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

ANet_high		99.57 9	99.67 5	99.28 71	99.89 7	98.09 105	99.14 44	99.93 1	99.82 2	99.93 2	99.81 4	99.17 14	99.94 20	99.31 30	100.00 1	99.82 10

PS-MVSNAJss		99.46 14	99.49 12	99.35 62	99.90 5	98.15 101	99.20 35	99.65 20	99.48 25	99.92 3	99.71 14	98.07 61	99.96 8	99.53 21	100.00 1	99.93 1

Anonymous20231211		99.71 2	99.70 3	99.74 2	99.97 2	99.52 2	99.74 4	99.82 4	99.73 6	99.91 4	99.89 2	99.27 9	99.94 20	99.02 49	99.94 33	99.75 21

mvs_tets		99.63 5	99.67 5	99.49 44	99.88 8	98.61 72	99.34 15	99.71 12	99.27 45	99.90 5	99.74 8	99.68 3	99.97 3	99.55 20	99.99 11	99.88 5

wuyk23d		96.06 258	97.62 183	91.38 337	98.65 265	98.57 76	98.85 72	96.95 301	96.86 210	99.90 5	99.16 98	99.18 12	98.40 348	89.23 324	99.77 104	77.18 353

wuykxyi23d		99.36 25	99.31 28	99.50 42	99.81 21	98.67 68	98.08 134	99.75 8	98.03 126	99.90 5	99.60 34	99.18 12	99.94 20	99.46 25	99.98 19	99.89 3

jajsoiax		99.58 8	99.61 7	99.48 45	99.87 12	98.61 72	99.28 29	99.66 19	99.09 68	99.89 8	99.68 19	99.53 4	99.97 3	99.50 22	99.99 11	99.87 6

pmmvs6		99.67 3	99.70 3	99.60 12	99.90 5	99.27 16	99.53 9	99.76 7	99.64 10	99.84 9	99.83 3	99.50 5	99.87 72	99.36 28	99.92 49	99.64 40

OurMVSNet-221017-0		99.37 24	99.31 28	99.53 32	99.91 4	98.98 50	99.63 6	99.58 36	99.44 30	99.78 10	99.76 6	96.39 173	99.92 34	99.44 26	99.92 49	99.68 30

LTVRE_ROB	98.40 1	99.67 3	99.71 2	99.56 18	99.85 18	99.11 42	99.90 1	99.78 5	99.63 12	99.78 10	99.67 21	99.48 6	99.81 142	99.30 31	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
TransMVSNet (Re)		99.44 15	99.47 15	99.36 57	99.80 22	98.58 75	99.27 31	99.57 43	99.39 33	99.75 12	99.62 28	99.17 14	99.83 117	99.06 47	99.62 156	99.66 33

NR-MVSNet		98.95 56	98.82 56	99.36 57	99.16 167	98.72 66	99.22 34	99.20 164	99.10 65	99.72 13	98.76 176	96.38 174	99.86 77	98.00 98	99.82 82	99.50 104

MIMVSNet1		99.38 23	99.32 27	99.55 20	99.86 16	99.19 25	99.41 12	99.59 34	99.59 19	99.71 14	99.57 39	97.12 125	99.90 47	99.21 38	99.87 68	99.54 86

test_djsdf		99.52 11	99.51 11	99.53 32	99.86 16	98.74 61	99.39 13	99.56 49	99.11 61	99.70 15	99.73 10	99.00 17	99.97 3	99.26 32	99.98 19	99.89 3

SixPastTwentyTwo		98.75 75	98.62 86	99.16 85	99.83 19	97.96 122	99.28 29	98.20 273	99.37 36	99.70 15	99.65 25	92.65 264	99.93 26	99.04 48	99.84 73	99.60 52

new-patchmatchnet		98.35 135	98.74 66	97.18 260	99.24 138	92.23 300	96.42 269	99.48 74	98.30 115	99.69 17	99.53 44	97.44 102	99.82 129	98.84 58	99.77 104	99.49 111

v748		99.44 15	99.48 13	99.33 67	99.88 8	98.43 87	99.42 11	99.53 59	99.63 12	99.69 17	99.60 34	97.99 69	99.91 43	99.60 14	99.96 28	99.66 33

LCM-MVSNet-Re		98.64 96	98.48 103	99.11 91	98.85 229	98.51 82	98.49 97	99.83 3	98.37 108	99.69 17	99.46 52	98.21 54	99.92 34	94.13 254	99.30 215	98.91 240

v7n		99.53 10	99.57 10	99.41 53	99.88 8	98.54 80	99.45 10	99.61 30	99.66 9	99.68 20	99.66 22	98.44 42	99.95 13	99.73 8	99.96 28	99.75 21

v52		99.59 6	99.60 8	99.55 20	99.87 12	99.00 48	99.59 7	99.56 49	99.56 22	99.68 20	99.72 11	98.57 34	99.93 26	99.85 1	99.99 11	99.72 24

V4		99.59 6	99.60 8	99.55 20	99.87 12	99.00 48	99.59 7	99.56 49	99.56 22	99.68 20	99.72 11	98.57 34	99.93 26	99.85 1	99.99 11	99.72 24

anonymousdsp		99.51 12	99.47 15	99.62 6	99.88 8	99.08 47	99.34 15	99.69 15	98.93 83	99.65 23	99.72 11	98.93 20	99.95 13	99.11 44	100.00 1	99.82 10

pm-mvs1		99.44 15	99.48 13	99.33 67	99.80 22	98.63 69	99.29 25	99.63 25	99.30 42	99.65 23	99.60 34	99.16 16	99.82 129	99.07 46	99.83 79	99.56 75

ACMH	96.65 7	99.25 30	99.24 35	99.26 76	99.72 33	98.38 90	99.07 52	99.55 54	98.30 115	99.65 23	99.45 56	99.22 10	99.76 198	98.44 76	99.77 104	99.64 40
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SD-MVS		98.40 132	98.68 79	97.54 248	98.96 206	97.99 115	97.88 162	99.36 111	98.20 121	99.63 26	99.04 125	98.76 24	95.33 354	96.56 174	99.74 115	99.31 179

v13		99.24 31	99.39 18	98.77 141	99.63 52	96.79 185	99.24 33	99.65 20	99.39 33	99.62 27	99.70 16	97.50 96	99.84 103	99.78 5	100.00 1	99.67 31

v12		99.21 32	99.37 20	98.74 149	99.60 55	96.72 190	99.19 39	99.65 20	99.35 39	99.62 27	99.69 17	97.43 103	99.83 117	99.76 6	100.00 1	99.66 33

V9		99.18 34	99.34 24	98.70 150	99.58 57	96.63 193	99.14 44	99.64 24	99.30 42	99.61 29	99.68 19	97.33 108	99.83 117	99.75 7	100.00 1	99.65 37

v11		99.12 40	99.31 28	98.53 178	99.59 56	96.11 213	99.08 49	99.65 20	99.15 56	99.60 30	99.69 17	97.26 116	99.83 117	99.81 3	100.00 1	99.66 33

V14		99.14 37	99.30 31	98.66 153	99.56 69	96.53 194	99.08 49	99.63 25	99.24 46	99.60 30	99.66 22	97.23 120	99.82 129	99.73 8	100.00 1	99.65 37

v15		99.11 41	99.27 33	98.62 159	99.52 81	96.43 198	99.01 55	99.63 25	99.18 55	99.59 32	99.64 26	97.13 124	99.81 142	99.71 10	100.00 1	99.64 40

PEN-MVS		99.41 20	99.34 24	99.62 6	99.73 28	99.14 35	99.29 25	99.54 58	99.62 16	99.56 33	99.42 59	98.16 57	99.96 8	98.78 59	99.93 39	99.77 16

DTE-MVSNet		99.43 18	99.35 22	99.66 4	99.71 34	99.30 12	99.31 20	99.51 64	99.64 10	99.56 33	99.46 52	98.23 50	99.97 3	98.78 59	99.93 39	99.72 24

EU-MVSNet		97.66 188	98.50 99	95.13 314	99.63 52	85.84 336	98.35 115	98.21 272	98.23 120	99.54 35	99.46 52	95.02 217	99.68 239	98.24 85	99.87 68	99.87 6

DeepC-MVS	97.60 4	98.97 54	98.93 51	99.10 93	99.35 125	97.98 119	98.01 150	99.46 82	97.56 162	99.54 35	99.50 46	98.97 18	99.84 103	98.06 93	99.92 49	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
TDRefinement		99.42 19	99.38 19	99.55 20	99.76 26	99.33 11	99.68 5	99.71 12	99.38 35	99.53 37	99.61 30	98.64 29	99.80 154	98.24 85	99.84 73	99.52 97

ACMH+	96.62 9	99.08 42	99.00 49	99.33 67	99.71 34	98.83 57	98.60 83	99.58 36	99.11 61	99.53 37	99.18 92	98.81 23	99.67 245	96.71 162	99.77 104	99.50 104

v8		99.01 47	99.16 41	98.57 169	99.47 99	96.31 205	98.90 67	99.47 80	99.03 72	99.52 39	99.57 39	96.93 137	99.81 142	99.60 14	99.98 19	99.60 52

VPA-MVSNet		99.30 28	99.30 31	99.28 71	99.49 92	98.36 92	99.00 59	99.45 85	99.63 12	99.52 39	99.44 57	98.25 48	99.88 63	99.09 45	99.84 73	99.62 45

K. test v3		98.00 165	97.66 179	99.03 106	99.79 24	97.56 153	99.19 39	92.47 345	99.62 16	99.52 39	99.66 22	89.61 279	99.96 8	99.25 34	99.81 88	99.56 75

no-one		97.98 168	98.10 150	97.61 243	99.55 73	93.82 280	96.70 253	98.94 217	96.18 234	99.52 39	99.41 61	95.90 195	99.81 142	96.72 159	99.99 11	99.20 202

tfpnnormal		98.90 60	98.90 52	98.91 122	99.67 44	97.82 136	99.00 59	99.44 88	99.45 29	99.51 43	99.24 82	98.20 55	99.86 77	95.92 205	99.69 137	99.04 223

testmv		98.51 120	98.47 105	98.61 162	99.24 138	96.53 194	96.66 256	99.73 10	98.56 105	99.50 44	99.23 86	97.24 118	99.87 72	96.16 196	99.93 39	99.44 135

WR-MVS_H		99.33 27	99.22 36	99.65 5	99.71 34	99.24 20	99.32 17	99.55 54	99.46 28	99.50 44	99.34 70	97.30 110	99.93 26	98.90 53	99.93 39	99.77 16

v17		99.07 43	99.22 36	98.61 162	99.50 86	96.42 199	99.01 55	99.60 32	99.15 56	99.48 46	99.61 30	97.05 128	99.81 142	99.64 12	99.98 19	99.61 49

v10		98.97 54	99.11 44	98.55 174	99.44 109	96.21 211	98.90 67	99.55 54	98.73 93	99.48 46	99.60 34	96.63 159	99.83 117	99.70 11	99.99 11	99.61 49

DP-MVS		98.93 57	98.81 58	99.28 71	99.21 150	98.45 86	98.46 109	99.33 126	99.63 12	99.48 46	99.15 102	97.23 120	99.75 204	97.17 133	99.66 152	99.63 44

N_pmnet		97.63 190	97.17 206	98.99 113	99.27 134	97.86 131	95.98 285	93.41 337	95.25 262	99.47 49	98.90 151	95.63 201	99.85 88	96.91 146	99.73 118	99.27 187

nrg030		99.40 21	99.35 22	99.54 25	99.58 57	99.13 38	98.98 62	99.48 74	99.68 7	99.46 50	99.26 79	98.62 30	99.73 218	99.17 43	99.92 49	99.76 19

v16		99.07 43	99.22 36	98.61 162	99.50 86	96.42 199	99.01 55	99.60 32	99.15 56	99.46 50	99.61 30	97.04 129	99.81 142	99.64 12	99.97 23	99.61 49

PS-CasMVS		99.40 21	99.33 26	99.62 6	99.71 34	99.10 43	99.29 25	99.53 59	99.53 24	99.46 50	99.41 61	98.23 50	99.95 13	98.89 55	99.95 30	99.81 12

v1240		98.55 113	98.62 86	98.32 203	99.22 144	95.58 232	97.51 204	99.45 85	97.16 200	99.45 53	99.24 82	96.12 181	99.85 88	99.60 14	99.88 64	99.55 83

testing_2		98.93 57	98.99 50	98.76 143	99.57 62	97.03 177	97.85 166	99.13 187	98.46 107	99.44 54	99.44 57	98.22 52	99.74 213	98.85 56	99.94 33	99.51 99

v18		99.02 46	99.17 39	98.57 169	99.45 106	96.31 205	98.94 64	99.58 36	99.06 70	99.43 55	99.58 38	96.91 138	99.80 154	99.60 14	99.97 23	99.59 58

FMVSNet1		99.17 35	99.17 39	99.17 82	99.55 73	98.24 95	99.20 35	99.44 88	99.21 47	99.43 55	99.55 41	97.82 79	99.86 77	98.42 78	99.89 63	99.41 145

pmmvs-eth3d		98.47 124	98.34 126	98.86 129	99.30 132	97.76 141	97.16 229	99.28 142	95.54 257	99.42 57	99.19 90	97.27 113	99.63 261	97.89 100	99.97 23	99.20 202

semantic-postprocess				96.87 272	99.27 134	91.16 320		99.25 153	99.10 65	99.41 58	99.35 68	92.91 260	99.96 8	98.65 66	99.94 33	99.49 111

test20.03		98.78 72	98.77 62	98.78 139	99.46 103	97.20 169	97.78 170	99.24 157	99.04 71	99.41 58	98.90 151	97.65 85	99.76 198	97.70 112	99.79 96	99.39 151

FC-MVSNet-test		99.27 29	99.25 34	99.34 65	99.77 25	98.37 91	99.30 24	99.57 43	99.61 18	99.40 60	99.50 46	97.12 125	99.85 88	99.02 49	99.94 33	99.80 13

EG-PatchMatch MVS		98.99 49	99.01 48	98.94 118	99.50 86	97.47 157	98.04 141	99.59 34	98.15 125	99.40 60	99.36 67	98.58 33	99.76 198	98.78 59	99.68 142	99.59 58

v1921920		98.54 116	98.60 91	98.38 199	99.20 159	95.76 228	97.56 198	99.36 111	97.23 196	99.38 62	99.17 97	96.02 184	99.84 103	99.57 18	99.90 57	99.54 86

IterMVS-LS		98.55 113	98.70 74	98.09 217	99.48 97	94.73 250	97.22 222	99.39 100	98.97 78	99.38 62	99.31 74	96.00 186	99.93 26	98.58 68	99.97 23	99.60 52
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
lessismore_v0				98.97 114	99.73 28	97.53 155		86.71 354		99.37 64	99.52 45	89.93 277	99.92 34	98.99 51	99.72 123	99.44 135

XXY-MVS		99.14 37	99.15 43	99.10 93	99.76 26	97.74 144	98.85 72	99.62 28	98.48 106	99.37 64	99.49 49	98.75 25	99.86 77	98.20 88	99.80 92	99.71 27

v1		98.63 98	98.70 74	98.41 193	99.39 117	95.96 220	97.64 186	99.20 164	97.92 130	99.36 66	99.07 117	96.63 159	99.78 184	99.25 34	99.90 57	99.50 104

TranMVSNet+NR-MVSNet		99.17 35	99.07 47	99.46 50	99.37 120	98.87 56	98.39 114	99.42 96	99.42 31	99.36 66	99.06 118	98.38 44	99.95 13	98.34 81	99.90 57	99.57 70

v1141		98.63 98	98.70 74	98.41 193	99.39 117	95.96 220	97.64 186	99.21 160	97.92 130	99.35 68	99.08 112	96.61 162	99.78 184	99.25 34	99.90 57	99.50 104

divwei89l23v2f112		98.63 98	98.70 74	98.41 193	99.39 117	95.96 220	97.64 186	99.21 160	97.92 130	99.35 68	99.08 112	96.61 162	99.78 184	99.25 34	99.90 57	99.50 104

APDe-MVS		98.99 49	98.79 59	99.60 12	99.21 150	99.15 34	98.87 69	99.48 74	97.57 160	99.35 68	99.24 82	97.83 76	99.89 56	97.88 102	99.70 130	99.75 21

PM-MVS		98.82 66	98.72 70	99.12 90	99.64 50	98.54 80	97.98 153	99.68 16	97.62 154	99.34 71	99.18 92	97.54 94	99.77 193	97.79 105	99.74 115	99.04 223

v1192		98.60 105	98.66 82	98.41 193	99.27 134	95.88 224	97.52 202	99.36 111	97.41 177	99.33 72	99.20 89	96.37 175	99.82 129	99.57 18	99.92 49	99.55 83

CP-MVSNet		99.21 32	99.09 45	99.56 18	99.65 47	98.96 54	99.13 46	99.34 121	99.42 31	99.33 72	99.26 79	97.01 133	99.94 20	98.74 63	99.93 39	99.79 14

IterMVS		97.73 183	98.11 148	96.57 282	99.24 138	90.28 321	95.52 311	99.21 160	98.86 85	99.33 72	99.33 72	93.11 256	99.94 20	98.49 74	99.94 33	99.48 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepPCF-MVS	96.93 5	98.32 138	98.01 158	99.23 79	98.39 285	98.97 51	95.03 322	99.18 174	96.88 209	99.33 72	98.78 172	98.16 57	99.28 329	96.74 158	99.62 156	99.44 135

COLMAP_ROB	96.50 10	98.99 49	98.85 54	99.41 53	99.58 57	99.10 43	98.74 75	99.56 49	99.09 68	99.33 72	99.19 90	98.40 43	99.72 227	95.98 203	99.76 113	99.42 143
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v144192		98.54 116	98.57 93	98.45 190	99.21 150	95.98 218	97.63 189	99.36 111	97.15 202	99.32 77	99.18 92	95.84 197	99.84 103	99.50 22	99.91 54	99.54 86

v148		98.45 127	98.60 91	98.00 226	99.44 109	94.98 246	97.44 208	99.06 196	98.30 115	99.32 77	98.97 139	96.65 158	99.62 263	98.37 80	99.85 71	99.39 151

HSP-MVS		98.34 136	97.94 163	99.54 25	99.57 62	99.25 19	98.57 86	98.84 235	97.55 163	99.31 79	97.71 266	94.61 232	99.88 63	96.14 198	99.19 232	99.48 117

VPNet		98.87 62	98.83 55	99.01 110	99.70 40	97.62 152	98.43 111	99.35 117	99.47 27	99.28 80	99.05 123	96.72 155	99.82 129	98.09 91	99.36 205	99.59 58

v2v482		98.56 109	98.62 86	98.37 200	99.42 114	95.81 227	97.58 196	99.16 183	97.90 138	99.28 80	99.01 131	95.98 190	99.79 174	99.33 29	99.90 57	99.51 99

ambc				98.24 210	98.82 237	95.97 219	98.62 81	99.00 215		99.27 82	99.21 87	96.99 134	99.50 300	96.55 175	99.50 195	99.26 190

Patchmatch-RL test		97.26 214	97.02 211	97.99 227	99.52 81	95.53 234	96.13 282	99.71 12	97.47 169	99.27 82	99.16 98	84.30 309	99.62 263	97.89 100	99.77 104	98.81 250

v1144		98.60 105	98.66 82	98.41 193	99.36 121	95.90 223	97.58 196	99.34 121	97.51 165	99.27 82	99.15 102	96.34 176	99.80 154	99.47 24	99.93 39	99.51 99

v6		98.70 82	98.76 63	98.52 179	99.47 99	96.30 207	98.03 142	99.18 174	97.92 130	99.27 82	99.08 112	96.91 138	99.78 184	99.19 40	99.82 82	99.48 117

Vis-MVSNet		99.34 26	99.36 21	99.27 74	99.73 28	98.26 94	99.17 41	99.78 5	99.11 61	99.27 82	99.48 50	98.82 22	99.95 13	98.94 52	99.93 39	99.59 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1neww		98.70 82	98.76 63	98.52 179	99.47 99	96.30 207	98.03 142	99.18 174	97.92 130	99.26 87	99.08 112	96.91 138	99.78 184	99.19 40	99.82 82	99.47 125

v7new		98.70 82	98.76 63	98.52 179	99.47 99	96.30 207	98.03 142	99.18 174	97.92 130	99.26 87	99.08 112	96.91 138	99.78 184	99.19 40	99.82 82	99.47 125

V42		98.78 72	98.78 60	98.76 143	99.44 109	97.04 176	98.27 118	99.19 170	97.87 142	99.25 89	99.16 98	96.84 145	99.78 184	99.21 38	99.84 73	99.46 129

TSAR-MVS + MP.		98.63 98	98.49 102	99.06 102	99.64 50	97.90 128	98.51 95	98.94 217	96.96 205	99.24 90	98.89 156	97.83 76	99.81 142	96.88 149	99.49 196	99.48 117

FIs		99.14 37	99.09 45	99.29 70	99.70 40	98.28 93	99.13 46	99.52 63	99.48 25	99.24 90	99.41 61	96.79 150	99.82 129	98.69 65	99.88 64	99.76 19

abl_6		98.99 49	98.78 60	99.61 9	99.45 106	99.46 4	98.60 83	99.50 65	98.59 99	99.24 90	99.04 125	98.54 37	99.89 56	96.45 182	99.62 156	99.50 104

TSAR-MVS + GP.		98.18 154	97.98 159	98.77 141	98.71 249	97.88 129	96.32 273	98.66 256	96.33 229	99.23 93	98.51 214	97.48 100	99.40 313	97.16 134	99.46 197	99.02 226

SMA-MVS		98.47 124	98.11 148	99.53 32	99.16 167	99.27 16	98.05 140	99.30 138	94.34 283	99.22 94	99.10 109	97.72 82	99.79 174	96.45 182	99.68 142	99.53 91

Baseline_NR-MVSNet		98.98 53	98.86 53	99.36 57	99.82 20	98.55 77	97.47 207	99.57 43	99.37 36	99.21 95	99.61 30	96.76 153	99.83 117	98.06 93	99.83 79	99.71 27

EI-MVSNet-UG-set		98.69 87	98.71 71	98.62 159	99.10 175	96.37 203	97.23 219	98.87 229	99.20 50	99.19 96	98.99 134	97.30 110	99.85 88	98.77 62	99.79 96	99.65 37

testgi		98.32 138	98.39 119	98.13 215	99.57 62	95.54 233	97.78 170	99.49 71	97.37 180	99.19 96	97.65 270	98.96 19	99.49 301	96.50 179	98.99 255	99.34 170

FMVSNet2		98.49 122	98.40 117	98.75 145	98.90 219	97.14 175	98.61 82	99.13 187	98.59 99	99.19 96	99.28 75	94.14 241	99.82 129	97.97 99	99.80 92	99.29 185

EI-MVSNet-Vis-set		98.68 90	98.70 74	98.63 157	99.09 178	96.40 201	97.23 219	98.86 233	99.20 50	99.18 99	98.97 139	97.29 112	99.85 88	98.72 64	99.78 100	99.64 40

Regformer-4		98.73 78	98.68 79	98.89 125	99.02 197	97.22 168	97.17 227	99.06 196	99.21 47	99.17 100	98.85 162	97.45 101	99.86 77	98.48 75	99.70 130	99.60 52

v7		98.67 92	98.73 67	98.50 184	99.43 113	96.21 211	98.00 151	99.31 131	97.58 158	99.17 100	99.18 92	96.63 159	99.80 154	99.42 27	99.88 64	99.48 117

TAMVS		98.24 150	98.05 156	98.80 135	99.07 182	97.18 171	97.88 162	98.81 241	96.66 220	99.17 100	99.21 87	94.81 226	99.77 193	96.96 145	99.88 64	99.44 135

UniMVSNet (Re)		98.87 62	98.71 71	99.35 62	99.24 138	98.73 64	97.73 177	99.38 103	98.93 83	99.12 103	98.73 178	96.77 151	99.86 77	98.63 67	99.80 92	99.46 129

VDD-MVS		98.56 109	98.39 119	99.07 97	99.13 173	98.07 110	98.59 85	97.01 298	99.59 19	99.11 104	99.27 77	94.82 224	99.79 174	98.34 81	99.63 155	99.34 170

XVG-OURS-SEG-HR		98.49 122	98.28 132	99.14 88	99.49 92	98.83 57	96.54 262	99.48 74	97.32 185	99.11 104	98.61 202	99.33 8	99.30 326	96.23 190	98.38 284	99.28 186

Regformer-3		98.61 104	98.61 89	98.63 157	99.02 197	96.53 194	97.17 227	98.84 235	99.13 60	99.10 106	98.85 162	97.24 118	99.79 174	98.41 79	99.70 130	99.57 70

LPG-MVS_test		98.71 80	98.46 108	99.47 48	99.57 62	98.97 51	98.23 120	99.48 74	96.60 223	99.10 106	99.06 118	98.71 27	99.83 117	95.58 223	99.78 100	99.62 45

LGP-MVS_train				99.47 48	99.57 62	98.97 51		99.48 74	96.60 223	99.10 106	99.06 118	98.71 27	99.83 117	95.58 223	99.78 100	99.62 45

EI-MVSNet		98.40 132	98.51 97	98.04 224	99.10 175	94.73 250	97.20 223	98.87 229	98.97 78	99.06 109	99.02 129	96.00 186	99.80 154	98.58 68	99.82 82	99.60 52

UniMVSNet_NR-MVSNet		98.86 64	98.68 79	99.40 55	99.17 165	98.74 61	97.68 181	99.40 98	99.14 59	99.06 109	98.59 204	96.71 156	99.93 26	98.57 70	99.77 104	99.53 91

DU-MVS		98.82 66	98.63 85	99.39 56	99.16 167	98.74 61	97.54 201	99.25 153	98.84 86	99.06 109	98.76 176	96.76 153	99.93 26	98.57 70	99.77 104	99.50 104

MVSTER		96.86 236	96.55 239	97.79 232	97.91 308	94.21 267	97.56 198	98.87 229	97.49 168	99.06 109	99.05 123	80.72 321	99.80 154	98.44 76	99.82 82	99.37 158

TinyColmap		97.89 171	97.98 159	97.60 244	98.86 226	94.35 264	96.21 278	99.44 88	97.45 176	99.06 109	98.88 157	97.99 69	99.28 329	94.38 249	99.58 169	99.18 208

test_part2					99.36 121	99.10 43				99.05 114

ESAPD		98.25 148	97.83 171	99.50 42	99.36 121	99.10 43	97.25 217	99.28 142	96.66 220	99.05 114	98.71 180	97.56 91	99.86 77	93.00 280	99.57 173	99.53 91

XVG-OURS		98.53 118	98.34 126	99.11 91	99.50 86	98.82 59	95.97 286	99.50 65	97.30 187	99.05 114	98.98 137	99.35 7	99.32 323	95.72 216	99.68 142	99.18 208

UA-Net		99.47 13	99.40 17	99.70 3	99.49 92	99.29 13	99.80 3	99.72 11	99.82 2	99.04 117	99.81 4	98.05 64	99.96 8	98.85 56	99.99 11	99.86 8

ACMM	96.08 12	98.91 59	98.73 67	99.48 45	99.55 73	99.14 35	98.07 136	99.37 107	97.62 154	99.04 117	98.96 142	98.84 21	99.79 174	97.43 125	99.65 153	99.49 111
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
APD-MVS_3200maxsize		98.84 65	98.61 89	99.53 32	99.19 160	99.27 16	98.49 97	99.33 126	98.64 95	99.03 119	98.98 137	97.89 74	99.85 88	96.54 176	99.42 200	99.46 129

Regformer-2		98.60 105	98.46 108	99.02 109	98.85 229	97.71 146	96.91 241	99.09 193	98.98 77	99.01 120	98.64 194	97.37 107	99.84 103	97.75 111	99.57 173	99.52 97

HyFIR lowres test		97.19 220	96.60 236	98.96 115	99.62 54	97.28 165	95.17 319	99.50 65	94.21 286	99.01 120	98.32 231	86.61 290	99.99 2	97.10 141	99.84 73	99.60 52

CVMVSNet		96.25 256	97.21 204	93.38 334	99.10 175	80.56 354	97.20 223	98.19 275	96.94 206	99.00 122	99.02 129	89.50 281	99.80 154	96.36 187	99.59 163	99.78 15

Regformer-1		98.55 113	98.44 112	98.87 127	98.85 229	97.29 163	96.91 241	98.99 216	98.97 78	98.99 123	98.64 194	97.26 116	99.81 142	97.79 105	99.57 173	99.51 99

PVSNet_Blended_VisFu		98.17 156	98.15 144	98.22 211	99.73 28	95.15 243	97.36 211	99.68 16	94.45 279	98.99 123	99.27 77	96.87 144	99.94 20	97.13 138	99.91 54	99.57 70

XVG-ACMP-BASELINE		98.56 109	98.34 126	99.22 80	99.54 77	98.59 74	97.71 178	99.46 82	97.25 191	98.98 125	98.99 134	97.54 94	99.84 103	95.88 206	99.74 115	99.23 196

IS-MVSNet		98.19 153	97.90 167	99.08 96	99.57 62	97.97 120	99.31 20	98.32 269	99.01 74	98.98 125	99.03 128	91.59 271	99.79 174	95.49 225	99.80 92	99.48 117

MP-MVS-pluss		98.57 108	98.23 134	99.60 12	99.69 42	99.35 9	97.16 229	99.38 103	94.87 270	98.97 127	98.99 134	98.01 66	99.88 63	97.29 130	99.70 130	99.58 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDDNet		98.21 151	97.95 161	99.01 110	99.58 57	97.74 144	99.01 55	97.29 293	99.67 8	98.97 127	99.50 46	90.45 276	99.80 154	97.88 102	99.20 228	99.48 117

USDC		97.41 206	97.40 194	97.44 253	98.94 209	93.67 284	95.17 319	99.53 59	94.03 288	98.97 127	99.10 109	95.29 211	99.34 320	95.84 212	99.73 118	99.30 182

GBi-Net		98.65 94	98.47 105	99.17 82	98.90 219	98.24 95	99.20 35	99.44 88	98.59 99	98.95 130	99.55 41	94.14 241	99.86 77	97.77 107	99.69 137	99.41 145

test1		98.65 94	98.47 105	99.17 82	98.90 219	98.24 95	99.20 35	99.44 88	98.59 99	98.95 130	99.55 41	94.14 241	99.86 77	97.77 107	99.69 137	99.41 145

FMVSNet3		97.50 197	97.24 203	98.29 207	98.08 301	95.83 226	97.86 165	98.91 225	97.89 139	98.95 130	98.95 143	87.06 288	99.81 142	97.77 107	99.69 137	99.23 196

test_0402		98.76 74	98.71 71	98.93 119	99.56 69	98.14 103	98.45 110	99.34 121	99.28 44	98.95 130	98.91 148	98.34 46	99.79 174	95.63 220	99.91 54	98.86 245

HPM-MVS_fast		99.01 47	98.82 56	99.57 16	99.71 34	99.35 9	99.00 59	99.50 65	97.33 183	98.94 134	98.86 160	98.75 25	99.82 129	97.53 119	99.71 127	99.56 75

Anonymous20231206		98.21 151	98.21 135	98.20 212	99.51 84	95.43 238	98.13 128	99.32 129	96.16 238	98.93 135	98.82 168	96.00 186	99.83 117	97.32 129	99.73 118	99.36 164

YYNet1		97.60 191	97.67 176	97.39 256	99.04 192	93.04 292	95.27 316	98.38 268	97.25 191	98.92 136	98.95 143	95.48 208	99.73 218	96.99 143	98.74 265	99.41 145

SteuartSystems-ACMMP		98.79 69	98.54 94	99.54 25	99.73 28	99.16 29	98.23 120	99.31 131	97.92 130	98.90 137	98.90 151	98.00 67	99.88 63	96.15 197	99.72 123	99.58 65
Skip Steuart: Steuart Systems R&D Blog.
RPSCF		98.62 103	98.36 123	99.42 51	99.65 47	99.42 5	98.55 89	99.57 43	97.72 149	98.90 137	99.26 79	96.12 181	99.52 294	95.72 216	99.71 127	99.32 175

zzz-MVS		98.79 69	98.52 96	99.61 9	99.67 44	99.36 7	97.33 212	99.20 164	98.83 87	98.89 139	98.90 151	96.98 135	99.92 34	97.16 134	99.70 130	99.56 75

MTAPA		98.88 61	98.64 84	99.61 9	99.67 44	99.36 7	98.43 111	99.20 164	98.83 87	98.89 139	98.90 151	96.98 135	99.92 34	97.16 134	99.70 130	99.56 75

WR-MVS		98.40 132	98.19 138	99.03 106	99.00 200	97.65 149	96.85 245	98.94 217	98.57 103	98.89 139	98.50 217	95.60 202	99.85 88	97.54 118	99.85 71	99.59 58

AllTest		98.44 128	98.20 136	99.16 85	99.50 86	98.55 77	98.25 119	99.58 36	96.80 212	98.88 142	99.06 118	97.65 85	99.57 281	94.45 243	99.61 160	99.37 158

TestCases				99.16 85	99.50 86	98.55 77		99.58 36	96.80 212	98.88 142	99.06 118	97.65 85	99.57 281	94.45 243	99.61 160	99.37 158

MDA-MVSNet_test_wron		97.60 191	97.66 179	97.41 255	99.04 192	93.09 289	95.27 316	98.42 266	97.26 190	98.88 142	98.95 143	95.43 209	99.73 218	97.02 142	98.72 266	99.41 145

VNet		98.42 129	98.30 131	98.79 136	98.79 242	97.29 163	98.23 120	98.66 256	99.31 41	98.85 145	98.80 170	94.80 227	99.78 184	98.13 90	99.13 242	99.31 179

CSCG		98.68 90	98.50 99	99.20 81	99.45 106	98.63 69	98.56 87	99.57 43	97.87 142	98.85 145	98.04 252	97.66 84	99.84 103	96.72 159	99.81 88	99.13 216

CHOSEN 1792x2688		97.49 198	97.14 209	98.54 177	99.68 43	96.09 216	96.50 263	99.62 28	91.58 316	98.84 147	98.97 139	92.36 266	99.88 63	96.76 157	99.95 30	99.67 31

test1235678		97.06 227	96.84 221	97.73 236	98.55 274	94.46 263	94.80 326	99.36 111	96.85 211	98.83 148	98.26 234	92.72 263	99.82 129	92.49 293	99.70 130	98.91 240

mvs_anonymous		97.83 181	98.16 142	96.87 272	98.18 298	91.89 302	97.31 214	98.90 226	97.37 180	98.83 148	99.46 52	96.28 177	99.79 174	98.90 53	98.16 293	98.95 234

MDA-MVSNet-bldmvs		97.94 169	97.91 166	98.06 222	99.44 109	94.96 247	96.63 258	99.15 186	98.35 109	98.83 148	99.11 107	94.31 238	99.85 88	96.60 168	98.72 266	99.37 158

PMMVS2		98.07 161	98.08 154	98.04 224	99.41 115	94.59 256	94.59 330	99.40 98	97.50 166	98.82 151	98.83 165	96.83 146	99.84 103	97.50 121	99.81 88	99.71 27

ACMMP		98.75 75	98.50 99	99.52 38	99.56 69	99.16 29	98.87 69	99.37 107	97.16 200	98.82 151	99.01 131	97.71 83	99.87 72	96.29 189	99.69 137	99.54 86
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
ACMP	95.32 15	98.41 130	98.09 151	99.36 57	99.51 84	98.79 60	97.68 181	99.38 103	95.76 248	98.81 153	98.82 168	98.36 45	99.82 129	94.75 234	99.77 104	99.48 117
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMMP_Plus		98.75 75	98.48 103	99.57 16	99.58 57	99.29 13	97.82 169	99.25 153	96.94 206	98.78 154	99.12 106	98.02 65	99.84 103	97.13 138	99.67 148	99.59 58

LFMVS		97.20 219	96.72 226	98.64 155	98.72 247	96.95 181	98.93 66	94.14 335	99.74 5	98.78 154	99.01 131	84.45 306	99.73 218	97.44 124	99.27 220	99.25 192

Patchmtry		97.35 207	96.97 213	98.50 184	97.31 330	96.47 197	98.18 124	98.92 223	98.95 82	98.78 154	99.37 65	85.44 301	99.85 88	95.96 204	99.83 79	99.17 212

UnsupCasMVSNet_eth		97.89 171	97.60 184	98.75 145	99.31 130	97.17 172	97.62 190	99.35 117	98.72 94	98.76 157	98.68 185	92.57 265	99.74 213	97.76 110	95.60 338	99.34 170

OPM-MVS		98.56 109	98.32 130	99.25 77	99.41 115	98.73 64	97.13 231	99.18 174	97.10 203	98.75 158	98.92 147	98.18 56	99.65 258	96.68 164	99.56 180	99.37 158

DeepC-MVS_fast	96.85 6	98.30 140	98.15 144	98.75 145	98.61 267	97.23 166	97.76 174	99.09 193	97.31 186	98.75 158	98.66 189	97.56 91	99.64 260	96.10 199	99.55 182	99.39 151
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APD-MVS		98.10 158	97.67 176	99.42 51	99.11 174	98.93 55	97.76 174	99.28 142	94.97 267	98.72 160	98.77 174	97.04 129	99.85 88	93.79 264	99.54 183	99.49 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		98.66 93	98.37 122	99.55 20	99.53 79	99.18 26	98.23 120	99.49 71	97.01 204	98.69 161	98.88 157	98.00 67	99.89 56	95.87 209	99.59 163	99.58 65

DI_MVS_plusplus_test		97.57 195	97.40 194	98.07 221	99.06 185	95.71 230	96.58 261	96.96 299	96.71 217	98.69 161	98.13 241	93.81 248	99.68 239	97.45 123	99.19 232	98.80 253

test_normal		97.58 193	97.41 193	98.10 216	99.03 195	95.72 229	96.21 278	97.05 297	96.71 217	98.65 163	98.12 245	93.87 245	99.69 234	97.68 116	99.35 207	98.88 243

OpenMVS_ROB	95.38 14	95.84 262	95.18 271	97.81 231	98.41 284	97.15 174	97.37 210	98.62 259	83.86 346	98.65 163	98.37 225	94.29 239	99.68 239	88.41 326	98.62 275	96.60 331

MS-PatchMatch		97.68 186	97.75 174	97.45 252	98.23 295	93.78 281	97.29 215	98.84 235	96.10 240	98.64 165	98.65 191	96.04 183	99.36 318	96.84 152	99.14 239	99.20 202

1111		93.99 305	93.72 301	94.80 317	99.33 128	85.20 340	95.97 286	99.39 100	97.88 140	98.64 165	98.56 209	57.79 360	99.80 154	96.02 200	99.87 68	99.40 150

.test1245		79.71 328	84.30 329	65.96 342	99.33 128	85.20 340	95.97 286	99.39 100	97.88 140	98.64 165	98.56 209	57.79 360	99.80 154	96.02 200	15.07 354	12.86 355

Test4		97.43 204	97.18 205	98.18 214	99.05 190	96.02 217	96.62 259	99.09 193	96.25 233	98.63 168	97.70 267	90.49 275	99.68 239	97.50 121	99.30 215	98.83 247

pmmvs5		97.64 189	97.49 188	98.08 220	99.14 172	95.12 245	96.70 253	99.05 200	93.77 290	98.62 169	98.83 165	93.23 253	99.75 204	98.33 83	99.76 113	99.36 164

ab-mvs		98.41 130	98.36 123	98.59 166	99.19 160	97.23 166	99.32 17	98.81 241	97.66 151	98.62 169	99.40 64	96.82 147	99.80 154	95.88 206	99.51 190	98.75 259

pmmvs4		97.58 193	97.28 202	98.51 183	98.84 232	96.93 182	95.40 315	98.52 262	93.60 292	98.61 171	98.65 191	95.10 216	99.60 270	96.97 144	99.79 96	98.99 229

HPM-MVS		98.79 69	98.53 95	99.59 15	99.65 47	99.29 13	99.16 42	99.43 93	96.74 214	98.61 171	98.38 224	98.62 30	99.87 72	96.47 180	99.67 148	99.59 58
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Gipuma		99.03 45	99.16 41	98.64 155	99.94 3	98.51 82	99.32 17	99.75 8	99.58 21	98.60 173	99.62 28	98.22 52	99.51 299	97.70 112	99.73 118	97.89 291
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
CDS-MVSNet		97.69 185	97.35 200	98.69 151	98.73 246	97.02 179	96.92 240	98.75 249	95.89 246	98.59 174	98.67 187	92.08 270	99.74 213	96.72 159	99.81 88	99.32 175
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPP-MVSNet		98.30 140	98.04 157	99.07 97	99.56 69	97.83 133	99.29 25	98.07 277	99.03 72	98.59 174	99.13 105	92.16 268	99.90 47	96.87 150	99.68 142	99.49 111

HFP-MVS		98.71 80	98.44 112	99.51 40	99.49 92	99.16 29	98.52 91	99.31 131	97.47 169	98.58 176	98.50 217	97.97 71	99.85 88	96.57 171	99.59 163	99.53 91

#test#		98.50 121	98.16 142	99.51 40	99.49 92	99.16 29	98.03 142	99.31 131	96.30 232	98.58 176	98.50 217	97.97 71	99.85 88	95.68 219	99.59 163	99.53 91

ACMMPR		98.70 82	98.42 115	99.54 25	99.52 81	99.14 35	98.52 91	99.31 131	97.47 169	98.56 178	98.54 212	97.75 81	99.88 63	96.57 171	99.59 163	99.58 65

new_pmnet		96.99 232	96.76 224	97.67 238	98.72 247	94.89 248	95.95 293	98.20 273	92.62 303	98.55 179	98.54 212	94.88 222	99.52 294	93.96 258	99.44 199	98.59 269

3Dnovator	98.27 2	98.81 68	98.73 67	99.05 103	98.76 243	97.81 138	99.25 32	99.30 138	98.57 103	98.55 179	99.33 72	97.95 73	99.90 47	97.16 134	99.67 148	99.44 135

OMC-MVS		97.88 173	97.49 188	99.04 105	98.89 223	98.63 69	96.94 237	99.25 153	95.02 265	98.53 181	98.51 214	97.27 113	99.47 305	93.50 273	99.51 190	99.01 227

jason		97.45 203	97.35 200	97.76 234	99.24 138	93.93 274	95.86 297	98.42 266	94.24 285	98.50 182	98.13 241	94.82 224	99.91 43	97.22 132	99.73 118	99.43 140
jason: jason.
MVP-Stereo		98.08 160	97.92 165	98.57 169	98.96 206	96.79 185	97.90 161	99.18 174	96.41 228	98.46 183	98.95 143	95.93 192	99.60 270	96.51 178	98.98 257	99.31 179
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DELS-MVS		98.27 144	98.20 136	98.48 186	98.86 226	96.70 191	95.60 308	99.20 164	97.73 148	98.45 184	98.71 180	97.50 96	99.82 129	98.21 87	99.59 163	98.93 237
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
region2R		98.69 87	98.40 117	99.54 25	99.53 79	99.17 27	98.52 91	99.31 131	97.46 174	98.44 185	98.51 214	97.83 76	99.88 63	96.46 181	99.58 169	99.58 65

BH-untuned		96.83 237	96.75 225	97.08 262	98.74 245	93.33 288	96.71 252	98.26 271	96.72 215	98.44 185	97.37 288	95.20 213	99.47 305	91.89 297	97.43 317	98.44 275

LS3D		98.63 98	98.38 121	99.36 57	97.25 331	99.38 6	99.12 48	99.32 129	99.21 47	98.44 185	98.88 157	97.31 109	99.80 154	96.58 169	99.34 209	98.92 238

xiu_mvs_v1_base_debu		97.86 175	98.17 139	96.92 269	98.98 203	93.91 275	96.45 266	99.17 180	97.85 144	98.41 188	97.14 295	98.47 39	99.92 34	98.02 95	99.05 247	96.92 321

xiu_mvs_v1_base		97.86 175	98.17 139	96.92 269	98.98 203	93.91 275	96.45 266	99.17 180	97.85 144	98.41 188	97.14 295	98.47 39	99.92 34	98.02 95	99.05 247	96.92 321

xiu_mvs_v1_base_debi		97.86 175	98.17 139	96.92 269	98.98 203	93.91 275	96.45 266	99.17 180	97.85 144	98.41 188	97.14 295	98.47 39	99.92 34	98.02 95	99.05 247	96.92 321

Patchmatch-test		96.55 248	96.34 245	97.17 261	98.35 287	93.06 290	98.40 113	97.79 282	97.33 183	98.41 188	98.67 187	83.68 313	99.69 234	95.16 227	99.31 214	98.77 256

MSDG		97.71 184	97.52 187	98.28 208	98.91 218	96.82 184	94.42 331	99.37 107	97.65 152	98.37 192	98.29 233	97.40 105	99.33 322	94.09 255	99.22 225	98.68 267

CP-MVS		98.70 82	98.42 115	99.52 38	99.36 121	99.12 40	98.72 77	99.36 111	97.54 164	98.30 193	98.40 223	97.86 75	99.89 56	96.53 177	99.72 123	99.56 75

UnsupCasMVSNet_bld		97.30 211	96.92 215	98.45 190	99.28 133	96.78 189	96.20 280	99.27 147	95.42 260	98.28 194	98.30 232	93.16 255	99.71 228	94.99 230	97.37 318	98.87 244

ITE_SJBPF				98.87 127	99.22 144	98.48 84		99.35 117	97.50 166	98.28 194	98.60 203	97.64 88	99.35 319	93.86 262	99.27 220	98.79 254

MVSFormer		98.26 146	98.43 114	97.77 233	98.88 224	93.89 278	99.39 13	99.56 49	99.11 61	98.16 196	98.13 241	93.81 248	99.97 3	99.26 32	99.57 173	99.43 140

lupinMVS		97.06 227	96.86 219	97.65 240	98.88 224	93.89 278	95.48 312	97.97 279	93.53 293	98.16 196	97.58 273	93.81 248	99.91 43	96.77 156	99.57 173	99.17 212

Vis-MVSNet (Re-imp)		97.46 202	97.16 207	98.34 202	99.55 73	96.10 214	98.94 64	98.44 265	98.32 114	98.16 196	98.62 200	88.76 284	99.73 218	93.88 261	99.79 96	99.18 208

TAPA-MVS	96.21 11	96.63 245	95.95 252	98.65 154	98.93 211	98.09 105	96.93 238	99.28 142	83.58 347	98.13 199	97.78 263	96.13 180	99.40 313	93.52 271	99.29 218	98.45 274
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_111021_LR		98.30 140	98.12 147	98.83 132	99.16 167	98.03 113	96.09 283	99.30 138	97.58 158	98.10 200	98.24 236	98.25 48	99.34 320	96.69 163	99.65 153	99.12 217

mPP-MVS		98.64 96	98.34 126	99.54 25	99.54 77	99.17 27	98.63 80	99.24 157	97.47 169	98.09 201	98.68 185	97.62 89	99.89 56	96.22 191	99.62 156	99.57 70

3Dnovator+	97.89 3	98.69 87	98.51 97	99.24 78	98.81 239	98.40 88	99.02 54	99.19 170	98.99 75	98.07 202	99.28 75	97.11 127	99.84 103	96.84 152	99.32 212	99.47 125

PHI-MVS		98.29 143	97.95 161	99.34 65	98.44 282	99.16 29	98.12 130	99.38 103	96.01 244	98.06 203	98.43 221	97.80 80	99.67 245	95.69 218	99.58 169	99.20 202

CLD-MVS		97.49 198	97.16 207	98.48 186	99.07 182	97.03 177	94.71 328	99.21 160	94.46 277	98.06 203	97.16 293	97.57 90	99.48 304	94.46 242	99.78 100	98.95 234
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_Test		98.18 154	98.36 123	97.67 238	98.48 278	94.73 250	98.18 124	99.02 208	97.69 150	98.04 205	99.11 107	97.22 122	99.56 284	98.57 70	98.90 261	98.71 261

FMVSNet5		96.01 259	95.20 270	98.41 193	97.53 321	96.10 214	98.74 75	99.50 65	97.22 199	98.03 206	99.04 125	69.80 352	99.88 63	97.27 131	99.71 127	99.25 192

MVS_111021_HR		98.25 148	98.08 154	98.75 145	99.09 178	97.46 158	95.97 286	99.27 147	97.60 157	97.99 207	98.25 235	98.15 59	99.38 317	96.87 150	99.57 173	99.42 143

MCST-MVS		98.00 165	97.63 182	99.10 93	99.24 138	98.17 100	96.89 243	98.73 252	95.66 249	97.92 208	97.70 267	97.17 123	99.66 253	96.18 195	99.23 224	99.47 125

MG-MVS		96.77 241	96.61 235	97.26 259	98.31 290	93.06 290	95.93 294	98.12 276	96.45 227	97.92 208	98.73 178	93.77 251	99.39 315	91.19 313	99.04 250	99.33 174

LP		96.60 247	96.57 238	96.68 277	97.64 317	91.70 304	98.11 131	97.74 284	97.29 189	97.91 210	99.24 82	88.35 285	99.85 88	97.11 140	95.76 337	98.49 272

tfpn1000		94.81 285	94.25 288	96.47 285	99.01 199	93.47 287	98.56 87	92.30 348	96.17 235	97.90 211	96.29 309	76.70 346	99.77 193	93.02 279	98.29 285	96.16 335

MSLP-MVS++		98.02 162	98.14 146	97.64 242	98.58 270	95.19 242	97.48 205	99.23 159	97.47 169	97.90 211	98.62 200	97.04 129	98.81 346	97.55 117	99.41 201	98.94 236

BH-RMVSNet		96.83 237	96.58 237	97.58 246	98.47 279	94.05 270	96.67 255	97.36 291	96.70 219	97.87 213	97.98 255	95.14 215	99.44 310	90.47 320	98.58 277	99.25 192

MIMVSNet		96.62 246	96.25 249	97.71 237	99.04 192	94.66 253	99.16 42	96.92 303	97.23 196	97.87 213	99.10 109	86.11 294	99.65 258	91.65 300	99.21 227	98.82 249

PNet_i23d		91.80 324	92.35 316	90.14 339	98.65 265	73.10 358	89.22 351	99.02 208	95.23 264	97.87 213	97.82 262	78.45 339	98.89 344	88.73 325	86.14 352	98.42 277

view600		94.87 278	94.41 280	96.26 288	99.22 144	91.37 310	98.49 97	94.45 324	98.75 89	97.85 216	95.98 313	80.38 323	99.75 204	86.06 334	98.49 279	97.66 304

view800		94.87 278	94.41 280	96.26 288	99.22 144	91.37 310	98.49 97	94.45 324	98.75 89	97.85 216	95.98 313	80.38 323	99.75 204	86.06 334	98.49 279	97.66 304

conf0.05thres1000		94.87 278	94.41 280	96.26 288	99.22 144	91.37 310	98.49 97	94.45 324	98.75 89	97.85 216	95.98 313	80.38 323	99.75 204	86.06 334	98.49 279	97.66 304

tfpn		94.87 278	94.41 280	96.26 288	99.22 144	91.37 310	98.49 97	94.45 324	98.75 89	97.85 216	95.98 313	80.38 323	99.75 204	86.06 334	98.49 279	97.66 304

LF4IMVS		97.90 170	97.69 175	98.52 179	99.17 165	97.66 148	97.19 226	99.47 80	96.31 231	97.85 216	98.20 240	96.71 156	99.52 294	94.62 238	99.72 123	98.38 279

CPTT-MVS		97.84 180	97.36 198	99.27 74	99.31 130	98.46 85	98.29 116	99.27 147	94.90 269	97.83 221	98.37 225	94.90 219	99.84 103	93.85 263	99.54 183	99.51 99

CMPMVS	75.91 23	96.29 254	95.44 263	98.84 131	96.25 346	98.69 67	97.02 233	99.12 189	88.90 335	97.83 221	98.86 160	89.51 280	98.90 343	91.92 296	99.51 190	98.92 238
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
E-PMN		94.17 300	94.37 285	93.58 331	96.86 337	85.71 338	90.11 349	97.07 296	98.17 124	97.82 223	97.19 291	84.62 305	98.94 341	89.77 322	97.68 314	96.09 339

conf0.01		94.82 283	94.07 289	97.06 264	99.21 150	94.53 257	98.47 103	92.69 339	95.61 250	97.81 224	95.54 320	77.71 340	99.80 154	91.49 305	98.11 296	96.86 324

conf0.002		94.82 283	94.07 289	97.06 264	99.21 150	94.53 257	98.47 103	92.69 339	95.61 250	97.81 224	95.54 320	77.71 340	99.80 154	91.49 305	98.11 296	96.86 324

thresconf0.02		94.70 287	94.07 289	96.58 278	99.21 150	94.53 257	98.47 103	92.69 339	95.61 250	97.81 224	95.54 320	77.71 340	99.80 154	91.49 305	98.11 296	95.42 343

tfpn_n400		94.70 287	94.07 289	96.58 278	99.21 150	94.53 257	98.47 103	92.69 339	95.61 250	97.81 224	95.54 320	77.71 340	99.80 154	91.49 305	98.11 296	95.42 343

tfpnconf		94.70 287	94.07 289	96.58 278	99.21 150	94.53 257	98.47 103	92.69 339	95.61 250	97.81 224	95.54 320	77.71 340	99.80 154	91.49 305	98.11 296	95.42 343

tfpnview11		94.70 287	94.07 289	96.58 278	99.21 150	94.53 257	98.47 103	92.69 339	95.61 250	97.81 224	95.54 320	77.71 340	99.80 154	91.49 305	98.11 296	95.42 343

CDPH-MVS		97.26 214	96.66 233	99.07 97	99.00 200	98.15 101	96.03 284	99.01 211	91.21 322	97.79 230	97.85 260	96.89 143	99.69 234	92.75 288	99.38 204	99.39 151

HQP_MVS		97.99 167	97.67 176	98.93 119	99.19 160	97.65 149	97.77 172	99.27 147	98.20 121	97.79 230	97.98 255	94.90 219	99.70 230	94.42 245	99.51 190	99.45 133

plane_prior3						97.78 140			97.41 177	97.79 230

MDTV_nov1_ep13_2view						74.92 356	97.69 180		90.06 331	97.75 233		85.78 297		93.52 271		98.69 264

pmmvs3		95.03 275	94.40 284	96.93 268	97.70 315	92.53 295	95.08 321	97.71 286	88.57 336	97.71 234	98.08 250	79.39 334	99.82 129	96.19 193	99.11 245	98.43 276

DP-MVS Recon		97.33 209	96.92 215	98.57 169	99.09 178	97.99 115	96.79 246	99.35 117	93.18 296	97.71 234	98.07 251	95.00 218	99.31 324	93.97 257	99.13 242	98.42 277

QAPM		97.31 210	96.81 222	98.82 133	98.80 241	97.49 156	99.06 53	99.19 170	90.22 328	97.69 236	99.16 98	96.91 138	99.90 47	90.89 317	99.41 201	99.07 220

Patchmatch-test1		96.44 253	96.72 226	95.60 309	98.24 293	88.35 327	95.85 299	96.88 305	96.11 239	97.67 237	98.57 206	93.10 257	99.69 234	94.79 233	99.22 225	98.77 256

Effi-MVS+-dtu		98.26 146	97.90 167	99.35 62	98.02 303	99.49 3	98.02 149	99.16 183	98.29 118	97.64 238	97.99 254	96.44 171	99.95 13	96.66 165	98.93 260	98.60 268

CNVR-MVS		98.17 156	97.87 170	99.07 97	98.67 259	98.24 95	97.01 234	98.93 220	97.25 191	97.62 239	98.34 228	97.27 113	99.57 281	96.42 185	99.33 210	99.39 151

PVSNet_BlendedMVS		97.55 196	97.53 186	97.60 244	98.92 215	93.77 282	96.64 257	99.43 93	94.49 275	97.62 239	99.18 92	96.82 147	99.67 245	94.73 235	99.93 39	99.36 164

PVSNet_Blended		96.88 235	96.68 230	97.47 251	98.92 215	93.77 282	94.71 328	99.43 93	90.98 323	97.62 239	97.36 289	96.82 147	99.67 245	94.73 235	99.56 180	98.98 230

alignmvs		97.35 207	96.88 218	98.78 139	98.54 275	98.09 105	97.71 178	97.69 287	99.20 50	97.59 242	95.90 317	88.12 287	99.55 287	98.18 89	98.96 258	98.70 263

MP-MVS		98.46 126	98.09 151	99.54 25	99.57 62	99.22 21	98.50 96	99.19 170	97.61 156	97.58 243	98.66 189	97.40 105	99.88 63	94.72 237	99.60 162	99.54 86

DSMNet-mixed		97.42 205	97.60 184	96.87 272	99.15 171	91.46 307	98.54 90	99.12 189	92.87 300	97.58 243	99.63 27	96.21 178	99.90 47	95.74 215	99.54 183	99.27 187

test0.0.03 1		94.51 292	93.69 302	96.99 267	96.05 347	93.61 285	94.97 323	93.49 336	96.17 235	97.57 245	94.88 339	82.30 318	99.01 340	93.60 269	94.17 347	98.37 281

PCF-MVS	92.86 18	94.36 294	93.00 312	98.42 192	98.70 253	97.56 153	93.16 342	99.11 191	79.59 350	97.55 246	97.43 284	92.19 267	99.73 218	79.85 350	99.45 198	97.97 290
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XVS		98.72 79	98.45 110	99.53 32	99.46 103	99.21 22	98.65 78	99.34 121	98.62 97	97.54 247	98.63 198	97.50 96	99.83 117	96.79 154	99.53 187	99.56 75

X-MVStestdata		94.32 296	92.59 313	99.53 32	99.46 103	99.21 22	98.65 78	99.34 121	98.62 97	97.54 247	45.85 353	97.50 96	99.83 117	96.79 154	99.53 187	99.56 75

旧先验2							95.76 301		88.56 337	97.52 249			99.66 253	94.48 241

PMVS	91.26 20	97.86 175	97.94 163	97.65 240	99.71 34	97.94 125	98.52 91	98.68 255	98.99 75	97.52 249	99.35 68	97.41 104	98.18 349	91.59 303	99.67 148	96.82 328
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-MVSNAJ		97.08 226	97.39 196	96.16 296	98.56 272	92.46 296	95.24 318	98.85 234	97.25 191	97.49 251	95.99 312	98.07 61	99.90 47	96.37 186	98.67 272	96.12 338

xiu_mvs_v2_base		97.16 222	97.49 188	96.17 294	98.54 275	92.46 296	95.45 313	98.84 235	97.25 191	97.48 252	96.49 304	98.31 47	99.90 47	96.34 188	98.68 271	96.15 337

canonicalmvs		98.34 136	98.26 133	98.58 167	98.46 280	97.82 136	98.96 63	99.46 82	99.19 54	97.46 253	95.46 327	98.59 32	99.46 307	98.08 92	98.71 269	98.46 273

testdata				98.09 217	98.93 211	95.40 239		98.80 243	90.08 330	97.45 254	98.37 225	95.26 212	99.70 230	93.58 270	98.95 259	99.17 212

thres600view7		94.45 293	93.83 298	96.29 286	99.06 185	91.53 306	97.99 152	94.24 331	98.34 110	97.44 255	95.01 333	79.84 328	99.67 245	84.33 340	98.23 287	97.66 304

EMVS		93.83 308	94.02 295	93.23 335	96.83 339	84.96 342	89.77 350	96.32 314	97.92 130	97.43 256	96.36 308	86.17 292	98.93 342	87.68 329	97.73 313	95.81 340

tfpn_ndepth		94.12 302	93.51 306	95.94 301	98.86 226	93.60 286	98.16 127	91.90 350	94.66 274	97.41 257	95.24 330	76.24 347	99.73 218	91.21 311	97.88 311	94.50 348

tfpn111		94.33 295	93.78 299	95.96 300	99.06 185	91.35 314	98.03 142	94.24 331	98.33 111	97.40 258	94.98 335	79.84 328	99.68 239	83.94 341	98.22 289	96.86 324

conf200view11		94.24 298	93.67 303	95.94 301	99.06 185	91.35 314	98.03 142	94.24 331	98.33 111	97.40 258	94.98 335	79.84 328	99.62 263	83.05 343	98.08 304	96.86 324

thres100view900		94.19 299	93.67 303	95.75 306	99.06 185	91.35 314	98.03 142	94.24 331	98.33 111	97.40 258	94.98 335	79.84 328	99.62 263	83.05 343	98.08 304	96.29 332

Fast-Effi-MVS+-dtu		98.27 144	98.09 151	98.81 134	98.43 283	98.11 104	97.61 192	99.50 65	98.64 95	97.39 261	97.52 277	98.12 60	99.95 13	96.90 148	98.71 269	98.38 279

API-MVS		97.04 230	96.91 217	97.42 254	97.88 310	98.23 99	98.18 124	98.50 263	97.57 160	97.39 261	96.75 300	96.77 151	99.15 335	90.16 321	99.02 251	94.88 347

testus		95.52 267	95.32 266	96.13 298	97.91 308	89.49 324	93.62 339	99.61 30	92.41 305	97.38 263	95.42 329	94.72 231	99.63 261	88.06 328	98.72 266	99.26 190

PatchMatch-RL		97.24 217	96.78 223	98.61 162	99.03 195	97.83 133	96.36 271	99.06 196	93.49 295	97.36 264	97.78 263	95.75 198	99.49 301	93.44 274	98.77 264	98.52 271

test12356		94.85 282	95.12 272	94.03 327	98.25 291	83.12 349	93.85 337	99.33 126	94.17 287	97.28 265	97.20 290	85.83 296	99.75 204	90.85 318	99.33 210	99.22 200

sss		97.21 218	96.93 214	98.06 222	98.83 234	95.22 241	96.75 250	98.48 264	94.49 275	97.27 266	97.90 259	92.77 262	99.80 154	96.57 171	99.32 212	99.16 215

MVS_0304		98.02 162	97.88 169	98.46 188	98.22 296	96.39 202	96.50 263	99.49 71	98.03 126	97.24 267	98.33 230	94.80 227	99.90 47	98.31 84	99.95 30	99.08 218

WTY-MVS		96.67 243	96.27 247	97.87 229	98.81 239	94.61 255	96.77 248	97.92 281	94.94 268	97.12 268	97.74 265	91.11 273	99.82 129	93.89 260	98.15 294	99.18 208

tfpn200view9		94.03 304	93.44 307	95.78 305	98.93 211	91.44 308	97.60 193	94.29 329	97.94 128	97.10 269	94.31 344	79.67 332	99.62 263	83.05 343	98.08 304	96.29 332

thres400		94.14 301	93.44 307	96.24 292	98.93 211	91.44 308	97.60 193	94.29 329	97.94 128	97.10 269	94.31 344	79.67 332	99.62 263	83.05 343	98.08 304	97.66 304

PatchmatchNet		95.58 265	95.67 258	95.30 313	97.34 329	87.32 331	97.65 185	96.65 309	95.30 261	97.07 271	98.69 183	84.77 303	99.75 204	94.97 231	98.64 273	98.83 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CNLPA		97.17 221	96.71 228	98.55 174	98.56 272	98.05 112	96.33 272	98.93 220	96.91 208	97.06 272	97.39 286	94.38 237	99.45 309	91.66 299	99.18 234	98.14 285

NCCC		97.86 175	97.47 192	99.05 103	98.61 267	98.07 110	96.98 235	98.90 226	97.63 153	97.04 273	97.93 258	95.99 189	99.66 253	95.31 226	98.82 263	99.43 140

TR-MVS		95.55 266	95.12 272	96.86 275	97.54 320	93.94 273	96.49 265	96.53 312	94.36 282	97.03 274	96.61 302	94.26 240	99.16 334	86.91 331	96.31 333	97.47 316

MDTV_nov1_ep13			95.22 269		97.06 334	83.20 348	97.74 176	96.16 315	94.37 281	96.99 275	98.83 165	83.95 311	99.53 290	93.90 259	97.95 309

CANet		97.87 174	97.76 173	98.19 213	97.75 311	95.51 235	96.76 249	99.05 200	97.74 147	96.93 276	98.21 239	95.59 203	99.89 56	97.86 104	99.93 39	99.19 207

EPMVS		93.72 309	93.27 309	95.09 315	96.04 348	87.76 329	98.13 128	85.01 355	94.69 273	96.92 277	98.64 194	78.47 338	99.31 324	95.04 228	96.46 332	98.20 283

AdaColmap		97.14 223	96.71 228	98.46 188	98.34 288	97.80 139	96.95 236	98.93 220	95.58 256	96.92 277	97.66 269	95.87 196	99.53 290	90.97 314	99.14 239	98.04 288

CR-MVSNet		96.28 255	95.95 252	97.28 257	97.71 313	94.22 265	98.11 131	98.92 223	92.31 307	96.91 279	99.37 65	85.44 301	99.81 142	97.39 127	97.36 320	97.81 297

RPMNet		96.82 239	96.66 233	97.28 257	97.71 313	94.22 265	98.11 131	96.90 304	99.37 36	96.91 279	99.34 70	86.72 289	99.81 142	97.53 119	97.36 320	97.81 297

HPM-MVS++		98.10 158	97.64 181	99.48 45	99.09 178	99.13 38	97.52 202	98.75 249	97.46 174	96.90 281	97.83 261	96.01 185	99.84 103	95.82 213	99.35 207	99.46 129

PatchT		96.65 244	96.35 244	97.54 248	97.40 327	95.32 240	97.98 153	96.64 310	99.33 40	96.89 282	99.42 59	84.32 308	99.81 142	97.69 114	97.49 315	97.48 315

1112_ss		97.29 213	96.86 219	98.58 167	99.34 127	96.32 204	96.75 250	99.58 36	93.14 297	96.89 282	97.48 280	92.11 269	99.86 77	96.91 146	99.54 183	99.57 70

test222					98.92 215	96.93 182	95.54 309	98.78 245	85.72 344	96.86 284	98.11 246	94.43 235			99.10 246	99.23 196

thres200		93.72 309	93.14 310	95.46 311	98.66 264	91.29 318	96.61 260	94.63 323	97.39 179	96.83 285	93.71 347	79.88 327	99.56 284	82.40 347	98.13 295	95.54 342

UGNet		98.53 118	98.45 110	98.79 136	97.94 306	96.96 180	99.08 49	98.54 261	99.10 65	96.82 286	99.47 51	96.55 165	99.84 103	98.56 73	99.94 33	99.55 83
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
Test_1112_low_res		96.99 232	96.55 239	98.31 205	99.35 125	95.47 237	95.84 300	99.53 59	91.51 318	96.80 287	98.48 220	91.36 272	99.83 117	96.58 169	99.53 187	99.62 45

新几何1				98.91 122	98.94 209	97.76 141		98.76 246	87.58 340	96.75 288	98.10 247	94.80 227	99.78 184	92.73 289	99.00 254	99.20 202

Effi-MVS+		98.02 162	97.82 172	98.62 159	98.53 277	97.19 170	97.33 212	99.68 16	97.30 187	96.68 289	97.46 282	98.56 36	99.80 154	96.63 167	98.20 290	98.86 245

GA-MVS		95.86 261	95.32 266	97.49 250	98.60 269	94.15 269	93.83 338	97.93 280	95.49 258	96.68 289	97.42 285	83.21 314	99.30 326	96.22 191	98.55 278	99.01 227

F-COLMAP		97.30 211	96.68 230	99.14 88	99.19 160	98.39 89	97.27 216	99.30 138	92.93 298	96.62 291	98.00 253	95.73 199	99.68 239	92.62 290	98.46 283	99.35 169

PAPM_NR		96.82 239	96.32 246	98.30 206	99.07 182	96.69 192	97.48 205	98.76 246	95.81 247	96.61 292	96.47 306	94.12 244	99.17 333	90.82 319	97.78 312	99.06 221

1121		96.73 242	96.00 250	98.91 122	98.95 208	97.76 141	98.07 136	98.73 252	87.65 339	96.54 293	98.13 241	94.52 234	99.73 218	92.38 294	99.02 251	99.24 195

test12				98.93 119	98.58 270	97.83 133		98.66 256		96.53 294		95.51 206	99.69 234		99.13 242	99.27 187

BH-w/o		95.13 273	94.89 277	95.86 303	98.20 297	91.31 317	95.65 306	97.37 290	93.64 291	96.52 295	95.70 318	93.04 258	99.02 338	88.10 327	95.82 336	97.24 319

ADS-MVSNet2		95.43 270	94.98 275	96.76 276	98.14 299	91.74 303	97.92 158	97.76 283	90.23 326	96.51 296	98.91 148	85.61 298	99.85 88	92.88 283	96.90 326	98.69 264

ADS-MVSNet		95.24 272	94.93 276	96.18 293	98.14 299	90.10 322	97.92 158	97.32 292	90.23 326	96.51 296	98.91 148	85.61 298	99.74 213	92.88 283	96.90 326	98.69 264

114514_t		96.50 251	95.77 254	98.69 151	99.48 97	97.43 160	97.84 167	99.55 54	81.42 349	96.51 296	98.58 205	95.53 204	99.67 245	93.41 275	99.58 169	98.98 230

PVSNet	93.40 17	95.67 264	95.70 256	95.57 310	98.83 234	88.57 325	92.50 344	97.72 285	92.69 302	96.49 299	96.44 307	93.72 252	99.43 311	93.61 268	99.28 219	98.71 261

mvs-test1		97.83 181	97.48 191	98.89 125	98.02 303	99.20 24	97.20 223	99.16 183	98.29 118	96.46 300	97.17 292	96.44 171	99.92 34	96.66 165	97.90 310	97.54 314

tpmrst		95.07 274	95.46 262	93.91 328	97.11 333	84.36 346	97.62 190	96.96 299	94.98 266	96.35 301	98.80 170	85.46 300	99.59 274	95.60 221	96.23 334	97.79 300

OpenMVS	96.65 7	97.09 225	96.68 230	98.32 203	98.32 289	97.16 173	98.86 71	99.37 107	89.48 332	96.29 302	99.15 102	96.56 164	99.90 47	92.90 282	99.20 228	97.89 291

diffmvs		97.49 198	97.36 198	97.91 228	98.38 286	95.70 231	97.95 156	99.31 131	94.87 270	96.14 303	98.78 172	94.84 223	99.43 311	97.69 114	98.26 286	98.59 269

Fast-Effi-MVS+		97.67 187	97.38 197	98.57 169	98.71 249	97.43 160	97.23 219	99.45 85	94.82 272	96.13 304	96.51 303	98.52 38	99.91 43	96.19 193	98.83 262	98.37 281

test_prior3		97.48 201	97.00 212	98.95 116	98.69 254	97.95 123	95.74 303	99.03 204	96.48 225	96.11 305	97.63 271	95.92 193	99.59 274	94.16 250	99.20 228	99.30 182

test_prior2							95.74 303		96.48 225	96.11 305	97.63 271	95.92 193		94.16 250	99.20 228

dp		93.47 311	93.59 305	93.13 336	96.64 340	81.62 353	97.66 183	96.42 313	92.80 301	96.11 305	98.64 194	78.55 337	99.59 274	93.31 276	92.18 351	98.16 284

原ACMM1				98.35 201	98.90 219	96.25 210		98.83 240	92.48 304	96.07 308	98.10 247	95.39 210	99.71 228	92.61 291	98.99 255	99.08 218

PMMVS		96.51 249	95.98 251	98.09 217	97.53 321	95.84 225	94.92 324	98.84 235	91.58 316	96.05 309	95.58 319	95.68 200	99.66 253	95.59 222	98.09 303	98.76 258

tpm		94.67 291	94.34 286	95.66 307	97.68 316	88.42 326	97.88 162	94.90 321	94.46 277	96.03 310	98.56 209	78.66 335	99.79 174	95.88 206	95.01 341	98.78 255

TEST9					98.71 249	98.08 108	95.96 290	99.03 204	91.40 319	95.85 311	97.53 275	96.52 166	99.76 198

train_agg		97.10 224	96.45 242	99.07 97	98.71 249	98.08 108	95.96 290	99.03 204	91.64 313	95.85 311	97.53 275	96.47 169	99.76 198	93.67 266	99.16 235	99.36 164

test_8					98.67 259	98.01 114	95.91 296	99.02 208	91.64 313	95.79 313	97.50 278	96.47 169	99.76 198

agg_prior3		96.95 234	96.27 247	99.00 112	98.68 256	97.91 126	95.96 290	99.01 211	90.74 325	95.60 314	97.45 283	96.14 179	99.74 213	93.67 266	99.16 235	99.36 164

agg_prior1		97.06 227	96.40 243	99.03 106	98.68 256	97.99 115	95.76 301	99.01 211	91.73 312	95.59 315	97.50 278	96.49 168	99.77 193	93.71 265	99.14 239	99.34 170

agg_prior					98.68 256	97.99 115		99.01 211		95.59 315			99.77 193

PLC	94.65 16	96.51 249	95.73 255	98.85 130	98.75 244	97.91 126	96.42 269	99.06 196	90.94 324	95.59 315	97.38 287	94.41 236	99.59 274	90.93 315	98.04 308	99.05 222
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HQP4-MVS										95.56 318			99.54 288			99.32 175

HQP-NCC					98.67 259		96.29 274		96.05 241	95.55 319

ACMP_Plane					98.67 259		96.29 274		96.05 241	95.55 319

HQP-MVS		97.00 231	96.49 241	98.55 174	98.67 259	96.79 185	96.29 274	99.04 202	96.05 241	95.55 319	96.84 298	93.84 246	99.54 288	92.82 285	99.26 222	99.32 175

MAR-MVS		96.47 252	95.70 256	98.79 136	97.92 307	99.12 40	98.28 117	98.60 260	92.16 310	95.54 322	96.17 310	94.77 230	99.52 294	89.62 323	98.23 287	97.72 303
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
tpmvs		95.02 276	95.25 268	94.33 322	96.39 345	85.87 335	98.08 134	96.83 306	95.46 259	95.51 323	98.69 183	85.91 295	99.53 290	94.16 250	96.23 334	97.58 312

test2356		91.64 325	90.19 328	96.00 299	94.30 353	89.58 323	90.84 347	96.68 308	91.76 311	95.48 324	93.69 348	67.05 355	99.52 294	84.83 339	97.08 325	98.91 240

MVS-HIRNet		94.32 296	95.62 259	90.42 338	98.46 280	75.36 355	96.29 274	89.13 353	95.25 262	95.38 325	99.75 7	92.88 261	99.19 332	94.07 256	99.39 203	96.72 330

PAPR		95.29 271	94.47 279	97.75 235	97.50 325	95.14 244	94.89 325	98.71 254	91.39 320	95.35 326	95.48 326	94.57 233	99.14 336	84.95 338	97.37 318	98.97 233

HY-MVS	95.94 13	95.90 260	95.35 265	97.55 247	97.95 305	94.79 249	98.81 74	96.94 302	92.28 308	95.17 327	98.57 206	89.90 278	99.75 204	91.20 312	97.33 322	98.10 286

CANet_DTU		97.26 214	97.06 210	97.84 230	97.57 318	94.65 254	96.19 281	98.79 244	97.23 196	95.14 328	98.24 236	93.22 254	99.84 103	97.34 128	99.84 73	99.04 223

cascas		94.79 286	94.33 287	96.15 297	96.02 349	92.36 299	92.34 346	99.26 152	85.34 345	95.08 329	94.96 338	92.96 259	98.53 347	94.41 248	98.59 276	97.56 313

CostFormer		93.97 306	93.78 299	94.51 321	97.53 321	85.83 337	97.98 153	95.96 316	89.29 334	94.99 330	98.63 198	78.63 336	99.62 263	94.54 240	96.50 331	98.09 287

CHOSEN 280x420		95.51 269	95.47 261	95.65 308	98.25 291	88.27 328	93.25 341	98.88 228	93.53 293	94.65 331	97.15 294	86.17 292	99.93 26	97.41 126	99.93 39	98.73 260

JIA-IIPM		95.52 267	95.03 274	97.00 266	96.85 338	94.03 271	96.93 238	95.82 317	99.20 50	94.63 332	99.71 14	83.09 315	99.60 270	94.42 245	94.64 342	97.36 317

MVS		93.19 314	92.09 318	96.50 284	96.91 336	94.03 271	98.07 136	98.06 278	68.01 351	94.56 333	96.48 305	95.96 191	99.30 326	83.84 342	96.89 328	96.17 334

PatchFormer-LS_test		94.08 303	93.91 296	94.59 320	96.93 335	86.86 333	97.55 200	96.57 311	94.27 284	94.38 334	93.64 349	80.96 320	99.59 274	96.44 184	94.48 345	97.31 318

1314		95.74 263	95.60 260	96.17 294	97.53 321	92.75 293	98.07 136	98.31 270	91.22 321	94.25 335	96.68 301	95.53 204	99.03 337	91.64 301	97.18 323	96.74 329

tpm cat1		93.29 313	93.13 311	93.75 329	97.39 328	84.74 343	97.39 209	97.65 288	83.39 348	94.16 336	98.41 222	82.86 317	99.39 315	91.56 304	95.35 340	97.14 320

test-LLR		93.90 307	93.85 297	94.04 325	96.53 341	84.62 344	94.05 334	92.39 346	96.17 235	94.12 337	95.07 331	82.30 318	99.67 245	95.87 209	98.18 291	97.82 295

test-mter		92.33 320	91.76 322	94.04 325	96.53 341	84.62 344	94.05 334	92.39 346	94.00 289	94.12 337	95.07 331	65.63 359	99.67 245	95.87 209	98.18 291	97.82 295

tpm2		93.09 315	92.58 314	94.62 319	97.56 319	86.53 334	97.66 183	95.79 318	86.15 343	94.07 339	98.23 238	75.95 348	99.53 290	90.91 316	96.86 329	97.81 297

TESTMET0.1,1		92.19 322	91.77 321	93.46 332	96.48 343	82.80 351	94.05 334	91.52 351	94.45 279	94.00 340	94.88 339	66.65 356	99.56 284	95.78 214	98.11 296	98.02 289

PVSNet_0	89.98 21	91.15 326	90.30 326	93.70 330	97.72 312	84.34 347	90.24 348	97.42 289	90.20 329	93.79 341	93.09 350	90.90 274	98.89 344	86.57 332	72.76 353	97.87 293

FPMVS		93.44 312	92.23 317	97.08 262	99.25 137	97.86 131	95.61 307	97.16 295	92.90 299	93.76 342	98.65 191	75.94 349	95.66 352	79.30 351	97.49 315	97.73 302

EPNet		96.14 257	95.44 263	98.25 209	90.76 356	95.50 236	97.92 158	94.65 322	98.97 78	92.98 343	98.85 162	89.12 283	99.87 72	95.99 202	99.68 142	99.39 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DWT-MVSNet_test		92.75 317	92.05 319	94.85 316	96.48 343	87.21 332	97.83 168	94.99 320	92.22 309	92.72 344	94.11 346	70.75 351	99.46 307	95.01 229	94.33 346	97.87 293

IB-MVS	91.63 19	92.24 321	90.90 324	96.27 287	97.22 332	91.24 319	94.36 332	93.33 338	92.37 306	92.24 345	94.58 343	66.20 357	99.89 56	93.16 278	94.63 343	97.66 304
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
gg-mvs-nofinetune		92.37 319	91.20 323	95.85 304	95.80 350	92.38 298	99.31 20	81.84 357	99.75 4	91.83 346	99.74 8	68.29 353	99.02 338	87.15 330	97.12 324	96.16 335

DeepMVS_CX				93.44 333	98.24 293	94.21 267		94.34 328	64.28 352	91.34 347	94.87 341	89.45 282	92.77 355	77.54 352	93.14 348	93.35 350

tpmp4_e23		92.91 316	92.45 315	94.29 323	97.41 326	85.62 339	97.95 156	96.77 307	87.55 341	91.33 348	98.57 206	74.21 350	99.59 274	91.62 302	96.64 330	97.65 311

testpf		89.08 327	90.27 327	85.50 340	94.03 354	82.85 350	96.87 244	91.09 352	91.61 315	90.96 349	94.86 342	66.15 358	95.83 351	94.58 239	92.27 350	77.82 352

PAPM		91.88 323	90.34 325	96.51 283	98.06 302	92.56 294	92.44 345	97.17 294	86.35 342	90.38 350	96.01 311	86.61 290	99.21 331	70.65 353	95.43 339	97.75 301

EPNet_dtu		94.93 277	94.78 278	95.38 312	93.58 355	87.68 330	96.78 247	95.69 319	97.35 182	89.14 351	98.09 249	88.15 286	99.49 301	94.95 232	99.30 215	98.98 230
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND				94.76 318	94.54 352	92.13 301	99.31 20	80.47 358		88.73 352	91.01 352	67.59 354	98.16 350	82.30 348	94.53 344	93.98 349

tmp_tt		78.77 329	78.73 330	78.90 341	58.45 357	74.76 357	94.20 333	78.26 359	39.16 353	86.71 353	92.82 351	80.50 322	75.19 356	86.16 333	92.29 349	86.74 351

MVE	83.40 22	92.50 318	91.92 320	94.25 324	98.83 234	91.64 305	92.71 343	83.52 356	95.92 245	86.46 354	95.46 327	95.20 213	95.40 353	80.51 349	98.64 273	95.73 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		17.12 332	20.53 333	6.87 345	12.05 358	4.20 360	93.62 339	6.73 360	4.62 355	10.41 355	24.33 354	8.28 363	3.56 358	9.69 355	15.07 354	12.86 355

test123		17.04 333	20.11 334	7.82 344	10.25 359	4.91 359	94.80 326	4.47 361	4.93 354	10.00 356	24.28 355	9.69 362	3.64 357	10.14 354	12.43 356	14.92 354

cdsmvs_eth3d_5k		24.66 331	32.88 332	0.00 346	0.00 360	0.00 361	0.00 352	99.10 192	0.00 356	0.00 357	97.58 273	99.21 11	0.00 359	0.00 356	0.00 357	0.00 357

pcd_1.5k_mvsjas		8.17 334	10.90 335	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	98.07 61	0.00 359	0.00 356	0.00 357	0.00 357

pcd1.5k->3k		41.59 330	44.35 331	33.30 343	99.87 12	0.00 361	0.00 352	99.58 36	0.00 356	0.00 357	0.00 358	99.70 2	0.00 359	0.00 356	99.99 11	99.91 2

sosnet-low-res		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

sosnet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uncertanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

Regformer		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

ab-mvs-re		8.12 335	10.83 336	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	97.48 280	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

uanet		0.00 336	0.00 337	0.00 346	0.00 360	0.00 361	0.00 352	0.00 362	0.00 356	0.00 357	0.00 358	0.00 364	0.00 359	0.00 356	0.00 357	0.00 357

GSMVS																98.81 250

test_part3							97.25 217		96.66 220		98.71 180		99.86 77	93.00 280

test_part1								99.28 142				97.56 91			99.57 173	99.53 91

sam_mvs1												84.74 304				98.81 250

sam_mvs												84.29 310

MTGPA								99.20 164

test_post1							97.59 195				20.48 357	83.07 316	99.66 253	94.16 250

test_post											21.25 356	83.86 312	99.70 230

patchmatchnet-post											98.77 174	84.37 307	99.85 88

MTMP								91.91 349

gm-plane-assit					94.83 351	81.97 352			88.07 338		94.99 334		99.60 270	91.76 298

test9_res														93.28 277	99.15 238	99.38 157

agg_prior2														92.50 292	99.16 235	99.37 158

test_prior4						97.97 120	95.86 297

test_prior				98.95 116	98.69 254	97.95 123		99.03 204					99.59 274			99.30 182

新几何2							95.93 294

旧先验1					98.82 237	97.45 159		98.76 246			98.34 228	95.50 207			99.01 253	99.23 196

无先验							95.74 303	98.74 251	89.38 333				99.73 218	92.38 294		99.22 200

原ACMM2							95.53 310

testdata2													99.79 174	92.80 287

segment_acmp												97.02 132

testdata1							95.44 314		96.32 230

plane_prior7					99.19 160	97.87 130

plane_prior6					98.99 202	97.70 147						94.90 219

plane_prior5								99.27 147					99.70 230	94.42 245	99.51 190	99.45 133

plane_prior4											97.98 255

plane_prior2							97.77 172		98.20 121

plane_prior1					99.05 190

plane_prior						97.65 149	97.07 232		96.72 215						99.36 205

n2								0.00 362

nn								0.00 362

door-mid								99.57 43

test11								98.87 229

door								99.41 97

HQP5-MVS						96.79 185

BP-MVS														92.82 285

HQP3-MVS								99.04 202							99.26 222

HQP2-MVS												93.84 246

NP-MVS					98.84 232	97.39 162					96.84 298

ACMMP++_ref															99.77 104

ACMMP++															99.68 142

Test By Simon												96.52 166

High-resolution multi-view benchmark