170	91.62 180	95.35 77	96.39 162	89.45 76	90.61 238	96.82 148	78.82 290	90.03 262	94.14 230	90.72 126	98.88 111	88.66 164	98.43 151	97.72 163

casdiffmvs		92.55 171	92.40 167	93.01 157	94.72 257	83.36 171	94.54 106	97.04 128	83.00 254	89.97 265	96.95 97	88.23 164	98.76 141	93.22 67	93.95 303	96.92 202

TAPA-MVS	88.58 10	92.49 172	91.75 179	94.73 97	96.50 153	89.69 73	92.91 155	97.68 73	78.02 294	92.79 204	94.10 231	90.85 120	97.96 217	84.76 217	98.16 181	96.54 213
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ab-mvs		92.40 173	92.62 160	91.74 208	97.02 122	81.65 190	95.84 57	95.50 209	86.95 212	92.95 202	97.56 67	90.70 127	97.50 250	79.63 265	97.43 222	96.06 239

CANet		92.38 174	91.99 173	93.52 144	93.82 280	83.46 170	91.14 224	97.00 131	89.81 150	86.47 312	94.04 233	87.90 177	99.21 63	89.50 145	98.27 169	97.90 150

DP-MVS Recon		92.31 175	91.88 174	93.60 138	97.18 114	86.87 124	91.10 226	97.37 101	84.92 237	92.08 222	94.08 232	88.59 157	98.20 205	83.50 226	98.14 183	95.73 249

F-COLMAP		92.28 176	91.06 196	95.95 52	97.52 102	91.90 51	93.53 135	97.18 121	83.98 243	88.70 290	94.04 233	88.41 161	98.55 175	80.17 259	95.99 263	97.39 183

OpenMVS	89.45 8	92.27 177	92.13 171	92.68 178	94.53 265	84.10 164	95.70 60	97.03 129	82.44 260	91.14 244	96.42 130	88.47 159	98.38 190	85.95 201	97.47 221	95.55 259

MVSFormer		92.18 178	92.23 168	92.04 202	94.74 253	80.06 214	97.15 13	97.37 101	88.98 162	88.83 282	92.79 260	77.02 275	99.60 8	96.41 6	96.75 243	96.46 224

diffmvs		92.17 179	92.73 157	90.49 238	92.22 301	77.47 266	92.53 166	95.74 199	90.43 139	88.32 295	96.48 124	89.76 144	97.38 260	92.63 84	96.50 256	96.63 212

HQP-MVS		92.09 180	91.49 185	93.88 132	96.36 168	84.89 155	91.37 217	97.31 111	87.16 206	88.81 284	93.40 251	84.76 221	98.60 163	86.55 194	97.73 205	98.14 134

DELS-MVS		92.05 181	92.16 169	91.72 209	94.44 266	80.13 212	87.62 301	97.25 117	87.34 204	92.22 220	93.18 256	89.54 147	98.73 146	89.67 143	98.20 179	96.30 230
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
TinyColmap		92.00 182	92.76 155	89.71 254	95.62 224	77.02 271	90.72 235	96.17 187	87.70 199	95.26 133	96.29 143	92.54 84	96.45 289	81.77 241	98.77 128	95.66 252

CLD-MVS		91.82 183	91.41 187	93.04 154	96.37 163	83.65 169	86.82 314	97.29 114	84.65 240	92.27 219	89.67 315	92.20 88	97.85 232	83.95 223	99.47 49	97.62 171
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CNLPA		91.72 184	91.20 193	93.26 149	96.17 185	91.02 61	91.14 224	95.55 207	90.16 144	90.87 246	93.56 246	86.31 209	94.40 324	79.92 264	97.12 230	94.37 284

PVSNet_Blended_VisFu		91.63 185	91.20 193	92.94 163	97.73 90	83.95 166	92.14 187	97.46 95	78.85 289	92.35 215	94.98 201	84.16 225	99.08 78	86.36 198	96.77 242	95.79 247

AdaColmap		91.63 185	91.36 189	92.47 189	95.56 226	86.36 134	92.24 185	96.27 180	88.88 166	89.90 268	92.69 264	91.65 99	98.32 194	77.38 289	97.64 211	92.72 319

pmmvs-eth3d		91.54 187	90.73 204	93.99 125	95.76 215	87.86 110	90.83 232	93.98 240	78.23 293	94.02 173	96.22 152	82.62 236	96.83 277	86.57 193	98.33 162	97.29 189

API-MVS		91.52 188	91.61 181	91.26 224	94.16 271	86.26 137	94.66 96	94.82 220	91.17 122	92.13 221	91.08 294	90.03 142	97.06 269	79.09 270	97.35 226	90.45 340

test_normal		91.49 189	91.44 186	91.62 212	95.21 239	79.44 235	90.08 258	93.84 242	82.60 256	94.37 163	94.74 210	86.66 204	98.46 185	88.58 167	96.92 237	96.95 201

xiu_mvs_v1_base_debu		91.47 190	91.52 182	91.33 221	95.69 218	81.56 191	89.92 263	96.05 189	83.22 248	91.26 234	90.74 300	91.55 101	98.82 125	89.29 148	95.91 264	93.62 304

xiu_mvs_v1_base		91.47 190	91.52 182	91.33 221	95.69 218	81.56 191	89.92 263	96.05 189	83.22 248	91.26 234	90.74 300	91.55 101	98.82 125	89.29 148	95.91 264	93.62 304

xiu_mvs_v1_base_debi		91.47 190	91.52 182	91.33 221	95.69 218	81.56 191	89.92 263	96.05 189	83.22 248	91.26 234	90.74 300	91.55 101	98.82 125	89.29 148	95.91 264	93.62 304

DI_MVS_plusplus_test		91.42 193	91.41 187	91.46 217	95.34 236	79.06 246	90.58 241	93.74 244	82.59 257	94.69 155	94.76 209	86.54 207	98.44 187	87.93 175	96.49 257	96.87 207

Test4		91.41 194	91.25 192	91.89 204	95.35 235	80.32 205	90.97 228	96.92 140	81.96 263	95.11 139	93.81 240	81.34 247	98.48 182	88.71 163	97.08 231	96.87 207

LFMVS		91.33 195	91.16 195	91.82 206	96.27 177	79.36 237	95.01 84	85.61 322	96.04 28	94.82 150	97.06 95	72.03 289	98.46 185	84.96 214	98.70 133	97.65 169

Fast-Effi-MVS+		91.28 196	90.86 199	92.53 187	95.45 230	82.53 182	89.25 284	96.52 165	85.00 235	89.91 267	88.55 322	92.94 75	98.84 123	84.72 218	95.44 276	96.22 233

MDA-MVSNet-bldmvs		91.04 197	90.88 198	91.55 215	94.68 258	80.16 208	85.49 323	92.14 274	90.41 141	94.93 148	95.79 169	85.10 219	96.93 273	85.15 208	94.19 302	97.57 173

PAPM_NR		91.03 198	90.81 201	91.68 211	96.73 137	81.10 197	93.72 132	96.35 178	88.19 190	88.77 288	92.12 280	85.09 220	97.25 263	82.40 237	93.90 304	96.68 211

MSDG		90.82 199	90.67 205	91.26 224	94.16 271	83.08 178	86.63 317	96.19 186	90.60 135	91.94 225	91.89 282	89.16 151	95.75 304	80.96 253	94.51 295	94.95 271

test20.03		90.80 200	90.85 200	90.63 236	95.63 223	79.24 239	89.81 269	92.87 258	89.90 149	94.39 160	96.40 132	85.77 214	95.27 316	73.86 309	99.05 97	97.39 183

FMVSNet3		90.78 201	90.32 208	92.16 198	93.03 291	79.92 222	92.54 164	94.95 217	86.17 219	95.10 140	96.01 160	69.97 295	98.75 142	86.74 188	98.38 155	97.82 158

X-MVStestdata		90.70 202	88.45 230	97.44 13	98.56 34	93.99 22	96.50 32	97.95 51	94.58 38	94.38 161	26.89 360	94.56 46	99.39 40	93.57 51	99.05 97	98.93 80

BH-untuned		90.68 203	90.90 197	90.05 251	95.98 204	79.57 234	90.04 259	94.94 218	87.91 193	94.07 172	93.00 257	87.76 178	97.78 237	79.19 269	95.17 282	92.80 317

114514_t		90.51 204	89.80 213	92.63 180	98.00 76	82.24 184	93.40 138	97.29 114	65.84 347	89.40 277	94.80 208	86.99 195	98.75 142	83.88 224	98.61 137	96.89 205

BH-RMVSNet		90.47 205	90.44 206	90.56 237	95.21 239	78.65 254	89.15 285	93.94 241	88.21 189	92.74 205	94.22 226	86.38 208	97.88 226	78.67 278	95.39 277	95.14 266

Vis-MVSNet (Re-imp)		90.42 206	90.16 209	91.20 227	97.66 97	77.32 268	94.33 112	87.66 305	91.20 121	92.99 200	95.13 194	75.40 282	98.28 196	77.86 282	99.19 83	97.99 141

PLC	85.34 15	90.40 207	88.92 224	94.85 93	96.53 152	90.02 69	91.58 213	96.48 167	80.16 274	86.14 314	92.18 277	85.73 215	98.25 201	76.87 292	94.61 294	96.30 230
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
testgi		90.38 208	91.34 190	87.50 299	97.49 104	71.54 321	89.43 276	95.16 214	88.38 184	94.54 158	94.68 213	92.88 78	93.09 335	71.60 324	97.85 203	97.88 152

mvs_anonymous		90.37 209	91.30 191	87.58 298	92.17 305	68.00 332	89.84 268	94.73 224	83.82 246	93.22 196	97.40 77	87.54 181	97.40 257	87.94 174	95.05 284	97.34 186

PVSNet_BlendedMVS		90.35 210	89.96 211	91.54 216	94.81 249	78.80 252	90.14 255	96.93 138	79.43 280	88.68 291	95.06 197	86.27 210	98.15 209	80.27 256	98.04 193	97.68 167

UnsupCasMVSNet_eth		90.33 211	90.34 207	90.28 243	94.64 261	80.24 206	89.69 271	95.88 193	85.77 226	93.94 174	95.69 173	81.99 240	92.98 336	84.21 221	91.30 331	97.62 171

MAR-MVS		90.32 212	88.87 226	94.66 99	94.82 248	91.85 52	94.22 115	94.75 223	80.91 268	87.52 306	88.07 326	86.63 205	97.87 228	76.67 293	96.21 261	94.25 286
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
1121		90.26 213	89.23 216	93.34 146	97.15 117	87.40 115	91.94 194	94.39 231	67.88 341	91.02 245	94.91 203	86.91 199	98.59 165	81.17 249	97.71 207	94.02 293

IterMVS		90.18 214	90.16 209	90.21 248	93.15 289	75.98 281	87.56 304	92.97 257	86.43 217	94.09 170	96.40 132	78.32 265	97.43 254	87.87 176	94.69 292	97.23 190
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		90.16 215	89.05 220	93.49 145	96.49 154	86.37 133	90.34 248	92.55 267	80.84 271	92.99 200	94.57 216	81.94 242	98.20 205	73.51 310	98.21 177	95.90 245

0601test		90.11 216	89.73 215	91.26 224	94.09 274	79.82 224	90.44 244	92.65 265	90.90 126	93.19 197	93.30 253	73.90 284	98.03 213	82.23 238	96.87 239	95.93 243

Patchmtry		90.11 216	89.92 212	90.66 235	90.35 326	77.00 272	92.96 153	92.81 259	90.25 143	94.74 153	96.93 100	67.11 301	97.52 249	85.17 206	98.98 104	97.46 178

MVP-Stereo		90.07 218	88.92 224	93.54 142	96.31 174	86.49 128	90.93 230	95.59 205	79.80 275	91.48 229	95.59 174	80.79 253	97.39 258	78.57 279	91.19 332	96.76 210
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CANet_DTU		89.85 219	89.17 218	91.87 205	92.20 304	80.02 218	90.79 233	95.87 194	86.02 221	82.53 336	91.77 284	80.01 256	98.57 168	85.66 203	97.70 208	97.01 198

EPNet		89.80 220	88.25 233	94.45 114	83.91 360	86.18 138	93.87 128	87.07 310	91.16 123	80.64 347	94.72 211	78.83 260	98.89 107	85.17 206	98.89 109	98.28 124
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		89.55 221	88.22 236	93.53 143	95.37 234	86.49 128	89.26 282	93.59 246	79.76 277	91.15 243	92.31 275	77.12 274	98.38 190	77.51 287	97.92 200	95.71 250
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		89.54 222	89.80 213	88.76 278	94.88 245	72.47 318	89.60 272	92.44 269	85.82 225	89.48 276	95.98 161	82.85 232	97.74 242	81.87 240	95.27 280	96.08 238

OpenMVS_ROB	85.12 16	89.52 223	89.05 220	90.92 233	94.58 264	81.21 196	91.10 226	93.41 250	77.03 300	93.41 184	93.99 237	83.23 228	97.80 235	79.93 263	94.80 289	93.74 301

MVSTER		89.32 224	88.75 227	91.03 229	90.10 328	76.62 274	90.85 231	94.67 228	82.27 261	95.24 135	95.79 169	61.09 336	98.49 179	90.49 120	98.26 170	97.97 145

RPMNet		89.30 225	89.00 222	90.22 246	91.01 315	78.93 247	92.52 167	87.85 304	91.91 98	89.10 279	96.89 103	68.84 296	97.64 246	90.17 133	92.70 320	94.08 288

PatchMatch-RL		89.18 226	88.02 241	92.64 179	95.90 210	92.87 42	88.67 294	91.06 284	80.34 272	90.03 262	91.67 286	83.34 227	94.42 323	76.35 296	94.84 288	90.64 339

jason		89.17 227	88.32 231	91.70 210	95.73 216	80.07 213	88.10 298	93.22 253	71.98 323	90.09 259	92.79 260	78.53 264	98.56 169	87.43 181	97.06 232	96.46 224
jason: jason.
PCF-MVS	84.52 17	89.12 228	87.71 247	93.34 146	96.06 191	85.84 145	86.58 318	97.31 111	68.46 339	93.61 180	93.89 238	87.51 182	98.52 177	67.85 337	98.11 187	95.66 252
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
USDC		89.02 229	89.08 219	88.84 277	95.07 243	74.50 298	88.97 288	96.39 173	73.21 317	93.27 191	96.28 144	82.16 238	96.39 292	77.55 286	98.80 126	95.62 254

xiu_mvs_v2_base		89.00 230	89.19 217	88.46 289	94.86 247	74.63 295	86.97 311	95.60 202	80.88 269	87.83 301	88.62 321	91.04 117	98.81 130	82.51 236	94.38 296	91.93 330

new-patchmatchnet		88.97 231	90.79 202	83.50 328	94.28 270	55.83 358	85.34 324	93.56 247	86.18 218	95.47 125	95.73 172	83.10 229	96.51 286	85.40 205	98.06 191	98.16 132

pmmvs4		88.95 232	87.70 248	92.70 177	94.30 269	85.60 149	87.22 308	92.16 273	74.62 307	89.75 273	94.19 227	77.97 268	96.41 291	82.71 233	96.36 258	96.09 237

N_pmnet		88.90 233	87.25 253	93.83 134	94.40 268	93.81 31	84.73 327	87.09 309	79.36 284	93.26 192	92.43 272	79.29 259	91.68 341	77.50 288	97.22 228	96.00 240

PS-MVSNAJ		88.86 234	88.99 223	88.48 288	94.88 245	74.71 293	86.69 315	95.60 202	80.88 269	87.83 301	87.37 334	90.77 121	98.82 125	82.52 235	94.37 297	91.93 330

Patchmatch-RL test		88.81 235	88.52 229	89.69 257	95.33 238	79.94 221	86.22 319	92.71 263	78.46 291	95.80 115	94.18 228	66.25 309	95.33 314	89.22 153	98.53 143	93.78 299

Anonymous20231206		88.77 236	88.29 232	90.20 249	96.31 174	78.81 251	89.56 274	93.49 249	74.26 311	92.38 213	95.58 177	82.21 237	95.43 311	72.07 319	98.75 131	96.34 228

PVSNet_Blended		88.74 237	88.16 238	90.46 240	94.81 249	78.80 252	86.64 316	96.93 138	74.67 306	88.68 291	89.18 319	86.27 210	98.15 209	80.27 256	96.00 262	94.44 283

ppachtmachnet_test		88.61 238	88.64 228	88.50 287	91.76 309	70.99 324	84.59 330	92.98 256	79.30 286	92.38 213	93.53 247	79.57 258	97.45 253	86.50 196	97.17 229	97.07 195

UnsupCasMVSNet_bld		88.50 239	88.03 240	89.90 252	95.52 228	78.88 249	87.39 306	94.02 239	79.32 285	93.06 198	94.02 235	80.72 254	94.27 326	75.16 306	93.08 316	96.54 213

testmv		88.46 240	88.11 239	89.48 258	96.00 197	76.14 278	86.20 320	93.75 243	84.48 241	93.57 181	95.52 181	80.91 252	95.09 317	63.97 346	98.61 137	97.22 191

1112_ss		88.42 241	87.41 250	91.45 218	96.69 139	80.99 198	89.72 270	96.72 156	73.37 316	87.00 310	90.69 303	77.38 272	98.20 205	81.38 245	93.72 307	95.15 265

lupinMVS		88.34 242	87.31 251	91.45 218	94.74 253	80.06 214	87.23 307	92.27 270	71.10 327	88.83 282	91.15 292	77.02 275	98.53 176	86.67 191	96.75 243	95.76 248

view600		88.32 243	87.94 242	89.46 260	96.49 154	73.31 306	93.95 122	84.46 335	93.02 67	94.18 165	92.68 265	63.33 326	98.56 169	75.87 300	97.50 216	96.51 215

view800		88.32 243	87.94 242	89.46 260	96.49 154	73.31 306	93.95 122	84.46 335	93.02 67	94.18 165	92.68 265	63.33 326	98.56 169	75.87 300	97.50 216	96.51 215

conf0.05thres1000		88.32 243	87.94 242	89.46 260	96.49 154	73.31 306	93.95 122	84.46 335	93.02 67	94.18 165	92.68 265	63.33 326	98.56 169	75.87 300	97.50 216	96.51 215

tfpn		88.32 243	87.94 242	89.46 260	96.49 154	73.31 306	93.95 122	84.46 335	93.02 67	94.18 165	92.68 265	63.33 326	98.56 169	75.87 300	97.50 216	96.51 215

YYNet1		88.17 247	88.24 234	87.93 294	92.21 303	73.62 303	80.75 345	88.77 294	82.51 259	94.99 146	95.11 195	82.70 234	93.70 330	83.33 227	93.83 305	96.48 223

MDA-MVSNet_test_wron		88.16 248	88.23 235	87.93 294	92.22 301	73.71 302	80.71 346	88.84 293	82.52 258	94.88 149	95.14 193	82.70 234	93.61 331	83.28 228	93.80 306	96.46 224

MS-PatchMatch		88.05 249	87.75 246	88.95 275	93.28 286	77.93 259	87.88 300	92.49 268	75.42 305	92.57 209	93.59 245	80.44 255	94.24 328	81.28 246	92.75 319	94.69 277

CR-MVSNet		87.89 250	87.12 257	90.22 246	91.01 315	78.93 247	92.52 167	92.81 259	73.08 318	89.10 279	96.93 100	67.11 301	97.64 246	88.80 160	92.70 320	94.08 288

pmmvs5		87.87 251	87.14 256	90.07 250	93.26 288	76.97 273	88.89 290	92.18 271	73.71 315	88.36 293	93.89 238	76.86 278	96.73 280	80.32 255	96.81 240	96.51 215

no-one		87.84 252	87.21 254	89.74 253	93.58 283	78.64 255	81.28 344	92.69 264	74.36 309	92.05 224	97.14 90	81.86 244	96.07 299	72.03 320	99.90 2	94.52 280

wuyk23d		87.83 253	90.79 202	78.96 339	90.46 324	88.63 92	92.72 159	90.67 287	91.65 113	98.68 11	97.64 64	96.06 14	77.53 360	59.84 350	99.41 61	70.73 357

FMVSNet5		87.82 254	86.56 268	91.62 212	92.31 300	79.81 225	93.49 136	94.81 222	83.26 247	91.36 232	96.93 100	52.77 355	97.49 252	76.07 297	98.03 194	97.55 176

GA-MVS		87.70 255	86.82 263	90.31 242	93.27 287	77.22 270	84.72 329	92.79 261	85.11 233	89.82 270	90.07 307	66.80 304	97.76 240	84.56 219	94.27 300	95.96 242

TR-MVS		87.70 255	87.17 255	89.27 269	94.11 273	79.26 238	88.69 293	91.86 277	81.94 264	90.69 250	89.79 312	82.82 233	97.42 255	72.65 317	91.98 328	91.14 335

thres600view7		87.66 257	87.10 259	89.36 267	96.05 192	73.17 310	92.72 159	85.31 325	91.89 99	93.29 189	90.97 295	63.42 322	98.39 188	73.23 312	96.99 235	96.51 215

PAPR		87.65 258	86.77 265	90.27 244	92.85 292	77.38 267	88.56 295	96.23 183	76.82 302	84.98 320	89.75 314	86.08 212	97.16 266	72.33 318	93.35 310	96.26 232

tfpn111		87.60 259	87.12 257	89.04 273	96.14 187	73.09 312	93.00 150	85.31 325	92.13 93	93.26 192	90.96 296	63.42 322	98.48 182	72.87 315	96.98 236	95.56 255

our_test_3		87.55 260	87.59 249	87.44 300	91.76 309	70.48 325	83.83 335	90.55 290	79.79 276	92.06 223	92.17 278	78.63 263	95.63 305	84.77 216	94.73 290	96.22 233

PatchT		87.51 261	88.17 237	85.55 313	90.64 319	66.91 336	92.02 190	86.09 315	92.20 91	89.05 281	97.16 89	64.15 318	96.37 294	89.21 154	92.98 318	93.37 310

Test_1112_low_res		87.50 262	86.58 267	90.25 245	96.80 134	77.75 262	87.53 305	96.25 181	69.73 335	86.47 312	93.61 244	75.67 281	97.88 226	79.95 261	93.20 312	95.11 267

conf200view11		87.41 263	86.89 261	88.97 274	96.14 187	73.09 312	93.00 150	85.31 325	92.13 93	93.26 192	90.96 296	63.42 322	98.28 196	71.27 327	96.54 251	95.56 255

EU-MVSNet		87.39 264	86.71 266	89.44 264	93.40 285	76.11 279	94.93 87	90.00 291	57.17 356	95.71 118	97.37 79	64.77 316	97.68 245	92.67 83	94.37 297	94.52 280

thres100view900		87.35 265	86.89 261	88.72 279	96.14 187	73.09 312	93.00 150	85.31 325	92.13 93	93.26 192	90.96 296	63.42 322	98.28 196	71.27 327	96.54 251	94.79 273

Patchmatch-test1		87.28 266	87.30 252	87.22 302	92.01 308	71.98 320	89.43 276	88.11 302	82.26 262	88.71 289	92.20 276	78.65 262	95.81 303	80.99 252	93.30 311	93.87 298

CMPMVS	68.83 22	87.28 266	85.67 285	92.09 200	88.77 341	85.42 151	90.31 249	94.38 232	70.02 334	88.00 299	93.30 253	73.78 285	94.03 329	75.96 299	96.54 251	96.83 209
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sss		87.23 268	86.82 263	88.46 289	93.96 275	77.94 258	86.84 313	92.78 262	77.59 295	87.61 305	91.83 283	78.75 261	91.92 340	77.84 283	94.20 301	95.52 260

BH-w/o		87.21 269	87.02 260	87.79 297	94.77 251	77.27 269	87.90 299	93.21 255	81.74 265	89.99 264	88.39 324	83.47 226	96.93 273	71.29 326	92.43 322	89.15 342

thres400		87.20 270	86.52 270	89.24 271	95.77 213	72.94 315	91.89 197	86.00 317	90.84 127	92.61 207	89.80 310	63.93 319	98.28 196	71.27 327	96.54 251	96.51 215

CHOSEN 1792x2688		87.19 271	85.92 284	91.00 232	97.13 119	79.41 236	84.51 331	95.60 202	64.14 350	90.07 261	94.81 205	78.26 266	97.14 267	73.34 311	95.38 278	96.46 224

HyFIR lowres test		87.19 271	85.51 286	92.24 195	97.12 120	80.51 203	85.03 325	96.06 188	66.11 346	91.66 228	92.98 258	70.12 294	99.14 71	75.29 305	95.23 281	97.07 195

MIMVSNet		87.13 273	86.54 269	88.89 276	96.05 192	76.11 279	94.39 109	88.51 296	81.37 267	88.27 297	96.75 111	72.38 287	95.52 307	65.71 344	95.47 275	95.03 268

tfpn200view9		87.05 274	86.52 270	88.67 280	95.77 213	72.94 315	91.89 197	86.00 317	90.84 127	92.61 207	89.80 310	63.93 319	98.28 196	71.27 327	96.54 251	94.79 273

cascas		87.02 275	86.28 274	89.25 270	91.56 312	76.45 275	84.33 332	96.78 151	71.01 328	86.89 311	85.91 341	81.35 246	96.94 272	83.09 230	95.60 270	94.35 285

conf0.01		86.95 276	86.04 276	89.70 255	95.99 198	75.66 285	93.28 140	82.70 342	88.81 167	91.26 234	88.01 327	58.77 341	97.89 220	78.93 271	96.60 245	95.56 255

conf0.002		86.95 276	86.04 276	89.70 255	95.99 198	75.66 285	93.28 140	82.70 342	88.81 167	91.26 234	88.01 327	58.77 341	97.89 220	78.93 271	96.60 245	95.56 255

WTY-MVS		86.93 278	86.50 272	88.24 291	94.96 244	74.64 294	87.19 309	92.07 276	78.29 292	88.32 295	91.59 289	78.06 267	94.27 326	74.88 307	93.15 314	95.80 246

tfpn1000		86.83 279	86.23 275	88.64 282	95.53 227	75.25 292	93.57 134	82.28 349	89.27 159	91.46 230	89.24 318	57.22 349	97.86 229	80.63 254	96.88 238	92.81 316

HY-MVS	82.50 18	86.81 280	85.93 283	89.47 259	93.63 282	77.93 259	94.02 118	91.58 281	75.68 303	83.64 329	93.64 242	77.40 271	97.42 255	71.70 323	92.07 327	93.05 313

thresconf0.02		86.69 281	86.04 276	88.64 282	95.99 198	75.66 285	93.28 140	82.70 342	88.81 167	91.26 234	88.01 327	58.77 341	97.89 220	78.93 271	96.60 245	92.36 323

tfpn_n400		86.69 281	86.04 276	88.64 282	95.99 198	75.66 285	93.28 140	82.70 342	88.81 167	91.26 234	88.01 327	58.77 341	97.89 220	78.93 271	96.60 245	92.36 323

tfpnconf		86.69 281	86.04 276	88.64 282	95.99 198	75.66 285	93.28 140	82.70 342	88.81 167	91.26 234	88.01 327	58.77 341	97.89 220	78.93 271	96.60 245	92.36 323

tfpnview11		86.69 281	86.04 276	88.64 282	95.99 198	75.66 285	93.28 140	82.70 342	88.81 167	91.26 234	88.01 327	58.77 341	97.89 220	78.93 271	96.60 245	92.36 323

1314		86.46 285	86.33 273	86.87 305	91.65 311	74.54 296	91.94 194	94.10 237	74.28 310	84.78 322	87.33 335	83.03 230	95.00 318	78.72 277	91.16 333	91.06 336

LP		86.29 286	85.35 287	89.10 272	87.80 343	76.21 277	89.92 263	90.99 285	84.86 238	87.66 303	92.32 274	70.40 293	96.48 287	81.94 239	82.24 352	94.63 278

Patchmatch-test		86.10 287	86.01 282	86.38 309	90.63 320	74.22 301	89.57 273	86.69 311	85.73 227	89.81 271	92.83 259	65.24 314	91.04 343	77.82 285	95.78 268	93.88 297

tfpn_ndepth		85.85 288	85.15 289	87.98 293	95.19 241	75.36 291	92.79 158	83.18 341	86.97 210	89.92 266	86.43 339	57.44 348	97.85 232	78.18 280	96.22 260	90.72 338

thres200		85.85 288	85.18 288	87.88 296	94.44 266	72.52 317	89.08 286	86.21 314	88.57 178	91.44 231	88.40 323	64.22 317	98.00 215	68.35 336	95.88 267	93.12 312

EPNet_dtu		85.63 290	84.37 292	89.40 266	86.30 353	74.33 300	91.64 212	88.26 298	84.84 239	72.96 358	89.85 308	71.27 291	97.69 244	76.60 294	97.62 212	96.18 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		85.22 291	84.64 291	86.98 304	89.51 334	69.83 329	90.52 242	87.34 308	78.87 288	87.22 308	92.74 262	66.91 303	96.53 284	81.77 241	86.88 343	94.58 279
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CVMVSNet		85.16 292	84.72 290	86.48 307	92.12 306	70.19 326	92.32 180	88.17 301	56.15 357	90.64 251	95.85 165	67.97 299	96.69 281	88.78 161	90.52 335	92.56 320

JIA-IIPM		85.08 293	83.04 301	91.19 228	87.56 345	86.14 139	89.40 278	84.44 339	88.98 162	82.20 338	97.95 50	56.82 351	96.15 297	76.55 295	83.45 348	91.30 334

MVS		84.98 294	84.30 293	87.01 303	91.03 314	77.69 264	91.94 194	94.16 236	59.36 355	84.23 326	87.50 333	85.66 216	96.80 278	71.79 321	93.05 317	86.54 348

test1235678		84.54 295	83.85 297	86.59 306	93.81 281	73.41 305	82.38 339	91.79 278	79.43 280	89.50 275	91.61 288	70.59 292	92.94 337	58.14 352	97.40 224	93.44 308

FPMVS		84.50 296	83.28 299	88.16 292	96.32 173	94.49 11	85.76 321	85.47 323	83.09 251	85.20 318	94.26 224	63.79 321	86.58 356	63.72 347	91.88 330	83.40 351

tpm		84.38 297	84.08 294	85.30 318	90.47 323	63.43 351	89.34 279	85.63 321	77.24 299	87.62 304	95.03 200	61.00 337	97.30 262	79.26 268	91.09 334	95.16 264

tpmvs		84.22 298	83.97 295	84.94 319	87.09 350	65.18 343	91.21 222	88.35 297	82.87 255	85.21 317	90.96 296	65.24 314	96.75 279	79.60 267	85.25 344	92.90 315

ADS-MVSNet2		84.01 299	82.20 305	89.41 265	89.04 338	76.37 276	87.57 302	90.98 286	72.71 321	84.46 323	92.45 269	68.08 297	96.48 287	70.58 332	83.97 345	95.38 262

test-LLR		83.58 300	83.17 300	84.79 321	89.68 331	66.86 338	83.08 336	84.52 333	83.07 252	82.85 334	84.78 345	62.86 331	93.49 332	82.85 231	94.86 286	94.03 291

IB-MVS	77.21 19	83.11 301	81.05 313	89.29 268	91.15 313	75.85 282	85.66 322	86.00 317	79.70 278	82.02 341	86.61 336	48.26 359	98.39 188	77.84 283	92.22 325	93.63 303
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
CostFormer		83.09 302	82.21 304	85.73 312	89.27 337	67.01 335	90.35 247	86.47 313	70.42 332	83.52 331	93.23 255	61.18 335	96.85 276	77.21 290	88.26 341	93.34 311

PMMVS		83.00 303	81.11 312	88.66 281	83.81 361	86.44 131	82.24 341	85.65 320	61.75 354	82.07 339	85.64 342	79.75 257	91.59 342	75.99 298	93.09 315	87.94 347

PVSNet	76.22 20	82.89 304	82.37 303	84.48 323	93.96 275	64.38 348	78.60 349	88.61 295	71.50 325	84.43 325	86.36 340	74.27 283	94.60 320	69.87 334	93.69 308	94.46 282

tpmrst		82.85 305	82.93 302	82.64 332	87.65 344	58.99 355	90.14 255	87.90 303	75.54 304	83.93 327	91.63 287	66.79 306	95.36 312	81.21 248	81.54 353	93.57 307

PatchFormer-LS_test		82.62 306	81.71 307	85.32 317	87.92 342	67.31 334	89.03 287	88.20 300	77.58 296	83.79 328	80.50 355	60.96 338	96.42 290	83.86 225	83.59 347	92.23 327

test0.0.03 1		82.48 307	81.47 310	85.48 314	89.70 330	73.57 304	84.73 327	81.64 351	83.07 252	88.13 298	86.61 336	62.86 331	89.10 353	66.24 343	90.29 336	93.77 300

ADS-MVSNet		82.25 308	81.55 309	84.34 324	89.04 338	65.30 342	87.57 302	85.13 331	72.71 321	84.46 323	92.45 269	68.08 297	92.33 339	70.58 332	83.97 345	95.38 262

DSMNet-mixed		82.21 309	81.56 308	84.16 325	89.57 333	70.00 328	90.65 237	77.66 358	54.99 358	83.30 332	97.57 66	77.89 269	90.50 347	66.86 340	95.54 272	91.97 329

gg-mvs-nofinetune		82.10 310	81.02 314	85.34 316	87.46 348	71.04 322	94.74 93	67.56 361	96.44 20	79.43 350	98.99 5	45.24 360	96.15 297	67.18 339	92.17 326	88.85 344

testus		82.09 311	81.78 306	83.03 330	92.35 299	64.37 349	79.44 347	93.27 252	73.08 318	87.06 309	85.21 344	76.80 279	89.27 351	53.30 355	95.48 274	95.46 261

PAPM		81.91 312	80.11 322	87.31 301	93.87 278	72.32 319	84.02 334	93.22 253	69.47 336	76.13 355	89.84 309	72.15 288	97.23 264	53.27 356	89.02 337	92.37 322

tpmp4_e23		81.87 313	80.41 318	86.27 310	89.29 336	67.84 333	91.58 213	87.61 306	67.42 342	78.60 351	92.71 263	56.42 352	96.87 275	71.44 325	88.63 339	94.10 287

tpm2		81.46 314	80.35 320	84.80 320	89.90 329	65.14 344	90.44 244	85.36 324	65.82 348	82.05 340	92.44 271	57.94 347	96.69 281	70.71 331	88.49 340	92.56 320

PMMVS2		81.31 315	83.44 298	74.92 343	90.52 322	46.49 360	69.19 357	85.23 330	84.30 242	87.95 300	94.71 212	76.95 277	84.36 358	64.07 345	98.09 189	93.89 296

new_pmnet		81.22 316	81.01 315	81.86 334	90.92 317	70.15 327	84.03 333	80.25 356	70.83 330	85.97 315	89.78 313	67.93 300	84.65 357	67.44 338	91.90 329	90.78 337

test-mter		81.21 317	80.01 323	84.79 321	89.68 331	66.86 338	83.08 336	84.52 333	73.85 314	82.85 334	84.78 345	43.66 363	93.49 332	82.85 231	94.86 286	94.03 291

EPMVS		81.17 318	80.37 319	83.58 327	85.58 356	65.08 345	90.31 249	71.34 360	77.31 298	85.80 316	91.30 290	59.38 339	92.70 338	79.99 260	82.34 351	92.96 314

pmmvs3		80.83 319	78.96 326	86.45 308	87.23 349	77.48 265	84.87 326	82.31 348	63.83 351	85.03 319	89.50 317	49.66 357	93.10 334	73.12 314	95.10 283	88.78 346

DWT-MVSNet_test		80.74 320	79.18 325	85.43 315	87.51 347	66.87 337	89.87 267	86.01 316	74.20 312	80.86 345	80.62 354	48.84 358	96.68 283	81.54 243	83.14 350	92.75 318

E-PMN		80.72 321	80.86 316	80.29 337	85.11 357	68.77 331	72.96 353	81.97 350	87.76 198	83.25 333	83.01 351	62.22 334	89.17 352	77.15 291	94.31 299	82.93 352

tpm cat1		80.61 322	79.46 324	84.07 326	88.78 340	65.06 346	89.26 282	88.23 299	62.27 353	81.90 342	89.66 316	62.70 333	95.29 315	71.72 322	80.60 354	91.86 332

1111		80.36 323	81.32 311	77.48 340	94.61 262	44.56 361	81.59 342	90.66 288	86.78 214	90.60 252	93.52 248	30.37 366	90.67 344	66.36 341	97.42 223	97.20 192

EMVS		80.35 324	80.28 321	80.54 336	84.73 359	69.07 330	72.54 355	80.73 353	87.80 197	81.66 343	81.73 352	62.89 330	89.84 349	75.79 304	94.65 293	82.71 353

CHOSEN 280x420		80.04 325	77.97 329	86.23 311	90.13 327	74.53 297	72.87 354	89.59 292	66.38 345	76.29 354	85.32 343	56.96 350	95.36 312	69.49 335	94.72 291	88.79 345

dp		79.28 326	78.62 327	81.24 335	85.97 355	56.45 357	86.91 312	85.26 329	72.97 320	81.45 344	89.17 320	56.01 354	95.45 310	73.19 313	76.68 356	91.82 333

TESTMET0.1,1		79.09 327	78.04 328	82.25 333	87.52 346	64.03 350	83.08 336	80.62 354	70.28 333	80.16 349	83.22 350	44.13 362	90.56 346	79.95 261	93.36 309	92.15 328

MVS-HIRNet		78.83 328	80.60 317	73.51 344	93.07 290	47.37 359	87.10 310	78.00 357	68.94 337	77.53 353	97.26 84	71.45 290	94.62 319	63.28 348	88.74 338	78.55 356

test12356		76.35 329	77.41 330	73.19 345	90.70 318	38.86 364	74.56 351	91.14 283	74.55 308	80.54 348	88.18 325	52.36 356	90.49 348	52.38 357	92.26 324	90.21 341

test2356		75.58 330	73.13 332	82.95 331	86.10 354	66.42 340	75.07 350	84.87 332	70.91 329	80.85 346	80.66 353	38.02 365	88.98 354	49.32 358	92.35 323	93.44 308

PVSNet_0	70.34 21	74.58 331	72.96 333	79.47 338	90.63 320	66.24 341	73.26 352	83.40 340	63.67 352	78.02 352	78.35 356	72.53 286	89.59 350	56.68 353	60.05 359	82.57 354

testpf		74.01 332	76.37 331	66.95 346	80.56 362	60.00 353	88.43 297	75.07 359	81.54 266	75.75 356	83.73 347	38.93 364	83.09 359	84.01 222	79.32 355	57.75 358

MVE	59.87 23	73.86 333	72.65 334	77.47 341	87.00 352	74.35 299	61.37 359	60.93 363	67.27 343	69.69 359	86.49 338	81.24 251	72.33 361	56.45 354	83.45 348	85.74 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		72.03 334	70.91 335	75.38 342	90.46 324	57.84 356	71.73 356	81.53 352	83.86 245	82.21 337	83.49 349	29.97 368	87.80 355	60.78 349	54.12 360	80.51 355

.test1245		64.72 335	70.88 336	46.22 348	94.61 262	44.56 361	81.59 342	90.66 288	86.78 214	90.60 252	93.52 248	30.37 366	90.67 344	66.36 341	3.45 362	3.44 362

pcd1.5k->3k		41.03 336	43.65 338	33.18 349	98.74 25	0.00 368	0.00 360	97.57 83	0.00 363	0.00 364	0.00 365	97.01 5	0.00 366	0.00 363	99.52 46	99.53 16

tmp_tt		37.97 337	44.33 337	18.88 350	11.80 364	21.54 365	63.51 358	45.66 366	4.23 360	51.34 361	50.48 359	59.08 340	22.11 363	44.50 359	68.35 358	13.00 360

cdsmvs_eth3d_5k		23.35 338	31.13 339	0.00 353	0.00 367	0.00 368	0.00 360	95.58 206	0.00 363	0.00 364	91.15 292	93.43 61	0.00 366	0.00 363	0.00 364	0.00 364

test123		9.49 339	12.01 340	1.91 351	2.87 365	1.30 366	82.38 339	1.34 368	1.36 361	2.84 362	6.56 362	2.45 369	0.97 364	2.73 361	5.56 361	3.47 361

testmvs		9.02 340	11.42 341	1.81 352	2.77 366	1.13 367	79.44 347	1.90 367	1.18 362	2.65 363	6.80 361	1.95 370	0.87 365	2.62 362	3.45 362	3.44 362

pcd_1.5k_mvsjas		7.56 341	10.09 342	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	90.77 121	0.00 366	0.00 363	0.00 364	0.00 364

ab-mvs-re		7.56 341	10.08 343	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	90.69 303	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet-low-res		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

sosnet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uncertanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

Regformer		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

uanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 364	0.00 364

GSMVS																94.75 275

test_part3							93.92 126		91.83 104		96.39 136		99.44 24	89.00 156

test_part2					98.21 61	89.41 77				96.72 69

test_part1								98.14 28				94.69 43			99.10 92	98.17 130

sam_mvs1												66.64 307				94.75 275

sam_mvs												66.41 308

semantic-postprocess				91.94 203	93.89 277	79.22 243		93.51 248	91.53 116	95.37 129	96.62 118	77.17 273	98.90 105	91.89 104	94.95 285	97.70 165

ambc				92.98 159	96.88 129	83.01 179	95.92 54	96.38 174		96.41 79	97.48 72	88.26 163	97.80 235	89.96 140	98.93 108	98.12 136

MTGPA								97.62 76

test_post1							90.21 251				5.85 364	65.36 312	96.00 300	79.61 266

test_post											6.07 363	65.74 311	95.84 302

patchmatchnet-post											91.71 285	66.22 310	97.59 248

GG-mvs-BLEND				83.24 329	85.06 358	71.03 323	94.99 86	65.55 362		74.09 357	75.51 357	44.57 361	94.46 322	59.57 351	87.54 342	84.24 350

MTMP							94.82 91	54.62 364

gm-plane-assit					87.08 351	59.33 354			71.22 326		83.58 348		97.20 265	73.95 308

test9_res														88.16 172	98.40 153	97.83 156

TEST9					96.45 160	89.46 74	90.60 239	96.92 140	79.09 287	90.49 254	94.39 221	91.31 107	98.88 111

test_8					96.37 163	89.14 82	90.51 243	96.89 144	79.37 282	90.42 256	94.36 223	91.20 113	98.82 125

agg_prior2														87.06 186	98.36 161	97.98 142

agg_prior					96.20 182	88.89 87		96.88 145		90.21 257			98.78 136

TestCases				96.00 50	98.02 74	92.17 45		98.43 8	90.48 136	95.04 144	96.74 112	92.54 84	97.86 229	85.11 211	98.98 104	97.98 142

test_prior4						89.91 71	90.74 234

test_prior2							90.21 251		89.33 157	90.77 247	94.81 205	90.41 132		88.21 169	98.55 140

test_prior				94.61 100	95.95 206	87.23 117		97.36 107					98.68 155			97.93 146

旧先验2							90.00 261		68.65 338	92.71 206			96.52 285	85.15 208

新几何2							90.02 260

新几何1				93.17 152	97.16 115	87.29 116		94.43 230	67.95 340	91.29 233	94.94 202	86.97 196	98.23 202	81.06 251	97.75 204	93.98 294

旧先验1					96.20 182	84.17 163		94.82 220			95.57 178	89.57 146			97.89 201	96.32 229

无先验							89.94 262	95.75 198	70.81 331				98.59 165	81.17 249		94.81 272

原ACMM2							89.34 279

原ACMM1				92.87 167	96.91 128	84.22 162		97.01 130	76.84 301	89.64 274	94.46 217	88.00 174	98.70 153	81.53 244	98.01 195	95.70 251

test222					96.95 124	85.27 153	88.83 291	93.61 245	65.09 349	90.74 249	94.85 204	84.62 223			97.36 225	93.91 295

testdata2													98.03 213	80.24 258

segment_acmp												92.14 89

testdata				91.03 229	96.87 130	82.01 185		94.28 234	71.55 324	92.46 210	95.42 185	85.65 217	97.38 260	82.64 234	97.27 227	93.70 302

testdata1							88.96 289		88.44 183

test12				94.43 115	95.95 206	86.75 126		96.24 182		89.76 272		89.79 143	98.79 132		97.95 198	97.75 162

plane_prior7					97.71 91	88.68 91

plane_prior6					97.21 113	88.23 104						86.93 197

plane_prior5								97.81 62					98.95 101	89.26 151	98.51 146	98.60 111

plane_prior4											95.59 174

plane_prior3						88.43 102			90.35 142	93.31 187

plane_prior2							94.56 103		91.74 111

plane_prior1					97.38 107

plane_prior						88.12 105	93.01 149		88.98 162						98.06 191

n2								0.00 369

nn								0.00 369

door-mid								92.13 275

lessismore_v0				93.87 133	98.05 72	83.77 168		80.32 355		97.13 55	97.91 54	77.49 270	99.11 76	92.62 85	98.08 190	98.74 100

LGP-MVS_train				96.84 36	98.36 53	92.13 47		98.25 18	91.78 107	97.07 56	97.22 86	96.38 11	99.28 54	92.07 98	99.59 35	99.11 53

test11								96.65 158

door								91.26 282

HQP5-MVS						84.89 155

HQP-NCC					96.36 168		91.37 217		87.16 206	88.81 284

ACMP_Plane					96.36 168		91.37 217		87.16 206	88.81 284

BP-MVS														86.55 194

HQP4-MVS										88.81 284			98.61 161			98.15 133

HQP3-MVS								97.31 111							97.73 205

HQP2-MVS												84.76 221

NP-MVS					96.82 132	87.10 120					93.40 251

MDTV_nov1_ep13_2view						42.48 363	88.45 296		67.22 344	83.56 330		66.80 304		72.86 316		94.06 290

MDTV_nov1_ep13			83.88 296		89.42 335	61.52 352	88.74 292	87.41 307	73.99 313	84.96 321	94.01 236	65.25 313	95.53 306	78.02 281	93.16 313

ACMMP++_ref															98.82 122

ACMMP++															99.25 77

Test By Simon												90.61 128

ITE_SJBPF				95.95 52	97.34 109	93.36 37		96.55 164	91.93 97	94.82 150	95.39 188	91.99 93	97.08 268	85.53 204	97.96 197	97.41 180

DeepMVS_CX				53.83 347	70.38 363	64.56 347		48.52 365	33.01 359	65.50 360	74.21 358	56.19 353	46.64 362	38.45 360	70.07 357	50.30 359

High-resolution multi-view benchmark