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		99.95 1	99.95 1	99.95 1	99.99 1	99.99 1	99.95 2	99.97 3	99.99 1	100.00 1	99.98 8	99.78 8	100.00 1	99.92 3	100.00 1	99.87 10

mvs_tets		99.90 2	99.90 2	99.90 4	99.96 5	99.79 34	99.72 26	99.88 18	99.92 5	99.98 3	99.93 14	99.94 2	99.98 7	99.77 30	100.00 1	99.92 3

jajsoiax		99.89 3	99.89 3	99.89 6	99.96 5	99.78 36	99.70 30	99.86 22	99.89 10	99.98 3	99.90 23	99.94 2	99.98 7	99.75 31	100.00 1	99.90 5

ANet_high		99.88 4	99.87 4	99.91 2	99.99 1	99.91 3	99.65 55	100.00 1	99.90 6	100.00 1	99.97 10	99.61 17	99.97 16	99.75 31	100.00 1	99.84 15

LTVRE_ROB	99.19 1	99.88 4	99.87 4	99.88 12	99.91 21	99.90 4	99.96 1	99.92 7	99.90 6	99.97 6	99.87 37	99.81 7	99.95 41	99.54 45	99.99 20	99.80 25
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
pmmvs6		99.86 6	99.86 6	99.83 25	99.94 15	99.90 4	99.83 8	99.91 11	99.85 19	99.94 20	99.95 12	99.73 10	99.90 110	99.65 35	99.97 47	99.69 57

v52		99.85 7	99.84 7	99.89 6	99.96 5	99.89 6	99.87 5	99.81 56	99.85 19	99.96 8	99.90 23	99.27 42	99.95 41	99.93 1	99.99 20	99.82 23

V4		99.85 7	99.84 7	99.88 12	99.96 5	99.89 6	99.87 5	99.81 56	99.85 19	99.96 8	99.90 23	99.27 42	99.95 41	99.93 1	100.00 1	99.82 23

PS-MVSNAJss		99.84 9	99.82 9	99.89 6	99.96 5	99.77 38	99.68 42	99.85 29	99.95 3	99.98 3	99.92 17	99.28 39	99.98 7	99.75 31	100.00 1	99.94 2

test_djsdf		99.84 9	99.81 10	99.91 2	99.94 15	99.84 19	99.77 14	99.80 60	99.73 42	99.97 6	99.92 17	99.77 9	99.98 7	99.43 54	100.00 1	99.90 5

Anonymous20231211		99.83 11	99.81 10	99.89 6	99.97 4	99.95 2	99.88 4	99.93 6	99.87 13	99.94 20	99.98 8	99.55 21	99.95 41	99.21 80	99.98 36	99.78 31

v7n		99.82 12	99.80 12	99.88 12	99.96 5	99.84 19	99.82 10	99.82 48	99.84 23	99.94 20	99.91 20	99.13 57	99.96 33	99.83 20	99.99 20	99.83 18

anonymousdsp		99.80 13	99.77 14	99.90 4	99.96 5	99.88 8	99.73 22	99.85 29	99.70 50	99.92 31	99.93 14	99.45 23	99.97 16	99.36 62	100.00 1	99.85 14

pm-mvs1		99.79 14	99.79 13	99.78 38	99.91 21	99.83 23	99.76 17	99.87 20	99.73 42	99.89 39	99.87 37	99.63 15	99.87 161	99.54 45	99.92 91	99.63 100

UA-Net		99.78 15	99.76 18	99.86 18	99.72 131	99.71 53	99.91 3	99.95 5	99.96 2	99.71 108	99.91 20	99.15 53	99.97 16	99.50 49	100.00 1	99.90 5

TransMVSNet (Re)		99.78 15	99.77 14	99.81 28	99.91 21	99.85 13	99.75 18	99.86 22	99.70 50	99.91 34	99.89 31	99.60 19	99.87 161	99.59 39	99.74 193	99.71 50

v748		99.76 17	99.74 21	99.84 21	99.95 13	99.83 23	99.82 10	99.80 60	99.82 27	99.95 16	99.87 37	98.72 112	99.93 66	99.72 34	99.98 36	99.75 41

v13		99.76 17	99.77 14	99.73 64	99.86 36	99.55 97	99.77 14	99.86 22	99.79 33	99.96 8	99.91 20	98.90 84	99.87 161	99.91 5	100.00 1	99.78 31

v12		99.75 19	99.77 14	99.72 69	99.85 40	99.53 100	99.75 18	99.86 22	99.78 34	99.96 8	99.90 23	98.88 87	99.86 181	99.91 5	100.00 1	99.77 34

v11		99.75 19	99.76 18	99.71 73	99.85 40	99.49 103	99.73 22	99.84 37	99.75 39	99.95 16	99.90 23	98.93 80	99.86 181	99.92 3	100.00 1	99.77 34

OurMVSNet-221017-0		99.75 19	99.71 25	99.84 21	99.96 5	99.83 23	99.83 8	99.85 29	99.80 31	99.93 26	99.93 14	98.54 141	99.93 66	99.59 39	99.98 36	99.76 38

Vis-MVSNet		99.75 19	99.74 21	99.79 35	99.88 29	99.66 72	99.69 39	99.92 7	99.67 59	99.77 88	99.75 93	99.61 17	99.98 7	99.35 63	99.98 36	99.72 47
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
V9		99.74 23	99.75 20	99.71 73	99.84 43	99.50 101	99.74 20	99.86 22	99.76 38	99.96 8	99.90 23	98.83 90	99.85 197	99.91 5	100.00 1	99.77 34

V14		99.73 24	99.74 21	99.69 80	99.83 47	99.48 106	99.72 26	99.85 29	99.74 40	99.96 8	99.89 31	98.79 98	99.85 197	99.91 5	100.00 1	99.76 38

v15		99.72 25	99.73 24	99.68 83	99.82 54	99.44 118	99.70 30	99.85 29	99.72 45	99.95 16	99.88 34	98.76 105	99.84 213	99.90 9	100.00 1	99.75 41

TDRefinement		99.72 25	99.70 28	99.77 40	99.90 26	99.85 13	99.86 7	99.92 7	99.69 54	99.78 83	99.92 17	99.37 30	99.88 141	98.93 122	99.95 67	99.60 125

XXY-MVS		99.71 27	99.67 32	99.81 28	99.89 28	99.72 52	99.59 66	99.82 48	99.39 112	99.82 66	99.84 50	99.38 28	99.91 93	99.38 59	99.93 88	99.80 25

nrg030		99.70 28	99.66 33	99.82 26	99.76 104	99.84 19	99.61 61	99.70 108	99.93 4	99.78 83	99.68 137	99.10 59	99.78 272	99.45 52	99.96 60	99.83 18

FC-MVSNet-test		99.70 28	99.65 34	99.86 18	99.88 29	99.86 12	99.72 26	99.78 70	99.90 6	99.82 66	99.83 51	98.45 154	99.87 161	99.51 48	99.97 47	99.86 12

v17		99.70 28	99.71 25	99.67 86	99.81 62	99.44 118	99.70 30	99.83 40	99.69 54	99.94 20	99.87 37	98.70 113	99.84 213	99.88 14	99.99 20	99.73 44

v16		99.70 28	99.71 25	99.67 86	99.81 62	99.43 124	99.70 30	99.83 40	99.70 50	99.94 20	99.87 37	98.69 115	99.84 213	99.88 14	99.99 20	99.73 44

v10		99.69 32	99.69 29	99.66 94	99.81 62	99.39 136	99.66 50	99.75 85	99.60 81	99.92 31	99.87 37	98.75 108	99.86 181	99.90 9	99.99 20	99.73 44

v18		99.68 33	99.69 29	99.65 98	99.79 83	99.40 133	99.68 42	99.83 40	99.66 63	99.93 26	99.85 45	98.65 124	99.84 213	99.87 18	99.99 20	99.71 50

v8		99.68 33	99.69 29	99.65 98	99.80 70	99.40 133	99.66 50	99.76 79	99.64 68	99.93 26	99.85 45	98.66 122	99.84 213	99.88 14	99.99 20	99.71 50

DTE-MVSNet		99.68 33	99.61 42	99.88 12	99.80 70	99.87 9	99.67 47	99.71 105	99.72 45	99.84 61	99.78 80	98.67 120	99.97 16	99.30 72	99.95 67	99.80 25

Anonymous20240521		99.67 36	99.62 38	99.84 21	99.91 21	99.85 13	99.81 12	99.76 79	99.72 45	99.92 31	99.83 51	98.10 181	99.90 110	99.58 41	99.97 47	99.77 34

VPA-MVSNet		99.66 37	99.62 38	99.79 35	99.68 150	99.75 45	99.62 57	99.69 114	99.85 19	99.80 75	99.81 62	98.81 91	99.91 93	99.47 51	99.88 115	99.70 54

PS-CasMVS		99.66 37	99.58 46	99.89 6	99.80 70	99.85 13	99.66 50	99.73 93	99.62 72	99.84 61	99.71 112	98.62 129	99.96 33	99.30 72	99.96 60	99.86 12

PEN-MVS		99.66 37	99.59 44	99.89 6	99.83 47	99.87 9	99.66 50	99.73 93	99.70 50	99.84 61	99.73 99	98.56 135	99.96 33	99.29 75	99.94 80	99.83 18

FMVSNet1		99.66 37	99.63 37	99.73 64	99.78 89	99.77 38	99.68 42	99.70 108	99.67 59	99.82 66	99.83 51	98.98 74	99.90 110	99.24 79	99.97 47	99.53 159

MIMVSNet1		99.66 37	99.62 38	99.80 30	99.94 15	99.87 9	99.69 39	99.77 73	99.78 34	99.93 26	99.89 31	97.94 193	99.92 84	99.65 35	99.98 36	99.62 114

FIs		99.65 42	99.58 46	99.84 21	99.84 43	99.85 13	99.66 50	99.75 85	99.86 16	99.74 100	99.79 71	98.27 169	99.85 197	99.37 61	99.93 88	99.83 18

wuykxyi23d		99.65 42	99.64 36	99.69 80	99.92 19	99.20 187	98.89 214	99.99 2	98.73 198	99.95 16	99.80 64	99.84 4	99.99 4	99.64 37	99.98 36	99.89 9

DeepC-MVS	98.90 4	99.62 44	99.61 42	99.67 86	99.72 131	99.44 118	99.24 141	99.71 105	99.27 124	99.93 26	99.90 23	99.70 12	99.93 66	98.99 109	99.99 20	99.64 96
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		99.61 45	99.53 62	99.87 16	99.80 70	99.83 23	99.67 47	99.75 85	99.58 84	99.85 58	99.69 125	98.18 179	99.94 55	99.28 77	99.95 67	99.83 18

ACMH	98.42 6	99.59 46	99.54 54	99.72 69	99.86 36	99.62 84	99.56 71	99.79 68	98.77 190	99.80 75	99.85 45	99.64 14	99.85 197	98.70 138	99.89 109	99.70 54
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testing_2		99.58 47	99.56 52	99.62 117	99.81 62	99.44 118	99.14 173	99.43 228	99.69 54	99.82 66	99.79 71	99.14 54	99.79 264	99.31 71	99.95 67	99.63 100

v1192		99.57 48	99.57 49	99.57 138	99.77 99	99.22 181	99.04 192	99.60 163	99.18 141	99.87 52	99.72 105	99.08 64	99.85 197	99.89 13	99.98 36	99.66 80

EG-PatchMatch MVS		99.57 48	99.56 52	99.62 117	99.77 99	99.33 155	99.26 135	99.76 79	99.32 120	99.80 75	99.78 80	99.29 37	99.87 161	99.15 93	99.91 101	99.66 80

Gipuma		99.57 48	99.59 44	99.49 161	99.98 3	99.71 53	99.72 26	99.84 37	99.81 28	99.94 20	99.78 80	98.91 83	99.71 298	98.41 152	99.95 67	99.05 278
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1921920		99.56 51	99.57 49	99.55 147	99.75 112	99.11 196	99.05 190	99.61 149	99.15 148	99.88 47	99.71 112	99.08 64	99.87 161	99.90 9	99.97 47	99.66 80

v1240		99.56 51	99.58 46	99.51 157	99.80 70	99.00 207	99.00 199	99.65 134	99.15 148	99.90 36	99.75 93	99.09 61	99.88 141	99.90 9	99.96 60	99.67 70

v7		99.56 51	99.54 54	99.61 120	99.80 70	99.39 136	99.30 122	99.59 167	99.14 150	99.82 66	99.72 105	98.75 108	99.84 213	99.83 20	99.94 80	99.61 119

V42		99.56 51	99.54 54	99.63 109	99.79 83	99.46 111	99.39 87	99.59 167	99.24 133	99.86 57	99.70 119	98.55 139	99.82 237	99.79 26	99.95 67	99.60 125

v144192		99.55 55	99.54 54	99.58 132	99.78 89	99.20 187	99.11 181	99.62 145	99.18 141	99.89 39	99.72 105	98.66 122	99.87 161	99.88 14	99.97 47	99.66 80

v1neww		99.55 55	99.54 54	99.61 120	99.80 70	99.39 136	99.32 112	99.61 149	99.18 141	99.87 52	99.69 125	98.64 127	99.82 237	99.79 26	99.94 80	99.60 125

v7new		99.55 55	99.54 54	99.61 120	99.80 70	99.39 136	99.32 112	99.61 149	99.18 141	99.87 52	99.69 125	98.64 127	99.82 237	99.79 26	99.94 80	99.60 125

v6		99.55 55	99.54 54	99.61 120	99.80 70	99.39 136	99.32 112	99.60 163	99.18 141	99.87 52	99.68 137	98.65 124	99.82 237	99.79 26	99.95 67	99.61 119

test20.03		99.55 55	99.54 54	99.58 132	99.79 83	99.37 145	99.02 195	99.89 15	99.60 81	99.82 66	99.62 168	98.81 91	99.89 126	99.43 54	99.86 129	99.47 188

v1144		99.54 60	99.53 62	99.59 128	99.79 83	99.28 164	99.10 182	99.61 149	99.20 139	99.84 61	99.73 99	98.67 120	99.84 213	99.86 19	99.98 36	99.64 96

v1141		99.54 60	99.52 64	99.57 138	99.78 89	99.27 168	99.15 168	99.61 149	99.26 128	99.89 39	99.69 125	98.56 135	99.82 237	99.82 23	99.97 47	99.63 100

divwei89l23v2f112		99.54 60	99.52 64	99.57 138	99.78 89	99.27 168	99.15 168	99.61 149	99.26 128	99.89 39	99.69 125	98.56 135	99.82 237	99.82 23	99.96 60	99.63 100

v1		99.54 60	99.52 64	99.58 132	99.77 99	99.28 164	99.15 168	99.61 149	99.26 128	99.88 47	99.68 137	98.56 135	99.82 237	99.82 23	99.97 47	99.63 100

CP-MVSNet		99.54 60	99.43 79	99.87 16	99.76 104	99.82 28	99.57 69	99.61 149	99.54 86	99.80 75	99.64 153	97.79 204	99.95 41	99.21 80	99.94 80	99.84 15

TranMVSNet+NR-MVSNet		99.54 60	99.47 70	99.76 43	99.58 177	99.64 78	99.30 122	99.63 142	99.61 76	99.71 108	99.56 200	98.76 105	99.96 33	99.14 99	99.92 91	99.68 63

testmv		99.53 66	99.51 67	99.59 128	99.73 121	99.31 158	98.48 261	99.92 7	99.57 85	99.87 52	99.79 71	99.12 58	99.91 93	99.16 92	99.99 20	99.55 148

v2v482		99.50 67	99.47 70	99.58 132	99.78 89	99.25 174	99.14 173	99.58 175	99.25 131	99.81 72	99.62 168	98.24 171	99.84 213	99.83 20	99.97 47	99.64 96

ACMH+	98.40 8	99.50 67	99.43 79	99.71 73	99.86 36	99.76 42	99.32 112	99.77 73	99.53 88	99.77 88	99.76 89	99.26 45	99.78 272	97.77 197	99.88 115	99.60 125

Baseline_NR-MVSNet		99.49 69	99.37 88	99.82 26	99.91 21	99.84 19	98.83 225	99.86 22	99.68 57	99.65 128	99.88 34	97.67 213	99.87 161	99.03 106	99.86 129	99.76 38

TAMVS		99.49 69	99.45 74	99.63 109	99.48 225	99.42 128	99.45 80	99.57 178	99.66 63	99.78 83	99.83 51	97.85 200	99.86 181	99.44 53	99.96 60	99.61 119

pmmvs-eth3d		99.48 71	99.47 70	99.51 157	99.77 99	99.41 132	98.81 229	99.66 125	99.42 109	99.75 92	99.66 147	99.20 48	99.76 280	98.98 111	99.99 20	99.36 222

EI-MVSNet-UG-set		99.48 71	99.50 68	99.42 180	99.57 186	98.65 239	99.24 141	99.46 220	99.68 57	99.80 75	99.66 147	98.99 73	99.89 126	99.19 84	99.90 103	99.72 47

APDe-MVS		99.48 71	99.36 91	99.85 20	99.55 199	99.81 29	99.50 75	99.69 114	98.99 165	99.75 92	99.71 112	98.79 98	99.93 66	98.46 150	99.85 132	99.80 25

PMMVS2		99.48 71	99.45 74	99.57 138	99.76 104	98.99 208	98.09 296	99.90 14	98.95 168	99.78 83	99.58 188	99.57 20	99.93 66	99.48 50	99.95 67	99.79 30

DSMNet-mixed		99.48 71	99.65 34	98.95 250	99.71 134	97.27 296	99.50 75	99.82 48	99.59 83	99.41 192	99.85 45	99.62 16	100.00 1	99.53 47	99.89 109	99.59 136

DP-MVS		99.48 71	99.39 83	99.74 56	99.57 186	99.62 84	99.29 130	99.61 149	99.87 13	99.74 100	99.76 89	98.69 115	99.87 161	98.20 169	99.80 169	99.75 41

EI-MVSNet-Vis-set		99.47 77	99.49 69	99.42 180	99.57 186	98.66 237	99.24 141	99.46 220	99.67 59	99.79 80	99.65 152	98.97 76	99.89 126	99.15 93	99.89 109	99.71 50

VPNet		99.46 78	99.37 88	99.71 73	99.82 54	99.59 89	99.48 79	99.70 108	99.81 28	99.69 112	99.58 188	97.66 217	99.86 181	99.17 89	99.44 253	99.67 70

ACMM	98.09 11	99.46 78	99.38 85	99.72 69	99.80 70	99.69 64	99.13 178	99.65 134	98.99 165	99.64 130	99.72 105	99.39 24	99.86 181	98.23 166	99.81 164	99.60 125
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-4		99.45 80	99.44 76	99.50 159	99.52 205	98.94 214	99.17 160	99.53 193	99.64 68	99.76 91	99.60 180	98.96 79	99.90 110	98.91 123	99.84 136	99.67 70

COLMAP_ROB	98.06 12	99.45 80	99.37 88	99.70 79	99.83 47	99.70 60	99.38 93	99.78 70	99.53 88	99.67 118	99.78 80	99.19 49	99.86 181	97.32 225	99.87 122	99.55 148
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpnnormal		99.43 82	99.38 85	99.60 126	99.87 33	99.75 45	99.59 66	99.78 70	99.71 48	99.90 36	99.69 125	98.85 89	99.90 110	97.25 231	99.78 177	99.15 253

HPM-MVS_fast		99.43 82	99.30 102	99.80 30	99.83 47	99.81 29	99.52 73	99.70 108	98.35 232	99.51 172	99.50 218	99.31 35	99.88 141	98.18 173	99.84 136	99.69 57

3Dnovator	99.15 2	99.43 82	99.36 91	99.65 98	99.39 249	99.42 128	99.70 30	99.56 181	99.23 135	99.35 208	99.80 64	99.17 51	99.95 41	98.21 168	99.84 136	99.59 136

SixPastTwentyTwo		99.42 85	99.30 102	99.76 43	99.92 19	99.67 69	99.70 30	99.14 283	99.65 66	99.89 39	99.90 23	96.20 265	99.94 55	99.42 58	99.92 91	99.67 70

GBi-Net		99.42 85	99.31 97	99.73 64	99.49 219	99.77 38	99.68 42	99.70 108	99.44 102	99.62 141	99.83 51	97.21 237	99.90 110	98.96 116	99.90 103	99.53 159

test1		99.42 85	99.31 97	99.73 64	99.49 219	99.77 38	99.68 42	99.70 108	99.44 102	99.62 141	99.83 51	97.21 237	99.90 110	98.96 116	99.90 103	99.53 159

Regformer-3		99.41 88	99.41 81	99.40 188	99.52 205	98.70 234	99.17 160	99.44 225	99.62 72	99.75 92	99.60 180	98.90 84	99.85 197	98.89 124	99.84 136	99.65 90

MVSFormer		99.41 88	99.44 76	99.31 210	99.57 186	98.40 248	99.77 14	99.80 60	99.73 42	99.63 134	99.30 257	98.02 188	99.98 7	99.43 54	99.69 208	99.55 148

IterMVS-LS		99.41 88	99.47 70	99.25 223	99.81 62	98.09 273	98.85 222	99.76 79	99.62 72	99.83 65	99.64 153	98.54 141	99.97 16	99.15 93	99.99 20	99.68 63
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		99.40 91	99.41 81	99.39 191	99.76 104	98.94 214	99.09 186	99.59 167	99.17 146	99.81 72	99.61 177	98.41 157	99.69 305	99.32 69	99.94 80	99.53 159

NR-MVSNet		99.40 91	99.31 97	99.68 83	99.43 241	99.55 97	99.73 22	99.50 208	99.46 100	99.88 47	99.36 244	97.54 220	99.87 161	98.97 115	99.87 122	99.63 100

PVSNet_Blended_VisFu		99.40 91	99.38 85	99.44 175	99.90 26	98.66 237	98.94 212	99.91 11	97.97 254	99.79 80	99.73 99	99.05 69	99.97 16	99.15 93	99.99 20	99.68 63

EU-MVSNet		99.39 94	99.62 38	98.72 275	99.88 29	96.44 307	99.56 71	99.85 29	99.90 6	99.90 36	99.85 45	98.09 182	99.83 229	99.58 41	99.95 67	99.90 5

CHOSEN 1792x2688		99.39 94	99.30 102	99.65 98	99.88 29	99.25 174	98.78 234	99.88 18	98.66 202	99.96 8	99.79 71	97.45 224	99.93 66	99.34 64	99.99 20	99.78 31

EI-MVSNet		99.38 96	99.44 76	99.21 228	99.58 177	98.09 273	99.26 135	99.46 220	99.62 72	99.75 92	99.67 143	98.54 141	99.85 197	99.15 93	99.92 91	99.68 63

UGNet		99.38 96	99.34 93	99.49 161	98.90 315	98.90 221	99.70 30	99.35 249	99.86 16	98.57 299	99.81 62	98.50 150	99.93 66	99.38 59	99.98 36	99.66 80
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
UniMVSNet_NR-MVSNet		99.37 98	99.25 115	99.72 69	99.47 230	99.56 94	98.97 207	99.61 149	99.43 107	99.67 118	99.28 261	97.85 200	99.95 41	99.17 89	99.81 164	99.65 90

UniMVSNet (Re)		99.37 98	99.26 113	99.68 83	99.51 209	99.58 91	98.98 206	99.60 163	99.43 107	99.70 110	99.36 244	97.70 208	99.88 141	99.20 83	99.87 122	99.59 136

CSCG		99.37 98	99.29 107	99.60 126	99.71 134	99.46 111	99.43 83	99.85 29	98.79 187	99.41 192	99.60 180	98.92 81	99.92 84	98.02 182	99.92 91	99.43 205

PM-MVS		99.36 101	99.29 107	99.58 132	99.83 47	99.66 72	98.95 209	99.86 22	98.85 179	99.81 72	99.73 99	98.40 159	99.92 84	98.36 156	99.83 146	99.17 251

abl_6		99.36 101	99.23 118	99.75 52	99.71 134	99.74 50	99.33 109	99.76 79	99.07 160	99.65 128	99.63 161	99.09 61	99.92 84	97.13 239	99.76 183	99.58 140

new-patchmatchnet		99.35 103	99.57 49	98.71 276	99.82 54	96.62 305	98.55 252	99.75 85	99.50 91	99.88 47	99.87 37	99.31 35	99.88 141	99.43 54	100.00 1	99.62 114

Anonymous20231206		99.35 103	99.31 97	99.47 166	99.74 118	99.06 206	99.28 131	99.74 90	99.23 135	99.72 104	99.53 209	97.63 219	99.88 141	99.11 101	99.84 136	99.48 183

MTAPA		99.35 103	99.20 121	99.80 30	99.81 62	99.81 29	99.33 109	99.53 193	99.27 124	99.42 186	99.63 161	98.21 174	99.95 41	97.83 194	99.79 172	99.65 90

FMVSNet2		99.35 103	99.28 109	99.55 147	99.49 219	99.35 152	99.45 80	99.57 178	99.44 102	99.70 110	99.74 95	97.21 237	99.87 161	99.03 106	99.94 80	99.44 199

3Dnovator+	98.92 3	99.35 103	99.24 116	99.67 86	99.35 257	99.47 107	99.62 57	99.50 208	99.44 102	99.12 245	99.78 80	98.77 104	99.94 55	97.87 191	99.72 204	99.62 114

TSAR-MVS + MP.		99.34 108	99.24 116	99.63 109	99.82 54	99.37 145	99.26 135	99.35 249	98.77 190	99.57 152	99.70 119	99.27 42	99.88 141	97.71 200	99.75 186	99.65 90

Regformer-2		99.34 108	99.27 111	99.53 152	99.41 245	99.10 199	98.99 202	99.53 193	99.47 97	99.66 122	99.52 211	98.80 95	99.89 126	98.31 161	99.74 193	99.60 125

DELS-MVS		99.34 108	99.30 102	99.48 164	99.51 209	99.36 148	98.12 292	99.53 193	99.36 116	99.41 192	99.61 177	99.22 47	99.87 161	99.21 80	99.68 210	99.20 244
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
DU-MVS		99.33 111	99.21 120	99.71 73	99.43 241	99.56 94	98.83 225	99.53 193	99.38 113	99.67 118	99.36 244	97.67 213	99.95 41	99.17 89	99.81 164	99.63 100

ab-mvs		99.33 111	99.28 109	99.47 166	99.57 186	99.39 136	99.78 13	99.43 228	98.87 177	99.57 152	99.82 59	98.06 185	99.87 161	98.69 139	99.73 199	99.15 253

Regformer-1		99.32 113	99.27 111	99.47 166	99.41 245	98.95 213	98.99 202	99.48 213	99.48 93	99.66 122	99.52 211	98.78 101	99.87 161	98.36 156	99.74 193	99.60 125

APD-MVS_3200maxsize		99.31 114	99.16 122	99.74 56	99.53 203	99.75 45	99.27 134	99.61 149	99.19 140	99.57 152	99.64 153	98.76 105	99.90 110	97.29 227	99.62 224	99.56 145

zzz-MVS		99.30 115	99.14 125	99.80 30	99.81 62	99.81 29	98.73 239	99.53 193	99.27 124	99.42 186	99.63 161	98.21 174	99.95 41	97.83 194	99.79 172	99.65 90

SteuartSystems-ACMMP		99.30 115	99.14 125	99.76 43	99.87 33	99.66 72	99.18 153	99.60 163	98.55 211	99.57 152	99.67 143	99.03 71	99.94 55	97.01 243	99.80 169	99.69 57
Skip Steuart: Steuart Systems R&D Blog.
testgi		99.29 117	99.26 113	99.37 197	99.75 112	98.81 230	98.84 223	99.89 15	98.38 225	99.75 92	99.04 301	99.36 33	99.86 181	99.08 103	99.25 280	99.45 194

ACMMP_Plus		99.28 118	99.11 134	99.79 35	99.75 112	99.81 29	98.95 209	99.53 193	98.27 241	99.53 168	99.73 99	98.75 108	99.87 161	97.70 201	99.83 146	99.68 63

LCM-MVSNet-Re		99.28 118	99.15 124	99.67 86	99.33 271	99.76 42	99.34 107	99.97 3	98.93 171	99.91 34	99.79 71	98.68 117	99.93 66	96.80 253	99.56 232	99.30 233

mvs_anonymous		99.28 118	99.39 83	98.94 251	99.19 292	97.81 284	99.02 195	99.55 184	99.78 34	99.85 58	99.80 64	98.24 171	99.86 181	99.57 43	99.50 246	99.15 253

MVS_Test		99.28 118	99.31 97	99.19 231	99.35 257	98.79 232	99.36 99	99.49 212	99.17 146	99.21 234	99.67 143	98.78 101	99.66 323	99.09 102	99.66 219	99.10 266

no-one		99.28 118	99.23 118	99.45 173	99.87 33	99.08 202	98.95 209	99.52 203	98.88 176	99.77 88	99.83 51	97.78 205	99.90 110	98.46 150	99.99 20	99.38 215

XVS		99.27 123	99.11 134	99.75 52	99.71 134	99.71 53	99.37 97	99.61 149	99.29 121	98.76 283	99.47 223	98.47 151	99.88 141	97.62 208	99.73 199	99.67 70

OPM-MVS		99.26 124	99.13 128	99.63 109	99.70 141	99.61 88	98.58 247	99.48 213	98.50 215	99.52 170	99.63 161	99.14 54	99.76 280	97.89 190	99.77 181	99.51 170

HFP-MVS		99.25 125	99.08 145	99.76 43	99.73 121	99.70 60	99.31 119	99.59 167	98.36 227	99.36 206	99.37 239	98.80 95	99.91 93	97.43 220	99.75 186	99.68 63

HPM-MVS		99.25 125	99.07 149	99.78 38	99.81 62	99.75 45	99.61 61	99.67 121	97.72 267	99.35 208	99.25 267	99.23 46	99.92 84	97.21 235	99.82 155	99.67 70
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		99.25 125	99.08 145	99.74 56	99.79 83	99.68 67	99.50 75	99.65 134	98.07 248	99.52 170	99.69 125	98.57 134	99.92 84	97.18 237	99.79 172	99.63 100
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
LS3D		99.24 128	99.11 134	99.61 120	98.38 345	99.79 34	99.57 69	99.68 117	99.61 76	99.15 242	99.71 112	98.70 113	99.91 93	97.54 214	99.68 210	99.13 260

SMA-MVS		99.23 129	99.06 151	99.74 56	99.46 234	99.76 42	99.13 178	99.58 175	97.62 273	99.68 114	99.64 153	99.02 72	99.83 229	97.61 210	99.82 155	99.63 100

xiu_mvs_v1_base_debu		99.23 129	99.34 93	98.91 254	99.59 174	98.23 262	98.47 262	99.66 125	99.61 76	99.68 114	98.94 313	99.39 24	99.97 16	99.18 86	99.55 238	98.51 307

xiu_mvs_v1_base		99.23 129	99.34 93	98.91 254	99.59 174	98.23 262	98.47 262	99.66 125	99.61 76	99.68 114	98.94 313	99.39 24	99.97 16	99.18 86	99.55 238	98.51 307

xiu_mvs_v1_base_debi		99.23 129	99.34 93	98.91 254	99.59 174	98.23 262	98.47 262	99.66 125	99.61 76	99.68 114	98.94 313	99.39 24	99.97 16	99.18 86	99.55 238	98.51 307

region2R		99.23 129	99.05 156	99.77 40	99.76 104	99.70 60	99.31 119	99.59 167	98.41 222	99.32 216	99.36 244	98.73 111	99.93 66	97.29 227	99.74 193	99.67 70

ACMMPR		99.23 129	99.06 151	99.76 43	99.74 118	99.69 64	99.31 119	99.59 167	98.36 227	99.35 208	99.38 238	98.61 131	99.93 66	97.43 220	99.75 186	99.67 70

XVG-ACMP-BASELINE		99.23 129	99.10 141	99.63 109	99.82 54	99.58 91	98.83 225	99.72 102	98.36 227	99.60 148	99.71 112	98.92 81	99.91 93	97.08 240	99.84 136	99.40 210

CP-MVS		99.23 129	99.05 156	99.75 52	99.66 155	99.66 72	99.38 93	99.62 145	98.38 225	99.06 252	99.27 263	98.79 98	99.94 55	97.51 216	99.82 155	99.66 80

DeepC-MVS_fast	98.47 5	99.23 129	99.12 131	99.56 144	99.28 280	99.22 181	98.99 202	99.40 237	99.08 158	99.58 150	99.64 153	98.90 84	99.83 229	97.44 219	99.75 186	99.63 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LPG-MVS_test		99.22 138	99.05 156	99.74 56	99.82 54	99.63 82	99.16 166	99.73 93	97.56 277	99.64 130	99.69 125	99.37 30	99.89 126	96.66 261	99.87 122	99.69 57

CDS-MVSNet		99.22 138	99.13 128	99.50 159	99.35 257	99.11 196	98.96 208	99.54 188	99.46 100	99.61 146	99.70 119	96.31 262	99.83 229	99.34 64	99.88 115	99.55 148
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_0402		99.22 138	99.14 125	99.45 173	99.79 83	99.43 124	99.28 131	99.68 117	99.54 86	99.40 196	99.56 200	99.07 66	99.82 237	96.01 286	99.96 60	99.11 262

AllTest		99.21 141	99.07 149	99.63 109	99.78 89	99.64 78	99.12 180	99.83 40	98.63 205	99.63 134	99.72 105	98.68 117	99.75 286	96.38 272	99.83 146	99.51 170

XVG-OURS		99.21 141	99.06 151	99.65 98	99.82 54	99.62 84	97.87 320	99.74 90	98.36 227	99.66 122	99.68 137	99.71 11	99.90 110	96.84 251	99.88 115	99.43 205

Fast-Effi-MVS+-dtu		99.20 143	99.12 131	99.43 178	99.25 283	99.69 64	99.05 190	99.82 48	99.50 91	98.97 258	99.05 298	98.98 74	99.98 7	98.20 169	99.24 282	98.62 300

VDD-MVS		99.20 143	99.11 134	99.44 175	99.43 241	98.98 209	99.50 75	98.32 318	99.80 31	99.56 159	99.69 125	96.99 247	99.85 197	98.99 109	99.73 199	99.50 176

PGM-MVS		99.20 143	99.01 166	99.77 40	99.75 112	99.71 53	99.16 166	99.72 102	97.99 252	99.42 186	99.60 180	98.81 91	99.93 66	96.91 247	99.74 193	99.66 80

pmmvs5		99.19 146	99.11 134	99.42 180	99.76 104	98.88 224	98.55 252	99.73 93	98.82 183	99.72 104	99.62 168	96.56 254	99.82 237	99.32 69	99.95 67	99.56 145

mPP-MVS		99.19 146	99.00 168	99.76 43	99.76 104	99.68 67	99.38 93	99.54 188	98.34 236	99.01 255	99.50 218	98.53 145	99.93 66	97.18 237	99.78 177	99.66 80

VNet		99.18 148	99.06 151	99.56 144	99.24 285	99.36 148	99.33 109	99.31 258	99.67 59	99.47 177	99.57 195	96.48 257	99.84 213	99.15 93	99.30 274	99.47 188

RPSCF		99.18 148	99.02 163	99.64 105	99.83 47	99.85 13	99.44 82	99.82 48	98.33 237	99.50 174	99.78 80	97.90 195	99.65 330	96.78 254	99.83 146	99.44 199

DeepPCF-MVS	98.42 6	99.18 148	99.02 163	99.67 86	99.22 287	99.75 45	97.25 340	99.47 217	98.72 199	99.66 122	99.70 119	99.29 37	99.63 334	98.07 181	99.81 164	99.62 114

MVS_0304		99.17 151	99.10 141	99.38 193	99.08 306	98.86 227	98.46 266	99.73 93	99.53 88	99.35 208	99.30 257	97.11 243	99.96 33	99.33 66	99.99 20	99.33 227

EPP-MVSNet		99.17 151	99.00 168	99.66 94	99.80 70	99.43 124	99.70 30	99.24 274	99.48 93	99.56 159	99.77 86	94.89 278	99.93 66	98.72 137	99.89 109	99.63 100

MVP-Stereo		99.16 153	99.08 145	99.43 178	99.48 225	99.07 204	99.08 187	99.55 184	98.63 205	99.31 218	99.68 137	98.19 177	99.78 272	98.18 173	99.58 231	99.45 194
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-OURS-SEG-HR		99.16 153	98.99 171	99.66 94	99.84 43	99.64 78	98.25 281	99.73 93	98.39 224	99.63 134	99.43 229	99.70 12	99.90 110	97.34 224	98.64 315	99.44 199

jason		99.16 153	99.11 134	99.32 208	99.75 112	98.44 245	98.26 280	99.39 240	98.70 200	99.74 100	99.30 257	98.54 141	99.97 16	98.48 149	99.82 155	99.55 148
jason: jason.
MP-MVS-pluss		99.14 156	98.92 181	99.80 30	99.83 47	99.83 23	98.61 243	99.63 142	96.84 300	99.44 180	99.58 188	98.81 91	99.91 93	97.70 201	99.82 155	99.67 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
pmmvs4		99.13 157	99.06 151	99.36 200	99.57 186	99.10 199	98.01 304	99.25 271	98.78 189	99.58 150	99.44 228	98.24 171	99.76 280	98.74 135	99.93 88	99.22 240

MVS_111021_LR		99.13 157	99.03 162	99.42 180	99.58 177	99.32 157	97.91 319	99.73 93	98.68 201	99.31 218	99.48 220	99.09 61	99.66 323	97.70 201	99.77 181	99.29 236

#test#		99.12 159	98.90 184	99.76 43	99.73 121	99.70 60	99.10 182	99.59 167	97.60 275	99.36 206	99.37 239	98.80 95	99.91 93	96.84 251	99.75 186	99.68 63

TSAR-MVS + GP.		99.12 159	99.04 161	99.38 193	99.34 267	99.16 191	98.15 288	99.29 262	98.18 245	99.63 134	99.62 168	99.18 50	99.68 313	98.20 169	99.74 193	99.30 233

MVS_111021_HR		99.12 159	99.02 163	99.40 188	99.50 214	99.11 196	97.92 317	99.71 105	98.76 193	99.08 248	99.47 223	99.17 51	99.54 343	97.85 193	99.76 183	99.54 156

CANet		99.11 162	99.05 156	99.28 213	98.83 325	98.56 240	98.71 241	99.41 231	99.25 131	99.23 230	99.22 276	97.66 217	99.94 55	99.19 84	99.97 47	99.33 227

WR-MVS		99.11 162	98.93 178	99.66 94	99.30 277	99.42 128	98.42 270	99.37 246	99.04 162	99.57 152	99.20 278	96.89 249	99.86 181	98.66 142	99.87 122	99.70 54

PHI-MVS		99.11 162	98.95 177	99.59 128	99.13 298	99.59 89	99.17 160	99.65 134	97.88 258	99.25 226	99.46 226	98.97 76	99.80 261	97.26 230	99.82 155	99.37 219

MSDG		99.08 165	98.98 174	99.37 197	99.60 171	99.13 194	97.54 330	99.74 90	98.84 182	99.53 168	99.55 205	99.10 59	99.79 264	97.07 241	99.86 129	99.18 249

Effi-MVS+-dtu		99.07 166	98.92 181	99.52 154	98.89 319	99.78 36	99.15 168	99.66 125	99.34 117	98.92 268	99.24 272	97.69 210	99.98 7	98.11 178	99.28 276	98.81 295

Effi-MVS+		99.06 167	98.97 175	99.34 202	99.31 273	98.98 209	98.31 278	99.91 11	98.81 184	98.79 280	98.94 313	99.14 54	99.84 213	98.79 130	98.74 310	99.20 244

MP-MVS		99.06 167	98.83 195	99.76 43	99.76 104	99.71 53	99.32 112	99.50 208	98.35 232	98.97 258	99.48 220	98.37 162	99.92 84	95.95 292	99.75 186	99.63 100
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MDA-MVSNet-bldmvs		99.06 167	99.05 156	99.07 242	99.80 70	97.83 283	98.89 214	99.72 102	99.29 121	99.63 134	99.70 119	96.47 258	99.89 126	98.17 175	99.82 155	99.50 176

MSLP-MVS++		99.05 170	99.09 143	98.91 254	99.21 288	98.36 252	98.82 228	99.47 217	98.85 179	98.90 271	99.56 200	98.78 101	99.09 353	98.57 144	99.68 210	99.26 237

1112_ss		99.05 170	98.84 192	99.67 86	99.66 155	99.29 162	98.52 257	99.82 48	97.65 272	99.43 184	99.16 280	96.42 260	99.91 93	99.07 104	99.84 136	99.80 25

ACMP	97.51 14	99.05 170	98.84 192	99.67 86	99.78 89	99.55 97	98.88 216	99.66 125	97.11 296	99.47 177	99.60 180	99.07 66	99.89 126	96.18 278	99.85 132	99.58 140
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		99.03 173	99.01 166	99.09 238	99.54 200	97.99 277	98.58 247	99.82 48	97.62 273	99.34 212	99.71 112	98.52 147	99.77 278	97.98 186	99.97 47	99.52 167

IS-MVSNet		99.03 173	98.85 190	99.55 147	99.80 70	99.25 174	99.73 22	99.15 282	99.37 114	99.61 146	99.71 112	94.73 280	99.81 256	97.70 201	99.88 115	99.58 140

xiu_mvs_v2_base		99.02 175	99.11 134	98.77 268	99.37 254	98.09 273	98.13 291	99.51 205	99.47 97	99.42 186	98.54 333	99.38 28	99.97 16	98.83 127	99.33 271	98.24 319

Fast-Effi-MVS+		99.02 175	98.87 187	99.46 169	99.38 252	99.50 101	99.04 192	99.79 68	97.17 291	98.62 294	98.74 326	99.34 34	99.95 41	98.32 160	99.41 262	98.92 287

canonicalmvs		99.02 175	99.00 168	99.09 238	99.10 305	98.70 234	99.61 61	99.66 125	99.63 71	98.64 293	97.65 350	99.04 70	99.54 343	98.79 130	98.92 295	99.04 279

MCST-MVS		99.02 175	98.81 197	99.65 98	99.58 177	99.49 103	98.58 247	99.07 286	98.40 223	99.04 253	99.25 267	98.51 149	99.80 261	97.31 226	99.51 245	99.65 90

HSP-MVS		99.01 179	98.76 201	99.76 43	99.78 89	99.73 51	99.35 100	99.31 258	98.54 212	99.54 165	98.99 302	96.81 250	99.93 66	96.97 245	99.53 243	99.61 119

SD-MVS		99.01 179	99.30 102	98.15 297	99.50 214	99.40 133	98.94 212	99.61 149	99.22 138	99.75 92	99.82 59	99.54 22	95.51 359	97.48 217	99.87 122	99.54 156

LF4IMVS		99.01 179	98.92 181	99.27 215	99.71 134	99.28 164	98.59 246	99.77 73	98.32 238	99.39 197	99.41 233	98.62 129	99.84 213	96.62 264	99.84 136	98.69 299

MS-PatchMatch		99.00 182	98.97 175	99.09 238	99.11 303	98.19 265	98.76 235	99.33 252	98.49 216	99.44 180	99.58 188	98.21 174	99.69 305	98.20 169	99.62 224	99.39 212

PS-MVSNAJ		99.00 182	99.08 145	98.76 269	99.37 254	98.10 272	98.00 306	99.51 205	99.47 97	99.41 192	98.50 335	99.28 39	99.97 16	98.83 127	99.34 269	98.20 323

CNVR-MVS		98.99 184	98.80 199	99.56 144	99.25 283	99.43 124	98.54 255	99.27 266	98.58 209	98.80 279	99.43 229	98.53 145	99.70 299	97.22 233	99.59 230	99.54 156

VDDNet		98.97 185	98.82 196	99.42 180	99.71 134	98.81 230	99.62 57	98.68 304	99.81 28	99.38 204	99.80 64	94.25 284	99.85 197	98.79 130	99.32 272	99.59 136

IterMVS		98.97 185	99.16 122	98.42 285	99.74 118	95.64 325	98.06 301	99.83 40	99.83 26	99.85 58	99.74 95	96.10 268	99.99 4	99.27 78	100.00 1	99.63 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TinyColmap		98.97 185	98.93 178	99.07 242	99.46 234	98.19 265	97.75 323	99.75 85	98.79 187	99.54 165	99.70 119	98.97 76	99.62 335	96.63 263	99.83 146	99.41 209

HPM-MVS++		98.96 188	98.70 204	99.74 56	99.52 205	99.71 53	98.86 219	99.19 278	98.47 218	98.59 297	99.06 297	98.08 184	99.91 93	96.94 246	99.60 229	99.60 125

lupinMVS		98.96 188	98.87 187	99.24 225	99.57 186	98.40 248	98.12 292	99.18 279	98.28 240	99.63 134	99.13 282	98.02 188	99.97 16	98.22 167	99.69 208	99.35 224

USDC		98.96 188	98.93 178	99.05 244	99.54 200	97.99 277	97.07 342	99.80 60	98.21 243	99.75 92	99.77 86	98.43 155	99.64 332	97.90 189	99.88 115	99.51 170

YYNet1		98.95 191	98.99 171	98.84 262	99.64 161	97.14 299	98.22 283	99.32 254	98.92 173	99.59 149	99.66 147	97.40 226	99.83 229	98.27 165	99.90 103	99.55 148

MDA-MVSNet_test_wron		98.95 191	98.99 171	98.85 260	99.64 161	97.16 298	98.23 282	99.33 252	98.93 171	99.56 159	99.66 147	97.39 228	99.83 229	98.29 163	99.88 115	99.55 148

Test_1112_low_res		98.95 191	98.73 202	99.63 109	99.68 150	99.15 193	98.09 296	99.80 60	97.14 293	99.46 179	99.40 234	96.11 267	99.89 126	99.01 108	99.84 136	99.84 15

diffmvs		98.94 194	98.87 187	99.13 235	99.37 254	98.90 221	99.25 139	99.64 139	97.55 279	99.04 253	99.58 188	97.23 236	99.64 332	98.73 136	99.44 253	98.86 291

test1235678		98.93 195	98.84 192	99.19 231	99.46 234	98.55 241	97.53 332	99.77 73	98.76 193	99.69 112	99.48 220	96.69 251	99.90 110	98.30 162	99.91 101	99.11 262

CANet_DTU		98.91 196	98.85 190	99.09 238	98.79 330	98.13 268	98.18 285	99.31 258	99.48 93	98.86 274	99.51 215	96.56 254	99.95 41	99.05 105	99.95 67	99.19 246

HyFIR lowres test		98.91 196	98.64 209	99.73 64	99.85 40	99.47 107	98.07 300	99.83 40	98.64 204	99.89 39	99.60 180	92.57 297	100.00 1	99.33 66	99.97 47	99.72 47

HQP_MVS		98.90 198	98.68 206	99.55 147	99.58 177	99.24 177	98.80 230	99.54 188	98.94 169	99.14 243	99.25 267	97.24 234	99.82 237	95.84 295	99.78 177	99.60 125

sss		98.90 198	98.77 200	99.27 215	99.48 225	98.44 245	98.72 240	99.32 254	97.94 256	99.37 205	99.35 249	96.31 262	99.91 93	98.85 126	99.63 223	99.47 188

OMC-MVS		98.90 198	98.72 203	99.44 175	99.39 249	99.42 128	98.58 247	99.64 139	97.31 289	99.44 180	99.62 168	98.59 133	99.69 305	96.17 279	99.79 172	99.22 240

ppachtmachnet_test		98.89 201	99.12 131	98.20 295	99.66 155	95.24 331	97.63 326	99.68 117	99.08 158	99.78 83	99.62 168	98.65 124	99.88 141	98.02 182	99.96 60	99.48 183

new_pmnet		98.88 202	98.89 185	98.84 262	99.70 141	97.62 290	98.15 288	99.50 208	97.98 253	99.62 141	99.54 207	98.15 180	99.94 55	97.55 213	99.84 136	98.95 284

ESAPD		98.87 203	98.58 214	99.74 56	99.62 168	99.67 69	98.74 236	99.53 193	97.71 268	99.55 162	99.57 195	98.40 159	99.90 110	94.47 325	99.68 210	99.66 80

K. test v3		98.87 203	98.60 211	99.69 80	99.93 18	99.46 111	99.74 20	94.97 356	99.78 34	99.88 47	99.88 34	93.66 288	99.97 16	99.61 38	99.95 67	99.64 96

APD-MVS		98.87 203	98.59 212	99.71 73	99.50 214	99.62 84	99.01 197	99.57 178	96.80 302	99.54 165	99.63 161	98.29 167	99.91 93	95.24 316	99.71 205	99.61 119
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
our_test_3		98.85 206	99.09 143	98.13 298	99.66 155	94.90 334	97.72 324	99.58 175	99.07 160	99.64 130	99.62 168	98.19 177	99.93 66	98.41 152	99.95 67	99.55 148

mvs-test1		98.83 207	98.70 204	99.22 227	98.89 319	99.65 76	98.88 216	99.66 125	99.34 117	98.29 310	98.94 313	97.69 210	99.96 33	98.11 178	98.54 326	98.04 327

UnsupCasMVSNet_eth		98.83 207	98.57 216	99.59 128	99.68 150	99.45 116	98.99 202	99.67 121	99.48 93	99.55 162	99.36 244	94.92 277	99.86 181	98.95 120	96.57 351	99.45 194

test_normal		98.82 209	98.67 207	99.27 215	99.56 197	98.83 229	98.22 283	98.01 322	99.03 163	99.49 176	99.24 272	96.21 264	99.76 280	98.69 139	99.56 232	99.22 240

NCCC		98.82 209	98.57 216	99.58 132	99.21 288	99.31 158	98.61 243	99.25 271	98.65 203	98.43 307	99.26 265	97.86 199	99.81 256	96.55 266	99.27 279	99.61 119

PMVS	92.94 21	98.82 209	98.81 197	98.85 260	99.84 43	97.99 277	99.20 151	99.47 217	99.71 48	99.42 186	99.82 59	98.09 182	99.47 347	93.88 333	99.85 132	99.07 276
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DI_MVS_plusplus_test		98.80 212	98.65 208	99.27 215	99.57 186	98.90 221	98.44 268	97.95 325	99.02 164	99.51 172	99.23 275	96.18 266	99.76 280	98.52 148	99.42 260	99.14 257

FMVSNet3		98.80 212	98.63 210	99.32 208	99.13 298	98.72 233	99.10 182	99.48 213	99.23 135	99.62 141	99.64 153	92.57 297	99.86 181	98.96 116	99.90 103	99.39 212

Patchmtry		98.78 214	98.54 219	99.49 161	98.89 319	99.19 189	99.32 112	99.67 121	99.65 66	99.72 104	99.79 71	91.87 303	99.95 41	98.00 185	99.97 47	99.33 227

Vis-MVSNet (Re-imp)		98.77 215	98.58 214	99.34 202	99.78 89	98.88 224	99.61 61	99.56 181	99.11 153	99.24 229	99.56 200	93.00 295	99.78 272	97.43 220	99.89 109	99.35 224

CLD-MVS		98.76 216	98.57 216	99.33 204	99.57 186	98.97 211	97.53 332	99.55 184	96.41 311	99.27 223	99.13 282	99.07 66	99.78 272	96.73 258	99.89 109	99.23 239
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CPTT-MVS		98.74 217	98.44 223	99.64 105	99.61 170	99.38 142	99.18 153	99.55 184	96.49 310	99.27 223	99.37 239	97.11 243	99.92 84	95.74 299	99.67 216	99.62 114

F-COLMAP		98.74 217	98.45 222	99.62 117	99.57 186	99.47 107	98.84 223	99.65 134	96.31 312	98.93 266	99.19 279	97.68 212	99.87 161	96.52 267	99.37 267	99.53 159

N_pmnet		98.73 219	98.53 220	99.35 201	99.72 131	98.67 236	98.34 275	94.65 357	98.35 232	99.79 80	99.68 137	98.03 186	99.93 66	98.28 164	99.92 91	99.44 199

PVSNet_Blended		98.70 220	98.59 212	99.02 247	99.54 200	97.99 277	97.58 329	99.82 48	95.70 323	99.34 212	98.98 305	98.52 147	99.77 278	97.98 186	99.83 146	99.30 233

PatchMatch-RL		98.68 221	98.47 221	99.30 212	99.44 239	99.28 164	98.14 290	99.54 188	97.12 295	99.11 246	99.25 267	97.80 203	99.70 299	96.51 268	99.30 274	98.93 286

Test4		98.65 222	98.44 223	99.27 215	99.57 186	98.86 227	98.43 269	99.41 231	98.85 179	99.57 152	98.95 312	93.05 293	99.75 286	98.57 144	99.56 232	99.19 246

test_prior3		98.62 223	98.34 236	99.46 169	99.35 257	99.22 181	97.95 313	99.39 240	97.87 259	98.05 323	99.05 298	97.90 195	99.69 305	95.99 288	99.49 248	99.48 183

CVMVSNet		98.61 224	98.88 186	97.80 311	99.58 177	93.60 339	99.26 135	99.64 139	99.66 63	99.72 104	99.67 143	93.26 291	99.93 66	99.30 72	99.81 164	99.87 10

Patchmatch-RL test		98.60 225	98.36 234	99.33 204	99.77 99	99.07 204	98.27 279	99.87 20	98.91 174	99.74 100	99.72 105	90.57 317	99.79 264	98.55 146	99.85 132	99.11 262

AdaColmap		98.60 225	98.35 235	99.38 193	99.12 300	99.22 181	98.67 242	99.42 230	97.84 263	98.81 277	99.27 263	97.32 232	99.81 256	95.14 317	99.53 243	99.10 266

WTY-MVS		98.59 227	98.37 233	99.26 220	99.43 241	98.40 248	98.74 236	99.13 285	98.10 247	99.21 234	99.24 272	94.82 279	99.90 110	97.86 192	98.77 306	99.49 182

CNLPA		98.57 228	98.34 236	99.28 213	99.18 294	99.10 199	98.34 275	99.41 231	98.48 217	98.52 301	98.98 305	97.05 245	99.78 272	95.59 307	99.50 246	98.96 283

1121		98.56 229	98.24 241	99.52 154	99.49 219	99.24 177	99.30 122	99.22 276	95.77 321	98.52 301	99.29 260	97.39 228	99.85 197	95.79 297	99.34 269	99.46 192

CDPH-MVS		98.56 229	98.20 245	99.61 120	99.50 214	99.46 111	98.32 277	99.41 231	95.22 329	99.21 234	99.10 288	98.34 164	99.82 237	95.09 319	99.66 219	99.56 145

UnsupCasMVSNet_bld		98.55 231	98.27 240	99.40 188	99.56 197	99.37 145	97.97 312	99.68 117	97.49 282	99.08 248	99.35 249	95.41 276	99.82 237	97.70 201	98.19 337	99.01 282

RPMNet		98.53 232	98.44 223	98.83 264	99.05 309	98.12 269	99.30 122	98.78 299	99.86 16	99.16 240	99.74 95	92.53 299	99.91 93	98.75 134	98.77 306	98.44 310

MG-MVS		98.52 233	98.39 230	98.94 251	99.15 295	97.39 295	98.18 285	99.21 277	98.89 175	99.23 230	99.63 161	97.37 230	99.74 290	94.22 329	99.61 228	99.69 57

DP-MVS Recon		98.50 234	98.23 242	99.31 210	99.49 219	99.46 111	98.56 251	99.63 142	94.86 335	98.85 275	99.37 239	97.81 202	99.59 340	96.08 281	99.44 253	98.88 289

CMPMVS	77.52 23	98.50 234	98.19 248	99.41 187	98.33 346	99.56 94	99.01 197	99.59 167	95.44 326	99.57 152	99.80 64	95.64 272	99.46 350	96.47 271	99.92 91	99.21 243
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
114514_t		98.49 236	98.11 251	99.64 105	99.73 121	99.58 91	99.24 141	99.76 79	89.94 350	99.42 186	99.56 200	97.76 206	99.86 181	97.74 199	99.82 155	99.47 188

PMMVS		98.49 236	98.29 239	99.11 236	98.96 312	98.42 247	97.54 330	99.32 254	97.53 281	98.47 306	98.15 340	97.88 198	99.82 237	97.46 218	99.24 282	99.09 269

MVSTER		98.47 238	98.22 243	99.24 225	99.06 308	98.35 253	99.08 187	99.46 220	99.27 124	99.75 92	99.66 147	88.61 327	99.85 197	99.14 99	99.92 91	99.52 167

LFMVS		98.46 239	98.19 248	99.26 220	99.24 285	98.52 243	99.62 57	96.94 341	99.87 13	99.31 218	99.58 188	91.04 308	99.81 256	98.68 141	99.42 260	99.45 194

PatchT		98.45 240	98.32 238	98.83 264	98.94 313	98.29 260	99.24 141	98.82 297	99.84 23	99.08 248	99.76 89	91.37 306	99.94 55	98.82 129	99.00 294	98.26 317

test12356		98.43 241	98.39 230	98.55 279	99.46 234	96.36 308	97.32 339	99.81 56	97.60 275	99.62 141	99.37 239	94.57 281	99.89 126	97.80 196	99.92 91	99.40 210

MIMVSNet		98.43 241	98.20 245	99.11 236	99.53 203	98.38 251	99.58 68	98.61 306	98.96 167	99.33 214	99.76 89	90.92 310	99.81 256	97.38 223	99.76 183	99.15 253

PVSNet	97.47 15	98.42 243	98.44 223	98.35 289	99.46 234	96.26 309	96.70 347	99.34 251	97.68 271	99.00 256	99.13 282	97.40 226	99.72 293	97.59 212	99.68 210	99.08 272

CHOSEN 280x420		98.41 244	98.41 228	98.40 287	99.34 267	95.89 319	96.94 343	99.44 225	98.80 186	99.25 226	99.52 211	93.51 289	99.98 7	98.94 121	99.98 36	99.32 231

BH-RMVSNet		98.41 244	98.14 250	99.21 228	99.21 288	98.47 244	98.60 245	98.26 319	98.35 232	98.93 266	99.31 254	97.20 240	99.66 323	94.32 327	99.10 288	99.51 170

QAPM		98.40 246	97.99 257	99.65 98	99.39 249	99.47 107	99.67 47	99.52 203	91.70 347	98.78 282	99.80 64	98.55 139	99.95 41	94.71 323	99.75 186	99.53 159

API-MVS		98.38 247	98.39 230	98.35 289	98.83 325	99.26 170	99.14 173	99.18 279	98.59 208	98.66 292	98.78 323	98.61 131	99.57 342	94.14 330	99.56 232	96.21 352

HQP-MVS		98.36 248	98.02 256	99.39 191	99.31 273	98.94 214	97.98 309	99.37 246	97.45 283	98.15 317	98.83 319	96.67 252	99.70 299	94.73 321	99.67 216	99.53 159

PAPM_NR		98.36 248	98.04 255	99.33 204	99.48 225	98.93 218	98.79 233	99.28 265	97.54 280	98.56 300	98.57 331	97.12 242	99.69 305	94.09 331	98.90 297	99.38 215

PLC	97.35 16	98.36 248	97.99 257	99.48 164	99.32 272	99.24 177	98.50 259	99.51 205	95.19 331	98.58 298	98.96 310	96.95 248	99.83 229	95.63 306	99.25 280	99.37 219
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		98.35 251	97.95 261	99.57 138	99.35 257	99.35 152	98.11 294	99.41 231	94.90 333	97.92 328	98.99 302	98.02 188	99.85 197	95.38 314	99.44 253	99.50 176

CR-MVSNet		98.35 251	98.20 245	98.83 264	99.05 309	98.12 269	99.30 122	99.67 121	97.39 286	99.16 240	99.79 71	91.87 303	99.91 93	98.78 133	98.77 306	98.44 310

LP		98.34 253	98.44 223	98.05 300	98.88 322	95.31 330	99.28 131	98.74 301	99.12 152	98.98 257	99.79 71	93.40 290	99.93 66	98.38 154	99.41 262	98.90 288

agg_prior1		98.33 254	97.92 263	99.57 138	99.35 257	99.36 148	97.99 308	99.39 240	94.85 336	97.76 338	98.98 305	98.03 186	99.85 197	95.49 309	99.44 253	99.51 170

alignmvs		98.28 255	97.96 260	99.25 223	99.12 300	98.93 218	99.03 194	98.42 315	99.64 68	98.72 286	97.85 343	90.86 313	99.62 335	98.88 125	99.13 286	99.19 246

agg_prior3		98.24 256	97.81 269	99.53 152	99.34 267	99.26 170	98.09 296	99.39 240	94.21 341	97.77 337	98.96 310	97.74 207	99.84 213	95.38 314	99.44 253	99.50 176

MAR-MVS		98.24 256	97.92 263	99.19 231	98.78 332	99.65 76	99.17 160	99.14 283	95.36 327	98.04 325	98.81 321	97.47 223	99.72 293	95.47 311	99.06 289	98.21 321
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
OpenMVS	98.12 10	98.23 258	97.89 267	99.26 220	99.19 292	99.26 170	99.65 55	99.69 114	91.33 348	98.14 321	99.77 86	98.28 168	99.96 33	95.41 313	99.55 238	98.58 304

BH-untuned		98.22 259	98.09 252	98.58 278	99.38 252	97.24 297	98.55 252	98.98 292	97.81 265	99.20 239	98.76 324	97.01 246	99.65 330	94.83 320	98.33 332	98.86 291

HY-MVS	98.23 9	98.21 260	97.95 261	98.99 248	99.03 311	98.24 261	99.61 61	98.72 302	96.81 301	98.73 285	99.51 215	94.06 285	99.86 181	96.91 247	98.20 335	98.86 291

testus		98.15 261	98.06 254	98.40 287	99.11 303	95.95 314	96.77 345	99.89 15	95.83 319	99.23 230	98.47 336	97.50 222	99.84 213	96.58 265	99.20 285	99.39 212

Patchmatch-test1		98.13 262	98.40 229	97.31 325	99.20 291	92.99 341	98.17 287	98.49 312	98.24 242	99.10 247	99.52 211	96.01 269	99.83 229	97.22 233	99.62 224	99.12 261

EPNet		98.13 262	97.77 273	99.18 234	94.57 359	97.99 277	99.24 141	97.96 323	99.74 40	97.29 344	99.62 168	93.13 292	99.97 16	98.59 143	99.83 146	99.58 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test		98.10 264	97.98 259	98.48 284	99.27 282	96.48 306	99.40 86	99.07 286	98.81 184	99.23 230	99.57 195	90.11 321	99.87 161	96.69 259	99.64 222	99.09 269

pmmvs3		98.08 265	97.80 270	98.91 254	99.41 245	97.69 288	97.87 320	99.66 125	95.87 318	99.50 174	99.51 215	90.35 319	99.97 16	98.55 146	99.47 250	99.08 272

JIA-IIPM		98.06 266	97.92 263	98.50 283	98.59 340	97.02 300	98.80 230	98.51 310	99.88 12	97.89 330	99.87 37	91.89 302	99.90 110	98.16 176	97.68 347	98.59 302

TAPA-MVS	97.92 13	98.03 267	97.55 279	99.46 169	99.47 230	99.44 118	98.50 259	99.62 145	86.79 351	99.07 251	99.26 265	98.26 170	99.62 335	97.28 229	99.73 199	99.31 232
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
1314		98.00 268	97.90 266	98.27 294	98.90 315	97.45 294	99.30 122	99.06 288	94.98 332	97.21 345	99.12 286	98.43 155	99.67 318	95.58 308	98.56 325	97.71 339

GA-MVS		97.99 269	97.68 276	98.93 253	99.52 205	98.04 276	97.19 341	99.05 289	98.32 238	98.81 277	98.97 308	89.89 324	99.41 351	98.33 159	99.05 290	99.34 226

MVS-HIRNet		97.86 270	98.22 243	96.76 329	99.28 280	91.53 351	98.38 272	92.60 358	99.13 151	99.31 218	99.96 11	97.18 241	99.68 313	98.34 158	99.83 146	99.07 276

FMVSNet5		97.80 271	97.25 282	99.42 180	98.83 325	98.97 211	99.38 93	99.80 60	98.87 177	99.25 226	99.69 125	80.60 359	99.91 93	98.96 116	99.90 103	99.38 215

ADS-MVSNet2		97.78 272	97.66 278	98.12 299	99.14 296	95.36 328	99.22 147	98.75 300	96.97 297	98.25 313	99.64 153	90.90 311	99.94 55	96.51 268	99.56 232	99.08 272

tpmrst		97.73 273	98.07 253	96.73 331	98.71 337	92.00 345	99.10 182	98.86 294	98.52 213	98.92 268	99.54 207	91.90 301	99.82 237	98.02 182	99.03 292	98.37 312

ADS-MVSNet		97.72 274	97.67 277	97.86 309	99.14 296	94.65 335	99.22 147	98.86 294	96.97 297	98.25 313	99.64 153	90.90 311	99.84 213	96.51 268	99.56 232	99.08 272

PatchmatchNet		97.65 275	97.80 270	97.18 326	98.82 328	92.49 343	99.17 160	98.39 316	98.12 246	98.79 280	99.58 188	90.71 315	99.89 126	97.23 232	99.41 262	99.16 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		97.62 276	97.79 272	97.11 328	96.67 358	92.31 344	98.51 258	98.04 320	99.24 133	95.77 353	99.47 223	93.78 287	99.66 323	98.98 111	99.62 224	99.37 219
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
wuyk23d		97.58 277	99.13 128	92.93 343	99.69 143	99.49 103	99.52 73	99.77 73	97.97 254	99.96 8	99.79 71	99.84 4	99.94 55	95.85 294	99.82 155	79.36 356

PAPR		97.56 278	97.07 284	99.04 245	98.80 329	98.11 271	97.63 326	99.25 271	94.56 339	98.02 326	98.25 339	97.43 225	99.68 313	90.90 340	98.74 310	99.33 227

TR-MVS		97.44 279	97.15 283	98.32 291	98.53 342	97.46 293	98.47 262	97.91 326	96.85 299	98.21 316	98.51 334	96.42 260	99.51 345	92.16 336	97.29 348	97.98 332

tpmvs		97.39 280	97.69 275	96.52 335	98.41 344	91.76 348	99.30 122	98.94 293	97.74 266	97.85 333	99.55 205	92.40 300	99.73 292	96.25 277	98.73 312	98.06 326

test0.0.03 1		97.37 281	96.91 291	98.74 274	97.72 352	97.57 291	97.60 328	97.36 340	98.00 250	99.21 234	98.02 341	90.04 322	99.79 264	98.37 155	95.89 354	98.86 291

OpenMVS_ROB	97.31 17	97.36 282	96.84 292	98.89 259	99.29 278	99.45 116	98.87 218	99.48 213	86.54 353	99.44 180	99.74 95	97.34 231	99.86 181	91.61 337	99.28 276	97.37 344

1111		97.29 283	96.71 302	99.04 245	99.65 159	97.72 285	98.35 273	99.80 60	99.40 110	99.66 122	99.43 229	75.10 363	99.87 161	98.98 111	99.98 36	99.52 167

tfpn1000		97.28 284	96.83 293	98.64 277	99.67 154	97.68 289	99.41 84	95.47 354	97.14 293	99.43 184	99.07 296	85.87 349	99.88 141	96.78 254	98.67 314	98.34 314

thresconf0.02		97.25 285	96.74 296	98.75 270	99.73 121	98.35 253	99.35 100	95.78 347	96.54 304	99.39 197	99.08 289	86.57 342	99.88 141	95.69 300	98.57 318	98.02 328

tfpn_n400		97.25 285	96.74 296	98.75 270	99.73 121	98.35 253	99.35 100	95.78 347	96.54 304	99.39 197	99.08 289	86.57 342	99.88 141	95.69 300	98.57 318	98.02 328

tfpnconf		97.25 285	96.74 296	98.75 270	99.73 121	98.35 253	99.35 100	95.78 347	96.54 304	99.39 197	99.08 289	86.57 342	99.88 141	95.69 300	98.57 318	98.02 328

tfpnview11		97.25 285	96.74 296	98.75 270	99.73 121	98.35 253	99.35 100	95.78 347	96.54 304	99.39 197	99.08 289	86.57 342	99.88 141	95.69 300	98.57 318	98.02 328

BH-w/o		97.20 289	97.01 287	97.76 312	99.08 306	95.69 324	98.03 303	98.52 309	95.76 322	97.96 327	98.02 341	95.62 273	99.47 347	92.82 335	97.25 349	98.12 325

conf0.01		97.19 290	96.74 296	98.51 280	99.73 121	98.35 253	99.35 100	95.78 347	96.54 304	99.39 197	99.08 289	86.57 342	99.88 141	95.69 300	98.57 318	97.30 345

conf0.002		97.19 290	96.74 296	98.51 280	99.73 121	98.35 253	99.35 100	95.78 347	96.54 304	99.39 197	99.08 289	86.57 342	99.88 141	95.69 300	98.57 318	97.30 345

test-LLR		97.15 292	96.95 289	97.74 314	98.18 349	95.02 332	97.38 335	96.10 343	98.00 250	97.81 334	98.58 329	90.04 322	99.91 93	97.69 206	98.78 304	98.31 315

tpm		97.15 292	96.95 289	97.75 313	98.91 314	94.24 337	99.32 112	97.96 323	97.71 268	98.29 310	99.32 252	86.72 340	99.92 84	98.10 180	96.24 353	99.09 269

E-PMN		97.14 294	97.43 280	96.27 337	98.79 330	91.62 350	95.54 351	99.01 291	99.44 102	98.88 272	99.12 286	92.78 296	99.68 313	94.30 328	99.03 292	97.50 341

PNet_i23d		97.02 295	97.87 268	94.49 342	99.69 143	84.81 361	95.18 354	99.85 29	97.83 264	99.32 216	99.57 195	95.53 275	99.47 347	96.09 280	97.74 346	99.18 249

cascas		96.99 296	96.82 294	97.48 319	97.57 355	95.64 325	96.43 349	99.56 181	91.75 346	97.13 346	97.61 351	95.58 274	98.63 356	96.68 260	99.11 287	98.18 324

EMVS		96.96 297	97.28 281	95.99 341	98.76 334	91.03 353	95.26 353	98.61 306	99.34 117	98.92 268	98.88 318	93.79 286	99.66 323	92.87 334	99.05 290	97.30 345

PatchFormer-LS_test		96.95 298	97.07 284	96.62 334	98.76 334	91.85 347	99.18 153	98.45 314	97.29 290	97.73 340	97.22 359	88.77 326	99.76 280	98.13 177	98.04 341	98.25 318

tfpn_ndepth		96.93 299	96.43 307	98.42 285	99.60 171	97.72 285	99.22 147	95.16 355	95.91 317	99.26 225	98.79 322	85.56 350	99.87 161	96.03 285	98.35 331	97.68 340

view600		96.86 300	96.52 303	97.88 305	99.69 143	95.87 320	99.39 87	97.68 329	99.11 153	98.96 260	97.82 345	87.40 328	99.79 264	89.78 341	98.83 299	97.98 332

view800		96.86 300	96.52 303	97.88 305	99.69 143	95.87 320	99.39 87	97.68 329	99.11 153	98.96 260	97.82 345	87.40 328	99.79 264	89.78 341	98.83 299	97.98 332

conf0.05thres1000		96.86 300	96.52 303	97.88 305	99.69 143	95.87 320	99.39 87	97.68 329	99.11 153	98.96 260	97.82 345	87.40 328	99.79 264	89.78 341	98.83 299	97.98 332

tfpn		96.86 300	96.52 303	97.88 305	99.69 143	95.87 320	99.39 87	97.68 329	99.11 153	98.96 260	97.82 345	87.40 328	99.79 264	89.78 341	98.83 299	97.98 332

dp		96.86 300	97.07 284	96.24 339	98.68 339	90.30 358	99.19 152	98.38 317	97.35 288	98.23 315	99.59 186	87.23 332	99.82 237	96.27 276	98.73 312	98.59 302

tpm cat1		96.78 305	96.98 288	96.16 340	98.85 324	90.59 357	99.08 187	99.32 254	92.37 345	97.73 340	99.46 226	91.15 307	99.69 305	96.07 282	98.80 303	98.21 321

PCF-MVS	96.03 18	96.73 306	95.86 318	99.33 204	99.44 239	99.16 191	96.87 344	99.44 225	86.58 352	98.95 264	99.40 234	94.38 283	99.88 141	87.93 349	99.80 169	98.95 284
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CostFormer		96.71 307	96.79 295	96.46 336	98.90 315	90.71 355	99.41 84	98.68 304	94.69 338	98.14 321	99.34 251	86.32 348	99.80 261	97.60 211	98.07 340	98.88 289

MVE	92.54 22	96.66 308	96.11 312	98.31 292	99.68 150	97.55 292	97.94 315	95.60 353	99.37 114	90.68 357	98.70 327	96.56 254	98.61 357	86.94 355	99.55 238	98.77 297
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
thres600view7		96.60 309	96.16 310	97.93 303	99.63 163	96.09 313	99.18 153	97.57 333	98.77 190	98.72 286	97.32 354	87.04 333	99.72 293	88.57 346	98.62 316	97.98 332

EPMVS		96.53 310	96.32 308	97.17 327	98.18 349	92.97 342	99.39 87	89.95 360	98.21 243	98.61 295	99.59 186	86.69 341	99.72 293	96.99 244	99.23 284	98.81 295

tfpn111		96.50 311	96.12 311	97.65 316	99.63 163	95.93 315	99.18 153	97.57 333	98.75 195	98.70 288	97.31 355	87.04 333	99.72 293	88.27 348	98.61 317	97.30 345

conf200view11		96.43 312	96.03 314	97.63 317	99.63 163	95.93 315	99.18 153	97.57 333	98.75 195	98.70 288	97.31 355	87.04 333	99.67 318	87.62 350	98.51 327	97.30 345

thres400		96.40 313	95.89 316	97.92 304	99.58 177	96.11 311	99.00 199	97.54 338	98.43 219	98.52 301	96.98 360	86.85 337	99.67 318	87.62 350	98.51 327	97.98 332

thres100view900		96.39 314	96.03 314	97.47 320	99.63 163	95.93 315	99.18 153	97.57 333	98.75 195	98.70 288	97.31 355	87.04 333	99.67 318	87.62 350	98.51 327	96.81 350

tpm2		96.35 315	96.22 309	96.73 331	98.88 322	91.75 349	99.21 150	98.51 310	93.27 344	97.89 330	99.21 277	84.83 351	99.70 299	96.04 284	98.18 338	98.75 298

FPMVS		96.32 316	95.50 323	98.79 267	99.60 171	98.17 267	98.46 266	98.80 298	97.16 292	96.28 349	99.63 161	82.19 354	99.09 353	88.45 347	98.89 298	99.10 266

tfpn200view9		96.30 317	95.89 316	97.53 318	99.58 177	96.11 311	99.00 199	97.54 338	98.43 219	98.52 301	96.98 360	86.85 337	99.67 318	87.62 350	98.51 327	96.81 350

TESTMET0.1,1		96.24 318	95.84 319	97.41 322	98.24 347	93.84 338	97.38 335	95.84 346	98.43 219	97.81 334	98.56 332	79.77 360	99.89 126	97.77 197	98.77 306	98.52 306

test-mter		96.23 319	95.73 321	97.74 314	98.18 349	95.02 332	97.38 335	96.10 343	97.90 257	97.81 334	98.58 329	79.12 361	99.91 93	97.69 206	98.78 304	98.31 315

tpmp4_e23		96.11 320	96.06 313	96.27 337	98.90 315	90.70 356	99.34 107	99.03 290	93.72 342	96.56 348	99.31 254	83.63 352	99.75 286	96.06 283	98.02 342	98.35 313

X-MVStestdata		96.09 321	94.87 329	99.75 52	99.71 134	99.71 53	99.37 97	99.61 149	99.29 121	98.76 283	61.30 363	98.47 151	99.88 141	97.62 208	99.73 199	99.67 70

thres200		96.09 321	95.68 322	97.33 324	99.48 225	96.22 310	98.53 256	97.57 333	98.06 249	98.37 309	96.73 362	86.84 339	99.61 339	86.99 354	98.57 318	96.16 353

DWT-MVSNet_test		96.03 323	95.80 320	96.71 333	98.50 343	91.93 346	99.25 139	97.87 327	95.99 316	96.81 347	97.61 351	81.02 356	99.66 323	97.20 236	97.98 343	98.54 305

test2356		95.99 324	95.26 327	98.18 296	96.93 357	95.53 327	95.31 352	98.71 303	95.67 324	98.48 305	97.83 344	80.72 357	99.88 141	95.47 311	98.21 334	99.11 262

gg-mvs-nofinetune		95.87 325	95.17 328	97.97 302	98.19 348	96.95 301	99.69 39	89.23 361	99.89 10	96.24 351	99.94 13	81.19 355	99.51 345	93.99 332	98.20 335	97.44 342

PVSNet_0	95.53 19	95.85 326	95.31 325	97.47 320	98.78 332	93.48 340	95.72 350	99.40 237	96.18 314	97.37 342	97.73 349	95.73 271	99.58 341	95.49 309	81.40 356	99.36 222

tmp_tt		95.75 327	95.42 324	96.76 329	89.90 360	94.42 336	98.86 219	97.87 327	78.01 354	99.30 222	99.69 125	97.70 208	95.89 358	99.29 75	98.14 339	99.95 1

MVS		95.72 328	94.63 331	98.99 248	98.56 341	97.98 282	99.30 122	98.86 294	72.71 356	97.30 343	99.08 289	98.34 164	99.74 290	89.21 345	98.33 332	99.26 237

PAPM		95.61 329	94.71 330	98.31 292	99.12 300	96.63 304	96.66 348	98.46 313	90.77 349	96.25 350	98.68 328	93.01 294	99.69 305	81.60 356	97.86 345	98.62 300

IB-MVS	95.41 20	95.30 330	94.46 332	97.84 310	98.76 334	95.33 329	97.33 338	96.07 345	96.02 315	95.37 355	97.41 353	76.17 362	99.96 33	97.54 214	95.44 355	98.22 320
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
testpf		94.48 331	95.31 325	91.99 344	97.22 356	89.64 359	98.86 219	96.52 342	94.36 340	96.09 352	98.76 324	82.21 353	98.73 355	97.05 242	96.74 350	87.60 355

.test1245		85.84 332	89.27 333	75.54 345	99.65 159	97.72 285	98.35 273	99.80 60	99.40 110	99.66 122	99.43 229	75.10 363	99.87 161	98.98 111	33.07 357	29.03 358

pcd1.5k->3k		49.97 333	55.52 334	33.31 346	99.95 13	0.00 364	0.00 355	99.81 56	0.00 359	0.00 360	100.00 1	99.96 1	0.00 362	0.00 359	100.00 1	99.92 3

test123		29.31 334	33.05 337	18.08 347	25.93 362	12.24 362	97.53 332	10.93 364	11.78 357	24.21 358	50.08 367	21.04 365	8.60 360	23.51 357	32.43 359	33.39 357

testmvs		28.94 335	33.33 335	15.79 348	26.03 361	9.81 363	96.77 345	15.67 363	11.55 358	23.87 359	50.74 366	19.03 366	8.53 361	23.21 358	33.07 357	29.03 358

cdsmvs_eth3d_5k		24.88 336	33.17 336	0.00 349	0.00 363	0.00 364	0.00 355	99.62 145	0.00 359	0.00 360	99.13 282	99.82 6	0.00 362	0.00 359	0.00 360	0.00 360

pcd_1.5k_mvsjas		16.61 337	22.14 338	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	100.00 1	99.28 39	0.00 362	0.00 359	0.00 360	0.00 360

sosnet-low-res		8.33 338	11.11 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	100.00 1	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

sosnet		8.33 338	11.11 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	100.00 1	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uncertanet		8.33 338	11.11 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	100.00 1	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

Regformer		8.33 338	11.11 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	100.00 1	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

uanet		8.33 338	11.11 339	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	100.00 1	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

ab-mvs-re		8.26 343	11.02 344	0.00 349	0.00 363	0.00 364	0.00 355	0.00 365	0.00 359	0.00 360	99.16 280	0.00 367	0.00 362	0.00 359	0.00 360	0.00 360

GSMVS																99.14 257

test_part3							98.74 236		97.71 268		99.57 195		99.90 110	94.47 325

test_part2					99.62 168	99.67 69				99.55 162

test_part1								99.53 193				98.40 159			99.68 210	99.66 80

sam_mvs1												90.81 314				99.14 257

sam_mvs												90.52 318

semantic-postprocess				98.51 280	99.75 112	95.90 318		99.84 37	99.84 23	99.89 39	99.73 99	95.96 270	99.99 4	99.33 66	100.00 1	99.63 100

ambc				99.20 230	99.35 257	98.53 242	99.17 160	99.46 220		99.67 118	99.80 64	98.46 153	99.70 299	97.92 188	99.70 207	99.38 215

MTGPA								99.53 193

test_post1							99.14 173				51.63 365	89.54 325	99.82 237	96.86 250

test_post											52.41 364	90.25 320	99.86 181

patchmatchnet-post											99.62 168	90.58 316	99.94 55

GG-mvs-BLEND				97.36 323	97.59 353	96.87 303	99.70 30	88.49 362		94.64 356	97.26 358	80.66 358	99.12 352	91.50 338	96.50 352	96.08 354

MTMP								98.59 308

gm-plane-assit					97.59 353	89.02 360			93.47 343		98.30 337		99.84 213	96.38 272

test9_res														95.10 318	99.44 253	99.50 176

TEST9					99.35 257	99.35 152	98.11 294	99.41 231	94.83 337	97.92 328	98.99 302	98.02 188	99.85 197

test_8					99.34 267	99.31 158	98.08 299	99.40 237	94.90 333	97.87 332	98.97 308	98.02 188	99.84 213

agg_prior2														94.58 324	99.46 252	99.50 176

agg_prior					99.35 257	99.36 148		99.39 240		97.76 338			99.85 197

TestCases				99.63 109	99.78 89	99.64 78		99.83 40	98.63 205	99.63 134	99.72 105	98.68 117	99.75 286	96.38 272	99.83 146	99.51 170

test_prior4						99.19 189	98.00 306

test_prior2							97.95 313		97.87 259	98.05 323	99.05 298	97.90 195		95.99 288	99.49 248

test_prior				99.46 169	99.35 257	99.22 181		99.39 240					99.69 305			99.48 183

旧先验2							97.94 315		95.33 328	98.94 265			99.88 141	96.75 256

新几何2							98.04 302

新几何1				99.52 154	99.50 214	99.22 181		99.26 268	95.66 325	98.60 296	99.28 261	97.67 213	99.89 126	95.95 292	99.32 272	99.45 194

旧先验1					99.49 219	99.29 162		99.26 268			99.39 237	97.67 213			99.36 268	99.46 192

无先验							98.01 304	99.23 275	95.83 319				99.85 197	95.79 297		99.44 199

原ACMM2							97.92 317

原ACMM1				99.37 197	99.47 230	98.87 226		99.27 266	96.74 303	98.26 312	99.32 252	97.93 194	99.82 237	95.96 291	99.38 265	99.43 205

test222					99.51 209	99.08 202	97.83 322	99.29 262	95.21 330	98.68 291	99.31 254	97.28 233			99.38 265	99.43 205

testdata2													99.89 126	95.99 288

segment_acmp												98.37 162

testdata				99.42 180	99.51 209	98.93 218		99.30 261	96.20 313	98.87 273	99.40 234	98.33 166	99.89 126	96.29 275	99.28 276	99.44 199

testdata1							97.72 324		97.86 262

test12				99.54 151	99.29 278	99.33 155		99.16 281		98.43 307		97.54 220	99.82 237		99.47 250	99.48 183

plane_prior7					99.58 177	99.38 142

plane_prior6					99.47 230	99.26 170						97.24 234

plane_prior5								99.54 188					99.82 237	95.84 295	99.78 177	99.60 125

plane_prior4											99.25 267

plane_prior3						99.31 158			98.36 227	99.14 243

plane_prior2							98.80 230		98.94 169

plane_prior1					99.51 209

plane_prior						99.24 177	98.42 270		97.87 259						99.71 205

n2								0.00 365

nn								0.00 365

door-mid								99.83 40

lessismore_v0				99.64 105	99.86 36	99.38 142		90.66 359		99.89 39	99.83 51	94.56 282	99.97 16	99.56 44	99.92 91	99.57 144

LGP-MVS_train				99.74 56	99.82 54	99.63 82		99.73 93	97.56 277	99.64 130	99.69 125	99.37 30	99.89 126	96.66 261	99.87 122	99.69 57

test11								99.29 262

door								99.77 73

HQP5-MVS						98.94 214

HQP-NCC					99.31 273		97.98 309		97.45 283	98.15 317

ACMP_Plane					99.31 273		97.98 309		97.45 283	98.15 317

BP-MVS														94.73 321

HQP4-MVS										98.15 317			99.70 299			99.53 159

HQP3-MVS								99.37 246							99.67 216

HQP2-MVS												96.67 252

NP-MVS					99.40 248	99.13 194					98.83 319

MDTV_nov1_ep13_2view						91.44 352	99.14 173		97.37 287	99.21 234		91.78 305		96.75 256		99.03 280

MDTV_nov1_ep13			97.73 274		98.70 338	90.83 354	99.15 168	98.02 321	98.51 214	98.82 276	99.61 177	90.98 309	99.66 323	96.89 249	98.92 295

ACMMP++_ref															99.94 80

ACMMP++															99.79 172

Test By Simon												98.41 157

ITE_SJBPF				99.38 193	99.63 163	99.44 118		99.73 93	98.56 210	99.33 214	99.53 209	98.88 87	99.68 313	96.01 286	99.65 221	99.02 281

DeepMVS_CX				97.98 301	99.69 143	96.95 301		99.26 268	75.51 355	95.74 354	98.28 338	96.47 258	99.62 335	91.23 339	97.89 344	97.38 343

High-resolution multi-view benchmark