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		99.93 1	99.92 1	99.94 1	99.99 1	99.97 1	99.90 1	99.89 2	99.98 1	99.99 1	99.96 1	99.77 1	100.00 1	99.81 3	100.00 1	99.85 9

Anonymous20231211		99.71 2	99.70 3	99.74 2	99.97 2	99.52 2	99.74 4	99.82 4	99.73 6	99.91 4	99.89 2	99.27 9	99.94 20	99.02 51	99.94 33	99.75 21

UA-Net		99.47 13	99.40 17	99.70 3	99.49 93	99.29 13	99.80 3	99.72 11	99.82 2	99.04 121	99.81 4	98.05 64	99.96 8	98.85 58	99.99 11	99.86 8

DTE-MVSNet		99.43 18	99.35 22	99.66 4	99.71 35	99.30 12	99.31 21	99.51 64	99.64 10	99.56 34	99.46 53	98.23 50	99.97 3	98.78 61	99.93 39	99.72 25

WR-MVS_H		99.33 28	99.22 37	99.65 5	99.71 35	99.24 20	99.32 18	99.55 54	99.46 28	99.50 46	99.34 72	97.30 110	99.93 26	98.90 55	99.93 39	99.77 16

anonymousdsp		99.51 12	99.47 15	99.62 6	99.88 8	99.08 47	99.34 16	99.69 15	98.93 85	99.65 23	99.72 11	98.93 20	99.95 13	99.11 46	100.00 1	99.82 10

PS-CasMVS		99.40 21	99.33 26	99.62 6	99.71 35	99.10 43	99.29 26	99.53 59	99.53 24	99.46 52	99.41 62	98.23 50	99.95 13	98.89 57	99.95 30	99.81 12

PEN-MVS		99.41 20	99.34 24	99.62 6	99.73 29	99.14 35	99.29 26	99.54 58	99.62 16	99.56 34	99.42 60	98.16 57	99.96 8	98.78 61	99.93 39	99.77 16

zzz-MVS		98.79 71	98.52 98	99.61 9	99.67 45	99.36 7	97.33 217	99.20 165	98.83 90	98.89 144	98.90 154	96.98 137	99.92 34	97.16 139	99.70 132	99.56 76

MTAPA		98.88 63	98.64 86	99.61 9	99.67 45	99.36 7	98.43 115	99.20 165	98.83 90	98.89 144	98.90 154	96.98 137	99.92 34	97.16 139	99.70 132	99.56 76

abl_6		98.99 50	98.78 62	99.61 9	99.45 107	99.46 4	98.60 86	99.50 65	98.59 102	99.24 92	99.04 128	98.54 37	99.89 56	96.45 188	99.62 159	99.50 105

MP-MVS-pluss		98.57 110	98.23 136	99.60 12	99.69 43	99.35 9	97.16 234	99.38 104	94.87 276	98.97 132	98.99 137	98.01 66	99.88 63	97.29 134	99.70 132	99.58 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs6		99.67 3	99.70 3	99.60 12	99.90 5	99.27 16	99.53 10	99.76 7	99.64 10	99.84 9	99.83 3	99.50 5	99.87 73	99.36 28	99.92 49	99.64 41

APDe-MVS		98.99 50	98.79 61	99.60 12	99.21 151	99.15 34	98.87 71	99.48 74	97.57 164	99.35 70	99.24 84	97.83 76	99.89 56	97.88 105	99.70 132	99.75 21

HPM-MVS		98.79 71	98.53 97	99.59 15	99.65 48	99.29 13	99.16 43	99.43 94	96.74 218	98.61 176	98.38 230	98.62 30	99.87 73	96.47 186	99.67 150	99.59 59
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP_Plus		98.75 77	98.48 105	99.57 16	99.58 58	99.29 13	97.82 174	99.25 154	96.94 210	98.78 159	99.12 108	98.02 65	99.84 104	97.13 143	99.67 150	99.59 59

HPM-MVS_fast		99.01 48	98.82 58	99.57 16	99.71 35	99.35 9	99.00 61	99.50 65	97.33 187	98.94 139	98.86 163	98.75 25	99.82 131	97.53 122	99.71 129	99.56 76

CP-MVSNet		99.21 33	99.09 46	99.56 18	99.65 48	98.96 55	99.13 47	99.34 122	99.42 32	99.33 74	99.26 81	97.01 135	99.94 20	98.74 65	99.93 39	99.79 14

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 18	99.85 18	99.11 42	99.90 1	99.78 5	99.63 12	99.78 10	99.67 21	99.48 6	99.81 144	99.30 32	99.97 23	99.77 16
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
v52		99.59 6	99.60 8	99.55 20	99.87 12	99.00 48	99.59 7	99.56 49	99.56 22	99.68 20	99.72 11	98.57 34	99.93 26	99.85 1	99.99 11	99.72 25

V4		99.59 6	99.60 8	99.55 20	99.87 12	99.00 48	99.59 7	99.56 49	99.56 22	99.68 20	99.72 11	98.57 34	99.93 26	99.85 1	99.99 11	99.72 25

PGM-MVS		98.66 95	98.37 124	99.55 20	99.53 80	99.18 26	98.23 124	99.49 71	97.01 208	98.69 166	98.88 160	98.00 67	99.89 56	95.87 215	99.59 166	99.58 66

MIMVSNet1		99.38 23	99.32 27	99.55 20	99.86 16	99.19 25	99.41 13	99.59 34	99.59 19	99.71 14	99.57 39	97.12 126	99.90 47	99.21 39	99.87 69	99.54 87

TDRefinement		99.42 19	99.38 19	99.55 20	99.76 27	99.33 11	99.68 5	99.71 12	99.38 36	99.53 39	99.61 30	98.64 29	99.80 156	98.24 87	99.84 74	99.52 98

HSP-MVS		98.34 138	97.94 166	99.54 25	99.57 63	99.25 19	98.57 89	98.84 240	97.55 167	99.31 81	97.71 272	94.61 237	99.88 63	96.14 204	99.19 237	99.48 118

nrg030		99.40 21	99.35 22	99.54 25	99.58 58	99.13 38	98.98 64	99.48 74	99.68 7	99.46 52	99.26 81	98.62 30	99.73 223	99.17 44	99.92 49	99.76 19

region2R		98.69 89	98.40 119	99.54 25	99.53 80	99.17 27	98.52 95	99.31 132	97.46 178	98.44 190	98.51 220	97.83 76	99.88 63	96.46 187	99.58 172	99.58 66

ACMMPR		98.70 84	98.42 117	99.54 25	99.52 82	99.14 35	98.52 95	99.31 132	97.47 173	98.56 183	98.54 218	97.75 81	99.88 63	96.57 177	99.59 166	99.58 66

MP-MVS		98.46 128	98.09 154	99.54 25	99.57 63	99.22 21	98.50 100	99.19 171	97.61 160	97.58 248	98.66 195	97.40 105	99.88 63	94.72 243	99.60 165	99.54 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		98.64 98	98.34 128	99.54 25	99.54 78	99.17 27	98.63 82	99.24 158	97.47 173	98.09 206	98.68 191	97.62 89	99.89 56	96.22 197	99.62 159	99.57 71

SteuartSystems-ACMMP		98.79 71	98.54 96	99.54 25	99.73 29	99.16 29	98.23 124	99.31 132	97.92 134	98.90 142	98.90 154	98.00 67	99.88 63	96.15 203	99.72 125	99.58 66
Skip Steuart: Steuart Systems R&D Blog.
Anonymous20240521		99.36 25	99.31 28	99.53 32	99.80 22	98.97 51	99.54 9	99.48 74	99.44 30	99.58 33	99.55 41	97.17 123	99.88 63	99.34 29	99.91 54	99.74 24

SMA-MVS		98.47 126	98.11 151	99.53 32	99.16 169	99.27 16	98.05 144	99.30 139	94.34 289	99.22 96	99.10 112	97.72 82	99.79 177	96.45 188	99.68 144	99.53 92

XVS		98.72 81	98.45 112	99.53 32	99.46 104	99.21 22	98.65 80	99.34 122	98.62 100	97.54 252	98.63 204	97.50 96	99.83 119	96.79 159	99.53 190	99.56 76

X-MVStestdata		94.32 302	92.59 319	99.53 32	99.46 104	99.21 22	98.65 80	99.34 122	98.62 100	97.54 252	45.85 359	97.50 96	99.83 119	96.79 159	99.53 190	99.56 76

APD-MVS_3200maxsize		98.84 67	98.61 91	99.53 32	99.19 161	99.27 16	98.49 101	99.33 127	98.64 98	99.03 124	98.98 140	97.89 74	99.85 89	96.54 182	99.42 204	99.46 130

test_djsdf		99.52 11	99.51 11	99.53 32	99.86 16	98.74 62	99.39 14	99.56 49	99.11 63	99.70 15	99.73 10	99.00 17	99.97 3	99.26 33	99.98 19	99.89 3

OurMVSNet-221017-0		99.37 24	99.31 28	99.53 32	99.91 4	98.98 50	99.63 6	99.58 36	99.44 30	99.78 10	99.76 6	96.39 175	99.92 34	99.44 26	99.92 49	99.68 31

CP-MVS		98.70 84	98.42 117	99.52 39	99.36 122	99.12 40	98.72 79	99.36 112	97.54 168	98.30 198	98.40 229	97.86 75	99.89 56	96.53 183	99.72 125	99.56 76

ACMMP		98.75 77	98.50 101	99.52 39	99.56 70	99.16 29	98.87 71	99.37 108	97.16 204	98.82 156	99.01 134	97.71 83	99.87 73	96.29 195	99.69 139	99.54 87
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
HFP-MVS		98.71 82	98.44 114	99.51 41	99.49 93	99.16 29	98.52 95	99.31 132	97.47 173	98.58 181	98.50 223	97.97 71	99.85 89	96.57 177	99.59 166	99.53 92

#test#		98.50 123	98.16 145	99.51 41	99.49 93	99.16 29	98.03 146	99.31 132	96.30 238	98.58 181	98.50 223	97.97 71	99.85 89	95.68 225	99.59 166	99.53 92

ESAPD		98.25 150	97.83 174	99.50 43	99.36 122	99.10 43	97.25 222	99.28 143	96.66 224	99.05 118	98.71 186	97.56 91	99.86 78	93.00 286	99.57 176	99.53 92

wuykxyi23d		99.36 25	99.31 28	99.50 43	99.81 21	98.67 69	98.08 138	99.75 8	98.03 130	99.90 5	99.60 34	99.18 12	99.94 20	99.46 25	99.98 19	99.89 3

mvs_tets		99.63 5	99.67 5	99.49 45	99.88 8	98.61 73	99.34 16	99.71 12	99.27 46	99.90 5	99.74 8	99.68 3	99.97 3	99.55 20	99.99 11	99.88 5

jajsoiax		99.58 8	99.61 7	99.48 46	99.87 12	98.61 73	99.28 30	99.66 19	99.09 70	99.89 8	99.68 19	99.53 4	99.97 3	99.50 22	99.99 11	99.87 6

HPM-MVS++		98.10 161	97.64 186	99.48 46	99.09 180	99.13 38	97.52 207	98.75 254	97.46 178	96.90 286	97.83 267	96.01 188	99.84 104	95.82 219	99.35 211	99.46 130

ACMM	96.08 12	98.91 61	98.73 69	99.48 46	99.55 74	99.14 35	98.07 140	99.37 108	97.62 158	99.04 121	98.96 145	98.84 21	99.79 177	97.43 128	99.65 156	99.49 112
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LPG-MVS_test		98.71 82	98.46 110	99.47 49	99.57 63	98.97 51	98.23 124	99.48 74	96.60 227	99.10 110	99.06 121	98.71 27	99.83 119	95.58 229	99.78 102	99.62 46

LGP-MVS_train				99.47 49	99.57 63	98.97 51		99.48 74	96.60 227	99.10 110	99.06 121	98.71 27	99.83 119	95.58 229	99.78 102	99.62 46

TranMVSNet+NR-MVSNet		99.17 36	99.07 48	99.46 51	99.37 121	98.87 57	98.39 118	99.42 97	99.42 32	99.36 68	99.06 121	98.38 44	99.95 13	98.34 83	99.90 58	99.57 71

APD-MVS		98.10 161	97.67 181	99.42 52	99.11 176	98.93 56	97.76 179	99.28 143	94.97 273	98.72 165	98.77 180	97.04 130	99.85 89	93.79 270	99.54 186	99.49 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
RPSCF		98.62 105	98.36 125	99.42 52	99.65 48	99.42 5	98.55 92	99.57 43	97.72 153	98.90 142	99.26 81	96.12 184	99.52 300	95.72 222	99.71 129	99.32 179

v7n		99.53 10	99.57 10	99.41 54	99.88 8	98.54 81	99.45 11	99.61 30	99.66 9	99.68 20	99.66 22	98.44 42	99.95 13	99.73 8	99.96 28	99.75 21

COLMAP_ROB	96.50 10	98.99 50	98.85 56	99.41 54	99.58 58	99.10 43	98.74 77	99.56 49	99.09 70	99.33 74	99.19 92	98.40 43	99.72 232	95.98 209	99.76 115	99.42 144
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UniMVSNet_NR-MVSNet		98.86 66	98.68 81	99.40 56	99.17 167	98.74 62	97.68 186	99.40 99	99.14 61	99.06 113	98.59 210	96.71 158	99.93 26	98.57 72	99.77 106	99.53 92

DU-MVS		98.82 68	98.63 87	99.39 57	99.16 169	98.74 62	97.54 206	99.25 154	98.84 89	99.06 113	98.76 182	96.76 155	99.93 26	98.57 72	99.77 106	99.50 105

TransMVSNet (Re)		99.44 15	99.47 15	99.36 58	99.80 22	98.58 76	99.27 32	99.57 43	99.39 34	99.75 12	99.62 28	99.17 14	99.83 119	99.06 49	99.62 159	99.66 34

NR-MVSNet		98.95 57	98.82 58	99.36 58	99.16 169	98.72 67	99.22 35	99.20 165	99.10 67	99.72 13	98.76 182	96.38 177	99.86 78	98.00 100	99.82 83	99.50 105

Baseline_NR-MVSNet		98.98 54	98.86 55	99.36 58	99.82 20	98.55 78	97.47 212	99.57 43	99.37 37	99.21 97	99.61 30	96.76 155	99.83 119	98.06 95	99.83 80	99.71 28

ACMP	95.32 15	98.41 132	98.09 154	99.36 58	99.51 85	98.79 61	97.68 186	99.38 104	95.76 254	98.81 158	98.82 173	98.36 45	99.82 131	94.75 240	99.77 106	99.48 118
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LS3D		98.63 100	98.38 123	99.36 58	97.25 337	99.38 6	99.12 49	99.32 130	99.21 49	98.44 190	98.88 160	97.31 109	99.80 156	96.58 175	99.34 213	98.92 243

Effi-MVS+-dtu		98.26 148	97.90 170	99.35 63	98.02 309	99.49 3	98.02 153	99.16 184	98.29 121	97.64 243	97.99 260	96.44 173	99.95 13	96.66 171	98.93 265	98.60 274

PS-MVSNAJss		99.46 14	99.49 12	99.35 63	99.90 5	98.15 104	99.20 36	99.65 20	99.48 25	99.92 3	99.71 14	98.07 61	99.96 8	99.53 21	100.00 1	99.93 1

UniMVSNet (Re)		98.87 64	98.71 73	99.35 63	99.24 139	98.73 65	97.73 182	99.38 104	98.93 85	99.12 106	98.73 184	96.77 153	99.86 78	98.63 69	99.80 94	99.46 130

FC-MVSNet-test		99.27 30	99.25 35	99.34 66	99.77 26	98.37 92	99.30 25	99.57 43	99.61 18	99.40 62	99.50 47	97.12 126	99.85 89	99.02 51	99.94 33	99.80 13

PHI-MVS		98.29 145	97.95 164	99.34 66	98.44 288	99.16 29	98.12 134	99.38 104	96.01 250	98.06 208	98.43 227	97.80 80	99.67 250	95.69 224	99.58 172	99.20 206

v748		99.44 15	99.48 13	99.33 68	99.88 8	98.43 88	99.42 12	99.53 59	99.63 12	99.69 17	99.60 34	97.99 69	99.91 43	99.60 14	99.96 28	99.66 34

pm-mvs1		99.44 15	99.48 13	99.33 68	99.80 22	98.63 70	99.29 26	99.63 25	99.30 43	99.65 23	99.60 34	99.16 16	99.82 131	99.07 48	99.83 80	99.56 76

ACMH+	96.62 9	99.08 43	99.00 50	99.33 68	99.71 35	98.83 58	98.60 86	99.58 36	99.11 63	99.53 39	99.18 94	98.81 23	99.67 250	96.71 168	99.77 106	99.50 105

FIs		99.14 38	99.09 46	99.29 71	99.70 41	98.28 94	99.13 47	99.52 63	99.48 25	99.24 92	99.41 62	96.79 152	99.82 131	98.69 67	99.88 65	99.76 19

VPA-MVSNet		99.30 29	99.30 32	99.28 72	99.49 93	98.36 93	99.00 61	99.45 86	99.63 12	99.52 41	99.44 58	98.25 48	99.88 63	99.09 47	99.84 74	99.62 46

DP-MVS		98.93 58	98.81 60	99.28 72	99.21 151	98.45 87	98.46 113	99.33 127	99.63 12	99.48 48	99.15 104	97.23 120	99.75 209	97.17 138	99.66 155	99.63 45

ANet_high		99.57 9	99.67 5	99.28 72	99.89 7	98.09 108	99.14 45	99.93 1	99.82 2	99.93 2	99.81 4	99.17 14	99.94 20	99.31 31	100.00 1	99.82 10

CPTT-MVS		97.84 184	97.36 204	99.27 75	99.31 131	98.46 86	98.29 120	99.27 148	94.90 275	97.83 226	98.37 231	94.90 224	99.84 104	93.85 269	99.54 186	99.51 100

Vis-MVSNet		99.34 27	99.36 21	99.27 75	99.73 29	98.26 95	99.17 42	99.78 5	99.11 63	99.27 84	99.48 51	98.82 22	99.95 13	98.94 54	99.93 39	99.59 59
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMH	96.65 7	99.25 31	99.24 36	99.26 77	99.72 34	98.38 91	99.07 53	99.55 54	98.30 118	99.65 23	99.45 57	99.22 10	99.76 203	98.44 78	99.77 106	99.64 41
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		98.56 111	98.32 132	99.25 78	99.41 116	98.73 65	97.13 236	99.18 175	97.10 207	98.75 163	98.92 150	98.18 56	99.65 263	96.68 170	99.56 183	99.37 160

3Dnovator+	97.89 3	98.69 89	98.51 99	99.24 79	98.81 242	98.40 89	99.02 55	99.19 171	98.99 77	98.07 207	99.28 77	97.11 128	99.84 104	96.84 157	99.32 216	99.47 126

DeepPCF-MVS	96.93 5	98.32 140	98.01 161	99.23 80	98.39 291	98.97 51	95.03 327	99.18 175	96.88 213	99.33 74	98.78 178	98.16 57	99.28 335	96.74 163	99.62 159	99.44 136

XVG-ACMP-BASELINE		98.56 111	98.34 128	99.22 81	99.54 78	98.59 75	97.71 183	99.46 83	97.25 195	98.98 130	98.99 137	97.54 94	99.84 104	95.88 212	99.74 117	99.23 200

CSCG		98.68 92	98.50 101	99.20 82	99.45 107	98.63 70	98.56 90	99.57 43	97.87 146	98.85 150	98.04 258	97.66 84	99.84 104	96.72 165	99.81 90	99.13 221

GBi-Net		98.65 96	98.47 107	99.17 83	98.90 221	98.24 97	99.20 36	99.44 89	98.59 102	98.95 135	99.55 41	94.14 246	99.86 78	97.77 110	99.69 139	99.41 146

test1		98.65 96	98.47 107	99.17 83	98.90 221	98.24 97	99.20 36	99.44 89	98.59 102	98.95 135	99.55 41	94.14 246	99.86 78	97.77 110	99.69 139	99.41 146

FMVSNet1		99.17 36	99.17 40	99.17 83	99.55 74	98.24 97	99.20 36	99.44 89	99.21 49	99.43 57	99.55 41	97.82 79	99.86 78	98.42 80	99.89 64	99.41 146

AllTest		98.44 130	98.20 138	99.16 86	99.50 87	98.55 78	98.25 123	99.58 36	96.80 216	98.88 147	99.06 121	97.65 85	99.57 287	94.45 249	99.61 163	99.37 160

TestCases				99.16 86	99.50 87	98.55 78		99.58 36	96.80 216	98.88 147	99.06 121	97.65 85	99.57 287	94.45 249	99.61 163	99.37 160

SixPastTwentyTwo		98.75 77	98.62 88	99.16 86	99.83 19	97.96 125	99.28 30	98.20 278	99.37 37	99.70 15	99.65 25	92.65 269	99.93 26	99.04 50	99.84 74	99.60 53

XVG-OURS-SEG-HR		98.49 124	98.28 134	99.14 89	99.49 93	98.83 58	96.54 267	99.48 74	97.32 189	99.11 107	98.61 208	99.33 8	99.30 332	96.23 196	98.38 290	99.28 190

F-COLMAP		97.30 217	96.68 236	99.14 89	99.19 161	98.39 90	97.27 221	99.30 139	92.93 304	96.62 296	98.00 259	95.73 204	99.68 244	92.62 296	98.46 288	99.35 171

Anonymous20240529		98.93 58	98.87 54	99.12 91	99.19 161	98.22 102	99.01 56	98.99 219	99.25 47	99.54 36	99.37 66	97.04 130	99.80 156	97.89 102	99.52 193	99.35 171

PM-MVS		98.82 68	98.72 72	99.12 91	99.64 51	98.54 81	97.98 157	99.68 16	97.62 158	99.34 73	99.18 94	97.54 94	99.77 198	97.79 108	99.74 117	99.04 228

LCM-MVSNet-Re		98.64 98	98.48 105	99.11 93	98.85 232	98.51 83	98.49 101	99.83 3	98.37 111	99.69 17	99.46 53	98.21 54	99.92 34	94.13 260	99.30 220	98.91 245

XVG-OURS		98.53 120	98.34 128	99.11 93	99.50 87	98.82 60	95.97 291	99.50 65	97.30 191	99.05 118	98.98 140	99.35 7	99.32 329	95.72 222	99.68 144	99.18 213

MCST-MVS		98.00 168	97.63 187	99.10 95	99.24 139	98.17 103	96.89 248	98.73 257	95.66 255	97.92 213	97.70 273	97.17 123	99.66 258	96.18 201	99.23 229	99.47 126

XXY-MVS		99.14 38	99.15 44	99.10 95	99.76 27	97.74 147	98.85 74	99.62 28	98.48 109	99.37 66	99.49 50	98.75 25	99.86 78	98.20 90	99.80 94	99.71 28

DeepC-MVS	97.60 4	98.97 55	98.93 52	99.10 95	99.35 126	97.98 122	98.01 154	99.46 83	97.56 166	99.54 36	99.50 47	98.97 18	99.84 104	98.06 95	99.92 49	99.49 112
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Anonymous202405211		97.90 173	97.50 193	99.08 98	98.90 221	98.25 96	98.53 94	96.16 320	98.87 87	99.11 107	98.86 163	90.40 282	99.78 187	97.36 131	99.31 218	99.19 211

IS-MVSNet		98.19 156	97.90 170	99.08 98	99.57 63	97.97 123	99.31 21	98.32 274	99.01 76	98.98 130	99.03 131	91.59 276	99.79 177	95.49 231	99.80 94	99.48 118

train_agg		97.10 230	96.45 248	99.07 100	98.71 253	98.08 111	95.96 295	99.03 207	91.64 319	95.85 317	97.53 281	96.47 171	99.76 203	93.67 272	99.16 240	99.36 166

VDD-MVS		98.56 111	98.39 121	99.07 100	99.13 175	98.07 113	98.59 88	97.01 303	99.59 19	99.11 107	99.27 79	94.82 229	99.79 177	98.34 83	99.63 158	99.34 173

CDPH-MVS		97.26 220	96.66 239	99.07 100	99.00 202	98.15 104	96.03 289	99.01 214	91.21 328	97.79 235	97.85 266	96.89 145	99.69 239	92.75 294	99.38 208	99.39 153

CNVR-MVS		98.17 159	97.87 173	99.07 100	98.67 265	98.24 97	97.01 239	98.93 225	97.25 195	97.62 244	98.34 234	97.27 113	99.57 287	96.42 191	99.33 214	99.39 153

EPP-MVSNet		98.30 142	98.04 160	99.07 100	99.56 70	97.83 136	99.29 26	98.07 282	99.03 74	98.59 179	99.13 107	92.16 273	99.90 47	96.87 155	99.68 144	99.49 112

TSAR-MVS + MP.		98.63 100	98.49 104	99.06 105	99.64 51	97.90 131	98.51 99	98.94 222	96.96 209	99.24 92	98.89 159	97.83 76	99.81 144	96.88 154	99.49 200	99.48 118

NCCC		97.86 179	97.47 198	99.05 106	98.61 273	98.07 113	96.98 240	98.90 231	97.63 157	97.04 278	97.93 264	95.99 192	99.66 258	95.31 232	98.82 268	99.43 141

3Dnovator	98.27 2	98.81 70	98.73 69	99.05 106	98.76 246	97.81 141	99.25 33	99.30 139	98.57 106	98.55 184	99.33 74	97.95 73	99.90 47	97.16 139	99.67 150	99.44 136

OMC-MVS		97.88 177	97.49 194	99.04 108	98.89 226	98.63 70	96.94 242	99.25 154	95.02 271	98.53 186	98.51 220	97.27 113	99.47 311	93.50 279	99.51 194	99.01 232

agg_prior1		97.06 233	96.40 249	99.03 109	98.68 262	97.99 118	95.76 306	99.01 214	91.73 318	95.59 321	97.50 284	96.49 170	99.77 198	93.71 271	99.14 244	99.34 173

WR-MVS		98.40 134	98.19 140	99.03 109	99.00 202	97.65 152	96.85 250	98.94 222	98.57 106	98.89 144	98.50 223	95.60 207	99.85 89	97.54 121	99.85 72	99.59 59

K. test v3		98.00 168	97.66 184	99.03 109	99.79 25	97.56 156	99.19 40	92.47 351	99.62 16	99.52 41	99.66 22	89.61 285	99.96 8	99.25 35	99.81 90	99.56 76

Regformer-2		98.60 107	98.46 110	99.02 112	98.85 232	97.71 149	96.91 246	99.09 195	98.98 79	99.01 125	98.64 200	97.37 107	99.84 104	97.75 114	99.57 176	99.52 98

VDDNet		98.21 154	97.95 164	99.01 113	99.58 58	97.74 147	99.01 56	97.29 298	99.67 8	98.97 132	99.50 47	90.45 281	99.80 156	97.88 105	99.20 233	99.48 118

VPNet		98.87 64	98.83 57	99.01 113	99.70 41	97.62 155	98.43 115	99.35 118	99.47 27	99.28 82	99.05 126	96.72 157	99.82 131	98.09 93	99.36 209	99.59 59

agg_prior3		96.95 240	96.27 253	99.00 115	98.68 262	97.91 129	95.96 295	99.01 214	90.74 331	95.60 320	97.45 289	96.14 182	99.74 218	93.67 272	99.16 240	99.36 166

N_pmnet		97.63 194	97.17 212	98.99 116	99.27 135	97.86 134	95.98 290	93.41 343	95.25 268	99.47 51	98.90 154	95.63 206	99.85 89	96.91 151	99.73 120	99.27 191

lessismore_v0				98.97 117	99.73 29	97.53 158		86.71 360		99.37 66	99.52 46	89.93 283	99.92 34	98.99 53	99.72 125	99.44 136

HyFIR lowres test		97.19 226	96.60 242	98.96 118	99.62 55	97.28 168	95.17 324	99.50 65	94.21 292	99.01 125	98.32 237	86.61 296	99.99 2	97.10 146	99.84 74	99.60 53

test_prior3		97.48 206	97.00 218	98.95 119	98.69 260	97.95 126	95.74 308	99.03 207	96.48 230	96.11 311	97.63 277	95.92 197	99.59 280	94.16 256	99.20 233	99.30 186

test_prior				98.95 119	98.69 260	97.95 126		99.03 207					99.59 280			99.30 186

EG-PatchMatch MVS		98.99 50	99.01 49	98.94 121	99.50 87	97.47 160	98.04 145	99.59 34	98.15 128	99.40 62	99.36 69	98.58 33	99.76 203	98.78 61	99.68 144	99.59 59

test12				98.93 122	98.58 276	97.83 136		98.66 261		96.53 299		95.51 211	99.69 239		99.13 247	99.27 191

HQP_MVS		97.99 170	97.67 181	98.93 122	99.19 161	97.65 152	97.77 177	99.27 148	98.20 124	97.79 235	97.98 261	94.90 224	99.70 235	94.42 251	99.51 194	99.45 134

test_0402		98.76 76	98.71 73	98.93 122	99.56 70	98.14 106	98.45 114	99.34 122	99.28 45	98.95 135	98.91 151	98.34 46	99.79 177	95.63 226	99.91 54	98.86 250

tfpnnormal		98.90 62	98.90 53	98.91 125	99.67 45	97.82 139	99.00 61	99.44 89	99.45 29	99.51 45	99.24 84	98.20 55	99.86 78	95.92 211	99.69 139	99.04 228

新几何1				98.91 125	98.94 211	97.76 144		98.76 251	87.58 346	96.75 293	98.10 253	94.80 232	99.78 187	92.73 295	99.00 259	99.20 206

1121		96.73 248	96.00 256	98.91 125	98.95 210	97.76 144	98.07 140	98.73 257	87.65 345	96.54 298	98.13 247	94.52 239	99.73 223	92.38 300	99.02 256	99.24 199

mvs-test1		97.83 185	97.48 197	98.89 128	98.02 309	99.20 24	97.20 228	99.16 184	98.29 121	96.46 305	97.17 298	96.44 173	99.92 34	96.66 171	97.90 316	97.54 320

Regformer-4		98.73 80	98.68 81	98.89 128	99.02 199	97.22 171	97.17 232	99.06 198	99.21 49	99.17 103	98.85 166	97.45 101	99.86 78	98.48 77	99.70 132	99.60 53

Regformer-1		98.55 115	98.44 114	98.87 130	98.85 232	97.29 166	96.91 246	98.99 219	98.97 80	98.99 128	98.64 200	97.26 116	99.81 144	97.79 108	99.57 176	99.51 100

ITE_SJBPF				98.87 130	99.22 145	98.48 85		99.35 118	97.50 170	98.28 199	98.60 209	97.64 88	99.35 325	93.86 268	99.27 225	98.79 260

pmmvs-eth3d		98.47 126	98.34 128	98.86 132	99.30 133	97.76 144	97.16 234	99.28 143	95.54 263	99.42 59	99.19 92	97.27 113	99.63 266	97.89 102	99.97 23	99.20 206

PLC	94.65 16	96.51 255	95.73 261	98.85 133	98.75 247	97.91 129	96.42 274	99.06 198	90.94 330	95.59 321	97.38 293	94.41 241	99.59 280	90.93 321	98.04 314	99.05 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CMPMVS	75.91 23	96.29 260	95.44 269	98.84 134	96.25 352	98.69 68	97.02 238	99.12 190	88.90 341	97.83 226	98.86 163	89.51 286	98.90 349	91.92 302	99.51 194	98.92 243
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MVS_111021_LR		98.30 142	98.12 150	98.83 135	99.16 169	98.03 116	96.09 288	99.30 139	97.58 162	98.10 205	98.24 242	98.25 48	99.34 326	96.69 169	99.65 156	99.12 222

QAPM		97.31 216	96.81 228	98.82 136	98.80 244	97.49 159	99.06 54	99.19 171	90.22 334	97.69 241	99.16 100	96.91 140	99.90 47	90.89 323	99.41 205	99.07 225

Fast-Effi-MVS+-dtu		98.27 146	98.09 154	98.81 137	98.43 289	98.11 107	97.61 197	99.50 65	98.64 98	97.39 266	97.52 283	98.12 60	99.95 13	96.90 153	98.71 274	98.38 285

TAMVS		98.24 152	98.05 159	98.80 138	99.07 184	97.18 174	97.88 167	98.81 246	96.66 224	99.17 103	99.21 89	94.81 231	99.77 198	96.96 150	99.88 65	99.44 136

VNet		98.42 131	98.30 133	98.79 139	98.79 245	97.29 166	98.23 124	98.66 261	99.31 42	98.85 150	98.80 175	94.80 232	99.78 187	98.13 92	99.13 247	99.31 183

UGNet		98.53 120	98.45 112	98.79 139	97.94 312	96.96 183	99.08 50	98.54 266	99.10 67	96.82 291	99.47 52	96.55 167	99.84 104	98.56 75	99.94 33	99.55 84
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
MAR-MVS		96.47 258	95.70 262	98.79 139	97.92 313	99.12 40	98.28 121	98.60 265	92.16 316	95.54 328	96.17 316	94.77 235	99.52 300	89.62 329	98.23 293	97.72 309
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
alignmvs		97.35 213	96.88 224	98.78 142	98.54 281	98.09 108	97.71 183	97.69 292	99.20 52	97.59 247	95.90 323	88.12 293	99.55 293	98.18 91	98.96 263	98.70 269

test20.03		98.78 74	98.77 64	98.78 142	99.46 104	97.20 172	97.78 175	99.24 158	99.04 73	99.41 60	98.90 154	97.65 85	99.76 203	97.70 115	99.79 98	99.39 153

v13		99.24 32	99.39 18	98.77 144	99.63 53	96.79 188	99.24 34	99.65 20	99.39 34	99.62 27	99.70 16	97.50 96	99.84 104	99.78 5	100.00 1	99.67 32

TSAR-MVS + GP.		98.18 157	97.98 162	98.77 144	98.71 253	97.88 132	96.32 278	98.66 261	96.33 235	99.23 95	98.51 220	97.48 100	99.40 319	97.16 139	99.46 201	99.02 231

testing_2		98.93 58	98.99 51	98.76 146	99.57 63	97.03 180	97.85 171	99.13 188	98.46 110	99.44 56	99.44 58	98.22 52	99.74 218	98.85 58	99.94 33	99.51 100

V42		98.78 74	98.78 62	98.76 146	99.44 110	97.04 179	98.27 122	99.19 171	97.87 146	99.25 91	99.16 100	96.84 147	99.78 187	99.21 39	99.84 74	99.46 130

UnsupCasMVSNet_eth		97.89 175	97.60 189	98.75 148	99.31 131	97.17 175	97.62 195	99.35 118	98.72 97	98.76 162	98.68 191	92.57 270	99.74 218	97.76 113	95.60 344	99.34 173

FMVSNet2		98.49 124	98.40 119	98.75 148	98.90 221	97.14 178	98.61 85	99.13 188	98.59 102	99.19 99	99.28 77	94.14 246	99.82 131	97.97 101	99.80 94	99.29 189

MVS_111021_HR		98.25 150	98.08 157	98.75 148	99.09 180	97.46 161	95.97 291	99.27 148	97.60 161	97.99 212	98.25 241	98.15 59	99.38 323	96.87 155	99.57 176	99.42 144

DeepC-MVS_fast	96.85 6	98.30 142	98.15 147	98.75 148	98.61 273	97.23 169	97.76 179	99.09 195	97.31 190	98.75 163	98.66 195	97.56 91	99.64 265	96.10 205	99.55 185	99.39 153
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v12		99.21 33	99.37 20	98.74 152	99.60 56	96.72 193	99.19 40	99.65 20	99.35 40	99.62 27	99.69 17	97.43 103	99.83 119	99.76 6	100.00 1	99.66 34

V9		99.18 35	99.34 24	98.70 153	99.58 58	96.63 196	99.14 45	99.64 24	99.30 43	99.61 29	99.68 19	97.33 108	99.83 119	99.75 7	100.00 1	99.65 38

114514_t		96.50 257	95.77 260	98.69 154	99.48 98	97.43 163	97.84 172	99.55 54	81.42 355	96.51 301	98.58 211	95.53 209	99.67 250	93.41 281	99.58 172	98.98 235

CDS-MVSNet		97.69 189	97.35 206	98.69 154	98.73 250	97.02 182	96.92 245	98.75 254	95.89 252	98.59 179	98.67 193	92.08 275	99.74 218	96.72 165	99.81 90	99.32 179
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
V14		99.14 38	99.30 32	98.66 156	99.56 70	96.53 198	99.08 50	99.63 25	99.24 48	99.60 30	99.66 22	97.23 120	99.82 131	99.73 8	100.00 1	99.65 38

TAPA-MVS	96.21 11	96.63 251	95.95 258	98.65 157	98.93 213	98.09 108	96.93 243	99.28 143	83.58 353	98.13 204	97.78 269	96.13 183	99.40 319	93.52 277	99.29 223	98.45 280
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LFMVS		97.20 225	96.72 232	98.64 158	98.72 251	96.95 184	98.93 68	94.14 341	99.74 5	98.78 159	99.01 134	84.45 312	99.73 223	97.44 127	99.27 225	99.25 196

Gipuma		99.03 46	99.16 42	98.64 158	99.94 3	98.51 83	99.32 18	99.75 8	99.58 21	98.60 178	99.62 28	98.22 52	99.51 305	97.70 115	99.73 120	97.89 297
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EI-MVSNet-Vis-set		98.68 92	98.70 76	98.63 160	99.09 180	96.40 205	97.23 224	98.86 238	99.20 52	99.18 102	98.97 142	97.29 112	99.85 89	98.72 66	99.78 102	99.64 41

Regformer-3		98.61 106	98.61 91	98.63 160	99.02 199	96.53 198	97.17 232	98.84 240	99.13 62	99.10 110	98.85 166	97.24 118	99.79 177	98.41 81	99.70 132	99.57 71

Effi-MVS+		98.02 165	97.82 175	98.62 162	98.53 283	97.19 173	97.33 217	99.68 16	97.30 191	96.68 294	97.46 288	98.56 36	99.80 156	96.63 173	98.20 296	98.86 250

EI-MVSNet-UG-set		98.69 89	98.71 73	98.62 162	99.10 177	96.37 207	97.23 224	98.87 234	99.20 52	99.19 99	98.99 137	97.30 110	99.85 89	98.77 64	99.79 98	99.65 38

v15		99.11 42	99.27 34	98.62 162	99.52 82	96.43 202	99.01 56	99.63 25	99.18 57	99.59 32	99.64 26	97.13 125	99.81 144	99.71 10	100.00 1	99.64 41

v17		99.07 44	99.22 37	98.61 165	99.50 87	96.42 203	99.01 56	99.60 32	99.15 58	99.48 48	99.61 30	97.05 129	99.81 144	99.64 12	99.98 19	99.61 50

v16		99.07 44	99.22 37	98.61 165	99.50 87	96.42 203	99.01 56	99.60 32	99.15 58	99.46 52	99.61 30	97.04 130	99.81 144	99.64 12	99.97 23	99.61 50

testmv		98.51 122	98.47 107	98.61 165	99.24 139	96.53 198	96.66 261	99.73 10	98.56 108	99.50 46	99.23 88	97.24 118	99.87 73	96.16 202	99.93 39	99.44 136

PatchMatch-RL		97.24 223	96.78 229	98.61 165	99.03 197	97.83 136	96.36 276	99.06 198	93.49 301	97.36 269	97.78 269	95.75 203	99.49 307	93.44 280	98.77 269	98.52 277

ab-mvs		98.41 132	98.36 125	98.59 169	99.19 161	97.23 169	99.32 18	98.81 246	97.66 155	98.62 174	99.40 65	96.82 149	99.80 156	95.88 212	99.51 194	98.75 265

canonicalmvs		98.34 138	98.26 135	98.58 170	98.46 286	97.82 139	98.96 65	99.46 83	99.19 56	97.46 258	95.46 333	98.59 32	99.46 313	98.08 94	98.71 274	98.46 279

1112_ss		97.29 219	96.86 225	98.58 170	99.34 128	96.32 208	96.75 255	99.58 36	93.14 303	96.89 287	97.48 286	92.11 274	99.86 78	96.91 151	99.54 186	99.57 71

Fast-Effi-MVS+		97.67 191	97.38 203	98.57 172	98.71 253	97.43 163	97.23 224	99.45 86	94.82 278	96.13 310	96.51 309	98.52 38	99.91 43	96.19 199	98.83 267	98.37 287

MVP-Stereo		98.08 163	97.92 168	98.57 172	98.96 208	96.79 188	97.90 166	99.18 175	96.41 233	98.46 188	98.95 146	95.93 196	99.60 276	96.51 184	98.98 262	99.31 183
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v18		99.02 47	99.17 40	98.57 172	99.45 107	96.31 209	98.94 66	99.58 36	99.06 72	99.43 57	99.58 38	96.91 140	99.80 156	99.60 14	99.97 23	99.59 59

v8		99.01 48	99.16 42	98.57 172	99.47 100	96.31 209	98.90 69	99.47 81	99.03 74	99.52 41	99.57 39	96.93 139	99.81 144	99.60 14	99.98 19	99.60 53

DP-MVS Recon		97.33 215	96.92 221	98.57 172	99.09 180	97.99 118	96.79 251	99.35 118	93.18 302	97.71 239	98.07 257	95.00 223	99.31 330	93.97 263	99.13 247	98.42 283

v10		98.97 55	99.11 45	98.55 177	99.44 110	96.21 215	98.90 69	99.55 54	98.73 96	99.48 48	99.60 34	96.63 161	99.83 119	99.70 11	99.99 11	99.61 50

HQP-MVS		97.00 237	96.49 247	98.55 177	98.67 265	96.79 188	96.29 279	99.04 205	96.05 247	95.55 325	96.84 304	93.84 251	99.54 294	92.82 291	99.26 227	99.32 179

CNLPA		97.17 227	96.71 234	98.55 177	98.56 278	98.05 115	96.33 277	98.93 225	96.91 212	97.06 277	97.39 292	94.38 242	99.45 315	91.66 305	99.18 239	98.14 291

CHOSEN 1792x2688		97.49 203	97.14 215	98.54 180	99.68 44	96.09 220	96.50 268	99.62 28	91.58 322	98.84 152	98.97 142	92.36 271	99.88 63	96.76 162	99.95 30	99.67 32

v11		99.12 41	99.31 28	98.53 181	99.59 57	96.11 217	99.08 50	99.65 20	99.15 58	99.60 30	99.69 17	97.26 116	99.83 119	99.81 3	100.00 1	99.66 34

v1neww		98.70 84	98.76 65	98.52 182	99.47 100	96.30 211	98.03 146	99.18 175	97.92 134	99.26 89	99.08 115	96.91 140	99.78 187	99.19 41	99.82 83	99.47 126

v7new		98.70 84	98.76 65	98.52 182	99.47 100	96.30 211	98.03 146	99.18 175	97.92 134	99.26 89	99.08 115	96.91 140	99.78 187	99.19 41	99.82 83	99.47 126

v6		98.70 84	98.76 65	98.52 182	99.47 100	96.30 211	98.03 146	99.18 175	97.92 134	99.27 84	99.08 115	96.91 140	99.78 187	99.19 41	99.82 83	99.48 118

LF4IMVS		97.90 173	97.69 180	98.52 182	99.17 167	97.66 151	97.19 231	99.47 81	96.31 237	97.85 221	98.20 246	96.71 158	99.52 300	94.62 244	99.72 125	98.38 285

pmmvs4		97.58 197	97.28 208	98.51 186	98.84 235	96.93 185	95.40 320	98.52 267	93.60 298	98.61 176	98.65 197	95.10 221	99.60 276	96.97 149	99.79 98	98.99 234

v7		98.67 94	98.73 69	98.50 187	99.43 114	96.21 215	98.00 155	99.31 132	97.58 162	99.17 103	99.18 94	96.63 161	99.80 156	99.42 27	99.88 65	99.48 118

Patchmtry		97.35 213	96.97 219	98.50 187	97.31 336	96.47 201	98.18 128	98.92 228	98.95 84	98.78 159	99.37 66	85.44 307	99.85 89	95.96 210	99.83 80	99.17 217

DELS-MVS		98.27 146	98.20 138	98.48 189	98.86 229	96.70 194	95.60 313	99.20 165	97.73 152	98.45 189	98.71 186	97.50 96	99.82 131	98.21 89	99.59 166	98.93 242
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
CLD-MVS		97.49 203	97.16 213	98.48 189	99.07 184	97.03 180	94.71 333	99.21 161	94.46 283	98.06 208	97.16 299	97.57 90	99.48 310	94.46 248	99.78 102	98.95 239
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_0304		98.02 165	97.88 172	98.46 191	98.22 302	96.39 206	96.50 268	99.49 71	98.03 130	97.24 272	98.33 236	94.80 232	99.90 47	98.31 86	99.95 30	99.08 223

AdaColmap		97.14 229	96.71 234	98.46 191	98.34 294	97.80 142	96.95 241	98.93 225	95.58 262	96.92 282	97.66 275	95.87 200	99.53 296	90.97 320	99.14 244	98.04 294

v144192		98.54 118	98.57 95	98.45 193	99.21 151	95.98 222	97.63 194	99.36 112	97.15 206	99.32 79	99.18 94	95.84 201	99.84 104	99.50 22	99.91 54	99.54 87

UnsupCasMVSNet_bld		97.30 217	96.92 221	98.45 193	99.28 134	96.78 192	96.20 285	99.27 148	95.42 266	98.28 199	98.30 238	93.16 260	99.71 233	94.99 236	97.37 324	98.87 249

PCF-MVS	92.86 18	94.36 300	93.00 318	98.42 195	98.70 257	97.56 156	93.16 349	99.11 193	79.59 356	97.55 251	97.43 290	92.19 272	99.73 223	79.85 356	99.45 202	97.97 296
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1192		98.60 107	98.66 84	98.41 196	99.27 135	95.88 228	97.52 207	99.36 112	97.41 181	99.33 74	99.20 91	96.37 178	99.82 131	99.57 18	99.92 49	99.55 84

v1144		98.60 107	98.66 84	98.41 196	99.36 122	95.90 227	97.58 201	99.34 122	97.51 169	99.27 84	99.15 104	96.34 179	99.80 156	99.47 24	99.93 39	99.51 100

v1141		98.63 100	98.70 76	98.41 196	99.39 118	95.96 224	97.64 191	99.21 161	97.92 134	99.35 70	99.08 115	96.61 164	99.78 187	99.25 35	99.90 58	99.50 105

divwei89l23v2f112		98.63 100	98.70 76	98.41 196	99.39 118	95.96 224	97.64 191	99.21 161	97.92 134	99.35 70	99.08 115	96.61 164	99.78 187	99.25 35	99.90 58	99.50 105

v1		98.63 100	98.70 76	98.41 196	99.39 118	95.96 224	97.64 191	99.20 165	97.92 134	99.36 68	99.07 120	96.63 161	99.78 187	99.25 35	99.90 58	99.50 105

FMVSNet5		96.01 265	95.20 276	98.41 196	97.53 327	96.10 218	98.74 77	99.50 65	97.22 203	98.03 211	99.04 128	69.80 358	99.88 63	97.27 135	99.71 129	99.25 196

v1921920		98.54 118	98.60 93	98.38 202	99.20 160	95.76 232	97.56 203	99.36 112	97.23 200	99.38 64	99.17 99	96.02 187	99.84 104	99.57 18	99.90 58	99.54 87

v2v482		98.56 111	98.62 88	98.37 203	99.42 115	95.81 231	97.58 201	99.16 184	97.90 142	99.28 82	99.01 134	95.98 193	99.79 177	99.33 30	99.90 58	99.51 100

原ACMM1				98.35 204	98.90 221	96.25 214		98.83 245	92.48 310	96.07 314	98.10 253	95.39 215	99.71 233	92.61 297	98.99 260	99.08 223

casdiffmvs		98.22 153	98.17 141	98.35 204	98.75 247	96.62 197	98.62 83	99.12 190	98.04 129	96.46 305	99.12 108	95.81 202	99.63 266	99.17 44	98.45 289	98.80 258

Vis-MVSNet (Re-imp)		97.46 207	97.16 213	98.34 206	99.55 74	96.10 218	98.94 66	98.44 270	98.32 117	98.16 201	98.62 206	88.76 290	99.73 223	93.88 267	99.79 98	99.18 213

v1240		98.55 115	98.62 88	98.32 207	99.22 145	95.58 236	97.51 209	99.45 86	97.16 204	99.45 55	99.24 84	96.12 184	99.85 89	99.60 14	99.88 65	99.55 84

OpenMVS	96.65 7	97.09 231	96.68 236	98.32 207	98.32 295	97.16 176	98.86 73	99.37 108	89.48 338	96.29 308	99.15 104	96.56 166	99.90 47	92.90 288	99.20 233	97.89 297

Test_1112_low_res		96.99 238	96.55 245	98.31 209	99.35 126	95.47 241	95.84 305	99.53 59	91.51 324	96.80 292	98.48 226	91.36 277	99.83 119	96.58 175	99.53 190	99.62 46

PAPM_NR		96.82 245	96.32 252	98.30 210	99.07 184	96.69 195	97.48 210	98.76 251	95.81 253	96.61 297	96.47 312	94.12 249	99.17 339	90.82 325	97.78 318	99.06 226

FMVSNet3		97.50 201	97.24 209	98.29 211	98.08 307	95.83 230	97.86 170	98.91 230	97.89 143	98.95 135	98.95 146	87.06 294	99.81 144	97.77 110	99.69 139	99.23 200

MSDG		97.71 188	97.52 192	98.28 212	98.91 220	96.82 187	94.42 338	99.37 108	97.65 156	98.37 197	98.29 239	97.40 105	99.33 328	94.09 261	99.22 230	98.68 273

EPNet		96.14 263	95.44 269	98.25 213	90.76 362	95.50 240	97.92 163	94.65 328	98.97 80	92.98 349	98.85 166	89.12 289	99.87 73	95.99 208	99.68 144	99.39 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ambc				98.24 214	98.82 240	95.97 223	98.62 83	99.00 218		99.27 84	99.21 89	96.99 136	99.50 306	96.55 181	99.50 199	99.26 194

PVSNet_Blended_VisFu		98.17 159	98.15 147	98.22 215	99.73 29	95.15 247	97.36 216	99.68 16	94.45 285	98.99 128	99.27 79	96.87 146	99.94 20	97.13 143	99.91 54	99.57 71

Anonymous20231206		98.21 154	98.21 137	98.20 216	99.51 85	95.43 242	98.13 132	99.32 130	96.16 244	98.93 140	98.82 173	96.00 189	99.83 119	97.32 133	99.73 120	99.36 166

CANet		97.87 178	97.76 176	98.19 217	97.75 317	95.51 239	96.76 254	99.05 202	97.74 151	96.93 281	98.21 245	95.59 208	99.89 56	97.86 107	99.93 39	99.19 211

Test4		97.43 209	97.18 211	98.18 218	99.05 192	96.02 221	96.62 264	99.09 195	96.25 239	98.63 173	97.70 273	90.49 280	99.68 244	97.50 124	99.30 220	98.83 252

testgi		98.32 140	98.39 121	98.13 219	99.57 63	95.54 237	97.78 175	99.49 71	97.37 184	99.19 99	97.65 276	98.96 19	99.49 307	96.50 185	98.99 260	99.34 173

test_normal		97.58 197	97.41 199	98.10 220	99.03 197	95.72 233	96.21 283	97.05 302	96.71 221	98.65 168	98.12 251	93.87 250	99.69 239	97.68 119	99.35 211	98.88 248

testdata				98.09 221	98.93 213	95.40 243		98.80 248	90.08 336	97.45 259	98.37 231	95.26 217	99.70 235	93.58 276	98.95 264	99.17 217

IterMVS-LS		98.55 115	98.70 76	98.09 221	99.48 98	94.73 254	97.22 227	99.39 101	98.97 80	99.38 64	99.31 76	96.00 189	99.93 26	98.58 70	99.97 23	99.60 53
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PMMVS		96.51 255	95.98 257	98.09 221	97.53 327	95.84 229	94.92 329	98.84 240	91.58 322	96.05 315	95.58 325	95.68 205	99.66 258	95.59 228	98.09 309	98.76 264

pmmvs5		97.64 193	97.49 194	98.08 224	99.14 174	95.12 249	96.70 258	99.05 202	93.77 296	98.62 174	98.83 170	93.23 258	99.75 209	98.33 85	99.76 115	99.36 166

DI_MVS_plusplus_test		97.57 199	97.40 200	98.07 225	99.06 187	95.71 234	96.58 266	96.96 304	96.71 221	98.69 166	98.13 247	93.81 253	99.68 244	97.45 126	99.19 237	98.80 258

MDA-MVSNet-bldmvs		97.94 172	97.91 169	98.06 226	99.44 110	94.96 251	96.63 263	99.15 187	98.35 112	98.83 153	99.11 110	94.31 243	99.85 89	96.60 174	98.72 271	99.37 160

sss		97.21 224	96.93 220	98.06 226	98.83 237	95.22 245	96.75 255	98.48 269	94.49 281	97.27 271	97.90 265	92.77 267	99.80 156	96.57 177	99.32 216	99.16 220

EI-MVSNet		98.40 134	98.51 99	98.04 228	99.10 177	94.73 254	97.20 228	98.87 234	98.97 80	99.06 113	99.02 132	96.00 189	99.80 156	98.58 70	99.82 83	99.60 53

PMMVS2		98.07 164	98.08 157	98.04 228	99.41 116	94.59 260	94.59 336	99.40 99	97.50 170	98.82 156	98.83 170	96.83 148	99.84 104	97.50 124	99.81 90	99.71 28

v148		98.45 129	98.60 93	98.00 230	99.44 110	94.98 250	97.44 213	99.06 198	98.30 118	99.32 79	98.97 142	96.65 160	99.62 269	98.37 82	99.85 72	99.39 153

Patchmatch-RL test		97.26 220	97.02 217	97.99 231	99.52 82	95.53 238	96.13 287	99.71 12	97.47 173	99.27 84	99.16 100	84.30 315	99.62 269	97.89 102	99.77 106	98.81 255

diffmvs		97.49 203	97.36 204	97.91 232	98.38 292	95.70 235	97.95 160	99.31 132	94.87 276	96.14 309	98.78 178	94.84 228	99.43 317	97.69 117	98.26 292	98.59 275

WTY-MVS		96.67 249	96.27 253	97.87 233	98.81 242	94.61 259	96.77 253	97.92 286	94.94 274	97.12 273	97.74 271	91.11 278	99.82 131	93.89 266	98.15 300	99.18 213

CANet_DTU		97.26 220	97.06 216	97.84 234	97.57 324	94.65 258	96.19 286	98.79 249	97.23 200	95.14 334	98.24 242	93.22 259	99.84 104	97.34 132	99.84 74	99.04 228

OpenMVS_ROB	95.38 14	95.84 268	95.18 277	97.81 235	98.41 290	97.15 177	97.37 215	98.62 264	83.86 352	98.65 168	98.37 231	94.29 244	99.68 244	88.41 332	98.62 280	96.60 337

MVSTER		96.86 242	96.55 245	97.79 236	97.91 314	94.21 271	97.56 203	98.87 234	97.49 172	99.06 113	99.05 126	80.72 327	99.80 156	98.44 78	99.82 83	99.37 160

MVSFormer		98.26 148	98.43 116	97.77 237	98.88 227	93.89 282	99.39 14	99.56 49	99.11 63	98.16 201	98.13 247	93.81 253	99.97 3	99.26 33	99.57 176	99.43 141

jason		97.45 208	97.35 206	97.76 238	99.24 139	93.93 278	95.86 302	98.42 271	94.24 291	98.50 187	98.13 247	94.82 229	99.91 43	97.22 137	99.73 120	99.43 141
jason: jason.
PAPR		95.29 277	94.47 285	97.75 239	97.50 331	95.14 248	94.89 330	98.71 259	91.39 326	95.35 332	95.48 332	94.57 238	99.14 342	84.95 344	97.37 324	98.97 238

test1235678		97.06 233	96.84 227	97.73 240	98.55 280	94.46 267	94.80 331	99.36 112	96.85 215	98.83 153	98.26 240	92.72 268	99.82 131	92.49 299	99.70 132	98.91 245

MIMVSNet		96.62 252	96.25 255	97.71 241	99.04 194	94.66 257	99.16 43	96.92 308	97.23 200	97.87 218	99.10 112	86.11 300	99.65 263	91.65 306	99.21 232	98.82 254

MVS_Test		98.18 157	98.36 125	97.67 242	98.48 284	94.73 254	98.18 128	99.02 211	97.69 154	98.04 210	99.11 110	97.22 122	99.56 290	98.57 72	98.90 266	98.71 267

new_pmnet		96.99 238	96.76 230	97.67 242	98.72 251	94.89 252	95.95 298	98.20 278	92.62 309	98.55 184	98.54 218	94.88 227	99.52 300	93.96 264	99.44 203	98.59 275

lupinMVS		97.06 233	96.86 225	97.65 244	98.88 227	93.89 282	95.48 317	97.97 284	93.53 299	98.16 201	97.58 279	93.81 253	99.91 43	96.77 161	99.57 176	99.17 217

PMVS	91.26 20	97.86 179	97.94 166	97.65 244	99.71 35	97.94 128	98.52 95	98.68 260	98.99 77	97.52 254	99.35 70	97.41 104	98.18 355	91.59 309	99.67 150	96.82 334
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MSLP-MVS++		98.02 165	98.14 149	97.64 246	98.58 276	95.19 246	97.48 210	99.23 160	97.47 173	97.90 216	98.62 206	97.04 130	98.81 352	97.55 120	99.41 205	98.94 241

no-one		97.98 171	98.10 153	97.61 247	99.55 74	93.82 284	96.70 258	98.94 222	96.18 240	99.52 41	99.41 62	95.90 199	99.81 144	96.72 165	99.99 11	99.20 206

PVSNet_BlendedMVS		97.55 200	97.53 191	97.60 248	98.92 217	93.77 286	96.64 262	99.43 94	94.49 281	97.62 244	99.18 94	96.82 149	99.67 250	94.73 241	99.93 39	99.36 166

TinyColmap		97.89 175	97.98 162	97.60 248	98.86 229	94.35 268	96.21 283	99.44 89	97.45 180	99.06 113	98.88 160	97.99 69	99.28 335	94.38 255	99.58 172	99.18 213

BH-RMVSNet		96.83 243	96.58 243	97.58 250	98.47 285	94.05 274	96.67 260	97.36 296	96.70 223	97.87 218	97.98 261	95.14 220	99.44 316	90.47 326	98.58 282	99.25 196

HY-MVS	95.94 13	95.90 266	95.35 271	97.55 251	97.95 311	94.79 253	98.81 76	96.94 307	92.28 314	95.17 333	98.57 212	89.90 284	99.75 209	91.20 318	97.33 328	98.10 292

SD-MVS		98.40 134	98.68 81	97.54 252	98.96 208	97.99 118	97.88 167	99.36 112	98.20 124	99.63 26	99.04 128	98.76 24	95.33 360	96.56 180	99.74 117	99.31 183

PatchT		96.65 250	96.35 250	97.54 252	97.40 333	95.32 244	97.98 157	96.64 315	99.33 41	96.89 287	99.42 60	84.32 314	99.81 144	97.69 117	97.49 321	97.48 321

GA-MVS		95.86 267	95.32 272	97.49 254	98.60 275	94.15 273	93.83 345	97.93 285	95.49 264	96.68 294	97.42 291	83.21 320	99.30 332	96.22 197	98.55 283	99.01 232

PVSNet_Blended		96.88 241	96.68 236	97.47 255	98.92 217	93.77 286	94.71 333	99.43 94	90.98 329	97.62 244	97.36 295	96.82 149	99.67 250	94.73 241	99.56 183	98.98 235

MS-PatchMatch		97.68 190	97.75 177	97.45 256	98.23 301	93.78 285	97.29 220	98.84 240	96.10 246	98.64 170	98.65 197	96.04 186	99.36 324	96.84 157	99.14 244	99.20 206

USDC		97.41 211	97.40 200	97.44 257	98.94 211	93.67 288	95.17 324	99.53 59	94.03 294	98.97 132	99.10 112	95.29 216	99.34 326	95.84 218	99.73 120	99.30 186

API-MVS		97.04 236	96.91 223	97.42 258	97.88 316	98.23 101	98.18 128	98.50 268	97.57 164	97.39 266	96.75 306	96.77 153	99.15 341	90.16 327	99.02 256	94.88 353

MDA-MVSNet_test_wron		97.60 195	97.66 184	97.41 259	99.04 194	93.09 293	95.27 321	98.42 271	97.26 194	98.88 147	98.95 146	95.43 214	99.73 223	97.02 147	98.72 271	99.41 146

YYNet1		97.60 195	97.67 181	97.39 260	99.04 194	93.04 296	95.27 321	98.38 273	97.25 195	98.92 141	98.95 146	95.48 213	99.73 223	96.99 148	98.74 270	99.41 146

CR-MVSNet		96.28 261	95.95 258	97.28 261	97.71 319	94.22 269	98.11 135	98.92 228	92.31 313	96.91 284	99.37 66	85.44 307	99.81 144	97.39 130	97.36 326	97.81 303

RPMNet		96.82 245	96.66 239	97.28 261	97.71 319	94.22 269	98.11 135	96.90 309	99.37 37	96.91 284	99.34 72	86.72 295	99.81 144	97.53 122	97.36 326	97.81 303

MG-MVS		96.77 247	96.61 241	97.26 263	98.31 296	93.06 294	95.93 299	98.12 281	96.45 232	97.92 213	98.73 184	93.77 256	99.39 321	91.19 319	99.04 255	99.33 178

new-patchmatchnet		98.35 137	98.74 68	97.18 264	99.24 139	92.23 304	96.42 274	99.48 74	98.30 118	99.69 17	99.53 45	97.44 102	99.82 131	98.84 60	99.77 106	99.49 112

Patchmatch-test		96.55 254	96.34 251	97.17 265	98.35 293	93.06 294	98.40 117	97.79 287	97.33 187	98.41 193	98.67 193	83.68 319	99.69 239	95.16 233	99.31 218	98.77 262

BH-untuned		96.83 243	96.75 231	97.08 266	98.74 249	93.33 292	96.71 257	98.26 276	96.72 219	98.44 190	97.37 294	95.20 218	99.47 311	91.89 303	97.43 323	98.44 281

FPMVS		93.44 318	92.23 323	97.08 266	99.25 138	97.86 134	95.61 312	97.16 300	92.90 305	93.76 348	98.65 197	75.94 355	95.66 358	79.30 357	97.49 321	97.73 308

conf0.01		94.82 289	94.07 295	97.06 268	99.21 151	94.53 261	98.47 107	92.69 345	95.61 256	97.81 229	95.54 326	77.71 346	99.80 156	91.49 311	98.11 302	96.86 330

conf0.002		94.82 289	94.07 295	97.06 268	99.21 151	94.53 261	98.47 107	92.69 345	95.61 256	97.81 229	95.54 326	77.71 346	99.80 156	91.49 311	98.11 302	96.86 330

JIA-IIPM		95.52 273	95.03 280	97.00 270	96.85 344	94.03 275	96.93 243	95.82 323	99.20 52	94.63 338	99.71 14	83.09 321	99.60 276	94.42 251	94.64 348	97.36 323

test0.0.03 1		94.51 298	93.69 308	96.99 271	96.05 353	93.61 289	94.97 328	93.49 342	96.17 241	97.57 250	94.88 345	82.30 324	99.01 346	93.60 275	94.17 353	98.37 287

pmmvs3		95.03 281	94.40 290	96.93 272	97.70 321	92.53 299	95.08 326	97.71 291	88.57 342	97.71 239	98.08 256	79.39 340	99.82 131	96.19 199	99.11 250	98.43 282

xiu_mvs_v1_base_debu		97.86 179	98.17 141	96.92 273	98.98 205	93.91 279	96.45 271	99.17 181	97.85 148	98.41 193	97.14 301	98.47 39	99.92 34	98.02 97	99.05 252	96.92 327

xiu_mvs_v1_base		97.86 179	98.17 141	96.92 273	98.98 205	93.91 279	96.45 271	99.17 181	97.85 148	98.41 193	97.14 301	98.47 39	99.92 34	98.02 97	99.05 252	96.92 327

xiu_mvs_v1_base_debi		97.86 179	98.17 141	96.92 273	98.98 205	93.91 279	96.45 271	99.17 181	97.85 148	98.41 193	97.14 301	98.47 39	99.92 34	98.02 97	99.05 252	96.92 327

semantic-postprocess				96.87 276	99.27 135	91.16 325		99.25 154	99.10 67	99.41 60	99.35 70	92.91 265	99.96 8	98.65 68	99.94 33	99.49 112

mvs_anonymous		97.83 185	98.16 145	96.87 276	98.18 304	91.89 306	97.31 219	98.90 231	97.37 184	98.83 153	99.46 53	96.28 180	99.79 177	98.90 55	98.16 299	98.95 239

DSMNet-mixed		97.42 210	97.60 189	96.87 276	99.15 173	91.46 311	98.54 93	99.12 190	92.87 306	97.58 248	99.63 27	96.21 181	99.90 47	95.74 221	99.54 186	99.27 191

TR-MVS		95.55 272	95.12 278	96.86 279	97.54 326	93.94 277	96.49 270	96.53 317	94.36 288	97.03 279	96.61 308	94.26 245	99.16 340	86.91 337	96.31 339	97.47 322

ppachtmachnet_test		97.50 201	97.74 178	96.78 280	98.70 257	91.23 324	94.55 337	99.05 202	96.36 234	99.21 97	98.79 177	96.39 175	99.78 187	96.74 163	99.82 83	99.34 173

ADS-MVSNet2		95.43 276	94.98 281	96.76 281	98.14 305	91.74 307	97.92 163	97.76 288	90.23 332	96.51 301	98.91 151	85.61 304	99.85 89	92.88 289	96.90 332	98.69 270

LP		96.60 253	96.57 244	96.68 282	97.64 323	91.70 308	98.11 135	97.74 289	97.29 193	97.91 215	99.24 84	88.35 291	99.85 89	97.11 145	95.76 343	98.49 278

thresconf0.02		94.70 293	94.07 295	96.58 283	99.21 151	94.53 261	98.47 107	92.69 345	95.61 256	97.81 229	95.54 326	77.71 346	99.80 156	91.49 311	98.11 302	95.42 349

tfpn_n400		94.70 293	94.07 295	96.58 283	99.21 151	94.53 261	98.47 107	92.69 345	95.61 256	97.81 229	95.54 326	77.71 346	99.80 156	91.49 311	98.11 302	95.42 349

tfpnconf		94.70 293	94.07 295	96.58 283	99.21 151	94.53 261	98.47 107	92.69 345	95.61 256	97.81 229	95.54 326	77.71 346	99.80 156	91.49 311	98.11 302	95.42 349

tfpnview11		94.70 293	94.07 295	96.58 283	99.21 151	94.53 261	98.47 107	92.69 345	95.61 256	97.81 229	95.54 326	77.71 346	99.80 156	91.49 311	98.11 302	95.42 349

IterMVS		97.73 187	98.11 151	96.57 287	99.24 139	90.28 326	95.52 316	99.21 161	98.86 88	99.33 74	99.33 74	93.11 261	99.94 20	98.49 76	99.94 33	99.48 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PAPM		91.88 329	90.34 331	96.51 288	98.06 308	92.56 298	92.44 352	97.17 299	86.35 348	90.38 356	96.01 317	86.61 296	99.21 337	70.65 359	95.43 345	97.75 307

MVS		93.19 320	92.09 324	96.50 289	96.91 342	94.03 275	98.07 140	98.06 283	68.01 357	94.56 339	96.48 311	95.96 195	99.30 332	83.84 348	96.89 334	96.17 340

tfpn1000		94.81 291	94.25 294	96.47 290	99.01 201	93.47 291	98.56 90	92.30 354	96.17 241	97.90 216	96.29 315	76.70 352	99.77 198	93.02 285	98.29 291	96.16 341

our_test_3		97.39 212	97.73 179	96.34 291	98.70 257	89.78 328	94.61 335	98.97 221	96.50 229	99.04 121	98.85 166	95.98 193	99.84 104	97.26 136	99.67 150	99.41 146

thres600view7		94.45 299	93.83 304	96.29 292	99.06 187	91.53 310	97.99 156	94.24 337	98.34 113	97.44 260	95.01 339	79.84 334	99.67 250	84.33 346	98.23 293	97.66 310

IB-MVS	91.63 19	92.24 327	90.90 330	96.27 293	97.22 338	91.24 323	94.36 339	93.33 344	92.37 312	92.24 351	94.58 349	66.20 363	99.89 56	93.16 284	94.63 349	97.66 310
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
view600		94.87 284	94.41 286	96.26 294	99.22 145	91.37 314	98.49 101	94.45 330	98.75 92	97.85 221	95.98 319	80.38 329	99.75 209	86.06 340	98.49 284	97.66 310

view800		94.87 284	94.41 286	96.26 294	99.22 145	91.37 314	98.49 101	94.45 330	98.75 92	97.85 221	95.98 319	80.38 329	99.75 209	86.06 340	98.49 284	97.66 310

conf0.05thres1000		94.87 284	94.41 286	96.26 294	99.22 145	91.37 314	98.49 101	94.45 330	98.75 92	97.85 221	95.98 319	80.38 329	99.75 209	86.06 340	98.49 284	97.66 310

tfpn		94.87 284	94.41 286	96.26 294	99.22 145	91.37 314	98.49 101	94.45 330	98.75 92	97.85 221	95.98 319	80.38 329	99.75 209	86.06 340	98.49 284	97.66 310

thres400		94.14 307	93.44 313	96.24 298	98.93 213	91.44 312	97.60 198	94.29 335	97.94 132	97.10 274	94.31 350	79.67 338	99.62 269	83.05 349	98.08 310	97.66 310

ADS-MVSNet		95.24 278	94.93 282	96.18 299	98.14 305	90.10 327	97.92 163	97.32 297	90.23 332	96.51 301	98.91 151	85.61 304	99.74 218	92.88 289	96.90 332	98.69 270

xiu_mvs_v2_base		97.16 228	97.49 194	96.17 300	98.54 281	92.46 300	95.45 318	98.84 240	97.25 195	97.48 257	96.49 310	98.31 47	99.90 47	96.34 194	98.68 276	96.15 343

1314		95.74 269	95.60 266	96.17 300	97.53 327	92.75 297	98.07 140	98.31 275	91.22 327	94.25 341	96.68 307	95.53 209	99.03 343	91.64 307	97.18 329	96.74 335

PS-MVSNAJ		97.08 232	97.39 202	96.16 302	98.56 278	92.46 300	95.24 323	98.85 239	97.25 195	97.49 256	95.99 318	98.07 61	99.90 47	96.37 192	98.67 277	96.12 344

cascas		94.79 292	94.33 293	96.15 303	96.02 355	92.36 303	92.34 353	99.26 153	85.34 351	95.08 335	94.96 344	92.96 264	98.53 353	94.41 254	98.59 281	97.56 319

testus		95.52 273	95.32 272	96.13 304	97.91 314	89.49 330	93.62 346	99.61 30	92.41 311	97.38 268	95.42 335	94.72 236	99.63 266	88.06 334	98.72 271	99.26 194

test2356		91.64 331	90.19 334	96.00 305	94.30 359	89.58 329	90.84 354	96.68 313	91.76 317	95.48 330	93.69 354	67.05 361	99.52 300	84.83 345	97.08 331	98.91 245

tfpn111		94.33 301	93.78 305	95.96 306	99.06 187	91.35 318	98.03 146	94.24 337	98.33 114	97.40 263	94.98 341	79.84 334	99.68 244	83.94 347	98.22 295	96.86 330

tfpn_ndepth		94.12 308	93.51 312	95.94 307	98.86 229	93.60 290	98.16 131	91.90 356	94.66 280	97.41 262	95.24 336	76.24 353	99.73 223	91.21 317	97.88 317	94.50 354

conf200view11		94.24 304	93.67 309	95.94 307	99.06 187	91.35 318	98.03 146	94.24 337	98.33 114	97.40 263	94.98 341	79.84 334	99.62 269	83.05 349	98.08 310	96.86 330

BH-w/o		95.13 279	94.89 283	95.86 309	98.20 303	91.31 321	95.65 311	97.37 295	93.64 297	96.52 300	95.70 324	93.04 263	99.02 344	88.10 333	95.82 342	97.24 325

gg-mvs-nofinetune		92.37 325	91.20 329	95.85 310	95.80 356	92.38 302	99.31 21	81.84 363	99.75 4	91.83 352	99.74 8	68.29 359	99.02 344	87.15 336	97.12 330	96.16 341

tfpn200view9		94.03 310	93.44 313	95.78 311	98.93 213	91.44 312	97.60 198	94.29 335	97.94 132	97.10 274	94.31 350	79.67 338	99.62 269	83.05 349	98.08 310	96.29 338

thres100view900		94.19 305	93.67 309	95.75 312	99.06 187	91.35 318	98.03 146	94.24 337	98.33 114	97.40 263	94.98 341	79.84 334	99.62 269	83.05 349	98.08 310	96.29 338

tpm		94.67 297	94.34 292	95.66 313	97.68 322	88.42 332	97.88 167	94.90 327	94.46 283	96.03 316	98.56 215	78.66 341	99.79 177	95.88 212	95.01 347	98.78 261

CHOSEN 280x420		95.51 275	95.47 267	95.65 314	98.25 297	88.27 334	93.25 348	98.88 233	93.53 299	94.65 337	97.15 300	86.17 298	99.93 26	97.41 129	99.93 39	98.73 266

Patchmatch-test1		96.44 259	96.72 232	95.60 315	98.24 299	88.35 333	95.85 304	96.88 310	96.11 245	97.67 242	98.57 212	93.10 262	99.69 239	94.79 239	99.22 230	98.77 262

PVSNet	93.40 17	95.67 270	95.70 262	95.57 316	98.83 237	88.57 331	92.50 351	97.72 290	92.69 308	96.49 304	96.44 313	93.72 257	99.43 317	93.61 274	99.28 224	98.71 267

thres200		93.72 315	93.14 316	95.46 317	98.66 270	91.29 322	96.61 265	94.63 329	97.39 183	96.83 290	93.71 353	79.88 333	99.56 290	82.40 353	98.13 301	95.54 348

EPNet_dtu		94.93 283	94.78 284	95.38 318	93.58 361	87.68 336	96.78 252	95.69 325	97.35 186	89.14 357	98.09 255	88.15 292	99.49 307	94.95 238	99.30 220	98.98 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		95.58 271	95.67 264	95.30 319	97.34 335	87.32 337	97.65 190	96.65 314	95.30 267	97.07 276	98.69 189	84.77 309	99.75 209	94.97 237	98.64 278	98.83 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EU-MVSNet		97.66 192	98.50 101	95.13 320	99.63 53	85.84 342	98.35 119	98.21 277	98.23 123	99.54 36	99.46 53	95.02 222	99.68 244	98.24 87	99.87 69	99.87 6

EPMVS		93.72 315	93.27 315	95.09 321	96.04 354	87.76 335	98.13 132	85.01 361	94.69 279	96.92 282	98.64 200	78.47 344	99.31 330	95.04 234	96.46 338	98.20 289

DWT-MVSNet_test		92.75 323	92.05 325	94.85 322	96.48 349	87.21 338	97.83 173	94.99 326	92.22 315	92.72 350	94.11 352	70.75 357	99.46 313	95.01 235	94.33 352	97.87 299

1111		93.99 311	93.72 307	94.80 323	99.33 129	85.20 346	95.97 291	99.39 101	97.88 144	98.64 170	98.56 215	57.79 366	99.80 156	96.02 206	99.87 69	99.40 152

GG-mvs-BLEND				94.76 324	94.54 358	92.13 305	99.31 21	80.47 364		88.73 358	91.01 358	67.59 360	98.16 356	82.30 354	94.53 350	93.98 355

tpm2		93.09 321	92.58 320	94.62 325	97.56 325	86.53 340	97.66 188	95.79 324	86.15 349	94.07 345	98.23 244	75.95 354	99.53 296	90.91 322	96.86 335	97.81 303

PatchFormer-LS_test		94.08 309	93.91 302	94.59 326	96.93 341	86.86 339	97.55 205	96.57 316	94.27 290	94.38 340	93.64 355	80.96 326	99.59 280	96.44 190	94.48 351	97.31 324

CostFormer		93.97 312	93.78 305	94.51 327	97.53 327	85.83 343	97.98 157	95.96 322	89.29 340	94.99 336	98.63 204	78.63 342	99.62 269	94.54 246	96.50 337	98.09 293

tpmvs		95.02 282	95.25 274	94.33 328	96.39 351	85.87 341	98.08 138	96.83 311	95.46 265	95.51 329	98.69 189	85.91 301	99.53 296	94.16 256	96.23 340	97.58 318

tpmp4_e23		92.91 322	92.45 321	94.29 329	97.41 332	85.62 345	97.95 160	96.77 312	87.55 347	91.33 354	98.57 212	74.21 356	99.59 280	91.62 308	96.64 336	97.65 317

MVE	83.40 22	92.50 324	91.92 326	94.25 330	98.83 237	91.64 309	92.71 350	83.52 362	95.92 251	86.46 360	95.46 333	95.20 218	95.40 359	80.51 355	98.64 278	95.73 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test-LLR		93.90 313	93.85 303	94.04 331	96.53 347	84.62 350	94.05 341	92.39 352	96.17 241	94.12 343	95.07 337	82.30 324	99.67 250	95.87 215	98.18 297	97.82 301

test-mter		92.33 326	91.76 328	94.04 331	96.53 347	84.62 350	94.05 341	92.39 352	94.00 295	94.12 343	95.07 337	65.63 365	99.67 250	95.87 215	98.18 297	97.82 301

test12356		94.85 288	95.12 278	94.03 333	98.25 297	83.12 355	93.85 344	99.33 127	94.17 293	97.28 270	97.20 296	85.83 302	99.75 209	90.85 324	99.33 214	99.22 204

tpmrst		95.07 280	95.46 268	93.91 334	97.11 339	84.36 352	97.62 195	96.96 304	94.98 272	96.35 307	98.80 175	85.46 306	99.59 280	95.60 227	96.23 340	97.79 306

tpm cat1		93.29 319	93.13 317	93.75 335	97.39 334	84.74 349	97.39 214	97.65 293	83.39 354	94.16 342	98.41 228	82.86 323	99.39 321	91.56 310	95.35 346	97.14 326

PVSNet_0	89.98 21	91.15 332	90.30 332	93.70 336	97.72 318	84.34 353	90.24 355	97.42 294	90.20 335	93.79 347	93.09 356	90.90 279	98.89 350	86.57 338	72.76 359	97.87 299

E-PMN		94.17 306	94.37 291	93.58 337	96.86 343	85.71 344	90.11 356	97.07 301	98.17 127	97.82 228	97.19 297	84.62 311	98.94 347	89.77 328	97.68 320	96.09 345

TESTMET0.1,1		92.19 328	91.77 327	93.46 338	96.48 349	82.80 357	94.05 341	91.52 357	94.45 285	94.00 346	94.88 345	66.65 362	99.56 290	95.78 220	98.11 302	98.02 295

DeepMVS_CX				93.44 339	98.24 299	94.21 271		94.34 334	64.28 358	91.34 353	94.87 347	89.45 288	92.77 361	77.54 358	93.14 354	93.35 356

CVMVSNet		96.25 262	97.21 210	93.38 340	99.10 177	80.56 360	97.20 228	98.19 280	96.94 210	99.00 127	99.02 132	89.50 287	99.80 156	96.36 193	99.59 166	99.78 15

EMVS		93.83 314	94.02 301	93.23 341	96.83 345	84.96 348	89.77 357	96.32 319	97.92 134	97.43 261	96.36 314	86.17 298	98.93 348	87.68 335	97.73 319	95.81 346

dp		93.47 317	93.59 311	93.13 342	96.64 346	81.62 359	97.66 188	96.42 318	92.80 307	96.11 311	98.64 200	78.55 343	99.59 280	93.31 282	92.18 357	98.16 290

wuyk23d		96.06 264	97.62 188	91.38 343	98.65 271	98.57 77	98.85 74	96.95 306	96.86 214	99.90 5	99.16 100	99.18 12	98.40 354	89.23 330	99.77 106	77.18 359

MVS-HIRNet		94.32 302	95.62 265	90.42 344	98.46 286	75.36 361	96.29 279	89.13 359	95.25 268	95.38 331	99.75 7	92.88 266	99.19 338	94.07 262	99.39 207	96.72 336

PNet_i23d		91.80 330	92.35 322	90.14 345	98.65 271	73.10 364	89.22 358	99.02 211	95.23 270	97.87 218	97.82 268	78.45 345	98.89 350	88.73 331	86.14 358	98.42 283

testpf		89.08 333	90.27 333	85.50 346	94.03 360	82.85 356	96.87 249	91.09 358	91.61 321	90.96 355	94.86 348	66.15 364	95.83 357	94.58 245	92.27 356	77.82 358

tmp_tt		78.77 335	78.73 336	78.90 347	58.45 363	74.76 363	94.20 340	78.26 365	39.16 359	86.71 359	92.82 357	80.50 328	75.19 362	86.16 339	92.29 355	86.74 357

.test1245		79.71 334	84.30 335	65.96 348	99.33 129	85.20 346	95.97 291	99.39 101	97.88 144	98.64 170	98.56 215	57.79 366	99.80 156	96.02 206	15.07 360	12.86 361

pcd1.5k->3k		41.59 336	44.35 337	33.30 349	99.87 12	0.00 367	0.00 359	99.58 36	0.00 362	0.00 363	0.00 364	99.70 2	0.00 365	0.00 362	99.99 11	99.91 2

test123		17.04 339	20.11 340	7.82 350	10.25 365	4.91 365	94.80 331	4.47 367	4.93 360	10.00 362	24.28 361	9.69 368	3.64 363	10.14 360	12.43 362	14.92 360

testmvs		17.12 338	20.53 339	6.87 351	12.05 364	4.20 366	93.62 346	6.73 366	4.62 361	10.41 361	24.33 360	8.28 369	3.56 364	9.69 361	15.07 360	12.86 361

cdsmvs_eth3d_5k		24.66 337	32.88 338	0.00 352	0.00 366	0.00 367	0.00 359	99.10 194	0.00 362	0.00 363	97.58 279	99.21 11	0.00 365	0.00 362	0.00 363	0.00 363

pcd_1.5k_mvsjas		8.17 340	10.90 341	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	98.07 61	0.00 365	0.00 362	0.00 363	0.00 363

sosnet-low-res		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

sosnet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uncertanet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

Regformer		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

ab-mvs-re		8.12 341	10.83 342	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	97.48 286	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

uanet		0.00 342	0.00 343	0.00 352	0.00 366	0.00 367	0.00 359	0.00 368	0.00 362	0.00 363	0.00 364	0.00 370	0.00 365	0.00 362	0.00 363	0.00 363

GSMVS																98.81 255

test_part3							97.25 222		96.66 224		98.71 186		99.86 78	93.00 286

test_part2					99.36 122	99.10 43				99.05 118

test_part1								99.28 143				97.56 91			99.57 176	99.53 92

sam_mvs1												84.74 310				98.81 255

sam_mvs												84.29 316

MTGPA								99.20 165

test_post1							97.59 200				20.48 363	83.07 322	99.66 258	94.16 256

test_post											21.25 362	83.86 318	99.70 235

patchmatchnet-post											98.77 180	84.37 313	99.85 89

MTMP							97.93 162	91.91 355

gm-plane-assit					94.83 357	81.97 358			88.07 344		94.99 340		99.60 276	91.76 304

test9_res														93.28 283	99.15 243	99.38 159

TEST9					98.71 253	98.08 111	95.96 295	99.03 207	91.40 325	95.85 317	97.53 281	96.52 168	99.76 203

test_8					98.67 265	98.01 117	95.91 301	99.02 211	91.64 319	95.79 319	97.50 284	96.47 171	99.76 203

agg_prior2														92.50 298	99.16 240	99.37 160

agg_prior					98.68 262	97.99 118		99.01 214		95.59 321			99.77 198

test_prior4						97.97 123	95.86 302

test_prior2							95.74 308		96.48 230	96.11 311	97.63 277	95.92 197		94.16 256	99.20 233

旧先验2							95.76 306		88.56 343	97.52 254			99.66 258	94.48 247

新几何2							95.93 299

旧先验1					98.82 240	97.45 162		98.76 251			98.34 234	95.50 212			99.01 258	99.23 200

无先验							95.74 308	98.74 256	89.38 339				99.73 223	92.38 300		99.22 204

原ACMM2							95.53 315

test222					98.92 217	96.93 185	95.54 314	98.78 250	85.72 350	96.86 289	98.11 252	94.43 240			99.10 251	99.23 200

testdata2													99.79 177	92.80 293

segment_acmp												97.02 134

testdata1							95.44 319		96.32 236

plane_prior7					99.19 161	97.87 133

plane_prior6					98.99 204	97.70 150						94.90 224

plane_prior5								99.27 148					99.70 235	94.42 251	99.51 194	99.45 134

plane_prior4											97.98 261

plane_prior3						97.78 143			97.41 181	97.79 235

plane_prior2							97.77 177		98.20 124

plane_prior1					99.05 192

plane_prior						97.65 152	97.07 237		96.72 219						99.36 209

n2								0.00 368

nn								0.00 368

door-mid								99.57 43

test11								98.87 234

door								99.41 98

HQP5-MVS						96.79 188

HQP-NCC					98.67 265		96.29 279		96.05 247	95.55 325

ACMP_Plane					98.67 265		96.29 279		96.05 247	95.55 325

BP-MVS														92.82 291

HQP4-MVS										95.56 324			99.54 294			99.32 179

HQP3-MVS								99.04 205							99.26 227

HQP2-MVS												93.84 251

NP-MVS					98.84 235	97.39 165					96.84 304

MDTV_nov1_ep13_2view						74.92 362	97.69 185		90.06 337	97.75 238		85.78 303		93.52 277		98.69 270

MDTV_nov1_ep13			95.22 275		97.06 340	83.20 354	97.74 181	96.16 320	94.37 287	96.99 280	98.83 170	83.95 317	99.53 296	93.90 265	97.95 315

ACMMP++_ref															99.77 106

ACMMP++															99.68 144

Test By Simon												96.52 168

High-resolution multi-view benchmark