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

pmmvs6		99.67 2	99.70 3	99.60 11	99.90 4	99.27 15	99.53 9	99.76 6	99.64 10	99.84 8	99.83 2	99.50 5	99.87 74	99.36 29	99.92 48	99.64 42

LTVRE_ROB	98.40 1	99.67 2	99.71 2	99.56 18	99.85 17	99.11 42	99.90 1	99.78 4	99.63 12	99.78 10	99.67 21	99.48 6	99.81 144	99.30 33	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
mvs_tets		99.63 4	99.67 4	99.49 44	99.88 7	98.61 72	99.34 16	99.71 11	99.27 46	99.90 4	99.74 7	99.68 3	99.97 3	99.55 20	99.99 11	99.88 5

v52		99.59 5	99.60 7	99.55 20	99.87 11	99.00 47	99.59 6	99.56 48	99.56 22	99.68 20	99.72 10	98.57 33	99.93 25	99.85 1	99.99 11	99.72 25

V4		99.59 5	99.60 7	99.55 20	99.87 11	99.00 47	99.59 6	99.56 48	99.56 22	99.68 20	99.72 10	98.57 33	99.93 25	99.85 1	99.99 11	99.72 25

jajsoiax		99.58 7	99.61 6	99.48 45	99.87 11	98.61 72	99.28 30	99.66 18	99.09 70	99.89 7	99.68 18	99.53 4	99.97 3	99.50 22	99.99 11	99.87 6

ANet_high		99.57 8	99.67 4	99.28 71	99.89 6	98.09 108	99.14 45	99.93 1	99.82 2	99.93 2	99.81 3	99.17 13	99.94 20	99.31 32	100.00 1	99.82 10

v7n		99.53 9	99.57 9	99.41 53	99.88 7	98.54 80	99.45 11	99.61 29	99.66 9	99.68 20	99.66 22	98.44 41	99.95 13	99.73 8	99.96 28	99.75 22

test_djsdf		99.52 10	99.51 10	99.53 32	99.86 15	98.74 61	99.39 14	99.56 48	99.11 63	99.70 15	99.73 9	99.00 16	99.97 3	99.26 34	99.98 19	99.89 3

anonymousdsp		99.51 11	99.47 14	99.62 5	99.88 7	99.08 46	99.34 16	99.69 14	98.93 85	99.65 23	99.72 10	98.93 19	99.95 13	99.11 48	100.00 1	99.82 10

UA-Net		99.47 12	99.40 16	99.70 2	99.49 92	99.29 12	99.80 3	99.72 10	99.82 2	99.04 121	99.81 3	98.05 63	99.96 8	98.85 59	99.99 11	99.86 8

PS-MVSNAJss		99.46 13	99.49 11	99.35 62	99.90 4	98.15 104	99.20 36	99.65 19	99.48 25	99.92 3	99.71 13	98.07 60	99.96 8	99.53 21	100.00 1	99.93 1

v748		99.44 14	99.48 12	99.33 67	99.88 7	98.43 87	99.42 12	99.53 58	99.63 12	99.69 17	99.60 34	97.99 68	99.91 43	99.60 14	99.96 28	99.66 34

pm-mvs1		99.44 14	99.48 12	99.33 67	99.80 21	98.63 69	99.29 26	99.63 24	99.30 43	99.65 23	99.60 34	99.16 15	99.82 131	99.07 50	99.83 80	99.56 77

TransMVSNet (Re)		99.44 14	99.47 14	99.36 57	99.80 21	98.58 75	99.27 32	99.57 42	99.39 34	99.75 12	99.62 28	99.17 13	99.83 118	99.06 51	99.62 160	99.66 34

DTE-MVSNet		99.43 17	99.35 21	99.66 3	99.71 34	99.30 11	99.31 21	99.51 63	99.64 10	99.56 34	99.46 53	98.23 49	99.97 3	98.78 63	99.93 38	99.72 25

TDRefinement		99.42 18	99.38 18	99.55 20	99.76 26	99.33 10	99.68 4	99.71 11	99.38 36	99.53 39	99.61 30	98.64 28	99.80 156	98.24 90	99.84 74	99.52 97

PEN-MVS		99.41 19	99.34 23	99.62 5	99.73 28	99.14 35	99.29 26	99.54 57	99.62 16	99.56 34	99.42 60	98.16 56	99.96 8	98.78 63	99.93 38	99.77 16

nrg030		99.40 20	99.35 21	99.54 25	99.58 57	99.13 38	98.98 64	99.48 74	99.68 7	99.46 52	99.26 81	98.62 29	99.73 226	99.17 46	99.92 48	99.76 20

PS-CasMVS		99.40 20	99.33 25	99.62 5	99.71 34	99.10 43	99.29 26	99.53 58	99.53 24	99.46 52	99.41 62	98.23 49	99.95 13	98.89 58	99.95 30	99.81 12

MIMVSNet1		99.38 22	99.32 26	99.55 20	99.86 15	99.19 24	99.41 13	99.59 33	99.59 19	99.71 14	99.57 39	97.12 124	99.90 47	99.21 40	99.87 69	99.54 88

OurMVSNet-221017-0		99.37 23	99.31 27	99.53 32	99.91 3	98.98 49	99.63 5	99.58 35	99.44 30	99.78 10	99.76 5	96.39 176	99.92 33	99.44 26	99.92 48	99.68 31

Anonymous20240521		99.36 24	99.31 27	99.53 32	99.80 21	98.97 50	99.54 8	99.48 74	99.44 30	99.58 33	99.55 41	97.17 121	99.88 64	99.34 30	99.91 53	99.74 24

wuykxyi23d		99.36 24	99.31 27	99.50 43	99.81 20	98.67 68	98.08 140	99.75 7	98.03 134	99.90 4	99.60 34	99.18 11	99.94 20	99.46 25	99.98 19	99.89 3

Vis-MVSNet		99.34 26	99.36 20	99.27 74	99.73 28	98.26 94	99.17 42	99.78 4	99.11 63	99.27 85	99.48 51	98.82 21	99.95 13	98.94 55	99.93 38	99.59 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
WR-MVS_H		99.33 27	99.22 37	99.65 4	99.71 34	99.24 18	99.32 18	99.55 53	99.46 28	99.50 46	99.34 72	97.30 108	99.93 25	98.90 56	99.93 38	99.77 16

VPA-MVSNet		99.30 28	99.30 31	99.28 71	99.49 92	98.36 92	99.00 61	99.45 86	99.63 12	99.52 41	99.44 58	98.25 47	99.88 64	99.09 49	99.84 74	99.62 47

Anonymous20231211		99.27 29	99.27 33	99.26 76	99.29 134	98.18 102	99.49 10	99.51 63	99.70 6	99.80 9	99.68 18	96.84 146	99.83 118	99.21 40	99.91 53	99.77 16

FC-MVSNet-test		99.27 29	99.25 35	99.34 65	99.77 25	98.37 91	99.30 25	99.57 42	99.61 18	99.40 63	99.50 47	97.12 124	99.85 88	99.02 53	99.94 33	99.80 13

ACMH	96.65 7	99.25 31	99.24 36	99.26 76	99.72 33	98.38 90	99.07 53	99.55 53	98.30 120	99.65 23	99.45 57	99.22 9	99.76 206	98.44 81	99.77 106	99.64 42
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v13		99.24 32	99.39 17	98.77 144	99.63 52	96.79 189	99.24 34	99.65 19	99.39 34	99.62 27	99.70 15	97.50 94	99.84 103	99.78 5	100.00 1	99.67 32

v12		99.21 33	99.37 19	98.74 152	99.60 55	96.72 194	99.19 40	99.65 19	99.35 40	99.62 27	99.69 16	97.43 101	99.83 118	99.76 6	100.00 1	99.66 34

CP-MVSNet		99.21 33	99.09 46	99.56 18	99.65 47	98.96 54	99.13 47	99.34 124	99.42 32	99.33 75	99.26 81	97.01 133	99.94 20	98.74 67	99.93 38	99.79 14

V9		99.18 35	99.34 23	98.70 153	99.58 57	96.63 197	99.14 45	99.64 23	99.30 43	99.61 29	99.68 18	97.33 106	99.83 118	99.75 7	100.00 1	99.65 39

TranMVSNet+NR-MVSNet		99.17 36	99.07 48	99.46 50	99.37 120	98.87 56	98.39 120	99.42 97	99.42 32	99.36 69	99.06 124	98.38 43	99.95 13	98.34 86	99.90 58	99.57 72

FMVSNet1		99.17 36	99.17 40	99.17 83	99.55 73	98.24 96	99.20 36	99.44 89	99.21 49	99.43 58	99.55 41	97.82 78	99.86 79	98.42 83	99.89 64	99.41 145

FIs		99.14 38	99.09 46	99.29 70	99.70 40	98.28 93	99.13 47	99.52 62	99.48 25	99.24 93	99.41 62	96.79 152	99.82 131	98.69 69	99.88 65	99.76 20

V14		99.14 38	99.30 31	98.66 156	99.56 69	96.53 199	99.08 50	99.63 24	99.24 48	99.60 30	99.66 22	97.23 118	99.82 131	99.73 8	100.00 1	99.65 39

XXY-MVS		99.14 38	99.15 44	99.10 95	99.76 26	97.74 147	98.85 74	99.62 27	98.48 111	99.37 67	99.49 50	98.75 24	99.86 79	98.20 93	99.80 94	99.71 28

v11		99.12 41	99.31 27	98.53 182	99.59 56	96.11 219	99.08 50	99.65 19	99.15 58	99.60 30	99.69 16	97.26 114	99.83 118	99.81 3	100.00 1	99.66 34

v15		99.11 42	99.27 33	98.62 162	99.52 81	96.43 203	99.01 56	99.63 24	99.18 57	99.59 32	99.64 26	97.13 123	99.81 144	99.71 10	100.00 1	99.64 42

ACMH+	96.62 9	99.08 43	99.00 50	99.33 67	99.71 34	98.83 57	98.60 88	99.58 35	99.11 63	99.53 39	99.18 95	98.81 22	99.67 253	96.71 171	99.77 106	99.50 104

v17		99.07 44	99.22 37	98.61 165	99.50 86	96.42 204	99.01 56	99.60 31	99.15 58	99.48 48	99.61 30	97.05 127	99.81 144	99.64 12	99.98 19	99.61 51

v16		99.07 44	99.22 37	98.61 165	99.50 86	96.42 204	99.01 56	99.60 31	99.15 58	99.46 52	99.61 30	97.04 128	99.81 144	99.64 12	99.97 23	99.61 51

Gipuma		99.03 46	99.16 42	98.64 158	99.94 2	98.51 82	99.32 18	99.75 7	99.58 21	98.60 180	99.62 28	98.22 51	99.51 310	97.70 119	99.73 120	97.89 301
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v18		99.02 47	99.17 40	98.57 173	99.45 106	96.31 210	98.94 66	99.58 35	99.06 72	99.43 58	99.58 38	96.91 139	99.80 156	99.60 14	99.97 23	99.59 60

v8		99.01 48	99.16 42	98.57 173	99.47 99	96.31 210	98.90 69	99.47 81	99.03 74	99.52 41	99.57 39	96.93 138	99.81 144	99.60 14	99.98 19	99.60 54

HPM-MVS_fast		99.01 48	98.82 58	99.57 15	99.71 34	99.35 8	99.00 61	99.50 65	97.33 193	98.94 140	98.86 167	98.75 24	99.82 131	97.53 125	99.71 129	99.56 77

APDe-MVS		98.99 50	98.79 61	99.60 11	99.21 153	99.15 33	98.87 71	99.48 74	97.57 169	99.35 71	99.24 84	97.83 75	99.89 56	97.88 108	99.70 132	99.75 22

abl_6		98.99 50	98.78 62	99.61 8	99.45 106	99.46 3	98.60 88	99.50 65	98.59 104	99.24 93	99.04 131	98.54 36	99.89 56	96.45 191	99.62 160	99.50 104

EG-PatchMatch MVS		98.99 50	99.01 49	98.94 121	99.50 86	97.47 160	98.04 147	99.59 33	98.15 131	99.40 63	99.36 69	98.58 32	99.76 206	98.78 63	99.68 144	99.59 60

COLMAP_ROB	96.50 10	98.99 50	98.85 56	99.41 53	99.58 57	99.10 43	98.74 77	99.56 48	99.09 70	99.33 75	99.19 93	98.40 42	99.72 235	95.98 212	99.76 115	99.42 143
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Baseline_NR-MVSNet		98.98 54	98.86 55	99.36 57	99.82 19	98.55 77	97.47 215	99.57 42	99.37 37	99.21 97	99.61 30	96.76 155	99.83 118	98.06 98	99.83 80	99.71 28

v10		98.97 55	99.11 45	98.55 178	99.44 109	96.21 216	98.90 69	99.55 53	98.73 97	99.48 48	99.60 34	96.63 161	99.83 118	99.70 11	99.99 11	99.61 51

DeepC-MVS	97.60 4	98.97 55	98.93 52	99.10 95	99.35 126	97.98 122	98.01 156	99.46 83	97.56 171	99.54 36	99.50 47	98.97 17	99.84 103	98.06 98	99.92 48	99.49 111
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NR-MVSNet		98.95 57	98.82 58	99.36 57	99.16 171	98.72 66	99.22 35	99.20 167	99.10 67	99.72 13	98.76 185	96.38 178	99.86 79	98.00 103	99.82 83	99.50 104

Anonymous20240529		98.93 58	98.87 54	99.12 91	99.19 163	98.22 101	99.01 56	98.99 221	99.25 47	99.54 36	99.37 66	97.04 128	99.80 156	97.89 105	99.52 192	99.35 170

testing_2		98.93 58	98.99 51	98.76 146	99.57 62	97.03 181	97.85 174	99.13 190	98.46 112	99.44 56	99.44 58	98.22 51	99.74 221	98.85 59	99.94 33	99.51 99

DP-MVS		98.93 58	98.81 60	99.28 71	99.21 153	98.45 86	98.46 115	99.33 129	99.63 12	99.48 48	99.15 106	97.23 118	99.75 212	97.17 141	99.66 155	99.63 46

ACMM	96.08 12	98.91 61	98.73 69	99.48 45	99.55 73	99.14 35	98.07 142	99.37 109	97.62 163	99.04 121	98.96 149	98.84 20	99.79 178	97.43 131	99.65 156	99.49 111
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnnormal		98.90 62	98.90 53	98.91 125	99.67 44	97.82 139	99.00 61	99.44 89	99.45 29	99.51 45	99.24 84	98.20 54	99.86 79	95.92 214	99.69 139	99.04 228

MTAPA		98.88 63	98.64 86	99.61 8	99.67 44	99.36 6	98.43 117	99.20 167	98.83 91	98.89 145	98.90 158	96.98 135	99.92 33	97.16 142	99.70 132	99.56 77

VPNet		98.87 64	98.83 57	99.01 113	99.70 40	97.62 155	98.43 117	99.35 120	99.47 27	99.28 83	99.05 129	96.72 157	99.82 131	98.09 96	99.36 209	99.59 60

UniMVSNet (Re)		98.87 64	98.71 73	99.35 62	99.24 141	98.73 64	97.73 185	99.38 105	98.93 85	99.12 106	98.73 187	96.77 153	99.86 79	98.63 71	99.80 94	99.46 129

UniMVSNet_NR-MVSNet		98.86 66	98.68 81	99.40 55	99.17 169	98.74 61	97.68 189	99.40 99	99.14 61	99.06 113	98.59 212	96.71 158	99.93 25	98.57 74	99.77 106	99.53 93

APD-MVS_3200maxsize		98.84 67	98.61 91	99.53 32	99.19 163	99.27 15	98.49 103	99.33 129	98.64 100	99.03 124	98.98 144	97.89 73	99.85 88	96.54 185	99.42 204	99.46 129

PM-MVS		98.82 68	98.72 72	99.12 91	99.64 50	98.54 80	97.98 159	99.68 15	97.62 163	99.34 74	99.18 95	97.54 90	99.77 200	97.79 111	99.74 117	99.04 228

DU-MVS		98.82 68	98.63 87	99.39 56	99.16 171	98.74 61	97.54 209	99.25 154	98.84 90	99.06 113	98.76 185	96.76 155	99.93 25	98.57 74	99.77 106	99.50 104

3Dnovator	98.27 2	98.81 70	98.73 69	99.05 106	98.76 249	97.81 141	99.25 33	99.30 140	98.57 108	98.55 186	99.33 74	97.95 72	99.90 47	97.16 142	99.67 150	99.44 135

zzz-MVS		98.79 71	98.52 98	99.61 8	99.67 44	99.36 6	97.33 220	99.20 167	98.83 91	98.89 145	98.90 158	96.98 135	99.92 33	97.16 142	99.70 132	99.56 77

HPM-MVS		98.79 71	98.53 97	99.59 14	99.65 47	99.29 12	99.16 43	99.43 94	96.74 224	98.61 178	98.38 232	98.62 29	99.87 74	96.47 189	99.67 150	99.59 60
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
SteuartSystems-ACMMP		98.79 71	98.54 96	99.54 25	99.73 28	99.16 28	98.23 126	99.31 134	97.92 138	98.90 143	98.90 158	98.00 66	99.88 64	96.15 206	99.72 125	99.58 67
Skip Steuart: Steuart Systems R&D Blog.
V42		98.78 74	98.78 62	98.76 146	99.44 109	97.04 180	98.27 124	99.19 173	97.87 150	99.25 92	99.16 102	96.84 146	99.78 189	99.21 40	99.84 74	99.46 129

test20.03		98.78 74	98.77 64	98.78 142	99.46 103	97.20 172	97.78 178	99.24 159	99.04 73	99.41 61	98.90 158	97.65 83	99.76 206	97.70 119	99.79 98	99.39 152

test_0402		98.76 76	98.71 73	98.93 122	99.56 69	98.14 106	98.45 116	99.34 124	99.28 45	98.95 136	98.91 155	98.34 45	99.79 178	95.63 229	99.91 53	98.86 252

ACMMP_Plus		98.75 77	98.48 105	99.57 15	99.58 57	99.29 12	97.82 177	99.25 154	96.94 216	98.78 160	99.12 111	98.02 64	99.84 103	97.13 146	99.67 150	99.59 60

SixPastTwentyTwo		98.75 77	98.62 88	99.16 86	99.83 18	97.96 125	99.28 30	98.20 281	99.37 37	99.70 15	99.65 25	92.65 270	99.93 25	99.04 52	99.84 74	99.60 54

ACMMP		98.75 77	98.50 101	99.52 38	99.56 69	99.16 28	98.87 71	99.37 109	97.16 210	98.82 157	99.01 137	97.71 81	99.87 74	96.29 198	99.69 139	99.54 88
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
Regformer-4		98.73 80	98.68 81	98.89 128	99.02 201	97.22 171	97.17 234	99.06 200	99.21 49	99.17 103	98.85 170	97.45 99	99.86 79	98.48 79	99.70 132	99.60 54

XVS		98.72 81	98.45 112	99.53 32	99.46 103	99.21 20	98.65 82	99.34 124	98.62 102	97.54 256	98.63 206	97.50 94	99.83 118	96.79 162	99.53 189	99.56 77

HFP-MVS		98.71 82	98.44 115	99.51 40	99.49 92	99.16 28	98.52 97	99.31 134	97.47 178	98.58 183	98.50 225	97.97 70	99.85 88	96.57 180	99.59 167	99.53 93

LPG-MVS_test		98.71 82	98.46 110	99.47 48	99.57 62	98.97 50	98.23 126	99.48 74	96.60 232	99.10 110	99.06 124	98.71 26	99.83 118	95.58 232	99.78 102	99.62 47

v1neww		98.70 84	98.76 65	98.52 183	99.47 99	96.30 212	98.03 148	99.18 177	97.92 138	99.26 90	99.08 118	96.91 139	99.78 189	99.19 43	99.82 83	99.47 125

v7new		98.70 84	98.76 65	98.52 183	99.47 99	96.30 212	98.03 148	99.18 177	97.92 138	99.26 90	99.08 118	96.91 139	99.78 189	99.19 43	99.82 83	99.47 125

v6		98.70 84	98.76 65	98.52 183	99.47 99	96.30 212	98.03 148	99.18 177	97.92 138	99.27 85	99.08 118	96.91 139	99.78 189	99.19 43	99.82 83	99.48 117

ACMMPR		98.70 84	98.42 119	99.54 25	99.52 81	99.14 35	98.52 97	99.31 134	97.47 178	98.56 185	98.54 220	97.75 80	99.88 64	96.57 180	99.59 167	99.58 67

CP-MVS		98.70 84	98.42 119	99.52 38	99.36 121	99.12 40	98.72 79	99.36 114	97.54 173	98.30 200	98.40 231	97.86 74	99.89 56	96.53 186	99.72 125	99.56 77

region2R		98.69 89	98.40 121	99.54 25	99.53 79	99.17 26	98.52 97	99.31 134	97.46 183	98.44 192	98.51 222	97.83 75	99.88 64	96.46 190	99.58 173	99.58 67

EI-MVSNet-UG-set		98.69 89	98.71 73	98.62 162	99.10 179	96.37 208	97.23 226	98.87 237	99.20 52	99.19 99	98.99 140	97.30 108	99.85 88	98.77 66	99.79 98	99.65 39

3Dnovator+	97.89 3	98.69 89	98.51 99	99.24 79	98.81 245	98.40 88	99.02 55	99.19 173	98.99 77	98.07 210	99.28 77	97.11 126	99.84 103	96.84 160	99.32 216	99.47 125

EI-MVSNet-Vis-set		98.68 92	98.70 76	98.63 160	99.09 182	96.40 206	97.23 226	98.86 241	99.20 52	99.18 102	98.97 146	97.29 110	99.85 88	98.72 68	99.78 102	99.64 42

CSCG		98.68 92	98.50 101	99.20 82	99.45 106	98.63 69	98.56 92	99.57 42	97.87 150	98.85 151	98.04 261	97.66 82	99.84 103	96.72 168	99.81 90	99.13 221

v7		98.67 94	98.73 69	98.50 188	99.43 113	96.21 216	98.00 157	99.31 134	97.58 167	99.17 103	99.18 95	96.63 161	99.80 156	99.42 28	99.88 65	99.48 117

PGM-MVS		98.66 95	98.37 126	99.55 20	99.53 79	99.18 25	98.23 126	99.49 71	97.01 214	98.69 167	98.88 164	98.00 66	99.89 56	95.87 218	99.59 167	99.58 67

GBi-Net		98.65 96	98.47 107	99.17 83	98.90 224	98.24 96	99.20 36	99.44 89	98.59 104	98.95 136	99.55 41	94.14 247	99.86 79	97.77 113	99.69 139	99.41 145

test1		98.65 96	98.47 107	99.17 83	98.90 224	98.24 96	99.20 36	99.44 89	98.59 104	98.95 136	99.55 41	94.14 247	99.86 79	97.77 113	99.69 139	99.41 145

LCM-MVSNet-Re		98.64 98	98.48 105	99.11 93	98.85 235	98.51 82	98.49 103	99.83 3	98.37 113	99.69 17	99.46 53	98.21 53	99.92 33	94.13 266	99.30 220	98.91 246

mPP-MVS		98.64 98	98.34 130	99.54 25	99.54 77	99.17 26	98.63 84	99.24 159	97.47 178	98.09 209	98.68 193	97.62 87	99.89 56	96.22 200	99.62 160	99.57 72

TSAR-MVS + MP.		98.63 100	98.49 104	99.06 105	99.64 50	97.90 131	98.51 101	98.94 225	96.96 215	99.24 93	98.89 163	97.83 75	99.81 144	96.88 157	99.49 200	99.48 117

v1141		98.63 100	98.70 76	98.41 197	99.39 117	95.96 226	97.64 194	99.21 163	97.92 138	99.35 71	99.08 118	96.61 165	99.78 189	99.25 36	99.90 58	99.50 104

divwei89l23v2f112		98.63 100	98.70 76	98.41 197	99.39 117	95.96 226	97.64 194	99.21 163	97.92 138	99.35 71	99.08 118	96.61 165	99.78 189	99.25 36	99.90 58	99.50 104

v1		98.63 100	98.70 76	98.41 197	99.39 117	95.96 226	97.64 194	99.20 167	97.92 138	99.36 69	99.07 123	96.63 161	99.78 189	99.25 36	99.90 58	99.50 104

LS3D		98.63 100	98.38 125	99.36 57	97.25 343	99.38 5	99.12 49	99.32 132	99.21 49	98.44 192	98.88 164	97.31 107	99.80 156	96.58 178	99.34 213	98.92 244

RPSCF		98.62 105	98.36 127	99.42 51	99.65 47	99.42 4	98.55 94	99.57 42	97.72 157	98.90 143	99.26 81	96.12 186	99.52 305	95.72 225	99.71 129	99.32 178

Regformer-3		98.61 106	98.61 91	98.63 160	99.02 201	96.53 199	97.17 234	98.84 243	99.13 62	99.10 110	98.85 170	97.24 116	99.79 178	98.41 84	99.70 132	99.57 72

v1192		98.60 107	98.66 84	98.41 197	99.27 136	95.88 230	97.52 210	99.36 114	97.41 187	99.33 75	99.20 91	96.37 179	99.82 131	99.57 18	99.92 48	99.55 85

v1144		98.60 107	98.66 84	98.41 197	99.36 121	95.90 229	97.58 204	99.34 124	97.51 174	99.27 85	99.15 106	96.34 180	99.80 156	99.47 24	99.93 38	99.51 99

Regformer-2		98.60 107	98.46 110	99.02 112	98.85 235	97.71 149	96.91 249	99.09 197	98.98 79	99.01 125	98.64 202	97.37 105	99.84 103	97.75 118	99.57 177	99.52 97

ESAPD		98.59 110	98.26 137	99.57 15	99.27 136	99.15 33	97.01 241	99.39 101	97.67 159	99.44 56	98.99 140	97.53 92	99.89 56	95.40 235	99.68 144	99.66 34

MP-MVS-pluss		98.57 111	98.23 139	99.60 11	99.69 42	99.35 8	97.16 236	99.38 105	94.87 281	98.97 133	98.99 140	98.01 65	99.88 64	97.29 137	99.70 132	99.58 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		98.56 112	98.32 134	99.25 78	99.41 115	98.73 64	97.13 238	99.18 177	97.10 213	98.75 164	98.92 154	98.18 55	99.65 267	96.68 173	99.56 182	99.37 159

VDD-MVS		98.56 112	98.39 123	99.07 100	99.13 177	98.07 113	98.59 90	97.01 307	99.59 19	99.11 107	99.27 79	94.82 230	99.79 178	98.34 86	99.63 159	99.34 172

v2v482		98.56 112	98.62 88	98.37 204	99.42 114	95.81 233	97.58 204	99.16 186	97.90 146	99.28 83	99.01 137	95.98 195	99.79 178	99.33 31	99.90 58	99.51 99

XVG-ACMP-BASELINE		98.56 112	98.34 130	99.22 81	99.54 77	98.59 74	97.71 186	99.46 83	97.25 201	98.98 130	98.99 140	97.54 90	99.84 103	95.88 215	99.74 117	99.23 199

Regformer-1		98.55 116	98.44 115	98.87 130	98.85 235	97.29 166	96.91 249	98.99 221	98.97 80	98.99 128	98.64 202	97.26 114	99.81 144	97.79 111	99.57 177	99.51 99

v1240		98.55 116	98.62 88	98.32 208	99.22 147	95.58 237	97.51 212	99.45 86	97.16 210	99.45 55	99.24 84	96.12 186	99.85 88	99.60 14	99.88 65	99.55 85

IterMVS-LS		98.55 116	98.70 76	98.09 223	99.48 97	94.73 257	97.22 229	99.39 101	98.97 80	99.38 65	99.31 76	96.00 191	99.93 25	98.58 72	99.97 23	99.60 54
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		98.54 119	98.57 95	98.45 194	99.21 153	95.98 224	97.63 197	99.36 114	97.15 212	99.32 80	99.18 95	95.84 203	99.84 103	99.50 22	99.91 53	99.54 88

v1921920		98.54 119	98.60 93	98.38 203	99.20 162	95.76 234	97.56 206	99.36 114	97.23 206	99.38 65	99.17 101	96.02 189	99.84 103	99.57 18	99.90 58	99.54 88

XVG-OURS		98.53 121	98.34 130	99.11 93	99.50 86	98.82 59	95.97 294	99.50 65	97.30 197	99.05 118	98.98 144	99.35 7	99.32 333	95.72 225	99.68 144	99.18 213

UGNet		98.53 121	98.45 112	98.79 139	97.94 318	96.96 184	99.08 50	98.54 269	99.10 67	96.82 297	99.47 52	96.55 168	99.84 103	98.56 77	99.94 33	99.55 85
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
testmv		98.51 123	98.47 107	98.61 165	99.24 141	96.53 199	96.66 264	99.73 9	98.56 110	99.50 46	99.23 88	97.24 116	99.87 74	96.16 205	99.93 38	99.44 135

#test#		98.50 124	98.16 148	99.51 40	99.49 92	99.16 28	98.03 148	99.31 134	96.30 243	98.58 183	98.50 225	97.97 70	99.85 88	95.68 228	99.59 167	99.53 93

casdiffmvs1		98.49 125	98.45 112	98.61 165	98.99 206	97.15 177	98.70 81	99.25 154	97.42 186	97.87 221	99.20 91	96.29 181	99.66 261	99.44 26	98.91 269	99.03 231

XVG-OURS-SEG-HR		98.49 125	98.28 136	99.14 89	99.49 92	98.83 57	96.54 270	99.48 74	97.32 195	99.11 107	98.61 210	99.33 8	99.30 336	96.23 199	98.38 294	99.28 189

FMVSNet2		98.49 125	98.40 121	98.75 148	98.90 224	97.14 179	98.61 87	99.13 190	98.59 104	99.19 99	99.28 77	94.14 247	99.82 131	97.97 104	99.80 94	99.29 188

pmmvs-eth3d		98.47 128	98.34 130	98.86 132	99.30 133	97.76 144	97.16 236	99.28 143	95.54 268	99.42 60	99.19 93	97.27 111	99.63 270	97.89 105	99.97 23	99.20 205

MP-MVS		98.46 129	98.09 157	99.54 25	99.57 62	99.22 19	98.50 102	99.19 173	97.61 165	97.58 252	98.66 197	97.40 103	99.88 64	94.72 248	99.60 166	99.54 88
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
v148		98.45 130	98.60 93	98.00 232	99.44 109	94.98 253	97.44 216	99.06 200	98.30 120	99.32 80	98.97 146	96.65 160	99.62 273	98.37 85	99.85 72	99.39 152

AllTest		98.44 131	98.20 141	99.16 86	99.50 86	98.55 77	98.25 125	99.58 35	96.80 222	98.88 148	99.06 124	97.65 83	99.57 291	94.45 255	99.61 164	99.37 159

VNet		98.42 132	98.30 135	98.79 139	98.79 248	97.29 166	98.23 126	98.66 264	99.31 42	98.85 151	98.80 179	94.80 233	99.78 189	98.13 95	99.13 248	99.31 182

ab-mvs		98.41 133	98.36 127	98.59 170	99.19 163	97.23 169	99.32 18	98.81 249	97.66 160	98.62 176	99.40 65	96.82 149	99.80 156	95.88 215	99.51 193	98.75 268

ACMP	95.32 15	98.41 133	98.09 157	99.36 57	99.51 84	98.79 60	97.68 189	99.38 105	95.76 259	98.81 159	98.82 177	98.36 44	99.82 131	94.75 245	99.77 106	99.48 117
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SMA-MVS		98.40 135	98.03 164	99.51 40	99.16 171	99.21 20	98.05 146	99.22 162	94.16 297	98.98 130	99.10 115	97.52 93	99.79 178	96.45 191	99.64 158	99.53 93

SD-MVS		98.40 135	98.68 81	97.54 256	98.96 211	97.99 118	97.88 170	99.36 114	98.20 127	99.63 26	99.04 131	98.76 23	95.33 364	96.56 183	99.74 117	99.31 182

EI-MVSNet		98.40 135	98.51 99	98.04 230	99.10 179	94.73 257	97.20 230	98.87 237	98.97 80	99.06 113	99.02 135	96.00 191	99.80 156	98.58 72	99.82 83	99.60 54

WR-MVS		98.40 135	98.19 143	99.03 109	99.00 204	97.65 152	96.85 253	98.94 225	98.57 108	98.89 145	98.50 225	95.60 209	99.85 88	97.54 124	99.85 72	99.59 60

diffmvs1		98.39 139	98.43 117	98.27 214	98.53 287	96.18 218	97.91 168	99.37 109	98.73 97	97.22 277	99.15 106	96.97 137	99.77 200	98.80 62	99.18 239	98.86 252

new-patchmatchnet		98.35 140	98.74 68	97.18 268	99.24 141	92.23 309	96.42 277	99.48 74	98.30 120	99.69 17	99.53 45	97.44 100	99.82 131	98.84 61	99.77 106	99.49 111

HSP-MVS		98.34 141	97.94 170	99.54 25	99.57 62	99.25 17	98.57 91	98.84 243	97.55 172	99.31 82	97.71 276	94.61 238	99.88 64	96.14 207	99.19 237	99.48 117

canonicalmvs		98.34 141	98.26 137	98.58 171	98.46 291	97.82 139	98.96 65	99.46 83	99.19 56	97.46 262	95.46 338	98.59 31	99.46 318	98.08 97	98.71 278	98.46 282

testgi		98.32 143	98.39 123	98.13 221	99.57 62	95.54 238	97.78 178	99.49 71	97.37 190	99.19 99	97.65 280	98.96 18	99.49 312	96.50 188	98.99 262	99.34 172

DeepPCF-MVS	96.93 5	98.32 143	98.01 165	99.23 80	98.39 296	98.97 50	95.03 330	99.18 177	96.88 219	99.33 75	98.78 182	98.16 56	99.28 339	96.74 166	99.62 160	99.44 135

MVS_111021_LR		98.30 145	98.12 154	98.83 135	99.16 171	98.03 116	96.09 291	99.30 140	97.58 167	98.10 208	98.24 245	98.25 47	99.34 330	96.69 172	99.65 156	99.12 222

EPP-MVSNet		98.30 145	98.04 163	99.07 100	99.56 69	97.83 136	99.29 26	98.07 285	99.03 74	98.59 181	99.13 110	92.16 274	99.90 47	96.87 158	99.68 144	99.49 111

DeepC-MVS_fast	96.85 6	98.30 145	98.15 150	98.75 148	98.61 276	97.23 169	97.76 182	99.09 197	97.31 196	98.75 164	98.66 197	97.56 89	99.64 269	96.10 208	99.55 184	99.39 152
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		98.29 148	97.95 168	99.34 65	98.44 293	99.16 28	98.12 136	99.38 105	96.01 255	98.06 211	98.43 229	97.80 79	99.67 253	95.69 227	99.58 173	99.20 205

Fast-Effi-MVS+-dtu		98.27 149	98.09 157	98.81 137	98.43 294	98.11 107	97.61 200	99.50 65	98.64 100	97.39 270	97.52 287	98.12 59	99.95 13	96.90 156	98.71 278	98.38 288

DELS-MVS		98.27 149	98.20 141	98.48 190	98.86 232	96.70 195	95.60 316	99.20 167	97.73 156	98.45 191	98.71 189	97.50 94	99.82 131	98.21 92	99.59 167	98.93 243
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
Effi-MVS+-dtu		98.26 151	97.90 174	99.35 62	98.02 315	99.49 2	98.02 155	99.16 186	98.29 123	97.64 247	97.99 263	96.44 174	99.95 13	96.66 174	98.93 268	98.60 278

MVSFormer		98.26 151	98.43 117	97.77 240	98.88 230	93.89 285	99.39 14	99.56 48	99.11 63	98.16 204	98.13 250	93.81 254	99.97 3	99.26 34	99.57 177	99.43 140

MVS_111021_HR		98.25 153	98.08 160	98.75 148	99.09 182	97.46 161	95.97 294	99.27 148	97.60 166	97.99 215	98.25 244	98.15 58	99.38 327	96.87 158	99.57 177	99.42 143

TAMVS		98.24 154	98.05 162	98.80 138	99.07 186	97.18 174	97.88 170	98.81 249	96.66 230	99.17 103	99.21 89	94.81 232	99.77 200	96.96 153	99.88 65	99.44 135

casdiffmvs		98.22 155	98.17 144	98.35 205	98.75 250	96.62 198	98.62 85	99.12 192	98.04 133	96.46 311	99.12 111	95.81 204	99.63 270	99.17 46	98.45 293	98.80 261

Anonymous20231206		98.21 156	98.21 140	98.20 218	99.51 84	95.43 243	98.13 134	99.32 132	96.16 249	98.93 141	98.82 177	96.00 191	99.83 118	97.32 136	99.73 120	99.36 165

VDDNet		98.21 156	97.95 168	99.01 113	99.58 57	97.74 147	99.01 56	97.29 302	99.67 8	98.97 133	99.50 47	90.45 282	99.80 156	97.88 108	99.20 233	99.48 117

IS-MVSNet		98.19 158	97.90 174	99.08 98	99.57 62	97.97 123	99.31 21	98.32 277	99.01 76	98.98 130	99.03 134	91.59 277	99.79 178	95.49 234	99.80 94	99.48 117

MVS_Test		98.18 159	98.36 127	97.67 245	98.48 289	94.73 257	98.18 130	99.02 213	97.69 158	98.04 213	99.11 113	97.22 120	99.56 294	98.57 74	98.90 270	98.71 270

TSAR-MVS + GP.		98.18 159	97.98 166	98.77 144	98.71 256	97.88 132	96.32 281	98.66 264	96.33 240	99.23 96	98.51 222	97.48 98	99.40 323	97.16 142	99.46 201	99.02 232

CNVR-MVS		98.17 161	97.87 177	99.07 100	98.67 268	98.24 96	97.01 241	98.93 228	97.25 201	97.62 248	98.34 236	97.27 111	99.57 291	96.42 194	99.33 214	99.39 152

PVSNet_Blended_VisFu		98.17 161	98.15 150	98.22 217	99.73 28	95.15 250	97.36 219	99.68 15	94.45 289	98.99 128	99.27 79	96.87 145	99.94 20	97.13 146	99.91 53	99.57 72

HPM-MVS++		98.10 163	97.64 189	99.48 45	99.09 182	99.13 38	97.52 210	98.75 257	97.46 183	96.90 292	97.83 271	96.01 190	99.84 103	95.82 222	99.35 211	99.46 129

APD-MVS		98.10 163	97.67 184	99.42 51	99.11 178	98.93 55	97.76 182	99.28 143	94.97 278	98.72 166	98.77 183	97.04 128	99.85 88	93.79 276	99.54 185	99.49 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVP-Stereo		98.08 165	97.92 172	98.57 173	98.96 211	96.79 189	97.90 169	99.18 177	96.41 238	98.46 190	98.95 150	95.93 198	99.60 280	96.51 187	98.98 264	99.31 182
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
diffmvs		98.08 165	98.14 152	97.88 235	98.37 297	95.22 247	97.93 163	98.99 221	98.87 87	95.93 322	99.18 95	96.63 161	99.79 178	98.45 80	98.95 266	98.64 277

PMMVS2		98.07 167	98.08 160	98.04 230	99.41 115	94.59 263	94.59 339	99.40 99	97.50 175	98.82 157	98.83 174	96.83 148	99.84 103	97.50 127	99.81 90	99.71 28

MVS_0304		98.02 168	97.88 176	98.46 192	98.22 308	96.39 207	96.50 271	99.49 71	98.03 134	97.24 276	98.33 239	94.80 233	99.90 47	98.31 89	99.95 30	99.08 223

Effi-MVS+		98.02 168	97.82 178	98.62 162	98.53 287	97.19 173	97.33 220	99.68 15	97.30 197	96.68 300	97.46 292	98.56 35	99.80 156	96.63 176	98.20 300	98.86 252

MSLP-MVS++		98.02 168	98.14 152	97.64 249	98.58 280	95.19 249	97.48 213	99.23 161	97.47 178	97.90 219	98.62 208	97.04 128	98.81 356	97.55 123	99.41 205	98.94 242

MCST-MVS		98.00 171	97.63 190	99.10 95	99.24 141	98.17 103	96.89 251	98.73 260	95.66 260	97.92 216	97.70 277	97.17 121	99.66 261	96.18 204	99.23 229	99.47 125

K. test v3		98.00 171	97.66 187	99.03 109	99.79 24	97.56 156	99.19 40	92.47 355	99.62 16	99.52 41	99.66 22	89.61 286	99.96 8	99.25 36	99.81 90	99.56 77

HQP_MVS		97.99 173	97.67 184	98.93 122	99.19 163	97.65 152	97.77 180	99.27 148	98.20 127	97.79 239	97.98 264	94.90 226	99.70 238	94.42 257	99.51 193	99.45 133

no-one		97.98 174	98.10 156	97.61 251	99.55 73	93.82 287	96.70 261	98.94 225	96.18 245	99.52 41	99.41 62	95.90 201	99.81 144	96.72 168	99.99 11	99.20 205

MDA-MVSNet-bldmvs		97.94 175	97.91 173	98.06 228	99.44 109	94.96 254	96.63 266	99.15 189	98.35 114	98.83 154	99.11 113	94.31 244	99.85 88	96.60 177	98.72 275	99.37 159

Anonymous202405211		97.90 176	97.50 196	99.08 98	98.90 224	98.25 95	98.53 96	96.16 324	98.87 87	99.11 107	98.86 167	90.40 283	99.78 189	97.36 134	99.31 218	99.19 211

LF4IMVS		97.90 176	97.69 183	98.52 183	99.17 169	97.66 151	97.19 233	99.47 81	96.31 242	97.85 225	98.20 249	96.71 158	99.52 305	94.62 249	99.72 125	98.38 288

UnsupCasMVSNet_eth		97.89 178	97.60 192	98.75 148	99.31 131	97.17 175	97.62 198	99.35 120	98.72 99	98.76 163	98.68 193	92.57 271	99.74 221	97.76 117	95.60 348	99.34 172

TinyColmap		97.89 178	97.98 166	97.60 252	98.86 232	94.35 271	96.21 286	99.44 89	97.45 185	99.06 113	98.88 164	97.99 68	99.28 339	94.38 261	99.58 173	99.18 213

OMC-MVS		97.88 180	97.49 197	99.04 108	98.89 229	98.63 69	96.94 245	99.25 154	95.02 276	98.53 188	98.51 222	97.27 111	99.47 316	93.50 285	99.51 193	99.01 233

CANet		97.87 181	97.76 179	98.19 219	97.75 323	95.51 240	96.76 257	99.05 204	97.74 155	96.93 286	98.21 248	95.59 210	99.89 56	97.86 110	99.93 38	99.19 211

xiu_mvs_v1_base_debu		97.86 182	98.17 144	96.92 278	98.98 208	93.91 282	96.45 274	99.17 183	97.85 152	98.41 195	97.14 305	98.47 38	99.92 33	98.02 100	99.05 254	96.92 331

xiu_mvs_v1_base		97.86 182	98.17 144	96.92 278	98.98 208	93.91 282	96.45 274	99.17 183	97.85 152	98.41 195	97.14 305	98.47 38	99.92 33	98.02 100	99.05 254	96.92 331

xiu_mvs_v1_base_debi		97.86 182	98.17 144	96.92 278	98.98 208	93.91 282	96.45 274	99.17 183	97.85 152	98.41 195	97.14 305	98.47 38	99.92 33	98.02 100	99.05 254	96.92 331

NCCC		97.86 182	97.47 201	99.05 106	98.61 276	98.07 113	96.98 243	98.90 234	97.63 162	97.04 283	97.93 267	95.99 194	99.66 261	95.31 236	98.82 272	99.43 140

PMVS	91.26 20	97.86 182	97.94 170	97.65 247	99.71 34	97.94 128	98.52 97	98.68 263	98.99 77	97.52 258	99.35 70	97.41 102	98.18 359	91.59 313	99.67 150	96.82 338
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
CPTT-MVS		97.84 187	97.36 207	99.27 74	99.31 131	98.46 85	98.29 122	99.27 148	94.90 280	97.83 230	98.37 233	94.90 226	99.84 103	93.85 275	99.54 185	99.51 99

mvs-test1		97.83 188	97.48 200	98.89 128	98.02 315	99.20 23	97.20 230	99.16 186	98.29 123	96.46 311	97.17 302	96.44 174	99.92 33	96.66 174	97.90 320	97.54 324

mvs_anonymous		97.83 188	98.16 148	96.87 281	98.18 310	91.89 311	97.31 222	98.90 234	97.37 190	98.83 154	99.46 53	96.28 182	99.79 178	98.90 56	98.16 303	98.95 240

IterMVS		97.73 190	98.11 155	96.57 292	99.24 141	90.28 331	95.52 319	99.21 163	98.86 89	99.33 75	99.33 74	93.11 262	99.94 20	98.49 78	99.94 33	99.48 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		97.71 191	97.52 195	98.28 213	98.91 223	96.82 188	94.42 341	99.37 109	97.65 161	98.37 199	98.29 242	97.40 103	99.33 332	94.09 267	99.22 230	98.68 276

CDS-MVSNet		97.69 192	97.35 208	98.69 154	98.73 253	97.02 183	96.92 248	98.75 257	95.89 257	98.59 181	98.67 195	92.08 276	99.74 221	96.72 168	99.81 90	99.32 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MS-PatchMatch		97.68 193	97.75 180	97.45 260	98.23 307	93.78 289	97.29 223	98.84 243	96.10 251	98.64 172	98.65 199	96.04 188	99.36 328	96.84 160	99.14 245	99.20 205

Fast-Effi-MVS+		97.67 194	97.38 206	98.57 173	98.71 256	97.43 163	97.23 226	99.45 86	94.82 282	96.13 315	96.51 313	98.52 37	99.91 43	96.19 202	98.83 271	98.37 290

EU-MVSNet		97.66 195	98.50 101	95.13 325	99.63 52	85.84 347	98.35 121	98.21 280	98.23 125	99.54 36	99.46 53	95.02 224	99.68 247	98.24 90	99.87 69	99.87 6

pmmvs5		97.64 196	97.49 197	98.08 226	99.14 176	95.12 252	96.70 261	99.05 204	93.77 301	98.62 176	98.83 174	93.23 259	99.75 212	98.33 88	99.76 115	99.36 165

N_pmnet		97.63 197	97.17 214	98.99 116	99.27 136	97.86 134	95.98 293	93.41 347	95.25 273	99.47 51	98.90 158	95.63 208	99.85 88	96.91 154	99.73 120	99.27 190

YYNet1		97.60 198	97.67 184	97.39 264	99.04 196	93.04 300	95.27 324	98.38 276	97.25 201	98.92 142	98.95 150	95.48 215	99.73 226	96.99 151	98.74 274	99.41 145

MDA-MVSNet_test_wron		97.60 198	97.66 187	97.41 263	99.04 196	93.09 297	95.27 324	98.42 274	97.26 200	98.88 148	98.95 150	95.43 216	99.73 226	97.02 150	98.72 275	99.41 145

test_normal		97.58 200	97.41 202	98.10 222	99.03 199	95.72 235	96.21 286	97.05 306	96.71 227	98.65 170	98.12 254	93.87 251	99.69 242	97.68 122	99.35 211	98.88 250

pmmvs4		97.58 200	97.28 210	98.51 187	98.84 238	96.93 186	95.40 323	98.52 270	93.60 303	98.61 178	98.65 199	95.10 223	99.60 280	96.97 152	99.79 98	98.99 235

DI_MVS_plusplus_test		97.57 202	97.40 203	98.07 227	99.06 189	95.71 236	96.58 269	96.96 308	96.71 227	98.69 167	98.13 250	93.81 254	99.68 247	97.45 129	99.19 237	98.80 261

PVSNet_BlendedMVS		97.55 203	97.53 194	97.60 252	98.92 220	93.77 290	96.64 265	99.43 94	94.49 285	97.62 248	99.18 95	96.82 149	99.67 253	94.73 246	99.93 38	99.36 165

ppachtmachnet_test		97.50 204	97.74 181	96.78 285	98.70 260	91.23 329	94.55 340	99.05 204	96.36 239	99.21 97	98.79 181	96.39 176	99.78 189	96.74 166	99.82 83	99.34 172

FMVSNet3		97.50 204	97.24 211	98.29 212	98.08 313	95.83 232	97.86 173	98.91 233	97.89 147	98.95 136	98.95 150	87.06 295	99.81 144	97.77 113	99.69 139	99.23 199

CHOSEN 1792x2688		97.49 206	97.14 217	98.54 181	99.68 43	96.09 222	96.50 271	99.62 27	91.58 327	98.84 153	98.97 146	92.36 272	99.88 64	96.76 165	99.95 30	99.67 32

CLD-MVS		97.49 206	97.16 215	98.48 190	99.07 186	97.03 181	94.71 336	99.21 163	94.46 287	98.06 211	97.16 303	97.57 88	99.48 315	94.46 254	99.78 102	98.95 240
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_prior3		97.48 208	97.00 220	98.95 119	98.69 263	97.95 126	95.74 311	99.03 209	96.48 235	96.11 316	97.63 281	95.92 199	99.59 284	94.16 262	99.20 233	99.30 185

Vis-MVSNet (Re-imp)		97.46 209	97.16 215	98.34 207	99.55 73	96.10 220	98.94 66	98.44 273	98.32 119	98.16 204	98.62 208	88.76 291	99.73 226	93.88 273	99.79 98	99.18 213

jason		97.45 210	97.35 208	97.76 241	99.24 141	93.93 281	95.86 305	98.42 274	94.24 294	98.50 189	98.13 250	94.82 230	99.91 43	97.22 140	99.73 120	99.43 140
jason: jason.
Test4		97.43 211	97.18 213	98.18 220	99.05 194	96.02 223	96.62 267	99.09 197	96.25 244	98.63 175	97.70 277	90.49 281	99.68 247	97.50 127	99.30 220	98.83 255

DSMNet-mixed		97.42 212	97.60 192	96.87 281	99.15 175	91.46 316	98.54 95	99.12 192	92.87 311	97.58 252	99.63 27	96.21 183	99.90 47	95.74 224	99.54 185	99.27 190

USDC		97.41 213	97.40 203	97.44 261	98.94 214	93.67 292	95.17 327	99.53 58	94.03 299	98.97 133	99.10 115	95.29 218	99.34 330	95.84 221	99.73 120	99.30 185

our_test_3		97.39 214	97.73 182	96.34 296	98.70 260	89.78 333	94.61 338	98.97 224	96.50 234	99.04 121	98.85 170	95.98 195	99.84 103	97.26 139	99.67 150	99.41 145

alignmvs		97.35 215	96.88 226	98.78 142	98.54 285	98.09 108	97.71 186	97.69 295	99.20 52	97.59 251	95.90 327	88.12 294	99.55 297	98.18 94	98.96 265	98.70 272

Patchmtry		97.35 215	96.97 221	98.50 188	97.31 342	96.47 202	98.18 130	98.92 231	98.95 84	98.78 160	99.37 66	85.44 309	99.85 88	95.96 213	99.83 80	99.17 217

DP-MVS Recon		97.33 217	96.92 223	98.57 173	99.09 182	97.99 118	96.79 254	99.35 120	93.18 307	97.71 243	98.07 260	95.00 225	99.31 334	93.97 269	99.13 248	98.42 286

QAPM		97.31 218	96.81 230	98.82 136	98.80 247	97.49 159	99.06 54	99.19 173	90.22 339	97.69 245	99.16 102	96.91 139	99.90 47	90.89 327	99.41 205	99.07 225

UnsupCasMVSNet_bld		97.30 219	96.92 223	98.45 194	99.28 135	96.78 193	96.20 288	99.27 148	95.42 271	98.28 201	98.30 241	93.16 261	99.71 236	94.99 240	97.37 328	98.87 251

F-COLMAP		97.30 219	96.68 238	99.14 89	99.19 163	98.39 89	97.27 225	99.30 140	92.93 309	96.62 302	98.00 262	95.73 206	99.68 247	92.62 300	98.46 292	99.35 170

1112_ss		97.29 221	96.86 227	98.58 171	99.34 128	96.32 209	96.75 258	99.58 35	93.14 308	96.89 293	97.48 290	92.11 275	99.86 79	96.91 154	99.54 185	99.57 72

CANet_DTU		97.26 222	97.06 218	97.84 237	97.57 330	94.65 261	96.19 289	98.79 252	97.23 206	95.14 340	98.24 245	93.22 260	99.84 103	97.34 135	99.84 74	99.04 228

Patchmatch-RL test		97.26 222	97.02 219	97.99 233	99.52 81	95.53 239	96.13 290	99.71 11	97.47 178	99.27 85	99.16 102	84.30 317	99.62 273	97.89 105	99.77 106	98.81 258

CDPH-MVS		97.26 222	96.66 241	99.07 100	99.00 204	98.15 104	96.03 292	99.01 216	91.21 333	97.79 239	97.85 270	96.89 144	99.69 242	92.75 298	99.38 208	99.39 152

PatchMatch-RL		97.24 225	96.78 231	98.61 165	99.03 199	97.83 136	96.36 279	99.06 200	93.49 306	97.36 273	97.78 273	95.75 205	99.49 312	93.44 286	98.77 273	98.52 280

sss		97.21 226	96.93 222	98.06 228	98.83 240	95.22 247	96.75 258	98.48 272	94.49 285	97.27 275	97.90 268	92.77 268	99.80 156	96.57 180	99.32 216	99.16 220

LFMVS		97.20 227	96.72 234	98.64 158	98.72 254	96.95 185	98.93 68	94.14 345	99.74 5	98.78 160	99.01 137	84.45 314	99.73 226	97.44 130	99.27 225	99.25 195

HyFIR lowres test		97.19 228	96.60 244	98.96 118	99.62 54	97.28 168	95.17 327	99.50 65	94.21 295	99.01 125	98.32 240	86.61 297	99.99 2	97.10 149	99.84 74	99.60 54

CNLPA		97.17 229	96.71 236	98.55 178	98.56 282	98.05 115	96.33 280	98.93 228	96.91 218	97.06 282	97.39 296	94.38 243	99.45 320	91.66 309	99.18 239	98.14 295

xiu_mvs_v2_base		97.16 230	97.49 197	96.17 305	98.54 285	92.46 305	95.45 321	98.84 243	97.25 201	97.48 261	96.49 314	98.31 46	99.90 47	96.34 197	98.68 280	96.15 347

AdaColmap		97.14 231	96.71 236	98.46 192	98.34 299	97.80 142	96.95 244	98.93 228	95.58 267	96.92 287	97.66 279	95.87 202	99.53 301	90.97 324	99.14 245	98.04 298

train_agg		97.10 232	96.45 250	99.07 100	98.71 256	98.08 111	95.96 298	99.03 209	91.64 324	95.85 323	97.53 285	96.47 172	99.76 206	93.67 278	99.16 241	99.36 165

OpenMVS	96.65 7	97.09 233	96.68 238	98.32 208	98.32 300	97.16 176	98.86 73	99.37 109	89.48 343	96.29 314	99.15 106	96.56 167	99.90 47	92.90 292	99.20 233	97.89 301

PS-MVSNAJ		97.08 234	97.39 205	96.16 307	98.56 282	92.46 305	95.24 326	98.85 242	97.25 201	97.49 260	95.99 322	98.07 60	99.90 47	96.37 195	98.67 281	96.12 348

agg_prior1		97.06 235	96.40 251	99.03 109	98.68 265	97.99 118	95.76 309	99.01 216	91.73 323	95.59 327	97.50 288	96.49 171	99.77 200	93.71 277	99.14 245	99.34 172

test1235678		97.06 235	96.84 229	97.73 243	98.55 284	94.46 270	94.80 334	99.36 114	96.85 221	98.83 154	98.26 243	92.72 269	99.82 131	92.49 303	99.70 132	98.91 246

lupinMVS		97.06 235	96.86 227	97.65 247	98.88 230	93.89 285	95.48 320	97.97 287	93.53 304	98.16 204	97.58 283	93.81 254	99.91 43	96.77 164	99.57 177	99.17 217

API-MVS		97.04 238	96.91 225	97.42 262	97.88 322	98.23 100	98.18 130	98.50 271	97.57 169	97.39 270	96.75 310	96.77 153	99.15 345	90.16 331	99.02 258	94.88 357

HQP-MVS		97.00 239	96.49 249	98.55 178	98.67 268	96.79 189	96.29 282	99.04 207	96.05 252	95.55 331	96.84 308	93.84 252	99.54 299	92.82 295	99.26 227	99.32 178

new_pmnet		96.99 240	96.76 232	97.67 245	98.72 254	94.89 255	95.95 301	98.20 281	92.62 314	98.55 186	98.54 220	94.88 229	99.52 305	93.96 270	99.44 203	98.59 279

Test_1112_low_res		96.99 240	96.55 247	98.31 210	99.35 126	95.47 242	95.84 308	99.53 58	91.51 329	96.80 298	98.48 228	91.36 278	99.83 118	96.58 178	99.53 189	99.62 47

agg_prior3		96.95 242	96.27 256	99.00 115	98.68 265	97.91 129	95.96 298	99.01 216	90.74 336	95.60 326	97.45 293	96.14 184	99.74 221	93.67 278	99.16 241	99.36 165

PVSNet_Blended		96.88 243	96.68 238	97.47 259	98.92 220	93.77 290	94.71 336	99.43 94	90.98 334	97.62 248	97.36 299	96.82 149	99.67 253	94.73 246	99.56 182	98.98 236

MVSTER		96.86 244	96.55 247	97.79 239	97.91 320	94.21 274	97.56 206	98.87 237	97.49 177	99.06 113	99.05 129	80.72 329	99.80 156	98.44 81	99.82 83	99.37 159

BH-untuned		96.83 245	96.75 233	97.08 270	98.74 252	93.33 296	96.71 260	98.26 279	96.72 225	98.44 192	97.37 298	95.20 220	99.47 316	91.89 307	97.43 327	98.44 284

BH-RMVSNet		96.83 245	96.58 245	97.58 254	98.47 290	94.05 277	96.67 263	97.36 299	96.70 229	97.87 221	97.98 264	95.14 222	99.44 321	90.47 330	98.58 286	99.25 195

RPMNet		96.82 247	96.66 241	97.28 265	97.71 325	94.22 272	98.11 137	96.90 313	99.37 37	96.91 289	99.34 72	86.72 296	99.81 144	97.53 125	97.36 330	97.81 307

PAPM_NR		96.82 247	96.32 254	98.30 211	99.07 186	96.69 196	97.48 213	98.76 254	95.81 258	96.61 303	96.47 316	94.12 250	99.17 343	90.82 329	97.78 322	99.06 226

MG-MVS		96.77 249	96.61 243	97.26 267	98.31 301	93.06 298	95.93 302	98.12 284	96.45 237	97.92 216	98.73 187	93.77 257	99.39 325	91.19 323	99.04 257	99.33 177

1121		96.73 250	96.00 259	98.91 125	98.95 213	97.76 144	98.07 142	98.73 260	87.65 350	96.54 304	98.13 250	94.52 240	99.73 226	92.38 304	99.02 258	99.24 198

0601test		96.69 251	96.29 255	97.90 234	98.28 302	95.24 246	97.29 223	97.36 299	98.21 126	98.17 203	97.86 269	86.27 299	99.55 297	94.87 243	98.32 295	98.89 249

WTY-MVS		96.67 252	96.27 256	97.87 236	98.81 245	94.61 262	96.77 256	97.92 289	94.94 279	97.12 278	97.74 275	91.11 279	99.82 131	93.89 272	98.15 304	99.18 213

PatchT		96.65 253	96.35 252	97.54 256	97.40 339	95.32 245	97.98 159	96.64 319	99.33 41	96.89 293	99.42 60	84.32 316	99.81 144	97.69 121	97.49 325	97.48 325

TAPA-MVS	96.21 11	96.63 254	95.95 261	98.65 157	98.93 216	98.09 108	96.93 246	99.28 143	83.58 358	98.13 207	97.78 273	96.13 185	99.40 323	93.52 283	99.29 223	98.45 283
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		96.62 255	96.25 258	97.71 244	99.04 196	94.66 260	99.16 43	96.92 312	97.23 206	97.87 221	99.10 115	86.11 302	99.65 267	91.65 310	99.21 232	98.82 257

LP		96.60 256	96.57 246	96.68 287	97.64 329	91.70 313	98.11 137	97.74 292	97.29 199	97.91 218	99.24 84	88.35 292	99.85 88	97.11 148	95.76 347	98.49 281

Patchmatch-test		96.55 257	96.34 253	97.17 269	98.35 298	93.06 298	98.40 119	97.79 290	97.33 193	98.41 195	98.67 195	83.68 321	99.69 242	95.16 237	99.31 218	98.77 265

PMMVS		96.51 258	95.98 260	98.09 223	97.53 333	95.84 231	94.92 332	98.84 243	91.58 327	96.05 320	95.58 330	95.68 207	99.66 261	95.59 231	98.09 313	98.76 267

PLC	94.65 16	96.51 258	95.73 264	98.85 133	98.75 250	97.91 129	96.42 277	99.06 200	90.94 335	95.59 327	97.38 297	94.41 242	99.59 284	90.93 325	98.04 318	99.05 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
114514_t		96.50 260	95.77 263	98.69 154	99.48 97	97.43 163	97.84 175	99.55 53	81.42 360	96.51 307	98.58 213	95.53 211	99.67 253	93.41 287	99.58 173	98.98 236

MAR-MVS		96.47 261	95.70 265	98.79 139	97.92 319	99.12 40	98.28 123	98.60 268	92.16 321	95.54 334	96.17 320	94.77 236	99.52 305	89.62 333	98.23 297	97.72 313
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
Patchmatch-test1		96.44 262	96.72 234	95.60 320	98.24 305	88.35 338	95.85 307	96.88 314	96.11 250	97.67 246	98.57 214	93.10 263	99.69 242	94.79 244	99.22 230	98.77 265

CMPMVS	75.91 23	96.29 263	95.44 272	98.84 134	96.25 358	98.69 67	97.02 240	99.12 192	88.90 346	97.83 230	98.86 167	89.51 287	98.90 353	91.92 306	99.51 193	98.92 244
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		96.28 264	95.95 261	97.28 265	97.71 325	94.22 272	98.11 137	98.92 231	92.31 318	96.91 289	99.37 66	85.44 309	99.81 144	97.39 133	97.36 330	97.81 307

CVMVSNet		96.25 265	97.21 212	93.38 345	99.10 179	80.56 365	97.20 230	98.19 283	96.94 216	99.00 127	99.02 135	89.50 288	99.80 156	96.36 196	99.59 167	99.78 15

EPNet		96.14 266	95.44 272	98.25 215	90.76 368	95.50 241	97.92 165	94.65 332	98.97 80	92.98 355	98.85 170	89.12 290	99.87 74	95.99 211	99.68 144	99.39 152
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		96.06 267	97.62 191	91.38 348	98.65 274	98.57 76	98.85 74	96.95 310	96.86 220	99.90 4	99.16 102	99.18 11	98.40 358	89.23 334	99.77 106	77.18 363

FMVSNet5		96.01 268	95.20 279	98.41 197	97.53 333	96.10 220	98.74 77	99.50 65	97.22 209	98.03 214	99.04 131	69.80 362	99.88 64	97.27 138	99.71 129	99.25 195

HY-MVS	95.94 13	95.90 269	95.35 274	97.55 255	97.95 317	94.79 256	98.81 76	96.94 311	92.28 319	95.17 339	98.57 214	89.90 285	99.75 212	91.20 322	97.33 332	98.10 296

GA-MVS		95.86 270	95.32 275	97.49 258	98.60 278	94.15 276	93.83 348	97.93 288	95.49 269	96.68 300	97.42 295	83.21 322	99.30 336	96.22 200	98.55 287	99.01 233

OpenMVS_ROB	95.38 14	95.84 271	95.18 280	97.81 238	98.41 295	97.15 177	97.37 218	98.62 267	83.86 357	98.65 170	98.37 233	94.29 245	99.68 247	88.41 336	98.62 284	96.60 341

1314		95.74 272	95.60 269	96.17 305	97.53 333	92.75 302	98.07 142	98.31 278	91.22 332	94.25 347	96.68 311	95.53 211	99.03 347	91.64 311	97.18 333	96.74 339

PVSNet	93.40 17	95.67 273	95.70 265	95.57 321	98.83 240	88.57 336	92.50 355	97.72 293	92.69 313	96.49 310	96.44 317	93.72 258	99.43 322	93.61 280	99.28 224	98.71 270

tttt0517		95.64 274	94.98 284	97.64 249	99.36 121	93.81 288	98.72 79	90.47 364	98.08 132	98.67 169	98.34 236	73.88 360	99.92 33	97.77 113	99.51 193	99.20 205

PatchmatchNet		95.58 275	95.67 267	95.30 324	97.34 341	87.32 342	97.65 193	96.65 318	95.30 272	97.07 281	98.69 191	84.77 311	99.75 212	94.97 241	98.64 282	98.83 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TR-MVS		95.55 276	95.12 281	96.86 284	97.54 332	93.94 280	96.49 273	96.53 321	94.36 292	97.03 284	96.61 312	94.26 246	99.16 344	86.91 341	96.31 343	97.47 326

testus		95.52 277	95.32 275	96.13 309	97.91 320	89.49 335	93.62 350	99.61 29	92.41 316	97.38 272	95.42 340	94.72 237	99.63 270	88.06 338	98.72 275	99.26 193

JIA-IIPM		95.52 277	95.03 283	97.00 274	96.85 350	94.03 278	96.93 246	95.82 327	99.20 52	94.63 344	99.71 13	83.09 323	99.60 280	94.42 257	94.64 352	97.36 327

CHOSEN 280x420		95.51 279	95.47 270	95.65 319	98.25 303	88.27 339	93.25 352	98.88 236	93.53 304	94.65 343	97.15 304	86.17 300	99.93 25	97.41 132	99.93 38	98.73 269

ADS-MVSNet2		95.43 280	94.98 284	96.76 286	98.14 311	91.74 312	97.92 165	97.76 291	90.23 337	96.51 307	98.91 155	85.61 306	99.85 88	92.88 293	96.90 336	98.69 273

PAPR		95.29 281	94.47 289	97.75 242	97.50 337	95.14 251	94.89 333	98.71 262	91.39 331	95.35 338	95.48 337	94.57 239	99.14 346	84.95 348	97.37 328	98.97 239

ADS-MVSNet		95.24 282	94.93 286	96.18 304	98.14 311	90.10 332	97.92 165	97.32 301	90.23 337	96.51 307	98.91 155	85.61 306	99.74 221	92.88 293	96.90 336	98.69 273

BH-w/o		95.13 283	94.89 287	95.86 314	98.20 309	91.31 326	95.65 314	97.37 298	93.64 302	96.52 306	95.70 329	93.04 264	99.02 348	88.10 337	95.82 346	97.24 329

tpmrst		95.07 284	95.46 271	93.91 339	97.11 345	84.36 357	97.62 198	96.96 308	94.98 277	96.35 313	98.80 179	85.46 308	99.59 284	95.60 230	96.23 344	97.79 310

pmmvs3		95.03 285	94.40 294	96.93 277	97.70 327	92.53 304	95.08 329	97.71 294	88.57 347	97.71 243	98.08 259	79.39 342	99.82 131	96.19 202	99.11 252	98.43 285

tpmvs		95.02 286	95.25 277	94.33 333	96.39 357	85.87 346	98.08 140	96.83 315	95.46 270	95.51 335	98.69 191	85.91 303	99.53 301	94.16 262	96.23 344	97.58 322

EPNet_dtu		94.93 287	94.78 288	95.38 323	93.58 367	87.68 341	96.78 255	95.69 329	97.35 192	89.14 363	98.09 258	88.15 293	99.49 312	94.95 242	99.30 220	98.98 236
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
view600		94.87 288	94.41 290	96.26 299	99.22 147	91.37 319	98.49 103	94.45 334	98.75 93	97.85 225	95.98 323	80.38 331	99.75 212	86.06 344	98.49 288	97.66 314

view800		94.87 288	94.41 290	96.26 299	99.22 147	91.37 319	98.49 103	94.45 334	98.75 93	97.85 225	95.98 323	80.38 331	99.75 212	86.06 344	98.49 288	97.66 314

conf0.05thres1000		94.87 288	94.41 290	96.26 299	99.22 147	91.37 319	98.49 103	94.45 334	98.75 93	97.85 225	95.98 323	80.38 331	99.75 212	86.06 344	98.49 288	97.66 314

tfpn		94.87 288	94.41 290	96.26 299	99.22 147	91.37 319	98.49 103	94.45 334	98.75 93	97.85 225	95.98 323	80.38 331	99.75 212	86.06 344	98.49 288	97.66 314

test12356		94.85 292	95.12 281	94.03 338	98.25 303	83.12 360	93.85 347	99.33 129	94.17 296	97.28 274	97.20 300	85.83 304	99.75 212	90.85 328	99.33 214	99.22 203

conf0.01		94.82 293	94.07 299	97.06 272	99.21 153	94.53 264	98.47 109	92.69 349	95.61 261	97.81 233	95.54 331	77.71 348	99.80 156	91.49 315	98.11 306	96.86 334

conf0.002		94.82 293	94.07 299	97.06 272	99.21 153	94.53 264	98.47 109	92.69 349	95.61 261	97.81 233	95.54 331	77.71 348	99.80 156	91.49 315	98.11 306	96.86 334

tfpn1000		94.81 295	94.25 298	96.47 295	99.01 203	93.47 295	98.56 92	92.30 358	96.17 246	97.90 219	96.29 319	76.70 354	99.77 200	93.02 291	98.29 296	96.16 345

cascas		94.79 296	94.33 297	96.15 308	96.02 361	92.36 308	92.34 357	99.26 153	85.34 356	95.08 341	94.96 349	92.96 265	98.53 357	94.41 260	98.59 285	97.56 323

thresconf0.02		94.70 297	94.07 299	96.58 288	99.21 153	94.53 264	98.47 109	92.69 349	95.61 261	97.81 233	95.54 331	77.71 348	99.80 156	91.49 315	98.11 306	95.42 353

tfpn_n400		94.70 297	94.07 299	96.58 288	99.21 153	94.53 264	98.47 109	92.69 349	95.61 261	97.81 233	95.54 331	77.71 348	99.80 156	91.49 315	98.11 306	95.42 353

tfpnconf		94.70 297	94.07 299	96.58 288	99.21 153	94.53 264	98.47 109	92.69 349	95.61 261	97.81 233	95.54 331	77.71 348	99.80 156	91.49 315	98.11 306	95.42 353

tfpnview11		94.70 297	94.07 299	96.58 288	99.21 153	94.53 264	98.47 109	92.69 349	95.61 261	97.81 233	95.54 331	77.71 348	99.80 156	91.49 315	98.11 306	95.42 353

tpm		94.67 301	94.34 296	95.66 318	97.68 328	88.42 337	97.88 170	94.90 331	94.46 287	96.03 321	98.56 217	78.66 343	99.79 178	95.88 215	95.01 351	98.78 264

test0.0.03 1		94.51 302	93.69 312	96.99 275	96.05 359	93.61 293	94.97 331	93.49 346	96.17 246	97.57 254	94.88 350	82.30 326	99.01 350	93.60 281	94.17 357	98.37 290

thres600view7		94.45 303	93.83 308	96.29 297	99.06 189	91.53 315	97.99 158	94.24 341	98.34 115	97.44 264	95.01 344	79.84 336	99.67 253	84.33 350	98.23 297	97.66 314

PCF-MVS	92.86 18	94.36 304	93.00 323	98.42 196	98.70 260	97.56 156	93.16 353	99.11 195	79.59 361	97.55 255	97.43 294	92.19 273	99.73 226	79.85 360	99.45 202	97.97 300
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tfpn111		94.33 305	93.78 309	95.96 311	99.06 189	91.35 323	98.03 148	94.24 341	98.33 116	97.40 267	94.98 346	79.84 336	99.68 247	83.94 351	98.22 299	96.86 334

X-MVStestdata		94.32 306	92.59 324	99.53 32	99.46 103	99.21 20	98.65 82	99.34 124	98.62 102	97.54 256	45.85 364	97.50 94	99.83 118	96.79 162	99.53 189	99.56 77

MVS-HIRNet		94.32 306	95.62 268	90.42 349	98.46 291	75.36 366	96.29 282	89.13 365	95.25 273	95.38 337	99.75 6	92.88 267	99.19 342	94.07 268	99.39 207	96.72 340

conf200view11		94.24 308	93.67 313	95.94 312	99.06 189	91.35 323	98.03 148	94.24 341	98.33 116	97.40 267	94.98 346	79.84 336	99.62 273	83.05 353	98.08 314	96.86 334

thres100view900		94.19 309	93.67 313	95.75 317	99.06 189	91.35 323	98.03 148	94.24 341	98.33 116	97.40 267	94.98 346	79.84 336	99.62 273	83.05 353	98.08 314	96.29 342

E-PMN		94.17 310	94.37 295	93.58 342	96.86 349	85.71 349	90.11 360	97.07 305	98.17 130	97.82 232	97.19 301	84.62 313	98.94 351	89.77 332	97.68 324	96.09 349

thres400		94.14 311	93.44 317	96.24 303	98.93 216	91.44 317	97.60 201	94.29 339	97.94 136	97.10 279	94.31 355	79.67 340	99.62 273	83.05 353	98.08 314	97.66 314

thisisatest0515		94.12 312	93.16 320	96.97 276	98.60 278	92.90 301	93.77 349	90.61 363	94.10 298	96.91 289	95.87 328	74.99 358	99.80 156	94.52 252	99.12 251	98.20 292

tfpn_ndepth		94.12 312	93.51 316	95.94 312	98.86 232	93.60 294	98.16 133	91.90 360	94.66 284	97.41 266	95.24 341	76.24 355	99.73 226	91.21 321	97.88 321	94.50 358

PatchFormer-LS_test		94.08 314	93.91 306	94.59 331	96.93 347	86.86 344	97.55 208	96.57 320	94.27 293	94.38 346	93.64 360	80.96 328	99.59 284	96.44 193	94.48 355	97.31 328

tfpn200view9		94.03 315	93.44 317	95.78 316	98.93 216	91.44 317	97.60 201	94.29 339	97.94 136	97.10 279	94.31 355	79.67 340	99.62 273	83.05 353	98.08 314	96.29 342

1111		93.99 316	93.72 311	94.80 328	99.33 129	85.20 351	95.97 294	99.39 101	97.88 148	98.64 172	98.56 217	57.79 370	99.80 156	96.02 209	99.87 69	99.40 151

CostFormer		93.97 317	93.78 309	94.51 332	97.53 333	85.83 348	97.98 159	95.96 326	89.29 345	94.99 342	98.63 206	78.63 344	99.62 273	94.54 251	96.50 341	98.09 297

test-LLR		93.90 318	93.85 307	94.04 336	96.53 353	84.62 355	94.05 344	92.39 356	96.17 246	94.12 349	95.07 342	82.30 326	99.67 253	95.87 218	98.18 301	97.82 305

EMVS		93.83 319	94.02 305	93.23 346	96.83 351	84.96 353	89.77 361	96.32 323	97.92 138	97.43 265	96.36 318	86.17 300	98.93 352	87.68 339	97.73 323	95.81 350

thres200		93.72 320	93.14 321	95.46 322	98.66 273	91.29 327	96.61 268	94.63 333	97.39 189	96.83 296	93.71 358	79.88 335	99.56 294	82.40 357	98.13 305	95.54 352

EPMVS		93.72 320	93.27 319	95.09 326	96.04 360	87.76 340	98.13 134	85.01 367	94.69 283	96.92 287	98.64 202	78.47 346	99.31 334	95.04 238	96.46 342	98.20 292

dp		93.47 322	93.59 315	93.13 347	96.64 352	81.62 364	97.66 191	96.42 322	92.80 312	96.11 316	98.64 202	78.55 345	99.59 284	93.31 288	92.18 361	98.16 294

FPMVS		93.44 323	92.23 328	97.08 270	99.25 140	97.86 134	95.61 315	97.16 304	92.90 310	93.76 354	98.65 199	75.94 357	95.66 362	79.30 361	97.49 325	97.73 312

tpm cat1		93.29 324	93.13 322	93.75 340	97.39 340	84.74 354	97.39 217	97.65 296	83.39 359	94.16 348	98.41 230	82.86 325	99.39 325	91.56 314	95.35 350	97.14 330

MVS		93.19 325	92.09 329	96.50 294	96.91 348	94.03 278	98.07 142	98.06 286	68.01 362	94.56 345	96.48 315	95.96 197	99.30 336	83.84 352	96.89 338	96.17 344

tpm2		93.09 326	92.58 325	94.62 330	97.56 331	86.53 345	97.66 191	95.79 328	86.15 354	94.07 351	98.23 247	75.95 356	99.53 301	90.91 326	96.86 339	97.81 307

tpmp4_e23		92.91 327	92.45 326	94.29 334	97.41 338	85.62 350	97.95 162	96.77 316	87.55 352	91.33 360	98.57 214	74.21 359	99.59 284	91.62 312	96.64 340	97.65 321

DWT-MVSNet_test		92.75 328	92.05 330	94.85 327	96.48 355	87.21 343	97.83 176	94.99 330	92.22 320	92.72 356	94.11 357	70.75 361	99.46 318	95.01 239	94.33 356	97.87 303

MVE	83.40 22	92.50 329	91.92 331	94.25 335	98.83 240	91.64 314	92.71 354	83.52 368	95.92 256	86.46 366	95.46 338	95.20 220	95.40 363	80.51 359	98.64 282	95.73 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
gg-mvs-nofinetune		92.37 330	91.20 334	95.85 315	95.80 362	92.38 307	99.31 21	81.84 369	99.75 4	91.83 358	99.74 7	68.29 363	99.02 348	87.15 340	97.12 334	96.16 345

test-mter		92.33 331	91.76 333	94.04 336	96.53 353	84.62 355	94.05 344	92.39 356	94.00 300	94.12 349	95.07 342	65.63 369	99.67 253	95.87 218	98.18 301	97.82 305

IB-MVS	91.63 19	92.24 332	90.90 335	96.27 298	97.22 344	91.24 328	94.36 342	93.33 348	92.37 317	92.24 357	94.58 354	66.20 367	99.89 56	93.16 290	94.63 353	97.66 314
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
TESTMET0.1,1		92.19 333	91.77 332	93.46 343	96.48 355	82.80 362	94.05 344	91.52 361	94.45 289	94.00 352	94.88 350	66.65 366	99.56 294	95.78 223	98.11 306	98.02 299

PAPM		91.88 334	90.34 336	96.51 293	98.06 314	92.56 303	92.44 356	97.17 303	86.35 353	90.38 362	96.01 321	86.61 297	99.21 341	70.65 363	95.43 349	97.75 311

PNet_i23d		91.80 335	92.35 327	90.14 350	98.65 274	73.10 369	89.22 362	99.02 213	95.23 275	97.87 221	97.82 272	78.45 347	98.89 354	88.73 335	86.14 362	98.42 286

test2356		91.64 336	90.19 339	96.00 310	94.30 365	89.58 334	90.84 358	96.68 317	91.76 322	95.48 336	93.69 359	67.05 365	99.52 305	84.83 349	97.08 335	98.91 246

PVSNet_0	89.98 21	91.15 337	90.30 337	93.70 341	97.72 324	84.34 358	90.24 359	97.42 297	90.20 340	93.79 353	93.09 361	90.90 280	98.89 354	86.57 342	72.76 363	97.87 303

testpf		89.08 338	90.27 338	85.50 351	94.03 366	82.85 361	96.87 252	91.09 362	91.61 326	90.96 361	94.86 353	66.15 368	95.83 361	94.58 250	92.27 360	77.82 362

.test1245		79.71 339	84.30 340	65.96 353	99.33 129	85.20 351	95.97 294	99.39 101	97.88 148	98.64 172	98.56 217	57.79 370	99.80 156	96.02 209	15.07 364	12.86 365

tmp_tt		78.77 340	78.73 341	78.90 352	58.45 369	74.76 368	94.20 343	78.26 371	39.16 364	86.71 365	92.82 362	80.50 330	75.19 366	86.16 343	92.29 359	86.74 361

pcd1.5k->3k		41.59 341	44.35 343	33.30 354	99.87 11	0.00 372	0.00 363	99.58 35	0.00 367	0.00 369	0.00 369	99.70 2	0.00 369	0.00 366	99.99 11	99.91 2

v1.0		41.09 342	54.78 342	0.00 357	99.36 121	0.00 372	0.00 363	99.28 143	96.66 230	99.05 118	98.71 189	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

cdsmvs_eth3d_5k		24.66 343	32.88 344	0.00 357	0.00 372	0.00 372	0.00 363	99.10 196	0.00 367	0.00 369	97.58 283	99.21 10	0.00 369	0.00 366	0.00 367	0.00 367

testmvs		17.12 344	20.53 345	6.87 356	12.05 370	4.20 371	93.62 350	6.73 372	4.62 366	10.41 367	24.33 365	8.28 373	3.56 368	9.69 365	15.07 364	12.86 365

test123		17.04 345	20.11 346	7.82 355	10.25 371	4.91 370	94.80 334	4.47 373	4.93 365	10.00 368	24.28 366	9.69 372	3.64 367	10.14 364	12.43 366	14.92 364

pcd_1.5k_mvsjas		8.17 346	10.90 347	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	98.07 60	0.00 369	0.00 366	0.00 367	0.00 367

ab-mvs-re		8.12 347	10.83 348	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	97.48 290	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

sosnet-low-res		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

sosnet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uncertanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

Regformer		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

GSMVS																98.81 258

test_part2					99.36 121	99.10 43				99.05 118

test_part1				0.00 357		0.00 372	0.00 363	99.28 143				0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

sam_mvs1												84.74 312				98.81 258

sam_mvs												84.29 318

semantic-postprocess				96.87 281	99.27 136	91.16 330		99.25 154	99.10 67	99.41 61	99.35 70	92.91 266	99.96 8	98.65 70	99.94 33	99.49 111

ambc				98.24 216	98.82 243	95.97 225	98.62 85	99.00 220		99.27 85	99.21 89	96.99 134	99.50 311	96.55 184	99.50 199	99.26 193

MTGPA								99.20 167

test_post1							97.59 203				20.48 368	83.07 324	99.66 261	94.16 262

test_post											21.25 367	83.86 320	99.70 238

patchmatchnet-post											98.77 183	84.37 315	99.85 88

GG-mvs-BLEND				94.76 329	94.54 364	92.13 310	99.31 21	80.47 370		88.73 364	91.01 363	67.59 364	98.16 360	82.30 358	94.53 354	93.98 359

MTMP							97.93 163	91.91 359

gm-plane-assit					94.83 363	81.97 363			88.07 349		94.99 345		99.60 280	91.76 308

test9_res														93.28 289	99.15 244	99.38 158

TEST9					98.71 256	98.08 111	95.96 298	99.03 209	91.40 330	95.85 323	97.53 285	96.52 169	99.76 206

test_8					98.67 268	98.01 117	95.91 304	99.02 213	91.64 324	95.79 325	97.50 288	96.47 172	99.76 206

agg_prior2														92.50 302	99.16 241	99.37 159

agg_prior					98.68 265	97.99 118		99.01 216		95.59 327			99.77 200

TestCases				99.16 86	99.50 86	98.55 77		99.58 35	96.80 222	98.88 148	99.06 124	97.65 83	99.57 291	94.45 255	99.61 164	99.37 159

test_prior4						97.97 123	95.86 305

test_prior2							95.74 311		96.48 235	96.11 316	97.63 281	95.92 199		94.16 262	99.20 233

test_prior				98.95 119	98.69 263	97.95 126		99.03 209					99.59 284			99.30 185

旧先验2							95.76 309		88.56 348	97.52 258			99.66 261	94.48 253

新几何2							95.93 302

新几何1				98.91 125	98.94 214	97.76 144		98.76 254	87.58 351	96.75 299	98.10 256	94.80 233	99.78 189	92.73 299	99.00 261	99.20 205

旧先验1					98.82 243	97.45 162		98.76 254			98.34 236	95.50 214			99.01 260	99.23 199

无先验							95.74 311	98.74 259	89.38 344				99.73 226	92.38 304		99.22 203

原ACMM2							95.53 318

原ACMM1				98.35 205	98.90 224	96.25 215		98.83 248	92.48 315	96.07 319	98.10 256	95.39 217	99.71 236	92.61 301	98.99 262	99.08 223

test222					98.92 220	96.93 186	95.54 317	98.78 253	85.72 355	96.86 295	98.11 255	94.43 241			99.10 253	99.23 199

testdata2													99.79 178	92.80 297

segment_acmp												97.02 132

testdata				98.09 223	98.93 216	95.40 244		98.80 251	90.08 341	97.45 263	98.37 233	95.26 219	99.70 238	93.58 282	98.95 266	99.17 217

testdata1							95.44 322		96.32 241

test12				98.93 122	98.58 280	97.83 136		98.66 264		96.53 305		95.51 213	99.69 242		99.13 248	99.27 190

plane_prior7					99.19 163	97.87 133

plane_prior6					98.99 206	97.70 150						94.90 226

plane_prior5								99.27 148					99.70 238	94.42 257	99.51 193	99.45 133

plane_prior4											97.98 264

plane_prior3						97.78 143			97.41 187	97.79 239

plane_prior2							97.77 180		98.20 127

plane_prior1					99.05 194

plane_prior						97.65 152	97.07 239		96.72 225						99.36 209

n2								0.00 374

nn								0.00 374

door-mid								99.57 42

lessismore_v0				98.97 117	99.73 28	97.53 158		86.71 366		99.37 67	99.52 46	89.93 284	99.92 33	98.99 54	99.72 125	99.44 135

LGP-MVS_train				99.47 48	99.57 62	98.97 50		99.48 74	96.60 232	99.10 110	99.06 124	98.71 26	99.83 118	95.58 232	99.78 102	99.62 47

test11								98.87 237

door								99.41 98

HQP5-MVS						96.79 189

HQP-NCC					98.67 268		96.29 282		96.05 252	95.55 331

ACMP_Plane					98.67 268		96.29 282		96.05 252	95.55 331

BP-MVS														92.82 295

HQP4-MVS										95.56 330			99.54 299			99.32 178

HQP3-MVS								99.04 207							99.26 227

HQP2-MVS												93.84 252

NP-MVS					98.84 238	97.39 165					96.84 308

MDTV_nov1_ep13_2view						74.92 367	97.69 188		90.06 342	97.75 242		85.78 305		93.52 283		98.69 273

MDTV_nov1_ep13			95.22 278		97.06 346	83.20 359	97.74 184	96.16 324	94.37 291	96.99 285	98.83 174	83.95 319	99.53 301	93.90 271	97.95 319

ACMMP++_ref															99.77 106

ACMMP++															99.68 144

Test By Simon												96.52 169

ITE_SJBPF				98.87 130	99.22 147	98.48 84		99.35 120	97.50 175	98.28 201	98.60 211	97.64 86	99.35 329	93.86 274	99.27 225	98.79 263

DeepMVS_CX				93.44 344	98.24 305	94.21 274		94.34 338	64.28 363	91.34 359	94.87 352	89.45 289	92.77 365	77.54 362	93.14 358	93.35 360

High-resolution multi-view benchmark